Estimating unbiased economies of scale of HIV prevention projects: a case study of Avahan.

PubMed

Lépine, Aurélia; Vassall, Anna; Chandrashekar, Sudha; Blanc, Elodie; Le Nestour, Alexis

2015-04-01

Governments and donors are investing considerable resources on HIV prevention in order to scale up these services rapidly. Given the current economic climate, providers of HIV prevention services increasingly need to demonstrate that these investments offer good 'value for money'. One of the primary routes to achieve efficiency is to take advantage of economies of scale (a reduction in the average cost of a health service as provision scales-up), yet empirical evidence on economies of scale is scarce. Methodologically, the estimation of economies of scale is hampered by several statistical issues preventing causal inference and thus making the estimation of economies of scale complex. In order to estimate unbiased economies of scale when scaling up HIV prevention services, we apply our analysis to one of the few HIV prevention programmes globally delivered at a large scale: the Indian Avahan initiative. We costed the project by collecting data from the 138 Avahan NGOs and the supporting partners in the first four years of its scale-up, between 2004 and 2007. We develop a parsimonious empirical model and apply a system Generalized Method of Moments (GMM) and fixed-effects Instrumental Variable (IV) estimators to estimate unbiased economies of scale. At the programme level, we find that, after controlling for the endogeneity of scale, the scale-up of Avahan has generated high economies of scale. Our findings suggest that average cost reductions per person reached are achievable when scaling-up HIV prevention in low and middle income countries. Copyright © 2015 Elsevier Ltd. All rights reserved.

Unbiased mean direction of paleomagnetic data and better estimate of paleolatitude

NASA Astrophysics Data System (ADS)

Hatakeyama, T.; Shibuya, H.

2010-12-01

In paleomagnetism, when we obtain only paleodirection data without paleointensities we calculate Fisher-mean directions (I, D) and Fisher-mean VGP positions as the description of the mean field. However, Kono (1997) and Hatakeyama and Kono (2001) indicated that these averaged directions does not show the unbiased estimated mean directions derived from the time-averaged field (TAF). Hatakeyama and Kono (2002) calculated the TAF and paleosecular variation (PSV) models for the past 5My with considering the biases due to the averaging of the nonlinear functions such as the summation of the unit vectors in the Fisher statistics process. Here we will show a zonal TAF model based on the Hatakeyama and Kono TAF model. Moreover, we will introduce the biased angles due to the PSV in the mean direction and a method for determining true paleolatitudes, which represents the TAF, from paleodirections. This method will helps tectonics studies, especially in the estimation of the accurate paleolatitude in the middle latitude regions.

Estimating Unbiased Land Cover Change Areas In The Colombian Amazon Using Landsat Time Series And Statistical Inference Methods

NASA Astrophysics Data System (ADS)

Arevalo, P. A.; Olofsson, P.; Woodcock, C. E.

2017-12-01

Unbiased estimation of the areas of conversion between land categories ("activity data") and their uncertainty is crucial for providing more robust calculations of carbon emissions to the atmosphere, as well as their removals. This is particularly important for the REDD+ mechanism of UNFCCC where an economic compensation is tied to the magnitude and direction of such fluxes. Dense time series of Landsat data and statistical protocols are becoming an integral part of forest monitoring efforts, but there are relatively few studies in the tropics focused on using these methods to advance operational MRV systems (Monitoring, Reporting and Verification). We present the results of a prototype methodology for continuous monitoring and unbiased estimation of activity data that is compliant with the IPCC Approach 3 for representation of land. We used a break detection algorithm (Continuous Change Detection and Classification, CCDC) to fit pixel-level temporal segments to time series of Landsat data in the Colombian Amazon. The segments were classified using a Random Forest classifier to obtain annual maps of land categories between 2001 and 2016. Using these maps, a biannual stratified sampling approach was implemented and unbiased stratified estimators constructed to calculate area estimates with confidence intervals for each of the stable and change classes. Our results provide evidence of a decrease in primary forest as a result of conversion to pastures, as well as increase in secondary forest as pastures are abandoned and the forest allowed to regenerate. Estimating areas of other land transitions proved challenging because of their very small mapped areas compared to stable classes like forest, which corresponds to almost 90% of the study area. Implications on remote sensing data processing, sample allocation and uncertainty reduction are also discussed.

Assessing the likely value of gravity and drawdown measurements to constrain estimates of hydraulic conductivity and specific yield during unconfined aquifer testing

USGS Publications Warehouse

Blainey, Joan B.; Ferré, Ty P.A.; Cordova, Jeffrey T.

2007-01-01

Pumping of an unconfined aquifer can cause local desaturation detectable with high‐resolution gravimetry. A previous study showed that signal‐to‐noise ratios could be predicted for gravity measurements based on a hydrologic model. We show that although changes should be detectable with gravimeters, estimations of hydraulic conductivity and specific yield based on gravity data alone are likely to be unacceptably inaccurate and imprecise. In contrast, a transect of low‐quality drawdown data alone resulted in accurate estimates of hydraulic conductivity and inaccurate and imprecise estimates of specific yield. Combined use of drawdown and gravity data, or use of high‐quality drawdown data alone, resulted in unbiased and precise estimates of both parameters. This study is an example of the value of a staged assessment regarding the likely significance of a new measurement method or monitoring scenario before collecting field data.

Unbiased estimation of chloroplast number in mesophyll cells: advantage of a genuine three-dimensional approach

PubMed Central

Kubínová, Zuzana

2014-01-01

Chloroplast number per cell is a frequently examined quantitative anatomical parameter, often estimated by counting chloroplast profiles in two-dimensional (2D) sections of mesophyll cells. However, a mesophyll cell is a three-dimensional (3D) structure and this has to be taken into account when quantifying its internal structure. We compared 2D and 3D approaches to chloroplast counting from different points of view: (i) in practical measurements of mesophyll cells of Norway spruce needles, (ii) in a 3D model of a mesophyll cell with chloroplasts, and (iii) using a theoretical analysis. We applied, for the first time, the stereological method of an optical disector based on counting chloroplasts in stacks of spruce needle optical cross-sections acquired by confocal laser-scanning microscopy. This estimate was compared with counting chloroplast profiles in 2D sections from the same stacks of sections. Comparing practical measurements of mesophyll cells, calculations performed in a 3D model of a cell with chloroplasts as well as a theoretical analysis showed that the 2D approach yielded biased results, while the underestimation could be up to 10-fold. We proved that the frequently used method for counting chloroplasts in a mesophyll cell by counting their profiles in 2D sections did not give correct results. We concluded that the present disector method can be efficiently used for unbiased estimation of chloroplast number per mesophyll cell. This should be the method of choice, especially in coniferous needles and leaves with mesophyll cells with lignified cell walls where maceration methods are difficult or impossible to use. PMID:24336344

Multifractals embedded in short time series: An unbiased estimation of probability moment

NASA Astrophysics Data System (ADS)

Qiu, Lu; Yang, Tianguang; Yin, Yanhua; Gu, Changgui; Yang, Huijie

2016-12-01

An exact estimation of probability moments is the base for several essential concepts, such as the multifractals, the Tsallis entropy, and the transfer entropy. By means of approximation theory we propose a new method called factorial-moment-based estimation of probability moments. Theoretical prediction and computational results show that it can provide us an unbiased estimation of the probability moments of continuous order. Calculations on probability redistribution model verify that it can extract exactly multifractal behaviors from several hundred recordings. Its powerfulness in monitoring evolution of scaling behaviors is exemplified by two empirical cases, i.e., the gait time series for fast, normal, and slow trials of a healthy volunteer, and the closing price series for Shanghai stock market. By using short time series with several hundred lengths, a comparison with the well-established tools displays significant advantages of its performance over the other methods. The factorial-moment-based estimation can evaluate correctly the scaling behaviors in a scale range about three generations wider than the multifractal detrended fluctuation analysis and the basic estimation. The estimation of partition function given by the wavelet transform modulus maxima has unacceptable fluctuations. Besides the scaling invariance focused in the present paper, the proposed factorial moment of continuous order can find its various uses, such as finding nonextensive behaviors of a complex system and reconstructing the causality relationship network between elements of a complex system.

Minimum mean squared error (MSE) adjustment and the optimal Tykhonov-Phillips regularization parameter via reproducing best invariant quadratic uniformly unbiased estimates (repro-BIQUUE)

NASA Astrophysics Data System (ADS)

Schaffrin, Burkhard

2008-02-01

In a linear Gauss-Markov model, the parameter estimates from BLUUE (Best Linear Uniformly Unbiased Estimate) are not robust against possible outliers in the observations. Moreover, by giving up the unbiasedness constraint, the mean squared error (MSE) risk may be further reduced, in particular when the problem is ill-posed. In this paper, the α-weighted S-homBLE (Best homogeneously Linear Estimate) is derived via formulas originally used for variance component estimation on the basis of the repro-BIQUUE (reproducing Best Invariant Quadratic Uniformly Unbiased Estimate) principle in a model with stochastic prior information. In the present model, however, such prior information is not included, which allows the comparison of the stochastic approach (α-weighted S-homBLE) with the well-established algebraic approach of Tykhonov-Phillips regularization, also known as R-HAPS (Hybrid APproximation Solution), whenever the inverse of the “substitute matrix” S exists and is chosen as the R matrix that defines the relative impact of the regularizing term on the final result.

Unbiased free energy estimates in fast nonequilibrium transformations using Gaussian mixtures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Procacci, Piero

2015-04-21

In this paper, we present an improved method for obtaining unbiased estimates of the free energy difference between two thermodynamic states using the work distribution measured in nonequilibrium driven experiments connecting these states. The method is based on the assumption that any observed work distribution is given by a mixture of Gaussian distributions, whose normal components are identical in either direction of the nonequilibrium process, with weights regulated by the Crooks theorem. Using the prototypical example for the driven unfolding/folding of deca-alanine, we show that the predicted behavior of the forward and reverse work distributions, assuming a combination of onlymore » two Gaussian components with Crooks derived weights, explains surprisingly well the striking asymmetry in the observed distributions at fast pulling speeds. The proposed methodology opens the way for a perfectly parallel implementation of Jarzynski-based free energy calculations in complex systems.« less

Estimating unbiased phenological trends by adapting site-occupancy models.

PubMed

Roth, Tobias; Strebel, Nicolas; Amrhein, Valentin

2014-08-01

As a response to climate warming, many animals and plants have been found to shift phenologies, such as appearance in spring or timing of reproduction. However, traditional measures for shifts in phenology that are based on observational data likely are biased due to a large influence of population size, observational effort, starting date of a survey, or other causes that may affect the probability of detecting a species. Understanding phenological responses of species to climate change, however, requires a robust measure that could be compared among studies and study years. Here, we developed a new method for estimating arrival and departure dates based on site-occupancy models. Using simulated data, we show that our method provided virtually unbiased estimates of phenological events even if detection probability or the number of sites occupied by the species is changing over time. To illustrate the flexibility of our method, we analyzed spring arrival of two long-distance migrant songbirds and the length of the flight period of two butterfly species, using data from a long-term biodiversity monitoring program in Switzerland. In contrast to many birds that migrate short distances, the two long-distance migrant songbirds tended to postpone average spring arrival by -0.5 days per year between 1995 and 2012. Furthermore, the flight period of the short-distance-flying butterfly species apparently became even shorter over the study period, while the flight period of the longer-distance-flying butterfly species remained relatively stable. Our method could be applied to temporally and spatially extensive data from a wide range of monitoring programs and citizen science projects, to help unravel how species and communities respond to global warming.

An unbiased risk estimator for image denoising in the presence of mixed poisson-gaussian noise.

PubMed

Le Montagner, Yoann; Angelini, Elsa D; Olivo-Marin, Jean-Christophe

2014-03-01

The behavior and performance of denoising algorithms are governed by one or several parameters, whose optimal settings depend on the content of the processed image and the characteristics of the noise, and are generally designed to minimize the mean squared error (MSE) between the denoised image returned by the algorithm and a virtual ground truth. In this paper, we introduce a new Poisson-Gaussian unbiased risk estimator (PG-URE) of the MSE applicable to a mixed Poisson-Gaussian noise model that unifies the widely used Gaussian and Poisson noise models in fluorescence bioimaging applications. We propose a stochastic methodology to evaluate this estimator in the case when little is known about the internal machinery of the considered denoising algorithm, and we analyze both theoretically and empirically the characteristics of the PG-URE estimator. Finally, we evaluate the PG-URE-driven parametrization for three standard denoising algorithms, with and without variance stabilizing transforms, and different characteristics of the Poisson-Gaussian noise mixture.

Estimating Unbiased Treatment Effects in Education Using a Regression Discontinuity Design

ERIC Educational Resources Information Center

Smith, William C.

2014-01-01

The ability of regression discontinuity (RD) designs to provide an unbiased treatment effect while overcoming the ethical concerns plagued by Random Control Trials (RCTs) make it a valuable and useful approach in education evaluation. RD is the only explicitly recognized quasi-experimental approach identified by the Institute of Education…

Infrasound Studies for Yield Estimation of HE Explosions

DTIC Science & Technology

2012-06-05

AFRL-RV-PS- AFRL-RV-PS- TR-2012-0084 TR-2012-0084 INFRASOUND STUDIES FOR YIELD ESTIMATION OF HE EXPLOSIONS Paul Golden, et al...05 Mar 2010 to 05 Mar 2012 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER FA9453-10-C-0212 Infrasound Studies for Yield Estimation of HE...report we discuss the capability of estimating the yield of an explosion from infrasound signals generated by low yield chemical explosions. We used

A Test-Length Correction to the Estimation of Extreme Proficiency Levels

ERIC Educational Resources Information Center

Magis, David; Beland, Sebastien; Raiche, Gilles

2011-01-01

In this study, the estimation of extremely large or extremely small proficiency levels, given the item parameters of a logistic item response model, is investigated. On one hand, the estimation of proficiency levels by maximum likelihood (ML), despite being asymptotically unbiased, may yield infinite estimates. On the other hand, with an…

Mutually unbiased product bases for multiple qudits

DOE Office of Scientific and Technical Information (OSTI.GOV)

McNulty, Daniel; Pammer, Bogdan; Weigert, Stefan

We investigate the interplay between mutual unbiasedness and product bases for multiple qudits of possibly different dimensions. A product state of such a system is shown to be mutually unbiased to a product basis only if each of its factors is mutually unbiased to all the states which occur in the corresponding factors of the product basis. This result implies both a tight limit on the number of mutually unbiased product bases which the system can support and a complete classification of mutually unbiased product bases for multiple qubits or qutrits. In addition, only maximally entangled states can be mutuallymore » unbiased to a maximal set of mutually unbiased product bases.« less

Estimating unbiased magnitudes for the announced DPRK nuclear tests, 2006-2016

NASA Astrophysics Data System (ADS)

Peacock, Sheila; Bowers, David

2017-04-01

The seismic disturbances generated from the five (2006-2016) announced nuclear test explosions by the Democratic People's Republic of Korea (DPRK) are of moderate magnitude (body-wave magnitude mb 4-5) by global earthquake standards. An upward bias of network mean mb of low- to moderate-magnitude events is long established, and is caused by the censoring of readings from stations where the signal was below noise level at the time of the predicted arrival. This sampling bias can be overcome by maximum-likelihood methods using station thresholds at detecting (and non-detecting) stations. Bias in the mean mb can also be introduced by differences in the network of stations recording each explosion - this bias can reduced by using station corrections. We apply a maximum-likelihood (JML) inversion that jointly estimates station corrections and unbiased network mb for the five DPRK explosions recorded by the CTBTO International Monitoring Network (IMS) of seismic stations. The thresholds can either be directly measured from the noise preceding the observed signal, or determined by statistical analysis of bulletin amplitudes. The network mb of the first and smallest explosion is reduced significantly relative to the mean mb (to < 4.0 mb) by removal of the censoring bias.

An evaluation of flow-stratified sampling for estimating suspended sediment loads

Treesearch

Robert B. Thomas; Jack Lewis

1995-01-01

Abstract - Flow-stratified sampling is a new method for sampling water quality constituents such as suspended sediment to estimate loads. As with selection-at-list-time (SALT) and time-stratified sampling, flow-stratified sampling is a statistical method requiring random sampling, and yielding unbiased estimates of load and variance. It can be used to estimate event...

Operation of the yield estimation subsystem

NASA Technical Reports Server (NTRS)

Mccrary, D. G.; Rogers, J. L.; Hill, J. D. (Principal Investigator)

1979-01-01

The organization and products of the yield estimation subsystem (YES) are described with particular emphasis on meteorological data acquisition, yield estimation, crop calendars, weekly weather summaries, and project reports. During the three phases of LACIE, YES demonstrated that it is possible to use the flow of global meteorological data and provide valuable information regarding global wheat production. It was able to establish a capability to collect, in a timely manner, detailed weather data from all regions of the world, and to evaluate and convert that data into information appropriate to the project's needs.

Empirical Bayes Estimation of Semi-parametric Hierarchical Mixture Models for Unbiased Characterization of Polygenic Disease Architectures

PubMed Central

Nishino, Jo; Kochi, Yuta; Shigemizu, Daichi; Kato, Mamoru; Ikari, Katsunori; Ochi, Hidenori; Noma, Hisashi; Matsui, Kota; Morizono, Takashi; Boroevich, Keith A.; Tsunoda, Tatsuhiko; Matsui, Shigeyuki

2018-01-01

Genome-wide association studies (GWAS) suggest that the genetic architecture of complex diseases consists of unexpectedly numerous variants with small effect sizes. However, the polygenic architectures of many diseases have not been well characterized due to lack of simple and fast methods for unbiased estimation of the underlying proportion of disease-associated variants and their effect-size distribution. Applying empirical Bayes estimation of semi-parametric hierarchical mixture models to GWAS summary statistics, we confirmed that schizophrenia was extremely polygenic [~40% of independent genome-wide SNPs are risk variants, most within odds ratio (OR = 1.03)], whereas rheumatoid arthritis was less polygenic (~4 to 8% risk variants, significant portion reaching OR = 1.05 to 1.1). For rheumatoid arthritis, stratified estimations revealed that expression quantitative loci in blood explained large genetic variance, and low- and high-frequency derived alleles were prone to be risk and protective, respectively, suggesting a predominance of deleterious-risk and advantageous-protective mutations. Despite genetic correlation, effect-size distributions for schizophrenia and bipolar disorder differed across allele frequency. These analyses distinguished disease polygenic architectures and provided clues for etiological differences in complex diseases. PMID:29740473

Unbiased estimation in seamless phase II/III trials with unequal treatment effect variances and hypothesis-driven selection rules.

PubMed

Robertson, David S; Prevost, A Toby; Bowden, Jack

2016-09-30

Seamless phase II/III clinical trials offer an efficient way to select an experimental treatment and perform confirmatory analysis within a single trial. However, combining the data from both stages in the final analysis can induce bias into the estimates of treatment effects. Methods for bias adjustment developed thus far have made restrictive assumptions about the design and selection rules followed. In order to address these shortcomings, we apply recent methodological advances to derive the uniformly minimum variance conditionally unbiased estimator for two-stage seamless phase II/III trials. Our framework allows for the precision of the treatment arm estimates to take arbitrary values, can be utilised for all treatments that are taken forward to phase III and is applicable when the decision to select or drop treatment arms is driven by a multiplicity-adjusted hypothesis testing procedure. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Yield estimation of sugarcane based on agrometeorological-spectral models

NASA Technical Reports Server (NTRS)

Rudorff, Bernardo Friedrich Theodor; Batista, Getulio Teixeira

1990-01-01

This work has the objective to assess the performance of a yield estimation model for sugarcane (Succharum officinarum). The model uses orbital gathered spectral data along with yield estimated from an agrometeorological model. The test site includes the sugarcane plantations of the Barra Grande Plant located in Lencois Paulista municipality in Sao Paulo State. Production data of four crop years were analyzed. Yield data observed in the first crop year (1983/84) were regressed against spectral and agrometeorological data of that same year. This provided the model to predict the yield for the following crop year i.e., 1984/85. The model to predict the yield of subsequent years (up to 1987/88) were developed similarly, incorporating all previous years data. The yield estimations obtained from these models explained 69, 54, and 50 percent of the yield variation in the 1984/85, 1985/86, and 1986/87 crop years, respectively. The accuracy of yield estimations based on spectral data only (vegetation index model) and on agrometeorological data only (agrometeorological model) were also investigated.

Infrasound Studies for Yield Estimation of HE Explosions

DTIC Science & Technology

2011-03-05

AFRL-RV-HA-TR-2011-1022 Infrasound Studies for Yield Estimation of HE Explosions Paul Golden Petru Negraru Southern Methodist...DATES COVERED (From - To) 5 Mar 2010 to 5 Mar 2011 4. TITLE AND SUBTITLE Infrasound Studies for Yield Estimation of HE Explosions 5a. CONTRACT NUMBER...conducting investigations to determine the yield of HE explosions from infrasound signals. In particular SMU is investigating how the period and amplitude

A Priori Estimation of Organic Reaction Yields

DOE Office of Scientific and Technical Information (OSTI.GOV)

Emami, Fateme S.; Vahid, Amir; Wylie, Elizabeth K.

2015-07-21

A thermodynamically guided calculation of free energies of substrate and product molecules allows for the estimation of the yields of organic reactions. The non-ideality of the system and the solvent effects are taken into account through the activity coefficients calculated at the molecular level by perturbed-chain statistical associating fluid theory (PC-SAFT). The model is iteratively trained using a diverse set of reactions with yields that have been reported previously. This trained model can then estimate a priori the yields of reactions not included in the training set with an accuracy of ca. ±15 %. This ability has the potential tomore » translate into significant economic savings through the selection and then execution of only those reactions that can proceed in good yields.« less

Quality control and gap-filling of PM10 daily mean concentrations with the best linear unbiased estimator.

PubMed

Sozzi, R; Bolignano, A; Ceradini, S; Morelli, M; Petenko, I; Argentini, S

2017-10-15

According to the European Directive 2008/50/CE, the air quality assessment consists in the measurement of the concentration fields, and the evaluation of the mean, number of exceedances, etc. of some chemical species dangerous to human health. The measurements provided by an air quality ground-based monitoring network are the main information source but the availability of these data is often limited by several technical and operational problems. In this paper, the best linear unbiased estimator (BLUE) is proposed to validate the pollutant concentration values and to fill the gaps in the measurement of time series collected by a monitoring network. The BLUE algorithm is tested using the daily mean concentrations of particulate matter having aerodynamic diameter less than 10 μ (PM 10 concentrations) measured by the air quality monitoring sensors operating in the Lazio Region in Italy. The comparison between the estimated and measured data evidences an error comparable with the measurement uncertainty. Due to its simplicity and reliability, the BLUE will be used in the routine quality test procedures of the Lazio air quality monitoring network measurements.

Simulation of relationship between river discharge and sediment yield in the semi-arid river watersheds

NASA Astrophysics Data System (ADS)

Khaleghi, Mohammad Reza; Varvani, Javad

2018-02-01

Complex and variable nature of the river sediment yield caused many problems in estimating the long-term sediment yield and problems input into the reservoirs. Sediment Rating Curves (SRCs) are generally used to estimate the suspended sediment load of the rivers and drainage watersheds. Since the regression equations of the SRCs are obtained by logarithmic retransformation and have a little independent variable in this equation, they also overestimate or underestimate the true sediment load of the rivers. To evaluate the bias correction factors in Kalshor and Kashafroud watersheds, seven hydrometric stations of this region with suitable upstream watershed and spatial distribution were selected. Investigation of the accuracy index (ratio of estimated sediment yield to observed sediment yield) and the precision index of different bias correction factors of FAO, Quasi-Maximum Likelihood Estimator (QMLE), Smearing, and Minimum-Variance Unbiased Estimator (MVUE) with LSD test showed that FAO coefficient increases the estimated error in all of the stations. Application of MVUE in linear and mean load rating curves has not statistically meaningful effects. QMLE and smearing factors increased the estimated error in mean load rating curve, but that does not have any effect on linear rating curve estimation.

A novel SURE-based criterion for parametric PSF estimation.

PubMed

Xue, Feng; Blu, Thierry

2015-02-01

We propose an unbiased estimate of a filtered version of the mean squared error--the blur-SURE (Stein's unbiased risk estimate)--as a novel criterion for estimating an unknown point spread function (PSF) from the degraded image only. The PSF is obtained by minimizing this new objective functional over a family of Wiener processings. Based on this estimated blur kernel, we then perform nonblind deconvolution using our recently developed algorithm. The SURE-based framework is exemplified with a number of parametric PSF, involving a scaling factor that controls the blur size. A typical example of such parametrization is the Gaussian kernel. The experimental results demonstrate that minimizing the blur-SURE yields highly accurate estimates of the PSF parameters, which also result in a restoration quality that is very similar to the one obtained with the exact PSF, when plugged into our recent multi-Wiener SURE-LET deconvolution algorithm. The highly competitive results obtained outline the great potential of developing more powerful blind deconvolution algorithms based on SURE-like estimates.

Mutually unbiased bases and semi-definite programming

NASA Astrophysics Data System (ADS)

Brierley, Stephen; Weigert, Stefan

2010-11-01

A complex Hilbert space of dimension six supports at least three but not more than seven mutually unbiased bases. Two computer-aided analytical methods to tighten these bounds are reviewed, based on a discretization of parameter space and on Gröbner bases. A third algorithmic approach is presented: the non-existence of more than three mutually unbiased bases in composite dimensions can be decided by a global optimization method known as semidefinite programming. The method is used to confirm that the spectral matrix cannot be part of a complete set of seven mutually unbiased bases in dimension six.

Losing the rose tinted glasses: neural substrates of unbiased belief updating in depression

PubMed Central

Garrett, Neil; Sharot, Tali; Faulkner, Paul; Korn, Christoph W.; Roiser, Jonathan P.; Dolan, Raymond J.

2014-01-01

Recent evidence suggests that a state of good mental health is associated with biased processing of information that supports a positively skewed view of the future. Depression, on the other hand, is associated with unbiased processing of such information. Here, we use brain imaging in conjunction with a belief update task administered to clinically depressed patients and healthy controls to characterize brain activity that supports unbiased belief updating in clinically depressed individuals. Our results reveal that unbiased belief updating in depression is mediated by strong neural coding of estimation errors in response to both good news (in left inferior frontal gyrus and bilateral superior frontal gyrus) and bad news (in right inferior parietal lobule and right inferior frontal gyrus) regarding the future. In contrast, intact mental health was linked to a relatively attenuated neural coding of bad news about the future. These findings identify a neural substrate mediating the breakdown of biased updating in major depression disorder, which may be essential for mental health. PMID:25221492

Density estimation in wildlife surveys

USGS Publications Warehouse

Bart, Jonathan; Droege, Sam; Geissler, Paul E.; Peterjohn, Bruce G.; Ralph, C. John

2004-01-01

Several authors have recently discussed the problems with using index methods to estimate trends in population size. Some have expressed the view that index methods should virtually never be used. Others have responded by defending index methods and questioning whether better alternatives exist. We suggest that index methods are often a cost-effective component of valid wildlife monitoring but that double-sampling or another procedure that corrects for bias or establishes bounds on bias is essential. The common assertion that index methods require constant detection rates for trend estimation is mathematically incorrect; the requirement is no long-term trend in detection "ratios" (index result/parameter of interest), a requirement that is probably approximately met by many well-designed index surveys. We urge that more attention be given to defining bird density rigorously and in ways useful to managers. Once this is done, 4 sources of bias in density estimates may be distinguished: coverage, closure, surplus birds, and detection rates. Distance, double-observer, and removal methods do not reduce bias due to coverage, closure, or surplus birds. These methods may yield unbiased estimates of the number of birds present at the time of the survey, but only if their required assumptions are met, which we doubt occurs very often in practice. Double-sampling, in contrast, produces unbiased density estimates if the plots are randomly selected and estimates on the intensive surveys are unbiased. More work is needed, however, to determine the feasibility of double-sampling in different populations and habitats. We believe the tension that has developed over appropriate survey methods can best be resolved through increased appreciation of the mathematical aspects of indices, especially the effects of bias, and through studies in which candidate methods are evaluated against known numbers determined through intensive surveys.

Explosion yield estimation from pressure wave template matching

PubMed Central

Arrowsmith, Stephen; Bowman, Daniel

2017-01-01

A method for estimating the yield of explosions from shock-wave and acoustic-wave measurements is presented. The method exploits full waveforms by comparing pressure measurements against an empirical stack of prior observations using scaling laws. The approach can be applied to measurements across a wide-range of source-to-receiver distances. The method is applied to data from two explosion experiments in different regions, leading to mean relative errors in yield estimates of 0.13 using prior data from the same region, and 0.2 when applied to a new region. PMID:28618805

Real-time yield estimation based on deep learning

NASA Astrophysics Data System (ADS)

Rahnemoonfar, Maryam; Sheppard, Clay

2017-05-01

Crop yield estimation is an important task in product management and marketing. Accurate yield prediction helps farmers to make better decision on cultivation practices, plant disease prevention, and the size of harvest labor force. The current practice of yield estimation based on the manual counting of fruits is very time consuming and expensive process and it is not practical for big fields. Robotic systems including Unmanned Aerial Vehicles (UAV) and Unmanned Ground Vehicles (UGV), provide an efficient, cost-effective, flexible, and scalable solution for product management and yield prediction. Recently huge data has been gathered from agricultural field, however efficient analysis of those data is still a challenging task. Computer vision approaches currently face diffident challenges in automatic counting of fruits or flowers including occlusion caused by leaves, branches or other fruits, variance in natural illumination, and scale. In this paper a novel deep convolutional network algorithm was developed to facilitate the accurate yield prediction and automatic counting of fruits and vegetables on the images. Our method is robust to occlusion, shadow, uneven illumination and scale. Experimental results in comparison to the state-of-the art show the effectiveness of our algorithm.

Yield estimation of corn with multispectral data and the potential of using imaging spectrometers

NASA Astrophysics Data System (ADS)

Bach, Heike

1997-05-01

In the frame of the special yield estimation, a regular procedure conducted for the European Union to more accurately estimate agricultural yield, a project was conducted for the state minister for Rural Environment, Food and Forestry of Baden-Wuerttemberg, Germany) to test remote sensing data with advanced yield formation models for accuracy and timelines of yield estimation of corn. The methodology employed uses field-based plant parameter estimation from atmospherically corrected multitemporal/multispectral LANDSAT-TM data. An agrometeorological plant-production-model is used for yield prediction. Based solely on 4 LANDSAT-derived estimates and daily meteorological data the grain yield of corn stands was determined for 1995. The modeled yield was compared with results independently gathered within the special yield estimation for 23 test fields in the Upper Rhine Valley. The agrement between LANDSAT-based estimates and Special Yield Estimation shows a relative error of 2.3 percent. The comparison of the results for single fields shows, that six weeks before harvest the grain yield of single corn fields was estimated with a mean relative accuracy of 13 percent using satellite information. The presented methodology can be transferred to other crops and geographical regions. For future applications hyperspectral sensors show great potential to further enhance the results or yield prediction with remote sensing.

Growth and Yield Estimation for Loblolly Pine in the West Gulf

Treesearch

Paul A. Murphy; Herbert S. Sternitzke

1979-01-01

An equation system is developed to estimate current yield, projected basal area, and projected volume for merchantable natural stands on a per-acre basis. These estimates indicate yields that can be expected from woods-run conditions.

Constructing statistically unbiased cortical surface templates using feature-space covariance

NASA Astrophysics Data System (ADS)

Parvathaneni, Prasanna; Lyu, Ilwoo; Huo, Yuankai; Blaber, Justin; Hainline, Allison E.; Kang, Hakmook; Woodward, Neil D.; Landman, Bennett A.

2018-03-01

The choice of surface template plays an important role in cross-sectional subject analyses involving cortical brain surfaces because there is a tendency toward registration bias given variations in inter-individual and inter-group sulcal and gyral patterns. In order to account for the bias and spatial smoothing, we propose a feature-based unbiased average template surface. In contrast to prior approaches, we factor in the sample population covariance and assign weights based on feature information to minimize the influence of covariance in the sampled population. The mean surface is computed by applying the weights obtained from an inverse covariance matrix, which guarantees that multiple representations from similar groups (e.g., involving imaging, demographic, diagnosis information) are down-weighted to yield an unbiased mean in feature space. Results are validated by applying this approach in two different applications. For evaluation, the proposed unbiased weighted surface mean is compared with un-weighted means both qualitatively and quantitatively (mean squared error and absolute relative distance of both the means with baseline). In first application, we validated the stability of the proposed optimal mean on a scan-rescan reproducibility dataset by incrementally adding duplicate subjects. In the second application, we used clinical research data to evaluate the difference between the weighted and unweighted mean when different number of subjects were included in control versus schizophrenia groups. In both cases, the proposed method achieved greater stability that indicated reduced impacts of sampling bias. The weighted mean is built based on covariance information in feature space as opposed to spatial location, thus making this a generic approach to be applicable to any feature of interest.

Within-subject template estimation for unbiased longitudinal image analysis.

PubMed

Reuter, Martin; Schmansky, Nicholas J; Rosas, H Diana; Fischl, Bruce

2012-07-16

Longitudinal image analysis has become increasingly important in clinical studies of normal aging and neurodegenerative disorders. Furthermore, there is a growing appreciation of the potential utility of longitudinally acquired structural images and reliable image processing to evaluate disease modifying therapies. Challenges have been related to the variability that is inherent in the available cross-sectional processing tools, to the introduction of bias in longitudinal processing and to potential over-regularization. In this paper we introduce a novel longitudinal image processing framework, based on unbiased, robust, within-subject template creation, for automatic surface reconstruction and segmentation of brain MRI of arbitrarily many time points. We demonstrate that it is essential to treat all input images exactly the same as removing only interpolation asymmetries is not sufficient to remove processing bias. We successfully reduce variability and avoid over-regularization by initializing the processing in each time point with common information from the subject template. The presented results show a significant increase in precision and discrimination power while preserving the ability to detect large anatomical deviations; as such they hold great potential in clinical applications, e.g. allowing for smaller sample sizes or shorter trials to establish disease specific biomarkers or to quantify drug effects. Copyright © 2012 Elsevier Inc. All rights reserved.

How does spatial and temporal resolution of vegetation index impact crop yield estimation?

USDA-ARS?s Scientific Manuscript database

Timely and accurate estimation of crop yield before harvest is critical for food market and administrative planning. Remote sensing data have long been used in crop yield estimation for decades. The process-based approach uses light use efficiency model to estimate crop yield. Vegetation index (VI) ...

Estimating yellow-poplar growth and yield

Treesearch

Donald E. Beck

1989-01-01

Yellow-poplar grows in essentially pure, even-aged stands, so you can make growth and yield estimates from relatively few stand characteristics. The tables and models described here require only measures of stand age, stand basal area in trees 4.5 inches and larger, and site index. They were developed by remeasuring (at 5-year intervals over a 20-year period) many...

Estimation of rice yield affected by drought and relation between rice yield and TVDI

NASA Astrophysics Data System (ADS)

Hongo, C.; Tamura, E.; Sigit, G.

2016-12-01

Impact of climate change is not only seen on food production but also on food security and sustainable development of society. Adaptation to climate change is a pressing issue throughout the world to reduce the risks along with the plans and strategies for food security and sustainable development. As a key adaptation to the climate change, agricultural insurance is expected to play an important role in stabilizing agricultural production through compensating the losses caused by the climate change. As the adaptation, the Government of Indonesia has launched agricultural insurance program for damage of rice by drought, flood and pest and disease. The Government started a pilot project in 2013 and this year the pilot project has been extended to 22 provinces. Having the above as background, we conducted research on development of new damage assessment method for rice using remote sensing data which could be used for evaluation of damage ratio caused by drought in West Java, Indonesia. For assessment of the damage ratio, estimation of rice yield is a key. As the result of our study, rice yield affected by drought in dry season could be estimated at level of 1 % significance using SPOT 7 data taken in 2015, and the validation result was 0.8t/ha. Then, the decrease ratio in rice yield about each individual paddy field was calculated using data of the estimated result and the average yield of the past 10 years. In addition, TVDI (Temperature Vegetation Dryness Index) which was calculated from Landsat8 data in heading season indicated the dryness in low yield area. The result suggests that rice yield was affected by irrigation water shortage around heading season as a result of the decreased precipitation by El Nino. Through our study, it becomes clear that the utilization of remote sensing data can be promising for assessment of the damage ratio of rice production precisely, quickly and quantitatively, and also it can be incorporated into the insurance procedures.

Noncommuting observables in quantum detection and estimation theory

NASA Technical Reports Server (NTRS)

Helstrom, C. W.

1972-01-01

Basing decisions and estimates on simultaneous approximate measurements of noncommuting observables in a quantum receiver is shown to be equivalent to measuring commuting projection operators on a larger Hilbert space than that of the receiver itself. The quantum-mechanical Cramer-Rao inequalities derived from right logarithmic derivatives and symmetrized logarithmic derivatives of the density operator are compared, and it is shown that the latter give superior lower bounds on the error variances of individual unbiased estimates of arrival time and carrier frequency of a coherent signal. For a suitably weighted sum of the error variances of simultaneous estimates of these, the former yield the superior lower bound under some conditions.

An examination of effect estimation in factorial and standardly-tailored designs

PubMed Central

Allore, Heather G; Murphy, Terrence E

2012-01-01

Background Many clinical trials are designed to test an intervention arm against a control arm wherein all subjects are equally eligible for all interventional components. Factorial designs have extended this to test multiple intervention components and their interactions. A newer design referred to as a ‘standardly-tailored’ design, is a multicomponent interventional trial that applies individual interventional components to modify risk factors identified a priori and tests whether health outcomes differ between treatment arms. Standardly-tailored designs do not require that all subjects be eligible for every interventional component. Although standardly-tailored designs yield an estimate for the net effect of the multicomponent intervention, it has not yet been shown if they permit separate, unbiased estimation of individual component effects. The ability to estimate the most potent interventional components has direct bearing on conducting second stage translational research. Purpose We present statistical issues related to the estimation of individual component effects in trials of geriatric conditions using factorial and standardly-tailored designs. The medical community is interested in second stage translational research involving the transfer of results from a randomized clinical trial to a community setting. Before such research is undertaken, main effects and synergistic and or antagonistic interactions between them should be identified. Knowledge of the relative strength and direction of the effects of the individual components and their interactions facilitates the successful transfer of clinically significant findings and may potentially reduce the number of interventional components needed. Therefore the current inability of the standardly-tailored design to provide unbiased estimates of individual interventional components is a serious limitation in their applicability to second stage translational research. Methods We discuss estimation of

Estimating sugarcane yield potential using an in-season determination of normalized difference vegetative index.

PubMed

Lofton, Josh; Tubana, Brenda S; Kanke, Yumiko; Teboh, Jasper; Viator, Howard; Dalen, Marilyn

2012-01-01

Estimating crop yield using remote sensing techniques has proven to be successful. However, sugarcane possesses unique characteristics; such as, a multi-year cropping cycle and plant height-limiting for midseason fertilizer application timing. Our study objective was to determine if sugarcane yield potential could be estimated using an in-season estimation of normalized difference vegetative index (NDVI). Sensor readings were taken using the GreenSeeker® handheld sensor from 2008 to 2011 in St. Gabriel and Jeanerette, LA, USA. In-season estimates of yield (INSEY) values were calculated by dividing NDVI by thermal variables. Optimum timing for estimating sugarcane yield was between 601-750 GDD. In-season estimated yield values improved the yield potential (YP) model compared to using NDVI. Generally, INSEY value showed a positive exponential relationship with yield (r(2) values 0.48 and 0.42 for cane tonnage and sugar yield, respectively). When models were separated based on canopy structure there was an increase the strength of the relationship for the erectophile varieties (r(2) 0.53 and 0.47 for cane tonnage and sugar yield, respectively); however, the model for planophile varieties weakened slightly. Results of this study indicate using an INSEY value for predicting sugarcane yield shows potential of being a valuable management tool for sugarcane producers in Louisiana.

Personalized recommendation based on unbiased consistence

NASA Astrophysics Data System (ADS)

Zhu, Xuzhen; Tian, Hui; Zhang, Ping; Hu, Zheng; Zhou, Tao

2015-08-01

Recently, in physical dynamics, mass-diffusion-based recommendation algorithms on bipartite network provide an efficient solution by automatically pushing possible relevant items to users according to their past preferences. However, traditional mass-diffusion-based algorithms just focus on unidirectional mass diffusion from objects having been collected to those which should be recommended, resulting in a biased causal similarity estimation and not-so-good performance. In this letter, we argue that in many cases, a user's interests are stable, and thus bidirectional mass diffusion abilities, no matter originated from objects having been collected or from those which should be recommended, should be consistently powerful, showing unbiased consistence. We further propose a consistence-based mass diffusion algorithm via bidirectional diffusion against biased causality, outperforming the state-of-the-art recommendation algorithms in disparate real data sets, including Netflix, MovieLens, Amazon and Rate Your Music.

Estimating national crop yield potential and the relevance of weather data sources

NASA Astrophysics Data System (ADS)

Van Wart, Justin

2011-12-01

To determine where, when, and how to increase yields, researchers often analyze the yield gap (Yg), the difference between actual current farm yields and crop yield potential. Crop yield potential (Yp) is the yield of a crop cultivar grown under specific management limited only by temperature and solar radiation and also by precipitation for water limited yield potential (Yw). Yp and Yw are critical components of Yg estimations, but are very difficult to quantify, especially at larger scales because management data and especially daily weather data are scarce. A protocol was developed to estimate Yp and Yw at national scales using site-specific weather, soils and management data. Protocol procedures and inputs were evaluated to determine how to improve accuracy of Yp, Yw and Yg estimates. The protocol was also used to evaluate raw, site-specific and gridded weather database sources for use in simulations of Yp or Yw. The protocol was applied to estimate crop Yp in US irrigated maize and Chinese irrigated rice and Yw in US rainfed maize and German rainfed wheat. These crops and countries account for >20% of global cereal production. The results have significant implications for past and future studies of Yp, Yw and Yg. Accuracy of national long-term average Yp and Yw estimates was significantly improved if (i) > 7 years of simulations were performed for irrigated and > 15 years for rainfed sites, (ii) > 40% of nationally harvested area was within 100 km of all simulation sites, (iii) observed weather data coupled with satellite derived solar radiation data were used in simulations, and (iv) planting and harvesting dates were specified within +/- 7 days of farmers actual practices. These are much higher standards than have been applied in national estimates of Yp and Yw and this protocol is a substantial step in making such estimates more transparent, robust, and straightforward. Finally, this protocol may be a useful tool for understanding yield trends and directing

A Comparison of Machine Learning Approaches for Corn Yield Estimation

NASA Astrophysics Data System (ADS)

Kim, N.; Lee, Y. W.

2017-12-01

Machine learning is an efficient empirical method for classification and prediction, and it is another approach to crop yield estimation. The objective of this study is to estimate corn yield in the Midwestern United States by employing the machine learning approaches such as the support vector machine (SVM), random forest (RF), and deep neural networks (DNN), and to perform the comprehensive comparison for their results. We constructed the database using satellite images from MODIS, the climate data of PRISM climate group, and GLDAS soil moisture data. In addition, to examine the seasonal sensitivities of corn yields, two period groups were set up: May to September (MJJAS) and July and August (JA). In overall, the DNN showed the highest accuracies in term of the correlation coefficient for the two period groups. The differences between our predictions and USDA yield statistics were about 10-11 %.

REML/BLUP and sequential path analysis in estimating genotypic values and interrelationships among simple maize grain yield-related traits.

PubMed

Olivoto, T; Nardino, M; Carvalho, I R; Follmann, D N; Ferrari, M; Szareski, V J; de Pelegrin, A J; de Souza, V Q

2017-03-22

Methodologies using restricted maximum likelihood/best linear unbiased prediction (REML/BLUP) in combination with sequential path analysis in maize are still limited in the literature. Therefore, the aims of this study were: i) to use REML/BLUP-based procedures in order to estimate variance components, genetic parameters, and genotypic values of simple maize hybrids, and ii) to fit stepwise regressions considering genotypic values to form a path diagram with multi-order predictors and minimum multicollinearity that explains the relationships of cause and effect among grain yield-related traits. Fifteen commercial simple maize hybrids were evaluated in multi-environment trials in a randomized complete block design with four replications. The environmental variance (78.80%) and genotype-vs-environment variance (20.83%) accounted for more than 99% of the phenotypic variance of grain yield, which difficult the direct selection of breeders for this trait. The sequential path analysis model allowed the selection of traits with high explanatory power and minimum multicollinearity, resulting in models with elevated fit (R 2 > 0.9 and ε < 0.3). The number of kernels per ear (NKE) and thousand-kernel weight (TKW) are the traits with the largest direct effects on grain yield (r = 0.66 and 0.73, respectively). The high accuracy of selection (0.86 and 0.89) associated with the high heritability of the average (0.732 and 0.794) for NKE and TKW, respectively, indicated good reliability and prospects of success in the indirect selection of hybrids with high-yield potential through these traits. The negative direct effect of NKE on TKW (r = -0.856), however, must be considered. The joint use of mixed models and sequential path analysis is effective in the evaluation of maize-breeding trials.

Entropic uncertainty relations and locking: Tight bounds for mutually unbiased bases

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ballester, Manuel A.; Wehner, Stephanie

We prove tight entropic uncertainty relations for a large number of mutually unbiased measurements. In particular, we show that a bound derived from the result by Maassen and Uffink [Phys. Rev. Lett. 60, 1103 (1988)] for two such measurements can in fact be tight for up to {radical}(d) measurements in mutually unbiased bases. We then show that using more mutually unbiased bases does not always lead to a better locking effect. We prove that the optimal bound for the accessible information using up to {radical}(d) specific mutually unbiased bases is log d/2, which is the same as can be achievedmore » by using only two bases. Our result indicates that merely using mutually unbiased bases is not sufficient to achieve a strong locking effect and we need to look for additional properties.« less

Spatial variability effects on precision and power of forage yield estimation

USDA-ARS?s Scientific Manuscript database

Spatial analyses of yield trials are important, as they adjust cultivar means for spatial variation and improve the statistical precision of yield estimation. While the relative efficiency of spatial analysis has been frequently reported in several yield trials, its application on long-term forage y...

Linear unmixing of multidate hyperspectral imagery for crop yield estimation

USDA-ARS?s Scientific Manuscript database

In this paper, we have evaluated an unsupervised unmixing approach, vertex component analysis (VCA), for the application of crop yield estimation. The results show that abundance maps of the vegetation extracted by the approach are strongly correlated to the yield data (the correlation coefficients ...

Estimation of Rice Crop Yields Using Random Forests in Taiwan

NASA Astrophysics Data System (ADS)

Chen, C. F.; Lin, H. S.; Nguyen, S. T.; Chen, C. R.

2017-12-01

Rice is globally one of the most important food crops, directly feeding more people than any other crops. Rice is not only the most important commodity, but also plays a critical role in the economy of Taiwan because it provides employment and income for large rural populations. The rice harvested area and production are thus monitored yearly due to the government's initiatives. Agronomic planners need such information for more precise assessment of food production to tackle issues of national food security and policymaking. This study aimed to develop a machine-learning approach using physical parameters to estimate rice crop yields in Taiwan. We processed the data for 2014 cropping seasons, following three main steps: (1) data pre-processing to construct input layers, including soil types and weather parameters (e.g., maxima and minima air temperature, precipitation, and solar radiation) obtained from meteorological stations across the country; (2) crop yield estimation using the random forests owing to its merits as it can process thousands of variables, estimate missing data, maintain the accuracy level when a large proportion of the data is missing, overcome most of over-fitting problems, and run fast and efficiently when handling large datasets; and (3) error verification. To execute the model, we separated the datasets into two groups of pixels: group-1 (70% of pixels) for training the model and group-2 (30% of pixels) for testing the model. Once the model is trained to produce small and stable out-of-bag error (i.e., the mean squared error between predicted and actual values), it can be used for estimating rice yields of cropping seasons. The results obtained from the random forests-based regression were compared with the actual yield statistics indicated the values of root mean square error (RMSE) and mean absolute error (MAE) achieved for the first rice crop were respectively 6.2% and 2.7%, while those for the second rice crop were 5.3% and 2

Determination of the optimal level for combining area and yield estimates

NASA Technical Reports Server (NTRS)

Bauer, M. E. (Principal Investigator); Hixson, M. M.; Jobusch, C. D.

1981-01-01

Several levels of obtaining both area and yield estimates of corn and soybeans in Iowa were considered: county, refined strata, refined/split strata, crop reporting district, and state. Using the CCEA model form and smoothed weather data, regression coefficients at each level were derived to compute yield and its variance. Variances were also computed with stratum level. The variance of the yield estimates was largest at the state and smallest at the county level for both crops. The refined strata had somewhat larger variances than those associated with the refined/split strata and CRD. For production estimates, the difference in standard deviations among levels was not large for corn, but for soybeans the standard deviation at the state level was more than 50% greater than for the other levels. The refined strata had the smallest standard deviations. The county level was not considered in evaluation of production estimates due to lack of county area variances.

Classical and Bayesian Seismic Yield Estimation: The 1998 Indian and Pakistani Tests

NASA Astrophysics Data System (ADS)

Shumway, R. H.

2001-10-01

- The nuclear tests in May, 1998, in India and Pakistan have stimulated a renewed interest in yield estimation, based on limited data from uncalibrated test sites. We study here the problem of estimating yields using classical and Bayesian methods developed by Shumway (1992), utilizing calibration data from the Semipalatinsk test site and measured magnitudes for the 1998 Indian and Pakistani tests given by Murphy (1998). Calibration is done using multivariate classical or Bayesian linear regression, depending on the availability of measured magnitude-yield data and prior information. Confidence intervals for the classical approach are derived applying an extension of Fieller's method suggested by Brown (1982). In the case where prior information is available, the posterior predictive magnitude densities are inverted to give posterior intervals for yield. Intervals obtained using the joint distribution of magnitudes are comparable to the single-magnitude estimates produced by Murphy (1998) and reinforce the conclusion that the announced yields of the Indian and Pakistani tests were too high.

Classical and Bayesian Seismic Yield Estimation: The 1998 Indian and Pakistani Tests

NASA Astrophysics Data System (ADS)

Shumway, R. H.

The nuclear tests in May, 1998, in India and Pakistan have stimulated a renewed interest in yield estimation, based on limited data from uncalibrated test sites. We study here the problem of estimating yields using classical and Bayesian methods developed by Shumway (1992), utilizing calibration data from the Semipalatinsk test site and measured magnitudes for the 1998 Indian and Pakistani tests given by Murphy (1998). Calibration is done using multivariate classical or Bayesian linear regression, depending on the availability of measured magnitude-yield data and prior information. Confidence intervals for the classical approach are derived applying an extension of Fieller's method suggested by Brown (1982). In the case where prior information is available, the posterior predictive magnitude densities are inverted to give posterior intervals for yield. Intervals obtained using the joint distribution of magnitudes are comparable to the single-magnitude estimates produced by Murphy (1998) and reinforce the conclusion that the announced yields of the Indian and Pakistani tests were too high.

Yield estimation of corn based on multitemporal LANDSAT-TM data as input for an agrometeorological model

NASA Astrophysics Data System (ADS)

Bach, Heike

1998-07-01

In order to test remote sensing data with advanced yield formation models for accuracy and timeliness of yield estimation of corn, a project was conducted for the State Ministry for Rural Environment, Food, and Forestry of Baden-Württemberg (Germany). This project was carried out during the course of the `Special Yield Estimation', a regular procedure conducted for the European Union, to more accurately estimate agricultural yield. The methodology employed uses field-based plant parameter estimation from atmospherically corrected multitemporal/multispectral LANDSAT-TM data. An agrometeorological plant-production-model is used for yield prediction. Based solely on four LANDSAT-derived estimates (between May and August) and daily meteorological data, the grain yield of corn fields was determined for 1995. The modelled yields were compared with results gathered independently within the Special Yield Estimation for 23 test fields in the upper Rhine valley. The agreement between LANDSAT-based estimates (six weeks before harvest) and Special Yield Estimation (at harvest) shows a relative error of 2.3%. The comparison of the results for single fields shows that six weeks before harvest, the grain yield of corn was estimated with a mean relative accuracy of 13% using satellite information. The presented methodology can be transferred to other crops and geographical regions. For future applications hyperspectral sensors show great potential to further enhance the results for yield prediction with remote sensing.

Construction of mutually unbiased bases with cyclic symmetry for qubit systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Seyfarth, Ulrich; Ranade, Kedar S.

2011-10-15

For the complete estimation of arbitrary unknown quantum states by measurements, the use of mutually unbiased bases has been well established in theory and experiment for the past 20 years. However, most constructions of these bases make heavy use of abstract algebra and the mathematical theory of finite rings and fields, and no simple and generally accessible construction is available. This is particularly true in the case of a system composed of several qubits, which is arguably the most important case in quantum information science and quantum computation. In this paper, we close this gap by providing a simple andmore » straightforward method for the construction of mutually unbiased bases in the case of a qubit register. We show that our construction is also accessible to experiments, since only Hadamard and controlled-phase gates are needed, which are available in most practical realizations of a quantum computer. Moreover, our scheme possesses the optimal scaling possible, i.e., the number of gates scales only linearly in the number of qubits.« less

Allowable SEM noise for unbiased LER measurement

NASA Astrophysics Data System (ADS)

Papavieros, George; Constantoudis, Vassilios; Gogolides, Evangelos

2018-03-01

Recently, a novel method for the calculation of unbiased Line Edge Roughness based on Power Spectral Density analysis has been proposed. In this paper first an alternative method is discussed and investigated, utilizing the Height-Height Correlation Function (HHCF) of edges. The HHCF-based method enables the unbiased determination of the whole triplet of LER parameters including besides rms the correlation length and roughness exponent. The key of both methods is the sensitivity of PSD and HHCF on noise at high frequencies and short distance respectively. Secondly, we elaborate a testbed of synthesized SEM images with controlled LER and noise to justify the effectiveness of the proposed unbiased methods. Our main objective is to find out the boundaries of the method in respect to noise levels and roughness characteristics, for which the method remains reliable, i.e the maximum amount of noise allowed, for which the output results cope with the controllable known inputs. At the same time, we will also set the extremes of roughness parameters for which the methods hold their accuracy.

Estimated loads and yields of suspended soils and water-quality constituents in Kentucky streams

USGS Publications Warehouse

Crain, Angela S.

2001-01-01

Loads and yields of suspended solids, nutrients, major ions, trace elements, organic carbon, fecal coliform, dissolved oxygen, and alkalinity were estimated for 22 streams in 11 major river basins in Kentucky. Mean daily discharge was estimated at ungaged stations or stations with incomplete discharge records using drainage-area ratio, regression analysis, or a combination of the two techniques. Streamflow was partitioned into total and base flow and used to estimate loads and yields for suspended solids and water-quality constituents by use of the ESTIMATOR and FLUX computer programs. The relative magnitude of constituent transport to streams from groundand surface-water sources was determined for the 22 stations. Nutrient and suspended solids yields for drainage basins with relatively homogenous land use were used to estimate the total-flow and base-flow yields of nutrient and suspended solids for forested, agricultural, and urban land. Yields of nutrients?nitrite plus nitrate, ammonia plus organic nitrogen, and total phosphorus?in forested drainage basins were generally less than 1 ton per square mile per year ((ton/mi2)/yr) and were generally less than 2 (ton/mi2)/yr in agricultural drainage basins. The smallest total-flow yields for nitrogen (nitrite plus nitrate) was estimated at Levisa Fork at Paintsville in which 95 percent of the land is forested. This site also had one of the smallest total-flow yields for ammonia plus organic nitrogen. In general, nutrient yields from forested lands were lower than those from urban and agricultural land. Some of the largest estimated total-flow yields of nutrients among agricultural basins were for streams in the Licking River Basin, the North Fork Licking River near Milford, and the South Fork Licking River at Cynthiana. Agricultural land constitutes greater than 75 percent of the drainage area in these two basins. Possible sources of nutrients discharging into the Licking River are farm and residential fertilizers

FAST TRACK COMMUNICATION: Affine constellations without mutually unbiased counterparts

NASA Astrophysics Data System (ADS)

Weigert, Stefan; Durt, Thomas

2010-10-01

It has been conjectured that a complete set of mutually unbiased bases in a space of dimension d exists if and only if there is an affine plane of order d. We introduce affine constellations and compare their existence properties with those of mutually unbiased constellations. The observed discrepancies make a deeper relation between the two existence problems unlikely.

Estimation of the simple correlation coefficient.

PubMed

Shieh, Gwowen

2010-11-01

This article investigates some unfamiliar properties of the Pearson product-moment correlation coefficient for the estimation of simple correlation coefficient. Although Pearson's r is biased, except for limited situations, and the minimum variance unbiased estimator has been proposed in the literature, researchers routinely employ the sample correlation coefficient in their practical applications, because of its simplicity and popularity. In order to support such practice, this study examines the mean squared errors of r and several prominent formulas. The results reveal specific situations in which the sample correlation coefficient performs better than the unbiased and nearly unbiased estimators, facilitating recommendation of r as an effect size index for the strength of linear association between two variables. In addition, related issues of estimating the squared simple correlation coefficient are also considered.

A statistical test of unbiased evolution of body size in birds.

PubMed

Bokma, Folmer

2002-12-01

Of the approximately 9500 bird species, the vast majority is small-bodied. That is a general feature of evolutionary lineages, also observed for instance in mammals and plants. The avian interspecific body size distribution is right-skewed even on a logarithmic scale. That has previously been interpreted as evidence that body size evolution has been biased. However, a procedure to test for unbiased evolution from the shape of body size distributions was lacking. In the present paper unbiased body size evolution is defined precisely, and a statistical test is developed based on Monte Carlo simulation of unbiased evolution. Application of the test to birds suggests that it is highly unlikely that avian body size evolution has been unbiased as defined. Several possible explanations for this result are discussed. A plausible explanation is that the general model of unbiased evolution assumes that population size and generation time do not affect the evolutionary variability of body size; that is, that micro- and macroevolution are decoupled, which theory suggests is not likely to be the case.

Toward unbiased estimations of the statefinder parameters

NASA Astrophysics Data System (ADS)

Aviles, Alejandro; Klapp, Jaime; Luongo, Orlando

2017-09-01

With the use of simulated supernova catalogs, we show that the statefinder parameters turn out to be poorly and biased estimated by standard cosmography. To this end, we compute their standard deviations and several bias statistics on cosmologies near the concordance model, demonstrating that these are very large, making standard cosmography unsuitable for future and wider compilations of data. To overcome this issue, we propose a new method that consists in introducing the series of the Hubble function into the luminosity distance, instead of considering the usual direct Taylor expansions of the luminosity distance. Moreover, in order to speed up the numerical computations, we estimate the coefficients of our expansions in a hierarchical manner, in which the order of the expansion depends on the redshift of every single piece of data. In addition, we propose two hybrids methods that incorporates standard cosmography at low redshifts. The methods presented here perform better than the standard approach of cosmography both in the errors and bias of the estimated statefinders. We further propose a one-parameter diagnostic to reject non-viable methods in cosmography.

Regional crop gross primary production and yield estimation using fused Landsat-MODIS data

NASA Astrophysics Data System (ADS)

He, M.; Kimball, J. S.; Maneta, M. P.; Maxwell, B. D.; Moreno, A.

2017-12-01

Accurate crop yield assessments using satellite-based remote sensing are of interest for the design of regional policies that promote agricultural resiliency and food security. However, the application of current vegetation productivity algorithms derived from global satellite observations are generally too coarse to capture cropland heterogeneity. Merging information from sensors with reciprocal spatial and temporal resolution can improve the accuracy of these retrievals. In this study, we estimate annual crop yields for seven important crop types -alfalfa, barley, corn, durum wheat, peas, spring wheat and winter wheat over Montana, United States (U.S.) from 2008 to 2015. Yields are estimated as the product of gross primary production (GPP) and a crop-specific harvest index (HI) at 30 m spatial resolution. To calculate GPP we used a modified form of the MOD17 LUE algorithm driven by a 30 m 8-day fused NDVI dataset constructed by blending Landsat (5 or 7) and MODIS Terra reflectance data. The fused 30-m NDVI record shows good consistency with the original Landsat and MODIS data, but provides better spatiotemporal information on cropland vegetation growth. The resulting GPP estimates capture characteristic cropland patterns and seasonal variations, while the estimated annual 30 m crop yield results correspond favorably with county-level crop yield data (r=0.96, p<0.05). The estimated crop yield performance was generally lower, but still favorable in relation to field-scale crop yield surveys (r=0.42, p<0.01). Our methods and results are suitable for operational applications at regional scales.

Model-assisted forest yield estimation with light detection and ranging

Treesearch

Jacob L. Strunk; Stephen E. Reutebuch; Hans-Erik Andersen; Peter J. Gould; Robert J. McGaughey

2012-01-01

Previous studies have demonstrated that light detection and ranging (LiDAR)-derived variables can be used to model forest yield variables, such as biomass, volume, and number of stems. However, the next step is underrepresented in the literature: estimation of forest yield with appropriate confidence intervals. It is of great importance that the procedures required for...

Comparing population size estimators for plethodontid salamanders

USGS Publications Warehouse

Bailey, L.L.; Simons, T.R.; Pollock, K.H.

2004-01-01

Despite concern over amphibian declines, few studies estimate absolute abundances because of logistic and economic constraints and previously poor estimator performance. Two estimation approaches recommended for amphibian studies are mark-recapture and depletion (or removal) sampling. We compared abundance estimation via various mark-recapture and depletion methods, using data from a three-year study of terrestrial salamanders in Great Smoky Mountains National Park. Our results indicate that short-term closed-population, robust design, and depletion methods estimate surface population of salamanders (i.e., those near the surface and available for capture during a given sampling occasion). In longer duration studies, temporary emigration violates assumptions of both open- and closed-population mark-recapture estimation models. However, if the temporary emigration is completely random, these models should yield unbiased estimates of the total population (superpopulation) of salamanders in the sampled area. We recommend using Pollock's robust design in mark-recapture studies because of its flexibility to incorporate variation in capture probabilities and to estimate temporary emigration probabilities.

Exoplanet Yield Estimation for Decadal Study Concepts using EXOSIMS

NASA Astrophysics Data System (ADS)

Morgan, Rhonda; Lowrance, Patrick; Savransky, Dmitry; Garrett, Daniel

2016-01-01

The anticipated upcoming large mission study concepts for the direct imaging of exo-earths present an exciting opportunity for exoplanet discovery and characterization. While these telescope concepts would also be capable of conducting a broad range of astrophysical investigations, the most difficult technology challenges are driven by the requirements for imaging exo-earths. The exoplanet science yield for these mission concepts will drive design trades and mission concept comparisons.To assist in these trade studies, the Exoplanet Exploration Program Office (ExEP) is developing a yield estimation tool that emphasizes transparency and consistent comparison of various design concepts. The tool will provide a parametric estimate of science yield of various mission concepts using contrast curves from physics-based model codes and Monte Carlo simulations of design reference missions using realistic constraints, such as solar avoidance angles, the observatory orbit, propulsion limitations of star shades, the accessibility of candidate targets, local and background zodiacal light levels, and background confusion by stars and galaxies. The python tool utilizes Dmitry Savransky's EXOSIMS (Exoplanet Open-Source Imaging Mission Simulator) design reference mission simulator that is being developed for the WFIRST Preliminary Science program. ExEP is extending and validating the tool for future mission concepts under consideration for the upcoming 2020 decadal review. We present a validation plan and preliminary yield results for a point design.

Mutually unbiased projectors and duality between lines and bases in finite quantum systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shalaby, M.; Vourdas, A., E-mail: a.vourdas@bradford.ac.uk

2013-10-15

Quantum systems with variables in the ring Z(d) are considered, and the concepts of weak mutually unbiased bases and mutually unbiased projectors are discussed. The lines through the origin in the Z(d)×Z(d) phase space, are classified into maximal lines (sets of d points), and sublines (sets of d{sub i} points where d{sub i}|d). The sublines are intersections of maximal lines. It is shown that there exists a duality between the properties of lines (resp., sublines), and the properties of weak mutually unbiased bases (resp., mutually unbiased projectors). -- Highlights: •Lines in discrete phase space. •Bases in finite quantum systems. •Dualitymore » between bases and lines. •Weak mutually unbiased bases.« less

Field design factors affecting the precision of ryegrass forage yield estimation

USDA-ARS?s Scientific Manuscript database

Field-based agronomic and genetic research relies heavily on the data generated from field evaluations. Therefore, it is imperative to optimize the precision and accuracy of yield estimates in cultivar evaluation trials to make reliable selections. Experimental error in yield trials is sensitive to ...

Double sampling to estimate density and population trends in birds

USGS Publications Warehouse

Bart, Jonathan; Earnst, Susan L.

2002-01-01

We present a method for estimating density of nesting birds based on double sampling. The approach involves surveying a large sample of plots using a rapid method such as uncorrected point counts, variable circular plot counts, or the recently suggested double-observer method. A subsample of those plots is also surveyed using intensive methods to determine actual density. The ratio of the mean count on those plots (using the rapid method) to the mean actual density (as determined by the intensive searches) is used to adjust results from the rapid method. The approach works well when results from the rapid method are highly correlated with actual density. We illustrate the method with three years of shorebird surveys from the tundra in northern Alaska. In the rapid method, surveyors covered ~10 ha h-1 and surveyed each plot a single time. The intensive surveys involved three thorough searches, required ~3 h ha-1, and took 20% of the study effort. Surveyors using the rapid method detected an average of 79% of birds present. That detection ratio was used to convert the index obtained in the rapid method into an essentially unbiased estimate of density. Trends estimated from several years of data would also be essentially unbiased. Other advantages of double sampling are that (1) the rapid method can be changed as new methods become available, (2) domains can be compared even if detection rates differ, (3) total population size can be estimated, and (4) valuable ancillary information (e.g. nest success) can be obtained on intensive plots with little additional effort. We suggest that double sampling be used to test the assumption that rapid methods, such as variable circular plot and double-observer methods, yield density estimates that are essentially unbiased. The feasibility of implementing double sampling in a range of habitats needs to be evaluated.

The influence of SO4 and NO3 to the acidity (pH) of rainwater using minimum variance quadratic unbiased estimation (MIVQUE) and maximum likelihood methods

NASA Astrophysics Data System (ADS)

Dilla, Shintia Ulfa; Andriyana, Yudhie; Sudartianto

2017-03-01

Acid rain causes many bad effects in life. It is formed by two strong acids, sulfuric acid (H2SO4) and nitric acid (HNO3), where sulfuric acid is derived from SO2 and nitric acid from NOx {x=1,2}. The purpose of the research is to find out the influence of So4 and NO3 levels contained in the rain to the acidity (pH) of rainwater. The data are incomplete panel data with two-way error component model. The panel data is a collection of some of the observations that observed from time to time. It is said incomplete if each individual has a different amount of observation. The model used in this research is in the form of random effects model (REM). Minimum variance quadratic unbiased estimation (MIVQUE) is used to estimate the variance error components, while maximum likelihood estimation is used to estimate the parameters. As a result, we obtain the following model: Ŷ* = 0.41276446 - 0.00107302X1 + 0.00215470X2.

A UNIFIED FRAMEWORK FOR VARIANCE COMPONENT ESTIMATION WITH SUMMARY STATISTICS IN GENOME-WIDE ASSOCIATION STUDIES.

PubMed

Zhou, Xiang

2017-12-01

Linear mixed models (LMMs) are among the most commonly used tools for genetic association studies. However, the standard method for estimating variance components in LMMs-the restricted maximum likelihood estimation method (REML)-suffers from several important drawbacks: REML requires individual-level genotypes and phenotypes from all samples in the study, is computationally slow, and produces downward-biased estimates in case control studies. To remedy these drawbacks, we present an alternative framework for variance component estimation, which we refer to as MQS. MQS is based on the method of moments (MoM) and the minimal norm quadratic unbiased estimation (MINQUE) criterion, and brings two seemingly unrelated methods-the renowned Haseman-Elston (HE) regression and the recent LD score regression (LDSC)-into the same unified statistical framework. With this new framework, we provide an alternative but mathematically equivalent form of HE that allows for the use of summary statistics. We provide an exact estimation form of LDSC to yield unbiased and statistically more efficient estimates. A key feature of our method is its ability to pair marginal z -scores computed using all samples with SNP correlation information computed using a small random subset of individuals (or individuals from a proper reference panel), while capable of producing estimates that can be almost as accurate as if both quantities are computed using the full data. As a result, our method produces unbiased and statistically efficient estimates, and makes use of summary statistics, while it is computationally efficient for large data sets. Using simulations and applications to 37 phenotypes from 8 real data sets, we illustrate the benefits of our method for estimating and partitioning SNP heritability in population studies as well as for heritability estimation in family studies. Our method is implemented in the GEMMA software package, freely available at www.xzlab.org/software.html.

Unbiased contaminant removal for 3D galaxy power spectrum measurements

NASA Astrophysics Data System (ADS)

Kalus, B.; Percival, W. J.; Bacon, D. J.; Samushia, L.

2016-11-01

We assess and develop techniques to remove contaminants when calculating the 3D galaxy power spectrum. We separate the process into three separate stages: (I) removing the contaminant signal, (II) estimating the uncontaminated cosmological power spectrum and (III) debiasing the resulting estimates. For (I), we show that removing the best-fitting contaminant (mode subtraction) and setting the contaminated components of the covariance to be infinite (mode deprojection) are mathematically equivalent. For (II), performing a quadratic maximum likelihood (QML) estimate after mode deprojection gives an optimal unbiased solution, although it requires the manipulation of large N_mode^2 matrices (Nmode being the total number of modes), which is unfeasible for recent 3D galaxy surveys. Measuring a binned average of the modes for (II) as proposed by Feldman, Kaiser & Peacock (FKP) is faster and simpler, but is sub-optimal and gives rise to a biased solution. We present a method to debias the resulting FKP measurements that does not require any large matrix calculations. We argue that the sub-optimality of the FKP estimator compared with the QML estimator, caused by contaminants, is less severe than that commonly ignored due to the survey window.

Graphical user interface for yield and dose estimations for cyclotron-produced technetium

NASA Astrophysics Data System (ADS)

Hou, X.; Vuckovic, M.; Buckley, K.; Bénard, F.; Schaffer, P.; Ruth, T.; Celler, A.

2014-07-01

The cyclotron-based 100Mo(p,2n)99mTc reaction has been proposed as an alternative method for solving the shortage of 99mTc. With this production method, however, even if highly enriched molybdenum is used, various radioactive and stable isotopes will be produced simultaneously with 99mTc. In order to optimize reaction parameters and estimate potential patient doses from radiotracers labeled with cyclotron produced 99mTc, the yields for all reaction products must be estimated. Such calculations, however, are extremely complex and time consuming. Therefore, the objective of this study was to design a graphical user interface (GUI) that would automate these calculations, facilitate analysis of the experimental data, and predict dosimetry. The resulting GUI, named Cyclotron production Yields and Dosimetry (CYD), is based on Matlab®. It has three parts providing (a) reaction yield calculations, (b) predictions of gamma emissions and (c) dosimetry estimations. The paper presents the outline of the GUI, lists the parameters that must be provided by the user, discusses the details of calculations and provides examples of the results. Our initial experience shows that the proposed GUI allows the user to very efficiently calculate the yields of reaction products and analyze gamma spectroscopy data. However, it is expected that the main advantage of this GUI will be at the later clinical stage when entering reaction parameters will allow the user to predict production yields and estimate radiation doses to patients for each particular cyclotron run.

Graphical user interface for yield and dose estimations for cyclotron-produced technetium.

PubMed

Hou, X; Vuckovic, M; Buckley, K; Bénard, F; Schaffer, P; Ruth, T; Celler, A

2014-07-07

The cyclotron-based (100)Mo(p,2n)(99m)Tc reaction has been proposed as an alternative method for solving the shortage of (99m)Tc. With this production method, however, even if highly enriched molybdenum is used, various radioactive and stable isotopes will be produced simultaneously with (99m)Tc. In order to optimize reaction parameters and estimate potential patient doses from radiotracers labeled with cyclotron produced (99m)Tc, the yields for all reaction products must be estimated. Such calculations, however, are extremely complex and time consuming. Therefore, the objective of this study was to design a graphical user interface (GUI) that would automate these calculations, facilitate analysis of the experimental data, and predict dosimetry. The resulting GUI, named Cyclotron production Yields and Dosimetry (CYD), is based on Matlab®. It has three parts providing (a) reaction yield calculations, (b) predictions of gamma emissions and (c) dosimetry estimations. The paper presents the outline of the GUI, lists the parameters that must be provided by the user, discusses the details of calculations and provides examples of the results. Our initial experience shows that the proposed GUI allows the user to very efficiently calculate the yields of reaction products and analyze gamma spectroscopy data. However, it is expected that the main advantage of this GUI will be at the later clinical stage when entering reaction parameters will allow the user to predict production yields and estimate radiation doses to patients for each particular cyclotron run.

Estimating yield gaps at the cropping system level.

PubMed

Guilpart, Nicolas; Grassini, Patricio; Sadras, Victor O; Timsina, Jagadish; Cassman, Kenneth G

2017-05-01

Yield gap analyses of individual crops have been used to estimate opportunities for increasing crop production at local to global scales, thus providing information crucial to food security. However, increases in crop production can also be achieved by improving cropping system yield through modification of spatial and temporal arrangement of individual crops. In this paper we define the cropping system yield potential as the output from the combination of crops that gives the highest energy yield per unit of land and time, and the cropping system yield gap as the difference between actual energy yield of an existing cropping system and the cropping system yield potential. Then, we provide a framework to identify alternative cropping systems which can be evaluated against the current ones. A proof-of-concept is provided with irrigated rice-maize systems at four locations in Bangladesh that represent a range of climatic conditions in that country. The proposed framework identified (i) realistic alternative cropping systems at each location, and (ii) two locations where expected improvements in crop production from changes in cropping intensity (number of crops per year) were 43% to 64% higher than from improving the management of individual crops within the current cropping systems. The proposed framework provides a tool to help assess food production capacity of new systems ( e.g. with increased cropping intensity) arising from climate change, and assess resource requirements (water and N) and associated environmental footprint per unit of land and production of these new systems. By expanding yield gap analysis from individual crops to the cropping system level and applying it to new systems, this framework could also be helpful to bridge the gap between yield gap analysis and cropping/farming system design.

Factors Affecting Firm Yield and the Estimation of Firm Yield for Selected Streamflow-Dominated Drinking-Water-Supply Reservoirs in Massachusetts

USGS Publications Warehouse

Waldron, Marcus C.; Archfield, Stacey A.

2006-01-01

Factors affecting reservoir firm yield, as determined by application of the Massachusetts Department of Environmental Protection's Firm Yield Estimator (FYE) model, were evaluated, modified, and tested on 46 streamflow-dominated reservoirs representing 15 Massachusetts drinking-water supplies. The model uses a mass-balance approach to determine the maximum average daily withdrawal rate that can be sustained during a period of record that includes the 1960s drought-of-record. The FYE methodology to estimate streamflow to the reservoir at an ungaged site was tested by simulating streamflow at two streamflow-gaging stations in Massachusetts and comparing the simulated streamflow to the observed streamflow. In general, the FYE-simulated flows agreed well with observed flows. There were substantial deviations from the measured values for extreme high and low flows. A sensitivity analysis determined that the model's streamflow estimates are most sensitive to input values for average annual precipitation, reservoir drainage area, and the soil-retention number-a term that describes the amount of precipitation retained by the soil in the basin. The FYE model currently provides the option of using a 1,000-year synthetic record constructed by randomly sampling 2-year blocks of concurrent streamflow and precipitation records 500 times; however, the synthetic record has the potential to generate records of precipitation and streamflow that do not reflect the worst historical drought in Massachusetts. For reservoirs that do not have periods of drawdown greater than 2 years, the bootstrap does not offer any additional information about the firm yield of a reservoir than the historical record does. For some reservoirs, the use of a synthetic record to determine firm yield resulted in as much as a 30-percent difference between firm-yield values from one simulation to the next. Furthermore, the assumption that the synthetic traces of streamflow are statistically equivalent to the

Study on paddy rice yield estimation based on multisource data and the Grey system theory

NASA Astrophysics Data System (ADS)

Deng, Wensheng; Wang, Wei; Liu, Hai; Li, Chen; Ge, Yimin; Zheng, Xianghua

2009-10-01

The paddy rice is our important crops. In study of the paddy rice yield estimation, compared with the scholars who usually only take the remote sensing data or meteorology as the influence factors, we combine the remote sensing and the meteorological data to make the monitoring result closer reality. Although the gray system theory has used in many aspects, it is applied very little in paddy rice yield estimation. This study introduces it to the paddy rice yield estimation, and makes the yield estimation model. This can resolve small data sets problem that can not be solved by deterministic model. It selects some regions in Jianghan plain for the study area. The data includes multi-temporal remote sensing image, meteorological and statistic data. The remote sensing data is the 16-day composite images (250-m spatial resolution) of MODIS. The meteorological data includes monthly average temperature, sunshine duration and rain fall amount. The statistical data is the long-term paddy rice yield of the study area. Firstly, it extracts the paddy rice planting area from the multi-temporal MODIS images with the help of GIS and RS. Then taking the paddy rice yield as the reference sequence, MODIS data and meteorological data as the comparative sequence, computing the gray correlative coefficient, it selects the yield estimation factor based on the grey system theory. Finally, using the factors, it establishes the yield estimation model and does the result test. The result indicated that the method is feasible and the conclusion is credible. It can provide the scientific method and reference value to carry on the region paddy rice remote sensing estimation.

Effects of social organization, trap arrangement and density, sampling scale, and population density on bias in population size estimation using some common mark-recapture estimators.

PubMed

Gupta, Manan; Joshi, Amitabh; Vidya, T N C

2017-01-01

Mark-recapture estimators are commonly used for population size estimation, and typically yield unbiased estimates for most solitary species with low to moderate home range sizes. However, these methods assume independence of captures among individuals, an assumption that is clearly violated in social species that show fission-fusion dynamics, such as the Asian elephant. In the specific case of Asian elephants, doubts have been raised about the accuracy of population size estimates. More importantly, the potential problem for the use of mark-recapture methods posed by social organization in general has not been systematically addressed. We developed an individual-based simulation framework to systematically examine the potential effects of type of social organization, as well as other factors such as trap density and arrangement, spatial scale of sampling, and population density, on bias in population sizes estimated by POPAN, Robust Design, and Robust Design with detection heterogeneity. In the present study, we ran simulations with biological, demographic and ecological parameters relevant to Asian elephant populations, but the simulation framework is easily extended to address questions relevant to other social species. We collected capture history data from the simulations, and used those data to test for bias in population size estimation. Social organization significantly affected bias in most analyses, but the effect sizes were variable, depending on other factors. Social organization tended to introduce large bias when trap arrangement was uniform and sampling effort was low. POPAN clearly outperformed the two Robust Design models we tested, yielding close to zero bias if traps were arranged at random in the study area, and when population density and trap density were not too low. Social organization did not have a major effect on bias for these parameter combinations at which POPAN gave more or less unbiased population size estimates. Therefore, the

Effects of social organization, trap arrangement and density, sampling scale, and population density on bias in population size estimation using some common mark-recapture estimators

PubMed Central

Joshi, Amitabh; Vidya, T. N. C.

2017-01-01

Mark-recapture estimators are commonly used for population size estimation, and typically yield unbiased estimates for most solitary species with low to moderate home range sizes. However, these methods assume independence of captures among individuals, an assumption that is clearly violated in social species that show fission-fusion dynamics, such as the Asian elephant. In the specific case of Asian elephants, doubts have been raised about the accuracy of population size estimates. More importantly, the potential problem for the use of mark-recapture methods posed by social organization in general has not been systematically addressed. We developed an individual-based simulation framework to systematically examine the potential effects of type of social organization, as well as other factors such as trap density and arrangement, spatial scale of sampling, and population density, on bias in population sizes estimated by POPAN, Robust Design, and Robust Design with detection heterogeneity. In the present study, we ran simulations with biological, demographic and ecological parameters relevant to Asian elephant populations, but the simulation framework is easily extended to address questions relevant to other social species. We collected capture history data from the simulations, and used those data to test for bias in population size estimation. Social organization significantly affected bias in most analyses, but the effect sizes were variable, depending on other factors. Social organization tended to introduce large bias when trap arrangement was uniform and sampling effort was low. POPAN clearly outperformed the two Robust Design models we tested, yielding close to zero bias if traps were arranged at random in the study area, and when population density and trap density were not too low. Social organization did not have a major effect on bias for these parameter combinations at which POPAN gave more or less unbiased population size estimates. Therefore, the

Empirical best linear unbiased prediction method for small areas with restricted maximum likelihood and bootstrap procedure to estimate the average of household expenditure per capita in Banjar Regency

NASA Astrophysics Data System (ADS)

Aminah, Agustin Siti; Pawitan, Gandhi; Tantular, Bertho

2017-03-01

So far, most of the data published by Statistics Indonesia (BPS) as data providers for national statistics are still limited to the district level. Less sufficient sample size for smaller area levels to make the measurement of poverty indicators with direct estimation produced high standard error. Therefore, the analysis based on it is unreliable. To solve this problem, the estimation method which can provide a better accuracy by combining survey data and other auxiliary data is required. One method often used for the estimation is the Small Area Estimation (SAE). There are many methods used in SAE, one of them is Empirical Best Linear Unbiased Prediction (EBLUP). EBLUP method of maximum likelihood (ML) procedures does not consider the loss of degrees of freedom due to estimating β with β ^. This drawback motivates the use of the restricted maximum likelihood (REML) procedure. This paper proposed EBLUP with REML procedure for estimating poverty indicators by modeling the average of household expenditures per capita and implemented bootstrap procedure to calculate MSE (Mean Square Error) to compare the accuracy EBLUP method with the direct estimation method. Results show that EBLUP method reduced MSE in small area estimation.

Estimating rice yield from MODIS-Landsat fusion data in Taiwan

NASA Astrophysics Data System (ADS)

Chen, C. R.; Chen, C. F.; Nguyen, S. T.

2017-12-01

Rice production monitoring with remote sensing is an important activity in Taiwan due to official initiatives. Yield estimation is a challenge in Taiwan because rice fields are small and fragmental. High spatiotemporal satellite data providing phenological information of rice crops is thus required for this monitoring purpose. This research aims to develop data fusion approaches to integrate daily Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat data for rice yield estimation in Taiwan. In this study, the low-resolution MODIS LST and emissivity data are used as reference data sources to obtain the high-resolution LST from Landsat data using the mixed-pixel analysis technique, and the time-series EVI data were derived the fusion of MODIS and Landsat spectral band data using STARFM method. The LST and EVI simulated results showed the close agreement between the LST and EVI obtained by the proposed methods with the reference data. The rice-yield model was established using EVI and LST data based on information of rice crop phenology collected from 371 ground survey sites across the country in 2014. The results achieved from the fusion datasets compared with the reference data indicated the close relationship between the two datasets with the correlation coefficient (R2) of 0.75 and root mean square error (RMSE) of 338.7 kgs, which were more accurate than those using the coarse-resolution MODIS LST data (R2 = 0.71 and RMSE = 623.82 kgs). For the comparison of total production, 64 towns located in the west part of Taiwan were used. The results also confirmed that the model using fusion datasets produced more accurate results (R2 = 0.95 and RMSE = 1,243 tons) than that using the course-resolution MODIS data (R2 = 0.91 and RMSE = 1,749 tons). This study demonstrates the application of MODIS-Landsat fusion data for rice yield estimation at the township level in Taiwan. The results obtained from the methods used in this study could be useful to policymakers

Estimating Latent Variable Interactions With Non-Normal Observed Data: A Comparison of Four Approaches

PubMed Central

Cham, Heining; West, Stephen G.; Ma, Yue; Aiken, Leona S.

2012-01-01

A Monte Carlo simulation was conducted to investigate the robustness of four latent variable interaction modeling approaches (Constrained Product Indicator [CPI], Generalized Appended Product Indicator [GAPI], Unconstrained Product Indicator [UPI], and Latent Moderated Structural Equations [LMS]) under high degrees of non-normality of the observed exogenous variables. Results showed that the CPI and LMS approaches yielded biased estimates of the interaction effect when the exogenous variables were highly non-normal. When the violation of non-normality was not severe (normal; symmetric with excess kurtosis < 1), the LMS approach yielded the most efficient estimates of the latent interaction effect with the highest statistical power. In highly non-normal conditions, the GAPI and UPI approaches with ML estimation yielded unbiased latent interaction effect estimates, with acceptable actual Type-I error rates for both the Wald and likelihood ratio tests of interaction effect at N ≥ 500. An empirical example illustrated the use of the four approaches in testing a latent variable interaction between academic self-efficacy and positive family role models in the prediction of academic performance. PMID:23457417

Added-values of high spatiotemporal remote sensing data in crop yield estimation

NASA Astrophysics Data System (ADS)

Gao, F.; Anderson, M. C.

2017-12-01

Timely and accurate estimation of crop yield before harvest is critical for food market and administrative planning. Remote sensing derived parameters have been used for estimating crop yield by using either empirical or crop growth models. The uses of remote sensing vegetation index (VI) in crop yield modeling have been typically evaluated at regional and country scales using coarse spatial resolution (a few hundred to kilo-meters) data or assessed over a small region at field level using moderate resolution spatial resolution data (10-100m). Both data sources have shown great potential in capturing spatial and temporal variability in crop yield. However, the added value of data with both high spatial and temporal resolution data has not been evaluated due to the lack of such data source with routine, global coverage. In recent years, more moderate resolution data have become freely available and data fusion approaches that combine data acquired from different spatial and temporal resolutions have been developed. These make the monitoring crop condition and estimating crop yield at field scale become possible. Here we investigate the added value of the high spatial and temporal VI for describing variability of crop yield. The explanatory ability of crop yield based on high spatial and temporal resolution remote sensing data was evaluated in a rain-fed agricultural area in the U.S. Corn Belt. Results show that the fused Landsat-MODIS (high spatial and temporal) VI explains yield variability better than single data source (Landsat or MODIS alone), with EVI2 performing slightly better than NDVI. The maximum VI describes yield variability better than cumulative VI. Even though VI is effective in explaining yield variability within season, the inter-annual variability is more complex and need additional information (e.g. weather, water use and management). Our findings augment the importance of high spatiotemporal remote sensing data and supports new moderate

Waveform inversion of acoustic waves for explosion yield estimation

DOE PAGES

Kim, K.; Rodgers, A. J.

2016-07-08

We present a new waveform inversion technique to estimate the energy of near-surface explosions using atmospheric acoustic waves. Conventional methods often employ air blast models based on a homogeneous atmosphere, where the acoustic wave propagation effects (e.g., refraction and diffraction) are not taken into account, and therefore, their accuracy decreases with increasing source-receiver distance. In this study, three-dimensional acoustic simulations are performed with a finite difference method in realistic atmospheres and topography, and the modeled acoustic Green's functions are incorporated into the waveform inversion for the acoustic source time functions. The strength of the acoustic source is related to explosionmore » yield based on a standard air blast model. The technique was applied to local explosions (<10 km) and provided reasonable yield estimates (<~30% error) in the presence of realistic topography and atmospheric structure. In conclusion, the presented method can be extended to explosions recorded at far distance provided proper meteorological specifications.« less

Waveform inversion of acoustic waves for explosion yield estimation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, K.; Rodgers, A. J.

We present a new waveform inversion technique to estimate the energy of near-surface explosions using atmospheric acoustic waves. Conventional methods often employ air blast models based on a homogeneous atmosphere, where the acoustic wave propagation effects (e.g., refraction and diffraction) are not taken into account, and therefore, their accuracy decreases with increasing source-receiver distance. In this study, three-dimensional acoustic simulations are performed with a finite difference method in realistic atmospheres and topography, and the modeled acoustic Green's functions are incorporated into the waveform inversion for the acoustic source time functions. The strength of the acoustic source is related to explosionmore » yield based on a standard air blast model. The technique was applied to local explosions (<10 km) and provided reasonable yield estimates (<~30% error) in the presence of realistic topography and atmospheric structure. In conclusion, the presented method can be extended to explosions recorded at far distance provided proper meteorological specifications.« less

Effect of Anisotropic Yield Function Evolution on Estimation of Forming Limit Diagram

NASA Astrophysics Data System (ADS)

Bandyopadhyay, K.; Basak, S.; Choi, H. J.; Panda, S. K.; Lee, M. G.

2017-09-01

In case of theoretical prediction of the FLD, the variations in yield stress and R-values along different material directions, were long been implemented to enhance the accuracy. Although influences of different yield models and hardening laws on formability were well addressed, anisotropic evolution of yield loci under monotonic loading with different deformation modes is yet to be explored. In the present study, Marciniak-Kuckzinsky (M-K) model was modified to incorporate the change in the shape of the initial yield function with evolution due to anisotropic hardening. Swift’s hardening law along with two different anisotropic yield criteria, namely Hill48 and Yld2000-2d were implemented in the model. The Hill48 yield model was applied with non-associated flow rule to comprehend the effect of variations in both yield stress and R-values. The numerically estimated FLDs were validated after comparing with FLD evaluated through experiments. A low carbon steel was selected, and hemispherical punch stretching test was performed for FLD evaluation. Additionally, the numerically estimated FLDs were incorporated in FE simulations to predict limiting dome heights for validation purpose. Other formability performances like strain distributions over the deformed cup surface were validated with experimental results.

Aspects of mutually unbiased bases in odd-prime-power dimensions

NASA Astrophysics Data System (ADS)

Chaturvedi, S.

2002-04-01

We rephrase the Wootters-Fields construction [W. K. Wootters and B. C. Fields, Ann. Phys. 191, 363 (1989)] of a full set of mutually unbiased bases in a complex vector space of dimensions N=pr, where p is an odd prime, in terms of the character vectors of the cyclic group G of order p. This form may be useful in explicitly writing down mutually unbiased bases for N=pr.

A spectral-spatial-dynamic hierarchical Bayesian (SSD-HB) model for estimating soybean yield

NASA Astrophysics Data System (ADS)

Kazama, Yoriko; Kujirai, Toshihiro

2014-10-01

A method called a "spectral-spatial-dynamic hierarchical-Bayesian (SSD-HB) model," which can deal with many parameters (such as spectral and weather information all together) by reducing the occurrence of multicollinearity, is proposed. Experiments conducted on soybean yields in Brazil fields with a RapidEye satellite image indicate that the proposed SSD-HB model can predict soybean yield with a higher degree of accuracy than other estimation methods commonly used in remote-sensing applications. In the case of the SSD-HB model, the mean absolute error between estimated yield of the target area and actual yield is 0.28 t/ha, compared to 0.34 t/ha when conventional PLS regression was applied, showing the potential effectiveness of the proposed model.

Yield gap analyses to estimate attainable bovine milk yields and evaluate options to increase production in Ethiopia and India.

PubMed

Mayberry, Dianne; Ash, Andrew; Prestwidge, Di; Godde, Cécile M; Henderson, Ben; Duncan, Alan; Blummel, Michael; Ramana Reddy, Y; Herrero, Mario

2017-07-01

Livestock provides an important source of income and nourishment for around one billion rural households worldwide. Demand for livestock food products is increasing, especially in developing countries, and there are opportunities to increase production to meet local demand and increase farm incomes. Estimating the scale of livestock yield gaps and better understanding factors limiting current production will help to define the technological and investment needs in each livestock sector. The aim of this paper is to quantify livestock yield gaps and evaluate opportunities to increase dairy production in Sub-Saharan Africa and South Asia, using case studies from Ethiopia and India. We combined three different methods in our approach. Benchmarking and a frontier analysis were used to estimate attainable milk yields based on survey data. Household modelling was then used to simulate the effects of various interventions on dairy production and income. We tested interventions based on improved livestock nutrition and genetics in the extensive lowland grazing zone and highland mixed crop-livestock zones of Ethiopia, and the intensive irrigated and rainfed zones of India. Our analyses indicate that there are considerable yield gaps for dairy production in both countries, and opportunities to increase production using the interventions tested. In some cases, combined interventions could increase production past currently attainable livestock yields.

Estimating variability in grain legume yields across Europe and the Americas

NASA Astrophysics Data System (ADS)

Cernay, Charles; Ben-Ari, Tamara; Pelzer, Elise; Meynard, Jean-Marc; Makowski, David

2015-06-01

Grain legume production in Europe has recently come under scrutiny. Although legume crops are often promoted to provide environmental services, European farmers tend to turn to non-legume crops. It is assumed that high variability in legume yields explains this aversion, but so far this hypothesis has not been tested. Here, we estimate the variability of major grain legume and non-legume yields in Europe and the Americas from yield time series over 1961-2013. Results show that grain legume yields are significantly more variable than non-legume yields in Europe. These differences are smaller in the Americas. Our results are robust at the level of the statistical methods. In all regions, crops with high yield variability are allocated to less than 1% of cultivated areas. Although the expansion of grain legumes in Europe may be hindered by high yield variability, some species display risk levels compatible with the development of specialized supply chains.

Hierarchical Bayes estimation of species richness and occupancy in spatially replicated surveys

USGS Publications Warehouse

Kery, M.; Royle, J. Andrew

2008-01-01

1. Species richness is the most widely used biodiversity metric, but cannot be observed directly as, typically, some species are overlooked. Imperfect detectability must therefore be accounted for to obtain unbiased species-richness estimates. When richness is assessed at multiple sites, two approaches can be used to estimate species richness: either estimating for each site separately, or pooling all samples. The first approach produces imprecise estimates, while the second loses site-specific information. 2. In contrast, a hierarchical Bayes (HB) multispecies site-occupancy model benefits from the combination of information across sites without losing site-specific information and also yields occupancy estimates for each species. The heart of the model is an estimate of the incompletely observed presence-absence matrix, a centrepiece of biogeography and monitoring studies. We illustrate the model using Swiss breeding bird survey data, and compare its estimates with the widely used jackknife species-richness estimator and raw species counts. 3. Two independent observers each conducted three surveys in 26 1-km(2) quadrats, and detected 27-56 (total 103) species. The average estimated proportion of species detected after three surveys was 0.87 under the HB model. Jackknife estimates were less precise (less repeatable between observers) than raw counts, but HB estimates were as repeatable as raw counts. The combination of information in the HB model thus resulted in species-richness estimates presumably at least as unbiased as previous approaches that correct for detectability, but without costs in precision relative to uncorrected, biased species counts. 4. Total species richness in the entire region sampled was estimated at 113.1 (CI 106-123); species detectability ranged from 0.08 to 0.99, illustrating very heterogeneous species detectability; and species occupancy was 0.06-0.96. Even after six surveys, absolute bias in observed occupancy was estimated at up to 0

Sustainable-yield estimation for the Sparta Aquifer in Union County, Arkansas

USGS Publications Warehouse

Hays, Phillip D.

2000-01-01

Options for utilizing alternative sources of water to alleviate overdraft from the Sparta aquifer and ensure that the aquifer can continue to provide abundant water of excellent quality for the future are being evaluated by water managers in Union County. Sustainable yield is a critical element in identifying and designing viable water supply alternatives. With sustainable yield defined and a knowledge of total water demand in an area, any unmet demand can be calculated. The ground-water flow model of the Sparta aquifer was used to estimate sustainable yield using an iterative approach. The Sparta aquifer is a confined aquifer of regional importance that comprises a sequence of unconsolidated sand units that are contained within the Sparta Sand. Currently, the rate of withdrawal in some areas greatly exceeds the rate of recharge to the aquifer and considerable water-level declines have occurred. Ground-water flow model results indicate that the aquifer cannot continue to meet growing water-use demands indefinitely and that water levels will drop below the top of the primary producing sand unit in Union County (locally termed the El Dorado sand) by 2008 if current water-use trends continue. Declines of that magnitude will initiate dewatering of the El Dorado sand. The sustainable yield of the aquifer was calculated by targeting a specified minimum acceptable water level within Union County and varying Union County pumpage within the model to achieve the target water level. Selection of the minimum target water level for sustainable-yield estimation was an important criterion for the modeling effort. In keeping with the State Critical Ground-Water Area designation criteria and the desire of water managers in Union County to improve aquifer conditions and bring the area out of the Critical Ground-Water Area designation, the approximate altitude of the top of the Sparta Sand in central Union County was used as the minimum water level target for estimation of

Evaluating accuracy of DSSAT model for soybean yield estimation using satellite weather data

NASA Astrophysics Data System (ADS)

Ovando, Gustavo; Sayago, Silvina; Bocco, Mónica

2018-04-01

Crop models allow simulating the development and yield of the crops, to represent and to evaluate the influence of multiple factors. The DSSAT cropping system model is one of the most widely used and contains CROPGRO module for soybean. This crop has a great importance for many southern countries of Latin America and for Argentina. Solar radiation and rainfall are necessary variables as inputs for crop models; however these data are not as readily available. The satellital products from Clouds and Earth's Radiant Energy System (CERES) and Tropic Rainfall Measurement Mission (TRMM) provide continuous spatial and temporal information of solar radiation and precipitation, respectively. This study evaluates and quantifies the uncertainty in estimating soybean yield using a DSSAT model, when recorded weather data are replaced with CERES and TRMM ones. Different percentages of data replacements, soybean maturity groups and planting dates are considered, for 2006-2016 period in Oliveros (Argentina). Results show that CERES and TRMM products can be used for soybean yield estimation with DSSAT considering that: percentage of data replacement, campaign, planting date and maturity group, determine the amounts and trends of yield errors. Replacements with CERES data up to 30% result in %RMSE lower than 10% in 87% of the cases; while the replacement with TRMM data presents the best statisticals in campaigns with high yields. Simulations based entirely on CERES solar radiation give better results than those with TRMM. In general, similar percentages of replacement show better performance in the estimation of soybean yield for solar radiation than the replacement of precipitation values.

Graph-state formalism for mutually unbiased bases

NASA Astrophysics Data System (ADS)

Spengler, Christoph; Kraus, Barbara

2013-11-01

A pair of orthonormal bases is called mutually unbiased if all mutual overlaps between any element of one basis and an arbitrary element of the other basis coincide. In case the dimension, d, of the considered Hilbert space is a power of a prime number, complete sets of d+1 mutually unbiased bases (MUBs) exist. Here we present a method based on the graph-state formalism to construct such sets of MUBs. We show that for n p-level systems, with p being prime, one particular graph suffices to easily construct a set of pn+1 MUBs. In fact, we show that a single n-dimensional vector, which is associated with this graph, can be used to generate a complete set of MUBs and demonstrate that this vector can be easily determined. Finally, we discuss some advantages of our formalism regarding the analysis of entanglement structures in MUBs, as well as experimental realizations.

Downscaling of a global climate model for estimation of runoff, sediment yield and dam storage: A case study of Pirapama basin, Brazil

NASA Astrophysics Data System (ADS)

Braga, Ana Cláudia F. Medeiros; Silva, Richarde Marques da; Santos, Celso Augusto Guimarães; Galvão, Carlos de Oliveira; Nobre, Paulo

2013-08-01

The coastal zone of northeastern Brazil is characterized by intense human activities and by large settlements and also experiences high soil losses that can contribute to environmental damage. Therefore, it is necessary to build an integrated modeling-forecasting system for rainfall-runoff erosion that assesses plans for water availability and sediment yield that can be conceived and implemented. In this work, we present an evaluation of an integrated modeling system for a basin located in this region with a relatively low predictability of seasonal rainfall and a small area (600 km2). The National Center for Environmental Predictions - NCEP’s Regional Spectral Model (RSM) nested within the Center for Weather Forecasting and Climate Studies - CPTEC’s Atmospheric General Circulation Model (AGCM) were investigated in this study, and both are addressed in the simulation work. The rainfall analysis shows that: (1) the dynamic downscaling carried out by the regional RSM model approximates the frequency distribution of the daily observed data set although errors were detected in the magnitude and timing (anticipation of peaks, for example) at the daily scale, (2) an unbiased precipitation forecast seemed to be essential for use of the results in hydrological models, and (3) the information directly extracted from the global model may also be useful. The simulated runoff and reservoir-stored volumes are strongly linked to rainfall, and their estimation accuracy was significantly improved at the monthly scale, thus rendering the results useful for management purposes. The runoff-erosion forecasting displayed a large sediment yield that was consistent with the predicted rainfall.

Similar Estimates of Temperature Impacts on Global Wheat Yield by Three Independent Methods

NASA Technical Reports Server (NTRS)

Liu, Bing; Asseng, Senthold; Muller, Christoph; Ewart, Frank; Elliott, Joshua; Lobell, David B.; Martre, Pierre; Ruane, Alex C.; Wallach, Daniel; Jones, James W.;

Similar estimates of temperature impacts on global wheat yield by three independent methods

NASA Astrophysics Data System (ADS)

Liu, Bing; Asseng, Senthold; Müller, Christoph; Ewert, Frank; Elliott, Joshua; Lobell, David B.; Martre, Pierre; Ruane, Alex C.; Wallach, Daniel; Jones, James W.; Rosenzweig, Cynthia; Aggarwal, Pramod K.; Alderman, Phillip D.; Anothai, Jakarat; Basso, Bruno; Biernath, Christian; Cammarano, Davide; Challinor, Andy; Deryng, Delphine; Sanctis, Giacomo De; Doltra, Jordi; Fereres, Elias; Folberth, Christian; Garcia-Vila, Margarita; Gayler, Sebastian; Hoogenboom, Gerrit; Hunt, Leslie A.; Izaurralde, Roberto C.; Jabloun, Mohamed; Jones, Curtis D.; Kersebaum, Kurt C.; Kimball, Bruce A.; Koehler, Ann-Kristin; Kumar, Soora Naresh; Nendel, Claas; O'Leary, Garry J.; Olesen, Jørgen E.; Ottman, Michael J.; Palosuo, Taru; Prasad, P. V. Vara; Priesack, Eckart; Pugh, Thomas A. M.; Reynolds, Matthew; Rezaei, Ehsan E.; Rötter, Reimund P.; Schmid, Erwin; Semenov, Mikhail A.; Shcherbak, Iurii; Stehfest, Elke; Stöckle, Claudio O.; Stratonovitch, Pierre; Streck, Thilo; Supit, Iwan; Tao, Fulu; Thorburn, Peter; Waha, Katharina; Wall, Gerard W.; Wang, Enli; White, Jeffrey W.; Wolf, Joost; Zhao, Zhigan; Zhu, Yan

2016-12-01

The potential impact of global temperature change on global crop yield has recently been assessed with different methods. Here we show that grid-based and point-based simulations and statistical regressions (from historic records), without deliberate adaptation or CO2 fertilization effects, produce similar estimates of temperature impact on wheat yields at global and national scales. With a 1 °C global temperature increase, global wheat yield is projected to decline between 4.1% and 6.4%. Projected relative temperature impacts from different methods were similar for major wheat-producing countries China, India, USA and France, but less so for Russia. Point-based and grid-based simulations, and to some extent the statistical regressions, were consistent in projecting that warmer regions are likely to suffer more yield loss with increasing temperature than cooler regions. By forming a multi-method ensemble, it was possible to quantify `method uncertainty’ in addition to model uncertainty. This significantly improves confidence in estimates of climate impacts on global food security.

Unbiased nonorthogonal bases for tomographic reconstruction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sainz, Isabel; Klimov, Andrei B.; Roa, Luis

2010-05-15

We have developed a general method for constructing a set of nonorthogonal bases with equal separations between all different basis states in prime dimensions. The results are that the corresponding biorthogonal counterparts are pairwise unbiased with the components of the original bases. Using these bases, we derive an explicit expression for the optimal tomography in nonorthogonal bases. A special two-dimensional case is analyzed separately.

Estimation of 305 Day Milk Yield from Cumulative Monthly and Bimonthly Test Day Records in Indonesian Holstein Cattle

NASA Astrophysics Data System (ADS)

Rahayu, A. P.; Hartatik, T.; Purnomoadi, A.; Kurnianto, E.

2018-02-01

The aims of this study were to estimate 305 day first lactation milk yield of Indonesian Holstein cattle from cumulative monthly and bimonthly test day records and to analyze its accuracy.The first lactation records of 258 dairy cows from 2006 to 2014 consisted of 2571 monthly (MTDY) and 1281 bimonthly test day yield (BTDY) records were used. Milk yields were estimated by regression method. Correlation coefficients between actual and estimated milk yield by cumulative MTDY were 0.70, 0.78, 0.83, 0.86, 0.89, 0.92, 0.94 and 0.96 for 2-9 months, respectively, meanwhile by cumulative BTDY were 0.69, 0.81, 0.87 and 0.92 for 2, 4, 6 and 8 months, respectively. The accuracy of fitting regression models (R2) increased with the increasing in the number of cumulative test day used. The used of 5 cumulative MTDY was considered sufficient for estimating 305 day first lactation milk yield with 80.6% accuracy and 7% error percentage of estimation. The estimated milk yield from MTDY was more accurate than BTDY by 1.1 to 2% less error percentage in the same time.

Soil Moisture as an Estimator for Crop Yield in Germany

NASA Astrophysics Data System (ADS)

Peichl, Michael; Meyer, Volker; Samaniego, Luis; Thober, Stephan

2015-04-01

Annual crop yield depends on various factors such as soil properties, management decisions, and meteorological conditions. Unfavorable weather conditions, e.g. droughts, have the potential to drastically diminish crop yield in rain-fed agriculture. For example, the drought in 2003 caused direct losses of 1.5 billion EUR only in Germany. Predicting crop yields allows to mitigate negative effects of weather extremes which are assumed to occur more often in the future due to climate change. A standard approach in economics is to predict the impact of climate change on agriculture as a function of temperature and precipitation. This approach has been developed further using concepts like growing degree days. Other econometric models use nonlinear functions of heat or vapor pressure deficit. However, none of these approaches uses soil moisture to predict crop yield. We hypothesize that soil moisture is a better indicator to explain stress on plant growth than estimations based on precipitation and temperature. This is the case because the latter variables do not explicitly account for the available water content in the root zone, which is the primary source of water supply for plant growth. In this study, a reduced form panel approach is applied to estimate a multivariate econometric production function for the years 1999 to 2010. Annual crop yield data of various crops on the administrative district level serve as depending variables. The explanatory variable of major interest is the Soil Moisture Index (SMI), which quantifies anomalies in root zone soil moisture. The SMI is computed by the mesoscale Hydrological Model (mHM, www.ufz.de/mhm). The index represents the monthly soil water quantile at a 4 km2 grid resolution covering entire Germany. A reduced model approach is suitable because the SMI is the result of a stochastic weather process and therefore can be considered exogenous. For the ease of interpretation a linear functionality is preferred. Meteorological

Remote Estimation of Vegetation Fraction and Yield in Oilseed Rape with Unmanned Aerial Vehicle Data

NASA Astrophysics Data System (ADS)

Peng, Y.; Fang, S.; Liu, K.; Gong, Y.

2017-12-01

This study developed an approach for remote estimation of Vegetation Fraction (VF) and yield in oilseed rape, which is a crop species with conspicuous flowers during reproduction. Canopy reflectance in green, red, red edge and NIR bands was obtained by a camera system mounted on an unmanned aerial vehicle (UAV) when oilseed rape was in the vegetative growth and flowering stage. The relationship of several widely-used Vegetation Indices (VI) vs. VF was tested and found to be different in different phenology stages. At the same VF when oilseed rape was flowering, canopy reflectance increased in all bands, and the tested VI decreased. Therefore, two algorithms to estimate VF were calibrated respectively, one for samples during vegetative growth and the other for samples during flowering stage. During the flowering season, we also explored the potential of using canopy reflectance or VIs to estimate Flower Fraction (FF) in oilseed rape. Based on FF estimates, rape yield can be estimated using canopy reflectance data. Our model was validated in oilseed rape planted under different nitrogen fertilization applications and in different phenology stages. The results showed that it was able to predict VF and FF accurately in oilseed rape with estimation error below 6% and predict yield with estimation error below 20%.

Annual Corn Yield Estimation through Multi-temporal MODIS Data

NASA Astrophysics Data System (ADS)

Shao, Y.; Zheng, B.; Campbell, J. B.

2013-12-01

This research employed 13 years of the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate annual corn yield for the Midwest of the United States. The overall objective of this study was to examine if annual corn yield could be accurately predicted using MODIS time-series NDVI (Normalized Difference Vegetation Index) and ancillary data such monthly precipitation and temperature. MODIS-NDVI 16-Day composite images were acquired from the USGS EROS Data Center for calendar years 2000 to 2012. For the same time-period, county level corn yield statistics were obtained from the National Agricultural Statistics Service (NASS). The monthly precipitation and temperature measures were derived from Precipitation-Elevation Regressions on Independent Slopes Model (PRISM) climate data. A cropland mask was derived using 2006 National Land Cover Database. For each county and within the cropland mask, the MODIS-NDVI time-series data and PRISM climate data were spatially averaged, at their respective time steps. We developed a random forest predictive model with the MODIS-NDVI and climate data as predictors and corn yield as response. To assess the model accuracy, we used twelve years of data as training and the remaining year as hold-out testing set. The training and testing procedures were repeated 13 times. The R2 ranged from 0.72 to 0.83 for testing years. It was also found that the inclusion of climate data did not improve the model predictive performance. MODIS-NDVI time-series data alone might provide sufficient information for county level corn yield prediction.

Estimating oak growth and yield

Treesearch

Martin E. Dale; Donald E. Hilt

1989-01-01

Yields from upland oak stands vary widely from stand to stand due to differences in age, site quality, species composition, and stand structure. Cutting history and other past disturbances such as grazing or fire also affect yields.

Hierarchical thinking in network biology: the unbiased modularization of biochemical networks.

PubMed

Papin, Jason A; Reed, Jennifer L; Palsson, Bernhard O

2004-12-01

As reconstructed biochemical reaction networks continue to grow in size and scope, there is a growing need to describe the functional modules within them. Such modules facilitate the study of biological processes by deconstructing complex biological networks into conceptually simple entities. The definition of network modules is often based on intuitive reasoning. As an alternative, methods are being developed for defining biochemical network modules in an unbiased fashion. These unbiased network modules are mathematically derived from the structure of the whole network under consideration.

Seismic Yield Estimates of UTTR Surface Explosions

NASA Astrophysics Data System (ADS)

Hayward, C.; Park, J.; Stump, B. W.

2016-12-01

Since 2007 the Utah Test and Training Range (UTTR) has used explosive demolition as a method to destroy excess solid rocket motors ranging in size from 19 tons to less than 2 tons. From 2007 to 2014, 20 high quality seismic stations within 180 km recorded most of the more than 200 demolitions. This provides an interesting dataset to examine seismic source scaling for surface explosions. Based upon observer records, shots were of 4 sizes, corresponding to the size of the rocket motors. Instrument corrections for the stations were quality controlled by examining the P-wave amplitudes of all magnitude 6.5-8 earthquakes from 30 to 90 degrees away. For each station recording, the instrument corrected RMS seismic amplitude in the first 10 seconds after the P-onset was calculated. Waveforms at any given station for all the observed explosions are nearly identical. The observed RMS amplitudes were fit to a model including a term for combined distance and station correction, a term for observed RMS amplitude, and an error term for the actual demolition size. The observed seismic yield relationship is RMS=k*Weight2/3 . Estimated yields for the largest shots vary by about 50% from the stated weights, with a nearly normal distribution.

Estimating tar and nicotine exposure: human smoking versus machine generated smoke yields.

PubMed

St Charles, F K; Kabbani, A A; Borgerding, M F

2010-02-01

Determine human smoked (HS) cigarette yields of tar and nicotine for smokers using their own brand in their everyday environment. A robust, filter analysis method was used to estimate the tar and nicotine yields for 784 subjects. Seventeen brands were chosen to represent a wide range of styles: 85 and 100 mm lengths; menthol and non-menthol; 17, 23, and 25 mm circumference; with tar yields [Federal Trade Commission (FTC) method] ranging from 1 to 18 mg. Tar bands chosen corresponded to yields of 1-3 mg, 4-6 mg, 7-12 mg, and 13+ mg. A significant difference (p<0.0001) in HS yields of tar and nicotine between tar bands was found. Machine-smoked yields were reasonable predictors of the HS yields for groups of subjects, but the relationship was neither exact nor linear. Neither the FTC, the Massachusetts (MA) nor the Canadian Intensive (CI) machine-smoking methods accurately reflect the HS yields across all brands. The FTC method was closest for the 7-12 mg and 13+ mg products and the MA method was closest for the 1-3mg products. The HS yields for the 4-6 mg products were approximately midway between the FTC and the MA yields. HS nicotine yields corresponded well with published urinary and plasma nicotine biomarker studies. 2009 Elsevier Inc. All rights reserved.

On the use and misuse of scalar scores of confounders in design and analysis of observational studies.

PubMed

Pfeiffer, R M; Riedl, R

2015-08-15

We assess the asymptotic bias of estimates of exposure effects conditional on covariates when summary scores of confounders, instead of the confounders themselves, are used to analyze observational data. First, we study regression models for cohort data that are adjusted for summary scores. Second, we derive the asymptotic bias for case-control studies when cases and controls are matched on a summary score, and then analyzed either using conditional logistic regression or by unconditional logistic regression adjusted for the summary score. Two scores, the propensity score (PS) and the disease risk score (DRS) are studied in detail. For cohort analysis, when regression models are adjusted for the PS, the estimated conditional treatment effect is unbiased only for linear models, or at the null for non-linear models. Adjustment of cohort data for DRS yields unbiased estimates only for linear regression; all other estimates of exposure effects are biased. Matching cases and controls on DRS and analyzing them using conditional logistic regression yields unbiased estimates of exposure effect, whereas adjusting for the DRS in unconditional logistic regression yields biased estimates, even under the null hypothesis of no association. Matching cases and controls on the PS yield unbiased estimates only under the null for both conditional and unconditional logistic regression, adjusted for the PS. We study the bias for various confounding scenarios and compare our asymptotic results with those from simulations with limited sample sizes. To create realistic correlations among multiple confounders, we also based simulations on a real dataset. Copyright © 2015 John Wiley & Sons, Ltd.

Comparison Between the Use of SAR and Optical Data for Wheat Yield Estimations Using Crop Model Assimilation

NASA Astrophysics Data System (ADS)

Silvestro, Paolo Cosmo; Yang, Hao; Jin, X. L.; Yang, Guijun; Casa, Raffaele; Pignatti, Stefano

2016-08-01

The ultimate aim of this work is to develop methods for the assimilation of the biophysical variables estimated by remote sensing in a suitable crop growth model. Two strategies were followed, one based on the use of Leaf Area Index (LAI) estimated by optical data, and the other based on the use of biomass estimated by SAR. The first one estimates LAI from the reflectance measured by the optical sensors on board of HJ1A, HJ1B and Landsat, using a method based on the training of artificial neural networks (ANN) with PROSAIL model simulations. The retrieved LAI is used to improve wheat yield estimation, using assimilation methods based on the Ensemble Kalman Filter, which assimilate the biophysical variables into growth crop model. The second strategy estimates biomass from SAR imagery. Polarimetric decomposition methods were used based on multi-temporal fully polarimetric Radarsat-2 data during the entire growing season. The estimated biomass was assimilating to FAO Aqua crop model for improving the winter wheat yield estimation, with the Particle Swarm Optimization (PSO) method. These procedures were used in a spatial application with data collected in the rural area of Yangling (Shaanxi Province) in 2014 and were validated for a number of wheat fields for which ground yield data had been recorded and according to statistical yield data for the area.

Wheat yield estimation at the farm level using TM Landsat and agrometeorological data

NASA Technical Reports Server (NTRS)

Rudorff, B. F. T.; Batista, G. T.

1991-01-01

A model for estimating wheat yields on the farm level was developed, that integrates the Landsat TM data and agrometeorological information. Results obtained for a test site in southern Brasil for years of 1986 and 1987 show that the vegetation index derived from Landsat TM could account for the 60 to 40 percent wheat-yield variability observed between the two crop years. Compared to results using either the Landsat TM vegetation index or the agrometeorological data alone, the joint use of both types of data in a single model yielded a significant improvement.

Estimates of Sputter Yields of Solar-Wind Heavy Ions of Lunar Regolith Materials

NASA Technical Reports Server (NTRS)

Barghouty, Abdulmasser F.; Adams, James H., Jr.

2008-01-01

At energies of approximately 1 keV/amu, solar-wind protons and heavy ions interact with the lunar surface materials via a number of microscopic interactions that include sputtering. Solar-wind induced sputtering is a main mechanism by which the composition of the topmost layers of the lunar surface can change, dynamically and preferentially. This work concentrates on sputtering induced by solar-wind heavy ions. Sputtering associated with slow (speeds the electrons speed in its first Bohr orbit) and highly charged ions are known to include both kinetic and potential sputtering. Potential sputtering enjoys some unique characteristics that makes it of special interest to lunar science and exploration. Unlike the yield from kinetic sputtering where simulation and approximation schemes exist, the yield from potential sputtering is not as easy to estimate. This work will present a preliminary numerical scheme designed to estimate potential sputtering yields from reactions relevant to this aspect of solar-wind lunar-surface coupling.

Extending unbiased stereology of brain ultrastructure to three-dimensional volumes

NASA Technical Reports Server (NTRS)

Fiala, J. C.; Harris, K. M.; Koslow, S. H. (Principal Investigator)

2001-01-01

OBJECTIVE: Analysis of brain ultrastructure is needed to reveal how neurons communicate with one another via synapses and how disease processes alter this communication. In the past, such analyses have usually been based on single or paired sections obtained by electron microscopy. Reconstruction from multiple serial sections provides a much needed, richer representation of the three-dimensional organization of the brain. This paper introduces a new reconstruction system and new methods for analyzing in three dimensions the location and ultrastructure of neuronal components, such as synapses, which are distributed non-randomly throughout the brain. DESIGN AND MEASUREMENTS: Volumes are reconstructed by defining transformations that align the entire area of adjacent sections. Whole-field alignment requires rotation, translation, skew, scaling, and second-order nonlinear deformations. Such transformations are implemented by a linear combination of bivariate polynomials. Computer software for generating transformations based on user input is described. Stereological techniques for assessing structural distributions in reconstructed volumes are the unbiased bricking, disector, unbiased ratio, and per-length counting techniques. A new general method, the fractional counter, is also described. This unbiased technique relies on the counting of fractions of objects contained in a test volume. A volume of brain tissue from stratum radiatum of hippocampal area CA1 is reconstructed and analyzed for synaptic density to demonstrate and compare the techniques. RESULTS AND CONCLUSIONS: Reconstruction makes practicable volume-oriented analysis of ultrastructure using such techniques as the unbiased bricking and fractional counter methods. These analysis methods are less sensitive to the section-to-section variations in counts and section thickness, factors that contribute to the inaccuracy of other stereological methods. In addition, volume reconstruction facilitates visualization

Probability Theory Plus Noise: Descriptive Estimation and Inferential Judgment.

PubMed

Costello, Fintan; Watts, Paul

2018-01-01

We describe a computational model of two central aspects of people's probabilistic reasoning: descriptive probability estimation and inferential probability judgment. This model assumes that people's reasoning follows standard frequentist probability theory, but it is subject to random noise. This random noise has a regressive effect in descriptive probability estimation, moving probability estimates away from normative probabilities and toward the center of the probability scale. This random noise has an anti-regressive effect in inferential judgement, however. These regressive and anti-regressive effects explain various reliable and systematic biases seen in people's descriptive probability estimation and inferential probability judgment. This model predicts that these contrary effects will tend to cancel out in tasks that involve both descriptive estimation and inferential judgement, leading to unbiased responses in those tasks. We test this model by applying it to one such task, described by Gallistel et al. ). Participants' median responses in this task were unbiased, agreeing with normative probability theory over the full range of responses. Our model captures the pattern of unbiased responses in this task, while simultaneously explaining systematic biases away from normatively correct probabilities seen in other tasks. Copyright © 2018 Cognitive Science Society, Inc.

Estimating the potential refolding yield of recombinant proteins expressed as inclusion bodies.

PubMed

Ho, Jason G S; Middelberg, Anton P J

2004-09-05

Recombinant protein production in bacteria is efficient except that insoluble inclusion bodies form when some gene sequences are expressed. Such proteins must undergo renaturation, which is an inefficient process due to protein aggregation on dilution from concentrated denaturant. In this study, the protein-protein interactions of eight distinct inclusion-body proteins are quantified, in different solution conditions, by measurement of protein second virial coefficients (SVCs). Protein solubility is shown to decrease as the SVC is reduced (i.e., as protein interactions become more attractive). Plots of SVC versus denaturant concentration demonstrate two clear groupings of proteins: a more aggregative group and a group having higher SVC and better solubility. A correlation of the measured SVC with protein molecular weight and hydropathicity, that is able to predict which group each of the eight proteins falls into, is presented. The inclusion of additives known to inhibit aggregation during renaturation improves solubility and increases the SVC of both protein groups. Furthermore, an estimate of maximum refolding yield (or solubility) using high-performance liquid chromatography was obtained for each protein tested, under different environmental conditions, enabling a relationship between "yield" and SVC to be demonstrated. Combined, the results enable an approximate estimation of the maximum refolding yield that is attainable for each of the eight proteins examined, under a selected chemical environment. Although the correlations must be tested with a far larger set of protein sequences, this work represents a significant move beyond empirical approaches for optimizing renaturation conditions. The approach moves toward the ideal of predicting maximum refolding yield using simple bioinformatic metrics that can be estimated from the gene sequence. Such a capability could potentially "screen," in silico, those sequences suitable for expression in bacteria from those

Estimation of corn yield using multi-temporal optical and radar satellite data and artificial neural networks

NASA Astrophysics Data System (ADS)

Fieuzal, R.; Marais Sicre, C.; Baup, F.

2017-05-01

The yield forecasting of corn constitutes a key issue in agricultural management, particularly in the context of demographic pressure and climate change. This study presents two methods to estimate yields using artificial neural networks: a diagnostic approach based on all the satellite data acquired throughout the agricultural season, and a real-time approach, where estimates are updated after each image was acquired in the microwave and optical domains (Formosat-2, Spot-4/5, TerraSAR-X, and Radarsat-2) throughout the crop cycle. The results are based on the Multispectral Crop Monitoring experimental campaign conducted by the CESBIO (Centre d'Études de la BIOsphère) laboratory in 2010 over an agricultural region in southwestern France. Among the tested sensor configurations (multi-frequency, multi-polarization or multi-source data), the best yield estimation performance (using the diagnostic approach) is obtained with reflectance acquired in the red wavelength region, with a coefficient of determination of 0.77 and an RMSE of 6.6 q ha-1. In the real-time approach the combination of red reflectance and CHH backscattering coefficients provides the best compromise between the accuracy and earliness of the yield estimate (more than 3 months before the harvest), with an R2 of 0.69 and an RMSE of 7.0 q ha-1 during the development of the central stem. The two best yield estimates are similar in most cases (for more than 80% of the monitored fields), and the differences are related to discrepancies in the crop growth cycle and/or the consequences of pests.

Soil Water Availability Modulation Over Estimated Relative Yield Losses in Wheat (Triticum aestivum L.) Due to Ozone Exposure

PubMed Central

De la Torre, Daniel; Sierra, Maria Jose

2007-01-01

The approach developed by Fuhrer in 1995 to estimate wheat yield losses induced by ozone and modulated by the soil water content (SWC) was applied to the data on Catalonian wheat yields. The aim of our work was to apply this approach and adjust it to Mediterranean environmental conditions by means of the necessary corrections. The main objective pursued was to prove the importance of soil water availability in the estimation of relative wheat yield losses as a factor that modifies the effects of tropospheric ozone on wheat, and to develop the algorithms required for the estimation of relative yield losses, adapted to the Mediterranean environmental conditions. The results show that this is an easy way to estimate relative yield losses just using meteorological data, without using ozone fluxes, which are much more difficult to calculate. Soil water availability is very important as a modulating factor of the effects of ozone on wheat; when soil water availability decreases, almost twice the amount of accumulated exposure to ozone is required to induce the same percentage of yield loss as in years when soil water availability is high. PMID:17619747

Yield Estimation for Semipalatinsk Underground Nuclear Explosions Using Seismic Surface-wave Observations at Near-regional Distances

NASA Astrophysics Data System (ADS)

Adushkin, V. V.

- A statistical procedure is described for estimating the yields of underground nuclear tests at the former Soviet Semipalatinsk test site using the peak amplitudes of short-period surface waves observed at near-regional distances (Δ < 150 km) from these explosions. This methodology is then applied to data recorded from a large sample of the Semipalatinsk explosions, including the Soviet JVE explosion of September 14, 1988, and it is demonstrated that it provides seismic estimates of explosion yield which are typically within 20% of the yields determined for these same explosions using more accurate, non-seismic techniques based on near-source observations.

Comparison of estimators of standard deviation for hydrologic time series

USGS Publications Warehouse

Tasker, Gary D.; Gilroy, Edward J.

1982-01-01

Unbiasing factors as a function of serial correlation, ρ, and sample size, n for the sample standard deviation of a lag one autoregressive model were generated by random number simulation. Monte Carlo experiments were used to compare the performance of several alternative methods for estimating the standard deviation σ of a lag one autoregressive model in terms of bias, root mean square error, probability of underestimation, and expected opportunity design loss. Three methods provided estimates of σ which were much less biased but had greater mean square errors than the usual estimate of σ: s = (1/(n - 1) ∑ (xi −x¯)2)½. The three methods may be briefly characterized as (1) a method using a maximum likelihood estimate of the unbiasing factor, (2) a method using an empirical Bayes estimate of the unbiasing factor, and (3) a robust nonparametric estimate of σ suggested by Quenouille. Because s tends to underestimate σ, its use as an estimate of a model parameter results in a tendency to underdesign. If underdesign losses are considered more serious than overdesign losses, then the choice of one of the less biased methods may be wise.

Use of vegetation health data for estimation of aus rice yield in bangladesh.

PubMed

Rahman, Atiqur; Roytman, Leonid; Krakauer, Nir Y; Nizamuddin, Mohammad; Goldberg, Mitch

2009-01-01

Rice is a vital staple crop for Bangladesh and surrounding countries, with interannual variation in yields depending on climatic conditions. We compared Bangladesh yield of aus rice, one of the main varieties grown, from official agricultural statistics with Vegetation Health (VH) Indices [Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and Vegetation Health Index (VHI)] computed from Advanced Very High Resolution Radiometer (AVHRR) data covering a period of 15 years (1991-2005). A strong correlation was found between aus rice yield and VCI and VHI during the critical period of aus rice development that occurs during March-April (weeks 8-13 of the year), several months in advance of the rice harvest. Stepwise principal component regression (PCR) was used to construct a model to predict yield as a function of critical-period VHI. The model reduced the yield prediction error variance by 62% compared with a prediction of average yield for each year. Remote sensing is a valuable tool for estimating rice yields well in advance of harvest and at a low cost.

Use of Vegetation Health Data for Estimation of Aus Rice Yield in Bangladesh

PubMed Central

Rahman, Atiqur; Roytman, Leonid; Krakauer, Nir Y.; Nizamuddin, Mohammad; Goldberg, Mitch

2009-01-01

Rice is a vital staple crop for Bangladesh and surrounding countries, with interannual variation in yields depending on climatic conditions. We compared Bangladesh yield of aus rice, one of the main varieties grown, from official agricultural statistics with Vegetation Health (VH) Indices [Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and Vegetation Health Index (VHI)] computed from Advanced Very High Resolution Radiometer (AVHRR) data covering a period of 15 years (1991–2005). A strong correlation was found between aus rice yield and VCI and VHI during the critical period of aus rice development that occurs during March–April (weeks 8–13 of the year), several months in advance of the rice harvest. Stepwise principal component regression (PCR) was used to construct a model to predict yield as a function of critical-period VHI. The model reduced the yield prediction error variance by 62% compared with a prediction of average yield for each year. Remote sensing is a valuable tool for estimating rice yields well in advance of harvest and at a low cost. PMID:22574057

Estimating soybean genetic gain for yield in the northern United States – Influence of cropping history

USDA-ARS?s Scientific Manuscript database

Mean on-farm USA soybean yield increased at a rate of 21.3 kg per ha per year between 1924 and 2010, due to adoption of yield-enhancing genetic and agronomic technologies. To estimate annual rates of genetic yield gain in three northern USA soybean maturity groups (MG) and determine if these estimat...

Seismic Methods of Identifying Explosions and Estimating Their Yield

NASA Astrophysics Data System (ADS)

Walter, W. R.; Ford, S. R.; Pasyanos, M.; Pyle, M. L.; Myers, S. C.; Mellors, R. J.; Pitarka, A.; Rodgers, A. J.; Hauk, T. F.

2014-12-01

Seismology plays a key national security role in detecting, locating, identifying and determining the yield of explosions from a variety of causes, including accidents, terrorist attacks and nuclear testing treaty violations (e.g. Koper et al., 2003, 1999; Walter et al. 1995). A collection of mainly empirical forensic techniques has been successfully developed over many years to obtain source information on explosions from their seismic signatures (e.g. Bowers and Selby, 2009). However a lesson from the three DPRK declared nuclear explosions since 2006, is that our historic collection of data may not be representative of future nuclear test signatures (e.g. Selby et al., 2012). To have confidence in identifying future explosions amongst the background of other seismic signals, and accurately estimate their yield, we need to put our empirical methods on a firmer physical footing. Goals of current research are to improve our physical understanding of the mechanisms of explosion generation of S- and surface-waves, and to advance our ability to numerically model and predict them. As part of that process we are re-examining regional seismic data from a variety of nuclear test sites including the DPRK and the former Nevada Test Site (now the Nevada National Security Site (NNSS)). Newer relative location and amplitude techniques can be employed to better quantify differences between explosions and used to understand those differences in term of depth, media and other properties. We are also making use of the Source Physics Experiments (SPE) at NNSS. The SPE chemical explosions are explicitly designed to improve our understanding of emplacement and source material effects on the generation of shear and surface waves (e.g. Snelson et al., 2013). Finally we are also exploring the value of combining seismic information with other technologies including acoustic and InSAR techniques to better understand the source characteristics. Our goal is to improve our explosion models

Statistical Properties of Maximum Likelihood Estimators of Power Law Spectra Information

NASA Technical Reports Server (NTRS)

Howell, L. W., Jr.

2003-01-01

A simple power law model consisting of a single spectral index, sigma(sub 2), is believed to be an adequate description of the galactic cosmic-ray (GCR) proton flux at energies below 10(exp 13) eV, with a transition at the knee energy, E(sub k), to a steeper spectral index sigma(sub 2) greater than sigma(sub 1) above E(sub k). The maximum likelihood (ML) procedure was developed for estimating the single parameter sigma(sub 1) of a simple power law energy spectrum and generalized to estimate the three spectral parameters of the broken power law energy spectrum from simulated detector responses and real cosmic-ray data. The statistical properties of the ML estimator were investigated and shown to have the three desirable properties: (Pl) consistency (asymptotically unbiased), (P2) efficiency (asymptotically attains the Cramer-Rao minimum variance bound), and (P3) asymptotically normally distributed, under a wide range of potential detector response functions. Attainment of these properties necessarily implies that the ML estimation procedure provides the best unbiased estimator possible. While simulation studies can easily determine if a given estimation procedure provides an unbiased estimate of the spectra information, and whether or not the estimator is approximately normally distributed, attainment of the Cramer-Rao bound (CRB) can only be ascertained by calculating the CRB for an assumed energy spectrum- detector response function combination, which can be quite formidable in practice. However, the effort in calculating the CRB is very worthwhile because it provides the necessary means to compare the efficiency of competing estimation techniques and, furthermore, provides a stopping rule in the search for the best unbiased estimator. Consequently, the CRB for both the simple and broken power law energy spectra are derived herein and the conditions under which they are stained in practice are investigated.

Integrating remote sensing, geographic information system and modeling for estimating crop yield

NASA Astrophysics Data System (ADS)

Salazar, Luis Alonso

This thesis explores various aspects of the use of remote sensing, geographic information system and digital signal processing technologies for broad-scale estimation of crop yield in Kansas. Recent dry and drought years in the Great Plains have emphasized the need for new sources of timely, objective and quantitative information on crop conditions. Crop growth monitoring and yield estimation can provide important information for government agencies, commodity traders and producers in planning harvest, storage, transportation and marketing activities. The sooner this information is available the lower the economic risk translating into greater efficiency and increased return on investments. Weather data is normally used when crop yield is forecasted. Such information, to provide adequate detail for effective predictions, is typically feasible only on small research sites due to expensive and time-consuming collections. In order for crop assessment systems to be economical, more efficient methods for data collection and analysis are necessary. The purpose of this research is to use satellite data which provides 50 times more spatial information about the environment than the weather station network in a short amount of time at a relatively low cost. Specifically, we are going to use Advanced Very High Resolution Radiometer (AVHRR) based vegetation health (VH) indices as proxies for characterization of weather conditions.

A photometric method for the estimation of the oil yield of oil shale

USGS Publications Warehouse

Cuttitta, Frank

1951-01-01

A method is presented for the distillation and photometric estimation of the oil yield of oil-bearing shales. The oil shale is distilled in a closed test tube and the oil extracted with toluene. The optical density of the toluene extract is used in the estimation of oil content and is converted to percentage of oil by reference to a standard curve. This curve is obtained by relating the oil yields determined by the Fischer assay method to the optical density of the toluene extract of the oil evolved by the new procedure. The new method gives results similar to those obtained by the Fischer assay method in a much shorter time. The applicability of the new method to oil-bearing shale and phosphatic shale has been tested.

Cotton yield estimation using very high-resolution digital images acquired on a low-cost small unmanned aerial vehicle

USDA-ARS?s Scientific Manuscript database

Yield estimation is a critical task in crop management. A number of traditional methods are available for crop yield estimation but they are costly, time-consuming and difficult to expand to a relatively large field. Remote sensing provides techniques to develop quick coverage over a field at any sc...

Estimation After a Group Sequential Trial.

PubMed

Milanzi, Elasma; Molenberghs, Geert; Alonso, Ariel; Kenward, Michael G; Tsiatis, Anastasios A; Davidian, Marie; Verbeke, Geert

2015-10-01

Group sequential trials are one important instance of studies for which the sample size is not fixed a priori but rather takes one of a finite set of pre-specified values, dependent on the observed data. Much work has been devoted to the inferential consequences of this design feature. Molenberghs et al (2012) and Milanzi et al (2012) reviewed and extended the existing literature, focusing on a collection of seemingly disparate, but related, settings, namely completely random sample sizes, group sequential studies with deterministic and random stopping rules, incomplete data, and random cluster sizes. They showed that the ordinary sample average is a viable option for estimation following a group sequential trial, for a wide class of stopping rules and for random outcomes with a distribution in the exponential family. Their results are somewhat surprising in the sense that the sample average is not optimal, and further, there does not exist an optimal, or even, unbiased linear estimator. However, the sample average is asymptotically unbiased, both conditionally upon the observed sample size as well as marginalized over it. By exploiting ignorability they showed that the sample average is the conventional maximum likelihood estimator. They also showed that a conditional maximum likelihood estimator is finite sample unbiased, but is less efficient than the sample average and has the larger mean squared error. Asymptotically, the sample average and the conditional maximum likelihood estimator are equivalent. This previous work is restricted, however, to the situation in which the the random sample size can take only two values, N = n or N = 2 n . In this paper, we consider the more practically useful setting of sample sizes in a the finite set { n 1 , n 2 , …, n L }. It is shown that the sample average is then a justifiable estimator , in the sense that it follows from joint likelihood estimation, and it is consistent and asymptotically unbiased. We also show why

Estimated winter wheat yield from crop growth predicted by LANDSAT

NASA Technical Reports Server (NTRS)

Kanemasu, E. T.

1977-01-01

An evapotranspiration and growth model for winter wheat is reported. The inputs are daily solar radiation, maximum temperature, minimum temperature, precipitation/irrigation and leaf area index. The meteorological data were obtained from National Weather Service while LAI was obtained from LANDSAT multispectral scanner. The output provides daily estimates of potential evapotranspiration, transpiration, evaporation, soil moisture (50 cm depth), percentage depletion, net photosynthesis and dry matter production. Winter wheat yields are correlated with transpiration and dry matter accumulation.

Exoplanet Classification and Yield Estimates for Direct Imaging Missions

NASA Astrophysics Data System (ADS)

Kopparapu, Ravi Kumar; Hébrard, Eric; Belikov, Rus; Batalha, Natalie M.; Mulders, Gijs D.; Stark, Chris; Teal, Dillon; Domagal-Goldman, Shawn; Mandell, Avi

2018-04-01

Future NASA concept missions that are currently under study, like the Habitable Exoplanet Imaging Mission (HabEx) and the Large Ultra-violet Optical Infra Red Surveyor, could discover a large diversity of exoplanets. We propose here a classification scheme that distinguishes exoplanets into different categories based on their size and incident stellar flux, for the purpose of providing the expected number of exoplanets observed (yield) with direct imaging missions. The boundaries of this classification can be computed using the known chemical behavior of gases and condensates at different pressures and temperatures in a planetary atmosphere. In this study, we initially focus on condensation curves for sphalerite ZnS, {{{H}}}2{{O}}, {CO}}2, and {CH}}4. The order in which these species condense in a planetary atmosphere define the boundaries between different classes of planets. Broadly, the planets are divided into rocky planets (0.5–1.0 R ⊕), super-Earths (1.0–1.75 R ⊕), sub-Neptunes (1.75–3.5 R ⊕), sub-Jovians (3.5–6.0 R ⊕), and Jovians (6–14.3 R ⊕) based on their planet sizes, and “hot,” “warm,” and “cold” based on the incident stellar flux. We then calculate planet occurrence rates within these boundaries for different kinds of exoplanets, η planet, using the community coordinated results of NASA’s Exoplanet Program Analysis Group’s Science Analysis Group-13 (SAG-13). These occurrence rate estimates are in turn used to estimate the expected exoplanet yields for direct imaging missions of different telescope diameters.

Estimation of joint stiffness with a compliant load.

PubMed

Ludvig, Daniel; Kearney, Robert E

2009-01-01

Joint stiffness defines the dynamic relationship between the position of the joint and the torque acting about it. It consists of two components: intrinsic and reflex stiffness. Many previous studies have investigated joint stiffness in an open-loop environment, because the current algorithm in use is an open-loop algorithm. This paper explores issues related to the estimation of joint stiffness when subjects interact with compliant loads. First, we show analytically how the bias in closed-loop estimates of joint stiffness depends on the properties of the load, the noise power, and length of the estimated impulse response functions (IRF). We then demonstrate with simulations that the open-loop analysis will fail completely for an elastic load but may succeed for an inertial load. We further show that the open-loop analysis can yield unbiased results with an inertial load and document IRF length, signal-to-noise ratio needed, and minimum inertia needed for the analysis to succeed. Thus, by using a load with a properly selected inertia, open-loop analysis can be used under closed-loop conditions.

Application of maximum-likelihood estimation in optical coherence tomography for nanometer-class thickness estimation

NASA Astrophysics Data System (ADS)

Huang, Jinxin; Yuan, Qun; Tankam, Patrice; Clarkson, Eric; Kupinski, Matthew; Hindman, Holly B.; Aquavella, James V.; Rolland, Jannick P.

2015-03-01

In biophotonics imaging, one important and quantitative task is layer-thickness estimation. In this study, we investigate the approach of combining optical coherence tomography and a maximum-likelihood (ML) estimator for layer thickness estimation in the context of tear film imaging. The motivation of this study is to extend our understanding of tear film dynamics, which is the prerequisite to advance the management of Dry Eye Disease, through the simultaneous estimation of the thickness of the tear film lipid and aqueous layers. The estimator takes into account the different statistical processes associated with the imaging chain. We theoretically investigated the impact of key system parameters, such as the axial point spread functions (PSF) and various sources of noise on measurement uncertainty. Simulations show that an OCT system with a 1 μm axial PSF (FWHM) allows unbiased estimates down to nanometers with nanometer precision. In implementation, we built a customized Fourier domain OCT system that operates in the 600 to 1000 nm spectral window and achieves 0.93 micron axial PSF in corneal epithelium. We then validated the theoretical framework with physical phantoms made of custom optical coatings, with layer thicknesses from tens of nanometers to microns. Results demonstrate unbiased nanometer-class thickness estimates in three different physical phantoms.

Estimation of genetic parameters for milk yield in Murrah buffaloes by Bayesian inference.

PubMed

Breda, F C; Albuquerque, L G; Euclydes, R F; Bignardi, A B; Baldi, F; Torres, R A; Barbosa, L; Tonhati, H

2010-02-01

Random regression models were used to estimate genetic parameters for test-day milk yield in Murrah buffaloes using Bayesian inference. Data comprised 17,935 test-day milk records from 1,433 buffaloes. Twelve models were tested using different combinations of third-, fourth-, fifth-, sixth-, and seventh-order orthogonal polynomials of weeks of lactation for additive genetic and permanent environmental effects. All models included the fixed effects of contemporary group, number of daily milkings and age of cow at calving as covariate (linear and quadratic effect). In addition, residual variances were considered to be heterogeneous with 6 classes of variance. Models were selected based on the residual mean square error, weighted average of residual variance estimates, and estimates of variance components, heritabilities, correlations, eigenvalues, and eigenfunctions. Results indicated that changes in the order of fit for additive genetic and permanent environmental random effects influenced the estimation of genetic parameters. Heritability estimates ranged from 0.19 to 0.31. Genetic correlation estimates were close to unity between adjacent test-day records, but decreased gradually as the interval between test-days increased. Results from mean squared error and weighted averages of residual variance estimates suggested that a model considering sixth- and seventh-order Legendre polynomials for additive and permanent environmental effects, respectively, and 6 classes for residual variances, provided the best fit. Nevertheless, this model presented the largest degree of complexity. A more parsimonious model, with fourth- and sixth-order polynomials, respectively, for these same effects, yielded very similar genetic parameter estimates. Therefore, this last model is recommended for routine applications. Copyright 2010 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Maximal Unbiased Benchmarking Data Sets for Human Chemokine Receptors and Comparative Analysis.

PubMed

Xia, Jie; Reid, Terry-Elinor; Wu, Song; Zhang, Liangren; Wang, Xiang Simon

2018-05-29

Chemokine receptors (CRs) have long been druggable targets for the treatment of inflammatory diseases and HIV-1 infection. As a powerful technique, virtual screening (VS) has been widely applied to identifying small molecule leads for modern drug targets including CRs. For rational selection of a wide variety of VS approaches, ligand enrichment assessment based on a benchmarking data set has become an indispensable practice. However, the lack of versatile benchmarking sets for the whole CRs family that are able to unbiasedly evaluate every single approach including both structure- and ligand-based VS somewhat hinders modern drug discovery efforts. To address this issue, we constructed Maximal Unbiased Benchmarking Data sets for human Chemokine Receptors (MUBD-hCRs) using our recently developed tools of MUBD-DecoyMaker. The MUBD-hCRs encompasses 13 subtypes out of 20 chemokine receptors, composed of 404 ligands and 15756 decoys so far and is readily expandable in the future. It had been thoroughly validated that MUBD-hCRs ligands are chemically diverse while its decoys are maximal unbiased in terms of "artificial enrichment", "analogue bias". In addition, we studied the performance of MUBD-hCRs, in particular CXCR4 and CCR5 data sets, in ligand enrichment assessments of both structure- and ligand-based VS approaches in comparison with other benchmarking data sets available in the public domain and demonstrated that MUBD-hCRs is very capable of designating the optimal VS approach. MUBD-hCRs is a unique and maximal unbiased benchmarking set that covers major CRs subtypes so far.

Estimating and testing interactions when explanatory variables are subject to non-classical measurement error.

PubMed

Murad, Havi; Kipnis, Victor; Freedman, Laurence S

2016-10-01

Assessing interactions in linear regression models when covariates have measurement error (ME) is complex.We previously described regression calibration (RC) methods that yield consistent estimators and standard errors for interaction coefficients of normally distributed covariates having classical ME. Here we extend normal based RC (NBRC) and linear RC (LRC) methods to a non-classical ME model, and describe more efficient versions that combine estimates from the main study and internal sub-study. We apply these methods to data from the Observing Protein and Energy Nutrition (OPEN) study. Using simulations we show that (i) for normally distributed covariates efficient NBRC and LRC were nearly unbiased and performed well with sub-study size ≥200; (ii) efficient NBRC had lower MSE than efficient LRC; (iii) the naïve test for a single interaction had type I error probability close to the nominal significance level, whereas efficient NBRC and LRC were slightly anti-conservative but more powerful; (iv) for markedly non-normal covariates, efficient LRC yielded less biased estimators with smaller variance than efficient NBRC. Our simulations suggest that it is preferable to use: (i) efficient NBRC for estimating and testing interaction effects of normally distributed covariates and (ii) efficient LRC for estimating and testing interactions for markedly non-normal covariates. © The Author(s) 2013.

Sediment yield estimation in mountain catchments of the Camastra reservoir, southern Italy: a comparison among different empirical methods

NASA Astrophysics Data System (ADS)

Lazzari, Maurizio; Danese, Maria; Gioia, Dario; Piccarreta, Marco

2013-04-01

Sedimentary budget estimation is an important topic for both scientific and social community, because it is crucial to understand both dynamics of orogenic belts and many practical problems, such as soil conservation and sediment accumulation in reservoir. Estimations of sediment yield or denudation rates in southern-central Italy are generally obtained by simple empirical relationships based on statistical regression between geomorphic parameters of the drainage network and the measured suspended sediment yield at the outlet of several drainage basins or through the use of models based on sediment delivery ratio or on soil loss equations. In this work, we perform a study of catchment dynamics and an estimation of sedimentary yield for several mountain catchments of the central-western sector of the Basilicata region, southern Italy. Sediment yield estimation has been obtained through both an indirect estimation of suspended sediment yield based on the Tu index (mean annual suspension sediment yield, Ciccacci et al., 1980) and the application of the Rusle (Renard et al., 1997) and the USPED (Mitasova et al., 1996) empirical methods. The preliminary results indicate a reliable difference between the RUSLE and USPED methods and the estimation based on the Tu index; a critical data analysis of results has been carried out considering also the present-day spatial distribution of erosion, transport and depositional processes in relation to the maps obtained from the application of those different empirical methods. The studied catchments drain an artificial reservoir (i.e. the Camastra dam), where a detailed evaluation of the amount of historical sediment storage has been collected. Sediment yield estimation obtained by means of the empirical methods have been compared and checked with historical data of sediment accumulation measured in the artificial reservoir of the Camastra dam. The validation of such estimations of sediment yield at the scale of large catchments

Simultaneous selection for cowpea (Vigna unguiculata L.) genotypes with adaptability and yield stability using mixed models.

PubMed

Torres, F E; Teodoro, P E; Rodrigues, E V; Santos, A; Corrêa, A M; Ceccon, G

2016-04-29

The aim of this study was to select erect cowpea (Vigna unguiculata L.) genotypes simultaneously for high adaptability, stability, and yield grain in Mato Grosso do Sul, Brazil using mixed models. We conducted six trials of different cowpea genotypes in 2005 and 2006 in Aquidauana, Chapadão do Sul, Dourados, and Primavera do Leste. The experimental design was randomized complete blocks with four replications and 20 genotypes. Genetic parameters were estimated by restricted maximum likelihood/best linear unbiased prediction, and selection was based on the harmonic mean of the relative performance of genetic values method using three strategies: selection based on the predicted breeding value, having considered the performance mean of the genotypes in all environments (no interaction effect); the performance in each environment (with an interaction effect); and the simultaneous selection for grain yield, stability, and adaptability. The MNC99542F-5 and MNC99-537F-4 genotypes could be grown in various environments, as they exhibited high grain yield, adaptability, and stability. The average heritability of the genotypes was moderate to high and the selective accuracy was 82%, indicating an excellent potential for selection.

Mutually unbiased bases in six dimensions: The four most distant bases

DOE Office of Scientific and Technical Information (OSTI.GOV)

Raynal, Philippe; Lue Xin; Englert, Berthold-Georg

2011-06-15

We consider the average distance between four bases in six dimensions. The distance between two orthonormal bases vanishes when the bases are the same, and the distance reaches its maximal value of unity when the bases are unbiased. We perform a numerical search for the maximum average distance and find it to be strictly smaller than unity. This is strong evidence that no four mutually unbiased bases exist in six dimensions. We also provide a two-parameter family of three bases which, together with the canonical basis, reach the numerically found maximum of the average distance, and we conduct a detailedmore » study of the structure of the extremal set of bases.« less

Estimation efficiency of usage satellite derived and modelled biophysical products for yield forecasting

NASA Astrophysics Data System (ADS)

Kolotii, Andrii; Kussul, Nataliia; Skakun, Sergii; Shelestov, Andrii; Ostapenko, Vadim; Oliinyk, Tamara

2015-04-01

Efficient and timely crop monitoring and yield forecasting are important tasks for ensuring of stability and sustainable economic development [1]. As winter crops pay prominent role in agriculture of Ukraine - the main focus of this study is concentrated on winter wheat. In our previous research [2, 3] it was shown that usage of biophysical parameters of crops such as FAPAR (derived from Geoland-2 portal as for SPOT Vegetation data) is far more efficient for crop yield forecasting to NDVI derived from MODIS data - for available data. In our current work efficiency of usage such biophysical parameters as LAI, FAPAR, FCOVER (derived from SPOT Vegetation and PROBA-V data at resolution of 1 km and simulated within WOFOST model) and NDVI product (derived from MODIS) for winter wheat monitoring and yield forecasting is estimated. As the part of crop monitoring workflow (vegetation anomaly detection, vegetation indexes and products analysis) and yield forecasting SPIRITS tool developed by JRC is used. Statistics extraction is done for landcover maps created in SRI within FP-7 SIGMA project. Efficiency of usage satellite based and modelled with WOFOST model biophysical products is estimated. [1] N. Kussul, S. Skakun, A. Shelestov, O. Kussul, "Sensor Web approach to Flood Monitoring and Risk Assessment", in: IGARSS 2013, 21-26 July 2013, Melbourne, Australia, pp. 815-818. [2] F. Kogan, N. Kussul, T. Adamenko, S. Skakun, O. Kravchenko, O. Kryvobok, A. Shelestov, A. Kolotii, O. Kussul, and A. Lavrenyuk, "Winter wheat yield forecasting in Ukraine based on Earth observation, meteorological data and biophysical models," International Journal of Applied Earth Observation and Geoinformation, vol. 23, pp. 192-203, 2013. [3] Kussul O., Kussul N., Skakun S., Kravchenko O., Shelestov A., Kolotii A, "Assessment of relative efficiency of using MODIS data to winter wheat yield forecasting in Ukraine", in: IGARSS 2013, 21-26 July 2013, Melbourne, Australia, pp. 3235 - 3238.

Reconfigurable generation and measurement of mutually unbiased bases for time-bin qudits

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lukens, Joseph M.; Islam, Nurul T.; Lim, Charles Ci Wen

Here, we propose a method for implementing mutually unbiased generation and measurement of time-bin qudits using a cascade of electro-optic phase modulator–coded fiber Bragg grating pairs. Our approach requires only a single spatial mode and can switch rapidly between basis choices. We obtain explicit solutions for dimensions d = 2, 3, and 4 that realize all d + 1 possible mutually unbiased bases and analyze the performance of our approach in quantum key distribution. Given its practicality and compatibility with current technology, our approach provides a promising springboard for scalable processing of high-dimensional time-bin states.

Reconfigurable generation and measurement of mutually unbiased bases for time-bin qudits

DOE PAGES

Lukens, Joseph M.; Islam, Nurul T.; Lim, Charles Ci Wen; ...

2018-03-12

Here, we propose a method for implementing mutually unbiased generation and measurement of time-bin qudits using a cascade of electro-optic phase modulator–coded fiber Bragg grating pairs. Our approach requires only a single spatial mode and can switch rapidly between basis choices. We obtain explicit solutions for dimensions d = 2, 3, and 4 that realize all d + 1 possible mutually unbiased bases and analyze the performance of our approach in quantum key distribution. Given its practicality and compatibility with current technology, our approach provides a promising springboard for scalable processing of high-dimensional time-bin states.

Reconfigurable generation and measurement of mutually unbiased bases for time-bin qudits

NASA Astrophysics Data System (ADS)

Lukens, Joseph M.; Islam, Nurul T.; Lim, Charles Ci Wen; Gauthier, Daniel J.

2018-03-01

We propose a method for implementing mutually unbiased generation and measurement of time-bin qudits using a cascade of electro-optic phase modulator-coded fiber Bragg grating pairs. Our approach requires only a single spatial mode and can switch rapidly between basis choices. We obtain explicit solutions for dimensions d = 2, 3, and 4 that realize all d + 1 possible mutually unbiased bases and analyze the performance of our approach in quantum key distribution. Given its practicality and compatibility with current technology, our approach provides a promising springboard for scalable processing of high-dimensional time-bin states.

Simple agrometeorological models for estimating Guineagrass yield in Southeast Brazil.

PubMed

Pezzopane, José Ricardo Macedo; da Cruz, Pedro Gomes; Santos, Patricia Menezes; Bosi, Cristiam; de Araujo, Leandro Coelho

2014-09-01

The objective of this work was to develop and evaluate agrometeorological models to simulate the production of Guineagrass. For this purpose, we used forage yield from 54 growing periods between December 2004-January 2007 and April 2010-March 2012 in irrigated and non-irrigated pastures in São Carlos, São Paulo state, Brazil (latitude 21°57'42″ S, longitude 47°50'28″ W and altitude 860 m). Initially we performed linear regressions between the agrometeorological variables and the average dry matter accumulation rate for irrigated conditions. Then we determined the effect of soil water availability on the relative forage yield considering irrigated and non-irrigated pastures, by means of segmented linear regression among water balance and relative production variables (dry matter accumulation rates with and without irrigation). The models generated were evaluated with independent data related to 21 growing periods without irrigation in the same location, from eight growing periods in 2000 and 13 growing periods between December 2004-January 2007 and April 2010-March 2012. The results obtained show the satisfactory predictive capacity of the agrometeorological models under irrigated conditions based on univariate regression (mean temperature, minimum temperature and potential evapotranspiration or degreedays) or multivariate regression. The response of irrigation on production was well correlated with the climatological water balance variables (ratio between actual and potential evapotranspiration or between actual and maximum soil water storage). The models that performed best for estimating Guineagrass yield without irrigation were based on minimum temperature corrected by relative soil water storage, determined by the ratio between the actual soil water storage and the soil water holding capacity.irrigation in the same location, in 2000, 2010 and 2011. The results obtained show the satisfactory predictive capacity of the agrometeorological models under

A Theoretical Model for Estimation of Yield Strength of Fiber Metal Laminate

NASA Astrophysics Data System (ADS)

Bhat, Sunil; Nagesh, Suresh; Umesh, C. K.; Narayanan, S.

2017-08-01

The paper presents a theoretical model for estimation of yield strength of fiber metal laminate. Principles of elasticity and formulation of residual stress are employed to determine the stress state in metal layer of the laminate that is found to be higher than the stress applied over the laminate resulting in reduced yield strength of the laminate in comparison with that of the metal layer. The model is tested over 4A-3/2 Glare laminate comprising three thin aerospace 2014-T6 aluminum alloy layers alternately bonded adhesively with two prepregs, each prepreg built up of three uni-directional glass fiber layers laid in longitudinal and transverse directions. Laminates with prepregs of E-Glass and S-Glass fibers are investigated separately under uni-axial tension. Yield strengths of both the Glare variants are found to be less than that of aluminum alloy with use of S-Glass fiber resulting in higher laminate yield strength than with the use of E-Glass fiber. Results from finite element analysis and tensile tests conducted over the laminates substantiate the theoretical model.

Poisson sampling - The adjusted and unadjusted estimator revisited

Treesearch

Michael S. Williams; Hans T. Schreuder; Gerardo H. Terrazas

1998-01-01

The prevailing assumption, that for Poisson sampling the adjusted estimator "Y-hat a" is always substantially more efficient than the unadjusted estimator "Y-hat u" , is shown to be incorrect. Some well known theoretical results are applicable since "Y-hat a" is a ratio-of-means estimator and "Y-hat u" a simple unbiased estimator...

Mixed models for selection of Jatropha progenies with high adaptability and yield stability in Brazilian regions.

PubMed

Teodoro, P E; Bhering, L L; Costa, R D; Rocha, R B; Laviola, B G

2016-08-19

The aim of this study was to estimate genetic parameters via mixed models and simultaneously to select Jatropha progenies grown in three regions of Brazil that meet high adaptability and stability. From a previous phenotypic selection, three progeny tests were installed in 2008 in the municipalities of Planaltina-DF (Midwest), Nova Porteirinha-MG (Southeast), and Pelotas-RS (South). We evaluated 18 families of half-sib in a randomized block design with three replications. Genetic parameters were estimated using restricted maximum likelihood/best linear unbiased prediction. Selection was based on the harmonic mean of the relative performance of genetic values method in three strategies considering: 1) performance in each environment (with interaction effect); 2) performance in each environment (with interaction effect); and 3) simultaneous selection for grain yield, stability and adaptability. Accuracy obtained (91%) reveals excellent experimental quality and consequently safety and credibility in the selection of superior progenies for grain yield. The gain with the selection of the best five progenies was more than 20%, regardless of the selection strategy. Thus, based on the three selection strategies used in this study, the progenies 4, 11, and 3 (selected in all environments and the mean environment and by adaptability and phenotypic stability methods) are the most suitable for growing in the three regions evaluated.

Unbiased reduced density matrices and electronic properties from full configuration interaction quantum Monte Carlo.

PubMed

Overy, Catherine; Booth, George H; Blunt, N S; Shepherd, James J; Cleland, Deidre; Alavi, Ali

2014-12-28

Properties that are necessarily formulated within pure (symmetric) expectation values are difficult to calculate for projector quantum Monte Carlo approaches, but are critical in order to compute many of the important observable properties of electronic systems. Here, we investigate an approach for the sampling of unbiased reduced density matrices within the full configuration interaction quantum Monte Carlo dynamic, which requires only small computational overheads. This is achieved via an independent replica population of walkers in the dynamic, sampled alongside the original population. The resulting reduced density matrices are free from systematic error (beyond those present via constraints on the dynamic itself) and can be used to compute a variety of expectation values and properties, with rapid convergence to an exact limit. A quasi-variational energy estimate derived from these density matrices is proposed as an accurate alternative to the projected estimator for multiconfigurational wavefunctions, while its variational property could potentially lend itself to accurate extrapolation approaches in larger systems.

Unbiased reduced density matrices and electronic properties from full configuration interaction quantum Monte Carlo

DOE Office of Scientific and Technical Information (OSTI.GOV)

Overy, Catherine; Blunt, N. S.; Shepherd, James J.

2014-12-28

Properties that are necessarily formulated within pure (symmetric) expectation values are difficult to calculate for projector quantum Monte Carlo approaches, but are critical in order to compute many of the important observable properties of electronic systems. Here, we investigate an approach for the sampling of unbiased reduced density matrices within the full configuration interaction quantum Monte Carlo dynamic, which requires only small computational overheads. This is achieved via an independent replica population of walkers in the dynamic, sampled alongside the original population. The resulting reduced density matrices are free from systematic error (beyond those present via constraints on the dynamicmore » itself) and can be used to compute a variety of expectation values and properties, with rapid convergence to an exact limit. A quasi-variational energy estimate derived from these density matrices is proposed as an accurate alternative to the projected estimator for multiconfigurational wavefunctions, while its variational property could potentially lend itself to accurate extrapolation approaches in larger systems.« less

Comparison of specific-yield estimates for calculating evapotranspiration from diurnal groundwater-level fluctuations

NASA Astrophysics Data System (ADS)

Gribovszki, Zoltán

2018-05-01

Methods that use diurnal groundwater-level fluctuations are commonly used for shallow water-table environments to estimate evapotranspiration (ET) and recharge. The key element needed to obtain reliable estimates is the specific yield (Sy), a soil-water storage parameter that depends on unsaturated soil-moisture and water-table fluxes, among others. Soil-moisture profile measurement down to the water table, along with water-table-depth measurements, can provide a good opportunity to calculate Sy values even on a sub-daily scale. These values were compared with Sy estimates derived by traditional techniques, and it was found that slug-test-based Sy values gave the most similar results in a sandy soil environment. Therefore, slug-test methods, which are relatively cheap and require little time, were most suited to estimate Sy using diurnal fluctuations. The reason for this is that the timeframe of the slug-test measurement is very similar to the dynamic of the diurnal signal. The dynamic characteristic of Sy was also analyzed on a sub-daily scale (depending mostly on the speed of drainage from the soil profile) and a remarkable difference was found in Sy with respect to the rate of change of the water table. When comparing constant and sub-daily (dynamic) Sy values for ET estimation, the sub-daily Sy application yielded higher correlation, but only a slightly smaller deviation from the control ET method, compared with the usage of constant Sy.

Remote sensing and modelling of vegetation dynamics for early estimation and spatial analysis of grain yields in semiarid context in central Tunisia

NASA Astrophysics Data System (ADS)

Chahbi, Aicha; Zribi, Mehrez; Lili-Chabaane, Zohra

2016-04-01

In arid and semi-arid areas, population growth, urbanization, food security and climate change have an impact on agriculture in general and particular on the cereal production. Therefore to improve food security in arid countries, crop canopy monitoring and yield forecasting cereals are needed. Many models, based on the use of remote sensing or agro-meteorological models, have been developed to estimate the biomass and grain yield of cereals. Through the use of a rich database, acquired over a period of two years for more than 80 test fields, and from optical satellite SPOT/HRV images, the aim of the present study is to evaluate the feasibility of two yield prediction approaches. The first approach is based on the application of the semi-empirical growth model SAFY, developed to simulate the dynamics of the LAI and the grain yield, at the field scale. The model is able to reproduce the time evolution of the leaf area index of all fields with acceptable error. However, an inter-comparison between ground yield measurements and SAFY model simulations reveals that the yields are under-estimated by this model. We can explain the limits of the semi-empirical model SAFY by its simplicity and also by various factors that were not considered (fertilization, irrigation,...). To improve the yield estimation, a new approach is proposed: the grain yield is estimated in function of the LAI in the growth period between 25 March and 5 April. The LAI of this period is estimated by SAFY model. A linear relationship is developed between the measured grain yield and the LAI area of the maximum growth period.This approach is robust, the measured and estimated grain yields are well correlated. Following the validation of this approach, yield estimations are proposed for the entire studied site using the SPOT/HRV images.

Usefulness of the HMRPGV method for simultaneous selection of upland cotton genotypes with greater fiber length and high yield stability.

PubMed

Farias, F J C; Carvalho, L P; Silva Filho, J L; Teodoro, P E

2016-08-19

The harmonic mean of the relative performance of genotypic predicted value (HMRPGV) method has been used to measure the genotypic stability and adaptability of various crops. However, its use in cotton is still restricted. This study aimed to use mixed models to select cotton genotypes that simultaneously result in longer fiber length, higher fiber yield, and phenotypic stability in both of these traits. Eight trials with 16 cotton genotypes were conducted in the 2008/2009 harvest in Mato Grosso State. The experimental design was randomized complete blocks with four replicates of each of the 16 genotypes. In each trial, we evaluated fiber yield and fiber length. The genetic parameters were estimated using the restricted maximum likelihood/best linear unbiased predictor method. Joint selection considering, simultaneously, fiber length, fiber yield, stability, and adaptability is possible with the HMRPGV method. Our results suggested that genotypes CNPA MT 04 2080 and BRS CEDRO may be grown in environments similar to those tested here and may be predicted to result in greater fiber length, fiber yield, adaptability, and phenotypic stability. These genotypes may constitute a promising population base in breeding programs aimed at increasing these trait values.

Unbiased methods for removing systematics from galaxy clustering measurements

NASA Astrophysics Data System (ADS)

Elsner, Franz; Leistedt, Boris; Peiris, Hiranya V.

2016-02-01

Measuring the angular clustering of galaxies as a function of redshift is a powerful method for extracting information from the three-dimensional galaxy distribution. The precision of such measurements will dramatically increase with ongoing and future wide-field galaxy surveys. However, these are also increasingly sensitive to observational and astrophysical contaminants. Here, we study the statistical properties of three methods proposed for controlling such systematics - template subtraction, basic mode projection, and extended mode projection - all of which make use of externally supplied template maps, designed to characterize and capture the spatial variations of potential systematic effects. Based on a detailed mathematical analysis, and in agreement with simulations, we find that the template subtraction method in its original formulation returns biased estimates of the galaxy angular clustering. We derive closed-form expressions that should be used to correct results for this shortcoming. Turning to the basic mode projection algorithm, we prove it to be free of any bias, whereas we conclude that results computed with extended mode projection are biased. Within a simplified setup, we derive analytical expressions for the bias and discuss the options for correcting it in more realistic configurations. Common to all three methods is an increased estimator variance induced by the cleaning process, albeit at different levels. These results enable unbiased high-precision clustering measurements in the presence of spatially varying systematics, an essential step towards realizing the full potential of current and planned galaxy surveys.

OCO-2 Solar-induced Fluorescence Data Portal and Applications to Crop Yield Estimation

NASA Astrophysics Data System (ADS)

Zhai, A. J.; Jiang, J. H.; Frankenberg, C.; Yung, Y. L.; Choi, Y. S.

2016-12-01

Solar-induced fluorescence (SIF) is a direct byproduct of photosynthesis and is an index that can represent overall plant productivity level of any region around the globe. Recently, in 2014, NASA launched the Orbiting Carbon Observatory 2 (OCO-2) satellite, which collects SIF measurements at a higher spatial resolution than any previous instrument has. We have first assembled a web-based data portal, which can be easily utilized by both farmers and researchers, to allow convenient access to the SIF data from OCO-2. One possible use of SIF is to estimate agricultural status of crop fields anywhere in the world. We are using OCO-2 level 2 measurements in conjunction with the USDA's Cropland Data Layer and reported crop yield data to study how effectively SIF can estimate agricultural yield on various types of landscape and various species of crops. Results, methods, and future implications will be presented.

Nonlinear unbiased minimum-variance filter for Mars entry autonomous navigation under large uncertainties and unknown measurement bias.

PubMed

Xiao, Mengli; Zhang, Yongbo; Fu, Huimin; Wang, Zhihua

2018-05-01

High-precision navigation algorithm is essential for the future Mars pinpoint landing mission. The unknown inputs caused by large uncertainties of atmospheric density and aerodynamic coefficients as well as unknown measurement biases may cause large estimation errors of conventional Kalman filters. This paper proposes a derivative-free version of nonlinear unbiased minimum variance filter for Mars entry navigation. This filter has been designed to solve this problem by estimating the state and unknown measurement biases simultaneously with derivative-free character, leading to a high-precision algorithm for the Mars entry navigation. IMU/radio beacons integrated navigation is introduced in the simulation, and the result shows that with or without radio blackout, our proposed filter could achieve an accurate state estimation, much better than the conventional unscented Kalman filter, showing the ability of high-precision Mars entry navigation algorithm. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Evaluation of Rgb-Based Vegetation Indices from Uav Imagery to Estimate Forage Yield in Grassland

NASA Astrophysics Data System (ADS)

Lussem, U.; Bolten, A.; Gnyp, M. L.; Jasper, J.; Bareth, G.

2018-04-01

Monitoring forage yield throughout the growing season is of key importance to support management decisions on grasslands/pastures. Especially on intensely managed grasslands, where nitrogen fertilizer and/or manure are applied regularly, precision agriculture applications are beneficial to support sustainable, site-specific management decisions on fertilizer treatment, grazing management and yield forecasting to mitigate potential negative impacts. To support these management decisions, timely and accurate information is needed on plant parameters (e.g. forage yield) with a high spatial and temporal resolution. However, in highly heterogeneous plant communities such as grasslands, assessing their in-field variability non-destructively to determine e.g. adequate fertilizer application still remains challenging. Especially biomass/yield estimation, as an important parameter in assessing grassland quality and quantity, is rather laborious. Forage yield (dry or fresh matter) is mostly measured manually with rising plate meters (RPM) or ultrasonic sensors (handheld or mounted on vehicles). Thus the in-field variability cannot be assessed for the entire field or only with potential disturbances. Using unmanned aerial vehicles (UAV) equipped with consumer grade RGB cameras in-field variability can be assessed by computing RGB-based vegetation indices. In this contribution we want to test and evaluate the robustness of RGB-based vegetation indices to estimate dry matter forage yield on a recently established experimental grassland site in Germany. Furthermore, the RGB-based VIs are compared to indices computed from the Yara N-Sensor. The results show a good correlation of forage yield with RGB-based VIs such as the NGRDI with R2 values of 0.62.

High levels of absorption in orientation-unbiased, radio-selected 3CR Active Galaxies

NASA Astrophysics Data System (ADS)

Wilkes, Belinda J.; Haas, Martin; Barthel, Peter; Leipski, Christian; Kuraszkiewicz, Joanna; Worrall, Diana; Birkinshaw, Mark; Willner, Steven P.

2014-08-01

A critical problem in understanding active galaxies (AGN) is the separation of intrinsic physical differences from observed differences that are due to orientation. Obscuration of the active nucleus is anisotropic and strongly frequency dependent leading to complex selection effects for observations in most wavebands. These can only be quantified using a sample that is sufficiently unbiased to test orientation effects. Low-frequency radio emission is one way to select a close-to orientation-unbiased sample, albeit limited to the minority of AGN with strong radio emission.Recent Chandra, Spitzer and Herschel observations combined with multi-wavelength data for a complete sample of high-redshift (1 24.2) = 2.5:1.4:1 in these high-luminosity (log L(0.3-8keV) ~ 44-46) sources. These ratios are consistent with current expectations based on modelingthe Cosmic X-ray Background. A strong correlation with radio orientation constrains the geometry of the obscuring disk/torus to have a ~60 degree opening angle and ~12 degree Compton-thick cross-section. The deduced ~50% obscured fraction of the population contrasts with typical estimates of ~20% obscured in optically- and X-ray-selected high-luminosity samples. Once the primary nuclear emission is obscured, AGN X-ray spectra are frequently dominated by unobscured non-nuclear or scattered nuclear emission which cannot be distinguished from direct nuclear emission with a lower obscuration level unless high quality data is available. As a result, both the level of obscuration and the estimated instrinsic luminosities of highly-obscured AGN are likely to be significantly (*10-1000) underestimated for 25-50% of the population. This may explain the lower obscured fractions reported for optical and X-ray samples which have no independent measure of the AGN

A Simple Joint Estimation Method of Residual Frequency Offset and Sampling Frequency Offset for DVB Systems

NASA Astrophysics Data System (ADS)

Kwon, Ki-Won; Cho, Yongsoo

This letter presents a simple joint estimation method for residual frequency offset (RFO) and sampling frequency offset (STO) in OFDM-based digital video broadcasting (DVB) systems. The proposed method selects a continual pilot (CP) subset from an unsymmetrically and non-uniformly distributed CP set to obtain an unbiased estimator. Simulation results show that the proposed method using a properly selected CP subset is unbiased and performs robustly.

Estimates of genetic and environmental (co)variances for first lactation on milk yield, survival, and calving interval.

PubMed

Dong, M C; van Vleck, L D

1989-03-01

Variance and covariance components for milk yield, survival to second freshening, calving interval in first lactation were estimated by REML with the expectation and maximization algorithm for an animal model which included herd-year-season effects. Cows without calving interval but with milk yield were included. Each of the four data sets of 15 herds included about 3000 Holstein cows. Relationships across herds were ignored to enable inversion of the coefficient matrix of mixed model equations. Quadratics and their expectations were accumulated herd by herd. Heritability of milk yield (.32) agrees with reports by same methods. Heritabilities of survival (.11) and calving interval(.15) are slightly larger and genetic correlations smaller than results from different methods of estimation. Genetic correlation between milk yield and calving interval (.09) indicates genetic ability to produce more milk is lightly associated with decreased fertility.

Effects of stage of pregnancy on variance components, daily milk yields and 305-day milk yield in Holstein cows, as estimated by using a test-day model.

PubMed

Yamazaki, T; Hagiya, K; Takeda, H; Osawa, T; Yamaguchi, S; Nagamine, Y

2016-08-01

Pregnancy and calving are elements indispensable for dairy production, but the daily milk yield of cows decline as pregnancy progresses, especially during the late stages. Therefore, the effect of stage of pregnancy on daily milk yield must be clarified to accurately estimate the breeding values and lifetime productivity of cows. To improve the genetic evaluation model for daily milk yield and determine the effect of the timing of pregnancy on productivity, we used a test-day model to assess the effects of stage of pregnancy on variance component estimates, daily milk yields and 305-day milk yield during the first three lactations of Holstein cows. Data were 10 646 333 test-day records for the first lactation; 8 222 661 records for the second; and 5 513 039 records for the third. The data were analyzed within each lactation by using three single-trait random regression animal models: one model that did not account for the stage of pregnancy effect and two models that did. The effect of stage of pregnancy on test-day milk yield was included in the model by applying a regression on days pregnant or fitting a separate lactation curve for each days open (days from calving to pregnancy) class (eight levels). Stage of pregnancy did not affect the heritability estimates of daily milk yield, although the additive genetic and permanent environmental variances in late lactation were decreased by accounting for the stage of pregnancy effect. The effects of days pregnant on daily milk yield during late lactation were larger in the second and third lactations than in the first lactation. The rates of reduction of the 305-day milk yield of cows that conceived fewer than 90 days after the second or third calving were significantly (P<0.05) greater than that after the first calving. Therefore, we conclude that differences between the negative effects of early pregnancy in the first, compared with later, lactations should be included when determining the optimal number of days open

Analytic model to estimate thermonuclear neutron yield in z-pinches using the magnetic Noh problem

NASA Astrophysics Data System (ADS)

Allen, Robert C.

The objective was to build a model which could be used to estimate neutron yield in pulsed z-pinch experiments, benchmark future z-pinch simulation tools and to assist scaling for breakeven systems. To accomplish this, a recent solution to the magnetic Noh problem was utilized which incorporates a self-similar solution with cylindrical symmetry and azimuthal magnetic field (Velikovich, 2012). The self-similar solution provides the conditions needed to calculate the time dependent implosion dynamics from which batch burn is assumed and used to calculate neutron yield. The solution to the model is presented. The ion densities and time scales fix the initial mass and implosion velocity, providing estimates of the experimental results given specific initial conditions. Agreement is shown with experimental data (Coverdale, 2007). A parameter sweep was done to find the neutron yield, implosion velocity and gain for a range of densities and time scales for DD reactions and a curve fit was done to predict the scaling as a function of preshock conditions.

Cancer Risk Estimates from Space Flight Estimated Using Yields of Chromosome Damage in Astronaut's Blood Lymphocytes

NASA Technical Reports Server (NTRS)

George, Kerry A.; Rhone, J.; Chappell, L. J.; Cucinotta, F. A.

2011-01-01

To date, cytogenetic damage has been assessed in blood lymphocytes from more than 30 astronauts before and after they participated in long-duration space missions of three months or more on board the International Space Station. Chromosome damage was assessed using fluorescence in situ hybridization whole chromosome analysis techniques. For all individuals, the frequency of chromosome damage measured within a month of return from space was higher than their preflight yield, and biodosimetry estimates were within the range expected from physical dosimetry. Follow up analyses have been performed on most of the astronauts at intervals ranging from around 6 months to many years after flight, and the cytogenetic effects of repeat long-duration missions have so far been assessed in four individuals. Chromosomal aberrations in peripheral blood lymphocytes have been validated as biomarkers of cancer risk and cytogenetic damage can therefore be used to characterize excess health risk incurred by individual crewmembers after their respective missions. Traditional risk assessment models are based on epidemiological data obtained on Earth in cohorts exposed predominantly to acute doses of gamma-rays, and the extrapolation to the space environment is highly problematic, involving very large uncertainties. Cytogenetic damage could play a key role in reducing uncertainty in risk estimation because it is incurred directly in the space environment, using specimens from the astronauts themselves. Relative cancer risks were estimated from the biodosimetry data using the quantitative approach derived from the European Study Group on Cytogenetic Biomarkers and Health database. Astronauts were categorized into low, medium, or high tertiles according to their yield of chromosome damage. Age adjusted tertile rankings were used to estimate cancer risk and results were compared with values obtained using traditional modeling approaches. Individual tertile rankings increased after space

Machine-smoking studies of cigarette filter color to estimate tar yield by visual assessment and through the use of a colorimeter.

PubMed

Morton, Michael J; Williams, David L; Hjorth, Heather B; Smith, Jennifer H

2010-04-01

This paper explores using the intensity of the stain on the end of the filter ("filter color") as a vehicle for estimating cigarette tar yield, both by instrument reading of the filter color and by visual comparison to a template. The correlation of machine-measured tar yield to filter color measured with a colorimeter was reasonably strong and was relatively unaffected by different puff volumes or different tobacco moistures. However, the correlation of filter color to machine-measured nicotine yield was affected by the moisture content of the cigarette. Filter color, as measured by a colorimeter, was generally comparable to filter extraction of either nicotine or solanesol in its correlation to machine-smoked tar yields. It was found that the color of the tar stain changes over time. Panelists could generally correctly order the filters from machine-smoked cigarettes by tar yield using the intensity of the tar stain. However, there was considerable variation in the panelist-to-panelist tar yield estimates. The wide person-to-person variation in tar yield estimates, and other factors discussed in the text could severely limit the usefulness and practicality of this approach for visually estimating the tar yield of machine-smoked cigarettes. Copyright 2009 Elsevier Inc. All rights reserved.

Temperature Increase Reduces Global Yields of Major Crops in Four Independent Estimates

NASA Technical Reports Server (NTRS)

Zhao, Chuang; Liu, Bing; Piao, Shilong; Wang, Xuhui; Lobell, David B.; Huang, Yao; Huang, Mengtian; Yao, Yitong; Bassu, Simona; Ciais, Philippe;

Temperature increase reduces global yields of major crops in four independent estimates

PubMed Central

Zhao, Chuang; Piao, Shilong; Wang, Xuhui; Lobell, David B.; Huang, Yao; Huang, Mengtian; Yao, Yitong; Bassu, Simona; Ciais, Philippe; Durand, Jean-Louis; Elliott, Joshua; Ewert, Frank; Janssens, Ivan A.; Li, Tao; Lin, Erda; Liu, Qiang; Martre, Pierre; Peng, Shushi; Wallach, Daniel; Wang, Tao; Wu, Donghai; Liu, Zhuo; Zhu, Yan; Zhu, Zaichun; Asseng, Senthold

2017-01-01

Wheat, rice, maize, and soybean provide two-thirds of human caloric intake. Assessing the impact of global temperature increase on production of these crops is therefore critical to maintaining global food supply, but different studies have yielded different results. Here, we investigated the impacts of temperature on yields of the four crops by compiling extensive published results from four analytical methods: global grid-based and local point-based models, statistical regressions, and field-warming experiments. Results from the different methods consistently showed negative temperature impacts on crop yield at the global scale, generally underpinned by similar impacts at country and site scales. Without CO2 fertilization, effective adaptation, and genetic improvement, each degree-Celsius increase in global mean temperature would, on average, reduce global yields of wheat by 6.0%, rice by 3.2%, maize by 7.4%, and soybean by 3.1%. Results are highly heterogeneous across crops and geographical areas, with some positive impact estimates. Multimethod analyses improved the confidence in assessments of future climate impacts on global major crops and suggest crop- and region-specific adaptation strategies to ensure food security for an increasing world population. PMID:28811375

Temperature increase reduces global yields of major crops in four independent estimates.

PubMed

Zhao, Chuang; Liu, Bing; Piao, Shilong; Wang, Xuhui; Lobell, David B; Huang, Yao; Huang, Mengtian; Yao, Yitong; Bassu, Simona; Ciais, Philippe; Durand, Jean-Louis; Elliott, Joshua; Ewert, Frank; Janssens, Ivan A; Li, Tao; Lin, Erda; Liu, Qiang; Martre, Pierre; Müller, Christoph; Peng, Shushi; Peñuelas, Josep; Ruane, Alex C; Wallach, Daniel; Wang, Tao; Wu, Donghai; Liu, Zhuo; Zhu, Yan; Zhu, Zaichun; Asseng, Senthold

2017-08-29

Wheat, rice, maize, and soybean provide two-thirds of human caloric intake. Assessing the impact of global temperature increase on production of these crops is therefore critical to maintaining global food supply, but different studies have yielded different results. Here, we investigated the impacts of temperature on yields of the four crops by compiling extensive published results from four analytical methods: global grid-based and local point-based models, statistical regressions, and field-warming experiments. Results from the different methods consistently showed negative temperature impacts on crop yield at the global scale, generally underpinned by similar impacts at country and site scales. Without CO 2 fertilization, effective adaptation, and genetic improvement, each degree-Celsius increase in global mean temperature would, on average, reduce global yields of wheat by 6.0%, rice by 3.2%, maize by 7.4%, and soybean by 3.1%. Results are highly heterogeneous across crops and geographical areas, with some positive impact estimates. Multimethod analyses improved the confidence in assessments of future climate impacts on global major crops and suggest crop- and region-specific adaptation strategies to ensure food security for an increasing world population.

Prioritizing causal disease genes using unbiased genomic features.

PubMed

Deo, Rahul C; Musso, Gabriel; Tasan, Murat; Tang, Paul; Poon, Annie; Yuan, Christiana; Felix, Janine F; Vasan, Ramachandran S; Beroukhim, Rameen; De Marco, Teresa; Kwok, Pui-Yan; MacRae, Calum A; Roth, Frederick P

2014-12-03

Cardiovascular disease (CVD) is the leading cause of death in the developed world. Human genetic studies, including genome-wide sequencing and SNP-array approaches, promise to reveal disease genes and mechanisms representing new therapeutic targets. In practice, however, identification of the actual genes contributing to disease pathogenesis has lagged behind identification of associated loci, thus limiting the clinical benefits. To aid in localizing causal genes, we develop a machine learning approach, Objective Prioritization for Enhanced Novelty (OPEN), which quantitatively prioritizes gene-disease associations based on a diverse group of genomic features. This approach uses only unbiased predictive features and thus is not hampered by a preference towards previously well-characterized genes. We demonstrate success in identifying genetic determinants for CVD-related traits, including cholesterol levels, blood pressure, and conduction system and cardiomyopathy phenotypes. Using OPEN, we prioritize genes, including FLNC, for association with increased left ventricular diameter, which is a defining feature of a prevalent cardiovascular disorder, dilated cardiomyopathy or DCM. Using a zebrafish model, we experimentally validate FLNC and identify a novel FLNC splice-site mutation in a patient with severe DCM. Our approach stands to assist interpretation of large-scale genetic studies without compromising their fundamentally unbiased nature.

Paddy crop yield estimation in Kashmir Himalayan rice bowl using remote sensing and simulation model.

PubMed

Muslim, Mohammad; Romshoo, Shakil Ahmad; Rather, A Q

2015-06-01

The Kashmir Himalayan region of India is expected to be highly prone to the change in agricultural land use because of its geo-ecological fragility, strategic location vis-à-vis the Himalayan landscape, its trans-boundary river basins, and inherent socio-economic instabilities. Food security and sustainability of the region are thus greatly challenged by these impacts. The effect of future climate change, increased competition for land and water, labor from non-agricultural sectors, and increasing population adds to this complex problem. In current study, paddy rice yield at regional level was estimated using GIS-based environment policy integrated climate (GEPIC) model. The general approach of current study involved combining regional level crop database, regional soil data base, farm management data, and climatic data outputs with GEPIC model. The simulated yield showed that estimated production to be 4305.55 kg/ha (43.05 q h(-1)). The crop varieties like Jhelum, K-39, Chenab, China 1039, China-1007, and Shalimar rice-1 grown in plains recorded average yield of 4783.3 kg/ha (47.83 q ha(-1)). Meanwhile, high altitude areas with varieties like Kohsaar, K-78 (Barkat), and K-332 recorded yield of 4102.2 kg/ha (41.02 q ha(-1)). The observed and simulated yield showed a good match with R (2) = 0.95, RMSE = 132.24 kg/ha, respectively.

Number of pins in two-stage stratified sampling for estimating herbage yield

Treesearch

William G. O' Regan; C. Eugene Conrad

1975-01-01

In a two-stage stratified procedure for sampling herbage yield, plots are stratified by a pin frame in stage one, and clipped. In stage two, clippings from selected plots are sorted, dried, and weighed. Sample size and distribution of plots between the two stages are determined by equations. A way to compute the effect of number of pins on the variance of estimated...

Repeatability estimates for oleoresin yield measurements in three species of the southern pines

Treesearch

James H. Roberds; Brain L. Strom

2006-01-01

Repeatability was estimated for constitutive oleoresin yield measurements in 10 stands of three species of pines native to southeastern United States. Trees of these species that discharge large quantities of oleoresin upon wounding are considered to be most resistant to attack by southern pine beetle (Dendroctonus frontalis Zimmermann). Oleoresin...

Control system estimation and design for aerospace vehicles

NASA Technical Reports Server (NTRS)

Stefani, R. T.; Williams, T. L.; Yakowitz, S. J.

1972-01-01

The selection of an estimator which is unbiased when applied to structural parameter estimation is discussed. The mathematical relationships for structural parameter estimation are defined. It is shown that a conventional weighted least squares (CWLS) estimate is biased when applied to structural parameter estimation. Two approaches to bias removal are suggested: (1) change the CWLS estimator or (2) change the objective function. The advantages of each approach are analyzed.

Four photon parametric amplification. [in unbiased Josephson junction

NASA Technical Reports Server (NTRS)

Parrish, P. T.; Feldman, M. J.; Ohta, H.; Chiao, R. Y.

1974-01-01

An analysis is presented describing four-photon parametric amplification in an unbiased Josephson junction. Central to the theory is the model of the Josephson effect as a nonlinear inductance. Linear, small signal analysis is applied to the two-fluid model of the Josephson junction. The gain, gain-bandwidth product, high frequency limit, and effective noise temperature are calculated for a cavity reflection amplifier. The analysis is extended to multiple (series-connected) junctions and subharmonic pumping.

Using normalized difference vegetation index (NDVI) to estimate sugarcane yield and yield components

USDA-ARS?s Scientific Manuscript database

Sugarcane (Saccharum spp.) yield and yield components are important traits for growers and scientists to evaluate and select cultivars. Collection of these yield data would be labor intensive and time consuming in the early selection stages of sugarcane breeding cultivar development programs with a ...

Analyzing self-controlled case series data when case confirmation rates are estimated from an internal validation sample.

PubMed

Xu, Stanley; Clarke, Christina L; Newcomer, Sophia R; Daley, Matthew F; Glanz, Jason M

2018-05-16

Vaccine safety studies are often electronic health record (EHR)-based observational studies. These studies often face significant methodological challenges, including confounding and misclassification of adverse event. Vaccine safety researchers use self-controlled case series (SCCS) study design to handle confounding effect and employ medical chart review to ascertain cases that are identified using EHR data. However, for common adverse events, limited resources often make it impossible to adjudicate all adverse events observed in electronic data. In this paper, we considered four approaches for analyzing SCCS data with confirmation rates estimated from an internal validation sample: (1) observed cases, (2) confirmed cases only, (3) known confirmation rate, and (4) multiple imputation (MI). We conducted a simulation study to evaluate these four approaches using type I error rates, percent bias, and empirical power. Our simulation results suggest that when misclassification of adverse events is present, approaches such as observed cases, confirmed case only, and known confirmation rate may inflate the type I error, yield biased point estimates, and affect statistical power. The multiple imputation approach considers the uncertainty of estimated confirmation rates from an internal validation sample, yields a proper type I error rate, largely unbiased point estimate, proper variance estimate, and statistical power. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Statistical Properties of Maximum Likelihood Estimators of Power Law Spectra Information

NASA Technical Reports Server (NTRS)

Howell, L. W.

2002-01-01

A simple power law model consisting of a single spectral index, a is believed to be an adequate description of the galactic cosmic-ray (GCR) proton flux at energies below 10(exp 13) eV, with a transition at the knee energy, E(sub k), to a steeper spectral index alpha(sub 2) greater than alpha(sub 1) above E(sub k). The Maximum likelihood (ML) procedure was developed for estimating the single parameter alpha(sub 1) of a simple power law energy spectrum and generalized to estimate the three spectral parameters of the broken power law energy spectrum from simulated detector responses and real cosmic-ray data. The statistical properties of the ML estimator were investigated and shown to have the three desirable properties: (P1) consistency (asymptotically unbiased). (P2) efficiency asymptotically attains the Cramer-Rao minimum variance bound), and (P3) asymptotically normally distributed, under a wide range of potential detector response functions. Attainment of these properties necessarily implies that the ML estimation procedure provides the best unbiased estimator possible. While simulation studies can easily determine if a given estimation procedure provides an unbiased estimate of the spectra information, and whether or not the estimator is approximately normally distributed, attainment of the Cramer-Rao bound (CRB) can only he ascertained by calculating the CRB for an assumed energy spectrum-detector response function combination, which can be quite formidable in practice. However. the effort in calculating the CRB is very worthwhile because it provides the necessary means to compare the efficiency of competing estimation techniques and, furthermore, provides a stopping rule in the search for the best unbiased estimator. Consequently, the CRB for both the simple and broken power law energy spectra are derived herein and the conditions under which they are attained in practice are investigated. The ML technique is then extended to estimate spectra information from

Metabolomic prediction of yield in hybrid rice.

PubMed

Xu, Shizhong; Xu, Yang; Gong, Liang; Zhang, Qifa

2016-10-01

Rice (Oryza sativa) provides a staple food source for more than 50% of the world's population. An increase in yield can significantly contribute to global food security. Hybrid breeding can potentially help to meet this goal because hybrid rice often shows a considerable increase in yield when compared with pure-bred cultivars. We recently developed a marker-guided prediction method for hybrid yield and showed a substantial increase in yield through genomic hybrid breeding. We now have transcriptomic and metabolomic data as potential resources for prediction. Using six prediction methods, including least absolute shrinkage and selection operator (LASSO), best linear unbiased prediction (BLUP), stochastic search variable selection, partial least squares, and support vector machines using the radial basis function and polynomial kernel function, we found that the predictability of hybrid yield can be further increased using these omic data. LASSO and BLUP are the most efficient methods for yield prediction. For high heritability traits, genomic data remain the most efficient predictors. When metabolomic data are used, the predictability of hybrid yield is almost doubled compared with genomic prediction. Of the 21 945 potential hybrids derived from 210 recombinant inbred lines, selection of the top 10 hybrids predicted from metabolites would lead to a ~30% increase in yield. We hypothesize that each metabolite represents a biologically built-in genetic network for yield; thus, using metabolites for prediction is equivalent to using information integrated from these hidden genetic networks for yield prediction. © 2016 The Authors The Plant Journal © 2016 John Wiley & Sons Ltd.

Unbiased estimation of oceanic mean rainfall from satellite borne radiometer measurements

NASA Technical Reports Server (NTRS)

Mittal, M. C.

1981-01-01

The statistical properties of the radar derived rainfall obtained during the GARP Atlantic Tropical Experiment (GATE) are used to derive quantitative estimates of the spatial and temporal sampling errors associated with estimating rainfall from brightness temperature measurements such as would be obtained from a satelliteborne microwave radiometer employing a practical size antenna aperture. A basis for a method of correcting the so called beam filling problem, i.e., for the effect of nonuniformity of rainfall over the radiometer beamwidth is provided. The method presented employs the statistical properties of the observations themselves without need for physical assumptions beyond those associated with the radiative transfer model. The simulation results presented offer a validation of the estimated accuracy that can be achieved and the graphs included permit evaluation of the effect of the antenna resolution on both the temporal and spatial sampling errors.

Forest inventory and stratified estimation: a cautionary note

Treesearch

John Coulston

2008-01-01

The Forest Inventory and Analysis (FIA) Program uses stratified estimation techniques to produce estimates of forest attributes. Stratification must be unbiased and stratification procedures should be examined to identify any potential bias. This note explains simple techniques for identifying potential bias, discriminating between sample bias and stratification bias,...

Biased and unbiased perceptual decision-making on vocal emotions.

PubMed

Dricu, Mihai; Ceravolo, Leonardo; Grandjean, Didier; Frühholz, Sascha

2017-11-24

Perceptual decision-making on emotions involves gathering sensory information about the affective state of another person and forming a decision on the likelihood of a particular state. These perceptual decisions can be of varying complexity as determined by different contexts. We used functional magnetic resonance imaging and a region of interest approach to investigate the brain activation and functional connectivity behind two forms of perceptual decision-making. More complex unbiased decisions on affective voices recruited an extended bilateral network consisting of the posterior inferior frontal cortex, the orbitofrontal cortex, the amygdala, and voice-sensitive areas in the auditory cortex. Less complex biased decisions on affective voices distinctly recruited the right mid inferior frontal cortex, pointing to a functional distinction in this region following decisional requirements. Furthermore, task-induced neural connectivity revealed stronger connections between these frontal, auditory, and limbic regions during unbiased relative to biased decision-making on affective voices. Together, the data shows that different types of perceptual decision-making on auditory emotions have distinct patterns of activations and functional coupling that follow the decisional strategies and cognitive mechanisms involved during these perceptual decisions.

Estimates of genetics and phenotypics parameters for the yield and quality of soybean seeds.

PubMed

Zambiazzi, E V; Bruzi, A T; Guilherme, S R; Pereira, D R; Lima, J G; Zuffo, A M; Ribeiro, F O; Mendes, A E S; Godinho, S H M; Carvalho, M L M

2017-09-27

Estimating genotype x environment (GxE) parameters for quality and yield in soybean seed grown in different environments in Minas Gerais State was the goal of this study, as well as to evaluate interaction effects of GxE for soybean seeds yield and quality. Seeds were produced in three locations in Minas Gerais State (Lavras, Inconfidentes, and Patos de Minas) in 2013/14 and 2014/15 seasons. Field experiments were conducted in randomized blocks in a factorial 17 x 6 (GxE), and three replications. Seed yield and quality were evaluated for germination in substrates paper and sand, seedling emergence, speed emergency index, mechanical damage by sodium hypochlorite, electrical conductivity, speed aging, vigor and viability of seeds by tetrazolium test in laboratory using completely randomized design. Quadratic component genotypic, GXE variance component, genotype determination coefficient, genetic variation coefficient and environmental variation coefficient were estimated using the Genes software. Percentage analysis of genotypes contribution, environments and genotype x environment interaction were conducted by sites combination two by two and three sites combination, using the R software. Considering genotypes selection of broad adaptation, TMG 1179 RR, CD 2737 RR, and CD 237 RR associated better yield performance at high physical and physiological potential of seed. Environmental effect was more expressive for most of the characters related to soybean seed quality. GxE interaction effects were expressive though genotypes did not present coincidental behavior in different environments.

Refinement and evaluation of the Massachusetts firm-yield estimator model version 2.0

USGS Publications Warehouse

Levin, Sara B.; Archfield, Stacey A.; Massey, Andrew J.

2011-01-01

The firm yield is the maximum average daily withdrawal that can be extracted from a reservoir without risk of failure during an extended drought period. Previously developed procedures for determining the firm yield of a reservoir were refined and applied to 38 reservoir systems in Massachusetts, including 25 single- and multiple-reservoir systems that were examined during previous studies and 13 additional reservoir systems. Changes to the firm-yield model include refinements to the simulation methods and input data, as well as the addition of several scenario-testing capabilities. The simulation procedure was adapted to run at a daily time step over a 44-year simulation period, and daily streamflow and meteorological data were compiled for all the reservoirs for input to the model. Another change to the model-simulation methods is the adjustment of the scaling factor used in estimating groundwater contributions to the reservoir. The scaling factor is used to convert the daily groundwater-flow rate into a volume by multiplying the rate by the length of reservoir shoreline that is hydrologically connected to the aquifer. Previous firm-yield analyses used a constant scaling factor that was estimated from the reservoir surface area at full pool. The use of a constant scaling factor caused groundwater flows during periods when the reservoir stage was very low to be overestimated. The constant groundwater scaling factor used in previous analyses was replaced with a variable scaling factor that is based on daily reservoir stage. This change reduced instability in the groundwater-flow algorithms and produced more realistic groundwater-flow contributions during periods of low storage. Uncertainty in the firm-yield model arises from many sources, including errors in input data. The sensitivity of the model to uncertainty in streamflow input data and uncertainty in the stage-storage relation was examined. A series of Monte Carlo simulations were performed on 22 reservoirs

Impacts of Different Assimilation Methodologies on Crop Yield Estimates Using Active and Passive Microwave Dataset at L-Band

NASA Astrophysics Data System (ADS)

Liu, P.; Bongiovanni, T. E.; Monsivais-Huertero, A.; Bindlish, R.; Judge, J.

2013-12-01

Accurate estimates of crop yield are important for managing agricultural production and food security. Although the crop growth models, such as the Decision Support System Agrotechnology Transfer (DSSAT), have been used to simulate crop growth and development, the crop yield estimates still diverge from the reality due to different sources of errors in the models and computation. Auxiliary observations may be incorporated into such dynamic models to improve predictions using data assimilation. Active and passive (AP) microwave observations at L-band (1-2 GHz) are sensitive to dielectric and geometric properties of soil and vegetation, including soil moisture (SM), vegetation water content (VWC), surface roughness, and vegetation structure. Because SM and VWC are one of the governing factors in estimating crop yield, microwave observations may be used to improve crop yield estimates. Current studies have shown that active observations are more sensitive to the surface roughness of soil and vegetation structure during the growing season, while the passive observations are more sensitive to the SM. Backscatter and emission models linked with the DSSAT model (DSSAT-A-P) allow assimilation of microwave observations of backscattering coefficient (σ0) and brightness temperature (TB) may provide biophysically realistic estimates of model states and parameters. The present ESA Soil Moisture Ocean Salinity (SMOS) mission provides passive observations at 1.41 GHz at 25 km every 2-3 days, and the NASA/CNDAE Aquarius mission provides L-band AP observations at spatial resolution of 150 km with a repeat coverage of 7 days for global SM products. In 2014, the planned NASA Soil Moisture Active Passive mission will provide AP observations at 1.26 and 1.41 GHz at the spatial resolutions of 3 and 30 km, respectively, with a repeat coverage of 2-3 days. The goal of this study is to understand the impacts of assimilation of asynchronous and synchronous AP observations on crop yield

Fast and unbiased estimator of the time-dependent Hurst exponent.

PubMed

Pianese, Augusto; Bianchi, Sergio; Palazzo, Anna Maria

2018-03-01

We combine two existing estimators of the local Hurst exponent to improve both the goodness of fit and the computational speed of the algorithm. An application with simulated time series is implemented, and a Monte Carlo simulation is performed to provide evidence of the improvement.

Fast and unbiased estimator of the time-dependent Hurst exponent

NASA Astrophysics Data System (ADS)

Pianese, Augusto; Bianchi, Sergio; Palazzo, Anna Maria

2018-03-01

We combine two existing estimators of the local Hurst exponent to improve both the goodness of fit and the computational speed of the algorithm. An application with simulated time series is implemented, and a Monte Carlo simulation is performed to provide evidence of the improvement.

Specific Yields Estimated from Gravity Change during Pumping Test

NASA Astrophysics Data System (ADS)

Chen, K. H.; Hwang, C.; Chang, L. C.

2017-12-01

Specific yield (Sy) is the most important parameter to describe available groundwater capacity in an unconfined aquifer. When estimating Sy by a field pumping test, aquifer heterogeneity and well performers will cause a large uncertainty. In this study, we use a gravity-based method to estimate Sy. At the time of pumping test, amounts of mass (groundwater) are forced to be taken out. If drawdown corn is big and close enough to high precision gravimeter, the gravity change can be detected. The gravity-based method use gravity observations that are independent from traditional flow computation. Only the drawdown corn should be modeled with observed head and hydrogeology data. The gravity method can be used in most groundwater field tests, such as locally pumping/injection tests initiated by active man-made or annual variations due to natural sources. We apply our gravity method at few sites in Taiwan situated over different unconfined aquifer. Here pumping tests for Sy determinations were also carried out. We will discuss why the gravity method produces different results from traditional pumping test, field designs and limitations of the gravity method.

Canopy Chlorophyll Density Based Index for Estimating Nitrogen Status and Predicting Grain Yield in Rice

PubMed Central

Liu, Xiaojun; Zhang, Ke; Zhang, Zeyu; Cao, Qiang; Lv, Zunfu; Yuan, Zhaofeng; Tian, Yongchao; Cao, Weixing; Zhu, Yan

2017-01-01

Canopy chlorophyll density (Chl) has a pivotal role in diagnosing crop growth and nutrition status. The purpose of this study was to develop Chl based models for estimating N status and predicting grain yield of rice (Oryza sativa L.) with Leaf area index (LAI) and Chlorophyll concentration of the upper leaves. Six field experiments were conducted in Jiangsu Province of East China during 2007, 2008, 2009, 2013, and 2014. Different N rates were applied to generate contrasting conditions of N availability in six Japonica cultivars (9915, 27123, Wuxiangjing 14, Wuyunjing 19, Yongyou 8, and Wuyunjing 24) and two Indica cultivars (Liangyoupei 9, YLiangyou 1). The SPAD values of the four uppermost leaves and LAI were measured from tillering to flowering growth stages. Two N indicators, leaf N accumulation (LNA) and plant N accumulation (PNA) were measured. The LAI estimated by LAI-2000 and LI-3050C were compared and calibrated with a conversion equation. A linear regression analysis showed significant relationships between Chl value and N indicators, the equations were as follows: PNA = (0.092 × Chl) − 1.179 (R2 = 0.94, P < 0.001, relative root mean square error (RRMSE) = 0.196), LNA = (0.052 × Chl) − 0.269 (R2 = 0.93, P < 0.001, RRMSE = 0.185). Standardized method was used to quantity the correlation between Chl value and grain yield, normalized yield = (0.601 × normalized Chl) + 0.400 (R2 = 0.81, P < 0.001, RRMSE = 0.078). Independent experimental data also validated the use of Chl value to accurately estimate rice N status and predict grain yield. PMID:29163568

Unbiased estimates of galaxy scaling relations from photometric redshift surveys

NASA Astrophysics Data System (ADS)

Rossi, Graziano; Sheth, Ravi K.

2008-06-01

Many physical properties of galaxies correlate with one another, and these correlations are often used to constrain galaxy formation models. Such correlations include the colour-magnitude relation, the luminosity-size relation, the fundamental plane, etc. However, the transformation from observable (e.g. angular size, apparent brightness) to physical quantity (physical size, luminosity) is often distance dependent. Noise in the distance estimate will lead to biased estimates of these correlations, thus compromising the ability of photometric redshift surveys to constrain galaxy formation models. We describe two methods which can remove this bias. One is a generalization of the Vmax method, and the other is a maximum-likelihood approach. We illustrate their effectiveness by studying the size-luminosity relation in a mock catalogue, although both methods can be applied to other scaling relations as well. We show that if one simply uses photometric redshifts one obtains a biased relation; our methods correct for this bias and recover the true relation.

Crop suitability monitoring for improved yield estimations with 100m PROBA-V data

NASA Astrophysics Data System (ADS)

Özüm Durgun, Yetkin; Gilliams, Sven; Gobin, Anne; Duveiller, Grégory; Djaby, Bakary; Tychon, Bernard

2015-04-01

This study has been realised within the framework of a PhD targeting to advance agricultural monitoring with improved yield estimations using SPOT VEGETATION remotely sensed data. For the first research question, the aim was to improve dry matter productivity (DMP) for C3 and C4 plants by adding a water stress factor. Additionally, the relation between the actual crop yield and DMP was studied. One of the limitations was the lack of crop specific maps which leads to the second research question on 'crop suitability monitoring'. The objective of this work is to create a methodological approach based on the spectral and temporal characteristics of PROBA-V images and ancillary data such as meteorology, soil and topographic data to improve the estimation of annual crop yields. The PROBA-V satellite was launched on 6th May 2013, and was designed to bridge the gap in space-borne vegetation measurements between SPOT-VGT (March 1998 - May 2014) and the upcoming Sentinel-3 satellites scheduled for launch in 2015/2016. PROBA -V has products in four spectral bands: BLUE (centred at 0.463 µm), RED (0.655 µm), NIR (0.845 µm), and SWIR (1.600 µm) with a spatial resolution ranging from 1km to 300m. Due to the construction of the sensor, the central camera can provide a 100m data product with a 5 to 8 days revisiting time. Although the 100m data product is still in test phase a methodology for crop suitability monitoring was developed. The multi-spectral composites, NDVI (Normalised Difference Vegetation Index) (NIR_RED/NIR+RED) and NDII (Normalised Difference Infrared Index) (NIR-SWIR/NIR+SWIR) profiles are used in addition to secondary data such as digital elevation data, precipitation, temperature, soil types and administrative boundaries to improve the accuracy of crop yield estimations. The methodology is evaluated on several FP7 SIGMA test sites for the 2014 - 2015 period. Reference data in the form of vector GIS with boundaries and cover type of agricultural fields are

Effects of Source RDP Models and Near-source Propagation: Implication for Seismic Yield Estimation

NASA Astrophysics Data System (ADS)

Saikia, C. K.; Helmberger, D. V.; Stead, R. J.; Woods, B. B.

- It has proven difficult to uniquely untangle the source and propagation effects on the observed seismic data from underground nuclear explosions, even when large quantities of near-source, broadband data are available for analysis. This leads to uncertainties in our ability to quantify the nuclear seismic source function and, consequently the accuracy of seismic yield estimates for underground explosions. Extensive deterministic modeling analyses of the seismic data recorded from underground explosions at a variety of test sites have been conducted over the years and the results of these studies suggest that variations in the seismic source characteristics between test sites may be contributing to the observed differences in the magnitude/yield relations applicable at those sites. This contributes to our uncertainty in the determination of seismic yield estimates for explosions at previously uncalibrated test sites. In this paper we review issues involving the relationship of Nevada Test Site (NTS) source scaling laws to those at other sites. The Joint Verification Experiment (JVE) indicates that a magnitude (mb) bias (δmb) exists between the Semipalatinsk test site (STS) in the former Soviet Union (FSU) and the Nevada test site (NTS) in the United States. Generally this δmb is attributed to differential attenuation in the upper-mantle beneath the two test sites. This assumption results in rather large estimates of yield for large mb tunnel shots at Novaya Zemlya. A re-examination of the US testing experiments suggests that this δmb bias can partly be explained by anomalous NTS (Pahute) source characteristics. This interpretation is based on the modeling of US events at a number of test sites. Using a modified Haskell source description, we investigated the influence of the source Reduced Displacement Potential (RDP) parameters ψ ∞ , K and B by fitting short- and long-period data simultaneously, including the near-field body and surface waves. In general

Development of estimation method for crop yield using MODIS satellite imagery data and process-based model for corn and soybean in US Corn-Belt region

NASA Astrophysics Data System (ADS)

Lee, J.; Kang, S.; Jang, K.; Ko, J.; Hong, S.

2012-12-01

Crop productivity is associated with the food security and hence, several models have been developed to estimate crop yield by combining remote sensing data with carbon cycle processes. In present study, we attempted to estimate crop GPP and NPP using algorithm based on the LUE model and a simplified respiration model. The state of Iowa and Illinois was chosen as the study site for estimating the crop yield for a period covering the 5 years (2006-2010), as it is the main Corn-Belt area in US. Present study focuses on developing crop-specific parameters for corn and soybean to estimate crop productivity and yield mapping using satellite remote sensing data. We utilized a 10 km spatial resolution daily meteorological data from WRF to provide cloudy-day meteorological variables but in clear-say days, MODIS-based meteorological data were utilized to estimate daily GPP, NPP, and biomass. County-level statistics on yield, area harvested, and productions were used to test model predicted crop yield. The estimated input meteorological variables from MODIS and WRF showed with good agreements with the ground observations from 6 Ameriflux tower sites in 2006. For examples, correlation coefficients ranged from 0.93 to 0.98 for Tmin and Tavg ; from 0.68 to 0.85 for daytime mean VPD; from 0.85 to 0.96 for daily shortwave radiation, respectively. We developed county-specific crop conversion coefficient, i.e. ratio of yield to biomass on 260 DOY and then, validated the estimated county-level crop yield with the statistical yield data. The estimated corn and soybean yields at the county level ranged from 671 gm-2 y-1 to 1393 gm-2 y-1 and from 213 gm-2 y-1 to 421 gm-2 y-1, respectively. The county-specific yield estimation mostly showed errors less than 10%. Furthermore, we estimated crop yields at the state level which were validated against the statistics data and showed errors less than 1%. Further analysis for crop conversion coefficient was conducted for 200 DOY and 280 DOY

Using NOAA/AVHRR based remote sensing data and PCR method for estimation of Aus rice yield in Bangladesh

NASA Astrophysics Data System (ADS)

Nizamuddin, Mohammad; Akhand, Kawsar; Roytman, Leonid; Kogan, Felix; Goldberg, Mitch

2015-06-01

Rice is a dominant food crop of Bangladesh accounting about 75 percent of agricultural land use for rice cultivation and currently Bangladesh is the world's fourth largest rice producing country. Rice provides about two-third of total calorie supply and about one-half of the agricultural GDP and one-sixth of the national income in Bangladesh. Aus is one of the main rice varieties in Bangladesh. Crop production, especially rice, the main food staple, is the most susceptible to climate change and variability. Any change in climate will, thus, increase uncertainty regarding rice production as climate is major cause year-to-year variability in rice productivity. This paper shows the application of remote sensing data for estimating Aus rice yield in Bangladesh using official statistics of rice yield with real time acquired satellite data from Advanced Very High Resolution Radiometer (AVHRR) sensor and Principal Component Regression (PCR) method was used to construct a model. The simulated result was compared with official agricultural statistics showing that the error of estimation of Aus rice yield was less than 10%. Remote sensing, therefore, is a valuable tool for estimating crop yields well in advance of harvest, and at a low cost.

Charge Yield at Low Electric Fields: Considerations for Bipolar Integrated Circuits

NASA Technical Reports Server (NTRS)

Johnston, A. H.; Swimm, R. T.; Thorbourn, D. O.

2013-01-01

A significant reduction in total dose damage is observed when bipolar integrated circuits are irradiated at low temperature. This can be partially explained by the Onsager theory of recombination, which predicts a strong temperature dependence for charge yield under low-field conditions. Reduced damage occurs for biased as well as unbiased devices because the weak fringing field in thick bipolar oxides only affects charge yield near the Si/SiO2 interface, a relatively small fraction of the total oxide thickness. Lowering the temperature of bipolar ICs - either continuously, or for time periods when they are exposed to high radiation levels - provides an additional degree of freedom to improve total dose performance of bipolar circuits, particularly in space applications.

Annual survival estimation of migratory songbirds confounded by incomplete breeding site-fidelity: Study designs that may help

USGS Publications Warehouse

Marshall, M.R.; Diefenbach, D.R.; Wood, L.A.; Cooper, R.J.

2004-01-01

, as an alternative protocol, we analyzed the data with subsets of three progressively larger areas surrounding the core. The data subsets provided four estimates of apparent survival that asymptotically approached true survival. This study design and analytical approach is likely to be logistically feasible in field settings and yields estimates of true survival unbiased (bias < 0.03) by incomplete breeding site-fidelity over a range of inter-annual territory movement patterns. The third approach we investigated used a robust design data collection and analysis approach. This approach resulted in estimates of survival that were unbiased (bias < 0.02), but were very imprecise and likely would not yield reliable estimates in field situations. The fourth approach utilized a fixed study area size, but modeled detection probability as a function of bird proximity to the study plot boundary (e.g., those birds closest to the edge are more likely to emigrate). This approach also resulted in estimates of survival that were unbiased (bias < 0.02), but because the individual covariates were normalized, the average capture probability was 0.50, and thus did not provide an accurate estimate of the true capture probability. Our results show that the core-area with surrounding resight-only can provide estimates of survival that are not biased by the effects of incomplete breeding site-fidelity. ?? 2004 Museu de Cie??ncies Naturals.

Empirical Likelihood-Based Estimation of the Treatment Effect in a Pretest-Posttest Study.

PubMed

Huang, Chiung-Yu; Qin, Jing; Follmann, Dean A

2008-09-01

The pretest-posttest study design is commonly used in medical and social science research to assess the effect of a treatment or an intervention. Recently, interest has been rising in developing inference procedures that improve efficiency while relaxing assumptions used in the pretest-posttest data analysis, especially when the posttest measurement might be missing. In this article we propose a semiparametric estimation procedure based on empirical likelihood (EL) that incorporates the common baseline covariate information to improve efficiency. The proposed method also yields an asymptotically unbiased estimate of the response distribution. Thus functions of the response distribution, such as the median, can be estimated straightforwardly, and the EL method can provide a more appealing estimate of the treatment effect for skewed data. We show that, compared with existing methods, the proposed EL estimator has appealing theoretical properties, especially when the working model for the underlying relationship between the pretest and posttest measurements is misspecified. A series of simulation studies demonstrates that the EL-based estimator outperforms its competitors when the working model is misspecified and the data are missing at random. We illustrate the methods by analyzing data from an AIDS clinical trial (ACTG 175).

Empirical Likelihood-Based Estimation of the Treatment Effect in a Pretest–Posttest Study

PubMed Central

Huang, Chiung-Yu; Qin, Jing; Follmann, Dean A.

2013-01-01

The pretest–posttest study design is commonly used in medical and social science research to assess the effect of a treatment or an intervention. Recently, interest has been rising in developing inference procedures that improve efficiency while relaxing assumptions used in the pretest–posttest data analysis, especially when the posttest measurement might be missing. In this article we propose a semiparametric estimation procedure based on empirical likelihood (EL) that incorporates the common baseline covariate information to improve efficiency. The proposed method also yields an asymptotically unbiased estimate of the response distribution. Thus functions of the response distribution, such as the median, can be estimated straightforwardly, and the EL method can provide a more appealing estimate of the treatment effect for skewed data. We show that, compared with existing methods, the proposed EL estimator has appealing theoretical properties, especially when the working model for the underlying relationship between the pretest and posttest measurements is misspecified. A series of simulation studies demonstrates that the EL-based estimator outperforms its competitors when the working model is misspecified and the data are missing at random. We illustrate the methods by analyzing data from an AIDS clinical trial (ACTG 175). PMID:23729942

Estimation of genomic breeding values for milk yield in UK dairy goats.

PubMed

Mucha, S; Mrode, R; MacLaren-Lee, I; Coffey, M; Conington, J

2015-11-01

The objective of this study was to estimate genomic breeding values for milk yield in crossbred dairy goats. The research was based on data provided by 2 commercial goat farms in the UK comprising 590,409 milk yield records on 14,453 dairy goats kidding between 1987 and 2013. The population was created by crossing 3 breeds: Alpine, Saanen, and Toggenburg. In each generation the best performing animals were selected for breeding, and as a result, a synthetic breed was created. The pedigree file contained 30,139 individuals, of which 2,799 were founders. The data set contained test-day records of milk yield, lactation number, farm, age at kidding, and year and season of kidding. Data on milk composition was unavailable. In total 1,960 animals were genotyped with the Illumina 50K caprine chip. Two methods for estimation of genomic breeding value were compared-BLUP at the single nucleotide polymorphism level (BLUP-SNP) and single-step BLUP. The highest accuracy of 0.61 was obtained with single-step BLUP, and the lowest (0.36) with BLUP-SNP. Linkage disequilibrium (r(2), the squared correlation of the alleles at 2 loci) at 50 kb (distance between 2 SNP) was 0.18. This is the first attempt to implement genomic selection in UK dairy goats. Results indicate that the single-step method provides the highest accuracy for populations with a small number of genotyped individuals, where the number of genotyped males is low and females are predominant in the reference population. Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Estimating genetic effects and quantifying missing heritability explained by identified rare-variant associations.

PubMed

Liu, Dajiang J; Leal, Suzanne M

2012-10-05

Next-generation sequencing has led to many complex-trait rare-variant (RV) association studies. Although single-variant association analysis can be performed, it is grossly underpowered. Therefore, researchers have developed many RV association tests that aggregate multiple variant sites across a genetic region (e.g., gene), and test for the association between the trait and the aggregated genotype. After these aggregate tests detect an association, it is only possible to estimate the average genetic effect for a group of RVs. As a result of the "winner's curse," such an estimate can be biased. Although for common variants one can obtain unbiased estimates of genetic parameters by analyzing a replication sample, for RVs it is desirable to obtain unbiased genetic estimates for the study where the association is identified. This is because there can be substantial heterogeneity of RV sites and frequencies even among closely related populations. In order to obtain an unbiased estimate for aggregated RV analysis, we developed bootstrap-sample-split algorithms to reduce the bias of the winner's curse. The unbiased estimates are greatly important for understanding the population-specific contribution of RVs to the heritability of complex traits. We also demonstrate both theoretically and via simulations that for aggregate RV analysis the genetic variance for a gene or region will always be underestimated, sometimes substantially, because of the presence of noncausal variants or because of the presence of causal variants with effects of different magnitudes or directions. Therefore, even if RVs play a major role in the complex-trait etiologies, a portion of the heritability will remain missing, and the contribution of RVs to the complex-trait etiologies will be underestimated. Copyright © 2012 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

On the degrees of freedom of reduced-rank estimators in multivariate regression

PubMed Central

Mukherjee, A.; Chen, K.; Wang, N.; Zhu, J.

2015-01-01

Summary We study the effective degrees of freedom of a general class of reduced-rank estimators for multivariate regression in the framework of Stein's unbiased risk estimation. A finite-sample exact unbiased estimator is derived that admits a closed-form expression in terms of the thresholded singular values of the least-squares solution and hence is readily computable. The results continue to hold in the high-dimensional setting where both the predictor and the response dimensions may be larger than the sample size. The derived analytical form facilitates the investigation of theoretical properties and provides new insights into the empirical behaviour of the degrees of freedom. In particular, we examine the differences and connections between the proposed estimator and a commonly-used naive estimator. The use of the proposed estimator leads to efficient and accurate prediction risk estimation and model selection, as demonstrated by simulation studies and a data example. PMID:26702155

Infrasound Propagation Modeling for Explosive Yield Estimation

NASA Astrophysics Data System (ADS)

Howard, J. E.; Golden, P.; Negraru, P.

2013-12-01

This study focuses on developing methods of estimating the size or yield of HE surface explosions from local and regional infrasound measurements in the southwestern United States. A munitions disposal facility near Mina, Nevada provides a repeating ground-truth source for this study, with charge weights ranging from 870 - 3800 lbs. Detonation logs and GPS synchronized videos were obtained for a sample of shots representing the full range of weights. These are used to calibrate a relationship between charge weight and spectral level from seismic waveforms recorded at the Nevada Seismic Array (NVAR) at a distance of 36 km. Origin times and yields for the remaining shots are inferred from the seismic recordings at NVAR. Infrasound arrivals from the detonations have been continuously recorded on three four-element, small aperture infrasound arrays since late 2009. NVIAR is collocated with NVAR at a range of approximately 36 km to the northeast. FALN and DNIAR are located at ranges of 154 km to the north, and 293 km to the southeast respectively. Travel times and amplitudes for stratospheric arrivals at DNIAR show strong seasonal variability with the largest amplitudes and celerities occurring during the winter months when the stratospheric winds are favorable. Stratospheric celerities for FNIAR to the north are more consistent as they are not strongly affected by the predominantly meridional stratospheric winds. Tropospheric arrivals at all three arrays show considerable variability that does not appear to be a seasonal effect. Naval Research Laboratory Ground to Space (NRL-G2S) Mesoscale models are used to specify the atmosphere along the propagation path for each detonation. Ray-tracing is performed for each source/receiver pair to identify events for which the models closely match the travel-time observations. This subset of events is used to establish preliminary wind correction formulas using wind values from the G2S profile for the entire propagation path. These

Estimating milk yield and value losses from increased somatic cell count on US dairy farms.

PubMed

Hadrich, J C; Wolf, C A; Lombard, J; Dolak, T M

2018-04-01

Milk loss due to increased somatic cell counts (SCC) results in economic losses for dairy producers. This research uses 10 mo of consecutive dairy herd improvement data from 2013 and 2014 to estimate milk yield loss using SCC as a proxy for clinical and subclinical mastitis. A fixed effects regression was used to examine factors that affected milk yield while controlling for herd-level management. Breed, milking frequency, days in milk, seasonality, SCC, cumulative months with SCC greater than 100,000 cells/mL, lactation, and herd size were variables included in the regression analysis. The cumulative months with SCC above a threshold was included as a proxy for chronic mastitis. Milk yield loss increased as the number of test days with SCC ≥100,000 cells/mL increased. Results from the regression were used to estimate a monetary value of milk loss related to SCC as a function of cow and operation related explanatory variables for a representative dairy cow. The largest losses occurred from increased cumulative test days with a SCC ≥100,000 cells/mL, with daily losses of $1.20/cow per day in the first month to $2.06/cow per day in mo 10. Results demonstrate the importance of including the duration of months above a threshold SCC when estimating milk yield losses. Cows with chronic mastitis, measured by increased consecutive test days with SCC ≥100,000 cells/mL, resulted in higher milk losses than cows with a new infection. This provides farm managers with a method to evaluate the trade-off between treatment and culling decisions as it relates to mastitis control and early detection. Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Test of mutually unbiased bases for six-dimensional photonic quantum systems

PubMed Central

D'Ambrosio, Vincenzo; Cardano, Filippo; Karimi, Ebrahim; Nagali, Eleonora; Santamato, Enrico; Marrucci, Lorenzo; Sciarrino, Fabio

2013-01-01

In quantum information, complementarity of quantum mechanical observables plays a key role. The eigenstates of two complementary observables form a pair of mutually unbiased bases (MUBs). More generally, a set of MUBs consists of bases that are all pairwise unbiased. Except for specific dimensions of the Hilbert space, the maximal sets of MUBs are unknown in general. Even for a dimension as low as six, the identification of a maximal set of MUBs remains an open problem, although there is strong numerical evidence that no more than three simultaneous MUBs do exist. Here, by exploiting a newly developed holographic technique, we implement and test different sets of three MUBs for a single photon six-dimensional quantum state (a “qusix”), encoded exploiting polarization and orbital angular momentum of photons. A close agreement is observed between theory and experiments. Our results can find applications in state tomography, quantitative wave-particle duality, quantum key distribution. PMID:24067548

Test of mutually unbiased bases for six-dimensional photonic quantum systems.

PubMed

D'Ambrosio, Vincenzo; Cardano, Filippo; Karimi, Ebrahim; Nagali, Eleonora; Santamato, Enrico; Marrucci, Lorenzo; Sciarrino, Fabio

2013-09-25

In quantum information, complementarity of quantum mechanical observables plays a key role. The eigenstates of two complementary observables form a pair of mutually unbiased bases (MUBs). More generally, a set of MUBs consists of bases that are all pairwise unbiased. Except for specific dimensions of the Hilbert space, the maximal sets of MUBs are unknown in general. Even for a dimension as low as six, the identification of a maximal set of MUBs remains an open problem, although there is strong numerical evidence that no more than three simultaneous MUBs do exist. Here, by exploiting a newly developed holographic technique, we implement and test different sets of three MUBs for a single photon six-dimensional quantum state (a "qusix"), encoded exploiting polarization and orbital angular momentum of photons. A close agreement is observed between theory and experiments. Our results can find applications in state tomography, quantitative wave-particle duality, quantum key distribution.

Unbiased classification of spatial strategies in the Barnes maze.

PubMed

Illouz, Tomer; Madar, Ravit; Clague, Charlotte; Griffioen, Kathleen J; Louzoun, Yoram; Okun, Eitan

2016-11-01

Spatial learning is one of the most widely studied cognitive domains in neuroscience. The Morris water maze and the Barnes maze are the most commonly used techniques to assess spatial learning and memory in rodents. Despite the fact that these tasks are well-validated paradigms for testing spatial learning abilities, manual categorization of performance into behavioral strategies is subject to individual interpretation, and thus to bias. We have previously described an unbiased machine-learning algorithm to classify spatial strategies in the Morris water maze. Here, we offer a support vector machine-based, automated, Barnes-maze unbiased strategy (BUNS) classification algorithm, as well as a cognitive score scale that can be used for memory acquisition, reversal training and probe trials. The BUNS algorithm can greatly benefit Barnes maze users as it provides a standardized method of strategy classification and cognitive scoring scale, which cannot be derived from typical Barnes maze data analysis. Freely available on the web at http://okunlab.wix.com/okunlab as a MATLAB application. eitan.okun@biu.ac.ilSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Unbiased and targeted mass spectrometry for the HDL proteome.

PubMed

Singh, Sasha A; Aikawa, Masanori

2017-02-01

Mass spectrometry is an ever evolving technology that is equipped with a variety of tools for protein research. Some lipoprotein studies, especially those pertaining to HDL biology, have been exploiting the versatility of mass spectrometry to understand HDL function through its proteome. Despite the role of mass spectrometry in advancing research as a whole, however, the technology remains obscure to those without hands on experience, but still wishing to understand it. In this review, we walk the reader through the coevolution of common mass spectrometry workflows and HDL research, starting from the basic unbiased mass spectrometry methods used to profile the HDL proteome to the most recent targeted methods that have enabled an unprecedented view of HDL metabolism. Unbiased global proteomics have demonstrated that the HDL proteome is organized into subgroups across the HDL size fractions providing further evidence that HDL functional heterogeneity is in part governed by its varying protein constituents. Parallel reaction monitoring, a novel targeted mass spectrometry method, was used to monitor the metabolism of HDL apolipoproteins in humans and revealed that apolipoproteins contained within the same HDL size fraction exhibit diverse metabolic properties. Mass spectrometry provides a variety of tools and strategies to facilitate understanding, through its proteins, the complex biology of HDL.

Estimates of nitrate loads and yields from groundwater to streams in the Chesapeake Bay watershed based on land use and geology

USGS Publications Warehouse

Terziotti, Silvia; Capel, Paul D.; Tesoriero, Anthony J.; Hopple, Jessica A.; Kronholm, Scott C.

2018-03-07

The water quality of the Chesapeake Bay may be adversely affected by dissolved nitrate carried in groundwater discharge to streams. To estimate the concentrations, loads, and yields of nitrate from groundwater to streams for the Chesapeake Bay watershed, a regression model was developed based on measured nitrate concentrations from 156 small streams with watersheds less than 500 square miles (mi2 ) at baseflow. The regression model has three predictive variables: geologic unit, percent developed land, and percent agricultural land. Comparisons of estimated and actual values within geologic units were closely matched. The coefficient of determination (R2 ) for the model was 0.6906. The model was used to calculate baseflow nitrate concentrations at over 83,000 National Hydrography Dataset Plus Version 2 catchments and aggregated to 1,966 total 12-digit hydrologic units in the Chesapeake Bay watershed. The modeled output geospatial data layers provided estimated annual loads and yields of nitrate from groundwater into streams. The spatial distribution of annual nitrate yields from groundwater estimated by this method was compared to the total watershed yields of all sources estimated from a Chesapeake Bay SPAtially Referenced Regressions On Watershed attributes (SPARROW) water-quality model. The comparison showed similar spatial patterns. The regression model for groundwater contribution had similar but lower yields, suggesting that groundwater is an important source of nitrogen for streams in the Chesapeake Bay watershed.

Estimating cell populations

NASA Technical Reports Server (NTRS)

White, B. S.; Castleman, K. R.

1981-01-01

An important step in the diagnosis of a cervical cytology specimen is estimating the proportions of the various cell types present. This is usually done with a cell classifier, the error rates of which can be expressed as a confusion matrix. We show how to use the confusion matrix to obtain an unbiased estimate of the desired proportions. We show that the mean square error of this estimate depends on a 'befuddlement matrix' derived from the confusion matrix, and how this, in turn, leads to a figure of merit for cell classifiers. Finally, we work out the two-class problem in detail and present examples to illustrate the theory.

Estimating daily fat yield from a single milking on test day for herds with a robotic milking system.

PubMed

Peeters, R; Galesloot, P J B

2002-03-01

The objective of this study was to estimate the daily fat yield and fat percentage from one sampled milking per cow per test day in an automatic milking system herd, when the milking times and milk yields of all individual milkings are recorded by the automatic milking system. Multiple regression models were used to estimate the 24-h fat percentage when only one milking is sampled for components and milk yields and milking times are known for all milkings in the 24-h period before the sampled milking. In total, 10,697 cow test day records, from 595 herd tests at 91 Dutch herds milked with an automatic milking system, were used. The best model to predict 24-h fat percentage included fat percentage, protein percentage, milk yield and milking interval of the sampled milking, milk yield, and milking interval of the preceding milking, and the interaction between milking interval and the ratio of fat and protein percentage of the sampled milking. This model gave a standard deviation of the prediction error (SE) for 24-h fat percentage of 0.321 and a correlation between the predicted and actual 24-h fat percentage of 0.910. For the 24-h fat yield, we found SE = 90 g and correlation = 0.967. This precision is slightly better than that of present a.m.-p.m. testing schemes. Extra attention must be paid to correctly matching the sample jars and the milkings. Furthermore, milkings with an interval of less than 4 h must be excluded from sampling as well as milkings that are interrupted or that follow an interrupted milking. Under these restrictions (correct matching, interval of at least 4 h, and no interrupted milking), one sampled milking suffices to get a satisfactory estimate for the test-day fat yield.

A sampling strategy to estimate the area and perimeter of irregularly shaped planar regions

Treesearch

Timothy G. Gregoire; Harry T. Valentine

1995-01-01

The length of a randomly oriented ray emanating from an interior point of a planar region can be used to unbiasedly estimate the region's area and perimeter. Estimators and corresponding variance estimators under various selection strategies are presented.

A cross-correlation-based estimate of the galaxy luminosity function

NASA Astrophysics Data System (ADS)

van Daalen, Marcel P.; White, Martin

2018-06-01

We extend existing methods for using cross-correlations to derive redshift distributions for photometric galaxies, without using photometric redshifts. The model presented in this paper simultaneously yields highly accurate and unbiased redshift distributions and, for the first time, redshift-dependent luminosity functions, using only clustering information and the apparent magnitudes of the galaxies as input. In contrast to many existing techniques for recovering unbiased redshift distributions, the output of our method is not degenerate with the galaxy bias b(z), which is achieved by modelling the shape of the luminosity bias. We successfully apply our method to a mock galaxy survey and discuss improvements to be made before applying our model to real data.

Estimation of dew yield from radiative condensers by means of an energy balance model

NASA Astrophysics Data System (ADS)

Maestre-Valero, J. F.; Ragab, R.; Martínez-Alvarez, V.; Baille, A.

2012-08-01

SummaryThis paper presents an energy balance modelling approach to predict the nightly water yield and the surface temperature (Tf) of two passive radiative dew condensers (RDCs) tilted 30° from horizontal. One was fitted with a white hydrophilic polyethylene foil recommended for dew harvest and the other with a black polyethylene foil widely used in horticulture. The model was validated in south-eastern Spain by comparing the simulation outputs with field measurements of Tf and dew yield. The results indicate that the model is robust and accurate in reproducing the behaviour of the two RDCs, especially in what refers to Tf, whose estimates were very close to the observations. The results were somewhat less precise for dew yield, with a larger scatter around the 1:1 relationship. A sensitivity analysis showed that the simulated dew yield was highly sensitive to changes in relative humidity and downward longwave radiation. The proposed approach provides a useful tool to water managers for quantifying the amount of dew that could be harvested as a valuable water resource in arid, semiarid and water stressed regions.

Spectroscopic observation of SN2017gkk by NUTS (NOT Un-biased Transient Survey)

NASA Astrophysics Data System (ADS)

Onori, F.; Benetti, S.; Cappellaro, E.; Losada, Illa R.; Gafton, E.; NUTS Collaboration

2017-09-01

The Nordic Optical Telescope (NOT) Unbiased Transient Survey (NUTS; ATel #8992) reports the spectroscopic classification of supernova SN2017gkk (=MASTER OT J091344.71762842.5) in host galaxy NGC 2748.

Frost trends and their estimated impact on yield in the Australian wheatbelt

PubMed Central

Zheng, Bangyou; Chapman, Scott C.; Christopher, Jack T.; Frederiks, Troy M.; Chenu, Karine

2015-01-01

Radiant spring frosts occurring during reproductive developmental stages can result in catastrophic yield loss for wheat producers. To better understand the spatial and temporal variability of frost, the occurrence and impact of frost events on rain-fed wheat production was estimated across the Australian wheatbelt for 1957–2013 using a 0.05 ° gridded weather data set. Simulated yield outcomes at 60 key locations were compared with those for virtual genotypes with different levels of frost tolerance. Over the last six decades, more frost events, later last frost day, and a significant increase in frost impact on yield were found in certain regions of the Australian wheatbelt, in particular in the South-East and West. Increasing trends in frost-related yield losses were simulated in regions where no significant trend of frost occurrence was observed, due to higher mean temperatures accelerating crop development and causing sensitive post-heading stages to occur earlier, during the frost risk period. Simulations indicated that with frost-tolerant lines the mean national yield could be improved by up to 20% through (i) reduced frost damage (~10% improvement) and (ii) the ability to use earlier sowing dates (adding a further 10% improvement). In the simulations, genotypes with an improved frost tolerance to temperatures 1 °C lower than the current 0 °C reference provided substantial benefit in most cropping regions, while greater tolerance (to 3 °C lower temperatures) brought further benefits in the East. The results indicate that breeding for improved reproductive frost tolerance should remain a priority for the Australian wheat industry, despite warming climates. PMID:25922479

Frost trends and their estimated impact on yield in the Australian wheatbelt.

PubMed

Zheng, Bangyou; Chapman, Scott C; Christopher, Jack T; Frederiks, Troy M; Chenu, Karine

2015-06-01

Radiant spring frosts occurring during reproductive developmental stages can result in catastrophic yield loss for wheat producers. To better understand the spatial and temporal variability of frost, the occurrence and impact of frost events on rain-fed wheat production was estimated across the Australian wheatbelt for 1957-2013 using a 0.05 ° gridded weather data set. Simulated yield outcomes at 60 key locations were compared with those for virtual genotypes with different levels of frost tolerance. Over the last six decades, more frost events, later last frost day, and a significant increase in frost impact on yield were found in certain regions of the Australian wheatbelt, in particular in the South-East and West. Increasing trends in frost-related yield losses were simulated in regions where no significant trend of frost occurrence was observed, due to higher mean temperatures accelerating crop development and causing sensitive post-heading stages to occur earlier, during the frost risk period. Simulations indicated that with frost-tolerant lines the mean national yield could be improved by up to 20% through (i) reduced frost damage (~10% improvement) and (ii) the ability to use earlier sowing dates (adding a further 10% improvement). In the simulations, genotypes with an improved frost tolerance to temperatures 1 °C lower than the current 0 °C reference provided substantial benefit in most cropping regions, while greater tolerance (to 3 °C lower temperatures) brought further benefits in the East. The results indicate that breeding for improved reproductive frost tolerance should remain a priority for the Australian wheat industry, despite warming climates. © The Author 2015. Published by Oxford University Press on behalf of the Society for Experimental Biology.

Estimated suspended-sediment loads and yields in the French and Brandywine Creek Basins, Chester County, Pennsylvania, water years 2008-09

USGS Publications Warehouse

Sloto, Ronald A.; Olson, Leif E.

2011-01-01

Turbidity and suspended-sediment concentration data were collected by the U.S. Geological Survey (USGS) at four stream stations--French Creek near Phoenixville, West Branch Brandywine Creek near Honey Brook, West Branch Brandywine Creek at Modena, and East Branch Brandywine Creek below Downingtown--in Chester County, Pa. Sedimentation and siltation is the leading cause of stream impairment in Chester County, and these data are critical for quantifying sediment transport. This study was conducted by the USGS in cooperation with the Chester County Water Resources Authority and the Chester County Health Department. Data from optical turbidity sensors deployed at the four stations were recorded at 15- or 30-minute intervals by a data logger and uploaded every 1 to 4 hours to the USGS database. Most of the suspended-sediment samples were collected using automated samplers. The use of optical sensors to continuously monitor turbidity provided an accurate estimate of sediment fluctuations without the collection and analysis costs associated with intensive sampling during storms. Turbidity was used as a surrogate for suspended-sediment concentration (SSC), which is a measure of sedimentation and siltation. Regression models were developed between SSC and turbidity for each of the monitoring stations using SSC data collected from the automated samplers and turbidity data collected at each station. Instantaneous suspended-sediment loads (SSL) were computed from time-series turbidity and discharge data for the 2008 and 2009 water years using the regression equations. The instantaneous computations of SSL were summed to provide daily, storm, and water year annual loads. The annual SSL contributed from each basin was divided by the upstream drainage area to estimate the annual sediment yield. For all four basins, storms provided more than 96 percent of the annual SSL. In each basin, four storms generally provided over half the annual SSL each water year. Stormflows with the

The limits of crop productivity: validating theoretical estimates and determining the factors that limit crop yields in optimal environments

NASA Technical Reports Server (NTRS)

Bugbee, B.; Monje, O.

1992-01-01

Plant scientists have sought to maximize the yield of food crops since the beginning of agriculture. There are numerous reports of record food and biomass yields (per unit area) in all major crop plants, but many of the record yield reports are in error because they exceed the maximal theoretical rates of the component processes. In this article, we review the component processes that govern yield limits and describe how each process can be individually measured. This procedure has helped us validate theoretical estimates and determine what factors limit yields in optimal environments.

Unbiased water and methanol maser surveys of NGC 1333

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lyo, A-Ran; Kim, Jongsoo; Byun, Do-Young

2014-11-01

We present the results of unbiased 22 GHz H{sub 2}O water and 44 GHz class I CH{sub 3}OH methanol maser surveys in the central 7' × 10' area of NGC 1333 and two additional mapping observations of a 22 GHz water maser in a ∼3' × 3' area of the IRAS4A region. In the 22 GHz water maser survey of NGC 1333 with a sensitivity of σ ∼ 0.3 Jy, we confirmed the detection of masers toward H{sub 2}O(B) in the region of HH 7-11 and IRAS4B. We also detected new water masers located ∼20'' away in the western directionmore » of IRAS4B or ∼25'' away in the southern direction of IRAS4A. We could not, however, find young stellar objects or molecular outflows associated with them. They showed two different velocity components of ∼0 and ∼16 km s{sup –1}, which are blue- and redshifted relative to the adopted systemic velocity of ∼7 km s{sup –1} for NGC 1333. They also showed time variabilities in both intensity and velocity from multi-epoch observations and an anti-correlation between the intensities of the blue- and redshifted velocity components. We suggest that the unidentified power source of these masers might be found in the earliest evolutionary stage of star formation, before the onset of molecular outflows. Finding this kind of water maser is only possible through an unbiased blind survey. In the 44 GHz methanol maser survey with a sensitivity of σ ∼ 0.5 Jy, we confirmed masers toward IRAS4A2 and the eastern shock region of IRAS2A. Both sources are also detected in 95 and 132 GHz methanol maser lines. In addition, we had new detections of methanol masers at 95 and 132 GHz toward IRAS4B. In terms of the isotropic luminosity, we detected methanol maser sources brighter than ∼5 × 10{sup 25} erg s{sup –1} from our unbiased survey.« less

Estimation of river and stream temperature trends under haphazard sampling

USGS Publications Warehouse

Gray, Brian R.; Lyubchich, Vyacheslav; Gel, Yulia R.; Rogala, James T.; Robertson, Dale M.; Wei, Xiaoqiao

2015-01-01

Long-term temporal trends in water temperature in rivers and streams are typically estimated under the assumption of evenly-spaced space-time measurements. However, sampling times and dates associated with historical water temperature datasets and some sampling designs may be haphazard. As a result, trends in temperature may be confounded with trends in time or space of sampling which, in turn, may yield biased trend estimators and thus unreliable conclusions. We address this concern using multilevel (hierarchical) linear models, where time effects are allowed to vary randomly by day and date effects by year. We evaluate the proposed approach by Monte Carlo simulations with imbalance, sparse data and confounding by trend in time and date of sampling. Simulation results indicate unbiased trend estimators while results from a case study of temperature data from the Illinois River, USA conform to river thermal assumptions. We also propose a new nonparametric bootstrap inference on multilevel models that allows for a relatively flexible and distribution-free quantification of uncertainties. The proposed multilevel modeling approach may be elaborated to accommodate nonlinearities within days and years when sampling times or dates typically span temperature extremes.

Spectroscopic observation of ASASSN-17he by NUTS (NOT Un-biased Transient Survey)

NASA Astrophysics Data System (ADS)

Kostrzewa-Rutkowska, Z.; Benetti, S.; Dong, S.; Stritzinger, M.; Stanek, K.; Brimacombe, J.; Sagues, A.; Galindo, P.; Losada, I. Rivero

2017-10-01

The Nordic Optical Telescope (NOT) Unbiased Transient Survey (NUTS; ATel #8992) reports the spectroscopic classification of ASASSN-17he. The candidate was discovered by by the All-Sky Automated Survey for Supernovae.

Use of Empirical Estimates of Shrinkage in Multiple Regression: A Caution.

ERIC Educational Resources Information Center

Kromrey, Jeffrey D.; Hines, Constance V.

1995-01-01

The accuracy of four empirical techniques to estimate shrinkage in multiple regression was studied through Monte Carlo simulation. None of the techniques provided unbiased estimates of the population squared multiple correlation coefficient, but the normalized jackknife and bootstrap techniques demonstrated marginally acceptable performance with…

Point estimation following two-stage adaptive threshold enrichment clinical trials.

PubMed

Kimani, Peter K; Todd, Susan; Renfro, Lindsay A; Stallard, Nigel

2018-05-31

Recently, several study designs incorporating treatment effect assessment in biomarker-based subpopulations have been proposed. Most statistical methodologies for such designs focus on the control of type I error rate and power. In this paper, we have developed point estimators for clinical trials that use the two-stage adaptive enrichment threshold design. The design consists of two stages, where in stage 1, patients are recruited in the full population. Stage 1 outcome data are then used to perform interim analysis to decide whether the trial continues to stage 2 with the full population or a subpopulation. The subpopulation is defined based on one of the candidate threshold values of a numerical predictive biomarker. To estimate treatment effect in the selected subpopulation, we have derived unbiased estimators, shrinkage estimators, and estimators that estimate bias and subtract it from the naive estimate. We have recommended one of the unbiased estimators. However, since none of the estimators dominated in all simulation scenarios based on both bias and mean squared error, an alternative strategy would be to use a hybrid estimator where the estimator used depends on the subpopulation selected. This would require a simulation study of plausible scenarios before the trial. © 2018 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Spectroscopic classification of Gaia18adv by NUTS (NOT Un-biased Transient Survey)

NASA Astrophysics Data System (ADS)

Gall, C.; Benetti, S.; Wyrzykowski, L.; Stritzinger, M.; Holmbo, S.; Dong, S.; Siltala, Lauri; NUTS Collaboration

2018-01-01

The Nordic Optical Telescope (NOT) Unbiased Transient Survey (NUTS; ATel #8992) collaboration reports the spectroscopic classification of Gaia18adv (SN2018hh) near the host galaxy SDSS J121341.37+282640.0.

Mutually unbiased coarse-grained measurements of two or more phase-space variables

NASA Astrophysics Data System (ADS)

Paul, E. C.; Walborn, S. P.; Tasca, D. S.; Rudnicki, Łukasz

2018-05-01

Mutual unbiasedness of the eigenstates of phase-space operators—such as position and momentum, or their standard coarse-grained versions—exists only in the limiting case of infinite squeezing. In Phys. Rev. Lett. 120, 040403 (2018), 10.1103/PhysRevLett.120.040403, it was shown that mutual unbiasedness can be recovered for periodic coarse graining of these two operators. Here we investigate mutual unbiasedness of coarse-grained measurements for more than two phase-space variables. We show that mutual unbiasedness can be recovered between periodic coarse graining of any two nonparallel phase-space operators. We illustrate these results through optics experiments, using the fractional Fourier transform to prepare and measure mutually unbiased phase-space variables. The differences between two and three mutually unbiased measurements is discussed. Our results contribute to bridging the gap between continuous and discrete quantum mechanics, and they could be useful in quantum-information protocols.

Estimation methods and parameter assessment for ethanol yields from total soluble solids of sweet sorghum

USDA-ARS?s Scientific Manuscript database

Estimation methods and evaluation of ethanol yield from sweet sorghum (Sorghum bicolor (L.) Moench.) based on agronomic production traits and juice characteristics is important for developing parents and inbred lines of sweet sorghum that can be used by the bio-ethanol industry. The objectives of th...

MODIS Data Assimilation in the CROPGRO model for improving soybean yield estimations

NASA Astrophysics Data System (ADS)

Richetti, J.; Monsivais-Huertero, A.; Ahmad, I.; Judge, J.

2017-12-01

Soybean is one of the main agricultural commodities in the world. Thus, having better estimates of its agricultural production is important. Improving the soybean crop models in Brazil is crucial for better understanding of the soybean market and enhancing decision making, because Brazil is the second largest soybean producer in the world, Parana state is responsible for almost 20% of it, and by itself would be the fourth greatest soybean producer in the world. Data assimilation techniques provide a method to improve spatio-temporal continuity of crops through integration of remotely sensed observations and crop growth models. This study aims to use MODIS EVI to improve DSSAT-CROPGRO soybean yield estimations in the Parana state, southern Brazil. The method uses the Ensemble Kalman filter which assimilates MODIS Terra and Aqua combined products (MOD13Q1 and MYD13Q1) into the CROPGRO model to improve the agricultural production estimates through update of light interception data over time. Expected results will be validated with monitored commercial farms during the period of 2013-2014.

Image Based Mango Fruit Detection, Localisation and Yield Estimation Using Multiple View Geometry

PubMed Central

Stein, Madeleine; Bargoti, Suchet; Underwood, James

2016-01-01

This paper presents a novel multi-sensor framework to efficiently identify, track, localise and map every piece of fruit in a commercial mango orchard. A multiple viewpoint approach is used to solve the problem of occlusion, thus avoiding the need for labour-intensive field calibration to estimate actual yield. Fruit are detected in images using a state-of-the-art faster R-CNN detector, and pair-wise correspondences are established between images using trajectory data provided by a navigation system. A novel LiDAR component automatically generates image masks for each canopy, allowing each fruit to be associated with the corresponding tree. The tracked fruit are triangulated to locate them in 3D, enabling a number of spatial statistics per tree, row or orchard block. A total of 522 trees and 71,609 mangoes were scanned on a Calypso mango orchard near Bundaberg, Queensland, Australia, with 16 trees counted by hand for validation, both on the tree and after harvest. The results show that single, dual and multi-view methods can all provide precise yield estimates, but only the proposed multi-view approach can do so without calibration, with an error rate of only 1.36% for individual trees. PMID:27854271

Estimating hazard ratios in cohort data with missing disease information due to death.

PubMed

Binder, Nadine; Herrnböck, Anne-Sophie; Schumacher, Martin

2017-03-01

In clinical and epidemiological studies information on the primary outcome of interest, that is, the disease status, is usually collected at a limited number of follow-up visits. The disease status can often only be retrieved retrospectively in individuals who are alive at follow-up, but will be missing for those who died before. Right-censoring the death cases at the last visit (ad-hoc analysis) yields biased hazard ratio estimates of a potential risk factor, and the bias can be substantial and occur in either direction. In this work, we investigate three different approaches that use the same likelihood contributions derived from an illness-death multistate model in order to more adequately estimate the hazard ratio by including the death cases into the analysis: a parametric approach, a penalized likelihood approach, and an imputation-based approach. We investigate to which extent these approaches allow for an unbiased regression analysis by evaluating their performance in simulation studies and on a real data example. In doing so, we use the full cohort with complete illness-death data as reference and artificially induce missing information due to death by setting discrete follow-up visits. Compared to an ad-hoc analysis, all considered approaches provide less biased or even unbiased results, depending on the situation studied. In the real data example, the parametric approach is seen to be too restrictive, whereas the imputation-based approach could almost reconstruct the original event history information. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Estimating crop yields and crop evapotranspiration distributions from remote sensing and geospatial agricultural data

NASA Astrophysics Data System (ADS)

Smith, T.; McLaughlin, D.

2017-12-01

Growing more crops to provide a secure food supply to an increasing global population will further stress land and water resources that have already been significantly altered by agriculture. The connection between production and resource use depends on crop yields and unit evapotranspiration (UET) rates that vary greatly, over both time and space. For regional and global analyses of food security it is appropriate to treat yield and UET as uncertain variables conditioned on climatic and soil properties. This study describes how probability distributions of these variables can be estimated by combining remotely sensed land use and evapotranspiration data with in situ agronomic and soils data, all available at different resolutions and coverages. The results reveal the influence of water and temperature stress on crop yield at large spatial scales. They also provide a basis for stochastic modeling and optimization procedures that explicitly account for uncertainty in the environmental factors that affect food production.

Estimating yields of salt- and water-stressed forages with remote sensing in the visible and near infrared.

PubMed

Poss, J A; Russell, W B; Grieve, C M

2006-01-01

In arid irrigated regions, the proportion of crop production under deficit irrigation with poorer quality water is increasing as demand for fresh water soars and efforts to prevent saline water table development occur. Remote sensing technology to quantify salinity and water stress effects on forage yield can be an important tool to address yield loss potential when deficit irrigating with poor water quality. Two important forages, alfalfa (Medicago sativa L.) and tall wheatgrass (Agropyron elongatum L.), were grown in a volumetric lysimeter facility where rootzone salinity and water content were varied and monitored. Ground-based hyperspectral canopy reflectance in the visible and near infrared (NIR) were related to forage yields from a broad range of salinity and water stress conditions. Canopy reflectance spectra were obtained in the 350- to 1000-nm region from two viewing angles (nadir view, 45 degrees from nadir). Nadir view vegetation indices (VI) were not as strongly correlated with leaf area index changes attributed to water and salinity stress treatments for both alfalfa and wheatgrass. From a list of 71 VIs, two were selected for a multiple linear-regression model that estimated yield under varying salinity and water stress conditions. With data obtained during the second harvest of a three-harvest 100-d growing period, regression coefficients for each crop were developed and then used with the model to estimate fresh weights for preceding and succeeding harvests during the same 100-d interval. The model accounted for 72% of the variation in yields in wheatgrass and 94% in yields of alfalfa within the same salinity and water stress treatment period. The model successfully predicted yield in three out of four cases when applied to the first and third harvest yields. Correlations between indices and yield increased as canopy development progressed. Growth reductions attributed to simultaneous salinity and water stress were well characterized, but the

Estimating climate change, CO2 and technology development effects on wheat yield in northeast Iran

NASA Astrophysics Data System (ADS)

Bannayan, M.; Mansoori, H.; Rezaei, E. Eyshi

2014-04-01

Wheat is the main food for the majority of Iran's population. Precise estimation of wheat yield change in future is essential for any possible revision of management strategies. The main objective of this study was to evaluate the effects of climate change, CO2 concentration, technology development and their integrated effects on wheat production under future climate change. This study was performed under two scenarios of the IPCC Special Report on Emission Scenarios (SRES): regional economic (A2) and global environmental (B1). Crop production was projected for three future time periods (2020, 2050 and 2080) in comparison with a baseline year (2005) for Khorasan province located in the northeast of Iran. Four study locations in the study area included Mashhad, Birjand, Bojnourd and Sabzevar. The effect of technology development was calculated by fitting a regression equation between the observed wheat yields against historical years considering yield potential increase and yield gap reduction as technology development. Yield relative increase per unit change of CO2 concentration (1 ppm-1) was considered 0.05 % and was used to implement the effect of elevated CO2. The HadCM3 general circulation model along with the CSM-CERES-Wheat crop model were used to project climate change effects on wheat crop yield. Our results illustrate that, among all the factors considered, technology development provided the highest impact on wheat yield change. Highest wheat yield increase across all locations and time periods was obtained under the A2 scenario. Among study locations, Mashhad showed the highest change in wheat yield. Yield change compared to baseline ranged from -28 % to 56 % when the integration of all factors was considered across all locations. It seems that achieving higher yield of wheat in future may be expected in northeast Iran assuming stable improvements in production technology.

Estimating climate change, CO2 and technology development effects on wheat yield in northeast Iran.

PubMed

Bannayan, M; Mansoori, H; Rezaei, E Eyshi

2014-04-01

Wheat is the main food for the majority of Iran's population. Precise estimation of wheat yield change in future is essential for any possible revision of management strategies. The main objective of this study was to evaluate the effects of climate change, CO2 concentration, technology development and their integrated effects on wheat production under future climate change. This study was performed under two scenarios of the IPCC Special Report on Emission Scenarios (SRES): regional economic (A2) and global environmental (B1). Crop production was projected for three future time periods (2020, 2050 and 2080) in comparison with a baseline year (2005) for Khorasan province located in the northeast of Iran. Four study locations in the study area included Mashhad, Birjand, Bojnourd and Sabzevar. The effect of technology development was calculated by fitting a regression equation between the observed wheat yields against historical years considering yield potential increase and yield gap reduction as technology development. Yield relative increase per unit change of CO2 concentration (1 ppm(-1)) was considered 0.05 % and was used to implement the effect of elevated CO2. The HadCM3 general circulation model along with the CSM-CERES-Wheat crop model were used to project climate change effects on wheat crop yield. Our results illustrate that, among all the factors considered, technology development provided the highest impact on wheat yield change. Highest wheat yield increase across all locations and time periods was obtained under the A2 scenario. Among study locations, Mashhad showed the highest change in wheat yield. Yield change compared to baseline ranged from -28 % to 56 % when the integration of all factors was considered across all locations. It seems that achieving higher yield of wheat in future may be expected in northeast Iran assuming stable improvements in production technology.

Block-circulant matrices with circulant blocks, Weil sums, and mutually unbiased bases. II. The prime power case

DOE Office of Scientific and Technical Information (OSTI.GOV)

Combescure, Monique

2009-03-15

In our previous paper [Combescure, M., 'Circulant matrices, Gauss sums and the mutually unbiased bases. I. The prime number case', Cubo A Mathematical Journal (unpublished)] we have shown that the theory of circulant matrices allows to recover the result that there exists p+1 mutually unbiased bases in dimension p, p being an arbitrary prime number. Two orthonormal bases B, B{sup '} of C{sup d} are said mutually unbiased if for all b(set-membership sign)B, for all b{sup '}(set-membership sign)B{sup '} one has that |b{center_dot}b{sup '}|=1/{radical}(d) (b{center_dot}b{sup '} Hermitian scalar product in C{sup d}). In this paper we show that the theorymore » of block-circulant matrices with circulant blocks allows to show very simply the known result that if d=p{sup n} (p a prime number and n any integer) there exists d+1 mutually unbiased bases in C{sup d}. Our result relies heavily on an idea of Klimov et al. [''Geometrical approach to the discrete Wigner function,'' J. Phys. A 39, 14471 (2006)]. As a subproduct we recover properties of quadratic Weil sums for p{>=}3, which generalizes the fact that in the prime case the quadratic Gauss sum properties follow from our results.« less

Correlation, path analysis and heritability estimation for agronomic traits contribute to yield on soybean

NASA Astrophysics Data System (ADS)

Sulistyo, A.; Purwantoro; Sari, K. P.

2018-01-01

Selection is a routine activity in plant breeding programs that must be done by plant breeders in obtaining superior plant genotypes. The use of appropriate selection criteria will determine the effectiveness of selection activities. The purpose of this study was to analysis the inheritable agronomic traits that contribute to soybean yield. A total of 91 soybean lines were planted in Muneng Experimental Station, Probolinggo District, East Java Province, Indonesia in 2016. All soybean lines were arranged in randomized complete block design with two replicates. Correlation analysis, path analysis and heritability estimation were performed on days to flowering, days to maturing, plant height, number of branches, number of fertile nodes, number of filled pods, weight of 100 seeds, and yield to determine selection criteria on soybean breeding program. The results showed that the heritability value of almost all agronomic traits observed is high except for the number of fertile nodes with low heritability. The result of correlation analysis shows that days to flowering, plant height and number of fertile nodes have positive correlation with seed yield per plot (0.056, 0.444, and 0.100, respectively). In addition, path analysis showed that plant height and number of fertile nodes have highest positive direct effect on soybean yield. Based on this result, plant height can be selected as one of selection criteria in soybean breeding program to obtain high yielding soybean variety.

Unbiased survival estimates and evidence for skipped breeding opportunities in females

USGS Publications Warehouse

Muths, Erin L.; Scherer, Rick D.; Lambert, Brad A.

2010-01-01

5. Establishing the occurrence of temporary emigration not only reduces bias in estimates of survival probabilities but also provides information about expected breeding attempts by females, a critical element in understanding the ecology of an organism and the impacts of outside stressors and conservation actions.

Evaluating the capabilities of watershed-scale models in estimating sediment yield at field-scale.

PubMed

Sommerlot, Andrew R; Nejadhashemi, A Pouyan; Woznicki, Sean A; Giri, Subhasis; Prohaska, Michael D

2013-09-30

Many watershed model interfaces have been developed in recent years for predicting field-scale sediment loads. They share the goal of providing data for decisions aimed at improving watershed health and the effectiveness of water quality conservation efforts. The objectives of this study were to: 1) compare three watershed-scale models (Soil and Water Assessment Tool (SWAT), Field_SWAT, and the High Impact Targeting (HIT) model) against calibrated field-scale model (RUSLE2) in estimating sediment yield from 41 randomly selected agricultural fields within the River Raisin watershed; 2) evaluate the statistical significance among models; 3) assess the watershed models' capabilities in identifying areas of concern at the field level; 4) evaluate the reliability of the watershed-scale models for field-scale analysis. The SWAT model produced the most similar estimates to RUSLE2 by providing the closest median and the lowest absolute error in sediment yield predictions, while the HIT model estimates were the worst. Concerning statistically significant differences between models, SWAT was the only model found to be not significantly different from the calibrated RUSLE2 at α = 0.05. Meanwhile, all models were incapable of identifying priorities areas similar to the RUSLE2 model. Overall, SWAT provided the most correct estimates (51%) within the uncertainty bounds of RUSLE2 and is the most reliable among the studied models, while HIT is the least reliable. The results of this study suggest caution should be exercised when using watershed-scale models for field level decision-making, while field specific data is of paramount importance. Copyright © 2013 Elsevier Ltd. All rights reserved.

Estimating the Effect of Climate Change on Crop Yields and Farmland Values: The Importance of Extreme Temperatures

EPA Pesticide Factsheets

This is a presentation titled Estimating the Effect of Climate Change on Crop Yields and Farmland Values: The Importance of Extreme Temperatures that was given for the National Center for Environmental Economics

On estimation in k-tree sampling

Treesearch

Christoph Kleinn; Frantisek Vilcko

2007-01-01

The plot design known as k-tree sampling involves taking the k nearest trees from a selected sample point as sample trees. While this plot design is very practical and easily applied in the field for moderate values of k, unbiased estimation remains a problem. In this article, we give a brief introduction to the...

Estimation and application of a growth and yield model for uneven-aged mixed conifer stands in California.

Treesearch

Jingjing Liang; J. Buongiorno; R.A. Monserud

2005-01-01

A growth model for uneven-aged mixed-conifer stands in California was developed with data from 205 permanent plots. The model predicts the number of softwood and hardwood trees in nineteen diameter classes, based on equations for diameter growth rates, mortality arid recruitment. The model gave unbiased predictions of the expected number of trees by diameter class and...

The estimation of rice paddy yield with GRAMI crop model and Geostationary Ocean Color Imager (GOCI) image over South Korea

NASA Astrophysics Data System (ADS)

Yeom, J. M.; Kim, H. O.

2014-12-01

In this study, we estimated the rice paddy yield with moderate geostationary satellite based vegetation products and GRAMI model over South Korea. Rice is the most popular staple food for Asian people. In addition, the effects of climate change are getting stronger especially in Asian region, where the most of rice are cultivated. Therefore, accurate and timely prediction of rice yield is one of the most important to accomplish food security and to prepare natural disasters such as crop defoliation, drought, and pest infestation. In the present study, GOCI, which is world first Geostationary Ocean Color Image, was used for estimating temporal vegetation indices of the rice paddy by adopting atmospheric correction BRDF modeling. For the atmospheric correction with LUT method based on Second Simulation of the Satellite Signal in the Solar Spectrum (6S), MODIS atmospheric products such as MOD04, MOD05, MOD07 from NASA's Earth Observing System Data and Information System (EOSDIS) were used. In order to correct the surface anisotropy effect, Ross-Thick Li-Sparse Reciprocal (RTLSR) BRDF model was performed at daily basis with 16day composite period. The estimated multi-temporal vegetation images was used for crop classification by using high resolution satellite images such as Rapideye, KOMPSAT-2 and KOMPSAT-3 to extract the proportional rice paddy area in corresponding a pixel of GOCI. In the case of GRAMI crop model, initial conditions are determined by performing every 2 weeks field works at Chonnam National University, Gwangju, Korea. The corrected GOCI vegetation products were incorporated with GRAMI model to predict rice yield estimation. The predicted rice yield was compared with field measurement of rice yield.

On the mathematical foundations of mutually unbiased bases

NASA Astrophysics Data System (ADS)

Thas, Koen

2018-02-01

In order to describe a setting to handle Zauner's conjecture on mutually unbiased bases (MUBs) (stating that in C^d, a set of MUBs of the theoretical maximal size d + 1 exists only if d is a prime power), we pose some fundamental questions which naturally arise. Some of these questions have important consequences for the construction theory of (new) sets of maximal MUBs. Partial answers will be provided in particular cases; more specifically, we will analyze MUBs with associated operator groups that have nilpotence class 2, and consider MUBs of height 1. We will also confirm Zauner's conjecture for MUBs with associated finite nilpotent operator groups.

Testcross additive and dominance effects in best linear unbiased prediction of maize single-cross performance.

PubMed

Bernardo, R

1996-11-01

Best linear unbiased prediction (BLUP) has been found to be useful in maize (Zea mays L.) breeding. The advantage of including both testcross additive and dominance effects (Intralocus Model) in BLUP, rather than only testcross additive effects (Additive Model), has not been clearly demonstrated. The objective of this study was to compare the usefulness of Intralocus and Additive Models for BLUP of maize single-cross performance. Multilocation data from 1990 to 1995 were obtained from the hybrid testing program of Limagrain Genetics. Grain yield, moisture, stalk lodging, and root lodging of untested single crosses were predicted from (1) the performance of tested single crosses and (2) known genetic relationships among the parental inbreds. Correlations between predicted and observed performance were obtained with a delete-one cross-validation procedure. For the Intralocus Model, the correlations ranged from 0.50 to 0.66 for yield, 0.88 to 0.94 for moisture, 0.47 to 0.69 for stalk lodging, and 0.31 to 0.45 for root lodging. The BLUP procedure was consistently more effective with the Intralocus Model than with the Additive Model. When the Additive Model was used instead of the Intralocus Model, the reductions in the correlation were largest for root lodging (0.06-0.35), smallest for moisture (0.00-0.02), and intermediate for yield (0.02-0.06) and stalk lodging (0.02-0.08). The ratio of dominance variance (v D) to total genetic variance (v G) was highest for root lodging (0.47) and lowest for moisture (0.10). The Additive Model may be used if prior information indicates that VD for a given trait has little contribution to VG. Otherwise, the continued use of the Intralocus Model for BLUP of single-cross performance is recommended.

Winter wheat yield estimation of remote sensing research based on WOFOST crop model and leaf area index assimilation

NASA Astrophysics Data System (ADS)

Chen, Yanling; Gong, Adu; Li, Jing; Wang, Jingmei

2017-04-01

Accurate crop growth monitoring and yield predictive information are significant to improve the sustainable development of agriculture and ensure the security of national food. Remote sensing observation and crop growth simulation models are two new technologies, which have highly potential applications in crop growth monitoring and yield forecasting in recent years. However, both of them have limitations in mechanism or regional application respectively. Remote sensing information can not reveal crop growth and development, inner mechanism of yield formation and the affection of environmental meteorological conditions. Crop growth simulation models have difficulties in obtaining data and parameterization from single-point to regional application. In order to make good use of the advantages of these two technologies, the coupling technique of remote sensing information and crop growth simulation models has been studied. Filtering and optimizing model parameters are key to yield estimation by remote sensing and crop model based on regional crop assimilation. Winter wheat of GaoCheng was selected as the experiment object in this paper. And then the essential data was collected, such as biochemical data and farmland environmental data and meteorological data about several critical growing periods. Meanwhile, the image of environmental mitigation small satellite HJ-CCD was obtained. In this paper, research work and major conclusions are as follows. (1) Seven vegetation indexes were selected to retrieve LAI, and then linear regression model was built up between each of these indexes and the measured LAI. The result shows that the accuracy of EVI model was the highest (R2=0.964 at anthesis stage and R2=0.920 at filling stage). Thus, EVI as the most optimal vegetation index to predict LAI in this paper. (2) EFAST method was adopted in this paper to conduct the sensitive analysis to the 26 initial parameters of the WOFOST model and then a sensitivity index was constructed

Circulating tumor cell detection: A direct comparison between negative and unbiased enrichment in lung cancer.

PubMed

Xu, Yan; Liu, Biao; Ding, Fengan; Zhou, Xiaodie; Tu, Pin; Yu, Bo; He, Yan; Huang, Peilin

2017-06-01

Circulating tumor cells (CTCs), isolated as a 'liquid biopsy', may provide important diagnostic and prognostic information. Therefore, rapid, reliable and unbiased detection of CTCs are required for routine clinical analyses. It was demonstrated that negative enrichment, an epithelial marker-independent technique for isolating CTCs, exhibits a better efficiency in the detection of CTCs compared with positive enrichment techniques that only use specific anti-epithelial cell adhesion molecules. However, negative enrichment techniques incur significant cell loss during the isolation procedure, and as it is a method that uses only one type of antibody, it is inherently biased. The detection procedure and identification of cell types also relies on skilled and experienced technicians. In the present study, the detection sensitivity of using negative enrichment and a previously described unbiased detection method was compared. The results revealed that unbiased detection methods may efficiently detect >90% of cancer cells in blood samples containing CTCs. By contrast, only 40-60% of CTCs were detected by negative enrichment. Additionally, CTCs were identified in >65% of patients with stage I/II lung cancer. This simple yet efficient approach may achieve a high level of sensitivity. It demonstrates a potential for the large-scale clinical implementation of CTC-based diagnostic and prognostic strategies.

Estimation of genetic parameters and selection of high-yielding, upright common bean lines with slow seed-coat darkening.

PubMed

Alvares, R C; Silva, F C; Melo, L C; Melo, P G S; Pereira, H S

2016-11-21

Slow seed coat darkening is desirable in common bean cultivars and genetic parameters are important to define breeding strategies. The aims of this study were to estimate genetic parameters for plant architecture, grain yield, grain size, and seed-coat darkening in common bean; identify any genetic association among these traits; and select lines that associate desirable phenotypes for these traits. Three experiments were set up in the winter 2012 growing season, in Santo Antônio de Goiás and Brasília, Brazil, including 220 lines obtained from four segregating populations and five parents. A triple lattice 15 x 15 experimental design was used. The traits evaluated were plant architecture, grain yield, grain size, and seed-coat darkening. Analyses of variance were carried out and genetic parameters such as heritability, gain expected from selection, and correlations, were estimated. For selection of superior lines, a "weight-free and parameter-free" index was used. The estimates of genetic variance, heritability, and gain expected from selection were high, indicating good possibility for success in selection of the four traits. The genotype x environment interaction was proportionally more important for yield than for the other traits. There was no strong genetic correlation observed among the four traits, which indicates the possibility of selection of superior lines with many traits. Considering simultaneous selection, it was not possible to join high genetic gains for the four traits. Forty-four lines that combined high yield, more upright plant architecture, slow darkening grains, and commercial grade size were selected.

Soviet test yields

NASA Astrophysics Data System (ADS)

Vergino, Eileen S.

Soviet seismologists have published descriptions of 96 nuclear explosions conducted from 1961 through 1972 at the Semipalatinsk test site, in Kazakhstan, central Asia [Bocharov et al., 1989]. With the exception of releasing news about some of their peaceful nuclear explosions (PNEs) the Soviets have never before published such a body of information.To estimate the seismic yield of a nuclear explosion it is necessary to obtain a calibrated magnitude-yield relationship based on events with known yields and with a consistent set of seismic magnitudes. U.S. estimation of Soviet test yields has been done through application of relationships to the Soviet sites based on the U.S. experience at the Nevada Test Site (NTS), making some correction for differences due to attenuation and near-source coupling of seismic waves.

Optimal estimation of diffusion coefficients from single-particle trajectories

NASA Astrophysics Data System (ADS)

Vestergaard, Christian L.; Blainey, Paul C.; Flyvbjerg, Henrik

2014-02-01

How does one optimally determine the diffusion coefficient of a diffusing particle from a single-time-lapse recorded trajectory of the particle? We answer this question with an explicit, unbiased, and practically optimal covariance-based estimator (CVE). This estimator is regression-free and is far superior to commonly used methods based on measured mean squared displacements. In experimentally relevant parameter ranges, it also outperforms the analytically intractable and computationally more demanding maximum likelihood estimator (MLE). For the case of diffusion on a flexible and fluctuating substrate, the CVE is biased by substrate motion. However, given some long time series and a substrate under some tension, an extended MLE can separate particle diffusion on the substrate from substrate motion in the laboratory frame. This provides benchmarks that allow removal of bias caused by substrate fluctuations in CVE. The resulting unbiased CVE is optimal also for short time series on a fluctuating substrate. We have applied our estimators to human 8-oxoguanine DNA glycolase proteins diffusing on flow-stretched DNA, a fluctuating substrate, and found that diffusion coefficients are severely overestimated if substrate fluctuations are not accounted for.

GT0 Explosion Sources for IMS Infrasound Calibration: Charge Design and Yield Estimation from Near-source Observations

NASA Astrophysics Data System (ADS)

Gitterman, Y.; Hofstetter, R.

2014-03-01

Three large-scale on-surface explosions were conducted by the Geophysical Institute of Israel (GII) at the Sayarim Military Range, Negev desert, Israel: about 82 tons of strong high explosives in August 2009, and two explosions of about 10 and 100 tons of ANFO explosives in January 2011. It was a collaborative effort between Israel, CTBTO, USA and several European countries, with the main goal to provide fully controlled ground truth (GT0) infrasound sources, monitored by extensive observations, for calibration of International Monitoring System (IMS) infrasound stations in Europe, Middle East and Asia. In all shots, the explosives were assembled like a pyramid/hemisphere on dry desert alluvium, with a complicated explosion design, different from the ideal homogenous hemisphere used in similar experiments in the past. Strong boosters and an upward charge detonation scheme were applied to provide more energy radiated to the atmosphere. Under these conditions the evaluation of the actual explosion yield, an important source parameter, is crucial for the GT0 calibration experiment. Audio-visual, air-shock and acoustic records were utilized for interpretation of observed unique blast effects, and for determination of blast wave parameters suited for yield estimation and the associated relationships. High-pressure gauges were deployed at 100-600 m to record air-blast properties, evaluate the efficiency of the charge design and energy generation, and provide a reliable estimation of the charge yield. The yield estimators, based on empirical scaled relations for well-known basic air-blast parameters—the peak pressure, impulse and positive phase duration, as well as on the crater dimensions and seismic magnitudes, were analyzed. A novel empirical scaled relationship for the little-known secondary shock delay was developed, consistent for broad ranges of ANFO charges and distances, which facilitates using this stable and reliable air-blast parameter as a new potential

Ethiopian Wheat Yield and Yield Gap Estimation: A Spatial Small Area Integrated Data Approach

NASA Astrophysics Data System (ADS)

Mann, M.; Warner, J.

2015-12-01

Despite the collection of routine annual agricultural surveys and significant advances in GIS and remote sensing products, little econometric research has been undertaken in predicting developing nation's agricultural yields. In this paper, we explore the determinants of wheat output per hectare in Ethiopia during the 2011-2013 Meher crop seasons aggregated to the woreda administrative area. Using a panel data approach, combining national agricultural field surveys with relevant GIS and remote sensing products, the model explains nearly 40% of the total variation in wheat output per hectare across the country. The model also identifies specific contributors to wheat yields that include farm management techniques (eg. area planted, improved seed, fertilizer, irrigation), weather (eg. rainfall), water availability (vegetation and moisture deficit indexes) and policy intervention. Our findings suggest that woredas produce between 9.8 and 86.5% of their potential wheat output per hectare given their altitude, weather conditions, terrain, and plant health. At the median, Amhara, Oromiya, SNNP, and Tigray produce 48.6, 51.5, 49.7, and 61.3% of their local attainable yields, respectively. This research has a broad range of applications, especially from a public policy perspective: identifying causes of yield fluctuations, remotely evaluating larger agricultural intervention packages, and analyzing relative yield potential. Overall, the combination of field surveys with spatial data can be used to identify management priorities for improving production at a variety of administrative levels.

Nearest neighbor density ratio estimation for large-scale applications in astronomy

NASA Astrophysics Data System (ADS)

Kremer, J.; Gieseke, F.; Steenstrup Pedersen, K.; Igel, C.

2015-09-01

In astronomical applications of machine learning, the distribution of objects used for building a model is often different from the distribution of the objects the model is later applied to. This is known as sample selection bias, which is a major challenge for statistical inference as one can no longer assume that the labeled training data are representative. To address this issue, one can re-weight the labeled training patterns to match the distribution of unlabeled data that are available already in the training phase. There are many examples in practice where this strategy yielded good results, but estimating the weights reliably from a finite sample is challenging. We consider an efficient nearest neighbor density ratio estimator that can exploit large samples to increase the accuracy of the weight estimates. To solve the problem of choosing the right neighborhood size, we propose to use cross-validation on a model selection criterion that is unbiased under covariate shift. The resulting algorithm is our method of choice for density ratio estimation when the feature space dimensionality is small and sample sizes are large. The approach is simple and, because of the model selection, robust. We empirically find that it is on a par with established kernel-based methods on relatively small regression benchmark datasets. However, when applied to large-scale photometric redshift estimation, our approach outperforms the state-of-the-art.

Estimation of genetic parameters for heat stress, including dominance gene effects, on milk yield in Thai Holstein dairy cattle.

PubMed

Boonkum, Wuttigrai; Duangjinda, Monchai

2015-03-01

Heat stress in tropical regions is a major cause that strongly negatively affects to milk production in dairy cattle. Genetic selection for dairy heat tolerance is powerful technique to improve genetic performance. Therefore, the current study aimed to estimate genetic parameters and investigate the threshold point of heat stress for milk yield. Data included 52 701 test-day milk yield records for the first parity from 6247 Thai Holstein dairy cattle, covering the period 1990 to 2007. The random regression test day model with EM-REML was used to estimate variance components, genetic parameters and milk production loss. A decline in milk production was found when temperature and humidity index (THI) exceeded a threshold of 74, also it was associated with the high percentage of Holstein genetics. All variance component estimates increased with THI. The estimate of heritability of test-day milk yield was 0.231. Dominance variance as a proportion to additive variance (0.035) indicated that non-additive effects might not be of concern for milk genetics studies in Thai Holstein cattle. Correlations between genetic and permanent environmental effects, for regular conditions and due to heat stress, were - 0.223 and - 0.521, respectively. The heritability and genetic correlations from this study show that simultaneous selection for milk production and heat tolerance is possible. © 2014 Japanese Society of Animal Science.

Can We Spin Straw Into Gold? An Evaluation of Immigrant Legal Status Imputation Approaches

PubMed Central

Van Hook, Jennifer; Bachmeier, James D.; Coffman, Donna; Harel, Ofer

2014-01-01

Researchers have developed logical, demographic, and statistical strategies for imputing immigrants’ legal status, but these methods have never been empirically assessed. We used Monte Carlo simulations to test whether, and under what conditions, legal status imputation approaches yield unbiased estimates of the association of unauthorized status with health insurance coverage. We tested five methods under a range of missing data scenarios. Logical and demographic imputation methods yielded biased estimates across all missing data scenarios. Statistical imputation approaches yielded unbiased estimates only when unauthorized status was jointly observed with insurance coverage; when this condition was not met, these methods overestimated insurance coverage for unauthorized relative to legal immigrants. We next showed how bias can be reduced by incorporating prior information about unauthorized immigrants. Finally, we demonstrated the utility of the best-performing statistical method for increasing power. We used it to produce state/regional estimates of insurance coverage among unauthorized immigrants in the Current Population Survey, a data source that contains no direct measures of immigrants’ legal status. We conclude that commonly employed legal status imputation approaches are likely to produce biased estimates, but data and statistical methods exist that could substantially reduce these biases. PMID:25511332

Growth and yield models for central hardwoods

Treesearch

Martin E. Dale; Donald E. Hilt

1989-01-01

Over the last 20 years computers have become an efficient tool to estimate growth and yield. Computerized yield estimates vary from simple approximation or interpolation of traditional normal yield tables to highly sophisticated programs that simulate the growth and yield of each individual tree.

Improved False Discovery Rate Estimation Procedure for Shotgun Proteomics.

PubMed

Keich, Uri; Kertesz-Farkas, Attila; Noble, William Stafford

2015-08-07

Interpreting the potentially vast number of hypotheses generated by a shotgun proteomics experiment requires a valid and accurate procedure for assigning statistical confidence estimates to identified tandem mass spectra. Despite the crucial role such procedures play in most high-throughput proteomics experiments, the scientific literature has not reached a consensus about the best confidence estimation methodology. In this work, we evaluate, using theoretical and empirical analysis, four previously proposed protocols for estimating the false discovery rate (FDR) associated with a set of identified tandem mass spectra: two variants of the target-decoy competition protocol (TDC) of Elias and Gygi and two variants of the separate target-decoy search protocol of Käll et al. Our analysis reveals significant biases in the two separate target-decoy search protocols. Moreover, the one TDC protocol that provides an unbiased FDR estimate among the target PSMs does so at the cost of forfeiting a random subset of high-scoring spectrum identifications. We therefore propose the mix-max procedure to provide unbiased, accurate FDR estimates in the presence of well-calibrated scores. The method avoids biases associated with the two separate target-decoy search protocols and also avoids the propensity for target-decoy competition to discard a random subset of high-scoring target identifications.

Improved False Discovery Rate Estimation Procedure for Shotgun Proteomics

PubMed Central

2016-01-01

Interpreting the potentially vast number of hypotheses generated by a shotgun proteomics experiment requires a valid and accurate procedure for assigning statistical confidence estimates to identified tandem mass spectra. Despite the crucial role such procedures play in most high-throughput proteomics experiments, the scientific literature has not reached a consensus about the best confidence estimation methodology. In this work, we evaluate, using theoretical and empirical analysis, four previously proposed protocols for estimating the false discovery rate (FDR) associated with a set of identified tandem mass spectra: two variants of the target-decoy competition protocol (TDC) of Elias and Gygi and two variants of the separate target-decoy search protocol of Käll et al. Our analysis reveals significant biases in the two separate target-decoy search protocols. Moreover, the one TDC protocol that provides an unbiased FDR estimate among the target PSMs does so at the cost of forfeiting a random subset of high-scoring spectrum identifications. We therefore propose the mix-max procedure to provide unbiased, accurate FDR estimates in the presence of well-calibrated scores. The method avoids biases associated with the two separate target-decoy search protocols and also avoids the propensity for target-decoy competition to discard a random subset of high-scoring target identifications. PMID:26152888

Assimilating Remote Sensing Observations of Leaf Area Index and Soil Moisture for Wheat Yield Estimates: An Observing System Simulation Experiment

NASA Technical Reports Server (NTRS)

Nearing, Grey S.; Crow, Wade T.; Thorp, Kelly R.; Moran, Mary S.; Reichle, Rolf H.; Gupta, Hoshin V.

2012-01-01

Observing system simulation experiments were used to investigate ensemble Bayesian state updating data assimilation of observations of leaf area index (LAI) and soil moisture (theta) for the purpose of improving single-season wheat yield estimates with the Decision Support System for Agrotechnology Transfer (DSSAT) CropSim-Ceres model. Assimilation was conducted in an energy-limited environment and a water-limited environment. Modeling uncertainty was prescribed to weather inputs, soil parameters and initial conditions, and cultivar parameters and through perturbations to model state transition equations. The ensemble Kalman filter and the sequential importance resampling filter were tested for the ability to attenuate effects of these types of uncertainty on yield estimates. LAI and theta observations were synthesized according to characteristics of existing remote sensing data, and effects of observation error were tested. Results indicate that the potential for assimilation to improve end-of-season yield estimates is low. Limitations are due to a lack of root zone soil moisture information, error in LAI observations, and a lack of correlation between leaf and grain growth.

Some New Results on Grubbs’ Estimators.

DTIC Science & Technology

1983-06-01

8217 ESTIMATORS DENNIS A. BRINDLEY AND RALPH A. BRADLEY* Consider a two-way classification with n rows and r columns and the usual model of analysis of variance...except that the error components of the model may have heterogeneous variances, by columns. -Grubbs provided unbiased estimators Q. of a . that depend...of observations yij, i = 1, ... , n, j 1, ... , r, and the model , Yij = Ili + ij + Ej, (1) when Vi represents the mean response of row i, . represents

Simple, Efficient Estimators of Treatment Effects in Randomized Trials Using Generalized Linear Models to Leverage Baseline Variables

PubMed Central

Rosenblum, Michael; van der Laan, Mark J.

2010-01-01

Models, such as logistic regression and Poisson regression models, are often used to estimate treatment effects in randomized trials. These models leverage information in variables collected before randomization, in order to obtain more precise estimates of treatment effects. However, there is the danger that model misspecification will lead to bias. We show that certain easy to compute, model-based estimators are asymptotically unbiased even when the working model used is arbitrarily misspecified. Furthermore, these estimators are locally efficient. As a special case of our main result, we consider a simple Poisson working model containing only main terms; in this case, we prove the maximum likelihood estimate of the coefficient corresponding to the treatment variable is an asymptotically unbiased estimator of the marginal log rate ratio, even when the working model is arbitrarily misspecified. This is the log-linear analog of ANCOVA for linear models. Our results demonstrate one application of targeted maximum likelihood estimation. PMID:20628636

The effect of flow data resolution on sediment yield estimation and channel design

NASA Astrophysics Data System (ADS)

Rosburg, Tyler T.; Nelson, Peter A.; Sholtes, Joel S.; Bledsoe, Brian P.

2016-07-01

The decision to use either daily-averaged or sub-daily streamflow records has the potential to impact the calculation of sediment transport metrics and stream channel design. Using bedload and suspended load sediment transport measurements collected at 138 sites across the United States, we calculated the effective discharge, sediment yield, and half-load discharge using sediment rating curves over long time periods (median record length = 24 years) with both daily-averaged and sub-daily streamflow records. A comparison of sediment transport metrics calculated with both daily-average and sub-daily stream flow data at each site showed that daily-averaged flow data do not adequately represent the magnitude of high stream flows at hydrologically flashy sites. Daily-average stream flow data cause an underestimation of sediment transport and sediment yield (including the half-load discharge) at flashy sites. The degree of underestimation was correlated with the level of flashiness and the exponent of the sediment rating curve. No consistent relationship between the use of either daily-average or sub-daily streamflow data and the resultant effective discharge was found. When used in channel design, computed sediment transport metrics may have errors due to flow data resolution, which can propagate into design slope calculations which, if implemented, could lead to unwanted aggradation or degradation in the design channel. This analysis illustrates the importance of using sub-daily flow data in the calculation of sediment yield in urbanizing or otherwise flashy watersheds. Furthermore, this analysis provides practical charts for estimating and correcting these types of underestimation errors commonly incurred in sediment yield calculations.

Applying network analysis and Nebula (neighbor-edges based and unbiased leverage algorithm) to ToxCast data.

PubMed

Ye, Hao; Luo, Heng; Ng, Hui Wen; Meehan, Joe; Ge, Weigong; Tong, Weida; Hong, Huixiao

2016-01-01

ToxCast data have been used to develop models for predicting in vivo toxicity. To predict the in vivo toxicity of a new chemical using a ToxCast data based model, its ToxCast bioactivity data are needed but not normally available. The capability of predicting ToxCast bioactivity data is necessary to fully utilize ToxCast data in the risk assessment of chemicals. We aimed to understand and elucidate the relationships between the chemicals and bioactivity data of the assays in ToxCast and to develop a network analysis based method for predicting ToxCast bioactivity data. We conducted modularity analysis on a quantitative network constructed from ToxCast data to explore the relationships between the assays and chemicals. We further developed Nebula (neighbor-edges based and unbiased leverage algorithm) for predicting ToxCast bioactivity data. Modularity analysis on the network constructed from ToxCast data yielded seven modules. Assays and chemicals in the seven modules were distinct. Leave-one-out cross-validation yielded a Q(2) of 0.5416, indicating ToxCast bioactivity data can be predicted by Nebula. Prediction domain analysis showed some types of ToxCast assay data could be more reliably predicted by Nebula than others. Network analysis is a promising approach to understand ToxCast data. Nebula is an effective algorithm for predicting ToxCast bioactivity data, helping fully utilize ToxCast data in the risk assessment of chemicals. Published by Elsevier Ltd.

Estimation of sediment yield from subsequent expanded landslides after heavy rainfalls : a case study in central Hokkaido, Japan

NASA Astrophysics Data System (ADS)

Koshimizu, K.; Uchida, T.

2015-12-01

Initial large-scale sediment yield caused by heavy rainfall or major storms have made a strong impression on us. Previous studies focusing on landslide management investigated the initial sediment movement and its mechanism. However, integrated management of catchment-scale sediment movements requires estimating the sediment yield, which is produced by the subsequent expanded landslides due to rainfall, in addition to the initial landslide movement. This study presents a quantitative analysis of expanded landslides by surveying the Shukushubetsu River basin, at the foot of the Hidaka mountain range in central Hokkaido, Japan. This area recorded heavy rainfall in 2003, reaching a maximum daily precipitation of 388 mm. We extracted the expanded landslides from 2003 to 2008 using aerial photographs taken over the river area. In particular, we calculated the probability of expansion for each landslide, the ratio of the landslide area in 2008 as compared with that in 2003, and the amount of the expanded landslide area corresponding to the initial landslide area. As a result, it is estimated 24% about probability of expansion for each landslide. In addition, each expanded landslide area is smaller than the initial landslide area. Furthermore, the amount of each expanded landslide area in 2008 is approximately 7% of their landslide area in 2003. Therefore, the sediment yield from subsequent expanded landslides is equal to or slightly greater than the sediment yield in a typical base flow. Thus, we concluded that the amount of sediment yield from subsequent expanded landslides is lower than that of initial large-scale sediment yield caused by a heavy rainfall in terms of effect on management of catchment-scale sediment movement.

Framework for making better predictions by directly estimating variables’ predictivity

PubMed Central

Chernoff, Herman; Lo, Shaw-Hwa

2016-01-01

We propose approaching prediction from a framework grounded in the theoretical correct prediction rate of a variable set as a parameter of interest. This framework allows us to define a measure of predictivity that enables assessing variable sets for, preferably high, predictivity. We first define the prediction rate for a variable set and consider, and ultimately reject, the naive estimator, a statistic based on the observed sample data, due to its inflated bias for moderate sample size and its sensitivity to noisy useless variables. We demonstrate that the I-score of the PR method of VS yields a relatively unbiased estimate of a parameter that is not sensitive to noisy variables and is a lower bound to the parameter of interest. Thus, the PR method using the I-score provides an effective approach to selecting highly predictive variables. We offer simulations and an application of the I-score on real data to demonstrate the statistic’s predictive performance on sample data. We conjecture that using the partition retention and I-score can aid in finding variable sets with promising prediction rates; however, further research in the avenue of sample-based measures of predictivity is much desired. PMID:27911830

Using operational data to estimate the reliable yields of water-supply wells

NASA Astrophysics Data System (ADS)

Misstear, Bruce D. R.; Beeson, Sarah

The reliable yield of a water-supply well depends on many different factors, including the properties of the well and the aquifer; the capacities of the pumps, raw-water mains, and treatment works; the interference effects from other wells; and the constraints imposed by ion licences, water quality, and environmental issues. A relatively simple methodology for estimating reliable yields has been developed that takes into account all of these factors. The methodology is based mainly on an analysis of water-level and source-output data, where such data are available. Good operational data are especially important when dealing with wells in shallow, unconfined, fissure-flow aquifers, where actual well performance may vary considerably from that predicted using a more analytical approach. Key issues in the yield-assessment process are the identification of a deepest advisable pumping water level, and the collection of the appropriate well, aquifer, and operational data. Although developed for water-supply operators in the United Kingdom, this approach to estimating the reliable yields of water-supply wells using operational data should be applicable to a wide range of hydrogeological conditions elsewhere. Résumé La productivité d'un puits capté pour l'adduction d'eau potable dépend de différents facteurs, parmi lesquels les propriétés du puits et de l'aquifère, la puissance des pompes, le traitement des eaux brutes, les effets d'interférences avec d'autres puits et les contraintes imposées par les autorisations d'exploitation, par la qualité des eaux et par les conditions environnementales. Une méthodologie relativement simple d'estimation de la productivité qui prenne en compte tous ces facteurs a été mise au point. Cette méthodologie est basée surtout sur une analyse des données concernant le niveau piézométrique et le débit de prélèvement, quand ces données sont disponibles. De bonnes données opérationnelles sont particuli

Comparison of methods for estimating the attributable risk in the context of survival analysis.

PubMed

Gassama, Malamine; Bénichou, Jacques; Dartois, Laureen; Thiébaut, Anne C M

2017-01-23

The attributable risk (AR) measures the proportion of disease cases that can be attributed to an exposure in the population. Several definitions and estimation methods have been proposed for survival data. Using simulations, we compared four methods for estimating AR defined in terms of survival functions: two nonparametric methods based on Kaplan-Meier's estimator, one semiparametric based on Cox's model, and one parametric based on the piecewise constant hazards model, as well as one simpler method based on estimated exposure prevalence at baseline and Cox's model hazard ratio. We considered a fixed binary exposure with varying exposure probabilities and strengths of association, and generated event times from a proportional hazards model with constant or monotonic (decreasing or increasing) Weibull baseline hazard, as well as from a nonproportional hazards model. We simulated 1,000 independent samples of size 1,000 or 10,000. The methods were compared in terms of mean bias, mean estimated standard error, empirical standard deviation and 95% confidence interval coverage probability at four equally spaced time points. Under proportional hazards, all five methods yielded unbiased results regardless of sample size. Nonparametric methods displayed greater variability than other approaches. All methods showed satisfactory coverage except for nonparametric methods at the end of follow-up for a sample size of 1,000 especially. With nonproportional hazards, nonparametric methods yielded similar results to those under proportional hazards, whereas semiparametric and parametric approaches that both relied on the proportional hazards assumption performed poorly. These methods were applied to estimate the AR of breast cancer due to menopausal hormone therapy in 38,359 women of the E3N cohort. In practice, our study suggests to use the semiparametric or parametric approaches to estimate AR as a function of time in cohort studies if the proportional hazards assumption appears

Genetic parameters for test-day yield of milk, fat and protein in buffaloes estimated by random regression models.

PubMed

Aspilcueta-Borquis, Rúsbel R; Araujo Neto, Francisco R; Baldi, Fernando; Santos, Daniel J A; Albuquerque, Lucia G; Tonhati, Humberto

2012-08-01

The test-day yields of milk, fat and protein were analysed from 1433 first lactations of buffaloes of the Murrah breed, daughters of 113 sires from 12 herds in the state of São Paulo, Brazil, born between 1985 and 2007. For the test-day yields, 10 monthly classes of lactation days were considered. The contemporary groups were defined as the herd-year-month of the test day. Random additive genetic, permanent environmental and residual effects were included in the model. The fixed effects considered were the contemporary group, number of milkings (1 or 2 milkings), linear and quadratic effects of the covariable cow age at calving and the mean lactation curve of the population (modelled by third-order Legendre orthogonal polynomials). The random additive genetic and permanent environmental effects were estimated by means of regression on third- to sixth-order Legendre orthogonal polynomials. The residual variances were modelled with a homogenous structure and various heterogeneous classes. According to the likelihood-ratio test, the best model for milk and fat production was that with four residual variance classes, while a third-order Legendre polynomial was best for the additive genetic effect for milk and fat yield, a fourth-order polynomial was best for the permanent environmental effect for milk production and a fifth-order polynomial was best for fat production. For protein yield, the best model was that with three residual variance classes and third- and fourth-order Legendre polynomials were best for the additive genetic and permanent environmental effects, respectively. The heritability estimates for the characteristics analysed were moderate, varying from 0·16±0·05 to 0·29±0·05 for milk yield, 0·20±0·05 to 0·30±0·08 for fat yield and 0·18±0·06 to 0·27±0·08 for protein yield. The estimates of the genetic correlations between the tests varied from 0·18±0·120 to 0·99±0·002; from 0·44±0·080 to 0·99±0·004; and from 0·41±0·080 to

Estimation of monthly water yields and flows for 1951-2012 for the United States portion of the Great Lakes Basin with AFINCH

USGS Publications Warehouse

Luukkonen, Carol L.; Holtschlag, David J.; Reeves, Howard W.; Hoard, Christopher J.; Fuller, Lori M.

2015-01-01

Monthly water yields from 105,829 catchments and corresponding flows in 107,691 stream segments were estimated for water years 1951–2012 in the Great Lakes Basin in the United States. Both sets of estimates were computed by using the Analysis of Flows In Networks of CHannels (AFINCH) application within the NHDPlus geospatial data framework. AFINCH provides an environment to develop constrained regression models to integrate monthly streamflow and water-use data with monthly climatic data and fixed basin characteristics data available within NHDPlus or supplied by the user. For this study, the U.S. Great Lakes Basin was partitioned into seven study areas by grouping selected hydrologic subregions and adjoining cataloguing units. This report documents the regression models and data used to estimate monthly water yields and flows in each study area. Estimates of monthly water yields and flows are presented in a Web-based mapper application. Monthly flow time series for individual stream segments can be retrieved from the Web application and used to approximate monthly flow-duration characteristics and to identify possible trends.

One-shot estimate of MRMC variance: AUC.

PubMed

Gallas, Brandon D

2006-03-01

One popular study design for estimating the area under the receiver operating characteristic curve (AUC) is the one in which a set of readers reads a set of cases: a fully crossed design in which every reader reads every case. The variability of the subsequent reader-averaged AUC has two sources: the multiple readers and the multiple cases (MRMC). In this article, we present a nonparametric estimate for the variance of the reader-averaged AUC that is unbiased and does not use resampling tools. The one-shot estimate is based on the MRMC variance derived by the mechanistic approach of Barrett et al. (2005), as well as the nonparametric variance of a single-reader AUC derived in the literature on U statistics. We investigate the bias and variance properties of the one-shot estimate through a set of Monte Carlo simulations with simulated model observers and images. The different simulation configurations vary numbers of readers and cases, amounts of image noise and internal noise, as well as how the readers are constructed. We compare the one-shot estimate to a method that uses the jackknife resampling technique with an analysis of variance model at its foundation (Dorfman et al. 1992). The name one-shot highlights that resampling is not used. The one-shot and jackknife estimators behave similarly, with the one-shot being marginally more efficient when the number of cases is small. We have derived a one-shot estimate of the MRMC variance of AUC that is based on a probabilistic foundation with limited assumptions, is unbiased, and compares favorably to an established estimate.

Markov state models from short non-equilibrium simulations—Analysis and correction of estimation bias

NASA Astrophysics Data System (ADS)

Nüske, Feliks; Wu, Hao; Prinz, Jan-Hendrik; Wehmeyer, Christoph; Clementi, Cecilia; Noé, Frank

2017-03-01

Many state-of-the-art methods for the thermodynamic and kinetic characterization of large and complex biomolecular systems by simulation rely on ensemble approaches, where data from large numbers of relatively short trajectories are integrated. In this context, Markov state models (MSMs) are extremely popular because they can be used to compute stationary quantities and long-time kinetics from ensembles of short simulations, provided that these short simulations are in "local equilibrium" within the MSM states. However, over the last 15 years since the inception of MSMs, it has been controversially discussed and not yet been answered how deviations from local equilibrium can be detected, whether these deviations induce a practical bias in MSM estimation, and how to correct for them. In this paper, we address these issues: We systematically analyze the estimation of MSMs from short non-equilibrium simulations, and we provide an expression for the error between unbiased transition probabilities and the expected estimate from many short simulations. We show that the unbiased MSM estimate can be obtained even from relatively short non-equilibrium simulations in the limit of long lag times and good discretization. Further, we exploit observable operator model (OOM) theory to derive an unbiased estimator for the MSM transition matrix that corrects for the effect of starting out of equilibrium, even when short lag times are used. Finally, we show how the OOM framework can be used to estimate the exact eigenvalues or relaxation time scales of the system without estimating an MSM transition matrix, which allows us to practically assess the discretization quality of the MSM. Applications to model systems and molecular dynamics simulation data of alanine dipeptide are included for illustration. The improved MSM estimator is implemented in PyEMMA of version 2.3.

Accurate and quantitative polarization-sensitive OCT by unbiased birefringence estimator with noise-stochastic correction

NASA Astrophysics Data System (ADS)

Kasaragod, Deepa; Sugiyama, Satoshi; Ikuno, Yasushi; Alonso-Caneiro, David; Yamanari, Masahiro; Fukuda, Shinichi; Oshika, Tetsuro; Hong, Young-Joo; Li, En; Makita, Shuichi; Miura, Masahiro; Yasuno, Yoshiaki

2016-03-01

Polarization sensitive optical coherence tomography (PS-OCT) is a functional extension of OCT that contrasts the polarization properties of tissues. It has been applied to ophthalmology, cardiology, etc. Proper quantitative imaging is required for a widespread clinical utility. However, the conventional method of averaging to improve the signal to noise ratio (SNR) and the contrast of the phase retardation (or birefringence) images introduce a noise bias offset from the true value. This bias reduces the effectiveness of birefringence contrast for a quantitative study. Although coherent averaging of Jones matrix tomography has been widely utilized and has improved the image quality, the fundamental limitation of nonlinear dependency of phase retardation and birefringence to the SNR was not overcome. So the birefringence obtained by PS-OCT was still not accurate for a quantitative imaging. The nonlinear effect of SNR to phase retardation and birefringence measurement was previously formulated in detail for a Jones matrix OCT (JM-OCT) [1]. Based on this, we had developed a maximum a-posteriori (MAP) estimator and quantitative birefringence imaging was demonstrated [2]. However, this first version of estimator had a theoretical shortcoming. It did not take into account the stochastic nature of SNR of OCT signal. In this paper, we present an improved version of the MAP estimator which takes into account the stochastic property of SNR. This estimator uses a probability distribution function (PDF) of true local retardation, which is proportional to birefringence, under a specific set of measurements of the birefringence and SNR. The PDF was pre-computed by a Monte-Carlo (MC) simulation based on the mathematical model of JM-OCT before the measurement. A comparison between this new MAP estimator, our previous MAP estimator [2], and the standard mean estimator is presented. The comparisons are performed both by numerical simulation and in vivo measurements of anterior and

Validation of abundance estimates from mark–recapture and removal techniques for rainbow trout captured by electrofishing in small streams

USGS Publications Warehouse

Rosenberger, Amanda E.; Dunham, Jason B.

2005-01-01

Estimation of fish abundance in streams using the removal model or the Lincoln - Peterson mark - recapture model is a common practice in fisheries. These models produce misleading results if their assumptions are violated. We evaluated the assumptions of these two models via electrofishing of rainbow trout Oncorhynchus mykiss in central Idaho streams. For one-, two-, three-, and four-pass sampling effort in closed sites, we evaluated the influences of fish size and habitat characteristics on sampling efficiency and the accuracy of removal abundance estimates. We also examined the use of models to generate unbiased estimates of fish abundance through adjustment of total catch or biased removal estimates. Our results suggested that the assumptions of the mark - recapture model were satisfied and that abundance estimates based on this approach were unbiased. In contrast, the removal model assumptions were not met. Decreasing sampling efficiencies over removal passes resulted in underestimated population sizes and overestimates of sampling efficiency. This bias decreased, but was not eliminated, with increased sampling effort. Biased removal estimates based on different levels of effort were highly correlated with each other but were less correlated with unbiased mark - recapture estimates. Stream size decreased sampling efficiency, and stream size and instream wood increased the negative bias of removal estimates. We found that reliable estimates of population abundance could be obtained from models of sampling efficiency for different levels of effort. Validation of abundance estimates requires extra attention to routine sampling considerations but can help fisheries biologists avoid pitfalls associated with biased data and facilitate standardized comparisons among studies that employ different sampling methods.

Complex sample survey estimation in static state-space

Treesearch

Raymond L. Czaplewski

2010-01-01

Increased use of remotely sensed data is a key strategy adopted by the Forest Inventory and Analysis Program. However, multiple sensor technologies require complex sampling units and sampling designs. The Recursive Restriction Estimator (RRE) accommodates this complexity. It is a design-consistent Empirical Best Linear Unbiased Prediction for the state-vector, which...

Estimating bottomland hardwood growth and yield

Treesearch

1989-01-01

Most bottomland hardwoods grow on very productive sites-site index 70 or more. A fully stocked immature stand (table 1, fig. 1) requires tending throughout its life. The goal is to attain a stand of approximately 50 high quality trees of commercial species per acre at maturity. Releasing these crop trees can result in the cumulative yield of 2,000-4,000 board feet per...

An unbiased method to build benchmarking sets for ligand-based virtual screening and its application to GPCRs.

PubMed

Xia, Jie; Jin, Hongwei; Liu, Zhenming; Zhang, Liangren; Wang, Xiang Simon

2014-05-27

Benchmarking data sets have become common in recent years for the purpose of virtual screening, though the main focus had been placed on the structure-based virtual screening (SBVS) approaches. Due to the lack of crystal structures, there is great need for unbiased benchmarking sets to evaluate various ligand-based virtual screening (LBVS) methods for important drug targets such as G protein-coupled receptors (GPCRs). To date these ready-to-apply data sets for LBVS are fairly limited, and the direct usage of benchmarking sets designed for SBVS could bring the biases to the evaluation of LBVS. Herein, we propose an unbiased method to build benchmarking sets for LBVS and validate it on a multitude of GPCRs targets. To be more specific, our methods can (1) ensure chemical diversity of ligands, (2) maintain the physicochemical similarity between ligands and decoys, (3) make the decoys dissimilar in chemical topology to all ligands to avoid false negatives, and (4) maximize spatial random distribution of ligands and decoys. We evaluated the quality of our Unbiased Ligand Set (ULS) and Unbiased Decoy Set (UDS) using three common LBVS approaches, with Leave-One-Out (LOO) Cross-Validation (CV) and a metric of average AUC of the ROC curves. Our method has greatly reduced the "artificial enrichment" and "analogue bias" of a published GPCRs benchmarking set, i.e., GPCR Ligand Library (GLL)/GPCR Decoy Database (GDD). In addition, we addressed an important issue about the ratio of decoys per ligand and found that for a range of 30 to 100 it does not affect the quality of the benchmarking set, so we kept the original ratio of 39 from the GLL/GDD.

Unbiased estimation of the eyeball volume using the Cavalieri principle on computed tomography images.

PubMed

Acer, Niyazi; Sahin, Bunyamin; Ucar, Tolga; Usanmaz, Mustafa

2009-01-01

The size of the eyeball has been the subject of a few studies. None of them used stereological methods to estimate the volume. In the current study, we estimated the volume of eyeball in normal men and women using the stereological methods. Eyeball volume (EV) was estimated using the Cavalieri principle as a combination of point-counting and planimetry techniques. We used computed tomography scans taken from 36 participants (15 men and 21 women) to estimate the EV. The mean (SD) EV values obtained by planimetry method were 7.49 (0.79) and 7.06 (0.85) cm in men and women, respectively. By using point-counting method, the mean (SD) values were 7.48 (0.85) and 7.21 (0.84) cm in men and women, respectively. There was no statistically significant difference between the findings from the 2 methods (P > 0.05). A weak correlation was found between the axial length of eyeball and the EV estimated by point counting and planimetry (P < 0.05, r = 0.494 and r = 0.523, respectively). The findings of the current study using the stereological methods could provide data for the evaluation of normal and pathologic volumes of the eyeball.

Estimating regional wheat yield from the shape of decreasing curves of green area index temporal profiles retrieved from MODIS data

NASA Astrophysics Data System (ADS)

Kouadio, Louis; Duveiller, Grégory; Djaby, Bakary; El Jarroudi, Moussa; Defourny, Pierre; Tychon, Bernard

2012-08-01

Earth observation data, owing to their synoptic, timely and repetitive coverage, have been recognized as a valuable tool for crop monitoring at different levels. At the field level, the close correlation between green leaf area (GLA) during maturation and grain yield in wheat revealed that the onset and rate of senescence appeared to be important factors for determining wheat grain yield. Our study sought to explore a simple approach for wheat yield forecasting at the regional level, based on metrics derived from the senescence phase of the green area index (GAI) retrieved from remote sensing data. This study took advantage of recent methodological improvements in which imagery with high revisit frequency but coarse spatial resolution can be exploited to derive crop-specific GAI time series by selecting pixels whose ground-projected instantaneous field of view is dominated by the target crop: winter wheat. A logistic function was used to characterize the GAI senescence phase and derive the metrics of this phase. Four regression-based models involving these metrics (i.e., the maximum GAI value, the senescence rate and the thermal time taken to reach 50% of the green surface in the senescent phase) were related to official wheat yield data. The performances of such models at this regional scale showed that final yield could be estimated with an RMSE of 0.57 ton ha-1, representing about 7% as relative RMSE. Such an approach may be considered as a first yield estimate that could be performed in order to provide better integrated yield assessments in operational systems.

Toward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sources.

PubMed

Yu, Sheng; Liao, Katherine P; Shaw, Stanley Y; Gainer, Vivian S; Churchill, Susanne E; Szolovits, Peter; Murphy, Shawn N; Kohane, Isaac S; Cai, Tianxi

2015-09-01

Analysis of narrative (text) data from electronic health records (EHRs) can improve population-scale phenotyping for clinical and genetic research. Currently, selection of text features for phenotyping algorithms is slow and laborious, requiring extensive and iterative involvement by domain experts. This paper introduces a method to develop phenotyping algorithms in an unbiased manner by automatically extracting and selecting informative features, which can be comparable to expert-curated ones in classification accuracy. Comprehensive medical concepts were collected from publicly available knowledge sources in an automated, unbiased fashion. Natural language processing (NLP) revealed the occurrence patterns of these concepts in EHR narrative notes, which enabled selection of informative features for phenotype classification. When combined with additional codified features, a penalized logistic regression model was trained to classify the target phenotype. The authors applied our method to develop algorithms to identify patients with rheumatoid arthritis and coronary artery disease cases among those with rheumatoid arthritis from a large multi-institutional EHR. The area under the receiver operating characteristic curves (AUC) for classifying RA and CAD using models trained with automated features were 0.951 and 0.929, respectively, compared to the AUCs of 0.938 and 0.929 by models trained with expert-curated features. Models trained with NLP text features selected through an unbiased, automated procedure achieved comparable or slightly higher accuracy than those trained with expert-curated features. The majority of the selected model features were interpretable. The proposed automated feature extraction method, generating highly accurate phenotyping algorithms with improved efficiency, is a significant step toward high-throughput phenotyping. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All

Differential estimates of southern flying squirrel (Glaucomys volans) population structure based on capture method

Treesearch

Kevin S. Laves; Susan C. Loeb

2005-01-01

It is commonly assumed that population estimates derived from trapping small mammals are accurate and unbiased or that estimates derived from different capture methods are comparable. We captured southern flying squirrels (Glaucmrtys volam) using two methods to study their effect on red-cockaded woodpecker (Picoides bumah) reproductive success. Southern flying...

Unbiased estimation of the calcaneus volume using the Cavalieri principle on computed tomography images.

PubMed

Acer, N; Bayar, B; Basaloglu, H; Oner, E; Bayar, K; Sankur, S

2008-11-20

The size and shape of tarsal bones are especially relevant when considering some orthopedic diseases such as clubfoot. For this reason, the measurements of the tarsal bones have been the subject of many studies, none of which has used stereological methods to estimate the volume. In the present stereological study, we estimated the volume of calcaneal bone of normal feet and dry bones. We used a combination of the Cavalieri principle and computer tomographic scans taken from eight males and nine dry calcanei to estimate the volumes of calcaneal bones. The mean volume of dry calcaneal bones was estimated, producing mean results using the point-counting method and Archimedes principle being 49.11+/-10.7 or 48.22+/-11.92 cm(3), respectively. A positive correlation was found between anthropometric measurements and the volume of calcaneal bones. The findings of the present study using the stereological methods could provide data for the evaluation of normal and pathological volumes of calcaneal bones.

Conjunctive-use optimization model and sustainable-yield estimation for the Sparta aquifer of southeastern Arkansas and north-central Louisiana

USGS Publications Warehouse

McKee, Paul W.; Clark, Brian R.; Czarnecki, John B.

2004-01-01

Conjunctive-use optimization modeling was done to assist water managers and planners by estimating the maximum amount of ground water that hypothetically could be withdrawn from wells within the Sparta aquifer indefinitely without violating hydraulic-head or stream-discharge constraints. The Sparta aquifer is largely a confined aquifer of regional importance that comprises a sequence of unconsolidated sand units that are contained within the Sparta Sand. In 2000, more than 35.4 million cubic feet per day (Mft3/d) of water were withdrawn from the aquifer by more than 900 wells, primarily for industry, municipal supply, and crop irrigation in Arkansas. Continued, heavy withdrawals from the aquifer have caused several large cones of depression, lowering hydraulic heads below the top of the Sparta Sand in parts of Union and Columbia Counties and several areas in north-central Louisiana. Problems related to overdraft in the Sparta aquifer can result in increased drilling and pumping costs, reduced well yields, and degraded water quality in areas of large drawdown. A finite-difference ground-water flow model was developed for the Sparta aquifer using MODFLOW, primarily in eastern and southeastern Arkansas and north-central Louisiana. Observed aquifer conditions in 1997 supported by numerical simulations of ground-water flow show that continued pumping at withdrawal rates representative of 1990 - 1997 rates cannot be sustained indefinitely without causing hydraulic heads to drop substantially below the top of the Sparta Sand in southern Arkansas and north-central Louisiana. Areas of ground-water levels below the top of the Sparta Sand have been designated as Critical Ground-Water Areas by the State of Arkansas. A steady-state conjunctive-use optimization model was developed to simulate optimized surface-water and ground-water withdrawals while maintaining hydraulic-head and streamflow constraints, thus determining the 'sustainable yield' for the aquifer. Initial attempts

Assessing Methods for Generalizing Experimental Impact Estimates to Target Populations

ERIC Educational Resources Information Center

Kern, Holger L.; Stuart, Elizabeth A.; Hill, Jennifer; Green, Donald P.

2016-01-01

Randomized experiments are considered the gold standard for causal inference because they can provide unbiased estimates of treatment effects for the experimental participants. However, researchers and policymakers are often interested in using a specific experiment to inform decisions about other target populations. In education research,…

Explosive Yield Estimation using Fourier Amplitude Spectra of Velocity Histories

NASA Astrophysics Data System (ADS)

Steedman, D. W.; Bradley, C. R.

2016-12-01

The Source Physics Experiment (SPE) is a series of explosive shots of various size detonated at varying depths in a borehole in jointed granite. The testbed includes an extensive array of accelerometers for measuring the shock environment close-in to the explosive source. One goal of SPE is to develop greater understanding of the explosion phenomenology in all regimes: from near-source, non-linear response to the far-field linear elastic region, and connecting the analyses from the respective regimes. For example, near-field analysis typically involves review of kinematic response (i.e., acceleration, velocity and displacement) in the time domain and looks at various indicators (e.g., peaks, pulse duration) to facilitate comparison among events. Review of far-field data more often is based on study of response in the frequency domain to facilitate comparison of event magnitudes. To try to "bridge the gap" between approaches, we have developed a scaling law for Fourier amplitude spectra of near-field velocity histories that successfully collapses data from a wide range of yields (100 kg to 5000 kg) and range to sensors in jointed granite. Moreover, we show that we can apply this scaling law to data from a new event to accurately estimate the explosive yield of that event. This approach presents a new way of working with near-field data that will be more compatible with traditional methods of analysis of seismic data and should serve to facilitate end-to-end event analysis. The goal is that this new approach to data analysis will eventually result in improved methods for discrimination of event type (i.e., nuclear or chemical explosion, or earthquake) and magnitude.

Potential for Bias When Estimating Critical Windows for Air Pollution in Children's Health.

PubMed

Wilson, Ander; Chiu, Yueh-Hsiu Mathilda; Hsu, Hsiao-Hsien Leon; Wright, Robert O; Wright, Rosalind J; Coull, Brent A

2017-12-01

Evidence supports an association between maternal exposure to air pollution during pregnancy and children's health outcomes. Recent interest has focused on identifying critical windows of vulnerability. An analysis based on a distributed lag model (DLM) can yield estimates of a critical window that are different from those from an analysis that regresses the outcome on each of the 3 trimester-average exposures (TAEs). Using a simulation study, we assessed bias in estimates of critical windows obtained using 3 regression approaches: 1) 3 separate models to estimate the association with each of the 3 TAEs; 2) a single model to jointly estimate the association between the outcome and all 3 TAEs; and 3) a DLM. We used weekly fine-particulate-matter exposure data for 238 births in a birth cohort in and around Boston, Massachusetts, and a simulated outcome and time-varying exposure effect. Estimates using separate models for each TAE were biased and identified incorrect windows. This bias arose from seasonal trends in particulate matter that induced correlation between TAEs. Including all TAEs in a single model reduced bias. DLM produced unbiased estimates and added flexibility to identify windows. Analysis of body mass index z score and fat mass in the same cohort highlighted inconsistent estimates from the 3 methods. © The Author(s) 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Unbiased simulation of near-Clifford quantum circuits

DOE PAGES

Bennink, Ryan S.; Ferragut, Erik M.; Humble, Travis S.; ...

2017-06-28

Modeling and simulation are essential for predicting and verifying the behavior of fabricated quantum circuits, but existing simulation methods are either impractically costly or require an unrealistic simplification of error processes. In this paper, we present a method of simulating noisy Clifford circuits that is both accurate and practical in experimentally relevant regimes. In particular, the cost is weakly exponential in the size and the degree of non-Cliffordness of the circuit. Our approach is based on the construction of exact representations of quantum channels as quasiprobability distributions over stabilizer operations, which are then sampled, simulated, and weighted to yield unbiasedmore » statistical estimates of circuit outputs and other observables. As a demonstration of these techniques, we simulate a Steane [[7,1,3

An Unbiased Method To Build Benchmarking Sets for Ligand-Based Virtual Screening and its Application To GPCRs

PubMed Central

2015-01-01

Benchmarking data sets have become common in recent years for the purpose of virtual screening, though the main focus had been placed on the structure-based virtual screening (SBVS) approaches. Due to the lack of crystal structures, there is great need for unbiased benchmarking sets to evaluate various ligand-based virtual screening (LBVS) methods for important drug targets such as G protein-coupled receptors (GPCRs). To date these ready-to-apply data sets for LBVS are fairly limited, and the direct usage of benchmarking sets designed for SBVS could bring the biases to the evaluation of LBVS. Herein, we propose an unbiased method to build benchmarking sets for LBVS and validate it on a multitude of GPCRs targets. To be more specific, our methods can (1) ensure chemical diversity of ligands, (2) maintain the physicochemical similarity between ligands and decoys, (3) make the decoys dissimilar in chemical topology to all ligands to avoid false negatives, and (4) maximize spatial random distribution of ligands and decoys. We evaluated the quality of our Unbiased Ligand Set (ULS) and Unbiased Decoy Set (UDS) using three common LBVS approaches, with Leave-One-Out (LOO) Cross-Validation (CV) and a metric of average AUC of the ROC curves. Our method has greatly reduced the “artificial enrichment” and “analogue bias” of a published GPCRs benchmarking set, i.e., GPCR Ligand Library (GLL)/GPCR Decoy Database (GDD). In addition, we addressed an important issue about the ratio of decoys per ligand and found that for a range of 30 to 100 it does not affect the quality of the benchmarking set, so we kept the original ratio of 39 from the GLL/GDD. PMID:24749745

Source spectral variation and yield estimation for small, near-source explosions

NASA Astrophysics Data System (ADS)

Yoo, S.; Mayeda, K. M.

2012-12-01

Significant S-wave generation is always observed from explosion sources which can lead to difficulty in discriminating explosions from natural earthquakes. While there are numerous S-wave generation mechanisms that are currently the topic of significant research, the mechanisms all remain controversial and appear to be dependent upon the near-source emplacement conditions of that particular explosion. To better understand the generation and partitioning of the P and S waves from explosion sources and to enhance the identification and discrimination capability of explosions, we investigate near-source explosion data sets from the 2008 New England Damage Experiment (NEDE), the Humble-Redwood (HR) series of explosions, and a Massachusetts quarry explosion experiment. We estimate source spectra and characteristic source parameters using moment tensor inversions, direct P and S waves multi-taper analysis, and improved coda spectral analysis using high quality waveform records from explosions from a variety of emplacement conditions (e.g., slow/fast burning explosive, fully tamped, partially tamped, single/ripple-fired, and below/above ground explosions). The results from direct and coda waves are compared to theoretical explosion source model predictions. These well-instrumented experiments provide us with excellent data from which to document the characteristic spectral shape, relative partitioning between P and S-waves, and amplitude/yield dependence as a function of HOB/DOB. The final goal of this study is to populate a comprehensive seismic source reference database for small yield explosions based on the results and to improve nuclear explosion monitoring capability.

Multiple-trait random regression models for the estimation of genetic parameters for milk, fat, and protein yield in buffaloes.

PubMed

Borquis, Rusbel Raul Aspilcueta; Neto, Francisco Ribeiro de Araujo; Baldi, Fernando; Hurtado-Lugo, Naudin; de Camargo, Gregório M F; Muñoz-Berrocal, Milthon; Tonhati, Humberto

2013-09-01

In this study, genetic parameters for test-day milk, fat, and protein yield were estimated for the first lactation. The data analyzed consisted of 1,433 first lactations of Murrah buffaloes, daughters of 113 sires from 12 herds in the state of São Paulo, Brazil, with calvings from 1985 to 2007. Ten-month classes of lactation days were considered for the test-day yields. The (co)variance components for the 3 traits were estimated using the regression analyses by Bayesian inference applying an animal model by Gibbs sampling. The contemporary groups were defined as herd-year-month of the test day. In the model, the random effects were additive genetic, permanent environment, and residual. The fixed effects were contemporary group and number of milkings (1 or 2), the linear and quadratic effects of the covariable age of the buffalo at calving, as well as the mean lactation curve of the population, which was modeled by orthogonal Legendre polynomials of fourth order. The random effects for the traits studied were modeled by Legendre polynomials of third and fourth order for additive genetic and permanent environment, respectively, the residual variances were modeled considering 4 residual classes. The heritability estimates for the traits were moderate (from 0.21-0.38), with higher estimates in the intermediate lactation phase. The genetic correlation estimates within and among the traits varied from 0.05 to 0.99. The results indicate that the selection for any trait test day will result in an indirect genetic gain for milk, fat, and protein yield in all periods of the lactation curve. The accuracy associated with estimated breeding values obtained using multi-trait random regression was slightly higher (around 8%) compared with single-trait random regression. This difference may be because to the greater amount of information available per animal. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Unbiased Rare Event Sampling in Spatial Stochastic Systems Biology Models Using a Weighted Ensemble of Trajectories

PubMed Central

Donovan, Rory M.; Tapia, Jose-Juan; Sullivan, Devin P.; Faeder, James R.; Murphy, Robert F.; Dittrich, Markus; Zuckerman, Daniel M.

2016-01-01

The long-term goal of connecting scales in biological simulation can be facilitated by scale-agnostic methods. We demonstrate that the weighted ensemble (WE) strategy, initially developed for molecular simulations, applies effectively to spatially resolved cell-scale simulations. The WE approach runs an ensemble of parallel trajectories with assigned weights and uses a statistical resampling strategy of replicating and pruning trajectories to focus computational effort on difficult-to-sample regions. The method can also generate unbiased estimates of non-equilibrium and equilibrium observables, sometimes with significantly less aggregate computing time than would be possible using standard parallelization. Here, we use WE to orchestrate particle-based kinetic Monte Carlo simulations, which include spatial geometry (e.g., of organelles, plasma membrane) and biochemical interactions among mobile molecular species. We study a series of models exhibiting spatial, temporal and biochemical complexity and show that although WE has important limitations, it can achieve performance significantly exceeding standard parallel simulation—by orders of magnitude for some observables. PMID:26845334

Estimating Seven Coefficients of Pairwise Relatedness Using Population-Genomic Data

PubMed Central

Ackerman, Matthew S.; Johri, Parul; Spitze, Ken; Xu, Sen; Doak, Thomas G.; Young, Kimberly; Lynch, Michael

2017-01-01

Population structure can be described by genotypic-correlation coefficients between groups of individuals, the most basic of which are the pairwise relatedness coefficients between any two individuals. There are nine pairwise relatedness coefficients in the most general model, and we show that these can be reduced to seven coefficients for biallelic loci. Although all nine coefficients can be estimated from pedigrees, six coefficients have been beyond empirical reach. We provide a numerical optimization procedure that estimates all seven reduced coefficients from population-genomic data. Simulations show that the procedure is nearly unbiased, even at 3× coverage, and errors in five of the seven coefficients are statistically uncorrelated. The remaining two coefficients have a negative correlation of errors, but their sum provides an unbiased assessment of the overall correlation of heterozygosity between two individuals. Application of these new methods to four populations of the freshwater crustacean Daphnia pulex reveal the occurrence of half siblings in our samples, as well as a number of identical individuals that are likely obligately asexual clone mates. Statistically significant negative estimates of these pairwise relatedness coefficients, including inbreeding coefficients that were typically negative, underscore the difficulties that arise when interpreting genotypic correlations as estimations of the probability that alleles are identical by descent. PMID:28341647

Unbiased Sampling of Globular Lattice Proteins in Three Dimensions

NASA Astrophysics Data System (ADS)

Jacobsen, Jesper Lykke

2008-03-01

We present a Monte Carlo method that allows efficient and unbiased sampling of Hamiltonian walks on a cubic lattice. Such walks are self-avoiding and visit each lattice site exactly once. They are often used as simple models of globular proteins, upon adding suitable local interactions. Our algorithm can easily be equipped with such interactions, but we study here mainly the flexible homopolymer case where each conformation is generated with uniform probability. We argue that the algorithm is ergodic and has dynamical exponent z=0. We then use it to study polymers of size up to 643=262144 monomers. Results are presented for the effective interaction between end points, and the interaction with the boundaries of the system.

Efficiency optimization in a correlation ratchet with asymmetric unbiased fluctuations

NASA Astrophysics Data System (ADS)

Ai, Bao-Quan; Wang, Xian-Ju; Liu, Guo-Tao; Wen, De-Hua; Xie, Hui-Zhang; Chen, Wei; Liu, Liang-Gang

2003-12-01

The efficiency of a Brownian particle moving in a periodic potential in the presence of asymmetric unbiased fluctuations is investigated. We found that even on the quasistatic limit there is a regime where the efficiency can be a peaked function of temperature, which proves that thermal fluctuations facilitate the efficiency of energy transformation, contradicting the earlier findings [H. Kamegawa et al., Phys. Rev. Lett. 80, 5251 (1998)]. It is also found that the mutual interplay between temporal asymmetry and spatial asymmetry may induce optimized efficiency at finite temperatures. The ratchet is not most efficient when it gives maximum current.

Random Regression Models Using Legendre Polynomials to Estimate Genetic Parameters for Test-day Milk Protein Yields in Iranian Holstein Dairy Cattle.

PubMed

Naserkheil, Masoumeh; Miraie-Ashtiani, Seyed Reza; Nejati-Javaremi, Ardeshir; Son, Jihyun; Lee, Deukhwan

2016-12-01

The objective of this study was to estimate the genetic parameters of milk protein yields in Iranian Holstein dairy cattle. A total of 1,112,082 test-day milk protein yield records of 167,269 first lactation Holstein cows, calved from 1990 to 2010, were analyzed. Estimates of the variance components, heritability, and genetic correlations for milk protein yields were obtained using a random regression test-day model. Milking times, herd, age of recording, year, and month of recording were included as fixed effects in the model. Additive genetic and permanent environmental random effects for the lactation curve were taken into account by applying orthogonal Legendre polynomials of the fourth order in the model. The lowest and highest additive genetic variances were estimated at the beginning and end of lactation, respectively. Permanent environmental variance was higher at both extremes. Residual variance was lowest at the middle of the lactation and contrarily, heritability increased during this period. Maximum heritability was found during the 12th lactation stage (0.213±0.007). Genetic, permanent, and phenotypic correlations among test-days decreased as the interval between consecutive test-days increased. A relatively large data set was used in this study; therefore, the estimated (co)variance components for random regression coefficients could be used for national genetic evaluation of dairy cattle in Iran.

Random Regression Models Using Legendre Polynomials to Estimate Genetic Parameters for Test-day Milk Protein Yields in Iranian Holstein Dairy Cattle

PubMed Central

Naserkheil, Masoumeh; Miraie-Ashtiani, Seyed Reza; Nejati-Javaremi, Ardeshir; Son, Jihyun; Lee, Deukhwan

2016-01-01

The objective of this study was to estimate the genetic parameters of milk protein yields in Iranian Holstein dairy cattle. A total of 1,112,082 test-day milk protein yield records of 167,269 first lactation Holstein cows, calved from 1990 to 2010, were analyzed. Estimates of the variance components, heritability, and genetic correlations for milk protein yields were obtained using a random regression test-day model. Milking times, herd, age of recording, year, and month of recording were included as fixed effects in the model. Additive genetic and permanent environmental random effects for the lactation curve were taken into account by applying orthogonal Legendre polynomials of the fourth order in the model. The lowest and highest additive genetic variances were estimated at the beginning and end of lactation, respectively. Permanent environmental variance was higher at both extremes. Residual variance was lowest at the middle of the lactation and contrarily, heritability increased during this period. Maximum heritability was found during the 12th lactation stage (0.213±0.007). Genetic, permanent, and phenotypic correlations among test-days decreased as the interval between consecutive test-days increased. A relatively large data set was used in this study; therefore, the estimated (co)variance components for random regression coefficients could be used for national genetic evaluation of dairy cattle in Iran. PMID:26954192

A Remote Sensing-Derived Corn Yield Assessment Model

NASA Astrophysics Data System (ADS)

Shrestha, Ranjay Man

Agricultural studies and food security have become critical research topics due to continuous growth in human population and simultaneous shrinkage in agricultural land. In spite of modern technological advancements to improve agricultural productivity, more studies on crop yield assessments and food productivities are still necessary to fulfill the constantly increasing food demands. Besides human activities, natural disasters such as flood and drought, along with rapid climate changes, also inflect an adverse effect on food productivities. Understanding the impact of these disasters on crop yield and making early impact estimations could help planning for any national or international food crisis. Similarly, the United States Department of Agriculture (USDA) Risk Management Agency (RMA) insurance management utilizes appropriately estimated crop yield and damage assessment information to sustain farmers' practice through timely and proper compensations. Through County Agricultural Production Survey (CAPS), the USDA National Agricultural Statistical Service (NASS) uses traditional methods of field interviews and farmer-reported survey data to perform annual crop condition monitoring and production estimations at the regional and state levels. As these manual approaches of yield estimations are highly inefficient and produce very limited samples to represent the entire area, NASS requires supplemental spatial data that provides continuous and timely information on crop production and annual yield. Compared to traditional methods, remote sensing data and products offer wider spatial extent, more accurate location information, higher temporal resolution and data distribution, and lower data cost--thus providing a complementary option for estimation of crop yield information. Remote sensing derived vegetation indices such as Normalized Difference Vegetation Index (NDVI) provide measurable statistics of potential crop growth based on the spectral reflectance and could

Genetic correlations among body condition score, yield, and fertility in first-parity cows estimated by random regression models.

PubMed

Veerkamp, R F; Koenen, E P; De Jong, G

2001-10-01

Twenty type classifiers scored body condition (BCS) of 91,738 first-parity cows from 601 sires and 5518 maternal grandsires. Fertility data during first lactation were extracted for 177,220 cows, of which 67,278 also had a BCS observation, and first-lactation 305-d milk, fat, and protein yields were added for 180,631 cows. Heritabilities and genetic correlations were estimated using a sire-maternal grandsire model. Heritability of BCS was 0.38. Heritabilities for fertility traits were low (0.01 to 0.07), but genetic standard deviations were substantial, 9 d for days to first service and calving interval, 0.25 for number of services, and 5% for first-service conception. Phenotypic correlations between fertility and yield or BCS were small (-0.15 to 0.20). Genetic correlations between yield and all fertility traits were unfavorable (0.37 to 0.74). Genetic correlations with BCS were between -0.4 and -0.6 for calving interval and days to first service. Random regression analysis (RR) showed that correlations changed with days in milk for BCS. Little agreement was found between variances and correlations from RR, and analysis including a single month (mo 1 to 10) of data for BCS, especially during early and late lactation. However, this was due to excluding data from the conventional analysis, rather than due to the polynomials used. RR and a conventional five-traits model where BCS in mo 1, 4, 7, and 10 was treated as a separate traits (plus yield or fertility) gave similar results. Thus a parsimonious random regression model gave more realistic estimates for the (co)variances than a series of bivariate analysis on subsets of the data for BCS. A higher genetic merit for yield has unfavorable effects on fertility, but the genetic correlation suggests that BCS (at some stages of lactation) might help to alleviate the unfavorable effect of selection for higher yield on fertility.

Maximum likelihood: Extracting unbiased information from complex networks

NASA Astrophysics Data System (ADS)

Garlaschelli, Diego; Loffredo, Maria I.

2008-07-01

The choice of free parameters in network models is subjective, since it depends on what topological properties are being monitored. However, we show that the maximum likelihood (ML) principle indicates a unique, statistically rigorous parameter choice, associated with a well-defined topological feature. We then find that, if the ML condition is incompatible with the built-in parameter choice, network models turn out to be intrinsically ill defined or biased. To overcome this problem, we construct a class of safely unbiased models. We also propose an extension of these results that leads to the fascinating possibility to extract, only from topological data, the “hidden variables” underlying network organization, making them “no longer hidden.” We test our method on World Trade Web data, where we recover the empirical gross domestic product using only topological information.

Using small area estimation and Lidar-derived variables for multivariate prediction of forest attributes

Treesearch

F. Mauro; Vicente Monleon; H. Temesgen

2015-01-01

Small area estimation (SAE) techniques have been successfully applied in forest inventories to provide reliable estimates for domains where the sample size is small (i.e. small areas). Previous studies have explored the use of either Area Level or Unit Level Empirical Best Linear Unbiased Predictors (EBLUPs) in a univariate framework, modeling each variable of interest...

Creel survey sampling designs for estimating effort in short-duration Chinook salmon fisheries

USGS Publications Warehouse

McCormick, Joshua L.; Quist, Michael C.; Schill, Daniel J.

2013-01-01

Chinook Salmon Oncorhynchus tshawytscha sport fisheries in the Columbia River basin are commonly monitored using roving creel survey designs and require precise, unbiased catch estimates. The objective of this study was to examine the relative bias and precision of total catch estimates using various sampling designs to estimate angling effort under the assumption that mean catch rate was known. We obtained information on angling populations based on direct visual observations of portions of Chinook Salmon fisheries in three Idaho river systems over a 23-d period. Based on the angling population, Monte Carlo simulations were used to evaluate the properties of effort and catch estimates for each sampling design. All sampling designs evaluated were relatively unbiased. Systematic random sampling (SYS) resulted in the most precise estimates. The SYS and simple random sampling designs had mean square error (MSE) estimates that were generally half of those observed with cluster sampling designs. The SYS design was more efficient (i.e., higher accuracy per unit cost) than a two-cluster design. Increasing the number of clusters available for sampling within a day decreased the MSE of estimates of daily angling effort, but the MSE of total catch estimates was variable depending on the fishery. The results of our simulations provide guidelines on the relative influence of sample sizes and sampling designs on parameters of interest in short-duration Chinook Salmon fisheries.

Framework for making better predictions by directly estimating variables' predictivity.

PubMed

Lo, Adeline; Chernoff, Herman; Zheng, Tian; Lo, Shaw-Hwa

2016-12-13

We propose approaching prediction from a framework grounded in the theoretical correct prediction rate of a variable set as a parameter of interest. This framework allows us to define a measure of predictivity that enables assessing variable sets for, preferably high, predictivity. We first define the prediction rate for a variable set and consider, and ultimately reject, the naive estimator, a statistic based on the observed sample data, due to its inflated bias for moderate sample size and its sensitivity to noisy useless variables. We demonstrate that the [Formula: see text]-score of the PR method of VS yields a relatively unbiased estimate of a parameter that is not sensitive to noisy variables and is a lower bound to the parameter of interest. Thus, the PR method using the [Formula: see text]-score provides an effective approach to selecting highly predictive variables. We offer simulations and an application of the [Formula: see text]-score on real data to demonstrate the statistic's predictive performance on sample data. We conjecture that using the partition retention and [Formula: see text]-score can aid in finding variable sets with promising prediction rates; however, further research in the avenue of sample-based measures of predictivity is much desired.

Canopy Temperature and Vegetation Indices from High-Throughput Phenotyping Improve Accuracy of Pedigree and Genomic Selection for Grain Yield in Wheat

PubMed Central

Rutkoski, Jessica; Poland, Jesse; Mondal, Suchismita; Autrique, Enrique; Pérez, Lorena González; Crossa, José; Reynolds, Matthew; Singh, Ravi

2016-01-01

Genomic selection can be applied prior to phenotyping, enabling shorter breeding cycles and greater rates of genetic gain relative to phenotypic selection. Traits measured using high-throughput phenotyping based on proximal or remote sensing could be useful for improving pedigree and genomic prediction model accuracies for traits not yet possible to phenotype directly. We tested if using aerial measurements of canopy temperature, and green and red normalized difference vegetation index as secondary traits in pedigree and genomic best linear unbiased prediction models could increase accuracy for grain yield in wheat, Triticum aestivum L., using 557 lines in five environments. Secondary traits on training and test sets, and grain yield on the training set were modeled as multivariate, and compared to univariate models with grain yield on the training set only. Cross validation accuracies were estimated within and across-environment, with and without replication, and with and without correcting for days to heading. We observed that, within environment, with unreplicated secondary trait data, and without correcting for days to heading, secondary traits increased accuracies for grain yield by 56% in pedigree, and 70% in genomic prediction models, on average. Secondary traits increased accuracy slightly more when replicated, and considerably less when models corrected for days to heading. In across-environment prediction, trends were similar but less consistent. These results show that secondary traits measured in high-throughput could be used in pedigree and genomic prediction to improve accuracy. This approach could improve selection in wheat during early stages if validated in early-generation breeding plots. PMID:27402362

The shared and unique values of optical, fluorescence, thermal and microwave satellite data for estimating large-scale crop yields

USDA-ARS?s Scientific Manuscript database

Large-scale crop monitoring and yield estimation are important for both scientific research and practical applications. Satellite remote sensing provides an effective means for regional and global cropland monitoring, particularly in data-sparse regions that lack reliable ground observations and rep...

Autocorrelation analysis for the unbiased determination of power-law exponents in single-quantum-dot blinking.

PubMed

Houel, Julien; Doan, Quang T; Cajgfinger, Thomas; Ledoux, Gilles; Amans, David; Aubret, Antoine; Dominjon, Agnès; Ferriol, Sylvain; Barbier, Rémi; Nasilowski, Michel; Lhuillier, Emmanuel; Dubertret, Benoît; Dujardin, Christophe; Kulzer, Florian

2015-01-27

We present an unbiased and robust analysis method for power-law blinking statistics in the photoluminescence of single nanoemitters, allowing us to extract both the bright- and dark-state power-law exponents from the emitters' intensity autocorrelation functions. As opposed to the widely used threshold method, our technique therefore does not require discriminating the emission levels of bright and dark states in the experimental intensity timetraces. We rely on the simultaneous recording of 450 emission timetraces of single CdSe/CdS core/shell quantum dots at a frame rate of 250 Hz with single photon sensitivity. Under these conditions, our approach can determine ON and OFF power-law exponents with a precision of 3% from a comparison to numerical simulations, even for shot-noise-dominated emission signals with an average intensity below 1 photon per frame and per quantum dot. These capabilities pave the way for the unbiased, threshold-free determination of blinking power-law exponents at the microsecond time scale.

Maximum unbiased validation (MUV) data sets for virtual screening based on PubChem bioactivity data.

PubMed

Rohrer, Sebastian G; Baumann, Knut

2009-02-01

Refined nearest neighbor analysis was recently introduced for the analysis of virtual screening benchmark data sets. It constitutes a technique from the field of spatial statistics and provides a mathematical framework for the nonparametric analysis of mapped point patterns. Here, refined nearest neighbor analysis is used to design benchmark data sets for virtual screening based on PubChem bioactivity data. A workflow is devised that purges data sets of compounds active against pharmaceutically relevant targets from unselective hits. Topological optimization using experimental design strategies monitored by refined nearest neighbor analysis functions is applied to generate corresponding data sets of actives and decoys that are unbiased with regard to analogue bias and artificial enrichment. These data sets provide a tool for Maximum Unbiased Validation (MUV) of virtual screening methods. The data sets and a software package implementing the MUV design workflow are freely available at http://www.pharmchem.tu-bs.de/lehre/baumann/MUV.html.

Signal detection theory and vestibular perception: III. Estimating unbiased fit parameters for psychometric functions.

PubMed

Chaudhuri, Shomesh E; Merfeld, Daniel M

2013-03-01

Psychophysics generally relies on estimating a subject's ability to perform a specific task as a function of an observed stimulus. For threshold studies, the fitted functions are called psychometric functions. While fitting psychometric functions to data acquired using adaptive sampling procedures (e.g., "staircase" procedures), investigators have encountered a bias in the spread ("slope" or "threshold") parameter that has been attributed to the serial dependency of the adaptive data. Using simulations, we confirm this bias for cumulative Gaussian parametric maximum likelihood fits on data collected via adaptive sampling procedures, and then present a bias-reduced maximum likelihood fit that substantially reduces the bias without reducing the precision of the spread parameter estimate and without reducing the accuracy or precision of the other fit parameters. As a separate topic, we explain how to implement this bias reduction technique using generalized linear model fits as well as other numeric maximum likelihood techniques such as the Nelder-Mead simplex. We then provide a comparison of the iterative bootstrap and observed information matrix techniques for estimating parameter fit variance from adaptive sampling procedure data sets. The iterative bootstrap technique is shown to be slightly more accurate; however, the observed information technique executes in a small fraction (0.005 %) of the time required by the iterative bootstrap technique, which is an advantage when a real-time estimate of parameter fit variance is required.

Reply to ''Comment on 'Mutually unbiased bases, orthogonal Latin squares, and hidden-variable models'''

DOE Office of Scientific and Technical Information (OSTI.GOV)

Paterek, Tomasz; Dakic, Borivoje; Brukner, Caslav

In this Reply to the preceding Comment by Hall and Rao [Phys. Rev. A 83, 036101 (2011)], we motivate terminology of our original paper and point out that further research is needed in order to (dis)prove the claimed link between every orthogonal Latin square of order being a power of a prime and a mutually unbiased basis.

Intercomparison of Soil Moisture, Evaporative Stress, and Vegetation Indices for Estimating Corn and Soybean Yields Over the U.S.

NASA Technical Reports Server (NTRS)

Mladenova, Iliana E.; Bolten, John D.; Crow, Wade T.; Anderson, Martha C.; Hain, C. R.; Johnson, David M.; Mueller, Rick

2017-01-01

This paper presents an intercomparative study of 12 operationally produced large-scale datasets describing soil moisture, evapotranspiration (ET), and or vegetation characteristics within agricultural regions of the contiguous United States (CONUS). These datasets have been developed using a variety of techniques, including, hydrologic modeling, satellite-based retrievals, data assimilation, and survey in-field data collection. The objectives are to assess the relative utility of each dataset for monitoring crop yield variability, to quantitatively assess their capacity for predicting end-of-season corn and soybean yields, and to examine the evolution of the yield-index correlations during the growing season. This analysis is unique both with regards to the number and variety of examined yield predictor datasets and the detailed assessment of the water availability timing on the end-of-season crop production during the growing season. Correlation results indicate that over CONUS, at state-level soil moisture and ET indices can provide better information for forecasting corn and soybean yields than vegetation-based indices such as normalized difference vegetation index. The strength of correlation with corn and soybean yields strongly depends on the interannual variability in yield measured at a given location. In this case study, some of the remotely derived datasets examined provide skill comparable to that of in situ field survey-based data further demonstrating the utility of these remote sensing-based approaches for estimating crop yield.

Brazilian Soybean Yields and Yield Gaps Vary with Farm Size

NASA Astrophysics Data System (ADS)

Jeffries, G. R.; Cohn, A.; Griffin, T. S.; Bragança, A.

2017-12-01

Understanding the farm size-specific characteristics of crop yields and yield gaps may help to improve yields by enabling better targeting of technical assistance and agricultural development programs. Linking remote sensing-based yield estimates with property boundaries provides a novel view of the relationship between farm size and yield structure (yield magnitude, gaps, and stability over time). A growing literature documents variations in yield gaps, but largely ignores the role of farm size as a factor shaping yield structure. Research on the inverse farm size-productivity relationship (IR) theory - that small farms are more productive than large ones all else equal - has documented that yield magnitude may vary by farm size, but has not considered other yield structure characteristics. We examined farm size - yield structure relationships for soybeans in Brazil for years 2001-2015. Using out-of-sample soybean yield predictions from a statistical model, we documented 1) gaps between the 95th percentile of attained yields and mean yields within counties and individual fields, and 2) yield stability defined as the standard deviation of time-detrended yields at given locations. We found a direct relationship between soy yields and farm size at the national level, while the strength and the sign of the relationship varied by region. Soybean yield gaps were found to be inversely related to farm size metrics, even when yields were only compared to farms of similar size. The relationship between farm size and yield stability was nonlinear, with mid-sized farms having the most stable yields. The work suggests that farm size is an important factor in understanding yield structure and that opportunities for improving soy yields in Brazil are greatest among smaller farms.

Genetic parameters for body condition score, body weight, milk yield, and fertility estimated using random regression models.

PubMed

Berry, D P; Buckley, F; Dillon, P; Evans, R D; Rath, M; Veerkamp, R F

2003-11-01

Genetic (co)variances between body condition score (BCS), body weight (BW), milk yield, and fertility were estimated using a random regression animal model extended to multivariate analysis. The data analyzed included 81,313 BCS observations, 91,937 BW observations, and 100,458 milk test-day yields from 8725 multiparous Holstein-Friesian cows. A cubic random regression was sufficient to model the changing genetic variances for BCS, BW, and milk across different days in milk. The genetic correlations between BCS and fertility changed little over the lactation; genetic correlations between BCS and interval to first service and between BCS and pregnancy rate to first service varied from -0.47 to -0.31, and from 0.15 to 0.38, respectively. This suggests that maximum genetic gain in fertility from indirect selection on BCS should be based on measurements taken in midlactation when the genetic variance for BCS is largest. Selection for increased BW resulted in shorter intervals to first service, but more services and poorer pregnancy rates; genetic correlations between BW and pregnancy rate to first service varied from -0.52 to -0.45. Genetic selection for higher lactation milk yield alone through selection on increased milk yield in early lactation is likely to have a more deleterious effect on genetic merit for fertility than selection on higher milk yield in late lactation.

Estimating temporary emigration and breeding proportions using capture-recapture data with Pollock's robust design

USGS Publications Warehouse

Kendall, W.L.; Nichols, J.D.; Hines, J.E.

1997-01-01

Statistical inference for capture-recapture studies of open animal populations typically relies on the assumption that all emigration from the studied population is permanent. However, there are many instances in which this assumption is unlikely to be met. We define two general models for the process of temporary emigration, completely random and Markovian. We then consider effects of these two types of temporary emigration on Jolly-Seber (Seber 1982) estimators and on estimators arising from the full-likelihood approach of Kendall et al. (1995) to robust design data. Capture-recapture data arising from Pollock's (1982) robust design provide the basis for obtaining unbiased estimates of demographic parameters in the presence of temporary emigration and for estimating the probability of temporary emigration. We present a likelihood-based approach to dealing with temporary emigration that permits estimation under different models of temporary emigration and yields tests for completely random and Markovian emigration. In addition, we use the relationship between capture probability estimates based on closed and open models under completely random temporary emigration to derive three ad hoc estimators for the probability of temporary emigration, two of which should be especially useful in situations where capture probabilities are heterogeneous among individual animals. Ad hoc and full-likelihood estimators are illustrated for small mammal capture-recapture data sets. We believe that these models and estimators will be useful for testing hypotheses about the process of temporary emigration, for estimating demographic parameters in the presence of temporary emigration, and for estimating probabilities of temporary emigration. These latter estimates are frequently of ecological interest as indicators of animal movement and, in some sampling situations, as direct estimates of breeding probabilities and proportions.

Specific yield: compilation of specific yields for various materials

USGS Publications Warehouse

Johnson, A.I.

1967-01-01

Specific yield is defined as the ratio of (1) the volume of water that a saturated rock or soil will yield by gravity to (2) the total volume of the rock or soft. Specific yield is usually expressed as a percentage. The value is not definitive, because the quantity of water that will drain by gravity depends on variables such as duration of drainage, temperature, mineral composition of the water, and various physical characteristics of the rock or soil under consideration. Values of specific yields nevertheless offer a convenient means by which hydrologists can estimate the water-yielding capacities of earth materials and, as such, are very useful in hydrologic studies. The present report consists mostly of direct or modified quotations from many selected reports that present and evaluate methods for determining specific yield, limitations of those methods, and results of the determinations made on a wide variety of rock and soil materials. Although no particular values are recommended in this report, a table summarizes values of specific yield, and their averages, determined for 10 rock textures. The following is an abstract of the table. [Table

Free energies from dynamic weighted histogram analysis using unbiased Markov state model.

PubMed

Rosta, Edina; Hummer, Gerhard

2015-01-13

The weighted histogram analysis method (WHAM) is widely used to obtain accurate free energies from biased molecular simulations. However, WHAM free energies can exhibit significant errors if some of the biasing windows are not fully equilibrated. To account for the lack of full equilibration, we develop the dynamic histogram analysis method (DHAM). DHAM uses a global Markov state model to obtain the free energy along the reaction coordinate. A maximum likelihood estimate of the Markov transition matrix is constructed by joint unbiasing of the transition counts from multiple umbrella-sampling simulations along discretized reaction coordinates. The free energy profile is the stationary distribution of the resulting Markov matrix. For this matrix, we derive an explicit approximation that does not require the usual iterative solution of WHAM. We apply DHAM to model systems, a chemical reaction in water treated using quantum-mechanics/molecular-mechanics (QM/MM) simulations, and the Na(+) ion passage through the membrane-embedded ion channel GLIC. We find that DHAM gives accurate free energies even in cases where WHAM fails. In addition, DHAM provides kinetic information, which we here use to assess the extent of convergence in each of the simulation windows. DHAM may also prove useful in the construction of Markov state models from biased simulations in phase-space regions with otherwise low population.

Nut crop yield records show that budbreak-based chilling requirements may not reflect yield decline chill thresholds

NASA Astrophysics Data System (ADS)

Pope, Katherine S.; Dose, Volker; Da Silva, David; Brown, Patrick H.; DeJong, Theodore M.

2015-06-01

Warming winters due to climate change may critically affect temperate tree species. Insufficiently cold winters are thought to result in fewer viable flower buds and the subsequent development of fewer fruits or nuts, decreasing the yield of an orchard or fecundity of a species. The best existing approximation for a threshold of sufficient cold accumulation, the "chilling requirement" of a species or variety, has been quantified by manipulating or modeling the conditions that result in dormant bud breaking. However, the physiological processes that affect budbreak are not the same as those that determine yield. This study sought to test whether budbreak-based chilling thresholds can reasonably approximate the thresholds that affect yield, particularly regarding the potential impacts of climate change on temperate tree crop yields. County-wide yield records for almond ( Prunus dulcis), pistachio ( Pistacia vera), and walnut ( Juglans regia) in the Central Valley of California were compared with 50 years of weather records. Bayesian nonparametric function estimation was used to model yield potentials at varying amounts of chill accumulation. In almonds, average yields occurred when chill accumulation was close to the budbreak-based chilling requirement. However, in the other two crops, pistachios and walnuts, the best previous estimate of the budbreak-based chilling requirements was 19-32 % higher than the chilling accumulations associated with average or above average yields. This research indicates that physiological processes beyond requirements for budbreak should be considered when estimating chill accumulation thresholds of yield decline and potential impacts of climate change.

Common Variable Immunodeficiency Non-Infectious Disease Endotypes Redefined Using Unbiased Network Clustering in Large Electronic Datasets.

PubMed

Farmer, Jocelyn R; Ong, Mei-Sing; Barmettler, Sara; Yonker, Lael M; Fuleihan, Ramsay; Sullivan, Kathleen E; Cunningham-Rundles, Charlotte; Walter, Jolan E

2017-01-01

Common variable immunodeficiency (CVID) is increasingly recognized for its association with autoimmune and inflammatory complications. Despite recent advances in immunophenotypic and genetic discovery, clinical care of CVID remains limited by our inability to accurately model risk for non-infectious disease development. Herein, we demonstrate the utility of unbiased network clustering as a novel method to analyze inter-relationships between non-infectious disease outcomes in CVID using databases at the United States Immunodeficiency Network (USIDNET), the centralized immunodeficiency registry of the United States, and Partners, a tertiary care network in Boston, MA, USA, with a shared electronic medical record amenable to natural language processing. Immunophenotypes were comparable in terms of native antibody deficiencies, low titer response to pneumococcus, and B cell maturation arrest. However, recorded non-infectious disease outcomes were more substantial in the Partners cohort across the spectrum of lymphoproliferation, cytopenias, autoimmunity, atopy, and malignancy. Using unbiased network clustering to analyze 34 non-infectious disease outcomes in the Partners cohort, we further identified unique patterns of lymphoproliferative (two clusters), autoimmune (two clusters), and atopic (one cluster) disease that were defined as CVID non-infectious endotypes according to discrete and non-overlapping immunophenotypes. Markers were both previously described {high serum IgE in the atopic cluster [odds ratio (OR) 6.5] and low class-switched memory B cells in the total lymphoproliferative cluster (OR 9.2)} and novel [low serum C3 in the total lymphoproliferative cluster (OR 5.1)]. Mortality risk in the Partners cohort was significantly associated with individual non-infectious disease outcomes as well as lymphoproliferative cluster 2, specifically (OR 5.9). In contrast, unbiased network clustering failed to associate known comorbidities in the adult USIDNET cohort

Airborne and ground-based remote sensing for the estimation of evapotranspiration and yield of bean, potato, and sugar beet crops

NASA Astrophysics Data System (ADS)

Jayanthi, Harikishan

The focus of this research was two-fold: (1) extend the reflectance-based crop coefficient approach to non-grain (potato and sugar beet), and vegetable crops (bean), and (2) develop vegetation index (VI)-yield statistical models for potato and sugar beet crops using high-resolution aerial multispectral imagery. Extensive crop biophysical sampling (leaf area index and aboveground dry biomass sampling) and canopy reflectance measurements formed the backbone of developing of canopy reflectance-based crop coefficients for bean, potato, and sugar beet crops in this study. Reflectance-based crop coefficient equations were developed for the study crops cultivated in Kimberly, Idaho, and subsequently used in water availability simulations in the plant root zone during 1998 and 1999 seasons. The simulated soil water profiles were compared with independent measurements of actual soil water profiles in the crop root zone in selected fields. It is concluded that the canopy reflectance-based crop coefficient technique can be successfully extended to non-grain crops as well. While the traditional basal crop coefficients generally expect uniform growth in a region the reflectance-based crop coefficients represent the actual crop growth pattern (in less than ideal water availability conditions) in individual fields. Literature on crop canopy interactions with sunlight states that there is a definite correspondence between leaf area index progression in the season and the final yield. In case of crops like potato and sugar beet, the yield is influenced not only on how early and how quickly the crop establishes its canopy but also on how long the plant stands on the ground in a healthy state. The integrated area under the crop growth curve has shown excellent correlations with hand-dug samples of potato and sugar beet crops in this research. Soil adjusted vegetation index-yield models were developed, and validated using multispectral aerial imagery. Estimated yield images were

A comparison of selection at list time and time-stratified sampling for estimating suspended sediment loads

Treesearch

Robert B. Thomas; Jack Lewis

1993-01-01

Time-stratified sampling of sediment for estimating suspended load is introduced and compared to selection at list time (SALT) sampling. Both methods provide unbiased estimates of load and variance. The magnitude of the variance of the two methods is compared using five storm populations of suspended sediment flux derived from turbidity data. Under like conditions,...

Estimating the impact of mineral aerosols on crop yields in food insecure regions using statistical crop models

NASA Astrophysics Data System (ADS)

Hoffman, A.; Forest, C. E.; Kemanian, A.

2016-12-01

A significant number of food-insecure nations exist in regions of the world where dust plays a large role in the climate system. While the impacts of common climate variables (e.g. temperature, precipitation, ozone, and carbon dioxide) on crop yields are relatively well understood, the impact of mineral aerosols on yields have not yet been thoroughly investigated. This research aims to develop the data and tools to progress our understanding of mineral aerosol impacts on crop yields. Suspended dust affects crop yields by altering the amount and type of radiation reaching the plant, modifying local temperature and precipitation. While dust events (i.e. dust storms) affect crop yields by depleting the soil of nutrients or by defoliation via particle abrasion. The impact of dust on yields is modeled statistically because we are uncertain which impacts will dominate the response on national and regional scales considered in this study. Multiple linear regression is used in a number of large-scale statistical crop modeling studies to estimate yield responses to various climate variables. In alignment with previous work, we develop linear crop models, but build upon this simple method of regression with machine-learning techniques (e.g. random forests) to identify important statistical predictors and isolate how dust affects yields on the scales of interest. To perform this analysis, we develop a crop-climate dataset for maize, soybean, groundnut, sorghum, rice, and wheat for the regions of West Africa, East Africa, South Africa, and the Sahel. Random forest regression models consistently model historic crop yields better than the linear models. In several instances, the random forest models accurately capture the temperature and precipitation threshold behavior in crops. Additionally, improving agricultural technology has caused a well-documented positive trend that dominates time series of global and regional yields. This trend is often removed before regression with

A spatially explicit capture-recapture estimator for single-catch traps.

PubMed

Distiller, Greg; Borchers, David L

2015-11-01

Single-catch traps are frequently used in live-trapping studies of small mammals. Thus far, a likelihood for single-catch traps has proven elusive and usually the likelihood for multicatch traps is used for spatially explicit capture-recapture (SECR) analyses of such data. Previous work found the multicatch likelihood to provide a robust estimator of average density. We build on a recently developed continuous-time model for SECR to derive a likelihood for single-catch traps. We use this to develop an estimator based on observed capture times and compare its performance by simulation to that of the multicatch estimator for various scenarios with nonconstant density surfaces. While the multicatch estimator is found to be a surprisingly robust estimator of average density, its performance deteriorates with high trap saturation and increasing density gradients. Moreover, it is found to be a poor estimator of the height of the detection function. By contrast, the single-catch estimators of density, distribution, and detection function parameters are found to be unbiased or nearly unbiased in all scenarios considered. This gain comes at the cost of higher variance. If there is no interest in interpreting the detection function parameters themselves, and if density is expected to be fairly constant over the survey region, then the multicatch estimator performs well with single-catch traps. However if accurate estimation of the detection function is of interest, or if density is expected to vary substantially in space, then there is merit in using the single-catch estimator when trap saturation is above about 60%. The estimator's performance is improved if care is taken to place traps so as to span the range of variables that affect animal distribution. As a single-catch likelihood with unknown capture times remains intractable for now, researchers using single-catch traps should aim to incorporate timing devices with their traps.

Spectroscopic observation of SN 2017jzp and SN 2018bf by NUTS (NOT Un-biased Transient Survey)

NASA Astrophysics Data System (ADS)

Kuncarayakti, H.; Mattila, S.; Kotak, R.; Harmanen, J.; Reynolds, T.; Wyrzykowski, L.; Stritzinger, M.; Onori, F.; Somero, A.; Kangas, T.; Lundqvist, P.; Taddia, F.; Ergon, M.

2018-01-01

The Nordic Optical Telescope (NOT) Unbiased Transient Survey (NUTS; ATel #8992) reports the spectroscopic classification of SNe 2017jzp and 2018bf in host galaxies KUG 1326+679 and SDSS J225746.53+253833.5, respectively.

Spectroscopic classification of supernovae SN 2018aei and SN 2018aej by NUTS (NOT Un-biased Transient Survey)

NASA Astrophysics Data System (ADS)

Cannizzaro, G.; Kuncarayakti, H.; Fraser, M.; Hamanowicz, A.; Jonker, P.; Kankare, E.; Kostrzewa-Rutkowska, Z.; Onori, F.; Wevers, T.; Wyrzykowski, L.; Galbany, L.

2018-03-01

The NOT Unbiased Transient Survey (NUTS; ATel #8992) collaboration reports the spectroscopic classification of supernovae SN 2018aei and SN 2018aej, discovered by PanSTARSS Survey for Transients (ATel #11408).

Accounting for missing data in the estimation of contemporary genetic effective population size (N(e) ).

PubMed

Peel, D; Waples, R S; Macbeth, G M; Do, C; Ovenden, J R

2013-03-01

Theoretical models are often applied to population genetic data sets without fully considering the effect of missing data. Researchers can deal with missing data by removing individuals that have failed to yield genotypes and/or by removing loci that have failed to yield allelic determinations, but despite their best efforts, most data sets still contain some missing data. As a consequence, realized sample size differs among loci, and this poses a problem for unbiased methods that must explicitly account for random sampling error. One commonly used solution for the calculation of contemporary effective population size (N(e) ) is to calculate the effective sample size as an unweighted mean or harmonic mean across loci. This is not ideal because it fails to account for the fact that loci with different numbers of alleles have different information content. Here we consider this problem for genetic estimators of contemporary effective population size (N(e) ). To evaluate bias and precision of several statistical approaches for dealing with missing data, we simulated populations with known N(e) and various degrees of missing data. Across all scenarios, one method of correcting for missing data (fixed-inverse variance-weighted harmonic mean) consistently performed the best for both single-sample and two-sample (temporal) methods of estimating N(e) and outperformed some methods currently in widespread use. The approach adopted here may be a starting point to adjust other population genetics methods that include per-locus sample size components. © 2012 Blackwell Publishing Ltd.

Empirically Driven Variable Selection for the Estimation of Causal Effects with Observational Data

ERIC Educational Resources Information Center

Keller, Bryan; Chen, Jianshen

2016-01-01

Observational studies are common in educational research, where subjects self-select or are otherwise non-randomly assigned to different interventions (e.g., educational programs, grade retention, special education). Unbiased estimation of a causal effect with observational data depends crucially on the assumption of ignorability, which specifies…

Water Quality in the Upper Anacostia River, Maryland: Continuous and Discrete Monitoring with Simulations to Estimate Concentrations and Yields, 2003-05

USGS Publications Warehouse

Miller, Cherie V.; Gutierrez-Magness, Angelica L.; Feit Majedi, Brenda L.; Foster, Gregory D.

2007-01-01

concentrations of total phosphorus and total nitrogen had lower values of multiple R2 than suspended sediment, but the estimated bias for all the models was similar. The models for total nitrogen and total phosphorus tended to under-predict high concentrations and to over-predict low concentrations as compared to measured values. Annual yields (loads per square area in kilograms per year per square kilometer) were estimated for suspended sediment, total nitrogen, and total phosphorus using the U.S. Geological Survey models ESTIMATOR and LOADEST. The model LOADEST used hourly time steps and allowed the use of turbidity, which is strongly correlated to concentrations of suspended sediment, as a predictor variable. Annual yields for total nitrogen and total phosphorus were slightly higher but similar to previous estimates for other watersheds of the Chesapeake Bay, but annual yields for suspended sediment were higher by an order of magnitude for the two Anacostia River stations. Annual yields of suspended sediment at the two Anacostia River stations ranged from 131,000 to 248,000 kilograms per year per square kilometer for 2004 and 2005. LOADEST estimates were similar to those determined with ESTIMATOR, but had reduced errors associated with the estimates.

Unbiased approaches to biomarker discovery in neurodegenerative diseases

PubMed Central

Chen-Plotkin, Alice S.

2014-01-01

Neurodegenerative diseases such as Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis, and frontotemporal dementia have several important features in common. They are progressive, they affect a relatively inaccessible organ, and we have no disease-modifying therapies for them. For these brain-based diseases, current diagnosis and evaluation of disease severity rely almost entirely on clinical examination, which may only be a rough approximation of disease state. Thus, the development of biomarkers – objective, relatively easily measured and precise indicators of pathogenic processes – could improve patient care and accelerate therapeutic discovery. Yet existing, rigorously tested neurodegenerative disease biomarkers are few, and even fewer biomarkers have translated into clinical use. To find new biomarkers for these diseases, an unbiased, high-throughput screening approach may be needed. In this review, I will describe the potential utility of such an approach to biomarker discovery, using Parkinson’s disease as a case example. PMID:25442938

Guidelines for Estimating Cone and Seed Yields of Southern Pines

Treesearch

James P. Barnett

1999-01-01

Our ability to predict cone and seed yields of southern pines (Pinus spp.) prior to collection is important when scheduling and allocating resources. Many managers have enough historical data to predict their orchards' yield; but such data are generally unavailable for some species and for collections outside of orchards. Guidelines are...

Benefits of seasonal forecasts of crop yields

NASA Astrophysics Data System (ADS)

Sakurai, G.; Okada, M.; Nishimori, M.; Yokozawa, M.

2017-12-01

Major factors behind recent fluctuations in food prices include increased biofuel production and oil price fluctuations. In addition, several extreme climate events that reduced worldwide food production coincided with upward spikes in food prices. The stabilization of crop yields is one of the most important tasks to stabilize food prices and thereby enhance food security. Recent development of technologies related to crop modeling and seasonal weather forecasting has made it possible to forecast future crop yields for maize and soybean. However, the effective use of these technologies remains limited. Here we present the potential benefits of seasonal crop-yield forecasts on a global scale for choice of planting day. For this purpose, we used a model (PRYSBI-2) that can well replicate past crop yields both for maize and soybean. This model system uses a Bayesian statistical approach to estimate the parameters of a basic process-based model of crop growth. The spatial variability of model parameters was considered by estimating the posterior distribution of the parameters from historical yield data by using the Markov-chain Monte Carlo (MCMC) method with a resolution of 1.125° × 1.125°. The posterior distributions of model parameters were estimated for each spatial grid with 30 000 MCMC steps of 10 chains each. By using this model and the estimated parameter distributions, we were able to estimate not only crop yield but also levels of associated uncertainty. We found that the global average crop yield increased about 30% as the result of the optimal selection of planting day and that the seasonal forecast of crop yield had a large benefit in and near the eastern part of Brazil and India for maize and the northern area of China for soybean. In these countries, the effects of El Niño and Indian Ocean dipole are large. The results highlight the importance of developing a system to forecast global crop yields.

Comparison of estimates of hardwood bole volume using importance sampling, the centroid method, and some taper equations

Treesearch

Harry V., Jr. Wiant; Michael L. Spangler; John E. Baumgras

2002-01-01

Various taper systems and the centroid method were compared to unbiased volume estimates made by importance sampling for 720 hardwood trees selected throughout the state of West Virginia. Only the centroid method consistently gave volumes estimates that did not differ significantly from those made by importance sampling, although some taper equations did well for most...

Full-Waveform Envelope Templates for Low Magnitude Discrimination and Yield Estimation at Local and Regional Distances with Application to the North Korean Nuclear Tests

NASA Astrophysics Data System (ADS)

Yoo, S. H.

2017-12-01

Monitoring seismologists have successfully used seismic coda for event discrimination and yield estimation for over a decade. In practice seismologists typically analyze long-duration, S-coda signals with high signal-to-noise ratios (SNR) at regional and teleseismic distances, since the single back-scattering model reasonably predicts decay of the late coda. However, seismic monitoring requirements are shifting towards smaller, locally recorded events that exhibit low SNR and short signal lengths. To be successful at characterizing events recorded at local distances, we must utilize the direct-phase arrivals, as well as the earlier part of the coda, which is dominated by multiple forward scattering. To remedy this problem, we have developed a new hybrid method known as full-waveform envelope template matching to improve predicted envelope fits over the entire waveform and account for direct-wave and early coda complexity. We accomplish this by including a multiple forward-scattering approximation in the envelope modeling of the early coda. The new hybrid envelope templates are designed to fit local and regional full waveforms and produce low-variance amplitude estimates, which will improve yield estimation and discrimination between earthquakes and explosions. To demonstrate the new technique, we applied our full-waveform envelope template-matching method to the six known North Korean (DPRK) underground nuclear tests and four aftershock events following the September 2017 test. We successfully discriminated the event types and estimated the yield for all six nuclear tests. We also applied the same technique to the 2015 Tianjin explosions in China, and another suspected low-yield explosion at the DPRK test site on May 12, 2010. Our results show that the new full-waveform envelope template-matching method significantly improves upon longstanding single-scattering coda prediction techniques. More importantly, the new method allows monitoring seismologists to extend

A hidden-process model for estimating prespawn mortality using carcass survey data

USGS Publications Warehouse

DeWeber, J. Tyrell; Peterson, James T.; Sharpe, Cameron; Kent, Michael L.; Colvin, Michael E.; Schreck, Carl B.

2017-01-01

After returning to spawning areas, adult Pacific salmon Oncorhynchus spp. often die without spawning successfully, which is commonly referred to as prespawn mortality. Prespawn mortality reduces reproductive success and can thereby hamper conservation, restoration, and reintroduction efforts. The primary source of information used to estimate prespawn mortality is collected through carcass surveys, but estimation can be difficult with these data due to imperfect detection and carcasses with unknown spawning status. To facilitate unbiased estimation of prespawn mortality and associated uncertainty, we developed a hidden-process mark–recovery model to estimate prespawn mortality rates from carcass survey data while accounting for imperfect detection and unknown spawning success. We then used the model to estimate prespawn mortality and identify potential associated factors for 3,352 adult spring Chinook Salmon O. tshawytscha that were transported above Foster Dam on the South Santiam River (Willamette River basin, Oregon) from 2009 to 2013. Estimated prespawn mortality was relatively low (≤13%) in most years (interannual mean = 28%) but was especially high (74%) in 2013. Variation in prespawn mortality estimates among outplanted groups of fish within each year was also very high, and some of this variation was explained by a trend toward lower prespawn mortality among fish that were outplanted later in the year. Numerous efforts are being made to monitor and, when possible, minimize prespawn mortality in salmon populations; this model can be used to provide unbiased estimates of spawning success that account for unknown fate and imperfect detection, which are common to carcass survey data.

Spectroscopic observation of ASASSN-17nb and CSS170922:172546+342249 by NUTS (NOT Un-biased Transient Survey)

NASA Astrophysics Data System (ADS)

Harmanen, J.; Mattila, S.; Kuncarayakti, H.; Reynolds, T.; Somero, A.; Kangas, T.; Lundqvist, P.; Taddia, F.; Ergon, M.; Dong, S.; Pastorello, A.; Pursimo, T.; NUTS Collaboration

2017-10-01

The Nordic Optical Telescope (NOT) Unbiased Transient Survey (NUTS; ATel #8992) reports the spectroscopic classification of ASASSN-17nb in MCG+06-17-007 and CSS170922:172546+342249 in an unknown host galaxy.

Spectroscopic observation of Gaia17dht and Gaia17diu by NUTS (NOT Un-biased Transient Survey)

NASA Astrophysics Data System (ADS)

Fraser, M.; Dyrbye, S.; Cappella, E.

2017-12-01

The Nordic Optical Telescope (NOT) Unbiased Transient Survey (NUTS; ATel #8992) reports the spectroscopic classification of Gaia17dht/SN2017izz and Gaia17diu/SN2017jdb (in host galaxies SDSS J145121.24+283521.6 and LEDA 2753585 respectively).

A Bayesian approach to parameter and reliability estimation in the Poisson distribution.

NASA Technical Reports Server (NTRS)

Canavos, G. C.

1972-01-01

For life testing procedures, a Bayesian analysis is developed with respect to a random intensity parameter in the Poisson distribution. Bayes estimators are derived for the Poisson parameter and the reliability function based on uniform and gamma prior distributions of that parameter. A Monte Carlo procedure is implemented to make possible an empirical mean-squared error comparison between Bayes and existing minimum variance unbiased, as well as maximum likelihood, estimators. As expected, the Bayes estimators have mean-squared errors that are appreciably smaller than those of the other two.

Indices estimated using REML/BLUP and introduction of a super-trait for the selection of progenies in popcorn.

PubMed

Vittorazzi, C; Amaral Junior, A T; Guimarães, A G; Viana, A P; Silva, F H L; Pena, G F; Daher, R F; Gerhardt, I F S; Oliveira, G H F; Pereira, M G

2017-09-27

Selection indices commonly utilize economic weights, which become arbitrary genetic gains. In popcorn, this is even more evident due to the negative correlation between the main characteristics of economic importance - grain yield and popping expansion. As an option in the use of classical biometrics as a selection index, the optimal procedure restricted maximum likelihood/best linear unbiased predictor (REML/BLUP) allows the simultaneous estimation of genetic parameters and the prediction of genotypic values. Based on the mixed model methodology, the objective of this study was to investigate the comparative efficiency of eight selection indices estimated by REML/BLUP for the effective selection of superior popcorn families in the eighth intrapopulation recurrent selection cycle. We also investigated the efficiency of the inclusion of the variable "expanded popcorn volume per hectare" in the most advantageous selection of superior progenies. In total, 200 full-sib families were evaluated in two different areas in the North and Northwest regions of the State of Rio de Janeiro, Brazil. The REML/BLUP procedure resulted in higher estimated gains than those obtained with classical biometric selection index methodologies and should be incorporated into the selection of progenies. The following indices resulted in higher gains in the characteristics of greatest economic importance: the classical selection index/values attributed by trial, via REML/BLUP, and the greatest genotypic values/expanded popcorn volume per hectare, via REML. The expanded popcorn volume per hectare characteristic enabled satisfactory gains in grain yield and popping expansion; this characteristic should be considered super-trait in popcorn breeding programs.

Assimilating remote sensing observations of leaf area index and soil moisture for wheat yield estimates: An observing system simulation experiment

USDA-ARS?s Scientific Manuscript database

We develop a robust understanding of the effects of assimilating remote sensing observations of leaf area index and soil moisture (in the top 5 cm) on DSSAT-CSM CropSim-Ceres wheat yield estimates. Synthetic observing system simulation experiments compare the abilities of the Ensemble Kalman Filter...

Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation

USDA-ARS?s Scientific Manuscript database

The scale mismatch between remotely sensed observations and crop growth models simulated state variables decreases the reliability of crop yield estimates. To overcome this problem, we used a two-step data assimilation phases: first we generated a complete leaf area index (LAI) time series by combin...

Mineral Nutritional Yield and Nutrient Density of Locally Adapted Wheat Genotypes under Organic Production

PubMed Central

Moreira-Ascarrunz, Sergio Daniel; Larsson, Hans; Prieto-Linde, Maria Luisa; Johansson, Eva

2016-01-01

The aim of the present investigation was to investigate the nutritional yield, nutrient density, stability, and adaptability of organically produced wheat for sustainable and nutritional high value food production. This study evaluated the nutritional yield of four minerals (Fe, Zn, Cu, and Mg) in 19 wheat genotypes, selected as being locally adapted under organic agriculture conditions. The new metric of nutritional yield was calculated for each genotype and they were evaluated for stability using the Additive Main effects and Multiplicative Interaction (AMMI) stability analysis and for genotypic value, stability, and adaptability using the Best Linear Unbiased Prediction (BLUP procedure). The results indicated that there were genotypes suitable for production under organic agriculture conditions with satisfactory yields (>4000 kg·ha−1). Furthermore, these genotypes showed high nutritional yield and nutrient density for the four minerals studied. Additionally, since these genotypes were stable and adaptable over three environmentally different years, they were designated “balanced genotypes” for the four minerals and for the aforementioned characteristics. Selection and breeding of such “balanced genotypes” may offer an alternative to producing nutritious food under low-input agriculture conditions. Furthermore, the type of evaluation presented here may also be of interest for implementation in research conducted in developing countries, following the objectives of producing enough nutrients for a growing population. PMID:28231184

EVALUATING PROBABILITY SAMPLING STRATEGIES FOR ESTIMATING REDD COUNTS: AN EXAMPLE WITH CHINOOK SALMON (Oncorhynchus tshawytscha)

EPA Science Inventory

Precise, unbiased estimates of population size are an essential tool for fisheries management. For a wide variety of salmonid fishes, redd counts from a sample of reaches are commonly used to monitor annual trends in abundance. Using a 9-year time series of georeferenced censuses...

Effects of sampling close relatives on some elementary population genetics analyses.

PubMed

Wang, Jinliang

2018-01-01

Many molecular ecology analyses assume the genotyped individuals are sampled at random from a population and thus are representative of the population. Realistically, however, a sample may contain excessive close relatives (ECR) because, for example, localized juveniles are drawn from fecund species. Our knowledge is limited about how ECR affect the routinely conducted elementary genetics analyses, and how ECR are best dealt with to yield unbiased and accurate parameter estimates. This study quantifies the effects of ECR on some popular population genetics analyses of marker data, including the estimation of allele frequencies, F-statistics, expected heterozygosity (H e ), effective and observed numbers of alleles, and the tests of Hardy-Weinberg equilibrium (HWE) and linkage equilibrium (LE). It also investigates several strategies for handling ECR to mitigate their impact and to yield accurate parameter estimates. My analytical work, assisted by simulations, shows that ECR have large and global effects on all of the above marker analyses. The naïve approach of simply ignoring ECR could yield low-precision and often biased parameter estimates, and could cause too many false rejections of HWE and LE. The bold approach, which simply identifies and removes ECR, and the cautious approach, which estimates target parameters (e.g., H e ) by accounting for ECR and using naïve allele frequency estimates, eliminate the bias and the false HWE and LE rejections, but could reduce estimation precision substantially. The likelihood approach, which accounts for ECR in estimating allele frequencies and thus target parameters relying on allele frequencies, usually yields unbiased and the most accurate parameter estimates. Which of the four approaches is the most effective and efficient may depend on the particular marker analysis to be conducted. The results are discussed in the context of using marker data for understanding population properties and marker properties. © 2017

Minimax Estimation of Functionals of Discrete Distributions

PubMed Central

Jiao, Jiantao; Venkat, Kartik; Han, Yanjun; Weissman, Tsachy

2017-01-01

We propose a general methodology for the construction and analysis of essentially minimax estimators for a wide class of functionals of finite dimensional parameters, and elaborate on the case of discrete distributions, where the support size S is unknown and may be comparable with or even much larger than the number of observations n. We treat the respective regions where the functional is nonsmooth and smooth separately. In the nonsmooth regime, we apply an unbiased estimator for the best polynomial approximation of the functional whereas, in the smooth regime, we apply a bias-corrected version of the maximum likelihood estimator (MLE). We illustrate the merit of this approach by thoroughly analyzing the performance of the resulting schemes for estimating two important information measures: 1) the entropy H(P)=∑i=1S−pilnpi and 2) Fα(P)=∑i=1Spiα, α > 0. We obtain the minimax L2 rates for estimating these functionals. In particular, we demonstrate that our estimator achieves the optimal sample complexity n ≍ S/ln S for entropy estimation. We also demonstrate that the sample complexity for estimating Fα(P), 0 < α < 1, is n ≍ S1/α/ln S, which can be achieved by our estimator but not the MLE. For 1 < α < 3/2, we show the minimax L2 rate for estimating Fα(P) is (n ln n)−2(α−1) for infinite support size, while the maximum L2 rate for the MLE is n−2(α−1). For all the above cases, the behavior of the minimax rate-optimal estimators with n samples is essentially that of the MLE (plug-in rule) with n ln n samples, which we term “effective sample size enlargement.” We highlight the practical advantages of our schemes for the estimation of entropy and mutual information. We compare our performance with various existing approaches, and demonstrate that our approach reduces running time and boosts the accuracy. Moreover, we show that the minimax rate-optimal mutual information estimator yielded by our framework leads to significant performance

Adoption of an unmanned helicopter for low-altitude remote sensing to estimate yield and total biomass of a rice crop

USDA-ARS?s Scientific Manuscript database

A radio-controlled unmanned helicopter-based LARS (Low-Altitude Remote Sensing) platform was used to acquire quality images of high spatial and temporal resolution, in order to estimate yield and total biomass of a rice crop (Oriza Sativa, L.). Fifteen rice field plots with five N-treatments (0, 33,...

Advances in regional crop yield estimation over the United States using satellite remote sensing data

NASA Astrophysics Data System (ADS)

Johnson, D. M.; Dorn, M. F.; Crawford, C.

2015-12-01

Since the dawn of earth observation imagery, particularly from systems like Landsat and the Advanced Very High Resolution Radiometer, there has been an overarching desire to regionally estimate crop production remotely. Research efforts integrating space-based imagery into yield models to achieve this need have indeed paralleled these systems through the years, yet development of a truly useful crop production monitoring system has been arguably mediocre in coming. As a result, relatively few organizations have yet to operationalize the concept, and this is most acute in regions of the globe where there are not even alternative sources of crop production data being collected. However, the National Agricultural Statistics Service (NASS) has continued to push for this type of data source as a means to complement its long-standing, traditional crop production survey efforts which are financially costly to the government and create undue respondent burden on farmers. Corn and soybeans, the two largest field crops in the United States, have been the focus of satellite-based production monitoring by NASS for the past decade. Data from the Moderate Resolution Imaging Spectroradiometer (MODIS) has been seen as the most pragmatic input source for modeling yields primarily based on its daily revisit capabilities and reasonable ground sample resolution. The research methods presented here will be broad but provides a summary of what is useful and adoptable with satellite imagery in terms of crop yield estimation. Corn and soybeans will be of particular focus but other major staple crops like wheat and rice will also be presented. NASS will demonstrate that while MODIS provides a slew of vegetation related products, the traditional normalized difference vegetation index (NDVI) is still ideal. Results using land surface temperature products, also generated from MODIS, will also be shown. Beyond the MODIS data itself, NASS research has also focused efforts on understanding a

Predicting red meat yields in carcasses from beef-type and calf-fed Holstein steers using the United States Department of Agriculture calculated yield grade.

PubMed

Lawrence, T E; Elam, N A; Miller, M F; Brooks, J C; Hilton, G G; VanOverbeke, D L; McKeith, F K; Killefer, J; Montgomery, T H; Allen, D M; Griffin, D B; Delmore, R J; Nichols, W T; Streeter, M N; Yates, D A; Hutcheson, J P

2010-06-01

Analyses were conducted to evaluate the ability of the USDA yield grade equation to detect differences in subprimal yield of beef-type steers and calf-fed Holstein steers that had been fed zilpaterol hydrochloride (ZH; Intervet Inc., Millsboro, DE) as well as those that had not been fed ZH. Beef-type steer (n = 801) and calf-fed Holstein steer (n = 235) carcasses were fabricated into subprimal cuts and trim. Simple correlations between calculated yield grades and total red meat yields ranged from -0.56 to -0.62 for beef-type steers. Reliable correlations from calf-fed Holstein steers were unobtainable; the probability of a type I error met or exceeded 0.39. Linear models were developed for the beef-type steers to predict total red meat yield based on calculated USDA yield grade within each ZH duration. At an average calculated USDA yield grade of 2.9, beef-type steer carcasses that had not been fed ZH had an estimated 69.4% red meat yield, whereas those fed ZH had an estimated 70.7% red meat yield. These results indicate that feeding ZH increased red meat yield by 1.3% at a constant calculated yield grade. However, these data also suggest that the calculated USDA yield grade score is a poor and variable estimator (adjusted R(2) of 0.31 to 0.38) of total red meat yield of beef-type steer carcasses, regardless of ZH feeding. Moreover, no relationship existed (adjusted R(2) of 0.00 to 0.01) for calf-fed Holstein steer carcasses, suggesting the USDA yield grade is not a valid estimate of calf-fed Holstein red meat yield.

Assessing Sediment Yield and the Effect of Best Management Practices on Sediment Yield Reduction for Tutuila Island, American Samoa

NASA Astrophysics Data System (ADS)

Leta, O. T.; Dulai, H.; El-Kadi, A. I.

2017-12-01

Upland soil erosion and sedimentation are the main threats for riparian and coastal reef ecosystems in Pacific islands. Here, due to small size of the watersheds and steep slope, the residence time of rainfall runoff and its suspended load is short. Fagaalu bay, located on the island of Tutuila (American Samoa) has been identified as a priority watershed, due to degraded coral reef condition and reduction of stream water quality from heavy anthropogenic activity yielding high nutrients and sediment loads to the receiving water bodies. This study aimed to estimate the sediment yield to the Fagaalu stream and assess the impact of Best Management Practices (BMP) on sediment yield reduction. For this, the Soil and Water Assessment Tool (SWAT) model was applied, calibrated, and validated for both daily streamflow and sediment load simulation. The model also estimated the sediment yield contributions from existing land use types of Fagaalu and identified soil erosion prone areas for introducing BMP scenarios in the watershed. Then, three BMP scenarios, such as stone bund, retention pond, and filter strip were treated on bare (quarry area), agricultural, and shrub land use types. It was found that the bare land with quarry activity yielded the highest annual average sediment yield of 133 ton per hectare (t ha-1) followed by agriculture (26.1 t ha-1) while the lowest sediment yield of 0.2 t ha-1 was estimated for the forested part of the watershed. Additionally, the bare land area (2 ha) contributed approximately 65% (207 ha) of the watershed's sediment yield, which is 4.0 t ha-1. The latter signifies the high impact as well as contribution of anthropogenic activity on sediment yield. The use of different BMP scenarios generally reduced the sediment yield to the coastal reef of Fagaalu watershed. However, treating the quarry activity area with stone bund showed the highest sediment yield reduction as compared to the other two BMP scenarios. This study provides an estimate

Hemifield-dependent N1 and event-related theta/delta oscillations: An unbiased comparison of surface Laplacian and common EEG reference choices

PubMed Central

Kayser, Jürgen; Tenke, Craig E.

2015-01-01

Surface Laplacian methodology has been used to reduce the impact of volume conduction and arbitrary choice of EEG recording reference for the analysis of surface potentials. However, the empirical implications of employing these different transformations to the same EEG data remain obscure. This study directly compared the statistical effects of four commonly-used (nose, linked mastoids, average) or recommended (reference electrode standardization technique [REST]) references and their spherical spline current source density (CSD) transformation for a large data set stemming from a well-understood experimental manipulation. ERPs (72 sites) recorded from 130 individuals during a visual half-field paradigm with highly-controlled emotional stimuli were characterized by mid-parietooccipital N1 (125 ms peak latency) and event-related synchronization (ERS) of theta/delta (160 ms), which were most robust over the contralateral hemisphere. All five data transformations were rescaled to the same covariance and submitted to a single temporal or time-frequency PCA (Varimax) to yield simplified estimates of N1 or theta/delta ERS. Unbiased nonparametric permutation tests revealed that these hemifield-dependent asymmetries were by far most focal and prominent for CSD data, despite all transformations showing maximum effects at mid-parietooccipital sites. Employing smaller subsamples (signal-to-noise) or window-based ERP/ERS amplitudes did not affect these comparisons. Furthermore, correlations between N1 and theta/delta ERS at these sites were strongest for CSD and weakest for nose-referenced data. Contrary to the common notion that the spatial high pass filter properties of a surface Laplacian reduce important contributions of neuronal generators to the EEG signal, the present findings demonstrate that instead volume conduction inherent in surface potentials weakens the representation of neuronal activation patterns at scalp that directly reflect regional brain activity. PMID

A Comparison of Latent Growth Models for Constructs Measured by Multiple Items

ERIC Educational Resources Information Center

Leite, Walter L.

2007-01-01

Univariate latent growth modeling (LGM) of composites of multiple items (e.g., item means or sums) has been frequently used to analyze the growth of latent constructs. This study evaluated whether LGM of composites yields unbiased parameter estimates, standard errors, chi-square statistics, and adequate fit indexes. Furthermore, LGM was compared…

Absorption and folding of melittin onto lipid bilayer membranes via unbiased atomic detail microsecond molecular dynamics simulation.

PubMed

Chen, Charles H; Wiedman, Gregory; Khan, Ayesha; Ulmschneider, Martin B

2014-09-01

Unbiased molecular simulation is a powerful tool to study the atomic details driving functional structural changes or folding pathways of highly fluid systems, which present great challenges experimentally. Here we apply unbiased long-timescale molecular dynamics simulation to study the ab initio folding and partitioning of melittin, a template amphiphilic membrane active peptide. The simulations reveal that the peptide binds strongly to the lipid bilayer in an unstructured configuration. Interfacial folding results in a localized bilayer deformation. Akin to purely hydrophobic transmembrane segments the surface bound native helical conformer is highly resistant against thermal denaturation. Circular dichroism spectroscopy experiments confirm the strong binding and thermostability of the peptide. The study highlights the utility of molecular dynamics simulations for studying transient mechanisms in fluid lipid bilayer systems. This article is part of a Special Issue entitled: Interfacially Active Peptides and Proteins. Guest Editors: William C. Wimley and Kalina Hristova. Copyright © 2014. Published by Elsevier B.V.

Unbiased Quantitative Models of Protein Translation Derived from Ribosome Profiling Data

PubMed Central

Gritsenko, Alexey A.; Hulsman, Marc; Reinders, Marcel J. T.; de Ridder, Dick

2015-01-01

Translation of RNA to protein is a core process for any living organism. While for some steps of this process the effect on protein production is understood, a holistic understanding of translation still remains elusive. In silico modelling is a promising approach for elucidating the process of protein synthesis. Although a number of computational models of the process have been proposed, their application is limited by the assumptions they make. Ribosome profiling (RP), a relatively new sequencing-based technique capable of recording snapshots of the locations of actively translating ribosomes, is a promising source of information for deriving unbiased data-driven translation models. However, quantitative analysis of RP data is challenging due to high measurement variance and the inability to discriminate between the number of ribosomes measured on a gene and their speed of translation. We propose a solution in the form of a novel multi-scale interpretation of RP data that allows for deriving models with translation dynamics extracted from the snapshots. We demonstrate the usefulness of this approach by simultaneously determining for the first time per-codon translation elongation and per-gene translation initiation rates of Saccharomyces cerevisiae from RP data for two versions of the Totally Asymmetric Exclusion Process (TASEP) model of translation. We do this in an unbiased fashion, by fitting the models using only RP data with a novel optimization scheme based on Monte Carlo simulation to keep the problem tractable. The fitted models match the data significantly better than existing models and their predictions show better agreement with several independent protein abundance datasets than existing models. Results additionally indicate that the tRNA pool adaptation hypothesis is incomplete, with evidence suggesting that tRNA post-transcriptional modifications and codon context may play a role in determining codon elongation rates. PMID:26275099

Unbiased Quantitative Models of Protein Translation Derived from Ribosome Profiling Data.

PubMed

Gritsenko, Alexey A; Hulsman, Marc; Reinders, Marcel J T; de Ridder, Dick

2015-08-01

Translation of RNA to protein is a core process for any living organism. While for some steps of this process the effect on protein production is understood, a holistic understanding of translation still remains elusive. In silico modelling is a promising approach for elucidating the process of protein synthesis. Although a number of computational models of the process have been proposed, their application is limited by the assumptions they make. Ribosome profiling (RP), a relatively new sequencing-based technique capable of recording snapshots of the locations of actively translating ribosomes, is a promising source of information for deriving unbiased data-driven translation models. However, quantitative analysis of RP data is challenging due to high measurement variance and the inability to discriminate between the number of ribosomes measured on a gene and their speed of translation. We propose a solution in the form of a novel multi-scale interpretation of RP data that allows for deriving models with translation dynamics extracted from the snapshots. We demonstrate the usefulness of this approach by simultaneously determining for the first time per-codon translation elongation and per-gene translation initiation rates of Saccharomyces cerevisiae from RP data for two versions of the Totally Asymmetric Exclusion Process (TASEP) model of translation. We do this in an unbiased fashion, by fitting the models using only RP data with a novel optimization scheme based on Monte Carlo simulation to keep the problem tractable. The fitted models match the data significantly better than existing models and their predictions show better agreement with several independent protein abundance datasets than existing models. Results additionally indicate that the tRNA pool adaptation hypothesis is incomplete, with evidence suggesting that tRNA post-transcriptional modifications and codon context may play a role in determining codon elongation rates.

Treatment Effect Estimation Using Nonlinear Two-Stage Instrumental Variable Estimators: Another Cautionary Note.

PubMed

Chapman, Cole G; Brooks, John M

2016-12-01

To examine the settings of simulation evidence supporting use of nonlinear two-stage residual inclusion (2SRI) instrumental variable (IV) methods for estimating average treatment effects (ATE) using observational data and investigate potential bias of 2SRI across alternative scenarios of essential heterogeneity and uniqueness of marginal patients. Potential bias of linear and nonlinear IV methods for ATE and local average treatment effects (LATE) is assessed using simulation models with a binary outcome and binary endogenous treatment across settings varying by the relationship between treatment effectiveness and treatment choice. Results show that nonlinear 2SRI models produce estimates of ATE and LATE that are substantially biased when the relationships between treatment and outcome for marginal patients are unique from relationships for the full population. Bias of linear IV estimates for LATE was low across all scenarios. Researchers are increasingly opting for nonlinear 2SRI to estimate treatment effects in models with binary and otherwise inherently nonlinear dependent variables, believing that it produces generally unbiased and consistent estimates. This research shows that positive properties of nonlinear 2SRI rely on assumptions about the relationships between treatment effect heterogeneity and choice. © Health Research and Educational Trust.

Optimizing rice yields while minimizing yield-scaled global warming potential.

PubMed

Pittelkow, Cameron M; Adviento-Borbe, Maria A; van Kessel, Chris; Hill, James E; Linquist, Bruce A

2014-05-01

To meet growing global food demand with limited land and reduced environmental impact, agricultural greenhouse gas (GHG) emissions are increasingly evaluated with respect to crop productivity, i.e., on a yield-scaled as opposed to area basis. Here, we compiled available field data on CH4 and N2 O emissions from rice production systems to test the hypothesis that in response to fertilizer nitrogen (N) addition, yield-scaled global warming potential (GWP) will be minimized at N rates that maximize yields. Within each study, yield N surplus was calculated to estimate deficit or excess N application rates with respect to the optimal N rate (defined as the N rate at which maximum yield was achieved). Relationships between yield N surplus and GHG emissions were assessed using linear and nonlinear mixed-effects models. Results indicate that yields increased in response to increasing N surplus when moving from deficit to optimal N rates. At N rates contributing to a yield N surplus, N2 O and yield-scaled N2 O emissions increased exponentially. In contrast, CH4 emissions were not impacted by N inputs. Accordingly, yield-scaled CH4 emissions decreased with N addition. Overall, yield-scaled GWP was minimized at optimal N rates, decreasing by 21% compared to treatments without N addition. These results are unique compared to aerobic cropping systems in which N2 O emissions are the primary contributor to GWP, meaning yield-scaled GWP may not necessarily decrease for aerobic crops when yields are optimized by N fertilizer addition. Balancing gains in agricultural productivity with climate change concerns, this work supports the concept that high rice yields can be achieved with minimal yield-scaled GWP through optimal N application rates. Moreover, additional improvements in N use efficiency may further reduce yield-scaled GWP, thereby strengthening the economic and environmental sustainability of rice systems. © 2013 John Wiley & Sons Ltd.

Human systems immunology: hypothesis-based modeling and unbiased data-driven approaches.

PubMed

Arazi, Arnon; Pendergraft, William F; Ribeiro, Ruy M; Perelson, Alan S; Hacohen, Nir

2013-10-31

Systems immunology is an emerging paradigm that aims at a more systematic and quantitative understanding of the immune system. Two major approaches have been utilized to date in this field: unbiased data-driven modeling to comprehensively identify molecular and cellular components of a system and their interactions; and hypothesis-based quantitative modeling to understand the operating principles of a system by extracting a minimal set of variables and rules underlying them. In this review, we describe applications of the two approaches to the study of viral infections and autoimmune diseases in humans, and discuss possible ways by which these two approaches can synergize when applied to human immunology. Copyright © 2012 Elsevier Ltd. All rights reserved.

Concentrations, and Estimated Loads and Yields of Total Nitrogen and Total Phosphorus at Selected Stations in Kentucky, 1979-2004

USGS Publications Warehouse

Crain, Angela S.; Martin, Gary R.

2009-01-01

To evaluate the State's water quality, the Kentucky Division of Water collects data from a statewide network of primary ambient stream water-quality monitoring stations and flexible, rotating watershed-monitoring stations. This ambient stream water-quality monitoring network program is directed to assess the conditions of surface waters throughout Kentucky. Water samples were collected monthly for the majority of the stations from 1979 to 1998, which represented agricultural, undeveloped (mainly forested), and areas of mixed land use/land cover. In 1998, the number of water samples collected was reduced to a collection frequency of six times per year (every 2 months) every 4 of 5 years, because a new monitoring network was implemented involving a 5-year rotating Basin Management Unit scheme of monitoring. This report presents the results of a study conducted by the U.S. Geological Survey, in cooperation with the Kentucky Energy and Environment Cabinet-Kentucky Division of Water, to summarize concentrations of total nitrogen and total phosphorus and provide estimates of total nitrogen and total phosphorus loads and yields in 55 selected streams in Kentucky's ambient stream water-quality monitoring network, which was operated from 1979 through 2004. Streams in predominately agricultural basins had higher concentrations of total nitrogen (TN) and concentrations of total phosphorus (TP) than streams in predominately undeveloped (forested) basins. Streams in basins in intensely developed karst areas characterized by caves, springs, sinkholes, and sinking streams had a higher median concentration of TN (1.5 milligrams per liter [mg/L]) than streams in basins with limited or no karst areas (0.63 mg/L). As with TN, median concentrations of TP also were higher in areas of intense karst (0.05 mg/L) than in areas with limited or no karst (0.02 mg/L). The U.S. Environmental Protection Agency (USEPA) has recommended ecoregional nutrient water-quality criteria as a starting

Towards an unbiased filter routine to determine precipitation and evapotranspiration from high precision lysimeter measurements

NASA Astrophysics Data System (ADS)

Peters, Andre; Groh, Jannis; Schrader, Frederik; Durner, Wolfgang; Vereecken, Harry; Pütz, Thomas

2017-06-01

Weighing lysimeters are considered to be the best means for a precise measurement of water fluxes at the interface between the soil-plant system and the atmosphere. Any decrease of the net mass of the lysimeter can be interpreted as evapotranspiration (ET), any increase as precipitation (P). However, the measured raw data need to be filtered to separate real mass changes from noise. Such filter routines typically apply two steps: (i) a low pass filter, like moving average, which smooths noisy data, and (ii) a threshold filter that separates significant from insignificant mass changes. Recent developments of these filters have identified and solved some problems regarding bias in the data processing. A remaining problem is that each change in flow direction is accompanied with a systematic flow underestimation due to the threshold scheme. In this contribution, we analyze this systematic effect and show that the absolute underestimation is independent of the magnitude of a flux event. Thus, for small events, like dew or rime formation, the relative error is high and can reach the same magnitude as the flux itself. We develop a heuristic solution to the problem by introducing a so-called "snap routine". The routine is calibrated and tested with synthetic flux data and applied to real measurements obtained with a precision lysimeter for a 10-month period. The heuristic snap routine effectively overcomes these problems and yields an almost unbiased representation of the real signal.

Three statistical models for estimating length of stay.

PubMed Central

Selvin, S

1977-01-01

The probability density functions implied by three methods of collecting data on the length of stay in an institution are derived. The expected values associated with these density functions are used to calculate unbiased estimates of the expected length of stay. Two of the methods require an assumption about the form of the underlying distribution of length of stay; the third method does not. The three methods are illustrated with hypothetical data exhibiting the Poisson distribution, and the third (distribution-independent) method is used to estimate the length of stay in a skilled nursing facility and in an intermediate care facility for patients enrolled in California's MediCal program. PMID:914532

Three statistical models for estimating length of stay.

PubMed

Selvin, S

1977-01-01

The probability density functions implied by three methods of collecting data on the length of stay in an institution are derived. The expected values associated with these density functions are used to calculate unbiased estimates of the expected length of stay. Two of the methods require an assumption about the form of the underlying distribution of length of stay; the third method does not. The three methods are illustrated with hypothetical data exhibiting the Poisson distribution, and the third (distribution-independent) method is used to estimate the length of stay in a skilled nursing facility and in an intermediate care facility for patients enrolled in California's MediCal program.

Effect of incomplete pedigrees on estimates of inbreeding and inbreeding depression for days to first service and summit milk yield in Holsteins and Jerseys.

PubMed

Cassell, B G; Adamec, V; Pearson, R E

2003-09-01

A method to measure completeness of pedigree information is applied to populations of Holstein (registered and grade) and Jersey (largely registered) cows. Inbreeding coefficients where missing ancestors make no contribution were compared to a method using average relationships for missing ancestors. Estimated inbreeding depression was from an animal model that simultaneously adjusted for breeding values. Inbreeding and its standard deviation increased with more information, from 0.04 +/- 0.84 to 1.65 +/- 2.05 and 2.06 +/- 2.22 for grade Holsteins with <31%, 31 to 70%, and 71 to 100% complete five-generation pedigrees. Inbreeding from the method of average relationships for missing ancestors was 2.75 +/- 1.06, 3.10 +/- 2.21, and 2.89 +/- 2.37 for the same groups. Pedigrees of registered Holsteins and Jerseys were over 97% and over 89% complete, respectively. Inbreeding depression in days to first service and summit milk yield was estimated from both methods. Inbreeding depression for days to first service was not consistently significant for grade Holsteins and ranged from -0.37 d/1% increase in inbreeding (grade Holstein pedigrees <31% complete) to 0.15 d for grade Holstein pedigrees >70% complete. Estimates were similar for both methods. Inbreeding depression for registered Holsteins and Jerseys were positive (undesirable) but not significant for days to first service. Inbreeding depressed summit milk yield significantly in all groups by both methods. Summit milk yield declined by -0.12 to -0.06 kg/d per 1% increase in inbreeding in Holsteins and by -0.08 kg/1% increase in inbreeding in Jerseys. Pedigrees of grade animals are frequently incomplete and can yield misleading estimates of inbreeding depression. This problem is not overcome by inserting average relationships for missing ancestors in calculation of inbreeding coefficients.

Optimal survey strategies and predicted planet yields for the Korean microlensing telescope network

DOE Office of Scientific and Technical Information (OSTI.GOV)

Henderson, Calen B.; Gaudi, B. Scott; Skowron, Jan

2014-10-10

The Korean Microlensing Telescope Network (KMTNet) will consist of three 1.6 m telescopes each with a 4 deg{sup 2} field of view (FoV) and will be dedicated to monitoring the Galactic Bulge to detect exoplanets via gravitational microlensing. KMTNet's combination of aperture size, FoV, cadence, and longitudinal coverage will provide a unique opportunity to probe exoplanet demographics in an unbiased way. Here we present simulations that optimize the observing strategy for and predict the planetary yields of KMTNet. We find preferences for four target fields located in the central Bulge and an exposure time of t {sub exp} = 120more » s, leading to the detection of ∼2200 microlensing events per year. We estimate the planet detection rates for planets with mass and separation across the ranges 0.1 ≤ M{sub p} /M {sub ⊕} ≤ 1000 and 0.4 ≤ a/AU ≤ 16, respectively. Normalizing these rates to the cool-planet mass function of Cassan et al., we predict KMTNet will be approximately uniformly sensitive to planets with mass 5 ≤ M{sub p} /M {sub ⊕} ≤ 1000 and will detect ∼20 planets per year per dex in mass across that range. For lower-mass planets with mass 0.1 ≤ M{sub p} /M {sub ⊕} < 5, we predict KMTNet will detect ∼10 planets per year. We also compute the yields KMTNet will obtain for free-floating planets (FFPs) and predict KMTNet will detect ∼1 Earth-mass FFP per year, assuming an underlying population of one such planet per star in the Galaxy. Lastly, we investigate the dependence of these detection rates on the number of observatories, the photometric precision limit, and optimistic assumptions regarding seeing, throughput, and flux measurement uncertainties.« less

Parameter estimation in linear models of the human operator in a closed loop with application of deterministic test signals

NASA Technical Reports Server (NTRS)

Vanlunteren, A.; Stassen, H. G.

1973-01-01

Parameter estimation techniques are discussed with emphasis on unbiased estimates in the presence of noise. A distinction between open and closed loop systems is made. A method is given based on the application of external forcing functions consisting of a sun of sinusoids; this method is thus based on the estimation of Fourier coefficients and is applicable for models with poles and zeros in open and closed loop systems.

Joint Bayesian inference for near-surface explosion yield

NASA Astrophysics Data System (ADS)

Bulaevskaya, V.; Ford, S. R.; Ramirez, A. L.; Rodgers, A. J.

2016-12-01

A near-surface explosion generates seismo-acoustic motion that is related to its yield. However, the recorded motion is affected by near-source effects such as depth-of-burial, and propagation-path effects such as variable geology. We incorporate these effects in a forward model relating yield to seismo-acoustic motion, and use Bayesian inference to estimate yield given recordings of the seismo-acoustic wavefield. The Bayesian approach to this inverse problem allows us to obtain the probability distribution of plausible yield values and thus quantify the uncertainty in the yield estimate. Moreover, the sensitivity of the acoustic signal falls as a function of the depth-of-burial, while the opposite relationship holds for the seismic signal. Therefore, using both the acoustic and seismic wavefield data allows us to avoid the trade-offs associated with using only one of these signals alone. In addition, our inference framework allows for correlated features of the same data type (seismic or acoustic) to be incorporated in the estimation of yield in order to make use of as much information from the same waveform as possible. We demonstrate our approach with a historical dataset and a contemporary field experiment.

PROMAB-GIS: A GIS based Tool for Estimating Runoff and Sediment Yield in running Waters

NASA Astrophysics Data System (ADS)

Jenewein, S.; Rinderer, M.; Ploner, A.; Sönser, T.

2003-04-01

In recent times settlements have expanded, traffic and tourist activities have increased in most alpine regions. As a consequence, on the one hand humans and goods are affected by natural hazard processes more often, while on the other hand the demand for protection by both technical constructions and planning measures carried out by public authorities is growing. This situation results in an ever stronger need of reproducibility, comparability, transparency of all methods applied in modern natural hazard management. As a contribution to a new way of coping this situation Promab-GIS Version 1.0 has been developed. Promab-Gis has been designed as a model for time- and space-dependent determination of both runoff and bedload transport in rivers of small alpine catchment areas. The estimation of the unit hydrograph relies upon the "rational formula" and the time-area curves of the watershed. The time area diagram is a graph of cumulative drainage area contributing to discharge at the watershed outlet within a specified time of travel. The sediment yield is estimated for each cell of the channel network by determining the actual process type (erosion, transport or accumulation). Two types of transport processes are considered, sediment transport and debris flows. All functions of Promab-GIS are integrated in the graphical user interface of ArcView as pull-up menus and tool buttons. Hence the application of Promab-GIS does not rely on a sophisticated knowledge of GIS in general, respectively the ArcView software. However, despite the use of computer assistance, Promab-GIS still is an expert support system. In order to obtain plausible results, the users must be familiar with all the relevant processes controlling runoff and sediment yield in torrent catchments.

Concentrations, and estimated loads and yields of nutrients and suspended sediment in the Little River basin, Kentucky, 2003-04

USGS Publications Warehouse

Crain, Angela S.

2006-01-01

Nutrients, primarily nitrogen and phosphorus compounds, naturally occur but also are applied to land in the form of commercial fertilizers and livestock waste to enhance plant growth. Concentrations, estimated loads and yields, and sources of nitrite plus nitrate, total phosphorus, and orthophosphate were evaluated in streams of the Little River Basin to assist the Commonwealth of Kentucky in developing 'total maximum daily loads' (TMDLs) for streams in the basin. The Little River Basin encompasses about 600 square miles in Christian and Trigg Counties, and a portion of Caldwell County in western Kentucky. Water samples were collected in streams in the Little River Basin during 2003-04 as part of a study conducted in cooperation with the Kentucky Department of Agriculture. A total of 92 water samples were collected at four fixed-network sites from March through November 2003 and from February through November 2004. An additional 20 samples were collected at five synoptic-network sites during the same period. Median concentrations of nitrogen, phosphorus, and suspended sediment varied spatially and seasonally. Concentrations of nitrogen were higher in the spring (March-May) after fertilizer application and runoff. The highest concentration of nitrite plus nitrate-5.7 milligrams per liter (mg/L)-was detected at the South Fork Little River site. The Sinking Fork near Cadiz site had the highest median concentration of nitrite plus nitrate (4.6 mg/L). The North Fork Little River site and the Little River near Cadiz site had higher concentrations of orthophosphate in the fall and lower concentrations in the spring. Concentrations of orthophosphate remained high during the summer (June-August) at the North Fork Little River site possibly because of the contribution of wastewater effluent to streamflow. Fifty-eight percent of the concentrations of total phosphorus at the nine sites exceeded the U.S. Environmental Protection Agency recommended maximum concentration limit of

Assimilation of Remotely Sensed Soil Moisture Profiles into a Crop Modeling Framework for Reliable Yield Estimations

NASA Astrophysics Data System (ADS)

Mishra, V.; Cruise, J.; Mecikalski, J. R.

2017-12-01

Much effort has been expended recently on the assimilation of remotely sensed soil moisture into operational land surface models (LSM). These efforts have normally been focused on the use of data derived from the microwave bands and results have often shown that improvements to model simulations have been limited due to the fact that microwave signals only penetrate the top 2-5 cm of the soil surface. It is possible that model simulations could be further improved through the introduction of geostationary satellite thermal infrared (TIR) based root zone soil moisture in addition to the microwave deduced surface estimates. In this study, root zone soil moisture estimates from the TIR based Atmospheric Land Exchange Inverse (ALEXI) model were merged with NASA Soil Moisture Active Passive (SMAP) based surface estimates through the application of informational entropy. Entropy can be used to characterize the movement of moisture within the vadose zone and accounts for both advection and diffusion processes. The Principle of Maximum Entropy (POME) can be used to derive complete soil moisture profiles and, fortuitously, only requires a surface boundary condition as well as the overall mean moisture content of the soil column. A lower boundary can be considered a soil parameter or obtained from the LSM itself. In this study, SMAP provided the surface boundary while ALEXI supplied the mean and the entropy integral was used to tie the two together and produce the vertical profile. However, prior to the merging, the coarse resolution (9 km) SMAP data were downscaled to the finer resolution (4.7 km) ALEXI grid. The disaggregation scheme followed the Soil Evaporative Efficiency approach and again, all necessary inputs were available from the TIR model. The profiles were then assimilated into a standard agricultural crop model (Decision Support System for Agrotechnology, DSSAT) via the ensemble Kalman Filter. The study was conducted over the Southeastern United States for the

Climate change impacts on crop yield: evidence from China.

PubMed

Wei, Taoyuan; Cherry, Todd L; Glomrød, Solveig; Zhang, Tianyi

2014-11-15

When estimating climate change impact on crop yield, a typical assumption is constant elasticity of yield with respect to a climate variable even though the elasticity may be inconstant. After estimating both constant and inconstant elasticities with respect to temperature and precipitation based on provincial panel data in China 1980-2008, our results show that during that period, the temperature change contributes positively to total yield growth by 1.3% and 0.4% for wheat and rice, respectively, but negatively by 12% for maize. The impacts of precipitation change are marginal. We also compare our estimates with other studies and highlight the implications of the inconstant elasticities for crop yield, harvest and food security. We conclude that climate change impact on crop yield would not be an issue in China if positive impacts of other socio-economic factors continue in the future. Copyright © 2014 Elsevier B.V. All rights reserved.

Estimation of genetic parameters and response to selection for a continuous trait subject to culling before testing.

PubMed

Arnason, T; Albertsdóttir, E; Fikse, W F; Eriksson, S; Sigurdsson, A

2012-02-01

The consequences of assuming a zero environmental covariance between a binary trait 'test-status' and a continuous trait on the estimates of genetic parameters by restricted maximum likelihood and Gibbs sampling and on response from genetic selection when the true environmental covariance deviates from zero were studied. Data were simulated for two traits (one that culling was based on and a continuous trait) using the following true parameters, on the underlying scale: h² = 0.4; r(A) = 0.5; r(E) = 0.5, 0.0 or -0.5. The selection on the continuous trait was applied to five subsequent generations where 25 sires and 500 dams produced 1500 offspring per generation. Mass selection was applied in the analysis of the effect on estimation of genetic parameters. Estimated breeding values were used in the study of the effect of genetic selection on response and accuracy. The culling frequency was either 0.5 or 0.8 within each generation. Each of 10 replicates included 7500 records on 'test-status' and 9600 animals in the pedigree file. Results from bivariate analysis showed unbiased estimates of variance components and genetic parameters when true r(E) = 0.0. For r(E) = 0.5, variance components (13-19% bias) and especially (50-80%) were underestimated for the continuous trait, while heritability estimates were unbiased. For r(E) = -0.5, heritability estimates of test-status were unbiased, while genetic variance and heritability of the continuous trait together with were overestimated (25-50%). The bias was larger for the higher culling frequency. Culling always reduced genetic progress from selection, but the genetic progress was found to be robust to the use of wrong parameter values of the true environmental correlation between test-status and the continuous trait. Use of a bivariate linear-linear model reduced bias in genetic evaluations, when data were subject to culling. © 2011 Blackwell Verlag GmbH.

Finite mixture model: A maximum likelihood estimation approach on time series data

NASA Astrophysics Data System (ADS)

Yen, Phoong Seuk; Ismail, Mohd Tahir; Hamzah, Firdaus Mohamad

2014-09-01

Recently, statistician emphasized on the fitting of finite mixture model by using maximum likelihood estimation as it provides asymptotic properties. In addition, it shows consistency properties as the sample sizes increases to infinity. This illustrated that maximum likelihood estimation is an unbiased estimator. Moreover, the estimate parameters obtained from the application of maximum likelihood estimation have smallest variance as compared to others statistical method as the sample sizes increases. Thus, maximum likelihood estimation is adopted in this paper to fit the two-component mixture model in order to explore the relationship between rubber price and exchange rate for Malaysia, Thailand, Philippines and Indonesia. Results described that there is a negative effect among rubber price and exchange rate for all selected countries.

OP-Yield Version 1.00 user's guide

Treesearch

Martin W. Ritchie; Jianwei Zhang

2018-01-01

OP-Yield is a Microsoft Excel™ spreadsheet with 14 specified user inputs to derive custom yield estimates using the original Oliver and Powers (1978) functions as the foundation. It presents yields for ponderosa pine (Pinus ponderosa Lawson & C. Lawson) plantations in northern California. The basic model forms for dominantand...

Evaluating uses of data mining techniques in propensity score estimation: a simulation study.

PubMed

Setoguchi, Soko; Schneeweiss, Sebastian; Brookhart, M Alan; Glynn, Robert J; Cook, E Francis

2008-06-01

In propensity score modeling, it is a standard practice to optimize the prediction of exposure status based on the covariate information. In a simulation study, we examined in what situations analyses based on various types of exposure propensity score (EPS) models using data mining techniques such as recursive partitioning (RP) and neural networks (NN) produce unbiased and/or efficient results. We simulated data for a hypothetical cohort study (n = 2000) with a binary exposure/outcome and 10 binary/continuous covariates with seven scenarios differing by non-linear and/or non-additive associations between exposure and covariates. EPS models used logistic regression (LR) (all possible main effects), RP1 (without pruning), RP2 (with pruning), and NN. We calculated c-statistics (C), standard errors (SE), and bias of exposure-effect estimates from outcome models for the PS-matched dataset. Data mining techniques yielded higher C than LR (mean: NN, 0.86; RPI, 0.79; RP2, 0.72; and LR, 0.76). SE tended to be greater in models with higher C. Overall bias was small for each strategy, although NN estimates tended to be the least biased. C was not correlated with the magnitude of bias (correlation coefficient [COR] = -0.3, p = 0.1) but increased SE (COR = 0.7, p < 0.001). Effect estimates from EPS models by simple LR were generally robust. NN models generally provided the least numerically biased estimates. C was not associated with the magnitude of bias but was with the increased SE.

Biased and unbiased strategies to identify biologically active small molecules.

PubMed

Abet, Valentina; Mariani, Angelica; Truscott, Fiona R; Britton, Sébastien; Rodriguez, Raphaël

2014-08-15

Small molecules are central players in chemical biology studies. They promote the perturbation of cellular processes underlying diseases and enable the identification of biological targets that can be validated for therapeutic intervention. Small molecules have been shown to accurately tune a single function of pluripotent proteins in a reversible manner with exceptional temporal resolution. The identification of molecular probes and drugs remains a worthy challenge that can be addressed by the use of biased and unbiased strategies. Hypothesis-driven methodologies employs a known biological target to synthesize complementary hits while discovery-driven strategies offer the additional means of identifying previously unanticipated biological targets. This review article provides a general overview of recent synthetic frameworks that gave rise to an impressive arsenal of biologically active small molecules with unprecedented cellular mechanisms. Copyright © 2014. Published by Elsevier Ltd.

National Variation in Crop Yield Production Functions

NASA Astrophysics Data System (ADS)

Devineni, N.; Rising, J. A.

2017-12-01

A new multilevel model for yield prediction at the county scale using regional climate covariates is presented in this paper. A new crop specific water deficit index, growing degree days, extreme degree days, and time-trend as an approximation of technology improvements are used as predictors to estimate annual crop yields for each county from 1949 to 2009. Every county in the United States is allowed to have unique parameters describing how these weather predictors are related to yield outcomes. County-specific parameters are further modeled as varying according to climatic characteristics, allowing the prediction of parameters in regions where crops are not currently grown and into the future. The structural relationships between crop yield and regional climate as well as trends are estimated simultaneously. All counties are modeled in a single multilevel model with partial pooling to automatically group and reduce estimation uncertainties. The model captures up to 60% of the variability in crop yields after removing the effect of technology, does well in out of sample predictions and is useful in relating the climate responses to local bioclimatic factors. We apply the predicted growing models in a cost-benefit analysis to identify the most economically productive crop in each county.

Yield and depth Estimation of Selected NTS Nuclear and SPE Chemical Explosions Using Source Equalization by modeling Local and Regional Seismograms (Invited)

NASA Astrophysics Data System (ADS)

Saikia, C. K.; Roman-nieves, J. I.; Woods, M. T.

2013-12-01

Source parameters of nuclear and chemical explosions are often estimated by matching either the corner frequency and spectral level of a single event or the spectral ratio when spectra from two events are available with known source parameters for one. In this study, we propose an alternative method in which waveforms from two or more events can be simultaneously equalized by setting the differential of the processed seismograms at one station from any two individual events to zero. The method involves convolving the equivalent Mueller-Murphy displacement source time function (MMDSTF) of one event with the seismogram of the second event and vice-versa, and then computing their difference seismogram. MMDSTF is computed at the elastic radius including both near and far-field terms. For this method to yield accurate source parameters, an inherent assumption is that green's functions for the any paired events from the source to a receiver are same. In the frequency limit of the seismic data, this is a reasonable assumption and is concluded based on the comparison of green's functions computed for flat-earth models at various source depths ranging from 100m to 1Km. Frequency domain analysis of the initial P wave is, however, sensitive to the depth phase interaction, and if tracked meticulously can help estimating the event depth. We applied this method to the local waveforms recorded from the three SPE shots and precisely determined their yields. These high-frequency seismograms exhibit significant lateral path effects in spectrogram analysis and 3D numerical computations, but the source equalization technique is independent of any variation as long as their instrument characteristics are well preserved. We are currently estimating the uncertainty in the derived source parameters assuming the yields of the SPE shots as unknown. We also collected regional waveforms from 95 NTS explosions at regional stations ALQ, ANMO, CMB, COR, JAS LON, PAS, PFO and RSSD. We are

Brazil soybean yield covariance model

NASA Technical Reports Server (NTRS)

Callis, S. L.; Sakamoto, C.

1984-01-01

A model based on multiple regression was developed to estimate soybean yields for the seven soybean-growing states of Brazil. The meteorological data of these seven states were pooled and the years 1975 to 1980 were used to model since there was no technological trend in the yields during these years. Predictor variables were derived from monthly total precipitation and monthly average temperature.

ESTIMATING TREATMENT EFFECTS ON HEALTHCARE COSTS UNDER EXOGENEITY: IS THERE A ‘MAGIC BULLET’?

PubMed Central

Polsky, Daniel; Manning, Willard G.

2011-01-01

Methods for estimating average treatment effects, under the assumption of no unmeasured confounders, include regression models; propensity score adjustments using stratification, weighting, or matching; and doubly robust estimators (a combination of both). Researchers continue to debate about the best estimator for outcomes such as health care cost data, as they are usually characterized by an asymmetric distribution and heterogeneous treatment effects,. Challenges in finding the right specifications for regression models are well documented in the literature. Propensity score estimators are proposed as alternatives to overcoming these challenges. Using simulations, we find that in moderate size samples (n= 5000), balancing on propensity scores that are estimated from saturated specifications can balance the covariate means across treatment arms but fails to balance higher-order moments and covariances amongst covariates. Therefore, unlike regression model, even if a formal model for outcomes is not required, propensity score estimators can be inefficient at best and biased at worst for health care cost data. Our simulation study, designed to take a ‘proof by contradiction’ approach, proves that no one estimator can be considered the best under all data generating processes for outcomes such as costs. The inverse-propensity weighted estimator is most likely to be unbiased under alternate data generating processes but is prone to bias under misspecification of the propensity score model and is inefficient compared to an unbiased regression estimator. Our results show that there are no ‘magic bullets’ when it comes to estimating treatment effects in health care costs. Care should be taken before naively applying any one estimator to estimate average treatment effects in these data. We illustrate the performance of alternative methods in a cost dataset on breast cancer treatment. PMID:22199462

Estimating agricultural yield gap in Africa using MODIS NDVI dataset

NASA Astrophysics Data System (ADS)

Luan, Y.; Zhu, W.; Luo, X.; Liu, J.; Cui, X.

2013-12-01

Global agriculture has undergone a period of rapid intensification characterized as 'Green Revolution', except for Africa, which is the region most affected by unreliable food access and undernourishment. Increasing crop production will be one of the most challenges and most effectual way to mitigate food insecurity there, as Africa's agricultural yield is on a much lower level comparing to global average. In this study we characterize cropland vegetation phenology in Africa based on MODIS NDVI time series between 2000 and 2012. Cumulated NDVI is a proxy for net primary productivity and used as an indicator for evaluating the potential yield gap in Africa. It is achieved via translating the gap between optimum attainable productivity level in each classification of cropping systems and actual productivity level by the relationship of cumulated NDVI and cereal-equivalent production. The results show most of cropland area in Africa have decreasing trend in cumulated NDVI, distributing in the Nile Delta, Eastern Africa and central of semi-arid to arid savanna area, except significant positive cumulated NDVI trends are mainly found between Senegal and Benin. Using cumulated NDVI and statistics of cereal equivalent production, we find remarkable potential yield gap at the Horn of East Africa (especially in Somalia), Northern Africa (Morocco, Algeria and Tunisia). Meanwhile, countries locating at the savanna area near Sahel desert and South Africa also show significant potential, though they already have a relatively high level of productivity. Our results can help provide policy recommendation for local government or NGO to tackle food security problems by identifying zones with high potential of yield improvement.

Assessment of uncertainties in soil erosion and sediment yield estimates at ungauged basins: an application to the Garra River basin, India

NASA Astrophysics Data System (ADS)

Swarnkar, Somil; Malini, Anshu; Tripathi, Shivam; Sinha, Rajiv

2018-04-01

High soil erosion and excessive sediment load are serious problems in several Himalayan river basins. To apply mitigation procedures, precise estimation of soil erosion and sediment yield with associated uncertainties are needed. Here, the revised universal soil loss equation (RUSLE) and the sediment delivery ratio (SDR) equations are used to estimate the spatial pattern of soil erosion (SE) and sediment yield (SY) in the Garra River basin, a small Himalayan tributary of the River Ganga. A methodology is proposed for quantifying and propagating uncertainties in SE, SDR and SY estimates. Expressions for uncertainty propagation are derived by first-order uncertainty analysis, making the method viable even for large river basins. The methodology is applied to investigate the relative importance of different RUSLE factors in estimating the magnitude and uncertainties in SE over two distinct morphoclimatic regimes of the Garra River basin, namely the upper mountainous region and the lower alluvial plains. Our results suggest that average SE in the basin is very high (23 ± 4.7 t ha-1 yr-1) with higher values in the upper mountainous region (92 ± 15.2 t ha-1 yr-1) compared to the lower alluvial plains (19.3 ± 4 t ha-1 yr-1). Furthermore, the topographic steepness (LS) and crop practice (CP) factors exhibit higher uncertainties than other RUSLE factors. The annual average SY is estimated at two locations in the basin - Nanak Sagar Dam (NSD) for the period 1962-2008 and Husepur gauging station (HGS) for 1987-2002. The SY at NSD and HGS are estimated to be 6.9 ± 1.2 × 105 t yr-1 and 6.7 ± 1.4 × 106 t yr-1, respectively, and the estimated 90 % interval contains the observed values of 6.4 × 105 t yr-1 and 7.2 × 106 t yr-1, respectively. The study demonstrated the usefulness of the proposed methodology for quantifying uncertainty in SE and SY estimates at ungauged basins.

Argentina soybean yield model

NASA Technical Reports Server (NTRS)

Callis, S. L.; Sakamoto, C.

1984-01-01

A model based on multiple regression was developed to estimate soybean yields for the country of Argentina. A meteorological data set was obtained for the country by averaging data for stations within the soybean growing area. Predictor variables for the model were derived from monthly total precipitation and monthly average temperature. A trend variable was included for the years 1969 to 1978 since an increasing trend in yields due to technology was observed between these years.

Unbiased Strain-Typing of Arbovirus Directly from Mosquitoes Using Nanopore Sequencing: A Field-forward Biosurveillance Protocol.

PubMed

Russell, Joseph A; Campos, Brittany; Stone, Jennifer; Blosser, Erik M; Burkett-Cadena, Nathan; Jacobs, Jonathan L

2018-04-03

The future of infectious disease surveillance and outbreak response is trending towards smaller hand-held solutions for point-of-need pathogen detection. Here, samples of Culex cedecei mosquitoes collected in Southern Florida, USA were tested for Venezuelan Equine Encephalitis Virus (VEEV), a previously-weaponized arthropod-borne RNA-virus capable of causing acute and fatal encephalitis in animal and human hosts. A single 20-mosquito pool tested positive for VEEV by quantitative reverse transcription polymerase chain reaction (RT-qPCR) on the Biomeme two3. The virus-positive sample was subjected to unbiased metatranscriptome sequencing on the Oxford Nanopore MinION and shown to contain Everglades Virus (EVEV), an alphavirus in the VEEV serocomplex. Our results demonstrate, for the first time, the use of unbiased sequence-based detection and subtyping of a high-consequence biothreat pathogen directly from an environmental sample using field-forward protocols. The development and validation of methods designed for field-based diagnostic metagenomics and pathogen discovery, such as those suitable for use in mobile "pocket laboratories", will address a growing demand for public health teams to carry out their mission where it is most urgent: at the point-of-need.

Fission yield and criticality excursion code

DOE Office of Scientific and Technical Information (OSTI.GOV)

Blanchard, A.

2000-06-30

The ANSI/ANS 8.3 standard allows a maximum yield not to exceed 2 x 10 fissions to calculate requiring the alarm system to be effective. It is common practice to use this allowance or to develop some other yield based on past criticality accident history or excursion experiments. The literature on the subject of yields discusses maximum yields larger and somewhat smaller than the ANS 8.3 permissive value. The ability to model criticality excursions and vary the various parameters to determine a credible maximum yield for operational specific cases has been available for some time but is not in common usemore » by criticality safety specialists. The topic of yields for various solution, metal, oxide powders, etc. in various geometry's and containers has been published by laboratory specialists or university staff and students for many decades but have not been available to practitioners. The need for best-estimate calculations of fission yields with a well-validated criticality excursion code has long been recognized. But no coordinated effort has been made so far to develop a generalized and well-validated excursion code for different types of systems. In this paper, the current practices to estimate fission yields are summarized along with its shortcomings for the 12-Rad zone (at SRS) and Criticality Alarm System (CAS) calculations. Finally the need for a user-friendly excursion code is reemphasized.« less

An unbiased Hessian representation for Monte Carlo PDFs.

PubMed

Carrazza, Stefano; Forte, Stefano; Kassabov, Zahari; Latorre, José Ignacio; Rojo, Juan

We develop a methodology for the construction of a Hessian representation of Monte Carlo sets of parton distributions, based on the use of a subset of the Monte Carlo PDF replicas as an unbiased linear basis, and of a genetic algorithm for the determination of the optimal basis. We validate the methodology by first showing that it faithfully reproduces a native Monte Carlo PDF set (NNPDF3.0), and then, that if applied to Hessian PDF set (MMHT14) which was transformed into a Monte Carlo set, it gives back the starting PDFs with minimal information loss. We then show that, when applied to a large Monte Carlo PDF set obtained as combination of several underlying sets, the methodology leads to a Hessian representation in terms of a rather smaller set of parameters (MC-H PDFs), thereby providing an alternative implementation of the recently suggested Meta-PDF idea and a Hessian version of the recently suggested PDF compression algorithm (CMC-PDFs). The mc2hessian conversion code is made publicly available together with (through LHAPDF6) a Hessian representations of the NNPDF3.0 set, and the MC-H PDF set.

Estimation of biogas and methane yields in an UASB treating potato starch processing wastewater with backpropagation artificial neural network.

PubMed

Antwi, Philip; Li, Jianzheng; Boadi, Portia Opoku; Meng, Jia; Shi, En; Deng, Kaiwen; Bondinuba, Francis Kwesi

2017-03-01

Three-layered feedforward backpropagation (BP) artificial neural networks (ANN) and multiple nonlinear regression (MnLR) models were developed to estimate biogas and methane yield in an upflow anaerobic sludge blanket (UASB) reactor treating potato starch processing wastewater (PSPW). Anaerobic process parameters were optimized to identify their importance on methanation. pH, total chemical oxygen demand, ammonium, alkalinity, total Kjeldahl nitrogen, total phosphorus, volatile fatty acids and hydraulic retention time selected based on principal component analysis were used as input variables, whiles biogas and methane yield were employed as target variables. Quasi-Newton method and conjugate gradient backpropagation algorithms were best among eleven training algorithms. Coefficient of determination (R 2 ) of the BP-ANN reached 98.72% and 97.93% whiles MnLR model attained 93.9% and 91.08% for biogas and methane yield, respectively. Compared with the MnLR model, BP-ANN model demonstrated significant performance, suggesting possible control of the anaerobic digestion process with the BP-ANN model. Copyright © 2016 Elsevier Ltd. All rights reserved.

[Regional scale remote sensing-based yield estimation of winter wheat by using MODIS-NDVI data: a case study of Jining City in Shandong Province].

PubMed

Ren, Jianqiang; Chen, Zhongxin; Tang, Huajun

2006-12-01

Taking Jining City of Shandong Province, one of the most important winter wheat production regions in Huanghuaihai Plain as an example, the winter wheat yield was estimated by using the 250 m MODIS-NDVI data smoothed by Savitzky-Golay filter. The NDVI values between 0. 20 and 0. 80 were selected, and the sum of NDVI value for each county was calculated to build its relation with winter wheat yield. By using stepwise regression method, the linear regression model between NDVI and winter wheat yield was established, with the precision validated by the ground survey data. The results showed that the relative error of predicted yield was between -3.6% and 3.9%, suggesting that the method was relatively accurate and feasible.

Improved gap size estimation for scaffolding algorithms.

PubMed

Sahlin, Kristoffer; Street, Nathaniel; Lundeberg, Joakim; Arvestad, Lars

2012-09-01

One of the important steps of genome assembly is scaffolding, in which contigs are linked using information from read-pairs. Scaffolding provides estimates about the order, relative orientation and distance between contigs. We have found that contig distance estimates are generally strongly biased and based on false assumptions. Since erroneous distance estimates can mislead in subsequent analysis, it is important to provide unbiased estimation of contig distance. In this article, we show that state-of-the-art programs for scaffolding are using an incorrect model of gap size estimation. We discuss why current maximum likelihood estimators are biased and describe what different cases of bias we are facing. Furthermore, we provide a model for the distribution of reads that span a gap and derive the maximum likelihood equation for the gap length. We motivate why this estimate is sound and show empirically that it outperforms gap estimators in popular scaffolding programs. Our results have consequences both for scaffolding software, structural variation detection and for library insert-size estimation as is commonly performed by read aligners. A reference implementation is provided at https://github.com/SciLifeLab/gapest. Supplementary data are availible at Bioinformatics online.

Estimating chlorophyll content and photochemical yield of photosystem II (ΦPSII) using solar-induced chlorophyll fluorescence measurements at different growing stages of attached leaves

PubMed Central

Tubuxin, Bayaer; Rahimzadeh-Bajgiran, Parinaz; Ginnan, Yusaku; Hosoi, Fumiki; Omasa, Kenji

2015-01-01

This paper illustrates the possibility of measuring chlorophyll (Chl) content and Chl fluorescence parameters by the solar-induced Chl fluorescence (SIF) method using the Fraunhofer line depth (FLD) principle, and compares the results with the standard measurement methods. A high-spectral resolution HR2000+ and an ordinary USB4000 spectrometer were used to measure leaf reflectance under solar and artificial light, respectively, to estimate Chl fluorescence. Using leaves of Capsicum annuum cv. ‘Sven’ (paprika), the relationships between the Chl content and the steady-state Chl fluorescence near oxygen absorption bands of O2B (686nm) and O2A (760nm), measured under artificial and solar light at different growing stages of leaves, were evaluated. The Chl fluorescence yields of ΦF 686nm/ΦF 760nm ratios obtained from both methods correlated well with the Chl content (steady-state solar light: R2 = 0.73; artificial light: R2 = 0.94). The SIF method was less accurate for Chl content estimation when Chl content was high. The steady-state solar-induced Chl fluorescence yield ratio correlated very well with the artificial-light-induced one (R2 = 0.84). A new methodology is then presented to estimate photochemical yield of photosystem II (ΦPSII) from the SIF measurements, which was verified against the standard Chl fluorescence measurement method (pulse-amplitude modulated method). The high coefficient of determination (R2 = 0.74) between the ΦPSII of the two methods shows that photosynthesis process parameters can be successfully estimated using the presented methodology. PMID:26071530

Modification of the Sandwich Estimator in Generalized Estimating Equations with Correlated Binary Outcomes in Rare Event and Small Sample Settings

PubMed Central

Rogers, Paul; Stoner, Julie

2016-01-01

Regression models for correlated binary outcomes are commonly fit using a Generalized Estimating Equations (GEE) methodology. GEE uses the Liang and Zeger sandwich estimator to produce unbiased standard error estimators for regression coefficients in large sample settings even when the covariance structure is misspecified. The sandwich estimator performs optimally in balanced designs when the number of participants is large, and there are few repeated measurements. The sandwich estimator is not without drawbacks; its asymptotic properties do not hold in small sample settings. In these situations, the sandwich estimator is biased downwards, underestimating the variances. In this project, a modified form for the sandwich estimator is proposed to correct this deficiency. The performance of this new sandwich estimator is compared to the traditional Liang and Zeger estimator as well as alternative forms proposed by Morel, Pan and Mancl and DeRouen. The performance of each estimator was assessed with 95% coverage probabilities for the regression coefficient estimators using simulated data under various combinations of sample sizes and outcome prevalence values with an Independence (IND), Autoregressive (AR) and Compound Symmetry (CS) correlation structure. This research is motivated by investigations involving rare-event outcomes in aviation data. PMID:26998504

Estimating the potential intensification of global grazing systems based on climate adjusted yield gap analysis

NASA Astrophysics Data System (ADS)

Sheehan, J. J.

2016-12-01

We report here a first-of-its-kind analysis of the potential for intensification of global grazing systems. Intensification is calculated using the statistical yield gap methodology developed previously by others (Mueller et al 2012 and Licker et al 2010) for global crop systems. Yield gaps are estimated by binning global pasture land area into 100 equal area sized bins of similar climate (defined by ranges of rainfall and growing degree days). Within each bin, grid cells of pastureland are ranked from lowest to highest productivity. The global intensification potential is defined as the sum of global production across all bins at a given percentile ranking (e.g. performance at the 90th percentile) divided by the total current global production. The previous yield gap studies focused on crop systems because productivity data on these systems is readily available. Nevertheless, global crop land represents only one-third of total global agricultural land, while pasture systems account for the remaining two-thirds. Thus, it is critical to conduct the same kind of analysis on what is the largest human use of land on the planet—pasture systems. In 2013, Herrero et al announced the completion of a geospatial data set that augmented the animal census data with data and modeling about production systems and overall food productivity (Herrero et al, PNAS 2013). With this data set, it is now possible to apply yield gap analysis to global pasture systems. We used the Herrero et al data set to evaluate yield gaps for meat and milk production from pasture based systems for cattle, sheep and goats. The figure included with this abstract shows the intensification potential for kcal per hectare per year of meat and milk from global cattle, sheep and goats as a function of increasing levels of performance. Performance is measured as the productivity achieved at a given ranked percentile within each bin.We find that if all pasture land were raised to their 90th percentile of

Systematic sampling of discrete and continuous populations: sample selection and the choice of estimator

Treesearch

Harry T. Valentine; David L. R. Affleck; Timothy G. Gregoire

2009-01-01

Systematic sampling is easy, efficient, and widely used, though it is not generally recognized that a systematic sample may be drawn from the population of interest with or without restrictions on randomization. The restrictions or the lack of them determine which estimators are unbiased, when using the sampling design as the basis for inference. We describe the...

Argentina wheat yield model

NASA Technical Reports Server (NTRS)

Callis, S. L.; Sakamoto, C.

1984-01-01

Five models based on multiple regression were developed to estimate wheat yields for the five wheat growing provinces of Argentina. Meteorological data sets were obtained for each province by averaging data for stations within each province. Predictor variables for the models were derived from monthly total precipitation, average monthly mean temperature, and average monthly maximum temperature. Buenos Aires was the only province for which a trend variable was included because of increasing trend in yield due to technology from 1950 to 1963.

Argentina corn yield model

NASA Technical Reports Server (NTRS)

Callis, S. L.; Sakamoto, C.

1984-01-01

A model based on multiple regression was developed to estimate corn yields for the country of Argentina. A meteorological data set was obtained for the country by averaging data for stations within the corn-growing area. Predictor variables for the model were derived from monthly total precipitation, average monthly mean temperature, and average monthly maximum temperature. A trend variable was included for the years 1965 to 1980 since an increasing trend in yields due to technology was observed between these years.

Wheat productivity estimates using LANDSAT data

NASA Technical Reports Server (NTRS)

Nalepka, R. F.; Colwell, J. E. (Principal Investigator); Rice, D. P.; Bresnahan, P. A.

1977-01-01

The author has identified the following significant results. Large area LANDSAT yield estimates were generated. These results were compared with estimates computed using a meteorological yield model (CCEA). Both of these estimates were compared with Kansas Crop and Livestock Reporting Service (KCLRS) estimates of yield, in an attempt to assess the relative and absolute accuracy of the LANDSAT and CCEA estimates. Results were inconclusive. A large area direct wheat prediction procedure was implemented. Initial results have produced a wheat production estimate comparable with the KCLRS estimate.

Global Crop Yields, Climatic Trends and Technology Enhancement

NASA Astrophysics Data System (ADS)

Najafi, E.; Devineni, N.; Khanbilvardi, R.; Kogan, F.

2016-12-01

During the last decades the global agricultural production has soared up and technology enhancement is still making positive contribution to yield growth. However, continuing population, water crisis, deforestation and climate change threaten the global food security. Attempts to predict food availability in the future around the world can be partly understood from the impact of changes to date. A new multilevel model for yield prediction at the country scale using climate covariates and technology trend is presented in this paper. The structural relationships between average yield and climate attributes as well as trends are estimated simultaneously. All countries are modeled in a single multilevel model with partial pooling and/or clustering to automatically group and reduce estimation uncertainties. El Niño Southern Oscillation (ENSO), Palmer Drought Severity Index (PDSI), Geopotential height (GPH), historical CO2 level and time-trend as a relatively reliable approximation of technology measurement are used as predictors to estimate annual agricultural crop yields for each country from 1961 to 2007. Results show that these indicators can explain the variability in historical crop yields for most of the countries and the model performs well under out-of-sample verifications.

Invited review: A commentary on predictive cheese yield formulas.

PubMed

Emmons, D B; Modler, H W

2010-12-01

Predictive cheese yield formulas have evolved from one based only on casein and fat in 1895. Refinements have included moisture and salt in cheese and whey solids as separate factors, paracasein instead of casein, and exclusion of whey solids from moisture associated with cheese protein. The General, Barbano, and Van Slyke formulas were tested critically using yield and composition of milk, whey, and cheese from 22 vats of Cheddar cheese. The General formula is based on the sum of cheese components: fat, protein, moisture, salt, whey solids free of fat and protein, as well as milk salts associated with paracasein. The testing yielded unexpected revelations. It was startling that the sum of components in cheese was <100%; the mean was 99.51% (N × 6.31). The mean predicted yield was only 99.17% as a percentage of actual yields (PY%AY); PY%AY is a useful term for comparisons of yields among vats. The PY%AY correlated positively with the sum of components (SofC) in cheese. The apparent low estimation of SofC led to the idea of adjusting upwards, for each vat, the 5 measured components in the formula by the observed SofC, as a fraction. The mean of the adjusted predicted yields as percentages of actual yields was 99.99%. The adjusted forms of the General, Barbano, and Van Slyke formulas gave predicted yields equal to the actual yields. It was apparent that unadjusted yield formulas did not accurately predict yield; however, unadjusted PY%AY can be useful as a control tool for analyses of cheese and milk. It was unexpected that total milk protein in the adjusted General formula gave the same predicted yields as casein and paracasein, indicating that casein or paracasein may not always be necessary for successful yield prediction. The use of constants for recovery of fat and protein in the adjusted General formula gave adjusted predicted yields equal to actual yields, indicating that analyses of cheese for protein and fat may not always be necessary for yield prediction

Estimating cosmic velocity fields from density fields and tidal tensors

NASA Astrophysics Data System (ADS)

Kitaura, Francisco-Shu; Angulo, Raul E.; Hoffman, Yehuda; Gottlöber, Stefan

2012-10-01

In this work we investigate the non-linear and non-local relation between cosmological density and peculiar velocity fields. Our goal is to provide an algorithm for the reconstruction of the non-linear velocity field from the fully non-linear density. We find that including the gravitational tidal field tensor using second-order Lagrangian perturbation theory based upon an estimate of the linear component of the non-linear density field significantly improves the estimate of the cosmic flow in comparison to linear theory not only in the low density, but also and more dramatically in the high-density regions. In particular we test two estimates of the linear component: the lognormal model and the iterative Lagrangian linearization. The present approach relies on a rigorous higher order Lagrangian perturbation theory analysis which incorporates a non-local relation. It does not require additional fitting from simulations being in this sense parameter free, it is independent of statistical-geometrical optimization and it is straightforward and efficient to compute. The method is demonstrated to yield an unbiased estimator of the velocity field on scales ≳5 h-1 Mpc with closely Gaussian distributed errors. Moreover, the statistics of the divergence of the peculiar velocity field is extremely well recovered showing a good agreement with the true one from N-body simulations. The typical errors of about 10 km s-1 (1σ confidence intervals) are reduced by more than 80 per cent with respect to linear theory in the scale range between 5 and 10 h-1 Mpc in high-density regions (δ > 2). We also find that iterative Lagrangian linearization is significantly superior in the low-density regime with respect to the lognormal model.

Estimation of quantum yields of weak fluorescence from eosin Y dimers formed in aqueous solutions.

PubMed

Enoki, Masami; Katoh, Ryuzi

2018-05-17

We studied the weak fluorescence from the dimer of eosin Y (EY) in aqueous solutions. We used a newly developed ultrathin optical cell with a thickness ranging from of the order of microns to several hundreds of microns to successfully measure the fluorescence spectra of highly concentrated aqueous solutions of EY without artifacts caused by the reabsorption of fluorescence. The spectra we obtained were similar to the fluorescence spectrum of the EY monomer; almost no fluorescence was observed from the EY dimer. By a careful comparison of the spectra of solutions at low and high concentrations of EY, we succeeded in extracting the fluorescence spectrum of the EY dimer. The fluorescence quantum yield of the EY dimer was estimated to be 0.005.

Estimating rice yield related traits and quantitative trait loci analysis under different nitrogen treatments using a simple tower-based field phenotyping system with modified single-lens reflex cameras

NASA Astrophysics Data System (ADS)

Naito, Hiroki; Ogawa, Satoshi; Valencia, Milton Orlando; Mohri, Hiroki; Urano, Yutaka; Hosoi, Fumiki; Shimizu, Yo; Chavez, Alba Lucia; Ishitani, Manabu; Selvaraj, Michael Gomez; Omasa, Kenji

2017-03-01

Application of field based high-throughput phenotyping (FB-HTP) methods for monitoring plant performance in real field conditions has a high potential to accelerate the breeding process. In this paper, we discuss the use of a simple tower based remote sensing platform using modified single-lens reflex cameras for phenotyping yield traits in rice under different nitrogen (N) treatments over three years. This tower based phenotyping platform has the advantages of simplicity, ease and stability in terms of introduction, maintenance and continual operation under field conditions. Out of six phenological stages of rice analyzed, the flowering stage was the most useful in the estimation of yield performance under field conditions. We found a high correlation between several vegetation indices (simple ratio (SR), normalized difference vegetation index (NDVI), transformed vegetation index (TVI), corrected transformed vegetation index (CTVI), soil-adjusted vegetation index (SAVI) and modified soil-adjusted vegetation index (MSAVI)) and multiple yield traits (panicle number, grain weight and shoot biomass) across a three trials. Among all of the indices studied, SR exhibited the best performance in regards to the estimation of grain weight (R2 = 0.80). Under our tower-based field phenotyping system (TBFPS), we identified quantitative trait loci (QTL) for yield related traits using a mapping population of chromosome segment substitution lines (CSSLs) and a single nucleotide polymorphism data set. Our findings suggest the TBFPS can be useful for the estimation of yield performance during early crop development. This can be a major opportunity for rice breeders whom desire high throughput phenotypic selection for yield performance traits.

An Unbiased Estimate of Global Interrater Agreement

ERIC Educational Resources Information Center

Cousineau, Denis; Laurencelle, Louis

2017-01-01

Assessing global interrater agreement is difficult as most published indices are affected by the presence of mixtures of agreements and disagreements. A previously proposed method was shown to be specifically sensitive to global agreement, excluding mixtures, but also negatively biased. Here, we propose two alternatives in an attempt to find what…

Quantitative Assessment of In-solution Digestion Efficiency Identifies Optimal Protocols for Unbiased Protein Analysis*

PubMed Central

León, Ileana R.; Schwämmle, Veit; Jensen, Ole N.; Sprenger, Richard R.

2013-01-01

The majority of mass spectrometry-based protein quantification studies uses peptide-centric analytical methods and thus strongly relies on efficient and unbiased protein digestion protocols for sample preparation. We present a novel objective approach to assess protein digestion efficiency using a combination of qualitative and quantitative liquid chromatography-tandem MS methods and statistical data analysis. In contrast to previous studies we employed both standard qualitative as well as data-independent quantitative workflows to systematically assess trypsin digestion efficiency and bias using mitochondrial protein fractions. We evaluated nine trypsin-based digestion protocols, based on standard in-solution or on spin filter-aided digestion, including new optimized protocols. We investigated various reagents for protein solubilization and denaturation (dodecyl sulfate, deoxycholate, urea), several trypsin digestion conditions (buffer, RapiGest, deoxycholate, urea), and two methods for removal of detergents before analysis of peptides (acid precipitation or phase separation with ethyl acetate). Our data-independent quantitative liquid chromatography-tandem MS workflow quantified over 3700 distinct peptides with 96% completeness between all protocols and replicates, with an average 40% protein sequence coverage and an average of 11 peptides identified per protein. Systematic quantitative and statistical analysis of physicochemical parameters demonstrated that deoxycholate-assisted in-solution digestion combined with phase transfer allows for efficient, unbiased generation and recovery of peptides from all protein classes, including membrane proteins. This deoxycholate-assisted protocol was also optimal for spin filter-aided digestions as compared with existing methods. PMID:23792921

Evaluating probability sampling strategies for estimating redd counts: an example with Chinook salmon (Oncorhynchus tshawytscha)

Treesearch

Jean-Yves Courbois; Stephen L. Katz; Daniel J. Isaak; E. Ashley Steel; Russell F. Thurow; A. Michelle Wargo Rub; Tony Olsen; Chris E. Jordan

2008-01-01

Precise, unbiased estimates of population size are an essential tool for fisheries management. For a wide variety of salmonid fishes, redd counts from a sample of reaches are commonly used to monitor annual trends in abundance. Using a 9-year time series of georeferenced censuses of Chinook salmon (Oncorhynchus tshawytscha) redds from central Idaho,...

Generalized approach for using unbiased symmetric metrics with negative values: normalized mean bias factor and normalized mean absolute error factor

EPA Science Inventory

Unbiased symmetric metrics provide a useful measure to quickly compare two datasets, with similar interpretations for both under and overestimations. Two examples include the normalized mean bias factor and normalized mean absolute error factor. However, the original formulations...

Software for the grouped optimal aggregation technique

NASA Technical Reports Server (NTRS)

Brown, P. M.; Shaw, G. W. (Principal Investigator)

1982-01-01

The grouped optimal aggregation technique produces minimum variance, unbiased estimates of acreage and production for countries, zones (states), or any designated collection of acreage strata. It uses yield predictions, historical acreage information, and direct acreage estimate from satellite data. The acreage strata are grouped in such a way that the ratio model over historical acreage provides a smaller variance than if the model were applied to each individual stratum. An optimal weighting matrix based on historical acreages, provides the link between incomplete direct acreage estimates and the total, current acreage estimate.

Demonstration of line transect methodologies to estimate urban gray squirrel density

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hein, E.W.

1997-11-01

Because studies estimating density of gray squirrels (Sciurus carolinensis) have been labor intensive and costly, I demonstrate the use of line transect surveys to estimate gray squirrel density and determine the costs of conducting surveys to achieve precise estimates. Density estimates are based on four transacts that were surveyed five times from 30 June to 9 July 1994. Using the program DISTANCE, I estimated there were 4.7 (95% Cl = 1.86-11.92) gray squirrels/ha on the Clemson University campus. Eleven additional surveys would have decreased the percent coefficient of variation from 30% to 20% and would have cost approximately $114. Estimatingmore » urban gray squirrel density using line transect surveys is cost effective and can provide unbiased estimates of density, provided that none of the assumptions of distance sampling theory are violated.« less

The Massachusetts Sustainable-Yield Estimator: A decision-support tool to assess water availability at ungaged stream locations in Massachusetts

USGS Publications Warehouse

Archfield, Stacey A.; Vogel, Richard M.; Steeves, Peter A.; Brandt, Sara L.; Weiskel, Peter K.; Garabedian, Stephen P.

2010-01-01

Federal, State and local water-resource managers require a variety of data and modeling tools to better understand water resources. The U.S. Geological Survey, in cooperation with the Massachusetts Department of Environmental Protection, has developed a statewide, interactive decision-support tool to meet this need. The decision-support tool, referred to as the Massachusetts Sustainable-Yield Estimator (MA SYE) provides screening-level estimates of the sustainable yield of a basin, defined as the difference between the unregulated streamflow and some user-specified quantity of water that must remain in the stream to support such functions as recreational activities or aquatic habitat. The MA SYE tool was designed, in part, because the quantity of surface water available in a basin is a time-varying quantity subject to competing demands for water. To compute sustainable yield, the MA SYE tool estimates a daily time series of unregulated, daily mean streamflow for a 44-year period of record spanning October 1, 1960, through September 30, 2004. Selected streamflow quantiles from an unregulated, daily flow-duration curve are estimated by solving six regression equations that are a function of physical and climate basin characteristics at an ungaged site on a stream of interest. Streamflow is then interpolated between the estimated quantiles to obtain a continuous daily flow-duration curve. A time series of unregulated daily streamflow subsequently is created by transferring the timing of the daily streamflow at a reference streamgage to the ungaged site by equating exceedence probabilities of contemporaneous flow at the two locations. One of 66 reference streamgages is selected by kriging, a geostatistical method, which is used to map the spatial relation among correlations between the time series of the logarithm of daily streamflows at each reference streamgage and the ungaged site. Estimated unregulated, daily mean streamflows show good agreement with observed

Spatial and Temporal Uncertainty of Crop Yield Aggregations

NASA Technical Reports Server (NTRS)

Porwollik, Vera; Mueller, Christoph; Elliott, Joshua; Chryssanthacopoulos, James; Iizumi, Toshichika; Ray, Deepak K.; Ruane, Alex C.; Arneth, Almut; Balkovic, Juraj; Ciais, Philippe;

Light- and water-use efficiency model synergy: a revised look at crop yield estimation for agricultural decision-making

NASA Astrophysics Data System (ADS)

Marshall, M.; Tu, K. P.

2015-12-01

Large-area crop yield models (LACMs) are commonly employed to address climate-driven changes in crop yield and inform policy makers concerned with climate change adaptation. Production efficiency models (PEMs), a class of LACMs that rely on the conservative response of carbon assimilation to incoming solar radiation absorbed by a crop contingent on environmental conditions, have increasingly been used over large areas with remote sensing spectral information to improve the spatial resolution of crop yield estimates and address important data gaps. Here, we present a new PEM that combines model principles from the remote sensing-based crop yield and evapotranspiration (ET) model literature. One of the major limitations of PEMs is that they are evaluated using data restricted in both space and time. To overcome this obstacle, we first validated the model using 2009-2014 eddy covariance flux tower Gross Primary Production data in a rice field in the Central Valley of California- a critical agro-ecosystem of the United States. This evaluation yielded a Willmot's D and mean absolute error of 0.81 and 5.24 g CO2/d, respectively, using CO2, leaf area, temperature, and moisture constraints from the MOD16 ET model, Priestley-Taylor ET model, and the Global Production Efficiency Model (GLOPEM). A Monte Carlo simulation revealed that the model was most sensitive to the Enhanced Vegetation Index (EVI) input, followed by Photosynthetically Active Radiation, vapor pressure deficit, and air temperature. The model will now be evaluated using 30 x 30m (Landsat resolution) biomass transects developed in 2011 and 2012 from spectroradiometric and other non-destructive in situ metrics for several cotton, maize, and rice fields across the Central Valley. Finally, the model will be driven by Daymet and MODIS data over the entire State of California and compared with county-level crop yield statistics. It is anticipated that the new model will facilitate agro-climatic decision-making in

Accounting for the decrease of photosystem photochemical efficiency with increasing irradiance to estimate quantum yield of leaf photosynthesis.

PubMed

Yin, Xinyou; Belay, Daniel W; van der Putten, Peter E L; Struik, Paul C

2014-12-01

Maximum quantum yield for leaf CO2 assimilation under limiting light conditions (Φ CO2LL) is commonly estimated as the slope of the linear regression of net photosynthetic rate against absorbed irradiance over a range of low-irradiance conditions. Methodological errors associated with this estimation have often been attributed either to light absorptance by non-photosynthetic pigments or to some data points being beyond the linear range of the irradiance response, both causing an underestimation of Φ CO2LL. We demonstrate here that a decrease in photosystem (PS) photochemical efficiency with increasing irradiance, even at very low levels, is another source of error that causes a systematic underestimation of Φ CO2LL. A model method accounting for this error was developed, and was used to estimate Φ CO2LL from simultaneous measurements of gas exchange and chlorophyll fluorescence on leaves using various combinations of species, CO2, O2, or leaf temperature levels. The conventional linear regression method under-estimated Φ CO2LL by ca. 10-15%. Differences in the estimated Φ CO2LL among measurement conditions were generally accounted for by different levels of photorespiration as described by the Farquhar-von Caemmerer-Berry model. However, our data revealed that the temperature dependence of PSII photochemical efficiency under low light was an additional factor that should be accounted for in the model.

Ground-Water Contributions to Reservoir Storage and the Effect on Estimates of Firm Yield for Reservoirs in Massachusetts

USGS Publications Warehouse

Archfield, Stacey A.; Carlson, Carl S.

2006-01-01

Potential ground-water contributions to reservoir storage were determined for nine reservoirs in Massachusetts that had shorelines in contact with sand and gravel aquifers. The effect of ground water on firm yield was not only substantial, but furthermore, the firm yield of a reservoir in contact with a sand and gravel aquifer was always greater when the ground-water contribution was included in the water balance. Increases in firm yield ranged from 2 to 113 percent, with a median increase in firm yield of 10 percent. Additionally, the increase in firm yield in two reservoirs was greater than 85 percent. This study identified a set of equations that are based on an analytical solution to the ground-water-flow equation for the case of one-dimensional flow in a finite-width aquifer bounded by a linear surface-water feature such as a stream. These equations, which require only five input variables, were incorporated into an existing firm-yield-estimator (FYE) model, and the potential effect of ground water on firm yield was evaluated. To apply the FYE model to a reservoir in Massachusetts, the model requires that the drainage area to the reservoir be clearly defined and that some surface water flows into the reservoir. For surface-water-body shapes having a more realistic representation of a reservoir shoreline than a stream, a comparison of ground-water-flow rates simulated by the ground-water equations with flow rates simulated by a two-dimensional, finite-difference ground-water-flow model indicate that the agreement between the simulated flow rates is within ?10 percent when the ratio of the distance from the reservoir shoreline to the aquifer boundary to the length of shoreline in contact with the aquifer is between values of 0.5 and 3.5. Idealized reservoir-aquifer systems were assumed to verify that the ground-water-flow equations were implemented correctly into the existing FYE model; however, the modified FYE model has not been validated through a comparison

Spectral considerations for modeling yield of canola

USDA-ARS?s Scientific Manuscript database

Conspicuous yellow flowers that are present in a Brassica oilseed crop such as canola require careful consideration when selecting a spectral index for yield estimation. This study evaluated spectral indices for multispectral sensors that correlate with the seed yield of Brassica oilseed crops. A ...

Estimates of spatial and temporal variation of energy crops biomass yields in the US

NASA Astrophysics Data System (ADS)

Song, Y.; Jain, A. K.; Landuyt, W.; Kheshgi, H. S.

2013-12-01

Perennial grasses, such as switchgrass (Panicum viragatum) and Miscanthus (Miscanthus x giganteus) have been identified for potential use as biomass feedstocks in the US. Current research on perennial grass biomass production has been evaluated on small-scale plots. However, the extent to which this potential can be realized at a landscape-scale will depend on the biophysical potential to grow these grasses with minimum possible amount of land that needs to be diverted from food to fuel production. To assess this potential three questions about the biomass yield for these grasses need to be answered: (1) how the yields for different grasses are varied spatially and temporally across the US; (2) whether the yields are temporally stable or not; and (3) how the spatial and temporal trends in yields of these perennial grasses are controlled by limiting factors, including soil type, water availability, climate, and crop varieties. To answer these questions, the growth processes of the perennial grasses are implemented into a coupled biophysical, physiological and biogeochemical model (ISAM). The model has been applied to quantitatively investigate the spatial and temporal trends in biomass yields for over the period 1980 -2010 in the US. The bioenergy grasses considered in this study include Miscanthus, Cave-in-Rock switchgrass and Alamo switchgrass. The effects of climate, soil and topography on the spatial and temporal trends of biomass yields are quantitatively analyzed using principal component analysis and GIS based geographically weighted regression. The spatial temporal trend results are evaluated further to classify each part of the US into four homogeneous potential yield zones: high and stable yield zone (HS), high but unstable yield zone (HU), low and stable yield zone (LS) and low but unstable yield zone (LU). Our preliminary results indicate that the yields for perennial grasses among different zones are strongly related to the different controlling factors

Unbiased split variable selection for random survival forests using maximally selected rank statistics.

PubMed

Wright, Marvin N; Dankowski, Theresa; Ziegler, Andreas

2017-04-15

The most popular approach for analyzing survival data is the Cox regression model. The Cox model may, however, be misspecified, and its proportionality assumption may not always be fulfilled. An alternative approach for survival prediction is random forests for survival outcomes. The standard split criterion for random survival forests is the log-rank test statistic, which favors splitting variables with many possible split points. Conditional inference forests avoid this split variable selection bias. However, linear rank statistics are utilized by default in conditional inference forests to select the optimal splitting variable, which cannot detect non-linear effects in the independent variables. An alternative is to use maximally selected rank statistics for the split point selection. As in conditional inference forests, splitting variables are compared on the p-value scale. However, instead of the conditional Monte-Carlo approach used in conditional inference forests, p-value approximations are employed. We describe several p-value approximations and the implementation of the proposed random forest approach. A simulation study demonstrates that unbiased split variable selection is possible. However, there is a trade-off between unbiased split variable selection and runtime. In benchmark studies of prediction performance on simulated and real datasets, the new method performs better than random survival forests if informative dichotomous variables are combined with uninformative variables with more categories and better than conditional inference forests if non-linear covariate effects are included. In a runtime comparison, the method proves to be computationally faster than both alternatives, if a simple p-value approximation is used. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

An Automatic Method for Generating an Unbiased Intensity Normalizing Factor in Positron Emission Tomography Image Analysis After Stroke.

PubMed

Nie, Binbin; Liang, Shengxiang; Jiang, Xiaofeng; Duan, Shaofeng; Huang, Qi; Zhang, Tianhao; Li, Panlong; Liu, Hua; Shan, Baoci

2018-06-07

Positron emission tomography (PET) imaging of functional metabolism has been widely used to investigate functional recovery and to evaluate therapeutic efficacy after stroke. The voxel intensity of a PET image is the most important indicator of cellular activity, but is affected by other factors such as the basal metabolic ratio of each subject. In order to locate dysfunctional regions accurately, intensity normalization by a scale factor is a prerequisite in the data analysis, for which the global mean value is most widely used. However, this is unsuitable for stroke studies. Alternatively, a specified scale factor calculated from a reference region is also used, comprising neither hyper- nor hypo-metabolic voxels. But there is no such recognized reference region for stroke studies. Therefore, we proposed a totally data-driven automatic method for unbiased scale factor generation. This factor was generated iteratively until the residual deviation of two adjacent scale factors was reduced by < 5%. Moreover, both simulated and real stroke data were used for evaluation, and these suggested that our proposed unbiased scale factor has better sensitivity and accuracy for stroke studies.

Future possible crop yield scenarios under multiple SSP and RCP scenarios.

NASA Astrophysics Data System (ADS)

Sakurai, G.; Yokozawa, M.; Nishimori, M.; Okada, M.

2016-12-01

Understanding the effect of future climate change on global crop yields is one of the most important tasks for global food security. Future crop yields would be influenced by climatic factors such as the changes of temperature, precipitation and atmospheric carbon dioxide concentration. On the other hand, the effect of the changes of agricultural technologies such as crop varieties, pesticide and fertilizer input on crop yields have large uncertainty. However, not much is available on the contribution ratio of each factor under the future climate change scenario. We estimated the future global yields of four major crops (maize, soybean, rice and wheat) under three Shared Socio Economic Pathways (SSPs) and four Representative Concentration Pathways (RCPs). For this purpose, firstly, we estimated a parameter of a process based model (PRYSBI2) using a Bayesian method for each 1.125 degree spatial grid. The model parameter is relevant to the agricultural technology (we call "technological parameter" here after). Then, we analyzed the relationship between the values of technological parameter and GDP values. We found that the estimated values of the technological parameter were positively correlated with the GDP. Using the estimated relationship, we predicted future crop yield during 2020 and 2100 under SSP1, SSP2 and SSP3 scenarios and RCP 2.6, 4.5, 6.0 and 8.5. The estimated crop yields were different among SSP scenarios. However, we found that the yield difference attributable to SSPs were smaller than those attributable to CO2 fertilization effects and climate change. Particularly, the estimated effect of the change of atmospheric carbon dioxide concentration on global yields was more than four times larger than that of GDP for C3 crops.

Development and Analysis of Global, High-Resolution Diagnostic Metrics for Vegetation Monitoring, Yield Estimation and Famine Mitigation

NASA Astrophysics Data System (ADS)

Anderson, B. T.; Zhang, P.; Myneni, R.

2008-12-01

Drought, through its impact on food scarcity and crop prices, can have significant economic, social, and environmental impacts - presently, up to 36 countries and 73 million people are facing food crises around the globe. Because of these adverse affects, there has been a drive to develop drought and vegetation- monitoring metrics that can quantify and predict human vulnerability/susceptibility to drought at high- resolution spatial scales over the entire globe. Here we introduce a new vegetation-monitoring index utilizing data derived from satellite-based instruments (the Moderate Resolution Imaging Spectroradiometer - MODIS) designed to identify the vulnerability of vegetation in a particular region to climate variability during the growing season. In addition, the index can quantify the percentage of annual grid-point vegetation production either gained or lost due to climatic variability in a given month. When integrated over the growing season, this index is shown to be better correlated with end-of-season crop yields than traditional remotely-sensed or meteorological indices. In addition, in-season estimates of the index, which are available in near real-time, provide yield forecasts comparable to concurrent in situ objective yield surveys, which are only available in limited regions of the world. Overall, the cost effectiveness and repetitive, near-global view of earth's surface provided by this satellite-based vegetation monitoring index can potentially improve our ability to mitigate human vulnerability/susceptibility to drought and its impacts upon vegetation and agriculture.

Comparison of volume estimation methods for pancreatic islet cells

NASA Astrophysics Data System (ADS)

Dvořák, JiřÃ.­; Å vihlík, Jan; Habart, David; Kybic, Jan

2016-03-01

In this contribution we study different methods of automatic volume estimation for pancreatic islets which can be used in the quality control step prior to the islet transplantation. The total islet volume is an important criterion in the quality control. Also, the individual islet volume distribution is interesting -- it has been indicated that smaller islets can be more effective. A 2D image of a microscopy slice containing the islets is acquired. The input of the volume estimation methods are segmented images of individual islets. The segmentation step is not discussed here. We consider simple methods of volume estimation assuming that the islets have spherical or ellipsoidal shape. We also consider a local stereological method, namely the nucleator. The nucleator does not rely on any shape assumptions and provides unbiased estimates if isotropic sections through the islets are observed. We present a simulation study comparing the performance of the volume estimation methods in different scenarios and an experimental study comparing the methods on a real dataset.

Estimation after classification using lot quality assurance sampling: corrections for curtailed sampling with application to evaluating polio vaccination campaigns.

PubMed

Olives, Casey; Valadez, Joseph J; Pagano, Marcello

2014-03-01

To assess the bias incurred when curtailment of Lot Quality Assurance Sampling (LQAS) is ignored, to present unbiased estimators, to consider the impact of cluster sampling by simulation and to apply our method to published polio immunization data from Nigeria. We present estimators of coverage when using two kinds of curtailed LQAS strategies: semicurtailed and curtailed. We study the proposed estimators with independent and clustered data using three field-tested LQAS designs for assessing polio vaccination coverage, with samples of size 60 and decision rules of 9, 21 and 33, and compare them to biased maximum likelihood estimators. Lastly, we present estimates of polio vaccination coverage from previously published data in 20 local government authorities (LGAs) from five Nigerian states. Simulations illustrate substantial bias if one ignores the curtailed sampling design. Proposed estimators show no bias. Clustering does not affect the bias of these estimators. Across simulations, standard errors show signs of inflation as clustering increases. Neither sampling strategy nor LQAS design influences estimates of polio vaccination coverage in 20 Nigerian LGAs. When coverage is low, semicurtailed LQAS strategies considerably reduces the sample size required to make a decision. Curtailed LQAS designs further reduce the sample size when coverage is high. Results presented dispel the misconception that curtailed LQAS data are unsuitable for estimation. These findings augment the utility of LQAS as a tool for monitoring vaccination efforts by demonstrating that unbiased estimation using curtailed designs is not only possible but these designs also reduce the sample size. © 2014 John Wiley & Sons Ltd.

Intrinsic Atomic Orbitals: An Unbiased Bridge between Quantum Theory and Chemical Concepts.

PubMed

Knizia, Gerald

2013-11-12

Modern quantum chemistry can make quantitative predictions on an immense array of chemical systems. However, the interpretation of those predictions is often complicated by the complex wave function expansions used. Here we show that an exceptionally simple algebraic construction allows for defining atomic core and valence orbitals, polarized by the molecular environment, which can exactly represent self-consistent field wave functions. This construction provides an unbiased and direct connection between quantum chemistry and empirical chemical concepts, and can be used, for example, to calculate the nature of bonding in molecules, in chemical terms, from first principles. In particular, we find consistency with electronegativities (χ), C 1s core-level shifts, resonance substituent parameters (σR), Lewis structures, and oxidation states of transition-metal complexes.

Bipartite entangled stabilizer mutually unbiased bases as maximum cliques of Cayley graphs

NASA Astrophysics Data System (ADS)

van Dam, Wim; Howard, Mark

2011-07-01

We examine the existence and structure of particular sets of mutually unbiased bases (MUBs) in bipartite qudit systems. In contrast to well-known power-of-prime MUB constructions, we restrict ourselves to using maximally entangled stabilizer states as MUB vectors. Consequently, these bipartite entangled stabilizer MUBs (BES MUBs) provide no local information, but are sufficient and minimal for decomposing a wide variety of interesting operators including (mixtures of) Jamiołkowski states, entanglement witnesses, and more. The problem of finding such BES MUBs can be mapped, in a natural way, to that of finding maximum cliques in a family of Cayley graphs. Some relationships with known power-of-prime MUB constructions are discussed, and observables for BES MUBs are given explicitly in terms of Pauli operators.

Genetic parameters of coagulation properties, milk yield, quality, and acidity estimated using coagulating and noncoagulating milk information in Brown Swiss and Holstein-Friesian cows.

PubMed

Cecchinato, A; Penasa, M; De Marchi, M; Gallo, L; Bittante, G; Carnier, P

2011-08-01

The aim of this study was to estimate heritabilities of rennet coagulation time (RCT) and curd firmness (a(30)) and their genetic correlations with test-day milk yield, composition (fat, protein, and casein content), somatic cell score, and acidity (pH and titratable acidity) using coagulating and noncoagulating (NC) milk information. Data were from 1,025 Holstein-Friesian (HF) and 1,234 Brown Swiss (BS) cows, which were progeny of 54 HF and 58 BS artificial insemination sires, respectively. Milk coagulation properties (MCP) of each cow were measured once using a computerized renneting meter and samples not exhibiting coagulation within 31 min after rennet addition were classified as NC milk. For NC samples, RCT was unobserved. Multivariate analyses, using Bayesian methodology, were performed to estimate the genetic relationships of RCT or a(30) with the other traits and statistical inference was based on the marginal posterior distributions of parameters of concern. For analyses involving RCT, a right-censored Gaussian linear model was used and records of NC milk samples, being censored records, were included as unknown parameters in the model implementing a data augmentation procedure. Rennet coagulation time was more heritable [heritability (h(2))=0.240 and h(2)=0.210 for HF and BS, respectively] than a(30) (h(2)=0.148 and h(2)=0.168 for HF and BS, respectively). Milk coagulation properties were more heritable than a single test-day milk yield (h(2)=0.103 and h(2)=0.097 for HF and BS, respectively) and less heritable than milk composition traits whose heritability ranged from 0.275 to 0.275, with the only exception of fat content of BS milk (h(2)=0.108). A negative genetic correlation, lower than -0.85, was estimated between RCT and a(30) for both breeds. Genetic relationships of MCP with yield and composition were low or moderate and favorable. The genetic correlation of somatic cell score with RCT in BS cows was large and positive and even more positive were

How Big Was It? Getting at Yield

NASA Astrophysics Data System (ADS)

Pasyanos, M.; Walter, W. R.; Ford, S. R.

2013-12-01

One of the most coveted pieces of information in the wake of a nuclear test is the explosive yield. Determining the yield from remote observations, however, is not necessarily a trivial thing. For instance, recorded observations of seismic amplitudes, used to estimate the yield, are significantly modified by the intervening media, which varies widely, and needs to be properly accounted for. Even after correcting for propagation effects such as geometrical spreading, attenuation, and station site terms, getting from the resulting source term to a yield depends on the specifics of the explosion source model, including material properties, and depth. Some formulas are based on assumptions of the explosion having a standard depth-of-burial and observed amplitudes can vary if the actual test is either significantly overburied or underburied. We will consider the complications and challenges of making these determinations using a number of standard, more traditional methods and a more recent method that we have developed using regional waveform envelopes. We will do this comparison for recent declared nuclear tests from the DPRK. We will also compare the methods using older explosions at the Nevada Test Site with announced yields, material and depths, so that actual performance can be measured. In all cases, we also strive to quantify realistic uncertainties on the yield estimation.

Maximum sustainable yield estimates of Ladypees, Sillago sihama (Forsskål), fishery in Pakistan using the ASPIC and CEDA packages

NASA Astrophysics Data System (ADS)

Panhwar, Sher Khan; Liu, Qun; Khan, Fozia; Siddiqui, Pirzada J. A.

2012-03-01

Using surplus production model packages of ASPIC (a stock-production model incorporating covariates) and CEDA (Catch effort data analysis), we analyzed the catch and effort data of Sillago sihama fishery in Pakistan. ASPIC estimates the parameters of MSY (maximum sustainable yield), F msy (fishing mortality), q (catchability coefficient), K (carrying capacity or unexploited biomass) and B1/K (maximum sustainable yield over initial biomass). The estimated non-bootstrapped value of MSY based on logistic was 598 t and that based on the Fox model was 415 t, which showed that the Fox model estimation was more conservative than that with the logistic model. The R 2 with the logistic model (0.702) is larger than that with the Fox model (0.541), which indicates a better fit. The coefficient of variation (cv) of the estimated MSY was about 0.3, except for a larger value 88.87 and a smaller value of 0.173. In contrast to the ASPIC results, the R 2 with the Fox model (0.651-0.692) was larger than that with the Schaefer model (0.435-0.567), indicating a better fit. The key parameters of CEDA are: MSY, K, q, and r (intrinsic growth), and the three error assumptions in using the models are normal, log normal and gamma. Parameter estimates from the Schaefer and Pella-Tomlinson models were similar. The MSY estimations from the above two models were 398 t, 549 t and 398 t for normal, log-normal and gamma error distributions, respectively. The MSY estimates from the Fox model were 381 t, 366 t and 366 t for the above three error assumptions, respectively. The Fox model estimates were smaller than those for the Schaefer and the Pella-Tomlinson models. In the light of the MSY estimations of 415 t from ASPIC for the Fox model and 381 t from CEDA for the Fox model, MSY for S. sihama is about 400 t. As the catch in 2003 was 401 t, we would suggest the fishery should be kept at the current level. Production models used here depend on the assumption that CPUE (catch per unit effort) data

Comparison of statistical models for analyzing wheat yield time series.

PubMed

Michel, Lucie; Makowski, David

2013-01-01

The world's population is predicted to exceed nine billion by 2050 and there is increasing concern about the capability of agriculture to feed such a large population. Foresight studies on food security are frequently based on crop yield trends estimated from yield time series provided by national and regional statistical agencies. Various types of statistical models have been proposed for the analysis of yield time series, but the predictive performances of these models have not yet been evaluated in detail. In this study, we present eight statistical models for analyzing yield time series and compare their ability to predict wheat yield at the national and regional scales, using data provided by the Food and Agriculture Organization of the United Nations and by the French Ministry of Agriculture. The Holt-Winters and dynamic linear models performed equally well, giving the most accurate predictions of wheat yield. However, dynamic linear models have two advantages over Holt-Winters models: they can be used to reconstruct past yield trends retrospectively and to analyze uncertainty. The results obtained with dynamic linear models indicated a stagnation of wheat yields in many countries, but the estimated rate of increase of wheat yield remained above 0.06 t ha⁻¹ year⁻¹ in several countries in Europe, Asia, Africa and America, and the estimated values were highly uncertain for several major wheat producing countries. The rate of yield increase differed considerably between French regions, suggesting that efforts to identify the main causes of yield stagnation should focus on a subnational scale.