Towards a new generation of agricultural system data, models and knowledge products: Information and communication technology.

PubMed

Janssen, Sander J C; Porter, Cheryl H; Moore, Andrew D; Athanasiadis, Ioannis N; Foster, Ian; Jones, James W; Antle, John M

2017-07-01

Agricultural modeling has long suffered from fragmentation in model implementation. Many models are developed, there is much redundancy, models are often poorly coupled, model component re-use is rare, and it is frequently difficult to apply models to generate real solutions for the agricultural sector. To improve this situation, we argue that an open, self-sustained, and committed community is required to co-develop agricultural models and associated data and tools as a common resource. Such a community can benefit from recent developments in information and communications technology (ICT). We examine how such developments can be leveraged to design and implement the next generation of data, models, and decision support tools for agricultural production systems. Our objective is to assess relevant technologies for their maturity, expected development, and potential to benefit the agricultural modeling community. The technologies considered encompass methods for collaborative development and for involving stakeholders and users in development in a transdisciplinary manner. Our qualitative evaluation suggests that as an overall research challenge, the interoperability of data sources, modular granular open models, reference data sets for applications and specific user requirements analysis methodologies need to be addressed to allow agricultural modeling to enter in the big data era. This will enable much higher analytical capacities and the integrated use of new data sources. Overall agricultural systems modeling needs to rapidly adopt and absorb state-of-the-art data and ICT technologies with a focus on the needs of beneficiaries and on facilitating those who develop applications of their models. This adoption requires the widespread uptake of a set of best practices as standard operating procedures.

Agricultural Model for the Nile Basin Decision Support System

NASA Astrophysics Data System (ADS)

van der Bolt, Frank; Seid, Abdulkarim

2014-05-01

To analyze options for increasing food supply in the Nile basin the Nile Agricultural Model (AM) was developed. The AM includes state-of-the-art descriptions of biophysical, hydrological and economic processes and realizes a coherent and consistent integration of hydrology, agronomy and economics. The AM covers both the agro-ecological domain (water, crop productivity) and the economic domain (food supply, demand, and trade) and allows to evaluate the macro-economic and hydrological impacts of scenarios for agricultural development. Starting with the hydrological information from the NileBasin-DSS the AM calculates the available water for agriculture, the crop production and irrigation requirements with the FAO-model AquaCrop. With the global commodity trade model MAGNET scenarios for land development and conversion are evaluated. The AM predicts consequences for trade, food security and development based on soil and water availability, crop allocation, food demand and food policy. The model will be used as a decision support tool to contribute to more productive and sustainable agriculture in individual Nile countries and the whole region.

Modelling the impacts of pests and diseases on agricultural systems.

PubMed

Donatelli, M; Magarey, R D; Bregaglio, S; Willocquet, L; Whish, J P M; Savary, S

2017-07-01

The improvement and application of pest and disease models to analyse and predict yield losses including those due to climate change is still a challenge for the scientific community. Applied modelling of crop diseases and pests has mostly targeted the development of support capabilities to schedule scouting or pesticide applications. There is a need for research to both broaden the scope and evaluate the capabilities of pest and disease models. Key research questions not only involve the assessment of the potential effects of climate change on known pathosystems, but also on new pathogens which could alter the (still incompletely documented) impacts of pests and diseases on agricultural systems. Yield loss data collected in various current environments may no longer represent a adequate reference to develop tactical, decision-oriented, models for plant diseases and pests and their impacts, because of the ongoing changes in climate patterns. Process-based agricultural simulation modelling, on the other hand, appears to represent a viable methodology to estimate the impacts of these potential effects. A new generation of tools based on state-of-the-art knowledge and technologies is needed to allow systems analysis including key processes and their dynamics over appropriate suitable range of environmental variables. This paper offers a brief overview of the current state of development in coupling pest and disease models to crop models, and discusses technical and scientific challenges. We propose a five-stage roadmap to improve the simulation of the impacts caused by plant diseases and pests; i) improve the quality and availability of data for model inputs; ii) improve the quality and availability of data for model evaluation; iii) improve the integration with crop models; iv) improve the processes for model evaluation; and v) develop a community of plant pest and disease modelers.

Using landscape typologies to model socioecological systems: Application to agriculture of the United States Gulf Coast

DOE PAGES

Preston, Benjamin L.; King, Anthony Wayne; Mei, Rui; ...

2016-02-11

Agricultural enterprises are vulnerable to the effects of climate variability and change. Improved understanding of the determinants of vulnerability and adaptive capacity in agricultural systems is important for projecting and managing future climate risk. At present, three analytical tools dominate methodological approaches to understanding agroecological vulnerability to climate: process-based crop models, empirical crop models, and integrated assessment models. A common weakness of these approaches is their limited treatment of socio-economic conditions and human agency in modeling agroecological processes and outcomes. This study proposes a framework that uses spatial cluster analysis to generate regional socioecological typologies that capture geographic variance inmore » regional agricultural production and enable attribution of that variance to climatic, topographic, edaphic, and socioeconomic components. This framework was applied to historical corn production (1986-2010) in the U.S. Gulf of Mexico region as a testbed. The results demonstrate that regional socioeconomic heterogeneity is an important driving force in human dominated ecosystems, which we hypothesize, is a function of the link between socioeconomic conditions and the adaptive capacity of agricultural systems. Meaningful representation of future agricultural responses to climate variability and change is contingent upon understanding interactions among biophysical conditions, socioeconomic conditions, and human agency their incorporation in predictive models.« less

Using landscape typologies to model socioecological systems: Application to agriculture of the United States Gulf Coast

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

Preston, Benjamin L.; King, Anthony Wayne; Mei, Rui

Agricultural enterprises are vulnerable to the effects of climate variability and change. Improved understanding of the determinants of vulnerability and adaptive capacity in agricultural systems is important for projecting and managing future climate risk. At present, three analytical tools dominate methodological approaches to understanding agroecological vulnerability to climate: process-based crop models, empirical crop models, and integrated assessment models. A common weakness of these approaches is their limited treatment of socio-economic conditions and human agency in modeling agroecological processes and outcomes. This study proposes a framework that uses spatial cluster analysis to generate regional socioecological typologies that capture geographic variance inmore » regional agricultural production and enable attribution of that variance to climatic, topographic, edaphic, and socioeconomic components. This framework was applied to historical corn production (1986-2010) in the U.S. Gulf of Mexico region as a testbed. The results demonstrate that regional socioeconomic heterogeneity is an important driving force in human dominated ecosystems, which we hypothesize, is a function of the link between socioeconomic conditions and the adaptive capacity of agricultural systems. Meaningful representation of future agricultural responses to climate variability and change is contingent upon understanding interactions among biophysical conditions, socioeconomic conditions, and human agency their incorporation in predictive models.« less

Exploring agricultural production systems and their fundamental components with system dynamics modeling

USDA-ARS?s Scientific Manuscript database

Agricultural production in the United States is undergoing marked changes due to rapid shifts in consumer demands, input costs, and concerns for food safety and environmental impact. Agricultural production systems are comprised of multidimensional components and drivers that interact in complex wa...

Searching for solutions to mitigate greenhouse gas emissions by agricultural policy decisions--Application of system dynamics modeling for the case of Latvia.

PubMed

Dace, Elina; Muizniece, Indra; Blumberga, Andra; Kaczala, Fabio

2015-09-15

European Union (EU) Member States have agreed to limit their greenhouse gas (GHG) emissions from sectors not covered by the EU Emissions Trading Scheme (non-ETS). That includes also emissions from agricultural sector. Although the Intergovernmental Panel on Climate Change (IPCC) has established a methodology for assessment of GHG emissions from agriculture, the forecasting options are limited, especially when policies and their interaction with the agricultural system are tested. Therefore, an advanced tool, a system dynamics model, was developed that enables assessment of effects various decisions and measures have on agricultural GHG emissions. The model is based on the IPCC guidelines and includes the main elements of an agricultural system, i.e. land management, livestock farming, soil fertilization and crop production, as well as feedback mechanisms between the elements. The case of Latvia is selected for simulations, as agriculture generates 22% of the total anthropogenic GHG emissions in the country. The results demonstrate that there are very limited options for GHG mitigation in the agricultural sector. Thereby, reaching the non-ETS GHG emission targets will be very challenging for Latvia, as the level of agricultural GHG emissions will be exceeded considerably above the target levels. Thus, other non-ETS sectors will have to reduce their emissions drastically to "neutralize" the agricultural sector's emissions for reaching the EU's common ambition to move towards low-carbon economy. The developed model may serve as a decision support tool for impact assessment of various measures and decisions on the agricultural system's GHG emissions. Although the model is applied to the case of Latvia, the elements and structure of the model developed are similar to agricultural systems in many countries. By changing numeric values of certain parameters, the model can be applied to analyze decisions and measures in other countries. Copyright © 2015 Elsevier B.V. All

Developing an Integrated Model Framework for the Assessment of Sustainable Agricultural Residue Removal Limits for Bioenergy Systems

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

David Muth, Jr.; Jared Abodeely; Richard Nelson

Agricultural residues have significant potential as a feedstock for bioenergy production, but removing these residues can have negative impacts on soil health. Models and datasets that can support decisions about sustainable agricultural residue removal are available; however, no tools currently exist capable of simultaneously addressing all environmental factors that can limit availability of residue. The VE-Suite model integration framework has been used to couple a set of environmental process models to support agricultural residue removal decisions. The RUSLE2, WEPS, and Soil Conditioning Index models have been integrated. A disparate set of databases providing the soils, climate, and management practice datamore » required to run these models have also been integrated. The integrated system has been demonstrated for two example cases. First, an assessment using high spatial fidelity crop yield data has been run for a single farm. This analysis shows the significant variance in sustainably accessible residue across a single farm and crop year. A second example is an aggregate assessment of agricultural residues available in the state of Iowa. This implementation of the integrated systems model demonstrates the capability to run a vast range of scenarios required to represent a large geographic region.« less

Risk Modelling of Agricultural Products

NASA Astrophysics Data System (ADS)

Nugrahani, E. H.

2017-03-01

In the real world market, agricultural commodity are imposed with fluctuating prices. This means that the price of agricultural products are relatively volatile, which means that agricultural business is a quite risky business for farmers. This paper presents some mathematical models to model such risks in the form of its volatility, based on certain assumptions. The proposed models are time varying volatility model, as well as time varying volatility with mean reversion and with seasonal mean equation models. Implementation on empirical data show that agricultural products are indeed risky.

Evaluation of the precision agricultural landscape modeling system (PALMS) in the semiarid Texas southern high plains

USDA-ARS?s Scientific Manuscript database

Accurate models to simulate the soil water balance in semiarid cropping systems are needed to evaluate management practices for soil and water conservation in both irrigated and dryland production systems. The objective of this study was to evaluate the application of the Precision Agricultural Land...

Evaluation of the Precision Agricultural Landscape Modeling System (PALMS) in the Semiarid Texas Southern High Plains

USDA-ARS?s Scientific Manuscript database

Accurate models to simulate the soil water balance in semiarid cropping systems are needed to evaluate management practices for soil and water conservation in both irrigated and dryland production systems. The objective of this study was to evaluate the application of the Precision Agricultural Land...

Environmental sub models for a macroeconomic model: agricultural contribution to climate change and acidification in Denmark.

PubMed

Jensen, Trine S; Jensen, Jørgen D; Hasler, Berit; Illerup, Jytte B; Andersen, Frits M

2007-01-01

Integrated modelling of the interaction between environmental pressure and economic development is a useful tool to evaluate environmental consequences of policy initiatives. However, the usefulness of such models is often restricted by the fact that these models only include a limited set of environmental impacts, which are often energy-related emissions. In order to evaluate the development in the overall environmental pressure correctly, these model systems must be extended. In this article an integrated macroeconomic model system of the Danish economy with environmental modules of energy related emissions is extended to include the agricultural contribution to climate change and acidification. Next to the energy sector, the agricultural sector is the most important contributor to these environmental themes and subsequently the extended model complex calculates more than 99% of the contribution to both climate change and acidification. Environmental sub-models are developed for agriculture-related emissions of CH(4), N(2)O and NH(3). Agricultural emission sources related to the production specific activity variables are mapped and emission dependent parameters are identified in order to calculate emission coefficients. The emission coefficients are linked to the economic activity variables of the Danish agricultural production. The model system is demonstrated by projections of agriculture-related emissions in Denmark under two alternative sets of assumptions: a baseline projection of the general economic development and a policy scenario for changes in the husbandry sector within the agricultural sector.

An AgMIP framework for improved agricultural representation in integrated assessment models

NASA Astrophysics Data System (ADS)

Ruane, Alex C.; Rosenzweig, Cynthia; Asseng, Senthold; Boote, Kenneth J.; Elliott, Joshua; Ewert, Frank; Jones, James W.; Martre, Pierre; McDermid, Sonali P.; Müller, Christoph; Snyder, Abigail; Thorburn, Peter J.

2017-12-01

Integrated assessment models (IAMs) hold great potential to assess how future agricultural systems will be shaped by socioeconomic development, technological innovation, and changing climate conditions. By coupling with climate and crop model emulators, IAMs have the potential to resolve important agricultural feedback loops and identify unintended consequences of socioeconomic development for agricultural systems. Here we propose a framework to develop robust representation of agricultural system responses within IAMs, linking downstream applications with model development and the coordinated evaluation of key climate responses from local to global scales. We survey the strengths and weaknesses of protocol-based assessments linked to the Agricultural Model Intercomparison and Improvement Project (AgMIP), each utilizing multiple sites and models to evaluate crop response to core climate changes including shifts in carbon dioxide concentration, temperature, and water availability, with some studies further exploring how climate responses are affected by nitrogen levels and adaptation in farm systems. Site-based studies with carefully calibrated models encompass the largest number of activities; however they are limited in their ability to capture the full range of global agricultural system diversity. Representative site networks provide more targeted response information than broadly-sampled networks, with limitations stemming from difficulties in covering the diversity of farming systems. Global gridded crop models provide comprehensive coverage, although with large challenges for calibration and quality control of inputs. Diversity in climate responses underscores that crop model emulators must distinguish between regions and farming system while recognizing model uncertainty. Finally, to bridge the gap between bottom-up and top-down approaches we recommend the deployment of a hybrid climate response system employing a representative network of sites to bias

Simulating Salt Movement using a Coupled Salinity Transport Model in a Variably Saturated Agricultural Groundwater System

NASA Astrophysics Data System (ADS)

Tavakoli Kivi, S.; Bailey, R. T.; Gates, T. K.

2017-12-01

Salinization is one of the major concerns in irrigated agricultural fields. Increasing salinity concentrations are due principally to a high water table that results from excessive irrigation, canal seepage, and a lack of efficient drainage systems, and lead to decreasing crop yield. High groundwater salinity loading to nearby river systems also impacts downstream areas, with saline river water diverted for application on irrigated fields. To assess the different strategies for salt remediation, we present a reactive transport model (UZF-RT3D) coupled with a salinity equilibrium chemistry module for simulating the fate and transport of salt ions in a variably-saturated agricultural groundwater system. The developed model accounts not for advection, dispersion, nitrogen and sulfur cycling, oxidation-reduction, sorption, complexation, ion exchange, and precipitation/dissolution of salt minerals. The model is applied to a 500 km2 region within the Lower Arkansas River Valley (LARV) in southeastern Colorado, an area acutely affected by salinization in the past few decades. The model is tested against salt ion concentrations in the saturated zone, total dissolved solid concentrations in the unsaturated zone, and salt groundwater loading to the Arkansas River. The model now can be used to investigate salinity remediation strategies.

Prospective and participatory integrated assessment of agricultural systems from farm to regional scales: Comparison of three modeling approaches.

PubMed

Delmotte, Sylvestre; Lopez-Ridaura, Santiago; Barbier, Jean-Marc; Wery, Jacques

2013-11-15

Evaluating the impacts of the development of alternative agricultural systems, such as organic or low-input cropping systems, in the context of an agricultural region requires the use of specific tools and methodologies. They should allow a prospective (using scenarios), multi-scale (taking into account the field, farm and regional level), integrated (notably multicriteria) and participatory assessment, abbreviated PIAAS (for Participatory Integrated Assessment of Agricultural System). In this paper, we compare the possible contribution to PIAAS of three modeling approaches i.e. Bio-Economic Modeling (BEM), Agent-Based Modeling (ABM) and statistical Land-Use/Land Cover Change (LUCC) models. After a presentation of each approach, we analyze their advantages and drawbacks, and identify their possible complementarities for PIAAS. Statistical LUCC modeling is a suitable approach for multi-scale analysis of past changes and can be used to start discussion about the futures with stakeholders. BEM and ABM approaches have complementary features for scenarios assessment at different scales. While ABM has been widely used for participatory assessment, BEM has been rarely used satisfactorily in a participatory manner. On the basis of these results, we propose to combine these three approaches in a framework targeted to PIAAS. Copyright © 2013 Elsevier Ltd. All rights reserved.

Understanding the Reach of Agricultural Impacts from Climate Extremes in the Agricultural Model Intercomparison and Improvement Project (AgMIP)

NASA Astrophysics Data System (ADS)

Ruane, A. C.

2016-12-01

The Agricultural Model Intercomparison and Improvement Project (AgMIP) has been working since 2010 to build a modeling framework capable of representing the complexities of agriculture, its dependence on climate, and the many elements of society that depend on food systems. AgMIP's 30+ activities explore the interconnected nature of climate, crop, livestock, economics, food security, and nutrition, using common protocols to systematically evaluate the components of agricultural assessment and allow multi-model, multi-scale, and multi-method analysis of intertwining changes in socioeconomic development, environmental change, and technological adaptation. AgMIP is now launching Coordinated Global and Regional Assessments (CGRA) with a particular focus on unforeseen consequences of development strategies, interactions between global and local systems, and the resilience of agricultural systems to extreme climate events. Climate extremes shock the agricultural system through local, direct impacts (e.g., droughts, heat waves, floods, severe storms) and also through teleconnections propagated through international trade. As the climate changes, the nature of climate extremes affecting agriculture is also likely to change, leading to shifting intensity, duration, frequency, and geographic extents of extremes. AgMIP researchers are developing new scenario methodologies to represent near-term extreme droughts in a probabilistic manner, field experiments that impose heat wave conditions on crops, increased resolution to differentiate sub-national drought impacts, new behavioral functions that mimic the response of market actors faced with production shortfalls, analysis of impacts from simultaneous failures of multiple breadbasket regions, and more detailed mapping of food and socioeconomic indicators into food security and nutrition metrics that describe the human impact in diverse populations. Agricultural models illustrate the challenges facing agriculture, allowing

Task-focused modeling in automated agriculture

NASA Astrophysics Data System (ADS)

Vriesenga, Mark R.; Peleg, K.; Sklansky, Jack

1993-01-01

Machine vision systems analyze image data to carry out automation tasks. Our interest is in machine vision systems that rely on models to achieve their designed task. When the model is interrogated from an a priori menu of questions, the model need not be complete. Instead, the machine vision system can use a partial model that contains a large amount of information in regions of interest and less information elsewhere. We propose an adaptive modeling scheme for machine vision, called task-focused modeling, which constructs a model having just sufficient detail to carry out the specified task. The model is detailed in regions of interest to the task and is less detailed elsewhere. This focusing effect saves time and reduces the computational effort expended by the machine vision system. We illustrate task-focused modeling by an example involving real-time micropropagation of plants in automated agriculture.

Comparing Supply-Side Specifications in Models of Global Agriculture and the Food System

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

Robinson, Sherman; van Meijl, Hans; Willenbockel, Dirk

This paper compares the theoretical specification of production and technical change across the partial equilibrium (PE) and computable general equilibrium (CGE) models of the global agricultural and food system included in the AgMIP model comparison study. The two modeling approaches have different theoretical underpinnings concerning the scope of economic activity they capture and how they represent technology and the behavior of supply and demand in markets. This paper focuses on their different specifications of technology and supply behavior, comparing their theoretical and empirical treatments. While the models differ widely in their specifications of technology, both within and between the PEmore » and CGE classes of models, we find that the theoretical responsiveness of supply to changes in prices can be similar, depending on parameter choices that define the behavior of supply functions over the domain of applicability defined by the common scenarios used in the AgMIP comparisons. In particular, we compare the theoretical specification of supply in CGE models with neoclassical production functions and PE models that focus on land and crop yields in agriculture. In practice, however, comparability of results given parameter choices is an empirical question, and the models differ in their sensitivity to variations in specification. To illustrate the issues, sensitivity analysis is done with one global CGE model, MAGNET, to indicate how the results vary with different specification of technical change, and how they compare with the results from PE models.« less

An AgMIP framework for improved agricultural representation in integrated assessment models

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

Ruane, Alex C.; Rosenzweig, Cynthia; Asseng, Senthold

Integrated assessment models (IAMs) hold great potential to assess how future agricultural systems will be shaped by socioeconomic development, technological innovation, and changing climate conditions. By coupling with climate and crop model emulators, IAMs have the potential to resolve important agricultural feedback loops and identify unintended consequences of socioeconomic development for agricultural systems. Here we propose a framework to develop robust representation of agricultural system responses within IAMs, linking downstream applications with model development and the coordinated evaluation of key climate responses from local to global scales. We survey the strengths and weaknesses of protocol-based assessments linked to the Agriculturalmore » Model Intercomparison and Improvement Project (AgMIP), each utilizing multiple sites and models to evaluate crop response to core climate changes including shifts in carbon dioxide concentration, temperature, and water availability, with some studies further exploring how climate responses are affected by nitrogen levels and adaptation in farm systems. Site-based studies with carefully calibrated models encompass the largest number of activities; however they are limited in their ability to capture the full range of global agricultural system diversity. Representative site networks provide more targeted response information than broadly-sampled networks, with limitations stemming from difficulties in covering the diversity of farming systems. Global gridded crop models provide comprehensive coverage, although with large challenges for calibration and quality control of inputs. Diversity in climate responses underscores that crop model emulators must distinguish between regions and farming system while recognizing model uncertainty. Finally, to bridge the gap between bottom-up and top-down approaches we recommend the deployment of a hybrid climate response system employing a representative network of sites to

Data model for the collaboration between land administration systems and agricultural land parcel identification systems.

PubMed

Inan, Halil Ibrahim; Sagris, Valentina; Devos, Wim; Milenov, Pavel; van Oosterom, Peter; Zevenbergen, Jaap

2010-12-01

The Common Agricultural Policy (CAP) of the European Union (EU) has dramatically changed after 1992, and from then on the CAP focused on the management of direct income subsidies instead of production-based subsidies. For this focus, Member States (MS) are expected to establish Integrated Administration and Control System (IACS), including a Land Parcel Identification System (LPIS) as the spatial part of IACS. Different MS have chosen different solutions for their LPIS. Currently, some MS based their IACS/LPIS on data from their Land Administration Systems (LAS), and many others use purpose built special systems for their IACS/LPIS. The issue with these different IACS/LPIS is that they do not have standardized structures; rather, each represents a unique design in each MS, both in the case of LAS based or special systems. In this study, we aim at designing a core data model for those IACS/LPIS based on LAS. For this purpose, we make use of the ongoing standardization initiatives for LAS (Land Administration Domain Model: LADM) and IACS/LPIS (LPIS Core Model: LCM). The data model we propose in this study implies the collaboration between LADM and LCM and includes some extensions. Some basic issues with the collaboration model are discussed within this study: registration of farmers, land use rights and farming limitations, geometry/topology, temporal data management etc. For further explanation of the model structure, sample instance level diagrams illustrating some typical situations are also included. Copyright © 2010 Elsevier Ltd. All rights reserved.

A hydro-sedimentary modeling system for flash flood propagation and hazard estimation under different agricultural practices

NASA Astrophysics Data System (ADS)

Kourgialas, N. N.; Karatzas, G. P.

2014-03-01

A modeling system for the estimation of flash flood flow velocity and sediment transport is developed in this study. The system comprises three components: (a) a modeling framework based on the hydrological model HSPF, (b) the hydrodynamic module of the hydraulic model MIKE 11 (quasi-2-D), and (c) the advection-dispersion module of MIKE 11 as a sediment transport model. An important parameter in hydraulic modeling is the Manning's coefficient, an indicator of the channel resistance which is directly dependent on riparian vegetation changes. Riparian vegetation's effect on flood propagation parameters such as water depth (inundation), discharge, flow velocity, and sediment transport load is investigated in this study. Based on the obtained results, when the weed-cutting percentage is increased, the flood wave depth decreases while flow discharge, velocity and sediment transport load increase. The proposed modeling system is used to evaluate and illustrate the flood hazard for different riparian vegetation cutting scenarios. For the estimation of flood hazard, a combination of the flood propagation characteristics of water depth, flow velocity and sediment load was used. Next, a well-balanced selection of the most appropriate agricultural cutting practices of riparian vegetation was performed. Ultimately, the model results obtained for different agricultural cutting practice scenarios can be employed to create flood protection measures for flood-prone areas. The proposed methodology was applied to the downstream part of a small Mediterranean river basin in Crete, Greece.

The General Ensemble Biogeochemical Modeling System (GEMS) and its applications to agricultural systems in the United States: Chapter 18

USGS Publications Warehouse

Liu, Shuguang; Tan, Zhengxi; Chen, Mingshi; Liu, Jinxun; Wein, Anne; Li, Zhengpeng; Huang, Shengli; Oeding, Jennifer; Young, Claudia; Verma, Shashi B.; Suyker, Andrew E.; Faulkner, Stephen P.

2012-01-01

The General Ensemble Biogeochemical Modeling System (GEMS) was es in individual models, it uses multiple site-scale biogeochemical models to perform model simulations. Second, it adopts Monte Carlo ensemble simulations of each simulation unit (one site/pixel or group of sites/pixels with similar biophysical conditions) to incorporate uncertainties and variability (as measured by variances and covariance) of input variables into model simulations. In this chapter, we illustrate the applications of GEMS at the site and regional scales with an emphasis on incorporating agricultural practices. Challenges in modeling soil carbon dynamics and greenhouse emissions are also discussed.

A system dynamics simulation model for sustainable water resources management and agricultural development in the Volta River Basin, Ghana.

PubMed

Kotir, Julius H; Smith, Carl; Brown, Greg; Marshall, Nadine; Johnstone, Ron

2016-12-15

In a rapidly changing water resources system, dynamic models based on the notion of systems thinking can serve as useful analytical tools for scientists and policy-makers to study changes in key system variables over time. In this paper, an integrated system dynamics simulation model was developed using a system dynamics modelling approach to examine the feedback processes and interaction between the population, the water resource, and the agricultural production sub-sectors of the Volta River Basin in West Africa. The objective of the model is to provide a learning tool for policy-makers to improve their understanding of the long-term dynamic behaviour of the basin, and as a decision support tool for exploring plausible policy scenarios necessary for sustainable water resource management and agricultural development. Structural and behavioural pattern tests, and statistical test were used to evaluate and validate the performance of the model. The results showed that the simulated outputs agreed well with the observed reality of the system. A sensitivity analysis also indicated that the model is reliable and robust to uncertainties in the major parameters. Results of the business as usual scenario showed that total population, agricultural, domestic, and industrial water demands will continue to increase over the simulated period. Besides business as usual, three additional policy scenarios were simulated to assess their impact on water demands, crop yield, and net-farm income. These were the development of the water infrastructure (scenario 1), cropland expansion (scenario 2) and dry conditions (scenario 3). The results showed that scenario 1 would provide the maximum benefit to people living in the basin. Overall, the model results could help inform planning and investment decisions within the basin to enhance food security, livelihoods development, socio-economic growth, and sustainable management of natural resources. Copyright © 2016 Elsevier B.V. All

How much detail and accuracy is required in plant growth sub-models to address questions about optimal management strategies in agricultural systems?

PubMed Central

Renton, Michael

2011-01-01

Background and aims Simulations that integrate sub-models of important biological processes can be used to ask questions about optimal management strategies in agricultural and ecological systems. Building sub-models with more detail and aiming for greater accuracy and realism may seem attractive, but is likely to be more expensive and time-consuming and result in more complicated models that lack transparency. This paper illustrates a general integrated approach for constructing models of agricultural and ecological systems that is based on the principle of starting simple and then directly testing for the need to add additional detail and complexity. Methodology The approach is demonstrated using LUSO (Land Use Sequence Optimizer), an agricultural system analysis framework based on simulation and optimization. A simple sensitivity analysis and functional perturbation analysis is used to test to what extent LUSO's crop–weed competition sub-model affects the answers to a number of questions at the scale of the whole farming system regarding optimal land-use sequencing strategies and resulting profitability. Principal results The need for accuracy in the crop–weed competition sub-model within LUSO depended to a small extent on the parameter being varied, but more importantly and interestingly on the type of question being addressed with the model. Only a small part of the crop–weed competition model actually affects the answers to these questions. Conclusions This study illustrates an example application of the proposed integrated approach for constructing models of agricultural and ecological systems based on testing whether complexity needs to be added to address particular questions of interest. We conclude that this example clearly demonstrates the potential value of the general approach. Advantages of this approach include minimizing costs and resources required for model construction, keeping models transparent and easy to analyse, and ensuring the model

Integration of agricultural and energy system models for biofuel assessment

EPA Science Inventory

This paper presents a coupled modeling framework to capture the dynamic linkages between agricultural and energy markets that have been enhanced through the expansion of biofuel production, as well as the environmental impacts resulting from this expansion. The framework incorpor...

Embedding an evolving agricultural system within a water resources planning model

NASA Astrophysics Data System (ADS)

Young, C.; Joyce, B.; Purkey, D.; Dale, L.; Mehta, V.

2008-12-01

The Water Evaluation and Planning (WEAP) system is a comprehensive, fully integrated water basin analysis tool. It is a simulation model that includes a robust and flexible representation of water demands from all sectors and flexible, programmable operating rules for infrastructure elements such as reservoirs, canals, and hydropower projects. Additionally, it has watershed rainfall-runoff modeling capabilities that allow all portions of the water infrastructure and demand to be dynamically nested within the underlying hydrological processes. WEAP also allows for linking with other models to provide feedback mechanisms whereby the management regime can be altered to respond to changing water supply conditions. This study presents an application wherein the year-to-year cropping decisions of farmers in California's Central Valley are reactive to changes in water supply conditions. To capture this dynamic, we have included in WEAP a link to an agricultural economics model (the Central Valley Production Model) that relates cropping decisions to water supply conditions (surface water allocations and depth to groundwater) and economic considerations (cost of electricity) at the time of planting. This linked model was used to evaluate changes in water supply and demand in the context of projected climate change over the next century.

Representing Extremes in Agricultural Models

NASA Technical Reports Server (NTRS)

Ruane, Alex

2015-01-01

AgMIP and related projects are conducting several activities to understand and improve crop model response to extreme events. This involves crop model studies as well as the generation of climate datasets and scenarios more capable of capturing extremes. Models are typically less responsive to extreme events than we observe, and miss several forms of extreme events. Models also can capture interactive effects between climate change and climate extremes. Additional work is needed to understand response of markets and economic systems to food shocks. AgMIP is planning a Coordinated Global and Regional Assessment of Climate Change Impacts on Agricultural Production and Food Security with an aim to inform the IPCC Sixth Assessment Report.

Stochastic and deterministic models for agricultural production networks.

PubMed

Bai, P; Banks, H T; Dediu, S; Govan, A Y; Last, M; Lloyd, A L; Nguyen, H K; Olufsen, M S; Rempala, G; Slenning, B D

2007-07-01

An approach to modeling the impact of disturbances in an agricultural production network is presented. A stochastic model and its approximate deterministic model for averages over sample paths of the stochastic system are developed. Simulations, sensitivity and generalized sensitivity analyses are given. Finally, it is shown how diseases may be introduced into the network and corresponding simulations are discussed.

Sustainable intensification in agricultural systems.

PubMed

Pretty, Jules; Bharucha, Zareen Pervez

2014-12-01

Agricultural systems are amended ecosystems with a variety of properties. Modern agroecosystems have tended towards high through-flow systems, with energy supplied by fossil fuels directed out of the system (either deliberately for harvests or accidentally through side effects). In the coming decades, resource constraints over water, soil, biodiversity and land will affect agricultural systems. Sustainable agroecosystems are those tending to have a positive impact on natural, social and human capital, while unsustainable systems feed back to deplete these assets, leaving fewer for the future. Sustainable intensification (SI) is defined as a process or system where agricultural yields are increased without adverse environmental impact and without the conversion of additional non-agricultural land. The concept does not articulate or privilege any particular vision or method of agricultural production. Rather, it emphasizes ends rather than means, and does not pre-determine technologies, species mix or particular design components. The combination of the terms 'sustainable' and 'intensification' is an attempt to indicate that desirable outcomes around both more food and improved environmental goods and services could be achieved by a variety of means. Nonetheless, it remains controversial to some. This review analyses recent evidence of the impacts of SI in both developing and industrialized countries, and demonstrates that both yield and natural capital dividends can occur. The review begins with analysis of the emergence of combined agricultural-environmental systems, the environmental and social outcomes of recent agricultural revolutions, and analyses the challenges for food production this century as populations grow and consumption patterns change. Emergent criticisms are highlighted, and the positive impacts of SI on food outputs and renewable capital assets detailed. It concludes with observations on policies and incentives necessary for the wider adoption of

A Systems Approach to Agricultural Biosecurity.

PubMed

Anand, Manish

This article highlights the importance of systems approaches in addressing agricultural biosecurity threats. On the basis of documentary analysis and stakeholder interaction, a brief survey of agricultural biosecurity threats and vulnerabilities from global and Indian perspectives is provided, followed by an exploration of technological and institutional capabilities. Finally, a perspective on the agricultural disease diagnostic networks is provided, drawing instances from global developments. Technical barriers to agroterrorism are lower than those to human-targeted bioterrorism, and the sector is unique as even a very small disease outbreak could prompt international export restrictions. Key vulnerabilities in the agriculture sector stem from, among others, the structure of agricultural production; insufficient monitoring, surveillance, and controls systems at the borders and in the food chain; inefficient systems for reporting unusual occurrences and outbreaks of disease; and lack of sufficiently trained human resources capable of recognizing or treating transboundary pathogens and diseases. An assessment of technology and institutions pertaining to crop and animal protection management suggests certain gaps. Investment in developing new technologies for civilian application in agriculture, as well as for legitimate actions pertaining to defense, detection, protection, and prophylaxis, and in upgrading laboratory facilities can increase the agricultural sector's level of preparedness for outbreaks. To address potential threats and vulnerabilities of agroterrorism effectively requires the development of a comprehensive strategy and a combined, interagency approach, ideally on an international level. It is proposed that a systems-oriented approach for developing knowledge and innovation networks and strengthening skills and capacities would enable a more resilient agricultural biosecurity system.

Designing a Model for Integration of Information and Communication Technologies (ICTs) in the Iranian Agricultural Research System

ERIC Educational Resources Information Center

Sharifzadeh, Aboulqasem; Abdollahzadeh, Gholam Hossein; Sharifi, Mahnoosh

2009-01-01

Capacity Development is needed in the Iranian Agricultural System. Integrating Information and Communication Technologies (ICTs) in the agricultural research system is an appropriate capacity development mechanism. The appropriate application of ICTs and information such as a National Agricultural Information System requires a systemically…

Integrating Sensory/Actuation Systems in Agricultural Vehicles

PubMed Central

Emmi, Luis; Gonzalez-de-Soto, Mariano; Pajares, Gonzalo; Gonzalez-de-Santos, Pablo

2014-01-01

In recent years, there have been major advances in the development of new and more powerful perception systems for agriculture, such as computer-vision and global positioning systems. Due to these advances, the automation of agricultural tasks has received an important stimulus, especially in the area of selective weed control where high precision is essential for the proper use of resources and the implementation of more efficient treatments. Such autonomous agricultural systems incorporate and integrate perception systems for acquiring information from the environment, decision-making systems for interpreting and analyzing such information, and actuation systems that are responsible for performing the agricultural operations. These systems consist of different sensors, actuators, and computers that work synchronously in a specific architecture for the intended purpose. The main contribution of this paper is the selection, arrangement, integration, and synchronization of these systems to form a whole autonomous vehicle for agricultural applications. This type of vehicle has attracted growing interest, not only for researchers but also for manufacturers and farmers. The experimental results demonstrate the success and performance of the integrated system in guidance and weed control tasks in a maize field, indicating its utility and efficiency. The whole system is sufficiently flexible for use in other agricultural tasks with little effort and is another important contribution in the field of autonomous agricultural vehicles. PMID:24577525

Integrating sensory/actuation systems in agricultural vehicles.

PubMed

Emmi, Luis; Gonzalez-de-Soto, Mariano; Pajares, Gonzalo; Gonzalez-de-Santos, Pablo

2014-02-26

In recent years, there have been major advances in the development of new and more powerful perception systems for agriculture, such as computer-vision and global positioning systems. Due to these advances, the automation of agricultural tasks has received an important stimulus, especially in the area of selective weed control where high precision is essential for the proper use of resources and the implementation of more efficient treatments. Such autonomous agricultural systems incorporate and integrate perception systems for acquiring information from the environment, decision-making systems for interpreting and analyzing such information, and actuation systems that are responsible for performing the agricultural operations. These systems consist of different sensors, actuators, and computers that work synchronously in a specific architecture for the intended purpose. The main contribution of this paper is the selection, arrangement, integration, and synchronization of these systems to form a whole autonomous vehicle for agricultural applications. This type of vehicle has attracted growing interest, not only for researchers but also for manufacturers and farmers. The experimental results demonstrate the success and performance of the integrated system in guidance and weed control tasks in a maize field, indicating its utility and efficiency. The whole system is sufficiently flexible for use in other agricultural tasks with little effort and is another important contribution in the field of autonomous agricultural vehicles.

Mapping of Biophysical Parameters of Rice Agriculture System from Hyperspectral Imagery

NASA Astrophysics Data System (ADS)

Moharana, Shreedevi; Duta, Subashisa

2017-04-01

Chlorophyll, nitrogen and leaf water content are the most essential parameters for paddy crop growth. Ground hyperspectral observations were collected at canopy level during critical growth period of rice by using hand held Spectroradiometer. Chemical analysis was carried out to quantify the total chlorophyll, nitrogen and leaf water content. By exploiting the in-situ hyperspectral measurements, regression models were established between each of the crop growth parameters and the spectral indices specifically designed for chlorophyll, nitrogen and water stress. Narrow band vegetation index models were developed for mapping these parameters from Hyperion imagery in an agriculture system. It was inferred that the modified simple ratio (SR) and leaf nitrogen concentration (LNC) predictive index models, which followed a linear and nonlinear relationship respectively, produced satisfactory results in predicting rice nitrogen content from hyperspectral imagery. The presently developed model was compared with other models proposed by researchers. It was ascertained that, nitrogen content varied widely from 1-4 percentage and only 2-3 percentage for paddy crop using present modified index models and well-known predicted Tian et al. (2011) model respectively. The modified present LNC index model performed better than the established Tian et al. (2011) model as far as the estimated nitrogen content from Hyperion imagery was concerned. Moreover, within the observed chlorophyll range attained from the rice genotypes cultivated in the studied rice agriculture system, the index models (LNC, OASVI, Gitelson, mSR and MTCI) accomplished satisfactory results in the spatial distribution of rice chlorophyll content from Hyperion imagery. Spatial distribution of total chlorophyll content widely varied from 1.77-5.81 mg/g (LNC), 3.0-13 mg/g (OASVI) and 2.90-5.40 mg/g (MTCI). Following the similar guideline, it was found that normalized difference water index (NDWI) and normalized

[Agricultural eco-economic system coupling in Zhifanggou watershed in hilly-gully region of Loess Plateau].

PubMed

Wang, Ji-Jun

2009-11-01

Agricultural eco-economic system coupling is an organic unit formed by the inherent interaction between agricultural ecosystem and economic system, and regulated and controlled by mankind moderate interference. Its status can be expressed by the circular chain-net structure of agricultural resources and agricultural industry. The agricultural eco-economic system in Zhifanggou watershed has gone through the process of system coupling, system conflict, system coupling, and partial conflict in high leverage, which is caused by the farmers' requirement and the state's macro-policy, economic means, and administrative means. To cope with the problems of agricultural eco-economics system coupling in Zhifanggou watershed, the optimal coupling model should be established, with tree-grass resources and related industries as the core.

Sustainable intensification in agricultural systems

PubMed Central

Pretty, Jules; Bharucha, Zareen Pervez

2014-01-01

Background Agricultural systems are amended ecosystems with a variety of properties. Modern agroecosystems have tended towards high through-flow systems, with energy supplied by fossil fuels directed out of the system (either deliberately for harvests or accidentally through side effects). In the coming decades, resource constraints over water, soil, biodiversity and land will affect agricultural systems. Sustainable agroecosystems are those tending to have a positive impact on natural, social and human capital, while unsustainable systems feed back to deplete these assets, leaving fewer for the future. Sustainable intensification (SI) is defined as a process or system where agricultural yields are increased without adverse environmental impact and without the conversion of additional non-agricultural land. The concept does not articulate or privilege any particular vision or method of agricultural production. Rather, it emphasizes ends rather than means, and does not pre-determine technologies, species mix or particular design components. The combination of the terms ‘sustainable’ and ‘intensification’ is an attempt to indicate that desirable outcomes around both more food and improved environmental goods and services could be achieved by a variety of means. Nonetheless, it remains controversial to some. Scope and Conclusions This review analyses recent evidence of the impacts of SI in both developing and industrialized countries, and demonstrates that both yield and natural capital dividends can occur. The review begins with analysis of the emergence of combined agricultural–environmental systems, the environmental and social outcomes of recent agricultural revolutions, and analyses the challenges for food production this century as populations grow and consumption patterns change. Emergent criticisms are highlighted, and the positive impacts of SI on food outputs and renewable capital assets detailed. It concludes with observations on policies and

Three Dimensional Modeling of Agricultural Contamination of Groundwater: a Case Study in the Nebraska Management Systems Evaluation Area (MSEA) Site

NASA Astrophysics Data System (ADS)

Akbariyeh, S.; Snow, D. D.; Bartelt-Hunt, S.; Li, X.; Li, Y.

2015-12-01

Contamination of groundwater from nitrogen fertilizers and pesticides in agricultural lands is an important environmental and water quality management issue. It is well recognized that in agriculturally intensive areas, fertilizers and pesticides may leach through the vadose zone and eventually reach groundwater, impacting future uses of this limited resource. While numerical models are commonly used to simulate fate and transport of agricultural contaminants, few models have been validated based on realistic three dimensional soil lithology, hydrological conditions, and historical changes in groundwater quality. In this work, contamination of groundwater in the Nebraska Management Systems Evaluation Area (MSEA) site was simulated based on extensive field data including (1) lithology from 69 wells and 11 test holes; (2) surface soil type, land use, and surface elevations; (3) 5-year groundwater level and flow velocity; (4) daily meteorological monitoring; (5) 5-year seasonal irrigation records; (6) 5-years of spatially intensive contaminant concentration in 40 multilevel monitoring wells; and (7) detailed cultivation records. Using this data, a three-dimensional vadose zone lithological framework was developed using a commercial software tool (RockworksTM). Based on the interpolated lithology, a hydrological model was developed using HYDRUS-3D to simulate water flow and contaminant transport. The model was validated through comparison of simulated atrazine and nitrate concentration with historical data from 40 wells and multilevel samplers. The validated model will be used to predict potential changes in ground water quality due to agricultural contamination under future climate scenarios in the High Plain Aquifer system.

Data Entities and Information System Matrix for Integrated Agriculture Information System (IAIS)

NASA Astrophysics Data System (ADS)

Budi Santoso, Halim; Delima, Rosa

2018-03-01

Integrated Agriculture Information System is a system that is developed to process data, information, and knowledge in Agriculture sector. Integrated Agriculture Information System brings valuable information for farmers: (1) Fertilizer price; (2) Agriculture technique and practise; (3) Pest management; (4) Cultivation; (5) Irrigation; (6) Post harvest processing; (7) Innovation in agriculture processing. Integrated Agriculture Information System contains 9 subsystems. To bring an integrated information to the user and stakeholder, it needs an integrated database approach. Thus, researchers describes data entity and its matrix relate to subsystem in Integrated Agriculture Information System (IAIS). As a result, there are 47 data entities as entities in single and integrated database.

Indicators of climate change in agricultural systems

USDA-ARS?s Scientific Manuscript database

Climate change affects all segments of the agricultural enterprise and there is mounting evidence that the continuing warming trend with shifting seasonality and intensity in precipitation will increase the vulnerability of agricultural systems. Agriculture is a complex system within the United Stat...

Against the Grain: The Influence of Changing Agricultural Management on the Earth System

NASA Astrophysics Data System (ADS)

Foley, J. A.

2007-12-01

The rise of modern agriculture was one of the most transformative events in human history, and has forever changed our relationship to the natural world. By clearing tropical forests, practicing subsistence agriculture on marginal lands and intensifying industrialized farmland production, agricultural practices are changing the worldês landscapes in pervasive ways. In the past decade, we have made tremendous progress in monitoring agricultural expansion from satellites, and modeling associated environmental impacts. In the past decade, the Earth System Science research community has begun to recognize the importance of agricultural lands, particularly as they continue expanding at the expense of important natural ecosystems, potentially altering the planetês carbon cycle and climate. With the advent of new remote sensing and global modeling methods, several efforts have documented the expansion of agricultural lands, the corresponding loss of natural ecosystems, and how this may influence the earth system. But the geographic expansion of agricultural lands is not the whole story. While significant agricultural expansion (or extensification) has occurred in the past few decades, the intensification of agricultural practices Ð under the aegis of the -Green Revolution" Ð has dramatically altered the relationship between humans and environmental systems across the world. Simply put, many of the worldês existing agricultural lands are being used much more intensively as opportunities for agricultural expansion are being exhausted elsewhere. In the last 40 years, global agricultural production has more than doubled Ð although global cropland has increased by only 12% Ð mainly through the use of high yielding varieties of grain, increased reliance on irrigation, massive increases in chemical fertilization, and increased mechanization. Indeed, in the past 40 years there has been a 700% increase in global fertilizer use and a 70% increase in irrigated cropland area

A coupled human-natural systems analysis of irrigated agriculture under changing climate

NASA Astrophysics Data System (ADS)

Giuliani, M.; Li, Y.; Castelletti, A.; Gandolfi, C.

2016-09-01

Exponentially growing water demands and increasingly uncertain hydrologic regimes due to changes in climate and land use are challenging the sustainability of agricultural water systems. Farmers must adapt their management strategies in order to secure food production and avoid crop failures. Investigating the potential for adaptation policies in agricultural systems requires accounting for their natural and human components, along with their reciprocal interactions. Yet this feedback is generally overlooked in the water resources systems literature. In this work, we contribute a novel modeling approach to study the coevolution of irrigated agriculture under changing climate, advancing the representation of the human component within agricultural systems by using normative meta-models to describe the behaviors of groups of farmers or institutional decisions. These behavioral models, validated against observational data, are then integrated into a coupled human-natural system simulation model to better represent both systems and their coevolution under future changing climate conditions, assuming the adoption of different policy adaptation options, such as cultivating less water demanding crops. The application to the pilot study of the Adda River basin in northern Italy shows that the dynamic coadaptation of water supply and demand allows farmers to avoid estimated potential losses of more than 10 M€/yr under projected climate changes, while unilateral adaptation of either the water supply or the demand are both demonstrated to be less effective. Results also show that the impact of the different policy options varies as function of drought intensity, with water demand adaptation outperforming water supply adaptation when drought conditions become more severe.

Research on prediction of agricultural machinery total power based on grey model optimized by genetic algorithm

NASA Astrophysics Data System (ADS)

Xie, Yan; Li, Mu; Zhou, Jin; Zheng, Chang-zheng

2009-07-01

Agricultural machinery total power is an important index to reflex and evaluate the level of agricultural mechanization. It is the power source of agricultural production, and is the main factors to enhance the comprehensive agricultural production capacity expand production scale and increase the income of the farmers. Its demand is affected by natural, economic, technological and social and other "grey" factors. Therefore, grey system theory can be used to analyze the development of agricultural machinery total power. A method based on genetic algorithm optimizing grey modeling process is introduced in this paper. This method makes full use of the advantages of the grey prediction model and characteristics of genetic algorithm to find global optimization. So the prediction model is more accurate. According to data from a province, the GM (1, 1) model for predicting agricultural machinery total power was given based on the grey system theories and genetic algorithm. The result indicates that the model can be used as agricultural machinery total power an effective tool for prediction.

Sensitivity of an Integrated Mesoscale Atmosphere and Agriculture Land Modeling System (WRF/CMAQ-EPIC) to MODIS Vegetation and Lightning Assimilation

NASA Astrophysics Data System (ADS)

Ran, L.; Cooter, E. J.; Gilliam, R. C.; Foroutan, H.; Kang, D.; Appel, W.; Wong, D. C.; Pleim, J. E.; Benson, V.; Pouliot, G.

2017-12-01

The combined meteorology and air quality modeling system composed of the Weather Research and Forecast (WRF) model and Community Multiscale Air Quality (CMAQ) model is an important decision support tool that is used in research and regulatory decisions related to emissions, meteorology, climate, and chemical transport. The Environmental Policy Integrated Climate (EPIC) is a cropping model which has long been used in a range of applications related to soil erosion, crop productivity, climate change, and water quality around the world. We have integrated WRF/CMAQ with EPIC using the Fertilizer Emission Scenario Tool for CMAQ (FEST-C) to estimate daily soil N information with fertilization for CMAQ bi-directional ammonia flux modeling. Driven by the weather and N deposition from WRF/CMAQ, FEST-C EPIC simulations are conducted on 22 different agricultural production systems ranging from managed grass lands (e.g. hay and alfalfa) to crop lands (e.g. corn grain and soybean) with rainfed and irrigated information across any defined conterminous United States (U.S.) CMAQ domain and grid resolution. In recent years, this integrated system has been enhanced and applied in many different air quality and ecosystem assessment projects related to land-water-atmosphere interactions. These enhancements have advanced this system to become a valuable tool for integrated assessments of air, land and water quality in light of social drivers and human and ecological outcomes. This presentation will focus on evaluating the sensitivity of precipitation and N deposition in the integrated system to MODIS vegetation input and lightning assimilation and their impacts on agricultural production and fertilization. We will describe the integrated modeling system and evaluate simulated precipitation and N deposition along with other weather information (e.g. temperature, humidity) for 2011 over the conterminous U.S. at 12 km grids from a coupled WRF/CMAQ with MODIS and lightning assimilation

Resilience of Socio-Hydrological Systems in Canadian Prairies to Agricultural Drainage: Policy Analysis and Modelling Approach

NASA Astrophysics Data System (ADS)

Wheater, H. S.; Xu, L.; Gober, P.; Pomeroy, J. W.; Wong, J.

2017-12-01

Extensive agricultural drainage of lakes and wetlands in the Canadian Prairies has led to benefits for agricultural production, but has had a substantial influence on hydrological regimes and wetland extent. There is need for the potential impacts of current policy in changing the socio-hydrological resilience of prairie wetland basins in response to agricultural drainage to be examined. Whilst wetland drainage can increase agricultural productivity, it can also reduce stocks of natural capital and decrease ecosystem services, such as pollutant retention, habitat for waterfowls, carbon sequestration, and downstream flood attenuation. Effective policies that balance drainage benefits and negative externalities have to consider pricing. This is explored here using the Cold Regions Hydrological Model for hydrological simulations and the Inclusive Wealth approach for modelling in support of cost-benefit analysis. Inclusive wealth aggregates the value of natural, human, and technological assets used to produce social welfare. A shadow price, defined as the marginal change in social value for a marginal change in the current stock quantity, is used to valuate assets that contribute to social welfare. The shadow price of each asset is estimated by taking into account the social and economic benefits and external losses of wetland services caused by wetland drainage. The coupled model was applied to the Smith Creek Research Basin in south-eastern Saskatchewan, Canada where wetland drainage has caused major alterations of the hydrological regime including increased peak flows, discharge volumes and duration of streamflow. Changes in depressional storage in wetlands was used to calculate the corresponding changes of inclusive wealth over a 30-year period under the impacts from the limitation proposed in the Agricultural Water Management Strategy of Saskatchewan. The adjusted societal values of drainage demonstrate the dynamics between changes in hydrological conditions of

Applications of computational fluid dynamics (CFD) in the modelling and design of ventilation systems in the agricultural industry: a review.

PubMed

Norton, Tomás; Sun, Da-Wen; Grant, Jim; Fallon, Richard; Dodd, Vincent

2007-09-01

The application of computational fluid dynamics (CFD) in the agricultural industry is becoming ever more important. Over the years, the versatility, accuracy and user-friendliness offered by CFD has led to its increased take-up by the agricultural engineering community. Now CFD is regularly employed to solve environmental problems of greenhouses and animal production facilities. However, due to a combination of increased computer efficacy and advanced numerical techniques, the realism of these simulations has only been enhanced in recent years. This study provides a state-of-the-art review of CFD, its current applications in the design of ventilation systems for agricultural production systems, and the outstanding challenging issues that confront CFD modellers. The current status of greenhouse CFD modelling was found to be at a higher standard than that of animal housing, owing to the incorporation of user-defined routines that simulate crop biological responses as a function of local environmental conditions. Nevertheless, the most recent animal housing simulations have addressed this issue and in turn have become more physically realistic.

An inexact risk management model for agricultural land-use planning under water shortage

NASA Astrophysics Data System (ADS)

Li, Wei; Feng, Changchun; Dai, Chao; Li, Yongping; Li, Chunhui; Liu, Ming

2016-09-01

Water resources availability has a significant impact on agricultural land-use planning, especially in a water shortage area such as North China. The random nature of available water resources and other uncertainties in an agricultural system present risk for land-use planning and may lead to undesirable decisions or potential economic loss. In this study, an inexact risk management model (IRM) was developed for supporting agricultural land-use planning and risk analysis under water shortage. The IRM model was formulated through incorporating a conditional value-at-risk (CVaR) constraint into an inexact two-stage stochastic programming (ITSP) framework, and could be used to control uncertainties expressed as not only probability distributions but also as discrete intervals. The measure of risk about the second-stage penalty cost was incorporated into the model so that the trade-off between system benefit and extreme expected loss could be analyzed. The developed model was applied to a case study in the Zhangweinan River Basin, a typical agricultural region facing serious water shortage in North China. Solutions of the IRM model showed that the obtained first-stage land-use target values could be used to reflect decision-makers' opinions on the long-term development plan. The confidence level α and maximum acceptable risk loss β could be used to reflect decisionmakers' preference towards system benefit and risk control. The results indicated that the IRM model was useful for reflecting the decision-makers' attitudes toward risk aversion and could help seek cost-effective agricultural land-use planning strategies under complex uncertainties.

Managing adaptively for multifunctionality in agricultural systems.

PubMed

Hodbod, Jennifer; Barreteau, Olivier; Allen, Craig; Magda, Danièle

2016-12-01

The critical importance of agricultural systems for food security and as a dominant global landcover requires management that considers the full dimensions of system functions at appropriate scales, i.e. multifunctionality. We propose that adaptive management is the most suitable management approach for such goals, given its ability to reduce uncertainty over time and support multiple objectives within a system, for multiple actors. As such, adaptive management may be the most appropriate method for sustainably intensifying production whilst increasing the quantity and quality of ecosystem services. However, the current assessment of performance of agricultural systems doesn't reward ecosystem service provision. Therefore, we present an overview of the ecosystem functions agricultural systems should and could provide, coupled with a revised definition for assessing the performance of agricultural systems from a multifunctional perspective that, when all satisfied, would create adaptive agricultural systems that can increase production whilst ensuring food security and the quantity and quality of ecosystem services. The outcome of this high level of performance is the capacity to respond to multiple shocks without collapse, equity and triple bottom line sustainability. Through the assessment of case studies, we find that alternatives to industrialized agricultural systems incorporate more functional goals, but that there are mixed findings as to whether these goals translate into positive measurable outcomes. We suggest that an adaptive management perspective would support the implementation of a systematic analysis of the social, ecological and economic trade-offs occurring within such systems, particularly between ecosystem services and functions, in order to provide suitable and comparable assessments. We also identify indicators to monitor performance at multiple scales in agricultural systems which can be used within an adaptive management framework to increase

Managing adaptively for multifunctionality in agricultural systems

USGS Publications Warehouse

Hodbod, Jennifer; Barreteau, Olivier; Allen, Craig R.; Magda, Danièle

2016-01-01

The critical importance of agricultural systems for food security and as a dominant global landcover requires management that considers the full dimensions of system functions at appropriate scales, i.e. multifunctionality. We propose that adaptive management is the most suitable management approach for such goals, given its ability to reduce uncertainty over time and support multiple objectives within a system, for multiple actors. As such, adaptive management may be the most appropriate method for sustainably intensifying production whilst increasing the quantity and quality of ecosystem services. However, the current assessment of performance of agricultural systems doesn’t reward ecosystem service provision. Therefore, we present an overview of the ecosystem functions agricultural systems should and could provide, coupled with a revised definition for assessing the performance of agricultural systems from a multifunctional perspective that, when all satisfied, would create adaptive agricultural systems that can increase production whilst ensuring food security and the quantity and quality of ecosystem services. The outcome of this high level of performance is the capacity to respond to multiple shocks without collapse, equity and triple bottom line sustainability. Through the assessment of case studies, we find that alternatives to industrialized agricultural systems incorporate more functional goals, but that there are mixed findings as to whether these goals translate into positive measurable outcomes. We suggest that an adaptive management perspective would support the implementation of a systematic analysis of the social, ecological and economic trade-offs occurring within such systems, particularly between ecosystem services and functions, in order to provide suitable and comparable assessments. We also identify indicators to monitor performance at multiple scales in agricultural systems which can be used within an adaptive management framework to

AgIIS, Agricultural Irrigation Imaging System, design and application

NASA Astrophysics Data System (ADS)

Haberland, Julio Andres

Remote sensing is a tool that is increasingly used in agriculture for crop management purposes. A ground-based remote sensing data acquisition system was designed, constructed, and implemented to collect high spatial and temporal resolution data in irrigated agriculture. The system was composed of a rail that mounts on a linear move irrigation machine, and a small cart that runs back and forth on the rail. The cart was equipped with a sensors package that measured reflectance in four discrete wavelengths (550 nm, 660 nm, 720 nm, and 810 nm, all 10 nm bandwidth) and an infrared thermometer. A global positioning system and triggers on the rail indicated cart position. The data was postprocessed in order to generate vegetation maps, N and water status maps and other indices relevant for site-specific crop management. A geographic information system (GIS) was used to generate images of the field on any desired day. The system was named AgIIS (A&barbelow;gricultural I&barbelow;rrigation I&barbelow;maging S&barbelow;ystem). This ground based remote sensing acquisition system was developed at the Agricultural and Biosystems Engineering Department at the University of Arizona in conjunction with the U.S. Water Conservation Laboratory in Phoenix, as part of a cooperative study primarily funded by the Idaho National Environmental and Engineering Laboratory. A second phase of the study utilized data acquired with AgIIS during the 1999 cotton growing season to model petiole nitrate (PNO3 -) and total leaf N. A latin square experimental design with optimal and low water and optimal and low N was used to evaluate N status under water and no water stress conditions. Multivariable models were generated with neural networks (NN) and multilinear regression (MLR). Single variable models were generated from chlorophyll meter readings (SPAD) and from the Canopy Chlorophyll Content Index (CCCI). All models were evaluated against observed PNO3- and total leaf N levels. The NN models

Environmental modelling of use of treated organic waste on agricultural land: a comparison of existing models for life cycle assessment of waste systems.

PubMed

Hansen, Trine Lund; Christensen, Thomas Højlund; Schmidt, Sonia

2006-04-01

Modelling of environmental impacts from the application of treated organic municipal solid waste (MSW) in agriculture differs widely between different models for environmental assessment of waste systems. In this comparative study five models were examined concerning quantification and impact assessment of environmental effects from land application of treated organic MSW: DST (Decision Support Tool, USA), IWM (Integrated Waste Management, U.K.), THE IFEU PROJECT (Germany), ORWARE (ORganic WAste REsearch, Sweden) and EASEWASTE (Environmental Assessment of Solid Waste Systems and Technologies, Denmark). DST and IWM are life cycle inventory (LCI) models, thus not performing actual impact assessment. The DST model includes only one water emission (biological oxygen demand) from compost leaching in the results and IWM considers only air emissions from avoided production of commercial fertilizers. THE IFEU PROJECT, ORWARE and EASEWASTE are life cycle assessment (LCA) models containing more detailed land application modules. A case study estimating the environmental impacts from land application of 1 ton of composted source sorted organic household waste was performed to compare the results from the different models and investigate the origin of any difference in type or magnitude of the results. The contributions from the LCI models were limited and did not depend on waste composition or local agricultural conditions. The three LCA models use the same overall approach for quantifying the impacts of the system. However, due to slightly different assumptions, quantification methods and environmental impact assessment, the obtained results varied clearly between the models. Furthermore, local conditions (e.g. soil type, farm type, climate and legal regulation) and waste composition strongly influenced the results of the environmental assessment.

The Development Model Electronic Commerce of Regional Agriculture

NASA Astrophysics Data System (ADS)

Kang, Jun; Cai, Lecai; Li, Hongchan

With the developing of the agricultural information, it is inevitable trend of the development of agricultural electronic commercial affairs. On the basis of existing study on the development application model of e-commerce, combined with the character of the agricultural information, compared with the developing model from the theory and reality, a new development model electronic commerce of regional agriculture base on the government is put up, and such key issues as problems of the security applications, payment mode, sharing mechanisms, and legal protection are analyzed, etc. The among coordination mechanism of the region is discussed on, it is significance for regulating the development of agricultural e-commerce and promoting the regional economical development.

Modeling of pesticide emissions from agricultural ecosystems

NASA Astrophysics Data System (ADS)

Li, Rong

2012-04-01

Pesticides are applied to crops and soils to improve agricultural yields, but the use of pesticides has become highly regulated because of concerns about their adverse effects on human health and environment. Estimating pesticide emission rates from soils and crops is a key component for risk assessment for pesticide registration, identification of pesticide sources to the contamination of sensitive ecosystems, and appreciation of transport and fate of pesticides in the environment. Pesticide emission rates involve processes occurring in the soil, in the atmosphere, and on vegetation surfaces and are highly dependent on soil texture, agricultural practices, and meteorology, which vary significantly with location and/or time. To take all these factors into account for simulating pesticide emissions from large agricultural ecosystems, this study coupled a comprehensive meteorological model with a dynamic pesticide emission model. The combined model calculates hourly emission rates from both emission sources: current applications and soil residues resulting from historical use. The coupled modeling system is used to compute a gridded (36 × 36 km) hourly toxaphene emission inventory for North America for the year 2000 using a published U.S. toxaphene residue inventory and a Mexican toxaphene residue inventory developed using its historical application rates and a cropland inventory. To my knowledge, this is the first such hourly toxaphene emission inventory for North America. Results show that modeled emission rates have strong diurnal and seasonal variations at a given location and over the entire domain. The simulated total toxaphene emission from contaminated agricultural soils in North America in 2000 was about 255 t, which compares reasonably well to a published annual estimate. Most emissions occur in spring and summer, with domain-wide emission rates in April, May and, June of 36, 51, and 35 t/month, respectively. The spatial distribution of emissions depends

Comparative analysis of environmental impacts of agricultural production systems, agricultural input efficiency, and food choice

NASA Astrophysics Data System (ADS)

Clark, Michael; Tilman, David

2017-06-01

Global agricultural feeds over 7 billion people, but is also a leading cause of environmental degradation. Understanding how alternative agricultural production systems, agricultural input efficiency, and food choice drive environmental degradation is necessary for reducing agriculture’s environmental impacts. A meta-analysis of life cycle assessments that includes 742 agricultural systems and over 90 unique foods produced primarily in high-input systems shows that, per unit of food, organic systems require more land, cause more eutrophication, use less energy, but emit similar greenhouse gas emissions (GHGs) as conventional systems; that grass-fed beef requires more land and emits similar GHG emissions as grain-feed beef; and that low-input aquaculture and non-trawling fisheries have much lower GHG emissions than trawling fisheries. In addition, our analyses show that increasing agricultural input efficiency (the amount of food produced per input of fertilizer or feed) would have environmental benefits for both crop and livestock systems. Further, for all environmental indicators and nutritional units examined, plant-based foods have the lowest environmental impacts; eggs, dairy, pork, poultry, non-trawling fisheries, and non-recirculating aquaculture have intermediate impacts; and ruminant meat has impacts ∼100 times those of plant-based foods. Our analyses show that dietary shifts towards low-impact foods and increases in agricultural input use efficiency would offer larger environmental benefits than would switches from conventional agricultural systems to alternatives such as organic agriculture or grass-fed beef.

Representative Agricultural Pathways: A Trans-Disciplinary Approach to Agricultural Model Inter-comparison, Improvement, Climate Impact Assessment and Stakeholder Engagement

NASA Astrophysics Data System (ADS)

Antle, J. M.; Valdivia, R. O.; Claessens, L.; Nelson, G. C.; Rosenzweig, C.; Ruane, A. C.; Vervoort, J.

2013-12-01

The global change research community has recognized that new pathway and scenario concepts are needed to implement impact and vulnerability assessment that is logically consistent across local, regional and global scales. For impact and vulnerability assessment, new socio-economic pathway and scenario concepts are being developed. Representative Agricultural Pathways (RAPs) are designed to extend global pathways to provide the detail needed for global and regional assessment of agricultural systems. In addition, research by the Agricultural Model Inter-comparison and Improvement Project (AgMIP) shows that RAPs provide a powerful way to engage stakeholders in climate-related research throughout the research process and in communication of research results. RAPs are based on the integrated assessment framework developed by AgMIP. This framework shows that both bio-physical and socio-economic drivers are essential components of agricultural pathways and logically precede the definition of adaptation and mitigation scenarios that embody associated capabilities and challenges. This approach is based on a trans-disciplinary process for designing pathways and then translating them into parameter sets for bio-physical and economic models that are components of agricultural integrated assessments of climate impact, adaptation and mitigation. RAPs must be designed to be part of a logically consistent set of drivers and outcomes from global to regional and local. Global RAPs are designed to be consistent with higher-level global socio-economic pathways, but add key agricultural drivers such as agricultural growth trends that are not specified in more general pathways, as illustrated in a recent inter-comparison of global agricultural models. To create pathways at regional or local scales, further detail is needed. At this level, teams of scientists and other experts with knowledge of the agricultural systems and regions work together through a step-wise process. Experiences

Towards a New Generation of Agricultural System Data, Models and Knowledge Products: Design and Improvement

NASA Technical Reports Server (NTRS)

Antle, John M.; Basso, Bruno; Conant, Richard T.; Godfray, H. Charles J.; Jones, James W.; Herrero, Mario; Howitt, Richard E.; Keating, Brian A.; Munoz-Carpena, Rafael; Rosenzweig, Cynthia

2016-01-01

This paper presents ideas for a new generation of agricultural system models that could meet the needs of a growing community of end-users exemplified by a set of Use Cases. We envision new data, models and knowledge products that could accelerate the innovation process that is needed to achieve the goal of achieving sustainable local, regional and global food security. We identify desirable features for models, and describe some of the potential advances that we envisage for model components and their integration. We propose an implementation strategy that would link a "pre-competitive" space for model development to a "competitive space" for knowledge product development and through private-public partnerships for new data infrastructure. Specific model improvements would be based on further testing and evaluation of existing models, the development and testing of modular model components and integration, and linkages of model integration platforms to new data management and visualization tools.

Towards a new generation of agricultural system data, models and knowledge products: Design and improvement.

PubMed

Antle, John M; Basso, Bruno; Conant, Richard T; Godfray, H Charles J; Jones, James W; Herrero, Mario; Howitt, Richard E; Keating, Brian A; Munoz-Carpena, Rafael; Rosenzweig, Cynthia; Tittonell, Pablo; Wheeler, Tim R

2017-07-01

This paper presents ideas for a new generation of agricultural system models that could meet the needs of a growing community of end-users exemplified by a set of Use Cases. We envision new data, models and knowledge products that could accelerate the innovation process that is needed to achieve the goal of achieving sustainable local, regional and global food security. We identify desirable features for models, and describe some of the potential advances that we envisage for model components and their integration. We propose an implementation strategy that would link a "pre-competitive" space for model development to a "competitive space" for knowledge product development and through private-public partnerships for new data infrastructure. Specific model improvements would be based on further testing and evaluation of existing models, the development and testing of modular model components and integration, and linkages of model integration platforms to new data management and visualization tools.

A New Trans-Disciplinary Approach to Regional Integrated Assessment of Climate Impact and Adaptation in Agricultural Systems (Invited)

NASA Astrophysics Data System (ADS)

Antle, J. M.; Valdivia, R. O.; Jones, J.; Rosenzweig, C.; Ruane, A. C.

2013-12-01

This presentation provides an overview of the new methods developed by researchers in the Agricultural Model Inter-comparison and Improvement Project (AgMIP) for regional climate impact assessment and analysis of adaptation in agricultural systems. This approach represents a departure from approaches in the literature in several dimensions. First, the approach is based on the analysis of agricultural systems (not individual crops) and is inherently trans-disciplinary: it is based on a deep collaboration among a team of climate scientists, agricultural scientists and economists to design and implement regional integrated assessments of agricultural systems. Second, in contrast to previous approaches that have imposed future climate on models based on current socio-economic conditions, this approach combines bio-physical and economic models with a new type of pathway analysis (Representative Agricultural Pathways) to parameterize models consistent with a plausible future world in which climate change would be occurring. Third, adaptation packages for the agricultural systems in a region are designed by the research team with a level of detail that is useful to decision makers, such as research administrators and donors, who are making agricultural R&D investment decisions. The approach is illustrated with examples from AgMIP's projects currently being carried out in Africa and South Asia.

Crop modeling applications in agricultural water management

USGS Publications Warehouse

Kisekka, Isaya; DeJonge, Kendall C.; Ma, Liwang; Paz, Joel; Douglas-Mankin, Kyle R.

2017-01-01

This article introduces the fourteen articles that comprise the “Crop Modeling and Decision Support for Optimizing Use of Limited Water” collection. This collection was developed from a special session on crop modeling applications in agricultural water management held at the 2016 ASABE Annual International Meeting (AIM) in Orlando, Florida. In addition, other authors who were not able to attend the 2016 ASABE AIM were also invited to submit papers. The articles summarized in this introductory article demonstrate a wide array of applications in which crop models can be used to optimize agricultural water management. The following section titles indicate the topics covered in this collection: (1) evapotranspiration modeling (one article), (2) model development and parameterization (two articles), (3) application of crop models for irrigation scheduling (five articles), (4) coordinated water and nutrient management (one article), (5) soil water management (two articles), (6) risk assessment of water-limited irrigation management (one article), and (7) regional assessments of climate impact (two articles). Changing weather and climate, increasing population, and groundwater depletion will continue to stimulate innovations in agricultural water management, and crop models will play an important role in helping to optimize water use in agriculture.

An Interoperable, Agricultural Information System Based on Satellite Remote Sensing Data

NASA Technical Reports Server (NTRS)

Teng, William; Chiu, Long; Doraiswamy, Paul; Kempler, Steven; Liu, Zhong; Pham, Long; Rui, Hualan

2005-01-01

Monitoring global agricultural crop conditions during the growing season and estimating potential seasonal production are critically important for market development of US. agricultural products and for global food security. The Goddard Space Flight Center Earth Sciences Data and Information Services Center Distributed Active Archive Center (GES DISC DAAC) is developing an Agricultural Information System (AIS), evolved from an existing TRMM Online Visualization and Analysis System (TOVAS), which will operationally provide satellite remote sensing data products (e.g., rainfall) and services. The data products will include crop condition and yield prediction maps, generated from a crop growth model with satellite data inputs, in collaboration with the USDA Agricultural Research Service. The AIS will enable the remote, interoperable access to distributed data, by using the GrADS-DODS Server (GDS) and by being compliant with Open GIS Consortium standards. Users will be able to download individual files, perform interactive online analysis, as well as receive operational data flows. AIS outputs will be integrated into existing operational decision support systems for global crop monitoring, such as those of the USDA Foreign Agricultural Service and the U.N. World Food Program.

[Development of APSIM (agricultural production systems simulator) and its application].

PubMed

Shen, Yuying; Nan, Zhibiao; Bellotti, Bill; Robertson, Michael; Chen, Wen; Shao, Xinqing

2002-08-01

Soil-crop simulator model is an effective tool for providing decision on agricultural management. APSIM (Agricultural Production Systems Simulator) was developed to simulate the biophysical process in farming system, and particularly in the economic and ecological features of the systems under climatic risk. The current literatures revealed that APSIM could be applied in wide zone, including temperate continental, temperate maritime, sub-tropic and arid climate, and Mediterranean climates, with the soil type of clay, duplex soil, vertisol, silt sandy, silt loam and silt clay loam. More than 20 crops have been simulated well. APSIM is powerful on describing crop structure, crop sequence, yield prediction, and quality control as well as erosion estimation under different planting pattern.

A seasonal agricultural drought forecast system for food-insecure regions of East Africa

USGS Publications Warehouse

Shukla, Shraddhanand; McNally, Amy; Husak, Gregory; Funk, Christopher C.

2014-01-01

 The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. More accurate seasonal agricultural drought forecasts for this region can inform better water and agricultural management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts and floods. Here we describe the development and implementation of a seasonal agricultural drought forecast system for East Africa (EA) that provides decision support for the Famine Early Warning Systems Network's science team. We evaluate this forecast system for a region of equatorial EA (2° S to 8° N, and 36° to 46° E) for the March-April-May growing season. This domain encompasses one of the most food insecure, climatically variable and socio-economically vulnerable regions in EA, and potentially the world: this region has experienced famine as recently as 2011. To assess the agricultural outlook for the upcoming season our forecast system simulates soil moisture (SM) scenarios using the Variable Infiltration Capacity (VIC) hydrologic model forced with climate scenarios for the upcoming season. First, to show that the VIC model is appropriate for this application we forced the model with high quality atmospheric observations and found that the resulting SM values were consistent with the Food and Agriculture Organization's (FAO's) Water Requirement Satisfaction Index (WRSI), an index used by FEWS NET to estimate crop yields. Next we tested our forecasting system with hindcast runs (1993–2012). We found that initializing SM forecasts with start-of-season (5 March) SM conditions resulted in useful SM forecast skill (> 0.5 correlation) at 1-month, and in some cases at 3 month lead times. Similarly, when the forecast was initialized with mid-season (i.e. 5 April) SM conditions the skill until the end-of-season improved. This shows that early-season rainfall

A New Extension Model: The Memorial Middle School Agricultural Extension and Education Center

ERIC Educational Resources Information Center

Skelton, Peter; Seevers, Brenda

2010-01-01

The Memorial Middle School Agricultural Extension and Education Center is a new model for Extension. The center applies the Cooperative Extension Service System philosophy and mission to developing public education-based programs. Programming primarily serves middle school students and teachers through agricultural and natural resource science…

Mining Environmental Data from a Coupled Modelling System to Examine the Impact of Agricultural Management Practices on Groundwater and Air Quality

NASA Astrophysics Data System (ADS)

Garcia, V.; Cooter, E. J.; Hayes, B.; Murphy, M. S.; Bash, J. O.

2014-12-01

Excess nitrogen (N) resulting from current agricultural management practices can leach into sources of drinking water as nitrate, increasing human health risks of 'blue baby syndrome', hypertension, and some cancers and birth defects. Nitrogen also enters the atmosphere from land surfaces forming air pollution increasing human health risks of pulmonary and cardio-vascular disease. Characterizing and attributing nitrogen from agricultural management practices is difficult due to the complex and inter-related chemical and biological reactions associated with the nitrogen cascade. Coupled physical process-based models, however, present new opportunities to investigate relationships among environmental variables on new scales; particularly because they link emission sources with meteorology and the pollutant concentration ultimately found in the environment. In this study, we applied a coupled meteorology (NOAA-WRF), agricultural (USDA-EPIC) and air quality modelling system (EPA-CMAQ) to examine the impact of nitrogen inputs from corn production on ecosystem and human health and wellbeing. The coupled system accounts for the nitrogen flux between the land surface and air, and the soil surface and groundwater, providing a unique opportunity to examine the effect of management practices such as type and timing of fertilization, tilling and irrigation on both groundwater and air quality across the conterminous US. In conducting the study, we first determined expected relationships based on literature searches and then identified model variables as direct or surrogate variables. We performed extensive and methodical multi-variate regression modelling and variable selection to examine associations between agricultural management practices and environmental condition. We then applied the regression model to predict and contrast pollution levels between two corn production scenarios (Figure 1). Finally, we applied published health functions (e.g., spina bifida and cardio

Integrating seasonal climate prediction and agricultural models for insights into agricultural practice

PubMed Central

Hansen, James W

2005-01-01

Interest in integrating crop simulation models with dynamic seasonal climate forecast models is expanding in response to a perceived opportunity to add value to seasonal climate forecasts for agriculture. Integrated modelling may help to address some obstacles to effective agricultural use of climate information. First, modelling can address the mismatch between farmers' needs and available operational forecasts. Probabilistic crop yield forecasts are directly relevant to farmers' livelihood decisions and, at a different scale, to early warning and market applications. Second, credible ex ante evidence of livelihood benefits, using integrated climate–crop–economic modelling in a value-of-information framework, may assist in the challenge of obtaining institutional, financial and political support; and inform targeting for greatest benefit. Third, integrated modelling can reduce the risk and learning time associated with adaptation and adoption, and related uncertainty on the part of advisors and advocates. It can provide insights to advisors, and enhance site-specific interpretation of recommendations when driven by spatial data. Model-based ‘discussion support systems’ contribute to learning and farmer–researcher dialogue. Integrated climate–crop modelling may play a genuine, but limited role in efforts to support climate risk management in agriculture, but only if they are used appropriately, with understanding of their capabilities and limitations, and with cautious evaluation of model predictions and of the insights that arises from model-based decision analysis. PMID:16433092

Nitrate in aquifers beneath agricultural systems

USGS Publications Warehouse

Burkart, M.R.; Stoner, J.D.

2002-01-01

Research from several regions of the world provides spatially anecdotal evidence to hypothesize which hydrologic and agricultural factors contribute to groundwater vulnerability to nitrate contamination. Analysis of nationally consistent measurements from the U.S. Geological Survey's NAWOA program confirms these hypotheses for a substantial range of agricultural systems. Shallow unconfined aquifers are most susceptible to nitrate contamination associated with agricultural systems. Alluvial and other unconsolidated aquifers are the most vulnerable and shallow carbonate aquifers provide a substantial but smaller contamination risk. Where any of these aquifers are overlain by permeable soils the risk of contamination is larger. Irrigated systems can compound this vulnerability by increasing leaching facilitated by additional recharge and additional nutrient applications. The agricultural system of corn, soybeans, and hogs produced significantly larger concentrations of groundwater nitrate than all other agricultural systems, although mean nitrate concentrations in counties with dairy, poultry, cattle and grains, and horticulture systems were similar. If trends in the relation between increased fertilizer use and groundwater nitrate in the United States are repeated in other regions of the world, Asia may experience increasing problems because of recent increases in fertilizer use. Groundwater monitoring in Western and Eastern Europe as well as Russia over the next decade may provide data to determine if the trend in increased nitrate contamination can be reversed. If the concentrated livestock trend in the United States is global, it may be accompanied by increasing nitrogen contamination in groundwater. Concentrated livestock provide both point sources in the confinement area and intense non-point sources as fields close to facilities are used for manure disposal. Regions where irrigated cropland is expanding, such as in Asia, may experience the greatest impact of

Higher Agricultural Universities Serve for "Sannong" by Offering English Human Resources Support System

ERIC Educational Resources Information Center

Yuan, Youqin; Cheng, Baole

2008-01-01

This paper puts higher agricultural English education how to serve for "Sannong" construction as priority, combining the actual market demand, based on teaching reform in the past few years, tries to explore English nurturing model and curriculum system for real delivery the agriculture-related qualified foreign language professionals.…

Analyzing Agricultural Technology Systems: A Research Report.

ERIC Educational Resources Information Center

Swanson, Burton E.

The International Program for Agricultural Knowledge Systems (INTERPAKS) research team is developing a descriptive and analytic framework to examine and assess agricultural technology systems. The first part of the framework is an inductive methodology that organizes data collection and orders data for comparison between countries. It requires and…

Benchmarking a Soil Moisture Data Assimilation System for Agricultural Drought Monitoring

NASA Technical Reports Server (NTRS)

Hun, Eunjin; Crow, Wade T.; Holmes, Thomas; Bolten, John

2014-01-01

Despite considerable interest in the application of land surface data assimilation systems (LDAS) for agricultural drought applications, relatively little is known about the large-scale performance of such systems and, thus, the optimal methodological approach for implementing them. To address this need, this paper evaluates an LDAS for agricultural drought monitoring by benchmarking individual components of the system (i.e., a satellite soil moisture retrieval algorithm, a soil water balance model and a sequential data assimilation filter) against a series of linear models which perform the same function (i.e., have the same basic inputoutput structure) as the full system component. Benchmarking is based on the calculation of the lagged rank cross-correlation between the normalized difference vegetation index (NDVI) and soil moisture estimates acquired for various components of the system. Lagged soil moistureNDVI correlations obtained using individual LDAS components versus their linear analogs reveal the degree to which non-linearities andor complexities contained within each component actually contribute to the performance of the LDAS system as a whole. Here, a particular system based on surface soil moisture retrievals from the Land Parameter Retrieval Model (LPRM), a two-layer Palmer soil water balance model and an Ensemble Kalman filter (EnKF) is benchmarked. Results suggest significant room for improvement in each component of the system.

The forest and agricultural sector optimization model (FASOM): model structure and policy applications.

Treesearch

Darius M. Adams; Ralph J. Alig; J.M. Callaway; Bruce A. McCarl; Steven M. Winnett

1996-01-01

The Forest and Agricultural Sector Optimization Model (FASOM) is a dynamic, nonlinear programming model of the forest and agricultural sectors in the United States. The FASOM model initially was developed to evaluate welfare and market impacts of alternative policies for sequestering carbon in trees but also has been applied to a wider range of forest and agricultural...

Representative Agricultural Pathways and Climate Impact Assessment for Pacific Northwest Agricultural Systems

NASA Astrophysics Data System (ADS)

MU, J.; Antle, J. M.; Zhang, H.; Capalbo, S. M.; Eigenbrode, S.; Kruger, C.; Stockle, C.; Wolfhorst, J. D.

2013-12-01

Representative Agricultural Pathways (RAPs) are projections of plausible future biophysical and socio-economic conditions used to carry out climate impact assessments for agriculture. The development of RAPs iss motivated by the fact that the various global and regional models used for agricultural climate change impact assessment have been implemented with individualized scenarios using various data and model structures, often without transparent documentation or public availability. These practices have hampered attempts at model inter-comparison, improvement, and synthesis of model results across studies. This paper aims to (1) present RAPs developed for the principal wheat-producing region of the Pacific Northwest, and to (2) combine these RAPs with downscaled climate data, crop model simulations and economic model simulations to assess climate change impacts on winter wheat production and farm income. This research was carried out as part of a project funded by the USDA known as the Regional Approaches to Climate Change in the Pacific Northwest (REACCH). The REACCH study region encompasses the major winter wheat production area in Pacific Northwest and preliminary research shows that farmers producing winter wheat could benefit from future climate change. However, the future world is uncertain in many dimensions, including commodity and input prices, production technology, and policies, as well as increased probability of disturbances (pests and diseases) associated with a changing climate. Many of these factors cannot be modeled, so they are represented in the regional RAPS. The regional RAPS are linked to global agricultural and shared social-economic pathways, and used along with climate change projections to simulate future outcomes for the wheat-based farms in the REACCH region.

Calibration of an agricultural-hydrological model (RZWQM2) using surrogate global optimization

DOE PAGES

Xi, Maolong; Lu, Dan; Gui, Dongwei; ...

2016-11-27

Robust calibration of an agricultural-hydrological model is critical for simulating crop yield and water quality and making reasonable agricultural management. However, calibration of the agricultural-hydrological system models is challenging because of model complexity, the existence of strong parameter correlation, and significant computational requirements. Therefore, only a limited number of simulations can be allowed in any attempt to find a near-optimal solution within an affordable time, which greatly restricts the successful application of the model. The goal of this study is to locate the optimal solution of the Root Zone Water Quality Model (RZWQM2) given a limited simulation time, so asmore » to improve the model simulation and help make rational and effective agricultural-hydrological decisions. To this end, we propose a computationally efficient global optimization procedure using sparse-grid based surrogates. We first used advanced sparse grid (SG) interpolation to construct a surrogate system of the actual RZWQM2, and then we calibrate the surrogate model using the global optimization algorithm, Quantum-behaved Particle Swarm Optimization (QPSO). As the surrogate model is a polynomial with fast evaluation, it can be efficiently evaluated with a sufficiently large number of times during the optimization, which facilitates the global search. We calibrate seven model parameters against five years of yield, drain flow, and NO 3-N loss data from a subsurface-drained corn-soybean field in Iowa. Results indicate that an accurate surrogate model can be created for the RZWQM2 with a relatively small number of SG points (i.e., RZWQM2 runs). Compared to the conventional QPSO algorithm, our surrogate-based optimization method can achieve a smaller objective function value and better calibration performance using a fewer number of expensive RZWQM2 executions, which greatly improves computational efficiency.« less

Calibration of an agricultural-hydrological model (RZWQM2) using surrogate global optimization

NASA Astrophysics Data System (ADS)

Xi, Maolong; Lu, Dan; Gui, Dongwei; Qi, Zhiming; Zhang, Guannan

2017-01-01

Robust calibration of an agricultural-hydrological model is critical for simulating crop yield and water quality and making reasonable agricultural management. However, calibration of the agricultural-hydrological system models is challenging because of model complexity, the existence of strong parameter correlation, and significant computational requirements. Therefore, only a limited number of simulations can be allowed in any attempt to find a near-optimal solution within an affordable time, which greatly restricts the successful application of the model. The goal of this study is to locate the optimal solution of the Root Zone Water Quality Model (RZWQM2) given a limited simulation time, so as to improve the model simulation and help make rational and effective agricultural-hydrological decisions. To this end, we propose a computationally efficient global optimization procedure using sparse-grid based surrogates. We first used advanced sparse grid (SG) interpolation to construct a surrogate system of the actual RZWQM2, and then we calibrate the surrogate model using the global optimization algorithm, Quantum-behaved Particle Swarm Optimization (QPSO). As the surrogate model is a polynomial with fast evaluation, it can be efficiently evaluated with a sufficiently large number of times during the optimization, which facilitates the global search. We calibrate seven model parameters against five years of yield, drain flow, and NO3-N loss data from a subsurface-drained corn-soybean field in Iowa. Results indicate that an accurate surrogate model can be created for the RZWQM2 with a relatively small number of SG points (i.e., RZWQM2 runs). Compared to the conventional QPSO algorithm, our surrogate-based optimization method can achieve a smaller objective function value and better calibration performance using a fewer number of expensive RZWQM2 executions, which greatly improves computational efficiency.

Calibration of an agricultural-hydrological model (RZWQM2) using surrogate global optimization

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

Xi, Maolong; Lu, Dan; Gui, Dongwei

Robust calibration of an agricultural-hydrological model is critical for simulating crop yield and water quality and making reasonable agricultural management. However, calibration of the agricultural-hydrological system models is challenging because of model complexity, the existence of strong parameter correlation, and significant computational requirements. Therefore, only a limited number of simulations can be allowed in any attempt to find a near-optimal solution within an affordable time, which greatly restricts the successful application of the model. The goal of this study is to locate the optimal solution of the Root Zone Water Quality Model (RZWQM2) given a limited simulation time, so asmore » to improve the model simulation and help make rational and effective agricultural-hydrological decisions. To this end, we propose a computationally efficient global optimization procedure using sparse-grid based surrogates. We first used advanced sparse grid (SG) interpolation to construct a surrogate system of the actual RZWQM2, and then we calibrate the surrogate model using the global optimization algorithm, Quantum-behaved Particle Swarm Optimization (QPSO). As the surrogate model is a polynomial with fast evaluation, it can be efficiently evaluated with a sufficiently large number of times during the optimization, which facilitates the global search. We calibrate seven model parameters against five years of yield, drain flow, and NO 3-N loss data from a subsurface-drained corn-soybean field in Iowa. Results indicate that an accurate surrogate model can be created for the RZWQM2 with a relatively small number of SG points (i.e., RZWQM2 runs). Compared to the conventional QPSO algorithm, our surrogate-based optimization method can achieve a smaller objective function value and better calibration performance using a fewer number of expensive RZWQM2 executions, which greatly improves computational efficiency.« less

Assessing the agricultural costs of climate change: Combining results from crop and economic models

NASA Astrophysics Data System (ADS)

Howitt, R. E.

2016-12-01

Any perturbation to a resource system used by humans elicits both technical and behavioral changes. For agricultural production, economic criteria and their associated models are usually good predictors of human behavior in agricultural production. Estimation of the agricultural costs of climate change requires careful downscaling of global climate models to the level of agricultural regions. Plant growth models for the dominant crops are required to accurately show the full range of trade-offs and adaptation mechanisms needed to minimize the cost of climate change. Faced with the shifts in the fundamental resource base of agriculture, human behavior can either exacerbate or offset the impact of climate change on agriculture. In addition, agriculture can be an important source of increased carbon sequestration. However the effectiveness and timing of this sequestration depends on agricultural practices and farmer behavior. Plant growth models and economic models have been shown to interact in two broad fashions. First there is the direct embedding of a parametric representation plant growth simulations in the economic model production function. A second and more general approach is to have plant growth and crop process models interact with economic models as they are simulated. The development of more general wrapper programs that transfer information between models rapidly and efficiently will encourage this approach. However, this method does introduce complications in terms of matching up disparate scales both in time and space between models. Another characteristic behavioral response of agricultural production is the distinction between the intensive margin which considers the quantity of resource, for example fertilizer, used for a given crop, and the extensive margin of adjustment that measures how farmers will adjust their crop proportions in response to climate change. Ideally economic models will measure the response to both these margins of adjustment

An inexact log-normal distribution-based stochastic chance-constrained model for agricultural water quality management

NASA Astrophysics Data System (ADS)

Wang, Yu; Fan, Jie; Xu, Ye; Sun, Wei; Chen, Dong

2018-05-01

In this study, an inexact log-normal-based stochastic chance-constrained programming model was developed for solving the non-point source pollution issues caused by agricultural activities. Compared to the general stochastic chance-constrained programming model, the main advantage of the proposed model is that it allows random variables to be expressed as a log-normal distribution, rather than a general normal distribution. Possible deviations in solutions caused by irrational parameter assumptions were avoided. The agricultural system management in the Erhai Lake watershed was used as a case study, where critical system factors, including rainfall and runoff amounts, show characteristics of a log-normal distribution. Several interval solutions were obtained under different constraint-satisfaction levels, which were useful in evaluating the trade-off between system economy and reliability. The applied results show that the proposed model could help decision makers to design optimal production patterns under complex uncertainties. The successful application of this model is expected to provide a good example for agricultural management in many other watersheds.

Evolution Model and Simulation of Profit Model of Agricultural Products Logistics Financing

NASA Astrophysics Data System (ADS)

Yang, Bo; Wu, Yan

2018-03-01

Agricultural products logistics financial warehousing business mainly involves agricultural production and processing enterprises, third-party logistics enterprises and financial institutions tripartite, to enable the three parties to achieve win-win situation, the article first gives the replication dynamics and evolutionary stability strategy between the three parties in business participation, and then use NetLogo simulation platform, using the overall modeling and simulation method of Multi-Agent, established the evolutionary game simulation model, and run the model under different revenue parameters, finally, analyzed the simulation results. To achieve the agricultural products logistics financial financing warehouse business to participate in tripartite mutually beneficial win-win situation, thus promoting the smooth flow of agricultural products logistics business.

Modeling the effect of social networks on adoption of multifunctional agriculture.

PubMed

Manson, Steven M; Jordan, Nicholas R; Nelson, Kristen C; Brummel, Rachel F

2016-01-01

Rotational grazing (RG) has attracted much attention as a cornerstone of multifunctional agriculture (MFA) in animal systems, potentially capable of producing a range of goods and services of value to diverse stakeholders in agricultural landscapes and rural communities, as well as broader societal benefits. Despite these benefits, global adoption of MFA has been uneven, with some places seeing active participation, while others have seen limited growth. Recent conceptual models of MFA emphasize the potential for bottom-up processes and linkages among social and environmental systems to promote multifunctionality. Social networks are critical to these explanations but how and why these networks matter is unclear. We investigated fifty-three farms in three states in the United States (New York, Wisconsin, Pennsylvania) and developed a stylized model of social networks and systemic change in the dairy farming system. We found that social networks are important to RG adoption but their impact is contingent on social and spatial factors. Effects of networks on farmer decision making differ according to whether they comprise weak-tie relationships, which bridge across disparate people and organizations, or strong-tie relationships, which are shared by groups in which members are well known to one another. RG adoption is also dependent on features of the social landscape including the number of dairy households, the probability of neighboring farmers sharing strong ties, and the role of space in how networks are formed. The model replicates features of real-world adoption of RG practices in the Eastern US and illustrates pathways toward greater multifunctionality in the dairy landscape. Such models are likely to be of heuristic value in network-focused strategies for agricultural development.

Modeling the effect of social networks on adoption of multifunctional agriculture

PubMed Central

Manson, Steven M.; Jordan, Nicholas R.; Nelson, Kristen C.; Brummel, Rachel F.

2014-01-01

Rotational grazing (RG) has attracted much attention as a cornerstone of multifunctional agriculture (MFA) in animal systems, potentially capable of producing a range of goods and services of value to diverse stakeholders in agricultural landscapes and rural communities, as well as broader societal benefits. Despite these benefits, global adoption of MFA has been uneven, with some places seeing active participation, while others have seen limited growth. Recent conceptual models of MFA emphasize the potential for bottom-up processes and linkages among social and environmental systems to promote multifunctionality. Social networks are critical to these explanations but how and why these networks matter is unclear. We investigated fifty-three farms in three states in the United States (New York, Wisconsin, Pennsylvania) and developed a stylized model of social networks and systemic change in the dairy farming system. We found that social networks are important to RG adoption but their impact is contingent on social and spatial factors. Effects of networks on farmer decision making differ according to whether they comprise weak-tie relationships, which bridge across disparate people and organizations, or strong-tie relationships, which are shared by groups in which members are well known to one another. RG adoption is also dependent on features of the social landscape including the number of dairy households, the probability of neighboring farmers sharing strong ties, and the role of space in how networks are formed. The model replicates features of real-world adoption of RG practices in the Eastern US and illustrates pathways toward greater multifunctionality in the dairy landscape. Such models are likely to be of heuristic value in network-focused strategies for agricultural development. PMID:26744579

Agricultural livelihoods in coastal Bangladesh under climate and environmental change--a model framework.

PubMed

Lázár, Attila N; Clarke, Derek; Adams, Helen; Akanda, Abdur Razzaque; Szabo, Sylvia; Nicholls, Robert J; Matthews, Zoe; Begum, Dilruba; Saleh, Abul Fazal M; Abedin, Md Anwarul; Payo, Andres; Streatfield, Peter Kim; Hutton, Craig; Mondal, M Shahjahan; Moslehuddin, Abu Zofar Md

2015-06-01

Coastal Bangladesh experiences significant poverty and hazards today and is highly vulnerable to climate and environmental change over the coming decades. Coastal stakeholders are demanding information to assist in the decision making processes, including simulation models to explore how different interventions, under different plausible future socio-economic and environmental scenarios, could alleviate environmental risks and promote development. Many existing simulation models neglect the complex interdependencies between the socio-economic and environmental system of coastal Bangladesh. Here an integrated approach has been proposed to develop a simulation model to support agriculture and poverty-based analysis and decision-making in coastal Bangladesh. In particular, we show how a simulation model of farmer's livelihoods at the household level can be achieved. An extended version of the FAO's CROPWAT agriculture model has been integrated with a downscaled regional demography model to simulate net agriculture profit. This is used together with a household income-expenses balance and a loans logical tree to simulate the evolution of food security indicators and poverty levels. Modelling identifies salinity and temperature stress as limiting factors to crop productivity and fertilisation due to atmospheric carbon dioxide concentrations as a reinforcing factor. The crop simulation results compare well with expected outcomes but also reveal some unexpected behaviours. For example, under current model assumptions, temperature is more important than salinity for crop production. The agriculture-based livelihood and poverty simulations highlight the critical significance of debt through informal and formal loans set at such levels as to persistently undermine the well-being of agriculture-dependent households. Simulations also indicate that progressive approaches to agriculture (i.e. diversification) might not provide the clear economic benefit from the perspective of

Using Multispectral Analysis in GIS to Model the Potential for Urban Agriculture in Philadelphia

NASA Astrophysics Data System (ADS)

Dmochowski, J. E.; Cooper, W. P.

2010-12-01

In the context of growing concerns about the international food system’s dependence on fossil fuels, soil degradation, climate change, and other diverse issues, a number of initiatives have arisen to develop and implement sustainable agricultural practices. Many seeking to reform the food system look to urban agriculture as a means to create localized, sustainable agricultural production, while simultaneously providing a locus for community building, encouraging better nutrition, and promoting the rebirth of depressed urban areas. The actual impact of such system, however, is not well understood, and many critics of urban agriculture regard its implementation as impractical and unrealistic. This project uses multispectral imagery from United States Department of Agriculture’s National Agricultural Imagery Program with a one-meter resolution to quantify the potential for increasing urban agriculture in an effort to create a sustainable food system in Philadelphia. Color infrared images are classified with a minimum distance algorithm in ArcGIS to generate baseline data on vegetative cover in Philadelphia. These data, in addition to mapping on the ground, form the basis of a model of land suitable for conversion to agriculture in Philadelphia, which will help address questions related to potential yields, workforce, and energy requirements. This research will help city planners, entrepreneurs, community leaders, and citizens understand how urban agriculture can contribute to creating a sustainable food system in a major North American city.

Evaluating the APEX model for simulating streamflow and water quality on ten agricultural watersheds in the U.S.

USDA-ARS?s Scientific Manuscript database

Simulation models are increasingly used to assess water quality constituent losses from agricultural systems. Mis-use often gives irrelevant or erroneous answers. The Agricultural Policy Environmental Extender (APEX) model is emerging as one of the premier modeling tools for fields, farms, and agr...

Agricultural biodiversity, social-ecological systems and sustainable diets.

PubMed

Allen, Thomas; Prosperi, Paolo; Cogill, Bruce; Flichman, Guillermo

2014-11-01

The stark observation of the co-existence of undernourishment, nutrient deficiencies and overweight and obesity, the triple burden of malnutrition, is inviting us to reconsider health and nutrition as the primary goal and final endpoint of food systems. Agriculture and the food industry have made remarkable advances in the past decades. However, their development has not entirely fulfilled health and nutritional needs, and moreover, they have generated substantial collateral losses in agricultural biodiversity. Simultaneously, several regions are experiencing unprecedented weather events caused by climate change and habitat depletion, in turn putting at risk global food and nutrition security. This coincidence of food crises with increasing environmental degradation suggests an urgent need for novel analyses and new paradigms. The sustainable diets concept proposes a research and policy agenda that strives towards a sustainable use of human and natural resources for food and nutrition security, highlighting the preeminent role of consumers in defining sustainable options and the importance of biodiversity in nutrition. Food systems act as complex social-ecological systems, involving multiple interactions between human and natural components. Nutritional patterns and environment structure are interconnected in a mutual dynamic of changes. The systemic nature of these interactions calls for multidimensional approaches and integrated assessment and simulation tools to guide change. This paper proposes a review and conceptual modelling framework that articulate the synergies and tradeoffs between dietary diversity, widely recognised as key for healthy diets, and agricultural biodiversity and associated ecosystem functions, crucial resilience factors to climate and global changes.

An enhanced export coefficient based optimization model for supporting agricultural nonpoint source pollution mitigation under uncertainty.

PubMed

Rong, Qiangqiang; Cai, Yanpeng; Chen, Bing; Yue, Wencong; Yin, Xin'an; Tan, Qian

2017-02-15

In this research, an export coefficient based dual inexact two-stage stochastic credibility constrained programming (ECDITSCCP) model was developed through integrating an improved export coefficient model (ECM), interval linear programming (ILP), fuzzy credibility constrained programming (FCCP) and a fuzzy expected value equation within a general two stage programming (TSP) framework. The proposed ECDITSCCP model can effectively address multiple uncertainties expressed as random variables, fuzzy numbers, pure and dual intervals. Also, the model can provide a direct linkage between pre-regulated management policies and the associated economic implications. Moreover, the solutions under multiple credibility levels can be obtained for providing potential decision alternatives for decision makers. The proposed model was then applied to identify optimal land use structures for agricultural NPS pollution mitigation in a representative upstream subcatchment of the Miyun Reservoir watershed in north China. Optimal solutions of the model were successfully obtained, indicating desired land use patterns and nutrient discharge schemes to get a maximum agricultural system benefits under a limited discharge permit. Also, numerous results under multiple credibility levels could provide policy makers with several options, which could help get an appropriate balance between system benefits and pollution mitigation. The developed ECDITSCCP model can be effectively applied to addressing the uncertain information in agricultural systems and shows great applicability to the land use adjustment for agricultural NPS pollution mitigation. Copyright © 2016 Elsevier B.V. All rights reserved.

Integrating NASA Earth Science Enterprise (ESE) Data Into Global Agricultural Decision Support Systems

NASA Astrophysics Data System (ADS)

Teng, W.; Kempler, S.; Chiu, L.; Doraiswamy, P.; Liu, Z.; Milich, L.; Tetrault, R.

2003-12-01

Monitoring global agricultural crop conditions during the growing season and estimating potential seasonal production are critically important for market development of U.S. agricultural products and for global food security. Two major operational users of satellite remote sensing for global crop monitoring are the USDA Foreign Agricultural Service (FAS) and the U.N. World Food Programme (WFP). The primary goal of FAS is to improve foreign market access for U.S. agricultural products. The WFP uses food to meet emergency needs and to support economic and social development. Both use global agricultural decision support systems that can integrate and synthesize a variety of data sources to provide accurate and timely information on global crop conditions. The Goddard Space Flight Center Earth Sciences Distributed Active Archive Center (GES DAAC) has begun a project to provide operational solutions to FAS and WFP, by fully leveraging results from previous work, as well as from existing capabilities of the users. The GES DAAC has effectively used its recently developed prototype TRMM Online Visualization and Analysis System (TOVAS) to provide ESE data and information to the WFP for its agricultural drought monitoring efforts. This prototype system will be evolved into an Agricultural Information System (AIS), which will operationally provide ESE and other data products (e.g., rainfall, land productivity) and services, to be integrated into and thus enhance the existing GIS-based, decision support systems of FAS and WFP. Agriculture-oriented, ESE data products (e.g., MODIS-based, crop condition assessment product; TRMM derived, drought index product) will be input to a crop growth model in collaboration with the USDA Agricultural Research Service, to generate crop condition and yield prediction maps. The AIS will have the capability for remotely accessing distributed data, by being compliant with community-based interoperability standards, enabling easy access to

Nitrate in aquifers beneath agricultural systems

USGS Publications Warehouse

Burkart, M.R.; Stoner, J.D.; ,

2007-01-01

Research from several regions of the world provides spatially anecdotal evidence to hypothesize which hydrologic and agricultural factors contribute to groundwater vulnerability to nitrate contamination. Analysis of nationally consistent measurements from the U.S. Geological Survey's NAWQA program confirms these hypotheses for a substantial range of agricultural systems. Shallow unconfined aquifers are most susceptible to nitrate contamination associated with agricultural systems. Alluvial and other unconsolidated aquifers are the most vulnerable and also shallow carbonate aquifers that provide a substantial but smaller contamination risk. Where any of these aquifers are overlain by permeable soils the risk of contamination is larger. Irrigated systems can compound this vulnerability by increasing leaching facilitated by additional recharge and additional nutrient applications. The system of corn, soybean, and hogs produced significantly larger concentrations of groundwater nitrate than all other agricultural systems because this system imports the largest amount of N-fertilizer per unit production area. Mean nitrate under dairy, poultry, horticulture, and cattle and grains systems were similar. If trends in the relation between increased fertilizer use and groundwater nitrate in the United States are repeated in other regions of the world, Asia may experience increasing problems because of recent increases in fertilizer use. Groundwater monitoring in Western and Eastern Europe as well as Russia over the next decade may provide data to determine if the trend in increased nitrate contamination can be reversed. If the concentrated livestock trend in the United States is global, it may be accompanied by increasing nitrogen contamination in groundwater. Concentrated livestock provide both point sources in the confinement area and intense non-point sources as fields close to facilities are used for manure disposal. Regions where irrigated cropland is expanding, such as

An integrated Modelling framework to monitor and predict trends of agricultural management (iMSoil)

NASA Astrophysics Data System (ADS)

Keller, Armin; Della Peruta, Raneiro; Schaepman, Michael; Gomez, Marta; Mann, Stefan; Schulin, Rainer

2014-05-01

Agricultural systems lay at the interface between natural ecosystems and the anthroposphere. Various drivers induce pressures on the agricultural systems, leading to changes in farming practice. The limitation of available land and the socio-economic drivers are likely to result in further intensification of agricultural land management, with implications on fertilization practices, soil and pest management, as well as crop and livestock production. In order to steer the development into desired directions, tools are required by which the effects of these pressures on agricultural management and resulting impacts on soil functioning can be detected as early as possible, future scenarios predicted and suitable management options and policies defined. In this context, the use of integrated models can play a major role in providing long-term predictions of soil quality and assessing the sustainability of agricultural soil management. Significant progress has been made in this field over the last decades. Some of these integrated modelling frameworks include biophysical parameters, but often the inherent characteristics and detailed processes of the soil system have been very simplified. The development of such tools has been hampered in the past by a lack of spatially explicit soil and land management information at regional scale. The iMSoil project, funded by the Swiss National Science Foundation in the national research programme NRP68 "soil as a resource" (www.nrp68.ch) aims at developing and implementing an integrated modeling framework (IMF) which can overcome the limitations mentioned above, by combining socio-economic, agricultural land management, and biophysical models, in order to predict the long-term impacts of different socio-economic scenarios on the soil quality. In our presentation we briefly outline the approach that is based on an interdisciplinary modular framework that builds on already existing monitoring tools and model components that are

Groundwater economics: An object-oriented foundation for integrated studies of irrigated agricultural systems

NASA Astrophysics Data System (ADS)

Steward, David R.; Peterson, Jeffrey M.; Yang, Xiaoying; Bulatewicz, Tom; Herrera-Rodriguez, Mauricio; Mao, Dazhi; Hendricks, Nathan

2009-05-01

An integrated foundation is presented to study the impacts of external forcings on irrigated agricultural systems. Individually, models are presented that simulate groundwater hydrogeology and econometric farm level crop choices and irrigated water use. The natural association between groundwater wells and agricultural parcels is employed to couple these models using geographic information science technology and open modeling interface protocols. This approach is used to study the collective action problem of the common pool. Three different policies (existing, regulation, and incentive based) are studied in the semiarid grasslands overlying the Ogallala Aquifer in the central United States. Results show that while regulation using the prior appropriation doctrine and incentives using a water buy-back program may each achieve the same level of water savings across the study region, each policy has a different impact on spatial patterns of groundwater declines and farm level economic activity. This represents the first time that groundwater and econometric models of irrigated agriculture have been integrated at the well-parcel level and provides methods for scientific investigation of this coupled natural-human system. Results are useful for science to inform decision making and public policy debate.

A socio-hydrologic model of coupled water-agriculture dynamics with emphasis on farm size.

NASA Astrophysics Data System (ADS)

Brugger, D. R.; Maneta, M. P.

2015-12-01

Agricultural land cover dynamics in the U.S. are dominated by two trends: 1) total agricultural land is decreasing and 2) average farm size is increasing. These trends have important implications for the future of water resources because 1) growing more food on less land is due in large part to increased groundwater withdrawal and 2) larger farms can better afford both more efficient irrigation and more groundwater access. However, these large-scale trends are due to individual farm operators responding to many factors including climate, economics, and policy. It is therefore difficult to incorporate the trends into watershed-scale hydrologic models. Traditional scenario-based approaches are valuable for many applications, but there is typically no feedback between the hydrologic model and the agricultural dynamics and so limited insight is gained into the how agriculture co-evolves with water resources. We present a socio-hydrologic model that couples simplified hydrologic and agricultural economic dynamics, accounting for many factors that depend on farm size such as irrigation efficiency and returns to scale. We introduce an "economic memory" (EM) state variable that is driven by agricultural revenue and affects whether farms are sold when land market values exceed expected returns from agriculture. The model uses a Generalized Mixture Model of Gaussians to approximate the distribution of farm sizes in a study area, effectively lumping farms into "small," "medium," and "large" groups that have independent parameterizations. We apply the model in a semi-arid watershed in the upper Columbia River Basin, calibrating to data on streamflow, total agricultural land cover, and farm size distribution. The model is used to investigate the sensitivity of the coupled system to various hydrologic and economic scenarios such as increasing market value of land, reduced surface water availability, and increased irrigation efficiency in small farms.

Merging remote sensing data and national agricultural statistics to model change in irrigated agriculture

USGS Publications Warehouse

Brown, Jesslyn; Pervez, Md Shahriar

2014-01-01

Over 22 million hectares (ha) of U.S. croplands are irrigated. Irrigation is an intensified agricultural land use that increases crop yields and the practice affects water and energy cycles at, above, and below the land surface. Until recently, there has been a scarcity of geospatially detailed information about irrigation that is comprehensive, consistent, and timely to support studies tying agricultural land use change to aquifer water use and other factors. This study shows evidence for a recent overall net expansion of 522 thousand ha across the U.S. (2.33%) and 519 thousand ha (8.7%) in irrigated cropped area across the High Plains Aquifer (HPA) from 2002 to 2007. In fact, over 97% of the net national expansion in irrigated agriculture overlays the HPA. We employed a modeling approach implemented at two time intervals (2002 and 2007) for mapping irrigated agriculture across the conterminous U.S. (CONUS). We utilized U.S. Department of Agriculture (USDA) county statistics, satellite imagery, and a national land cover map in the model. The model output, called the Moderate Resolution Imaging Spectroradiometer (MODIS) Irrigated Agriculture Dataset for the U.S. (MIrAD-US), was then used to reveal relatively detailed spatial patterns of irrigation change across the nation and the HPA. Causes for the irrigation increase in the HPA are complex, but factors include crop commodity price increases, the corn ethanol industry, and government policies related to water use. Impacts of more irrigation may include shifts in local and regional climate, further groundwater depletion, and increasing crop yields and farm income.

Sustainability of Agricultural Systems: Concept to Application

EPA Science Inventory

Agriculture not only feeds the planet, it also is the biggest overall factor affecting the environment. Thus, innovative sustainable farming systems that produce healthy food and protect the environment at the same time are very much needed. We, as agricultural engineers, need ...

Agricultural Drainage Management Systems Task Force (ADMSTF)

USDA-ARS?s Scientific Manuscript database

The Agricultural Drainage Management Systems (ADMS) Task Force was initiated during a Charter meeting in the fall of 2002 by dedicated professional employees of Federal, State, and Local Government Agencies and Universities. The Agricultural Drainage Management (ADM) Coalition was established in 200...

Restrictive Factors and Output Forecast of Green Development of Agricultural Industry Based on Gray System

NASA Astrophysics Data System (ADS)

Sun, Fengru

2018-01-01

This paper analyzes the characteristics of agricultural products from the perspective of agricultural production, farmers’ income, adjustment of agricultural structure and environmental improvement, and analyzes the characteristics of agricultural products in LanZhou area. Through data mining and empirical analysis, the regional agriculture (1) forecasting model of gray system with dynamic data processing, combined with the output data of lily in 2004-2003, the yield prediction is predicted and the fitting state is good and the error is small. Finally, combined with the relevant characteristics of the local characteristics of the agricultural industry to make reference, by changing the characteristics of agricultural production as the center of the mindset, and agricultural industrialization and organic combination, take the characteristics of efficient industrialization of agricultural products.

Evaluating agricultural nonpoint-source pollution using integrated geographic information systems and hydrologic/water quality model

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

Tim, U.S.; Jolly, R.

1994-01-01

Considerable progress has been made in developing physically based, distributed parameter, hydrologic/water quality (HIWQ) models for planning and control of nonpoint-source pollution. The widespread use of these models is often constrained by the excessive and time-consuming input data demands and the lack of computing efficiencies necessary for iterative simulation of alternative management strategies. Recent developments in geographic information systems (GIS) provide techniques for handling large amounts of spatial data for modeling nonpoint-source pollution problems. Because a GIS can be used to combine information from several sources to form an array of model input data and to examine any combinations ofmore » spatial input/output data, it represents a highly effective tool for HiWQ modeling. This paper describes the integration of a distributed-parameter model (AGNPS) with a GIS (ARC/INFO) to examine nonpoint sources of pollution in an agricultural watershed. The ARC/INFO GIS provided the tools to generate and spatially organize the disparate data to support modeling, while the AGNPS model was used to predict several water quality variables including soil erosion and sedimentation within a watershed. The integrated system was used to evaluate the effectiveness of several alternative management strategies in reducing sediment pollution in a 417-ha watershed located in southern Iowa. The implementation of vegetative filter strips and contour buffer (grass) strips resulted in a 41 and 47% reduction in sediment yield at the watershed outlet, respectively. In addition, when the integrated system was used, the combination of the above management strategies resulted in a 71% reduction in sediment yield. In general, the study demonstrated the utility of integrating a simulation model with GIS for nonpoini-source pollution control and planning. Such techniques can help characterize the diffuse sources of pollution at the landscape level. 52 refs., 6 figs., 1 tab.« less

[Model-based biofuels system analysis: a review].

PubMed

Chang, Shiyan; Zhang, Xiliang; Zhao, Lili; Ou, Xunmin

2011-03-01

Model-based system analysis is an important tool for evaluating the potential and impacts of biofuels, and for drafting biofuels technology roadmaps and targets. The broad reach of the biofuels supply chain requires that biofuels system analyses span a range of disciplines, including agriculture/forestry, energy, economics, and the environment. Here we reviewed various models developed for or applied to modeling biofuels, and presented a critical analysis of Agriculture/Forestry System Models, Energy System Models, Integrated Assessment Models, Micro-level Cost, Energy and Emission Calculation Models, and Specific Macro-level Biofuel Models. We focused on the models' strengths, weaknesses, and applicability, facilitating the selection of a suitable type of model for specific issues. Such an analysis was a prerequisite for future biofuels system modeling, and represented a valuable resource for researchers and policy makers.

Natuculture Systems: Addressing Students' STEM and Agriculture Knowledge

NASA Astrophysics Data System (ADS)

Joyce, Alexander Augusto

The purpose of this study was to assess the inclusion of a Natuculture systems learning experience into selected high school STEM courses to determine high school students' interests in majoring in STEM and for pursuing careers in agricultural sciences. Natuculture is defined as "any human-made system that mimics nature in human-disturbed landscapes". The research occurred at an urban area high school located in the Piedmont region of North Carolina. Fifty-three students in grades 9-12 participated during an academic semester learning experience which included planting, maintenance, & harvesting for an oasissofa. Data was collected using a questionnaire and reflective journals to gather students' attitudes towards agriculture and science and knowledge towards agriculture. Results showed that while the experiences did not improve students' interest in pursuing careers in agricultural sciences, overall, they did increase their knowledge of concepts related to agriculture. It was concluded that students benefit from experiential learning experiences. Based on the study, it is recommended that future research follow up with students to learn of their educational and career choices in agriculture and future learning experiences include curricula that integrates agricultural topics with STEM courses.

Traceability System For Agricultural Productsbased on Rfid and Mobile Technology

NASA Astrophysics Data System (ADS)

Sugahara, Koji

In agriculture, it is required to establish and integrate food traceability systems and risk management systems in order to improve food safety in the entire food chain. The integrated traceability system for agricultural products was developed, based on innovative technology of RFID and mobile computing. In order to identify individual products on the distribution process efficiently,small RFID tags with unique ID and handy RFID readers were applied. On the distribution process, the RFID tags are checked by using the readers, and transit records of the products are stored to the database via wireless LAN.Regarding agricultural production, the recent issues of pesticides misuse affect consumer confidence in food safety. The Navigation System for Appropriate Pesticide Use (Nouyaku-navi) was developed, which is available in the fields by Internet cell-phones. Based on it, agricultural risk management systems have been developed. These systems collaborate with traceability systems and they can be applied for process control and risk management in agriculture.

Beyond climate-smart agriculture: toward safe operating spaces for global food systems

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

Gulledge, Jay; Neufeldt, Heinrich; Jahn, Margaret M

Agriculture is considered to be climate-smart when it contributes to increasing food security, adaptation and mitigation in a sustainable way. This new concept now dominates current discussions in agricultural development because of its capacity to unite the agendas of the agriculture, development and climate change communities under one brand. In this opinion piece authored by scientists from a variety of international agricultural and climate research communities, we argue that the concept needs to be evaluated critically because the relationship between the three dimensions is poorly understood, such that practically any improved agricultural practice can be considered climate-smart. This lack ofmore » clarity may have contributed to the broad appeal of the concept. From the understanding that we must hold ourselves accountable to demonstrably better meet human needs in the short and long term within foreseeable local and planetary limits, we develop a conceptualization of climate-smart agriculture as agriculture that can be shown to bring us closer to safe operating spaces for agricultural and food systems across spatial and temporal scales. Improvements in the management of agricultural systems that bring us significantly closer to safe operating spaces will require transformations in governance and use of our natural resources, underpinned by enabling political, social and economic conditions beyond incremental changes. Establishing scientifically credible indicators and metrics of long-term safe operating spaces in the context of a changing climate and growing social-ecological challenges is critical to creating the societal demand and political will required to motivate deep transformations. Answering questions on how the needed transformational change can be achieved will require actively setting and testing hypotheses to refine and characterize our concepts of safer spaces for social-ecological systems across scales. This effort will demand prioritizing

Benchmarking the performance of a land data assimilation system for agricultural drought monitoring

USDA-ARS?s Scientific Manuscript database

The application of land data assimilation systems to operational agricultural drought monitoring requires the development of (at least) three separate system sub-components: 1) a retrieval model to invert satellite-derived observations into soil moisture estimates, 2) a prognostic soil water balance...

Climate change impacts utilizing regional models for agriculture, hydrology and natural ecosystems

NASA Astrophysics Data System (ADS)

Kafatos, M.; Asrar, G. R.; El-Askary, H. M.; Hatzopoulos, N.; Kim, J.; Kim, S.; Medvigy, D.; Prasad, A. K.; Smith, E.; Stack, D. H.; Tremback, C.; Walko, R. L.

2012-12-01

Climate change impacts the entire Earth but with crucial and often catastrophic impacts at local and regional levels. Extreme phenomena such as fires, dust storms, droughts and other natural hazards present immediate risks and challenges. Such phenomena will become more extreme as climate change and anthropogenic activities accelerate in the future. We describe a major project funded by NIFA (Grant # 2011-67004-30224), under the joint NSF-DOE-USDA Earth System Models (EaSM) program, to investigate the impacts of climate variability and change on the agricultural and natural (i.e. rangeland) ecosystems in the Southwest USA using a combination of historical and present observations together with climate, and ecosystem models, both in hind-cast and forecast modes. The applicability of the methodology to other regions is relevant (for similar geographic regions as well as other parts of the world with different agriculture and ecosystems) and should advance the state of knowledge for regional impacts of climate change. A combination of multi-model global climate projections from the decadal predictability simulations, to downscale dynamically these projections using three regional climate models, combined with remote sensing MODIS and other data, in order to obtain high-resolution climate data that can be used with hydrological and ecosystem models for impacts analysis, is described in this presentation. Such analysis is needed to assess the future risks and potential impacts of projected changes on these natural and managed ecosystems. The results from our analysis can be used by scientists to assist extended communities to determine agricultural coping strategies, and is, therefore, of interest to wide communities of stakeholders. In future work we will be including surface hydrologic modeling and water resources, extend modeling to higher resolutions and include significantly more crops and geographical regions with different weather and climate conditions

A Resource-Based Modelling Framework to Assess Habitat Suitability for Steppe Birds in Semiarid Mediterranean Agricultural Systems

PubMed Central

Cardador, Laura; De Cáceres, Miquel; Bota, Gerard; Giralt, David; Casas, Fabián; Arroyo, Beatriz; Mougeot, François; Cantero-Martínez, Carlos; Moncunill, Judit; Butler, Simon J.; Brotons, Lluís

2014-01-01

European agriculture is undergoing widespread changes that are likely to have profound impacts on farmland biodiversity. The development of tools that allow an assessment of the potential biodiversity effects of different land-use alternatives before changes occur is fundamental to guiding management decisions. In this study, we develop a resource-based model framework to estimate habitat suitability for target species, according to simple information on species’ key resource requirements (diet, foraging habitat and nesting site), and examine whether it can be used to link land-use and local species’ distribution. We take as a study case four steppe bird species in a lowland area of the north-eastern Iberian Peninsula. We also compare the performance of our resource-based approach to that obtained through habitat-based models relating species’ occurrence and land-cover variables. Further, we use our resource-based approach to predict the effects that change in farming systems can have on farmland bird habitat suitability and compare these predictions with those obtained using the habitat-based models. Habitat suitability estimates generated by our resource-based models performed similarly (and better for one study species) than habitat based-models when predicting current species distribution. Moderate prediction success was achieved for three out of four species considered by resource-based models and for two of four by habitat-based models. Although, there is potential for improving the performance of resource-based models, they provide a structure for using available knowledge of the functional links between agricultural practices, provision of key resources and the response of organisms to predict potential effects of changing land-uses in a variety of context or the impacts of changes such as altered management practices that are not easily incorporated into habitat-based models. PMID:24667825

Landscape evolution and agricultural land salinization in coastal area: A conceptual model.

PubMed

Bless, Aplena Elen; Colin, François; Crabit, Armand; Devaux, Nicolas; Philippon, Olivier; Follain, Stéphane

2018-06-01

Soil salinization is a major threat to agricultural lands. Among salt-affected lands, coastal areas could be considered as highly complex systems, where salinization degradation due to anthropogenic pressure and climate-induced changes could significantly alter system functioning. For such complex systems, conceptual models can be used as evaluation tools in a preliminary step to identify the main evolutionary processes responsible for soil and water salinization. This study aimed to propose a conceptual model for water fluxes in a coastal area affected by salinity, which can help to identify the relationships between agricultural landscape evolution and actual salinity. First, we conducted field investigations from 2012 to 2016, mainly based on both soil (EC 1/5 ) and water (EC w ) electrical conductivity survey. This allowed us to characterize spatial structures for EC 1/5 and EC w and to identify the river as a preponderant factor in land salinization. Subsequently, we proposed and used a conceptual model for water fluxes and conducted a time analysis (1962-2012) for three of its main constitutive elements, namely climate, river, and land systems. When integrated within the conceptual model framework, it appeared that the evolution of all constitutive elements since 1962 was responsible for the disruption of system equilibrium, favoring overall salt accumulation in the soil root zone. Copyright © 2017 Elsevier B.V. All rights reserved.

Climate-Agriculture-Modeling and Decision Tool for Disease (CAMDT-Disease) for seasonal climate forecast-based crop disease risk management in agriculture

NASA Astrophysics Data System (ADS)

Kim, K. H.; Lee, S.; Han, E.; Ines, A. V. M.

2017-12-01

Climate-Agriculture-Modeling and Decision Tool (CAMDT) is a decision support system (DSS) tool that aims to facilitate translations of probabilistic seasonal climate forecasts (SCF) to crop responses such as yield and water stress. Since CAMDT is a software framework connecting different models and algorithms with SCF information, it can be easily customized for different types of agriculture models. In this study, we replaced the DSSAT-CSM-Rice model originally incorporated in CAMDT with a generic epidemiological model, EPIRICE, to generate a seasonal pest outlook. The resulting CAMDT-Disease generates potential risks for selected fungal, viral, and bacterial diseases of rice over the next months by translating SCFs into agriculturally-relevant risk information. The integrated modeling procedure of CAMDT-Disease first disaggregates a given SCF using temporal downscaling methods (predictWTD or FResampler1), runs EPIRICE with the downscaled weather inputs, and finally visualizes the EPIRICE outputs as disease risk compared to that of the previous year and the 30-year-climatological average. In addition, the easy-to-use graphical user interface adopted from CAMDT allows users to simulate "what-if" scenarios of disease risks over different planting dates with given SCFs. Our future work includes the simulation of the effect of crop disease on yields through the disease simulation models with the DSSAT-CSM-Rice model, as disease remains one of the most critical yield-reducing factors in the field.

Teaching Diversified Organic Crop Production Using the Community Supported Agriculture Farming System Model

ERIC Educational Resources Information Center

Falk, Constance L.; Pao, Pauline; Cramer, Christopher S.

2005-01-01

An organic garden operated as a community supported agriculture (CSA) venture on the New Mexico State University (NMSU) main campus was begun in January 2002. Students enroll in an organic vegetable production class during spring and fall semesters to help manage and work on the project. The CSA model of farming involves the sale of shares to…

Modeling applications for precision agriculture in the California Central Valley

NASA Astrophysics Data System (ADS)

Marklein, A. R.; Riley, W. J.; Grant, R. F.; Mezbahuddin, S.; Mekonnen, Z. A.; Liu, Y.; Ying, S.

2017-12-01

Drought in California has increased the motivation to develop precision agriculture, which uses observations to make site-specific management decisions throughout the growing season. In agricultural systems that are prone to drought, these efforts often focus on irrigation efficiency. Recent improvements in soil sensor technology allow the monitoring of plant and soil status in real-time, which can then inform models aimed at improving irrigation management. But even on farms with resources to deploy soil sensors across the landscape, leveraging that sensor data to design an efficient irrigation scheme remains a challenge. We conduct a modeling experiment aimed at simulating precision agriculture to address several questions: (1) how, when, and where does irrigation lead to optimal yield? and (2) What are the impacts of different precision irrigation schemes on yields, soil organic carbon (SOC), and total water use? We use the ecosys model to simulate precision agriculture in a conventional tomato-corn rotation in the California Central Valley with varying soil water content thresholds for irrigation and soil water sensor depths. This model is ideal for our question because it includes explicit process-based functions for the plant growth, plant water use, soil hydrology, and SOC, and has been tested extensively in agricultural ecosystems. Low irrigation thresholds allows the soil to become drier before irrigating compared to high irrigation thresholds; as such, we found that the high irrigation thresholds use more irrigation over the course of the season, have higher yields, and have lower water use efficiency. The irrigation threshold did not affect SOC. Yields and water use are highest at sensor depths of 0.5 to 0.15 m, but water use efficiency was also lowest at these depths. We found SOC to be significantly affected by sensor depth, with the highest SOC at the shallowest sensor depths. These results will help regulate irrigation water while maintaining yield

Modelling Mediterranean agro-ecosystems by including agricultural trees in the LPJmL model

NASA Astrophysics Data System (ADS)

Fader, M.; von Bloh, W.; Shi, S.; Bondeau, A.; Cramer, W.

2015-06-01

Climate and land use change in the Mediterranean region is expected to affect natural and agricultural ecosystems by decreases in precipitation, increases in temperature as well as biodiversity loss and anthropogenic degradation of natural resources. Demographic growth in the Eastern and Southern shores will require increases in food production and put additional pressure on agro-ecosystems and water resources. Coping with these challenges requires informed decisions that, in turn, require assessments by means of a comprehensive agro-ecosystem and hydrological model. This study presents the inclusion of 10 Mediterranean agricultural plants, mainly perennial crops, in an agro-ecosystem model (LPJmL): nut trees, date palms, citrus trees, orchards, olive trees, grapes, cotton, potatoes, vegetables and fodder grasses. The model was successfully tested in three model outputs: agricultural yields, irrigation requirements and soil carbon density. With the development presented in this study, LPJmL is now able to simulate in good detail and mechanistically the functioning of Mediterranean agriculture with a comprehensive representation of ecophysiological processes for all vegetation types (natural and agricultural) and in a consistent framework that produces estimates of carbon, agricultural and hydrological variables for the entire Mediterranean basin. This development pave the way for further model extensions aiming at the representation of alternative agro-ecosystems (e.g. agroforestry), and opens the door for a large number of applications in the Mediterranean region, for example assessments on the consequences of land use transitions, the influence of management practices and climate change impacts.

Research on Agriculture Domain Meta-Search Engine System

NASA Astrophysics Data System (ADS)

Xie, Nengfu; Wang, Wensheng

The rapid growth of agriculture web information brings a fact that search engine can not return a satisfied result for users’ queries. In this paper, we propose an agriculture domain search engine system, called ADSE, that can obtains results by an advance interface to several searches and aggregates them. We also discuss two key technologies: agriculture information determination and engine.

Ecosystem Services Provided by Agricultural Land as Modeled by Broad Scale Geospatial Analysis

NASA Astrophysics Data System (ADS)

Kokkinidis, Ioannis

Agricultural ecosystems provide multiple services including food and fiber provision, nutrient cycling, soil retention and water regulation. Objectives of the study were to identify and quantify a selection of ecosystem services provided by agricultural land, using existing geospatial tools and preferably free and open source data, such as the Virginia Land Use Evaluation System (VALUES), the North Carolina Realistic Yield Expectations (RYE) database, and the land cover datasets NLCD and CDL. Furthermore I sought to model tradeoffs between provisioning and other services. First I assessed the accuracy of agricultural land in NLCD and CDL over a four county area in eastern Virginia using cadastral parcels. I uncovered issues concerning the definition of agricultural land. The area and location of agriculture saw little change in the 19 years studied. Furthermore all datasets have significant errors of omission (11.3 to 95.1%) and commission (0 to 71.3%). Location of agriculture was used with spatial crop yield databases I created and combined with models I adapted to calculate baseline values for plant biomass, nutrient composition and requirements, land suitability for and potential production of biofuels and the economic impact of agriculture for the four counties. The study area was then broadened to cover 97 counties in eastern Virginia and North Carolina, investigating the potential for increased regional grain production through intensification and extensification of agriculture. Predicted yield from geospatial crop models was compared with produced yield from the NASS Survey of Agriculture. Area of most crops in CDL was similar to that in the Survey of Agriculture, but a yield gap is present for most years, partially due to weather, thus indicating potential for yield increase through intensification. Using simple criteria I quantified the potential to extend agriculture in high yield land in other uses and modeled the changes in erosion and runoff should

Evaluating the sensitivity of agricultural model performance to different climate inputs

PubMed Central

Glotter, Michael J.; Moyer, Elisabeth J.; Ruane, Alex C.; Elliott, Joshua W.

2017-01-01

Projections of future food production necessarily rely on models, which must themselves be validated through historical assessments comparing modeled to observed yields. Reliable historical validation requires both accurate agricultural models and accurate climate inputs. Problems with either may compromise the validation exercise. Previous studies have compared the effects of different climate inputs on agricultural projections, but either incompletely or without a ground truth of observed yields that would allow distinguishing errors due to climate inputs from those intrinsic to the crop model. This study is a systematic evaluation of the reliability of a widely-used crop model for simulating U.S. maize yields when driven by multiple observational data products. The parallelized Decision Support System for Agrotechnology Transfer (pDSSAT) is driven with climate inputs from multiple sources – reanalysis, reanalysis bias-corrected with observed climate, and a control dataset – and compared to observed historical yields. The simulations show that model output is more accurate when driven by any observation-based precipitation product than when driven by un-bias-corrected reanalysis. The simulations also suggest, in contrast to previous studies, that biased precipitation distribution is significant for yields only in arid regions. However, some issues persist for all choices of climate inputs: crop yields appear oversensitive to precipitation fluctuations but undersensitive to floods and heat waves. These results suggest that the most important issue for agricultural projections may be not climate inputs but structural limitations in the crop models themselves. PMID:29097985

Biophysical system models advance agricultural research and technology: Some examples and further research needs

USDA-ARS?s Scientific Manuscript database

Environmental concerns of the general public, droughts, and climate change effects require continual adaptation and optimization of agricultural systems through changes in cropping and management. Advancement of science and technology to achieve these changes requires cutting-edge field research, us...

Sensitivity Analysis in Agent-Based Models of Socio-Ecological Systems: An Example in Agricultural Land Conservation for Lake Water Quality Improvement

NASA Astrophysics Data System (ADS)

Ligmann-Zielinska, A.; Kramer, D. B.; Spence Cheruvelil, K.; Soranno, P.

2012-12-01

Socio-ecological systems are dynamic and nonlinear. To account for this complexity, we employ agent-based models (ABMs) to study macro-scale phenomena resulting from micro-scale interactions among system components. Because ABMs typically have many parameters, it is challenging to identify which parameters contribute to the emerging macro-scale patterns. In this paper, we address the following question: What is the extent of participation in agricultural land conservation programs given heterogeneous landscape, economic, social, and individual decision making criteria in complex lakesheds? To answer this question, we: [1] built an ABM for our model system; [2] simulated land use change resulting from agent decision making, [3] estimated the uncertainty of the model output, decomposed it and apportioned it to each of the parameters in the model. Our model system is a freshwater socio-ecological system - that of farmland and lake water quality within a region containing a large number of lakes and high proportions of agricultural lands. Our study focuses on examining how agricultural land conversion from active to fallow reduces freshwater nutrient loading and improves water quality. Consequently, our ABM is composed of farmer agents who make decisions related to participation in a government-sponsored Conservation Reserve Program (CRP) managed by the Farm Service Agency (FSA). We also include an FSA agent, who selects enrollment offers made by farmers and announces the signup results leading to land use change. The model is executed in a Monte Carlo simulation framework to generate a distribution of maps of fallow lands that are used for calculating nutrient loading to lakes. What follows is a variance-based sensitivity analysis of the results. We compute sensitivity indices for individual parameters and their combinations, allowing for identification of the most influential as well as the insignificant inputs. In the case study, we observe that farmland

The Climate-Agriculture-Modeling and Decision Tool (CAMDT) for Climate Risk Management in Agriculture

NASA Astrophysics Data System (ADS)

Ines, A. V. M.; Han, E.; Baethgen, W.

2017-12-01

Advances in seasonal climate forecasts (SCFs) during the past decades have brought great potential to improve agricultural climate risk managements associated with inter-annual climate variability. In spite of popular uses of crop simulation models in addressing climate risk problems, the models cannot readily take seasonal climate predictions issued in the format of tercile probabilities of most likely rainfall categories (i.e, below-, near- and above-normal). When a skillful SCF is linked with the crop simulation models, the informative climate information can be further translated into actionable agronomic terms and thus better support strategic and tactical decisions. In other words, crop modeling connected with a given SCF allows to simulate "what-if" scenarios with different crop choices or management practices and better inform the decision makers. In this paper, we present a decision support tool, called CAMDT (Climate Agriculture Modeling and Decision Tool), which seamlessly integrates probabilistic SCFs to DSSAT-CSM-Rice model to guide decision-makers in adopting appropriate crop and agricultural water management practices for given climatic conditions. The CAMDT has a functionality to disaggregate a probabilistic SCF into daily weather realizations (either a parametric or non-parametric disaggregation method) and to run DSSAT-CSM-Rice with the disaggregated weather realizations. The convenient graphical user-interface allows easy implementation of several "what-if" scenarios for non-technical users and visualize the results of the scenario runs. In addition, the CAMDT also translates crop model outputs to economic terms once the user provides expected crop price and cost. The CAMDT is a practical tool for real-world applications, specifically for agricultural climate risk management in the Bicol region, Philippines, having a great flexibility for being adapted to other crops or regions in the world. CAMDT GitHub: https://github.com/Agro-Climate/CAMDT

Ecohydrological modeling: the consideration of agricultural trees is essential in the Mediterranean area

NASA Astrophysics Data System (ADS)

Fader, Marianela; von Bloh, Werner; Shi, Sinan; Bondeau, Alberte; Cramer, Wolfgang

2016-04-01

In the Mediterranean region, climate and land use change are expected to impact on natural and agricultural ecosystems by warming, reduced rainfall and direct degradation of ecosystems. Human population growth and socioeconomic changes, notably on the Eastern and Southern shores, will require increases in food production and put additional pressure on agro-ecosystems and water resources. Coping with these challenges requires informed decisions that, in turn, require assessments by means of a comprehensive ecohydrological model. Here we present here the inclusion of 10 Mediterranean agricultural plants, mainly perennial crops, in an agro-ecosystem model (LPJmL, "Lund-Potsdam-Jena managed Land"): nut trees, date palms, citrus trees, orchards, olive trees, grapes, cotton, potatoes, vegetables and fodder grasses. The model was then successfully tested in three model outputs: agricultural yields, irrigation requirements and soil carbon density. A first application of the model indicates that, currently, agricultural trees consume in average more irrigation water per hectare than annual crops. Also, different crops show different magnitude of changes in net irrigation requirements due to climate change, being the increases most pronounced in agricultural trees. This is very relevant since the Mediterranean area as a whole might face an increase in gross irrigation requirements between 4% and 74% from climate change and population growth if irrigation systems and conveyance are not improved. Additionally, future water scarcity might pose further challenges to the agricultural sector: Algeria, Libya, Israel, Jordan, Lebanon, Syria, Serbia, Morocco, Tunisia and Spain have a high risk of not being able to sustainably meet future irrigation water requirements in some scenarios by the end of the century (1). The importance of including agricultural trees in the ecohydrological models is also shown in the results concerning soil organic carbon (SOC). Since in former model

The Agricultural Model Intercomparison and Improvement Project: Phase I Activities by a Global Community of Science. Chapter 1

NASA Technical Reports Server (NTRS)

Rosenzweig, Cynthia E.; Jones, James W.; Hatfield, Jerry L.; Antle, John M.; Ruane, Alexander C.; Mutter, Carolyn Z.

2015-01-01

The Agricultural Model Intercomparison and Improvement Project (AgMIP) was founded in 2010. Its mission is to improve substantially the characterization of world food security as affected by climate variability and change, and to enhance adaptation capacity in both developing and developed countries. The objectives of AgMIP are to: Incorporate state-of-the-art climate, crop/livestock, and agricultural economic model improvements into coordinated multi-model regional and global assessments of future climate impacts and adaptation and other key aspects of the food system. Utilize multiple models, scenarios, locations, crops/livestock, and participants to explore uncertainty and the impact of data and methodological choices. Collaborate with regional experts in agronomy, animal sciences, economics, and climate to build a strong basis for model applications, addressing key climate related questions and sustainable intensification farming systems. Improve scientific and adaptive capacity in modeling for major agricultural regions in the developing and developed world, with a focus on vulnerable regions. Improve agricultural data and enhance data-sharing based on their intercomparison and evaluation using best scientific practices. Develop modeling frameworks to identify and evaluate promising adaptation technologies and policies and to prioritize strategies.

Modelling Mediterranean agro-ecosystems by including agricultural trees in the LPJmL model

NASA Astrophysics Data System (ADS)

Fader, M.; von Bloh, W.; Shi, S.; Bondeau, A.; Cramer, W.

2015-11-01

In the Mediterranean region, climate and land use change are expected to impact on natural and agricultural ecosystems by warming, reduced rainfall, direct degradation of ecosystems and biodiversity loss. Human population growth and socioeconomic changes, notably on the eastern and southern shores, will require increases in food production and put additional pressure on agro-ecosystems and water resources. Coping with these challenges requires informed decisions that, in turn, require assessments by means of a comprehensive agro-ecosystem and hydrological model. This study presents the inclusion of 10 Mediterranean agricultural plants, mainly perennial crops, in an agro-ecosystem model (Lund-Potsdam-Jena managed Land - LPJmL): nut trees, date palms, citrus trees, orchards, olive trees, grapes, cotton, potatoes, vegetables and fodder grasses. The model was successfully tested in three model outputs: agricultural yields, irrigation requirements and soil carbon density. With the development presented in this study, LPJmL is now able to simulate in good detail and mechanistically the functioning of Mediterranean agriculture with a comprehensive representation of ecophysiological processes for all vegetation types (natural and agricultural) and in a consistent framework that produces estimates of carbon, agricultural and hydrological variables for the entire Mediterranean basin. This development paves the way for further model extensions aiming at the representation of alternative agro-ecosystems (e.g. agroforestry), and opens the door for a large number of applications in the Mediterranean region, for example assessments of the consequences of land use transitions, the influence of management practices and climate change impacts.

Agricultural model intercomparison and improvement project: Overview of model intercomparisons

USDA-ARS?s Scientific Manuscript database

Improvement of crop simulation models to better estimate growth and yield is one of the objectives of the Agricultural Model Intercomparison and Improvement Project (AgMIP). The overall goal of AgMIP is to provide an assessment of crop model through rigorous intercomparisons and evaluate future clim...

BioEarth: Envisioning and developing a new regional earth system model to inform natural and agricultural resource management

DOE PAGES

Adam, Jennifer C.; Stephens, Jennie C.; Chung, Serena H.; ...

2014-04-24

Uncertainties in global change impacts, the complexities associated with the interconnected cycling of nitrogen, carbon, and water present daunting management challenges. Existing models provide detailed information on specific sub-systems (e.g., land, air, water, and economics). An increasing awareness of the unintended consequences of management decisions resulting from interconnectedness of these sub-systems, however, necessitates coupled regional earth system models (EaSMs). Decision makers’ needs and priorities can be integrated into the model design and development processes to enhance decision-making relevance and “usability” of EaSMs. BioEarth is a research initiative currently under development with a focus on the U.S. Pacific Northwest region thatmore » explores the coupling of multiple stand-alone EaSMs to generate usable information for resource decision-making. Direct engagement between model developers and non-academic stakeholders involved in resource and environmental management decisions throughout the model development process is a critical component of this effort. BioEarth utilizes a bottom-up approach for its land surface model that preserves fine spatial-scale sensitivities and lateral hydrologic connectivity, which makes it unique among many regional EaSMs. Here, we describe the BioEarth initiative and highlights opportunities and challenges associated with coupling multiple stand-alone models to generate usable information for agricultural and natural resource decision-making.« less

A satellite-driven, client-server hydro-economic model prototype for agricultural water management

NASA Astrophysics Data System (ADS)

Maneta, Marco; Kimball, John; He, Mingzhu; Payton Gardner, W.

2017-04-01

Anticipating agricultural water demand, land reallocation, and impact on farm revenues associated with different policy or climate constraints is a challenge for water managers and for policy makers. While current integrated decision support systems based on programming methods provide estimates of farmer reaction to external constraints, they have important shortcomings such as the high cost of data collection surveys necessary to calibrate the model, biases associated with inadequate farm sampling, infrequent model updates and recalibration, model overfitting, or their deterministic nature, among other problems. In addition, the administration of water supplies and the generation of policies that promote sustainable agricultural regions depend on more than one bureau or office. Unfortunately, managers from local and regional agencies often use different datasets of variable quality, which complicates coordinated action. To overcome these limitations, we present a client-server, integrated hydro-economic modeling and observation framework driven by satellite remote sensing and other ancillary information from regional monitoring networks. The core of the framework is a stochastic data assimilation system that sequentially ingests remote sensing observations and corrects the parameters of the hydro-economic model at unprecedented spatial and temporal resolutions. An economic model of agricultural production, based on mathematical programming, requires information on crop type and extent, crop yield, crop transpiration and irrigation technology. A regional hydro-climatologic model provides biophysical constraints to an economic model of agricultural production with a level of detail that permits the study of the spatial impact of large- and small-scale water use decisions. Crop type and extent is obtained from the Cropland Data Layer (CDL), which is multi-sensor operational classification of crops maintained by the United States Department of Agriculture. Because

Adaptation Options for Land Drainage Systems Towards Sustainable Agriculture and Environment: A Czech Perspective

NASA Astrophysics Data System (ADS)

Kulhavý, Zbyněk; Fučík, Petr

2015-04-01

In this paper, issues of agricultural drainage systems are introduced and discussed from the views of their former, current and future roles and functioning in the Czech Republic (CR). A methodologically disparate survey was done on thirty-nine model localities in CR with different intensity and state of land drainage systems, aimed at description of commonly occurred problems and possible adaptations of agricultural drainage as perceived by farmers, land owners, landscape managers or by protective water management. The survey was focused on technical state of drainage, fragmentation of land ownership within drained areas as well as on possible conflicts between agricultural and environmental interests in a landscape. Achieved results confirmed that there is obviously an increasing need to reassess some functions of prevailingly single-purpose agricultural drainage systems. Drainage intensity and detected unfavourable technical state of drainage systems as well as the risks connected with the anticipated climate change from the view of possible water scarcity claims for a complex solution. An array of adaptation options for agricultural drainage systems is presented, aiming at enhancement of water retention time and improvement of water quality. It encompasses additional flow-controlling measures on tiles or ditches, or facilities for making selected parts of a drainage system inoperable in order to retain or slow down the drainage runoff, to establish water accumulation zones and to enhance water self-cleaning processes. However, it was revealed that the question of landowner parcels fragmentation on drained land in CR would dramatically complicate design and realization of these measures. Presented solutions and findings are propounded with a respect to contemporary and future state policies and international strategies for sustainable agriculture, water management and environment.

Modelling carbon dioxide emissions from agricultural soils in Canada.

PubMed

Yadav, Dhananjay; Wang, Junye

2017-11-01

Agricultural soils are a leading source of atmospheric greenhouse gas (GHG) emissions and are major contributors to global climate change. Carbon dioxide (CO 2 ) makes up 20% of the total GHG emitted from agricultural soil. Therefore, an evaluation of CO 2 emissions from agricultural soil is necessary in order to make mitigation strategies for environmental efficiency and economic planning possible. However, quantification of CO 2 emissions through experimental methods is constrained due to the large time and labour requirements for analysis. Therefore, a modelling approach is needed to achieve this objective. In this paper, the DeNitrification-DeComposition (DNDC), a process-based model, was modified to predict CO 2 emissions for Canada from regional conditions. The modified DNDC model was applied at three experimental sites in the province of Saskatchewan. The results indicate that the simulations of the modified DNDC model are in good agreement with observations. The agricultural management of fertilization and irrigation were evaluated using scenario analysis. The simulated total annual CO 2 flux changed on average by ±13% and ±1% following a ±50% variance of the total amount of N applied by fertilising and the total amount of water through irrigation applications, respectively. Therefore, careful management of irrigation and applications of fertiliser can help to reduce CO 2 emissions from the agricultural sector. Copyright © 2017 Elsevier Ltd. All rights reserved.

Evaluation of the AnnAGNPS model for predicting runoff and sediment yield in a small Mediterranean agricultural watershed in Navarre (Spain)

USDA-ARS?s Scientific Manuscript database

AnnAGNPS (Annualized Agricultural Non-Point Source Pollution Model) is a system of computer models developed to predict non-point source pollutant loadings within agricultural watersheds. It contains a daily time step distributed parameter continuous simulation surface runoff model designed to assis...

76 FR 29083 - Agriculture Priorities and Allocations System

Federal Register 2010, 2011, 2012, 2013, 2014

2011-05-19

... Vol. 76 Thursday, No. 97 May 19, 2011 Part III Department of Agriculture Farm Service Agency 7 CFR Part 789 Agriculture Priorities and Allocations System; Proposed Rule #0;#0;Federal Register / Vol. 76 , No. 97 / Thursday, May 19, 2011 / Proposed Rules#0;#0; [[Page 29084

The Effects of Liberalizing World Agricultural Trade: A Review of Modeling Studies

DTIC Science & Technology

2006-06-01

agricultural policy issues. Like the LINKAGE model analysis, the GTAP-AGR model analysis uses version 6.05 of the GTAP database with a base year of...the 2006 study—fully liberal- izing agricultural policy in equal increments from 2005 through 2010—would increase world economic welfare in 2015 by...Burfisher, ed., Agricultural Policy Reform in the WTO—The Road Ahead, Agricultural Economic Report No. 802 (U.S. Department of Agriculture, Economic

Emergy assessment of three home courtyard agriculture production systems in Tibet Autonomous Region, China*

PubMed Central

Guan, Fa-chun; Sha, Zhi-peng; Zhang, Yu-yang; Wang, Jun-feng; Wang, Chao

2016-01-01

Home courtyard agriculture is an important model of agricultural production on the Tibetan plateau. Because of the sensitive and fragile plateau environment, it needs to have optimal performance characteristics, including high sustainability, low environmental pressure, and high economic benefit. Emergy analysis is a promising tool for evaluation of the environmental-economic performance of these production systems. In this study, emergy analysis was used to evaluate three courtyard agricultural production models: Raising Geese in Corn Fields (RGICF), Conventional Corn Planting (CCP), and Pea-Wheat Rotation (PWR). The results showed that the RGICF model produced greater economic benefits, and had higher sustainability, lower environmental pressure, and higher product safety than the CCP and PWR models. The emergy yield ratio (EYR) and emergy self-support ratio (ESR) of RGICF were 0.66 and 0.11, respectively, lower than those of the CCP production model, and 0.99 and 0.08, respectively, lower than those of the PWR production model. The impact of RGICF (1.45) on the environment was lower than that of CCP (2.26) and PWR (2.46). The emergy sustainable indices (ESIs) of RGICF were 1.07 and 1.02 times higher than those of CCP and PWR, respectively. With regard to the emergy index of product safety (EIPS), RGICF had a higher safety index than those of CCP and PWR. Overall, our results suggest that the RGICF model is advantageous and provides higher environmental benefits than the CCP and PWR systems. PMID:27487808

Emergy assessment of three home courtyard agriculture production systems in Tibet Autonomous Region, China.

PubMed

Guan, Fa-Chun; Sha, Zhi-Peng; Zhang, Yu-Yang; Wang, Jun-Feng; Wang, Chao

2016-08-01

Home courtyard agriculture is an important model of agricultural production on the Tibetan plateau. Because of the sensitive and fragile plateau environment, it needs to have optimal performance characteristics, including high sustainability, low environmental pressure, and high economic benefit. Emergy analysis is a promising tool for evaluation of the environmental-economic performance of these production systems. In this study, emergy analysis was used to evaluate three courtyard agricultural production models: Raising Geese in Corn Fields (RGICF), Conventional Corn Planting (CCP), and Pea-Wheat Rotation (PWR). The results showed that the RGICF model produced greater economic benefits, and had higher sustainability, lower environmental pressure, and higher product safety than the CCP and PWR models. The emergy yield ratio (EYR) and emergy self-support ratio (ESR) of RGICF were 0.66 and 0.11, respectively, lower than those of the CCP production model, and 0.99 and 0.08, respectively, lower than those of the PWR production model. The impact of RGICF (1.45) on the environment was lower than that of CCP (2.26) and PWR (2.46). The emergy sustainable indices (ESIs) of RGICF were 1.07 and 1.02 times higher than those of CCP and PWR, respectively. With regard to the emergy index of product safety (EIPS), RGICF had a higher safety index than those of CCP and PWR. Overall, our results suggest that the RGICF model is advantageous and provides higher environmental benefits than the CCP and PWR systems.

Study on an agricultural environment monitoring server system using Wireless Sensor Networks.

PubMed

Hwang, Jeonghwan; Shin, Changsun; Yoe, Hyun

2010-01-01

This paper proposes an agricultural environment monitoring server system for monitoring information concerning an outdoors agricultural production environment utilizing Wireless Sensor Network (WSN) technology. The proposed agricultural environment monitoring server system collects environmental and soil information on the outdoors through WSN-based environmental and soil sensors, collects image information through CCTVs, and collects location information using GPS modules. This collected information is converted into a database through the agricultural environment monitoring server consisting of a sensor manager, which manages information collected from the WSN sensors, an image information manager, which manages image information collected from CCTVs, and a GPS manager, which processes location information of the agricultural environment monitoring server system, and provides it to producers. In addition, a solar cell-based power supply is implemented for the server system so that it could be used in agricultural environments with insufficient power infrastructure. This agricultural environment monitoring server system could even monitor the environmental information on the outdoors remotely, and it could be expected that the use of such a system could contribute to increasing crop yields and improving quality in the agricultural field by supporting the decision making of crop producers through analysis of the collected information.

Progress in modelling agricultural impacts of and adaptations to climate change.

PubMed

Rötter, R P; Hoffmann, M P; Koch, M; Müller, C

2018-06-01

Modelling is a key tool to explore agricultural impacts of and adaptations to climate change. Here we report recent progress made especially referring to the large project initiatives MACSUR and AgMIP; in particular, in modelling potential crop impacts from field to global using multi-model ensembles. We identify two main fields where further progress is necessary: a more mechanistic understanding of climate impacts and management options for adaptation and mitigation; and focusing on cropping systems and integrative multi-scale assessments instead of single season and crops, especially in complex tropical and neglected but important cropping systems. Stronger linking of experimentation with statistical and eco-physiological crop modelling could facilitate the necessary methodological advances. Copyright © 2018 Elsevier Ltd. All rights reserved.

An inverse problem for a mathematical model of aquaponic agriculture

NASA Astrophysics Data System (ADS)

Bobak, Carly; Kunze, Herb

2017-01-01

Aquaponic agriculture is a sustainable ecosystem that relies on a symbiotic relationship between fish and macrophytes. While the practice has been growing in popularity, relatively little mathematical models exist which aim to study the system processes. In this paper, we present a system of ODEs which aims to mathematically model the population and concetrations dynamics present in an aquaponic environment. Values of the parameters in the system are estimated from the literature so that simulated results can be presented to illustrate the nature of the solutions to the system. As well, a brief sensitivity analysis is performed in order to identify redundant parameters and highlight those which may need more reliable estimates. Specifically, an inverse problem with manufactured data for fish and plants is presented to demonstrate the ability of the collage theorem to recover parameter estimates.

Agricultural and Social Resiliency of Small-Scale Agriculture to Economic and Climatic Shocks: A Comparison of Subsistence versus Market-Based Agricultural Approaches in Rural Guatemala

NASA Astrophysics Data System (ADS)

Malard, J. J.; Melgar-Quiñonez, H.; Pineda, P.; Gálvez, J.; Adamowski, J. F.

2014-12-01

Agricultural production is heavily dependent not only on climate but also on markets as well as on the social and community systems managing the agroecosystem. In addition, the ultimate goal of agricultural production, human food security, is also affected not only by net agricultural production but also by similar economic and social factors. These complex feedbacks assume a particular importance in the case of smallholder farms in the tropics, where alternative rural development policies have led to different and contrasting agricultural management systems. Current approaches at comparing such systems generally study their environmental, economic or social components in isolation, potentially missing important interconnections. This research uses a participatory systems dynamics modelling (SDM) framework to compare two small-scale agricultural approaches in rural Guatemala which differ in their social, economic and ecosystem management decisions. The first case study community, in Quiché, has adopted a subsistence-based system that aims to use low levels of outside inputs to produce food for their own consumption, while the second, in Sololá, has opted for market-based agriculture that uses high input levels to obtain marketable crops in order to assure income for the purchase of food and other necessities. Each of these systems has its respective vulnerabilities; while the Sololá community suffers from more environmental degradation issues (soils and pests), the Quiché community, given lower monetary incomes, is more vulnerable to events whose responses require a significant monetary expenditure. Through the SDM approach, we incorporate local stakeholder knowledge of the respective systems, including biophysical and socioeconomic variables, into a joint biophysical and socioeconomic model for each community. These models then allow for the comparison of the resilience of both types of socio-agroecosystems in the face of climatic, economic and biological

Ecosystem Services from Edible Insects in Agricultural Systems: A Review.

PubMed

Payne, Charlotte L R; Van Itterbeeck, Joost

2017-02-17

Many of the most nutritionally and economically important edible insects are those that are harvested from existing agricultural systems. Current strategies of agricultural intensification focus predominantly on increasing crop yields, with no or little consideration of the repercussions this may have for the additional harvest and ecology of accompanying food insects. Yet such insects provide many valuable ecosystem services, and their sustainable management could be crucial to ensuring future food security. This review considers the multiple ecosystem services provided by edible insects in existing agricultural systems worldwide. Directly and indirectly, edible insects contribute to all four categories of ecosystem services as outlined by the Millennium Ecosystem Services definition: provisioning, regulating, maintaining, and cultural services. They are also responsible for ecosystem disservices, most notably significant crop damage. We argue that it is crucial for decision-makers to evaluate the costs and benefits of the presence of food insects in agricultural systems. We recommend that a key priority for further research is the quantification of the economic and environmental contribution of services and disservices from edible insects in agricultural systems.

Ecosystem Services from Edible Insects in Agricultural Systems: A Review

PubMed Central

Payne, Charlotte L. R.; Van Itterbeeck, Joost

2017-01-01

Many of the most nutritionally and economically important edible insects are those that are harvested from existing agricultural systems. Current strategies of agricultural intensification focus predominantly on increasing crop yields, with no or little consideration of the repercussions this may have for the additional harvest and ecology of accompanying food insects. Yet such insects provide many valuable ecosystem services, and their sustainable management could be crucial to ensuring future food security. This review considers the multiple ecosystem services provided by edible insects in existing agricultural systems worldwide. Directly and indirectly, edible insects contribute to all four categories of ecosystem services as outlined by the Millennium Ecosystem Services definition: provisioning, regulating, maintaining, and cultural services. They are also responsible for ecosystem disservices, most notably significant crop damage. We argue that it is crucial for decision-makers to evaluate the costs and benefits of the presence of food insects in agricultural systems. We recommend that a key priority for further research is the quantification of the economic and environmental contribution of services and disservices from edible insects in agricultural systems. PMID:28218635

Groundwater economics: An object-oriented foundation for integrated studies of irrigated agricultural systems

USDA-ARS?s Scientific Manuscript database

An integrated foundation is presented to study the impacts of external forcings on irrigated agricultural systems. Individually, models are presented that simulate groundwater hydrogeology and econometric farm level crop choices and irrigated water use. The natural association between groundwater we...

Sustainable Uses of FGD Gypsum in Agricultural Systems: Introduction.

PubMed

Watts, Dexter B; Dick, Warren A

2014-01-01

Interest in using gypsum as a management tool to improve crop yields and soil and water quality has recently increased. Abundant supply and availability of flue gas desulfurization (FGD) gypsum, a by-product of scrubbing sulfur from combustion gases at coal-fired power plants, in major agricultural producing regions within the last two decades has attributed to this interest. Currently, published data on the long-term sustainability of FGD gypsum use in agricultural systems is limited. This has led to organization of the American Society of Agronomy's Community "By-product Gypsum Uses in Agriculture" and a special collection of nine technical research articles on various issues related to FGD gypsum uses in agricultural systems. A brief review of FGD gypsum, rationale for the special collection, overviews of articles, knowledge gaps, and future research directions are presented in this introductory paper. The nine articles are focused in three general areas: (i) mercury and other trace element impacts, (ii) water quality impacts, and (iii) agronomic responses and soil physical changes. While this is not an exhaustive review of the topic, results indicate that FGD gypsum use in sustainable agricultural production systems is promising. The environmental impacts of FGD gypsum are mostly positive, with only a few negative results observed, even when applied at rates representing cumulative 80-year applications. Thus, FGD gypsum, if properly managed, seems to represent an important potential input into agricultural systems. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.

A generic bio-economic farm model for environmental and economic assessment of agricultural systems.

PubMed

Janssen, Sander; Louhichi, Kamel; Kanellopoulos, Argyris; Zander, Peter; Flichman, Guillermo; Hengsdijk, Huib; Meuter, Eelco; Andersen, Erling; Belhouchette, Hatem; Blanco, Maria; Borkowski, Nina; Heckelei, Thomas; Hecker, Martin; Li, Hongtao; Oude Lansink, Alfons; Stokstad, Grete; Thorne, Peter; van Keulen, Herman; van Ittersum, Martin K

2010-12-01

Bio-economic farm models are tools to evaluate ex-post or to assess ex-ante the impact of policy and technology change on agriculture, economics and environment. Recently, various BEFMs have been developed, often for one purpose or location, but hardly any of these models are re-used later for other purposes or locations. The Farm System Simulator (FSSIM) provides a generic framework enabling the application of BEFMs under various situations and for different purposes (generating supply response functions and detailed regional or farm type assessments). FSSIM is set up as a component-based framework with components representing farmer objectives, risk, calibration, policies, current activities, alternative activities and different types of activities (e.g., annual and perennial cropping and livestock). The generic nature of FSSIM is evaluated using five criteria by examining its applications. FSSIM has been applied for different climate zones and soil types (criterion 1) and to a range of different farm types (criterion 2) with different specializations, intensities and sizes. In most applications FSSIM has been used to assess the effects of policy changes and in two applications to assess the impact of technological innovations (criterion 3). In the various applications, different data sources, level of detail (e.g., criterion 4) and model configurations have been used. FSSIM has been linked to an economic and several biophysical models (criterion 5). The model is available for applications to other conditions and research issues, and it is open to be further tested and to be extended with new components, indicators or linkages to other models.

A Generic Bio-Economic Farm Model for Environmental and Economic Assessment of Agricultural Systems

PubMed Central

Louhichi, Kamel; Kanellopoulos, Argyris; Zander, Peter; Flichman, Guillermo; Hengsdijk, Huib; Meuter, Eelco; Andersen, Erling; Belhouchette, Hatem; Blanco, Maria; Borkowski, Nina; Heckelei, Thomas; Hecker, Martin; Li, Hongtao; Oude Lansink, Alfons; Stokstad, Grete; Thorne, Peter; van Keulen, Herman; van Ittersum, Martin K.

2010-01-01

Bio-economic farm models are tools to evaluate ex-post or to assess ex-ante the impact of policy and technology change on agriculture, economics and environment. Recently, various BEFMs have been developed, often for one purpose or location, but hardly any of these models are re-used later for other purposes or locations. The Farm System Simulator (FSSIM) provides a generic framework enabling the application of BEFMs under various situations and for different purposes (generating supply response functions and detailed regional or farm type assessments). FSSIM is set up as a component-based framework with components representing farmer objectives, risk, calibration, policies, current activities, alternative activities and different types of activities (e.g., annual and perennial cropping and livestock). The generic nature of FSSIM is evaluated using five criteria by examining its applications. FSSIM has been applied for different climate zones and soil types (criterion 1) and to a range of different farm types (criterion 2) with different specializations, intensities and sizes. In most applications FSSIM has been used to assess the effects of policy changes and in two applications to assess the impact of technological innovations (criterion 3). In the various applications, different data sources, level of detail (e.g., criterion 4) and model configurations have been used. FSSIM has been linked to an economic and several biophysical models (criterion 5). The model is available for applications to other conditions and research issues, and it is open to be further tested and to be extended with new components, indicators or linkages to other models. PMID:21113782

Assessing the mitigation potential of agricultural systems by optimization of the agricultural management: A modeling study on 8 agricultural observation sites across Europe with the process based model LandscapeDNDC

NASA Astrophysics Data System (ADS)

Molina Herrera, Saul; Haas, Edwin; Klatt, Steffen; Kraus, David; Kiese, Ralf; Butterbach-Bahl, Klaus

2014-05-01

The use of mineral nitrogen (N) fertilizers increase crop yields but cause the biggest anthropogenic source of nitrous oxide (N2O) emissions and strongly contribute to surface water eutrophication (e.g. nitrate leaching). The necessity to identify affordable strategies that improve crop production while improving ecosystem services are in continuous debate between policy decision makers and farmers. In this line, a lack commitment from farmers to enforce laws might result in the reduction of benefits. For this reason, farmers should aim to increase crop production and to reduce environmental harm by the adoption of precision climate smart agriculture tools applied to management practices for instance. In this study we present optimized strategies for 8 sites (agricultural and grassland ecosystems) with long term field observation across Europe to show the mitigation potential to reduce reactive nitrogen losses under the constrain of keeping yields at observed levels. LandscapeDNDC simulations of crop yields and associated nitrogen losses (N2O emissions and NO3 leaching) were evaluated against long term field measurements. The sites presented different management regimes including the main commodity crops (maize, wheat, barley, rape seeds, etc) and fertilization amendments (synthetic and organic fertilizers) in Europe. The simulations reproduced the observed yields, captured N2O emissions and NO3 leaching losses with high statistical presicion (r2), acurrency (ME) and agreement (RMSPEn). The mitigation potentials to reduce N losses while keeping yields at observed levels for all 8 sites were assesed by Monte Carlo optimizations of the individual underlying multi year agricultural management options (timings of planting and harvest, fertilization & manure applications and rates, residues management). In this study we present for all 8 agricultural observations sites their individual mitigation potentials to reduce N losses for multi year rotations. The conclusions

Agricultural production and stability of settlement systems in Upper Mesopotamia during the Early Bronze Age (third millennium BCE)

NASA Astrophysics Data System (ADS)

Kalayci, Tuna

This study investigates the relationship between rainfall variation and rain-fed agricultural production in Upper Mesopotamia with a specific focus on Early Bronze Age urban settlements. In return, the variation in production is used to explore stability of urban settlement systems. The organization of the flow of agricultural goods is the key to sustaining the total settlement system. The vulnerability of a settlement system increases due to the increased demand for more output from agricultural lands. This demand is the key for the success of urbanization project. However, without estimating how many foodstuffs were available at the end of a production cycle, further discussions on the forces that shaped and sustained urban settlement systems will be lacking. While large scale fluctuations in the flow of agricultural products between settlements are not the only determinants of hierarchical structures, the total available agricultural yield for each urban settlement in a hierarchy must have influenced settlement relations. As for the methodology, first, Early Bronze Age precipitation levels are estimated by using modern day associations between the eastern Mediterranean coastal areas and the inner regions of Upper Mesopotamia. Next, these levels are integrated into a remote-sensing based biological growth model. Also, a CORONA satellite imagery based archaeological survey is conducted in order to map the Early Bronze Age settlement system in its entirety as well as the ancient markers of agricultural intensification. Finally, ancient agricultural production landscapes are modeled in a GIS. The study takes a critical position towards the traditionally held assumption that large urban settlements (cities) in Upper Mesopotamia were in a state of constant demand for food. The results from this study also suggest that when variations in ancient precipitation levels are translated into the variations in production levels, the impact of climatic aridification on ancient

Implementation of AN Agricultural Environmental Information System (aeis) for the Sanjiang Plain, Ne-China

NASA Astrophysics Data System (ADS)

Zhao, Q.; Brocks, S.; Lenz-Wiedemann, V.; Miao, Y.; Jiang, R.; Chen, X.; Zhang, F.; Bareth, G.

2012-07-01

The Sino-German Project between the China Agricultural University and the University of Cologne, Germany, focuses on regional agro-ecosystem modelling. One major focus of the cooperation activity is the establishment of joint rice field experiment research in Jiansanjiang, located in the Sanjiang Plain (Heilongjiang Province, north-eastern part of China), to investigate the different agricultural practices and their impact on yield and environment. An additional task is to set-up an Agricultural Environmental Information System (AEIS) for the Sanjiang Plain (SJP), which covers more than 100 000 km2. Research groups from Geography (e.g. GIS & Remote Sensing) and Plant Nutrition (e.g. Precision Agriculture) are involved in the project. The major aim of the AEIS for the SJP is to provide information about (i) agriculture in the region, (ii) the impact of agricultural practices on the environment, and (iii) simulation scenarios for sustainable strategies. Consequently, the AEIS for the SJP provides information for decision support and therefore could be regarded as a Spatial Decision Support System (SDSS), too. The investigation of agricultural and environmental issues has a spatial context, which requires the management, handling, and analysis of spatial data. The use of GIS enables the capture, storage, analysis and presentation of spatial data. Therefore, GIS is the major tool for the set-up of the AEIS for the SJP. This contribution presents the results of linking agricultural statistics with GIS to provide information about agriculture in the SJP and discusses the benefits of this method as well as the integration of methods to produce new data.

Direct Nitrous Oxide Emissions From Tropical And Sub-Tropical Agricultural Systems - A Review And Modelling Of Emission Factors.

PubMed

Albanito, Fabrizio; Lebender, Ulrike; Cornulier, Thomas; Sapkota, Tek B; Brentrup, Frank; Stirling, Clare; Hillier, Jon

2017-03-10

There has been much debate about the uncertainties associated with the estimation of direct and indirect agricultural nitrous oxide (N 2 O) emissions in developing countries and in particular from tropical regions. In this study, we report an up-to-date review of the information published in peer-review journals on direct N 2 O emissions from agricultural systems in tropical and sub-tropical regions. We statistically analyze net-N 2 O-N emissions to estimate tropic-specific annual N 2 O emission factors (N 2 O-EFs) using a Generalized Additive Mixed Model (GAMM) which allowed the effects of multiple covariates to be modelled as linear or smooth non-linear continuous functions. Overall the mean N 2 O-EF was 1.2% for the tropics and sub-tropics, thus within the uncertainty range of IPCC-EF. On a regional basis, mean N 2 O-EFs were 1.4% for Africa, 1.1%, for Asia, 0.9% for Australia and 1.3% for Central &South America. Our annual N 2 O-EFs, estimated for a range of fertiliser rates using the available data, do not support recent studies hypothesising non-linear increase N 2 O-EFs as a function of applied N. Our findings highlight that in reporting annual N 2 O emissions and estimating N 2 O-EFs, particular attention should be paid in modelling the effect of study length on response of N 2 O.

Direct Nitrous Oxide Emissions From Tropical And Sub-Tropical Agricultural Systems - A Review And Modelling Of Emission Factors

PubMed Central

Albanito, Fabrizio; Lebender, Ulrike; Cornulier, Thomas; Sapkota, Tek B.; Brentrup, Frank; Stirling, Clare; Hillier, Jon

2017-01-01

There has been much debate about the uncertainties associated with the estimation of direct and indirect agricultural nitrous oxide (N2O) emissions in developing countries and in particular from tropical regions. In this study, we report an up-to-date review of the information published in peer-review journals on direct N2O emissions from agricultural systems in tropical and sub-tropical regions. We statistically analyze net-N2O-N emissions to estimate tropic-specific annual N2O emission factors (N2O-EFs) using a Generalized Additive Mixed Model (GAMM) which allowed the effects of multiple covariates to be modelled as linear or smooth non-linear continuous functions. Overall the mean N2O-EF was 1.2% for the tropics and sub-tropics, thus within the uncertainty range of IPCC-EF. On a regional basis, mean N2O-EFs were 1.4% for Africa, 1.1%, for Asia, 0.9% for Australia and 1.3% for Central & South America. Our annual N2O-EFs, estimated for a range of fertiliser rates using the available data, do not support recent studies hypothesising non-linear increase N2O-EFs as a function of applied N. Our findings highlight that in reporting annual N2O emissions and estimating N2O-EFs, particular attention should be paid in modelling the effect of study length on response of N2O. PMID:28281637

Direct Nitrous Oxide Emissions From Tropical And Sub-Tropical Agricultural Systems - A Review And Modelling Of Emission Factors

NASA Astrophysics Data System (ADS)

Albanito, Fabrizio; Lebender, Ulrike; Cornulier, Thomas; Sapkota, Tek B.; Brentrup, Frank; Stirling, Clare; Hillier, Jon

2017-03-01

There has been much debate about the uncertainties associated with the estimation of direct and indirect agricultural nitrous oxide (N2O) emissions in developing countries and in particular from tropical regions. In this study, we report an up-to-date review of the information published in peer-review journals on direct N2O emissions from agricultural systems in tropical and sub-tropical regions. We statistically analyze net-N2O-N emissions to estimate tropic-specific annual N2O emission factors (N2O-EFs) using a Generalized Additive Mixed Model (GAMM) which allowed the effects of multiple covariates to be modelled as linear or smooth non-linear continuous functions. Overall the mean N2O-EF was 1.2% for the tropics and sub-tropics, thus within the uncertainty range of IPCC-EF. On a regional basis, mean N2O-EFs were 1.4% for Africa, 1.1%, for Asia, 0.9% for Australia and 1.3% for Central & South America. Our annual N2O-EFs, estimated for a range of fertiliser rates using the available data, do not support recent studies hypothesising non-linear increase N2O-EFs as a function of applied N. Our findings highlight that in reporting annual N2O emissions and estimating N2O-EFs, particular attention should be paid in modelling the effect of study length on response of N2O.

Diffuse nitrogen loss simulation and impact assessment of stereoscopic agriculture pattern by integrated water system model and consideration of multiple existence forms

NASA Astrophysics Data System (ADS)

Zhang, Yongyong; Gao, Yang; Yu, Qiang

2017-09-01

Agricultural nitrogen loss becomes an increasingly important source of water quality deterioration and eutrophication, even threatens water safety for humanity. Nitrogen dynamic mechanism is still too complicated to be well captured at watershed scale due to its multiple existence forms and instability, disturbance of agricultural management practices. Stereoscopic agriculture is a novel agricultural planting pattern to efficiently use local natural resources (e.g., water, land, sunshine, heat and fertilizer). It is widely promoted as a high yield system and can obtain considerable economic benefits, particularly in China. However, its environmental quality implication is not clear. In our study, Qianyanzhou station is famous for its stereoscopic agriculture pattern of Southern China, and an experimental watershed was selected as our study area. Regional characteristics of runoff and nitrogen losses were simulated by an integrated water system model (HEQM) with multi-objective calibration, and multiple agriculture practices were assessed to find the effective approach for the reduction of diffuse nitrogen losses. Results showed that daily variations of runoff and nitrogen forms were well reproduced throughout watershed, i.e., satisfactory performances for ammonium and nitrate nitrogen (NH4-N and NO3-N) loads, good performances for runoff and organic nitrogen (ON) load, and very good performance for total nitrogen (TN) load. The average loss coefficient was 62.74 kg/ha for NH4-N, 0.98 kg/ha for NO3-N, 0.0004 kg/ha for ON and 63.80 kg/ha for TN. The dominating form of nitrogen losses was NH4-N due to the applied fertilizers, and the most dramatic zones aggregated in the middle and downstream regions covered by paddy and orange orchard. In order to control diffuse nitrogen losses, the most effective practices for Qianyanzhou stereoscopic agriculture pattern were to reduce farmland planting scale in the valley by afforestation, particularly for orchard in the

Psychiatric agriculture: systemic nutritional modification and mental health in the developing world.

PubMed

London, Douglas S; Stoll, Andrew L; Manning, Bruce B

2006-01-01

Modernization of agricultural systems to increase output causes changes to the nutritional content of food entire populations consume. Human nutritional needs differ from their "food", thus producing healthy agricultural products is not equivalent to providing agricultural products that are healthy for humans. Inclusion of the food production system as a factor in the increase of neuropsychiatric disorders and other chronic diseases helps explain negative trends in modern chronic diseases that remain unchecked despite stunning advances in modern medicine. Diseases in which our own technology plays a significant role include obesity and resulting disorders, such as diabetes, heart disease, hypertension, stroke and arthritis. Modernization's lure leads to importation of modern agricultural practices into a nutritionally vulnerable, malnourished and sometimes starving developing world. Wealthier nations hedge their food portfolio by having access to a wider variety of foods. The developing world's reliance on staple foods means even a minor widespread nutritional modification of one key food can have profound effects. New agricultural techniques may improve or exacerbate neuropsychiatric disorders through nutritional modification in regions where populations walk a nutritional tightrope with little margin for error. In most of the developing world western psychiatric interventions have failed to make inroads. People's consumption of fish has a demonstrated beneficial effect on their mental health and the omega-3 fatty acid content is a significant factor. Epidemiological, biological and agricultural studies implicate a lack of dietary omega-3s as a factor in certain mental disorders. Replenishing omega-3s has improved mental illnesses in controlled clinical trials. This article's detailed tilapia fish-farming model demonstrates how aquaculture/agriculture techniques can function as a public health intervention by increasing dietary omega-3s through creation of

The role of country-to-region assignments in global integrated modeling of energy, agriculture, land use, and climate

NASA Astrophysics Data System (ADS)

Kyle, P.; Patel, P.; Calvin, K. V.

2014-12-01

Global integrated assessment models used for understanding the linkages between the future energy, agriculture, and climate systems typically represent between 8 and 30 geopolitical macro-regions, balancing the benefits of geographic resolution with the costs of additional data collection, processing, analysis, and computing resources. As these models are continually being improved and updated in order to address new questions for the research and policy communities, it is worth examining the consequences of the country-to-region mapping schemes used for model results. This study presents an application of a data processing system built for the GCAM integrated assessment model that allows any country-to-region assignments, with a minimum of four geopolitical regions and a maximum of 185. We test ten different mapping schemes, including the specific mappings used in existing major integrated assessment models. We also explore the impacts of clustering nations into regions according to the similarity of the structure of each nation's energy and agricultural sectors, as indicated by multivariate analysis. Scenarios examined include a reference scenario, a low-emissions scenario, and scenarios with agricultural and buildings sector climate change impacts. We find that at the global level, the major output variables (primary energy, agricultural land use) are surprisingly similar regardless of regional assignments, but at finer geographic scales, differences are pronounced. We suggest that enhancing geographic resolution is advantageous for analysis of climate impacts on the buildings and agricultural sectors, due to the spatial heterogeneity of these drivers.

Effects of dynamic agricultural decision making in an ecohydrological model

NASA Astrophysics Data System (ADS)

Reichenau, T. G.; Krimly, T.; Schneider, K.

2012-04-01

Due to various interdependencies between the cycles of water, carbon, nitrogen, and energy the impacts of climate change on ecohydrological systems can only be investigated in an integrative way. Furthermore, the human intervention in the environmental processes makes the system even more complex. On the one hand human impact affects natural systems. On the other hand the changing natural systems have a feedback on human decision making. One of the most important examples for this kind of interaction can be found in the agricultural sector. Management dates (planting, fertilization, harvesting) are chosen based on meteorological conditions and yield expectations. A faster development of crops under a warmer climate causes shorter cropping seasons. The choice of crops depends on their profitability, which is mainly determined by market prizes, the agro-political framework, and the (climate dependent) crop yield. This study investigates these relations for the district Günzburg located in the Upper Danube catchment in southern Germany. The modeling system DANUBIA was used to perform dynamically coupled simulations of plant growth, surface and soil hydrological processes, soil nitrogen transformations, and agricultural decision making. The agro-economic model simulates decisions on management dates (based on meteorological conditions and the crops' development state), on fertilization intensities (based on yield expectations), and on choice of crops (based on profitability). The environmental models included in DANUBIA are to a great extent process based to enable its use in a climate change scenario context. Scenario model runs until 2058 were performed using an IPCC A1B forcing. In consecutive runs, dynamic crop management, dynamic crop selection, and a changing agro-political framework were activated. Effects of these model features on hydrological and ecological variables were analyzed separately by comparing the results to a model run with constant crop

Estimating the agricultural fertilizer NH3 emission in China based on the bi-directional CMAQ model and an agro-ecosystem model

NASA Astrophysics Data System (ADS)

Wang, S.

2014-12-01

Atmospheric ammonia (NH3) plays an important role in fine particle formation. Accurate estimates of ammonia can reduce uncertainties in air quality modeling. China is one of the largest countries emitting ammonia with the majority of NH3 emissions coming from the agricultural practices, such as fertilizer applications and animal operations. The current ammonia emission estimates in China are mainly based on pre-defined emission factors. Thus, there are considerable uncertainties in estimating NH3 emissions, especially in time and space distribution. For example, fertilizer applications vary in the date of application and amount by geographical regions and crop types. In this study, the NH3 emission from the agricultural fertilizer use in China of 2011 was estimated online by an agricultural fertilizer modeling system coupling a regional air-quality model and an agro-ecosystem model, which contains three main components 1) the Environmental Policy Integrated Climate (EPIC) model, 2) the meso-scale meteorology Weather Research and Forecasting (WRF) model and 3) the CMAQ air quality model with bi-directional ammonia fluxes. The EPIC output information about daily fertilizer application and soil characteristics would be the input of the CMAQ model. In order to run EPIC model, much Chinese local information is collected and processed. For example, Crop land data are computed from the MODIS land use data at 500-m resolution and crop categories at Chinese county level; the fertilizer use rate for different fertilizer types, crops and provinces are obtained from Chinese statistic materials. The system takes into consideration many influencing factors on agriculture ammonia emission, including weather, the fertilizer application method, timing, amount, and rate for specific pastures and crops. The simulated fertilizer data is compared with the NH3 emissions and fertilizer application data from other sources. The results of CMAQ modeling are also discussed and analyzed with

Efficient mapping of agricultural soils using a novel electromagnetic measurement system

NASA Astrophysics Data System (ADS)

Trinks, Immo; Pregesbauer, Michael

2016-04-01

depth. Instead of being towed several metres behind the tractor, as common with traditional EMI systems used in precision farming, the novel device is conveniently mounted on the front hitch of a tractor and operated from a terminal in the driver's cabin. A major improvement compared with existing EMI systems is the system's capability to cope with the induced noise from the tractor, through integration of a mechanical shielding mechanism into the sensor housing. Any remaining vehicle induced high-frequency electromagnetic noise is filtered out on-the-fly by the data acquisition software, logging the data and positioning information on a ruggedized small computer. The main purpose of this system is to permit the land owner or farmer the efficient mapping of the electrical soil conductivity across agricultural fields on the scale of the entire acreage. The main objective of the measurements is to obtain detailed information on the long wavelength variability of soil structure, while eliminating short wavelength variations. The calculation of the depth of the agricultural layer, or topsoil thickness, has been implemented by inverting the cumulative response function for all coil configurations. The resulting inverted models of the soil conductivity display the vertical distribution of agriculturally relevant soil parameters and improve the chances to identify different subsoil features. By providing this information on the shallow subsurface in real-time, while passing across the field, permits the agriculturist to variably adjust for instance tillage depth or to control other agricultural implements and machines based to the derived information, rendering the soil cultivation both ecologically as well as economically more efficient. We present the TSM system as well as derived data examples.

An overview of crop growing condition monitoring in China agriculture remote sensing monitoring system

NASA Astrophysics Data System (ADS)

Huang, Qing; Zhou, Qing-bo; Zhang, Li

2009-07-01

China is a large agricultural country. To understand the agricultural production condition timely and accurately is related to government decision-making, agricultural production management and the general public concern. China Agriculture Remote Sensing Monitoring System (CHARMS) can monitor crop acreage changes, crop growing condition, agriculture disaster (drought, floods, frost damage, pest etc.) and predict crop yield etc. quickly and timely. The basic principles, methods and regular operation of crop growing condition monitoring in CHARMS are introduced in detail in the paper. CHARMS can monitor crop growing condition of wheat, corn, cotton, soybean and paddy rice with MODIS data. An improved NDVI difference model was used in crop growing condition monitoring in CHARMS. Firstly, MODIS data of every day were received and processed, and the max NDVI values of every fifteen days of main crop were generated, then, in order to assessment a certain crop growing condition in certain period (every fifteen days, mostly), the system compare the remote sensing index data (NDVI) of a certain period with the data of the period in the history (last five year, mostly), the difference between NDVI can indicate the spatial difference of crop growing condition at a certain period. Moreover, Meteorological data of temperature, precipitation and sunshine etc. as well as the field investigation data of 200 network counties were used to modify the models parameters. Last, crop growing condition was assessment at four different scales of counties, provinces, main producing areas and nation and spatial distribution maps of crop growing condition were also created.

The Agriculture Model Intercomparison and Improvement Project (AgMIP) (Invited)

NASA Astrophysics Data System (ADS)

Rosenzweig, C.

2010-12-01

The Agricultural Model Intercomparison and Improvement Project (AgMIP) is a distributed climate-scenario simulation exercise for historical model intercomparison and future climate change conditions with participation of multiple crop and world agricultural trade modeling groups around the world. The goals of AgMIP are to improve substantially the characterization of risk of hunger and world food security due to climate change and to enhance adaptation capacity in both developing and developed countries. Historical period results will spur model improvement and interaction among major modeling groups, while future period results will lead directly to tests of adaptation and mitigation strategies across a range of scales. AgMIP will consist of a multi-scale impact assessment utilizing the latest methods for climate and agricultural scenario generation. Scenarios and modeling protocols will be distributed on the web, and multi-model results will be collated and analyzed to ensure the widest possible coverage of agricultural crops and regions. AgMIP will place regional changes in agricultural production in a global context that reflects new trading opportunities, imbalances, and shortages in world markets resulting from climate change and other driving forces for food supply. Such projections are essential inputs from the Vulnerability, Impacts, and Adaptation (VIA) research community to the Intergovernmental Panel on Climate Change Fifth Assessment (AR5), now underway, and the UN Framework Convention on Climate Change. They will set the context for local-scale vulnerability and adaptation studies, supply test scenarios for national-scale development of trade policy instruments, provide critical information on changing supply and demand for water resources, and elucidate interactive effects of climate change and land use change. AgMIP will not only provide crucially-needed new global estimates of how climate change will affect food supply and hunger in the

Evaluating the Sensitivity of Agricultural Model Performance to Different Climate Inputs: Supplemental Material

NASA Technical Reports Server (NTRS)

Glotter, Michael J.; Ruane, Alex C.; Moyer, Elisabeth J.; Elliott, Joshua W.

2015-01-01

Projections of future food production necessarily rely on models, which must themselves be validated through historical assessments comparing modeled and observed yields. Reliable historical validation requires both accurate agricultural models and accurate climate inputs. Problems with either may compromise the validation exercise. Previous studies have compared the effects of different climate inputs on agricultural projections but either incompletely or without a ground truth of observed yields that would allow distinguishing errors due to climate inputs from those intrinsic to the crop model. This study is a systematic evaluation of the reliability of a widely used crop model for simulating U.S. maize yields when driven by multiple observational data products. The parallelized Decision Support System for Agrotechnology Transfer (pDSSAT) is driven with climate inputs from multiple sources reanalysis, reanalysis that is bias corrected with observed climate, and a control dataset and compared with observed historical yields. The simulations show that model output is more accurate when driven by any observation-based precipitation product than when driven by non-bias-corrected reanalysis. The simulations also suggest, in contrast to previous studies, that biased precipitation distribution is significant for yields only in arid regions. Some issues persist for all choices of climate inputs: crop yields appear to be oversensitive to precipitation fluctuations but under sensitive to floods and heat waves. These results suggest that the most important issue for agricultural projections may be not climate inputs but structural limitations in the crop models themselves.

Energy for agriculture. A computerized information retrieval system

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

Stout, B.A.; Myers, C.A.

Energy may come from the sun or the earth or be the product of plant materials or agricultural wastes. Whatever its source, energy is indispensable to our way of life, beginning with the production, processing, and distribution of abundant, high quality food and fiber supplies. This specialized bibliography on the subject of energy for agriculture contains 2613 citations to the literature for 1973 through May 1979. Originally issued by Michigan State University (MSU), it is being reprinted and distributed by the U.S. Department of Agriculture. The literature citations will be incorporated into AGRICOLA (Agricultural On-Line Access), the comprehensive bibliographic datamore » base maintained by Technical Information Systems (TIS), a component of USDA's Science and Education Administration (SEA). The citations and the listing of research projects will be combined with other relevant references to provide a continuously updated source of information on energy programs in the agricultural field. No abstracts are included.« less

The agroecological matrix as alternative to the land-sparing/agriculture intensification model.

PubMed

Perfecto, Ivette; Vandermeer, John

2010-03-30

Among the myriad complications involved in the current food crisis, the relationship between agriculture and the rest of nature is one of the most important yet remains only incompletely analyzed. Particularly in tropical areas, agriculture is frequently seen as the antithesis of the natural world, where the problem is framed as one of minimizing land devoted to agriculture so as to devote more to conservation of biodiversity and other ecosystem services. In particular, the "forest transition model" projects an overly optimistic vision of a future where increased agricultural intensification (to produce more per hectare) and/or increased rural-to-urban migration (to reduce the rural population that cuts forest for agriculture) suggests a near future of much tropical aforestation and higher agricultural production. Reviewing recent developments in ecological theory (showing the importance of migration between fragments and local extinction rates) coupled with empirical evidence, we argue that there is little to suggest that the forest transition model is useful for tropical areas, at least under current sociopolitical structures. A model that incorporates the agricultural matrix as an integral component of conservation programs is proposed. Furthermore, we suggest that this model will be most successful within a framework of small-scale agroecological production.

Identifying Hydrologic Processes in Agricultural Watersheds Using Precipitation-Runoff Models

USGS Publications Warehouse

Linard, Joshua I.; Wolock, David M.; Webb, Richard M.T.; Wieczorek, Michael

2009-01-01

Understanding the fate and transport of agricultural chemicals applied to agricultural fields will assist in designing the most effective strategies to prevent water-quality impairments. At a watershed scale, the processes controlling the fate and transport of agricultural chemicals are generally understood only conceptually. To examine the applicability of conceptual models to the processes actually occurring, two precipitation-runoff models - the Soil and Water Assessment Tool (SWAT) and the Water, Energy, and Biogeochemical Model (WEBMOD) - were applied in different agricultural settings of the contiguous United States. Each model, through different physical processes, simulated the transport of water to a stream from the surface, the unsaturated zone, and the saturated zone. Models were calibrated for watersheds in Maryland, Indiana, and Nebraska. The calibrated sets of input parameters for each model at each watershed are discussed, and the criteria used to validate the models are explained. The SWAT and WEBMOD model results at each watershed conformed to each other and to the processes identified in each watershed's conceptual hydrology. In Maryland the conceptual understanding of the hydrology indicated groundwater flow was the largest annual source of streamflow; the simulation results for the validation period confirm this. The dominant source of water to the Indiana watershed was thought to be tile drains. Although tile drains were not explicitly simulated in the SWAT model, a large component of streamflow was received from lateral flow, which could be attributed to tile drains. Being able to explicitly account for tile drains, WEBMOD indicated water from tile drains constituted most of the annual streamflow in the Indiana watershed. The Nebraska models indicated annual streamflow was composed primarily of perennial groundwater flow and infiltration-excess runoff, which conformed to the conceptual hydrology developed for that watershed. The hydrologic

A history of wind erosion prediction models in the United States Department of Agriculture: The Wind Erosion Prediction System (WEPS)

USDA-ARS?s Scientific Manuscript database

Development of the Wind Erosion Prediction System (WEPS) was officially inaugurated in 1985 by United States Department of Agriculture-Agricultural Research Service (USDA-ARS) scientists in response to customer requests, particularly those coming from the USDA Soil Conservation Service (SCS), for im...

An analysis of yield stability in a conservation agriculture system

USDA-ARS?s Scientific Manuscript database

Climate models predict increasing growing-season weather variability, with negative consequences for crop production. Maintaining agricultural productivity despite variability in weather (i.e., crop yield stability) will be critical to meeting growing global demand. Conservation agriculture is an ...

Resolving Multi-Stakeholder Robustness Asymmetries in Coupled Agricultural and Urban Systems

NASA Astrophysics Data System (ADS)

Li, Yu; Giuliani, Matteo; Castelletti, Andrea; Reed, Patrick

2016-04-01

The evolving pressures from a changing climate and society are increasingly motivating decision support frameworks that consider the robustness of management actions across many possible futures. Focusing on robustness is helpful for investigating key vulnerabilities within current water systems and for identifying potential tradeoffs across candidate adaptation responses. To date, most robustness studies assume a social planner perspective by evaluating highly aggregated measures of system performance. This aggregate treatment of stakeholders does not explore the equity or intrinsic multi-stakeholder conflicts implicit to the system-wide measures of performance benefits and costs. The commonly present heterogeneity across complex management interests, however, may produce strong asymmetries for alternative adaptation options, designed to satisfy system-level targets. In this work, we advance traditional robustness decision frameworks by replacing the centralized social planner with a bottom-up, agent-based approach, where stakeholders are modeled as individuals, and represented as potentially self-interested agents. This agent-based model enables a more explicit exploration of the potential inequities and asymmetries in the distribution of the system-wide benefit. The approach is demonstrated by exploring the potential conflicts between urban flooding and agricultural production in the Lake Como system (Italy). Lake Como is a regulated lake that is operated to supply water to the downstream agricultural district (Muzza as the pilot study area in this work) composed of a set of farmers with heterogeneous characteristics in terms of water allocation, cropping patterns, and land properties. Supplying water to farmers increases the risk of floods along the lakeshore and therefore the system is operated based on the tradeoff between these two objectives. We generated an ensemble of co-varying climate and socio-economic conditions and evaluated the robustness of the

A statistical model for radar images of agricultural scenes

NASA Technical Reports Server (NTRS)

Frost, V. S.; Shanmugan, K. S.; Holtzman, J. C.; Stiles, J. A.

1982-01-01

The presently derived and validated statistical model for radar images containing many different homogeneous fields predicts the probability density functions of radar images of entire agricultural scenes, thereby allowing histograms of large scenes composed of a variety of crops to be described. Seasat-A SAR images of agricultural scenes are accurately predicted by the model on the basis of three assumptions: each field has the same SNR, all target classes cover approximately the same area, and the true reflectivity characterizing each individual target class is a uniformly distributed random variable. The model is expected to be useful in the design of data processing algorithms and for scene analysis using radar images.

Flood damage modeling based on expert knowledge: Insights from French damage model for agricultural sector

NASA Astrophysics Data System (ADS)

Grelot, Frédéric; Agenais, Anne-Laurence; Brémond, Pauline

2015-04-01

In France, since 2011, it is mandatory for local communities to conduct cost-benefit analysis (CBA) of their flood management projects, to make them eligible for financial support from the State. Meanwhile, as a support, the French Ministry in charge of Environment proposed a methodology to fulfill CBA. Like for many other countries, this methodology is based on the estimation of flood damage. However, existing models to estimate flood damage were judged not convenient for a national-wide use. As a consequence, the French Ministry in charge of Environment launched studies to develop damage models for different sectors, such as: residential sector, public infrastructures, agricultural sector, and commercial and industrial sector. In this presentation, we aim at presenting and discussing methodological choices of those damage models. They all share the same principle: no sufficient data from past events were available to build damage models on a statistical analysis, so modeling was based on expert knowledge. We will focus on the model built for agricultural activities and more precisely for agricultural lands. This model was based on feedback from 30 agricultural experts who experienced floods in their geographical areas. They were selected to have a representative experience of crops and flood conditions in France. The model is composed of: (i) damaging functions, which reveal physiological vulnerability of crops, (ii) action functions, which correspond to farmers' decision rules for carrying on crops after a flood, and (iii) economic agricultural data, which correspond to featured characteristics of crops in the geographical area where the flood management project studied takes place. The two first components are generic and the third one is specific to the area studied. It is, thus, possible to produce flood damage functions adapted to different agronomic and geographical contexts. In the end, the model was applied to obtain a pool of damage functions giving

Flood damage modeling based on expert knowledge: Insights from French damage model for agricultural sector

NASA Astrophysics Data System (ADS)

Grelot, Frédéric; Agenais, Anne-Laurence; Brémond, Pauline

2014-05-01

In France, since 2011, it is mandatory for local communities to conduct cost-benefit analysis (CBA) of their flood management projects, to make them eligible for financial support from the State. Meanwhile, as a support, the French Ministry in charge of Environment proposed a methodology to fulfill CBA. Like for many other countries, this methodology is based on the estimation of flood damage. Howerver, existing models to estimate flood damage were judged not convenient for a national-wide use. As a consequence, the French Ministry in charge of Environment launched studies to develop damage models for different sectors, such as: residential sector, public infrastructures, agricultural sector, and commercial and industrial sector. In this presentation, we aim at presenting and discussing methodological choices of those damage models. They all share the same principle: no sufficient data from past events were available to build damage models on a statistical analysis, so modeling was based on expert knowledge. We will focus on the model built for agricultural activities and more precisely for agricultural lands. This model was based on feedback from 30 agricultural experts who experienced floods in their geographical areas. They were selected to have a representative experience of crops and flood conditions in France. The model is composed of: (i) damaging functions, which reveal physiological vulnerability of crops, (ii) action functions, which correspond to farmers' decision rules for carrying on crops after a flood, and (iii) economic agricultural data, which correspond to featured characteristics of crops in the geographical area where the flood management project studied takes place. The two first components are generic and the third one is specific to the area studied. It is, thus, possible to produce flood damage functions adapted to different agronomic and geographical contexts. In the end, the model was applied to obtain a pool of damage functions giving

Design of agricultural product quality safety retrospective supervision system of Jiangsu province

NASA Astrophysics Data System (ADS)

Wang, Kun

2017-08-01

In store and supermarkets to consumers can trace back agricultural products through the electronic province card to query their origin, planting, processing, packaging, testing and other important information and found that the problems. Quality and safety issues can identify the responsibility of the problem. This paper designs a retroactive supervision system for the quality and safety of agricultural products in Jiangsu Province. Based on the analysis of agricultural production and business process, the goal of Jiangsu agricultural product quality safety traceability system construction is established, and the specific functional requirements and non-functioning requirements of the retroactive system are analyzed, and the target is specified for the specific construction of the retroactive system. The design of the quality and safety traceability system in Jiangsu province contains the design of the overall design, the trace code design and the system function module.

Modeling GHG Emissions and Carbon Changes in Agricultural and Forest Systems to Guide Mitigation and Adaptation: Synthesis and Future Needs

USDA-ARS?s Scientific Manuscript database

Agricultural production systems and land use change for agriculture and forestry are important sources of anthropogenic greenhouse gas (GHG) emissions. Recent commitments by the European Union, the United States, and China to reduce GHG emissions highlight the need to improve estimates of current em...

Integrating a Detailed Agricultural Model in a Global Economic Framework: New methods for assessment of climate mitigation and adaptation opportunities

NASA Astrophysics Data System (ADS)

Thomson, A. M.; Izaurralde, R. C.; Calvin, K.; Zhang, X.; Wise, M.; West, T. O.

2010-12-01

Climate change and food security are global issues increasingly linked through human decision making that takes place across all scales from on-farm management actions to international climate negotiations. Understanding how agricultural systems can respond to climate change, through mitigation or adaptation, while still supplying sufficient food to feed a growing global population, thus requires a multi-sector tool in a global economic framework. Integrated assessment models are one such tool, however they are typically driven by historical aggregate statistics of production in combination with exogenous assumptions of future trends in agricultural productivity; they are not yet capable of exploring agricultural management practices as climate adaptation or mitigation strategies. Yet there are agricultural models capable of detailed biophysical modeling of farm management and climate impacts on crop yield, soil erosion and C and greenhouse gas emissions, although these are typically applied at point scales that are incompatible with coarse resolution integrated assessment modeling. To combine the relative strengths of these modeling systems, we are using the agricultural model EPIC (Environmental Policy Integrated Climate), applied in a geographic data framework for regional analyses, to provide input to the global economic model GCAM (Global Change Assessment Model). The initial phase of our approach focuses on a pilot region of the Midwest United States, a highly productive agricultural area. We apply EPIC, a point based biophysical process model, at 60 m spatial resolution within this domain and aggregate the results to GCAM agriculture and land use subregions for the United States. GCAM is then initialized with multiple management options for key food and bioenergy crops. Using EPIC to distinguish these management options based on grain yield, residue yield, soil C change and cost differences, GCAM then simulates the optimum distribution of the available

The PEDA Model. An advocacy tool modeling the interrelationships between population, development, the environment and agriculture in Africa.

PubMed

1999-01-01

This article reports on the PEDA (population changes, environment, socioeconomic development and agriculture) model and its implication for policy-making in Africa. PEDA is an interactive computer simulation model (developed for a Windows environment) demonstrating the long-term impacts of alternative national policies on food security status of the population. The model is based on multistate demographic techniques, projecting at the same time 8 different subgroups (by age and sex) in the population, and based on 3 dichotomous individual characteristics: urban/rural place of residence; literacy status; and food security status. Through the manipulation of scenario variables, the model enables the user to project the proportion of the population that will be food secure and food insecure for a chosen point in time. This model developed by Dr. W. Lutz, Director of the International Institute for Applied Systems Analysis, will serve as an advocacy tool to help convince policy-makers and country experts in Africa of the negative synergy arising from the interconnections of population growth, environmental deterioration, and declining agricultural production.

Linking knowledge and action through mental models of sustainable agriculture.

PubMed

Hoffman, Matthew; Lubell, Mark; Hillis, Vicken

2014-09-09

Linking knowledge to action requires understanding how decision-makers conceptualize sustainability. This paper empirically analyzes farmer "mental models" of sustainability from three winegrape-growing regions of California where local extension programs have focused on sustainable agriculture. The mental models are represented as networks where sustainability concepts are nodes, and links are established when a farmer mentions two concepts in their stated definition of sustainability. The results suggest that winegrape grower mental models of sustainability are hierarchically structured, relatively similar across regions, and strongly linked to participation in extension programs and adoption of sustainable farm practices. We discuss the implications of our findings for the debate over the meaning of sustainability, and the role of local extension programs in managing knowledge systems.

Sustainable agriculture, renewable energy and rural development: An analysis of bio-energy systems used by small farms in China

NASA Astrophysics Data System (ADS)

Zhou, Aiming

Renewable energy needs to be incorporated into the larger picture of sustainable agriculture and rural development if it is to serve the needs of the 3.25 billion human beings whose livelihoods and based on rural economies and ecologies. For rural communities, increasing agriculture production is key to raising income generation and improving social well-being, but this linkage depends also upon not harming natural resources. This dissertation provides an overview of recent Chinese agriculture history, discusses the role of energy in contemporary's China's agriculture and rural development, and introduces a new approach---the integrated agricultural bio-energy (IAB) system---to address the challenge of sustainable agriculture and rural development. IAB is an innovative design and offers a renewable energy solution for improving agricultural productivity, realizing efficient resource management, and enhancing social well-being for rural development. In order to understand how the IAB system can help to achieve sustainable agricultural and rural development in China, a comprehensive evaluation methodology is developed from health, ecological, energy and economic (HE3) perspectives. With data from surveys of 200 small farm households, a detailed study of IAB and conventional agricultural energy (CAE) system applications (in China's Liaoning and Yunnan Province) is conducted. The HE3 impacts of IAB systems in China's rural areas (compared to existing CAE systems) are quantified. The dissertation analyzes the full life-cycle costs and benefits of IAB systems, including their contributions to energy savings, CO2 emissions reduction, agricultural waste reduction, increased rural incomes, better rural health, and improved ecosystem sustainability. The analysis relies upon qualitative and quantitative modeling in order to produce a comprehensive assessment of IAB system impacts. Finally, the dissertation discusses the barriers to greater diffusion of the IAB systems

Modelling of agricultural diffuse pollution and mitigation measures effectiveness in Wallonia (Belgium)

NASA Astrophysics Data System (ADS)

Sohier, C.; Deraedt, D.; Degré, A.

2012-04-01

Implementation of European directives in the environmental field and, specially, in the water management field, generates a request from policy-makers for news tools able to evaluate impact of management measures aiming at reducing pressures on ecosystems. In Wallonia (Southern Region of Belgium), the Nitrate Directive (EEC/676/91) was transposed into the "Walloon action plan for nitrogen sustainable management in agriculture" (PGDA1) in 2002. In 2007, a second plan was launched to reinforce some topics (PGDA2). Furthermore, the goal of "good quality" of surface waters and groundwater imposed by the Water Framework Directive poses new challenges in water management. In this context, a "soil and vadose" hydrological model is used in order to evaluate diffuse pollutions and efficiency of mitigation measures. This model, called EPICgrid, has been developed at catchment scale with an original modular concept on the basis of the field scale "water-soil-plant" EPIC model (Williams J.R., Jones C.A., Dyke P.T. (1984). A modelling approach to determining the relationship between erosion and soil productivity. Transactions of the ASAE. 27, 129-144). The model estimates, for each HRU identified into a 1km2 grid, water and nutrients flows into the plant-soil-vadose zone system (Sohier C., Degré A., Dautrebande S. (2009). From root zone modelling to regional forecasting of nitrate concentration in recharge flows - The case of the Walloon Region (Belgium). Journal of Hydrology, Volume 369, Issues 3-4, 15 May 2009, Pages 350-359). The model is used to make prospective simulations in order to evaluate the impact of measures currently performed to reduce the effect of diffuse pollution on water surface quality and groundwater quality, at regional scale. Response of the soil-vadose zone to agricultural practices modification is analyzed for the deadlines of the Water Framework Directive: 2015, 2021 and 2027, taking into account two climatic scenarios. Simulations results showed

Changes in Information Systems in Czech Agriculture

ERIC Educational Resources Information Center

Slavik, Milan

2004-01-01

A study carried out in 1998 (reported in the Journal of Agricultural Education and Extension, 2003) of the information systems used by farmers in the Czech Republic to access information and advice was repeated in 2003. The research aim was to assess whether, and how, the systems had changed during these five years. The perceived importance of 10…

Software for pest-management science: computer models and databases from the United States Department of Agriculture-Agricultural Research Service.

PubMed

Wauchope, R Don; Ahuja, Lajpat R; Arnold, Jeffrey G; Bingner, Ron; Lowrance, Richard; van Genuchten, Martinus T; Adams, Larry D

2003-01-01

We present an overview of USDA Agricultural Research Service (ARS) computer models and databases related to pest-management science, emphasizing current developments in environmental risk assessment and management simulation models. The ARS has a unique national interdisciplinary team of researchers in surface and sub-surface hydrology, soil and plant science, systems analysis and pesticide science, who have networked to develop empirical and mechanistic computer models describing the behavior of pests, pest responses to controls and the environmental impact of pest-control methods. Historically, much of this work has been in support of production agriculture and in support of the conservation programs of our 'action agency' sister, the Natural Resources Conservation Service (formerly the Soil Conservation Service). Because we are a public agency, our software/database products are generally offered without cost, unless they are developed in cooperation with a private-sector cooperator. Because ARS is a basic and applied research organization, with development of new science as our highest priority, these products tend to be offered on an 'as-is' basis with limited user support except for cooperating R&D relationship with other scientists. However, rapid changes in the technology for information analysis and communication continually challenge our way of doing business.

Challenges of agricultural monitoring: integration of the Open Farm Management Information System into GEOSS and Digital Earth

NASA Astrophysics Data System (ADS)

Řezník, T.; Kepka, M.; Charvát, K.; Charvát, K., Jr.; Horáková, S.; Lukas, V.

2016-04-01

From a global perspective, agriculture is the single largest user of freshwater resources, each country using an average of 70% of all its surface water supplies. An essential proportion of agricultural water is recycled back to surface water and/or groundwater. Agriculture and water pollution is therefore the subject of (inter)national legislation, such as the Clean Water Act in the United States of America, the European Water Framework Directive, and the Law of the People's Republic of China on the Prevention and Control of Water Pollution. Regular monitoring by means of sensor networks is needed in order to provide evidence of water pollution in agriculture. This paper describes the benefits of, and open issues stemming from, regular sensor monitoring provided by an Open Farm Management Information System. Emphasis is placed on descriptions of the processes and functionalities available to users, the underlying open data model, and definitions of open and lightweight application programming interfaces for the efficient management of collected (spatial) data. The presented Open Farm Management Information System has already been successfully registered under Phase 8 of the Global Earth Observation System of Systems (GEOSS) Architecture Implementation Pilot in order to support the wide variety of demands that are primarily aimed at agriculture pollution monitoring. The final part of the paper deals with the integration of the Open Farm Management Information System into the Digital Earth framework.

Systems Biology for Smart Crops and Agricultural Innovation: Filling the Gaps between Genotype and Phenotype for Complex Traits Linked with Robust Agricultural Productivity and Sustainability

PubMed Central

Pathak, Rajesh Kumar; Gupta, Sanjay Mohan; Gaur, Vikram Singh; Pandey, Dinesh

2015-01-01

Abstract In recent years, rapid developments in several omics platforms and next generation sequencing technology have generated a huge amount of biological data about plants. Systems biology aims to develop and use well-organized and efficient algorithms, data structure, visualization, and communication tools for the integration of these biological data with the goal of computational modeling and simulation. It studies crop plant systems by systematically perturbing them, checking the gene, protein, and informational pathway responses; integrating these data; and finally, formulating mathematical models that describe the structure of system and its response to individual perturbations. Consequently, systems biology approaches, such as integrative and predictive ones, hold immense potential in understanding of molecular mechanism of agriculturally important complex traits linked to agricultural productivity. This has led to identification of some key genes and proteins involved in networks of pathways involved in input use efficiency, biotic and abiotic stress resistance, photosynthesis efficiency, root, stem and leaf architecture, and nutrient mobilization. The developments in the above fields have made it possible to design smart crops with superior agronomic traits through genetic manipulation of key candidate genes. PMID:26484978

The agricultural model intercomparison and improvement project (AgMIP): Protocols and pilot studies

USDA-ARS?s Scientific Manuscript database

The Agricultural Model Intercomparison and Improvement Project (AgMIP) is a distributed climate-scenario simulation research activity for historical period model intercomparison and future climate change conditions with participation of multiple crop and agricultural economic model groups around the...

A component-based system for agricultural drought monitoring by remote sensing.

PubMed

Dong, Heng; Li, Jun; Yuan, Yanbin; You, Lin; Chen, Chao

2017-01-01

In recent decades, various kinds of remote sensing-based drought indexes have been proposed and widely used in the field of drought monitoring. However, the drought-related software and platform development lag behind the theoretical research. The current drought monitoring systems focus mainly on information management and publishing, and cannot implement professional drought monitoring or parameter inversion modelling, especially the models based on multi-dimensional feature space. In view of the above problems, this paper aims at fixing this gap with a component-based system named RSDMS to facilitate the application of drought monitoring by remote sensing. The system is designed and developed based on Component Object Model (COM) to ensure the flexibility and extendibility of modules. RSDMS realizes general image-related functions such as data management, image display, spatial reference management, image processing and analysis, and further provides drought monitoring and evaluation functions based on internal and external models. Finally, China's Ningxia region is selected as the study area to validate the performance of RSDMS. The experimental results show that RSDMS provide an efficient and scalable support to agricultural drought monitoring.

Nanotechnology Applications and Implications of Agrochemicals toward Sustainable Agriculture and Food Systems.

PubMed

Scott, Norman R; Chen, Hongda; Cui, Haixin

2018-06-08

The first international conference on Nanotechnology Applications and Implications of Agrochemicals toward Sustainable Agriculture and Food Systems was held in Beijing, China on November 17-18, 2016 to address and exchange latest knowledge and developments in nanotechnology of agrochemicals toward sustainable agriculture and food systems. World-leading scientists gathered to discuss a wide range of relevant topics. The purposes of this paper are to provide: an introduction to the international conference, summarize in brief the contributions of papers that follow within this special issue of Journal of Agricultural and Food Chemistry, provide a synthesis of conference outcomes, suggest future directions including an important role of converging science and technologies to advance sustainable agriculture, food, and natural resource systems.

Autonomous agricultural remote sensing systems with high spatial and temporal resolutions

NASA Astrophysics Data System (ADS)

Xiang, Haitao

In this research, two novel agricultural remote sensing (RS) systems, a Stand-alone Infield Crop Monitor RS System (SICMRS) and an autonomous Unmanned Aerial Vehicles (UAV) based RS system have been studied. A high-resolution digital color and multi-spectral camera was used as the image sensor for the SICMRS system. An artificially intelligent (AI) controller based on artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS) was developed. Morrow Plots corn field RS images in the 2004 and 2006 growing seasons were collected by the SICMRS system. The field site contained 8 subplots (9.14 m x 9.14 m) that were planted with corn and three different fertilizer treatments were used among those subplots. The raw RS images were geometrically corrected, resampled to 10cm resolution, removed soil background and calibrated to real reflectance. The RS images from two growing seasons were studied and 10 different vegetation indices were derived from each day's image. The result from the image processing demonstrated that the vegetation indices have temporal effects. To achieve high quality RS data, one has to utilize the right indices and capture the images at the right time in the growing season. Maximum variations among the image data set are within the V6-V10 stages, which indicated that these stages are the best period to identify the spatial variability caused by the nutrient stress in the corn field. The derived vegetation indices were also used to build yield prediction models via the linear regression method. At that point, all of the yield prediction models were evaluated by comparing the R2-value and the best index model from each day's image was picked based on the highest R 2-value. It was shown that the green normalized difference vegetation (GNDVI) based model is more sensitive to yield prediction than other indices-based models. During the VT-R4 stages, the GNDVI based models were able to explain more than 95% potential corn yield

Modeling the cadmium balance in Australian agricultural systems in view of potential impacts on food and water quality.

PubMed

de Vries, W; McLaughlin, M J

2013-09-01

The historical build up and future cadmium (Cd) concentrations in top soils and in crops of four Australian agricultural systems are predicted with a mass balance model, focusing on the period 1900-2100. The systems include a rotation of dryland cereals, a rotation of sugarcane and peanuts/soybean, intensive dairy production and intensive horticulture. The input of Cd to soil is calculated from fertilizer application and atmospheric deposition and also examines options including biosolid and animal manure application in the sugarcane rotation and dryland cereal production systems. Cadmium output from the soil is calculated from leaching to deeper horizons and removal with the harvested crop or with livestock products. Parameter values for all Cd fluxes were based on a number of measurements on Australian soil-plant systems. In the period 1900-2000, soil Cd concentrations were predicted to increase on average between 0.21 mg kg(-1) in dryland cereals, 0.42 mg kg(-1) in intensive agriculture and 0.68 mg kg(-1) in dairy production, which are within the range of measured increases in soils in these systems. Predicted soil concentrations exceed critical soil Cd concentrations, based on food quality criteria for Cd in crops during the simulation period in clay-rich soils under dairy production and intensive horticulture. Predicted dissolved Cd concentrations in soil pore water exceed a ground water quality criterion of 2 μg l(-1) in light textured soils, except for the sugarcane rotation due to large water leaching fluxes. Results suggest that the present fertilizer Cd inputs in Australia are in excess of the long-term critical loads in heavy-textured soils for dryland cereals and that all other systems are at low risk. Calculated critical Cd/P ratios in P fertilizers vary from <50 to >1000 mg Cd kg P(-1) for the different soil, crop and environmental conditions applied. Copyright © 2013 Elsevier B.V. All rights reserved.

What good are unmanned aircraft systems for agricultural remote sensing and precision agriculture?

USDA-ARS?s Scientific Manuscript database

Civilian applications of unmanned aircraft systems (UAS, also called drones) are rapidly expanding into crop production. UAS acquire high spatial resolution remote sensing imagery that can be used three different ways in agriculture. One is to assist crop scouts looking for problems in crop fields....

How to design a targeted agricultural subsidy system: efficiency or equity?

PubMed

Cong, Rong-Gang; Brady, Mark

2012-01-01

In this paper we appraise current agricultural subsidy policy in the EU. Several sources of its inefficiency are identified: it is inefficient for supporting farmers' incomes or guaranteeing food security, and irrational transfer payments decoupled from actual performance that may be negative for environmental protection, social cohesion, etc. Based on a simplified economic model, we prove that there is "reverse redistribution" in the current tax-subsidy system, which cannot be avoided. To find a possible way to distribute subsidies more efficiently and equitably, several alternative subsidy systems (the pure loan, the harvest tax and the income contingent loan) are presented and examined.

Application of the WEPS and SWEEP models to non-agricultural disturbed lands.

PubMed

Tatarko, J; van Donk, S J; Ascough, J C; Walker, D G

2016-12-01

Wind erosion not only affects agricultural productivity but also soil, air, and water quality. Dust and specifically particulate matter ≤10 μm (PM-10) has adverse effects on respiratory health and also reduces visibility along roadways, resulting in auto accidents. The Wind Erosion Prediction System (WEPS) was developed by the USDA-Agricultural Research Service to simulate wind erosion and provide for conservation planning on cultivated agricultural lands. A companion product, known as the Single-Event Wind Erosion Evaluation Program (SWEEP), has also been developed which consists of the stand-alone WEPS erosion submodel combined with a graphical interface to simulate soil loss from single (i.e., daily) wind storm events. In addition to agricultural lands, wind driven dust emissions also occur from other anthropogenic sources such as construction sites, mined and reclaimed areas, landfills, and other disturbed lands. Although developed for agricultural fields, WEPS and SWEEP are useful tools for simulating erosion by wind for non-agricultural lands where typical agricultural practices are not employed. On disturbed lands, WEPS can be applied for simulating long-term (i.e., multi-year) erosion control strategies. SWEEP on the other hand was developed specifically for disturbed lands and can simulate potential soil loss for site- and date-specific planned surface conditions and control practices. This paper presents novel applications of WEPS and SWEEP for developing erosion control strategies on non-agricultural disturbed lands. Erosion control planning with WEPS and SWEEP using water and other dust suppressants, wind barriers, straw mulch, re-vegetation, and other management practices is demonstrated herein through the use of comparative simulation scenarios. The scenarios confirm the efficacy of the WEPS and SWEEP models as valuable tools for supporting the design of erosion control plans for disturbed lands that are not only cost-effective but also incorporate

Analysis And Assistant Planning System Ofregional Agricultural Economic Inform

NASA Astrophysics Data System (ADS)

Han, Jie; Zhang, Junfeng

For the common problems existed in regional development and planning, we try to design a decision support system for assisting regional agricultural development and alignment as a decision-making tool for local government and decision maker. The analysis methods of forecast, comparative advantage, liner programming and statistical analysis are adopted. According to comparative advantage theory, the regional advantage can be determined by calculating and comparing yield advantage index (YAI), Scale advantage index (SAI), Complicated advantage index (CAI). Combining with GIS, agricultural data are presented as a form of graph such as area, bar and pie to uncover the principle and trend for decision-making which can't be found in data table. This system provides assistant decisions for agricultural structure adjustment, agro-forestry development and planning, and can be integrated to information technologies such as RS, AI and so on.

Automatic Positioning System of Small Agricultural Robot

NASA Astrophysics Data System (ADS)

Momot, M. V.; Proskokov, A. V.; Natalchenko, A. S.; Biktimirov, A. S.

2016-08-01

The present article discusses automatic positioning systems of agricultural robots used in field works. The existing solutions in this area have been analyzed. The article proposes an original solution, which is easy to implement and is characterized by high- accuracy positioning.

Modeling interactions of agriculture and groundwater nitrate contaminants: application of The STICS-Eau-Dyssée coupled models over the Seine River Basin

NASA Astrophysics Data System (ADS)

Tavakoly, A. A.; Habets, F.; Saleh, F.; Yang, Z. L.

2017-12-01

Human activities such as the cultivation of N-fixing crops, burning of fossil fuels, discharging of industrial and domestic effluents, and extensive usage of fertilizers have recently accelerated the nitrogen loading to watersheds worldwide. Increasing nitrate concentration in surface water and groundwater is a major concern in watersheds with extensive agricultural activities. Nutrient enrichment is one of the major environmental problems in the French coastal zone. To understand and predict interactions between agriculture, surface water and groundwater nitrate contaminants, this study presents a modeling framework that couples the agronomic STICS model with Eau-Dyssée, a distributed hydrologic modeling system to simulate groundwater-surface water interaction. The coupled system is implemented on the Seine River Basin with an area of 88,000 km2 to compute daily nitrate contaminants. Representing a sophisticated hydrosystem with several aquifers and including the megalopolis of Paris, the Seine River Basin is well-known as one of the most productive agricultural areas in France. The STICS-EauDyssée framework is evaluated for a long-term simulation covering 39 years (1971-2010). Model results show that the simulated nitrate highly depends on the inflow produced by surface and subsurface waters. Daily simulation shows that the model captures the seasonal variation of observations and that the overall long-term simulation of nitrate contaminant is satisfactory at the regional scale.

A Modernized System for Agricultural Monitoring for Food Security in Tanzania

NASA Astrophysics Data System (ADS)

Dempewolf, J.; Nakalembe, C. L.; Becker-Reshef, I.; Justice, C. J.; Tumbo, S.; Mbilinyi, B.; Maurice, S.; Mtalo, M.

2016-12-01

Accurate and timely information on agriculture, particularly in many countries dominated by complex smallholder, subsistence agricultural systems is often difficult to obtain or not available. This includes up-to-date information during the growing season on crop type, crop area and crop condition such as developmental stage, damage from pests and diseases, drought or flooding. These data are critical for government decision making on production forecasts, planning for commodity market transactions, food aid delivery, responding to disease outbreaks and for implementing agricultural extension and development efforts. In Tanzania we have been working closely with the National Food Security Division (NFSD) at the Ministry of Agriculture, Livestock and Fisheries (MALF) on designing and implementing an advanced agricultural monitoring system, utilizing satellite remote sensing, smart phone and internet technologies. Together with our local implementing partner, the Sokoine University of Agriculture we trained a large number of agricultural extension agents in different regions of Tanzania to deliver field data in near-realtime. Using our collaborative internet portal (Crop Monitor) the team of analysts compiles pertinent information on current crop and weather conditions from throughout the country in a standardized, consistent manner. Using the portal traditionally collected data are combined with electronically collected field data and MODIS satellite image time series from GLAM East-Africa (Global Agricultural Monitoring System, customized for stakeholders in East Africa). The main outcome of this work has been the compilation of the National Food Security Bulletin for Tanzania with plans for a public release and the intention for it to become the main avenue to dispense current updates and analysis on agriculture in the country. The same information is also a potential contribution to the international Early Warning Crop Monitor, which currently covers Tanzania

Introduction The Role of the Agricultural Model Intercomparison and Improvement Project

NASA Technical Reports Server (NTRS)

Rosenzweig, Cynthia; Hillel, Daniel

2015-01-01

Climate impacts on agriculture are of increasing concern in both the scientific and policy communities because of the need to ensure food security for a growing population. A special challenge is posed by the changes in the frequency and intensity of heat-waves, droughts, and episodic rainstorms already underway in many parts of the world. Changes in production are directly linked to such variations in temperature and precipitation during the growing season, and often to offseason changes in weather affecting soil-water storage and availability to crops. This is not an isolated problem but one of both global and regional importance, because of impacts on the livelihoods of smallholder farmers as well as consequences for the world food trade system. This two-part set the Agricultural Model Intercomparison and Improvement Project (AgMIP): Integrated Crop and Economic Assessments is the first to be entirely devoted to AgMIP (www.agmip.org). AgMIP is a major international research program focused on climate change and agriculture. The goal of the two parts is to advance the field by providing detailed information on new simulation techniques and assessments being conducted by this program. It presents information about new methods of global and regional integrated assessment, results from agricultural regions, and adaptation strategies for maintaining food security under changing climate conditions.

Sustaining the Earth's watersheds, agricultural research data system

USDA-ARS?s Scientific Manuscript database

The USDA-ARS water resources program has developed a web-based data system, STEWARDS: Sustaining the Earth’s Watersheds, Agricultural Research Data System to support research that encompasses a broad range of topics such as water quality, hydrology, conservation, land use, and soils. The data syst...

Water quality modeling using geographic information system (GIS) data

NASA Technical Reports Server (NTRS)

Engel, Bernard A

1992-01-01

Protection of the environment and natural resources at the Kennedy Space Center (KSC) is of great concern. The potential for surface and ground water quality problems resulting from non-point sources of pollution was examined using models. Since spatial variation of parameters required was important, geographic information systems (GIS) and their data were used. The potential for groundwater contamination was examined using the SEEPAGE (System for Early Evaluation of the Pollution Potential of Agricultural Groundwater Environments) model. A watershed near the VAB was selected to examine potential for surface water pollution and erosion using the AGNPS (Agricultural Non-Point Source Pollution) model.

Market assessment of photovoltaic power systems for agricultural applications in Nigeria

NASA Technical Reports Server (NTRS)

Staples, D.; Steingass, H.; Nolfi, J.

1981-01-01

The market potential for stand-alone photovoltaic systems in agriculture was studied. Information is presented on technical and economically feasible applications, and assessments of the business, government and financial climate for photovoltaic sales. It is concluded that the market for stand-alone systems will be large because of the availability of captial and the high premium placed on high reliability, low maintenance power systems. Various specific applications are described, mostly related to agriculture.

Market assessment of photovoltaic power systems for agricultural applications in Nigeria

NASA Astrophysics Data System (ADS)

Staples, D.; Steingass, H.; Nolfi, J.

1981-10-01

The market potential for stand-alone photovoltaic systems in agriculture was studied. Information is presented on technical and economically feasible applications, and assessments of the business, government and financial climate for photovoltaic sales. It is concluded that the market for stand-alone systems will be large because of the availability of captial and the high premium placed on high reliability, low maintenance power systems. Various specific applications are described, mostly related to agriculture.

A model for inventory of ammonia emissions from agriculture in the Netherlands

NASA Astrophysics Data System (ADS)

Velthof, G. L.; van Bruggen, C.; Groenestein, C. M.; de Haan, B. J.; Hoogeveen, M. W.; Huijsmans, J. F. M.

2012-01-01

Agriculture is the major source of ammonia (NH 3). Methodologies are needed to quantify national NH 3 emissions and to identify the most effective options to mitigate NH 3 emissions. Generally, NH 3 emissions from agriculture are quantified using a nitrogen (N) flow approach, in which the NH 3 emission is calculated from the N flows and NH 3 emission factors. Because of the direct dependency between NH 3 volatilization and Total Ammoniacal N (TAN; ammonium-N + N compounds readily broken down to ammonium) an approach based on TAN is preferred to calculate NH 3 emission instead of an approach based on total N. A TAN-based NH 3-inventory model was developed, called NEMA (National Emission Model for Ammonia). The total N excretion and the fraction of TAN in the excreted N are calculated from the feed composition and N digestibility of the components. TAN-based emission factors were derived or updated for housing systems, manure storage outside housing, manure application techniques, N fertilizer types, and grazing. The NEMA results show that the total NH 3 emission from agriculture in the Netherlands in 2009 was 88.8 Gg NH 3-N, of which 50% from housing, 37% from manure application, 9% from mineral N fertilizer, 3% from outside manure storage, and 1% from grazing. Cattle farming was the dominant source of NH 3 in the Netherlands (about 50% of the total NH 3 emission). The NH 3 emission expressed as percentage of the excreted N was 22% of the excreted N for poultry, 20% for pigs, 15% for cattle, and 12% for other livestock, which is mainly related to differences in emissions from housing systems. The calculated ammonia emission was most sensitive to changes in the fraction of TAN in the excreted manure and to the emission factor of manure application. From 2011, NEMA will be used as official methodology to calculate the national NH 3 emission from agriculture in the Netherlands.

Nanoagroparticles emerging trends and future prospect in modern agriculture system.

PubMed

Baker, Syed; Volova, Tatiana; Prudnikova, Svetlana V; Satish, S; Prasad M N, Nagendra

2017-07-01

Increment of technical knowledge has remarkably uplifted logical thinking among scientific communities to shape the theoretical concepts into near product-oriented research. The concept of nanotechnology has overwhelmed almost all forms of lives and has traded its applications in myriad fields. Despite rapid expansion of nanotechnology, sustainable competitions still do exist in the field of agriculture. In current scenario, agriculture is a manifestation demand to provide adequate nutrition for relentless growing global population. It is estimated that nearly one-third of the global crop production is destroyed annually. The loss owes to various stresses such as pest infestation, microbial pathogens, weeds, natural calamities, lack of soil fertility and much more. In order to overcome these limitations, various technological strategies are implemented but a majority of these have their own repercussions. Hence there is a scrawling progress on the evaluation of nanoparticles into agriculture sector which can reform the modern agricultural system. Applications of these nanomaterials can add tremendous value in the current scenario of a global food scarcity. Nanotechnology can address the adverse effects posed by the abundant use of chemical agrochemicals which are reported to cause biomagnification in an ecosystem. Based on these facts and consideration, present review envisages on nanoparticles as nanoherbicides, nanopesticides, onsite detection agro-pathogens and nanoparticles in post harvest management. The review also elucidates on the importance of nanoparticles in soil fertility, irrigation management and its influence on improving crop yield. With scanty reports available on nanotechnology in agriculture system, present review attributes toward developing nanoagroparticles as the future prospect which can give new facelift for existing agriculture system. Copyright © 2017 Elsevier B.V. All rights reserved.

Vulnerability and risk evaluation of agricultural nitrogen pollution for Hungary's main aquifer using DRASTIC and GLEAMS models.

PubMed

Leone, A; Ripa, M N; Uricchio, V; Deák, J; Vargay, Z

2009-07-01

In recent years, the significant improvement in point source depuration technologies has highlighted problems regarding, in particular, phosphorus and nitrogen pollution of surface and groundwater caused by agricultural non-point (diffuse) sources (NPS). Therefore, there is an urgent need to determine the relationship between agriculture and chemical and ecological water quality. This is a worldwide problem, but it is particularly relevant in countries, such as Hungary, that have recently become members of the European Community. The Italian Foreign Ministry has financed the PECO (Eastern Europe Countries Project) projects, amongst which is the project that led to the present paper, aimed at agricultural sustainability in Hungary, from the point of view of NPS. Specifically, the aim of the present work has been to study nitrates in Hungary's main aquifer. This study compares a model showing aquifer intrinsic vulnerability to pollution (using the DRASTIC parameter method; Aller et al. [Aller, L., Truman, B., Leher, J.H., Petty, R.J., 1986. DRASTIC: A Standardized System for Evaluating Ground Water Pollution Potential Using Hydrogeologic Settings. US NTIS, Springfield, VA.]) with a field-scale model (GLEAMS; Knisel [Knisel, W.G. (Ed.), 1993. GLEAMS--Groudwater Leaching Effects of Agricultural Management Systems, Version 3.10. University of Georgia, Coastal Plain Experimental Station, Tifton, GA.]) developed to evaluate the effects of agricultural management systems within and through the plant root zone. Specifically, GLEAMS calculates nitrate nitrogen lost by runoff, sediment and leachate. Groundwater monitoring probes were constructed for the project to measure: (i) nitrate content in monitored wells; (ii) tritium (3H) hydrogen radioisotope, as a tool to estimate the recharge conditions of the shallow groundwater; (iii) nitrogen isotope ratio delta15N, since nitrogen of organic and inorganic origin can easily be distinguished. The results obtained are satisfactory

Agriculture and Climate Change in Global Scenarios: Why Don't the Models Agree

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

Nelson, Gerald; van der Mensbrugghe, Dominique; Ahammad, Helal

Agriculture is unique among economic sectors in the nature of impacts from climate change. The production activity that transforms inputs into agricultural outputs makes direct use of weather inputs. Previous studies of the impacts of climate change on agriculture have reported substantial differences in outcomes of key variables such as prices, production, and trade. These divergent outcomes arise from differences in model inputs and model specification. The goal of this paper is to review climate change results and underlying determinants from a model comparison exercise with 10 of the leading global economic models that include significant representation of agriculture. Bymore » providing common productivity drivers that include climate change effects, differences in model outcomes are reduced. All models show higher prices in 2050 because of negative productivity shocks from climate change. The magnitude of the price increases, and the adaptation responses, differ significantly across the various models. Substantial differences exist in the structural parameters affecting demand, area, and yield, and should be a topic for future research.« less

Development of a Global Agricultural Hotspot Detection and Early Warning System

NASA Astrophysics Data System (ADS)

Lemoine, G.; Rembold, F.; Urbano, F.; Csak, G.

2015-12-01

The number of web based platforms for crop monitoring has grown rapidly over the last years and anomaly maps and time profiles of remote sensing derived indicators can be accessed online thanks to a number of web based portals. However, while these systems make available a large amount of crop monitoring data to the agriculture and food security analysts, there is no global platform which provides agricultural production hotspot warning in a highly automatic and timely manner. Therefore a web based system providing timely warning evidence as maps and short narratives is currently under development by the Joint Research Centre. The system (called "HotSpot Detection System of Agriculture Production Anomalies", HSDS) will focus on water limited agricultural systems worldwide. The automatic analysis of relevant meteorological and vegetation indicators at selected administrative units (Gaul 1 level) will trigger warning messages for the areas where anomalous conditions are observed. The level of warning (ranging from "watch" to "alert") will depend on the nature and number of indicators for which an anomaly is detected. Information regarding the extent of the agricultural areas concerned by the anomaly and the progress of the agricultural season will complement the warning label. In addition, we are testing supplementary detailed information from other sources for the areas triggering a warning. These regard the automatic web-based and food security-tailored analysis of media (using the JRC Media Monitor semantic search engine) and the automatic detection of active crop area using Sentinel 1, upcoming Sentinel-2 and Landsat 8 imagery processed in Google Earth Engine. The basic processing will be fully automated and updated every 10 days exploiting low resolution rainfall estimates and satellite vegetation indices. Maps, trend graphs and statistics accompanied by short narratives edited by a team of crop monitoring experts, will be made available on the website on a

Linking knowledge and action through mental models of sustainable agriculture

PubMed Central

Hoffman, Matthew; Lubell, Mark; Hillis, Vicken

2014-01-01

Linking knowledge to action requires understanding how decision-makers conceptualize sustainability. This paper empirically analyzes farmer “mental models” of sustainability from three winegrape-growing regions of California where local extension programs have focused on sustainable agriculture. The mental models are represented as networks where sustainability concepts are nodes, and links are established when a farmer mentions two concepts in their stated definition of sustainability. The results suggest that winegrape grower mental models of sustainability are hierarchically structured, relatively similar across regions, and strongly linked to participation in extension programs and adoption of sustainable farm practices. We discuss the implications of our findings for the debate over the meaning of sustainability, and the role of local extension programs in managing knowledge systems. PMID:25157158

The use of geographic information system and 1860s cadastral data to model agricultural suitability before heavy mechanization. A case study from Malta.

PubMed

Alberti, Gianmarco; Grima, Reuben; Vella, Nicholas C

2018-01-01

The present study seeks to understand the determinants of land agricultural suitability in Malta before heavy mechanization. A GIS-based Logistic Regression model is built on the basis of the data from mid-1800s cadastral maps (cabreo). This is the first time that such data are being used for the purpose of building a predictive model. The maps record the agricultural quality of parcels (ranging from good to lowest), which is represented by different colours. The study treats the agricultural quality as a depended variable with two levels: optimal (corresponding to the good class) vs. non-optimal quality (mediocre, bad, low, and lowest classes). Seventeen predictors are isolated on the basis of literature review and data availability. Logistic Regression is used to isolate the predictors that can be considered determinants of the agricultural quality. Our model has an optimal discriminatory power (AUC: 0.92). The positive effect on land agricultural quality of the following predictors is considered and discussed: sine of the aspect (odds ratio 1.42), coast distance (2.46), Brown Rendzinas (2.31), Carbonate Raw (2.62) and Xerorendzinas (9.23) soils, distance to minor roads (4.88). Predictors resulting having a negative effect are: terrain elevation (0.96), slope (0.97), distance to the nearest geological fault lines (0.09), Terra Rossa soil (0.46), distance to secondary roads (0.19) and footpaths (0.41). The model isolates a host of topographic and cultural variables, the latter related to human mobility and landscape accessibility, which differentially contributed to the agricultural suitability, providing the bases for the creation of the fragmented and extremely variegated agricultural landscape that is the hallmark of the Maltese Islands. Our findings are also useful to suggest new questions that may be posed to the more meagre evidence from earlier periods.

The use of geographic information system and 1860s cadastral data to model agricultural suitability before heavy mechanization. A case study from Malta

PubMed Central

Grima, Reuben; Vella, Nicholas C.

2018-01-01

The present study seeks to understand the determinants of land agricultural suitability in Malta before heavy mechanization. A GIS-based Logistic Regression model is built on the basis of the data from mid-1800s cadastral maps (cabreo). This is the first time that such data are being used for the purpose of building a predictive model. The maps record the agricultural quality of parcels (ranging from good to lowest), which is represented by different colours. The study treats the agricultural quality as a depended variable with two levels: optimal (corresponding to the good class) vs. non-optimal quality (mediocre, bad, low, and lowest classes). Seventeen predictors are isolated on the basis of literature review and data availability. Logistic Regression is used to isolate the predictors that can be considered determinants of the agricultural quality. Our model has an optimal discriminatory power (AUC: 0.92). The positive effect on land agricultural quality of the following predictors is considered and discussed: sine of the aspect (odds ratio 1.42), coast distance (2.46), Brown Rendzinas (2.31), Carbonate Raw (2.62) and Xerorendzinas (9.23) soils, distance to minor roads (4.88). Predictors resulting having a negative effect are: terrain elevation (0.96), slope (0.97), distance to the nearest geological fault lines (0.09), Terra Rossa soil (0.46), distance to secondary roads (0.19) and footpaths (0.41). The model isolates a host of topographic and cultural variables, the latter related to human mobility and landscape accessibility, which differentially contributed to the agricultural suitability, providing the bases for the creation of the fragmented and extremely variegated agricultural landscape that is the hallmark of the Maltese Islands. Our findings are also useful to suggest new questions that may be posed to the more meagre evidence from earlier periods. PMID:29415059

How to Design a Targeted Agricultural Subsidy System: Efficiency or Equity?

PubMed Central

Cong, Rong-Gang; Brady, Mark

2012-01-01

In this paper we appraise current agricultural subsidy policy in the EU. Several sources of its inefficiency are identified: it is inefficient for supporting farmers’ incomes or guaranteeing food security, and irrational transfer payments decoupled from actual performance that may be negative for environmental protection, social cohesion, etc. Based on a simplified economic model, we prove that there is “reverse redistribution” in the current tax-subsidy system, which cannot be avoided. To find a possible way to distribute subsidies more efficiently and equitably, several alternative subsidy systems (the pure loan, the harvest tax and the income contingent loan) are presented and examined. PMID:22876283

From LACIE to GEOGLAM: Integrating Earth Observations into Operational Agricultural Monitoring Systems

NASA Astrophysics Data System (ADS)

Becker-Reshef, I.; Justice, C. O.

2012-12-01

Earth observation data, owing to their synoptic, timely and repetitive coverage, have long been recognized as an indispensible tool for agricultural monitoring at local to global scales. Research and development over the past several decades in the field of agricultural remote sensing has led to considerable capacity for crop monitoring within the current operational monitoring systems. These systems are relied upon nationally and internationally to provide crop outlooks and production forecasts as the growing season progresses. This talk will discuss the legacy and current state of operational agricultural monitoring using earth observations. In the US, the National Aeronautics and Space Administration (NASA) and the US Department of Agriculture (USDA) have been collaborating to monitor global agriculture from space since the 1970s. In 1974, the USDA, NASA and National Oceanic and Atmospheric Administration (NOAA) initiated the Large Area Crop Inventory Experiment (LACIE) which demonstrated that earth observations could provide vital information on crop production, with unprecedented accuracy and timeliness, prior to harvest. This experiment spurred many agencies and researchers around the world to further develop and evaluate remote sensing technologies for timely, large area, crop monitoring. The USDA and NASA continue to closely collaborate. More recently they jointly initiated the Global Agricultural Monitoring Project (GLAM) to enhance the agricultural monitoring and the crop-production estimation capabilities of the USDA Foreign Agricultural Service by using the new generation of NASA satellite observations including from MODIS and the Visible Infrared Imaging Radiometer Suite (VIIRS) instruments. Internationally, in response to the growing calls for improved agricultural information, the Group on Earth Observations (partnership of governments and international organizations) developed the Global Agricultural Monitoring (GEOGLAM) initiative which was adopted

A component-based system for agricultural drought monitoring by remote sensing

PubMed Central

Yuan, Yanbin; You, Lin; Chen, Chao

2017-01-01

In recent decades, various kinds of remote sensing-based drought indexes have been proposed and widely used in the field of drought monitoring. However, the drought-related software and platform development lag behind the theoretical research. The current drought monitoring systems focus mainly on information management and publishing, and cannot implement professional drought monitoring or parameter inversion modelling, especially the models based on multi-dimensional feature space. In view of the above problems, this paper aims at fixing this gap with a component-based system named RSDMS to facilitate the application of drought monitoring by remote sensing. The system is designed and developed based on Component Object Model (COM) to ensure the flexibility and extendibility of modules. RSDMS realizes general image-related functions such as data management, image display, spatial reference management, image processing and analysis, and further provides drought monitoring and evaluation functions based on internal and external models. Finally, China’s Ningxia region is selected as the study area to validate the performance of RSDMS. The experimental results show that RSDMS provide an efficient and scalable support to agricultural drought monitoring. PMID:29236700

Agricultural intensification escalates future conservation costs.

PubMed

Phelps, Jacob; Carrasco, Luis Roman; Webb, Edward L; Koh, Lian Pin; Pascual, Unai

2013-05-07

The supposition that agricultural intensification results in land sparing for conservation has become central to policy formulations across the tropics. However, underlying assumptions remain uncertain and have been little explored in the context of conservation incentive schemes such as policies for Reducing Emissions from Deforestation and forest Degradation, conservation, sustainable management, and enhancement of carbon stocks (REDD+). Incipient REDD+ forest carbon policies in a number of countries propose agricultural intensification measures to replace extensive "slash-and-burn" farming systems. These may result in conservation in some contexts, but will also increase future agricultural land rents as productivity increases, creating new incentives for agricultural expansion and deforestation. While robust governance can help to ensure land sparing, we propose that conservation incentives will also have to increase over time, tracking future agricultural land rents, which might lead to runaway conservation costs. We present a conceptual framework that depicts these relationships, supported by an illustrative model of the intensification of key crops in the Democratic Republic of Congo, a leading REDD+ country. A von Thünen land rent model is combined with geographic information systems mapping to demonstrate how agricultural intensification could influence future conservation costs. Once postintensification agricultural land rents are considered, the cost of reducing forest sector emissions could significantly exceed current and projected carbon credit prices. Our analysis highlights the importance of considering escalating conservation costs from agricultural intensification when designing conservation initiatives.

Agricultural intensification escalates future conservation costs

PubMed Central

Phelps, Jacob; Carrasco, Luis Roman; Webb, Edward L.; Koh, Lian Pin; Pascual, Unai

2013-01-01

The supposition that agricultural intensification results in land sparing for conservation has become central to policy formulations across the tropics. However, underlying assumptions remain uncertain and have been little explored in the context of conservation incentive schemes such as policies for Reducing Emissions from Deforestation and forest Degradation, conservation, sustainable management, and enhancement of carbon stocks (REDD+). Incipient REDD+ forest carbon policies in a number of countries propose agricultural intensification measures to replace extensive “slash-and-burn” farming systems. These may result in conservation in some contexts, but will also increase future agricultural land rents as productivity increases, creating new incentives for agricultural expansion and deforestation. While robust governance can help to ensure land sparing, we propose that conservation incentives will also have to increase over time, tracking future agricultural land rents, which might lead to runaway conservation costs. We present a conceptual framework that depicts these relationships, supported by an illustrative model of the intensification of key crops in the Democratic Republic of Congo, a leading REDD+ country. A von Thünen land rent model is combined with geographic information systems mapping to demonstrate how agricultural intensification could influence future conservation costs. Once postintensification agricultural land rents are considered, the cost of reducing forest sector emissions could significantly exceed current and projected carbon credit prices. Our analysis highlights the importance of considering escalating conservation costs from agricultural intensification when designing conservation initiatives. PMID:23589860

Successes and challenges in a novel doctoral program in systems agriculture: a case example.

PubMed

Lust, D; Topliff, D; Deotte, R

2010-01-01

A doctoral program in Systems Agriculture was initiated at West Texas A&M University, Canyon, TX, in September, 2003. The stated objective of the program was "..to prepare leaders for the agricultural industry that are trained in a multidisciplinary, research-based curriculum that emphasizes a systems approach to problem solving". The program offers a single doctoral degree in Agriculture and accepts qualified students with a master's or professional degree in agricultural or related disciplines. Courses related to systems methodologies, leadership, agricultural economics, plant and soil science, and animal science are required. Additional program requirements include a systems research project and dissertation, leadership training, and written and oral exams. The program has exceeded enrollment and graduation targets, suggesting interest in this approach to a doctoral degree. Students have entered the program with M.S. backgrounds in education, traditional agricultural disciplines, veterinary medicine, business, and physics. Graduates have gained employment in industry, university teaching and research, government research/administration, and extension. Doctoral student projects in systems agriculture contributed to curriculum changes and to the conceptual framework adopted by a multi-state research group. Designing and teaching courses for students with diverse backgrounds has been challenging. Development of a common understanding of systems agriculture was identified by a third-party program review as an issue for faculty. Development and maintenance of program standards and administrative procedures posed additional challenges. Leadership, administrative support, and timely and continuing program assessment are suggested as necessary components for a nontraditional doctoral program.

Adaptation to Interannual and Interdecadal Climate Variability in Agricultural Production Systems of the Argentine Pampas

NASA Astrophysics Data System (ADS)

Podestá, G. P.; Bert, F.; Weber, E.; Laciana, C.; Rajagopalan, B.; Letson, D.

2007-05-01

Agricultural ecosystems play a central role in world food production and food security, and involve one of the most climate-sensitive sectors of society-agriculture. We focus on crop production in the Argentine Pampas, one of the world's major agricultural regions. Climate of the Pampas shows marked variability at both interannual and decadal time scales. We explored the scope for adaptive management in response to climate information on interannual scales. We show that different assumptions about what decision makers are trying to achieve (i.e., their objective functions) may change what actions are considered as "optimal" for a given climate context. Optimal actions also were used to estimate the economic value of forecasts of an ENSO phase. Decision constraints (e.g., crop rotations) have critical influence on value of the forecasting system. Gaps in knowledge or misconceptions about climate variability were identified in open-ended "mental model" interviews. Results were used to design educational interventions. A marked increase in precipitation since the 1970s, together with new production technologies, led to major changes in land use patterns in the Pampas. Continuous cropping has widely replaced agriculture-pasture rotations. Nevertheless, production systems that evolved partly in response to increased rainfall may not be viable if climate reverts to a drier epoch. We use historical data to define a range of plausible climate trajectories 20-30 years hence. Regional scenarios are downscaled using semi-parametric weather generators to produce multiple realizations of daily weather consistent with decadal scenarios. Finally, we use the synthetic climate, crop growth models, and realistic models of decision-making under risk to compute risk metrics (e.g., probability of yields or profits being below a threshold). Climatically optimal and marginal locations show differential responses: probabilities of negative economic results are much higher in currently

National Agricultural Library | United States Department of Agriculture

Science.gov Websites

Skip to main content Home National Agricultural Library United States Department of Agriculture Ag is a data access system maintained by the US Department of Agriculture's (USDA) National Agricultural websites. The Ag Data Commons provides access to a wide variety of open data relevant to agricultural

The Agricultural Model Intercomparison and Improvement Project (AgMIP): Protocols and Pilot Studies

NASA Technical Reports Server (NTRS)

Rosenzweig, C.; Jones, J. W.; Hatfield, J. L.; Ruane, A. C.; Boote, K. J.; Thorburn, P.; Antle, J. M.; Nelson, G. C.; Porter, C.; Janssen, S.;

Reducing agricultural greenhouse gas emissions: role of biotechnology, organic systems, and consumer behavior

USDA-ARS?s Scientific Manuscript database

All agricultural systems have environmental and societal costs and benefits that should be objectively quantified before recommending specific management practices. Agricultural biotechnology, which takes advantage of genetically engineered organisms (GEOs), along with organic cropping systems, econ...

Evaluation of the Agro-EcoSystem-Watershed (AgES-W)model for estimating nutrient dynamics on a midwest agricultural watershed

USDA-ARS?s Scientific Manuscript database

In order to satisfy the requirements of Conservation Effects Assessment Project (CEAP) Watershed Assessment Study (WAS) Objective 5 (“develop and verify regional watershed models that quantify environmental outcomes of conservation practices in major agricultural regions”), a new watershed model dev...

Optical modeling of agricultural fields and rough-textured rock and mineral surfaces

NASA Technical Reports Server (NTRS)

Suits, G. H.; Vincent, R. K.; Horwitz, H. M.; Erickson, J. D.

1973-01-01

Review was made of past models for describing the reflectance and/or emittance properties of agricultural/forestry and geological targets in an effort to select the best theoretical models. An extension of the six parameter Allen-Gayle-Richardson model was chosen as the agricultural plant canopy model. The model is used to predict the bidirectional reflectance of a field crop from known laboratory spectra of crop components and approximate plant geometry. The selected geological model is based on Mie theory and radiative transfer equations, and will assess the effect of textural variations of the spectral emittance of natural rock surfaces.

Representing natural and manmade drainage systems in an earth system modeling framework

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

Li, Hongyi; Wu, Huan; Huang, Maoyi

Drainage systems can be categorized into natural or geomorphological drainage systems, agricultural drainage systems and urban drainage systems. They interact closely among themselves and with climate and human society, particularly under extreme climate and hydrological events such as floods. This editorial articulates the need to holistically understand and model drainage systems in the context of climate change and human influence, and discusses the requirements and examples of feasible approaches to representing natural and manmade drainage systems in an earth system modeling framework.

Market assessment of photovoltaic power systems for agricultural applications in the Philippines

NASA Technical Reports Server (NTRS)

Cabraal, R. A.; Delasanta, D.; Burrill, G.

1981-01-01

The market potential in the Philippines for stand alone photovoltaic (P/V) systems in agriculture was assessed. Applications include: irrigation, postharvest operation, food and fiber processing and storage, and livestock and fisheries operations. Power and energy use profiles for many applications as well as assessments of business, government and financial climate for P/V sales are described. Many characteristics of the Philippine agriculture and energy sector favorably influence the use of P/V systems. However, serious and significant barriers prevent achieving the technically feasible, cost competitive market for P/V systems in the agricultural sector. The reason for the small market is the limited availability capital for financing P/V systems. It is suggested that innovative financing schemes and promotional campaigns should be devised.

Market assessment of photovoltaic power systems for agricultural applications in the Philippines

NASA Astrophysics Data System (ADS)

Cabraal, R. A.; Delasanta, D.; Burrill, G.

1981-04-01

The market potential in the Philippines for stand alone photovoltaic (P/V) systems in agriculture was assessed. Applications include: irrigation, postharvest operation, food and fiber processing and storage, and livestock and fisheries operations. Power and energy use profiles for many applications as well as assessments of business, government and financial climate for P/V sales are described. Many characteristics of the Philippine agriculture and energy sector favorably influence the use of P/V systems. However, serious and significant barriers prevent achieving the technically feasible, cost competitive market for P/V systems in the agricultural sector. The reason for the small market is the limited availability capital for financing P/V systems. It is suggested that innovative financing schemes and promotional campaigns should be devised.

AgMIP's Transdisciplinary Agricultural Systems Approach to Regional Integrated Assessment of Climate Impacts, Vulnerability, and Adaptation

NASA Technical Reports Server (NTRS)

Antle, John M.; Valdivia, Roberto O.; Boote, Kenneth J.; Janssen, Sander; Jones, James W.; Porter, Cheryl H.; Rosenzweig, Cynthia; Ruane, Alexander C.; Thorburn, Peter J.

2015-01-01

This chapter describes methods developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) to implement a transdisciplinary, systems-based approach for regional-scale (local to national) integrated assessment of agricultural systems under future climate, biophysical, and socio-economic conditions. These methods were used by the AgMIP regional research teams in Sub-Saharan Africa and South Asia to implement the analyses reported in their respective chapters of this book. Additional technical details are provided in Appendix 1.The principal goal that motivates AgMIP's regional integrated assessment (RIA) methodology is to provide scientifically rigorous information needed to support improved decision-making by various stakeholders, ranging from local to national and international non-governmental and governmental organizations.

Online hyperspectral imaging system for evaluating quality of agricultural products

NASA Astrophysics Data System (ADS)

Mo, Changyeun; Kim, Giyoung; Lim, Jongguk

2017-06-01

The consumption of fresh-cut agricultural produce in Korea has been growing. The browning of fresh-cut vegetables that occurs during storage and foreign substances such as worms and slugs are some of the main causes of consumers' concerns with respect to safety and hygiene. The purpose of this study is to develop an on-line system for evaluating quality of agricultural products using hyperspectral imaging technology. The online evaluation system with single visible-near infrared hyperspectral camera in the range of 400 nm to 1000 nm that can assess quality of both surfaces of agricultural products such as fresh-cut lettuce was designed. Algorithms to detect browning surface were developed for this system. The optimal wavebands for discriminating between browning and sound lettuce as well as between browning lettuce and the conveyor belt were investigated using the correlation analysis and the one-way analysis of variance method. The imaging algorithms to discriminate the browning lettuces were developed using the optimal wavebands. The ratio image (RI) algorithm of the 533 nm and 697 nm images (RI533/697) for abaxial surface lettuce and the ratio image algorithm (RI533/697) and subtraction image (SI) algorithm (SI538-697) for adaxial surface lettuce had the highest classification accuracies. The classification accuracy of browning and sound lettuce was 100.0% and above 96.0%, respectively, for the both surfaces. The overall results show that the online hyperspectral imaging system could potentially be used to assess quality of agricultural products.

Desert agricultural terrace systems at EBA Jawa (Jordan) - Layout, water availability and efficiency

NASA Astrophysics Data System (ADS)

Meister, Julia; Krause, Jan; Müller-Neuhof, Bernd; Portillo, Marta; Reimann, Tony; Schütt, Brigitta

2016-04-01

Located in the arid basalt desert of northeastern Jordan, the Early Bronze Age (EBA) settlement of Jawa is by far the largest and best preserved archaeological EBA site in the region. Recent surveys in the close vicinity revealed well-preserved remains of three abandoned agricultural terrace systems. In the presented study these archaeological features are documented by detailed mapping and the analysis of the sediment records in a multi-proxy approach. To study the chronology of the terrace systems optically stimulated luminescence (OSL) is used. In order to evaluate the efficiency of the water management techniques and its impact on harvest yields, a crop simulation model (CropSyst) under today's climatic conditions is applied, simulating crop yields with and without (runoff) irrigation. In order to do so, a runoff time series for each agricultural terrace system and its catchment is generated, applying the SCS runoff curve number method (CN) based on rainfall and soil data. Covering a total area of 38 ha, irrigated terrace agriculture was practiced on slopes, small plateaus, and valleys in the close vicinity of Jawa. Floodwater from nearby wadis or runoff from adjacent slopes was collected and diverted via surface canals. The terraced fields were arranged in cascades, allowing effective water exploitation through a system of risers, canals and spillways. The examined terrace profiles show similar stratigraphic sequences of mixed unstratified fine sediments that are composed of small-scale relocated sediments with local origin. The accumulation of these fines is associated with the construction of agricultural terraces, forcing infiltration and storage of the water within the terraces. Two OSL ages of terrace fills indicate that the construction of these terrace systems started as early as 5300 ± 300 a, which fits well to the beginning of the occupation phase of Jawa at around 3.500 calBC, thus making them to the oldest examples of its kind in the Middle East

Integrated Systems Mitigate Land Degradation and Improve Agricultural System Sustainability

NASA Astrophysics Data System (ADS)

Landblom, Douglas; Senturklu, Songul; Cihacek, Larry; Brevik, Eric

2017-04-01

Rain-fed agricultural production supported by exogenous inputs is not sustainable because a continuous influx of expensive inputs (fertilizer, chemicals, fossil fuel, labor, tillage, and other) is required. Alternatives to traditional management allow natural occurring dynamic soil processes to provide the necessary microbial activity that supports nutrient cycling in balance with nature. Research designed to investigate the potential for integrated systems to replace expensive inputs has shown that healthy soils rich in soil organic matter (SOM) are the foundation upon which microbial nutrient cycling can reduce and eventually replace expensive fertilizer. No-till seed placement technology effectively replaces multiple-pass cultivation conserving stored soil water in semi-arid farming systems. In multi-crop rotations, cool- and warm-season crops are grown in sequence to meet goals of the integrated farming and ranching system, and each crop in the rotation complements the subsequent crop by supplying a continuous flow of essential SOM for soil nutrient cycling. Grazing animals serve an essential role in the system's sustainability as non-mechanized animal harvesters that reduce fossil fuel consumption and labor, and animal waste contributes soil nutrients to the system. Integrated systems' complementarity has contributed to greater soil nutrient cycling and crop yields, fertilizer reduction or elimination, greater yearling steer grazing net return, reduced cow wintering costs grazing crop residues, increased wildlife sightings, and reduced environmental footprint. Therefore, integrating crop and animal systems can reverse soil quality decline and adopting non-traditional procedures has resulted in a wider array of opportunities for sustainable agriculture and profitability.

Modeling the Heterogeneous Effects of GHG Mitigation Policies on Global Agriculture and Forestry

NASA Astrophysics Data System (ADS)

Golub, A.; Henderson, B.; Hertel, T. W.; Rose, S. K.; Sohngen, B.

2010-12-01

Agriculture and forestry are envisioned as potentially key sectors for climate change mitigation policy, yet the depth of analysis of mitigation options and their economic consequences remains remarkably shallow in comparison to that for industrial mitigation. Farming and land use change - much of it induced by agriculture -account for one-third of global greenhouse gas (GHG) emissions. Any serious attempt to curtail these emissions will involve changes in the way farming is conducted, as well as placing limits on agricultural expansion into areas currently under more carbon-intensive land cover. However, agriculture and forestry are extremely heterogeneous, both in the technology and intensity of production, as well as in the GHG emissions intensity of these activities. And these differences, in turn, give rise to significant changes in the distribution of agricultural production, trade and consumption in the wake of mitigation policies. This paper assesses such distributional impacts via a global economic analysis undertaken with a modified version of the GTAP model. The paper builds on a global general equilibrium GTAP-AEZ-GHG model (Golub et al., 2009). This is a unified modeling framework that links the agricultural, forestry, food processing and other sectors through land, and other factor markets and international trade, and incorporates different land-types, land uses and related CO2 and non-CO2 GHG emissions and sequestration. The economic data underlying this work is the global GTAP data base aggregated up to 19 regions and 29 sectors. The model incorporates mitigation cost curves for different regions and sectors based on information from the US-EPA. The forestry component of the model is calibrated to the results of the state of the art partial equilibrium global forestry model of Sohngen and Mendelson (2007). Forest carbon sequestration at both the extensive and intensive margins are modeled separately to better isolate land competition between

A Food Systems Approach To Healthy Food And Agriculture Policy.

PubMed

Neff, Roni A; Merrigan, Kathleen; Wallinga, David

2015-11-01

Food has become a prominent focus of US public health policy. The emphasis has been almost exclusively on what Americans eat, not what is grown or how it is grown. A field of research, policy, and practice activities addresses the food-health-agriculture nexus, yet the work is still often considered "alternative" to the mainstream. This article outlines the diverse ways in which agriculture affects public health. It then describes three policy issues: farm-to-school programming, sustainability recommendations in the Dietary Guidelines for Americans, and antibiotic use in animal agriculture. These issues illustrate the progress, challenges, and public health benefits of taking a food systems approach that brings together the food, agriculture, and public health fields. Project HOPE—The People-to-People Health Foundation, Inc.

A distribution model for the aerial application of granular agricultural particles

NASA Technical Reports Server (NTRS)

Fernandes, S. T.; Ormsbee, A. I.

1978-01-01

A model is developed to predict the shape of the distribution of granular agricultural particles applied by aircraft. The particle is assumed to have a random size and shape and the model includes the effect of air resistance, distributor geometry and aircraft wake. General requirements for the maintenance of similarity of the distribution for scale model tests are derived and are addressed to the problem of a nongeneral drag law. It is shown that if the mean and variance of the particle diameter and density are scaled according to the scaling laws governing the system, the shape of the distribution will be preserved. Distributions are calculated numerically and show the effect of a random initial lateral position, particle size and drag coefficient. A listing of the computer code is included.

Interval Optimization Model Considering Terrestrial Ecological Impacts for Water Rights Transfer from Agriculture to Industry in Ningxia, China.

PubMed

Sun, Lian; Li, Chunhui; Cai, Yanpeng; Wang, Xuan

2017-06-14

In this study, an interval optimization model is developed to maximize the benefits of a water rights transfer system that comprises industry and agriculture sectors in the Ningxia Hui Autonomous Region in China. The model is subjected to a number of constraints including water saving potential from agriculture and ecological groundwater levels. Ecological groundwater levels serve as performance indicators of terrestrial ecology. The interval method is applied to present the uncertainty of parameters in the model. Two scenarios regarding dual industrial development targets (planned and unplanned ones) are used to investigate the difference in potential benefits of water rights transfer. Runoff of the Yellow River as the source of water rights fluctuates significantly in different years. Thus, compensation fees for agriculture are calculated to reflect the influence of differences in the runoff. Results show that there are more available water rights to transfer for industrial development. The benefits are considerable but unbalanced between buyers and sellers. The government should establish a water market that is freer and promote the interest of agriculture and farmers. Though there has been some success of water rights transfer, the ecological impacts and the relationship between sellers and buyers require additional studies.

A dynamic econometric model of agricultural wage determination in Bangladesh.

PubMed

Boyce, J K; Ravallion, M

1991-11-01

Economists applied data from 1949-1950 and 1980-1981 to a new dynamic model to examine the dynamics of determinants of agricultural wages in Bangladesh, particularly the effect of changes in relative prices of rice (the staple food) and productivity. Just a 20% rise in the price or rice was passed on in the agricultural wage rate within the current year. About 50% was passed on in the long run, however. Therefore an increase in the price of rice reduced the rice purchasing power of agricultural wages in the short and long term. In fact, the importance given to rice in the long run real wage rate was almost the same as the mean proportion of expenditure that an agricultural laborer in Bangladesh committed to rice and closely related food staples. Thus arise in the price of rice in comparison to other goods had limited effects on the long run real wage in terms of the bundle of goods typically consumed, but very adverse effects in the short run placing a high burden on the rural poor. On the other hand, the long run real wage rate fell considerably between the mid 1960s-early 1980s when overall agricultural productivity increased. The economists pointed out that this increased productivity may not have lowered long run real wage rates, but instead mitigating factors may have contributed to this fall. For example, population growth, rising landlessness, and insufficient economic growth in nonagricultural sectors resulted in a consistent growth in the labor supply. In conclusion, this new dynamic model showed that Bangladesh cannot depend only on agricultural growth to reduce the poverty of farmers.

Modeling the Dynamics of Soil Structure and Water in Agricultural Soil

NASA Astrophysics Data System (ADS)

Weller, U.; Lang, B.; Rabot, E.; Stössel, B.; Urbanski, L.; Vogel, H. J.; Wiesmeier, M.; Wollschlaeger, U.

2017-12-01

The impact of agricultural management on soil functions is manifold and severe. It has both positive and adverse influence. Our goal is to develop model tools quantifying the agricultural impact on soil functions based on a mechanistic understanding of soil processes to support farmers and decision makers. The modeling approach is based on defining relevant soil components, i.e. soil matrix, macropores, organisms, roots and organic matter. They interact and form the soil's macroscopic properties and functions including water and gas dynamics, and biochemical cycles. Based on existing literature information we derive functional interaction processes and combine them in a network of dynamic soil components. In agricultural soils, a major issue is linked to changes in soil structure and their influence on water dynamics. Compaction processes are well studied in literature, but for the resilience due to root growth and activity of soil organisms the information is scarcer. We implement structural dynamics into soil water and gas simulations using a lumped model that is both coarse enough to allow extensive model runs while still preserving some important, yet rarely modeled phenomenons like preferential flow, hysteretic and dynamic behavior. For simulating water dynamics, at each depth, the model assumes water at different binding energies depending on soil structure, i.e. the pore size distribution. Non-equilibrium is postulated, meaning that free water may occur even if the soil is not fully saturated. All energy levels are interconnected allowing water to move, both within a spatial node, and between neighboring nodes (adding gravity). Structure dynamics alters the capacity of this water compartments, and the conductance of its connections. Connections are switched on and off depending on whether their sources contain water or their targets have free capacity. This leads to piecewise linear system behavior that allows fast calculation for extended time steps. Based

Reduction of solids and nutrient loss from agricultural land by tailwater recovery systems

USGS Publications Warehouse

Omer, A.R.; Miranda, Leandro E.; Moore, M. T.; Krutz, L. J.; Prince Czarnecki, J. M.; Kröger, R.; Baker, B. H.; Hogue, J.; Allen, P. J.

2018-01-01

Best management practices are being implemented throughout the Lower Mississippi River Alluvial Valley with the aim of alleviating pressures placed on downstream aquatic systems by sediment and nutrient losses from agricultural land; however, research evaluating the performance of tailwater recovery (TWR) systems, an increasingly important practice, is limited. This study evaluated the ability of TWR systems to retain sediment and nutrients draining from agricultural landscapes. Composite flow-based samples were collected during flow events (precipitation or irrigation) over a two-year period in six TWR systems. Performance was evaluated by comparing concentrations and loads in water entering TWR systems (i.e., runoff or influent) from agricultural fields to water overflow exiting TWR systems (effluent). Tailwater recovery systems did not reduce concentrations of solids and nutrients, but did reduce loads of solids, phosphorus (P), and nitrogen (N) by 43%, 32%, and 44%, respectively. Annual mean load reductions were 1,142 kg solids, 0.7 kg of P, and 3.8 kg of N. Performance of TWR systems was influenced by effluent volume, system fullness, time since the previous event, and capacity of the TWR system. Mechanistically, TWR systems retain runoff on the agricultural landscape, thereby reducing the amount of sediment and nutrients entering downstream waterbodies. System performance can be improved through manipulation of influential parameters.

[Integrated evaluation of circular agriculture system: a life cycle perspective].

PubMed

Liang, Long; Chen, Yuan-Quan; Gao, Wang-Sheng

2010-11-01

For the point of view that recycling economy system is one of ways to achieve the low-carbon economy, we have made an evaluation on a typical circular agriculture duck industry in Hunan Province, China, through improving the framework of life cycle assessment (LCA). The analysis indicated that the consumption of non-renewable resources, land and water were 48.629 MJ, 2.36 m2 and 1 321.41 kg, while the potential greenhouse gas (GHGs), acidification, eutrophication, human toxicity, freshwater ecotoxicity and terrestrial ecotoxicity were 11 543.26 g (CO2 eq), 52.36g (SO2eq), 25.83g (PO4eq), 1.26, 60.74 and 24.65 g (1,4-DCBeq), respectively. The potential damage of aquatic eutrophication, freshwater ecotoxicity and terrestrial ecotoxicity was more serious than that of GHGs. Main results were following: i. the circular agricultural chain promoted the principle of "moderate circulation", which based on the traditional production methods; ii. circular agriculture could not blindly pursue low carbon development. Instead, soil and biological carbon sequestration should be considered, in addition to reducing carbon emissions; iii. circular economy and circular agriculture should take other potential environmental impacts into account such as acidification, eutrophication and ecotoxicity,with the exception to carbon emissions,to developed integrated system assessment; iv. LCA could provide a comprehensive assessment of circular agriculture, and it was worth of further study.

Model Course of Study for Agricultural Programs in Iowa. Preparing for the Future.

ERIC Educational Resources Information Center

Martin, Robert A.; And Others

Each section contained in this packet is necessary for designing an effective program of agriculture education. The curriculum guide that is developed from this model should include the same sections. The model includes: (1) community description; (2) school description; (3) goals and objectives of education in agriculture; (4) evaluation policy;…

Machine learning algorithms for modeling groundwater level changes in agricultural regions of the U.S.

DOE PAGES

Sahoo, S.; Russo, T. A.; Elliott, J.; ...

2017-05-13

Climate, groundwater extraction, and surface water flows have complex nonlinear relationships with groundwater level in agricultural regions. To better understand the relative importance of each driver and predict groundwater level change, we develop a new ensemble modeling framework based on spectral analysis, machine learning, and uncertainty analysis, as an alternative to complex and computationally expensive physical models. We apply and evaluate this new approach in the context of two aquifer systems supporting agricultural production in the United States: the High Plains aquifer (HPA) and the Mississippi River Valley alluvial aquifer (MRVA). We select input data sets by using a combinationmore » of mutual information, genetic algorithms, and lag analysis, and then use the selected data sets in a Multilayer Perceptron network architecture to simulate seasonal groundwater level change. As expected, model results suggest that irrigation demand has the highest influence on groundwater level change for a majority of the wells. The subset of groundwater observations not used in model training or cross-validation correlates strongly (R > 0.8) with model results for 88 and 83% of the wells in the HPA and MRVA, respectively. In both aquifer systems, the error in the modeled cumulative groundwater level change during testing (2003-2012) was less than 2 m over a majority of the area. Here, we conclude that our modeling framework can serve as an alternative approach to simulating groundwater level change and water availability, especially in regions where subsurface properties are unknown.« less

Machine learning algorithms for modeling groundwater level changes in agricultural regions of the U.S.

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

Sahoo, S.; Russo, T. A.; Elliott, J.

Climate, groundwater extraction, and surface water flows have complex nonlinear relationships with groundwater level in agricultural regions. To better understand the relative importance of each driver and predict groundwater level change, we develop a new ensemble modeling framework based on spectral analysis, machine learning, and uncertainty analysis, as an alternative to complex and computationally expensive physical models. We apply and evaluate this new approach in the context of two aquifer systems supporting agricultural production in the United States: the High Plains aquifer (HPA) and the Mississippi River Valley alluvial aquifer (MRVA). We select input data sets by using a combinationmore » of mutual information, genetic algorithms, and lag analysis, and then use the selected data sets in a Multilayer Perceptron network architecture to simulate seasonal groundwater level change. As expected, model results suggest that irrigation demand has the highest influence on groundwater level change for a majority of the wells. The subset of groundwater observations not used in model training or cross-validation correlates strongly (R > 0.8) with model results for 88 and 83% of the wells in the HPA and MRVA, respectively. In both aquifer systems, the error in the modeled cumulative groundwater level change during testing (2003-2012) was less than 2 m over a majority of the area. Here, we conclude that our modeling framework can serve as an alternative approach to simulating groundwater level change and water availability, especially in regions where subsurface properties are unknown.« less

Agrochemical fate models applied in agricultural areas from Colombia

NASA Astrophysics Data System (ADS)

Garcia-Santos, Glenda; Yang, Jing; Andreoli, Romano; Binder, Claudia

2010-05-01

The misuse application of pesticides in mainly agricultural catchments can lead to severe problems for humans and environment. Especially in developing countries where there is often found overuse of agrochemicals and incipient or lack of water quality monitoring at local and regional levels, models are needed for decision making and hot spots identification. However, the complexity of the water cycle contrasts strongly with the scarce data availability, limiting the number of analysis, techniques, and models available to researchers. Therefore there is a strong need for model simplification able to appropriate model complexity and still represent the processes. We have developed a new model so-called Westpa-Pest to improve water quality management of an agricultural catchment located in the highlands of Colombia. Westpa-Pest is based on the fully distributed hydrologic model Wetspa and a fate pesticide module. We have applied a multi-criteria analysis for model selection under the conditions and data availability found in the region and compared with the new developed Westpa-Pest model. Furthermore, both models were empirically calibrated and validated. The following questions were addressed i) what are the strengths and weaknesses of the models?, ii) which are the most sensitive parameters of each model?, iii) what happens with uncertainties in soil parameters?, and iv) how sensitive are the transfer coefficients?

Agricultural climate impacts assessment for economic modeling and decision support

NASA Astrophysics Data System (ADS)

Thomson, A. M.; Izaurralde, R. C.; Beach, R.; Zhang, X.; Zhao, K.; Monier, E.

2013-12-01

A range of approaches can be used in the application of climate change projections to agricultural impacts assessment. Climate projections can be used directly to drive crop models, which in turn can be used to provide inputs for agricultural economic or integrated assessment models. These model applications, and the transfer of information between models, must be guided by the state of the science. But the methodology must also account for the specific needs of stakeholders and the intended use of model results beyond pure scientific inquiry, including meeting the requirements of agencies responsible for designing and assessing policies, programs, and regulations. Here we present methodology and results of two climate impacts studies that applied climate model projections from CMIP3 and from the EPA Climate Impacts and Risk Analysis (CIRA) project in a crop model (EPIC - Environmental Policy Indicator Climate) in order to generate estimates of changes in crop productivity for use in an agricultural economic model for the United States (FASOM - Forest and Agricultural Sector Optimization Model). The FASOM model is a forward-looking dynamic model of the US forest and agricultural sector used to assess market responses to changing productivity of alternative land uses. The first study, focused on climate change impacts on the UDSA crop insurance program, was designed to use available daily climate projections from the CMIP3 archive. The decision to focus on daily data for this application limited the climate model and time period selection significantly; however for the intended purpose of assessing impacts on crop insurance payments, consideration of extreme event frequency was critical for assessing periodic crop failures. In a second, coordinated impacts study designed to assess the relative difference in climate impacts under a no-mitigation policy and different future climate mitigation scenarios, the stakeholder specifically requested an assessment of a

Denitrification 'Woodchip' Bioreactors for Productive and Sustainable Agricultural Systems

NASA Astrophysics Data System (ADS)

Christianson, L. E.; Summerfelt, S.; Sharrer, K.; Lepine, C.; Helmers, M. J.

2014-12-01

Growing alarm about negative cascading effects of reactive nitrogen in the environment has led to multifaceted efforts to address elevated nitrate-nitrogen levels in water bodies worldwide. The best way to mitigate N-related impacts, such as hypoxic zones and human health concerns, is to convert nitrate to stable, non-reactive dinitrogen gas through the natural process of denitrification. This means denitrification technologies need to be one of our major strategies for tackling the grand challenge of managing human-induced changes to our global nitrogen cycle. While denitrification technologies have historically been focused on wastewater treatment, there is great interest in new lower-tech options for treating effluent and drainage water from one of our largest reactive nitrogen emitters -- agriculture. Denitrification 'woodchip' bioreactors are able to enhance this natural N-conversion via addition of a solid carbon source (e.g., woodchips) and through designs that facilitate development of anoxic conditions required for denitrification. Wood-based denitrification technologies such as woodchip bioreactors and 'sawdust' walls for groundwater have been shown to be effective at reducing nitrate loads in agricultural settings around the world. Designing these systems to be low-maintenance and to avoid removing land from agricultural production has been a primary focus of this "farmer-friendly" technology. This presentation provides a background on woodchip bioreactors including design considerations, N-removal performance, and current research worldwide. Woodchip bioreactors for the agricultural sector are an accessible new option to address society's interest in improving water quality while simultaneously allowing highly productive agricultural systems to continue to provide food in the face of increasing demand, changing global diets, and fluctuating weather.

Test of a simplified modeling approach for nitrogen transfer in agricultural subsurface-drained catchments

NASA Astrophysics Data System (ADS)

Henine, Hocine; Julien, Tournebize; Jaan, Pärn; Ülo, Mander

2017-04-01

In agricultural areas, nitrogen (N) pollution load to surface waters depends on land use, agricultural practices, harvested N output, as well as the hydrology and climate of the catchment. Most of N transfer models need to use large complex data sets, which are generally difficult to collect at larger scale (>km2). The main objective of this study is to carry out a hydrological and a geochemistry modeling by using a simplified data set (land use/crop, fertilizer input, N losses from plots). The modelling approach was tested in the subsurface-drained Orgeval catchment (Paris Basin, France) based on following assumptions: Subsurface tile drains are considered as a giant lysimeter system. N concentration in drain outlets is representative for agricultural practices upstream. Analysis of observed N load (90% of total N) shows 62% of export during the winter. We considered prewinter nitrate (NO3) pool (PWNP) in soils at the beginning of hydrological drainage season as a driving factor for N losses. PWNP results from the part of NO3 not used by crops or the mineralization part of organic matter during the preceding summer and autumn. Considering these assumptions, we used PWNP as simplified input data for the modelling of N transport. Thus, NO3 losses are mainly influenced by the denitrification capacity of soils and stream water. The well-known HYPE model was used to perform water and N losses modelling. The hydrological simulation was calibrated with the observation data at different sub-catchments. We performed a hydrograph separation validated on the thermal and isotopic tracer studies and the general knowledge of the behavior of Orgeval catchment. Our results show a good correlation between the model and the observations (a Nash-Sutcliffe coefficient of 0.75 for water discharge and 0.7 for N flux). Likewise, comparison of calibrated PWNP values with the results from a field survey (annual PWNP campaign) showed significant positive correlation. One can conclude that

Socially optimal drainage system and agricultural biodiversity: a case study for Finnish landscape.

PubMed

Saikkonen, Liisa; Herzon, Irina; Ollikainen, Markku; Lankoski, Jussi

2014-12-15

This paper examines the socially optimal drainage choice (surface/subsurface) for agricultural crop cultivation in a landscape with different land qualities (fertilities) when private profits and nutrient runoff damages are taken into account. We also study the measurable social costs to increase biodiversity by surface drainage when the locations of the surface-drained areas in a landscape affect the provided biodiversity. We develop a general theoretical model and apply it to empirical data from Finnish agriculture. We find that for low land qualities the measurable social returns are higher to surface drainage than to subsurface drainage, and that the profitability of subsurface drainage increases along with land quality. The measurable social costs to increase biodiversity by surface drainage under low land qualities are negative. For higher land qualities, these costs depend on the land quality and on the biodiversity impacts. Biodiversity conservation plans for agricultural landscapes should focus on supporting surface drainage systems in areas where the measurable social costs to increase biodiversity are negative or lowest. Copyright © 2014 Elsevier Ltd. All rights reserved.

The Role of Cash Flow in Financial Early Warning of Agricultural Enterprises Based on Logistic Model

NASA Astrophysics Data System (ADS)

Sun, Fengru

2018-01-01

This paper chooses the agricultural listed companies as the research object, compares the financial situation of the enterprise and the theory of financial early warning, combines the financial status of the agricultural listed companies, selects the relevant cash flow indicators, discusses the application of the Logistic financial early warning model in the agricultural listed companies, Agricultural enterprises get better development. Research on financial early warning of agricultural listed companies will help the agricultural listed companies to predict the financial crisis. Financial early warning model is simple to establish, operational and strong, the use of financial early warning model, to help enterprises in the financial crisis before taking rapid and effective measures, which can avoid losses. Help enterprises to discover signs of deterioration of the financial situation in time to maintain the sustainable development of agricultural enterprises. In addition, through the financial early warning model, investors can correctly identify the financial situation of agricultural enterprises, and can evaluate the financial situation of agricultural enterprises and to help investors to invest in scientific and rational, beneficial to investors to analyze the safety of investment. But also help the relevant regulatory agencies to effectively monitor the market and promote the healthy and stable development of the market.

An Overview of Agricultural Extension Systems: The Territory, Recent Developments and Recommended Directions.

ERIC Educational Resources Information Center

Rivera, William M.

This overview is composed of four major sections. Part I is a map of agricultural extension's "territory," that is, the definitions and systems. It discusses extension functions in agricultural production institutions and varying institutional settings, describes types of extension systems, and considers farmers' degree of influence on extension…

Bushland Evapotranspiration and Agricultural Remote Sensing System (BEARS) software

NASA Astrophysics Data System (ADS)

Gowda, P. H.; Moorhead, J.; Brauer, D. K.

2017-12-01

Evapotranspiration (ET) is a major component of the hydrologic cycle. ET data are used for a variety of water management and research purposes such as irrigation scheduling, water and crop modeling, streamflow, water availability, and many more. Remote sensing products have been widely used to create spatially representative ET data sets which provide important information from field to regional scales. As UAV capabilities increase, remote sensing use is likely to also increase. For that purpose, scientists at the USDA-ARS research laboratory in Bushland, TX developed the Bushland Evapotranspiration and Agricultural Remote Sensing System (BEARS) software. The BEARS software is a Java based software that allows users to process remote sensing data to generate ET outputs using predefined models, or enter custom equations and models. The capability to define new equations and build new models expands the applicability of the BEARS software beyond ET mapping to any remote sensing application. The software also includes an image viewing tool that allows users to visualize outputs, as well as draw an area of interest using various shapes. This software is freely available from the USDA-ARS Conservation and Production Research Laboratory website.

Ecological and economic dynamics of the Shunde agricultural system under China's small city development strategy.

PubMed

Lu, Hongfang; Campbell, Daniel E

2009-06-01

The agricultural and industrial development of small cities is the primary environmental management strategy employed to make full use of extra labor in the rural areas of China. The ecological and economic consequences of this development strategy will affect over 100 million people and change the organization of the Chinese landscape. In this study, we examined the agricultural development of Shunde, a small city in Guangdong Province, over the period 1978 until 2000. Our analysis of the ecological and economic dynamics of the agricultural system revealed the dominant role of labor in the intensification of agricultural production, even though the use of fuels, fertilizers and machines also increased during this time. The Shunde agricultural system was examined from both biophysical or donor-based and human utility or receiver-based perspectives, using emergy and economic methods, respectively. After 22 years of urbanization, the Shunde agricultural system was still able to fill 96% of the local demand for agricultural products using only 6% of its total yield compared to using 14% of the total yield in 1978. Aquaculture developed quickly during the study period as grain production decreased. In 2000, the production of fish, pork, and vegetables accounted for 92% of the total emergy output of the system; however, the emergy buying power of the money received in exchange was lower than the emergy contained in the products exported. The excess emergy exported is the basis for a high quality diet delivered to city dwellers at a relatively low price. In the 1980s, the productivity of both land and labor increased; but after 1992 the productivity of labor decreased, causing the efficiency of the whole agricultural system to decrease. We recommend that processing plants be established for the main agricultural products of Shunde to decrease the emergy loss in trading and to increase employment. The effect of including monetized ecosystem services in the balance between

Design of System Scheme and Operationmechanism on Agricultural Science &Technology Information Service System `110'

NASA Astrophysics Data System (ADS)

Wu, Yongchang; Hu, Zhiquan; Xiao, Bilin; Li, Quanxin

Agricultural science & technology information service system ‘110’ (ASTISS-110), connected through unitary telephone hotline as well as multipurpose service of the network, television and video etc, is one of the most characteristic content of the Chinese rural informatization. ASTISS-110 is a low cost and high efficiency way to make the agricultural science & technology achievements extension and achieve the combination of science & technology with farmers in the rural area. This paper would primary focus on the ASTISS-110 foundation and system principle. On basis of its main functions and system objectives, we put forward the combination of the ‘Sky- Land-People’ technical solution, and analyze the management operation mechanism from commonweal service, enterprise management and commercialization operation.

Ecological mechanisms underlying the sustainability of the agricultural heritage rice-fish coculture system.

PubMed

Xie, Jian; Hu, Liangliang; Tang, Jianjun; Wu, Xue; Li, Nana; Yuan, Yongge; Yang, Haishui; Zhang, Jiaen; Luo, Shiming; Chen, Xin

2011-12-13

For centuries, traditional agricultural systems have contributed to food and livelihood security throughout the world. Recognizing the ecological legacy in the traditional agricultural systems may help us develop novel sustainable agriculture. We examine how rice-fish coculture (RF), which has been designated a "globally important agricultural heritage system," has been maintained for over 1,200 y in south China. A field survey demonstrated that although rice yield and rice-yield stability are similar in RF and rice monoculture (RM), RF requires 68% less pesticide and 24% less chemical fertilizer than RM. A field experiment confirmed this result. We documented that a mutually beneficial relationship between rice and fish develops in RF: Fish reduce rice pests and rice favors fish by moderating the water environment. This positive relationship between rice and fish reduces the need for pesticides in RF. Our results also indicate a complementary use of nitrogen (N) between rice and fish in RF, resulting in low N fertilizer application and low N release into the environment. These findings provide unique insights into how positive interactions and complementary use of resource between species generate emergent ecosystem properties and how modern agricultural systems might be improved by exploiting synergies between species.

Comparison of models used for national agricultural ammonia emission inventories in Europe: Litter-based manure systems

NASA Astrophysics Data System (ADS)

Reidy, B.; Webb, J.; Misselbrook, T. H.; Menzi, H.; Luesink, H. H.; Hutchings, N. J.; Eurich-Menden, B.; Döhler, H.; Dämmgen, U.

Six N-flow models, used to calculate national ammonia (NH 3) emissions from agriculture in different European countries, were compared using standard data sets. Scenarios for litter-based systems were run separately for beef cattle and for broilers, with three different levels of model standardisation: (a) standardized inputs to all models (FF scenario); (b) standard N excretion, but national values for emission factors (EFs) (FN scenario); (c) national values for N excretion and EFs (NN scenario). Results of the FF scenario for beef cattle produced very similar estimates of total losses of total ammoniacal-N (TAN) (±6% of the mean total), but large differences in NH 3 emissions (±24% of the mean). These differences arose from the different approaches to TAN immobilization in litter, other N losses and mineralization in the models. As a result of those differences estimates of TAN available at spreading differed by a factor of almost 3. Results of the FF scenario for broilers produced a range of estimates of total changes in TAN (±9% of the mean total), and larger differences in the estimate of NH 3 emissions (±17% of the mean). The different approaches among the models to TAN immobilization, other N losses and mineralization, produced estimates of TAN available at spreading which differed by a factor of almost 1.7. The differences in estimates of NH 3 emissions decreased as estimates of immobilization and other N losses increased. Since immobilization and denitrification depend also on the C:N ratio in manure, there would be advantages to include C flows in mass-flow models. This would also provide an integrated model for the estimation of emissions of methane, non-methane VOCs and carbon dioxide. Estimation of these would also enable an estimate of mass loss, calculation of the N and TAN concentrations in litter-based manures and further validation of model outputs.

Nitrate-Nitrogen Leaching and Modeling in Intensive Agriculture Farmland in China

PubMed Central

Xu, Ligang; Xu, Jin

2013-01-01

Protecting water resources from nitrate-nitrogen (NO3-N) contamination is an important public health concern and a major national environmental issue in China. Loss of NO3-N in soils due to leaching is not only one of the most important problems in agriculture farming, but is also the main factor causing nitrogen pollution in aquatic environments. Three typical intensive agriculture farmlands in Jiangyin City in China are selected as a case study for NO3-N leaching and modeling in the soil profile. In this study, the transport and fate of NO3-N within the soil profile and nitrate leaching to drains were analyzed by comparing field data with the simulation results of the LEACHM model. Comparisons between measured and simulated data indicated that the NO3-N concentrations in the soil and nitrate leaching to drains are controlled by the fertilizer practice, the initial conditions and the rainfall depth and distribution. Moreover, the study reveals that the LEACHM model gives a fair description of the NO3-N dynamics in the soil and subsurface drainage at the field scale. It can also be concluded that the model after calibration is a useful tool to optimize as a function of the combination “climate-crop-soil-bottom boundary condition” the nitrogen application strategy resulting for the environment in an acceptable level of nitrate leaching. The findings in this paper help to demonstrate the distribution and migration of nitrogen in intensive agriculture farmlands, as well as to explore the mechanism of groundwater contamination resulting from agricultural activities. PMID:23983629

Theme--Achieving 2020. Goal 3: All Students Are Conversationally Literate in Agriculture, Food, Fiber, and Natural Resource Systems.

ERIC Educational Resources Information Center

Trexler, Cary, Ed.

2000-01-01

Nine theme articles focus on the need for students to be conversationally literate about agriculture, food, fiber, and natural resources systems. Discusses the definition of conversational literacy, the human and institutional resources needed, and exemplary models for promoting literacy. (JOW)

The Agricultural Model Intercomparison and Improvement Project (AgMIP) Town Hall

NASA Technical Reports Server (NTRS)

Ruane, Alex; Rosenzweig, Cynthia; Kyle, Page; Basso, Bruno; Winter, Jonathan; Asseng, Senthold

2015-01-01

AgMIP (www.agmip.org) is an international community of climate, crop, livestock, economics, and IT experts working to further the development and application of multi-model, multi-scale, multi-disciplinary agricultural models that can inform policy and decision makers around the world. This meeting will engage the AGU community by providing a brief overview of AgMIP, in particular its new plans for a Coordinated Global and Regional Assessment of climate change impacts on agriculture and food security for AR6. This Town Hall will help identify opportunities for participants to become involved in AgMIP and its 30+ activities.

Development of functional agricultural products utilizing the new health claim labeling system in Japan.

PubMed

Maeda-Yamamoto, Mari; Ohtani, Toshio

2018-04-01

In April 2015, Consumer Affairs Agency of Japan launched a new food labeling system known as "Foods with Function Claims (FFC)." Under this system, the food industry independently evaluates scientific evidence on foods and describes their functional properties. As of May 23, 2017, 1023 FFC containing 8 fresh foods have been launched. Meanwhile, to clarify the health-promoting effects of agricultural products, National Agriculture and Food Research Organization (NARO) implemented the "Research Project on Development of Agricultural Products" and demonstrated the risk reduction of osteoporosis of β-cryptoxanthin rich Satsuma mandarins and the anti-allergic effect of the O-methylated catechin rich tea cultivar Benifuuki. These foods were subsequently released as FFC. Moreover, NARO elucidated the health-promoting effects of various functional agricultural products (β-glucan rich barley, β-conglycinin rich soybean, quercetin rich onion, etc.) and a healthy boxed lunch. This review focuses on new food labeling system or research examining functional aspects of agricultural products.

A commentary on domestic animals as dual-purpose models that benefit agricultural and biomedical research.

PubMed

Ireland, J J; Roberts, R M; Palmer, G H; Bauman, D E; Bazer, F W

2008-10-01

Research on domestic animals (cattle, swine, sheep, goats, poultry, horses, and aquatic species) at land grant institutions is integral to improving the global competitiveness of US animal agriculture and to resolving complex animal and human diseases. However, dwindling federal and state budgets, years of stagnant funding from USDA for the Competitive State Research, Education, and Extension Service National Research Initiative (CSREES-NRI) Competitive Grants Program, significant reductions in farm animal species and in numbers at land grant institutions, and declining enrollment for graduate studies in animal science are diminishing the resources necessary to conduct research on domestic species. Consequently, recruitment of scientists who use such models to conduct research relevant to animal agriculture and biomedicine at land grant institutions is in jeopardy. Concerned stakeholders have addressed this critical problem by conducting workshops, holding a series of meetings with USDA and National Institutes of Health (NIH) officials, and developing a white paper to propose solutions to obstacles impeding the use of domestic species as dual-purpose animal models for high-priority problems common to agriculture and biomedicine. In addition to shortfalls in research support and human resources, overwhelming use of mouse models in biomedicine, lack of advocacy from university administrators, long-standing cultural barriers between agriculture and human medicine, inadequate grantsmanship by animal scientists, and a scarcity of key reagents and resources are major roadblocks to progress. Solutions will require a large financial enhancement of USDA's Competitive Grants Program, educational programs geared toward explaining how research using agricultural animals benefits both animal agriculture and human health, and the development of a new mind-set in land grant institutions that fosters greater cooperation among basic and applied researchers. Recruitment of

Chitosan nanoparticle based delivery systems for sustainable agriculture.

PubMed

Kashyap, Prem Lal; Xiang, Xu; Heiden, Patricia

2015-01-01

Development of technologies that improve food productivity without any adverse impact on the ecosystem is the need of hour. In this context, development of controlled delivery systems for slow and sustained release of agrochemicals or genetic materials is crucial. Chitosan has emerged as a valuable carrier for controlled delivery of agrochemicals and genetic materials because of its proven biocompatibility, biodegradability, non-toxicity, and adsorption abilities. The major advantages of encapsulating agrochemicals and genetic material in a chitosan matrix include its ability to function as a protective reservoir for the active ingredients, protecting the ingredients from the surrounding environment while they are in the chitosan domain, and then controlling their release, allowing them to serve as efficient gene delivery systems for plant transformation or controlled release of pesticides. Despite the great progress in the use of chitosan in the area of medical and pharmaceutical sciences, there is still a wide knowledge gap regarding the potential application of chitosan for encapsulation of active ingredients in agriculture. Hence, the present article describes the current status of chitosan nanoparticle-based delivery systems in agriculture, and to highlight challenges that need to be overcome. Copyright © 2015 Elsevier B.V. All rights reserved.

Monitoring and modeling agricultural drought for famine early warning (Invited)

NASA Astrophysics Data System (ADS)

Verdin, J. P.; Funk, C.; Budde, M. E.; Lietzow, R.; Senay, G. B.; Smith, R.; Pedreros, D.; Rowland, J.; Artan, G. A.; Husak, G. J.; Michaelsen, J.; Adoum, A.; Galu, G.; Magadzire, T.; Rodriguez, M.

2009-12-01

The Famine Early Warning Systems Network (FEWS NET) makes quantitative estimates of food insecure populations, and identifies the places and periods during which action must be taken to assist them. Subsistence agriculture and pastoralism are the predominant livelihood systems being monitored, and they are especially drought-sensitive. At the same time, conventional climate observation networks in developing countries are often sparse and late in reporting. Consequently, remote sensing has played a significant role since FEWS NET began in 1985. Initially there was heavy reliance on vegetation index imagery from AVHRR to identify anomalies in landscape greenness indicative of drought. In the latter part of the 1990s, satellite rainfall estimates added a second, independent basis for identification of drought. They are used to force crop water balance models for the principal rainfed staple crops in twenty FEWS NET countries. Such models reveal seasonal moisture deficits associated with yield reduction on a spatially continuous basis. In 2002, irrigated crops in southwest Asia became a concern, and prompted the implementation of a gridded energy balance model to simulate the seasonal mountain snow pack, the main source of irrigation water. MODIS land surface temperature data are also applied in these areas to directly estimate actual seasonal evapotranspiration on the irrigated lands. The approach reveals situations of reduced irrigation water supply and crop production due to drought. The availability of MODIS data after 2000 also brought renewed interest in vegetation index imagery. MODIS NDVI data have proven to be of high quality, thanks to significant spectral and spatial resolution improvements over AVHRR. They are vital to producing rapid harvest assessments for drought-impacted countries in Africa and Asia. The global food crisis that emerged in 2008 has led to expansion of FEWS NET monitoring to over 50 additional countries. Unlike previous practice, these

The relationship between carbon dioxide and agriculture in Ghana: a comparison of VECM and ARDL model.

PubMed

Asumadu-Sarkodie, Samuel; Owusu, Phebe Asantewaa

2016-06-01

In this paper, the relationship between carbon dioxide and agriculture in Ghana was investigated by comparing a Vector Error Correction Model (VECM) and Autoregressive Distributed Lag (ARDL) Model. Ten study variables spanning from 1961 to 2012 were employed from the Food Agricultural Organization. Results from the study show that carbon dioxide emissions affect the percentage annual change of agricultural area, coarse grain production, cocoa bean production, fruit production, vegetable production, and the total livestock per hectare of the agricultural area. The vector error correction model and the autoregressive distributed lag model show evidence of a causal relationship between carbon dioxide emissions and agriculture; however, the relationship decreases periodically which may die over-time. All the endogenous variables except total primary vegetable production lead to carbon dioxide emissions, which may be due to poor agricultural practices to meet the growing food demand in Ghana. The autoregressive distributed lag bounds test shows evidence of a long-run equilibrium relationship between the percentage annual change of agricultural area, cocoa bean production, total livestock per hectare of agricultural area, total pulses production, total primary vegetable production, and carbon dioxide emissions. It is important to end hunger and ensure people have access to safe and nutritious food, especially the poor, orphans, pregnant women, and children under-5 years in order to reduce maternal and infant mortalities. Nevertheless, it is also important that the Government of Ghana institutes agricultural policies that focus on promoting a sustainable agriculture using environmental friendly agricultural practices. The study recommends an integration of climate change measures into Ghana's national strategies, policies and planning in order to strengthen the country's effort to achieving a sustainable environment.

The Urban Food-Water Nexus: Modeling Water Footprints of Urban Agriculture using CityCrop

NASA Astrophysics Data System (ADS)

Tooke, T. R.; Lathuilliere, M. J.; Coops, N. C.; Johnson, M. S.

2014-12-01

Urban agriculture provides a potential contribution towards more sustainable food production and mitigating some of the human impacts that accompany volatility in regional and global food supply. When considering the capacity of urban landscapes to produce food products, the impact of urban water demand required for food production in cities is often neglected. Urban agricultural studies also tend to be undertaken at broad spatial scales, overlooking the heterogeneity of urban form that exerts an extreme influence on the urban energy balance. As a result, urban planning and management practitioners require, but often do not have, spatially explicit and detailed information to support informed urban agricultural policy, especially as it relates to potential conflicts with sustainability goals targeting water-use. In this research we introduce a new model, CityCrop, a hybrid evapotranspiration-plant growth model that incorporates detailed digital representations of the urban surface and biophysical impacts of the built environment and urban trees to account for the daily variations in net surface radiation. The model enables very fine-scale (sub-meter) estimates of water footprints of potential urban agricultural production. Results of the model are demonstrated for an area in the City of Vancouver, Canada and compared to aspatial model estimates, demonstrating the unique considerations and sensitivities for current and future water footprints of urban agriculture and the implications for urban water planning and policy.

A comprehensive review of thin-layer drying models used in agricultural products.

PubMed

Ertekin, Can; Firat, M Ziya

2017-03-04

Drying is one of the widely used methods of grain, fruit, and vegetable preservation. The important aim of drying is to reduce the moisture content and thereby increase the lifetime of products by limiting enzymatic and oxidative degradation. In addition, by reducing the amount of water, drying reduces the crop losses, improves the quality of dried products, and facilitates its transportation, handling, and storage requirements. Drying is a process comprising simultaneous heat and mass transfer within the material, and between the surface of the material and the surrounding media. Many models have been used to describe the drying process for different agricultural products. These models are used to estimate drying time of several products under different drying conditions, and how to increase the drying process efficiency and also to generalize drying curves, for the design and operation of dryers. Several investigators have proposed numerous mathematical models for thin-layer drying of many agricultural products. This study gives a comprehensive review of more than 100 different semitheoretical and empirical thin-layer drying models used in agricultural products and evaluates the statistical criteria for the determination of appropriate model.

Collaboration between nurses and agricultural teachers to prevent adolescent agricultural injuries: the Agricultural Disability Awareness and Risk Education Model.

PubMed

Reed, Deborah B; Kidd, Pamela S

2004-01-01

Nearly 2 million children live or work on America's farms and ranches. Despite the increasing mechanization of production agriculture in the United States, children still constitute a considerable portion of the work force on farms and ranches. When adjusted for actual work exposure time, adolescent injury rates on agricultural establishments surpass those of adults (Castillo, D. N., Landen, D. D., & Layne, L. A. (1994). American Journal of Public Health, 84, 646-649). This project, headed by two public health nurses, developed and tested an agricultural safety curriculum [Agricultural Disability Awareness and Risk Education (AgDARE)] for use in high school agriculture classes. Students who participated in AgDARE scored significantly higher in farm safety attitude and intent to change work behavior than the control group. School and public health nurses, working together with agriculture teachers, may make an effective team in reducing injuries among teen agricultural workers.

Establishment and application of the estimation model for pollutant concentrfation in agriculture drain

NASA Astrophysics Data System (ADS)

Li, Qiangkun; Hu, Yawei; Jia, Qian; Song, Changji

2018-02-01

It is the key point of quantitative research on agricultural non-point source pollution load, the estimation of pollutant concentration in agricultural drain. In the guidance of uncertainty theory, the synthesis of fertilization and irrigation is used as an impulse input to the farmland, meanwhile, the pollutant concentration in agricultural drain is looked as the response process corresponding to the impulse input. The migration and transformation of pollutant in soil is expressed by Inverse Gaussian Probability Density Function. The law of pollutants migration and transformation in soil at crop different growth periods is reflected by adjusting parameters of Inverse Gaussian Distribution. Based on above, the estimation model for pollutant concentration in agricultural drain at field scale was constructed. Taking the of Qing Tong Xia Irrigation District in Ningxia as an example, the concentration of nitrate nitrogen and total phosphorus in agricultural drain was simulated by this model. The results show that the simulated results accorded with measured data approximately and Nash-Sutcliffe coefficients were 0.972 and 0.964, respectively.

A steady state model of agricultural waste pyrolysis: A mini review.

PubMed

Trninić, M; Jovović, A; Stojiljković, D

2016-09-01

Agricultural waste is one of the main renewable energy resources available, especially in an agricultural country such as Serbia. Pyrolysis has already been considered as an attractive alternative for disposal of agricultural waste, since the technique can convert this special biomass resource into granular charcoal, non-condensable gases and pyrolysis oils, which could furnish profitable energy and chemical products owing to their high calorific value. In this regard, the development of thermochemical processes requires a good understanding of pyrolysis mechanisms. Experimental and some literature data on the pyrolysis characteristics of corn cob and several other agricultural residues under inert atmosphere were structured and analysed in order to obtain conversion behaviour patterns of agricultural residues during pyrolysis within the temperature range from 300 °C to 1000 °C. Based on experimental and literature data analysis, empirical relationships were derived, including relations between the temperature of the process and yields of charcoal, tar and gas (CO2, CO, H2 and CH4). An analytical semi-empirical model was then used as a tool to analyse the general trends of biomass pyrolysis. Although this semi-empirical model needs further refinement before application to all types of biomass, its prediction capability was in good agreement with results obtained by the literature review. The compact representation could be used in other applications, to conveniently extrapolate and interpolate these results to other temperatures and biomass types. © The Author(s) 2016.

Climate Forcing Datasets for Agricultural Modeling: Merged Products for Gap-Filling and Historical Climate Series Estimation

NASA Technical Reports Server (NTRS)

Ruane, Alex C.; Goldberg, Richard; Chryssanthacopoulos, James

2014-01-01

The AgMERRA and AgCFSR climate forcing datasets provide daily, high-resolution, continuous, meteorological series over the 1980-2010 period designed for applications examining the agricultural impacts of climate variability and climate change. These datasets combine daily resolution data from retrospective analyses (the Modern-Era Retrospective Analysis for Research and Applications, MERRA, and the Climate Forecast System Reanalysis, CFSR) with in situ and remotely-sensed observational datasets for temperature, precipitation, and solar radiation, leading to substantial reductions in bias in comparison to a network of 2324 agricultural-region stations from the Hadley Integrated Surface Dataset (HadISD). Results compare favorably against the original reanalyses as well as the leading climate forcing datasets (Princeton, WFD, WFD-EI, and GRASP), and AgMERRA distinguishes itself with substantially improved representation of daily precipitation distributions and extreme events owing to its use of the MERRA-Land dataset. These datasets also peg relative humidity to the maximum temperature time of day, allowing for more accurate representation of the diurnal cycle of near-surface moisture in agricultural models. AgMERRA and AgCFSR enable a number of ongoing investigations in the Agricultural Model Intercomparison and Improvement Project (AgMIP) and related research networks, and may be used to fill gaps in historical observations as well as a basis for the generation of future climate scenarios.

Web-based information system design of agricultural management towards self-sufficiency local food in North Aceh

NASA Astrophysics Data System (ADS)

Salahuddin; Husaini; Anwar

2018-01-01

The agricultural sector, especially food crops and horticulture, is one of the sectors driving regional economic pillars in Aceh Utara Regency of Aceh Province. Some agricultural products and food crops that become excellent products in North Aceh regency are: rice, corn, peanuts, long beans, cassava and soybeans. The Local Government of North Aceh Regency has not been optimal in empowering and maximizing the potential of agriculture resources. One of the obstacles is caused by the North Aceh Regency Government does not have an adequate database and web information system/GIS (Geographic Information System) for data management of agricultural centre in North Aceh Regency. This research is expected to assist local government of North Aceh Regency in managing agriculture sector to realize local food independence the region in supporting national food security program. The method in this research is using waterfall method for designing and making information system by conducting sequential process starting from data collection stage, requirement analysis, design, coding, testing and implementation system. The result of this research is a web-based information system for the management of agriculture superior agricultural product centre in North Aceh. This application provides information mapping the location of agricultural superior product producers and mapping of potential locations for the development of certain commodities in North Aceh Regency region in realizing food self-sufficiency in the region.

A New Paradigm for Assessing the Role of Agriculture in the Climate System and in Climate Change

NASA Technical Reports Server (NTRS)

Pielke, Roger A., Sr.; Adegoke, Jimmy O.; Chase, Thomas N.; Marshall, Curtis H.; Matsui, Toshihisa; Niyogi, Dev

2007-01-01

This paper discusses the diverse climate forcings that impact agricultural systems, and contrasts the current paradigm of using global models downscaled to agricultural areas (a top-down approach) with a new paradigm that first assesses the vulnerability of agricultural activities to the spectrum of environmental risk including climate (a bottom-up approach). To illustrate the wide spectrum of climate forcings, regional climate forcings are presented including land-use/land-cover change and the influence of aerosols on radiative and biogeochemical fluxes and cloud/precipitation processes, as well as how these effects can be teleconnected globally. Examples are presented of the vulnerability perspective, along with a small survey of the perceived drought impacts in a local area, in which a wide range of impacts for the same precipitation deficits are found. This example illustrates why agricultural assessments of risk to climate change and variability and of other environmental risks should start with a bottom-up perspective.

Forest and Agricultural Sector Optimization Model Greenhouse Gas Version (FASOM-GHG)

EPA Science Inventory

FASOM-GHG is a dynamic, multi-period, intertemporal, price-endogenous, mathematical programming model depicting land transfers and other resource allocations between and within the agricultural and forest sectors in the US. The model solution portrays simultaneous market equilibr...

3-D Imaging Systems for Agricultural Applications—A Review

PubMed Central

Vázquez-Arellano, Manuel; Griepentrog, Hans W.; Reiser, David; Paraforos, Dimitris S.

2016-01-01

Efficiency increase of resources through automation of agriculture requires more information about the production process, as well as process and machinery status. Sensors are necessary for monitoring the status and condition of production by recognizing the surrounding structures such as objects, field structures, natural or artificial markers, and obstacles. Currently, three dimensional (3-D) sensors are economically affordable and technologically advanced to a great extent, so a breakthrough is already possible if enough research projects are commercialized. The aim of this review paper is to investigate the state-of-the-art of 3-D vision systems in agriculture, and the role and value that only 3-D data can have to provide information about environmental structures based on the recent progress in optical 3-D sensors. The structure of this research consists of an overview of the different optical 3-D vision techniques, based on the basic principles. Afterwards, their application in agriculture are reviewed. The main focus lays on vehicle navigation, and crop and animal husbandry. The depth dimension brought by 3-D sensors provides key information that greatly facilitates the implementation of automation and robotics in agriculture. PMID:27136560

Modeling technical change in climate analysis: evidence from agricultural crop damages.

PubMed

Ahmed, Adeel; Devadason, Evelyn S; Al-Amin, Abul Quasem

2017-05-01

This study accounts for the Hicks neutral technical change in a calibrated model of climate analysis, to identify the optimum level of technical change for addressing climate changes. It demonstrates the reduction to crop damages, the costs to technical change, and the net gains for the adoption of technical change for a climate-sensitive Pakistan economy. The calibrated model assesses the net gains of technical change for the overall economy and at the agriculture-specific level. The study finds that the gains of technical change are overwhelmingly higher than the costs across the agriculture subsectors. The gains and costs following technical change differ substantially for different crops. More importantly, the study finds a cost-effective optimal level of technical change that potentially reduces crop damages to a minimum possible level. The study therefore contends that the climate policy for Pakistan should consider the role of technical change in addressing climate impacts on the agriculture sector.

AgBase: supporting functional modeling in agricultural organisms

PubMed Central

McCarthy, Fiona M.; Gresham, Cathy R.; Buza, Teresia J.; Chouvarine, Philippe; Pillai, Lakshmi R.; Kumar, Ranjit; Ozkan, Seval; Wang, Hui; Manda, Prashanti; Arick, Tony; Bridges, Susan M.; Burgess, Shane C.

2011-01-01

AgBase (http://www.agbase.msstate.edu/) provides resources to facilitate modeling of functional genomics data and structural and functional annotation of agriculturally important animal, plant, microbe and parasite genomes. The website is redesigned to improve accessibility and ease of use, including improved search capabilities. Expanded capabilities include new dedicated pages for horse, cat, dog, cotton, rice and soybean. We currently provide 590 240 Gene Ontology (GO) annotations to 105 454 gene products in 64 different species, including GO annotations linked to transcripts represented on agricultural microarrays. For many of these arrays, this provides the only functional annotation available. GO annotations are available for download and we provide comprehensive, species-specific GO annotation files for 18 different organisms. The tools available at AgBase have been expanded and several existing tools improved based upon user feedback. One of seven new tools available at AgBase, GOModeler, supports hypothesis testing from functional genomics data. We host several associated databases and provide genome browsers for three agricultural pathogens. Moreover, we provide comprehensive training resources (including worked examples and tutorials) via links to Educational Resources at the AgBase website. PMID:21075795

An Exploration of the Formal Agricultural Education System in Trinidad and Tobago

ERIC Educational Resources Information Center

Hurst, Sara D.; Conner, Nathan W.; Stripling, Christopher T.; Blythe, Jessica; Giorgi, Aaron; Rubenstein, Eric D.; Futrell, Angel; Jenkins, Jenny; Roberts, T. Grady

2015-01-01

A team of nine researchers from the United States spent 10 days exploring the formal agricultural education system in Trinidad and Tobago from primary education through postgraduate education. Data were collected from interviews and observations from students, teachers/instructors, and agricultural producers. The team concluded that (a) the people…

Modelling mitigation options to reduce diffuse nitrogen water pollution from agriculture.

PubMed

Bouraoui, Fayçal; Grizzetti, Bruna

2014-01-15

Agriculture is responsible for large scale water quality degradation and is estimated to contribute around 55% of the nitrogen entering the European Seas. The key policy instrument for protecting inland, transitional and coastal water resources is the Water Framework Directive (WFD). Reducing nutrient losses from agriculture is crucial to the successful implementation of the WFD. There are several mitigation measures that can be implemented to reduce nitrogen losses from agricultural areas to surface and ground waters. For the selection of appropriate measures, models are useful for quantifying the expected impacts and the associated costs. In this article we review some of the models used in Europe to assess the effectiveness of nitrogen mitigation measures, ranging from fertilizer management to the construction of riparian areas and wetlands. We highlight how the complexity of models is correlated with the type of scenarios that can be tested, with conceptual models mostly used to evaluate the impact of reduced fertilizer application, and the physically-based models used to evaluate the timing and location of mitigation options and the response times. We underline the importance of considering the lag time between the implementation of measures and effects on water quality. Models can be effective tools for targeting mitigation measures (identifying critical areas and timing), for evaluating their cost effectiveness, for taking into consideration pollution swapping and considering potential trade-offs in contrasting environmental objectives. Models are also useful for involving stakeholders during the development of catchments mitigation plans, increasing their acceptability. © 2013.

Assessment of nitrogen and phosphorus flows in agricultural and urban systems in a small island under limited data availability.

PubMed

Firmansyah, I; Spiller, M; de Ruijter, F J; Carsjens, G J; Zeeman, G

2017-01-01

Nitrogen (N) and phosphorus (P) are two essential macronutrients required in agricultural production. The major share of this production relies on chemical fertilizer that requires energy and relies on limited resources (P). Since these nutrients are lost to the environment, there is a need to shift from this linear urban metabolism to a circular metabolism in which N and P from domestic waste and wastewater are reused in agriculture. A first step to facilitate a transition to more circular urban N and P management is to understand the flows of these resources in a coupled urban-agricultural system. For the first time this paper presents a Substance Flow Analysis (SFA) approach for the assessment of the coupled agricultural and urban systems under limited data availability in a small island. The developed SFA approach is used to identify intervention points that can provide N and P stocks for agricultural production. The island of St. Eustatius, a small island in the Caribbean, was used as a case study. The model developed in this study consists of eight sub-systems: agricultural and natural lands, urban lands, crop production, animal production, market, household consumption, soakage pit and open-dump landfill. A total of 26 flows were identified and quantified for a period of one year (2013). The results showed that the agricultural system is a significant source for N and P loss because of erosion/run-off and leaching. Moreover, urban sanitation systems contribute to deterioration of the island's ecosystem through N and P losses from domestic waste and wastewater by leaching and atmospheric emission. Proposed interventions are the treatment of blackwater and greywater for the recovery of N and P. In conclusion, this study allows for identification of potential N and P losses and proposes mitigation measures to improve nutrient management in a small island context. Copyright © 2016 Elsevier B.V. All rights reserved.

Application of methane fermentation technology into organic wastes in closed agricultural system

NASA Astrophysics Data System (ADS)

Endo, Ryosuke; Kitaya, Yoshiaki

Sustainable and recycling-based systems are required in space agriculture which takes place in an enclosed environment. Methane fermentation is one of the most major biomass conversion technologies, because (1) it provides a renewable energy source as biogas including methane, suitable for energy production, (2) the nutrient-rich solids left after digestion can be used as compost for agriculture. In this study, the effect of the application of methane fermentation technology into space agriculture on the material and energy cycle was investigated.

Environmental Indicator Principium with Case References to Agricultural Soil, Water, and Air Quality and Model-Derived Indicators.

PubMed

Zhang, T Q; Zheng, Z M; Lal, R; Lin, Z Q; Sharpley, A N; Shober, A L; Smith, D; Tan, C S; Van Cappellen, P

2018-03-01

Environmental indicators are powerful tools for tracking environmental changes, measuring environmental performance, and informing policymakers. Many diverse environmental indicators, including agricultural environmental indicators, are currently in use or being developed. This special collection of technical papers expands on the peer-reviewed literature on environmental indicators and their application to important current issues in the following areas: (i) model-derived indicators to indicate phosphorus losses from arable land to surface runoff and subsurface drainage, (ii) glutathione-ascorbate cycle-related antioxidants as early-warning bioindicators of polybrominated diphenyl ether toxicity in mangroves, and (iii) assessing the effectiveness of using organic matrix biobeds to limit herbicide dissipation from agricultural fields, thereby controlling on-farm point-source pollution. This introductory review also provides an overview of environmental indicators, mainly for agriculture, with examples related to the quality of the agricultural soil-water-air continuum and the application of model-derived indicators. Current knowledge gaps and future lines of investigation are also discussed. It appears that environmental indicators, particularly those for agriculture, work efficiently at the field, catchment, and local scales and serve as valuable metrics of system functioning and response; however, these indicators need to be refined or further developed to comprehensively meet community expectations in terms of providing a consistent picture of relevant issues and/or allowing comparisons to be made nationally or internationally. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.

Sidewalks and City Streets: A Model for Vibrant Agricultural Education in Urban American Communities

ERIC Educational Resources Information Center

Brown, Nicholas R.; Kelsey, Kathleen D.

2013-01-01

In 2005, The National Council for Agricultural Education (NCAE) unveiled The Long Range Goal for Agricultural Education also known as 10 x 15. According to NCAE, the primary goal of 10 x 15 was to create 10,000 new agricultural education programs by 2015 that focused on an integrated model of classroom and laboratory instruction, experiential…

The Agricultural Model Intercomparison and Improvement Project (AgMIP): Progress and Preliminary Results

NASA Astrophysics Data System (ADS)

Rosenzweig, C.

2011-12-01

The Agricultural Model Intercomparison and Improvement Project (AgMIP) is a distributed climate-scenario simulation exercise for historical model intercomparison and future climate change conditions with participation of multiple crop and agricultural trade modeling groups around the world. The goals of AgMIP are to improve substantially the characterization of risk of hunger and world food security due to climate change and to enhance adaptation capacity in both developing and developed countries. Recent progress and the current status of AgMIP will be presented, highlighting three areas of activity: preliminary results from crop pilot studies, outcomes from regional workshops, and emerging scientific challenges. AgMIP crop modeling efforts are being led by pilot studies, which have been established for wheat, maize, rice, and sugarcane. These crop-specific initiatives have proven instrumental in testing and contributing to AgMIP protocols, as well as creating preliminary results for aggregation and input to agricultural trade models. Regional workshops are being held to encourage collaborations and set research activities in motion for key agricultural areas. The first of these workshops was hosted by Embrapa and UNICAMP and held in Campinas, Brazil. Outcomes from this meeting have informed crop modeling research activities within South America, AgMIP protocols, and future regional workshops. Several scientific challenges have emerged and are currently being addressed by AgMIP researchers. Areas of particular interest include geospatial weather generation, ensemble methods for climate scenarios and crop models, spatial aggregation of field-scale yields to regional and global production, and characterization of future changes in climate variability.

Capability of Hyperspectral data in Spatial Variability Distribution of Chlorophyll and Water Stress in Rice Agriculture System

NASA Astrophysics Data System (ADS)

Moharana, S.; Dutta, S.

2016-12-01

Abstract : The mapping and analysis of spatial variability within the field is a challenging task. However, field variability of a single vegetation cover does not give satisfactory results mainly due to low spectral resolution and non-availability of remote sensing data. From the NASA Earth Observing-1 (EO-1) satellite data, spatial distribution of biophysical parameters like chlorophyll and relative water content in a rice agriculture system is carried out in the present study. Hyperion L1R product composed of 242 spectral bands with 30m spatial resolution was acquired for Assam, India. This high dimensional data is allowed for pre-processing to get an atmospherically corrected imagery. Moreover, ground based hyperspectral measurements are collected from experimental rice fields from the study site using hand held ASD spectroradiometer (350-1050 nm). Published indices specifically designed for chlorophyll (OASVI, mSR, and MTCI indices) and water content (WI and WBI indices) are selected based on stastical performance of the in-situ hyperspectral data. Index models are established for the respective biophysical parameters and observed that the aforementioned indices followed different linear and nonlinear relationships which are completely different from the published indices. By employing the presently developed relationships, spatial variation of total chlorophyll and water stress are mapped for a rice agriculture system from Hyperion imagery. The findings showed that, the variation of chlorophyll and water content ranged from 1.77-10.61mg/g and 40-90% respectively for the studied rice agriculture system. The spatial distribution of these parameters resulted from presently developed index models are well captured from Hyperion imagery and they have good agreement with observed field based chlorophyll (1.14-7.26 mg/g) and water content (60-95%) of paddy crop. This study can be useful in providing essential information to assess the paddy field heterogeneity in an

Using the Health Belief Model to Comparatively Examine the Welding Safety Beliefs of Postsecondary Agricultural Education Students and Their Non-Agricultural Education Peers

ERIC Educational Resources Information Center

Anderson, Ryan; Velez, Jonathan; Anderson, Shawn

2014-01-01

The purpose of this descriptive correlational research was to investigate postsecondary agriculture students' perceptions regarding the safe use of agricultural mechanics equipment. Students enrolled in a university metals and welding course were surveyed using an adapted instrument to assess constructs of the Health Beliefs Model, self-efficacy…

Probabilistic Decision Tools for Determining Impacts of Agricultural Development Policy on Household Nutrition

NASA Astrophysics Data System (ADS)

Whitney, Cory W.; Lanzanova, Denis; Muchiri, Caroline; Shepherd, Keith D.; Rosenstock, Todd S.; Krawinkel, Michael; Tabuti, John R. S.; Luedeling, Eike

2018-03-01

Governments around the world have agreed to end hunger and food insecurity and to improve global nutrition, largely through changes to agriculture and food systems. However, they are faced with a lot of uncertainty when making policy decisions, since any agricultural changes will influence social and biophysical systems, which could yield either positive or negative nutrition outcomes. We outline a holistic probability modeling approach with Bayesian Network (BN) models for nutritional impacts resulting from agricultural development policy. The approach includes the elicitation of expert knowledge for impact model development, including sensitivity analysis and value of information calculations. It aims at a generalizable methodology that can be applied in a wide range of contexts. To showcase this approach, we develop an impact model of Vision 2040, Uganda's development strategy, which, among other objectives, seeks to transform the country's agricultural landscape from traditional systems to large-scale commercial agriculture. Model results suggest that Vision 2040 is likely to have negative outcomes for the rural livelihoods it intends to support; it may have no appreciable influence on household hunger but, by influencing preferences for and access to quality nutritional foods, may increase the prevalence of micronutrient deficiency. The results highlight the trade-offs that must be negotiated when making decisions regarding agriculture for nutrition, and the capacity of BNs to make these trade-offs explicit. The work illustrates the value of BNs for supporting evidence-based agricultural development decisions.

MODELING LONG-TERM NITRATE BASE-FLOW LOADING FROM TWO AGRICULTURAL WATERSHEDS

EPA Science Inventory

Nitrate contamination of ground water from agricultural practices may be contributing to the eutrophication of the Chesapeake Bay, degrading water quality and aquatic habitats. Groundwater flow and nitrate transport and fate are modeled, using MODFLOW and MT3D computer models, in...

Design on automatic rolling system for agricultural greenhouse

NASA Astrophysics Data System (ADS)

Fu, Li; Fu, Xiuwei; Zhang, Yanxiao

2018-03-01

The automatic rolling system of agricultural greenhouse is introduced in this paper. The opening degree of greenhouse according to changes in light intensity and temperature is adjusted. When the current is too large or the motor is blocked or lost, the buzzer is alarmed and warned someone the controlling system badly. When the temperature is higher than the default value, the fan is moved by the micro-controller controls, otherwise the heating rod so that the temperature reaches the preset range.

Systems assessment of water savings impact of controlled environment agriculture (CEA) utilizing wirelessly networked Sense•Decide•Act•Communicate (SDAC) systems.

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

Campbell, Jonathan T.; Baynes, Edward E., Jr.; Aguirre,Carlos

Reducing agricultural water use in arid regions while maintaining or improving economic productivity of the agriculture sector is a major challenge. Controlled environment agriculture (CEA, or, greenhouse agriculture) affords advantages in direct resource use (less land and water required) and productivity (i.e., much higher product yield and quality per unit of resources used) relative to conventional open-field practices. These advantages come at the price of higher operating complexity and costs per acre. The challenge is to implement and apply CEA such that the productivity and resource use advantages will sufficiently outweigh the higher operating costs to provide for overall benefitmore » and viability. This project undertook an investigation of CEA for livestock forage production as a water-saving alternative to open-field forage production in arid regions. Forage production is a large consumer of fresh water in many arid regions of the world, including the southwestern U.S. and northern Mexico. With increasing competition among uses (agriculture, municipalities, industry, recreation, ecosystems, etc.) for limited fresh water supplies, agricultural practice alternatives that can potentially maintain or enhance productivity while reducing water use warrant consideration. The project established a pilot forage production greenhouse facility in southern New Mexico based on a relatively modest and passive (no active heating or cooling) system design pioneered in Chihuahua, Mexico. Experimental operations were initiated in August 2004 and carried over into early-FY05 to collect data and make initial assessments of operational and technical system performance, assess forage nutrition content and suitability for livestock, identify areas needing improvement, and make initial assessment of overall feasibility. The effort was supported through the joint leveraging of late-start FY04 LDRD funds and bundled CY2004 project funding from the New Mexico Small Business

Socio-hydrological model to inform community adaptation to seasonal drought and climate variability in rural agricultural watersheds in Costa Rica

NASA Astrophysics Data System (ADS)

Hund, S. V.; Johnson, M. S.; Morillas, L.; McDaniels, T.; Romero Valpreda, J.; Allen, D. M.

2017-12-01

Climate variability and seasonal droughts associated with ENSO (El Niño Southern Oscillation) and increasing water demand due to growing population are leading to serious water conflicts in the wet-dry tropics of Central America. Integrated methods are needed to understand the linkages of these complex socio-hydrological systems and design reliable adaption strategies in a period of global change. With increasing pressure on surface and groundwater resources during long annual dry seasons, rural agricultural communities suffer water shortages, especially in those years preceded by wet seasons with lower rainfall (and reduced groundwater recharge). To support community resilience to rainfall variability and droughts, we conducted a combination of fieldwork (development of hydrologic monitoring system and local stakeholder cooperation), and hydrological modeling for two watersheds with a shared aquifer (Potrero and Caimital) in Northwestern Costa Rica. The agricultural land use of the region and the many rural villages that draw directly on their local water resource and live in close interaction with their watersheds necessitated a socio-hydrological systems approach. In this talk we present results from our hydrologic modeling, for which we used the WEAP (Water Evaluation and Planning) model and locally recorded data. With the integrated water supply and demand features of the WEAP model, we were able to synthesize both the hydrological system and the societal system (specifically, household and agricultural water use), and show feedbacks such as that water use tends to increase during the dry season, likely exacerbating water shortages issues. Further, applying a range of ENSO related rainfall scenarios to the model demonstrated that community adaptation will become in particular important in response to lower water availability in future El Niño years. In collaboration with local stakeholders, we identified a set of feasible adaptation strategies to seasonal

Pedoarchaeology of Early Agricultural Period Irrigation Systems in the Tucson Basin of the American Southwest

NASA Astrophysics Data System (ADS)

Homburg, Jeffrey; Nials, Fred

2017-04-01

Pedoarchaeological studies were conducted at the Las Capas and Sunset Road sites in the Tucson Basin of Arizona in order to document and evaluate soil productivity and hydraulic soil properties of ancient agricultural irrigation systems. These ancient irrigated fields are on the margin of the Santa Cruz River floodplain, between two alluvial fans where high water tables and stable to aggrading geomorphic conditions facilitated diverting water from drainages and directing it to fields by gravity-fed canal irrigation. Archaeological investigations at these sites recently provided opportunities for documenting the configuration and evolution of the oldest irrigation systems yet identified in the United States, the earliest dating to more than three millennia in age. This research is significant archaeologically because of: (1) the antiquity ( 575-1225 B.C.) of the Early Agricultural period irrigation systems at these sites, (2) the fact that irrigation systems dated to different times are separated stratigraphically within the sites, and (3) the fact that extensive, well-preserved gridded irrigation features were identified using mechanical stripping, with nearly 100 ancient footprints preserved on a buried agricultural surface at Sunset Road. The stratigraphic separation of buried surfaces that were irrigated and the abundant cultivated irrigation plots facilitated soil sampling so that field, border, and uncultivated control samples could be compared in order to measure the anthropogenic effects of agriculture on soil quality in the irragric soils. Long-term indicators of agricultural soil quality such as organic carbon, nutrient content, and hydraulic soil water properties such as available water capacity and saturated hydraulic conductivity, indicate that soil changes were generally favorable for agricultural production and that these ancient irrigation systems were sustainable. Canals regularly supplied water to the fields, but they also supplied nutrient

Modelling adaptation to climate change of Ecuadorian agriculture and associated water resources: uncertainties in coastal and highland cropping systems

NASA Astrophysics Data System (ADS)

Ruiz-Ramos, Margarita; Bastidas, Wellington; Cóndor, Amparo; Villacís, Marcos; Calderón, Marco; Herrera, Mario; Zambrano, José Luis; Lizaso, Jon; Hernández, Carlos; Rodríguez, Alfredo; Capa-Morocho, Mirian

2016-04-01

Climate change threatens sustainability of farms and associated water resources in Ecuador. Although the last IPCC report (AR5) provides a general framework for adaptation, , impact assessment and especially adaptation analysis should be site-specific, taking into account both biophysical and social aspects. The objective of this study is to analyse the climate change impacts and to sustainable adaptations to optimize the crop yield. Furthermore is also aimed to weave agronomical and hydrometeorological aspects, to improve the modelling of the coastal ("costa") and highland ("sierra") cropping systems in Ecuador, from the agricultural production and water resources points of view. The final aim is to support decision makers, at national and local institutions, for technological implementation of structural adaptation strategies, and to support farmers for their autonomous adaptation actions to cope with the climate change impacts and that allow equal access to resources and appropriate technologies. . A diagnosis of the current situation in terms of data availability and reliability was previously done, and the main sources of uncertainty for agricultural projections have been identified: weather data, especially precipitation projections, soil data below the upper 30 cm, and equivalent experimental protocol for ecophysiological crop field measurements. For reducing these uncertainties, several methodologies are being discussed. This study was funded by PROMETEO program from Ecuador through SENESCYT (M. Ruiz-Ramos contract), and by the project COOP-XV-25 funded by Universidad Politécnica de Madrid.

Modelling the water-agricultural sector in Rosetta, Egypt: exploring the interaction between water and food

NASA Astrophysics Data System (ADS)

Sušnik, Janez; Vamvakeridou-Lyroudia, Lydia; Savic, Dragan; Kapelan, Zoran

2014-05-01

An integrated System Dynamics Model for the Rosetta region, Egypt, assessing local water balance and agricultural yield to 2050, is presented. Fifty-seven simulations are analysed to better understand potential impacts on water and food security resulting from climate and social change and local/regional policy decisions related to the agricultural sector. Water limitation is a national issue: Egypt relies on the Nile for >95% of supply, and the flow of which is regulated by the Aswan High Dam. Egypt's share water of Aswan water is limited to 55 x 19 m3 yr-1. Any reduction in supply to the reservoir or increase in demand (e.g. from an expanding agricultural sector), has the potential to lead to a serious food and water supply situation. Results show current water resource over-exploitation. The remaining suite of 56 simulations, divided into seven scenarios, also mostly show resource overexploitation. Only under significant increases to Nile flow volumes was the trend reversed. Despite this, by threading together multiple local policies to reduce demand and improve/maintain supply, water resource exploitation can be mitigated while allowing for agricultural development. By changing cropping patterns, it is possible to improve yield and revenue, while using up to 21% less water in 2050 when compared with today. The results are useful in highlighting pathways to improving future water resource availability. Many policies should be considered in parallel, introducing redundancy into the policy framework. We do not suggest actual policy measures; this was beyond the scope of the work. This work highlights the utility of systems modelling of complex systems such as the water-food nexus, with the potential to extend the methodology to other studies and scales. In particular, the benefit of being able to easily modify and extend existing models in light of results from initial modelling efforts is cited. Analysis of initial results led to the hypothesis that by producing

eFarm: A Tool for Better Observing Agricultural Land Systems

PubMed Central

Yu, Qiangyi; Shi, Yun; Tang, Huajun; Yang, Peng; Xie, Ankun; Liu, Bin; Wu, Wenbin

2017-01-01

Currently, observations of an agricultural land system (ALS) largely depend on remotely-sensed images, focusing on its biophysical features. While social surveys capture the socioeconomic features, the information was inadequately integrated with the biophysical features of an ALS and the applications are limited due to the issues of cost and efficiency to carry out such detailed and comparable social surveys at a large spatial coverage. In this paper, we introduce a smartphone-based app, called eFarm: a crowdsourcing and human sensing tool to collect the geotagged ALS information at the land parcel level, based on the high resolution remotely-sensed images. We illustrate its main functionalities, including map visualization, data management, and data sensing. Results of the trial test suggest the system works well. We believe the tool is able to acquire the human–land integrated information which is broadly-covered and timely-updated, thus presenting great potential for improving sensing, mapping, and modeling of ALS studies. PMID:28245554

Assessing Agricultural Intensification Strategies with a Sustainable Agriculture Matrix

NASA Astrophysics Data System (ADS)

Zhang, X.; Davidson, E. A.

2017-12-01

To meet the growing global demand for food and bioenergy, agricultural production must nearly double by 2050, placing additional pressures on the environment and the society. Thus, how to efficiently use limited land, water, and nutrient resources to produce more food with low pollution (MoFoLoPo) is clearly one of the major challenges of this century. The increasingly interconnected global market provides a great opportunity for reallocating crop production to the countries and regions that use natural resources more efficiently. For example, it is estimated that optimizing the allocation of crop production around the world can mitigate 41% of nitrogen lost to the environment. However, higher efficiency in nutrients use does not necessarily lead to higher efficiency in land use or water use. In addition, the increasing share of international trade in food supply may introduce additional systemic risk and affect the resilience of global food system. Using the data/indicator from a Sustainable Agriculture Matrix and an international trade matrix, we developed a simple model to assess the trade-offs of international trade considering resource use efficiencies (including water, land, nitrogen, and phosphorus), economic costs and benefits, and the resilience of food system.

[Energy flow characteristics of the compound agriculture-fruit farming system in Xipo Village, Shaanxi, Northwest China].

PubMed

Wu, Fa-Qi; Zhu, Li; Wang, Hong-Hong

2014-01-01

Taking the crop-fruit farming system in Xipo Village in Chunhua, Shaanxi Province as a case, the energy flow path, input and output structure, and the indices of energy cycle for the agriculture, fruit, stockbreeding and human subsystems were compared between 2008 and 2010. Results showed that during the study period the total investment to the agriculture-fruit farming system (CAF) decreased by 1.6%, while the total output increased by 56.7%, which led to a 59.4% increase of the output/input ratio. Energy output/input ratio of the agriculture, fruit, stockbreeding, human subsystems increased by 36.6%, 21.0%, 10.0% and 3.8%, respectively. The Xipo Village still needed to stabilize the agriculture, develop stockbreeding and strengthen fruit to upgrade the compound agriculture-fruit farming system.

Irrigated Agriculture in Morocco: An Agent-Based Model of Adaptation and Decision Making Amid Increasingly Frequent Drought Events

NASA Astrophysics Data System (ADS)

Norton, M.

2015-12-01

In the past 100 years, Morocco has undertaken a heavy investment in developing water infrastructure that has led to a dramatic expansion of irrigated agriculture. Irrigated agriculture is the primary user of water in many arid countries, often accounting for 80-90% of total water usage. Irrigation is adopted by farmers not only because it leads to increased production, but also because it improves resilience to an uncertain climate. However, the Mediterranean region as a whole has also seen an increase in the frequency and severity of drought events. These droughts have had a dramatic impact on farmer livelihoods and have led to a number of coping strategies, including the adoption or disadoption of irrigation. In this study, we use a record of the annual extent of irrigated agriculture in Morocco to model the effect of drought on the extent of irrigated agriculture. Using an agent-based socioeconomic model, we seek to answer the following questions: 1) Do farmers expand irrigated agriculture in response to droughts? 2) Do drought events entail the removal of perennial crops like orchards? 3) Can we detect the retreat of irrigated agriculture in the more fragile watersheds of Morocco? Understanding the determinants of irrigated crop expansion and contractions will help us understand how agro-ecological systems transition from 20th century paradigms of expansion of water supply to a 21st century paradigm of water use efficiency. The answers will become important as countries learn how to manage water in new climate regimes characterized by less reliable and available precipitation.

Leaf chlorophyll constraint on model simulated gross primary productivity in agricultural systems

NASA Astrophysics Data System (ADS)

Houborg, Rasmus; McCabe, Matthew F.; Cescatti, Alessandro; Gitelson, Anatoly A.

2015-12-01

Leaf chlorophyll content (Chll) may serve as an observational proxy for the maximum rate of carboxylation (Vmax), which describes leaf photosynthetic capacity and represents the single most important control on modeled leaf photosynthesis within most Terrestrial Biosphere Models (TBMs). The parameterization of Vmax is associated with great uncertainty as it can vary significantly between plants and in response to changes in leaf nitrogen (N) availability, plant phenology and environmental conditions. Houborg et al. (2013) outlined a semi-mechanistic relationship between Vmax25 (Vmax normalized to 25 °C) and Chll based on inter-linkages between Vmax25, Rubisco enzyme kinetics, N and Chll. Here, these relationships are parameterized for a wider range of important agricultural crops and embedded within the leaf photosynthesis-conductance scheme of the Community Land Model (CLM), bypassing the questionable use of temporally invariant and broadly defined plant functional type (PFT) specific Vmax25 values. In this study, the new Chll constrained version of CLM is refined with an updated parameterization scheme for specific application to soybean and maize. The benefit of using in-situ measured and satellite retrieved Chll for constraining model simulations of Gross Primary Productivity (GPP) is evaluated over fields in central Nebraska, U.S.A between 2001 and 2005. Landsat-based Chll time-series records derived from the Regularized Canopy Reflectance model (REGFLEC) are used as forcing to the CLM. Validation of simulated GPP against 15 site-years of flux tower observations demonstrate the utility of Chll as a model constraint, with the coefficient of efficiency increasing from 0.91 to 0.94 and from 0.87 to 0.91 for maize and soybean, respectively. Model performances particularly improve during the late reproductive and senescence stage, where the largest temporal variations in Chll (averaging 35-55 μg cm-2 for maize and 20-35 μg cm-2 for soybean) are observed. While

Assessment of future crop yield and agricultural sustainable water use in north china plain using multiple crop models

NASA Astrophysics Data System (ADS)

Huang, G.

2016-12-01

Currently, studying crop-water response mechanism has become an important part in the development of new irrigation technology and optimal water allocation in water-scarce regions, which is of great significance to crop growth guidance, sustainable utilization of agricultural water, as well as the sustainable development of regional agriculture. Using multiple crop models(AquaCrop,SWAP,DNDC), this paper presents the results of simulating crop growth and agricultural water consumption of the winter-wheat and maize cropping system in north china plain. These areas are short of water resources, but generates about 23% of grain production for China. By analyzing the crop yields and the water consumption of the traditional flooding irrigation, the paper demonstrates quantitative evaluation of the potential amount of water use that can be reduced by using high-efficient irrigation approaches, such as drip irrigation. To maintain food supply and conserve water resources, the research concludes sustainable irrigation methods for the three provinces for sustainable utilization of agricultural water.

The landscape model: A model for exploring trade-offs between agricultural production and the environment.

PubMed

Coleman, Kevin; Muhammed, Shibu E; Milne, Alice E; Todman, Lindsay C; Dailey, A Gordon; Glendining, Margaret J; Whitmore, Andrew P

2017-12-31

We describe a model framework that simulates spatial and temporal interactions in agricultural landscapes and that can be used to explore trade-offs between production and environment so helping to determine solutions to the problems of sustainable food production. Here we focus on models of agricultural production, water movement and nutrient flow in a landscape. We validate these models against data from two long-term experiments, (the first a continuous wheat experiment and the other a permanent grass-land experiment) and an experiment where water and nutrient flow are measured from isolated catchments. The model simulated wheat yield (RMSE 20.3-28.6%), grain N (RMSE 21.3-42.5%) and P (RMSE 20.2-29% excluding the nil N plots), and total soil organic carbon particularly well (RMSE3.1-13.8%), the simulations of water flow were also reasonable (RMSE 180.36 and 226.02%). We illustrate the use of our model framework to explore trade-offs between production and nutrient losses. Copyright © 2017 Rothamsted Research. Published by Elsevier B.V. All rights reserved.

Private Agricultural Extension System in Kenya: Practice and Policy Lessons

ERIC Educational Resources Information Center

Muyanga, Milu; Jayne, T. S.

2008-01-01

Private extension system has been at the centre of a debate triggered by inefficient public agricultural extension. The debate is anchored on the premise that the private sector is more efficient in extension service delivery. This study evaluates the private extension system in Kenya. It employs qualitative and quantitative methods. The results…

Modeling Spatial and Temporal Variability in Ammonia Emissions from Agricultural Fertilization

NASA Astrophysics Data System (ADS)

Balasubramanian, S.; Koloutsou-Vakakis, S.; Rood, M. J.

2013-12-01

Ammonia (NH3), is an important component of the reactive nitrogen cycle and a precursor to formation of atmospheric particulate matter (PM). Predicting regional PM concentrations and deposition of nitrogen species to ecosystems requires representative emission inventories. Emission inventories have traditionally been developed using top down approaches and more recently from data assimilation based on satellite and ground based ambient concentrations and wet deposition data. The National Emission Inventory (NEI) indicates agricultural fertilization as the predominant contributor (56%) to NH3 emissions in Midwest USA, in 2002. However, due to limited understanding of the complex interactions between fertilizer usage, farm practices, soil and meteorological conditions and absence of detailed statistical data, such emission estimates are currently based on generic emission factors, time-averaged temporal factors and coarse spatial resolution. Given the significance of this source, our study focuses on developing an improved NH3 emission inventory for agricultural fertilization at finer spatial and temporal scales for air quality modeling studies. Firstly, a high-spatial resolution 4 km x 4 km NH3 emission inventory for agricultural fertilization has been developed for Illinois by modifying spatial allocation of emissions based on combining crop-specific fertilization rates with cropland distribution in the Sparse Matrix Operator Kernel Emissions model. Net emission estimates of our method are within 2% of NEI, since both methods are constrained by fertilizer sales data. However, we identified localized crop-specific NH3 emission hotspots at sub-county resolutions absent in NEI. Secondly, we have adopted the use of the DeNitrification-DeComposition (DNDC) Biogeochemistry model to simulate the physical and chemical processes that control volatilization of nitrogen as NH3 to the atmosphere after fertilizer application and resolve the variability at the hourly scale

Pain involving the motor system and serum vitamin D concentration in postmenopausal women working in agriculture.

PubMed

Raczkiewicz, Dorota; Owoc, Alfred; Sarecka-Hujar, Beata; Bojar, Iwona

2017-03-22

Since the role of vitamin D is essential in numerous biological processes its deficiency was suggested to be a risk factor for e.g. osteoporosis, musculoskeletal pain and spine pain. The purpose of the study was to analyse whether serum vitamin D concentration is related to pain involving the motor system in Polish postmenopausal women working in agriculture. The study group consisted of 1,751 post-menopausal women, aged 45-65, at least 12 months from the last menstrual period, living in rural areas and working in agriculture. The research method was self-assessment of pain involving the motor system using VAS, laboratory test of serum vitamin D concentration and a medical interview. Statistical methods included generalized linear models, analysis of variance, t test for two means in two independents, χ2 test of stochastic independence. Postmenopausal women working in agriculture and suffering from pain in at least one part of the motor system were younger and lower educated, they also had higher abdominal obesity and lower serum vitamin D, compared to those without pain in any part of the motor system. Decreased serum vitamin D concentration in postmenopausal women working in agriculture is important from the aspect of a higher prevalence of pain in the thoracic spine and more severe pain in the neck spine, but not for severity of pain in the lumbar spine; higher occurrence of pain in both hands or wrists; higher prevalence and more severe pain in at least one knee; and no prevalence or severity of pain in the shoulders and elbows. Serum vitamin D concentration is important for the prevalence and severity of pain in the neck and thoracic spine, knees and hands or wrists, but not for the lumbar spine, shoulders and elbows.

Developing an automatic classification system of vegetation anomalies for early warning with the ASAP (Anomaly hot Spots of Agricultural Production) system

NASA Astrophysics Data System (ADS)

Meroni, M.; Rembold, F.; Urbano, F.; Lemoine, G.

2016-12-01

Anomaly maps and time profiles of remote sensing derived indicators relevant to monitor crop and vegetation stress can be accessed online thanks to a rapidly growing number of web based portals. However, timely and systematic global analysis and coherent interpretation of such information, as it is needed for example for SDG 2 related monitoring, remains challenging. With the ASAP system (Anomaly hot Spots of Agricultural Production) we propose a two-step analysis to provide monthly warning of production deficits in water-limited agriculture worldwide. The first step is fully automated and aims at classifying each administrative unit (1st sub-national level) into a number of possible warning levels, ranging from "none" to "watch" and up to "extended alarm". The second step involves the verification of the automatic warnings and integration into a short national level analysis by agricultural analysts. In this paper we describe the methodological development of the automatic vegetation anomaly classification system. Warnings are triggered only during the crop growing season, defined by a remote sensing based phenology. The classification takes into consideration the fraction of the agricultural and rangelands area for each administrative unit that is affected by a severe anomaly of two rainfall-based indicators (the Standardized Precipitation Index (SPI), computed at 1 and 3-month scale) and one biophysical indicator (the cumulative NDVI from the start of the growing season). The severity of the warning thus depends on the timing, the nature and the number of indicators for which an anomaly is detected. The prototype system is using global NDVI images of the METOP sensor, while a second version is being developed based on 1km Modis NDVI with temporal smoothing and near real time filtering. Also a specific water balance model is under development to include agriculture water stress information in addition to the SPI. The monthly warning classification and crop

Research on agricultural ecology and environment analysis and modeling based on RS and GIS

NASA Astrophysics Data System (ADS)

Zhang, Wensheng; Chen, Hongfu; Wang, Mingsheng

2009-07-01

Analysis of agricultural ecology and environment is based on the data of agricultural resources, which are obtained by RS monitoring. The over-exploitation of farmlands will cause structural changes of the soil composition, and damage the planting environment and the agro-ecosystem. Through the research on the dynamic monitoring methods of multitemporal RS images and GIS technology, the crop growth status, crop acreage and other relevant information in agricultural production are extracted based on the monitor and analysis of the conditions of the fields and crop growth. The agro-ecological GIS platform is developed with the establishment of the agricultural resources management database, which manages spatial data, RS data and attribute data of agricultural resources. Using the RS, GIS analysis results, the reasons of agro-ecological destruction are analyzed and the evaluation methods are established. This paper puts forward the concept of utilization capacity of farmland, which describes farmland space for development and utilization that is influenced by the conditions of the land, water resources, climate, pesticides and chemical fertilizers and many other agricultural production factors. Assessment model of agricultural land use capacity is constructed with the help of Fuzzy. Assessing the utilization capacity of farmland can be helpful to agricultural production and ecological protection of farmland. This paper describes the application of the capacity evaluation model with simulated data in two aspects, namely, in evaluating the status of farmland development and utilization and in optimal planting.

Benchmarking a soil moisture data assimilation system for agricultural drought monitoring

USDA-ARS?s Scientific Manuscript database

Despite considerable interest in the application of land surface data assimilation systems (LDAS) for agricultural drought applications, relatively little is known about the large-scale performance of such systems and, thus, the optimal methodological approach for implementing them. To address this ...

Mathematical and computational modeling simulation of solar drying Systems

USDA-ARS?s Scientific Manuscript database

Mathematical modeling of solar drying systems has the primary aim of predicting the required drying time for a given commodity, dryer type, and environment. Both fundamental (Fickian diffusion) and semi-empirical drying models have been applied to the solar drying of a variety of agricultural commo...

Implementation of Sentinel-2 Data in the M4Land System for the Generation of Continuous Information Products in Agriculture

NASA Astrophysics Data System (ADS)

Klug, P.; Schlenz, F.; Hank, T.; Migdall, S.; Weiß, I.; Danner, M.; Bach, H.; Mauser, W.

2016-08-01

The analysis system developed in the frame of the M4Land project (Model based, Multi-temporal, Multi scale and Multi sensorial retrieval of continuous land management information) has proven its capabilities of classifying crop type and creating products on the intensity of agricultural production using optical remote sensing data from Landsat and RapidEye. In this study, Sentinel-2 data is used for the first time together with Landsat 7 ETM+ and 8 OLI data within the M4Land analysis system to derive continuously crop type and the agricultural intensity of fields in an area north of Munich, Germany and the year 2015.

Knowledge Integration to Make Decisions About Complex Systems: Sustainability of Energy Production from Agriculture

ScienceCinema

Danuso, Francesco

2017-12-22

A major bottleneck for improving the governance of complex systems, rely on our ability to integrate different forms of knowledge into a decision support system (DSS). Preliminary aspects are the classification of different types of knowledge (a priori or general, a posteriori or specific, with uncertainty, numerical, textual, algorithmic, complete/incomplete, etc.), the definition of ontologies for knowledge management and the availability of proper tools like continuous simulation models, event driven models, statistical approaches, computational methods (neural networks, evolutionary optimization, rule based systems etc.) and procedure for textual documentation. Following these views at University of Udine, a computer language (SEMoLa, Simple, Easy Modelling Language) for knowledge integration has been developed.  SEMoLa can handle models, data, metadata and textual knowledge; it implements and extends the system dynamics ontology (Forrester, 1968; Jørgensen, 1994) in which systems are modelled by the concepts of material, group, state, rate, parameter, internal and external events and driving variables. As an example, a SEMoLa model to improve management and sustainability (economical, energetic, environmental) of the agricultural farms is presented. The model (X-Farm) simulates a farm in which cereal and forage yield, oil seeds, milk, calves and wastes can be sold or reused. X-Farm is composed by integrated modules describing fields (crop and soil), feeds and materials storage, machinery management, manpower  management, animal husbandry, economic and energetic balances, seed oil extraction, manure and wastes management, biogas production from animal wastes and biomasses.

An alternative agriculture system is defined by a distinct expression profile of select gene transcripts and proteins

PubMed Central

Kumar, Vinod; Mills, Douglas J.; Anderson, James D.; Mattoo, Autar K.

2004-01-01

Conventional agriculture has relied heavily on chemical inputs that have negatively impacted the environment and increased production costs. Transition to agricultural sustainability is a major challenge and requires that alternative agricultural practices are scientifically analyzed to provide a sufficiently informative knowledge base in favor of alternative farming practices. We show a molecular basis for delayed leaf senescence and tolerance to diseases in tomato plants cultivated in a legume (hairy vetch) mulch-based alternative agricultural system. In the hairy vetch-cultivated plants, expression of specific and select classes of genes is up-regulated compared to those grown on black polyethylene mulch. These include N-responsive genes such as NiR, GS1, rbcL, rbcS, and G6PD; chaperone genes such as hsp70 and BiP; defense genes such as chitinase and osmotin; a cytokinin-responsive gene CKR; and gibberellic acid 20 oxidase. We present a model of how their protein products likely complement one another in a field scenario to effect efficient utilization and mobilization of C and N, promote defense against disease, and enhance longevity. PMID:15249656

Using historical and projected future climate model simulations as drivers of agricultural and biological models (Invited)

NASA Astrophysics Data System (ADS)

Stefanova, L. B.

2013-12-01

Climate model evaluation is frequently performed as a first step in analyzing climate change simulations. Atmospheric scientists are accustomed to evaluating climate models through the assessment of model climatology and biases, the models' representation of large-scale modes of variability (such as ENSO, PDO, AMO, etc) and the relationship between these modes and local variability (e.g. the connection between ENSO and the wintertime precipitation in the Southeast US). While these provide valuable information about the fidelity of historical and projected climate model simulations from an atmospheric scientist's point of view, the application of climate model data to fields such as agriculture, ecology and biology may require additional analyses focused on the particular application's requirements and sensitivities. Typically, historical climate simulations are used to determine a mapping between the model and observed climate, either through a simple (additive for temperature or multiplicative for precipitation) or a more sophisticated (such as quantile matching) bias correction on a monthly or seasonal time scale. Plants, animals and humans however are not directly affected by monthly or seasonal means. To assess the impact of projected climate change on living organisms and related industries (e.g. agriculture, forestry, conservation, utilities, etc.), derivative measures such as the heating degree-days (HDD), cooling degree-days (CDD), growing degree-days (GDD), accumulated chill hours (ACH), wet season onset (WSO) and duration (WSD), among others, are frequently useful. We will present a comparison of the projected changes in such derivative measures calculated by applying: (a) the traditional temperature/precipitation bias correction described above versus (b) a bias correction based on the mapping between the historical model and observed derivative measures themselves. In addition, we will present and discuss examples of various application-based climate

Systems and methods for autonomously controlling agricultural machinery

DOEpatents

Hoskinson, Reed L.; Bingham, Dennis N.; Svoboda, John M.; Hess, J. Richard

2003-07-08

Systems and methods for autonomously controlling agricultural machinery such as a grain combine. The operation components of a combine that function to harvest the grain have characteristics that are measured by sensors. For example, the combine speed, the fan speed, and the like can be measured. An important sensor is the grain loss sensor, which may be used to quantify the amount of grain expelled out of the combine. The grain loss sensor utilizes the fluorescence properties of the grain kernels and the plant residue to identify when the expelled plant material contains grain kernels. The sensor data, in combination with historical and current data stored in a database, is used to identify optimum operating conditions that will result in increased crop yield. After the optimum operating conditions are identified, an on-board computer can generate control signals that will adjust the operation of the components identified in the optimum operating conditions. The changes result in less grain loss and improved grain yield. Also, because new data is continually generated by the sensor, the system has the ability to continually learn such that the efficiency of the agricultural machinery is continually improved.

Implementation of Wireless Sensor Networks Based Pig Farm Integrated Management System in Ubiquitous Agricultural Environments

NASA Astrophysics Data System (ADS)

Hwang, Jeonghwan; Lee, Jiwoong; Lee, Hochul; Yoe, Hyun

The wireless sensor networks (WSN) technology based on low power consumption is one of the important technologies in the realization of ubiquitous society. When the technology would be applied to the agricultural field, it can give big change in the existing agricultural environment such as livestock growth environment, cultivation and harvest of agricultural crops. This research paper proposes the 'Pig Farm Integrated Management System' based on WSN technology, which will establish the ubiquitous agricultural environment and improve the productivity of pig-raising farmers. The proposed system has WSN environmental sensors and CCTV at inside/outside of pig farm. These devices collect the growth-environment related information of pigs, such as luminosity, temperature, humidity and CO2 status. The system collects and monitors the environmental information and video information of pig farm. In addition to the remote-control and monitoring of the pig farm facilities, this system realizes the most optimum pig-raising environment based on the growth environmental data accumulated for a long time.

An L-system model for root system mycorrhization

NASA Astrophysics Data System (ADS)

Schnepf, Andrea; Schweiger, Peter; Jansa, Jan; Leitner, Daniel

2014-05-01

Mineral phosphate fertilisers are a non-renewable resource; rock phosphate reserves are estimated to be depleted in 50 to 100 years. In order to prevent a severe phosphate crisis in the 21st century, there is a need to decrease agricultural inputs such as P fertilisers by making use of plant mechanisms that increase P acquisition efficiency. Most plants establish mycorrhizal symbiosis as an adaptation to increase/economize their P acquisition from the soil. However, there is a great functional diversity in P acquisition mechanisms among different fungal species that colonize the roots (Thonar et al. 2011), and the composition of mycorrhizal community is known to depend strongly on agricultural management practices. Thus, the agroecosystem management may substantially affect the mycorrhizal functioning and also the use of P fertilizers. To date, it is still difficult to quantify the potential input savings for the agricultural crops through manipulation of their symbiotic microbiome, mainly due to lack of mechanistic understanding of P uptake dynamics by the fungal hyphae. In a first attempt, Schnepf et al. (2008b) have used mathematical modelling to show on the single root scale how different fungal growth pattern influence root P uptake. However, their approach was limited by the fact that it was restricted to the scale of a single root. The goal of this work is to advance the dynamic, three-dimensional root architecture model of Leitner et al. (2010) to include root system infection with arbuscular mycorrhizal fungi and growth of external mycelium. The root system infection model assumes that there is an average probability of infection (primary infection), that the probability of infection of a new root segment immediately adjacent to an existing infection is much higher than the average (secondary infection), that infected root segments have entry points that are the link between internal and external mycelium, that only uninfected root segments are susceptible

Integrating predictive information into an agro-economic model to guide agricultural management

NASA Astrophysics Data System (ADS)

Zhang, Y.; Block, P.

2016-12-01

Skillful season-ahead climate predictions linked with responsive agricultural planning and management have the potential to reduce losses, if adopted by farmers, particularly for rainfed-dominated agriculture such as in Ethiopia. Precipitation predictions during the growing season in major agricultural regions of Ethiopia are used to generate predicted climate yield factors, which reflect the influence of precipitation amounts on crop yields and serve as inputs into an agro-economic model. The adapted model, originally developed by the International Food Policy Research Institute, produces outputs of economic indices (GDP, poverty rates, etc.) at zonal and national levels. Forecast-based approaches, in which farmers' actions are in response to forecasted conditions, are compared with no-forecast approaches in which farmers follow business as usual practices, expecting "average" climate conditions. The effects of farmer adoption rates, including the potential for reduced uptake due to poor predictions, and increasing forecast lead-time on economic outputs are also explored. Preliminary results indicate superior gains under forecast-based approaches.

1986 Agricultural Chartbook. Agriculture Handbook No. 663.

ERIC Educational Resources Information Center

Department of Agriculture, Washington, DC.

This book contains 310 charts, tables, and graphs containing statistical information about agriculture-related commodities and services, primarily in the United States, in 1986. The book is organized in seven sections that cover the following topics: (1) the farm (farm income, farm population, farm workers, food and fiber system, agriculture and…

Feasibility study of a microwave radar system for agricultural inspection

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

Okelo-Odongo, S.

1994-10-03

The feasibility of an impulse radar system for agricultural inspection is investigated. This system would be able to quickly determine the quality of foodstuffs that are passed through the system. A prototype was designed at the Lawrence Livermore National Laboratory and this report discusses it`s evaluation. A variety of apples were used to test the system and preliminary data suggests that this technology holds promise for successful application on a large scale in food processing plants.

Spatial Numeric Classification Model Suitability with Landuse Change in Sustainable Food Agriculture Zone in Kediri Sub-district, Tabanan Regency, Indonesia

NASA Astrophysics Data System (ADS)

Trigunasih, N. M.; Lanya, I.; Hutauruk, J.; Arthagama, I. D. M.

2017-12-01

The development of rapid population will make the availability and utilization of land resources is increasingly shrinking in number, especially occurs in rice field. Since the last 5 years the numbers of farmland is decrasing by industry, infrastructure development, tourism development and other services. The agricultural problems facing at the moment is the occurrence of a change of use of agricultural land into farming now is not more popular is called over the function of agricultural land into non-farming. According to the Central Bureau of statistics (BPS) of the province of Bali (2013) within a period of 14 years (1999-2013), there has been a change of use of agricultural land be not agriculture/wetland functions over the 4,906 hectares. When averaged over the function flatten paddy fields per year occurred in Bali approximately 350 ha (0.41%). The highest paddy fields over the function during a period of fourteen years there is in Tabanan area of 1,230 ha. To maintain the existence of the rice fields or subak in Bali in particular, need to be done protection against agricultural lands sustainable. Ninth District/Town in Bali today, haven’t had a Perda on protection of agricultural land sustainable food that is mandated by law 41 Year 2009. This will have an impact on food security of the region, and the world’s cultural heritage as the water will lose its existence as a system of irrigation organization in Bali. The purpose of this research was done to (1) determine the numerical classification of spatial parameters of sustainable food farm in Tabanan Regency Kediri Subdistrict, (2) determine the model of the zoning of agricultural land area of sustainable food that fits on Years 2020, 2030, 2040, and in district of Kediri, Tabanan Regency. The method used is the kuantitaif method includes the focus group discussion, the development of spatial data, analysis geoprosessing (spatial analysis and analysis of proximity), and statistical analysis

Agricultural production and water use scenarios in Cyprus under global change

NASA Astrophysics Data System (ADS)

Bruggeman, Adriana; Zoumides, Christos; Camera, Corrado; Pashiardis, Stelios; Zomeni, Zomenia

2014-05-01

In many countries of the world, food demand exceeds the total agricultural production. In semi-arid countries, agricultural water demand often also exceeds the sustainable supply of water resources. These water-stressed countries are expected to become even drier, as a result of global climate change. This will have a significant impact on the future of the agricultural sector and on food security. The aim of the AGWATER project consortium is to provide recommendations for climate change adaptation for the agricultural sector in Cyprus and the wider Mediterranean region. Gridded climate data sets, with 1-km horizontal resolution were prepared for Cyprus for 1980-2010. Regional Climate Model results were statistically downscaled, with the help of spatial weather generators. A new soil map was prepared using a predictive modelling and mapping technique and a large spatial database with soil and environmental parameters. Stakeholder meetings with agriculture and water stakeholders were held to develop future water prices, based on energy scenarios and to identify climate resilient production systems. Green houses, including also hydroponic systems, grapes, potatoes, cactus pears and carob trees were the more frequently identified production systems. The green-blue-water model, based on the FAO-56 dual crop coefficient approach, has been set up to compute agricultural water demand and yields for all crop fields in Cyprus under selected future scenarios. A set of agricultural production and water use performance indicators are computed by the model, including green and blue water use, crop yield, crop water productivity, net value of crop production and economic water productivity. This work is part of the AGWATER project - AEIFORIA/GEOGRO/0311(BIE)/06 - co-financed by the European Regional Development Fund and the Republic of Cyprus through the Research Promotion Foundation.

An engineering approach to modelling, decision support and control for sustainable systems.

PubMed

Day, W; Audsley, E; Frost, A R

2008-02-12

Engineering research and development contributes to the advance of sustainable agriculture both through innovative methods to manage and control processes, and through quantitative understanding of the operation of practical agricultural systems using decision models. This paper describes how an engineering approach, drawing on mathematical models of systems and processes, contributes new methods that support decision making at all levels from strategy and planning to tactics and real-time control. The ability to describe the system or process by a simple and robust mathematical model is critical, and the outputs range from guidance to policy makers on strategic decisions relating to land use, through intelligent decision support to farmers and on to real-time engineering control of specific processes. Precision in decision making leads to decreased use of inputs, less environmental emissions and enhanced profitability-all essential to sustainable systems.

Evaluating the impacts of agricultural land management practices on water resources: A probabilistic hydrologic modeling approach.

PubMed

Prada, A F; Chu, M L; Guzman, J A; Moriasi, D N

2017-05-15

Evaluating the effectiveness of agricultural land management practices in minimizing environmental impacts using models is challenged by the presence of inherent uncertainties during the model development stage. One issue faced during the model development stage is the uncertainty involved in model parameterization. Using a single optimized set of parameters (one snapshot) to represent baseline conditions of the system limits the applicability and robustness of the model to properly represent future or alternative scenarios. The objective of this study was to develop a framework that facilitates model parameter selection while evaluating uncertainty to assess the impacts of land management practices at the watershed scale. The model framework was applied to the Lake Creek watershed located in southwestern Oklahoma, USA. A two-step probabilistic approach was implemented to parameterize the Agricultural Policy/Environmental eXtender (APEX) model using global uncertainty and sensitivity analysis to estimate the full spectrum of total monthly water yield (WYLD) and total monthly Nitrogen loads (N) in the watershed under different land management practices. Twenty-seven models were found to represent the baseline scenario in which uncertainty of up to 29% and 400% in WYLD and N, respectively, is plausible. Changing the land cover to pasture manifested the highest decrease in N to up to 30% for a full pasture coverage while changing to full winter wheat cover can increase the N up to 11%. The methodology developed in this study was able to quantify the full spectrum of system responses, the uncertainty associated with them, and the most important parameters that drive their variability. Results from this study can be used to develop strategic decisions on the risks and tradeoffs associated with different management alternatives that aim to increase productivity while also minimizing their environmental impacts. Copyright © 2017 Elsevier Ltd. All rights reserved.

Development of a global Agricultural Stress Index System (ASIS) based on remote sensing data

NASA Astrophysics Data System (ADS)

Van Hoolst, R.

2016-12-01

According to the 2012 IPCC SREX report, extreme drought events are projected to become more frequent and intense in several regions of the world. Wide and timely monitoring systems are required to mitigate the impact of agricultural drought. Therefore, FAO's Global Information and Early Warning System (GIEWS) and the Climate, Energy and Tenure Division (NRC) have established the `Agricultural Stress Index System' (ASIS). The ASIS is a remote sensing application that provides early warnings of agricultural drought at a global scale. The ASIS has first been designed and described by Rojas et al. (2011). This study focused on the African continent and was based on the back processing of low resolution data of the NOAA-satellites. In the current setup, developed by VITO (Flemish Institute for Technological Research), the system operates in Near Real Time using data from the METOP-AVHRR sensor. The Agricultural Stress Index (ASI) is the percentage of agricultural area affected by drought in the course of the growing season within a given administrative unit. The start and end of the growing season are derived per pixel from the long term NDVI average of SPOT-VEGETATION. The Global Administrative Unit Layer (GAUL) defines the administrative boundaries at level 0, 1 and 2. A global cropland and grassland map eliminates non-agricultural areas. Temperature and NDVI anomalies are used as drought indicators and calculated at a per pixel base. The ASIS aggregates this information and produces every dekad global maps to highlight hotspots of drought stress. New developments are ongoing to strengthen the ASIS to produce country specific outputs, improve existing drought indicators and estimate production deficits using a probabilistic approach.

Building agribusiness model of LEISA to achieve sustainable agriculture in Surian Subdistrict of Sumedang Regency West Java Indonesia

NASA Astrophysics Data System (ADS)

Djuwendah, E.; Priyatna, T.; Kusno, K.; Deliana, Y.; Wulandari, E.

2018-03-01

Building agribusiness model of LEISA is needed as a prototype of sustainable regional and economic development (SRRED) in the watersheds (DAS) of West Java Province. Agribusiness model of LEISA is a sustainable agribusiness system applying low external input. The system was developed in the framework of optimizing local-based productive resources including soil, water, vegetation, microclimate, renewable energy, appropriate technology, social capital, environment and human resources by combining various subsystems including integrated production subsystems of crops, livestock and fish to provide a maximum synergy effect, post-harvest subsystem and processing of results, marketing subsystems and supporting subsystems. In this study, the ecological boundary of Cipunegara sub-watershed ecosystem, administrative boundaries are Surian Subdistricts in Sumedang. The purpose of this study are to identify the potency of natural resources and local agricultural technologies that could support the LEISA model in Surian and to identify the potency of internal and external inputs in the LEISA model. The research used qualitative descriptive method and technical action research. Data were obtained through interviews, documentation, and observation. The results showed that natural resources in the form of agricultural land, water resources, livestock resources, and human labor are sufficient to support agribusiness model of LEISA. LEISA agribusiness model that has been applied in the research location is the integration of beef cattle, agroforestry, and agrosilvopasture. By building LEISA model, agribusiness can optimize the utilization of locally based productive resources, reduce dependence on external resources, and support sustainable food security.

A modelling approach for the assessment of the effects of Common Agricultural Policy measures on farmland biodiversity in the EU27.

PubMed

Overmars, Koen P; Helming, John; van Zeijts, Henk; Jansson, Torbjörn; Terluin, Ida

2013-09-15

In this paper we describe a methodology to model the impacts of policy measures within the Common Agricultural Policy (CAP) on farm production, income and prices, and on farmland biodiversity. Two stylised scenarios are used to illustrate how the method works. The effects of CAP measures, such as subsidies and regulations, are calculated and translated into changes in land use and land-use intensity. These factors are then used to model biodiversity with a species-based indicator on a 1 km scale in the EU27. The Common Agricultural Policy Regionalised Impact Modelling System (CAPRI) is used to conduct the economic analysis and Dyna-CLUE (Conversion of Land Use and its Effects) is used to model land use changes. An indicator that expresses the relative species richness was used as the indicator for biodiversity in agricultural areas. The methodology is illustrated with a baseline scenario and two scenarios that include a specific policy. The strength of the methodology is that impacts of economic policy instruments can be linked to changes in agricultural production, prices and incomes, on the one hand, and to biodiversity effects, on the other - with land use and land-use intensity as the connecting drivers. The method provides an overall assessment, but for detailed impact assessment at landscape, farm or field level, additional analysis would be required. Copyright © 2013 Elsevier Ltd. All rights reserved.

Use of computer models to assess exposure to agricultural chemicals via drinking water.

PubMed

Gustafson, D I

1995-10-27

Surveys of drinking water quality throughout the agricultural regions of the world have revealed the tendency of certain crop protection chemicals to enter water supplies. Fortunately, the trace concentrations that have been detected are generally well below the levels thought to have any negative impact on human health or the environment. However, the public expects drinking water to be pristine and seems willing to bear the costs involved in further regulating agricultural chemical use in such a way so as to eliminate the potential for such materials to occur at any detectable level. Of all the tools available to assess exposure to agricultural chemicals via drinking water, computer models are one of the most cost-effective. Although not sufficiently predictive to be used in the absence of any field data, such computer programs can be used with some degree of certainty to perform quantitative extrapolations and thereby quantify regional exposure from field-scale monitoring information. Specific models and modeling techniques will be discussed for performing such exposure analyses. Improvements in computer technology have recently made it practical to use Monte Carlo and other probabilistic techniques as a routine tool for estimating human exposure. Such methods make it possible, at least in principle, to prepare exposure estimates with known confidence intervals and sufficient statistical validity to be used in the regulatory management of agricultural chemicals.

Vulnerability of Rehabilitated Agricultural Production Systems to Invasion by Nontarget Plant Species

NASA Astrophysics Data System (ADS)

Baer, Sara G.; Engle, David M.; Knops, Johannes M. H.; Langeland, Kenneth A.; Maxwell, Bruce D.; Menalled, Fabian D.; Symstad, Amy J.

2009-02-01

Vast areas of arable land have been retired from crop production and “rehabilitated” to improved system states through landowner incentive programs in the United States (e.g., Conservation and Wetland Reserve Programs), as well as Europe (i.e., Agri-Environment Schemes). Our review of studies conducted on invasion of rehabilitated agricultural production systems by nontarget species elucidates several factors that may increase the vulnerability of these systems to invasion. These systems often exist in highly fragmented and agriculturally dominated landscapes, where propagule sources of target species for colonization may be limited, and are established under conditions where legacies of past disturbance persist and prevent target species from persisting. Furthermore, rehabilitation approaches often do not include or successfully attain all target species or historical ecological processes (e.g., hydrology, grazing, and/or fire cycles) key to resisting invasion. Uncertainty surrounds ways in which nontarget species may compromise long term goals of improving biodiversity and ecosystem services through rehabilitation efforts on former agricultural production lands. This review demonstrates that more studies are needed on the extent and ecological impacts of nontarget species as related to the goals of rehabilitation efforts to secure current and future environmental benefits arising from this widespread conservation practice.

A New Era in Agriculture: Reinventing Agricultural Education for the Year 2020.

ERIC Educational Resources Information Center

National Council for Agricultural Education, Alexandria, VA.

The Reinventing Agricultural Education for the Year 2020 initiative brought together a diverse group of people from across the nation to create a new vision for agriculture education. The group envisioned a system of agricultural education beginning in early childhood and continuing throughout life. The group examined agricultural education's…

Modeling the impacts of dryland agricultural reclamation on groundwater resources in Northern Egypt using sparse data

NASA Astrophysics Data System (ADS)

Switzman, Harris; Coulibaly, Paulin; Adeel, Zafar

2015-01-01

Demand for freshwater in many dryland environments is exerting negative impacts on the quality and availability of groundwater resources, particularly in areas where demand is high due to irrigation or industrial water requirements to support dryland agricultural reclamation. Often however, information available to diagnose the drivers of groundwater degradation and assess management options through modeling is sparse, particularly in low and middle-income countries. This study presents an approach for generating transient groundwater model inputs to assess the long-term impacts of dryland agricultural land reclamation on groundwater resources in a highly data-sparse context. The approach was applied to the area of Wadi El Natrun in Northern Egypt, where dryland reclamation and the associated water use has been aggressive since the 1960s. Statistical distributions of water use information were constructed from a variety of sparse field and literature estimates and then combined with remote sensing data in spatio-temporal infilling model to produce the groundwater model inputs of well-pumping and surface recharge. An ensemble of groundwater model inputs were generated and used in a 3D groundwater flow (MODFLOW) of Wadi El Natrun's multi-layer aquifer system to analyze trends in water levels and water budgets over time. Validation of results against monitoring records, and model performance statistics demonstrated that despite the extremely sparse data, the approach used in this study was capable of simulating the cumulative impacts of agricultural land reclamation reasonably well. The uncertainty associated with the groundwater model itself was greater than that associated with the ensemble of well-pumping and surface recharge estimates. Water budget analysis of the groundwater model output revealed that groundwater recharge has not changed significantly over time, while pumping has. As a result of these trends, groundwater was estimated to be in a deficit of

The Role of Aerospace Technology in Agriculture. The 1977 Summer Faculty Fellowship Program in Engineering Systems Design

NASA Technical Reports Server (NTRS)

1977-01-01

Possibilities were examined for improving agricultural productivity through the application of aerospace technology. An overview of agriculture and of the problems of feeding a growing world population are presented. The present state of agriculture, of plant and animal culture, and agri-business are reviewed. Also analyzed are the various systems for remote sensing, particularly applications to agriculture. The report recommends additional research and technology in the areas of aerial application of chemicals, of remote sensing systems, of weather and climate investigations, and of air vehicle design. Also considered in detail are the social, legal, economic, and political results of intensification of technical applications to agriculture.

MODELING OF MACROSCALE AGRICULTURAL ELEMENTS IN PESTICIDE EXPOSURE

EPA Science Inventory

Yuma County, Arizona, is the site of year around agriculture. To understand the role of agricultural pesticide exposures experienced by children, urinary metabolite concentrations were compared with agricultural use of pesticides. The urinary metabolite and household data wer...

Environmental impacts of organic and conventional agricultural products--are the differences captured by life cycle assessment?

PubMed

Meier, Matthias S; Stoessel, Franziska; Jungbluth, Niels; Juraske, Ronnie; Schader, Christian; Stolze, Matthias

2015-02-01

Comprehensive assessment tools are needed that reliably describe environmental impacts of different agricultural systems in order to develop sustainable high yielding agricultural production systems with minimal impacts on the environment. Today, Life Cycle Assessment (LCA) is increasingly used to assess and compare the environmental sustainability of agricultural products from conventional and organic agriculture. However, LCA studies comparing agricultural products from conventional and organic farming systems report a wide variation in the resource efficiency of products from these systems. The studies show that impacts per area farmed land are usually less in organic systems, but related to the quantity produced impacts are often higher. We reviewed 34 comparative LCA studies of organic and conventional agricultural products to analyze whether this result is solely due to the usually lower yields in organic systems or also due to inaccurate modeling within LCA. Comparative LCAs on agricultural products from organic and conventional farming systems often do not adequately differentiate the specific characteristics of the respective farming system in the goal and scope definition and in the inventory analysis. Further, often only a limited number of impact categories are assessed within the impact assessment not allowing for a comprehensive environmental assessment. The most critical points we identified relate to the nitrogen (N) fluxes influencing acidification, eutrophication, and global warming potential, and biodiversity. Usually, N-emissions in LCA inventories of agricultural products are based on model calculations. Modeled N-emissions often do not correspond with the actual amount of N left in the system that may result in potential emissions. Reasons for this may be that N-models are not well adapted to the mode of action of organic fertilizers and that N-emission models often are built on assumptions from conventional agriculture leading to even greater

Nitrogen and phosphorus losses from agricultural systems in China: a meta-analysis.

PubMed

Cao, Di; Cao, Wenzhi; Fang, Jing; Cai, Longyan

2014-08-30

Studies worldwide have indicated that agricultural pollution is the main source of nitrogen and phosphorus (N and P) in surface waters. A systematic understanding of N and P sources and sinks in agricultural systems is important for selecting the appropriate remedial strategies to control nutrient losses and water pollution. Based on nationwide data and a long-term monitoring program in Southeast China, the nationwide spatial and temporal patterns of N and P losses and the relationships between such losses and N and P inputs and rainfall were analyzed. The results showed that the annual nutrient losses from agricultural systems in China strongly varied, and the N/P values ranged from 0.01 to 51.0, with a majority at approximately 0-20, and an arithmetic mean of 9.73; these values mostly overlap the suitable range of N/P (6-15) for red bloom algae. Copyright © 2014 Elsevier Ltd. All rights reserved.

A decision support system for rainfed agricultural areas of Mexico

USDA-ARS?s Scientific Manuscript database

Rural inhabitants of arid lands lack sufficient water to fulfill their agricultural and household needs. They do not have readily available technical information to support decisions regarding the course of action they should follow to handle the agro-climatic risk. In this paper, a computer model (...

Remote sensing with unmanned aircraft systems for precision agriculture applications

USDA-ARS?s Scientific Manuscript database

The Federal Aviation Administration is revising regulations for using unmanned aircraft systems (UAS) in the national airspace. An important potential application of UAS may be as a remote-sensing platform for precision agriculture, but simply down-scaling remote sensing methodologies developed usi...

Evaluation of the Food and Agriculture Sector Criticality Assessment Tool (FASCAT) and the Collected Data.

PubMed

Huff, Andrew G; Hodges, James S; Kennedy, Shaun P; Kircher, Amy

2015-08-01

To protect and secure food resources for the United States, it is crucial to have a method to compare food systems' criticality. In 2007, the U.S. government funded development of the Food and Agriculture Sector Criticality Assessment Tool (FASCAT) to determine which food and agriculture systems were most critical to the nation. FASCAT was developed in a collaborative process involving government officials and food industry subject matter experts (SMEs). After development, data were collected using FASCAT to quantify threats, vulnerabilities, consequences, and the impacts on the United States from failure of evaluated food and agriculture systems. To examine FASCAT's utility, linear regression models were used to determine: (1) which groups of questions posed in FASCAT were better predictors of cumulative criticality scores; (2) whether the items included in FASCAT's criticality method or the smaller subset of FASCAT items included in DHS's risk analysis method predicted similar criticality scores. Akaike's information criterion was used to determine which regression models best described criticality, and a mixed linear model was used to shrink estimates of criticality for individual food and agriculture systems. The results indicated that: (1) some of the questions used in FASCAT strongly predicted food or agriculture system criticality; (2) the FASCAT criticality formula was a stronger predictor of criticality compared to the DHS risk formula; (3) the cumulative criticality formula predicted criticality more strongly than weighted criticality formula; and (4) the mixed linear regression model did not change the rank-order of food and agriculture system criticality to a large degree. © 2015 Society for Risk Analysis.

Requirement analysis for the one-stop logistics management of fresh agricultural products

NASA Astrophysics Data System (ADS)

Li, Jun; Gao, Hongmei; Liu, Yuchuan

2017-08-01

Issues and concerns for food safety, agro-processing, and the environmental and ecological impact of food production have been attracted many research interests. Traceability and logistics management of fresh agricultural products is faced with the technological challenges including food product label and identification, activity/process characterization, information systems for the supply chain, i.e., from farm to table. Application of one-stop logistics service focuses on the whole supply chain process integration for fresh agricultural products is studied. A collaborative research project for the supply and logistics of fresh agricultural products in Tianjin was performed. Requirement analysis for the one-stop logistics management information system is studied. The model-driven business transformation, an approach uses formal models to explicitly define the structure and behavior of a business, is applied for the review and analysis process. Specific requirements for the logistic management solutions are proposed. Development of this research is crucial for the solution of one-stop logistics management information system integration platform for fresh agricultural products.

Impacts of Stratospheric Black Carbon on Agriculture

NASA Astrophysics Data System (ADS)

Xia, L.; Robock, A.; Elliott, J. W.

2017-12-01

A regional nuclear war between India and Pakistan could inject 5 Tg of soot into the stratosphere, which would absorb sunlight, decrease global surface temperature by about 1°C for 5-10 years and have major impacts on precipitation and the amount of solar radiation reaching Earth's surface. Using two global gridded crop models forced by one global climate model simulation, we investigate the impacts on agricultural productivity in various nations. The crop model in the Community Land Model 4.5 (CLM-crop4.5) and the parallel Decision Support System for Agricultural Technology (pDSSAT) in the parallel System for Integrating Impact Models and Sectors are participating in the Global Gridded Crop Model Intercomparison. We force these two crop models with output from the Whole Atmospheric Community Climate Model to characterize the global agricultural impact from climate changes due to a regional nuclear war. Crops in CLM-crop4.5 include maize, rice, soybean, cotton and sugarcane, and crops in pDSSAT include maize, rice, soybean and wheat. Although the two crop models require a different time frequency of weather input, we downscale the climate model output to provide consistent temperature, precipitation and solar radiation inputs. In general, CLM-crop4.5 simulates a larger global average reduction of maize and soybean production relative to pDSSAT. Global rice production shows negligible change with climate anomalies from a regional nuclear war. Cotton and sugarcane benefit from a regional nuclear war from CLM-crop4.5 simulation, and global wheat production would decrease significantly in the pDSSAT simulation. The regional crop yield responses to a regional nuclear conflict are different for each crop, and we present the changes in production on a national basis. These models do not include the crop responses to changes in ozone, ultraviolet radiation, or diffuse radiation, and we would like to encourage more modelers to improve crop models to account for those

Application of the Doppler lidar system to agricultural burning and air-sea interactions

NASA Technical Reports Server (NTRS)

Fitzjarrald, D.

1980-01-01

The Doppler lidar system is potentially a very powerful measurement system. Three areas concerning the system are discussed: (1) error analysis of the system to verify the results; (2) application of the system to agricultural burning in California central valley; and (3) oceanographic possibilities of the system.

Research on the performance evaluation of agricultural products supply chain integrated operation

NASA Astrophysics Data System (ADS)

Jiang, Jiake; Wang, Xifu; Liu, Yang

2017-04-01

The agricultural product supply chain integrated operation can ensure the quality and efficiency of agricultural products, and achieve the optimal goal of low cost and high service. This paper establishes a performance evaluation index system of agricultural products supply chain integration operation based on the development status of agricultural products and SCOR, BSC and KPI model. And then, we constructing rough set theory and BP neural network comprehensive evaluation model with the aid of Rosetta and MATLAB tools and the case study is about the development of agricultural products integrated supply chain in Jing-Jin-Ji region. And finally, we obtain the corresponding performance results, and give some improvement measures and management recommendations to the managers.

Integrated Modeling to Assess the Impacts of Changes in Climate and Socio Economics on Agriculture in the Columbia River Basin

NASA Astrophysics Data System (ADS)

Rajagopalan, K.; Chinnayakanahalli, K.; Adam, J. C.; Malek, K.; Nelson, R.; Stockle, C.; Brady, M.; Dinesh, S.; Barber, M. E.; Yorgey, G.; Kruger, C.

2012-12-01

The objective of this work is to assess the impacts of climate change and socio economics on agriculture in the Columbia River basin (CRB) in the Pacific Northwest region of the U.S. and a portion of Southwestern Canada. The water resources of the CRB are managed to satisfy multiple objectives including agricultural withdrawal, which is the largest consumptive user of CRB water with 14,000 square kilometers of irrigated area. Agriculture is an important component of the region's economy, with an annual value over 5 billion in Washington State alone. Therefore, the region is relevant for applying a modeling framework that can aid agriculture decision making in the context of a changing climate. To do this, we created an integrated biophysical and socio-economic regional modeling framework that includes human and natural systems. The modeling framework captures the interactions between climate, hydrology, crop growth dynamics, water management and socio economics. The biophysical framework includes a coupled macro-scale physically-based hydrology model (the Variable Infiltration Capacity, VIC model), and crop growth model (CropSyst), as well as a reservoir operations simulation model. Water rights data and instream flow target requirements are also incorporated in the model to simulate the process of curtailment during water shortage. The economics model informs the biophysical model of the short term agricultural producer response to water shortage as well as the long term agricultural producer response to domestic growth and international trade in terms of an altered cropping pattern. The modeling framework was applied over the CRB for the historical period 1976-2006 and compared to a future 30-year period centered on the 2030s. Impacts of climate change on irrigation water availability, crop irrigation demand, frequency of curtailment, and crop yields are quantified and presented. Sensitivity associated with estimates of water availability, irrigation demand, crop

Development of the ClearSky smoke dispersion forecast system for agricultural field burning in the Pacific Northwest

NASA Astrophysics Data System (ADS)

Jain, Rahul; Vaughan, Joseph; Heitkamp, Kyle; Ramos, Charleston; Claiborn, Candis; Schreuder, Maarten; Schaaf, Mark; Lamb, Brian

The post-harvest burning of agricultural fields is commonly used to dispose of crop residue and provide other desired services such as pest control. Despite careful regulation of burning, smoke plumes from field burning in the Pacific Northwest commonly degrade air quality, particularly for rural populations. In this paper, ClearSky, a numerical smoke dispersion forecast system for agricultural field burning that was developed to support smoke management in the Inland Pacific Northwest, is described. ClearSky began operation during the summer through fall burn season of 2002 and continues to the present. ClearSky utilizes Mesoscale Meteorological Model version 5 (MM5v3) forecasts from the University of Washington, data on agricultural fields, a web-based user interface for defining burn scenarios, the Lagrangian CALPUFF dispersion model and web-served animations of plume forecasts. The ClearSky system employs a unique hybrid source configuration, which treats the flaming portion of a field as a buoyant line source and the smoldering portion of the field as a buoyant area source. Limited field observations show that this hybrid approach yields reasonable plume rise estimates using source parameters derived from recent field burning emission field studies. The performance of this modeling system was evaluated for 2003 by comparing forecast meteorology against meteorological observations, and comparing model-predicted hourly averaged PM 2.5 concentrations against observations. Examples from this evaluation illustrate that while the ClearSky system can accurately predict PM 2.5 surface concentrations due to field burning, the overall model performance depends strongly on meteorological forecast error. Statistical evaluation of the meteorological forecast at seven surface stations indicates a strong relationship between topographical complexity near the station and absolute wind direction error with wind direction errors increasing from approximately 20° for sites in

A novel model for estimating organic chemical bioconcentration in agricultural plants

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

Hung, H.; Mackay, D.; Di Guardo, A.

1995-12-31

There is increasing recognition that much human and wildlife exposure to organic contaminants can be traced through the food chain to bioconcentration in vegetation. For risk assessment, there is a need for an accurate model to predict organic chemical concentrations in plants. Existing models range from relatively simple correlations of concentrations using octanol-water or octanol-air partition coefficients, to complex models involving extensive physiological data. To satisfy the need for a relatively accurate model of intermediate complexity, a novel approach has been devised to predict organic chemical concentrations in agricultural plants as a function of soil and air concentrations, without themore » need for extensive plant physiological data. The plant is treated as three compartments, namely, leaves, roots and stems (including fruit and seeds). Data readily available from the literature, including chemical properties, volume, density and composition of each compartment; metabolic and growth rate of plant; and readily obtainable environmental conditions at the site are required as input. Results calculated from the model are compared with observed and experimentally-determined concentrations. It is suggested that the model, which includes a physiological database for agricultural plants, gives acceptably accurate predictions of chemical partitioning between plants, air and soil.« less