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United States Department of Agriculture

Agricultural Research Service

Title: An Improved Agricultural System Model for Space-Time Simulation of Agricultural Landscape Variability

Authors
item Ascough, James
item Ma, Liwang
item Green, Timothy
item Flerchinger, Gerald
item McMaster, Gregory
item Ahuja, Lajpat
item Vandenberg, Bruce

Submitted to: World Congress of Soil Science
Publication Type: Abstract Only
Publication Acceptance Date: December 2, 2005
Publication Date: July 9, 2006
Citation: Ascough II, J.C., Ma, L., Green, T.R., Flerchinger, G.N., Mcmaster, G.S., Ahuja, L.R., Vandenberg, B.C. 2006. An improved agricultural system model for space-time simulation of agricultural landscape variability. World Congress of Soil Science. July 9-15,2006.

Interpretive Summary: The ARS Root Zone Water Quality Model (RZWQM) simulates the physical, chemical, and biological responses of the soil system within the root zone under various agricultural management practices. The model has been tested extensively under various soil-weather-management conditions and is being used widely for assessment of agricultural management effects on crop production (plant growth and development) and water quality related issues (vertical soil water dynamics, nutrient cycling and chemical transport). RZWQM strengths include the ability to characterize macropore/preferential flow, pesticide/nitrate transport in water (runoff/percolation/tile drains), and detailed carbon/nitrogen dynamics with consideration of microbial populations. However, RZWQM, like most field-scale agricultural system models, was designed for homogeneous land units (e.g., one soil type, cropping system etc.) with little or no consideration of hydrological interaction between land units. In the past two years, RZWQM has undergone a transformation from a point-based architecture towards a spatial framework for integrating a complex, agricultural system water quality model with interaction between simulated land areas via overland runoff and runon. The framework also provides the increased interface sophistication necessary for distributed hydrologic modeling. Modifications and improvements include the following: 1) integrated ESRI ArcGIS 9.1 modeling framework; 2) space-time output visualization tool; 3) linkage to the DSSAT 3.5 crop growth models; 4) kinematic/diffusive wave overland flow routing; 5) process simulation beyond the root zone; 6) canopy interception and surface depressional storage; and 7) SHAW model full energy balance. This new distributed agroecosystem water quality tool for predicting space-time planning scenarios across spatially variable agricultural landscapes is now called MARIA (Management of Agricultural Resources through Integrated Assessment). Component development is ongoing with new modules for soil erosion (including chemical transport), plant growth [DSSAT 4 Cropping System Model (CSM) and a new generic Unified Plant Growth Model (UPGM)], and saturated/unsaturated lateral subsurface flow scheduled for integration into the MARIA system framework. This poster presents evaluation of the MARIA model with: a) experimental corn datasets with various nitrogen and irrigation treatments distributed with DSSAT (Decision Support System for Agrotechnology Transfer) Version 3.5, and b) experimental soybean datasets taken from the literature representing various drought conditions. Simulation results were compared to a previous version of RZWQM incorporating only a linkage to the DSSAT 3.5 CERES-Maize and CROPGRO models without the additional modifications as listed above. Special emphasis in the comparison is placed on evaluation of the effect of the SHAW model based energy balance (e.g., soil temperature) routines on improved crop yield predictions.

Technical Abstract: The ARS Root Zone Water Quality Model (RZWQM) simulates the physical, chemical, and biological responses of the soil system within the root zone under various agricultural management practices. The model has been tested extensively under various soil-weather-management conditions and is being used widely for assessment of agricultural management effects on crop production (plant growth and development) and water quality related issues (vertical soil water dynamics, nutrient cycling and chemical transport). RZWQM strengths include the ability to characterize macropore/preferential flow, pesticide/nitrate transport in water (runoff/percolation/tile drains), and detailed carbon/nitrogen dynamics with consideration of microbial populations. However, RZWQM, like most field-scale agricultural system models, was designed for homogeneous land units (e.g., one soil type, cropping system etc.) with little or no consideration of hydrological interaction between land units. In the past two years, RZWQM has undergone a transformation from a point-based architecture towards a spatial framework for integrating a complex, agricultural system water quality model with interaction between simulated land areas via overland runoff and runon. The framework also provides the increased interface sophistication necessary for distributed hydrologic modeling. Modifications and improvements include the following: 1) integrated ESRI ArcGIS 9.1 modeling framework; 2) space-time output visualization tool; 3) linkage to the DSSAT 3.5 crop growth models; 4) kinematic/diffusive wave overland flow routing; 5) process simulation beyond the root zone; 6) canopy interception and surface depressional storage; and 7) SHAW model full energy balance. This new distributed agroecosystem water quality tool for predicting space-time planning scenarios across spatially variable agricultural landscapes is now called MARIA (Management of Agricultural Resources through Integrated Assessment). Component development is ongoing with new modules for soil erosion (including chemical transport), plant growth [DSSAT 4 Cropping System Model (CSM) and a new generic Unified Plant Growth Model (UPGM)], and saturated/unsaturated lateral subsurface flow scheduled for integration into the MARIA system framework. This poster presents evaluation of the MARIA model with: a) experimental corn datasets with various nitrogen and irrigation treatments distributed with DSSAT (Decision Support System for Agrotechnology Transfer) Version 3.5, and b) experimental soybean datasets taken from the literature representing various drought conditions. Simulation results were compared to a previous version of RZWQM incorporating only a linkage to the DSSAT 3.5 CERES-Maize and CROPGRO models without the additional modifications as listed above. Special emphasis in the comparison is placed on evaluation of the effect of the SHAW model based energy balance (e.g., soil temperature) routines on improved crop yield predictions.

Last Modified: 10/22/2014
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