Crop models often require extensive input data sets to realistically simulate crop growth. Development of such input data sets can be difficult for some model users. The objective of this study was to evaluate the imp...Crop models often require extensive input data sets to realistically simulate crop growth. Development of such input data sets can be difficult for some model users. The objective of this study was to evaluate the importance of variables in input data sets for crop modeling. Based on published hybrid performance trials in eight Texas counties, we developed standard data sets of 10-year simulations of maize and sorghum for these eight counties with the ALMANAC (Agricultural Land Management Alternatives with Numerical Assessment Criteria) model. The simulation results were close to the measured county yields with relative error only 2.6% for maize, and - 0.6% for sorghum. We then analyzed the sensitivity of grain yield to solar radiation, rainfall, soil depth, soil plant available water, and runoff curve number, comparing simulated yields to those with the original, standard data sets. Runoff curve number changes had the greatest impact on simulated maize and sorghum yields for all the counties. The next most critical input was rainfall, and then solar radiation for both maize and sorghum, especially for the dryland condition. For irrigated sorghum, solar radiation was the second most critical input instead of rainfall. The degree of sensitivity of yield to all variables for maize was larger than for sorghum except for solar radiation. Many models use a USDA curve number approach to represent soil water redistribution, so it will be important to have accurate curve numbers, rainfall, and soil depth to realistically simulate yields.展开更多
Water quality modeling requires across-scale support of combined digital soil elements and simulation parameters. This paper presents the unprecedented development of a large spatial scale (1:250,000) ArcGIS geodataba...Water quality modeling requires across-scale support of combined digital soil elements and simulation parameters. This paper presents the unprecedented development of a large spatial scale (1:250,000) ArcGIS geodatabase coverage designed as a functional repository of soil-parameters for modeling and comparison of water quality outcomes in the United States. The set of target models include: SWAT (Soil and Water Assessment Tool), APEX (Agricultural Policy Environmental Extender), and ALMANAC (Agricultural Land Management Alternatives with Numerical Assessment Criteria). This development relies on the Digital General Soil Map (DGSM) as the source of soil information, and leverages on architectural design and associated tools created for a companion product at higher resolution from which also was extended a procedure for refilling a large number of missing derived parameters. Outlined by regional watershed layouts and supported by GIS land use layers, the core product is developed using the File Geodatabase (FGDB) data structure, which brings, via customized Python-based tools, the data directly into geoprocessing workflows. The FGDB implement efficiently stores spatial soil features, tabular model elements and linked relationships, while seamlessly providing the environment for the extraction, spatial analysis, and mapping of the models’ parameters. As an alternative, the composing spatial elements, polygons and multi-resolution rasters, and the models’ elements are offered as a file-folder system of data with completely Open Source formats. Finally, this geographic database coverage provides support for the traditional large-scale and harmonized application of the models as well as an alternative to the higher resolution companion for areas where this information is still under development.展开更多
文摘Crop models often require extensive input data sets to realistically simulate crop growth. Development of such input data sets can be difficult for some model users. The objective of this study was to evaluate the importance of variables in input data sets for crop modeling. Based on published hybrid performance trials in eight Texas counties, we developed standard data sets of 10-year simulations of maize and sorghum for these eight counties with the ALMANAC (Agricultural Land Management Alternatives with Numerical Assessment Criteria) model. The simulation results were close to the measured county yields with relative error only 2.6% for maize, and - 0.6% for sorghum. We then analyzed the sensitivity of grain yield to solar radiation, rainfall, soil depth, soil plant available water, and runoff curve number, comparing simulated yields to those with the original, standard data sets. Runoff curve number changes had the greatest impact on simulated maize and sorghum yields for all the counties. The next most critical input was rainfall, and then solar radiation for both maize and sorghum, especially for the dryland condition. For irrigated sorghum, solar radiation was the second most critical input instead of rainfall. The degree of sensitivity of yield to all variables for maize was larger than for sorghum except for solar radiation. Many models use a USDA curve number approach to represent soil water redistribution, so it will be important to have accurate curve numbers, rainfall, and soil depth to realistically simulate yields.
文摘Water quality modeling requires across-scale support of combined digital soil elements and simulation parameters. This paper presents the unprecedented development of a large spatial scale (1:250,000) ArcGIS geodatabase coverage designed as a functional repository of soil-parameters for modeling and comparison of water quality outcomes in the United States. The set of target models include: SWAT (Soil and Water Assessment Tool), APEX (Agricultural Policy Environmental Extender), and ALMANAC (Agricultural Land Management Alternatives with Numerical Assessment Criteria). This development relies on the Digital General Soil Map (DGSM) as the source of soil information, and leverages on architectural design and associated tools created for a companion product at higher resolution from which also was extended a procedure for refilling a large number of missing derived parameters. Outlined by regional watershed layouts and supported by GIS land use layers, the core product is developed using the File Geodatabase (FGDB) data structure, which brings, via customized Python-based tools, the data directly into geoprocessing workflows. The FGDB implement efficiently stores spatial soil features, tabular model elements and linked relationships, while seamlessly providing the environment for the extraction, spatial analysis, and mapping of the models’ parameters. As an alternative, the composing spatial elements, polygons and multi-resolution rasters, and the models’ elements are offered as a file-folder system of data with completely Open Source formats. Finally, this geographic database coverage provides support for the traditional large-scale and harmonized application of the models as well as an alternative to the higher resolution companion for areas where this information is still under development.
