A Regression Metamodel To Replace SWAT In Crop Yield Prediction For Big Creek Watershed

Climate change will have a significant impact on the productivity of agricultural lands and ecosystem services in the coming decades. Variability in temperature and precipitation will alter many central U.S. watersheds. Simulation models such as the Soil and Water Assessment Tool (SWAT) offer the...

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Main Author: Johnson, Andrew
Format: Others
Published: OpenSIUC 2013
Online Access:https://opensiuc.lib.siu.edu/theses/1238
https://opensiuc.lib.siu.edu/cgi/viewcontent.cgi?article=2249&context=theses
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spelling ndltd-siu.edu-oai-opensiuc.lib.siu.edu-theses-22492018-12-20T04:35:15Z A Regression Metamodel To Replace SWAT In Crop Yield Prediction For Big Creek Watershed Johnson, Andrew Climate change will have a significant impact on the productivity of agricultural lands and ecosystem services in the coming decades. Variability in temperature and precipitation will alter many central U.S. watersheds. Simulation models such as the Soil and Water Assessment Tool (SWAT) offer the ability to model changes in watersheds by varying inputs. Unfortunately, SWAT requires a large number of input parameters and computation time to process the output data. Regression metamodels offer an alternative that seeks to replace the simulation model with a regression equation. This research created a linear regression metamodel to approximate SWAT in crop yield prediction. Results show that regression models can account for 45-84 percent of variance in yields for corn, soybean, alfalfa, switchgrass, and cotton in Big Creek Watershed. The coefficient of variation for each of these models ranged from 13 to 41 percent. These metamodels were able to reduce simulation time from hours to minutes. The tradeoff for utilizing metamodels is computation time versus accuracy. The results of this research indicate that the considerable reduction in computation time coupled with a moderate degree of accuracy in predicting crop yields necessitates the use of metamodels over SWAT. Regression coefficients for each metamodel can reveal how various weather and farm management techniques impact crop yields. These metamodels will be utilized by the Agent Based Model to determine how farmers will respond to future economic policies and crop prices based on a series of climate scenarios. 2013-08-01T07:00:00Z text application/pdf https://opensiuc.lib.siu.edu/theses/1238 https://opensiuc.lib.siu.edu/cgi/viewcontent.cgi?article=2249&context=theses Theses OpenSIUC
collection NDLTD
format Others
sources NDLTD
description Climate change will have a significant impact on the productivity of agricultural lands and ecosystem services in the coming decades. Variability in temperature and precipitation will alter many central U.S. watersheds. Simulation models such as the Soil and Water Assessment Tool (SWAT) offer the ability to model changes in watersheds by varying inputs. Unfortunately, SWAT requires a large number of input parameters and computation time to process the output data. Regression metamodels offer an alternative that seeks to replace the simulation model with a regression equation. This research created a linear regression metamodel to approximate SWAT in crop yield prediction. Results show that regression models can account for 45-84 percent of variance in yields for corn, soybean, alfalfa, switchgrass, and cotton in Big Creek Watershed. The coefficient of variation for each of these models ranged from 13 to 41 percent. These metamodels were able to reduce simulation time from hours to minutes. The tradeoff for utilizing metamodels is computation time versus accuracy. The results of this research indicate that the considerable reduction in computation time coupled with a moderate degree of accuracy in predicting crop yields necessitates the use of metamodels over SWAT. Regression coefficients for each metamodel can reveal how various weather and farm management techniques impact crop yields. These metamodels will be utilized by the Agent Based Model to determine how farmers will respond to future economic policies and crop prices based on a series of climate scenarios.
author Johnson, Andrew
spellingShingle Johnson, Andrew
A Regression Metamodel To Replace SWAT In Crop Yield Prediction For Big Creek Watershed
author_facet Johnson, Andrew
author_sort Johnson, Andrew
title A Regression Metamodel To Replace SWAT In Crop Yield Prediction For Big Creek Watershed
title_short A Regression Metamodel To Replace SWAT In Crop Yield Prediction For Big Creek Watershed
title_full A Regression Metamodel To Replace SWAT In Crop Yield Prediction For Big Creek Watershed
title_fullStr A Regression Metamodel To Replace SWAT In Crop Yield Prediction For Big Creek Watershed
title_full_unstemmed A Regression Metamodel To Replace SWAT In Crop Yield Prediction For Big Creek Watershed
title_sort regression metamodel to replace swat in crop yield prediction for big creek watershed
publisher OpenSIUC
publishDate 2013
url https://opensiuc.lib.siu.edu/theses/1238
https://opensiuc.lib.siu.edu/cgi/viewcontent.cgi?article=2249&context=theses
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