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Evaluating Animo Model for Predicting Nitrogen Leaching in Rice and Wheat

Evaluating Animo Model for Predicting Nitrogen Leaching in Rice and Wheat Nitrogen (N) leaching could be a serious problem for coarse-textured soils. Various models are created to simulate N leaching in coarse-textured soils, but their testing is required to evaluate the accuracy of simulations. This study evaluated the Soil Water Atmosphere Plant (SWAP) and Agricultural NItrogen MOdel (ANIMO) models to predict soil water content and N leaching. Calibration was performed with soil water content data from wheat crop and validation was conducted with NO3-N and NH4-N concentrations data from rice crop of the same field. Hydraulic and various other N rate constants were calibrated using the PEST (Parameter EStimation Technique) model. The calibration of the SWAP model showed differences between measured and predicted soil water content at different soil depths were less than 2/5th of the measured absolute error (MAE). The calibration of the ANIMO model showed relatively large differences between measured and predicted NO3-N concentrations that could be attributed to erratic values of NO3-N concentrations found in the soil profiles. The validation of ANIMO model showed that differences between measured and predicted NH4-N concentrations at different soil depths were less than 2/5th of the MAE and R2 was greater than 0.76. These results suggest that SWAP/ANIMO model has the potential to predict N leaching in rice and wheat crops in testing environments and their performance could be further improved with the availability of more field data. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Arid Land Research and Management Taylor & Francis

Evaluating Animo Model for Predicting Nitrogen Leaching in Rice and Wheat

Farmaha, Bhupinder S.
Arid Land Research and Management , Volume 28 (1): 11 – Jan 2, 2014
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Evaluating Animo Model for Predicting Nitrogen Leaching in Rice and Wheat

Abstract

Nitrogen (N) leaching could be a serious problem for coarse-textured soils. Various models are created to simulate N leaching in coarse-textured soils, but their testing is required to evaluate the accuracy of simulations. This study evaluated the Soil Water Atmosphere Plant (SWAP) and Agricultural NItrogen MOdel (ANIMO) models to predict soil water content and N leaching. Calibration was performed with soil water content data from wheat crop and validation was conducted with NO3-N and NH4-N...
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Publisher
Taylor & Francis
Copyright
Copyright Taylor & Francis Group, LLC
ISSN
1532-4990
eISSN
1532-4982
DOI
10.1080/15324982.2013.801371
Publisher site
See Article on Publisher Site

Abstract

Nitrogen (N) leaching could be a serious problem for coarse-textured soils. Various models are created to simulate N leaching in coarse-textured soils, but their testing is required to evaluate the accuracy of simulations. This study evaluated the Soil Water Atmosphere Plant (SWAP) and Agricultural NItrogen MOdel (ANIMO) models to predict soil water content and N leaching. Calibration was performed with soil water content data from wheat crop and validation was conducted with NO3-N and NH4-N concentrations data from rice crop of the same field. Hydraulic and various other N rate constants were calibrated using the PEST (Parameter EStimation Technique) model. The calibration of the SWAP model showed differences between measured and predicted soil water content at different soil depths were less than 2/5th of the measured absolute error (MAE). The calibration of the ANIMO model showed relatively large differences between measured and predicted NO3-N concentrations that could be attributed to erratic values of NO3-N concentrations found in the soil profiles. The validation of ANIMO model showed that differences between measured and predicted NH4-N concentrations at different soil depths were less than 2/5th of the MAE and R2 was greater than 0.76. These results suggest that SWAP/ANIMO model has the potential to predict N leaching in rice and wheat crops in testing environments and their performance could be further improved with the availability of more field data.

Journal

Arid Land Research and Management – Taylor & Francis

Published: Jan 2, 2014

Keywords: Agricultural Nitrogen Model (ANIMO); ammonium; groundwater; leaching; modeling; nitrate; Parameter Estimation Technique (PEST); rice; Soil Water Atmosphere Plant (SWAP); wheat

References

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