Generic optimization approach of soil hydraulic parameters for site-specific model applications

Site-specific crop management is based on the postulate of varying soil and crop requirements in a field. Therefore, a field is separated into homogenous management zones, using available data to adapt management practices environment to maximize productivity and profitability while reducing environ...

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Bibliographic Details
Published in:	Precision agriculture Vol. 25; no. 2; pp. 654 - 680
Main Authors:	Trenz, Jonas, Memic, Emir, Batchelor, William D., Graeff-Hönninger, Simone
Format:	Journal Article
Language:	English
Published:	New York Springer US 01-04-2024 Springer Nature B.V
Subjects:	Agriculture Atmospheric Sciences Biomedical and Life Sciences Chemistry and Earth Sciences Computer Science Crop growth Crop management Crops Decision support systems Economics Environmental impact Growth models Life Sciences Mathematical models Parameters Physics Precision farming Profitability Remote Sensing/Photogrammetry Root-mean-square errors Soil profiles Soil properties Soil Science & Conservation Soils Statistics for Engineering Precision agriculture Soil profile parameters optimization Site-specific management Crop models Inverse modelling Hydraulic parameters
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Summary:	Site-specific crop management is based on the postulate of varying soil and crop requirements in a field. Therefore, a field is separated into homogenous management zones, using available data to adapt management practices environment to maximize productivity and profitability while reducing environmental impacts. Due to advancing sensor technologies, crop growth and yield data on more minor scales are common, but soil data often needs to be more appropriate. Crop growth models have shown promise as a decision support tool for site-specific farming. The Decision Support System for Agrotechnology Transfer (DSSAT) is a widely used point-based model. To overcome the problem of inappropriate soil input data problem, this study introduces an external plug-in program called Soil Profile Optimizer (SPO), which uses the current DSSAT v4.8 to calibrate soil profile parameters on a site-specific level. Developed as an inverse modelling approach, the SPO can calibrate selected soil profile parameters by targeting available in-season plant data. Root Mean Square Error (RMSE) and normalized RMSE as error minimization criteria are used. The SPO was tested and evaluated by comparing different simulation scenarios in a case study of a 3-yr field trial with maize. The scenario with optimized soil profiles, conducted with the SPO, resulted in an R 2 of 0.76 between simulated and observed yield and led to significant improvements compared to the scenario conducted with field scale soil profile information (R 2 0.03). The SPO showed promise in using spatial plant measurements to estimate management zone scale soil parameters required for the DSSAT model.
ISSN:	1385-2256 1573-1618
DOI:	10.1007/s11119-023-10087-9