Profitability of On-Farm Precipitation Data for Nitrogen Management Based on Crop Simulation

Profitability of On-Farm Precipitation Data for Nitrogen Management Based on Crop Simulation The purpose of this study was to determine the utility of on-farm precipitation measurement for nitrogen management decisions on an Indiana farm. Site-specific farming has led some producers to measure on-farm precipitation at multiple sites, but the profitability of such intense sampling for non-irrigated agriculture is not clear. The CERES-Maize model in Decision Support System for Agrotechnology Transfer (DSSAT) version 3.5 was used to simulate corn yield for a farm in east-central Indiana for 20years of weather data from three precipitation data sources—an on-farm station, the nearest non-urban National Weather Service (NWS) station, and the weighted mean of the three nearest such stations. Stochastic dominance and descriptive statistics were used to compare simulated yield and profitability for four nitrogen strategies: variable-rate versus whole-field fertilizer application and split application (starter urea-ammonium nitrate mixture at planting and sidedressed ammonia 37days later) versus sidedress application only. Off-farm data never led to a different choice of nitrogen strategy than on-farm data, but the ability to categorize a choice as risk averse or risk neutral depended on the precipitation data source used. This suggested that although on-farm precipitation measurement could be useful for risk management decisionmaking, it might not be profitable on average. The nearest NWS station would be the most profitable source of precipitation data, if it leads to the same management strategy as on-farm data. Precision Agriculture Springer Journals

Profitability of On-Farm Precipitation Data for Nitrogen Management Based on Crop Simulation

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Kluwer Academic Publishers
Copyright © 2004 by Kluwer Academic Publishers
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
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