Simulation of Cotton Production for Precision Farming

Simulation of Cotton Production for Precision Farming Most crop simulation models do not directly consider the spatial variability of inputs nor do they produce outputs that show the expected spatial variability of yield across a field. If such models were available for precision farming, then researchers could much better evaluate the effects of soil sampling densities to determine the number of samples necessary to adequately model a particular field. The objectives of this study were: (1) to design and implement a spatial simulation methodology for examining details of precision farming and (2) use this to evaluate the effects of different soil sampling resolutions on predicted yield and residual nitrates through spatially variable nitrogen applications. The GOSSYM/COMAX cotton growth model/expert system and the GRASS geographic information system were used to develop a spatial simulation that produces spatially variable outputs. Inputs to the model were collected from a 3.9-ha cotton field. Soil nitrate, a primary driver in fertilizer recommendations, was sampled on a 15.2-m regular grid for depths to 15 cm and on a 30.5-m regular grid at six 15-cm depth intervals (down to 90 cm). COMAX was used to determine spatially variable fertilizer recommendations. GOSSYM was used to simulate perfect application of these recommendations and predicted spatially variable yield and residual nitrates. Reductions in sampling density or resolution were simulated by systematically reducing the amount of data available to COMAX for calculating spatially variable fertilizer recommendations. GOSSYM subsequently used these recommendations (based upon less and less knowledge of soil nitrates) to simulate the effects of differing sampling resolutions on predicted yield and residual nitrates. For recommendations based upon a 15.2-m grid of inputs, 41.4 kg/ha of nitrate fertilizer produced 801.7 kg/ha of cotton and left an average of 9.4 ppm of nitrate in the soil profile. For a 30.5-m grid, 42.8 kg/ha of nitrate fertilizer resulted in a yield of 811.2 kg/ha and residual soil nitrate of 8.3 ppm. For 45.7-m and 61.0-m grids, the results were 43.3 kg/ha and 41.2 kg/ha of nitrate fertilizer, 755.3 kg/ha and 794.3 kg/ha of cotton, and 11.5 ppm and 8.1 ppm of residual soil nitrate, respectively. This study concluded that crop simulations and geographic information systems are a valuable combination for modeling the effects of precision farming and planning variable rate treatments. Simulation results indicate that excessive fertilization, while potentially damaging to the environment, may also have a negative impact on yield. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Simulation of Cotton Production for Precision Farming

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Publisher
Springer Journals
Copyright
Copyright © 1999 by Kluwer Academic Publishers
Subject
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
ISSN
1385-2256
eISSN
1573-1618
D.O.I.
10.1023/A:1009930020042
Publisher site
See Article on Publisher Site

Abstract

Most crop simulation models do not directly consider the spatial variability of inputs nor do they produce outputs that show the expected spatial variability of yield across a field. If such models were available for precision farming, then researchers could much better evaluate the effects of soil sampling densities to determine the number of samples necessary to adequately model a particular field. The objectives of this study were: (1) to design and implement a spatial simulation methodology for examining details of precision farming and (2) use this to evaluate the effects of different soil sampling resolutions on predicted yield and residual nitrates through spatially variable nitrogen applications. The GOSSYM/COMAX cotton growth model/expert system and the GRASS geographic information system were used to develop a spatial simulation that produces spatially variable outputs. Inputs to the model were collected from a 3.9-ha cotton field. Soil nitrate, a primary driver in fertilizer recommendations, was sampled on a 15.2-m regular grid for depths to 15 cm and on a 30.5-m regular grid at six 15-cm depth intervals (down to 90 cm). COMAX was used to determine spatially variable fertilizer recommendations. GOSSYM was used to simulate perfect application of these recommendations and predicted spatially variable yield and residual nitrates. Reductions in sampling density or resolution were simulated by systematically reducing the amount of data available to COMAX for calculating spatially variable fertilizer recommendations. GOSSYM subsequently used these recommendations (based upon less and less knowledge of soil nitrates) to simulate the effects of differing sampling resolutions on predicted yield and residual nitrates. For recommendations based upon a 15.2-m grid of inputs, 41.4 kg/ha of nitrate fertilizer produced 801.7 kg/ha of cotton and left an average of 9.4 ppm of nitrate in the soil profile. For a 30.5-m grid, 42.8 kg/ha of nitrate fertilizer resulted in a yield of 811.2 kg/ha and residual soil nitrate of 8.3 ppm. For 45.7-m and 61.0-m grids, the results were 43.3 kg/ha and 41.2 kg/ha of nitrate fertilizer, 755.3 kg/ha and 794.3 kg/ha of cotton, and 11.5 ppm and 8.1 ppm of residual soil nitrate, respectively. This study concluded that crop simulations and geographic information systems are a valuable combination for modeling the effects of precision farming and planning variable rate treatments. Simulation results indicate that excessive fertilization, while potentially damaging to the environment, may also have a negative impact on yield.

Journal

Precision AgricultureSpringer Journals

Published: Oct 6, 2004

References

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