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Crop model- and satellite imagery-based recommendation tool for variable rate N fertilizer application for the US Corn system

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Abstract

Precision nitrogen (N) management for corn has gained popularity due to both economic and environmental considerations. There is sufficient evidence demonstrating that N fertilizer efficiency can be improved by implementing sidedress and variable rate fertilization. In this paper, a crop model- and satellite imagery-based decision-support tool for recommending variable rate N fertilization at a high resolution of 5 m × 5 m is introduced. The sub-field management zones were delineated by overlapping the soil survey geographic (SSURGO) soil map units with wide dynamic range vegetation index (WDRVI)-derived relative productivity zones. The calibrated Agricultural Production Systems sIMulator (APSIM) was used to simulate a range of soil N processes, corn growth and N uptake by assimilating real-time weather data from the National Climate Data Center (NCDC). Sidedress N rates were estimated based on the target rate, N loss via leaching and denitrification, plant uptake and leftover N in the soil. The tool was tested on a 66 ha corn field in Illinois, USA for the growing season of 2015. Results showed that N-Prescription was able to give reasonable management zone delineation and sidedress N recommendation. The recommended sidedress N ranged from 60 to over 120 kg ha−1. Corn yield was greater in areas with higher sidedress recommendation, but the benefit from sidedress decreased with the increasing rate and plateaued above 110 kg ha−1. Sensitivity analysis suggested that soil hydraulic properties and soil organic matter content were critical to the sidedress accounting. Corn growth, and hence the cumulative N uptake, can be well simulated by calibrating the WDRVI derived leaf area index. This tool could serve as a good foundation for further development in precision N management.

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Acknowledgement

We thank the Backend team at FarmLogs and the Information Technology at Purdue Research Computing (RCAC) for computing support. This study is financially supported through projects funded to Q. Zhuang by the NASA Land Use and Land Cover Change program (NASA-NNX09AI26G), the NSF Division of Information and Intelligent Systems (NSF-1028291).

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Correspondence to Qianlai Zhuang.

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Jin, Z., Prasad, R., Shriver, J. et al. Crop model- and satellite imagery-based recommendation tool for variable rate N fertilizer application for the US Corn system. Precision Agric 18, 779–800 (2017). https://doi.org/10.1007/s11119-016-9488-z

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