Abstract
Precision agriculture offers the technologies to manage for infield variability and incorporate variability into irrigation management decisions. The major limitation of this technology often lies in the reconciliation of disparate data sources and the generation of irrigation prescription maps. Here the authors explore the utility of the cosmic-ray neutron probe (CRNP) which measures volumetric soil water content (SWC) in the top ~ 30 cm of the soil profile. The key advantages of CRNP is that the sensor is passive, non-invasive, mobile and soil temperature-invariant, making data collection more compatible with existing farm operations and extending the mapping period. The objectives of this study were to: (1) improve the delineation of irrigation management zones within a field and (2) estimate spatial soil hydraulic properties to make effective irrigation prescriptions. Ten CRNP SWC surveys were collected in a 53-ha field in Nebraska. The SWC surveys were analyzed using Empirical Orthogonal Functions (EOFs) to isolate the underlying spatial structure. A statistical bootstrapping analysis confirmed the CRNP + EOF provided superior soil hydraulic property estimates, compared to other hydrogeophysical datasets, when linearly correlated to laboratory measured soil hydraulic properties (field capacity estimates reduced 20–25% in root mean square error). The authors propose a soil sampling strategy for better quantifying soil hydraulic properties using CRNP + EOF methods. Here, five CRNP surveys and 6–8 sample locations for laboratory analysis were sufficient to describe the spatial distribution of soil hydraulic properties within this field. While the proposed strategy may increase overall effort, rising scrutiny for agricultural water-use could make this technology cost-effective.
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Data availability
The datasets and data products used for this study are available from the corresponding author on reasonable request.
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Acknowledgements
This research was supported by the University of Nebraska Extension. The authors would also like to thank Paulman Farms for access to the field site and historical datasets and Matthew Russell for assistance collecting soil samples. TEF, DMH, and JL would also like to acknowledge the financial support of the United States Department of Agriculture National Institute of Food and Agriculture, Hatch Project #1009760. Trade names or commercial products are given solely for the purpose of providing information on the exact equipment used in this study and do not imply recommendation or endorsement by the University of Nebraska.
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Finkenbiner, C.E., Franz, T.E., Gibson, J. et al. Integration of hydrogeophysical datasets and empirical orthogonal functions for improved irrigation water management. Precision Agric 20, 78–100 (2019). https://doi.org/10.1007/s11119-018-9582-5
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DOI: https://doi.org/10.1007/s11119-018-9582-5