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Agronomy 2019, 9(4), 174; https://doi.org/10.3390/agronomy9040174

Soil Water Content and High-Resolution Imagery for Precision Irrigation: Maize Yield

1
Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO 80523, USA
2
Agriculture and Agri-Food Canada, St-Jean-sur-Richelieu, QC J3B 3E6, Canada
*
Author to whom correspondence should be addressed.
Received: 31 January 2019 / Revised: 18 March 2019 / Accepted: 1 April 2019 / Published: 5 April 2019
(This article belongs to the Special Issue Increasing Agricultural Water Productivity in a Changing Environment)
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Abstract

Improvement in water use efficiency of crops is a key component in addressing the increasing global water demand. The time and depth of the soil water monitoring are essential when defining the amount of water to be applied to irrigated crops. Precision irrigation (PI) is a relatively new concept in agriculture, and it provides a vast potential for enhancing water use efficiency, while maintaining or increasing grain yield. Neutron probes (NPs) have consistently been used as a robust and accurate method to estimate soil water content (SWC). Remote sensing derived vegetation indices have been successfully used to estimate variability of Leaf Area Index and biomass, which are related to root water uptake. Crop yield has not been evaluated on a basis of SWC, as explained by NPs in time and at different depths. The objectives of this study were (1) to determine the optimal time and depth of SWC and its relationship to maize grain yield (2) to determine if satellite-derived vegetation indices coupled with SWC could further improve the relationship between maize grain yield and SWC. Soil water and remote sensing data were collected throughout the crop season and analyzed. The results from the automated model selection of SWC readings, used to assess maize yield, consistently selected three dates spread around reproductive growth stages for most depths (p value < 0.05). SWC readings at the 90 cm depth had the highest correlation with maize yield, followed closely by the 120 cm. When coupled with remote sensing data, models improved by adding vegetation indices representing the crop health status at V9, right before tasseling. Thus, SWC monitoring at reproductive stages combined with vegetation indices could be a tool for improving maize irrigation management. View Full-Text
Keywords: soil water content; water use efficiency; precision irrigation; remote sensing; maize yield soil water content; water use efficiency; precision irrigation; remote sensing; maize yield
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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de Lara, A.; Longchamps, L.; Khosla, R. Soil Water Content and High-Resolution Imagery for Precision Irrigation: Maize Yield. Agronomy 2019, 9, 174.

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