Among one of the many challenges in implementing precision irrigation is to obtain an accurate characterization of the soil water content (SWC) across spatially variable fields along the crop growing season. The accuracy of characterizing SWC has been tested primarily on a small-scale and has received little attention from the scientific community at the field scale. Hence, the objective of this study was to assess the characterization of the spatial distribution of soil water content at the field scale by the apparent electrical conductivity (ECa). In evaluating the current aim, ECa survey was compared against repeated measurements of SWC at five depths using neutron probe. Results showed that mean SWC was different across ECa derived management zones, which indicates that on a macro-scale, soil ECa could effectively characterize the mean differences in SWC across management zones. Results also showed that deep ECa (0-150 cm) survey outperformed shallow survey (0-75 cm). Considering other soil properties, such as organic matter content and salt content, further improved the relationship between SWC and ECa.

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.

Applying at the economic optimal nitrogen rate (EONR) has the potential to increase nitrogen (N) fertilization efficiency and profits while reducing negative environmental impacts. On-farm precision experimentation (OFPE) provides the opportunity to collect large amounts of data to estimate the EONR. Machine learning (ML) methods such as generalized additive models (GAM) and random forest (RF) are promising methods for estimating yields and EONR. Twenty OFPE N trials in wheat and barley were conducted and analyzed with soil, terrain and remote-sensed variables to address the following objectives: (1) to quantify the spatial variability of winter crops yield and the yield response to N using OFPE, (2) to evaluate and compare the performance of GAM and RF models to predict yield and yield response to N and, (3) to quantify the impact of soil, crop and field characteristics on the EONR estimation. Machine learning techniques were able to model wheat and barley yield with an average error of 13.7% (624 kg ha−1). However, similar yield prediction accuracy from RF and GAM resulted in widely different economic optimal nitrogen rates. Across sites, soil available phosphorus and soil organic matter were the most influential variables; however, the magnitude and direction of the effect varied between fields. These indicate that training a model using data coming from different fields may lead to unreliable site-specific EONR when it is applied to another field. Further evaluation of ML methods is needed to ensure a robust automation of N recommendation while producers transition into the digital ag era.

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