Meeting the ever-increasing global food, feed, and fiber demands while conserving the quantity and quality of limited agricultural water resources and maintaining the sustainability of irrigated agriculture requires optimizing irrigation management using advanced technologies such as soil moisture sensors. In this study, the performance of five different soil moisture sensors was evaluated for their accuracy in two irrigated cropping systems, one each in central and southwest Oklahoma, with variable levels of soil salinity and clay content. With factory calibrations, three of the sensors had sufficient accuracies at the site with lower levels of salinity and clay, while none of them performed satisfactorily at the site with higher levels of salinity and clay. The study also investigated the performance of different approaches (laboratory, sensor-based, and the Rosetta model) to determine soil moisture thresholds required for irrigation scheduling, i.e., field capacity (FC) and wilting point (WP). The estimated FC and WP by the Rosetta model were closest to the laboratory-measured data using undisturbed soil cores, regardless of the type and number of input parameters used in the Rosetta model. The sensor-based method of ranking the readings resulted in overestimation of FC and WP. Finally, soil moisture depletion, a critical parameter in effective irrigation scheduling, was calculated by combining sensor readings and FC estimates. Ranking-based FC resulted in overestimation of soil moisture depletion, even for accurate sensors at the site with lower levels of salinity and clay.

Highlights The progress made in agricultural irrigation scheduling in the past ten years and the current challenges are discussed. The main scientific scheduling strategies are based on soil water status, plant characteristics, and crop modeling. Challenges include large time and data requirements and availability of decision support systems. Opportunities include integration of scheduling strategies and demonstrating their effectiveness through local studies. Abstract. Irrigation scheduling is the process of determining the appropriate amount and timing of water application to achieve desired crop yield and quality, maximize water conservation, and minimize possible negative effects on the environment, such as nutrient leaching below the crop root zone. Effective irrigation scheduling has been shown to save water, save energy, and help agricultural producers achieve improved yields and quality. However, scientific irrigation scheduling methods generally have remained limited to mostly research applications with relatively low adoption by irrigators. There are several main approaches to irrigation scheduling, including those based on soil water status, plant characteristics, and/or crop modeling. Each of these approaches has advantages as well as limitations and sources of uncertainty and variability, depending on application conditions. This article summarizes progress made in the U.S. in each of the main scheduling approaches in the past ten years (since the 2010 Decennial Irrigation Symposium) and existing challenges and opportunities that should be considered moving forward. This article is intended to guide future research and extension projects in improving adoption of scientific irrigation scheduling approaches. Keywords: Computer modeling, Plant characteristics, Soil water status.

A new agricultural drought index was developed for monitoring drought impacts on agriculture in Oklahoma. This new index, called the Soil Moisture Evapotranspiration Index (SMEI), estimates the departure of aggregated root zone moisture from reference evapotranspiration. The SMEI was estimated at five locations across Oklahoma representing different climates. The results showed good agreement with existing soil moisture-based (SM) and meteorological drought indices. In addition, the SMEI had improved performance compared to other indices in capturing the effects of temporal and spatial variations in drought. The relationship with crop production is a key characteristic of any agricultural drought index. The correlations between winter wheat production and studied drought indices estimated during the growing period were investigated. The correlation coefficients were largest for SMEI (r > 0.9) during the critical crop growth stages when compared to other drought indices, and r decreased by moving from semi-arid to more humid regions across Oklahoma. Overall, the results suggest that the SMEI can be used effectively for monitoring the effects of drought on agriculture in Oklahoma.

This paper presents the potential of remotely sensed data in addressing spatially distributed irrigation equity, adequacy, and sustainability. The surface energy balance algorithm for land (SEBAL) was implemented to map actual evapotranspiration (ET) over an irrigation district in southern California. Potential ET was also mapped based on the Priestley–Taylor method, modified to account for the effect of horizontally transported energy on enhancing/suppressing ET. Remotely sensed products were integrated with ground-based data in a Geographical Information System (GIS) environment to quantify several irrigation and drainage performance indicators. The among- and within-field coefficients of variation of actual ET were comparable to previous studies, suggesting that water consumption was uniform across the irrigation district. The relative ET was high, indicating that irrigation supply was adequate. The extensive network of open drains was also found to be functioning at an optimal level according to the results of two performance indicators based on the magnitude and uniformity of groundwater depth.