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Decision support systems intended for precision irrigation aim at reducing irrigation applications while optimizing crop yield to achieve maximum crop water productivity (CWP). These systems incorporate on-site sensor data, remote sensing inputs, and advanced algorithms with spatial and temporal characteristics to compute precise crop water needs. The availability of variable rate irrigation (VRI) systems enables irrigation applications at a sub-field scale. The combination of an appropriate VRI system along with a precise decision support system would be ideal for improved CWP. The objective of this study was to compare and evaluate two decision support systems in terms of seasonal applied irrigation, crop yield, and CWP. This study implemented the Spatial EvapoTranspiration Modeling Interface (SETMI) model and the Irrigation Scheduling Supervisory Control and Data Acquisition (ISSCADA) system for management of a center pivot irrigation system in a 58-ha maize-soybean field during the 2020 and 2021 growing seasons. The irrigation scheduling methods included: ISSCADA plant feedback, ISSCADA hybrid, common practice, and SETMI. These methods were applied at irrigation levels of 0, 50, 100, and 150% of the full irrigation prescribed by the respective irrigation scheduling method. Data from infrared thermometers (IRTs), soil water sensors, weather stations, and satellites were used in the irrigation methods. Mean seasonal irrigation prescribed was different among the irrigation levels and methods for the 2 years. The ISSCADA plant feedback prescribed the least irrigation among the methods for majority of the cases. The common practice prescribed the largest seasonal irrigation depth among the methods for three crop-year cases. The maize yield in rainfed was found to be significantly lower than the irrigated levels in 2020 since 2020 was a dry year. No significant differences were observed in crop yield among the different irrigation methods for both years. The CWP among the different irrigation methods ranged between 2.72 and 3.15 kg m−3 for 2020 maize, 1.03 and 1.13 kg m−3 for 2020 soybean, 3.57 and 4.24 kg m−3 for 2021 maize, and 1.19 and 1.48 kg m−3 for 2021 soybean. Deficit level (50%) had the largest irrigation water productivity in all crop-year cases in this study. The ISSCADA and SETMI systems were found to reduce irrigation applications as compared to the common practice while maintaining crop yield. This study was the first to implement the newly developed integrated crop water stress index (iCWSI) thresholds and the ISSCADA system for site-specific irrigation of maize and soybean in Nebraska. [ABSTRACT FROM AUTHOR]

Crop nitrogen (N) status is known to affect crop water status and crop water use. To investigate further the N effects on soil water changes and on canopy temperature, three water levels × four N levels were imposed on two growing seasons of maize in west central Nebraska, USA. Soil water changes were measured using a neutron probe, whereas canopy temperature was measured using infrared thermometers on a ground-based mobile platform. At all water levels, soil water losses over month-long intervals were generally greater as N levels increased. Given equal water levels, early afternoon canopy temperatures were usually lower with higher N levels, but no trend or even the opposite trend was occasionally observed. Jointly considering canopy reflectance and soil water depletion shows potential to explain much of the variation in estimated instantaneous water use among plots. However, determining the relative contributions of the canopy and soil factors on a particular day may require season-to-date knowledge of the crop. Further research on assimilating such sensor data for a combined stress coefficient would improve crop modeling and irrigation scheduling when variable water sufficiency and variable N sufficiency are simultaneously significant. [ABSTRACT FROM AUTHOR]

Automated monitoring of the soil-plant-atmospheric continuum at a high spatiotemporal resolution is a key to transform the labor-intensive, experience-based decision making to an automatic, data-driven approach in agricultural production. Growers could make better management decisions by leveraging the real-time field data while researchers could utilize these data to answer key scientific questions. Traditionally, data collection in agricultural fields, which largely relies on human labor, can only generate limited numbers of data points with low resolution and accuracy. During the last two decades, crop monitoring has drastically evolved with the advancement of modern sensing technologies. Most importantly, the introduction of IoT (Internet of Things) into crop, soil, and microclimate sensing has transformed crop monitoring into a quantitative and data-driven work from a qualitative and experience-based task. Ag-IoT systems enable a data pipeline for modern agriculture that includes data collection, transmission, storage, visualization, analysis, and decision-making. This review serves as a technical guide for Ag-IoT system design and development for crop, soil, and microclimate monitoring. It highlighted Ag-IoT platforms presented in 115 academic publications between 2011 and 2021 worldwide. These publications were analyzed based on the types of sensors and actuators used, main control boards, types of farming, crops observed, communication technologies and protocols, power supplies, and energy storage used in Ag-IoT platforms. The result showed that 33 variables measured by various sensors were demonstrated in these studies while 10 actuations were successfully integrated with Ag-IoT platforms. Perennial crops, which introduced less disturbance to Ag-IoT platforms than annual crops, were selected by 64% of researchers. Furthermore, studies in Ag-IoT system development were more focused on outdoor than indoor environments. Ag-IoT systems based on Arduino were most common among the studies while commercial platforms were least adopted, likely due to their inflexibility in customized developments. More researchers focused on agricultural applications than the IoT technology itself. Soil water content-based irrigation scheduling and controlled environment monitoring and controlling were the main applications. Other application areas included soil nutrient estimation, crop monitoring based on multiple vegetation indices, pest identification, and chemigation. Several potential future research directions were identified at the end of the review, including integration of satellite-based internet connectivity to improve the IoT networks in non-connected farms, development of mobile IoT platforms (drones and autonomous ground vehicles) with continuous connectivity, and the use of edge-computing and machine-learning/deep-learning to enhance the capability of the Ag-IoT systems. [Display omitted] • We searched and reviewed the most relevant 115 papers on Ag-IoT published between 2011 and 2021. • The papers were analyzed focusing on sensors, actuators, main boards, crops, communication protocols, and power supplies. • Ag-IoT components, challenges, potential solutions, and supporting technologies were presented and discussed. • The benefits of Ag-IoT for farming systems analyses and management were discussed. • We concluded with the future direction of designing an Ag-IoT system with completeness, robustness, and compatibility. [ABSTRACT FROM AUTHOR]

Irrigation has a great impact on global food security as it contributes to the majority of the world's agricultural food supply. It is essential to judiciously utilize water resources through efficient irrigation management since the majority of U.S. groundwater aquifers are rapidly depleting. Thus, quantification of the relationships between water depletion and environmental factors is important for understanding crop response to varying levels of water stresses that depletion can cause. The objectives of this research were to: 1) investigate the relationship between root zone water depletion (D rw) and canopy temperature differential (ΔT) at different ranges of D rw ; and 2) develop upper (water stressed) and lower (non-water stressed) baselines for quantification of crop water stress index (CWSI) in a sub-humid climate. The research was conducted over maize and soybean during 2018, 2019, and 2020 growing seasons. Sensor node stations comprising of an infrared thermometer and three soil water sensors were installed at various sites over maize and soybean fields. ΔT tends to increase with the increase in D rw when the range of D rw includes values greater than 170 mm for maize and values greater than 160 mm for soybean. The results indicate that ΔT and D rw are unrelated until a soil-water depletion threshold is attained, and these D rw threshold values could be considered as indicators to trigger irrigation for efficient agricultural water management. To the best of the authors' knowledge, the research is the first to develop upper and lower CWSI baselines for east-central Nebraska. The baselines developed in this study could facilitate the quantification of CWSI for irrigation scheduling of maize and soybean in east-Central Nebraska. Future work should aim to investigate the potential in using D rw and/or ΔT to determine efficient water allocation and if a threshold CWSI could be used for timing of irrigation to prevent yield loss. • Inter-relationship between water depletion and temperature differential was studied. • Water-stressed and non-water-stressed baselines were developed for sub-humid regime. • The research was carried on maize and soybean over three growing seasons. • Temperature differential increases with increasing water depletion beyond threshold. • The threshold value could be considered as indicator to prescribe irrigation regime. [ABSTRACT FROM AUTHOR]