The intensified development of aquaculture and excessive use of agricultural fertilizers pose a threat to natural resource availability and deteriorate the environment. Utilizing aquaculture wastewater from mariculture for agricultural irrigation can mitigate pollution and alleviate the pressure on natural resources. This study investigated the comprehensive benefits of using freshwater and mariculture wastewater for irrigation, employing two concentration levels of mariculture wastewater in a pot experiment with saline-tolerant rice. Furthermore, we quantitively assessed the integrated benefits for farmland by utilizing an ecosystem service function value assessment and emergy value theory. The results indicate a significant enhancement in the growth and yield of saline-tolerant rice when irrigated with mariculture wastewater. Specifically, the theoretical yield increased by 9.01% relative to freshwater irrigation. Irrigation using aquaculture wastewater significantly enhanced the nutrient concentrations in the soil, including soil organic carbon (SOC), avail-K (AK), Olsen-P (OP), and alkali-N (AN). Additionally, the uptake of these nutrients by salt-tolerant crops during their late reproductive stage effectively mitigated the rise in soil salinity induced by the wastewater irrigation practice. Under experimental conditions, wastewater irrigation conferred superior ecological benefits compared with freshwater irrigation. The comprehensive benefits of wastewater irrigation, valued at 104,439.10 RMB/hm2, exceeded those of freshwater irrigation by 188.8%. The utilization of mariculture wastewater effectively enhances the coastal environment, augments crop yields, and diminishes treatment costs. From the perspectives of revenue enhancement, environmental compatibility, and sustainability, the model of utilizing salt-tolerant crops irrigated with mariculture wastewater holds substantial promotional and practical significance.

Integrating water and nitrogen (N) management is critical to addressing contemporary challenges in agricultural development. This research explored using multispectral sensors mounted on unmanned aerial vehicles (UAVs) to monitor N demand via the normalized difference red-edge (NDRE) vegetation index and consequently schedule fertigation. The experiment included eight treatments with four fertilizer levels under both excessive and full irrigation. The four fertilizer levels comprised: high reference treatment based on commercial lab soil tests, sensor-based treatment triggered by an NDRE saturation threshold of 0.95, deficit treatment with base rate at pre-plant and side-dress, and a control treatment without any N application. The performance of each treatment was evaluated through a comprehensive comparison of yield, water productivity (WP), and nitrogen use efficiency (NUE). The sufficiency index (SI) of sensor-based treatment plots reached a threshold of 0.95, allowing spatially variable adjustment of N application for optimal yield with reduced total N input. Reducing N fertilizer in sensor-based treatments resulted in a substantial reduction of 50 %-60 %, though it led to a yield loss up to 12 %. However, NUE parameters such as partial factor productivity, agronomic efficiency, recovery efficiency, and physiological efficiency improved with sensor-based treatments, alongside reduced N leaching. Combining sensor-based treatment with full irrigation demonstrated the best ecological return, showing relatively lower yield reduction but significant improvements in NUE and WP. Further research into economic returns, saturation threshold algorithms for SI, adaptability to diverse environments, and virtual saturation reference is recommended for the widespread adoption of UAV-based N split management among growers.

Irrigation water management using automated irrigation decision support system (IDSS) as a smart irrigation scheduling tool can improve water use efficiency and crop production, especially under circumstances of limited water supply. The current study evaluated the performance of different artificial intelligence (AI) algorithms and their ensembles in forecasting Crop Evapotranspiration (ETc) and Crop Water Stress Index (CWSI) against calculated single crop coefficient FAO56 ETc and Jackson's theoretical CWSI, respectively. Soil moisture, canopy temperatures (Tc) and Normalized Difference Vegetation Index (NDVI) were all measured from irrigated and non-irrigated maize plots in West Central Nebraska during 2020 and 2021 growing seasons. There were fifteen and twelve input combinations used for ETc and CWSI predictions, respectively, having input variables such as weather and soil moisture as well as ancillary variables, including NDVI, reference evapotranspiration (ETr), and cumulative growing degree days (CGDDs). While evaluating the models, four statistical performance indicators including coefficient of determination (r2), root mean square error (RMSE), mean absoluter error (MAE), and mean absolute percentage error (MAPE) were used. Furthermore, ranking scores were performed on statistical results to find the overall best model across all the input combinations. Based on total ranking scores, CatBoost (RMSE ranging between 0.06 – 0.09 unitless) was the best model in predicting CWSI, while Stacked Regression (RMSE ranging between 0.27 – 0.72 mm d−1) was the best model for ETc estimation. Future research will consider designing and evaluating an IDSS using identified best machine learning models to establish soil water and plant stress feedback for automated irrigation scheduling.

Canopy temperature (Tc) measurements are increasingly being used to compute crop thermal indices for water stress estimation and improved irrigation management. Conventionally monitoring crop thermal response requires maintenance of a well-watered crop from which non-stressed canopy temperature (Tcns) is measured as a reference for thermal index computation. This study alternatively evaluated the performance of 36 weather data driven model combinations to predict peak time (12:00–17:00 h) Tcns in maize grown in semi-arid climates at the West Central Research, Extension, and Education Center (WCREEC) in North Platte, NE, and at the Limited Irrigation Research Farm (LIRF) in Greeley, CO. Data-driven models considered were multilinear regression (MLR), forward feed neural network (NN), recurrent neural network (RNN), multivariate adoptive regression splines (MARS), random forest (RF), and k-nearest neighbor (KNN). For each of these models, the following weather data combinations were tested: average air temperature (Ta), average relative humidity (RH), wind speed (U2), and solar radiation (Rs) (combination 1); RH, U2, Rs (combination 2), Ta, RH, Rs (combination 3); Ta, RH (combination 4); RH, Rs (combination 5); and Ta, Rs (combination 6). Ranking the performance of weather data × model combinations across both climate sites showed that MARS model with combination 1 was a better predictor of Tcns with R2 of 0.866 and RMSE value of 0.966 °C at WCREEC and R2 of 0.910 and RMSE value of 0.693 °C at LIRF. The performance of site specific (localized) and generalized model combinations was compared and indicated that cross site prediction of Tcns was primarily determined by weather data combinations, rather than model specificity.

Irrigation scheduling methods have been used to determine the timing and amount of water applied to crops. Scheduling techniques can include measurement of soil water content, quantification of crop water use, and monitoring of crop physiological response to water stress. The aim of this study was to evaluate the performance of a simplified crop canopy temperature measurement (CTM) method as a technique to schedule irrigation for maize. Specifically, the Degrees Above Non-Stressed (DANS) index, which suggests water stress when canopy temperature exceeds the non-stressed canopy temperature (Tcns), was determined by estimating Tcns from a weather based multilinear regression model. The modeled Tcns had a strong correlation with observed Tcns with a pooled R2 values of 0.94 across the 2018, 2019, and 2020 growing seasons. This DANS index was also highly correlated with the conventionally used Crop Water Stress Index (CWSI) with R2 values of 0.67, 0.59, and 0.76 in 2018, 2019, and 2020, respectively. Furthermore, DANS had a strong linear relationship with soil water depletion above 60% in the 0.60 m soil profile with an R2 of 0.78. The CTM method was also compared to more commonly used scheduling methods namely: soil moisture monitoring (SMM) and crop evapotranspiration modeling (ETM). Grain yield was significantly lower for the CTM method than for the ETM method in 2018 and 2020 but not in 2019. No significant differences were observed in Irrigation Water Productivity (IWP) in 2018; however, all treatments were significantly different with the CTM method having the greatest IWP in 2020. For attempting to trigger full irrigation with the CTM method, a fixed DANS threshold of 0.5°C was found to be more appropriate than the literature value of 1.0°C, but consideration of crop growth stage would further improve scheduling. Ultimately, the reactive nature of the CTM method and the inevitable uncertainty of modeled Tcns suggest that the CTM method may be more suited to deficit than full irrigation.

Mobile infrared thermometers (IRTs) mounted on moving platforms provide one-time-of-day radiometric mea-surements (Tr), which can be used to calculate instantaneous actual evapotranspiration (ETa) using the two -source energy balance (TSEB) model. However, irrigation scheduling decisions utilize daily ETa estimates, hence the need for time scaling. This study evaluated different upscaling methods to calculate daily maize ETa using one-time-of day Tr under varying water stress conditions. Mobile IRTs were mounted on a high clearance mobile sensing platform and collected Tr in remote locations under full, deficit and rainfed conditions. Seven scaling methods via two pathways were employed to obtain daily ETa. First pathway was scaling one-time-of-day Tr (SC) whereas the second pathway involved use of six upscaling methods of instantaneous ETa including: original and modified evaporative factor ((EF)o, (EF)m) as well as crop coefficient ((Kc)o, (Kc)m), direct canopy resistance (Direct-rc), and solar radiation ratio (Rn/Rs); and all were compared to a neutron-based soil water balance (SWB) determined ETa. From the results, SC outperformed other methods in comparison to SWB ETa across all the selected treatments with smaller discrepancies and lower RMSE (0.9-1.7 mm d-1 vs. 0.7-4.3 mm d-1 for other methods). Furthermore, methods including SC, (EF)o, (EF)m, and Rn/Rs had their daily average ETa values in close agreement to SWB ETa with mean ETa differences ranging between 0.2 and 1.6 mm d-1. Overall, SC method performed better in fully irrigated maize (r2 = 0.52, RMSE = 0.9 mm d-1) than in deficit irrigated maize ( r2 = 0.48, RMSE = 1.4 mm d-1) but worst in rainfed maize (r2 = 0.16, RMSE = 1.7 mm d-1). This implies that SC is more suited for irrigated rather than rainfed settings. Importantly, the choice of any method depends on data requirements, irrigation water management strategy, and ETa estimation accuracy.

There is a growing interest of using canopy temperature (T-c) based methods, including crop water stress index (CWSI), for irrigation management. However, different approaches exist to normalize T-c to microclimatic conditions, which can influence the accuracy and suitability of CWSI for irrigation scheduling. This study evaluated the performance of CWSI computation approaches and their sensitivity to changes in soil water depletion under different water stress levels. There were six different approaches - two empirical methods using developed lower baseline (i.e., CWSI-EB1, CWSI-EB2), two empirical methods using either artificial (CWSI-EA) or actual/natural (CWSI-EN) canopy reference surfaces, and two theoretical approaches which differ by how aerodynamic and canopy resistances are determined (CWSI-Th1, CWSI-Th2). Stationary infrared thermometers (IRTs) provided continuous T-c to calculate CWSI-EB, CWSI-Th, and CWSI-EN; whereas mobile IRTs and a thermal camera provided one-point-in-time T-c and temperatures of artificial canopy reference surfaces to calculate CWSI-EA. These measurements were all collected from full and deficit irrigated and rainfed maize plots in West Central Nebraska. Day-to-day variations within and across CWSI approaches were evident and their sensitivity to soil water depletion varied. Greater sensitivity and correlation strength to depletion (D-r,D-i) were observed with CWSI-Th and CWSI-EB under severe stress (i.e., D-r,D-i > 80%) at deeper soil depths of 1.8 and 2.1 m, producing r(2) which ranged from 0.61 to 0.80 (slope: 0.03-0.05) and 0.69-0.79 (slope: 0.03-0.04), respectively. Observed differences in stress magnitudes among approaches and treatments, warrants a specific irrigation triggering threshold for each approach. Additionally, developing a robust index coupling both CWSI and soil water depletion is desirable to improve irrigation water management by accounting for both soil and plant water status.