Plant responses to salinity can be used to manage irrigation with brackish water. This study evaluated effects of brackish water irrigation on tomato ( Solanum lycopersicum L.) plants, proposed a new model to describe plant relative evapotranspiration (ET) and relative yield as a function of electrical conductivity of soil saturated paste extract (EC _e ), and compared the estimations of the new model with four other models (linear model of Mass and Hoffman, and three nonlinear models of van Genuchten, Dirksen, and Homaee). A greenhouse experiment was conducted with five salinity treatments: tap water of EC 0.6, brackish groundwater of EC 2 and 3, and reverse osmosis (RO) of EC 4 and 6 dS m ⁻¹ . Results indicated no changes in leaf photosynthesis rate ( P _n ), transpiration rate ( T _r ), and stomatal conductance ( G _s ) up to 2 dS m ⁻¹ irrigation water salinity. Nonlinear models better described tomato yield and seasonal ET. The proposed new model performed best when compared with four previous models to predict tomato yield and ET responses to salinity. The calculated soil salinity threshold values (EC _e ^* ) from the new model were 1.73 dS m ⁻¹ for yield and 2.52 dS m ⁻¹ for ET. Data from published studies on effects of irrigation water salinity (EC _w ) on tomato yield and ET were analyzed to validate models. The results showed that the new model was simpler and superior in estimating plant responses to EC _w . Tomato is a salt‐sensitive crop, and irrigation with brackish waters adversely influences growth and ET.

Soil moisture prediction remains a major challenge in arid and semi‐arid agroecosystems, where highly variable rainfall, intermittent irrigation, and heterogeneous soil properties create strong nonlinearities in soil‐water dynamics. Reliable forecasts and interpretable models are needed for optimizing irrigation scheduling and improving water‐use efficiency under such conditions. The purpose was development of a novel, dual‐resolution framework that integrates machine learning (ML) and deep learning (DL) approaches with explainable artificial intelligence to evaluate soil moisture dynamics under different cover crop treatments. A 2‐year field experiment was conducted in Clovis, NM, where soil moisture, temperature, and meteorological variables were continuously monitored across five treatments: fallow, pea, oat, pea–oat mixture, and a six‐species mixture. ML models (random forest, light gradient boosting machine [LGBM], and eXtreme gradient boosting) and DL models, such as long short‐term memory and Transformer models, were trained on both daily and 5‐min datasets. SHapley Additive exPlanations (SHAP) indicates that features from the three most recent days had the strongest influence on daily predictions, informing a 72‐h lookback for short‐term modeling. LGBM achieved the highest accuracy, while Transformer reduced temporal lag in high‐frequency predictions. SHAP analysis further identified treatment‐specific sensitivities and scale‐dependent relationships between environmental variables and soil moisture content, with historical soil moisture and total water input dominating daily predictions, and solar radiation, temperature, and recent water events prevailing at finer scales. This framework improves both predictive accuracy and interpretability of soil water dynamics, supporting data‐driven irrigation and crop management in arid and semi‐arid regions.

Water scarcity is a major problem for crop production around the world including Southwestern United States and growers are increasingly using groundwater for agriculture in Southern New Mexico. Most of the groundwater in New Mexico is brackish and continuous long-term use could lead to salt accumulation in the soil. Reverse osmosis (RO) can desalinate brackish groundwater (BGW), however, environmentally safe disposal of RO concentrate is costly. This greenhouse study evaluated the effects of BGW and RO concentrate at various growth stages of two chile pepper cultivars, NuMex Joe E. Parker and NuMex Sandia Select. Five salinity treatments were applied to plants, three of them used saline waters of 0.6 (control), 4.0 (BGW), and 8.0 dS/m (RO) throughout the growing season, whereas the other two changed waters of 4.0 and 8.0 dS/m to waters of 2.0 and 6.0 dS/m from the beginning of the flowering stage. Number of flowers, days to flowering, relative plant heights, relative fresh biomass, fruit yields, photosynthetic rate (Pn), stomatal conductivity ( g _S ), and actual evapotranspiration (ETa) significantly decreased with increasing irrigation water salinity levels. Concentrations of Mg ²⁺ , Na ⁺ , and Cl ⁻ in plants increased with increasing water salinity levels. Changing to irrigation with reduced salinity waters of 2.0 and 6.0 dS/m at the flowering stage initiated reproductive development more rapidly and alleviated the adverse influence of salinity on the number of flowers of chile pepper, plant height, Pn, as well as fresh shoot and fruit weight than that with continuous irrigation with electrical conductivity (EC) of 4.0 dS/m and 8.0 dS/m beyond the flowering stage. Irrigation that practices a change from high salinity to lower salinity at the flowering stage can optimize the use of saline irrigation water for growing chile peppers.

Crop coefficient (K_c), the ratio of crop evapotranspiration (ET_c) to reference evapotranspiration (ET_o), is used to schedule an efficient irrigation regime. This research was conducted to investigate variations in ET_c and growth-stage-specific K_c in flood-irrigated winter wheat as a forage crop from 2021 to 2023 in the Lower Rio Grande Valley of southern New Mexico, USA, and evaluate the performances of two temperature-based ET_o estimation methods of Hargreaves–Samani and Blaney–Criddle with the widely used Penman–Monteith method. The results indicated that the total ET_c over the whole growth stage for flood-irrigated winter wheat was 556.4 mm on a two-year average, while the average deep percolation (DP) was 2.93 cm and 2.77 cm, accounting for 28.8% and 27.2% of applied irrigation water in the 2021–2022 and 2022–2023 growing seasons, respectively. The ET_o over the growing season, computed using Penman–Monteith, Hargreaves–Samani, and Blaney–Criddle equations, were 867.0 mm, 1015.0 mm, and 856.2 mm in 2021–2022, and 785.6 mm, 947.0 mm, and 800.1 mm in 2022–2023, respectively. The result of global sensitivity analysis showed that the mean temperature is the main driving factor for estimated ET_o based on Blaney–Criddle and Hargreaves–Samani methods, but the sensitivity percentage for Blaney–Criddle was 76.9%, which was much higher than that of 48.9% for Hargreaves–Samani, given that Blaney–Criddle method is less accurate in ET_o estimation for this area, especially during the hottest season from May to August. In contrast, wind speed and maximum temperature were the main driving factors for the Penman–Monteith method, with sensitivity percentages of 70.9% and 21.9%, respectively. The two-year average crop coefficient (K_c) values at the initial, mid, and late growth stage were 0.54, 1.1, and 0.54 based on Penman–Monteith, 0.51, 1.0 and 0.46 based on Blaney–Criddle, and 0.52, 1.2 and 0.56 based on Hargreaves–Samani. The results showed that the Hargreaves–Samani equation serves as an alternative tool to predict ET_o when fewer meteorological variables are available. The calculated local growth-stage-specific K_c can help improve irrigation water management in this region.

Crop coefficient (Kc), the ratio of crop evapotranspiration (ETc) to reference evapotranspiration (ETo), is used to schedule an efficient irrigation regime. This research was conducted to investigate variations of ETc and growth-stage-specific Kc in a flood-irrigated winter wheat as a forage crop from 2021 to 2023 in the Lower Rio Grande Valley of southern New Mexico, USA, and evaluate the performances of the two temperature-based ETo estimation methods of Hargreaves-Samani and Blaney-Criddle with the widely used Penman-Monteith method. Results indicated that the total ETc over the whole growth stage for flood-irrigated winter wheat was 556.4 mm on a two-year average, while the average deep percolation (DP) was 2.93 cm and 2.77 cm, accounting for 28.8% and 27.2% of applied irrigation water, respectively in the 2021-22 and 2022-23 growing seasons. The ETo over the growing season computed by Penman-Monteith, Hargreaves-Samani, and Blaney-Criddle equations were 867.0 mm, 1015.0 mm, and 856.2 mm in 2021-22, and 785.6 mm, 947.0 mm, and 800.1 mm in 2022-23, respectively. The Hargreaves-Samani method performed better than the Blaney-Criddle method with higher statistical indicators of model efficiency coefficient (CE), and smaller mean bias error (MBE), mean absolute error (MAE), and the root mean square error (RMSE) in both growing seasons. The result of a global sensitivity analysis showed that the mean temper-ature is main driving factor for estimated ETo based on Blaney-Criddle and Hargreaves-Samani, but the sensitivity percentage for Blaney-Criddle was 76.9%, which was much higher than that of 48.9% for Hargreaves-Samani, given that Blaney-Criddle method is less accurate in ETo estimation for this area especially during the hottest season from May to August. In contrast, wind speed and maximum temperature were the main driving factors for the Penman-Monteith method, with sensitivity percentages of 70.9% and 21.9%, respectively. The two-year average crop coefficient (Kc) values at the initial, mid, and late growth stage were 0.54, 1.1, and 0.54 based on Penman-Monteith, 0.51, 1.0 and 0.46 based on Blaney-Criddle, and 0.52, 1.2 and 0.56 based on Hargreaves-Samani. Results showed that the Hargreaves-Samani equation serves as an alternative tool to predict ETo when fewer meteorological variables are available. The calculated local growth-stage-specific Kc can help improve irrigation water management in this region.