The quantification of evaporation is very important for water resource management, determination of water availability and environmental modeling. Evaporation (E) is a key component of the hydrological cycle, which becomes more relevant under a changing climate scenario, where increases in temperature and E are projected on a regional scale. This study evaluated the hourly applicability of 21 methods to estimate E in a mountain lake. Models were grouped as combination, solar radiation-temperature and mass transfer methods. Estimated E by all models were compared to three measurement campaigns with an eddy covariance system in Lake Laja during the month of January (22 to 28), March (17 to 23) and November (09 to 15) of 2016. The average evaporated water during the measurement sessions were 3.40, 3.38 and 1.89 mm d⁻¹, respectively. Best model performance was obtained with models whose main E principles are heat flow and water vapor flow. The best performance in this group was the Penman model using a calibrated wind function with a determination coefficient (R²) of 0.91, Nash–Sutcliffe coefficient (NS) of 0.9, and index of agreement (W) of 0.98. Comparing daytime and nighttime hours, it was found that the daytime E in the lake is best explained by the product of the vapor pressure deficit and wind speed, while the nighttime E was explained by the flow of heat in the water. The results highlight the importance of the analysis of diurnal dynamics of water flux and energy stored in water to better understand the E in water bodies.

Water availability, a vital factor in agriculture, is being threatened by shortages caused by droughts, desertification, and climate change. In this context, hydrogels, polymers with high water absorption and retention capacity, emerge as key solutions to improve water use efficiency in irrigation. This study investigated the effect of a natural superabsorbent hydrogel (TG) at different concentrations (0.3%, 0.5%, 0.8%, and 1.0%) on the water retention capacity in three coarse-textured soils and one medium-textured soil. Results are compared to an existing commercial polymer (COM). The evaluation shows that both polymers increase the values of the water retention curve; however, higher values were observed with TG. The addition of both hydrogels was compared in the increase of the available water capacity (AWC). For sand soil, the TG obtained a statistically significant difference of a 26% greater AWC when compared to the COM superabsorbent. Soils with a higher percentage of sand tend to obtain a greater improvement in the AWC. There is a proportional relationship of the increase in the AWC when increasing hydrogel concentration ($R^2$ = 0.74). Sand soil with a concentration of 0.5% of the TG showed 17.8% of the AWC, while for a concentration of 1.0% of this hydrogel, a value of 35.8% of the AWC was obtained. However, in soils with higher clay content, such as silt loam, the effect was smaller. Finally, the time required to reach a permanent wilting point (PWP) was compared. The loam sand soil increased the time to reach the PWP by more than three times using the TG or COM when compared to the same soil without any hydrogel concentration. In conclusion, the TG significantly improves water retention in coarse and medium-textured soils, making it a promising solution for improving water efficiency in agriculture, especially in drought-affected regions. However, additional field studies are needed to evaluate its long-term durability and environmental compatibility, ensuring its applicability under real agricultural conditions.

Crop yield forecasting allows farmers to make decisions in advance to improve farm management and logistics during and after harvest. In this sense, crop yield potential maps are an asset for farmers making decisions about farm management and planning. Although scientific efforts have been made to determine crop yields from in situ information and through remote sensing, most studies are limited to evaluating data from a single date just before harvest. This has a direct negative impact on the quality and predictability of these estimates, especially for logistics. This study proposes a methodology for the early prediction of tomato yield using decision tree ensembles, vegetation spectral indices, and shape factors from images captured by multispectral sensors on board an unmanned aerial vehicle (UAV) during different phenological stages of crop development. With the predictive model developed and based on the collection of training characteristics for 6 weeks before harvest, the tomato yield was estimated for a 0.4 ha plot, obtaining an error rate of $9.28$%.

A surface energy balance model was conceived to estimate crop transpiration and soil evaporation in orchards and vineyards where the floor is partially wetted by micro-irrigation systems. The proposed surface energy balance model for partial wetting (SEB-PW) builds upon previous multiple-layer modelling approaches to estimate the latent, sensible, and soil heat fluxes, while partitioning the total evapotranspiration ( $ET$ ) into dry and wet soil evaporation ( $\mathsf{\lambda }{\mathrm{E}}_{soil}$ ) and crop transpiration ( $T$ ). The model estimates the energy balance and flux resistances for the evaporation from dry and wet soil areas below the canopy, evaporation from dry and wet soil areas between plant rows, crop transpiration, and total crop $ET$ . This article describes the model development, sensitivity analysis and a preliminary model evaluation. The evaluation shows that simulated hourly $ET$ values have a good correlation with field measurements conducted with the surface renewal method and micro-lysimeter measurements in a micro-irrigated winegrape vineyard of Northern California for a range of fractional crop canopy cover conditions. Evaluation showed that hourly $LE$ estimates had root mean square error ( $RMSE$ ) of 58.6 W m⁻², mean absolute error ( $MAE$ ) of 35.6 W m⁻², Nash-Sutcliffe coefficient ( $C_{NS}$ ) of 0.85, and index of agreement ( $d_a$ ) of 0.94. Daily soil evaporation ( $E_s$ ) estimations had $RMSE$ of 0.30 mm d⁻¹, $MAE$ of 0.24 mm d⁻¹, $C_{NS}$ of 0.87, and $d_a$ of 0.94. $E_s$ estimation had a coefficient of determination ( $r^2$ ) of 0.95, when compared with the micro-lysimeter measurements, which showed that $E_s$ can reach values from 28% to 46% of the total $ET$ after an irrigation event. The proposed SEB-PW model can be used to estimate the effect and significance of soil evaporation from wet and dry soil areas on the total $ET$ , and to inform water balance studies for optimizing irrigation management. Further evaluation is needed to test the model in other partially wetted orchards and to test the model performance during all growing seasons and for different environmental conditions.

This study proposes a nonlinear programming model for the optimization of water and land allocation in a 1000 ha orchard over a 20-year period to maximize farmers’ net profits. Different scenarios were evaluated, including equitable and unrestricted land allocation, and the risks associated with fruit production were considered. Additionally, a sensitivity analysis that focused on the variability of labor and water availability was conducted. The results reveal that with equitable land allocations and no constraints on the cultivated area, cherry emerges as the most profitable crop, although there are large risks associated with its price volatility. The introduction of risk and land allocation constraints highlights the importance of crop diversification in mitigating economic risks. A sensitivity analysis indicated that reductions in water and labor availability significantly affect the optimal cropping pattern of an orchard, suggesting that the efficient and adaptive management of resources is required. The proposed optimal cropping pattern maintains the economic viability of the orchard even with 70% and 24% reductions in water and labor, respectively. This approach underscores the importance of implementing resilient and sustainable agricultural strategies to ensure food security and increase economic stability in the face of changing climatic and labor conditions.