In the face of the climate change crisis, the increase in air temperature negatively impacts rice crop productivity due to stress from water scarcity. The objective of this study was to determine the rice crop water stress index (CWSI) and stomatal conductance (Gs) under different irrigation regimes, specifically continuous flood irrigation treatments (CF) and irrigations with alternating wetting and drying (AWD) at water levels of 5 cm, 10 cm, and 20 cm below the soil surface (${\mathrm{AWD}}_5{, \mathrm{AWD}}_{10}$, and ${\mathrm{AWD}}_{20}$) in an experimental area of INIA-Vista Florida and in six commercial areas of the Lambayeque region using thermal images captured with thermal sensors. The results indicated that AWD irrigation generated more water stress, with CWSI values between 0.4 and 1.0. Despite this, the yields were similar in CF and ${\mathrm{AWD}}_{20}.$In the commercial areas, CWSI values between 0.38 and 0.51 were obtained, with Santa Julia having the highest values. Furthermore, a strong Pearson correlation (R) of 0.91 was established between the CWSI and Gs, representing a reference scale based on Gs values for evaluating water stress levels.

Predicting rice yield accurately is crucial for enhancing farming practices and securing food supplies. This research aims to estimate rice yield in Peru’s Lambayeque region by utilizing spectral and textural indices derived from unmanned aerial vehicle (UAV) imagery, which offers a cost-effective alternative to traditional approaches. UAV data collection in commercial areas involved seven flights in 2022 and ten in 2023, focusing on key growth stages such as flowering, milk, and dough, each showing significant predictive capability. Vegetation indices like NDVI, SP, DVI, NDRE, GNDVI, and EVI2, along with textural features from the gray-level co-occurrence matrix (GLCM) such as ENE, ENT, COR, IDM, CON, SA, and VAR, were combined to form a comprehensive dataset for model training. Among the machine learning models tested, including Multiple Linear Regression (MLR), Support Vector Machines (SVR), and Random Forest (RF), MLR demonstrated high reliability for annual data with an R² of 0.69 during the flowering and milk stages, and an R² of 0.78 for the dough stage in 2022. The RF model excelled in the combined analysis of 2022–2023 data, achieving an R² of 0.58 for the dough stage, all confirmed through cross-validation. Integrating spectral and textural data from UAV imagery enhances early yield prediction, aiding precision agriculture and informed decision-making in rice management. These results emphasize the need to incorporate climate variables to refine predictions under diverse environmental conditions, offering a scalable solution to improve agricultural management and market planning.

In the context of global warming, rising air temperatures are increasing evapotranspiration (${\mathrm{ET}}_{\mathrm{c}}$) in all agricultural crops, including rice, a staple food worldwide. Simultaneously, the occurrence of droughts is reducing water availability, affecting traditional irrigation methods for rice cultivation (flood irrigation). The objective of this study was to determine ${\mathrm{ET}}_{\mathrm{c}}$ (water use) and yield performance in rice crop under different irrigation regimes: treatments with continuous flood irrigation (CF) and irrigations with alternating wetting and drying (${\mathrm{A}\mathrm{W}\mathrm{D}}_5$, ${\mathrm{A}\mathrm{W}\mathrm{D}}_{10}$, and ${\mathrm{A}\mathrm{W}\mathrm{D}}_{20}$) in an experimental area in INIA–Vista Florida. Water balance, rice physiological data, and yield were measured in the field, and local weather data and thermal and multispectral images were collected with a meteorological station and a UAV (a total of 13 flights). ${\mathrm{ET}}_{\mathrm{c}}$ values obtained by applying the METRIC^TM (Mapping Evapotranspiration at High Resolution using Internalized Calibration) energy balance model ranged from 2.4 to 8.9 mm d⁻¹ for the AWD and CF irrigation regimes. In addition, ${\mathrm{ET}}_{\mathrm{c}}$ was estimated by a water balance using the AquaCrop model, previously parameterized with RGB image data and field weather data, soil, irrigation water, and crops, obtaining values between 4.3 and 7.1 mm d⁻¹ for the AWD and CF irrigation regimes. The results indicated that AWD irrigation allows for water savings of 27 to 28%, although it entails a yield reduction of from 2 to 15%, which translates into an increase in water use efficiency (WUE) of from 18 to 36%, allowing for optimizing water use and improving irrigation management.

Accurate rice yield prediction is essential for optimizing water management and supporting decision-making in agricultural systems, particularly in arid environments where irrigation efficiency is critical. This study assessed five machine learning algorithms—Multiple Linear Regression (MLR), Support Vector Regression (SVR, linear and RBF), Partial Least Squares Regression (PLSR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—for plot-scale rice yield estimation using Sentinel-2 vegetation indices (VIs) during the 2022 and 2023 seasons in the Chancay–Lambayeque Valley, Peru. VIs sensitive to canopy vigor, water status, and structure were derived in Google Earth Engine and optimized via Sequential Forward Selection to identify the most relevant predictors per phenological stage. Models were trained and validated against field yields using leave-one-out cross-validation (LOOCV). Intermediate stages (Flowering, Milk, Dough) yielded the strongest relationships, with water-sensitive indices (NDMI, MSI) consistently ranked as key predictors. MLR and PLSR achieved the highest generalization (R²_CV up to 0.68; RMSE_CV ≈ 1.3 t ha⁻¹), while RF and XGBoost showed high training accuracy but lower validation performance, indicating overfitting. Model accuracy decreased in 2023 due to climatic variability and limited satellite observations. Findings confirm that Sentinel-2–based VI modeling offers a cost-effective, scalable alternative to UAV data for operational rice yield monitoring, supporting water resource management and decision-making in data-scarce agricultural regions.

In view of the growing scarcity of water for agriculture, the increase in food demand and future drought scenarios posed by climate change, it is essential to design new technologies that contribute to lower water consumption. In this research, high-resolution images have been used to estimate evapotranspiration in rice fields by applying the METRICTM (Mapping Evapotranspiration at High Resolution using Internalized Calibration) energy balance model. For this purpose, 5900 m2 of crop were monitored under continuous flood irrigation (CF) and 2600 m2 under alternate wetting and drying irrigation (AWD), in addition to some plots with lateral filtration. Ten flights were conducted between tillering and flowering phases, five flights with a Matrice 210 UAV equipped with a Parrot Sequoia multispectral camera, and five flights with a Matrice 300 RTK equipped with a H20T thermal camera. Field data were collected from vegetation indices (NDVI and LAI), and readings from a radiometer, to adjust information from multispectral and thermal images, respectively, and to obtain the components of the surface energy balance. Mean values for crop evapotranspiration (ETc) of 6.34 ± 1.49 and 5.84 ± 0.41 mm d-1 were obtained for IC irrigation and AWD irrigation, respectively, obtaining a water saving of 42% with a yield reduction of 14%, providing a guide for proper irrigation management, however, it is suggested to use the model to optimize yield by obtaining critical thresholds for optimal application of AWD in the face of water resource scarcity.

Introduction. Estimating crop evapotranspiration (ETc) helps determine water requirements, enabling the proposal of irrigation techniques that save water. Objective. To use remotely piloted aircraft system (RPAs) for greater precision in measuring evapotranspiration in rice fields, aiming to reduce water consumption. Materials and methods. The study utilized a randomized complete block design with a factorial structure of two experiments: flooded irrigation (E1) and irrigation with controlled drying (E2), and three rice varieties (IR43, IR71706, Sahod Ulan 12). The study was conducted at the Experimental Irrigation Area (AER) of the Universidad Nacional Agraria La Molina, Peru. Eight RPAS flights were carried out between January and February 2019, distributed between the tillering and cotton point stages. Results. The combined analysis of treatments using analysis of variance (ANOVA) and Duncan’s test with p < 0.05 revealed a significant difference in ETc between E1 and E2. However, no significant difference was found between the rice varieties. Maximum values of ETc and yield were obtained for E1 at 4.50 mm/day and 10389 kg/ha, and for E2 at 3.7 mm/day and 9710 kg/ha, respectively. Conclusions. The use of a remotely piloted aircraft system improved the temporal and spatial resolution of multispectral and thermal images, providing greater accuracy in crop evapotranspiration (ETc) under two irrigation regimes. Controlled drying irrigation resulted in a A 24% reduction in ETc, allowing for a water saving of 855 m3/ha.