The cultivation of rice is one of the main sources of greenhouse gas (GHG) emissions due to continuously flooded irrigation (CF), which demands large volumes of water. As an alternative solution, alternate wetting and drying (AWD) irrigation has been developed as a water-saving strategy. This study was conducted at the Experimental Agricultural Station (EEA) in Vista, Florida, in the Lambayeque region located on the northern coast of Peru. Thus, it was analyzed the effect of AWD irrigation at different depths (5, 10, and less than 20 cm below the surface) compared to CF control on methane (CH₄) and nitrous oxide (N₂O) emissions and rice grain yield. AWD treatments reduced CH₄ emissions by 84% to 99% but increased N₂O emissions by 66% to 273%. In terms of Global Warming Potential (GWP), the AWD₁₀ treatment demonstrated a 77% reduction and a Water Use Efficiency (WUE) of 0.96, affecting only a 2% decrease in rice grain yield, which ranged between 11.85 and 14.01 ${\mathrm{t} \mathrm{ha}}^{-1}$. Likewise, this study provides sufficient evidence for the adoption of AWD irrigation as a strategy for the efficient use of water resources and the mitigation of GHG emissions in rice cultivation in the study area, compared to continuous flooded irrigation.

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.

Global warming is influenced by an increase in greenhouse gas (GHG) concentration in the atmosphere. Consequently, Net Ecosystem Exchange (NEE) is the main factor that influences the exchange of carbon (C) between the atmosphere and the soil. As a result, agricultural ecosystems are a potential carbon dioxide (CO₂) sink, particularly rice paddies (Oryza sativa). Therefore, a static chamber with a portable CO₂ analyzer was designed and implemented for three rice plots to monitor CO₂ emissions. Furthermore, a weather station was installed to record meteorological variables. The vegetative, reproductive, and maturation phases of the crop lasted 95, 35, and 42 days post-sowing (DPS), respectively. In total, the crop lasted 172 DPS. Diurnal NEE had the highest CO₂ absorption capacity at 10:00 a.m. for the tillering stage (82 and 89 DPS), floral primordium (102 DPS), panicle initiation (111 DPS), and flowering (126 DPS). On the other hand, the maximum CO₂ emission at 82, 111, and 126 DPS occurred at 6:00 p.m. At 89 and 102 DPS, it occurred at 4:00 and 6:00 a.m., respectively. NEE in the vegetative stage was −25 $\mathsf{\mu }\mathrm{m}\mathrm{o}\mathrm{l}\mathrm{C}\mathrm{O}2 {\mathrm{m}}^2 {\mathrm{s}}^{-1}$, and in the reproductive stage, it was −35 $\mathsf{\mu }\mathrm{m}\mathrm{o}\mathrm{l}\mathrm{C}\mathrm{O}2 {\mathrm{m}}^2 {\mathrm{s}}^{-1}$, indicating the highest absorption capacity of the plots. The seasonal dynamics of NEE were mainly controlled by the air temperature inside the chamber (Tc) (R = −0.69), the relative humidity inside the chamber (RHc) (R = −0.66), and net radiation (R_n) (R = −0.75). These results are similar to previous studies obtained via chromatographic analysis and eddy covariance (EC), which suggests that the portable analyzer could be an alternative for CO₂ monitoring.

Alternate wetting and drying (AWD) irrigation reduces CH₄ emissions from flooded rice but amplifies N₂O pulses; identifying candidate phenological windows for the remote screening of greenhouse gas (GHG) fluxes remains challenging with small datasets. In a single-site, single-season exploratory study at INIA Vista Florida (Lambayeque, Peru), eight UAV flights were paired with eight PlanetScope SuperDove scenes ($|\Delta |\le 1$ d) and closed-chamber CH₄, N₂O and CO₂ fluxes under four water regimes (CF, AWD₅, AWD₁₀, AWD₂₀; 96 sub-plot × date observations). Multivariate explanatory power was assessed by bootstrap Ridge regression on each sensor’s native predictors (VI + GLCM + $T_{\mathrm{mean}}$ for the UAV, VI for PlanetScope). Maximum tillering (79 DAS) emerged as a candidate UAV window, ranking in the top three for all gases through GLCM textures, whereas PlanetScope peaked at Mid-boot and Late-boot (103–107 DAS), with median ${\widetilde{R^2}}_{\mathrm{UAV}}$ at $0.34$–$0.71$ and ${\widetilde{R^2}}_{\mathrm{Planet}}$ at $0.20$–$0.60$. Nested Leave-One-Plot-Out (LOPO) validation gave $R_{\mathrm{CV}}^2$ between $+0.57$ and $+0.69$ for four of six platform × gas combinations (UAV-CH₄ and Planet-N₂O stayed weak), and $T_{\mathrm{mean}}$ was decisive for N₂O on the UAV ($\Delta R^2=+0.48$). Repeating the stage selection inside every LOPO fold preserved the leading combinations and their ranking. These exploratory windows and sensor-native descriptors need multi-site, multi-season validation before operational use.

Precise estimation of evapotranspiration (ET) is essential for sustainable water management in arid agroecosystems, particularly for high-water-demand crops such as rice. This study integrated very-high-resolution UAV thermal–multispectral imagery with a Two-Source Energy Balance model (UAV–TSEB) and a field-calibrated AquaCrop model to quantify daily ET and its components under continuous flooding on the arid Peruvian coast during the 2024–2025 season. A network of 24 drainage lysimeters provided an independent observational benchmark (ET_lys); to represent the treatment-level response, lysimeter observations were aggregated as the mean across the 24 units for each UAV campaign. Thirteen UAV surveys supplied radiometric surface temperature and biophysical inputs (e.g., NDVI and fractional cover) to derive spatially explicit ET, while AquaCrop provided continuous daily simulations between flight dates. Direct lysimeter-based validation indicated high agreement for AquaCrop (R² = 0.85; RMSE = 0.26 mm d⁻¹; MBE = 0.01 mm d⁻¹) and moderate agreement for UAV–TSEB (R² = 0.66; RMSE = 0.81 mm d⁻¹; MBE = 1.01 mm d⁻¹). Model intercomparison further showed consistent temporal dynamics of ET (R² = 0.70; RMSE = 1.35 mm d⁻¹) and robust partitioning of crop transpiration (R² = 0.79; RMSE = 0.99 mm d⁻¹) and soil evaporation (R² = 0.76; RMSE = 1.03 mm d⁻¹) while revealing a systematic divergence under near-complete canopy cover: AquaCrop tended to suppress evaporation, whereas UAV–TSEB detected residual evaporation from the flooded surface. Overall, the results highlight the complementarity of both approaches—UAV–TSEB as a spatial diagnostic tool and AquaCrop as a temporally continuous simulator—providing a robust framework for ET monitoring, flux partitioning, and water-use-efficiency assessment in water-scarce rice systems.

Popping expansion is the main quality trait determining the commercial value of popcorn maize, yet its evaluation requires destructive grain sampling. We investigated whether multitemporal UAV multispectral and textural features could predict grain yield and popping expansion in a native population of Peruvian popcorn and its five purple-kernel corn hybrids grown in 16 drainage lysimeters (80 subplots) under controlled irrigation in Lima, Peru. Eight UAV flights were conducted between 50 and 117 days after sowing, and 8 vegetation indices plus 5 GLCM texture metrics were extracted from canopy-masked imagery. Six regression algorithms were trained using Sequential Forward Selection (SFS; applied to five of six algorithms) and validated by Leave-One-Lysimeter-Out cross-validation (LOGO). Early grain, grain filling, and maturity were the most informative stages for yield prediction. The best model, obtained at maturity, was SVR-rbf using SCCCI and Homogeneity, reaching R² = 0.66 and RMSE = 1.23 t ha⁻¹. SCCCI was the most consistently selected predictor across models. By contrast, popping expansion was poorly predicted (R² = 0.17), indicating that this post-harvest quality trait is only weakly linked to canopy-level spectral information. Multitemporal UAV phenotyping therefore shows promise for non-destructive yield screening, but not for replacing direct popping expansion measurements.