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.

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.

Twelve rice genotypes were studied in field conditions, in flooded conditions and in moderate and severe drought environments. Split-parcel design with two repetitions was used, locating in parcels the treatment irrigation managements (three) and in sub-parcel the genotypes (twelve), with a separation of ten meters for controlling the lateral filtration.Direct seeding was done in rows, using a density of 80 Kg. ha-1. Yield in grain was adjusted to 14 % of moisture. For each genotype was determined the tolerance to drought using the following selection indexes: Stress Susceptibility Index (SSI), Geometric Mean Productivity (GMP), Mean Productivity (MP), Harmonic Mean (HM), Stress Tolerance Index (STI), Yield Index (YI), Yield Stability Index (YSI), and Index of Drought Resistance (IDR).Significant differences were observed between yield in drought conditions and stress in the Geometric Mean Productivity (GMP), Harmonic Media (HM), and Mean Productivity (MP). The correlation coefficients indicate that these indexes could be used to identify rice genotypes tolerant to drought. The analysis of tridimensional graphics indicate that IR71706 is the most tolerant genotype to drought, and can be sown in irrigation conditions and dryness.Producción Agropecuaria y Desarrollo Sostenible, Vol. 2, 2014: 11-31

El objetivo fue evaluarel desempeño económico del uso de recursos productivos y del agua en sistemas de siembra directa e indirecta (trasplante) en el cultivo del arroz, además contrastar experimentos de asignación de agua de riego y su aplicación en parcelas de agricultores. El estudio abordó las campañas agrícolas 2019, 2020 y 2021 en la Provincia de Ferreñafe, Lambayeque-Perú y usa datos primarios de encuestas aplicadas a 272 productores, registros de experimentos con tres tipos de riegoyde parcelas de productores,con ellos se calcularon costos de producción y rentabilidad del cultivo. Se evidencia que en la cosecha 2019 la siembra directa mostró menor costo de producción (1.228,6USD/ha)que la siembra indirecta (1.734,1USD/ha), ygeneró mayor utilidad económica (599,0USD/ha) comparado con siembra indirecta (289,6USD/ha). Parael 2021 el costo de producción fue similar al 2019, pero los rendimientos fueron superiores debido a la ausencia de plagas, el 2020 los experimentos tuvieron costos elevados. Se concluye que las políticas públicas deben promover la siembra directa pues utilizanmenos agua de riego y tienen mayor rentabilidad, y para el uso delagua de riego en siembra indirecta debenaplicarse instrumentos económicos para la asignación delrecurso.