The cultivation of potatoes is essential for rural food security, and the use of Unmanned Aerial Vehicle Red-Green-Blue (UAV-RGB) imagery allows for precise and cost-effective estimation of yield and identification of varieties, overcoming the limitations of manual assessment. We evaluated four INIA varieties (Bicentenario, Canchán, Shulay and Tahuaqueña) by integrating agronomic measurements (height, number and weight of tubers, leaf health) with color and textural indices derived from RGB orthomosaics. Yield prediction was modeled using Random Forest (RF) and Gradient Boosting (GB); varietal identification was approached with (i) a Convolutional Neural Network (CNN) that classifies RGB images and (ii) classical models such as Random Forest, Support Vector Machines (SVMs), K-Nearest Neighbors (KNNs), Decision Trees and Logistic Regression trained on EfficientNetB0 embeddings. The results showed significant genotypic differences in yield (p < 0.001): Tahuaqueña 13.86 ± 0.27 t ha⁻¹ and Bicentenario 6.65 ± 0.27 t ha⁻¹. The number of tubers (r = 0.52) and plant height (r = 0.23) correlated with yield; RGB indices showed low correlations (r < 0.3) and high redundancy (r > 0.9). RF achieved a better fit (Coefficient of determination, R² = 0.54; Root Mean Square Error, RMSE = 2.72 t ha⁻¹), excelling in stolon development (R² = 0.66) and losing precision in maturation due to foliar senescence. In classification, the CNN and RF on embeddings achieved F1-macro ≈ 0.69 and 0.66 (Receiver Operating Characteristic—Area Under the Curve, ROC AUC RF = 0.89), with better identification of Bicentenario and Shulay. We conclude that UAV-RGB is a cost-effective alternative for phenotypic monitoring and varietal selection in high Andean contexts. These findings support the integration of UAV-RGB imagery into breeding and monitoring pipelines in resource-limited Andean systems.

Organic quinoa (Chenopodium quinoa Wild) cultivation embodies a holistic agricultural approach, integrating biological fertilizers to curtail reliance on insecticides and synthetic fertilizers and low levels of greenhouse gases. The objective of this paper was to identify the socioeconomic factors that determine smallholder farmer organic quinoa production. The socioeconomic factors of organic quinoa farmer in the district of San Jerónimo, Apurímac in Southern Peru associated with five groups of organic quinoa farmers. Primary data were collected from 109 smallholder farmers belonging to quinoa producers' associations, using a non-experimental and cross-sectional study design, that was analyzed with descriptive, correlational statistics and a logistic regression method involving the evaluation of 13 independent variables. Motivational factors are identified through the application of a qualitative and quantitative sequence of mixed methods design. The results show that high price is the most important explanatory variable, and it is also the one that smallholders primarily consider when cultivating organic quinoa. The second most important variables motivating such farmers are social factors, mainly those related to health benefits, food quality and lastly environmental benefits. The variables: quinoa income, distance to the land, membership in an association, technical assistance and mixed (own and hired) labor had an inverse relationship with organic quinoa production while land ownership had a positive but less significant effect on the production of organic quinoa (p < 0.1). In conclusion, organic quinoa producers are primarily influenced by financial reasons, followed by considerations of sustainability and the desire to obtain healthy, pesticide-free food for self-consumption. These findings of important factors in the adoption of organic agriculture by producer associations and the motivational aspects found for its continued production could be considered in agricultural policy proposals in the face of a world with greater demands for food and environmental protection.

Introduction. Avocado cultivation is susceptible to water stress during the flowering stage and initial fruit growth. This stress can cause premature fruit drop and accentuate the alternation in production. Objective. To determine the crop water stress index (CWSI) in three avocado cultivars (Hass, Fuerte, and Zutano) under different irrigation conditions using infrared thermography. Materials and methods. The study was conducted in the autumn of 2023, at the Estación Experimental Agraria Canaán, Ayacucho, Peru. A total of 170 four-year-old avocado trees in the reproductive phase were selected. Five evaluations were carried out under different irrigation conditions, where data was collected after 14 and 7 days without irrigation (WI-14d and WI-7d) and at 12, 2, and 1 hour post-irrigation (AI-12h, AI-2h, and AI-1h, respectively). Leaf temperature (Th), stomatal conductance (gs), soil moisture (θ), and CWSI were recorded. Results. The three evaluated cultivars showed significant differences in Th depending on the irrigation management. These variations, influenced by ambient temperature and humidity, established stress thresholds that defined the CWSI within a range of 0.34 to 0.96. By correlating the CWSI with gs and θ, values of R2 = 0,51 y R2 = 0,61 were obtained, respectively. The Zutano cultivar exhibited the highest CWSI values during prolonged water restriction periods and showed a limited ability to reduce these values after rehydration, differing from the other cultivars. Conclusions. Infrared thermography proved to be an effective too for measuring the CWSI in avocado cultivars, reveling significant variations in Th based on irrigation conditions. Adicionally, the Zutano cultivar was the lest resilient to water stress.