Legumes represent a sustainable alternative for the production of nutritious foods, contributing to agroecosystem resilience, biodiversity conservation, and climate change mitigation. Nonetheless, the productivity and nutritional quality of legumes are strongly influenced by environmental factors. These environmental conditions, together with crop management practices, explain a large proportion of the variability observed in chemical composition, highlighting the importance of genotype × environment interactions. Therefore, the aim of this study was to evaluate the effects of contrasting environments on the protein, fat, and quinolizidine alkaloid contents of Andean lupin (Lupinus mutabilis) seeds. Six genotypes of different origins and alkaloid contents were studied. The genotypes were evaluated across five contrasting field and greenhouse environments using a randomized complete block design with three replications. Chemical analyses of the legume grain were carried out in the Nutrition and Quality laboratory at INIAP’s Santa Catalina Experimental Station (INIAP-EESC). The results of the study indicate that the protein content was affected primarily by the environment, followed by the genotype × environment interaction and, to a lesser extent, by genotype. Fat content was similarly influenced by both genotype and environmental conditions, whereas total alkaloid content was predominantly controlled by genotype, which explained approximately 90.7% of the observed variation. Protein content ranged from 49.9 to 56.8 g 100 g⁻¹ DW across environments, while fat content varied between 14.0 and 18.3 g 100 g⁻¹ DW. Among the evaluated genotypes, AndxPro1 exhibited the highest average protein content (54.96 g 100 g⁻¹ DW), whereas LmPro17 combined the highest fat content (19.78 g 100 g⁻¹ DW) with the lowest total alkaloid concentration (0.13 g 100 g⁻¹ DW). Protein content showed high sensitivity to environmental conditions, whereas alkaloid accumulation exhibited strong genetic control and comparatively limited environmental influence. These findings suggest the importance of conducting evaluations of the different varieties of L. mutabilis in different environments to support breeding programs aimed at improving seed quality.

Operational production planning has become a key factor in a company’s competitiveness, enabling efficient resource management and a rapid response to fluctuations in demand. However, many manufacturing organizations base their decisions on a conceptual perspective, creating imbalances between supply and demand. In this context, this study proposes a hybrid framework that integrates demand forecasting techniques and discrete-event simulation to optimize production planning at a dairy plant under scenarios involving random demand. First, a predictive model was developed using ARIMA as a baseline for comparison, and then parameters were adjusted in neural networks to estimate demand. The results showed that neural networks performed better, specifically the RN2 model, in generating future projections. Subsequently, the estimates were used as inputs for the simulation model to represent the company’s current system, enabling the identification of bottlenecks and the evaluation of improvement scenarios. The findings indicated that the company’s current capacity is insufficient to meet current and projected demand. Similarly, the “Assembly” and “Quality Control” stations were identified as the main operational constraints. The proposed improvement increased production capacity by 27.7%, enabling the company to meet demand. The evidence shows that integrating forecasting with simulation is an effective tool, as it provides data and scenarios that closely reflect reality, allowing companies to anticipate constraints and, most importantly, reduce risks and make more informed investment decisions.