An agent-based model to evaluate agricultural vulnerability and risk facing climate change in strawberry production

Human activity has created an environmental imbalance, which is reflected in climate change and resulting temperature variations, harmful levels of air quality, and more frequent and more intense meteorological phenomena, thus having significant consequences on the agricultural sector, for example, changes in soil, climate, and water characteristics as well as in available resources for agricultural production. This paper presents an agent-based model (ABM) to evaluate the behavior of the strawberry's (Fragaria x ananassa) agricultural system in Veracruz, Mexico, to determine the degree of existing vulnerability through a dynamic assessment of agricultural risk. Development of the ABM allows the evaluation of variations in the active entities (critical variables of the model) to determine the factors that present a better crop yield. The research showed that temperature and precipitation are determining factors in strawberry quality, as they have a direct relationship with the growth and different phenological stages of the crop from an early age to a stage of greater maturity from the budding, flowering, and fruiting stages of the strawberry. The application of artificial intelligence plays a key role in optimizing parameters related to planting and harvesting.The agent-based model allows the identification of parameters of interest to increase soil quality.The convolutional neural networks make it possible to detect areas of potential risk affecting strawberry crop yields.