Datasets Horticulturae 2023. Predictive Model

Abstract

Agriculture is the main driver of depletion resources worldwide, and its duty is to ensure food security within a rapidly increasing demographic and urbanization, so it is important to transi-tion to sustainable production systems. Vertical crops (VCs) can reduce the pressure on conven-tional agriculture because they save water and nutrients and increase crop yield. Therefore, this study aimed to validate a proposed predictive model (PM) to simulate water and nutrient uptake in vertical crops under greenhouse conditions. Based on the Penman-Monteith equation, PM es-timates transpiration, while nutrient uptake was estimated using the Carmassi-Sonneveld sub-model. PM was experimentally evaluated for vertically grown lettuce under Mediterranean greenhouse conditions, during spring 2023. The irrigation technique was a closed-loop fertiga-tion circuit. The experimental consisted of testing two densities (50 and 80 plants·m-2), where each unit of the experiment unit was divided into three heights (lower, medium, and upper). It performed ANOVA with a value of p < 0.05 and R2 to assess PM performance. The results sug-gest a high degree of PM, since R2 ranged from 0.6 to 0.8 for the uptake of water and nutrients. Both densities had a yield between 17-20 times higher than conventional lettuce production and significant savings in water, between 85-88%. In this sense, PM has great potential to intelli-gently manage VC fertigation, saving water and nutrients, which represents an advance towards reaching SDG 6 and SDG 12, within the 2030