A framework based on an input-yield model for greenhouse optimisation for varying environmental conditions

Localised food production in closed-environment agriculture, particularly greenhouses, ensures year-round food availability but requires resource optimisation, especially in arid climates. Therefore, this study proposes a framework based on an analytical input-yield model that dynamically determines the electricity, water, light, and CO2 requirements, along with the estimated yield, hourly throughout the crop's growth cycle. Furthermore, the framework is optimised to determine the decision variables that lead to the most cost-effective yield. Results from the study indicate the optimal decision variables: Al Ruwais as the preferred location, glass as the covering material, day and night temperatures set at 24 °C and 18 °C, plant density of 3.7 plants m−2, 80 % relative humidity, and CO2 concentration of 1050 ppm, lead to a yield of $2.70 kg−1. Moreover, parametric analysis demonstrates that greenhouse temperature and CO2 have the most significant impact on the yield. The overall results establish the potential for cost-effective food production in arid climates by optimising the greenhouse parameters. The proposed framework is flexible as it can be used to determine optimum parameters for food production across different geographical locations and climatic conditions.