Irrigation management is a key factor in attaining optimal yields, as different irrigation strategies lead to different yields even when using the same amount of water or under the same weather conditions. Our research aimed to simulate the water-use efficiency (WUE) of crops considering different irrigation strategies in the Central Valley of Chile. By means of AquaCrop-OS, we simulated expected yields for combinations of crops (maize, sugar beet, wheat), soil (clay loam, loam, silty clay loam, and silty loam), and bulk density. Thus, we tested four watering strategies: rainfed, soil moisture-based irrigation, irrigation with a fixed interval every 1, 3, 5, and 7 days, and an algorithm for optimal irrigation scheduling under water supply constraints (GET-OPTIS). The results showed that an efficient irrigation strategy must account for soil and crop characteristics. Among the tested strategies, GET-OPTIS led to the best performance for crop yield, water use, water-use efficiency, and profit, followed by the soil moisture-based strategy. Thus, soil type has an important influence on the yield and performance of different irrigation strategies, as it provides a significant storage and buffer for plants, making it possible to produce “more crop per drop”. This work can serve as a methodological guide for simulating the water-use efficiency of crops and can be used alongside evidence from the field.

We propose a methodology to estimate the yield response factor (i.e., the slope of the water-yield function) under local conditions for a given crop, weather, sowing date, and management at each growth stage using AquaCrop-OS. The methodology was applied to three crops (maize, sugar beet, and wheat) and four soil types (clay loam, loam, silty clay loam, and silty loam), considering three levels of bulk density: low, medium, and high. Yields are estimated for different weather and management scenarios using a problem-specific algorithm for optimal irrigation scheduling with limited water supply (GET-OPTIS). Our results show a good agreement between benchmarking (mathematical approach) and benchmark (estimated by AquaCrop-OS) using the Normalised Root Mean Square Error (NRMSE), allowing us to estimate reliable yield response factors ( $Ky$ ) under local conditions and to dispose of the typical simple mathematical approach, which estimates the yield reduction as a result of water scarcity at each growth stage.

We propose a nonlinear optimisation model which maximises profits by resource allocation on a monthly time scale, considering a monthly crop yield model. The proposed model was applied to six management scenarios (two seasonal and four monthly), nine conditions of water availability, and two situations of resource availability under Chilean conditions. These situations provided the same seasonal amount of resources, but different distributions over time. The model included improvements in water resource management such as water storage and water transactions, being the latter a monthly decision variable that can increase farmers’ profits. According to our results, monthly scenarios gave high profits, even better with appropriate resource distribution. When water costs are high, water transactions allow loss reduction of up to 50%. Regarding labour, the lack of availability is more critical than the wages.

This study proposes a nonlinear programming model for the optimization of water and land allocation in a 1000 ha orchard over a 20-year period to maximize farmers’ net profits. Different scenarios were evaluated, including equitable and unrestricted land allocation, and the risks associated with fruit production were considered. Additionally, a sensitivity analysis that focused on the variability of labor and water availability was conducted. The results reveal that with equitable land allocations and no constraints on the cultivated area, cherry emerges as the most profitable crop, although there are large risks associated with its price volatility. The introduction of risk and land allocation constraints highlights the importance of crop diversification in mitigating economic risks. A sensitivity analysis indicated that reductions in water and labor availability significantly affect the optimal cropping pattern of an orchard, suggesting that the efficient and adaptive management of resources is required. The proposed optimal cropping pattern maintains the economic viability of the orchard even with 70% and 24% reductions in water and labor, respectively. This approach underscores the importance of implementing resilient and sustainable agricultural strategies to ensure food security and increase economic stability in the face of changing climatic and labor conditions.

The super high-density (SHD) production system has recently been introduced to the Chilean European plum (Prunus domestica L.) industry, but the potential of applying regulated deficit irrigation (RDI) in this system remains unexplored. As irrigation water availability in Chile has been strongly jeopardized by climate change, there is an urgent need to validate water-conserving practices in modern production systems. A field study was conducted in a commercial SHD European plum orchard (cv. French grafted on Rootpac-20 rootstock) for two consecutive seasons in Peralillo, O’Higgins Region, Chile. The objective of this study was to assess the impact of a late water deficit (LD) on water productivity, fruit quality, plant water relations, and soil microbiota. The results showed that implementing LD enhanced water productivity by 40% without compromising fresh and dry fruit quality. Moderate to severe water stress induced no changes in physiological parameters such as stomatal conductance and photochemical efficiency. Additionally, the LD treatment significantly reduced soil moisture but increased the abundance of certain groups of beneficial soil microbiota and fine roots. These results highlight the potential of LD as a viable water-conserving practice in modern SHD European plum orchards, particularly in regions facing water scarcity due to climate change.