Dynamic Modeling of Crop–Soil Systems to Design Monitoring and Automatic Irrigation Processes: A Review with Worked Examples

A soil water indicator for a dynamic model of crop and soil water interaction

Water scarcity is a critical issue in agriculture, and the development of reliable methods for determining soil water content is crucial for effective water management. This study proposes a novel, theoretical, non-physiological indicator of soil water content obtained by applying the next-generation matrix method, which reflects the water-soil-crop dynamics and identifies the minimum viable value of soil water content for crop growth. The development of this indicator is based on a two-dimensional, nonlinear dynamic that considers two different irrigation scenarios: the first scenario involves constant irrigation, and the second scenario irrigates in regular periods by assuming each irrigation as an impulse in the system. The analysis considers the study of the local stability of the system by incorporating parameters involved in the water-soil-crop dynamics. We established a criterion for identifying the minimum viable value of soil water content for crop growth over time. Finally, the model was calibrated and validated using data from an independent field study on apple orchards and a tomato crop obtained from a previous field study. Our results suggest the advantages of using this theoretical approach in modeling the plants' conditions under water scarcity as the first step before an empirical model. The proposed indicator has some limitations, suggesting the need for future studies that consider other factors that affect soil water content.

Model predictive control for precision irrigation of a Quinoa crop

Traditional High Andean agriculture is rainfed, and irrigation is commonly carried out in an open loop, that is, without measuring variables such as soil moisture content or plant development to define water consumption. This article presents model predictive control applied to irrigation systems under real conditions, whose purpose is the efficient use of water in rainfed crops with improved yield and crop productivity at minimum water consumption. The article presents a control strategy applying a model of predictive control that calculates the optimal amount of water for daily irrigation under real conditions. The most important attraction of the model is the prediction and future behavior of the controlled variables as a function of the changes in the manipulated variables. The objective is to improve the yield of the crop at minimum water consumption, for this, it will be necessary to use models that link with the Aquacrop software and allow it to be a source of data, and for the prediction of future values. The predictive controller is evaluated in the Quinoa crop (Chenopodium Quinoa Willdenow), and the performance is compared against existing traditional irrigation data in the literature. The results indicate that the predictive controller can achieve higher crop efficiency and reduce irrigation water supplies considerably.

Smart Irrigation System Considering Optimal Energy Management Based on Model Predictive Control (MPC)

Traditional irrigation techniques usually cause the wasting of water resources. In addition, crops that are located in rural areas require water pumps that are powered by environmentally unfriendly fossil fuels. This research proposes a smart irrigation system energized by a microgrid. The proposal includes two stages: the first generates the daily irrigation profile based on an expert system for the adequate use of the water. Then, considering the irrigation profile, the power required for the water pump is measured—the optimal daily profile of electricity demand is determined in the second stage. The energy system is a microgrid composed of solar energy, a battery energy storage system (BESS) and a diesel generator. The microgrid is managed by an energy management system (EMS) that is based on model predictive control (MPC). The system selects the optimal start-up time of the water pump considering the technical aspects of irrigation and of the microgrid. The proposed methodology is validated by a simulation with real data from an alfalfa crop in an area of Ecuador. The results show that the smart irrigation proposed considers technical aspects that benefit the growth of the crops being studied and also avoids the waste of water.