An Agent-Based Crop Model Framework for Heterogeneous Soils

Abstract

Climate change and the efficient use of freshwater for irrigation pose a challenge for sustainable agriculture. Traditionally, the prediction of agricultural production is carried out through crop-growth models and historical records of the climatic variables. However, one of the main flaws of these models is that they do not consider the variability of the soil throughout the cultivation area. In addition, with the availability of new information sources (i.e., aerial or satellite images) and low-cost meteorological stations, it is convenient that the models incorporate prediction capabilities to enhance the representation of production scenarios. In this work, an agent-based model (ABM) that considers the soil heterogeneity and water exchanges is proposed. Soil heterogeneity is associated to the combination of individual behaviours of uniform portions of land (agents), while water fluxes are related to the topography. Each agent is characterized by an individual dynamic model, which describes the local crop growth. Moreover, this model considers positive and negative effects of water level, i.e., drought and waterlogging, on the biomass production. The development of the global ABM is oriented to the future use of control strategies and optimal irrigation policies. The model is built bottom-up starting with the definition of agents, and the Python environment Mesa is chosen for the implementation. The validation is carried out using three topographic scenarios in Colombia. Results of potential production cases are discussed, and some practical recommendations on the implementation are presented.

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

“…The model prediction is shown in Figure 5. Rainfall and evapotranspiration were retrieved from [123].…”

“…The results show that the model with its previously estimated parameters is able to reproduce the dynamic behavior of the soil for other climatic conditions with an error of less than 3%, as shown in Figure 7. Cross-validation with a previously unseen (for the identification procedure) dataset [123] for (a) transpiration and (b) available water in soil.…”

“…Instead, other approaches are emerging, such as the stochastic distribution approach [162] and agent-based modeling [123]. In [162], the authors discussed modeling and monitoring complex systems that vary in both space and time given a limited number of agents or sensors providing measurements that are spread out across a large area.…”

“…They described the land using Gaussian process modeling and addressed data incorporation with a machine learning technique named Kernel Observers. In [123], the idea was to represent the changes across the surface thanks to a discretization of the land into small patches that were linked by the side exchange of water due to the saturation of its retention capacity and the runoff. This latter representation is well suited for crops located in mountain slopes or rugged terrains.…”

“…Figure 5. Direct validation: (a) transpiration, (b) runoff, (c) drainage, and (d) available water in soil.Rainfall and evapotranspiration were retrieved from[123].…”