How does research address the design of innovative agricultural production systems at the farm level? A review

Farmers must yearly allocate fields to different crops and choose crop management options. Far from being obvious, these decisions are critical because they modify farm productivity and profitability in the short and long run. To support farmers and efficiently allocate scarce resources, decision support models are developed. Decision support models are mainly based on two concepts, the cropping plan and the crop rotation decisions. These decisions concern crop choice, crop spatial distribution within the farmland and crop temporal successions over years. Decisions are at the core of the farm management. Decisions have strong impacts on resource use efficiency and on environmental processes at both farm and landscape scales. We review here more than 120 references where cropping plan and crop rotation decision concepts were incorporated into models. Our aim is to review how these two concepts have been formalised and used in agronomic, economic and land-use studies. We found that cropping plan decisions selection and design have been done using many approaches based on different objectives and handled at very different scales. The main results show that (1) cropping plan design decisions have mainly been tackled as a static concept, i.e. as if they were a single decision made only once a year or once a rotation; (2) modelling the achievement of a suitable cropping plan is often based on a single monetary criterion optimization procedure instead of a multi-criteria assessment; and (3) when considered, uncertainty of information is defined as stochastic factors or probability of occurrence, but this probability is kept static whatever the knowledge of the dynamic evolution of various constraints. We argue that cropping plan and crop rotation decisions are on the contrary dynamic processes incorporated into a succession of other planned and adaptive decisions made at annual and long-term horizons. For supporting farmers in their decisions, new cropping plan decision models will require new modelling paradigm. A promising improvement could be reached by including explicitly the simulation of the farmers' decision-making processes, based on the simulation of the decision-making processes rather than on single normative approaches.

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Models to support cropping plan and crop rotation decisions. A review

This study presents a linear and a mixed-integer linear (MIL) model for optimizing an irrigation water allocation and a multicrop planning problem. The main objective was to maximize net benefit for all cultivated crops within irrigated areas in a reservoir-irrigation system in Iran. The linear model was optimized with linear programming (LP) method and continuous particle swarm optimization (CPSO) algorithm to make a detailed comparison between the LP method and CPSO algorithm results. The optimal solution obtained by the CPSO algorithm and LP method in the linear model were comparable. However, the optimal allocated areas for both crops and orchards in the linear model obtained by the LP method and CPSO algorithm were not directly applicable in real crop planning situations. Consequently, the MIL model was developed for which a discrete particle swarm optimization (DPSO) algorithm was used to obtain an applicable and allowable solution for the problem. Contrary to LP and CPSO, the DPSO algorithm...

Optimizing Irrigation Water Allocation and Multicrop Planning Using Discrete PSO Algorithm

Determination of optimal regulated deficit irrigation strategies for maize in a semi-arid environment

In an effort to improve plant growth and to achieve high yield and/or quality, irrigation scheduling (IS) seeks to provide plants with appropriate quantities of water at appropriate times. ...

Irrigation Scheduling Approaches and Applications: A Review

Water is a natural, sometimes scarce, and fundamental resource for life, essential both for agriculture in many regions of the world and also to achieve sustainability in production systems. Maximizing net returns with the available resources is of the utmost importance, but doing so is a complex problem, owing to the many factors that affect this process (e.g. climatic variability, irrigation system configuration, production costs, subsidy policies). The MOPECO model is a tool for identifying optimal production plans, and water irrigation management strategies. The model estimates crop yield, production and gross margin as a function of the irrigation depth. Finally, these gross margin functions are used to determine an optimum cropping pattern and irrigation strategy to maximize the gross margin on a farm in a specific scenario. Since the relationships between the variables are generally non-linear and the number of alternative strategies is quite large, the optimum process is complex and computationally intensive. Genetic algorithms are therefore used to identify optimal strategies. This paper describes the MOPECO model, which comprises three computing modules: (1) estimation of net water requirements; (2) derivation of the relationship between gross margin and irrigation depth; and (3) identification of the crop planning and the water volumes to be applied. The results obtained by applying the MOPECO model to a specific irrigable area in a semi-arid area of Spain, with great deficits and high water costs, are also included and discussed. These results usually show that the irrigation depth for maximum benefits is lower than that necessary to obtain maximum production. In some areas of Spain, horticultural crops are nearly always part of the optimum alternative. The crops that become part of the optimum alternative are mainly horticultural crops with a high gross margin and low water requirements. The irrigation depths selected for the ideal crop rotation are included among the irrigation depth of maximum economic efficiency and the maximum gross margin irrigation depth. Both are lower than that necessary for the maximum yield. This model helps farmers, extension services, and other agents to analyse, make decisions and optimize water management.

Eulogio López Mata

Papers

MOPECO: an economic optimization model for irrigation water management