Crop Water Models

Agricultural planning is a wide management field [88].

Irrigation management consists of determining when to irrigate, the amount to apply at each irrigation and during each stage of plant growth, and the operation and maintenance of the irrigation system. The primary objective is to manage the production system for profit without compromising the environment. The major management activity involves irrigation scheduling. Most irrigation water management concepts include salinity control and the improvement of soil-air-water environment. The management strategy for an irrigated farm or region normally is dictated by economics. The benefits include increased and predictable yields, enhanced crop quality, and reduced farming risks. The principal economic variables include the price of irrigation water, the cost of applying the water, and the price received for the crop. The optimal level of applied water depends on the crop-water production function. Generally speaking, the management of irrigation systems aims to achieve optimal crop production and efficient water use or, in other terms, a reliable, predictable, and equitable irrigation water supply to farmers.

The management of irrigation systems for highest benefit requires an understanding of the many physical, biological, technical, and socioeconomic factors involved in crop production. The more information that water managers have, and the better that information is, the better the decisions they can make. During the past 25 years, the development and application of computers and computer models to describe main aspects of processes and links relative to crop growth and yield as influenced by water availability have been relevant. This development has resulted from an increased understanding of soil-plant-atmospheric processes and the rapid expansion of personal computers. However, only a few of the models have been implemented, with varying success. Limitations of most models are that they aim to support only one aspect of irrigation management, or that they are programmed for one irrigation system alone, or that their user-friendliness does not fit the skills of the users.

The development of computer simulation tools leads to several applications of modeling in irrigation management. Crop-water models focusing on the crop and the farm scales probably have been the most important development in the area of irrigation management, as evidenced in models presented by Pereira et al. [89, 90].

Irrigation modeling can be focused on three levels according the technical detail and application scope [90]: irrigation scheduling models, crop growth simulation models, and models for irrigation management at the system level.

The first group of models places primary emphasis on irrigation scheduling. Irrigation scheduling provides information that can be used to develop strategies for different crops under varying soil and climatic conditions. These strategies can be determined using long-term data representing average conditions or in-season factors based on real-time information and short-term predictions. Several computer-assisted irrigation scheduling techniques relying on the water-balance method are available [89-94]. The computer programs usually generate an irrigation schedule such that the irrigation event takes place when the estimated soil moisture of the root zone is depleted to a certain level. Crop water relations are described by simple yield-response functions to predict relative yield loss based on simulated evapotranspiration relative to simulated potential evapotranspiration. Some of these models are currently utilized in field practice, with interactive communication with users in real or near-real time. In general, these models apply to the field scale for irrigated crops; however, procedures usually can be expanded to enlarge the scale of application. In addition, the irrigation scheduling models are usually not crop specific and can apply to different types of irrigation methods.

A second group of models includes models that aim at a more detailed description of soil-water fluxes and/or a more sophisticated simulation of crop growth and yield formation. Thus, these models have more state variables than the former models have, but they vary considerably in level of detail. These models require specific soil and crop characteristics for a more detailed description of each physical and biological process. Consequently, the models developed at this level are used mostly in research and special applications [89, 90].

Crop systems are highly complex. The crop in the field is affected by weather, soil physicochemical factors, pests, diseases, weeds, and interactions of these many factors. A classification of systems of crop production, based on growth-limiting factors, has been proposed by de Wit [95].

Most crop simulation models are a mixture of empiricism and mechanism, and even the most mechanistic models make use of empiricisms at some hierarchical level. A mechanistic crop model generally is considered to be based on physiological and physical processes and considers cause and effect at the process level. Material (carbon, nitrogen, water) and energy balance usually are included. However, the most useful models for studying irrigation management of crops under several weather and soil conditions have been largely functional models. The term dynamic is used to mean that the crop model responds to daily (or more frequent) changes in the environment.

In the past decades, much research effort has been devoted to the development of crop-growth models [96]. The CERES [97, 98] and GRO [99] families of models are among the most widely used. These models simulate crop growth, development, and yield for specific genotypes, taking into account weather, soil water, and nitrogen dynamics in the soil and crop in a mechanistic manner. Because these models are based on physiological and biological concepts along with experimental data, it is believed that the simulation provides a reasonable estimation of the relationships between management practices, weather conditions, and yield.

A number of crop modeling groups have attempted to optimize water and nitrogen management over long-term historical weather data [100-104]. Models used for this purpose must have reliable soil-water and soil-nitrogen-balance components. These optimization efforts can suggest the best long-term strategy for water or nitrogen-applications (often growth-stage dependent) and also show that enhanced decisions can be made if additional information, such as weather forecast, is available. In addition to phenology, dry-matter, and final-yield data, SOYGRO [105, 106], CERES-maize [107, 108], and CERES-wheat [109] models simulate daily values of leaf area index; root-length density; biomass of leaves, stems, grain, and roots; number of leaves; soil water content; evapotranspiration; potential evapotranspiration; transpiration; yield components; and water stress. They include processes that describe the development of a reproductive structure, photosynthesis, respiration, and tissue senescence. The irrigation management components allow users to specify different strategies for managing the crop such as specific dates and amounts of irrigation for comparison and selection of the best strategies.

Crop growth models have an advantage over the response functions in that they are designed to be more robust for use in other weather conditions and they include the possibilities for studying irrigation decisions in combination with other management decisions, such as planting date, row spacing, and nitrogen fertilizer use. However, all models have certain limitations because they do not comprise all possible parameters and influences that represent the biophysical environment. Moreover, they require local calibration and validation. These models are not a panacea for all agricultural problems, and care must be used in incorporating them into research studies, but many efforts have been made to improve the general application and accuracy of the crop-growth models, particularly for design or management of irrigation systems [90].

Crop models also can be integrated with optimization procedures to allow the computer to automatically search for the irrigation strategy that maximizes profit or satisfies other optimization objective functions. Other objective functions may include considerations for energy use, WUE, nitrogen leaching, and constraints on water availability.

Management practices have to be selected so that the levels of salinity in the soil are not harmful for the crop. The selection of appropriate practices for salinity control requires quantification of the movement of salts and water in the soil, the response of the crop to soil water and salinity, and how the environment and management conditions affect these interactions. There are a larger number of research and management models reported in scientific literature that deal with water and solute movement. Seasonal models assume steady-state conditions for crop response [110]. However, these models are not suitable for irrigation management in saline conditions [111].

Transient models compute water and solute flux in the soil and include a water-uptake term. Available transient models differ in their conceptual approach, degree of complexity, and in their application for research or management purposes.

Current models for quantifying the stress imposed on crops by excessive soil water conditions are based on water-table depth [112, 113].

Several computer models have been proposed to simulate the behavior of drainage and irrigation systems [114]. These include numerical solutions of Richards' equation for combined saturated-unsaturated flow in two or three dimensions.

A useful approach is provided by SWATRE, a model for transient vertical water flow in soil [115, 116]. The model includes a sink term for water uptake by roots and two functions for flow to drains. A modern version [117] incorporates solute transport. The advantage of this approach is that it is based on sound theory for vertical water movement in the unsaturated zone. Because most of the unsaturated water movement tends to be in the vertical direction, even in drained soil, this approach should provide reliable predictions of the soil water conditions above the water table. Another advantage is that it also can be applied for soils without water tables. Because SWATRE combines concepts for soil water flow, soil salinity, and drainage, it can be used to analyze problems of waterlogging and salinity in irrigated agriculture.

A third approach to modeling drainage systems is based on numerical solutions to Boussinesq's equation. This approach normally is applied for watershed-scale systems or where the horizontal water-table variation is critical [118, 119]. These models may be used in large nonuniform areas where lateral differences in soils, surface evaluations and water-table depths are important [119].

DRAINMOD [120, 121] is a drainage and subirrigation design and operation model for drainage-subirrigation systems and it is probably the best-known and most accepted water management model in humid areas. It includes methods to simulate subsurface drainage, surface drainage, subirrigation, controlled drainage, and surface irrigation. Input data include soil properties, crop parameters, drainage-system parameters, and climatological and irrigation information. The model can be used to simulate the performance of a water management system over a long period of climatological record.

The SWARD model [122] predicts the effects of drainage on grass yield. The model estimates daily soil water budget, including drainage and irrigation, and then links with a physiological model to predict a daily grass budget.

The recently developed drainage module of model MUST [123] is capable of simulating drainage and subirrigation in relation to ET and vertical flow in the unsaturated zone. The latest version of MUST has been used to design a subsurface irrigation system and to establish operational rules.

More complex models, such as the leaching estimation and chemistry model (LEACHM) [124], PRZM [125], the groundwater-loading effects of agricultural management systems (GLEAMS) model [126], and ADAPT [127], include leaching of agro-chemical pesticides and soil fertilizer elements such as nitrate. The OPUS model [128] treats the water, plant, nutrients, and pesticide systems in the agricultural ecosystem.

It includes surface-water flow and transport, processes of water flow, nitrogen cycling, pesticide movement and decay within the soil profile, and crop usage of nitrogen, phosphorus, and water. Soil water flow is simulated by using Richards' equation adapted to a multiple-horizon soil profile.

Crop models also can be used to help schedule irrigation or control pests. Use of the entire crop-growth models in this mode is not very common, except for the experience of the GOSSYM cotton-modeling group [129]. GOSSYM predicts the response of crops to variations in both environment and cultural management. The model estimates the ratio of appearance of several plant organs and takes into account the placement of every organ, the content of nitrogen and carbon, the water content, and the thermal exposition. (Crop Management Expert) COMAX is a framework of expert systems explicitly developed for working in crop-simulation models. COMAX is based on rules that imply a reasoning system, a file-storage system for the simulation-model requirements, a database system for the knowledge base, and a system for interaction with the user. It is based on menus. COMAX was developed to allow farmers and technicians using the GOSSYM model to incorporate water, nitrogen, and chemical management.

The goal of the International Benchmark Sites Network for Agrotechnology Transfer Project (IBSNAT) [130] is to accelerate the flow of agrotechnology and to increase the success rate of technology transfer from agricultural research centers to farmers' fields. To do this, IBSNAT has developed computer software that helps to match crop requirements to land characteristics using crop simulation models, databases, and strategy evaluation programs. The resulting system is called Decision Support System for Agrotechnology Transfer (DSSAT). In the DSSAT, users select as many combinations of alternative irrigation (or other) management practices as they wish; then, simulations are performed for all that were selected to allow users to select the best one.

Cropping Systems Simulation Model (CropSyst) [131] is an existing multiyear, multicrop, daily time-step model with a mechanistic approach, including a variety of agronomic management options (irrigation, fertilization, tillage, residue management, cultivar selection, and rotation selection) and environmental impact analysis capabilities (erosion and chemical leaching). CropSyst simulates the soil water budget, soil-plant nitrogen budget, crop phenology, crop canopy and root growth, biomass production, crop yield, residue production and decomposition, and soil erosion by water. CropSyst was modified for assessing crop response and water management under saline conditions [132].

The ever-increasing developments in geographical information systems (GIS) and decision support systems (DSS) may contribute to the set of input data for simulation studies in irrigation management. The package HYDRA [133] is an example. It was designed to be a versatile software package that can be employed for both strategic and operational irrigation and management purposes.

To utilize the water resources fully, to match water supply and requirements, and to reach the maximum economic benefit, the crop's response to applied water, the technical efficiency of the system, the farm's resource constraints, economic considerations at both the micro- and sectoral level, institutional factors along with the decision-maker's objectives and risk attitudes need to be considered [85]. There are many techniques that can be used to evaluate alternative irrigation equipment and strategies [134]. Crop models can be used to evaluate economic risk [135], as considered with prices and complete enterprise production costs. Bogges and Ritchie [135] also showed that irrigation reduces weather-related variability in yield, and thus may be preferred by risk-averse producers.

Crop models that predict yield and irrigation demand can be used in planning regional and watershed-level strategies for water withdrawals for irrigation [136]. In regions where water resources are limited, it is particularly important to plan the permitted area of irrigated crops and water demand for drought years.

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