Crop models are the representation of the crop growth interaction with the environment in an mathematical way where the information are predicted quantitatively. Different abiotic factors and biotic factors, factors within crop and environment, are equally used to test or assume the reality. The objective of the crop models remain in its ability to support in assistance in decision making.
The earliest crop simulation models began in the 1960s where the mathematical relationships were developed between biomass growth and solar radiation. Publication in 1970s dealt biomass and its relation with the intercepted light. Crop models ARCWHEAT 1, CERES-WHEAT and the Dutch Models Simple and Universal Crop Growth Simulator were developed in 1980s and 1990s. Such models had the prime objective to compare the models with the experimental data to improve the cropping simulations.
Around 1990s, multiple crop models were developed for different crops such as Decision Support System for Agrotechnology Transfer (DSSAT), Agricultural Production Systems Simulator (APSIM), CropSyst, Wageningen crop models, Simulateur multidisciplinaire pour les Cultures Standard (STICS), Environmental Policy Integrated Climate model (EPIC). In 2000s more complex model with various approaches and complexities (System Approach to Land Use Sustainability (SALUS), AquaCrop, HERMES, Model of Nitrogen and Carbon Dynamics in Agro-ecosystems, and Lund-Potsdam-Jena Managed Land Dynamic Global Vegetation and Water Balance Model) were developed. The enhancements in the previous single crop models (SIRIUS model, WheatGrow model) were also seen. Crop models are primarily applied in simulating experiments.
The processes in the crop models are mainly in crop developments, biomass accumulation, yield, water and nutrient uptake.
The most cited crop models include DSSAT, APSIM, CropSyst, Wageningen models, EPIC. The crop models have modular structure where the crop modules are linked to the other modules such as soil water, soil nitrogen, carbon modules. Crop models may often be lined with the other systems such as human-landscape interactive models, DSS, farm economic models.
3D crop model related to the simulation based on the plant architecture which described the 3D architecture of the plants without consideration of the plant physiological processes and interactions with the environment. Such models are now being incorporated with the physiological algorithms for functional structural models.
Further developments in different disciplines such as genetics, plant physiology, agri. meteorolgy, climatology, soil science, remote sensing, computer science led to crop modeling towards more interdisciplinary approaches. The goals are always to reduce the negative impacts in the environment and more production even in the unit area
Study of N application in the field and their effectiveness is the other important goal in the crop model. The long term effects in the crops due to the N on the crop growth are simulated in the crop models. N use efficency has been found to be varied due to weather data such as rainfall, soil data, soil water conditions. Simulation studies are focused in the crop growth and grain quality.
Crop yield potential (highest achievable quantitative yield) and water-limited yield potential (in rain-fed conditions) have been estimated by the use of the crop models. The best attempts are in the understanding of the yield gaps (difference between the actual and potential yield).
Crop water balance and crops' yield response to water is another important application of the crop model. Management of the irrigation of the crops to help maximum yield has been the real application in the northern Africa based on the limited irrigation.
Sowing depth, planting density, time of sowing, also differs the crop yield. The best variety for the specific location, for the maximization of the economic returns is the other most important objective of the crop model.
Seasonal variability (the variation of the rainfall) is the another most hindrance in the agricultural yields. Crop models can combine the long term historical weather data for the best management practices.
Spatial Variability (differences in weather, soil, nutrients, according to the place, topography) needs location specific management strategies. This includes integration of the remote sensing data around the region to collect crop information.
Rotation mainly done to avoid pests and diseases, and for the soil health is best strategy in agricultural field. Crop models help in the evaluation of the effect of the crops rotation, how the nutrition flow is being carried from one crop to the other.
Crop pest interaction has also been attempted to be modeled which requires different physical factors such as soil temperature, pest species, soil surface, water and nutrient availability and so on. The objective is, for instance, if the pest attacks the leaf, the objective is to study the impact in the leaf area which can later reduce light distribution activity and crop structures.
In intercropping, the crop models attempts to understand how different species compete for the resources, which can be the best case in the weed and crops.
Crop models help in the resource management with the prime objective to develop the strategies for increasing the crop production while reducing the negative impacts in the environment.
For soil, soil erosion model are applied to study crop productivity, while other models have been in the effects of the deep tillage. Other such cases include in the study of the soil salinity, where the salt movements have been simulated. The potential damage from drainage has also been quantified through crop models.
Nitrate leaching has been the major reason for the ground water pollution. Crop models have predicted in the impacts of crop managements in N leaching. Studies include nitrogen is lost earlier in sandy soils and if the rainfall is high before reaching to plants.
Soil carbon has also been incorporated in the crop models which include factors such as presence of the fresh organic matter and so on. The objective is in the soil carbon in different tillage and fertilizer management practices.
Phenotypic traits have been used to estimate the performance and how traits are related to genes. Different traits have been linked to increase yield such as in maize yield suggesting in the size of the primary ears.
Crop models are also been incorporated in the genetic details with more estimation of the trait values. However, the current understandings of the gene expression does not help in the prediction of the majority of the phenotypic traits.
Crop models have been limited to develop improved cultivars and has not been fully able to estimate effects of phenotypic traits quantitatively.
Crop models have also been helpful to study the impacts of climate change in agriculture from past, and the prediction of the future climate change. The models have been used in the improvements of the global and regional assessments of the future climate impacts.
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