Crop modeling: A tool for agricultural research – A review

Sel'skokhozyaistvennaya Biologiya, 2017

Mechanistic (eco-physiological), or process-oriented, approach to simulation modeling of the production process of plants assumes considering the essences of processes and cause-effect relationships in the agroecosystem with a description of their dynamics based on physically interpreted dependencies (as opposed to logically interpreted dependencies at the empirical approach) (A. Di Paola et al., 2016; R.A. Poluektov, 2010). The analysis of the possible use of dynamic simulation models of agro-ecosystems in the mechanistic nature of applied and theoretical research of agricultural biology is presented. The current practice of the development and usage of these models shows their highest suitability for research purposes in comparison to the potential usefulness and relevance to the practical problems of agronomy. Specific examples of model applications demonstrate the possibility of computer-based model experiments to get nontrivial results, which are not directly incorporated into the logic of the model algorithms (V. Badenko et al., 2014; S. Medvedev et al., 2015). The role of simulation model as a tool of obtaining new knowledge and interpretation of the empirically observed phenomena has been showed. To demonstrate the potentials of simulation models for agricultural biology, some results of authors' studies have been reviewed, including analyze of the appearance of a non-monotonic response function of crop yield on the doses of nitrogen fertilizer, the results of computer experiments on interpretation of the effect of the time delay during management of nitrogen feeding «on the leaf», and the joint impact of combined water and nitrogen stresses. Based on analysis of recent publications, conclusions of perspectives of models application to accelerate the plant breeding process were justified. It is concluded, i) further «biologization» of existing models is a prerequisite for a successful development of the dynamic crop growth modeling, and ii) it is necessary to increase the level of scientific validity of model approaches, which are used to describe the biotic processes in the soil-plant-atmosphere system.

Crop/soil simulation models basically applied in three sections (1) tools for research, (2) tools for decision-making, and (3) tools for education, training and technology-transfer. The greatest use of crop/soil models so far has been by the research community, as models are primarily tools for organizing knowledge gained in experimentation. However, there is an urgent need to make the use of models in research more relevant to problems in the real world, and find effective means of dissemination of results from work using models to potential beneficiaries. Nevertheless, crop models can be used for a wide range of applications. As research tools, model development and application can contribute to identify gaps in our knowledge, thus enabling more efficient and targeted research planning. Models that are based on sound physiological data are capable of supporting extrapolation to alternative cropping cycles and locations, thus permitting the quantification of temporal and spatial variability. Over a relatively short time span and at comparatively low costs, the modeler can investigate a large number of management strategies that would not be possible using traditional methodologies. Despite some limitations, the modelling approach remains the best means of assessing the effects of future global climate change, thus helping in the formulation of national policies for mitigation purposes. Other policy issues, like yield forecasting, industry planning, operations management, consequences of management decisions on environmental issues, are also well supported by modelling. Models are not simple mechanisms to archive and synthesize information for producing forecasts. Modelling represents a better way of synthesizing knowledge about different components of a system, summarizing data, and transferring research results to users.

Scientia Agricola, 1998

With the purpose of presenting to scientists the implications of the objective in model development and a basic vision of modeling, with its potential applications and limitations in agriculture, an integration of crop modeling professionals with agricultural professionals is suggested. Models mean modernization of the information, of the measurement process and of an efficient way to learn more about complex systems. They are one of the best mechanisms of transforming information in useful knowledge and of transferring this knowledge to others. One of the problems that impede a larger progress in modeling is the lack of communication between modelers and a frequent appearance of modelers without a global vision of reality.

MOJ Food Processing & Technology

Fosu 8 reiterated that the APSIM model simulated the trend of maize yield fairly well inside the experiments of inorganic N and P fertilizer applications. In addition, similar result was reported by

2021

A crop simulation model is a computerized program which is used to describe the process of growth and developmental stages of crop in relation to weather data, crop conditions and soil conditions to solve the real-world problems. Crop simulation models plays an important role in decision making process as these models can save time and resources. The prediction accuracy of simulation models is one of the most vital components in decision making process. Our review shows the prediction accuracy and efficiency of the simulation models like DSSAT and APSIM. We have compared the prediction accuracy of these models on various growth and development stages of crops along with yield prediction. Both the models have performed well while predicting various growth and developmental stages of crops. The present scenario of traditional research is site-specific, Resource consuming and time consuming. Hence the information obtained through traditional research by qualitative analysis has many li...

Scientia Agricola, 1998

Modeling techniques applied to agriculture can be useful to define research priorities and understanding the basic interactions of the soil-plant-atmosphere system. Using a model to estimate the importance and the effect of certain parameters, a researcher can notice which factors can be most useful. The modeler should define his objectives before beginning his work and construct a model that fulfills the proposed objectives.

The Journal of Agricultural Science

During the past decade, the interest in using crop models for research, education, extension, outreach and in the private sector has rapidly increased. The iCROPM 2020 Symposium entitled ‘Crop Modeling for the Future’, held in February 2020, therefore, provided a great opportunity for over 400 scientists from 50 different countries to exchange information on crop model development, evaluation with experimental data and implementation. A key outcome was the understanding that crop models simulate the dynamics of the soil-plant-atmosphere continuum. Thus, the models can be used for a quantitative and multi-criteria assessment of cropping system functioning, agronomic performance and environmental impact. This should result in more practical applications that can provide actionable information for stakeholders.

Scientia Horticulturae, 1998

For the last two decades, crop modelling has become a major research tool in horticulture as in other areas of plant production. A reason for such a success is the versatility of this technique. Scientists look for conceptual frameworks, and horticulture has offered original case studies on, e.g. photosynthesis or plant architecture. Engineers want procedures to solve practical problems, and horticulture is a field where yield prediction, policy evaluation or process optimisation can be very important. Horticulture is characterised by a high diversity of cultivation systems and fruit, vegetable and ornamental species. Till now, few of them have been modelled and efforts have focused on a limited number of processes of crop growth and development. The water balance of plants, the uptake of minerals, the interaction with pests, diseases and genetics, the interplant variability, and the formation of product quality have been poorly addressed. To face the challenge of diversity, modellers will certainly have to adopt more generic approaches. For decision making, crop models should be integrated in a model of the whole system under control and connected to a model of the decision system. Even though a lot remains to be done, a major achievement of crop Ž modelling in horticulture has been a significant increase of communication in terms of concepts . and modelling tools in a field where the high diversity of species and cultivation systems can be an obstacle. q 1998 Elsevier Science B.V.

Research & Reviews: Journal of Agriculture and Allied Sciences, 2013

Crop/soil simulation models basically applied in three sections (1) tools for research, (2) tools for decision-making, and (3) tools for education, training and technology-transfer. The greatest use of crop/soil models so far has been by the research community, as models are primarily tools for organizing knowledge gained in experimentation. However, there is an urgent need to make the use of models in research more relevant to problems in the real world, and find effective means of dissemination of results from work using models to potential beneficiaries. Nevertheless, crop models can be used for a wide range of applications. As research tools, model development and application can contribute to identify gaps in our knowledge, thus enabling more efficient and targeted research planning. Models that are based on sound physiological data are capable of supporting extrapolation to alternative cropping cycles and locations, thus permitting the quantification of temporal and spatial va...