Crop Growth Modeling: A ReviewP Thimme Gowda*, Sunil A Satyareddi, and SB Manjunath
Ph.D.Scholars, Department of Agronomy, College of Agriculture, University of Agricultural Sciences,Dharwad- 580 005, Karnataka, India.
- *Corresponding Author:
- P Thimme Gowda
Ph.D. Scholars, Department of Agronomy,
College of Agriculture,
University of Agricultural Sciences,
Dharwad- 580 005, Karnataka, India.
Phone No.: +91-9972067066
Received date: 04 January 2013; Revised date: 02 February 2013; Accepted date: 11 February 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 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.