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Title
Abstract
Introduction
Study Site and Field Experiment
Economic Analysis
Conclusion
References

Crop Models as Decision Support Systems in Crop Production

Profile image of Simone GraeffSimone GraeffProfile image of Johanna LinkJohanna Link
https://doi.org/10.5772/28976
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Abstract
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Crop models are increasingly recognized as vital tools in improving agricultural practices by providing decision support systems that can help farmers optimize their management strategies. This paper discusses the challenges faced in crop production, such as the need for increased yields and the reduction of inputs like fertilizer and water, while highlighting the usefulness of these models in managing agricultural systems under changing climatic and regulatory conditions. It presents case studies from China and Germany to illustrate the practical applications of crop growth models in supporting decision-making for yield variability and resource management.

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Ravi Patil

Journal of Farm Sciences, 2019

Models are built on the assumption that any given process and relation can be expressed in a formal mathematical statement(s) or equations, and they all can be interconnected to run iteratively. Development of crop models started first in 1940s, but very few in the scientific community believed that the complex bio-physical and morphological processes of plant growth could be described mathematically, except possibly under controlled environments. In the early 1980s for the first time models were used as Decision Support System (DSS) tool. Soon after enhanced understanding of agro-ecosystem processes and synthesization of this to predict outcomes helped apply models to control and manage outcomes. The first product that came out of this effort was DSSAT model followed by APSIM model. Soon CGIAR centres started to develop and work with models. Development in modeling science and natural progression of scientific research went hand-in-hand. Since then models have been used in crop management, yield gap analysis, abiotic stress impact studies, potential yield, plant disease forecasting, inter-cropping and livestock-crop interaction studies. Soon models were used to predict climate change impact on food security and socioeconomic issues at regional and global scale. Since 2010 modeling work expanded to multi-model inter-comparison, climate change and climatic variability impact assessment, quantifying uncertainties in models for further development, genomics studies, crop improvement programs, regional and global food security issues, GxExM and GxExMxS interactions, conservation agriculture (CA), agro-forestry and tree-crop-livestock interactions and tuber crops, and also as DSS tool for crop management under current and future climates.

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Role of Modelling in International Crop Research: Overview and Some Case Studies
Gideon Kruseman

Agronomy

Crop modelling has the potential to contribute to global food and nutrition security. This paper briefly examines the history of crop modelling by international crop research centres of the CGIAR (formerly Consultative Group on International Agricultural Research but now known simply as CGIAR), whose primary focus is on less developed countries. Basic principles of crop modelling building up to a Genotype × Environment × Management × Socioeconomic (G × E × M × S) paradigm, are explained. Modelling has contributed to better understanding of crop performance and yield gaps, better prediction of pest and insect outbreaks, and improving the efficiency of crop management including irrigation systems and optimization of planting dates. New developments include, for example, use of remote sensed data and mobile phone technology linked to crop management decision support models, data sharing in the new era of big data, and the use of genomic selection and crop simulation models linked to en...

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Crop modelling : a tool for agricultural research- A review
Eric Owusu Danquah

The Earth's land resources are finite, whereas the number of people that the land must support continues to grow rapidly. This creates a major problem for agriculture. Production (productivity) must be increased to meet rapidly growing demands while natural resources must be protected. New agricultural research is needed to supply information to farmers, policy makers and other decision makers on how to accomplish sustainable agriculture over the wide variations in climate around the world. In this direction the use of crop models in research is being encouraged.

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Crop modeling: A tool for agricultural research – A review
Patricia Amankwaa-Yeboah

2013

The Earth’s land resources are finite, whereas the number of people that the land must support continues to grow rapidly. This creates a major problem for agriculture. Production (productivity) must be increased to meet rapidly growing demands while natural resources must be protected. New agricultural research is needed to supply information to farmers, policy makers and other decision makers on how to accomplish sustainable agriculture over the wide variations in climate around the world. In this direction the use of crop models in research is being encouraged.

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