|Ale, S - Texas A&M Agrilife|
|Bange, M - Commonwealth Scientific And Industrial Research Organisation (CSIRO)|
|Barnes, E - Cotton, Inc|
|Hoogenboom, G - Washington State University|
|Mccarthy, A - University Of Queensland|
|Nair, S - Texas Tech University|
|Paz, J - Mississippi State University|
|Rajan, N - Texas A&M University|
|Reddy, K - Mississippi State University|
|Wall, Gerard - Gary|
Submitted to: Journal of Cotton Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/14/2014
Publication Date: 3/1/2014
Citation: Thorp, K.R., Ale, S., Bange, M.P., Barnes, E.M., Hoogenboom, G., Lascano, R.J., Mccarthy, A.C., Nair, S., Paz, J.O., Rajan, N., Reddy, K.R., Wall, G.W., White, J.W. 2014. Development and application of process-based simulation models for cotton production: A review of past, present, and future directions. Journal of Cotton Science. 18:10-47.
Interpretive Summary: Crop simulation models are increasingly being used as tools to address a variety of agricultural issues and other related topics: crop water use and irrigation water management, nitrogen dynamics and fertilizer management, genetics and crop improvement, climatology and global climate change, precision agriculture, remote sensing, economics, and classroom instruction. In the United States, development of crop simulation models began in the late 1960's and focused on cotton. Several cotton simulation models have been developed since then, but the perception has been that cotton simulation modeling efforts have languished in recent times. This article reviewed the current state-of-the-art of cotton simulation modeling, providing a brief history of model development efforts and an in-depth review of cotton modeling applications in the new century. While the development of new cotton simulation models has languished, application of existing models in a variety of areas remains strong and continues to grow. A main outcome of this research was the cooperation among several scientists, both nationally and internationally, who have great interest and expertise in cotton simulation modeling activities. This activity will provide a springboard for future collaboration to intercompare and improve existing cotton simulation models. The published article will be useful to many people in the cotton industry, who have interest in understanding how cotton simulation models can be used to improve cotton production.
Technical Abstract: The development and application of cropping system simulation models for cotton production has a long and rich history, beginning in the southeast United States in the 1960's and now expanded to major cotton production regions globally. This paper briefly reviews the history of cotton simulation models, examines applications of the models since the turn of the century, and identifies opportunities for improving models and their use in cotton research and decision support. Cotton models reviewed include those specific to cotton (GOSSYM, Cotton2K, COTCO2, OZCOT, and CROPGRO-Cotton) and generic crop models that have been applied to cotton production (EPIC, WOFOST, SUCROS, GRAMI, CropSyst, and AquaCrop). Model application areas included crop water use and irrigation water management, nitrogen dynamics and fertilizer management, genetics and crop improvement, climatology, global climate change, precision agriculture, model integration with sensor data, economics, and classroom instruction. Generally, the literature demonstrated increased emphasis on cotton model development in the previous century and on cotton model application in the current century. Although efforts to develop cotton models have a 40-year history, no comparisons among cotton models were reported. Such efforts would be advisable as an initial step to evaluate current cotton simulation strategies. Increasingly, cotton simulation models are being applied by non-traditional crop modelers, who are not trained agronomists but wish to use the models for broad economic or life cycle analyses. While this trend demonstrates the interest in the models and their potential utility for a variety of applications, it necessitates the development of models with appropriate complexity and ease-of-use for a given application, and improved documentation and teaching materials are needed to educate potential model users. Spatial scaling issues are also increasingly prominent, as models originally developed for use at the field scale are being implemented for regional simulations over large geographic areas. Research steadily progresses toward the advanced goal of model integration with variable-rate control systems, which use real-time crop status and environmental information to spatially and temporally optimize applications of crop inputs, while also considering potential environmental impacts, resource limitations, and climate forecasts. Overall, the review demonstrates a languished effort in cotton simulation model development, but the application of existing models in a variety of research areas remains strong and continues to grow.