Progress 10/01/00 to 09/30/05
Outputs
Crop models use mathematical equations to simulate growth, development and yield as a function of weather, soil conditions and crop management. Such models integrate scientific knowledge from diverse agronomic disciplines, ranging from plant breeding to soil physics. Most crop models use one or more cultivar-specific parameters to identify differences in performance among cultivars. Until recently, however, there was little relation between cultivar-specific parameters and genotypes. The GeneGro model simulates the impact of seven genes on physiological processes in common bean, specifying cultivar differences through the presence or absence of the seven genes. The model was based on the bean model BEANGRO. GeneGro has now been incorporated into the Cropping System Model (CSM), which can simulate growth and development for more than 20 different crops, although the CSM-GeneGro version is currently implemented only for common bean and soybean. Gene-based models can
provide a well-structured linkage between functional genomics and crop physiology, especially as more genes are identified and their functions are clarified. Incorporating genetic information strengthens underlying physiological assumptions of the model, improving its utility for research in crop improvement, crop management, global change, and other fields. The CSM-GeneGro model was used to show how specific genes can simulate both yield levels and yield variability for three locations in the USA. The model is also used to examine how single genes can affect crop response to global change. Gene-based modeling approaches could significantly enhance our ability to predict how global change will impact agricultural production, but modelers and physiologists will have to be proactive in accessing information and tools being developed in the plant genomics community.
Impacts
Crop simulation models and system analysis play critical roles in helping to determine research priorities and to identify research gaps. The application of crop simulation can result in outputs that can be used by policy and decision makers.
Publications
- Nunez-Barrios, A., G. Hoogenboom, and S. NeSmith. 2005. Drought stress and the distribution of vegetative and reproductive traits of a bean cultivar. Scientia Agricola 62(1):18-22.
- White, J.W., and G. Hoogenboom. 2005. Integrated viewing and analysis of phenotypic, genotypic, and environmental data with "GenPhEn arrays." European Journal of Agronomy 23:170-182.
- White, J.W, G. Hoogenboom, and L.A. Hunt. 2005. A structured procedure for assessing how crop models respond to temperature. Agronomy Journal 96(2):426-439.
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Progress 01/01/04 to 12/31/04
Outputs
Crop models use mathematical equations to simulate growth, development and yield as a function of weather, soil conditions and crop management. Such models integrate scientific knowledge from diverse agronomic disciplines, ranging from plant breeding to soil physics. Most crop models use one or more cultivar-specific parameters to identify differences in performance among cultivars. Until recently, however, there was little relation between cultivar-specific parameters and genotypes. The GeneGro model simulates the impact of seven genes on physiological processes in common bean, specifying cultivar differences through the presence or absence of the seven genes. The model was based on the bean model BEANGRO. GeneGro has now been incorporated into the Cropping System Model (CSM), which can simulate growth and development for more than 20 different crops, although the CSM-GeneGro version is currently implemented only for common bean and soybean. Gene-based models can
provide a well-structured linkage between functional genomics and crop physiology, especially as more genes are identified and their functions are clarified. Incorporating genetic information strengthens underlying physiological assumptions of the model, improving its utility for research in crop improvement, crop management, global change, and other fields. The CSM-GeneGro model was used to show how specific genes can simulate both yield levels and yield variability for three locations in the USA. The model is also used to examine how single genes can affect crop response to global change. Gene-based modeling approaches could significantly enhance our ability to predict how global change will impact agricultural production, but modelers and physiologists will have to be proactive in accessing information and tools being developed in the plant genomics community.
Impacts
Crop simulation models and system analysis play critical roles in helping to determine research priorities and to identify research gaps. The application of crop simulation can result in outputs that can be used by policy and decision makers.
Publications
- Boote, K.J., J.W. Jones, G. Hoogenboom, A. DuToit, E.L. Piper, P.J. Sexton and F. Sau. 2004. The origin of the temperature functions for processes in CROPGRO-Soybean. p. 19-20. In: Proceedings Biological Systems Simulation Conference. University of Florida, Gainesville, Florida.
- Bannayan, M., G. Hoogenboom, and N.M.J. Crout. 2004. Photothermal impact on maize performance: a simulation approach. Ecological Modeling 180(2-3):277-290.
- Banterng, P., A. Patanothai, K. Pannangpetch, S. Jogloy, and G. Hoogenboom. 2004. Determination of genetic coefficients of peanut lines for breeding applications. European Journal of Agronomy 21(3):297-310.
- Hoogenboom, G., J.W. White, and C.D. Messina. 2004. From genome to crop: Integration through simulation modeling. Field Crops Research 90(1):145-163.
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Progress 01/01/03 to 12/31/03
Outputs
A model that can predict the impact of gene action on dry bean growth, development and yield, called GeneGro, was linked to the Cropping System Model (CSM). The model CSM is the next generation crop simulation model that replaced the generic grain legume model CROPGRO and the generic grain cereal model CERES. CSM uses a modular approach, which means those computer modules that simulate certain processes can be easily replaced. CSM simulates crop growth, development and yield, as well as the plant and soil water, nitrogen and carbon balance. Inputs include daily weather data, soil surface and profile information, and crop management as well as genetic information in the form of cultivar and species parameters and functions. The CSM-Genegro model was able to account for seven genes and their physiological traits and impacts on dry bean growth, development and yield. The model was run for three dominant dry bean production regions to determine which gene combinations
would produce the highest yield. In addition, the CSM-Genegro model was used to determine the potential impact of climate change and potential adaptation with respect to gene combinations that produce the highest yield and associated crop parameters, such as the number of days to anthesis and physiological maturity, and are adapted to different local environments. A second modeling study was conducted for Brazil, using the CSM-Dry Bean model, to determine the impact of different combinations of irrigation and nitrogen fertilizer not only on yield, but also on environmental variables such nitrogen leaching.
Impacts
Crop simulation models and system analysis play critical roles in helping to determine research priorities and to identify research gaps. The application of crop simulation can result in outputs that can be used by policy and decision makers.
Publications
- Garcia y Garcia, A., D. Dourado Neto and G. Hoogenboom. 2003. The effect of nitrogen fertilizer on dry bean production under rainfed and irrigated conditions. In: 395-396. Annals of the XIII Brazilian Congress of Agrometeorology, Volume 1. Santa Maria, RS. Brazil. Santa Maria: Soceidade Brasileira de Agrometeorologia/Univesidade de Federal de Santa Maria.
- Garcia y Garcia, A., C. Tojo Soler, D. Dourado Neto and G. Hoogenboom. 2003. Seasonal analysis of dry bean productivity for different nitrogen fertilizer levels in Piracicaba, Sao Paulo, Brazil. Annual Report of the Bean Improvement Cooperative, Volume 46:107-108.
- Hoogenboom, G., J.W. White, and C. Messina. 2003. From genome to crop integration through simulation modeling. Annual Meetings Abstracts (CD-ROM). American Society of Agronomy, Madison, WI.
- White, J.W., G. Hoogenboom, and C. D. Messina. 2003. From genome to crop: using gene-based models to assess impact of global change in agroecosystems. GCTE/Land Meeting, Morelia, Mexico.
- Hoogenboom, G., and J.W. White. 2003. Improving physiological assumptions of simulation models by using gene-based approaches. Agronomy Journal 95(1):82-89.
- Jones, J.W., G. Hoogenboom, C.H. Porter, K.J. Boote, W.D. Batchelor, L.A. Hunt, P.W. Wilkens, U. Singh, A.J. Gijsman, and J.T. Ritchie. 2003. DSSAT Cropping System Model. European Journal of Agronomy 18:235-265.
- White, J.W., and G. Hoogenboom. 2003. Gene-based approaches to crop simulation: past experiences and future opportunities. Agronomy Journal 95(1):52-64.
- Hoogenboom, G. 2003. Crop growth and development. p. 655-691. In: [D.K. Bendi and R. Nieder] Handbook of Processes and Modeling in the Soil-Plant System. The Haworth Press, Binghamton, New York.
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Progress 01/01/02 to 12/31/02
Outputs
The generic grain legume model CROPGRO, which includes options to simulate growth, development and yield for soybean, peanut and common bean, was modified in order to be able to simulate yield for velvet bean or mucuna as function of environmental conditions and crop management factors. The model was evaluated with data obtained from different ecoregions and it showed that it was able to predict growth and development fairly accurately. This demonstrated that the generic approach implemented in the grain legume model CROPGRO can easily adopted and applied to other legume species. Velvet bean is a cover crop and can be used to help protect fallow land against erosion and degradation and improve soil quality and long-term sustainability. In a separate project, the spatial variability of crop yield and water use was evaluated. The CROPGRO dry bean model was linked to a Geographic Information System (GIS) and soil and weather data were presented as spatial inputs to the
model. The model was used to predict yield for each unique grid cell or polygon and final results were scaled up across the region. The linkage of the crop simulation model with GIS showed to be an effective tool for understanding the interaction between spatial variability of environmental variables, including weather and soil conditions and crop management. A third project studied the use and applicability of regional yield trial data on determining genetic coefficients of the crop simulation models. In many cases, cultivar or genetic coefficients are not readily available, especially for new or recently released cultivars. Procedures are therefore needed to help determine these coefficients based on existing yield trials. It was found that yield trial data conducted in Georgia and North Carolina for soybean could be used to determine the cultivar coefficients of the CROPGRO-Soybean model fairly accurately.
Impacts
Crop simulation models and system analysis play critical roles in helping to determine research priorities and to identify research gaps. The application of crop simulation can result in outputs that can be used by policy and decision makers.
Publications
- Faria, R.T., D. de. Oliveira and G. Hoogenboom. 2002. Drybean development and yield simulation by CROPGRO. Annual Report of the Bean Improvement Cooperative, Volume 45:206-207.
- Hartkamp, A.D., G. Hoogenboom, and J.W. White. 2002. Adaptation of the CROPGRO growth model to velvet bean as a green manure cover crop: I. Model development. Field Crops Research 78(1):9-25.
- Hartkamp, A.D., G. Hoogenboom, R. Gilbert, T. Benson, S.A. Tarawali, A. Gijsman, W. Bowen, and J.W. White. 2002. Adaptation of the CROPGRO growth model to velvet bean as a green manure cover crop: II. Model evaluation and testing. Field Crops Research 78(1):27-40.
- Heinemann, A.B., G. Hoogenboom, and R.T. de Faria. 2002. Determination of spatial water requirements at county and regional levels using crop models and GIS: An example for the State of Parana. Agricultural Water Management 52(3):177-196.
- Mavromatis, T., K.J. Boote, J.W. Jones, G.G. Wilkerson, and G. Hoogenboom. 2002. Repeatability of model genetic coefficients derived from soybean performance trails across different states. Crop Science 42(1)76-89.
- Nielsen, D.C., L. Ma, L.R. Ahuja, and G. Hoogenboom. 2002. Simulating soybean water stress effects with RZWQM and CROPGRO models. Agronomy Journal.94(6):1234-1243.
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Progress 01/01/01 to 12/31/01
Outputs
The Decision Support System for Agrotechnology Transfer (DSSAT) encompasses computer models that simulate growth, development and yield for more than 17 agronomic crops, including common bean. In order to facilitate the analysis of cropping systems, rather than single crops, a DSSAT Cropping System Model is being developed. The DSSAT-CSM is a unique approach and provides a uniform platform for the simulation of the soil water, nitrogen and carbon balance and at the same provides the capabilities to simulate growth for different crops. Especially for the simulation of crop rotations and sequencing, this new approach is critical, as it allows for a dynamic and continuous simulation of all soil processes. This has application potential to address issues such as carbon sequestration. The concept and design of the DSSAT-CSM have been finalized. The existing generic grain legume model CROPGRO and grain cereal model CERES, as well as the potato model SUBSTOR have been
combined into one model that can simulate growth and development for most of the important agronomic crops. The computer code is currently being developed and will be followed by an evaluation with measured data to establish the credibility of this modeling system. One of the critical inputs for crop simulation models are the genetic or cultivar coefficients. These coefficients correspond to some of the unique cultivar characteristics, such as the number of days to flowering, first seed and physiological maturity, sensitivity to photoperiod, seed size and other physiological traits. In many cases modelers are not in a position to obtain this information, due to the requirements of detailed experiments. A new method was developed in which the measurements taken in variety trials can be used to determine these coefficients. This provides for a unique interaction between variety evaluation trials, breeders, crop physiologists and crop modelers to help understand the impact of weather,
soil and other environmental conditions on crop yield. This approach was tested for soybean and is now being evaluated for peanut and other grain legumes.
Impacts
Crop simulation models and system analysis play critical roles in helping to determine research priorities and to identify research gaps. The application of crop simulation can result in outputs that can be used by policy and decision makers.
Publications
- Hoogenboom, G. 2001. Crop growth and development. [In: D.K. Bendi and R. Nieder] Processes in the Soil-Plant System: Modeling Concepts and Applications. The Haworth Press, Binghamton, NY. (in press).
- Hunt, L.A., J.W. White, and G. Hoogenboom. 2001. Agronomic data: Advances in documentation and protocols for exchange and use. Agricultural Systems 70:477-492.
- Mavromatis, T., K.J. Boote, J.W. Jones, A. Irmak, D. Shinde, and G. Hoogenboom. 2001. Developing genetic coefficients for crop simulation models with data from crop performance trials. Crop Science 41(1):40-51.
- Hoogenboom, G., G.R. Rodriguez, P.K. Thornton, and S. Tarawali. 1999. Development of a forage model for livestock production systems in Africa. In: Proceedings - Third International Symposium on Systems Approaches for Agricultural Development [CD-ROM Computer File], International Potato Center (CIP), Lima, Peru.
- Boote, K.J., F. Sau, J.W. Jones and G. Hoogenboom. 2001. Modeling growth and yield of grain legumes: soybean, phaseolus, and faba bean. p. 38-39. In: Proceedings of the 4th European Conference on Grain Legumes, 8-12 July, 2001, Cracow, Poland.
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