HarvestChoice | Crop Growth Models

Four different crop models are currently being used in HarvestChoice. The relevance of individual models to evaluating the impacts of specific scenarios and intervention possibilities is conditioned by the original goal, conceptual design and operational versatility of each model, as well as by the experience and creativity of the analyst. Some models perform better than others in specific contexts, e.g. when applied to particular climates, crops, cropping patterns/rotations, soil quality indicators, and potential management interventions.

	DSSAT	APSIM	ORYZA	WOFOST
Correspondent	Jawoo Koo (IFPRI)	John Dimes (ICRISAT)	Robert Hijmans (IRRI)	Robert Hijmans (IRRI)
Purpose	To simulate crop growth and yield responses to scenarios of field management changes	To simulate crop growth and yield responses to scenarios of field management changes	To simulate rice growth and yield responses to scenarios of field management changes	To simulate potential crop growth and yield under limited and unlimited nutrients and water supplies
Crops	Maize Wheat Potato Sorghum Millet Cassava	Dry bean Cowpea Pigeon pea Chick pea Groundnut Weed	Rice	Wheat Maize Potato Sweet potato Sorghum Millet Cassava Dry bean Cowpea Pigeon pea Chick pea Groundnut Weed
Scenarios	Yield responses to planting window, germplasm, and nitrogen and water managements	Yield responses to planting window, germplasm, and nitrogen and water managements	Yield responses to planting window, germplasm, and nitrogen and water managements	Yield responses to planting window and germplasm
Note	Soil, weather, and climate data in sub-Saharan Africa at 30' grid resolution	Capable of simulating phosphorus dynamics and intercroppings	Modified ORYZA 2000 runs from the DIVA-GIS	Relatively simple model that can be used in parallel for developing qualitative and simplified index development
Recent Application Examples	Estimating soil carbon sequestration potential¹ Assessing regional climate change impacts²Gene-based modeling to simulate yield responses to environment³	Optimizing irrigation strategy⁴ Quantifying benefits of climate forecasts⁵ Groundwater management⁶	Assessing climate change impacts⁷ Nutrient management⁸ Water management⁹	Seasonal yield forecast¹⁰ Regional yield forecast¹¹ Quantifying yield potential¹²

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Crop Modeling Control Points

Together with the high-resolution sub-national agricultural statistics data (e.g., Crop Allocation Model), important datasets to evaluate the results of crop growth modeling are the databases of region-wide field trials managed by agricultural research institutes, including CGIAR centers and FAO. HarvestChoice georeferences and compiles the dataset in an easily accessible format to be used in a range of crop systems modeling platforms (e.g, ICASA format for DSSAT model).

The following map show the location of CIMMYT maize trials in sub-Saharan Africa. Each trial location reports the performance of local and improved varieties in response to a range of treatments, including their tolerance to local biotic/abiotic constraints.

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Crop Nutrient Responses

FAO FERTIBASE is a compilation of crop nutrient response data managed by the FAO Fertilizer Programme. The aim of this database is "...to allow for the extraction of yield data per agro-ecological zone for the main food crops in a specific country." For example, the following map shows the location of FAO maize trial locations and reported yield response to different types/amount of fertilizers.

References

1. Koo, J., W.M. Bostick, J.B. Naab, J.W. Jones, W.D. Graham, and A.J. Gijsman. 2007. Estimating soil carbon in agricultural systems using ensemble Kalman filter and DSSAT-century. Transactions of the Asabe 50:1851-1865.

2. Xiong, W., I. Holman, D. Conway, E. Lin, and Y. Li. 2008. A crop model cross calibration for use in regional climate impacts studies. Ecological Modelling 213:365-380.

3. Messina, C.D., J.W. Jones, K.J. Boote, and C.E. Vallejos. 2006. A gene-based model to simulate soybean development and yield responses to environment. Crop Science 46:456-466.

4. Peake, A.S., M.J. Robertson, and R.J. Bidstrup. 2008. Optimising maize plant population and irrigation strategies on the Darling Downs using the APSIM crop simulation model. Australian Journal of Experimental Agriculture 48:313-325.

5. Yu, Q., E.L. Wang, and C.J. Smith. 2008. A modelling investigation into the economic and environmental values of 'perfect' climate forecasts for wheat production under contrasting rainfall conditions. International Journal of Climatology 28:255-266.

6. Qureshi, M.E., S.E. Qureshi, K. Bajracharya, and M. Kirby. 2008. Integrated biophysical and economic modelling framework to assess impacts of alternative groundwater management options. Water Resources Management 22:321-341.

7. Das, L., D. Lohar, I. Sadhukhan, S.A. Khan, A. Saha, and S. Sarkar. 2007. Evaluation of the performance of ORYZA2000 and assessing the impact of climate change on rice production in Gangetic West Bengal. Journal of Agrometeorology 9:1-10.

8. Jing, Q., B.A.M. Bouman, H. Hengsdijk, H. Van Keulen, and W. Cao. 2007. Exploring options to combine high yields with high nitrogen use efficiencies in irrigated rice in China. European Journal of Agronomy 26:166-177.

9. Feng, L.P., B.A.M. Bouman, T.P. Tuong, R.J. Cabangon, Y.L. Li, G.A. Lu, and Y.H. Feng. 2007. Exploring options to grow rice using less water in northern China using a modelling approach - I. Field experiments and model evaluation. Agricultural Water Management 88:1-13.

10. Marletto, V., F. Ventura, G. Fontana, and F. Tomei. 2007. Wheat growth simulation and yield prediction with seasonal forecasts and a numerical model. Agricultural and Forest Meteorology 147:71-79.

11. de Wit, A.M., and C.A. van Diepen. 2007. Crop model data assimilation with the Ensemble Kalman filter for improving regional crop yield forecasts. Agricultural and Forest Meteorology 146:38-56.

12. Wu, D.R., Q. Yu, C.H. Lu, and H. Hengsdijk. 2006. Quantifying production potentials of winter wheat in the North China Plain. European Journal of Agronomy 24:226-235.

Production Section

Crop Growth Models