Theoretical and Applied Climatology

, Volume 96, Issue 3–4, pp 275–280 | Cite as

Crop yield model validation for Cameroon

  • Munang TingemEmail author
  • Mike Rivington
  • Gianni Bellocchi
  • Jeremy Colls
Original Paper


A crop simulation model must first be capable of representing the actual performance of crops grown in any region before it can be applied to the prediction of climate variability and change impacts. A cropping systems model (CropSyst) simulations of crop productivity in the sub-Saharan Central African (using Cameroon as the case study) region, under the current climate were compared with observed yields of maize, sorghum, groundnut, bambara groundnut and soybean from eight sites. The model produced both over-and-under estimates, but with a mean percentage difference of only –2.8%, ranging from –0.6% to –4.5%. Based on these results, we judged the CropSyst simulations sufficiently reliable to justify use of the model in assessing crop growth vulnerability to climatic changes in Cameroon and else where.


Sorghum Thermal Time Crop Model Equatorial Zone Bambara Groundnut 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We acknowledge the help and assistance provided by Claudio O. Stöckle and Roger L. Nelson (Biological Systems Engineering Department, Pullman WA, USA) in using CropSyst.


  1. Abraha MG, Savage MJ (2006) Potential impacts of climate change on the grain yield of maize for the midland of Kwazulu-Natal, South Africa. Agric Ecosys Environ 115:150–160CrossRefGoogle Scholar
  2. AGRISTAT (2001) Semi-annual bulletin of the statistics of agricultural sector 2000/2001, DEPA, Ministry of Agriculture. Yaounde, CameroonGoogle Scholar
  3. Badini O, Stöckle CO, Franz EH (1997) Application of crop simulation modelling and GIS to agroclimatic assessment in Burkina Faso. Agric Ecosys Environ 64:233–244CrossRefGoogle Scholar
  4. Badini O, Stöckle CO, Jones JW, Nelson R, Kodio A, Keita M (2007) A simulation-based analysis of productivity and soil carbon in response to time-controlled rotational grazing in the West African Sahel region. Agric Syst 94:87–96CrossRefGoogle Scholar
  5. Batjes N (1995) A homogenised soil data file for global environmental research: a subset of FAO, ISRIC and NRCS profiles (version 1.0). Working paper 95/10, International Soil Reference Information Center (ISRIC), Wageningen, The NetherlandsGoogle Scholar
  6. Brassard JE (2003) Valuation des impacts de la hausse de la concentration atmospherique du CO2 et des changements climatiques sur la production agricole du Quebec. Me‘moire de Maitrise, Departement de Geographie, Universite de Montreal, QC, 193 ppGoogle Scholar
  7. DeLancey M, Mike D (2000) Historical dictionary of the Republic of Cameroon, 3rd edn. Scarecrow, Lanham, MDGoogle Scholar
  8. Farre I (1998) Maize (Zea mays L.) and sorghum (Sorghum bicolor L. Moench) response to deficit irrigation. Agronomy and Modelling, PhD Thesis, University of Lieida, Spain, 150 ppGoogle Scholar
  9. Fischer G, Shah M, Tubiello F, Van Velthuizen HT (2005) Socio-economic and climate change impacts on agriculture: an integrated assessment, 1990–2080. Philos Trans Roy Soc B 360:2067–2083CrossRefGoogle Scholar
  10. IPCC (2007) Working Group II Contribution to the Intergovernmental Panel on Climate Change Fourth Assessment Report: Climate Change 2007: impacts, adaptation and vulnerability. Brussels, BelgiumGoogle Scholar
  11. Molua EL (2003) Global climate change and Cameroon’s Agriculture: evaluating the economic impacts. PhD Thesis, Institute of Agricultural Economics, Georg-August University, Goettingen, Germany, 94 ppGoogle Scholar
  12. Moriondo M, Maselli F, Bindi M (2007) A simple model of regional wheat yield based on NDVI data. Eur J Agron 26:266–274CrossRefGoogle Scholar
  13. Ndemah RN (1999) Towards an integrated crop management strategy for the African stalk borer Busseola fusca (Fuller) (Lepidoptera: Noctuidae) in maize systems in Cameroon. PhD Thesis, University of Hannover, Hannover, Germany, 145 ppGoogle Scholar
  14. Neba A (1999) Modern geography of the Republic of Cameroon, 3rd edn. Neba, Bamenda, CameroonGoogle Scholar
  15. Priestly CHB, Taylor RJ (1972) On the assessment of surface heat flux and evaporation using large-scale parameters. Mon Weather Rev 100:81–82CrossRefGoogle Scholar
  16. Richards LA (1931) Capillary conduction of liquids in porous mediums. Physics 1:318–333CrossRefGoogle Scholar
  17. Ritchie JT, Singh U, Godwin DC, Bowen WT (1998) Cereal growth, development and yield. In: Tsuji GY, Hoogenboom G, Thornton PK (eds) Understanding options for agricultural production. Kluwer, Dordrecht, pp 79–98Google Scholar
  18. Rivington M, Matthews KB, Bellocchi G, Buchan K, Stöckle CO, Donatelli M (2006) An integrated assessment approach to conduct analyses of climate change impacts on whole-farm systems. Environ Model Softw 22:202–210CrossRefGoogle Scholar
  19. Rosenzweig C, Hillel D (1998) Climate change and the global harvest. PBD: 1998. United States, Oxford University Press, New York, 323 ppGoogle Scholar
  20. Stöckle CO, Donatelli M, Nelson R (2003) CropSyst, a cropping systems simulation model. Eur J Agron 18:289–307CrossRefGoogle Scholar
  21. Thornton PK, Jones PG (2003) The potential impacts of climate change on maize production in Africa and Latin America in 2055. Glob Environ Change 13:51–59CrossRefGoogle Scholar
  22. Thornton PK, Jones PG, Owiyo TM, Kruska RL, Herero M, Kristjanson P, Notenbaert A, Bekele N (2006) Mapping climate vulnerability and poverty in Africa. Report to the Department for International Development, ILRI, Nairobi, Kenya, 200 ppGoogle Scholar
  23. Tingem M, Rivington M, Azam Ali SN, Colls JJ (2007) Assessment of the ClimGen stochastic weather generator at Cameroon sites. Afr J Environ Sci Technol 1:86–92Google Scholar
  24. Tingem M, Rivington M, Azam Ali SN, Colls JJ (2008) Climate variability and maize production in Cameroon: simulating the effects of extreme dry and wet years. Singapore J Trop Geogr (in press)Google Scholar
  25. Worldbank (2007) World Development Indicators Database. Avaliable at Cited 24 December 2007
  26. World Fact Book (2007) (Cameroon): United States Central Intelligence Agency (CIA). Aavailable at factbook/geos/cm. Cited 9 December 2007

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Munang Tingem
    • 1
    Email author
  • Mike Rivington
    • 2
  • Gianni Bellocchi
    • 3
  • Jeremy Colls
    • 1
  1. 1.Agriculture and Environmental Science Division, School of BiosciencesUniversity of NottinghamNottinghamUK
  2. 2.Macaulay Institute, CraigiebucklerScotlandUK
  3. 3.Agrichiana FarmingMontepulcianoItaly

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