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Comparing Maize Potential yields Predicted using Actual and Interpolated Weather data in Uganda

Author Affiliations

  • 1Dept. of Environmental management, Makerere University, Kampala, UGANDA
  • 2 Dept. of Agricultural and Bio-systems Engineering, Makerere University, Kampala, UGANDA

Int. Res. J. Environment Sci., Volume 2, Issue (10), Pages 63-70, October,22 (2013)


Acquisition of measured weather data in Uganda for crop growth modeling is a challenge due to the low number of weather stations. Often, rainfall, maximum and minimum temperatures are measured. Total solar radiation is only measured at few weather stations due to shortage of sunshine duration recorders, the time graded paper strips or the newer automated weather stations (AWOS). A number of agencies do fill this void and provide on-line interpolated daily weather data to enable long-term simulations. A dynamic crop growth model CERES within the DSSAT modeling suite was used in order to evaluate simulation results obtained using actual and interpolated weather data from Kawanda, Central and Mbarara, south-western Uganda. Generic coefficients for very short, short, medium and long season maize varieties with in DSSAT were used. Farmer planting dates for the two cropping seasons were used to start the simulation. Results showed that at Kawanda, the average actual and interpolated maximum temperature were comparable, while at Mbarara, maximum temperatures were underestimated with a deviation of 3°C. At both sites, actual and interpolated minimum temperatures were comparable. The average actual total solar radiation at Kawanda was lower, probably indicating a shift in the AWOS radiation sensors. At Mbarara, the interpolated and measured values are comparable, indicating that the solarimeter method is still very reliable. RMSEs between actual and predicted potential yields at Kawanda were larger; very short (942 kg ha 1), short (1176 kg ha 1), medium (1864 kg ha 1) and long season maize (3055 kg ha 1). Actual radiation measurements at this site were lower, which emphasizes the importance of re-calibrating radiation sensors at least every two years. At Mbarara, the RMSEs for very short (418 kg ha 1), short (618 kg ha 1), medium (1056 kg ha 1) and long season (1896 kg ha 1) were low and acceptable. Interpolated data from the NASA can be used to predict potential yields and for long-term simulations in absence of measured weather data.


  1. Wassmann R., Jagadish S.V. K., Heuer S., IsmailA., Redona E., Serraj R., Singh R.K., Howell G.,Pathak H. and Sumeth K., Climate ChangeAecting Rice Production: The Physiological andAgronomic Basis for Possible Adaptation Strategies, Adv. Agron., 101, 59–122 (2009)
  2. Lövenstein H., LantingaE.A., Rabbinge R. and van Keulen H., Principles of production ecology. Department of Theoretical production ecology and Centre for Agrobiological Research (CABO-DLO), Wageningen, The Netherlands, 7 (1995)
  3. Adger W.N., Huq S., Brown K., Conway D. and Hulme M., Adaptation to climate change in the developing world, Progress Develop. Stud., 3(3), 179 195 (2003)
  4. Hepworth N. and Goulden M., Climate Change in Uganda: Understanding the implications and upraising response, LTS International Edinburg (2008)
  5. van Asten P.J.A., Fermont A.M. and Taulya G., Drought is a major yield loss factor for rain-fed East Africa highland banana, Agric. Water Manag., 98(4), 541 552 (2011)
  6. Traerup S.L.M. and Mertz O., Rainfall variability and household coping strategies in northern Tanzania: a motivation for district-level strategies, Reg. Environ. Change, 11(3), 471 481 (2011)
  7. Stampone M.D., Hartter J., Chapman C.A. and Ryan S.J., Trends and variability in localized precipitation around Kibale National Park, western Uganda, Africa, Res. J Environ. Earth Scie.3(1), 14 23 (2011)
  8. Nonhebel S., Inaccuracies in weather data and their effects on crop growth simulation results, 1. Potential production, Climate Res. , 47 60 (1994)
  9. Angstrom, A. Solar and terrestrial radiation, Q.J.R. Meteorol.Soc., 50,121 125 (1924)
  10. Hoogenboom G., Jones J.W., Porter C.H., Wilkens P.W., Boote K.J., Batchelor W.D., Hunt L.A. and Tsuji G.Y. (Eds). Decision Support System for Agrotechnology Transfer Version 4.0. Vol. 1. University of Hawaii, Honolulu, HI (2003)
  11. Jones C.A. and Kiniry J.R., CERES-Maize, a simulation model of maize growth and development, Texas A&M University Press, College Station (1986)
  12. Walker N.J. and Schulze R.E., An assessment of sustainable maize productdifferent management and climate scenarios for smallholder agro-ecosystemNatal, South Africa, Phys. Chem. Earth, 31, 995 1002 (2006)
  13. Wasige J.E., Assessment of the impact of climate change and climate variability on crop production in Uganda. Project report submitted to the Global change system for analysis, research and training (START) and the National science foundation (NFS) (2009)
  14. Davis Solar radiation sensor, standard, industrial and vantage versions. Rev C manual (1/12/01), Davis instruments corporation, Hayward, CA, USA (2000)
  15. Jennings S.B., Brown N.D. and Sheil D., Assessing forest canopies and understory illumination: canopy closure, canopy cover and other measures, Forestry, 72(1), 60 73 (1999)
  16. Van Wart J., Kersebaumb K.C., Peng S., Milnera M. and Cassman K.G., Estimating crop yield potential at regional to national scales, Field Crops Res., 143, 34 43 (2013)
  17. Van Wart J., Grassini P., and Cassman K.G., Impact of derived global weather data on simulated crop yields, Global Chang. Biol.doi: 10.1111/gcb.12302 (2013)