Assessing the impact of agricultural drought on maize prices in Kenya with the approach of the SPOT-VEGETATION NDVI remote sensing

Sakirat M. Shuaibu, John A. Ogbodo, Ejiet J. Wasige, Sani A. Mashi


The high cost of maize in Kenya is basically driven by East African regional commodity demand forces and agricultural drought. The production of maize, which is a common staple food in Kenya, is greatly affected by agricultural drought. However, calculations of drought risk and impact on maize production in Kenya is limited by the scarcity of reliable rainfall data. The objective of this study was to apply a novel hyperspectral remote sensing method to modelling temporal fluctuations of maize production and prices in five markets in Kenya. SPOT-VEGETATION NDVI time series were corrected for seasonal effects by computing the standardized NDVI anomalies. The maize residual price time series was further related to the NDVI seasonal anomalies using a multiple linear regression modelling approach. The result shows a moderately strong positive relationship (0.67) between residual price series and global maize prices. Maize prices were high during drought periods (i.e. negative NDVI anomalies) and low during wet seasons (i.e. positive NDVI anomalies). This study concludes that NDVI is a good index for monitoring the evolution of maize prices and food security emergency planning in Kenya. To obtain a very strong correlation for the relationship between the wholesale maize price and the global maize price, future research could consider adding other price-driving factors into the regression models.

Key words: Seasonal anomalies, Drought, Food security, NDVI, Multiple linear regression

Data of the article

First received : 25 May2016 | Last revision received : 25 October 2016
Accepted : 14 November 2016 | Published online : 23 December 2016

Full Text:



AATF - African Agricultural Technology Foundation. (2010, May 4). Policy Brief. Reducing Maize Insecurity in Kenya. Retrieved from

Andrea, M. B., Prakash, N.K. D., & Zhuohua, T. (2011). Strengthening Institutional Capacity for Integrated Climate Change Adaptation and Comprehensive National Development Planning in Kenya. Millenium Institute, Washington DC, USA. pp 51.

Ariga, J., Jayne, T.S. & Nyoro, J. (2006). Factors driving the growth in fertilizer consumption in Kenya, 1990 -2005: Sustaining the momentum in Kenya and lessons for broader replicability in Sub-Saharan Africa. Tegemeo working paper 24/2006, Tegemeo Institute of Agricultural Policy and Development, Egerton University, Nairobi, Kenya.

Blackburn, G.A. (1998). Quantifying chlorophylls and ceroteniods at leaf and canopy scales: An evaluation of some hyperspectral approaches. Remote sensing of Environment, 66(2), 273 - 285.

Boken, V. K., Cracknell, A. P., Heathcote, R. L., & World Meteorological, O. (2005). Monitoring and predicting agricultural drought: a global study (Eds.). GB: Oxford University Press.

Brown, M. E., Pinzon, J. E., & Prince, S. D. (2006). The effect of vegetation productivity on millet prices in the informal markets of Mali, Burkina Faso and Niger. Climatic Change, 78(1), 181-202.

Chatfield C. (1995). Model uncertainty, data mining and statistical inference. Journal of the Royal Satistical Society, A(158), 419-466.

Dinku, T., Ceccato, P., Grover-Kopec, E. Lemma, M., Conner, S.J., & Ropelewski, C.F., (2007). Validation of satellite rainfall products over East Africa’s complex topography. International Journal of Remote Sensing 28(7), 1503 - 1524.

FAO - Food and Agriculture Organization (2011, June 5). The state of food security in the World: How does international price volatility affect domestic economies and food security. Retrieved from

FAO - Food and Agriculture Organization (2013, June). Kenyan domestic staple food price volatility for the period 1995-2012. Retrieved from

FEWSNET - Famine Early Warning Systems Network (2011, June). Kenya Food Security Outlook. Retrieved from

FEWSNET - Famine Early Warning Systems Network (2013, December). Kenya Food Security Brief.

FEWSNET - Famine Early Warning Systems Network (2011, June). Kenya Food Security Outlook. Retrieved from

FEWSNET - Famine Early Warning Systems Network (2010, May). Food Security Framework. Retrieved from

FEWSNET - Famine Early Warning Systems Network (2009, April). KENYA Food Security Outlook Update. Retrieved May 29, 2011, from

Gitelson, A.A. & Merzlyak, M.N. (1997). Remote estimation of chlorophyl content in higher plant leaves. International Journal of Remote Sensing, 18 (12), 2691 - 2697.

Grace, K., Brown, M. & McNally, A. (2014). Examining the link between food prices and food insecurity: A multi-level analysis of maize price and birthweight in Kenya. Journal of Food Policy, 46, 56–65.

High Level Panel of Experts on Food Security and Nutrition. (2013, June). Biofuels and food security. A report by the HLPE of the Committee on World Food Security, Rome .

High Level Panel of Experts on Food Security and Nutrition. (2011, July). Price volatility and food security. A report by the HLPE of the Committee on World Food Security, Rome.

Huete, A. R., Karl, F. H., Tomoaki, M., Xiangming, X., Didan, K., Willem, L., et al. (2006). Vegetation Index greenness global data set. National Aeronautics and Space Administration. USA. pp 56.

Ifejika, S., Chinwe, Kiteme, B., & Wiesmann, U. (2008). Droughts and famines: The underlying factors and the causal links among agro-pastoral households in semi-arid Makueni district, Kenya. Global Environmental Change Journal, 18 (1), 220-233.

Jayne, T. S., Myers, Robert, J., & James Nyoro. (2008). The effects of NCPB marketing policies on maize market prices in Kenya. Journal of International Association of Agricultural Economics, 38, 313–325.

Kangasniemi, J., Staatz, J., Phillips, C., Diskin, P., & Diagne, A. (1993). Food Sector Instability and Food Aid in Sub-Saharan Africa: Michigan State University,East Lansing, Michigan.

Kangethe, E. (2011, October 10). Situational Analysis: Improving food safety in the maize value chain in Kenya. FAO Technical Report. Retrieved from

Kibaara, B. W. (2005). Technical efficiency in kenyan maize production: An application of the stochastic frontier approach. Colorado State University, Fort Collins. pp 23 - 28.

Kogan, F. N. (1995). Application of vegetation index and brightness temperature for drought detection. Advances in Space Research, 15, 91-100.

Jacquin, A., Sheeren, D., & Lacombe, J.P. (2010). Vegetation cover degradation assessment in Madagascar savvana based on trend analysis of MODIS NDVI time series. International Journal of Applied Earth Observation and Geoinformation, 12(S1), 3-10.

Lewis, J. E., Rowland, J., & Nadeau, A. (1998). Estimating maize production in Kenya using NDVI: Some statistical considerations. International Journal of Remote Sensing, 19(13), 2609-2617.

Malmström, C. M., Thompson, M. V., Juday, G. P., Los, S. O., Randerson, J. T., & Field, C. B. (1997). Interannual variation in global-scale net primary production: Testing model estimates. Global Biogeochem. Cycles, 11(3), 367-392.

Metcalfe, A. V., & Cowpertwait, P. S. P. (2009). Time Series Data. Introductory Time Series with R (pp. 1-25). New York, NY: Springer.

Minot, N. (2010). Transmission of World Food Price Changes to Markets in sub-Saharan Africa. Washington: International Food Policy Research Institute.

Mishra, A. K., & Singh, V. P. (2010). A review of drought concepts. Journal of Hydrology, 391(1-2), 202-216.

Mutanga, O. & Skidmore, A.K. (2004). Narrow band vegetation indices overcome the saturation problem in biomass estimation. International Journal of Remote Sensing, 57, 263 - 272.

Mutanga, O. & Skidmore, A.K. (2004). Narrow band vegetation indices overcome the saturation problem in biomass estimation. International Journal of Remote Sensing, 57, 263 - 272.

Nguyen, H.T. & Lee, B. W. (2006). Assessment of rice leaf growth and nitrogen status by hyperspectral canopy reflectance and partial least square regression. European Journal of Agronomy, 24(4), 349 - 356.

Nyoro, J. K. (2002). Kenya’s Competitiveness in Domestic Maize Production: Implications for Food Security. Nairobi: Tegemeo Institute, Egerton University, Kenya.

Nyoro, J. K., Kirimi, L., & Jayne T.S. (2004). Competitiveness of Kenyan and Ugandan Maize Production: Challenges for the Future. Nairobi: Tegemeo Institute of Agricultural Policy and Development.

Pettorelli, N., Vik, J. O., Mysterud, A., Gaillard, J.-M., Tucker, C. J., & Stenseth, N. C. (2005). Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in Ecology & Evolution, 20(9), 503-510.

Prince, S. D., & Goward, S. N. (1995). Global Primary Production: A Remote Sensing Approach. Journal of Biogeography, 22(4/5), 815-835.

Rojas, O., Vrieling, A., & Rembold, F. (2011). Assessing drought probability for agricultural areas in Africa with coarse resolution remote sensing imagery. Remote Sensing of Environment, 115(2), 343-352.

Rouse, J. W., Haas, R.H., Schell, J.A., Deering, D.W. & Harlan, J.C. (1974). Monitoring the vernal advancement and retrogradation of natural vegetation. NASA/GSFC, Type III Final Report, M.D. Greenbelt, pp. 371.

Rowland, J. D., Brock , J. C., Nadeau, C. A., Klaver, R. W., Moore, D. G., & Lewis, J. E. (1996). Use of Vegetative index to characterize drought patterns in East Africa. In Raster Imagery in Geographic Information System ( Santa Fe, NM: Onward Press. pp. 247-255.

Singh, R. P., Roy, S., & Kogan, F. (2003). Vegetation and temperature condition indices from NOAA AVHRR data for drought monitoring over India. International Journal of Remote Sensing, 24(GEOBASE), 4393-4402.

Short C., Mulinge W. & Witwer M. (2012). Analysis of incentives and disincentives for maize in Kenya. Technical notes series, MAFAP, FAO, Rome

Udelhoven, T., Stellmes, M., del Barrio, G., & Hill, J. (2009). Assessment of rainfall and NDVI anomalies in Spain (1989–1999) using distributed lag models. International Journal of Remote Sensing, 30(8), 1961-1976.

Wilhite, D., (2005). Drought and water crisis: science, technology and management issues. Taylor and Francis Group.

World Resources Institute. (2007). Nature’s benefits in Kenya: An atlas of ecosystems and human well-being. World Resources Institute. Washington DC (USA) and Nairobi (Kenya).

Wylie, B.K., Meyer, D.J., Tieszen, L.L. & Mannel, S. (2002). Satellite mapping of surface biophysical parameters at the biome scale over the North American grasslands: A case study. Remote sensing of Environment, 79(2-3), 266-278.


  • There are currently no refbacks.

Comments on this article

View all comments



 Google Scholar H5 index 3 

Sponsoring Organisations

Logo Agrarekologie Uni Kassel