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P. J. M. Cooper, J. Dimes, K. P. C. Rao, B. Shapiro, B. Shiferaw and S. Twomlow, “Coping Better with Current Climatic Variability in the Rain-Fed Farming Systems of the Sub-Sahara Africa: An Essential First Step in Adapting to Future Climate Change,” Agriculture Ecosystem and Environment, Vol. 126, No. 1-2, 2008, pp. 24-35. doi:10.1016/j.agee.2008.01.007

has been cited by the following article:

• TITLE: Rainfall Variability and Its Impact on Normalized Difference Vegetation Index in Arid and Semi-Arid Lands of Kenya

  AUTHORS: C. A. Shisanya, C. Recha, A. Anyamba

  KEYWORDS: Semi-Arid, NDVI Time Series, Southeast Kenya

  JOURNAL NAME: International Journal of Geosciences, Vol.2 No.1, February 24, 2011

  ABSTRACT: Agriculture in arid and semi-arid lands of Kenya is depends on seasonal characteristics of rainfall. This study seeks to distinguish components of regional climate variability, especially El Niño Southern Oscillation events and their impact on the growing season normalized difference vegetation index (NDVI). Datasets used were: 1) rainfall (1961-2003) and 2) NDVI (1981-2003). Results indicate that climate variability is persistent in the arid and semi-arid lands of Kenya and continues to affect vegetation condition and consequently crop production. Correlation calculations between seasonal NDVI and rainfall shows that the October-December (OND) growing season is more reliable than March-May (MAM) season. Results show that observed biomass trends are not solely explained by rainfall variability but also changes in land cover and land use. Results show that El Niño and La Niña events in southeast Kenya vary in magnitude, both in time and space as is their impact on vegetation; and that variation in El Niño intensity is higher than during La Niña events. It is suggested that farmers should be encouraged to increase use of farm input in their agricultural enterprises during the OND season; particularly when above normal rains are forecast. The close relationship between rainfall and NDVI yield ground for improvement in the prediction of local level rainfall. Effective dissemination of this information to stakeholders will go along way to ameliorate the suffering of many households and enable government to plan ahead of a worse season. This would greatly reduce the vulnerability of livelihoods to climate related disasters by improving their management.