regard the amount of significant negative trend of ΣEVI pixels (in percentage) among the whole acting scope. Colored dots describe the agro-regional zones according to the Tegemeo survey in close relation to the AEZ-approach by FAO.
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2.3 Interplays among biophysical and socio-economic variables
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Figure IV.5 shows ΣEVI and ΣRFE over time in the study area. Looking at the actual development of ΣEVI and ΣRFE the dependencies again become clear despite an obvious decreasing peak from 2007 to 2009. A drop in ΣEVI is observed in 2009 which is most likely not related to slightly decreasing rainfall trends only. Certain trigger events are identified in 2007 and 2008 which had an impact on productivity trends, especially decreasing trends: the post-election crisis in 2007 and the world economy crisis in 2008. As mentioned both will be addressed in the following analysis.
Especially in the High Potential Maize Zone a decrease in productivity could be observed57. The decreasing trend in 2011 again can be related to the maize disease, called Maize lethal necrosis (MLN), occurring in Eastern Africa58.
Based on the pixel information all pixels with negative, positive or stable trends were calculated.
Analyzing the interplay among other biophysical variables on the pixel-level furthermore showed a positive correlation of 0.5084 between AI and EVI and a negative correlation of -0.6632 between SRTM and RFE. According to the IPCC 2014 only low vulnerability of ecosystems to biome shifts are expected in the study area, western Kenya in particular (Field et al., 2014, Figure 22-4)59. Climate change is nevertheless mentioned to affect crop production worldwide. In Kenya, and Eastern Africa in general, climate change can improve also maize production by warmer climate conditions in locations of high elevation such as in the high potential maize zones in the study area referring to the A1F1 scenario (Field et al., 2014; Thornton, 2014). With regard to these prospects climate change was not highlighted in this study.
Impact of the three main biophysical variables – AI, PET and RFE – and their relationship to negative and positive trends are listed in Table IV.2.
Table IV.2: Correlations between biophysical variables – Aridity Index, Potential Evapotranspiration and Rainfall Estimates – and productivity trends on the village level.
Neg_0.05 Signi Neg Pos_0.05 Signi Pos Stable
AI 0.68 0.56 -0.67 -0.29 -0.66
PET 0.54 0.28 -0.03 0.02 -0.02
RFE -0.03 0.22 0.35 0.03 0.35
57 See also Annex 8.
58 Information during field research from several sources and stated by the international maize and wheat
improvement Center (http://www.cimmyt.org/en/where-we-work/africa/item/maize-lethal-necrosis-mln-disease-in-kenya-and-tanzania-facts-and-actions) (last accessed: 08.02.2015).
59 The model to calculate vulnerability of ecosystems to biomes shifts is based on historical climate data (1901-2002) and projected vegetation (2071-2100) (Field et al. 2014, Figure 22-4: 1215)
101 Socio-economic interplay
The Tegemeo household panel survey provides different information that helped to get insights in social and economic activities within the households of the study area. Data are collected for agricultural inputs such as amount of fertilizer use, field size, land tenure or the cultivation system such as e.g. rainfed agriculture versus irrigation. All different dimensions of marginality are also fully represented in the local approach by extracting and including information on education, health, income, ownership of assets, access to the next agglomeration and market, infrastructure and information, and use of agricultural technologies.
Data merging and analysis was made with STATA12 and R. After extracting the information for all four years (2000, 2004, 2007 and 2010) trends were calculated for each village within the given time period of the survey (2000-2010). Several relationships could be observed by pair wise correlation among the different trends of the socio-economic indicators. Most of the relationships were already expected such as a positive correlation between income (whether from crop or livestock or in general) and ownership of assets. People having own land make use of credits, represented by a positive correlation of 0.4328 between these two variables. Farmers could either use the credit for buying own land or to afford seeds and fertilizer to guarantee further income.
Around 90% of all farms in the research area are based on rainfed agriculture while only about 10%
of farms are irrigated in 2007. In 2010 a slight decrease in rainfed agriculture among the villages in the study area of around 5% could be observed which has an exact increase in irrigated agriculture involving60. A relationship was found among having rainfed or irrigated agriculture in combination with livestock income. People that can afford irrigation on their fields are having more livestock or so to say those farmers who can also gather income from livestock are able to irrigate their fields61. Accessibility should play a key role when it comes to productivity even if it has to be kept in mind that the study area is already characterized by a good infrastructure. This is particularly valid for the northern area with commercial maize farming on large-scale farms (WRI, 2007). It could be observed that the longer it takes a farmer to get to the next agglomeration the less hybrid maize and fertilizer is used. Also the crop diversification index is higher, meaning a higher number of different crops planted, the closer a village was located to the next bigger agglomeration. This is again linked to the factor having access according to the definition of marginality. Accessibility seems to also have a relationship to ownership of land as the further away a village is located according to the definition of accessibility the fewer farmers do own their own land. Self-evident positive correlations between distance and price of seed or fertilizer are mentioned for the sake of completeness.
Seed prices go in line with manure use and show a positive correlation. The higher the price the more capital a farmer has to afford to make use of improved varieties which may lead to less available
60 Average percentage based on Tegemeo survey data from 2007 and 2010. Data on irrigation and rainfed based agriculture were only available for the years 2007 and 2010.
61 Positive correlation (0.5) between trend in irrigation and livestock income. Negative correlation (-0.52) between trend in rainfed agriculture and livestock income.
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capital for other agricultural inputs such as fertilizer. A negative correlation was observed among trends of land ownership and seed prices. The higher the price trend the fewer farmers do own land but rather rent land. But both relationships are not showing high correlations, especially the one between renting land and seed prices.
Education and Mortality had the expected correlation by showing a decrease in education and an increase in decreasing productivity trends while also an increasing mortality showed higher amounts of decreasing productivity. Especially in high productive areas where innovations such as hybrid seeds or chemical fertilizer are used basic knowledge is necessary. The variable of education was represented by the years the members of a household attended school. Mortality again let reflect on health and was represented by the number of households that experienced prime-age mortality since the previous survey.
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3. The Crucial Triangle: Interplay of Land Degradation, Land Use/Land
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decreasing vegetation trends and price increase for fertilizer and seeds showing that the higher the price trend was the bigger the area affected by decreasing vegetation trends. Consequently a reduction in planting and a decrease in fertilizer use was the outcome.
Figure IV.6 shows the development of prices of seed and fertilizer, particularly for maize and vegetables, based on the Tegemeo household survey. The increase of prices with regard to fertilizer and seeds can be clearly identified from 2007 to 2010. But there is also a general tendency for price increase. The seed price (red line) has a very sharp increase from 2007 to 2010 which most likely concludes to less cultivation or poor land management with regard to fewer or no fertilizer use.
Figure IV.6: Development of Price Trends of seeds and fertilizer in the study area based on information derived from the Tegemeo Survey 2000-2010. The red line shows the seed price, the grey and black line seed costs.
The post-election crisis affected the western and Rift Valley area of Kenya the most. Among the 1,133 reported deaths due to the violence, 744 came from former Rift Valley and 134 from former Nyanza Province (Kriegler & Waki, 2009). Violence was concentrated here based on the ethnical group distribution within the country. Besides the mentioned deaths also around 500,000 people who had to leave their homes were reported after the post-election violence (Gibson &
Long, 2009). Map IV.5 shows the areas that were affected during the election phase (right) and those which are generally affected by violence because of ethnic affiliation (left). High rates of
116 118 120 122 124 126
0 10 20 30 40 50 60
2000 2004 2007 2010
Seedprice KES/kg
KES/kg
Market Trend for seed and fertilizer
in Local Study Area (2000-2010) Average farmgate fertilzer price, maize (KES/kg)
Average district median fertilzer price, maize (KES/kg)
Average farmgate fertilzer price, veg (KES/kg)
Average district median fertilzer price, veg (KES/kg)
Average farmgate fertilzer price, tea (KES/kg)
Average district median fertilzer price, tea (KES/kg)
Seed cost per kg/maize sales price per kg (actual grain price)
Seed cost per kg/maize sales price per kg (deistrict median grain price)
Seed price (seed cost per kg)
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violence can be found in central/southern Rift Valley including the counties Laikipia, Nyeri, Nyandarua, Muranga, Kirinyaga, Embu, Machakos, and Kiambu.
Map IV.5: Violence in Kenya; modified from the Armed Conflict Location and Event Data Project