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    In our research we assess the sustainability performance of 400 smallholder farms practicing organic (i.e. certified or non-certified) and non-organic agriculture (i.e. conventional or other) using the Sustainability Monitoring and Assessment RouTine (SMART)-Farm Tool and examine differences between these farm categories using multivariate analyses. We also identify general gaps in sustainability performance for all farms. Murang'a County, Kajiado County.

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    This set of interviews is part of the 'Farmer Empowerment' project that focused on the impact of farmer organizations (FOs) on the socio-economic development of their FO-members. The interviews were conducted within a second field research in order to gather information on specific impact pathways of selected FOs that work to empower respective FO-members.

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    Using the methodology developed by Graw and Husmann (2014), the map overlays three indicators (high agricultural potential, high poverty mass and high yield gaps) to identify areas with high potential for agricultural development and poverty reduction in Kenya. Data sources and thresholds: Agricultural potential: Suitability of currently available land area for rainfed crops, using maximising crop and technology mix, FGGD map 6.61 (2005), High: top 3 suitability classes (medium high, high and very high) Poverty mass: Number of poor people in Kenya (by district), KIHBS (2005/06), High: >300,000 per district Yield gap: Yield gap for a combination of major crops, FAO/IIASA - GAEZ (2000/05), High: < 0.25 (on a scale from 0-1, with the highest value in Kenya ca. 0.44) District boundaries: Kenya Central Bureau of Statistics (2003)

  • Categories    

    In our research we assess the sustainability performance of 400 smallholder farms practicing organic (i.e. certified or non-certified) and non-organic agriculture (i.e. conventional or other) using the Sustainability Monitoring and Assessment RouTine (SMART)-Farm Tool and examine differences between these farm categories using multivariate analyses. We also identify general gaps in sustainability performance for all farms.

  • Categories  

    This set of interviews is part of the 'Farmer Empowerment' project that focused on the impact of farmer organizations (FOs) on the socio-economic development of their FO-members. The interviews were conducted within a second field research in order to gather information on specific impact pathways of selected FOs that work to empower respective FO-members.

  • Categories  

    This set of interviews is part of the 'Farmer Empowerment' project that focused on the impact of farmer organizations (FOs) on the socio-economic development of their FO-members. The interviews were conducted within a second field research in order to gather information on specific impact pathways of selected FOs that work to empower respective FO-members.

  • This survey was conducted by the Kenya National Bureau of Statistics. The Kenya Integrated Household Budget Survey (KIHBS)2005-2006 is a baseline survey (the last one done nationally was in the early 1980s) designed to monitor on a regular basis and in an integrated way progress being made towards the improvement of welfare in the Kenyan society so as to monitor effects of economic policies and national development strategies on the well-being of society. The interview of households in the KIHBS was spread over a period of 12 months. This was done to enable analysts take into account the different conditions that household’s experience, particularly farming households, in both the rainy season and in the dry season and prices. A combination of two methods (the personal interview method and the account book/diary method) were used for different components of the questionnaire. Three questionnaires were administered during the survey period. One focused on the household while the other two were on the community as a whole. The three relevant survey instruments that were used are: Household questionnaire Community questionnaire Market price questionnaire

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    The Map shows the overlay of the number of land cover changes with NDVI decrease and increase between 2001 and 2011 referring to NDVI trends. Three classes among the trends are built. Besides a “tolerance class” meaning NDVI trends between -0.005 and +0.005 the dataset was classified into “decreasing” (NDVI Trend <-0.005) and “increasing” (NDVI trend >0.005) vegetation trends. The overlay highlights the southern part of Kenya, especially the counties Narok and Kajiado where a stable land cover and decreasing trends overlap. Within this overlap are also Kitui and Isiolo – both counties that were also highlighted in the OLS-regression output as underpredicting –, parts of Marsabit and some small areas along the coastline. Also again the northwestern area, mainly Turkana Region but also West Pokot and Baringo are expressing increasing trends and seem to be linked to a more stable land cover.

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    This set of interviews is part of the 'Farmer Empowerment' project that focused on the impact of farmer organizations (FO) on the socio-economic development of their FO-member. The interviews were conducted within a first field research in order to gather information on institutional settings of FOs and to identify empowering approaches and empowering areas of Farmer Organizations.

  • Categories  

    This set of interviews is part of the 'Farmer Empowerment' project that focused on the impact of farmer organizations (FOs) on the socio-economic development of their FO-members. The interviews were conducted within a second field research in order to gather information on specific impact pathways of selected FOs that work to empower respective FO-members.