CIS-derived measures of location and space have increasingly been used in models of land use and ecology. However, they have made few inroads into the literature on technology adoption in developing countries, which continues to rely mainly on survey-derived information. Location, with all its dimensions of market access, demographics and agro-climate, nevertheless remains key to understanding potential for technology use. The measures of location typically used in the adoption literature, such as locational dummy variables that proxy a range of locational factors, now appear relatively crude given the increased availability of more explicit GIs-derived measures. This paper attempts to demonstrate the usefulness of integrating CIS-measures into analysis of technology uptake, for better differentiating and understanding locational effects. A set of GIs-derived measures of market access and agro-climate are included in a standard household model of technology uptake, applied to smallholder dairy farms in Kenya, using a sample of 3330 geo-referenced farm households. The three technologies examined are keeping of dairy cattle, planting of specialised fodder, and use of concentrate feed. Logit estimations are conducted that significantly differentiate effects of individual household characteristics from those related to location. The predicted values of the locational variables are then used to make spatial predictions of technology potential. Comparisons are made with estimations based only on survey data, which demonstrate that while overall explanatory power may not improve with CIS-derived variables, the latter yield more practical interpretations, which is further demonstrated through predictions of technology uptake change with a shift in infrastructure policy. Although requiring large geo-referenced data sets and high resolution GIS layers, the methodology demonstrates the potential to better unravel the multiple effects of location on farmer decisions on technology and land use.

CIS‐derived measures of location and space have increasingly been used in models of land use and ecology. However, they have made few inroads into the literature on technology adoption in developing countries, which continues to rely mainly on survey‐derived information. Location, with all its dimensions of market access, demographics and agro‐climate, nevertheless remains key to understanding potential for technology use. The measures of location typically used in the adoption literature, such as locational dummy variables that proxy a range of locational factors, now appear relatively crude given the increased availability of more explicit GIs‐derived measures. This paper attempts to demonstrate the usefulness of integrating CIS‐measures into analysis of technology uptake, for better differentiating and understanding locational effects. A set of GIs‐derived measures of market access and agro‐climate are included in a standard household model of technology uptake, applied to smallholder dairy farms in Kenya, using a sample of 3330 geo‐referenced farm households. The three technologies examined are keeping of dairy cattle, planting of specialised fodder, and use of concentrate feed. Logit estimations are conducted that significantly differentiate effects of individual household characteristics from those related to location. The predicted values of the locational variables are then used to make spatial predictions of technology potential. Comparisons are made with estimations based only on survey data, which demonstrate that while overall explanatory power may not improve with CIS‐derived variables, the latter yield more practical interpretations, which is further demonstrated through predictions of technology uptake change with a shift in infrastructure policy. Although requiring large geo‐referenced data sets and high resolution GIS layers, the methodology demonstrates the potential to better unravel the multiple effects of location on farmer decisions on technology and land use.

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The purpose of this study was to analyze the ways in which community participation do influence sustainability of government funded water projects in Nzambani area which is a semi arid area in Kitui county in Kenya. The design used was mixed method design and the sampling technique was stratified sampling since four areas in Nzambani namely; Katoteni, Kwa Mai, Kavalula and Kyambenzi were analyzed. The sample size used was 185 respondents of whom 170 were the local community members who filled the questionnaire whereas 15 were members of water management committee in the area who were interviewed. The study revealed out that participation influence sustainability in the following discussed ways. First is that it promotes community contributions as indicated by 84.1% of the respondents .Secondly, is through effective representation of men and women as shown by 70% of the respondents. Decision making is also another way in which sustainably is influenced as a result of participation as indicated by 64.7% of the respondents. Furthermore, the results from the interview also affirmed that sustainability is also possible through the above ways. The study recommended that there is need for governments and other actors dealing with water projects in semi arid areas to continue creating a conducive environment through which community participation will be fostered so that sustainability can be assured.

The purpose of this paper is to understand the extent to which farmers’ participation in NGO interventions affect their household food security in Yatta Sub County of Machakos County, Kenya. This study assessed farmers’ participation in needs identification, selection of interventions, implementation and monitoring. The study applied mixed method approach to collect both quantitative and qualitative data. This involved a cross-sectional survey of 357 farmers selected from 100 farmers’ groups that had worked with NGOs for more than three years, 6 focus group discussions (5 with farmers and one with NGOs), 33 key informant interviews, as well as 2 case studies. Quantitative data was analysed using logistic regression model. Findings reveal that there is a significant relationship between farmers’ participation in NGO interventions and household food security outcomes. In needs identification, NGOs mainly engaged farmers in joint meetings as opposed to formalized assessments. Selection of interventions was done through consultative meetings, while implementation was carried out in farmer plots and demonstration plots. Farmers participated in monitoring through project management committees, quarterly review meetings and feedback sessions. However, there was no standardized protocols of applying participation among NGOs. Sometimes unstructured needs assessment, hurried selection of interventions and lack of skills among farmers to negotiate with NGOs for preferred interventions negatively impacted farmers’ participation. The study is useful in informing NGOs and funding agencies to strengthen farmers’ participation in NGO interventions. NGOs should develop standardized participation protocols, which are engrained in their contracts with farmers to enhance uniformity and accountability.