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Kassie, M.

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Kassie
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Kassie, M.
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0000-0002-5898-3877

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AGROVOC: STATISTICAL METHODS ×

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Now showing 1 - 3 of 3
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    Different ways to cut a cake: comparing expert-based and statistical typologies to target sustainable intensification technologies, a case-study in Southern Ethiopia
    (Cambridge University Press, 2019) Berre, D.; Baudron, F.; Kassie, M.; Craufurd, P.; Lopez-Ridaura, S.
    Understanding farm diversity is essential to delineate recommendation domains for new technologies, but diversity is a subjective concept, and can be described differently depending on the way it is perceived. Historically, new technologies have been targeted primarily based on agro-ecological conditions, largely ignoring socioeconomic conditions. Based on 273 farm households’ surveys in Ethiopia, we compare two approaches for the delineation of farm type recommendation domains for crop and livestock technologies: one based on expert knowledge and one based on statistical methods. The expert-based typology used a simple discriminant key for stakeholders in the field to define four farm types based on Tropical Livestock Unit, total cultivated surface and the ratio of these two indicators. This simple key took only a few minutes to make inferences about the potential of adoption of crop and livestock technologies. The PCA-HC analysis included a greater number of variables describing the farm (land use, household size, cattle, fertilizer, off-farm work, hiring labour, production). This analysis emphasized the multi-dimensional potential of such a statistical approach and, in principle, its usefulness to grasp the full complexity of farming systems to identify their needs in crop and livestock technologies. A sub-sampling approach was used to test the impact of data selection on the diversity represented in the statistical approach. Our results show that diversity structure is significantly impacted according to the choice of a sub-sample of 15 of the 20 variables available. This paper shows the complementarity of the two approaches and demonstrates the influence of data selection within large baseline data sets on the total diversity represented in the clusters identified.
    Publication
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    Response to climate risks among smallholder farmers in Malawi: a multivariate probit assessment of the role of information, household demographics, and farm characteristics
    (Elsevier, 2017) Mulwa, C.; Marenya, P.P.; Rahut, D.B.; Kassie, M.
    Why do many smallholder farmers fail to adopt what appear to be relatively simple agronomic or management practices which can help them cope with climate-induced stressors? Using household and plot level data collected in 2011, we implement a multivariate probit model to assess the determinants of farmer adaptation behavior to climatic risks and the relative contribution of information, credit and education on the probability of adopting specific practices in response to adverse changes in weather patterns. We find that plot characteristics, credit constraints and availability of climate-related information explain the adoption of several of these practices. In relative terms, we also find that even when financial limitations are binding, making climate-related information available can still motivate farmers to adapt. Policy implications are that the deepening of extension access with information on the appropriate adaptation strategies is crucial to help farmers make adaptation choices. The need to foster credit markets for easy accessibility and affordability by farmers or otherwise strengthening access to assets is also important.
    Publication
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    Household-level determinants of soil and water conservation adoption phases: evidence from North-Western Ethiopian Highlands
    (Springer Verlag, 2016) Addis Teshome Kebede; Graaff, J.; Kassie, M.
    Soil and water conservation (SWC) practices have been promoted in the highlands of Ethiopia during the last four decades. However, the level of adoption of SWC practices varies greatly. This paper examines the drivers of different stages of adoption of SWC technologies in the north-western highlands of Ethiopia. This study is based on a detailed farm survey among 298 households in three watersheds. Simple descriptive statistics were applied to analyze the stages of adoption. An ordered probit model was used to analyze the drivers of different stages of adoption of SWC. This model is used to analyze more than two outcomes of an ordinal dependent variable. The results indicate that sampled households are found in different phases of adoption, i.e., dis-adoption/non-adoption (18.5 %), initial adoption (30.5 %), actual adoption (20.1 %), and final adoption (30.9 %). The results of the ordered probit model show that some socio-economic and institutional factors affect the adoption phases of SWC differently. Farm labor, parcel size, ownership of tools, training in SWC, presence of SWC program, social capital (e.g., cooperation with adjacent farm owners), labor sharing scheme, and perception of erosion problem have a significant positive influence on actual and final adoption phases of SWC. In addition, the final adoption phase of SWC is positively associated with tenure security, cultivated land sizes, parcel slope, and perception on SWC profitability. Policy makers should take into consideration factors affecting (continued) adoption of SWC such as profitability, tenure security, social capital, technical support, and resource endowments (e.g., tools and labor) when designing and implementing SWC policies and programs.
    Publication