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Research Collection
Doctoral thesis . 2018
Research Collection
Doctoral thesis . 2018
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Drivers of Climate Sensitive Farm Level Adjustments: Climate Change, Economic and Technical Factors, and Resource Availability

Authors: Tessema, Yibekal;

Drivers of Climate Sensitive Farm Level Adjustments: Climate Change, Economic and Technical Factors, and Resource Availability

Abstract

This thesis is aimed at contributing to an overarching research question of what new interventions are required in agriculture to adapt to climate change. In a similar manner to most previous studies, the thesis attempted to answer this question by examining how farmers are currently responding to climate change and the factors influencing the process or its determinants. Earlier studies have produced a long list of farm-level adaptations to climate change and their socio-economic determinants. However, the adjustments identified by the studies along with their determinants are not unique to climate change, and therefore do not indicate the importance of new interventions. For example, the commonly identified adjustments such as soil conservation and irrigation are already at the center of existing policies in agriculture. Like wise the importance of determinants such as access to credit and education which are singled out by the studies as key variables for adaptation are already at the hub of the literature on agricultural development. Studies in the past have failed in suggesting distinct interventions, which are necessitated by climate change. I attribute this shortcoming to weak methodological approach in disentangling adjustments strongly motivated by climate change and to lack of appropriate typologies with implications on resource needs. Studies in the past often used an enquiry method that directly asks farmers to list their adaptation strategies. Such approach is susceptible to response biases, and does not also put non-climatic drivers in perspective. Farmers normally make adjustments simultaneously addressing multiple drivers. Therefore for an adjustment to be identified as an adaptation strategy, it should be shown that it is strongly liked to climate change than other drivers. This is particularly true for contemporary farmers who are responding for a number of drivers including market and technological dynamics. Previous studies also treated all farm-level adaptations as one homogenous group of adjustments that are influenced by one set of determinants. This study, however, argues that adaptations that use traditionally used inputs, here called non-technological adaptation, should be separately examined, as they are likely to have distinctive resource needs. Examples could be changing planting date and crop diversification. While technological adaptations can benefit from the extensive literature on agricultural technology adoption in the last decades, non-technological adaptations can greatly benefit from further study as one distinct group of adjustments. In this Doctoral study, I employed a household survey of farm households in a district in central Ethiopia and attempted to address the existing flaws in the literature on farm-level adaptation in agriculture. The first paper was aimed at identifying farm adjustments that are primarily motivated by climate change. A new methodological approach that puts non-climatic drivers in perspective while reducing the likelihood of response biases was applied along commonly used methods. It was shown that in the study area three adjustments are strongly associated to climate change: changing planting date, crop switching (changing crop type) and crop diversification. In the second paper, a second-round survey was carried out to closely examine crop switching as an adaptation strategy. I examined the permanent adoption and abandonment of crops by farmers in the last two decades. It was found that crop adoption is primarily induced by price changes while crop abandonment is strongly motivated by climate change. The trend of crop abandonment is also inline with predictions by studies on ecological change in climate change. In the third paper, I showed that non-technological adaptations are likely to be more dependent on accumulated farm experience than level of schooling and availability of finance. The major implication of this study, for our study area, is that non-technological adaptations such as changing planting date and crop switching are adjustments that are primarily induced by climate change. The broader implication of my findings is that non-technological adaptations should be the primary focus of adaptation policies in agriculture. The findings implied that adaptation in agriculture is essentially the reallocation of resources accessible to a community. For which reason, accumulated experience of farmers on the broader community context like farming conditions could be instrumental. Since climate change just contracts or expands existing agro-ecologies without creating unique conditions, it is unlikely that new technological adoptions are necessitated by climate change. Therefore, mechanisms that facilitate non-technological adaptations such as experience-sharing sessions composed of farmers from diverse agro-ecologies and farming conditions can be vital for adaptation in agriculture. Our study also highlighted that mainstreaming climate change in farm adjustments primarily induced by non-climatic drivers can be crucial.

Country
Switzerland
Related Organizations
Keywords

crop switching, non-technological adaptations, determinants, Agriculture, adaptation, climate change; adaptation; agriculture; Adjustment; Ethiopia; determinants; crop switching; non-technological adaptations, climate change, Adjustment, Ethiopia, info:eu-repo/classification/ddc/630, agriculture, ddc: ddc:630

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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