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Record W3083477847 · doi:10.22215/etd/2016-11636

Understanding smallholder farmers’ food security and institutional arrangements in view of climate dynamics: Lessons from Mt. Kenya region

2016· dissertation· en· W3083477847 on OpenAlexaff
Beth Mburu

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicClimate change impacts on agriculture
Canadian institutionsCarleton University
FundersInternational Fund for Agricultural Development
KeywordsLivelihoodFood securityAgricultureAgricultural productivityBusinessFocus groupProductivityClimate changeFood processingNatural resource economicsGeographyEnvironmental resource managementEconomic growthPolitical scienceEconomicsMarketingEcology

Abstract

fetched live from OpenAlex

Globally, an estimated 800 million people are currently experiencing hunger.Food insecurity remains a major concern, especially in developing countries.In sub-Saharan Africa, smallholder farmers, who are both food producers and consumers, manage 80% of all farms but face many challenges including; shrinking farm sizes, limited financial resources and dynamic farming environments.Food insecurity persists among smallholders as various uncertainties together with climate change impacts exacerbate existing vulnerabilities.However, smallholders have the potential to contribute significantly to food security at community through national scales.This research aims to provide a better understanding of food security determinants among smallholders with a focus on how they use various institutional arrangements to augment their livelihoods.Through focus group discussions and on-farm interviews, the research engaged with smallholders and non-farmers from Embu, Mt.Kenya region in central Kenya.Guided by insights from political ecology, the research assessed smallholders' narratives on perceptions of and experiences with food security, institutional arrangements, climate variability, and climate change.The findings offer strong evidence that smallholders' livelihoods are inextricably reliant on their food production.Fulfilling other livelihood needs, e.g.school fees, often receives priority over satisfying a household's dietary requirements.Cropping seasons rather than longer timescales dictate smallholders' decision-making and planning.Resultantly, everyday uncertainties and challenges tend to subsume climate change threats while climate variability poses more impacts on their seasonal productivity.The conventional definition of food security posed by the Food and Agricultural Organization aligns only partially with the realities of farmers in the Embu region.Smallholders place greater emphasis on two food security dimensions, iii availability and access, paying considerably less attention to utilization and stability.While engaging formal institutions, smallholders have greater agency as members of informal groups than as individuals.This research proposes leveraging current formal and informal institutional arrangements to bolster smallholders' food security outcomes as well as improve their adaptive capacity to climate variability and change.It recommends supporting smallholders through providing relevant agro-climatic information, offering functional financing, brokering new knowledge, assisting in scenario planning for risk management, and reducing access barriers in pre-production processes.This journey has revealed my ability to adapt and reinvent myself.Whatever direction my career takes hereafter, the art of balancing looming deadlines while reining in my stellar procrastination skills will be an asset.I would like to acknowledge the people who have shaped my path and ensured I steered steadily towards my True North.I am deeply obliged to my supervisor Prof. Mike Brklacich for his patience and guidance.Mike, your mentorship throughout my doctoral journey has helped me unmask many blind spots.As the African adage states, "What an elder sees while seated, a child cannot see while standing on their toes".In your unique way, you have allowed me to grow into scholarship and find my voice as I embraced new concepts in social science.Your support has fashioned my time at Carleton to be truly interdisciplinary and helped shape my insights on global-to-local perspectives.To members of my doctoral advisory committee, Prof. Blair Rutherford and Dr.Evans Kituyi, I remain indebted to your succinct and helpful guidance that has helped refine the scholar in me.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.126

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.004
Scholarly communication0.0030.004
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.115
GPT teacher head0.283
Teacher spread0.168 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2016
Admission routes1
Has abstractyes

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