Essays on climate-smart agriculture (CSA) technologies, youth involvement in agricultural activities, and agricultural finance
Bibliographic record
Abstract
The thesis presents four essays addressing key issues in smallholder agriculture in Tanzania, such as CSA technologies, youth involvement in farming, and agricultural finance. The thesis examines the effects of climate-smart agriculture (CSA) adoption on crop productivity and income. The thesis addresses the impact of climate change to enhance farm performance and improve farmers’ resilience. Also, the thesis analyzes youth engagement in agriculture. Under this theme, the thesis seeks to understand how we can attract and keep youths in the agricultural sector as a response to alarming youth unemployment. Further, the thesis examines agricultural finance constraints and their impacts on farm performance. Access to agricultural finance by farmers is ideal for poor farm resource farmers to improve farm input usage and resilience. Chapter 1 consists of the introductory part. Chapter 2 investigates the impacts of CSA on crop productivity and income using nationally representative data that sampled 1862 smallholder household farmers who cultivate less than 2 hectares. The chapter reports that smallholder farmers who practice CSA augment crop productivity per acre and income more than nonadopters. Interestingly, non-adopters, had they adopted, would have remarkably gained in both. The results survived robust checks and remained consistent. We used the endogenous switching regression (ESR) model, instrument variables, and other control variables to address the endogeneity and selection bias issues. The propensity score matching (PSM) is adopted for comparison and for a consistency check of the results. At least the results are consistent in both models. The implication of the findings is that plausible programs, promotions, campaigns, or policy support initiatives for scaling up CSA adoption have a significant contribution to food security and poverty reduction through increased crop productivity and income augmentation. Chapter 3 contains two research papers. The first paper investigates youth involvement in agricultural activities, using a sample of 6419 Tanzanian youths aged 15-35 years old. The paper highlights the critical problems and challenges faced by youth in the agricultural sector. Further, the paper elucidates the crucial drivers of youth’s full involvement in agriculture. The statistically significant drivers include access to and usage of farm machines (e.g., tractors), irrigation facilities, land ownership, presence of agro-product processing, profits, access to agricultural credit, youth membership in the farmers’ cooperatives and organizations, access to and use of extension services, use of information source channels to access agricultural information, and distance to the nearest market. At the same time, off-farm income, general education, and age reduce the propensity of youth’s involvement in agriculture. A series of robust checks were performed to ensure that the assumptions of the ordered logit model are met to ensure unbiased and consistent results. The second paper examines the impact of youths’ intensive participation in agriculture on farm performance. We used nationally representative cross-sectional data of 3399 small youth farmers in Tanzania. We employed a doubly robust IPWRA estimator, and we compared these empirical results with the results from ESR and PSM models. The results remain statistically and quantitatively consistent in all models: that youth intensive involvement in agriculture significantly impacts maize yields, net returns, and returns on investment (ROI). Overall, both papers propose policy actions, academic interventions, and parental interventions to promote and retain many youths in agriculture. Finally, chapter 4 contains one paper that used a sample of 1042 smallholder farmers. The paper examines the significant obstacles to agricultural financing that both the demand and supply sides of agricultural credit encounter. Further, the paper demonstrates the impact of agricultural finance on farm performance for credit-constrained smallholder farmers. We applied the ESR model to address the endogeneity problem and selection bias. Furthermore, PSM was used to compare the results of the ESR model. The results in both models are consistent that credit access augments smallholder farmers’ crop productivity. Interestingly, farmers without credit counterfactual would increase their crop production if they were assumed to be credit unconstrained. The findings carry important policy implications in favour of smallholder farmers’ credit access.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".