Does uptake of post-harvest handling technologies lead to better household nutrition? Empirical evidence from a project-based intervention in Northern Uganda
Bibliographic record
Abstract
Adoption of improved agricultural technologies is important for increasing agricultural productivity, household income, food security, and reducing poverty. This paper assessed the impact of adopting improved post-harvest technologies and practices on the dietary diversity of smallholder agricultural households in northern Uganda. The study used cross-sectional survey data from 722 smallholder households across nine districts of northern Uganda. These were districts where a donor funded project, Project for the Restoration of Livelihoods in Northern Uganda was implemented. The focus was on post-harvest (harvesting, drying, and storage) technologies and practices promoted under this project. Data was analyzed using descriptive statistics, as well as, inferential statistics. In the case of inferential statistics, binary probit regression analysis was used to assess factors influencing adoption of each technology, while, propensity score matching technique was used to compare household dietary diversity (HDD) of adopters and non-adopters. Results showed that most farmers decided to harvest at the right maturity, while, the use of tarpaulins was the only adopted drying technology. Less than a quarter of farmers had adopted PICS bags, super gunny bags, and polypropylene bags, as storage facilities. Regression results showed that age, gender, and education level of the household head, household labor, size of land owned, land acreage under crop production, access to credit, use of ox-plough, and access to agricultural markets had significant impact on farmer’s decision to adopt the technologies. Average Treatment Effects on the Treated (ATT) for HDD were positive and significant when households knew how to assess maturity, and also used tarpaulin and PICS bags. These findings indicate that these technologies were associated with increased levels of HDD. On the other hand, ATT was negative and significant for households that harvested at the right time, at the right maturity, used super gunny, and used polypropylene bags for storage. Findings of the study support the existence of a strong relationship between dietary diversity and the adoption of post-harvest technologies and practices. These findings highlight the need for targeted extension services and technology promotion to improve food security through dietary diversity.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".