Contribution of Agroecological Practices to Household Food Availability: A Case Study of Singida District
Bibliographic record
Abstract
Globally, there are urgent calls for transformation of agriculture and food systems to address food unavailability, food insecurity and environmental challenges. Agroecological practices have been promoted as one of the solutions, which have potential to address these challenges. Nonetheless, there is limited evidence regarding the question whether agroecology can indeed enhance food availability among smallholder farmers in Sub-Saharan Africa, Tanzania inclusive. Thus, the study was conducted to examines how the implementation of agroecological practices contribute to achieving food availability at household level by comparing between farmers who are members of Farmer Research Network (FRN) (implementers) and non-FRN farmers (non-implementers) using a case of Singida district. The study employed a cross-sectional research design, and an integration of both quantitative and qualitative data collection and analysis methods. Interviews involved a total of 160 respondents who were randomly selected from household sampling frameworks. Focus group discussions (FGDs) and in-depth interviews were conducted to gather complementary data. On one hand, quantitative data were analyzed using Statistical Package for Social Sciences (SPSS) software whereby descriptive statistics and an inferential statistic (multiple linear regression) were determined. On the other hand, qualitative data were analyzed using thematic content analysis. The findings revealed, more than half (68%) and 15% of FRN farmers were in the medium and high categories of implementation of agroecological practices, contrary to non-FRN farmers, the majority (76%) were in low and none in high categories respectively. Based on alaysed data, the most common applied practices by both FRN and non-FRN farmers were: (i) the use of organic fertilizers (farm yard manure and compost manure (96%); (ii) intercropping (88%); (iii) crop rotation (82%); and integration of crop and livestock (79%). In addition, results indicate a significant association between the level of implementation of agroecological practices and food availability (p-value = 0.000). FRNs’ farmers were food secure as compared to non-FRN farmers just due to campaign and capacity building training offered to them. There is a need for a capacity development program to speed up agroecological intensification for sustainable food systems. Thus, it is very essential for public and private organizations to develop capacity building strategies or programmes to impart farmers with knowledge and skills on agroecological revolution.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".