Exploring Crop Choices: Benefits, Challenges, and Rationale Among Rwandan Farmers
Bibliographic record
Abstract
Farming decisions on crop choices are guided by different factors including natural conditions, household needs, traditions, stakeholder recommendations, and productivity. The best decision varies for each farmer based on specific circumstances. There are both benefits and challenges in farmers’ crop growing experience. In Rwanda, agriculture employs 70% of the population, contributing 33% to the GDP across three main agricultural seasons. However, food and nutritional insecurity remain pressing issue affecting both human and economic progress. This study explored the rationale, benefits, and challenges of farmers’ choices. This study used a qualitative descriptive approach, conducting six focus group discussions (FGDs) in each participating district. Each FGD comprised 10 participants, ensuring gender balance. Recruitment was facilitated by local community health workers (CHWs), with participants providing informed consent. Trained data collectors utilized voice recorders to collect the data. The researchers transcribed the data verbatim, anonymized the data, and translated the same data into English. Data analysis revealed four key themes: reasons for cultivation, factors influencing crop choice, farmers’ livelihoods, and farming challenges. Findings highlight the need for holistic and context-specific solutions in Rwandan agricultural development, emphasizing stakeholder collaboration to support informed decision-making and sustainable agriculture.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".