Profitability and Farmer Acceptability of Selected Climate Smart Technologies and Practices for Maize-Beans Production in Drought-Prone Areas, Uganda
Bibliographic record
Abstract
Climate change is increasingly affecting farm-level decisions on when to plant and which climate smart agriculture (CSA) options to use. This study was conducted to determine the profitability and farmer acceptability of different CSA options for maize-bean production in drought-prone areas of Uganda. It was conducted on-farm in Rakai and Nakasongola districts during 2020 and 2021. Variables included: planting date (early vs late); varieties (common beans: NABE 4 and NAROBEAN 2, and maize: Longe 5 and Bazooka); intercropping versus pure stand; and fertiliser use (manure, Diammonium phosphate (DAP) or combination). The experimental design was split-split plot, replicated six times. Over two years, early planting caused 16% and up to 46% higher yields of maize and beans, respectively, than late planting, resulting in 14% - 28% and 18% - 43% higher Benefit/Cost (B/C) ratio for maize and beans, respectively. Intercropping reduced maize and beans yield by 16% - 25% and 52% - 57%, respectively. The B/C was highest for sole maize; intercropping was more profitable than sole beans. Fertilizer (DAP) was most profitable when Bazooka was early-planted as sole crop followed by intercrop. For late planted-crop, manure was better. These practices were more beneficial when applied simultaneously for both crops excluding bean variety. Farmers’ lessons stressed the importance of early planting and fertilizer use; however, majority indicated they were to adopt more than two of the practices tested.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".