Resource Use Efficiency as a Climate Smart Approach: Case of Smallholder Maize Farmers in Nyando, Kenya
Bibliographic record
Abstract
To simultaneously enhance agricultural productivity and lower negative impacts on the environment, food systems need to be much more efficient in using resources such as land, water, and fertilizer. This study examines resource use efficiency of maize production among smallholder farmers in Nyando, Kenya. The main objective is to assess the degree of technical efficiency of smallholder farmers and identify the impact of so-called “climate smart practices” on technical efficiency. The method of Stochastic Frontier Analysis is used to simultaneously estimate a stochastic production frontier and a technical inefficiency effect model. Data for 324 subplots farmed by 170 households were available for this analysis. The study reveals that maize production in Nyando is associated with mean technical efficiency of 45% and that soil conservation practices such as residue management, legume intercropping, and improved varieties significantly increase farmers’ technical efficiency. Soil carbon is found to be a critical factor of production. These results imply that there is potential to more than double production using the same resources and that soil conservation practices can be very “climate smart,” at once increasing soil carbon, production, climate resilience, and technical efficiency.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".