A Stochastic Frontier Analysis of Technical Efficiency of Maize Production Under Minimum Tillage in Zambia
Bibliographic record
Abstract
Minimum tillage and other conservation agriculture practices are not only associated with income gains but are also claimed to be the panacea to the declining agricultural productivity and soil degradation problems in Africa and across the world. The few studies on technical efficiency related to the agricultural sector performance in Zambia have not attempted to determine how technically efficient smallholder farmers that produce maize under minimum tillage are. This study used stochastic frontier analysis based on both the half-normal and exponential model distributions on 2008 cross-sectional nationally representative data of 160 smallholder maize farm households that adopted minimum tillage in Zambia. Results indicate that maize farmers face increasing returns to scale (1.074) implying that there were opportunities for them to improve their technical efficiency as they were operating in stage I of their production functions. The half-normal and exponential model distributions indicate average technical efficiency scores of 60 and 71.7 percent, respectively. Their respective lowest efficiency scores were 9.3 and 8.5 percent. The highest efficiency scores for the half-normal and exponential model distributions were 89.3 and 90.9 percent. Maximum likelihood estimation results show that marital status, level of education of household head, square of household size, off farm income, agro-ecological region III, distance to vehicular road and access to loans are statistically significant factors that affect technical efficiency of smallholder maize farmers that practice minimum tillage in Zambia. The study calls for increased infrastructural development through construction of improved road network, schools and colleges in remote areas as a means to increasing accesss to knowledge and other agricultural services in order to enhance their technical efficiency levels. It also recommends promotion of minimum tillage practices in recommended agro-ecological regions to improve their technical efficiency. The study further acclaims for increased access to loans by smallholder maize farmers that practice minimum tillage as this would in one way induce them to invest in improved varieties and equipment that would help enhance their technical efficiency in Zambia.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".