Effects of Crop Insurance and Finance on Small-Scale Maize Productivity in Zambia
Bibliographic record
Abstract
Productivity of maize is dependent on facilitative and competitive interactive effects on resource availability and other moderating factors. The study investigated the impact crop insurance and financing had on the productivity of small-scale maize farmers in Southern province of Zambia. It also sought to see the effect moderating factors have on maize productivity. The relationship between crop insurance and financing, and maize productivity was explored by interviewing 602 farmers in Mazabuka, Monze, Choma and Kalomo districts through a structured questionnaire. This also included interviews with insurance and finance providers. SPSS and hierarchical multiple regression analysis were used to evaluate the data after making some assumptions. The regression analysis was run to determine the relationship between maize productivity, loan, insurance and the interaction between loan and insurance over and above the control variables. The results showed that the relationship was not supported (t = -0.750, p > 0.05) and that insurance and financing in the four districts studied did not have any effect on productivity. There was no significant relationship between crop insurance and productivity (t = -1.741, p > 0.05). The model used to analyze the data excluded financing as it did not bring any additional significant information. The results further indicated that there was linearity as determined by partial regression plots, as well as residual independence as determined by the Durbin-Watson statistic of 1.745. Results showed no evidence of multicollinearity based on correlations and Variance Inflation Factors (VIFs) and that farmers relied heavily on the government subsidy program, the FISP which resulting in less or no effect of commercial crop insurance and financing on productivity.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".