Estimation of Optimal Land Use Allocation Among Small Holder (A1) Farmer Households in Zimbabwe. A Case Study of Long Croft Farm, in Mazowe District
Bibliographic record
Abstract
This study aimed to develop an optimal land use plan for Long Croft farm (LCF) by assessing the available land resources. It evaluates the potential of the land resources for recommending optimum, sustainable and appropriate land utilisation in agriculture development. Possible ways in improving farming systems and more efficient utilization of the scarce available agricultural resources especially arable land were determined. The study used quantitative analytical approach in optimal land use allocation analysis. A total of thirty six structured questionnaires were used to gather data necessary for analysis. Every household at Long Croft farm was taken as a sampling unit and the area consisted of 36 A1 farmer households. The Linear programming model with an objective function that seeks to maximise net farm income subject to land, labour, capital and consumption constraints was run using Microsoft excel premium solver. The results of the study showed that A1 households are insecure with respect to their land holdings; they cannot use their land as collateral security which is affecting farmer access to credit. The households are also much constrained in land, labour, capital and consumption and as such their land allocation has been found to be not optimal. The LP (linear programming) model has depicted maize, soya beans and sugar beans for LCF when they are no factor markets. However introduction of factor markets (land and labour markets) and farmer performance groups depicted production of small grains and maize. The land reform programme promulgated by the government in 2000 should have focused on equitable right of use, optimising sustainable production, protection of natural resources/ environment against degradation and increase the effectiveness of economic growth of rural and urban population. Most of the fast track land reform developmental activities were done without proper resource surveying for optimum and sustainable resource utilization, especially land, hence great competition and losses of good agricultural uses to inefficient agricultural uses. Resource surveying, land evaluation and land use planning should be employed in Zimbabwe’s agriculture to boast production.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".