Analyzing the Cost and Returns of Smallholder Farmers: A Case of Asante Akim South in Ghana
Bibliographic record
Abstract
Managing crop production as a business among smallholder farmers is a challenge. This farmers’ survey therefore assessed farm activities and their economic implications to smallholder farmers, with special reference on cocoa farmers, using structured questionnaire. Farmers and their household were found to be greatly involved in providing labour for all key farm activities such as weed management, pesticide application and harvesting. This labour was not priced by most farmers and therefore estimated expenditure on managing the farms were lower than actual cost incurred. Cocoa formed 75% of total landholdings with 3-4 acres and < 3acres being the modal farm size for cocoa and supplementary crops (vegetables, plantain, oil palm, cassava and maize) respectively. Although applying fertilizer to cocoa increased yield by over 144%, majority of the farmers did not consistently apply it due to purported high price. Vegetables were the only crop that fertilizer was consistently applied to, and even that it was below recommended rates. Yearly variable margin from cocoa treated with fertilizer was GHS 837 (US$ 190)/acre compared to GHS 548 (US$ 125)/acre of cocoa without fertilizer, thereby justifying the use of fertilizer. The annual variable margin from supplementary crops ranged GHS 162-274 (US$37-62)/acre/year, depending on the type of crop. Based on the statistical mode of 4-6 persons per household, 3-4 acres of cocoa and 0.5-2.5 acres of supplementary crops, the yearly returns of GHS 2,511-3,348 (US$ 570-761) from cocoa (with fertilizer) and GHS 81-685 (US$ 18.41-155.68) from supplementary crops was inadequate. Promoting other low capital input ventures like snail rearing, mushroom production and bee keeping will be of immense support to the farmer’s household.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".