Profitability of Smallholder Sugarcane Farming in Swaziland: The case of Komati Downstream Development Programme (KDDP) Sugar Farmers’ Associations, 2005-2011
Bibliographic record
Abstract
Smallholder sugarcane growing is central to rural development and poverty alleviation in Swaziland. The main objective of the study was to investigate the profitability of smallholder sugarcane farmers’ associations under KDDP and to explain the determinants of sugarcane profitability. The study used data from 2004/05 to 2010/11 production seasons for 15 smallholder sugarcane farmers’ associations under KDDP. A structured questionnaire was used to solicit production and financial data. Secondary data were obtained from accounting records of the farmers. The associations were purposively selected because of their experience in sugarcane production. Descriptive statistics such as mean, standard deviation, minimum and maximum values were used in data analysis. The cost and returns analysis was used to assess the profitability, whilst multiple linear regression analysis was used in identifying the determinants of profitability.The associations were found to be profitable with a mean profit per hectare of E5080.00.The further results indicated that variables such as farm size, farming experience, sucrose price, labour cost per hectare and fertilizer cost per hectare significantly (p<0.01) influence the profitability of smallholder sugarcane farmers’ associations in the study area. The adjusted R2 was 0.623, suggesting that about 62.3% in the variation in profit per hectare is explained by the explanatory variables. It is, therefore recommended that good crop husbandry practices like timely weeding, fertilization, and irrigation should be adopted to produce a good crop which will enhance profitability. There is need for the promotion of collective action as an institutional means to improve bargaining power of farmers, especially when procuring inputs. Collective action will enable smallholder sugarcane farmers to buy in bulk and be entitled to discounts and that can enhance sustainability of profitability of the farmers.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".