A Stepwise Analysis of Production Returns and Cost Distribution for Chinese Cabbage Produced Under Irrigation in South Africa
Bibliographic record
Abstract
Cultivation of indigenous crops for food and nutritional security has emerged as a topic of interest in South Africa. Commercial cultivation of indigenous crops is promoted especially among smallholder farmers because of their nutritional value and their ability to adapt to marginal soil and climatic conditions. Support for commercial production of specific crops among farmers necessitates the need for optimum use of inputs in production. In order to evaluate optimum input use in production, this study established the profitability and production costs of one of the indigenised leafy vegetables in South Africa, Chinese cabbage, using gross margin analysis. Production costs and profitability evaluations are fundamental tools for analysing cash flow and investment options. The study was based on field trials on different levels of fertilizer (NPK application). The results of the study show that at low production level (10.1 t ha-1), gross income is less than total variable costs (TVC), resulting in a negative gross margin. A movement from low production to medium production (26.1 t ha-1) results in an increase in gross margin, from -R16,664.19 to R29,091.99. The highest gross margin of R82,807.07 is obtained at high production level (44.5 t ha-1). The study supports an interdisciplinary evaluation approach (agronomy and economics) when analysing field trials.
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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 | 0.002 | 0.002 |
| 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) | 0.002 | 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".