Assessment of Differences in Small Farmer Uses of Produce and Determinants of Marketed Surplus of Green Grams and Pigeon Peas in Semi-arid Machakos County, Kenya
Bibliographic record
Abstract
Small farmers in semi-arid areas of eastern Kenya produce green grams and pigeon peas for both subsistence and income. Because of this, it was important for a detailed investigation on the difficulty of raising marketed surplus (produce) of green grams and pigeon peas. The literature with respect to how small farmers differed in the uses of the produce and the determinants of marketed surplus is sparse. The objectives of the study therefore were to assess the differences in farm retention (for self-consumption, seed and payments in kind) and marketed surplus and to determine the factors affecting marketed surplus. Survey research design was selected for making the observations. Sampling of the households was done using a multistage technique. There were 364 households selected in two agro-ecological zones, each with 182 respondents. Data collection was done using a well-structured questionnaire. Data analysis was done using the IBM SPSS Statistics 21. One-way analysis of variance results indicated that farmers of green grams and pigeon peas differed significantly in the produce consumed, marketed and used as seed and in-kind transactions. Green grams farmers differed significantly at F(3, 1 452) = 11.420, p = 0.000, whereas pigeon peas small farmers at F(3, 1 452) = 21.680, p = 0.000. Multiple linear regression analysis results showed factors influencing farmers in marketed produce of green grams as area cultivated, yield and region of production at p = 0.01. Seeds and in-kind transactions showed positive effect at p = 0.05, whereas access to market information at p = 0.1. The factors influencing farmers on marketed production of pigeon peas were seeds and in-kind transactions and the price of output (p = 0.01). The study concluded that the design of policy interventions therefore needed to take account of small farmer variations and the factors influencing the marketed production of green grams and pigeon peas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".