Smallholder orange farmer access to markets in Uganda
Bibliographic record
Abstract
Orange (Citrus sinensis L.) is a major dietary component globally, responsible for supplying nutrients and phytochemicals of biological and health influence such as minerals, vitamins, fiber, flavonoids, limonoids, and carotenoids and antioxidant. It accounts for more than 50% of the citrus fruits produced world-over. It is a popular fruit in sub-Saharan Africa, though its level of consumption per capita is by global standards very low. In Uganda, orange production is mostly concentrated in eastern and northern parts; mostly grown by small holder farmers who are plagued by a milliard of production and marketing constraints. The objective of this study was to evaluate the effect of institutional, infrastructural and socio-economic factors on smallholder access to orange markets in Uganda. The study was conducted in Kaberamaido, Kumi and Soroti Districts in eastern Uganda, using cross sectional data, during 2011-2012. Probit model results showed that the key institutional factor that affected smallholder access to markets was institutional belonging; the infrastructural factors entailed ownership of mobile phone and location of household; while age of household head, household size and output price constituted the socio-economic factors. Mobile phone, household size and age of household head elicited the highest effect on the probability for smallholder market access, and the magnitude of effect is shown by flexibilities of 0.5, -0.06 and 0.02, respectively. Tobit model estimates showed that market information, and household location constituted institutional and infrastructural factors affecting market access, respectively; while age of trees, output, output price and occupation of household head constituted the socio-economic factors. The critical factors that affect the extent of market access include location, market information, primary occupation of household head and quantity of output as shown by flexibilities of -0.6, 0.5, 0.5, and 0.03, respectively. Based on the Probit and Tobit model estimates, market information, mobile phone and quantity of outputs constitute critical institutional, infrastructural and socio-economic factors that affect smallholder market access. Therefore, opportunity for unlocking the potential for smallholders to access orange markets exists in boosting the level of output and facilitating linkage to markets. Key words: Citrus sinensis, infrastructural, institutional
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".