Factors Influencing Change of Smallholder Organic Horticultural Farmer Organisations under Nongovernmental Organisations in Two Selected Regions in Tanzania
Bibliographic record
Abstract
There has been the persistent failure of organic horticultural production to meet its full potential in various aspects including productivity, technological and marketing areas in various Sub Saharan countries in Africa including Tanzania. Thus, this study intended to determine whether a change of Smallholder Organic Horticultural Farmer Organisations (SOHFOs) under the local umbrella Non-governmental Organisations (NGOs)with the mandate to work within the country in coordinating SOHFOs is influenced by relational factors (their networks with other Organic Horticultural Value Chain Actors (OHVCAs)) or non-relational (other) factors. The study was conducted in Morogoro and Kilimanjaro regions in Tanzania. A study included a total of one hundred fifty nine organizations (159) that were represented with three hundred fifty one (351) respondents. From one hundred and forty nine (149) SOHFOs under local umbrella NGOs selected by simple random sampling technique and further proportionate random sampling, quantitative data were collected from two hundred and eighty nine (289) respondents and qualitative data were collected from forty four (44) SOHFOs participants. Moreover, from ten (10) managing organisations represented by eighteen (18) Key Informants qualitative data were collected. Quantitative data (relational data) were analysed using the social network analysis approach using Ghephi 0.9.2 software. For non-relational data, Statistical Packages for Social Science (SPSS) version 21 was used whereby descriptive statistics such as measures of centralities (that is closeness centralities (CCs) and betweenness centralities (BCs)) and mean scores were used to establish some of the variables of the study. Binary logistic regression model was used to predict the factors influencing change (which is regarded as use of manure) at SOHFOs under local umbrella NGOs. Qualitative data were analysed using content analysis. Results from binary logistic regression model and content analysis indicate that SOHFOs under local umbrella NGOs are experiencing change in technological area whereby, soil erosion control measures are the most used technological practice as opposed to the use of organic manure. Again, the results on predictor factors for use of manure at SOHFOs under local umbrella NGOs indicate that relational factors; that is capacity of SOHFO to access and disseminate knowledge and information to other SOHFOs and to access and spread organic horticultural products and farm inputs to other OHVCAs are the significant factors over individual organisational attribute of SOHFOs under the local umbrella NGOs in Tanzania. The study recommends policies and systems that put emphasis on relational measures for more effective organic horticultural agriculture via SOHFOs under the local umbrella NGOs in Tanzania.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".