Drivers to Utilize Farm Credits: Lessons From Tea Farmers of the Nyaruguru District in Southern Province, Rwanda
Bibliographic record
Abstract
This paper examines the factors of credit utilization for tea enterprise production and the conditions to inform stakeholders and policymakers in the Rwandan tea sector. Through purposive and random techniques, the study used data collected from 358 tea-farming households. A fractional regression model was utilized in the analysis. Factors like access to credit in group (p<0.01), training on tea agricultural practices and credit management (p<0.01), level of production costs (p<0.01) and type of lending sources (p<0.01) were shown to influence the rate of credit allocated for tea production projects while engagement of tea-farming households in off-farm businesses (p<0.01) and larger size of credit (p<0.01) both increased incidences of credit diversion to other than tea farming uses. Policymakers can intervene for mechanisms that improve management and accountability of tea farmers’ organizations as emerging players in the tea sector. Also, public policies should integrate other economic and social attributes that may have real-valued utilities for rural tea-farming households to sustain living needs if they have the right to choose, and engage in, certain range of income activities. Keywords: tea credit, credit diversion, credit utilization, fractional regression model, tea-farming household interest _________________________________ Facteurs d'utilisation des credits agricoles : lecons des producteurs de the du district de Nyaruguru dans la province du Sud du Rwanda Cet article examine les facteurs d'utilisation du credit pour la production des entreprises de the et les conditions pour informer les parties prenantes et les decideurs du secteur rwandais du the. Grâce a des techniques intentionnelles et aleatoires, l'etude a utilise des donnees recueillies aupres de 358 menages producteurs de the. Un modele de regression fractionnaire a ete utilise dans l'analyse. Il a ete demontre que des facteurs tels que l'acces au credit en groupe (p<0,01), la formation sur les pratiques agricoles du the et la gestion du credit (p<0,01), le niveau des couts de production (p<0,01) et le type de sources de pret (p<0,01) influencent le taux de credit alloue aux projets de production de the tandis que l'engagement des menages producteurs de the dans des entreprises non agricoles (p<0,01) et une plus grande taille de credit (p<0,01) ont tous deux augmente les incidences de detournement de credit vers d'autres utilisations que la culture du the. Les decideurs politiques peuvent intervenir pour des mecanismes qui ameliorent la gestion et la responsabilite des organisations de producteurs de the en tant qu'acteurs emergents dans le secteur du the. En outre, les politiques publiques devraient integrer d'autres attributs economiques et sociaux qui peuvent avoir des utilites reelles pour les menages ruraux producteurs de the afin de subvenir a leurs besoins vitaux s'ils ont le droit de choisir et de s'engager dans une certaine gamme d'activites remuneratrices. Mots cles : credit de the, detournement de credit, utilisation du credit, modele de regression fractionnaire, interet des menages producteurs de the
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| 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".