Fish-farming Value Chain Analysis: Policy Implications for Transformations and Robust Growth in Tanzania
Bibliographic record
Abstract
This article sets out data and issues in relation to fish-farming in Tanzania with the objective of generating information to inform policy decisions required for the transformations in the fish-farming into a viable commercial activity. It identifies challenges within the sector that should be addressed through policy reform. Fish farming in Tanzania is governed by the Fishery Act 2003 No. 22 and the National Fisheries Sector Policy of 1997. The guiding research problem statement was despite the National policy objective to develop a robust, competitive and efficient fishery sub-sector, fish farming in Tanzania is underdeveloped at subsistence production that contributes to only 1.2% of GDP. A survey design was used for collecting primary data from 293 respondents randomly sampled from 8 regions of Dar Es Salaam, Coastal, Morogoro, Njombe, Mbeya, Ruvuma, Kagera and Kilimanjaro. This data was collected using questionnaire and interviews. These were triangulated with secondary data obtained from desk top review. Descriptive statistics and content analysis method were used to report findings. The study found that the major constraints were lack of value chain in the fish farming. We examined the value chain in terms of sources of production, inputs, extension services, technology, and marketing and found that 60% of fish farmers obtain fingerlings from local sources such as friendship network. These sources have no scientific production of fingerlings suitable for commercial fish-farming. In the overall, farmers don't have good and reliable sources for fingerlings. It was also found that 76% of fish farmers make their own feeds using the locally obtained materials like maize and paddy husks, remains of vegetables from garden, cocoyam leaves, and cattle dung. However, it was found that the home made feeds lack quality due to inadequate basic knowledge of producing right fish feeds Lack of appropriate technology application in the fish-farming was a critical constraint that minimizes the chance of transforming the sub-sector into a commercial entity. Technology in fish farming industry include proper pond size, species, sex selection fingerlings, improved fish feeds, hatchery and storage facilities. Furthermore, the study found high demand for extension services in the fish-farming agribusiness, but there is insufficient or non-availability of the extension services, to impart knowledge, proper use of medicines, fish farm management practices and appropriate technology application. For the fish-farming sub-sector to grow from the current 1.2% to the targeted 5% contribution to GDP, it is recommended that policy actions should be undertaken for providing capacity building for small farmers in terms of skills for best practice of fish-farming, credit and or subsidy facility for fish farming infrastructure and inputs, extension services for knowledge and technology transfer to small farmers and encouraging public-private partnership along fish-farming value chain for ensuring availability of quality fingerlings, fish feeds, transportation, and marketing. Keywords: fish farming, small farmers, constraints, latent potential, policy implications making. ------------------------------------------------------- Resume Cet article presente les donnees et les problemes relatifs a la pisciculture en Tanzanie, en relation avec l'objectif de generation d'informations, pour mieux eclairer les decisions politiques requises pour des changements de la pisciculture a une activite commerciale viable. Il identifie les defis a l'interieur du secteur et auxquels il faudrait repondre via une reforme politique. La pisciculture en Tanzanie est regie par la Loi 2003 No. 22 sur la peche (Act. 2003 No. 22) et la Politique nationale relative au secteur de la peche de 1997 (National Fisheries Sector Policy of 1997). La recherche repose sur le probleme que malgre les objectifs de la politique nationale de developper des filieres piscicoles robustes, competitives et efficientes, la pisciculture en Tanzanie est sous-developpee et ne permet qu'une production de subsistance qui ne contribue qu'a 1.2% du PIB. Un plan de sondage a ete utilise pour collecter les donnees primaires de 293 repondants sondes au hasard dans les huit regions du Dar Es Salaam, Coastal, Morogoro, Njombe, Mbeya, Ruvuma, Kagera et Kilimandjaro. Ces donnees ont ete collectees a l'aide de questionnaires et d'entrevues. Elles ont ete recoupees avec des donnees secondaires provenant d'examens documentaires. Des statistiques descriptives et la methode d'analyse de contenu ont ete utilisees pour les conclusions. L'etude montre que le principal obstacle a ete le manque de chaine de valeur dans la pisciculture. Nous avons examine la chaine des valeurs en termes de sources de production, des entrees, de services auxiliaires, de technologie, de marketing et avons trouve que 60% des fermiers piscicoles obtiennent des alevins de sources locales, par exemple dans un reseau d'amis. Ces sources n'ont pas de production scientifique d'alevins adaptee a une pisciculture commerciale. En general, les fermiers n'ont pas de sources d'alevins de qualite ou stables. De plus, 76% des fermiers piscicoles font leurs propres nourritures en utilisant les produits obtenus localement tels que le mais et les balles de riz, restes vegetaux des jardins, les feuilles de taro et du fumier de betail. Cependant, il a ete prouve que cette nourriture faite maison manquait de qualite du fait d'un manque de connaissances de base dans la production d'une bonne nourriture pour poisson. Le manque d'applications technologiques appropriees dans la pisciculture est un obstacle critique qui minimise les chances de transformer les filieres en une entite commerciale. La technologie dans la pisciculture inclut des bassins de taille adequate, des especes, une selection sexuelle des alevins, une amelioration de la nourriture des poissons, un incubateur et des installations de stockage. De plus, l'etude montre une forte demande pour des services auxiliaires dans la pisciculture agroalimentaire mais il y a une insuffisance ou une non-disponibilite de l'extension des services pour faire connaitre la bonne utilisation des medicaments, les pratiques de gestion piscicoles et l'application appropriee des technologies. Pour que les filieres piscicoles passent de 1.2 % a l'objectif de 5% de contribution au PIB, il est recommande que les actions politiques soient entreprises dans le but de fournir la capacite de construire pour les petits fermiers en termes de competences pour les meilleures pratiques de pisciculture, credit ou bonification d'interet pour les infrastructures piscicoles et les entrees, des services auxiliaires pour le transfert des savoirs et des technologies aux petits fermiers et encourager le partenariat public-prive avec les valeurs de la chaine piscicole pour s'assurer de la disponibilite de la qualite des alevins, de la nourriture des poissons, des transports et du marketing.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".