Tilling the Soil in Tanzania: What Do Emerging Economies Have to Offer?
Notice bibliographique
Résumé
Close to 70% of Tanzanian farmers are small scale resource-poor subsistence operators, cultivating an average of less than 1 to 3 hectares of mainly rain-fed land, deteriorated by continuous cropping and lack of fertility management. In the farmers’ effort to move up the commercialisation continuum and alleviate poverty through increased output and incomes, innovation and technical change is key. However, liquidity constraints and prohibitive prices have in the past discouraged farmer investment in capital goods (power tillers and tractors). This is a limiting factor for increased cropping area and timeliness of operation which has the potential to positively affect crop output and incomes. In the face of these difficulties, the farmer is prepared to trade-off quality and variety, for relatively low priced capital goods, provided they are good enough and rely less on heavily built infrastructure. In recent decades, the capital goods market for power tillers and tractors has become dynamic with respect to cost, quality and origin of production. With new entrants like China, India and Pakistan joining Western Europe, USA and Japan in the supply of farm machinery, the range of choice for the Tanzanian farmer is increasing. Chinese, Indian and Pakistani power tillers and tractors have some distinctiveness in their engineering, acquisition cost, operational cost and their supply chains which may be useful in more ways to the small farmer in Tanzania. This thesis appraises the pro-poor nature of emerging economy tillage capital goods, placing particular emphases on how an optimal technological choice is made. It examines the role that cost innovators’ from emerging economies (China/India/Pakistan) are playing in meeting the farmers’ choice objective particularly with regard to cost, labour intensity and scale of operation. In as far as Tanzanian farmers are concerned the study discusses the role that local institutions can play to enhance choice, access and efficient use of such capital goods for higher productivity which may translate into increased incomes. The study draws on both qualitative and quantitative data to compare advanced country tractors and power tillers with those from emerging economies and finds that; First, aid/government support, trade and FDI/licencing are key conduits for technology imports into Tanzania. However, trade has been very important for emerging economy machines whilst aid/government support has been found to be key for advanced country machines. Second, in terms of penetration and extent of use among Tanzanian farmers emerging economy machines are more popular than advanced country ones when it comes to power tillers. Nevertheless, the total stock of advanced country tractors in Tanzania are known to be larger than emerging economy ones; though we are recently witnessing a recent rapid increase in the former than the latter. Third, advanced country machines are generally superior in terms of engineering performance and work efficiency when compared with emerging economy ones. That said, it is worth noting that the advanced country machines are capital intensive and involve higher maintenance costs because of higher spare parts and repair cost. Finally, emerging economy machines are more pro-poor than matured market ones since they create more opportunities for employment and capability building among capital constrained users and dealers.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».