Rubber Cash Crop and Changes in Livelihoods Strategies in a Village in Northeastern Thailand
Notice bibliographique
Résumé
This research article aims to explain the changes of livelihoods strategies in a village in the Northeast region of Thailand after the introduction of rubber as cash crop. Northeast region is considered a new rubber growing area of Thailand, the world’s largest rubber exporter. A village in Udornthani province was selected as a case study for a qualitative research conducted in 2012-2013. In order to get a full understanding of these changes, the research studied livelihood changes in the village over the last 62 years or since the village establishment. We have classified livelihood strategies into three periods, based on the dominant mode of production and livelihoods. The first period was the livelihoods before cash crops, in which people were involved in subsistence mode of production. The second period was marked by the introduction of cash crops and market economy into the village in the early 1960s. During this period, subsistence economy had been transformed into market oriented. Forest lands were changed into cash crop farms, so much that the natural capital lost its balance and became less dependable. The third period, starting in 2001, was the rubber period, the focus of this research. Rubber came to the northeastern region as a result of the promotion of the state. Strong research, development and extension supports that used to work well in the Southern region were applied to promote rubber growing here. However, the adoption of rubber was not without problems. People had to adjust themselves to the capitalist standardized farming practices of rubber. In addition, there was a misbalance of and unequal access to capital assets, particularly financial capital. Within the changing socio-economic context and constraining status of capital assets and access, rural households adopted integrated livelihood strategies, including 1) economic diversification or pluri-activities to reduce risks and increase income 2) maintaining rice cultivation to guarantee food security 3) keeping the ownership of agricultural land as an important capital asset 4) investment in higher education of the younger generation of the household. The article also discusses the implication of these livelihood strategies on rural society, and provides policy and research recommendation.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».