Impact of Communal Violent Conflict on Farmer’s Livelihood Activities in Two Agro-Ecological Zones of Nigeria
Notice bibliographique
Résumé
In Nigeria there is hardly a year where there are no major violent conflicts. However, much has not been published on the quantitative impact of the conflicts on farmers’ livelihood the manager of crops, domesticated and wild life animals. Hence, this study tend to provide information for understanding how conflict handling styles employed by conflicting parties made most of the communal conflicts degenerate into destruction of farmers livelihood activities. Two violent communal conflicts ridden states one in rainforest and derive savannah region of Nigeria were purposively selected to reflect discrepancy in impact of the conflict on livelihood activities the means of generating livelihood in two main agro-ecological regions of Nigeria. Based on the conflict severity the two agro-ecological zones were stratified into core and outside conflict areas. Using farmers register as sampling frame work 60 and 67 farmers were randomly selected in core and outside violent conflict areas of rainforest and savannah zones respectively. Interview schedule instrument was used to collect data while frequency count, percentage t-test and ANOVA were statistical tools used for data analysis. The findings revealed that in Core Violent Conflict Area (CVCA) of rainforest and derived savannah areas 72.1% and 23.8% of the farmers were displaced from their farms respectively. Consequently tree (cocoa) crops production level were severely affected as reflected in lower ( 295) and higher mean ( 697) cocoa production level in tons recorded in CVCA and Outside Violent Conflict Areas (OVCA) respectively in rainforest areas. The severity of conflict impact was not reflected in derived savannah area because yam production level means gap in tons between CVCA ( 423.0) and OVCA ( 629) were very close. However, the savannah area felt the impact of the conflict on sheep and goat production because CVCA recorded lower mean ( 180) numbers of sheep and goats as against higher mean ( 2007) number of sheep and goat recorded in OVCA. The decline in production of sheep and goat could be attributed to conflict because majority (78.4%) of the farmers claimed that they have lost their productive land to conflict. Farmers’ means of generating livelihood activities such as crops production level, sheep and goat number produced were statistically different across conflict zones at P < 0.05 in rainforest and savannah zones. The conflict had severe impact on crops, sheep and goat production hence, a sustainable capacity building program, as a post conflict coping strategies should be organised for conflict victims.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| 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 | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».