Impact of Communal Violent Conflict on Farmer’s Livelihood Activities in Two Agro-Ecological Zones of Nigeria
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".