The Effect of Backyard Agriculture on Household Income in the COVID-19 Era in Southeast Nigeria
Bibliographic record
Abstract
Poverty is a major menace in Nigeria, and the onset of COVID-19 complicated the issue by limiting people's economic activities and livelihoods. Thus, there is a need for households to engage in sustainable economic activities to cope with economic shocks. Backyard agriculture could play a critical role in enhancing household income, especially during economic shocks; yet there exists a dearth of empirical information on this. Hence the need for this study, which investigated the effect of backyard agriculture on household income in southeast Nigeria. The study employed a multistage sampling procedure to get to the respondents. The data collected from randomly selected 480 households were analysed using descriptive statistics, t-tests, and multiple regression. The study revealed that households that engaged in backyard agriculture had a higher income (N130,125=USD 204.84) than households that did not engage in backyard agriculture (N64,700=USD 101.85). Thus, there is a difference of N65,425 (USD 102.99) between the average income of households that engaged in backyard agriculture and households that did not engage in backyard agriculture. The t-test results indicate that the difference between their income was significant at 1%. The regression result further shows that backyard agriculture significantly increased the income level of households. Thus, backyard agriculture is a crucial tool to enhance household economic status and livelihood during health and economic shocks. Based on these, this study recommends the promotion of backyard agriculture among households to boost household income by the government through agricultural extension agents.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".