CASSAVA VALUE CHAIN IN NIGERIA: ATOOL FOR HUNGER REDUCTION AND EMPLOYMENT GENERATION IN POST COVID19 ERA
Bibliographic record
Abstract
Agricultural production is an engine for poverty reduction and food security of any developing country like Nigeria if potentials are fully harnessed. The term value chain is used to explain the series of farming activities it takes a farmer to create a product from a startto finish. The key actors in value chain include input suppliers,producers/farmers, processors, traders, transporters and consumers who are the end users of the chain. The COVID-19 pandemic has seriously affected the value chain from farmers to the retailers, this created local restrictions and travel bans that led to limited access to inputs which have resulted to low agricultural production. The global pandemic caused a shortage of workers for cultivation of crops like cassava, potato,sweetpotato amongothers. However; Canada, USA and Europe experienced a shortage of about 1 million migrant workers from Eastern Europe and African countries. In Nigeria and other countries, the nationwide lockdown affected the production and harvest of crops seriously. Itforced the farmers and other business agencies to shut down their farms, business and return to home for safety, this has led to food shortage and labour in many countries of the world today. Cassava value chains have latent potentials for employing over two million Nigerian if opportunities are well harnessed. However, investments to enhance cassava production have resulted to increased output and also boosted the rural economy. Local processing of cassava has created jobs for many rural women and the local fabricators; this has significantly boosted the rural economy in sub-Saharan Africa and also attracted the agricultural input supply market. Therefore, cassava value chain contributes to capital formation and securing markets for the agro-industry in Nigeria. Cassava is a choice crop for rural development, poverty alleviation, employment generation, economic growth and food security.Sequel to these, mechanization, commercialization and industrialization of cassava value addition in the post COVID-19 Erawill not only create food security in the country but also will bring about agricultural reformation that can sustain the county in the next 20 years. Therefore, agriculture is a key solution to food insecurity, unemployment rate, poverty escalation and economic dwindling that posed challenges to the country in the post COVID-19 Era. In view of the above, this paper reviewed cassava value chain in Nigeria: a tool for hunger Reduction and employment generation. We therefore recommend that; integration of information and supply of various inputs is necessary to fill up the gaps created by COVID-19 pandemic; group approachpreventive measures against COVID-19 should be encouraged and promoted to caution the effect of further occurrence.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".