Clipped Cowpea (Vigna unguiculata (L.) Walp) Fodder Utilization: A Potential for Income Growths of Resource Poor Farmers in the Savannah Regions of Nigeria
Bibliographic record
Abstract
<p>Cowpea features prominently in the farming systems of the semi-arid tropics where they are grown for fodder used as feed for livestock. Traditional farm varieties are the indeterminate, spreading type which grow fast; cover the soil surface and produce large quantities of biomass. Studies have focused on the use of such green crop materials for soil fertility improvement. But, it’s been highlighted that a green manure crop should also be a cover crop of economic value. Such crops should raise the farmers’ income not only indirectly by improving soil fertility but also directly by yielding products of economic importance such as food and fodder. In addition, trading in these residues (fodder/ haulms) can be highly remunerating. It is against this background that this study was carried out at the Institute for Agricultural Research, Ahmadu Bello University, Samaru, Zaria, Nigeria; During the 2002-2005 wet seasons; to determining the influence of intra-row spacing, clipping height and time on the productivity and income growth potential of the dual purpose cowpea (<em>Vigna unguiculata</em> (L.) Walp). The experimental lay out was a Randomized Complete Block Design (RCBD); replicated three times. The data was analyzed statistically using the analysis of variance test (ANOVA); and the means compared using the Duncan Multiple Range Test (DMRT). Results showed that, clipping management, facilitated the production of large (15 t ha<sup>-1</sup>) amounts of green plant biomass on-farm. This could be put to various uses by the resource poor farmer who usually is cash strapped at the peak of the farming (dry) season. The farmer could choose to keep and use the fodder to feed animals, and/ or trade it off and use the proceeds (<s>N</s> 41, 000.00 - <s>N</s> 46, 000.00 - Nigerian Naira equivalent) to subsidize on fertilizer requirement and/ or meet up with immediate socio-economic family demands. Additionally, considering the high amount (187 kg<sup>-1</sup>) of N-added to the soil (i.e. about 4 bags of Urea fertilizer), a cost saving of about <s>N</s> 24, 000.00 was made by the resource poor farmer; which could have otherwise been invested into the procurement of N-fertilizer input. It is concluded that such pro-poor income growths originating from the adoption of such innovative clipping management technological farming practices as this, needs to be encouraged and supported; as this hold immense potential of increasing the income of resource poor, low income, and low technology farmers. Indeed, it is a veritable tool for the reduction of food insecurity and poverty viz. economic empowerment in the region.</p><p> </p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".