Performance of black soldier fly frass fertiliser on maize (Zea mays L.) growth, yield, nutritional quality, and economic returns
Why this work is in the frame
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Bibliographic record
Abstract
Although the black soldier fly frass fertiliser (BSFFF) is globally recognised as a promising and potential high-quality organic fertiliser, there is inadequate information on its impact on the growth performance and nutritional status of maize. Furthermore, no information exist on their combination with mineral fertiliser (NPK) as well as economic impact on maize production. This study evaluated the comparative impact of BSFFF, NPK and commercial organic fertiliser (Evergrow ® ) on growth, nitrogen use efficiency, yield, nutritional quality, and profitability of maize under greenhouse conditions. The treatments included: (1) sole application of BSFFF, conventional compost of brewers’ spent grain (BSG), Evergrow, and NPK at rates equivalent to 100 kg nitrogen (N) per hectare; (2) BSFFF and BSG combined with NPK so that each fertiliser supplies 50% of the N required; and (3) the control (unamended soil). Our findings revealed that BSFFF increased maize grain yield at higher rates: 2-25, 25-113 and 153-212% than NPK, BSG and Evergrow, respectively. Similarly, the BSF frass application led to higher maize growth and yield than the control treatment. The agronomic N use efficiency of maize grown using BSFFF was 2 and 3 times higher compared to that of BSG and Evergrow, respectively. Maize grown using BSFFF and NPK had higher crude protein and crude fibre content compared to the other treatments. The net income generated from the commercialisation of maize grown using a combination of BSFFF and NPK was 2, 163 and 173% higher than those achieved using sole NPK, mixture of BSG and NPK, and sole BSFFF, respectively. Our results imply that developing and promoting BSFFF alone or in integration with NPK can enhance the food security and livelihoods of smallholders, while safeguarding planetary health.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it