Co‐composted biochar to decrease fertilization rates in cotton–maize rotation in Burkina Faso
Bibliographic record
Abstract
Abstract Agriculture in Burkina Faso relies on mineral fertilizers to reach decent crop production. Therefore, there is an urgent need to implement sustainable solutions that improve soil nutrient status while maintaining crop yields. Here we experiment with the recycling of nutrients through the production of biochar from cotton ( Gossypium hirsutum L.) stalks and its mixing with compost to improve soil properties of highly weathered Lixisol. The trials included three treatments: conventional compost (COMP‐100), co‐composted biochar (COMPBI‐100), each with recommended fertilization rates [cotton = 16.3 kg N ha –1 , 15.1 kg P ha –1 and 17.4 kg of K ha –1 ; maize ( Zea mays L.) = 21.8 kg N ha –1 , 20.1 kg P ha –1 and 23.2 kg K ha –1 ], and co‐composted biochar with 75% of recommended NPK fertilizer rate (COMPBI‐75). We amended the soil with compost at conventional rates used in Burkina Faso, that is, 2.5 t ha –1 at each crop year (2018 and 2019). We measured the effect of the amendments on cotton and maize yield cropped in rotation using a randomized block design with four replicates for each of the studied treatments. Our results showed that the soil properties and crop yield in COMPBI‐75 were not significantly lowered compared to COMPBI‐100, which did not differ compared to soil and plant responses in COMP‐100. Even not significant, COMPBI‐100 and COMPBI‐75 tend to have higher grain yields for cotton and maize. Our results highlight that co‐composted biochar may be a promising amendment to increase crop productivity parameters in Burkina Faso while decreasing the NPK doses. The reduction of fertilizer rates can have essential implications considering the socio‐economic and environmental advantages of reducing by quarter fertilizer doses in the Sudanese climatic region of Burkina Faso.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".