Impact of fertilizer applications on grain and vegetable crops in smallholder Mixed Crop-Livestock (MCL) systems in West Africa
Bibliographic record
Abstract
Mixed crop-livestock (MCL) systems can enhance crop yield, and improve nutrient cycling while reducing chemical fertilizer use. However, only a limited number of studies that reported this assumption were conducted under real-world conditions of small-scale farmers or followed an integrated approach. A survey was conducted in the 2021/2022 and 2022/2023 cropping seasons in Ghana and Burkina Faso, respectively, to determine the impact of fertilizer application practices on the yield of grain and vegetable crops in real-world MCL systems. Detailed information on fertilizer management practice and yield was collected from 317 MCL system farms distributed across three (3) districts/provinces in the Upper East region of Ghana and over the Plateau central of Burkina Faso, respectively summarising data on their grain and vegetable yields under (1) major fertilizer sources; organic, chemical, and combined (organic + chemical), (2) N fertilizer rate (crop x country specific N kg ha −1 recommendation), (3) application timing of fertilizer sources (recommended crop x country specific time of application), and (4) fertilizer placement methods (broadcast versus side placement versus furrow). Results show that the use of different fertilizer source increased (P < 0.05) yields of all grain crops (in Burkina Faso) and maize, rice, sorghum, millet, cowpea and all vegetable crops (in Ghana). The application of crop and country specific recommended N rates significantly influenced (P < 0.05) yields of sorghum, cowpea and green beans in Burkina Faso and rice, sorghum, millet, cowpea and pepper in Ghana compared to low N application rates. The contribution of manure application and appropriate timing on yield mostly differed between countries, such that high tendencies of increased yields were recorded when manure was applied for 0–3 weeks before planting (WBP) in Burkina Faso, while in Ghana, the highest yield improvements were observed when application periods exceeded 3 WBP. Not broadcasting chemical fertilizer only increased (P < 0.05) yields of millet and green beans (in Burkina Faso) and vegetable crops in both countries. These results help improve our understanding of fertilizer practices in mixed crop-livestock systems of Burkina Faso and Ghana, and may help guide recommended fertilizer management in MCL systems of these countries and similar ecologies in West Africa. Fig. 1. An illustrative summary of the Impact of fertilizer applications on grain and vegetable crops. Graphic vector elements designed and downloaded from Freepik ( https://www.freepik.com/ .) • Combined application of manure and chemical fertilizer increased grain and vegetable yields. • Applying chemical fertilizers in furrows proved to be the most effective method of fertilizer placement. • Yield response to the correct timing of fertilizer application differed depending on crop type and country. • Increased amount of fertilizer led to greater yield as N rate (kg/ha) increased. • The most important source of nitrogen (N) in MCLS was applied through manure.
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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 | 0.001 | 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 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".