Optimizing split-fertilizer applications for enhanced maize yield and nutrient use efficiency in Nigeria's Middle-belt
Bibliographic record
Abstract
Inadequate and imbalanced fertilizer application is a significant barrier to achieving higher maize yields in Nigeria's Middle Belt. This study hypothesized that optimizing fertilizer types and application rates, particularly through split applications of straight fertilizers, can significantly enhance maize yield and nutrient use efficiency compared to conventional NPK blends and farmer's practices. This experiment evaluated the effects of optimizing types and amounts of fertilizer on maize growth and yield, soil characteristics, and nutrient use efficiencies in the mid-belt region of Nigeria. A field experiment was conducted at two locations using a randomized complete block design with four replications. The treatments included national and regional fertilizer recommendations, applied as NPK blends and straight fertilizers, along with a farmer's practice and control. Soil samples were collected before and after the experiment, and data on yield, yield attributes, grain, and leaf samples, were collected for analysis. The results showed that split applications of straight fertilizers increased grain yield by 22 %–46 %, achieving yields ranging from 2.37 to 3.08 t ha −1 , compared to yields from NPK blends. Nitrogen uptake efficiency improved by up to 52 %, while potassium uptake exceeded 100 % in certain treatments. Despite higher input costs, split applications yielded gross margins up to 35 % greater than those obtained with NPK blends, underscoring their economic viability. Split application of regional recommendation of 119:38:20 kg ha −1 of N, P, and K from straight fertilizer shows higher yields and better nutrient efficiency than NPK blends, proving effective for optimum maize production in the region. No significant changes in soil physio-chemical properties, suggesting that long-term studies are needed to fully understand the impact of fertilizer practices on soil health . These findings strongly support the adoption of site-specific nutrient management strategies, particularly the use of straight fertilizers in split applications, to maximize maize production in Nigeria's Middle-Belt.
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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 | 0.000 | 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 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".