Understanding the underlying factors of soybean yield variation from field-managed interventions in northern Nigeria: Meta-regression approaches
Bibliographic record
Abstract
Soybean ( Glycine max , L.) is an important grain legume cultivated in northern Nigeria for human food, animal fodders and as a source of income. However, yield is often low and unpredictable, and our understanding of the factors explaining yield variability is limited. We examine the factors influencing soybean yield variability from field-managed interventions. A search using Web of Science, Google Scholar, and Scopus extracted studies on Rhizobia (Rh) inoculated and phosphorus (P) fertilizer or Rh × P combination treatments across three agroecological zones (AEZs): Sudan Savanna (SS), Northern Guinea Savanna (NGS), and Southern Guinea Savanna (SGS). The yield responses to management interventions and across AEZs were analyzed using effect size. Meta-regression models were used to fit yield change with various soil properties such as exchangeable potassium (K), nitrogen (N), and organic carbon (OC). The yield change was higher (39.1 ± 4.0 %) for the Rh × P combination than the Rh or P application. The NGS exhibited a lower yield change (23.1 ± 0.15 %) compared to SS (38.0 ± 0.2 %) and SGS (39.0 ± 0.2 %). The model identified a minimum soil exchangeable-K concentration of 0.54 cmol (+) kg −1 to increase yield under Rh and Rh × P treatments, while a minimum soil-N content of 1.50 g. kg −1 increased yield for the Rh and Rh × P interventions. The required OC content for significant yield responses ranged from 6 to 9 g kg −1 under the SGS agroecology. We discuss the impacts of soil OC and N levels on soybean yield variations, aiming to advance sustainable farming practices among smallholder farmers in Nigeria. • Systematic studies on the use of rhizobia (Rh) inoculants and P fertilizer in Nigeria are limited. • Meta-Regression models were used to analyze soybean yield responses to Rh, P, and Rh × P combination. • The yield increase was higher for the Rh × P treatment than for Rh inoculation or P treatment. • The soil organic carbon content needed for significant soybean yield responses is 6–9 g/kg −1 .
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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 | 0.024 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.031 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".