Enhancing land nutrient through rhizobia biofertilization: modeling the joint effects of rhizobium inoculants and improved soybean varieties on soybean productivity in North Central, Nigeria
Bibliographic record
Abstract
Improving bacterial nitrogen fixation in grain legumes is central to the sustainable intensification of agriculture using rhizobia biofertilization. However, few studies have evaluated their joint impact on productivity using improved soybean varieties. Using household-level data from North Central Nigeria, this study explored the joint effects of the adoption of improved soybean varieties and the use of rhizobium inoculants on soybean yield and farm income. As both observed and unobserved factors may affect farmers’ decisions to adopt improved soybean varieties, a recursive bivariate probit (RBP) model is used to address the selection bias issue associated with the adoption of improved soybean varieties. Furthermore, a selectivity-corrected ordinary least square (OLS) model is applied to estimate the joint effects of the adoption of improved soybean varieties and usage of rhizobium inoculants on soybean yield and farm income. The results of the RBP model reveal a negative selection bias due to unobserved factors. After controlling for this selection bias, the results show that the adoption of improved soybean varieties increases the probability of using rhizobium inoculant by 25.2% as a complementary technological package. Soybean yield and farm income are positively and statistically significantly impacted by the adoption of improved soybean varieties (ISV). In the same vein, the adoption of rhizobium inoculants shows a positive and statistically significant effect on the yield and income from soybean production. This implies that farmers’ use of rhizobium inoculants helps them increase their farm yield while also improving their income. To provide more robust insights into this study, a robustness check, using unconditional quantile regression at different quantiles, was estimated. The findings demonstrate the heterogeneous effects of rhizobium inoculants and the adoption of improved soybean varieties adoption on soybean yield and farm income. Our finding generally confirms the significant role of the adoption of improved soybean varieties in facilitating farmers’ use of rhizobium inoculants as a complementary package.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".