Evaluation of the Effect of Stone Lines and Microdosing Adoption on Sorghum Yield and Income: A Case of Smallholder Farmers in Burkina Faso
Bibliographic record
Abstract
This present study aims to investigate the factors influencing the combined adoption of stone lines and microdosing and its effect on sorghum yields and net income. By adjusting for biases in observable and unobservable factors, the multinomial endogenous switching regression (MESR) model was employed to estimate the net effects of adoption on outcomes. The average treatment effect on treated (ATT) was also employed to evaluate the effects of stone lines and microdosing adoption. For a more accurate estimation of farmer output and farming income, the inverse probability weighted regression adjustment (IPWRA) was also estimated. In achieving our purpose, we collected data from 420 farm households which had 1280 plots for four main crops. The total sample identified 368 sorghum plots with stone lines and microdosing adoption. The MESR results indicated that the number of extension visits, level of education, access to agricultural credit, access to subsidies, household size, family labour and tropical livestock unit (TLU), and perception of soil fertility all played significant roles in the adoption of the stone lines and microdosing combination. The ATT revealed that adopters of the stone lines and combined microdosing had a higher sorghum yield than their counterfactual. The adoption of the stone lines and microdosing increased sorghum yield and net sorghum income, respectively, by 70% (p < 0.001) and 60% (p < 0.001). This result shows a strong synergy in agricultural productivity between the stone lines and the microdosing. However, the sorghum yield was positively and significantly affected by the microdosing adoption, but the effect on net income was non-significant. The results demonstrate that adopting both techniques would be more effective, and this would let smallholder farmers improve their sorghum yield and income. The study recommends intensifying efforts to promote the use of both technologies simultaneously, educating smallholder farmers on the proper use of microdosing, and encouraging fertilizer subsidies for smallholder farmers in order to farm yield and maintain food security.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".