Adoption of NPS Fertilizer on Sorghum Crop Production by Smallholder Farmers in Gemechis and Mieso Districts of West Hararghe Zone, Oromia Regional State, Ethiopia
Bibliographic record
Abstract
The adoption of inorganic fertilizer such as NPS which is concerned by development clients and government is different from one farmer to another farmer and this makes productivity of agricultural crops to vary from one plot to another plot due to socio-economic, institutional and other factors. Therefore, this study was intended to know the socio-economic factors that significantly affect utilization of inorganic fertilizer NPS. Primary data was collected from 201 sampled households of selected districts. Secondary data was collected from stakeholders related with production of sorghum and inorganic fertilizer NPS in the study areas. In the sampling procedure, two stage simple random sampling was used. In the first stage, kebeles were randomly taken from total kebeles in the two districts. In the second stage, households were randomly selected from the selected kebeles. Data was analyzed using descriptive, inferential statistics and econometric models methods of data analysis. In econometric models Double Hurdle model was use to know factors affect adoption decision of inorganic fertilizer NPS and intensity use of inorganic fertilizer NPS. Double Hurdle model result confirms that district of the household, education level, family size, extension visit, expectation of the coming rainfall by the household, number of farm plot owned, total farm land owned and off/non-farm income earned by the household significantly affect adoption decision inorganic fertilizer NPS. Double hurdle model result also reveals that, district of the household, livestock holding, number of farm plot owned, participation on agricultural training by the household significantly affect intensity use of inorganic fertilizer NPS. Government and concerned stakeholders should give attention on these significant socio-economic factors so that utilization inorganic fertilizer can be improved to sorghum crop productivity.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".