Adoption of Horticultural Innovations by Smallholder Farmers in North Lombok - Indonesia
Bibliographic record
Abstract
Various approaches have been promoted by the Innovative Farm Systems and Capability for Agribusiness Activity (IFSCA) Program to improve small farmers' the livelihoods in North Lombok District -Indonesia.Through this collaborative action-research program involving Mataram University (Lombok -Indonesia), Massey University (New Zealand), and the local government, some horticulture innovations had been introduced to the smallholder farmers such as growth hormone, growing safe, high beds, plastic mulch, compost, pruning technique, and drip irrigation.A study was conducted to understand (1) farmers' adoption behavior of these innovations, (2) the impacts of the innovation adoption the horticulture production, job creation, and farmers' income, (3) factors affecting farmers' adoption behaviorsimpacts of earthquake and COVID-19 pandemic.A mix method approach was used for the study interviewing 60 farmers from 8 farmer groups participated in the IFSCA Program.The results show 100% farmers have adopted the horticulture innovations, increased the production and smallholder farmers' income.Changes in work patterns were identified as the results of the adoption behavior and changes of farming system, from previously food crop farming system to hoticulture farming.The adoption of horticulture innovations has created new jobs along the value chain.Higher profit, continuous production, job creation, and doing horticultural business are important factors affecting farmers' adoption behaviors.However, a big earthquake in July -August 2018, and COVID-19 pandemic that took place early 2020 have had significant impacts on the smallholder farmers' adoption rate.Horticulture farming scale decreased due to the big earthquake and COVID-19 pandemic, and another challenging effort is needed to bring the horticulture business back to normal.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".