The Sustainability Model of Dryland Farming in Food-Insecure Regions: Structural Equation Modeling (SEM) Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Agricultural sustainability is a prerequisite for reducing poverty and food insecurity. The readiness of food is closely linked to food security and the sustainability of dryland farming. It shows a vital position in food-insecure zones. This article purposes at presenting the analyses of the sustainability model of dryland farming in food-insecure regions. The research was carried out in East Nusa Tenggara Province, which is a region with a relatively high food insecurity level in Indonesia. The samples of farmers include 240 respondents taken using the combination of purposive and snowball samplings. Survey, interviews, and observation methods were applied to gather the data, which include main and supporting data. Data were examined with Structural Equation Modeling. The research model was built based on inputs, processes, outputs, food security, both directly and indirectly, affecting the sustainability of dryland farming. The outcomes of the study have shown that the sustainability of dryland farming can be improved by using government inputs and environmental inputs, reducing family resource inputs, using appropriate farming system models, utilizing government policies, increasing output, and strengthening the food security of farmers' households. Farmers are rational in making decisions about the sustainability of their farming management which is challenged with limitations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it