Management of Gogo Rice Production in Realizing the Commercialization of Marginal Land Farming Households in Yogyakarta
Bibliographic record
Abstract
The land is an important factor for people whose lives depend on the agricultural sector. The need for land for various uses has resulted in decreasing agricultural land which could have implications for decreasing food production. Alternative options which are expected to increase the potential for food production are the utilization of marginal land. Farmers with marginal land need to manage their production to meet household needs. So it is important to examine the marketable surplus, the level of commercialization, and the carrying capacity of marginal land. The research area was taken by purposive sampling method in Gunungkidul Yogyakarta. The samples taken were upland rice farmer households with the simple random sampling method. The marketable surplus analysis uses a marketable surplus formula, then the percentage is used to determine the level of farm commercialization. The carrying capacity analysis is carried out using the carrying capacity formula. The results showed that farmer households manage rice production by allocating an average of 59.1% for marketed and 40.9% for household consumption. The allocation of marketable surplus is greater than for household consumption, this shows that gogo rice farming households are towards commercially. The marginal land carrying capacity of 0.641 indicates that the land cannot be developed in an expansive and exploratory manner. The implication is in increasing upland rice production on marginal land, namely by an intensification of farming.
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.
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 | 0.000 | 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 | 0.001 | 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) | 0.001 | 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".