Rainwater Harvesting-Based Marginal Land Irrigation Technology: A Case Study in Ngawen Sub-district of Gunungkidul Regency, Indonesia
Bibliographic record
Abstract
Gunungkidul Regency is an area that has both potential and problems in achieving food stability.Though agriculture in this region makes the highest contribution to Gross Regional Domestic Product, the productivity of this sector is still low.Drought is a classic problem and represents the largest barrier in agricultural development, despite high precipitation.This paper describes the design of an efficient irrigation technology to increase agricultural productivity.Specifically, this research aims to determine marginal-land suitability, analyze and design a suitable model of rainwater-harvesting-based irrigation technology.Using the method of combining field study and desktop analysis, the results indicate that the land in the research site is considered suitable given the conditions of a particular treatment for the commodities of upland rice, soybean, corn, green beans, peanuts and cassava.The model rainwater irrigation reservoir is built by considering the drainage flow and the contour of the rainwater catchment area.The feasible irrigation distribution models are the pitcher irrigation system and perforated pipe irrigation system.The pitcher system from the existing reservoir can support a maximum of 24.75 m 2 of land, 120 plants and at least 66 service days.The optimum range of pitcher water is around 25 cm with a 50-cm space between plants and one pitcher serving 4 plants.Meanwhile, the perforated pipe is mounted near the root zone (10 -25 cm) at the depth of 17.5 cm, with 25 cm left-right spacing between plants.An L-shaped pipe can serve 10 plants; one side is mounted underground while the other side is above the land surface for water intake.The pipe system from a reservoir can serve a maximum of 129.5 m 2 land.The study results lead to the conclusion that the most suitable irrigation model in the study area is the perforated pipe system.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".