Effects of Farmland Landscape Fragmentation on Agricultural Irrigation in Hotan Oasis
Bibliographic record
Abstract
Farmland landscape fragmentation is an important problem affecting the agricultural modernization process in China. However, farmland landscape fragmentation leads to land being wasted and increases management costs, particularly in the dryland’s oasis regions. Therefore, investigating the impact of farmland landscape fragmentation on agricultural irrigation is of great significance in developing oasis agriculture. This paper used the landscape quantitative index (DIVISION), the moving window method, and gradient analysis methods to study the temporal and spatial pattern changes in farmland fragmentation in the Hotan Oasis. Additionally, the impact of fragmentation on irrigation in the oasis was elaborated upon by exploring the relationship between evapotranspiration and its components in farmland fragmentation. The results showed that the farmland area of the Hotan Oasis increased from 1546.19 km2 in 2000 to 2068.23 km2 in 2020, and farmland landscape fragmentation increased with the expansion of the Hotan Oasis. In addition, a significant relationship between farmland fragmentation and evapotranspiration and its components was evident. A lower DIVISION value corresponded to a higher ET value, a lower ETs/ETc ratio, and a higher water use efficiency. When the total farmland area is assumed to remain unchanged, the irrigation water consumption is reduced by 4.82 × 108 m3 according to the size and proportion of arable land with the lowest degree of fragmentation (L1, division value of 0.46). In addition, with the increase in the proportion of farmland, the scale of oasis decreases by 2431.56 km2 for the reduction in field roads, shelterbelt, and bare land. These findings suggest that solving the problem of farmland fragmentation can effectively reduce irrigation water consumption, realize the internal expansion of the oasis through intensive land use, and relieve the pressure of the external expansion of the oasis.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".