Agronomic technology to promote sustainable utilization of newly created farmland in the Chinese Loess Plateau
Bibliographic record
Abstract
Abstract The new farmland created by land consolidation often faces the problems of poor soil structure and low productivity, which cause potential degradation risk. The Gully Land Consolidation Program (GLCP) significantly increased the quantity of farmland in the Chinese Loess Plateau (LP), but the research on a comprehensive method of simultaneously improving soil quality, agricultural profit, and utilization efficiency of newly created farmland (NCF) is relatively scant. This study explored an agronomic technology to improve soil quality, agricultural profit, and utilization efficiency NCF by the GLCP in the LP. Our field experiment was carried out in Yangjuangou catchment with seven soil treatments and planting Brassica napus (B. napus) on these soils: dry mixing Malan Loess and red clay at volumetric ratios of 1:0 (MR10), 5:1 (MR51), 2:1 (MR21), 1:1 (MR11), 1:2 (MR12), 1:5 (MR15), and 0:1 (MR01). The results showed that: the soil microstructure, physico‐chemical properties, and productivity of NCF were significantly improved after soil reconstruction by dry mixing Malan Loess and red clay. More specifically, the MR51 boosted the root thickness and fresh weight of B. napus by 78.69% and 45.01% compared to that of red clay (MR01). Crop optimization by the B. napus helped to increase the agricultural profits of NCF. The proposed three portfolios of B. napus' silage, vegetable plus rapeseed, and vegetable plus silage enhanced the profits by 35.39%, 57.05%, and 66.93% in comparison with that of traditional crop planting, respectively. Therefore, industrial integration through effective, ecological and economic (3E) agriculture could advance sustainable utilization of NCF. Further, developing efficient agriculture, animal husbandry, agricultural products processing industry, and ecological tourism would enhance the multi‐functional value of farmland. Our study suggests that targeted agronomic technology based on agricultural geographical engineering oriented to human‐environment interaction can provide technical support for minimizing the degradation risk of NCF and generating more sustainable development in ecologically fragile areas.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".