Alternative Management Practices Improve Soil Health Indices in Intensive Vegetable Cropping Systems: A Review
Bibliographic record
Abstract
An increase in intensive cropping would benefit society by providing food to a growing population, and vegetable production is an excellent example of intensive cropping systems that are indeed on the rise. Vegetable cropping systems are high-input and generally require large quantities of fertilization, frequent irrigation, and repeated tillage operations. Consequently, an increase in global vegetable production may have seriously negative impacts on soil health and ecosystem services. Yet, not only maintaining but improving soil health is critical to enhancing the sustainability of food production systems. Previous agricultural research mainly focused on field crop systems and largely ignored vegetable cropping systems; consequently, this represents a conspicuous research gap, one that must be addressed in order to make progress towards sustainable food production. Here, we review the literature to gain a better understanding of how management has influenced various soil health indices (soil biology, chemistry, and physical dynamics) and to evaluate the implications for soil ecosystem services in vegetable cropping systems. We found that alternative modifications to conventional vegetable production systems, which resemble methods used in organic or conservation agriculture, tended to improve aspects of soil health. For example, soil amendments generally improved soil chemical indices of health – soil carbon levels and nitrogen reserves in particular. Incorporation of cover crops to vegetable crop rotations tended to improve nitrogen recycling via reduced nitrate leaching risks, increased soil carbon levels, and weed suppression. Reduced tillage systems were rare, presenting an important challenge and opportunity for further improving soil health dynamics in vegetable production. Notably, adopting alternative practices generally had no effect on crop yields, which implies little risk of yield penalties when agronomic management is carefully planned. Our results indicate that future sustainable vegetable cropping systems may embody a blend between organic and conventional ideologies to better maintain or improve soil ecosystem functioning.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".