Synergism between production and soil health through crop diversification, organic amendments and crop protection in wheat‐based systems
Bibliographic record
Abstract
Abstract One of the critical challenges in agriculture is enhancing yield without compromising its foundation, a healthy environment and, particularly, soils. Hence, there is an urgent need to identify management practices that simultaneously support soil health and production and help achieve environmentally sound production systems. To investigate how management influences production and soil health under realistic agronomic conditions, we conducted an on‐farm study involving 60 wheat fields managed conventionally, under no‐till or organically. We assessed 68 variables defining management, production and soil health properties. We examined how management systems and individual practices describing crop diversification, fertiliser inputs, agrochemical use and soil disturbance influenced production—quantity and quality—and soil health focusing on aspects ranging from soil organic matter over soil structure to microbial abundance and diversity. Our on‐farm comparison showed marked differences between soil health and production in the current system: organic management resulted in the best overall soil health (+47%) but the most significant yield gap (−34%) compared to conventional management. No‐till systems were generally intermediate, exhibiting a smaller yield gap (−17%) and only a marginally improved level of soil health (+5%) compared to conventional management. Yet, the overlap between management systems in production and soil health properties was considerably large. Our results further highlight the importance of soil health for productivity by revealing positive associations between crop yield and soil health properties, particularly under conventional management, whereas factors such as weed pressure were more dominant in organic systems. None of the three systems showed advantages in supporting production‐soil health‐based multifunctionality. In contrast, a cross‐system analysis suggests that multifunctional agroecosystems could be achieved through a combination of crop diversification and organic amendments with effective crop protection. Synthesis and applications : Our on‐farm study implies that current trade‐offs in managing production and soil health could be overcome through more balanced systems incorporating conventional and alternative approaches. Such multifunctionality supporting systems could unlock synergies between vital ecosystem services and help achieve productive yet environmentally sound agriculture supported by healthy soils.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".