Integrated strategies for enhancing agrifood productivity, lowering greenhouse gas emissions, and improving soil health
Bibliographic record
Abstract
Global agrifood systems face three interconnected challenges: ensuring food security, promoting environmental sustainability, and restoring soil health in the face of climate change. Conventional practices have prioritized productivity over ecological resilience, leading to soil degradation, increased greenhouse gas (GHG) emissions, and inefficient resource utilization. Here, we introduce a "triple-goal" agrifood framework that enhances food production, soil health, and GHG mitigation simultaneously through integrated innovations. Using a second-order meta-analysis of 104 meta-analyses that cover 39,162 studies and 300,139 global field comparisons, we identified key interventions, including optimized fertigation, diversified cropping systems, organic amendments, and precision N management, that increased productivity by 14%–28% while reducing environmental impacts. Diversified systems boosted yields by 19.6% and reduced land use by 19%. Integrating legumes and cover crops lowered N 2 O emissions by 18%–65%, while organic amendments increased soil organic carbon stocks by 7%–13%. Structural equation modeling identified nitrogen use efficiency and microbial activity as central to the food-soil-emissions nexus. However, tradeoffs remain; yield-focused strategies can elevate emissions if not tailored to local conditions. By integrating agronomic, biological, and technological interventions such as conservation tillage, biofertilization, and digital agriculture, this triple-goal framework supports a 15%–30% reduction in anthropogenic CO 2 -equivalent emissions. These findings underscore the need for policy reform and multi-stakeholder collaboration to scale up the adaptation of integrated strategies in alignment with the UN's Sustainable Development Goals and the "One Health" initiative. The triple-goal framework provides a transformative pathway to climate-smart, equitable, and resilient agrifood systems that strike a balance between productivity and planetary health.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".