Agro-Ecosystem Function and Prediction

Agro-ecosystems are managed systems in which crop and livestock production interact with soil, water and atmospheric processes to deliver food, fibre, fodder and fuel alongside regulating and supporting services. Their functioning reflects the interplay of spatial patterns in soil properties, temporal weather variability, biodiversity and human interventions. Advances in remote and proximal sensing, decision-support tools and process-based simulation models are now being combined to predict yields, resource-use efficiency and environmental impacts at field and landscape scales. By coupling crop physiology, soil hydrology, nutrient cycling and climate drivers in integrated platforms, it becomes feasible to explore scenarios for irrigation, fertilisation and land-use change to optimise productivity, minimise losses and avert critical thresholds. At the same time, machine-learning and agent-based modelling approaches are emerging to recognise complex patterns, issue early warnings of abrupt shifts and guide adaptive management for sustainable intensification.

Research from Nature Portfolio

A deep-learning framework has been introduced for forecasting rate-induced tipping in nonlinear dynamical systems exposed to rapid external forcing and noise, extracting characteristic fingerprints that allow early warnings even when conventional indicators fail. In crop-rotation research, a six-year field trial in the North China Plain demonstrated that diversifying a traditional wheat–maize sequence with sweet potato, peanut and soybean increased equivalent yields by up to 38 %, reduced N₂O emissions by 39 % and raised soil organic carbon stocks, thereby aligning productivity gains with greenhouse-gas mitigation. A global meta-analysis of 11 000 yield comparisons from 462 experiments further revealed that legume-based rotations lift subsequent main-crop yields by an average of 20 %, with the largest benefits in low-input or low-diversity systems, underscoring the key role of biological nitrogen inputs and functional diversity in enhancing global crop production.

Agro-Ecosystem Function and Prediction publication trend

The graph below shows the total number of articles in agro-ecosystem function and prediction across all publications each year (not limited to Nature Index journals).

Technical terms

Agro-ecosystem: A managed ecological system in which crops, livestock, soils and water interact with human activities to produce goods and ecosystem services.

Radiation use efficiency (RUE): The ratio of biomass produced per unit of intercepted photosynthetically active radiation, typically expressed in grams per megajoule.

Rate-induced tipping: A sudden transition in system state triggered when the rate of change in external forcing exceeds the system’s internal response time.

Agent-based model (ABM): A computational framework in which autonomous ‘agents’ follow behavioural rules and interact locally, giving rise to emergent system-level patterns.

Legume-based rotation: A cropping sequence incorporating nitrogen-fixing legume crops to enhance soil fertility and reduce reliance on synthetic fertilizers.

References

1. Deep learning for predicting rate-induced tipping. Nature Machine Intelligence (2024).
2. Diversifying crop rotation increases food production, reduces net greenhouse gas emissions and improves soil health. Nature Communications (2024).
3. Global systematic review with meta-analysis reveals yield advantage of legume-based rotations and its drivers. Nature Communications (2022).
4. Sustainable farming strategies for mixed crop-livestock farms in Luxembourg simulated with a hybrid agent-based and life-cycle assessment model. Journal of Cleaner Production (2023).
5. Data-driven agent-based modelling of incentives for carbon sequestration: The case of sown biodiverse pastures in Portugal. Journal of Environmental Management (2023).