14 MAY 2026 (WED) 15:35 - 16:05

Improved Cropland Management and Crop Spatial Distribution for Climate Mitigation and Food Security

Mr LIU Xinran  

( Supervisor: Prof Peng Zhu )

Abstract:

Agrifood systems generated 16.2 Pg CO₂eq of greenhouse gas emissions, accounting for 29.7% of anthropogenic emissions. Among these, cropping activities—including the application of synthetic nitrogen fertilizers, rice cultivation, and the management of crop residues and manure—contributed 1.5 Pg CO₂eq. Improved management practices can capture atmospheric CO₂ and store it in soils, thereby transforming croplands into a carbon sink. Moreover, crop yields and greenhouse gas emissions per unit area also exhibit pronounced spatial variability. To feed a growing population projected to reach 9.66 billion by 2050, it is essential to improve cropland management and optimize crop spatial distribution to enhance production, rather than expanding cropland, which would further increase greenhouse gas emissions.

Numerous field experiments have measured the impacts of diverse management practices on crop yields, greenhouse gas emissions, and soil organic carbon (SOC) stocks. However, the results are subject to complex interactions among climate, soil, and management practices, leading to substantial variability. Furthermore, few studies have simultaneously considered production gains, climate mitigation, and implementation costs, which makes it difficult to identify optimal practice combinations. Although optimizing crop spatial distribution could further enhance climate mitigation potential, existing studies have primarily emphasized production maximization and water-use minimization, while largely neglecting climate mitigation.

We compiled 7,753 yield observations, 4,424 N₂O observations, 1,900 CH₄ observations, and 3,716 SOC observations from 1,473 studies, and extrapolated these results globally at 5‑arc‑minute resolution using machine learning. After estimating the remaining adoption area of each practice, we calculated the combined benefits of production increase and climate mitigation based on grain and carbon prices. We then incorporated practice-specific costs to derive net benefits and identified optimal practice combinations under three scenarios: maximization of climate mitigation potential (S1), maximization of gross benefits (S2), and maximization of net benefits (S3). Finally, based on the optimal practice combinations, we apply linear programming to maximize production, climate mitigation potential, and net benefits under given constraints.