简报

On August 6, 2026, Wenjiao Shi, the core member of the RESIL FARM project and a research professor at the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (IGSNRR, CAS), held an academic exchange with Zhanli Sun, Senior Researcher and Coordinator of the China Research Group at the Leibniz Institute of Agricultural Development in Transition Economies (IAMO), Germany. The discussion focused on progress under RESIL FARM; the effects of well-facilitated farmland construction on stable crop production, carbon sequestration, and climate resilience; spatiotemporal modelling of complex agricultural systems; and feedbacks, thresholds, and regime shifts in the evolution of land systems.

The workshop examined how complex-system perspectives on land-system evolution can inform the project. For RESIL FARM's research on the coordinated improvement of yield stability, carbon sequestration, and climate resilience, the two researchers discussed how internal feedbacks and nonlinear change within land systems can help explain the combined effects of agricultural infrastructure, field management, and external climate shocks. They also considered how field surveys, remote-sensing observations, process models, and policy-scenario analysis could be linked to form a research chain extending from plot-level effect identification to regional land-system evolution.

Professor Shi presented the overall progress of RESIL FARM and the research task undertaken by IGSNRR, CAS, namely, examining how irrigation and drainage, infrastructure, and field management affect grain production capacity and resilience to disasters. She also outlined the team's progress in monitoring and optimizing agricultural land systems. Using multi-source remote sensing, agricultural surveys, and statistical data, the team identifies crop distribution patterns and agricultural resource elements at fine spatial scales and analyzes how climate change, irrigation conditions, and field management affect grain production and its spatial variation. Under water, land, and ecological constraints, the team also simulates grain production capacity, agricultural water use, nitrogen inputs, and carbon effects under different development scenarios, and conducts multi-objective trade-off analysis and spatial optimization. This work provides a basis for zoning well-facilitated farmland construction, investing in agricultural infrastructure, and adjusting regional cropping patterns.

Sun introduced IAMO's research on agricultural and rural development, climate change, ecosystem services, and land-system change, together with applications of spatial econometrics, agent-based modelling, system dynamics, and Bayesian networks. He described land systems as coupled social-ecological complex systems whose evolution may involve feedbacks, resilience, tipping points, regime shifts, path dependence, and hysteresis. Linear or reductionist approaches alone cannot adequately explain persistent changes in system structure and function. Drawing on forest transition in China and land-use transitions in the Argentine Chaco and Southeast Asia, the presentation examined how socioeconomic development, policy interventions, agricultural expansion, and ecological processes interact to drive land-system change. Causal-loop diagrams and multi-scale agent-based models illustrated ways to represent these processes and simulate alternative scenarios. Sun also noted that views of human-nature relationships influence research paradigms and governance measures. Land governance therefore needs to respect natural processes and local conditions and address the core drivers of system change.

The discussion focused on two connections between the two sides' work. First, well-facilitated farmland construction can be treated as an important intervention in a land system. In addition to estimating average effects on crop production and disaster resistance, evaluation should consider long-term feedbacks, critical thresholds, and possible changes in system state under extreme climate shocks. Second, high-resolution remote-sensing data, field surveys, and construction-project information can be combined with system dynamics, agent-based models, and spatial analysis to represent interactions among infrastructure, farmer behavior, resource constraints, and policy, and to support cross-scale scenario simulation.

The exchange highlighted the complementarity between RESIL FARM's evaluation of well-facilitated farmland and research on complex agricultural systems, and IAMO's work on land-system science and complex-system modelling. Future work could focus on representative cases in China and other transition economies, strengthen the sharing of multi-source data and modelling methods, and compare the long-term evolution and resilience of farmland systems under different policy and climate scenarios. Joint research, papers, and bilingual policy briefs could provide scientific support for optimizing agricultural infrastructure and advancing sustainable land governance.