Identifying the relationships and drivers of agro-ecosystem services using a constraint line approach in the agro-pastoral transitional zone of China

Understanding the relationships and drivers of agro-ecosystem services (AES) can promote the management of sustainable agricultural ecosystems. In contrast to previous studies that analyzed the trade-off or synergistic relationships between ecosystem services, the constraint effects and the thresholds between ecosystem services were quantified using a constraint line method in this study. The constraint effects of climatic (maximum temperature (T-max) and minimum temperature (T-min), relative humidity, solar radiation, precipitation, wind speed), management (fertilization and irrigation), soil texture (sand, silt, and clay content), and terrain (elevation and slope) factors on AES were evaluated. The results indicated that two types of constraint relationships (hump-shaped and logarithmic) between ecosystem services of maize yield, soil organic carbon (SOC), nitrate (N) leaching, water and wind erosion were identified in the agro-pastoral transitional zone of China. The maize yield had hump-shaped constraint effects on SOC, N leaching, and water erosion, while other paired AES were mutually exclusive with logarithmic constraint curves. By contrast, seven types of constraint effects of drivers on AES, including positive linear, hump-shaped, negative and positive convex, logarithmic, exponential and S-shaped curves, were identified. Among all the drivers, except for precipitation and solar radiation which had constraint effects on the lower boundary of maize yield, others had constraint effects on the upper boundary of AES. The drivers that played major roles in determining the yield, SOC, water erosion, wind erosion, and N leaching were Tmin, water availability and slope. The constraint line method could deepen our comprehension of relationship between ecosystem services and is particularly helpful in identifying the constraint drivers on AES from the perspective of optimizing the agricultural ecosystem.