Taking Shandong Province as the research area, we explored the willingness of farmers to grow grain and the influencing factors. By constructing the evaluation system of their willingness with 6 levels and 15 indicators based on field investigation, and quantitatively analyzing the influence degree and impact assessment of factors through a logistic regression model, the regional differences in farmers’ willingness were summarized, and the influencing factors were recognized. This study indicates that there were obvious regional differences in farmers’ willingness, which were the highest in the western region, the second in the eastern region, and the lowest in the central region. Specifically, the willingness varies significantly among cities, among which Laiwu has the highest willingness (0.76), while Tai’an has the lowest (0.41). The level of urbanization in different regions and the main influencing factors are different, and the same factor has different degrees of influence on cities, leading to regional differences. In terms of urbanization level, the main influencing factors in areas with high urbanization levels are the proportion of grain income and grain expenditure. However, in areas with a low urbanization level, it is the farmers’ planting attitude. From the perspective of influence mode, different factors have positive and negative differences in the willingness. Additionally, farmers’ willingness is becoming more and more rational, and more consideration is given to economic benefits. Among the influencing factors, the land planting mode, the proportion of grain income, and the proportion of grain expenditure are the most important factors, and 82% (11) of the cities are affected by the above three factors. Finally, the corresponding incentive measures are proposed by the regional differences in the influencing factors in various cities.

With the intensification of conflicts between different ecosystem services, how to achieve a win-win situation between socio-economic development and ecological protection is an important issue that needs to be addressed nowadays. In particular, how to better quantify and assess the intensity of ecosystem service trade-offs and their relative benefits, and to identify the influencing factors are issues that need to be studied in depth. Based on the INVEST model, this paper analyzed the evolution of spatial and temporal patterns of ecosystem services such as Carbon Storage (CS), Food Production (FP), Habitat Quality (HQ), and Water Yield (WY) in the Shandong Yellow River Basin (SYRB) in 2000, 2010 and 2020. Next, we quantitatively measured the trade-off intensity and revealed the key influencing factors of the trade-off intensity evolution using automatic linear models, root mean square deviation, and geographically weighted regressions. Subsequently, we further analyzed the impact of the correlation between environmental and socio-economic factors on the trade-off intensity of ecosystem services. The results indicated that the temporal and spatial changes of the four main ecosystem services in SYRB area were inconsistent. WY showed a fluctuating trend, with a large interannual gap. CS and FP are on the rise, while HQ is on the decline. Spatially, WY and HQ showed a decreasing distribution from the center to the periphery, while FP and CS showed a decreasing distribution from the southwest to the northeast. The location characteristics of SYRB’s four ecosystem services and their trade-offs were obvious. FP had absolute location advantage in ecosystem service trade-offs. Most of the four ecosystem services showed significant trade-offs, and the trade-off intensity had significant spatial heterogeneity, but the trade-off between FP and CS was relatively weak. At the same time, there were also differences in the trends of trade-off intensities. Counties with low trade-off intensity were mostly located in mountainous areas; these areas are less disturbed by human activities, and most of them are areas without prominent services. Counties with high trade-off intensities were mostly concentrated in areas with relatively developed agriculture; these areas are more disturbed by human activities and are mostly prominent in FP. The trade-off intensity of ecosystem services in SYRB was affected by several factors together, and there were difference characteristics in the degree and direction of influence of each factor. Moreover, these influencing factors have gradually changed over 20 years. In terms of the spatial distribution at the county scale, the influence areas of the dominant drivers of different trade-off types varied greatly, among which the areas with NDVI, CON, and PRE as the dominant factors were the largest. In the future, in effectively balancing regional economic development and ecological environmental protection, quantifiable correspondence strategies should be developed from the administrative perspective of counties and regions based on comprehensive consideration of the locational advantages of each ecosystem service and changes in trade-offs.