Agricultural eco-efficiency (AEE) considers economic and environmental benefits and is a key indicator of green agricultural development. To achieve the multiple goals of improving agricultural production efficiency, reducing agricultural environmental damage, and reducing the input of agricultural resources, this study enriches the case study of agricultural production performance evaluation at the county level by measuring the AEE of 44 agricultural counties in Liaoning Province based on panel data and a super-efficient slacks-based measure model including undesired outputs. A two-way fixed-effects model was used to analyze the impact of agricultural development, macro-environment, and policy support on AEE. We found that the average AEE of the counties in Liaoning Province in 2014, 2016, 2018, and 2020 was 0.716, 0.735, 0.749, and 0.813, respectively, indicating a cumulative improvement rate of 13.55%. The average AEE levels gradually improved during the study period. Notably, the development of AEE among the counties was uneven. AEE was distributed in a “block-like” manner, and its local correlation presents a phenomenon of “small agglomeration and large dispersion”. In addition, the level of the agricultural economy, industrialization, and urbanization significantly promoted the improvement of AEE, and the promoting effects varied between different income levels and regions. Therefore, Liaoning Province needs to improve the AEE of each county according to local conditions and narrow the differences in AEE between counties. To continuously improve the level of rural economic development, lead the development of agricultural modernization with new urbanization, and comprehensively improve the overall AEE of counties. The research results are of guiding significance for deepening the study of AEE and can provide decision-making support for optimizing the mode of agricultural production and promoting the green development of regional agriculture.

Since land use/cover change profoundly impacts climate change and global warming has become an irreversible trend in the Anthropocene, there have been numerous global studies on the impact of climate change on vegetation growth (VG). However, the effects of climate extremes on the growth and direction of various vegetation types need to be better investigated, especially in the climate transition zones. In this paper, we examined the effect of diurnal warming on the growth of various types of vegetation in China’s north–south transition zone. Based on the daily observation data of 92 meteorological stations in the Qinling-Daba (Qinba) mountainous area from 1982 to 2015, coupled with the Normalized Difference Vegetation Index (NDVI) and data on the type of vegetation. This research examined the temporal changes in the highest and lowest temperatures during the last 33 years using trend analysis. Second-order correlation analysis was used to investigate vegetation NDVI response characteristics to diurnal warming and to examine the effect of diurnal warming on the growth of different vegetation types. Our results showed that maximum temperature (T_max) and minimum temperature (T_min) showed an obvious upward trend, with the daytime temperature increase rate 1.2 times that at night, but failing the t-test. In addition, diurnal warming promoted vegetation growth, with NDVI associated positively correlated with T_max at approximately 91.2% of the sites and 3492 rasters and with T_min at roughly 53.25% of the sites and 2864 rasters. Spatial significance analysis showed an apparent difference, but few areas passed the t-test. Furthermore, daytime warming enhanced the growth of grasses, shrubs, deciduous broad-leaved forests, crops, and conifers, while the effect of nighttime warming on VG had a positive effect only on the growth of evergreen broad-leaved forest vegetation. These findings reveal the mechanisms of the impact of climate extremes on VG under global change, particularly the extent to which different vegetation types in climatic transitional zones respond to climate extremes.