Based on the microdata of 11,547 farmers from the China Labor Dynamics Survey (CLDS) database in 2017, an ordered multi-classification logistic model was constructed to empirically test the impact of social capital (i.e., social networks, social participation, and social trust) on farmers’ willingness to adopt agricultural technology. The moderating effect of demographic changes (i.e., the number of instances of hukou migration) on social capital and farmers’ willingness to adopt new agricultural technology was further investigated. The results show that the following: (1) Social trust has a significant positive impact on farmers’ willingness to adopt new agricultural technologies, while social participation has no significant impact on farmers’ willingness to adopt new technologies. (2) Social networks influence farmers’ technology adoption behavior differently, e.g., the scope of relatives’ wedding gifts has a significant and positive influence on farmers’ technology adoption behavior, while the scope of non-relatives’ wedding gifts has no significant influence on farmers’ technology adoption behavior. (3) Demographic change plays a moderating role in the impact of social capital on farmers’ willingness to adopt new agricultural technologies. In other words, the greater the number of instances of hukou migration, the less the promoting effect of social capital on farmers’ willingness to adopt agricultural technology. (4) In the eastern and central regions of China, social capital has a significant positive impact on farmers’ adoption of new agricultural technologies. In the western region of China, social capital has a significant negative impact on farmers’ adoption of new agricultural technology. In the northeast region of China, social capital has no significant impact on farmers’ adoption of new agricultural technologies. Social capital and population changes are important factors that affect farmers’ willingness to adopt new agricultural technologies. Therefore, attention should be paid to cultivating and promoting farmers’ social capital to improve farmers’ willingness to adopt new agricultural technologies.

Promoting the integrated development of rural industries represents a crucial pathway for accelerating the modernization of the entire industrial chain and consolidating and enhancing agricultural resilience. This study constructs provincial-level panel data for China spanning 2012–2022 and employs a multidimensional, visualization-based, and spatial research paradigm to comprehensively examine the impact effects and mechanisms through which rural industrial integration empowers agricultural resilience in China. The findings reveal that, first, rural industrial integration can effectively enhance agricultural resilience levels, with stable economic returns and diversified functional development serving as key pathways for improving agricultural resilience. Second, the eastern and western regions have unleashed agricultural resilience potential through superior policy and environmental advantages, while the relatively homogeneous grain structure and path dependence in the central and northeastern regions have prevented agricultural industrial integration from effectively achieving expected outcomes. Third, structural rationalization has improved resource allocation efficiency, but industrial structure advancement and industrial structure sophistication have actually diminished the enhancement effects on agricultural resilience. Fourth, urbanization has led to adverse resource competition, reducing the positive impact of rural industrial integration on agricultural resilience levels. Fifth, the enhancement of agricultural resilience through rural industrial integration demonstrates geographically-distant resource spillover effects to neighboring regions. This research provides an important theoretical framework and practical paradigm for global agricultural transformation, offering particularly significant strategic guidance for developing countries in building resilient agricultural systems, addressing climate change impacts, and ensuring food security.

The construction of ecological barriers in the Yellow River Basin represents a significant step toward reducing agricultural carbon emissions, achieving carbon neutrality, and reaching carbon peaking in China. The diverse agrarian development objectives of various regions within the basin have resulted in a heterogeneous approach to greening agriculture. Therefore, this paper will evaluate the development of carbon sink agriculture across 34 cities and municipalities in the lower Yellow River basin from 2008 to 2021 based on the EBM-GML model, and analyze the spatial-temporal evolution of agricultural green total factor productivity (AGTFP) in each region through the application of the Moran index, kernel density estimation, and spatial Markov chain analysis. The results demonstrate that agricultural carbon emissions in the Lower Yellow River Basin gradually decreased throughout the study period. Furthermore, overall carbon emission efficiency improved, indicating significant potential for further emission reduction. In addition, Agricultural Green Technology Progress (AGTC) has become a primary driver of AGTFP growth, while Agricultural Green Technology Efficiency (AGEC) has demonstrated a gradual upward trend. Locally, most areas are weakly connected and display an isolated development trend. The results of the kernel density analysis demonstrate a notable degree of mobility in the distributional dynamics of AGTFP growth, characterized by a gradual narrowing of the gap between locations. The transfer of (AGTFP) types in the lower reaches of the Yellow River Basin is stable, with a noticeable “club convergence” phenomenon, while geographical conditions significantly influence the transfer of AGTFP types in this region. Based on long-term trend predictions, the future trajectory of AGTFP in the lower Yellow River Basin appears optimistic and is expected to improve progressively, with the overall distribution tending toward equilibrium.

Achieving common prosperity is a long-term and challenging historical process globally, with particularly arduous tasks facing rural areas. Digital rural development provides material guarantees for realizing common prosperity in rural areas. This paper calculates the digital rural development and rural common prosperity levels across provinces from 2011 to 2021, creates Kernel density plots, and preliminarily demonstrates the dynamic evolutionary trends of China’s digital rural development and common prosperity. Furthermore, panel data analysis is employed to investigate the impact effects, mechanisms, and spatial spillovers of digital rural development on rural common prosperity. The research shows that the development of digital rural areas exhibits significant heterogeneity across different domains, periods, and regions. Additionally, government central transfer payment policies can effectively enhance rural common prosperity levels. However, excessively strict environmental regulatory policies (pollution discharge fees) may inhibit the effectiveness of digital rural development on rural common prosperity. Finally, spatial modeling reveals that digital rural development has significant spillover effects on improving rural common prosperity, generating positive “dividend” effects for adjacent regions, though these dividends gradually diminish with increasing distance.

An in-depth examination of the effects of climate change on rice yield in China, encompassing various rice types, is crucial for ensuring the nation’s food security. This study develops an “economy-climate” theoretical model and employs Panel Corrected Standard Error Estimation (PCSE) on panel data spanning from 1978 to 2018, sourced from China’s primary grain-producing regions. The analysis delves into the impact of climate variables, including precipitation, temperature, and sunshine duration, on overall rice production and different rice types. Overall, the findings reveal a nonlinear relationship between precipitation, temperature, sunshine duration, and rice yield, characterized by an “inverted U-shaped” pattern. However, significant variations exist in the effects on different rice types across China’s main grain-producing areas. Increasing precipitation generally enhances early rice production across provinces and also augments mid-season and one-season-late rice production in the Inner Mongolia Autonomous Region, Hebei, Jilin, Heilongjiang, and Shandong Province. Conversely, it reduces mid-season and one-season-late rice output in Liaoning, Jiangsu, Anhui, Jiangxi, Henan, Hubei, and Hunan. Sichuan Province sees a rise in temperature favoring early and double-season-late rice production, unlike other provinces. For mid-season and one-season-late rice, temperature increases benefit output in Heilongjiang Province but not in other regions. Additionally, prolonged sunshine duration boosts early and double-season-late rice production across all provinces but does not have the same effect on mid-season and one-season-late rice in China’s primary grain-producing areas.