The improvement of agricultural green total factor productivity (AGTFP) is crucial to achieve sustainable agricultural development. By matching China’s provincial digital financial inclusion index and agricultural production data from 2011 to 2020, and on the basis of using the DEA–Malmquist productivity index to measure AGTFP, the fixed effect model and Mesomeric effect model are used to empirically test the impact and mechanism of digital financial development on China’s AGTFP. Our research found that from 2011 to 2020, China experienced consistent improvement in AGTFP, which was largely attributed to advancements in technology. Interestingly, the AGTFP in non-major grain-producing areas surpassed that in major grain producing areas. Additionally, digital finance has proven to be an effective tool in boosting China’s AGTFP, the coverage subdimension, the depth of use subdimension and the digitization subdimension all significantly promote the AGTFP. Digital finance can significantly promote the efficiency and progress of agricultural green technology, which shows that digital finance promotes AGTFP in a “dual wheel” driven manner. However, the impact of digital finance on agricultural green technology efficiency and progress is more pronounced in major grain-producing areas than in non-major areas. The impact mechanism demonstrates that digital finance has the potential to stimulate AGTFP in two key ways. First, it can improve the mismatch of agricultural production resources; second, it can promote agricultural technology innovation. Therefore, it is necessary to further promote the rapid development of digital finance, optimize the rational allocation of financial resources, and formulate tailored digital finance development strategies to promote green development of agriculture.

The article aims to analyze the effect of training programs on the yield of smallholder farmers. The empirical analysis employed a sample of data collected from a rice farming household in the Mvomero district of Tanzania. The results indicate that the yield outcome among trained and non-trained farmers with water access for irrigation was significantly more than double; however, the yield difference between trained and non-trained farmers was insignificant in non-irrigated plots. Our findings have policy implications for agricultural development in developing countries where training programs alone may not be a panacea for smallholder farmers’ productivity improvement. Therefore, respective governments, policymakers, and other agricultural stakeholders, should consider both farm and non-farm factors altogether, which may increase agricultural training effectiveness to address the challenges of low yields.

The carbon trading system affects all aspects of the economy and society profoundly. Agriculture, as a high-carbon-emitting industry, has been hard-hit. China’s agricultural activities will emit about 820 million tons of carbon dioxide equivalents, accounting for 7% of the country’s total carbon emissions. In order to develop a green and low-carbon economy and control greenhouse gas emissions, China officially launched the pilot carbon emissions trading policy in 2013. The effects and mechanism of this on agricultural carbon emissions are still unclear. Herein, this paper uses China’s provincial panel data from 2000 to 2019 to measure agricultural green total factor productivity regarding the implementation of China’s carbon emissions trading pilot policy in 2013 as a quasi-natural experiment, and uses PSM-DID robustness analysis to evaluate the effect of China’s carbon emission rights trading pilot policy on agricultural green total factor productivity in pilot areas. The propensity score method is a type of statistical method that uses nonexperimental or observational data for intervention-effect analysis, which reduces the effects of bias and allows for more reasonable comparisons between treatment and control groups. “Difference in difference” is an approach to policy-effect evaluation based on a counterfactual framework to assess the change in the observed factors in both cases of policy occurrence and nonoccurrence. PSM-DID is a combination of PSM and DID using the PSM method to match each treatment group sample to a specific control group sample, which can solve the problem of self-selection bias in the DID method and assess the policy implementation effect more accurately. This study found that China’s carbon emissions trading pilot policy has significantly improved China’s agricultural green total factor productivity. Further impact mechanism tests show that China’s carbon emissions trading pilot policy will improve agricultural green total factor productivity through environmental protection policies and technological innovation. Finally, this paper puts forward corresponding countermeasures and suggestions based on the research results.

The China–Tanzania Village-based Learning Center for Poverty Reduction project aims to demonstrate China’s experience in poverty reduction through developing smallholder agriculture at the village level, and through the promotion of improved technologies with the purpose of increasing agricultural productivity and improving village-level agricultural production. However, to promote technology application effectively, a better understanding of farmers’ behavioral intention toward improved maize technology is needed. This study uses microdata from 282 Tanzanian maize farmers. Compared with previous research, the innovation of our study is that the methods of structural equation model (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) are applied to study the influencing factors of farmers’ intention to adopt improved maize technology and the combination paths that affect farmers’ intention. The analysis results show that farmers’ behavior perceptions and compatibility perceptions positively impact their intention to choose agricultural technology. Three modes can trigger farmers’ intention to adopt technology: “behavior perception, compatibility perception, non-self-efficacy, facilitation conditions,” “behavior perception, social impact, non-self-efficacy, facilitation conditions,” and “compatibility perception, social impact, self-efficacy, facilitation conditions.” To increase maize yield and promote the extension of improved agricultural technology through the China–Tanzania Village-based Learning Center for Poverty Reduction project, it is necessary to choose an effective combination path to influence farmers’ intention to adopt the proposed changes. If farmers’ intention to adopt improved maize technology can be increased to a greater extent, it can accelerate the improvement of agricultural technology in Tanzania, thereby increasing agricultural productivity, improving agricultural production at the village level, and reducing poverty.

Leveraging the transformative potential of digital technologies to support the farmers’ green production transformation (FGPT) is a critical catalyst for facilitating the accelerated green transformation and upgrading of agricultural systems, thereby achieving high-quality agricultural development. Using survey data from major watermelon- and muskmelon-producing regions in Henan, Shandong, and Xinjiang, and employing Ordinary Least Squares (OLS), Propensity Score Matching (PSM), and the Logit model, this study examines the mechanisms through which digital technology influences FGPT. The findings reveal that the adoption of digital technology significantly reduces farmers’ use of pesticides and fertilizers, with a particularly pronounced effect on pesticide reduction. Heterogeneity analysis indicates that the impact of digital technology on reducing pesticide and fertilizer usage varies across regions: while its adoption leads to significant reductions in Henan and Shandong, the positive effects in Xinjiang remain statistically insignificant. Mechanism analysis shows that digital technology enhances FGPT by reducing information asymmetry, improving market access, and enhancing precision management practices. Based on these findings, this study recommends expanding awareness campaigns to accelerate the adoption of digital technology, enhancing digital infrastructure to bridge the urban–rural digital divide, and optimizing digital technology promotion systems. These actions can be taken alongside the implementation of economic incentives and compensation mechanisms. The insights and policy recommendations from this research provide valuable guidance for China and other countries seeking to leverage digital technology for sustainable agricultural development.

Agricultural technology transfer plays a chief role in transforming agricultural productivity in rural areas especially in the current setting where food demand surpasses the production capacity. Technology transfer facilitates the movement of soft and hard skills essential for improving farm production. Yet, the technical cooperation projects in Africa have been suffering from effectiveness and sustainability challenges while lacking responsiveness to local demand. This study applies a system dynamic method and a literature review to bring lessons from Japan and China’s experiences in agricultural technology transfer projects to Africa. Three cases in agricultural technical cooperation projects are presented: China—(Agricultural Technology Demonstration Center (ATDC) in Tanzania), Japan—(Smallholder Horticulture Empowerment Project (SHEP), and Rice Industry Development Support (RIDS) in Kenya and Tanzania, respectively). Japanese and Chinese agricultural technology has the potential to improve productivity and the livelihood of rural households. Nevertheless, strong linkages, commitment, and participation of all stakeholders in the design and implementation of agricultural technology transfer projects play an important role in enhancing project sustainability in the recipient countries. Further studies are recommended such as, to explain the agricultural technology transfer mechanism that fits well to equip beneficiary autonomy in terms of knowledge and capacity of production in the recipient country. The local governments need to set policy environments and institutional frameworks that encourage and support the agricultural technology transfer to benefit the rural farmers.