In the era of the digital economy, where digitization permeates all sectors of society, digital transformation in agriculture stands as a crucial solution for addressing the growing challenges in agricultural production. Amid the competition to enhance the resilience of sustainable food systems, China sets an exemplary model with its achievements in digital agricultural transformation, providing a blueprint for developing countries in Asia and the Pacific. Primarily based on statistical data and typical case studies, this paper presents analytical findings on how digital transformation of agriculture enhances the adoption of green agricultural practices and promotes inclusive development in China. In light of the intricate challenges faced by China’s food system, the adoption of digitization emerges to facilitate the transformation from conventional agriculture to smart and sustainable practices. The pathways by which digital transformation of agriculture have the potential to address the over-application of chemical fertilizer and irrigation water, mitigation of carbon emissions, and the challenge of climate change and contribute to environmental sustainability of agriculture have been discussed. The implementation of digital transformation in sustainable agriculture—which enhances green practices and social inclusiveness by promoting digital literacy, reducing workload, creating job opportunities for low-skilled labor, and developing rural inclusive finance—has been completely explored. The challenges in digital transformation of agriculture are explained in this paper, which also provides evidence-based policy recommendations for its sustainable development applicable to developing countries.

Rice production holds a significant position in the Thai economy. Although it is the world’s largest rice exporter, Thailand’s increase in rice production is the result of an expansion in the cultivation area rather than an increase in yield per unit area. The present study was designed to estimate the technical efficiency and its governing factors for certified organic rice-growing farms in Yasothon Province, Thailand. A data envelopment model was employed to assess the technical efficiency of 328 farmer groups. The data revealed that the average technical efficiency was 23% and 28% under constant returns to scale (CRS) and variable returns to scale (VRS) specifications, respectively. Farmers can reduce the use of machinery, fertilizer, seed, and labor as input factors by about 80.1%, 25.62%, 24.72%, and 19.15%, respectively, while still achieving the same level of output. Multiple regression analysis was applied to estimate factors that affect the pure technical efficiency score (PTES) in the test regions. Results show that household size, farm size, water source, market accessibility, health symptoms, income, and labor were highly related to the TES and the amount of organic rice production. The regression coefficients of the predictors show that the income was the best predictor of the PTES at a significance level of p < 0.05. It is concluded that the farmers can potentially increase their yields by up to 72%–77% under current management practices.

Planting green manure in fallow croplands in winter can bring various economic and environmental benefits, including increased food production, carbon capture and sequestration, soil retention, sandstorm prevention, water retention, and provision of habitat for biodiversity. However, the increased production cost of planting green manure reduces farmers’ willingness to adopt this approach, which is unfavorable for its sustainability. This research aims to investigate the influence of instrumental variables on farmers’ perceptions of sustainable agriculture practices, especially the use of rotation fallow, and tries to understand the relationship between farmers’ perceptions of using rotation fallow and planting green manure under incentive measures adopted by local authorities in Guangxi Province, China. Using simultaneous equation models, the results show that subsidies and planting training were the most important drivers for restoring green manure planting in the target region. These incentive measures could be further enhanced as a priority to restore green manure planting. The study also finds that socioeconomic factors such as farmer’s income, area of farmland, and labor for agricultural production have a certain influence on planting green manure planting and on farmers’ perceptions of using rotation fallow as a form of sustainable agriculture practice.

We propose the concept of inclusive economic resilience to examine intra-regional economic recovery in American metropolitan areas after the Great Recession. Previous studies have treated regional and municipal economic resilience separately, with little attention to within-region variations in economic resilience. We contribute to the understanding of regional economic resilience by focusing on intra-regional economic recovery in cities. We also introduce an important yet overlooked regional factor in the context of American federalism – fragmentation of local governments. Examining US metropolitan areas from 2007 to 2017, we find that different dimensions of local fragmentation exert different impacts on intra-regional economic resilience. Our results indicate that a large number of municipal governments and greater service responsibilities borne by special and school districts lead to uneven economic recovery. In contrast, similar fiscal responsibilities taken by municipal governments promote inclusive income recovery.

The COVID-19 outbreak at the end of December 2019 spread rapidly all around the world. The objective of this study is to investigate and understand the relationship between public health measures and the development of the pandemic through Google search behaviors in the United States. Our collected data includes Google search queries related to COVID-19 from 1 January to 4 April 2020. After using unit root tests (ADF test and PP test) to examine the stationary and a Hausman test to choose a random effect model, a panel data analysis is conducted to investigate the key query terms with the newly added cases. In addition, a full sample regression and two sub-sample regressions are proposed to explain: (1) The changes in COVID-19 cases number are partly related to search variables related to treatments and medical resources, such as ventilators, hospitals, and masks, which correlate positively with the number of new cases. In contrast, regarding public health measures, social distancing, lockdown, stay-at-home, and self-isolation measures were negatively associated with the number of new cases in the US. (2) In mild states, which ranked one to twenty by the average daily new cases from least to most in 50 states, the query terms about public health measures (quarantine, lockdown, and self-isolation) have a significant negative correlation with the number of new cases. However, only the query terms about lockdown and self-isolation are also negatively associated with the number of new cases in serious states (states ranking 31 to 50). Furthermore, public health measures taken by the government during the COVID-19 outbreak are closely related to the situation of controlling the pandemic.