Huijie Zhang’s research while affiliated with Chinese Academy of Agricultural Sciences and other places

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Reducing carbon emissions in crop production not only aligns with the goal of high-quality agricultural development but also contributes to achieving the “dual carbon goals”. Based on panel data from 31 provinces in China between 2010 and 2019, this paper explores the impact of Agricultural Socialized Services on carbon emissions in China’s crop production. Utilizing the classical IPCC carbon emission calculation model and spatial econometrics models, this study analyzes the temporal and spatial distribution characteristics of crop production carbon emissions and their driving factors, with a particular focus on evaluating the role of Agricultural Socialized Services in reducing carbon emissions in crop production. The empirical results reveal a “reverse U-shaped” curve for carbon emissions in crop production from 2010 to 2019, with a peak in 2015. Agricultural Socialized Services significantly reduced carbon emissions in crop production, especially in terms of emissions reductions from fertilizer and pesticide use, although the impact on other carbon sources such as plastic mulch, diesel, and tillage was relatively limited. Furthermore, Agricultural Socialized Services exhibited significant spatial spillover effects, effectively reducing local carbon emissions and generating positive carbon reduction effects in neighboring regions through cross-regional services. Based on these findings, the paper suggests improving the Agricultural Socialized Services system according to regional conditions to fully leverage its positive role in reducing carbon emissions in crop production. It also advocates accelerating the innovation of low-carbon agricultural technologies, encouraging farmers’ participation, and utilizing the organizational advantages of village collectives to jointly promote the development of Agricultural Socialized Services and achieve carbon reduction goals.

Soybean production, integral to sustainable agriculture and reliant on imports, faces vulnerability to international risk factors impacting domestic food security. The 2021 Central Rural Work Conference advocated strategic adjustments for sustainable soybean production amidst resource constraints, trade conflicts, and the lingering impact of COVID-19. This study scrutinizes the developmental framework of China’s soybean industry within current domestic policies. This paper uses the soybean sown area and soybean production in each province of China from 1995 to 2020 to measure China’s soybean concentration, production layout, and comparative advantage of regional scale, to analyze in depth the overall situation of China’s soybean production as well as the differences between regions, and to pave the way for the subsequent soybean production forecast. Further, taking the domestic soybean supply and demand situation in China in 2020 as the base period, while considering the growth values of soybean production under different scenarios, the potential forecasting model is utilized to estimate China’s soybean production under different scenarios. The results show that, in terms of inter-annual variability, revealing an upward trajectory from 1995 to 2020, with a shift to major soybean producing areas, notably in the northeast and the Huanghe-Huaihe-Haihe area. From inter-provincial changes, Soybean production on the left and right sides of the “Hu-Huanyong line” has significant heterogeneity; specifically, east of the “Hu-Huanyong line,” soybean production advantages are more obvious. Additionally, the study considered the actual planting situation of soybeans and envisaged two scenarios of compound planting and strip planting. The soybean production range in 2030 is expected to be 20.73–22.32 million tons and 21.15–27.55 million tons, with self-sufficiency rates varying from 18.57 to 19.98% and 18.95 to 24.68%, respectively. The research aims to provide a comprehensive understanding of China’s soybean industry and its potential trajectories, employing a model combining historical trends, policy analysis, and technological advancements. Results suggest a promising future with strategic adjustments in planting structures. Recommendations emphasize policymakers’ prioritization of technological investments and sustainable planting practices to achieve projected production targets. Policy interventions must address challenges tied to resource limitations, trade conflicts, and the ongoing COVID-19 effects, ensuring soybean industry resilience.

Climate change (CC) is a worldwide environmental issue affecting all economic sectors, especially agriculture. Pakistan is one of the countries most affected by CC due to the country’s vulnerability to catastrophic events and limited ability to adapt. Assessing existing activities for adaptation to CC at the farm level is critical to understanding their success and recommending additional government measures. This study analyzes possible farming practice modifications that Pakistani farmers may adopt to reduce the loss of agricultural output due to the rising prevalence of dangerous weather events by CC. Data for the current research were gathered from 432 wheat farmers in rural Pakistan. This article investigates many factors that impact farmers’ decisions to CC adaptation in crop production utilizing binary logit (BL) and multivariate probit (MVP) models. Gender, education level, farming experience, farm size, level of damage, access to finance, and training participation are characteristics that substantially affect farmers’ likelihood of adapting to CC. Farm size and participation in CC training were the most critical factors influencing farmers’ CC adaptation decisions. Policy recommendations were presented to increase the farmers’ resilience in the study areas to CC. These comprise expanding CC training courses, developing regulations to encourage agricultural integration, and integrating CC and adaptation to CC principles into the operations of regional organizations. Finally, based on the findings, policymakers will be better equipped to address the challenges posed by CC and create a more resilient agricultural sector. This, in turn, will contribute to improving food security, ensuring sustainable agricultural growth.

Agricultural production and advancement is significantly affected by climate change (CC), especially in drought-prone regions where farmers depend primarily on rainfall for their livelihoods. One of the main threats to the agricultural sector for decades has been global warming, and the sector is particularly susceptible to climatic circumstances. CC has become a crucial concern impeding sustainable development due to rapid changes in urbanization, industry, and agricultural systems. Food security, which is a worldwide concern, is another essential component connected to a country’s economy and people’s livelihoods. In view of these adverse consequences, the main objective of this study was to examine the impact of CC and food security on sustainable agricultural development in Pakistan. The study area was selected from twenty-four districts in two provinces of Pakistan (Khyber Pakhtunkhwa and Balochistan). Collectively, the current research offers possible solutions to the dilemmas described above, which could have a significant impact on improving efficiency and technological progress. To address these issues, we introduced a slack-based approach to quantify inefficiencies in overall agricultural production. In addition, this study further scrutinized the decomposition of specific variables. The results show that Pakistan has an agricultural inefficiency of 0.62 under CC and food security constraints, of which 0.29 is primarily attributable to food security limitations. In the end, this study will help policymakers to make appropriate decisions to minimize the impact of CC on sustainable agriculture growth and improve human living standards and food security.

This paper summarizes the spatial–temporal characteristics of China’s rural energy carbon emission efficiency and then uses the Tobit model to explore its influencing factors. The results show that the rural energy carbon emission efficiency had experienced a growing trend in China during 2005 and 2020, with an annual growth rate of 4.82%. The growth is more affected by technological changes than by improvements in technical efficiency. Although all 30 provinces were in a state of improvement in rural energy carbon productivity during the period under review, there were significant differences between them. Technological change played a significant important role in promoting rural energy carbon productivity in the majority of Chinese provinces, while technical efficiency not only played a slightly less important role but also deteriorated in many provinces. Rural energy carbon emission efficiency is positively influenced by the level of agricultural development, the structure of rural labor force, and the urbanization level. However, it is negatively affected by the structure of cultivated land use, the rural human capital and rural residents’ consumption level. As such, policy formulation should support and promote the overall improvement of rural energy carbon emission efficiency.

Early prediction of winter wheat yield at the regional scale is essential for food policy making and food security, especially in the context of population growth and climate change. Agricultural big data and artificial intelligence (AI) are key technologies for smart agriculture, bringing cost-effective solutions to the agricultural sector. Deep learning-based crop yield forecast has currently emerged as one of the key methods for guiding agricultural production. In this study, we proposed a Bayesian optimization-based long- and short-term memory model (BO-LSTM) to construct a multi-source data fusion-driven crop growth feature extraction algorithm for winter wheat yield prediction. The yield prediction performance of BO-LSTM, support vector machine (SVM), and least absolute shrinkage and selection operator (Lasso) was then compared with multi-source data as input variables. The results showed that effective deep learning hyperparameter optimization is made possible by Bayesian optimization. The BO-LSTM (RMSE = 177.84 kg/ha, R² = 0.82) model had the highest accuracy of yield prediction with the input combination of “GPP + Climate + LAI + VIs”. BO-LSTM and SVM (RMSE = 185.7 kg/ha, R² = 0.80) methods outperformed linear regression Lasso (RMSE = 214.5 kg/ha, R² = 0.76) for winter wheat yield estimation. There were also differences between machine learning and deep learning, BO-LSTM outperformed SVM. indicating that the BO-LSTM model was more effective at capturing data correlations. In order to further verify the robustness of the BO-LSTM method, we explored the performance estimation performance of BO-LSTM in different regions. The results demonstrated that the BO-LSTM model could obtain higher estimation accuracy in regions with concentrated distribution of winter wheat cultivation and less influence of human factors. The approach used in this study can be expected to forecast crop yields, both in regions with a deficit of data and globally; it can also simply and effectively forecast winter wheat yields in a timely way utilizing publicly available multi-source data.

Advances in crop breeding techniques and economic evaluation are critical to ensuring and improving crop yields and sustainable development. Based on the provincial data on food legumes breeding (FLB) in China from 2001 to 2020, the research and development (R&D) expenditure distribution, FLB contribution rate, and R&D expenditure on FLB were estimated. The economic benefits of output per unit area and R&D expenses were evaluated, and relevant policy suggestions were put forward. The study found that: (i) the R&D expenditure on FLB’s showed a significant upward trend, and the development can be divided into a starting, stable, and rapid growth period. There were significant differences in R&D expenditures across eight provinces of China with relatively high R&D expenditures for FLB; (ii) the R&D expenditure on FLB had a significant lag in the improvement of per mu yield (1 mu = 0.067 hectares). For every 10,000 yuan increase in R&D funding for FLB, the yield per mu will increase by 0.145 kg/mu in the next five years, and the regional spillover effect of breeding costs is significant; and (iii) the marginal revenue of R&D expenditures FLB from 2008 to 2020 is 34.91 yuan, that is, every 1 yuan invested in breeding R&D can bring a short-term marginal revenue of 34.91 yuan and a long-term marginal revenue of 337.23 yuan. Based on the above analysis, some recommendations were proposed and discussed, including further increasing the R&D investment of FLB, strengthening industry–university–research cooperation in breeding, improving the breeding research system, and promoting the multi-dimensional development of FLB industry–university–research services.

Minor beans other than soybeans or peanuts are edible beans (EBs) that significantly contribute to the Chinese agricultural sector and play a vital role in the sustainability of agricultural production, diversification of food consumption, and income generation for producers. These beans are an important source of protein in a healthy diet, helping to improve national food security. In addition, adjusting and optimizing the industrial structure promotes the sustainable development of agriculture and diversifies staple food crops and introduction of new revenue streams for EB products. The current study examines the responses of mung bean and broad bean producers to environmental and internal input constraints. This study uses the production function with a multilevel mixed-effects method and is based on 848 households from two major EB-producing provinces of China in 2018 and 2019. The results show that local climatic conditions influence planting behavior. These types of beans are considered as a supplement and backup crop to the staple crop. Commercialization encourages cultivation. Producers show variable price responses to output prices, but very strong responses to product costs. Minor bean production is favored by small households because of its low labor intensity. For households growing these beans for consumption, soil fertility and environmental outcomes are improved. Findings from research on planting behavior have strong policy implications for guiding research and development for drought and pest resistance, market monitoring for price stabilization, promoting EB production through low-cost technologies, and encouraging sustainable agriculture.

The development of introduced pulse varieties has made valuable contributions to the development of the global agricultural industry, and China is one of the largest pulse producers in the international market. A special type of pulse, the red kidney bean, has made a major contribution to improving the rural economy. Taking Kelan County, Shanxi Province, as an example, this paper expounds on the formation of the kidney bean industry and its impact on local development. The existing research used a qualitative case study (QCS) method to examine the driver and impact of kidney beans in the agricultural industry. This study found that (1) the development of the kidney bean industry has benefited from its adherence to a market demand-oriented strategy, focusing on breeding and retaining excellent varieties, and vigorously supporting the construction of technical systems and the cultivation of the main body of the industrial chain. Developing new varieties, creating brands, and industrial integration are the key driving forces for development. (2) The kidney bean industry promotes local development by increasing farmers’ income, forming a more complete kidney bean supply chain, highlighting the brand effect, and promoting sustainable rural development. This study suggests that disease-resistant and mechanized-adapted varieties need to be developed in the future. Market and demand trends should be constantly monitored when determining reproductive paths.