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.

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.

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.

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.

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.

Evaluating agricultural ecology can help us to understand regional environmental status and contribute to the sustainable development of agricultural ecosystems. Furthermore, the results of eco-environmental assessment can provide data support for policy-making and agricultural production. The application of multi-source remote-sensing technology has the advantages of being fast, accurate and wide ranging. It can reveal the status of regional ecological environments, and is of great significance to monitoring their quality. In this paper, an agroecological efficiency evaluation model was constructed by combining remote sensing data and ecological index (EI). Multi-source remote-sensing data were used to obtain the evaluation index. Indicators collected from satellites, such as biological richness, vegetation cover, water network density, land stress, and pollution load, were used to quantitatively evaluate the agroecological efficiency of Rangtang County in the Tibetan Plateau. The results showed that the EI of Rangtang County increased from 61.77 to 65.10 during 2000–2020, which means that the eco-environmental quality of this area was good, and it has shown an obviously improving trend over the past 20 years. Rangtang County has converted more than 30 km²of grassland into woodland over the past 20 years. Climate change and human activities have had combined effects on the ecological environment of this area. The change in ecological environment quality is greatly affected by human disturbance. Policymakers should continue setting up nature reserves and should implement the policy of returning farmland to forests. Unreasonable grazing and rational allocation of land resources are still critical points of concern for future ecological environment construction. EI, combined with remote sensing and statistical data, is proven to be able to reasonably represent changes in ecological environment in Rangtang County, thus providing more possibilities for ecological evaluation on the Tibetan Plateau, and even the whole world.