Amid climate change and food security challenges, transforming agricultural systems in middle- and low-income countries is crucial for carbon neutrality and sustainable development. China and Africa, responsible for 32% of global agricultural emissions, share agricultural similarities despite different development stages. China’s modernization efforts offer valuable insights for Africa, highlighting opportunities for increased Sino-African cooperation. This study, analyzing FAO data from 2000 to 2021, compares emission trends, sources, inputs, and mitigation policies in China and Africa. It reveals that Africa’s emissions remained consistently higher than China’s, which grew faster compared to Africa’s 40% increase. Notably, Africa shows regional disparities in emissions, with the highest increases in East and Central Africa (56% and 54%, respectively), while North and South Africa show slower growth. Structurally, China’s emissions have transitioned from production to pre- and post-production stages. Conversely, Africa’s emissions mainly stem from agricultural production (42%) and land-use changes (43%), emphasizing challenges in resource management and reliance on land expansion. The rapid growth of Africa’s pre- and post-production emissions highlights the supply chain’s growing role in emissions, with regional variations, such as livestock and rice cultivation driving emissions in East and West Africa, and land-use changes in Central Africa. China and Africa’s agricultural policies differ significantly. China adopts multi-objective policies promoting green, low-carbon development, whereas Africa focuses on short-term yield increases with heavy reliance on fertilizers, conflicting with low-carbon objectives. Learning from China could help Africa balance food security, income stability, and environmental sustainability, providing a pathway to achieve both food security and carbon reduction.

The development of Internet information technology has given digital agricultural technology extension services advantages over earlier agricultural technology extension models, rendering them more conducive to the pursuit of sustainable and environmentally friendly agricultural development. This study leveraged survey data from 1167 farmers in Shaanxi and Gansu Provinces and used the propensity score matching method to elucidate the impact and mechanism of the digital agricultural technology extension service on the adoption of organomineral fertilizer. The results indicate that farmers who had used digital agricultural technology extension services had a 7.2% to 10.2% increase in the probability of adopting organomineral fertilizer compared with their non-user counterparts. In addition, adoption intensity increased from 7.0% to 9.9%. Secondly, digital agricultural technology extension services indirectly influence farmer adoption behavior by shaping their perceptions of benefits and reducing transaction costs. Also, this study examined the heterogeneity in the adoption of organomineral fertilizer facilitated by digital agricultural technology extension services. The findings of this study provide policy recommendations for advancing the use of digital agricultural technology extension services and enhancing organomineral fertilize adoption rates of farmers.

Tanzania’s food supply relies heavily on crop production from its breadbasket regions (BBRs). Despite their central role in national agriculture, the 2018 National Nutrition Survey revealed a troubling paradox: five of the regions with the highest rates of child stunting and severe malnutrition are located within these BBRs. This paper investigates the underlying causes of this paradox. Using data from the 2017–2018 National Household Budget Survey, the 2020–2021 National Integrated Labor Force Survey, and the 2020–2021 National Panel Survey, instrumental variable probit models were used to assess the impact of household crop production on children’s growth status. The findings confirm that children in BBRs are more likely to experience stunting than those in non-BBRs. Overall, higher crop production is associated with a lower risk of stunting and improved height-for-age z-scores. However, these benefits appear more pronounced in non-BBRs than in BBRs. Further analysis shows a positive relationship between increased crop production and household dietary diversity, although this relationship is also weaker in BBRs. These results indicate that factors beyond food availability, such as dietary practices and household-level conditions, may contribute to the observed paradox, highlighting the need for more nuanced policy discussions.

School feeding programs (SFPs) have been extensively implemented as a public policy investment aimed at fostering the development of children across various countries. This study evaluated the impact of the implementation of SFPs on the physical development of school-age children in rural China using a data set comprising over 400 thousand observations. It was found that participation in the Nutrition Improvement Program (NIP) in China promoted the development of height and weight of children in rural schools, and reduced the gap in growth between urban and rural schoolchildren. However, the physical examination performance of children in rural schools was better than that of children in urban schools, and this advantage is gradually expanding with the increase of participation time. This research provides insights for the development and evaluation of SFPs in African countries, including the importance of long-term program participation, targeted interventions to address regional disparities and the use of comprehensive evaluation measures such as physical examination performance. These results provide a framework for enhancing SFPs in Africa, supporting their role in addressing malnutrition, improving education outcomes and driving long-term socioeconomic development.

The COVID-19 pandemic and geopolitical tensions have introduced significant uncertainty into global value chains, posing challenges to food security in both African countries and China. Agricultural trade offers a solution to the mismatches between food supply and demand. This study used a structural multi-country general equilibrium trade model to assess the welfare impacts of agricultural trade between China and Africa. It examines key patterns in China–Africa agricultural trade, followed by a model framework and empirical analysis. The results indicate that African countries have yet to fully exploit their agricultural trade potential. Greater trade liberalization between China and Africa could substantially enhance welfare in African countries by increasing real incomes and improving household access to food. While a small number of African countries currently dominate exports to China, the removal of trade barriers and deeper agricultural cooperation could enable a broader range of countries to benefit over time.

Agrifood systems in China and Africa are subject to increasing pressures due to resource limitations, environmental degradation and climate change. Both regions confront challenges such as soil degradation, water scarcity and biodiversity loss, while also experiencing the adverse impacts of climate change on agricultural productivity and food security. This paper examines these challenges and outlines strategic actions with a systems approach being taken in both regions to transform their agrifood systems. Key strategies include enhancing agricultural efficiency, strengthening resilience to climate-related shocks, and promoting sustainability through innovative practices. Also, the paper emphasizes the importance of China–Africa cooperation in climate-smart production, trade and market optimization, and sustainable diets. The role of China–Africa food policy network in supporting these efforts are also discussed. The paper concludes with a call for continued collaboration to facilitate the sustainable transformation of agrifood systems in developing countries, particularly in China and Africa, ensuring food security and resilience for the future.

Agricultural subsidies have been a vital component of agricultural policies in many Asian and African countries since the 1960s, acting as a key driving factor for facilitating the sustainable transformation of agrifood systems. China and Africa are chosen as case studies because they represent two distinct regions with large population sizes and facing common challenges. This study reviews the evolution, design and implementation of agricultural subsidy policies in China and Africa, highlighting their successes and challenges. The results show that China and Africa aim to enhance agricultural productivity and ensure food security, offering incentives to farmers to increase production and address challenges, such as poverty reduction. However, there are significant differences in the structure and scale of agricultural subsidies. China’s policy is comprehensive and oriented toward sustainable development, while African policies tend to be more targeted and often focus on specific areas such as fertilizer subsidies and seed distribution. While both regions have made significant progress in transforming their agrifood systems, they continue to grapple with common, but context-specific, challenges. This study developed recommendations to guide future efforts toward sustainable transformation of agrifood systems in China and Africa. This will involve repurposing agricultural subsidies to promote green sustainability, enhancing support for agrifood policies and collaboration between China and African countries, and strengthening investments in agrifood systems in both regions.

The productivity and yield of rice crops are continually threatened by various biotic and abiotic stressors, with weed infestations being a primary concern. Among the many types of weeds that challenge rice cultivation, grass weeds are particularly troublesome due to their competitive nature and fast growth, which can lead to significant yield losses if not managed effectively. Normally, the detection and control of grass weeds in rice fields have relied on labor-intensive visual methods, such as visual inspections and hand-weeding. These approaches are not only time-consuming but also prone to human error, making them inefficient and costly. In recent years, remote sensing, particularly hyperspectral imaging, has emerged as a promising technology for addressing this challenge. Hyperspectral imaging systems capture a vast amount of spectral information across numerous narrow wavelength bands, enabling the differentiation of various objects and materials based on their unique spectral signatures. The objective of this review was to examine the principles of hyperspectral imaging, its advantages over current methods, and the various techniques and approaches used in weed detection and classification. Also, this paper examines the challenges and limitations associated with this technology and identify potential areas for future research and development.

Drying is a critical process in postharvest handling of agricultural products, significantly impacting their preservation, quality and market value. This review explores the application of artificial neural networks (ANNs) in the drying of agricultural products, focusing on four key areas: moisture ratio prediction, quality detection, process optimization, and control systems. ANNs have remarkable potential for accurately modeling the highly nonlinear drying processes, optimizing energy consumption and improving product quality. Despite their advantages, challenges, such as data dependency, computational complexity and model interpretability, remain. This review highlights recent advancements in ANN-based drying models, discusses their limitations, and envisions future directions, including the integration of ANNs with other artificial intelligence (AI) technologies and the development of hybrid models. Through literature research in recent years, it has been found that ANN have achieved development from standard ANN to machine learning and even deep learning. Deep learning is gradually becoming the mainstream direction in agricultural product drying. The findings underscore the transformative potential of AI in revolutionizing the agricultural drying industry, paving the way for more efficient, sustainable and intelligent drying systems.

Based on survey data from 832 maize growers of four major maize production regions in China, on-farm maize losses were assessed and the effects of Internet access to agricultural information on the losses were evaluated. The results showed that on average 4.5%, 4.0% and 3.7% of maize was lost in the harvest, drying and storage stages, respectively. This indicated that at the farm level, 19 Mt of maize is lost annually in China, and these losses have considerable economic, resource and environmental impacts. Additionally, Internet-based access to agricultural information was found to be positively related to reduced maize losses. Compared with farmers who do not access agricultural information from the Internet, farmers with Internet-based access to agricultural information experienced 3.2%, 1.5% and 3.6% lower losses in the maize harvest, drying, and storage stages, respectively. Therefore, enhancing farmers access to agricultural information could increase the maize supply by 7.89 Mt and enhance the utilization of 1.02 Mha of farmland. Additionally, 0.39 Mt of fertilizer could be saved, and the carbon emission and water usage could be reduced. It is recommended that the government accelerate the removal of barriers to farmers using the Internet to access agricultural information.

Accurate predictions of future temperature and humidity in poultry houses are essential for environmental control strategies. Given the complex dynamics of environmental changes, a gated feature fusion (GFF) module was developed to capture multiscale temporal features. This module is integrated with a transformer model to develop a GFF-transformer model. The GFF-transformer model leverages environmental data from the past 24 h (temperature, humidity, CO₂ and static air pressure) to predict the temperature and humidity of the next 6, 12, 18 or 24 h. Compared to the long short-term memory method, gated recurrent unit and transformer models, the GFF-transformer model exhibits improved performance. For prediction intervals of 6, 12, 18 and 24 h, the model achieves R² values between 0.88 and 0.92 for temperature in the range of 20.1 and 31.5 °C, with mean absolute error (MAE) ranging from 0.48 to 0.62 °C, and root mean square error (RMSE) ranging from 0.68 to 0.85 °C. For humidity in the range of 18% and 97%, the R² ranges of the model from 0.86 to 0.94, with MAE between 2.9% and 4.7%, and RMSE between 4.3% and 6.4%. Overall, the proposed GFF-transformer model provides a highly accurate and low-error solution for multistep temperature and humidity predictions in poultry houses, offering an effective tool for optimizing environmental control strategies.

Cropland provides the base for the rising food demand. Recent studies highlighted the more systematic cropland management from ecosystem services (ESs) perspective, rather than solely depending on the provisioning services. However, the driving mechanism of cropland related ESs has not been well understood, hindering the co-benefits of sustainable crop-management practices. Taking the world’s largest grain producer, China, as a study case, this work contributed to determine the dynamic changes and driving mechanism of cropland related ESs by integrating InVEST model, geographically weighted regression and partial least squares-structural equation modeling (PLS-SEM). It was found that, from 2000 to 2020, the increased water purification (WP) was along with the decreased grain production (GP), soil retention (SR), carbon storage (CS) and water retention (WR) which mainly occurred in southern China. Aggregation of ESs were identified with strong spatial heterogeneity, while CS-WR and WR-WP almost showed persistent trade-offs. The direct effects of climate on WR and WP, vegetation on GP, terrain on SR and human activities on CS were found to be most significant. While the opposite effects of climate on GP during the study period were also observed. The positive effect of human activities on GP and WR was lessen by the indirect effect of terrain, highlighting the synergy management of interaction mechanisms among drivers. Finally, implications of these findings were discussed.

Due to its high-temperature and high-pressure operating environment, food/feed puffing machines are prone to faults such as cavity blockage and cutter wear. This paper presents the design of a fault diagnosis system for puffing machines (food/feed processing equipment that expands or puffs agricultural products), based on a convolutional neural network and a multi-head attention mechanism model, which incorporates Bayesian optimization. The system combines multi-source information fusion, capturing patterns and characteristics associated with fault states by monitoring various sources of information, such as temperature, noise signals, main motor current and vibration signals from key components. Hyperparameters are optimized through Bayesian optimization to obtain the optimal parameter model. The integration of convolutional neural networks and multi-head attention mechanisms enables the simultaneous capture of both local and global information, thereby enhancing data comprehension. Experimental results demonstrate that the system successfully diagnoses puffing machine faults, achieving an average recognition accuracy of 98.8% across various operating conditions, highlighting its high accuracy, generalization ability and robustness.

Tibetan pigs are known for their excellent fat deposition capacity and greater backfat thickness. In this study, scRNA-seq was performed to reveal the cellular heterogeneity of stromal vascular fraction (SVF) cells within porcine neck adipose tissues. Diverse cell types in neck adipose tissue were identified, including mesenchymal stem cells, preadipocytes, mature adipocytes, macrophages, endothelial cells and vascular smooth muscle cells. Tibetan pigs had a higher proportion of mature adipocytes and a greater tendency for preadipocytes to differentiate into mature adipocytes by pseudo-time analysis. Gene ontology analysis highlighted augment pathways related to fatty acid transport and thermogenesis in Tibetan pigs. In vitro experiments further confirmed the superior fat accumulation and fatty acid transport capacities of Tibetan pig SVF cells during adipocyte differentiation, supporting their enhanced fat deposition. Despite their superior adipogenesis, Tibetan pigs had less metabolic activity and oxygen consumption at both the SVF cells and mature adipocyte stages, indicating an adaptation to hypoxic environments at high elevations. This study provides valuable insights into the mechanisms of fat deposition in pigs and highlights the critical role of Tibetan pig adipose cells in hypoxia adaptation, offering guidance for improving fat content and stress resistance in pig breeding programs.

Early detection of subclinical mastitis (SM) in dairy cows is important to minimize economic losses and improve dairy cow comfort. This study focused on the identification of threshold temperature of California mastitis test for being positive for SM and determined if it was affected by temperature-humidity index (THI). Six hundred and fifty-eight small and medium scale dairy farms were selected in four regions of Sri Lanka (UP, Up Country; MC, Mid Country; CT, Coconut Triangle; and WP, Western Province) and 4274 udder quarters were captured using thermal camera. SM positive udder quarters had a higher temperature (as mean ± standard deviation; UP, 36.4 ± 1.79 °C; MC, 36.3 ± 1.79 °C; WP, 37.7 ± 1.84 °C; and CT, 38.3 ± 1.01 °C) compared to SM negative samples. The prevalence of SM was statistically significant for udder skin surface temperature (P < 0.05) and the difference between it and flank skin temperature was statistically significant with prevalence of SM (P < 0.05). The threshold temperature differences were (in ΔT °C): UP 2.49; MC, 2.17; WP, 1.90; and CT, 1.86, and these were statistically different (B = −0.080, R = 0.957, R² = 0.916, P < 0.05) from environmental temperature and tended to be significantly related to the THI. Thus, the threshold value of temperature difference can be applied for early detection of SM taking into consideration that threshold temperature varies with the environmental temperature and THI.

The integration of hyperspectral imaging and deep learning for foreign fiber detection has primarily focused on plastic film. However, detecting various foreign fibers in long-staple cotton, particularly those that are white, transparent or similar in color, remains a significant challenge. The spectral response differences of various foreign fibers across different wavelengths are significant, which makes hyperspectral multi-target detection more complex. To address this challenge, a hyperspectral identification algorithm is proposed. First, hyperspectral image of the experimental samples are captured, and principal component analysis (PCA) is applied to select the optimal feature bands for recognition by a convolutional neural network. Next, the AlexNet model is fine-tuned to optimal parameters using the primary feature bands. After multidimensional experimental validation, the PCA-AlexNet model efficiently identifies foreign fibers. Finally, after analyzing the experimental results from multiple perspectives, the fiber identification model is identified as PCA-AlexNet-23. The results show that the PCA-AlexNet-23 model excels in identifying multiple fibers, achieving an overall accuracy of 97.2%, an average accuracy of 95.2%, and a Kappa coefficient of 93.1%. These accuracy rates outperform those of a support vector machine, artificial neural network, LDA-VGGNet and LDA-LeNet models. In the experimental tests, the overall foreign fiber removal rate exceeds 85%.

Greenhouse gas (GHG) emissions and their mitigation in food crop production, particularly in tropical regions such as Thailand, remain a knowledge gap in advancing sustainable agricultural systems. This study used a 47-year field experiment to assess the effects of diverse fertilizer application practices on GHG emissions, soil properties and cassava yield. The results revealed that carbon inputs from crop residues (CR) and compost (CP) significantly elevated carbon dioxide emissions, primarily due to enhanced soil microbial respiration. Nitrogen applications, whether from mineral or organic sources, significantly stimulated nitrous oxide (N₂O) emissions, with greater N inputs leading to higher N₂O releases. At equivalent N application rates, mineral N fertilizers induced greater N₂O emissions, having a mean emission factor (EF) of 0.75% compared to CR-derived N with an EF of 0.56%. Additionally, mineral fertilizers led to soil acidification and nutrient accumulation. CR and CP inputs increased soil organic carbon stocks by 42.1% and 53.3%, respectively, relative to the control. CP addition also improved soil pH and significantly enhanced phosphorus and potassium availability. Notably, the combined inputs of NPK fertilizers and CR achieved the lowest GHG emissions per unit yield, highlighting the potential of integrated fertilizer application strategies to mitigate GHG emissions while sustaining crop productivity.