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Agricultural production has dual functions as a carbon emitter and sink, and fully exploiting the carbon sink function of agriculture is highly valuable for ensuring carbon neutrality. The layout of net carbon in agriculture shows an imbalance, i.e., underscaling in areas with a net carbon advantage or overscaling in areas with a net carbon disadvantage, which restricts the fulfillment of agricultural carbon sinks. Clarifying the global pattern of net carbon imbalances in agriculture and their drivers is highly important for improving the overall net carbon. In this regard, this paper focuses on the global net carbon in agriculture in 155 countries and regions, and a spatial mismatch index of net carbon in agriculture (MNCA) is constructed based on the relative difference between net carbon and cultivated land. Spatial autocorrelation analysis and the standard deviation ellipse are used to reflect its spatiotemporal evolution pattern. In addition, through geographically weighted regression, we explored the effects of agricultural structure, agricultural economic development, the scale of labor and cultivated land and organized the mismatches into carbon and area effects. The results show that (1) global agricultural production represents net carbon emissions, while developing countries represent net carbon sinks, and there is a mismatch of net agricultural carbon in most regions; (2) the MNCA is characterized by low-low clustering in North America, Asia and Europe, with its center of gravity falling in western Africa and migrating northeastward; and (3) the main factors influencing the MNCA are the agricultural structure and economic level. Underscaling in areas with a net carbon advantage is a major problem. To increase the global agricultural carbon sink capacity, we suggest that North America, South Asia and other regions strictly implement agricultural emission reduction policies and that Central America, South America and Africa tentatively upgrade agricultural technology and their economies to alleviate farmland pressure through transnational cooperation. In this paper, the global net carbon imbalance in agriculture is identified and the overall implementation path of increasing carbon sinks is explored to provide a reference for achieving carbon neutrality. This study reports a global mismatch in net carbon in agriculture and identifies the wasted carbon sink advantage of developing countries as the root problem. • It is a new indicator to measure the efficiency of net carbon allocation in the agricultural production process. • Agricultural production in developing countries shows a net carbon sink advantage. • Under-scaling of net carbon advantage is major problem. [ABSTRACT FROM AUTHOR]

This paper examines the economic viability of biochar application in crop production, focusing on varying application rates of 0, 4, 8, 12, 16, 20, 24, and 28 tons per hectare. Biochar, produced through biomass pyrolysis, has shown promise in improving crop yields and contributing to environmental sustainability. The biochar production financial metrics were analyzed, and it shows that the economic parameters such as Operational Expenditure (OPEX) amounting to $2339.40, feedstock cost of $2420.25, and transport cost of $293.71. The total biochar production cost was $232.87 per ton, with labour costs comprising 27.07 % of the overall production and application expenses. The analysis revealed that the maximum annual revenue from crop production (peas in the rabi season and maize in the kharif season) reached $525.88 per hectare at a biochar application rate of 12 tons/ha. Carbon sequestration revenue was estimated at $186.6 per ton of biochar. Economic indicators revealed that the benefit-cost ratio (BCR) was the highest at 1.476, with an 8 ton/ha biochar application rate. At the same time, the net present worth (NPW) and internal rate of return (IRR) also maximized at this rate, reaching $932.85 and 85.71 %, respectively. Conversely, higher biochar application rates of 24 and 28 tons/ha resulted in negative NPW and IRR values and BCR values below 1, indicating economic infeasibility at these levels. These findings highlight the economic viability and potential agronomic benefits of biochar application at optimal rates of 8 tons/ha for promoting sustainable crop production. • Biochar production cost is $232.87 per ton of biochar. • One ton of biochar production mitigates 6.22 tons of CO 2. • Biochar can support a circular bioeconomy in crop production by reusing agricultural crop residue. • Biochar application of 8 tons/ha is feasible; policymakers should support this rate and discourage rates over 20 tons/ha. [ABSTRACT FROM AUTHOR]

Fresh vegetables are prone to foodborne pathogen infection due to improper cultivation methods and poor post-harvest handling practices. This paper describes the prevalence of foodborne pathogens in three vegetables (tomato, brinjal, and lablab) cultivated by small-scale producers (SSP) of India. We assessed the possible entry of pathogens into the vegetable production chain. Samples from soil, manure, irrigation water, harvesting bins, and workers' hands were assessed as sources of possible pathogen entry. In addition, further sampling was done to determine prevalence of these pathogens in the produce from the agricultural fields and markets. Total plate counts, yeast and mold count, total coliforms like general microbial indices, and possible foodborne pathogens like Salmonella , Escherichia coli O157, Staphylococcus aureus , Pseudomonas aeruginosa , and norovirus were assessed in all the samples by standard microbiological, biochemical, and molecular methods. The results revealed that among the three vegetables we studied, brinjal had the highest load of general microbial indicators. Moreover, the total coliforms count of market vegetables was higher than that of on-farm vegetables, indicating poor hygiene in post-harvest operations. The pathogen Pseudomonas aeruginosa was found in the market tomatoes (0.35 log 10 cfu/g), and E. coli was detected in the market samples of brinjal (2.98–3.89 log 10 cfu/g) and lablab (0.59–2.21 log 10 cfu/g), thereby revealing fecal cross-contamination during pre or post-harvest operations. None of the vegetable samples were contaminated with E. coli O157:H7, Staphylococcus aureus , Salmonella sp., and norovirus. However, the microbiological source tracking of primary production farms showed that E. coli O157:H7 was present in manure (9/27), soil (6/27), harvest bins (6/27), and irrigation water samples (3/27) of SSP farms. In addition, soil (33%), manure (11%), and harvest bins (11%) had Pseudomonas aeruginosa. In conclusion, our study suggests that adequate knowledge of good agricultural practices for small-scale producers to cultivate microbiologically safe vegetable production and proper handling strategies for vegetable vendors is crucial to mitigate foodborne pathogen outbreaks through fresh vegetables. • The work delineates food safety issues of fresh vegetables of small-scale producers. • Field and market samples of tomato, brinjal and lablab had high microbial indices. • Pseudomonas aeruginosa and Escherichia coli found in fresh produces. • Manure, soil, irrigation water, and harvest bin are sources of contamination. • Good agricultural practices for microbial safe vegetable production identified. [ABSTRACT FROM AUTHOR]

• AI-driven tools are pivotal for optimizing agriculture productivity in developing and emerging economies. • Big data and ML serve as catalysts for sustainable practices. •.Standardized frameworks is essential for comparing AI in agriculture, fostering collaboration among stakeholders. • Tailored digital solutions are imperative for overcoming specific agricultural challenges. • Collaboration of government, private sector, and educational institutions is essential to bring innovative technologies and methodologies. Agriculture is the backbone of numerous developing and emerging economies, supporting millions of livelihoods and playing a crucial role. The transformative integration of AI-driven tools, big-data analytics, and advanced technologies, such as drones and satellites, can transform agricultural decision-making in these developing and emerging economies and globally. Acknowledging the intricate web of biophysical and socioeconomic factors that influence agricultural systems, we highlight the pressing need for innovative approaches. We explore the critical role of big-data analytics, Computational Fluid Dynamics (CFD), Machine Learning (ML), and remote sensing in transforming the sector, with a focus on enhancing efficiency, sustainability, and productivity. In this context, continuous real-time monitoring becomes essential, allowing farmers to manage their agricultural systems with precision. Implementing AI-driven tools and devices enables the collection and analysis of data in real time, leading to timely decisions that can significantly improve crop yields and resource management. This study discusses the impacts of big-data analytics on agriculture, including reduced pesticide use and improved crop yields. It presents concrete examples of AI applications in crop and animal farming, emphasizing precision irrigation, fertilizer management, animal health monitoring, and reproductive efficiency. We advocate for customizing these technologies to suit local contexts, which can lead to ensuring deep integration into developing and emerging economies' agricultural practices. In conclusion, the paper calls for collaborative efforts from governments, the private sector, and educational institutions to make these technologies accessible, affordable, and culturally relevant. [ABSTRACT FROM AUTHOR]

Agroforestry represents a potentially multi-beneficial approach to land management as it may enhance a farming household's income and income stability simultaneously with nutrition security and climate change mitigation. However, our comprehension of the specific characteristics within agroforestry systems that bolster economic productivity remains limited, exacerbating the challenges of land management for smallholder livelihoods. This complexity is compounded by the potential influence of carbon revenue, adding an additional layer of intricacy to the equation. This paper aimed to identify the key economic determinants of multistrata agroforestry in sub-Saharan Africa with and without potential carbon revenue. A quantitative survey of 135 farmers was performed regarding their multistrata agroforestry systems with contrasting histories in three regions of Ethiopia. The carbon sequestration rate and carbon stocks in agroforestry were assessed on one-fifth (27) of the farms. The relative importance of hypothetical determinants of agroforestry systems' economic performance was modeled using descriptive statistics and generalized linear models. Farm size and fertilizer usage were the primary drivers of the farm economy, but farm net income was also highly influenced by the richness and diversity of the income sources. In addition, tree diversity had a positive impact on the net income, whereas the proportion of legume trees and trees with a large diameter correlated negatively with the income. Potential carbon revenue at prices of US$40 tCO 2 −1 and US$100 tCO 2 −1 increased income for multistrata agroforestry farms without significantly changing the magnitude of the identified key determinants. This suggests that the most economically viable agroforestry systems inherently possess a strong capacity for carbon sequestration, effectively serving as carbon sinks. Consequently, carbon revenue serves as a compelling financial incentive for the adoption of these agroforestry systems. Ultimately, this research underscores the pivotal role of biological and product diversity in shaping economic productivity within multistrata agroforestry systems. Moreover, it highlights the accessibility of diversity management for smallholder farmers, even under conditions of resource constraints. [Display omitted] • The economic determinants of agroforestry systems remain not well understood. • The determinants with and without potential access to carbon market were identified. • The farm income was mainly affected by farm size and fertilizer use. • Diversity of the income sources was key determinant regardless of carbon revenue. • Diversity is manageable for all smallholder farmers. [ABSTRACT FROM AUTHOR]

• China's sustainable development relies on agricultural trade and carbon emissions. • Carbon emission transfer index in agricultural trade is measured. • Decoupling-recoupling model and integrated prediction model are constructed. • A unified framework for trade, environment and technology is established. Under the new development pattern of domestic and international "dual circulation" in China, studying the relationship among green technology innovation, agricultural trade deficit (ATD) and carbon emission transfer embodied in agricultural trade (ATCT) becomes crucial for achieving coordinated and unified high-quality economic development and efficient environmental protection. Based on this, the paper uses environmental input–output model, "two-stage" decoupling and recoupling model, panel vector auto-regressive model, spatial econometric model and "rolling" Grey-BP neural network prediction model to explore present status of ATD, ATCT and their relationship, driving role of green technology innovation, as well as whether that relationship will be improving in the future. The results reveal the following: (1) China's ATD and ATCT show an increasing trend year by year, and their inter-provincial differences are gradually significant over time. The overall relationship between ATD and ATCT does not display ideally, which makes it necessary to quickly transform that relationship into reasonable and ideal state. (2) Green technology innovation has significant positive "U-shaped" and inverted "U-shaped" local effects on ATD and ATCT respectively, with a reasonable range of ATD reduction while ATCT increase valuing from 0 to 0.290, and has significant positive "U-shaped" spatial spillover effect on both without any reasonable range. The short-term effect of green technology innovation on coordinated promotion between ATD and ATCT is superior to the long-term one. (3) Green technology innovation has a significant regional heterogeneity. Regional differences must be taken into consideration seriously, and the measures adapted to local conditions are crucial. The role of fundamental and progressive green technology innovation has heterogeneity, which means that targeted development and utilization of green technology products are important. (4) Regardless of the time horizon, the relationship between ATD and ATCT will improve, which can provide a key step for coordinated development of economy and environment in agricultural trade. Therefore, in order for China to implement and promote further opening-up, reduce agricultural trade dependence, ensure food security and important agricultural product supply, as well as achieve the unity of high-quality economic development and ecological efficient protection, it is necessary to reasonably reverse trade deficit and increase carbon emission transfer in agriculture. [ABSTRACT FROM AUTHOR]