Digital technologies are considered to hold transformative potential for agriculture by enhancing productivity, reducing environmental impacts, improving market access, and strengthening farmer livelihoods (Trendov et al. 2019; Klerkx et al. 2019; Prause et al. 2021; Huang et al. 2023). Mobile phones can provide real-time weather information and market prices, reducing information asymmetries that have long disadvantaged small-scale producers (Aker and Fafchamps 2014). Precision agriculture technologies, including drones and sensor systems, can optimize input use and reduce costs per unit of output (Wolfert et al. 2017). Digital financial services can improve access to credit and insurance, helping farmers manage risks and invest in productivity-enhancing technologies (Suri and Jack 2016). E-commerce platforms may connect smallholders directly to consumers and processors, potentially capturing higher value and margins for producers by bypassing intermediaries (Dannenberg and Lakes 2013; Feng 2024) .

Meanwhile, smallholder farmers, while playing a critical role in ensuring food security, face distinct disadvantages in accessing and adopting digital technologies as most of them do not reach the critical operational size beyond which technology becomes profitable. High upfront costs of digital devices, connectivity and capacity building remain prohibitive for many resource-constrained households (Fabregas et al. 2019). Furthermore, digital interfaces and technical requirements often exceed the educational backgrounds and technical skills of smallholder farmers (Nakasone and Torero, 2016). Risk aversion, common among vulnerable farming households, can further discourage adoption of unfamiliar technologies (Foster and Rosenzweig 2010). These barriers contribute to a growing digital divide in agriculture, where the benefits of technological advancement increasingly accrue to larger, better-resourced farms while smallholders remain excluded (Jouanjean et al. 2017).

China, being both the largest producer and consumer of agricultural products worldwide, features an agricultural sector that traditionally was characterized by high input of manual labor and agrochemicals, but low input of technology. This structure can be considered a heritage of ample rural labor on the one hand and lack of private rural finances on the other hand. For millennia, agriculture served as major source of income for China’s rural population. During the shift from a largely agricultural society to a modern, industrially-dominated society, agriculture remained a fall-back option and essentially a social security for the rural population. Currently though, demographic change (i.e., shrinking and rapidly aging population), deterioration and scarcity of natural resources (i.e., soil, water resources) as well as strategic concerns over food security have driven a fundamental need to modernize China’s agricultural sector.

China is currently aiming to modernize farming by expanding rural digital infrastructure, integrating IT and AI into production, strengthening big-data platforms, and upgrading rural industries. With a series of policies, beginning with the Opinions of the Ministry of Agriculture on Promoting the Development of Agricultural and Rural Big Data (2015) and the Internet Plus Agriculture Plan (2016), China laid the groundwork for spreading digital infrastructure, e-commerce, and precision agriculture. More recent frameworks - including the Smart Agriculture Development Plan, the Development Plan for Digital Agriculture and Rural Areas (2019–2025), the National Smart Agriculture Action Plan (2024–2028), and the Digital Village Strategy - set concrete targets for national data platforms, digital-skills training, and integrated rural digital ecosystems. The newly unveiled China’s 15th Five-Year Plan (2026–2030) further emphasizes accelerating smart agriculture and digitalization technologies (e.g., AI, Drones, and IoT) to achieve food security, agricultural modernization, and rural revitalization. Together, these policies create a comprehensive roadmap for accelerating digital transformation across China’s agri-food sector, and demonstrate the determination of the Chinese government in promoting digitalization in agrifood systems. Compared to other countries, where smallholder-systems are dominating agricultural production, China is leading in digitalization efforts and could offer valuable experiences towards a successful and sustainable digital transformation of the agricultural sectors.

At the same time, the current political framework has yet to achieve successful incorporation of smallholders into the digitalization process. Empirical evidence showed that family farms in China remain mostly excluded from digitalization processes at present. The digitalization level of most family farms, particularly on the production stage, is still low in China. Considering the barriers to smallholder digitalization laid out earlier, an integration of these producers will likely require active policy support. Otherwise, digitalization is likely to act as a catalyst to already ongoing structural change, crowding out smallholders from commercial farming within a matter of years. While farm consolidation processes worldwide since the 1960s showed that agricultural labor can mostly be absorbed by industrial and service sectors, the social consequences of a high-speed, unchanneled sector transformation will be difficult to predict. To direct policymaking towards not just supporting but also guiding this digitalization process in an inclusive fashion, current smallholder-specific empirical evidence into the conditions, drivers and impacts of digitalization are of high need.

General research established that in general, high upfront costs and investment risks remain substantial barriers for digital innovation adoption among smallholders (Pivoto et al. 2019; Geng and Liao 2024). Beyond fundamental economic incentives, social and behavioral factors play a critical role, with younger, more educated farmers and those with strong social networks or frequent extension contact demonstrating higher adoption intensity (Pivoto et al. 2019; DeLay et al. 2021; FAO 2022). From a technological perspective, ease of use and robust digital infrastructure - such as reliable internet and technical support - are essential enablers, with “embodied-knowledge” technologies like automated guidance seeing faster diffusion than complex information-intensive systems (Ofori et al. 2020; Damasceno et al. 2025). Furthermore, institutional support through government subsidies and targeted policy interventions effectively mitigates financial constraints, whereas environmental motivations currently appear as secondary or inconclusive drivers compared to direct economic benefits (Geng and Liao 2024; Ruslan 2024; Tian et al. 2025).

Education and digital literacy emerge as critical factors to reduce adoption costs among smallholders and act as prerequisite for their engagement in agricultural digitalization (Li et al. 2025). Digital literacy encompasses not only basic technical skills for using devices and applications, but also the ability to critically evaluate information, adapt digital tools to local contexts, and integrate digital resources into decision-making processes (Salemink et al. 2017). Research has shown that farmers with higher digital literacy are more likely to adopt beneficial agricultural technologies, make informed and adaptation production decisions, and achieve better economic outcomes and resilience (Deichmann et al. 2016; Bai et al. 2026). Conversely, limited digital literacy can lead to misuse of digital information, inappropriate technology choices, and potentially harmful agricultural practices (Birner et al. 2021).

The actual or expected impact of agricultural digitalization under smallholders context is another critical research topic. Literature reviews for the grain sector show that both main motivation and impact of digitalization lies within saving labor and other agricultural inputs at given rates of productivity. Meanwhile, international studies have also documented productivity gains from precision agriculture technologies (Lowenberg-DeBoer and Erickson 2019), improved market outcomes from mobile phone adoption (Jensen 2007), and enhanced financial inclusion through digital payment systems (Suri and Jack 2016). However, research has also identified unintended consequences, including increased input use in some contexts (Fabregas et al. 2019) and growing inequality between adopters and non-adopters (Klerkx et al. 2019). These mixed findings underscore the importance of understanding not just whether digital technologies work, but how and under what conditions they benefit different types of farmers.

This special focus contributes new empirical evidence to these ongoing discussions, with particular attention to the Chinese context, where rapid digital transformation intersects with the world’s largest population of smallholder farmers. The six papers are organized around three complementary themes: behavioral aspects of innovation adoption (Section 1), aspects of digital knowledge and literacy within innovation adoption (Section 2), followed by the actual and expected impacts of digitalization (Section 3).

Section 1: Behavioral adoption drivers

Two papers in this section provide detailed insights into behavioral adoption drivers of agricultural drones, or unmanned aerial vehicles (UAVs), under the smallholder context in China. Although these two papers address the farmers’ adoption of same technologies, i.e., agricultural drones, the authors take distinct approaches and pay different attention on the adoption factors.

Zhang et al. (2026) strived to provide a specific perspective on behavioral aspect by examining farmers’ preferences for agricultural drone services rather than ownership, an organizational innovation which allows small holders to use drones without purchasing them. Using a discrete choice experiment among rice producers in Hubei Province, they show that most farmers are willing to adopt drone services under collective hiring arrangements. The study reveals that farmers are willing to pay for drone services, with localness of suppliers valued more highly than contractual arrangements. Farmers strongly prefer local suppliers and contractual agreements, indicating that supplier uncertainty is a major concern in service-based adoption. These findings suggest that service-based models may offer more inclusive pathways for smallholder access to advanced digital technologies, but require careful attention to trust, reliability, local capacity building, and more importantly, the standardization of the services.

Zhou et al. (2026) examined the drivers and barriers to UAV adoption among rice farmers in Jiangxi Province, China, using a structural equation model grounded in the Technology Acceptance Model. The study reveals that perceived usefulness and ease of use strongly predict adoption intention, while perceived risk acts as a significant barrier. Importantly, the paper identifies network externalities as a key social factor, with peer influence amplifying adoption likelihood by reducing the perceived risk of innovation adoption. As expected, farm size matters significantly, with larger farms showing higher adoption rates. With their research, the authors provide novel evidence towards the social and psychological process shaped by perceptions, peer effects, and risk considerations behind drone adoption among Chinese smallholders.

Section 2: Digital literacy as adoption driver

Beyond economic and behavioral drivers, digital knowledge and capabilities emerge as a cross-cutting theme throughout this special focus, warranting dedicated attention. Two papers in this section provide particularly detailed insights into how digital knowledge shapes technology valuation, information use, and agricultural production behaviors. Collectively, these two articles suggest that digital knowledge and literacy are foundational capabilities that determine whether smallholders can successfully participate in and benefit from agricultural digitalization.

Bai et al. (2026) focused on climate adaptation, examining whether digital literacy promotes adaptive production behaviors among grain farmers in Sichuan Province, China. The study finds that digital literacy significantly increases adoption of climate-adaptive practices by improving farmers’ perception of climate disaster risks. Importantly, the positive effects of digital literacy are stronger where government support is present, including internet training, climate information services, and agricultural infrastructure development. This paper demonstrates that digital literacy can contribute to agricultural resilience, but requires supportive policy environments to realize its full potential.

Amolegbe et al. (2026) shifted the focus to Nigeria, Africa, and investigated the relationship between digital technology knowledge and e-commerce valuation among farmers. Using objective measures of digital knowledge rather than self-reported experience, the study reveals significant gaps in basic digital skills despite widespread mobile phone ownership. However, farmers with stronger digital knowledge show substantially higher willingness to pay for digital marketing services. The heterogeneity analysis reveals that digital knowledge benefits vary by age and gender, with young adults and men showing larger increases in e-commerce valuation. These findings underscore that the digital divide extends beyond device access to encompass fundamental differences in capability and knowledge.

Section 3: Impacts and effects

While existing policy may support in leveling adoption barriers and provide innovation incentives for smallholders, research may also guide policy makers with respect to how policy may facilitate digital innovation processes towards reducing undesired effects of digitalization and shaping positive benefit incidence among different user groups. Two papers in this special focus address different dimensions of impacts of digitalization: agrochemical use decisions and income stability.

Liu et al. (2026) focused on the socioeconomic perspective by investigating how digital technology use affects income stability of marginalized farm households. Using panel data and multiple estimation approaches, the study shows that digital technology use significantly improves both income levels and income stability among relocated households participating in China’s poverty alleviation programs, with stronger effects at higher levels of use. The analysis identifies information acquisition and human capital accumulation as key mechanisms, showing that digital technology helps households access employment opportunities and develop skills that contribute to stable livelihoods. The paper also reveals that digital technology is particularly effective at stabilizing income for households experiencing downward volatility, suggesting an important insurance-like function.

Hu et al. (2026), on the other hand, provided a sobering assessment of how online agricultural information affects input use decisions among Chinese farmers. Using propensity score matching with data from 1,833 farms across five provinces, the study finds that online information use increases rather than decreases chemical fertilizer expenditure, particularly among smallholders. This counterintuitive finding highlights potential problems with the quality and targeting of existing digital agricultural information. The authors suggest that much online agricultural content originates from input suppliers with commercial interests in high application rates, and that smallholders may lack the digital literacy needed to critically evaluate such information. This paper serves as an important reminder that digitalization is not automatically beneficial and that information quality and farmer capability are crucial mediating factors.

Concluding remarks

The digitalization of smallholder agriculture represents both an opportunity and a challenge. The papers in this special focus show that digital technologies can indeed contribute to more productive, resilient, and inclusive agricultural systems. However, they also demonstrate that realizing this potential requires careful attention to farmer capabilities, information quality, service delivery models, and supportive policy environments. As the digital transformation of agriculture continues, ensuring that its benefits reach smallholder farmers will require sustained effort across multiple domains of policy and practice.

Future research should continue to examine these complex relationships between digital technologies, farmer capabilities, and agricultural outcomes. Particular attention should be paid to long-term impacts, inequality effects, and the institutional conditions that enable inclusive digitalization. Mixed-method approaches that combine quantitative impact assessment with qualitative investigation of farmer experiences and institutional dynamics would be especially valuable. Further research into processes, trends and projections of digitalization therefore remains of urgent need.

Special care should be given to the interaction between digitalization, structural change and sustainability. Providing a critical advantage to large producers with respect to total factor productivity, digitalization is likely to accelerate structural change. By shifting the production function and the efficient scale of production to a level where smallholders are no longer competitive, commercial smallholder production is critically endangered. While a structural change within China’s smallholder farming system is both needed and ultimately inevitable, the challenges lie in ensuring the meaningful engagement of small and disadvantaged farmers and guiding the sustainable transformation of farming system towards digitalization and sustainability.

This special focus provides a glimpse into the dynamic and emerging field of agricultural digitalization in smallholder contexts, which represents both a compelling scientific frontier and an urgent challenge for sustainable agricultural transformation and global food security. The featured papers collectively offer robust empirical evidence on the heterogeneous effects of digital technologies across different farmer types, technologies, and contexts, moving beyond simple adoption narratives to examine nuanced patterns of use and impact. Additionally, the contributions also shed light on the critical but often overlooked role of digital literacy and information quality in determining whether digitalization benefits or potentially harms smallholder farmers. By demonstrating that digital technologies can both enhance and undermine agricultural outcomes - depending on their design, delivery, and use - this collection underscores the need for more sophisticated approaches that prioritize farmer capabilities alongside technological advancement. We envision this special focus will stimulate further research on agricultural digitalization in smallholder contexts in China and beyond.

Agricultural drones can improve productivity, save labor, and reduce environmental impacts by offering digital multifunctionality in agricultural production.  Yet, the lagging adoption among smallholders is still prevalent.  Existing literature commonly explains it by the lack of capital, land fragmentation, and digital illiteracy, with little delving into specific adoption modes under uncertainty.  In this study, we demonstrate the potential of hiring agricultural drone services and investigate the role of supplier uncertainty in the adoption decision.  We conduct a discrete choice experiment among 338 farmers in Hubei Province, China.  Mixed logit models are used to analyze farmers’ preferences for the agricultural drone service (ADS) and its attributes.  The results show that the large majority of sampled farmers are willing to adopt ADS.  Besides low prices, farmers prefer services with local suppliers and contracts.  Potential adopters in this choice experiment are characterized by youth, higher education, owning poor-topography farms, drone learning via word-of-mouth, and adoption experience.  The willingness to pay analysis indicates that farmers would like to spend 25 CNY per mu (53 USD per ha) on average for ADS.  Notably, farmers value the localness of suppliers more than the form of agreements when choosing a particular drone service.  These findings suggest that the mode of hiring ADS can effectively motivate farmers’ adoption intention, thereby requiring supply-side incentives and uncertainty-reducing promotion strategies to enhance smallholders’ access to and adoption of agricultural drones.

The adoption of plant-protection unmanned aerial vehicles (UAVs) in agriculture is gaining attention, yet empirical evidence on the factors affecting their uptake remains limited.  This study investigates the factors influencing the adoption of UAVs among rice farmers in China’s Jiangxi Province (n=260), utilizing a structural equation model grounded in the Technology Acceptance Model (TAM).  Results indicate that perceived usefulness (β=1.274, P<0.001) and ease of use (β=0.146, P<0.001) have a positive influence on adoption intention, while perceived risk (β=–0.731, P<0.001) acts as a barrier.  Network externalities also play a significant role, with peer influence amplifying the likelihood of adoption.  Mediation analysis reveals that perceived risk mediates the relationship between network externalities and adoption (β=–0.569, P<0.05), underscoring the interplay between social factors and risk perception.  Additionally, farm size (β=0.001, P<0.001) has a significant positive effect on adoption decisions, whereas education level, age, and planting experience show no significant impact.  These findings provide critical insights for policymakers and industry stakeholders in designing targeted interventions, such as subsidies, training programs, and risk mitigation strategies, to promote the adoption of UAVs in agriculture.

Climate disasters lead to substantial economic losses and grain yield losses, emphasizing the need for adaptation to ensure food security.  As digital technologies advance, it is imperative to investigate how digital literacy among grain farmers affects their adaptive production behaviors in the face of climate disasters.  Drawing on survey data from 505 grain-producing smallholders in Sichuan Province, China, this study constructs a theoretical framework linking digital literacy, climate disaster risk perception, and adaptive production behaviors.  Empirical analysis shows that digital literacy positively impacts the adaptive production behaviors of grain-producing smallholders.  Our results are robust across various models and tests.  An analysis of the mediation mechanism reveals that digital literacy contributes to climate disaster-adaptive production behaviors by improving the awareness of climate disaster risks.  Heterogeneity analysis shows that the positive impact of digital literacy is more pronounced for smallholders that receive internet skills training and climate information services, and this impact intensifies as the level of agricultural infrastructure improves.  The findings suggest that digital literacy plays a key role in reducing production risks, thereby contributing to increased sustainable agricultural development among smallholders.

The growth of digital marketing platforms is reshaping the food systems and offers a potential solution to farmers’ output market access problem.  However, the digital transformation also leads to a growing digital divide, driven by disparities in digital literacy, which may hinder rural farmers from exploiting opportunities in the digital market.  This paper investigates the relationship between e-commerce valuation and digital technology knowledge in rural Nigeria.  We used contingent valuation questions to measure farmers’ willingness to pay for digital platform services in commercializing their agricultural products.  We performed a basic digital technology knowledge test to assess farmers’ proficiency in using digital tools, focusing on practical digital skills.  Using the treatment effects framework associated with panel data, we found significant positive and heterogeneous effects of digital technology knowledge on e-commerce valuation in rural Nigeria.  To fully realize the potential of rural e-commerce, urgent policy interventions are needed to enhance digital literacy and make internet access more affordable for rural farming households.

Ensuring the income stability of relocated households is essential for advancing rural revitalization and achieving common prosperity.  While existing research has explored the impact of digital technology on income, few studies have addressed how digital technology use affects income stability.  To fill this gap, based on survey data from relocated households in 16 counties across 8 provincial-level regions in China, this study examines the impact of digital technology use on the income stability of relocated households using Ordinary Least Squares (OLS) and Propensity Score Matching (PSM).  The results show that digital technology use improves both income and income stability, with stronger effects at higher levels of use.  This impact is driven by better access to information acquisition and enhanced human capital.  Additionally, digital technology helps stabilize the income of households facing downward volatility.  The income stabilizing effect is particularly significant among relocated households in central regions, rural resettlement areas, and those with higher education levels.  Furthermore, digital literacy amplifies the positive impact of digital technology on income stability.  These findings offer valuable insights for policymakers aiming to promote digital technology use to ensure the income stability of relocated households and foster common prosperity.

Motivated by growing concerns about excessive agrochemical use and the resulting environmental pollution in China, this study explores the importance of online agricultural information for chemical fertilizer and pesticide use decisions among grain farmers.  In particular, we focus on the functional agricultural information used for productive purposes for smallholders.  Based on a survey dataset of 1,833 family farms across five Chinese provinces, we employ a propensity score matching (PSM) approach to estimate treatment effects of online agricultural information.  The results reveal that online acquisition of agricultural information does not reduce the expenses of chemical fertilizers and pesticides in our sample; rather, the opposite is true.  The use of online agricultural information significantly increased agrochemical expenses, particularly among smallholders.  Within our sample region, the limited evolution of online information content and the inherent challenges faced by smallholder farmers are the major barriers to the beneficial effects of online agricultural information in reducing agrochemical use.  Our findings emphasize the need for targeted interventions and educational efforts to bridge the knowledge gaps of smallholders.  Furthermore, there is a need to raise awareness among information providers to ensure that their recommendations avoid encouraging overdoses of agrochemicals.  In addition, enhancing farmers’ digital literacy will be a future task of development policy.

 

Goats (Capra hircus) provide a rich source of products, such as meat, milk, and wool, and are important domestic animals in many parts of the world.  Goats were one of the first domesticated livestock species during the late Neolithic period, approximately 11,000 years ago, in the Fertile Crescent.  In the past decades, genomic studies of goats have provided insight into their domestication and genetic basis of economically important traits.  This review outlines the latest advancements that have been made in reference to domestication and genetic improvement of production traits such as meat and carcass quality, reproduction, milk, cashmere, and functional traits such as environmental adaptation and disease-resistance.  Genomic research is entering a new era with the availability of graphical pan-genomics and telomere-to-telomere (T2T) gap-free genome assembly, which will extend our understanding of domestication and molecular mechanistic dissection of economic traits in goats.  We provide new perspectives and future directions for genomics and suggest how the ever-increasing multi-omics dataset will facilitate future studies and molecular breeding in goat.

Seed germination, which initiates the plant life cycle, exhibits high sensitivity to salt stress, a significant environmental factor limiting rice production.  Brassinosteroid (BR), a growth-promoting phytohormone, mitigates various stresses including salt, drought, and extreme temperatures in rice.  However, the mechanisms by which BR alleviates salt stress during seed germination remain inadequately characterized.  This study demonstrates that seed-specific overexpression of OsDWF4, a rate-limiting gene in BR biosynthesis, enhances rice germination.  The DWF4-OX lines, which increase endogenous BR content in seeds, promote germination under salt stress, corroborating results obtained through exogenous BR application.  Antioxidant enzyme analyses demonstrate that BR enhances the activities of superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT).  Metabolomic analysis reveals that BR mitigates salt stress primarily through the biosynthesis of phenylpropanoids and secondary metabolites.  Transcriptomic analysis indicates that both endogenous and exogenous BR share five co-regulated target genes and utilize a common biosynthetic pathway for stilbenoids, diarylheptanoids, and gingerols.  These findings confirm BR's capacity to enhance seed germination under salt stress and identify several BR-mediated targets for developing salt-tolerant rice varieties suitable for direct seeding cultivation.

Wheat (Triticum aestivum L.) quality is a major focus of wheat breeding, which is influenced by multiple factors. The Huang-Huai wheat region, one of the main wheat-producing areas in China, provides favourable conditions for cultivating wheat cultivars with strong-gluten and medium-strong-gluten. In this study, a systematic assessment of seven crucial quality traits and important genetic loci (Glu-1 and Sec-1) in 436 wheat cultivars in the Huang-Huai wheat region of China by principal component analysis (PCA) and fuzzy comprehensive evaluation (FCE) methods showed that the stability time (ST), stretch area (SA), and maximum resistance (MAXR) were identified as three key factors, which significantly influenced wheat quality. Glu-1 and Sec-1 primarily impacted these three traits and subsequently influenced wheat quality. Compared to Glu-A1 and Glu-B1, Glu-D1 has a more significant impact on the comprehensive evaluation value D, principal components PC1-PC3, and the main traits ST, SA and MAXR of PC1. Wheat cultivars carrying the high-molecular-weight glutenin subunit (HMW-GS) Dx5+Dy10 exhibited a notable improvement in ST, SA, and MAXR traits compared with those carrying HMW-GS Dx2+Dy12, suggesting that Dx5+Dy10 may enhance wheat quality by improving ST, SA, and MAXR. By combining the results of D value, GYT (genotype by yield×trait) index, and HMW-GS score, 20 high-quality and high yield wheat cultivars were identified, which can be used as elite parents for wheat quality breeding.

Cadmium (Cd) contamination in wheat farmland is increasing at an alarming rate, posing threats to food security and public health.  Breeding and utilizing wheat varieties characterized by low Cd accumulation levels constitute an effective strategy in the battle against wheat Cd contamination.  The adoption of molecular marker-assisted approaches can greatly expedite the selection and enhancement of wheat varieties with low Cd accumulation.  Nonetheless, research concerning the genes associated with wheat cadmium accumulation remains scarce.  In this study, a high-density 660K SNP array was employed for conducting a genome-wide association study (GWAS) on the grain Cd concentration (GCdC), bioconcentration factor (BCF) and translocation factor (TF) in 175 wheat germplasms.  The findings revealed 401 significant SNPs identified across three diverse environments.  Linkage disequilibrium analysis revealed 30 core quantitative trait loci (QTLs) capable of reliably modulating wheat Cd accumulation phenotypes.  Through gene annotation, transcriptomics, and gene molecular features, four candidate genes (TraesCS7B02G000200, TraesCS4A02G035900, TraesCS4A02G040900, and TraesCS5D02G564000) were identified as potential constituents in the biological process of wheat Cd accumulation.  Furthermore, six wheat germplasms exhibiting low grain Cd accumulation were isolated, and two kompetitive allele specific PCR (KASP) markers conducive to breeding selection were developed.  These findings provide valuable genetic resources for cultivating wheat with low Cd accumulation and establish a foundation for understanding the molecular mechanisms underlying low Cd accumulation in wheat.  The candidate genes and KASP markers elucidated in this research have potential for effective use in genetic enhancement and marker-assisted selection in the breeding of wheat with low Cd accumulation.

Peanut (Arachis hypogaea L.) is an important oil and edible protein crop.  Its fatty acid composition not only influences the quality of peanut oil but also impacts flavor, shelf life, and consumer health.  Peanut oil is comprised of approximately 80% oleic acid (C18:1) and linoleic acid (C18:2), 10% palmitic acid (C16:0), and the remaining 10% includes stearic acid (C18:0), arachidic acid (C20:0), gadoleic acid (C20:1), behenic acid (C22:0), and lignoceric acid (C24:0).  To unravel the genetic foundation of fatty acid content and delve into QTL localization, high-density SNP microarrays were used to genotype the RIL population of ‘SunOleic 97R’ × ‘NC94022’.  A genetic linkage map was constructed with 3,141 SNP markers, covering a total genetic distance of 3,051.81 cM.  Sixty quantitative trait loci (QTLs) associated with fatty acids were distributed in 11 linkage groups, with phenotypic variance explained (PVE) ranging from 1.37 to 44.92%.  Notably, the QTLs qFAT_A05.1 and qFAT_A08.1 are multiple-effect loci contributing to various fatty acid compositions.  Moreover, 15 haplotypes for the QTLs qFAT_A05.1 and qFAT_A08.1 were identified through genotyping 178 peanut germplasms.  Haplotype analysis in a natural population confirmed the close relationship of the QTLs with the contents of oil, oleic acid, lignoceric acid, palmitic acid and behenic acid.  This study serves as a valuable reference for selecting improved peanut genotypes with superior oil quality and desirable fatty acid composition.

Vegetable soybean ((Glycine max (L.) Merr.), commonly referred to as edamame, holds significant agricultural importance in China as a legume vegetable harvested at the pod-filling stage (R6).  The visual appeal of vegetable soybeans is crucial for consumer preference and marketability, and it depends on factors such as pod length, pod width, and pod color.  This study cultivated 264 vegetable soybeans in Nanjing, Huai’an, and Nantong, Jiangsu Province, China to assess pod traits using PlantPhenoM, a system for pod phenotypic identification and analysis.  The results revealed a variability range of 8.64 to 30.00% in appearance quality traits among the vegetable soybeans.  Leveraging phenotypic data and employing a genome-wide association study (GWAS) identified 525 SNPs significantly linked to the appearance quality traits in different regions.  In addition, five candidate genes (Glyma.04G004700, Glyma.15G051600, Glyma.18G225700, Glyma.18G225900, and Glyma.18G272300) associated with target traits were identified, and KASP markers for S04_372771 (pod length), S18_51477324 (pod width), and S18_55553200 (pod color) were developed.  This study offers valuable insights for breeding superior vegetable soybean varieties and lays the groundwork for exploring candidate genes and molecular markers related to appearance and quality traits in vegetable soybeans.

The change in leaf color during the later reproductive period of rice is directly related to photoassimilate accumulation and nutrient reuse, and it ultimately affects grain filling and yield.  This study aimed to explore an assessment model that depicts the leaf color change process, and extract parameters that can precisely distinguish differences in leaf color changes among different treatments and varieties.  A total of 31 rice varieties were selected as the field experiment materials in 2019 and 2023.  The SPAD values of the flag, 2nd and 3rd leaves were measured after heading, and they were normalized to the leaf color index (CI).  A functional model for the variation of leaf CI with time (t) in the late reproductive stage of rice was established based on CI=at²+bt+c, and seven color change parameters were extracted for the quantitative comparison and assessment of leaf color changes, including three time related parameters for color change (onset time, T₀; midpoint time, T₅₀; and color change duration, T₁₀₀); one leaf color index (final value of CI, CI_f); and three parameters related to the color change rate (the rate during T₀−T₅₀, R₁; the rate during T₅₀−T₁₀₀, R₂; and the mean color change rate, R_m).  In 2023, Chunyou 927 (CY927) with a dark leaf color and Yongyou 1540 (YY1540) with a normal leaf color were used as materials, and three N fertilizer amounts were applied to explore the effects of N fertilizer on the leaf color change process through the established assessment system.  The T₀ of the flag leaf was delayed by 2.6−3.0 d compared to the 2nd and 3rd leaves.  The CI_f of the flag leaf was 12.12 and 21.15% higher than those of 2nd and 3rd leaves, respectively.  In addition, the R₁, R₂ and R_m of the 3rd leaf were 10.75–19.82%, 17.99–20.09% and 18.23–11.61% higher than the flag and 2nd leaves, respectively.  Rice yield was significantly positively correlated with T₀, positively correlated with T₅₀ and T₁₀₀, and negatively correlated with R₁, R₂ and R_m.  The average T₀, T₅₀, and T₁₀₀ of rice varieties with yields higher than 8,000 kg ha⁻¹ were 6.8, 22.2, and 31.8 d, respectively, with a CI_f of 0.563 and an R_m of 0.015 d^–1.  N applications delayed T₀ by 4.5–6.2 d, reduced R_m by 30.06–32.33%, and increased CI_f by 35.78–39.69%.  The established leaf color change model and extracted parameters quantitatively depicted the leaf color change process during the later reproductive period.  They also effectively distinguished the differences in leaf color change among leaf positions, rice varieties and N treatments.  This approach is valuable for selecting and cultivating high-yield and nutrient-efficient rice varieties, as well as for analyzing the underlying mechanisms.

In the face of agricultural labor shortages, reducing labor and costs in rice production while meeting demand or increasing yield is crucial for sustainable agricultural development.  Using crop straw boards and raising seedlings at a high-density can reduce labor demand and enhance rice yield.  This study investigated the effects of seeding density and transplanting age on tillering patterns, panicle formation rates, and yield to determine the optimal cultivation practices for maximizing rice yield.  Two-year field experiments were conducted in Sihong County, Jiangsu Province, China, using the japonica rice variety Nanjing 5718.  Five seeding densities (150–350 g/tray) and four transplanting ages (10–25 days) were evaluated to assess their impacts on tillering patterns, panicle formation rates, and yield.  Innovative crop straw boards were employed to enhance planting efficiency and reduce dependence on soil for raising seedlings.  This approach also lessened tillage layer destruction, promoting sustainable practices.  The results indicated that increasing seeding density significantly altered tillering and panicle formation patterns by reducing the occurrence and panicle formation rates of lower-position tillers.  Although the occurrence of middle- and high-position tillers increased, the overall number of panicles per hill decreased, especially at higher densities, negatively affecting yield.  Reducing the transplanting age promoted the emergence and panicle formation of lower-position tillers, thus mitigating these negative effects.  Specifically, compared to traditional methods (150 g/tray, 20-day seedlings), the higher seeding density (300 g/tray) and reduced transplanting age (15-day seedlings) increased total panicle number by 3.79–4.73% and yield by 3.38–5.05%.  Combining higher seeding densities with reduced transplanting ages offers significant advantages over conventional practices by enhancing resource utilization and improving tillering efficiency.  These findings provide actionable recommendations for optimizing rice cultivation practices and contribute to sustainable agricultural development.

Nitrogen (N) and potassium (K) are key elements for crop growth, yet studies on the impact of N–K interactions on plant N and K status and yield are lacking.  This study aimed to develop effective indicators for diagnosing N and K nutrition and predicting the yield of wheat under N–K interactions based on the theoretical framework of a critical nutrient dilution curve.  A 4-year N–K interaction experiment involving three wheat cultivars was employed for building and validating nutrient indices (NIs) based on the critical N dilution curve (CNDC) and the critical K dilution curve (CKDC).  In addition, relevant data from the literature were collected for supplementary validation.  The results revealed that changes in parameter A1 of the critical K dilution curves (CKDCs) can reflect the impact of nitrogen application on K absorption and utilization.  However, the difference in K nutrition index (KNI) values calculated by CKDC under different N levels was not significant.  Based on the aboveground biomass (AGB), a universal CKDC was established and defined as K_c=3.63AGB^–0.37 under N–K interactions.  The results showed that the direct effects of N or K deficiency on crops could be quantified by the N–K interaction index (NKI) calculated by integrating CNDC and CKDC, and the changes in crop growth in response to proportional N and K concentrations could be determined by NKI as well.  In addition, topdressing N fertilizer at the jointing stage significantly improved the N–K interaction effect on the N nutrition index (NNI) and NKI at the booting stage (P<0.05), but it had no significant N–K interaction effect on the KNI.  All indicators at the heading stage demonstrated the best predictive capability for relative yield (RY) compared to other stages.  Compared with NNI and KNI, the prediction accuracy of yield with NKI improved by 11.63 and 17.44%, respectively.  The NKI has better performance in diagnosing N and K nutrition and predicting yield under N–K interactions than either NNI or KNI.  This result enhances our understanding of the effects of N–K interactions on wheat growth and has important applications for improving the accuracy of N and K nutrition diagnosis and yield prediction.

To evaluate the impact of climate change on maize production, accurately measuring the radiation use efficiency (RUE) of maize is critical.  This study focused on three maize cultivars in Jilin Province, China: Zhengdan 958 (ZD958), Xianyu 335 (XY335), and Liangyu 99 (LY99).  Under the optimal growing conditions for high density planting (9 plants m^–2), the maize RUE was determined during the vegetative and reproductive phases, and the entire growth period.  The results showed that the canopy light interception for maize peaked during anthesis.  After anthesis, maize plant biomass continued to accumulate.  The maize RUE was calculated based on the absorbed photosynthetically active radiation (APAR).  During the entire growth period, maize RUE averaged 5.71 g MJ^–1 APAR among the three cultivars, with a high-to-low order of ZD958 (5.85 g MJ^–1 APAR)>XY335 (5.64 g MJ^–1 APAR)>LY99 (5.07 g MJ^–1 APAR).  Within the vegetative and reproductive growth periods, maize RUE averaged 6.85 and 5.64 g MJ^–1 APAR, respectively.  When utilizing maize models that depend on RUE to predict aboveground biomass accumulation, such as APSIM, the current RUE value of 3.6 g MJ^–1 APAR is considerably lower than the measured value obtained under high-density optimal growing conditions.  Consequently, to derive the optimal potential yield for maize in such planting conditions, we recommend adjusting the RUE to a range of 5.07–5.85 g MJ^–1 APAR.

Timely and accurate forecasting of crop yields is critical for food management and trade.  However, only limited research has explored the impact of integrating crop phenotypic parameters (CPPs) with unmanned aerial vehicle (UAV) data across different phenological stages on maize yield prediction.  The extent to which multi-temporal data enhances the accuracy and reliability of yield projections compared to mono-temporal data has yet to be systematically investigated.  To attain the proper balance between accuracy and cost in crop yield estimation, this study proposed a structured framework for identifying the optimal phenological periods for summer maize yield prediction using UAV-based multispectral data.  Three classical methods of custom mean decrease accuracy (C-MDA), optimal parameters-based geographical detector (OPGD), and grey relational analysis (GRA) were first used to sort and screen both the CPPs and vegetation indices (VIs) derived from UAV-based information over six growth stages.  Ridge regression models based on multi-temporal data combinations and mono-temporal data were established separately, and their performance in yield prediction were compared to identify the optimal phenological stages and the corresponding key factors.  Our results showed that C-MDA was much better at factor screening and ranking compared to OPGD and GRA.  The green normalized difference vegetation index (GNDVI), normalized difference vegetation index (NDVI), and normalized difference red edge index (NDRE) emerged as the top-performing VIs, while the leaf area index (LAI) and above ground biomass (AGB) proved to be the most effective CPPs.  When predicting yield using only mono-temporal data, the dough stage delivered the highest predictive accuracy (R²=0.871, RMSE=0.407 t ha^–1), while the tasseling stage was the earliest that achieved yield estimates with acceptable precision (R²=0.810, RMSE=0.493 t ha^–1).  In contrast, the integration of UAV data from different crop growth stages markedly enhanced the accuracy of yield estimation.  Combinations of data from the tasseling, silking, and dough stages were recommended as the best option (R²=0.942, RMSE=0.291 t ha^–1).  These findings indicate that the precise estimation of maize yields in smallholder fields may be attainable, and present both substantial theoretical insights and practical benefits for the advancement of precision agriculture.

Flowering is a necessary condition and basis for yield in the life cycle of woody fruit trees.  Although there has been considerable interest in the regulatory mechanisms underlying floral induction and flowering, the associated epigenetic modifications remain poorly characterized.  We identified genome-wide DNA methylation changes and the transcriptional responses in axillary buds of ‘Qinguan’ (QA) and ‘Fuji’ (FA) varieties with contrasting flowering behaviors.  The DNA methylation levels were 19.35, 62.96 and 17.68% in FA, and 19.64, 62.49 and 17.86% in QA in the CG, CHG and CHH contexts, respectively.  The number of hypermethylated and hypomethylated differentially methylated regions (DMRs) in different regions contributed to significantly up- and downregulated gene expression.  DNA methylation can positively or negatively regulate gene expression depending on the CG, CHG and CHH contexts and their locations in different regions.  Additionally, the huge differences in transcription of MIKC^c-type MADS-box genes, and multiple flowering genes in multiple flowering pathways (i.e., light, aging, GA and sugar) by changing DNA methylation, contributed to contrasting flowering behaviors in both QA and FA.  Specifically, the floral meristem identity genes (i.e., FT, LEAFY, AP1 and SOC1) exhibited significantly higher expression in QA than FA, but the floral repressors (i.e., SVP, AGL15, and AGL18) showed the opposite trend.  Significant differences in multiple hormone levels were due to differentially expressed genes (DEGs) and their DMRs in hormone synthesis pathways, leading to both contrasting axillary bud outgrowth and flowering behaviors.  These findings reflect the diversity in the epigenetic regulation of gene expression and may be helpful for elucidating the epigenetic regulatory mechanism underlying the axillary bud flowering in apple.

Vapor pressure deficit (VPD), defined as the difference between the actual water vapor pressure and the saturation vapor pressure in the air, is a core indicator of atmospheric aridity.  High VPD induces intensified water loss via plant transpiration, thereby constraining water uptake and photosynthetic capacity.  The dynamic functions and molecular regulatory mechanisms of plasma membrane intrinsic proteins (PIPs), key aquaporins mediating rapid transmembrane water transport, remain unclear during plant responses to high VPD stress.  In this study, we elucidated the regulatory role of SlPIP1;7 in regulating the multi-level adaptation strategy of tomato (Solanum lycopersicum) at the morphological, physiological, and molecular levels under high VPD conditions.  The results indicate that, compared to wild-type (WT) plants, SlPIP1;7 overexpressing (OE) plants exhibit superior growth performance under high VPD conditions.  The overexpression of SlPIP1;7 significantly enhances the reactive oxygen species (ROS) scavenging efficiency, effectively protecting plant cells from oxidative damage.  This protective mechanism for maintaining ROS homeostasis is closely associated with stomatal function.  The overexpression of SlPIP1;7 can regulate stomatal morphology, size, and aperture dynamics, thereby promoting efficient utilization of water and carbon dioxide and enhancing the overall physiological regulatory capacity of plants under stress conditions.  Additionally, we identified the ethylene response factor SlERF4 as an upstream regulatory factor in this adaptive network.  Yeast one-hybrid (Y1H) and dual-luciferase (LUC) assays demonstrate that the transcription factor SlERF4 can bind to the SlPIP1;7 promoter, enhancing its expression and functionality.  This interaction further underscores the pivotal role of SlPIP1;7 in combating high VPD stress.  In summary, our study elucidates the crucial function of SlPIP1;7 in plant response and acclimation to high VPD stress.  These findings expand our understanding of the molecular mechanisms underlying plant acclimation to environmental stresses and provide a reference for future breeding strategies aimed at developing drought-resistant crops.

Bacterial blight (BB) of rice caused by the phytopathogenic bacterium Xanthomonas oryzae pv. oryzae (Xoo) is a disease of global importance.  Xoo utilizes the type III secretion system (T3SS) and its effectors for virulence, and XopM is a conserved T3SS effector in Xanthomonas spp.  However, the virulence function of XopM is largely unknown.  In this study, we show that XopM contributes to Xoo virulence in rice.  We demonstrate that XopM interacts with allene oxide synthase OsAOS3, a key enzyme involved in jasmonic acid (JA) biosynthesis.  The expression levels of OsAOS3 and three homologues of OsAOS were elevated after Xoo infection.  Knockout mutants of OsAOS3 exhibited decreased JA accumulation and reduced resistance to Xoo and X. oryzae pv. oryzicola.  Moreover, JA-related defense genes were downregulated in osaos3 mutants during Xoo infection.  Based on our results, we propose a model showing how XopM hijacks OsAOS3 to interfere with JA-mediated defenses, leading to a suppression of rice immunity.  Our findings reveal a novel virulence strategy where Xanthomonas pathogens interfere with the JA pathway and modulate the host defense response.

The efficient colonization of plant-beneficial Pseudomonas spp. is a prerequisite for their biocontrol capacity.  Prior work revealed that the PcoI/PcoR quorum-sensing (QS) system plays a pivotal role in the root colonization of P. fluorescens 2P24.  During the colonization, strain 2P24 has faced diverse impacts from plant-derived reactive oxygen species and other environmental stress.  However, the molecular mechanism by which the PcoI/PcoR QS system is regulated under unfavored conditions remains unclear.  Thus, in this study, the role of the (p)ppGpp synthetase RelA and the bifunctional (p)ppGpp synthase/hydrolase SpoT in the PcoI/PcoR QS system of P. fluorescens was investigated.  Our data indicated that the deficiency of relA and spoT genes remarkably improved the expression of the pcoI gene, whereas the mutation of the spoT gene significantly repressed the expression of the pcoI gene.  We further demonstrated that the regulation of the PcoI/PcoR QS system by (p)ppGpp was dependent on the function of the trmE gene, which encodes a tRNA modification GTPase.  Furthermore, the mutation of relA, spoT, or both significantly influenced the motility, biofilm formation, oxidative stress, osmotic tolerance, and rhizosphere colonization.  Collectively, our data indicated that the (p)ppGpp signaling pathway mediated by the relA gene and spoT gene was important to the function of the PcoI/PcoR QS system and had important implications for the understanding of the molecular mechanism of (p)ppGpp in epiphytic fitness via TrmE of P. fluorescens.

Root-knot nematodes (RKNs) are the most economically damaging plant-parasitic nematodes globally.  Xinjiang, encompassing one-sixth of China’s landmass, currently lacks comprehensive data regarding the occurrence, distribution, and genetic variation of RKNs infecting vegetables within its borders.  Hence, identifying RKNs species and genetic diversity is crucial for devising comprehensive management strategies.  Between 2021 and 2023, we present a survey of 130 samples, collected from 86 counties across 14 cities in Xinjiang, China, aiming to comprehensively understand the occurrence, distribution, damage, and species of vegetable RKNs.  The results indicated that 57 out of 130 samples collected from the regions of Hami, Tulufan, Ili, Bayingol, Hotan, Aksu, Kashgar, and Kizilsu in Xinjiang were infected by RKNs, suggesting an expansion of RKN disease in the vegetable-producing regions of Xinjiang.  The infected vegetable roots were found to harbor Meloidogyne incognita and M. hapla, with M. incognita being the most prevalent species.  A phylogenetic analysis of the COI region revealed significant evolutionary divergence between M. incognita populations from Xinjiang and those from southeastern provinces.  Haplotype analysis of the COI gene revealed that M. incognita populations are categorized into three major lineages: Asia, Europe, and a combined lineage encompassing both America and Africa.  Notable gene flow patterns were observed among M. incognita populations, with significant migrations from Europe and America to Asia, specifically from the southeastern China towards Xinjiang.  This study’s findings indicate a consistent increase in the detrimental effects of vegetables production caused by RKNs in Xinjiang.  Implementing effective prevention and control measures is crucial to mitigate the spread of RKNs.

The diamondback moth, Plutella xylostella represents a worldwide threat to Brassicaceae crops and has developed substantial resistance to conventional insecticides.  Entomopathogenic fungi (EPF) have emerged as environmentally sustainable alternatives to chemical insecticides.  Since insect immunity constitutes the primary defense against fungal pathogens, understanding these mechanisms could advance biocontrol strategies.  Nevertheless, research on the immune functions of galectins in insects remains limited.  This study identifies a Galectin-4 homolog in P. xylostella (PxGalectin-4) and systematically examines its immunological functions against an EPF Isaria cicadae infection.  The open reading frame of PxGalectin-4 encoded 338 amino acids with a carbohydrate recognition domain (CRD).  PxGalectin-4 expression exhibited peak levels in late-instar larval stages and fat body, and increased significantly following I. cicadae challenge.  Functional characterization demonstrated that recombinant PxGalectin-4 (rPxGalectin-4) directly bound cells and cell wall components of microbes, and displayed Ca²⁺-dependent microbial agglutination.  Additionally, rPxGalectin-4 enhanced hemocyte-mediated immune responses by promoting nodulation and encapsulation, and increased phenoloxidase activity of hemolymph.  Knockdown of PxGalectin-4 significantly increased the susceptibility of P. xylostella larvae to I. cicadae infection.  In conclusion, PxGalectin-4 serves a vital immune function in P. xylostella defense against I.cicadae, and presents a potential target for novel pest control strategies.

The development of novel stimuli-responsive pesticide delivery systems is a highly effective strategy for improving pesticide utilization efficiency while minimizing environmental risks.  A pH-, glutathione-, and chitinase-responsive pesticide delivery system (PYR@MONs-COS) was designed by conjugating chitosan oligosaccharide (COS) with biodegradable disulfide bond-bridged mesoporous silica nanoparticles (MONs) loaded with pyraclostrobin (PYR).  The loading capacity of PYR in the nanoparticles was approximately 13.6%.  The covalent attachment of COS to the modified MONs could effectively protect the active ingredient from photodegradation and prevent premature release of PYR.  During the infection process, physiological and biochemical changes at the infection site, including reduced pH values, increased glutathione levels, and enhanced chitinase activity, facilitated the rapid degradation of disulfide bonds and COS in PYR@MONs-COS, resulting in the rapid release of PYR.  Furthermore, PYR@MONs-COS significantly enhanced the foliar penetration of PYR, improved the adhesion of pesticide droplets, and stimulated callose deposition in rice leaves, thereby enhancing rice immunity.  In antifungal activity assays, PYR@MONs-COS exhibited superior efficacy and prolonged efficacy against Magnaporthe oryzae compared to PYR microcapsules in both in vitro and in vivo experiments.  The phytotoxicity assessment indicated that PYR@MONs-COS was safe for rice plants.  More importantly, PYR@MONs-COS demonstrated a 7.3-fold reduction in acute toxicity to zebrafish compared to PYR technical.  Therefore, the triple-stimuli pesticide delivery system has great potential for rice disease management and provides a promising pathway for the development of sustainable agriculture.

Streptococcus suis is a significant zoonotic agent affecting both human and pig health and poses a substantial public health concern. The pathogenicity of S. suis is intricately linked to its ability to form biofilms and express virulence factors, which are regulated by the LuxS/AI-2 quorum sensing (QS) system. Herein, we uncover a novel therapeutic avenue by demonstrating that 5-fluorouracil (5-FU), an FDA-approved anti-cancer agent, effectively mitigates biofilm formation and attenuates the virulence of S. suis. Mechanistically, we observe a significant reduction in capsular polysaccharide and extracellular polysaccharide production upon 5-FU treatment, elucidating a potential mechanism for biofilm weakening. Additionally, 5-FU down-regulates virulence traits, diminishing S. suis's ability to adhere to host cells and evade phagocytosis. Crucially, our study identifies the thymidylate synthase regulatory gene thyA as a key mediator of 5-FU's effects on the LuxS/AI-2 QS system. Virtual molecular docking and gene knockout experiments provide compelling evidence that 5-FU modulates the LuxS/AI-2 QS system by targeting thyA. In vivo experiments further validate the therapeutic potential of 5-FU, showcasing a significant reduction in bacterial load and mitigation of tissue damage in a mouse model. In conclusion, our investigation unveils 5-FU as a potent disruptor of S. suis's biofilm formation and virulence, offering a promising avenue for the control of this devastating pathogen.

Newcastle disease virus (NDV) is a highly lethal and contagious viral pathogen, and it is also a potent oncolytic virus that selectively replicates in tumor cells.  NDV demonstrates high replication efficiency in avian and tumor cells, causing various types of cell death, including ferroptosis, necrosis, apoptosis and autophagic cell death, with apoptosis being the most thoroughly studied.  Organelles play critical and distinctive roles in the regulation and execution of apoptosis.  However, the involvement of peroxisomes, an important organelle that regulates redox balance and lipid biosynthesis, in virus-induced apoptosis remains unclear.  Our findings reveal that NDV infection promotes the downregulation of several peroxisome biogenesis factors (PEXs) at the mRNA level.  Peroxisomal biogenesis factor 5 (PEX5), a critical peroxisomal shuttle protein, was identified to be significantly downregulated at both the mRNA and protein levels.  Further, gain- and loss-of-function experiments demonstrated the negative regulation of NDV-induced apoptosis by PEX5.  In addition, PEX5 inhibits NDV-induced apoptosis by regulating the anti-apoptotic protein B-cell lymphoma-2 (Bcl-2) expression.  These findings reveal a novel mechanism by which NDV-induced apoptosis is modulated through the downregulation of PEXs, particularly PEX5, shedding light on the potential role of peroxisome in apoptosis regulation in response to virus infection.

Computer vision is widely recognized as an influential technology in the field of precision management of animals.  Emerging studies have demonstrated the potential to improve pig health and welfare through animal surveillance systems and computer vision (CV) algorithms.  However, the lack of benchmark datasets and robust fundamental algorithms restrict CV applications for the commercial use.  This study aims to bridge the gap between technology development and commercial applications in pig farming scenarios by introducing a general-purpose dataset (PigLife), comparing benchmark performances of foundational CV algorithms and model development workflows.  The PigLife dataset contains video clips and images (38 short video clips, 2K image frames, 22K pig instances) across most pig production phases in a typical commercial pig farm: Breeding and Gestation, Farrow to Wean, Weaning & Nursery, and Growth to Finish.  Three detection algorithms (Faster R-CNN, RetinaNet, TridentNet) and three segmentation algorithms (Mask R-CNN, MViTv2, PointRend) were trained on the PigLife dataset from scratch.  Fine-tuning of pre-trained models (YOLO8-m, Faster-RCNN-r50) and no-training from zero-shot models (CLIP-SAM, Grouddino-HQSAM) were also evaluated to suggest faster CV development workflows for commercial applications in pig farming.  This study emphasizes the necessity of a benchmark dataset for evaluating the robustness of algorithms and identifying the remaining difficulties and challenges across various algorithms.  Furthermore, developing CV models from pre-trained algorithms or zero-shot models showed better performance and a faster process, which could reduce barriers when developing high-performance CV products in pig production industry.

The integration of green manure (GM) crops into traditional cropping systems has regained attention for its potential to improve soil organic carbon (SOC) content in an environmentally sustainable way. However, the effects of carbon (C) input from different GM species on the SOC accumulation and recalcitrant C fractions across soil profile remain inadequately understood. This three-year North China Plain study assessed SOC changes and C fractions of easily oxidizable organic carbon (EOC) and recalcitrant organic carbon (ROC) in fallow, rye, rapeseed, and vetch systems, with δ¹³C analysis for GM-derived C fraction and microbial C-decomposition functional genes. Our results show that SOC was significantly increased by GMs. Rapeseed was the only species that improved SOC at 20-40 cm, the rapeseed-derived C contributed 2.48% of the SOC. Rye enhanced EOC and ROC at topsoil, rapeseed increased ROC at 20-60 cm, and vetch increased EOC at 40-60 cm. At the topsoil, the abundances of cellulose- and pectin-decomposition gene were increased in vetch and decreased in rye. At 20-40 cm, the pectin- and lignin-decomposition genes were markedly improved by rapeseed, while at 40-60 cm, the chitin-decomposition gene was increased in vetch, indicating the microbial promoting effects by deep roots of vetch and rapeseed. Our results suggest GM species influence SOC deposition depth and the recalcitrance of SOC decomposition, thereby affecting the distribution of SOC accumulation through microbial-driven C decomposition activities.

Home plant–soil feedbacks (home-PSFs) typically demonstrate negative effects in vegetable crops, substantially inhibiting their growth.  Phosphorus (P), an essential plant nutrient crucial for growth, influences vegetable crop growth patterns through soil availability levels.  However, the relationship between soil available P levels and home-PSFs in vegetable crops requires further investigation.  This study established a home PSF system incorporating 12 vegetable crops from 6 families to examine growth responses under two P conditions (low P level: 40 mg P kg^–1 soil; high P level: 200 mg P kg^–1 soil).  The findings revealed that low P conditions significantly decreased overall biomass across all vegetables, with preferential biomass allocation to root development.  Furthermore, low P conditions enhanced mycorrhizal colonization and rhizosphere acid phosphatase activity while notably decreasing root length.  While vegetables generally exhibited negative home PSFs, allium and nonmycorrhizal plants demonstrated positive responses under high P conditions.  Wild tomatoes displayed greater variation in feedback values across P levels compared to common tomatoes.  Under high-P conditions, mycorrhizal colonization showed positive correlations with feedback values of biomass and P concentration.  Root diameter and mycorrhizal colonization demonstrated distinct correlations with these feedback values under low-P conditions.  The research concludes that high P levels effectively mitigate negative home-PSFs in vegetables while increasing biomass production.  Additionally, high P levels demonstrated superior efficacy in alleviating negative home-PSFs in wild tomatoes compared to common tomatoes.

Phosphorus (P) availability influences the spatial distribution of carbon (C)-cycling enzyme activities in the rhizosphere through its effects on plant growth and microbial activity.  However, the influence of P availability on the spatial patterns of C and P hydrolase activities remains unclear in the rhizosphere of Maize (Zea mays L.) and narrow-leaf lupine (Lupinus angustifolius L.), which exhibit contrasting P deficiency adaptation and acquisition strategies.  This study analyzed the spatial patterns of C and P hydrolase activities through zymography and correlated them with bacterial community structure in maize and lupine rhizospheres.  Under P-deficient conditions, maize exhibited severe growth restriction while demonstrating a 2.2–9.6-fold increase in root exudation compared to P-sufficient conditions.  The enhanced exudation under P deficiency promoted r-strategist bacterial proliferation (e.g., Ktedonobacteria and Xanthomonadales) while reducing K-strategist abundance (Actinobacteriota, Chloroflexia, and Alphaproteobacteria).  Maize rhizosphere enzyme activities and hotspot areas demonstrated positive correlation with K-strategist abundance and negative correlation with r-strategist abundance.  P-sufficient maize exhibited 15–550% higher C- and P-cycle-related enzyme activity and hotspot areas, attributed to its enhanced root system and predominance of K-strategists with superior enzyme synthesis capabilities.  Lupine demonstrated superior P deficiency adaptation, producing 2–19 times more DOC and organic acids than maize.  Consequently, lupine showed no significant alterations in enzyme activity, hotspot areas, or bacterial community composition in response to P availability.  These findings demonstrate that plant-specific P deficiency adaptation mechanisms distinctly influence the spatial distribution of C-cycling enzyme activity and bacterial community structure in the rhizosphere.

Denitrification plays a critical role in mitigating anthropogenic nitrate (NO₃^–) accumulation in ecosystems.  The isotopic composition of NO₃^– (δ¹⁵N and δ¹⁸O) serves as a powerful tracer for identifying N sources and transformation processes.  Denitrification often superimposed on the isotope effects of NO₂^– oxidation, resulting in parallel enrichment of δ¹⁵N- and δ¹⁸O-NO₃^– (Δδ¹⁸O:Δδ¹⁵N trajectory) that causes them to be either below or above 1.  This study compared the Δδ¹⁸O:Δδ¹⁵N trajectory during denitrification, functional genes (narG, napA, and nxrA), and carbon sources from metabolites in the Δδ¹⁸O:Δδ¹⁵N trajectories below or above 1 in unsaturated zones.  The results revealed that NO₃^– reduction was more important for variation in the Δδ¹⁸O:Δδ¹⁵N trajectory because the difference in isotope effects (¹⁵ε_{NO3 reduction} and ¹⁸ε_{NO3 reduction}) between the two Δδ¹⁸O:Δδ¹⁵N trajectory groups was significant, whereas the difference in isotope effects (¹⁵ε_nxr and ¹⁸ε_nxr) upon NO₂^– oxidation was not.  Carbon sources in the group with Δδ¹⁸O:Δδ¹⁵N trajectories below 1 facilitated more efficient electron production to promote NO₃^– reduction because of their low molecular weight and simple structure.  Conversely, the lower electron production efficiency due to the high molecular weight and complex structures of carbon sources in the group with Δδ¹⁸O:Δδ¹⁵N trajectories above 1 downregulated the expression of the three functional genes (narG, napA, and nxrA).  The group with Δδ¹⁸O:Δδ¹⁵N trajectories below 1 showed significantly higher levels of ¹⁵ε_{NO3 reduction}, ¹⁸ε_{NO3 reduction}, NO₂^– oxidation ratio, and copy numbers of narG, napA, and nxrA genes compared to the other group, revealing that NO₃^– reduction at the cellular level was more active in the former group.  This study elucidated the integrated influence of isotope effects, NO₃^– reductase and NO₂^– oxidoreductase activities, and carbon sources from metabolites.  These findings are significant for understanding the Δδ¹⁸O:Δδ¹⁵N trajectories of N cycling in terrestrial ecosystems and support groundwater conservation by improving carbon supplementation approaches that stimulate denitrification, with Δδ¹⁸O:Δδ¹⁵N trajectories serving as effective tracers for assessing denitrification performance in terrestrial environments.

Composting represents a crucial component of sustainable waste management, providing significant resource recovery and environmental advantages.  However, nitrogen loss during composting remains a significant challenge, necessitating the development of a predictive model for nitrogen loss during the composting process.  This investigation implemented five machine learning models, utilizing 307 data points encompassing composting strategies, physicochemical properties, and composting time stages, to predict nitrogen loss during organic solid waste composting.  The findings demonstrated that the adaptive boosting (AdaBoost) algorithm achieved optimal performance with a coefficient of determination of 0.847 after eliminating redundant features (scale and C/N).  Moreover, Shapley additive explanation analysis identified several key factors significantly influencing nitrogen losses during composting, including composting time stages, bulking agents, raw materials, and ammonium nitrogen levels.  Notably, the initial phase of composting emerged as the most critical period for nitrogen loss.  The utilization of sawdust, rice husk, and corn stalk as bulking agents enhanced nitrogen retention in compost.  Furthermore, implementing static aeration for ventilation and applying chemical additives effectively reduced nitrogen losses during the composting process.  These results provide a scientific foundation for identifying optimal composting conditions to minimize nitrogen loss, thereby offering practical guidance for effective composting operations.

Soil salinization represents a primary manifestation of land degradation and presents a significant threat to sustainable agricultural development.  Remote sensing-based methodologies currently constitute the preferred approach for salinization monitoring.  Environmental factors’ spatial heterogeneity substantially constrains the modeling process in accurately capturing the soil salt content (SSC)-modeling factor relationship, thereby affecting monitoring accuracy.  This study proposes a classification modeling framework based on dominant salinization factors, establishing distinct remote sensing inversion models through categorization of soil texture and surface drainage conditions.  Results indicate that classification modeling substantially improves the capture of SSC-modeling factor relationships.  The efficacy of identical modeling indicators and methods varies significantly across different classification scenarios.  Among the three modeling approaches, random forest demonstrates superior overall robustness.  Of the three variable selection methods, light gradient boosting machine (LightGBM) shows the strongest compatibility with the modeling approaches.  The classification strategy significantly enhances model accuracy: compared to non-classified modeling (R²_V=0.62), the testing set R² increases by up to 24% (R²_V=0.77).  Models under poor surface drainage category demonstrate optimal performance, with coupled models achieving R²_C=0.82 (training set) and R²_V=0.77 (testing set).  This research provides valuable insights for remote sensing monitoring of soil salinization in precision agriculture contexts.