ABSTRACT Oat and Sesbania have complementary growth, enabling eco‐friendly rotation for autumn‐sown oat. Nevertheless, how different Sesbania utilization practices regulate soil conditions, crop productivity and comprehensive benefits remains poorly quantified. A four‐year field experiment was conducted at two sites (WJ and CZ), encompassing three cropping treatments: oat‐fallow (O + F, control), oat‐ Sesbania rotation with Sesbania harvested as forage (O + SFH) and oat– Sesbania incorporated as green manure (O + SGM). We measured soil physicochemical properties, oat yield, quality and economic benefits, and adopted partial least squares path modelling (PLS‐PM) to reveal driving mechanisms. Compared with O + F, O + SGM and O + SFH significantly increased soil organic matter (SOM) and total and available nutrients (N, P, K) in 0–20 cm soil at WJ. Oat dry matter yield (DMY) increased by 6.86–39.23% under O + SGM and 3.96–29.21% under O + SFH, consistent trends were found at CZ. Improved soil nutrient accumulation facilitated crop nutrient absorption and dry matter production. Both rotations boosted economic returns, with O + SFH demonstrating greater economic performance due to extra forage gains. This study quantitatively compares different Sesbania utilization modes in oat rotations and clarifies soil–crop‐economic synergistic mechanisms. The findings support region rotation schemes to lower chemical fertiliser input and improve land use efficiency.
ABSTRACT Using cryo‐electron microscopy and spectroscopic analyses, demonstrated that Chl f at the A −1B site of the far‐red photosystem I reaction centre directly participates in primary charge separation, thereby providing an atomic‐level structural basis for understanding photochemical reactions beyond the conventional red‐light region. This finding further offers potential design targets for engineering spectral expansion in crop photosynthesis.
ABSTRACT Sweetness is the most important determinant of the overall desirability of tomato (Solanum lycopersicum) fruits. In cultivated tomatoes, sweetness is mainly determined by the contents of fructose and glucose, which are the major carbohydrates in the fruits. In recent years, many key regulators of sugar accumulation in tomato fruit, including sugar metabolism enzymes, sugar transporters, transcription factors, and protein kinases, have been identified through map‐based cloning, genome‐wide association studies, and reverse genetics. A growing number of studies have also indicated that aromatic volatiles, such as apocarotenoid‐ and phenylalanine‐derived volatiles (PHEVs), can improve tomato sweetness independently of sugar content. In this perspective, we summarise recent advances in our understanding of sugar metabolism in tomatoes and discuss progress in identifying aromatic substances that can affect tomato sweetness. We also discuss challenges in increasing tomato sweetness and propose new breeding strategies to further improve sugar content and sweetness in tomatoes.
ABSTRACT Gum Arabic (GA) is an edible dried sticky exudate from the stems and branches of Acacia seyal and Senegal that is rich in non‐viscous soluble fibre and has been used widely in the food and pharmaceutical field. It has been used as an oral hygiene substance by communities in North Africa and the Middle East. However, the molecular mechanisms underlying its therapeutic effects, particularly the modulation of gene expression in metabolic disorders, remain underexplored. Therefore, the objective of this review was to review the potentiality of GA in gene expression for diabetic and obesity diseases and conditions as therapeutics. Different research articles have described that GA modified gene expression in Obesity, diabetes, inflammation, and fertility. Moreover, these GAs were capable of downregulating or upregulating different types of genes in other metabolic diseases such as infertility, insulin resistance, hyperglycemia, hyperlipidaemia, obesity, and type‐2 diabetes. Despite encouraging results, human translational data remain limited, and direct causal relationships among GA, chromatin remodelling, and metabolic gene networks necessitate additional clarification. This review emphasises the promise of GA as a functional food aimed at modulating gene expression to improve diabetic and obese phenotypes, while pinpointing essential knowledge gaps to inform future nutrigenomic research.
ABSTRACT Soil salinisation poses a global threat to agricultural sustainability, affecting about one billion hectares of farmland. This review highlights integrated strategies—combining water management, agronomic practices, and biochemical interventions—to mitigate salinity while improving overall productivity. Precision irrigation methods, such as subsurface drip and microsprinklers, raise water‐use efficiency by 25%–40% and reduce surface salt buildup. Agronomic approaches—deep tillage, land levelling, and organic or inorganic amendments—enhance soil structure and increase soil organic carbon by 18%–32%. Biochemical tools, including salt‐tolerant germplasm and rhizosphere microorganisms (e.g., plant growth‐promoting bacteria and arbuscular mycorrhizal fungi), can boost yields by up to 50%. Unlike prior reviews focussing on isolated tactics, this work emphasises interdisciplinary synergies, such as subsurface drip irrigation creating favourable conditions for microbial inoculants and salt‐tolerant crops. It also addresses key socioeconomic barriers, including high initial costs and technical expertise gaps, and proposes future research on landscape‐scale modelling, circular resource use, and climate‐resilient saline agroecosystems.
ABSTRACT Hairy root transformation has emerged as a promising method for assessing genome editing efficiency in dicot plants. However, the absence of a universally applicable protocol has posed a significant challenge. In our study, we addressed this challenge by investigating the impact of explant age and Agrobacterium rhizogenes strain. We employed five distinct Agrobacterium rhizogenes (A. rhizogenes) strains, namely K599, MSU440, Ar.1193, Ar.Qual and C58C1,to infect Arabidopsis seedlings at 2‐, 4‐, 6‐, 8‐ and 10‐day‐old stages, as well as seedlings of other dicot species at 1‐, 2‐ and 3‐day‐old stages. We successfully established a universal protocol, which utilizes explants derived from 2‐day‐old Arabidopsis seedlings and 1‐day‐old seedlings of other dicots, in conjunction with the A. rhizogenes strain K599. Leveraging this optimized system, we achieved reliable evaluation of gene‐editing vector efficiency in dicot plants prior to tissue culture, through the combined application of Sanger sequencing, PCR/restriction enzyme (PCR/RE) assay and deep sequencing techniques.
ABSTRACT As a vital production carrier of efficient and intensive modern agriculture, the plant factories with artificial lighting (PFALs) represents an important application scenario for the development of smart agriculture. To achieve autonomy and intelligence in environmental control, crop growth management and production decision‐making in PFALs, we developed an artificial intelligence agent (AI Agent), Chuiyuan (垂元), tailored for PFALs scenarios. Chuiyuan integrates the capabilities of large language models (LLMs) in semantic understanding, logical reasoning and interaction, and the technical advantages of deep learning in feature extraction and predictive modelling. Through LLMs, knowledge interaction in PFALs is realized, endowing Chuiyuan with professional consultation and continuous iterative optimization capabilities. In a comparative evaluation involving 104 PFAL‐related questions, Chuiyuan achieved a high score compared to a general large model. This paper further discusses the development paths and potential values of future research on PFALs AI Agent. AI Agents are expected to enhance operational efficiency, resource utilization, and production stability in PFALs. It provides a scalable intelligent decision‐making framework for digital transformation and smart agriculture applications, and offers meaningful insights for advancing controlled‐environment agriculture towards high‐efficiency, precise, and unmanned operation.
ABSTRACT Plant factories are an innovative agricultural model that leverage controlled environments and advanced regulation technologies to improve land‐use efficiency and reduce resource dependence. However, their development is constrained by high energy consumption. Given the high costs and environmental impacts associated with fossil‐fuel‐based electricity, renewable energy sources such as solar power have emerged as promising alternatives. In this study, a model vertical plant factory consisting of 20 stories (area = 100 m2) was applied to 21 Chinese cities with populations exceeding 5 million. Three power supply modes were considered: a grid‐powered system, a standalone solar‐powered system, and a grid‐solar hybrid system. The net present cost (NPC), levelized cost of energy (COE), and carbon dioxide emissions were assessed for each mode. Among the three systems, the hybrid system substantially reduced economic costs (40.87%–65.68% lower NPC than the grid‐powered system), whereas the standalone solar‐powered system most effectively reduced carbon dioxide emissions (85.99%–97.93% lower than the grid‐powered system). By comprehensively analysing solar resources, system design, economic indicators, and emission reduction benefits, this study provides scientific evidence to support decision‐making and implementation of vertical agriculture farming projects, promoting the coordinated advancement of agriculture and environmental protection.
ABSTRACT To identify critical knowledge gaps and define strategic research priorities for microplastic and nanoplastic contamination in agricultural soils. Current evidence on agricultural microplastics was critically synthesized, with emphasis on analytical methodologies, environmental fate, ecological effects, plant uptake, food‐chain transfer, and remediation approaches. Key knowledge gaps were evaluated to formulate priority research questions. Ten essential scientific questions were identified, spanning four thematic areas: extraction and quantification, environmental realism and ecosystem effects, plastic–pollutant–microbe interactions, and crop uptake, human exposure, and mitigation strategies. Major challenges include the lack of standardized analytical protocols, limited understanding of long‐term environmental behavior and biological impacts, uncertainties regarding biodegradable plastics, and the absence of scalable remediation technologies. Future agricultural microplastic research should move beyond contamination surveys toward mechanistic understanding, predictive assessment, and field‐validated mitigation strategies. Addressing these priorities is critical for protecting soil health, food security, and environmental sustainability.
ABSTRACT Pyropia/Porphyra algae are an important mariculture species, yet genome‐wide association studies (GWAS) on agronomic traits remain scarce. To bridge this gap, we conducted a comprehensive genetic analysis of 280 double‐haploid offspring strains derived from a previously constructed population. Eleven phenotypic traits—including thallus length, width, fresh weight, and maturity days—were systematically recorded, with heritability ranging from 0.08 to 0.39, except for thickness (0.57). Whole‐genome sequencing and GWAS identified 454 candidate genes (37 annotated), with major loci predominantly clustered on chromosome 4, collectively explaining 7.00%–58.02% of phenotypic variation. Sequence site validation revealed that RPN5 (encoding 26S proteasome regulatory subunit N5) might control thallus length, a C/G mutation at Chr4:4,319,644 (G for long, C for short); DWF1 (encoding Delta24‐sterol reductase) might regulate thallus maturation, a T/G mutation at Chr4:4,485,590 (T for early, G for late). Additionally, physiological phenotypic analysis combined with real‐time quantitative PCR suggested that PhmIDH might regulate thallus thickness by controlling the synthesis of cell wall components. These findings provide functional markers and mechanistic insights to advance marker‐assisted breeding and genetic improvement in Pyropia haitanensis.
ABSTRACT Global food security demands innovative strategies to enhance crop resilience and productivity amidst escalating environmental pressures. While genome‐wide association studies (GWAS) have proven powerful in identifying genetic variants underlying complex agronomic traits, their resolution is often limited by the polygenic nature of traits and genotype‐by‐environment (G × E) interactions. The integration of multi‐omics technologies—including genomics, transcriptomics, proteomics, and metabolomics—with GWAS provides a transformative framework to decipher the molecular mechanisms governing stress adaptation and growth regulation. This review critically examines the synergistic potential of multi‐omics‐augmented GWAS in elucidating genetic architectures, uncovering candidate genes, and reconstructing regulatory networks. We highlight computational and experimental strategies for data integration, address persistent challenges such as polygenic trait dissection and environmental contextualisation, and discuss emerging opportunities through single‐cell omics and machine learning. This multi‐omics‐augmented approach significantly boosts GWAS resolution to uncover candidate genes and reconstruct regulatory networks, thereby addressing the persistent challenges of polygenic trait dissection and environmental contextualisation.
Cotton, as a globally significant economic crop, necessitates accurate yield prediction for field management and market regulation. This study aims to enhance the precision of cotton yield prediction by integrating hyperspectral feature fusion techniques with machine learning modelling approaches. A method widely used in finance and stock markets (MF-DFA) is introduced into the agricultural sector to extract multifractal features from cotton canopy spectra. These features are fused with other spectral characteristics, and machine learning algorithms are employed to construct yield prediction models. The results indicate that (1) building prediction models based on fused features effectively improves the accuracy and stability of cotton yield predictions, outperforming models based on single features; (2) the fusion of Vegetation Index (VI) and Fractal Parameters (FP) features yields the best prediction results during the flower and boll period, with R 2 values of 0.6681 and 0.7009 for two consecutive years, respectively; (3) the Random Forest modelling approach exhibits higher prediction accuracy, and feature fusion parameters such as VI&FP and VI&FP&TR also perform well when applied to other model methods, demonstrating excellent generalisation capabilities. In conclusion, the prediction model based on hyperspectral feature fusion and machine learning provides a new and effective approach for cotton yield prediction.
The enhancement of irrigation infrastructure is essential for boosting agricultural productivity, particularly in developing nations. Nevertheless, the accumulation of sediment in irrigation channels presents a major obstacle, reducing water flow efficiency and escalating maintenance expenses. This study introduces a numerical model designed for the hydrodynamic removal of bed sediment in an open irrigation channel located in Guanajuato, Mexico. Fluid dynamics are resolved using a finite difference method to simulate three-dimensional velocity fields, while sediment transport is analysed through the particle-in-cell method (PICM). The model incorporates key factors influencing particle behaviour, including velocity field, turbulent dispersion, and particle-specific properties such as size, shape, and settling velocity. Validation experiments were performed in a laboratory setting, utilizing a 1:20 scale irrigation channel with three distinct configurations of submerged structures. The results confirmed the model's capability to predict sediment transport, pinpoint erosion and deposition zones, and evaluate alterations in the hydraulic cross-section. These outcomes underscore the critical role of effective sediment management in ensuring the optimal performance and maintenance of irrigation channels.
Microplastics (MPs) have drawn great interest in aquaculture studies due to their health effects. But the underneath relationships among MPs and other environmental factors are still unclear, particularly the synergetic health effects with environmental and fish microbes. To respond, we have investigated the impacts of MPs on tilapia along with microorganisms in the biofloc technology (BFT) system. Our results demonstrate that MPs reduce probiotic levels while increasing pathogenic bacteria in both the biofloc technology (BFT) system and in tilapia. In addition, a 38.24% reduction in tilapia gut microbiota was observed following MP exposure. This study elucidates that MPs impair fish health primarily through compromised water quality, disrupted intestinal microbiota, and induced oxidative stress. Consequently, we propose optimising suspended solid management to maintain probiotic stability and mitigate MP contamination. These findings provide a scientific basis for improving aquaculture environmental management and MP pollution control and advancing sustainable fisheries development.
Marginal agricultural lands play a critical role in global food security, especially for smallholder farmers in developing countries. However, these farmers are disproportionately affected by food insecurity and malnutrition and often rely on marginal lands for their livelihoods. These lands, characterized by poor soil quality, limited rainfall, steep slopes and other constraints, pose significant challenges such as low yields, high input costs, and environmental degradation. Although, there are suitable technologies for almost every marginal land, not many have used the appropriate technologies to harness the untapped potential in a sustainable manner. This perspective examines the key challenges of marginal agriculture while exploring emerging opportunities to boost both productivity and sustainability. We highlight innovations in agricultural technologies, the cultivation of alternative crops, climate change adaptation strategies, and incentives for sustainable practices that could transform areas into productive agriculture landscapes. Realizing this potential requires coordinated efforts of policymakers, researchers, plant breeders, farmers, and consumers. By tackling these challenges, we can improve smallholder livelihoods and make meaningful contributions towards global food security and sustainable development goals.
Plant phenomics has emerged as a critical bridge between genotype and phenotype, addressing a significant bottleneck in crop breeding and functional genomics studies. Hyperspectral imaging, a key technology in this field, has been instrumental in high-throughput, non-destructive phenotyping. Compared to other imaging technologies, hyperspectral imaging stands out for its continuous and fine spectral resolution, capturing subtle changes in plant biochemical and physiological states, which is essential for precise identification and analysis of plant characteristics. Recent advances in deep learning have further expedited hyperspectral data analysis, fostered multi-omics research and enhanced our ability to integrate diverse datasets. Despite challenges in establishing standards of data acquisition and processing, a significant proposal has emerged for the scientific community to collaboratively build a vast hyperspectral database. Integrated with reducing the cost of hyperspectral sensors and promoting more open-source analysis pipelines for hyperspectral data, these initiatives promise to lay the groundwork for robust big data analytics, potentially revolutionising plant research and breeding.
