Search Results (802)

Natamycin is widely used in other countries for the postharvest treatment of agricultural commodities to prevent fungal growth. However, since no MRL has been set in Korea, natamycin residues are regulated under the Positive List System (PLS) with a uniform limit of 0.01 mg/kg, requiring the development of highly sensitive and reliable analytical methods. In this study, a QuEChERS-based analytical method was developed and validated for the quantification of natamycin in five agricultural commodities—soybean, mandarin, hulled rice, green pepper, and potato—using liquid chromatography–tandem mass spectrometry (LC-MS/MS). Extraction using methanol with 3 g of MgSO₄ resulted in high recoveries without crystallization, and clean-up with MgSO₄ and C18 effectively reduced matrix interferences blow <50%. Natamycin was detected in all five matrices at 6.8 min without any interfering peaks. The MLOQ was determined at 0.01 mg/kg for all five matrices. The mean recoveries (82.2–115.4%) and %CV values (1.1–4.6%) values were within the acceptance criteria defined by the CODEX guidelines. Matrix effects were classified as “soft” for mandarin (|ME| < 20%) and “medium” for soybean, hulled rice, green pepper, and potato (20% ≤ |ME| < 50%). The analytical method for natamycin was validated as suitable for regulatory safety monitoring under the Korean PLS. Full article

Bioavailable phosphorus is essential for sustaining high crop productivity, yet excessive inorganic P fertilization often leads to P accumulation in stable soil forms, reducing utilization efficiency. Straw serves as an organic P source and enhances P availability by stimulating microbial activity. However, systematic studies on how organic P inputs (straw returning) and inorganic P fertilizers regulate soil bioavailable P through microbial and enzymatic processes remain limited. A 16-year field experiment was carried out in a rice–wheat rotation system, including five fertilization treatments: no fertilization (CK), optimized fertilization (OPT), increased N (OPTN), increased P (OPTP), and optimized fertilization combined with straw mulching/returning (OPTM). This study evaluates the impacts of long-term organic and inorganic P sources on soil P fractions, extracellular enzyme activities, and the composition of microbial communities, alongside their collective contributions to crop yield. In this study, based on soil samples collected in 2023, we found that fertilization led to significant increases in Citrate-P and HCl-P, enhanced the activities of β-1,4-glucosidase (BG), β-D-cellobiosidase (CBH), and β-1,4-N-acetylglucosaminidase (NAG), and altered both microbial diversity and co-occurrence network complexity. The OPTM treatment showed the highest yield and improved microbial diversity and network complexity, with Enzyme-P, Citrate-P, and HCl-P increasing by 62.64%, 11.24%, and 9.49%, and BG, CBH, and NAG activities rising by 22.74%, 40.90%, and 18.09% compared to OPT. Mantel tests and random forest analyses revealed significant associations between microbial community and biochemical properties, while partial least squares path modeling (PLS-PM) indicated that inorganic P source enhanced yield primarily through altering soil P dynamics and enzymatic processes, while microbial communities under organic P source acted as key mediators to increase crop productivity. These findings deepen insights into how microbial communities and enzymatic stoichiometry synergistically regulate phosphorus bioavailability and wheat yield, providing a theoretical basis for sustainable fertilization practices in rice–wheat rotation systems. Full article

In order to alleviate the constraints of global warming and sustainable development, digitalization has made significant contributions to promoting agricultural carbon reduction through resources, technology, and platforms. Under this situation, China insists on developing agricultural scale management. However, what impact will scale management in agricultural digital emission reduction have on mechanisms and pathways? Based on three rounds of follow-up surveys conducted by the Digital Countryside Research Institute of Nanjing Agricultural University in Jiangsu Province from 2022 to 2024, in this study a total of 258 valid questionnaires on the rice and wheat industry were collected. Methods such as member checking and audit trail were employed to ensure data reliability and validity. Using econometric approaches including Tobit, mediation, and moderation models, this study quantified the Scale Management Level (SML), examined the mechanism pathways of digital emission reduction in a scaled environment, further demonstrated the impact of scale management on digital emission reduction, and verified the mediating and moderating effects of internal and external scale management. We found that: (1) In scale and carbon reduction, the SBM-DEA model calculates that the scale of agricultural land in Jiangsu showed an “inverted S” trend with SML and an “inverted W” trend with the overall agricultural green production efficiency (AGPE), and the highest agricultural green production efficiency is 0.814 in the moderate scale range of 20–36.667 hm². (2) In digitalization and carbon reduction, the Tobit regression model results indicate that Network Platform Empowerment (NPE) significantly promotes carbon reduction (p < 1%), but its squared terms exhibit an inverted U-shaped relationship with agricultural green production efficiency (p < 1%), and SML is significant at the 5% level. From a local regression perspective, the strength of SML’s impact on the three core variables is: NPE > DRE > DTE. (3) Adding scale in agricultural digital emission reduction, the intermediary mechanism results showed that the significant intensity (p < 5%) of the mediating role of Agricultural Mechanization Level (AML) is NPE > DTE > DRE, and that of the Employment of Labor (EOL) is DRE > NPE > DTE. (4) Adding scale in agricultural digital emission reduction, the regulatory effect results showed that the Organized Management Level (OML) and Social Service System (SSS) significantly positively regulate the inhibitory effect of DRE and DTE on AGPE. Finally, we suggest controlling the scale of land management reasonably and developing moderate agricultural scale management according to local conditions, enhancing the digital literacy and agricultural machinery training of scale entities while encouraging the improvement of organizational level and social service innovation, and reasonably reducing labor and mechanization inputs in order to standardize the digital emission reduction effect of agriculture under the background of scale. Full article

Rice (Oryza sativa L.) is a salt-sensitive crop, where even moderate soil salinity (electrical conductivity ≥ 3.5 dS/m) can cause significant yield reduction. During the seedling stage, the underdeveloped root system has limited capacity for salt uptake and translocation, making root system architecture (RSA) a crucial trait for enhancing salinity tolerance. In this study, we used 165 individuals from the 3K Rice Genome Project to comprehensively measure multidimensional root morphological traits at the early seedling stage under salt stress, thereby overcoming the limitations of conventional methods that mainly rely on root length and biomass. We identified 78 quantitative trait nucleotides (QTNs) associated with eight root morphological traits through genome-wide association studies (GWAS) of 3VmrMLM. Among these, 12 QTNs co-localized within genomic regions of previously cloned salt tolerance-related genes. Additionally, six salt-tolerant lines were selected based on significantly increased root volume (RV) and surface area (SA), suggesting that their adaptive mechanism under salinity involves optimized spatial root distribution rather than radial thickening. Our findings show that high-resolution root scanning-based phenotyping provides a reliable platform for screening and breeding salt-tolerant rice varieties, offering valuable indicators for assessing seedling-stage salt tolerance. Full article

The nourishment of the human population depends on a handful of staple crops, such as maize, rice, wheat, soybeans, potatoes, tomatoes, and cassava. However, all crop plants are affected by at least one virus causing diseases that reduce yield, and in some parts of the world, this leads to food insecurity. Conventional management practices need to be improved to incorporate recent scientific and technological developments such as antiviral gene silencing, the use of double-stranded RNA (dsRNA) to activate an antiviral response, and nanobiotechnology. dsRNA with antiviral activity disrupt viral replication, limit infection, and its use represents a promising option for virus management. However, currently, the biggest limitation for viral diseases management is that dsRNA is unstable in the environment. This review is focused on the potential of nanoparticles and nanocarriers to deliver dsRNA, enhance stability, and activate antiviral gene silencing. Effective carriers include metal-based nanoparticles, including silver, zinc oxide, and copper oxide. The stability of dsRNA and the efficiency of gene-silencing activation are enhanced by nanocarriers, including layered double hydroxides, chitosan, and carbon nanotubes, which protect and transport dsRNA to plant cells. The integration of nanocarriers and gene silencing represents a sustainable, precise, and scalable option for the management of viral diseases in crops. It is essential to continue interdisciplinary research to optimize delivery systems and ensure biosafety in large-scale agricultural applications. Full article

Damping-off disease hinders rice seedling growth and reduces yield. Current control methods, such as seed or soil sterilization, rely on chemicals that cause environmental pollution and promote pathogen resistance. As a sustainable alternative, we targeted the damping-off resistance-related gene OsDGTq1 using CRISPR/Cas9. Field experiments first verified OsDGTq1’s significance in resistance. The CRISPR/Cas9 system, delivered via Agrobacterium-mediated transformation, was used to edit OsDGTq1 in rice cultivar Ilmi. Lesions from major damping-off pathogens, Rhizoctonia solani and Pythium graminicola, were observed on G₀ plants. All 37 regenerated plants contained T-DNA insertions. Among them, edits generated by sgRNA1-1, sgRNA1-2, and sgRNA1-3 resulted in the insertion of two thymine bases as target mutations. Edited lines were assigned names and evaluated for agronomic traits, seed-setting rates, and pathogen responses. Several lines with edited target genes showed distinct disease responses and altered gene expression compared to Ilmi, likely due to CRISPR/Cas9-induced sequence changes. Further studies in subsequent generations are needed to confirm the stability of these edits and their association with resistance. These results confirm that genome editing of OsDGTq1 alters resistance to damping-off. The approach demonstrates that gene-editing technology can accelerate rice breeding, offering an environmentally friendly strategy to develop resistant varieties. Such varieties can reduce chemical inputs, prevent pollution, and minimize seedling loss, ultimately enhancing food self-sufficiency and stabilizing rice supply. Full article

Advances in sensor technology have significantly improved the efficiency and precision of agricultural spraying. Unmanned aerial vehicles (UAVs) are widely utilized for applying plant protection products (PPPs) and fertilizers, offering enhanced spatial control and operational flexibility. This study evaluated the performance of an autonomous UAV-based precision spraying system that applies variable rates based on zone levels defined in a prescription map. The system integrates real-time kinematic global navigation satellite system positioning with a proximity-triggered spray algorithm. Field experiments on a rice field were conducted to assess spray accuracy and fertilization efficacy with liquid fertilizer. Spray deposition patterns on water-sensitive paper showed that the graded strategy distinguished among zone levels, with the highest deposition in high-spray zones, moderate in medium zones, and minimal in no-spray zones. However, entry and exit deviations—used to measure system response delays—averaged 0.878 m and 0.955 m, respectively, indicating slight lags in spray activation and deactivation. Fertilization results showed that higher application levels significantly increased the grain-filling rate and thousand-grain weight (both p < 0.001), but had no significant effect on panicle number or grain count per panicle (p > 0.05). This suggests that increased fertilization primarily enhances grain development rather than overall plant structure. Overall, the system shows strong potential to optimize inputs and yields, though UAV path tracking errors and system response delays require further refinement to enhance spray uniformity and accuracy under real-world applications. Full article

Plants have evolved a complex, multilayered immune system that integrates molecular recognition, signaling pathways, epigenetic regulation, and small RNA-mediated control. Recent studies have shown that DNA-level regulatory mechanisms, such as RNA-directed DNA methylation (RdDM), histone modifications, and chromatin remodeling, are critical for modulating immune gene expression, allowing for rapid and accurate pathogen-defense responses. The epigenetic landscape not only maintains immunological homeostasis but also promotes stress-responsive transcription via stable chromatin modifications. These changes contribute to immunological priming, a process in which earlier exposure to pathogens or abiotic stress causes a heightened state of preparedness for future encounters. Small RNAs, including siRNAs, miRNAs, and phasiRNAs, are essential for gene silencing before and after transcription, fine-tuning immune responses, and inhibiting negative regulators. These RNA molecules interact closely with chromatin features, influencing histone acetylation/methylation (e.g., H3K4me3, H3K27me3) and guiding DNA methylation patterns. Epigenetically encoded immune memory can be stable across multiple generations, resulting in the transgenerational inheritance of stress resilience. Such memory effects have been observed in rice, tomato, maize, and Arabidopsis. This review summarizes new findings on short RNA biology, chromatin-level immunological control, and epigenetic memory in plant defense. Emerging technologies, such as ATAC-seq (Assay for Transposase-Accessible Chromatin using Sequencing), ChIP-seq (Chromatin Immunoprecipitation followed by Sequencing), bisulfite sequencing, and CRISPR/dCas9-based epigenome editing, are helping researchers comprehend these pathways. These developments hold an opportunity for establishing epigenetic breeding strategies that target the production of non-GMO, stress-resistant crops for sustainable agriculture. Full article

Accurate rice yield prediction is essential for optimizing water management and supporting decision-making in agricultural systems, particularly in arid environments where irrigation efficiency is critical. This study assessed five machine learning algorithms—Multiple Linear Regression (MLR), Support Vector Regression (SVR, linear and RBF), Partial Least Squares Regression (PLSR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—for plot-scale rice yield estimation using Sentinel-2 vegetation indices (VIs) during the 2022 and 2023 seasons in the Chancay–Lambayeque Valley, Peru. VIs sensitive to canopy vigor, water status, and structure were derived in Google Earth Engine and optimized via Sequential Forward Selection to identify the most relevant predictors per phenological stage. Models were trained and validated against field yields using leave-one-out cross-validation (LOOCV). Intermediate stages (Flowering, Milk, Dough) yielded the strongest relationships, with water-sensitive indices (NDMI, MSI) consistently ranked as key predictors. MLR and PLSR achieved the highest generalization (R²_CV up to 0.68; RMSE_CV ≈ 1.3 t ha⁻¹), while RF and XGBoost showed high training accuracy but lower validation performance, indicating overfitting. Model accuracy decreased in 2023 due to climatic variability and limited satellite observations. Findings confirm that Sentinel-2–based VI modeling offers a cost-effective, scalable alternative to UAV data for operational rice yield monitoring, supporting water resource management and decision-making in data-scarce agricultural regions. Full article

Annual rice growing lands are mainly threatened by soil loss. High-yielding perennial rice cultivars with great socioeconomic values are developed to stabilize fragile rice farms. Nitrogen balance in perennial rice fields can be facilitated by its no-tillage-based management system. However, systematic studies on nitrogen transformation and its distribution pattern are lacking. This study has therefore been conducted to look for the merits of no-tillage-based perennial rice farming on maintaining balanced nitrogen under perennial rice field conditions. From 2021 to 2023, a field experiment was conducted for six successive seasons, and the effect of no-tillage-based perennial rice plantation on apparent nitrogen balance was assessed. Plant nitrogen dry matter production efficiency and nitrogen recovery efficiency under the perennial rice production system were higher than the annual rice farming system by 10.32% (p < 0.05) and 14.17% (p < 0.05) per annum, respectively. Perennial rice systems exhibit higher nitrogen use efficiency and soil nitrogen potential for crops, sustain soil nitrogen balance and enhance soil fertility for long-term rice productivity. Perennial rice farming system is conducive to green and sustainable production in farmland. Full article

Base editing represents a major breakthrough in the field of genome editing in recent years. By fusing deaminases with the CRISPR/Cas system, it enables precise single-base modifications of DNA. This review systematically summarizes the development of base editing technologies, including cytosine base editors (CBEs), adenine base editors (ABEs), and glycosylase base editors (GBEs), with a particular focus on their applications in crop improvement as well as future trends and prospects. We highlight advances in the creation of novel germplasm with enhanced stress resistance and desirable agronomic traits through base editing in rice, wheat, maize, potato, and other crops, particularly for improving herbicide resistance, disease resistance, and grain quality. Furthermore, we analyze factors that influence base editing efficiency, noting that challenges remain, such as PAM sequence constraints, limited base conversion types, off-target effects, narrow editing windows, and efficiency variation. Future efforts should aim to optimize deaminase activity, expand PAM compatibility, and develop versatile tools to facilitate the broad application of base editing in molecular breeding. This review provides a timely reference for researchers and breeders, offering theoretical guidance and practical insights into harnessing base editing for crop genetic improvement. Full article

Straw return is essential for improving soil fertility, recycling organic matter, and sustaining productivity in rice–wheat systems. This study focuses on the conceptual design and systematic analysis of the spatial and temporal variability of straw return methods and their classification. We proposed and analyzed 36 technical models for straw return by integrating spatial distribution (depth and horizontal placement) with temporal variability (decomposition period managed through mulching or decomposers). The models of straw return were categorized into five classes: mixed burial, even spreading, strip mulching, deep burial, and ditch burial. Field experiments were conducted in Babaiqiao Town, Nanjing, China, using clay loam soils typical of intensive rice–wheat rotation. Soil properties (bulk density, porosity, and moisture content) and straw characteristics (length and density) were evaluated to determine their influence on decomposition efficiency and nutrient release. Results showed that shallow incorporation (0–5 cm) accelerated straw breakdown and microbial activity, while deeper incorporation (15–20 cm) enhanced long-term organic matter accumulation. Temporal control using mulching films and decomposer agents further improved moisture retention, aeration, and nutrient availability. For the rice–wheat system study area, four typical straw return modes were selected based on spatial distribution and soil physical parameters: straw even spreading, rotary plowing, conventional tillage with mulching, and straw plowing with burying. This study added to the growing body of literature on straw return by providing a systematic analysis of the parameters influencing straw decomposition and the incorporation. The results have significant implications for sustainable agricultural practices, offering practical recommendations for optimizing straw return strategies to improve soil health. Full article

Advancements in unmanned aerial vehicle (UAV) multispectral systems offer robust technical support for the precise and efficient estimation of japonica rice yield in cold regions within the framework of precision agriculture. These innovations also present a viable alternative to conventional yield estimation methods. However, recent research suggests that reliance solely on vegetation indices (VIs) may result in inaccurate yield estimations due to variations in crop cultivars, growth stages, and environmental conditions. This study investigated six fertilization gradient experiments involving two conventional japonica rice varieties (KY131, SJ22) and two hybrid japonica rice varieties (CY31, TLY619) at Yanjiagang Farm in Heilongjiang Province during 2023. By integrating UAV multispectral data with machine learning techniques, this research aimed to derive critical phenotypic parameters of rice and estimate yield. This study was conducted in two phases: In the first phase, models for assessing phenotypic traits such as leaf area index (LAI), canopy cover (CC), plant height (PH), and above-ground biomass (AGB) were developed using remote sensing spectral indices and machine learning algorithms, including Random Forest (RF), XGBoost, Support Vector Regression (SVR), and Backpropagation Neural Network (BPNN). In the second phase, plot yields for hybrid rice and conventional rice were predicted using key phenotypic parameters at critical growth stages through linear (Multiple Linear Regression, MLR) and nonlinear regression models (RF). The findings revealed that (1) Phenotypic traits at critical growth stages exhibited a strong correlation with rice yield, with correlation coefficients for LAI and CC exceeding 0.85 and (2) the accuracy of phenotypic trait evaluation using multispectral data was high, demonstrating practical applicability in production settings. Remarkably, the R² for CC based on the RF algorithm exceeded 0.9, while R² values for PH and AGB using the RF algorithm and for LAI using the XGBoost algorithm all surpassed 0.8. (3) Yield estimation performance was optimal at the heading (HD) stage, with the RF model achieving superior accuracy (R² = 0.86, RMSE = 0.59 t/ha) compared to other growth stages. These results underscore the immense potential of combining UAV multispectral data with machine learning techniques to enhance the accuracy of yield estimation for cold-region japonica rice. This innovative approach significantly supports optimized decision-making for farmers in precision agriculture and holds substantial practical value for rice yield estimation and the sustainable advancement of rice production. Full article

This study explores the use of agricultural waste rice husk powder (RH) as a sustainable alternative to the petrochemical-derived carbon source, pentaerythritol (PER), in expandable flame retardants. RH is combined with halogen-free ammonium polyphosphate (APP), which serves as both an acid and a gas source. The resulting APP/RH system is incorporated into bio-based thermoplastic polyurethane (Biobased TPU) to prepare a halogen-free, flame-retardant composite material consistent with circular economy principles and environmental sustainability. The optimal APP-to-RH ratio in bio-based TPU was determined to be 2:1, with the best flame-retardant performance observed in the composite containing 20 wt% APP/RH. This formulation achieved a limiting oxygen index (LOI) of 27% and a UL-94 V-0 rating, indicating excellent flame resistance. Thermogravimetric analysis (TGA) showed a significant increase in char residue—from 0.51 wt% in pure TPU to 26.1 wt%—demonstrating improved thermal stability. Further characterization using cone calorimetry, thermogravimetric analysis–Fourier transform infrared spectroscopy (TGA-FTIR), X-ray photoelectron spectroscopy (XPS), and Raman spectroscopy confirmed that the addition of APP/RH significantly enhances the flame-retardant properties of the TPU composite. Consequently, the application of TPU in construction materials can be advanced through improved fire safety performance and alignment with sustainability goals. Full article

This study aimed to assess the effect of organic amendment-based integrated nitrogen (N) application on major soil macronutrients, carbon (C) accumulation, crop productivity and N use efficiency (NUE) of different rice-based cropping patterns. This experiment was composed of various organic amendments ((i): control (no organic amendment, application of 100% N from urea); (ii): 25% N from compost + 75% N from urea; (iii): 25% N from cowdung + 75% N from urea; iv: 25% N from vermicompost + 75% N from urea) and rice-based cropping patterns ((i) rice–rice–rice, (ii) rice–fallow–rice–mustard, and (iii) rice–vegetables–rice). Organic amendments and soils (0–20 cm) were collected from farmers’ fields and were analyzed for major nutrients: N and organic C (OC), phosphorus (P), potassium (K) and sulphur (S). Soil OC accumulation potential, system productivity and partial factor productivity of N were also calculated. The results indicate that organic amendment application significantly enhanced soil OC (0.957–1.604%) compared to control (0.916–1.292%), with vermicompost attaining the highest OC content and OC accumulation potential (up to 24.15%), especially in the rice–vegetables–rice pattern. Vermicompost also predominantly increased N (22–62%) and S (51–78%) level in soils, while cowdung significantly amended P levels (155–178%) and contributed steadily to improved K levels in soil. Overall, nutrient improvements and soil fertility were highest under the rice–vegetables–rice system, followed by rice–fallow–mustard–rice and rice–rice–rice. System productivity was maximum in the rice–vegetables–rice pattern (up to 85.7 t ha⁻¹), with remarkable enhancements in NUE when organic amendments were applied. Cowdung and vermicompost both matched or exceeded the performance of chemical fertilizer in these cases. These results demonstrate the advantages of integrated N management and diversified cropping to improve nutrient cycling, soil health and sustainable productivity in rice-based agroecosystems. Full article