Safety Of Agricultural Products Research Articles

Quantitative assessment of ecological risk from pollution source based on geostatistical analysis and APCS-MLR model.

The safety of human health and agricultural production depends on the quality of farmland soil. Risk assessment of heavy metal pollution sources could effectively reduce the hazard of soil pollution from various sources. This study has identified and quantitatively analyzed pollution sources with geostatistical analysis and the APCS-MLR model. The potential ecological risk index was combined with the APCS-MLR model which has quantitatively calculated the source contribution. The results revealed that As, Cr, Cd, Pb, Zn, and Cu were enriched in soil. Geostatistical analysis and the APCS-MLR model have apportioned four pollution sources. The Mn and Ni were attributed to natural sources; As and Cr were from agricultural activities; Cu and Zn were originated from natural sources; Cd and Pb were derived from atmospheric deposition. Atmospheric deposition and agricultural activities were the largest contributors to ecological risk of heavy metals in soil, which accounted for 56.21% and 36.01% respectively. Atmospheric deposition and agricultural activities are classified as priority sources of pollution. The combination of source analysis receptor model and risk assessment is an effective method to quantify source contribution. This study has quantified the ecological risks of soil heavy metals from different sources, which will provide a reliable method for the identification of primary harmfulness sources of pollution for future studies.

Agricultural performance of biomaterials mulching based on nitrosamine-free natural rubber latex

Natural rubber mulching material is generally more environmentally friendly than a conventional mulching. Artificial straw and mulch derived from natural rubber latex (NRL) with no presence of nitrosamines were used as sustainable alternatives to substitute for conventional materials like rice straw and polyethylene plastic. A real-time recorded field study employed Internet of Things (IoTs) with tracking devices and sensors to monitor the growth and quality of cucumber plants (Cucumis sativus L.) over a 50-day period. Results of the study highlight the superior efficiency of artificial straw, showcasing enhanced growth and quality. NRL mulching from natural rubber latex contained nanocellulose (CNC) filler and was vulcanized at room temperature. The addition of 3 parts per hundred rubber (phr) of CNC significantly enhanced the mechanical properties of the NRL formulation, particularly in tensile (22.13±2.26 MPa) and tear strength (39.13±4.58 N/mm). The NRL mulching was demonstrated to promote plant growth and increase yield. It has been developed to ensure the safety of agricultural products and to support environmental conservation.

Simultaneous Analysis of 272 Pesticides in Agricultural Products by the QuEChERS Method and Gas Chromatography with Tandem Mass Spectrometry.

The aim of this study is to develop a rapid and accurate method for simultaneous analysis of multi-residue pesticides and conduct pesticide monitoring in agricultural products produced by the production and distribution stage in Korea. The representative agricultural products were selected as brown rice, soybean, potato, mandarin, and green pepper and developed using gas chromatography with tandem mass (GC-MS/MS) for the analysis of 272 pesticide residues. The experimental samples were extracted by the QuEChERS-EN method and then cleaned up by using d-SPE, including MgSO4 and primary secondary amine (PSA) sorbents. The established method was validated in accordance with Codex CAC-GL/40, and the limit of quantitation (LOQ) was determined to be 0.01 mg/kg. A total of 243 pesticides satisfied the guidelines in five samples at three levels with values of 60 to 120% (recovery) and ≤45% (coefficient of variation, CV). The remaining 29 pesticides did not satisfy the guidelines, and these pesticides are expected to be used as a screening method for the routine inspection of agricultural products. As a result of analyzing 223 agricultural products in South Korea by applying the simultaneous analysis method, none of the detected levels in the samples exceeded the standard values based on maximum residue limits (MRLs). The developed method in this study will be used to inspect residual pesticides in agricultural products, and it is anticipated to contribute to the distribution of safe agricultural products to consumers.

Multiple pathway exposure risks and driving factors of heavy metals in soil-crop system in a Pb/Zn smelting city, China

Heavy metals (HMs) in soil near metal smelting areas have attracted great interest due to their crucial threat to agricultural product safety and human health. We investigated HMs in agricultural soils and crops (wheat and maize) in a Pb/Zn smelting city, central China; analysed the exposure risk of HMs through multiple pathways in a soil-crop system through Monte Carlo simulations; and identified the key driving factors using random forest (RF) model. The results showed that most soil HMs exceeded their background level. Cd had the highest exceeding rate compared with the screening values and the highest exceeding rate (40%, in wheat) compared with the food safety limit. Additionally, Cd had the highest bioaccumulation capacity in both maize and wheat. In identifying the key driving factors of soil HMs, RF had a satisfactory prediction performance for As, Cd, and Pb, with corresponding R2 values of 0.47, 0.51, and 0.60, respectively, with the dominant factors being distance to the Pb/Zn smelter, coal washery, and road area, respectively. However, Cr and Hg exhibited lower R2 values which may be due to their limited influence from the selected anthropogenic, traffic, and industrial variables. Probabilistic health risk assessments through Monte Carlo simulations revealed that the non-carcinogenic risk was around the risk limit (HI=1), while the carcinogenic risk for both children and adults exceeded the risk limit (TCR=1E-04), and adults had higher carcinogenic risk than children, which was due to a longer duration of exposure and higher ingestion rate of crop grains in adults. Cd had the highest carcinogenic risk and the highest probability of exceeding the limiting risk. Briefly, levels of Cd in both soil and crops should be controlled, especially for the source of smelting emissions. The results of this study could be treated as valuable references for policymakers to formulate control strategies for soil HM pollution in agricultural areas near Pb/Zn smelting activities.

DSADNet: A Dual-Source Attention Dynamic Neural Network for Precipitation Nowcasting

Accurate precipitation nowcasting is of great significance for flood prevention, agricultural production, and public safety. In recent years, spatiotemporal sequence models based on deep learning have been widely used for precipitation nowcasting and have achieved better prediction results than traditional methods. These models commonly use radar echo extrapolation and utilize the Z-R relationship between radar and rainfall to predict rainfall. However, radar echo data can be affected by various noises, and the Z-R correlation linking radar and rainfall encompasses several variables influenced by factors like terrain, climate, and seasonal variations. To solve this problem, we propose a dual-source attention dynamic neural network (DSADNet) for precipitation nowcasting, which is a network model that utilizes a fusion module to extract valid information from radar maps and rainfall maps, together with dynamic convolution and the attention mechanism, to directly predict future rainfall through encoding and decoding structure. We conducted experiments on a real dataset in Jiangsu, China, and the experimental results show that our model had better performance than the other examined models.

Novel Aminocoumarin Derivatives against Phytopathogenic Fungi: Design, Synthesis and Structure-Activity Relationships.

Phytopathogenic fungi is the most devastating reason for the decrease of the agricultural production and food safety. To develop new fungicidal agents for resistance concerning, a novel series of aminocoumarin derivatives were synthesized and their fungicidal activity were investigated both in vitro and in vivo. Transmission electron microscope (TEM), scanning electron microscope (SEM), RNA-Seq, 3D-QSAR and molecular docking were applied to reveal the underlying anti-fungal mechanisms. Most of the compounds exhibited significant fungicidal activity. Notably, compound 10c had a more extensive fungicidal effect than positive control. TEM indicated that compound 10c could cause abnormal morphology of cell walls, vacuoles and release of cellular contents. Transcriptional analysis data indicated that 895 and 653 out of 1548 differential expressed genes (DEGs) were up-regulated and down-regulated respectively. The Go and KEGG enrichment indicated that the coumarin derivatives could induce significant changes of succinate dehydrogenase (SDH), Acetyl-coenzyme A synthetase (ACCA) and pyruvate dehydrogenase (PDH) genes, which contributed to the disorders of glucolipid metabolism and the dysfunction of mitochondrial. The results demonstrated that aminocoumarins with schiff-base as core moieties could be the promising lead compounds for the discovery of novel fungicides.

Research of blockchain-embedded agricultural quality credit regulation influencing factors

PurposeThe credit of agricultural product quality and safety reflects the ability of the main actors involved in the supply chain to provide reliable agricultural products to consumers. To fundamentally solve the problem of agricultural product quality and safety, it is worth studying how to make the credit awareness and integrity self-discipline of the supply chain agriculture-related subjects strengthened and the role and value of credit supervision given full play. Starting from the application of blockchain in the agricultural product supply chain, this paper aims to investigate the main factors affecting the credit regulation of agricultural product quality.Design/methodology/approachUsing the DEMATEL-ISM (decision-making trial and evaluation laboratory–interpretative structural modeling) method, we analyze the credit influencing factors of agricultural quality and safety empowered by blockchain technology, find the causal relationship between the crucial influencing factors and deeply explore the hierarchical transmission relationship between the influencing factors. Then, the path analysis in structural equation modeling is utilized to verify and measure the significance and effect value of the transmission relationship among the crucial influencing factors of credit regulation.FindingsThe results show that the quality and safety credit regulation of agricultural products is influenced by a combination of direct and deep influencing factors. Long-term stable cooperative relationship, Quality and safety credit evaluation, Supply chain risk control ability, Quality and safety testing, Constraints of the smart contract are the main influence path of blockchain embedded in agricultural product supply chain quality and safety credit supervision.Originality/valueCredit supervision is an important means to improve the ability and level of social governance and standardize the market order. From the perspective of blockchain embedded in the agricultural supply chain, the regulatory body is transformed from the product body to the supply chain body. Take the credit supervision of supply chain subjects as the basis of agricultural product quality supervision. With the help of blockchain technology to improve the effectiveness of agricultural product quality and safety credit supervision, credit supervision is used to constrain and incentivize the behavior of agricultural subjects.

Swellable microneedle-coupled light-addressable photoelectrochemical sensor for in-situ tracking of multiple pesticides pollution in vivo

In vivo monitoring of multiple pesticide contamination is of great significance for evaluating the health risks of different pesticides, agricultural production safety, and ecological and environmental assessment. Here, we report a hydrogel microneedle array coupled light-addressable photoelectrochemical sensor for tracking multiple pesticide uptake and elimination in living animals and plants, holding three prominent merits: i) enables in-situ detection of in vivo pesticides, avoiding cumbersome and complex sample transportation and handling processes; ii) allows repeated in vivo sampling of the same organism, improving tracking test controllability and accuracy; iii) avoids lethal sampling, providing a better understanding of the pesticides fate in living organisms. The coupled sensor is mechanically robust for withstanding more than 0.35 N per needle and highly swellable (800 %) for timely extraction of sufficient in vivo solution for analysis. For proof-of-concept, it achieves in-situ detection of atrazine, acetamiprid, and carbendazim efficiently and quantitatively in artificial agarose skin models, mouse skin interstitial fluids, and plant leaves with little inflammatory reaction. This simple, highly integrated, minimally invasive, and high-throughput in vivo monitoring method is ideal for future field environmental monitoring and plant and animal disease diagnosis.

Behavioral decision-making of government, agricultural product producers, and consumers on agricultural product quality and safety regulation in a digital environment.

The quality and safety of agricultural products are related to people's lives and health, economic development, and social stability, and have always been a hot issue of concern to the government and society. The rapid development of digital traceability technology in the digital environment has brought new opportunities for the supervision of agricultural product quality and safety, but the frequent occurrence of agricultural product safety incidents in recent years has exposed many problems such as the lack of governmental supervision, unstandardized production process of enterprises, and weak consumer awareness. To improve the cooperation efficiency of stakeholders and ensure the quality and safety of agricultural products, this paper proposes a dynamic model based on evolutionary game theory. The model incorporates the government, agricultural product producers, and farmers, and evaluates the stability and effectiveness of the system under different circumstances. The results of the study show that there are multiple evolutionary stabilization strategies in the tripartite evolutionary game model of agricultural product quality and safety supervision, and there are corresponding evolutionary stabilization conditions. There are several factors affecting the stability of the system, the most important of which are government regulation, severe penalties for agricultural product producers, and incentives. When these factors reach a certain threshold, the stakeholder cooperation mechanism can establish an evolutionarily stable strategy. This study contributes to the understanding of the operational mechanism of stakeholder cooperation in agricultural product quality and safety regulation in the digital environment and provides decision support and policy recommendations for stakeholders to promote the sustainable development and optimization of agricultural product quality and safety regulation.

Design of Safety Evaluation and Risk Traceability System for Agricultural Product Quality

The quality and safety of agricultural products involve a variety of risk factors, a large amount of risk information data, and multiple circulation and disposal processes, making it difficult to accurately trace the source of risks. To achieve precise traceability of agricultural product quality and safety, the monitoring and supervision level of agricultural product traceability systems needs to be improved. In this paper, the agricultural product quality- and safety-related detection data are taken as the research object, the agricultural product supply chain and business process are sorted out, the agricultural product risk factors under the influence of heavy metals are analyzed, and the agricultural product quality and safety risk evaluation model is established. The agricultural product risk traceability system was designed based on the six-layer architecture of the equipment layer, data layer, service layer, application layer, display layer, and user layer. Combined with the agricultural product quality and safety risk evaluation model, the system data were stored and modified by using the MySQL relational database, and the agricultural product risk traceability system was developed, which realized the accurate traceability of the risk link of the quality and safety of agricultural products. The research results of this paper provide an effective information means for the quality and safety supervision of agricultural products, and provide a technical reference for the development of a traceability system of risk factors related to agricultural products, which has good practical significance.

Crater-Spectrum Feature Fusion Method for Panax notoginseng Cadmium Detection Using Laser-Induced Breakdown Spectroscopy.

Panax notoginseng (P. notoginseng) is a valuable herbal medicine, as well as a dietary food supplement known for its satisfactory clinical efficacy in alleviating blood stasis, reducing swelling, and relieving pain. However, the ability of P. notoginseng to absorb and accumulate cadmium (Cd) poses a significant environmental pollution risk and potential health hazards to humans. In this study, we employed laser-induced breakdown spectroscopy (LIBS) for the rapid detection of Cd. It is important to note that signal uncertainty can impact the quantification performance of LIBS. Hence, we proposed the crater-spectrum feature fusion method, which comprises ablation crater morphology compensation and characteristic peak ratio correction (CPRC), to explore the feasibility of signal uncertainty reduction. The crater morphology compensation method, namely, adding variables using multiple linear regression (MLR) analysis, decreased the root-mean-square error of the prediction set (RMSEP) from 7.0233 μg/g to 5.4043 μg/g. The prediction results were achieved after CPRC pretreatment using the calibration curve model with an RMSEP of 3.4980 μg/g, a limit of detection of 1.92 μg/g, and a limit of quantification of 6.41 μg/g. The crater-spectrum feature fusion method reached the lowest RMSEP of 2.8556 μg/g, based on a least-squares support vector machine (LSSVM) model. The preliminary results suggest the effectiveness of the crater-spectrum feature fusion method for detecting Cd. Furthermore, this method has the potential to be extended to detect other toxic metals in addition to Cd, which significantly contributes to ensuring the quality and safety of agricultural production.

Assessing Essential Parameters for Soil Fertility in Remote Agricultural Areas of PDUAM, Dalgaon, Darrang, Assam

Soil is the upper layer of the earth’s surface, consisting of a mixture of organic matter, clay, and rock particles, and it serves as the medium in which plants grow. The quality of soil plays a crucial role in agricultural activities and is vital for ensuring the safety of agricultural products. This study focuses on the assessment of soil properties within various remote areas of Pandit Deendayal Upadhyaya Adarsha Mahavidyalaya (PDUAM) located in Dalgaon, Darrang, Assam. The investigation seeks to comprehensively evaluate some of the key soil characteristics, including pH levels, electrical conductivity (EC), and some of the important ions present in the soil matrix using basic chemical laboratory experiments. By undertaking this detailed analysis, the aim is to gain an understanding of the soil fertility in these remote regions, where extensive agricultural practices are carried out. The study reveals that more than 80% of the soil samples have required pH within the fertile range. The observed conductivity value indicates the presence of sufficient soluble ions. Recognizing the pivotal role of soil in sustaining agricultural ecosystems, this study contributes to the broader discourse on optimizing agricultural practices. The findings from the assessment will not only enhance knowledge on soil health but also shall offer valuable insights for agricultural stakeholders in this area.

Towards interpretable machine learning for observational quantification of soil heavy metal concentrations under environmental constraints

Monitoring heavy metal concentrations in soils is central to assessing agricultural production safety. Satellite observations permit inferring concentrations from spectrum, thereby contributing to the prevention and control of soil heavy metal pollution. However, heavy metals exhibit weak spectral responses, particularly at low and medium concentrations, and are predominantly influenced by other soil components. Machine learning (ML)-driven modelling can produce predictions but lacks interpretability. Here, we present an interpretable ML framework for concentration quantification modelling and investigated the contributions of spectral and environmental factors—pH and organic carbon—to the estimation of metals with multiple concentration gradients, as analysed through SHAP (SHapley Additive exPlanations) data derived from four learning-based scenarios. The results indicated that scenarios SHC (spectral, pH, and organic carbon) and SH (spectral and pH) were the most optimal for chromium (Cr) [RPD = 1.42, Adj R2 = 0.62], and cadmium (Cd) [RPD = 1.80, Adj R2 = 0.80]. Under environmental constraints, the spectral predictability for Cr and Cd was improved by 67 % and 87 %, respectively. We concluded that interpretable modelling, utilising both spectral and soil environmental factors, holds significant potential for estimating heavy metals across concentration gradients. It is recommended that samples with higher organic carbon content and lower pH be selected to enhance Cr and Cd predictions. An advanced grasp of interpretable predictions facilitates earlier warning of heavy metal contamination and guides the formulation of robust sampling strategies.

Bumblebee Foraging Dynamics and Pollination Outcomes for Cherry Tomato and Pear Varieties in Northern China.

Bumblebees (Bombus terrestris) have strong environmental adaptability and high pollen transfer efficiency, making them well-suited pollinators of economic crops. However, bumblebee pollination is still not widely applied in northern China due to the lack of data on foraging behavior and pollination effects. We conducted a three-year experiment involving cherry tomatoes (Solanum lycopersicum L.) and pears (Pyrus spp.) treated with bumblebee pollination to evaluate the foraging behavior and pollination effects on these two crops. Results showed that B. terrestris had enhanced foraging activities as daytime temperatures rose from 18 °C to 26 °C, as indicated by the increased number of bees leaving the hive and returning bees carrying pollen in greenhouses in winter. There were two peaks in the foraging activity of bumblebees in pear orchards in early spring, which was closely related to the temperature change in the daytime. Undoubtedly, cherry tomatoes treated with B. terrestris had higher fruit setting rate, weight, seed number, and fruit yields compared to those with hormone 2,4-dichlorophenoxyacetic acid treatments, as well as a lower rate of deformed fruits. B. terrestris pollination can significantly increase the fruit setting rate and fruit yield of pears, compared with open pollination, and can fully achieve the effect of hand pollination. B. terrestris pollination can improve cultivation efficiency, increase yield, and produce more economic benefits. Moreover, it can also contribute to reducing hormone residues and ensure the safety of agricultural products. We recommend its application to cherry tomatoes in greenhouses in winter and potential application to pears in orchards in early spring in northern China. However, the risk to local bumblebee species of introducing commercially available bumblebees into orchards should be considered and evaluated in future research. This study provides both empirical support and a theoretical basis for the selection of bumblebees as pollinators in the production of economically important crops and the improvement of crop cultivation management in northern China.

A Deep Vision Sensing-Based Smart Control Method for Apple-Picking Robots Under the Context of Agricultural E-Commerce

The growing development of the agricultural e-commerce industry is promoting the Digital transformation of agricultural production and logistics. Aiming at the problem of labor shortage in apple picking, this paper proposes a performance control method for robot apple picking based on a nonlinear wide convolutional neural network (NLWCNN) and multi-objective cooperative distance transformation algorithm (MOCDTA). This method utilizes deep learning algorithms and visual sensing technology to enable robots to accurately recognize apples and perform autonomous picking operations based on their maturity and position. We have designed a machine learning-based algorithm that enables robots to accurately recognize and distinguish apples of different maturity levels through training on a large amount of sample data. At the same time, we also combined the motion control algorithm of the robot to enable efficient picking operations based on the position and maturity information of the apples. The experimental results indicate that the NLWCNN method can significantly improve the efficiency and accuracy of apple picking, ensure the safety and traceability of agricultural products, increase consumer trust, and promote agricultural product transactions.

The factors affecting microplastic pollution in farmland soil for different agricultural uses: A case study of China

Microplastics (MPs) accumulation in agricultural soil seriously affects soil quality and grain production, and endangers ecological environment and human safety. The factors contributing to the MPs accumulation in various agricultural land-use soils remain unclear. In this study, a meta-analysis was conducted to analyze the influence of meteorological factors. Furthermore, the accumulation process of MPs pollution in different agricultural land-use soils in China was explored. The conclusions are as follows: (1) The MPs abundance varied from 23 to 41,741 n/kg in Chinese agricultural soil. Especially, the moderate and severe pollution (≥4,000 n/kg) were found in the Yunnan-Kweichow Plateau. (2) The order of MPs abundance in soil was as follows: vegetable > orchard > food crops. Compared to non-mulching, MPs abundance in mulching food crops, vegetable, and orchard soil was approximately 1.4-fold, 11.4-fold, and 1.1-fold, respectively. (3) In orchard and food-crop soils, MPs abundance was negatively associated with rain and wind speed, but positively correlated with altitude and temperature. Whereas MPs abundance in vegetable soil was significantly positively affected with temperature, altitude, sunshine hours, wind speed, and rainfall. (4) The MPs pollution accumulation in soil was mainly dominated by mulching agricultural measures under different agricultural land-use. Meteorological factors have an indirect impact by affecting MPs fragmentation and migration. The results provide theoretical support for soil pollution control, agricultural production safety, and watershed management.

Blocking Effects of Foliar Conditioners on Cadmium, Arsenic, and Lead Accumulation in Wheat Grain in Compound-contaminated Farmland

Heavy metal contamination of soil has become a hot issue of social concern due to its impact on the safety of agricultural products in recent years. Wheat is one of the most dominant staple food crops worldwide and has become a major source of toxic metals in human diets. Foliar application was considered to be a more efficient and economical method of heavy metal remediation. Field experiments were carried out in Cd-, As-, and Pb-contaminated farmland soils. The effects of foliar conditioners on the accumulation of Cd, As, and Pb in wheat grains were investigated after being sprayed with Zn (0.2% ZnSO4), Mg (0.4% MgSO4), and Mn (0.2% MnSO4) separately and in combination. Thus, the effective foliar conditioners were selected to block the accumulation of Cd, As, and Pb in wheat grains grown in combined heavy metal-contaminated farmland in north China. The results showed that, compared with that in the control, the Cd, As, and Pb contents in wheat grains of the Zn+Mg+Mn foliar treatment were significantly decreased by 18.96%, 23.87%, and 51.31%, respectively, and TFgrain/straw decreased by 14.62%, 27.73%, and 47.70%, respectively. Thus, spraying the compound foliar conditioner of Zn+Mg+Mn could effectively reduce heavy metal accumulation in wheat grains through inhibition translocation of those metals from stem leaves to grain. In addition, the results indicated that Cd and As were mainly distributed at the central endosperm (34.08%-37.08%), whereas Pb was primarily distributed at the pericarp and seed coat (27.78%) of the wheat grain. Compared with that in the control, spraying the compound foliar conditioner of Zn+Mg+Mn extremely decreased Cd and As accumulation in the aleurone layer of the wheat grain by 81.10% and 82.24%, respectively. Except for the pericarp, seed coat, and central endosperm layers, the Pb content in each grain layer was dramatically decreased by 42.85% to 91.15%. There was only a significant negative correlation between heavy metal content and Zn content in the aleurone layer (P2) of wheat flour. In summary, the accumulation of Cd, As, and Pb in wheat grains, especially in the aleurone layer, could be effectively reduced by foliar conditioner application at the jointing, booting, and early filling stages of wheat, separately. Furthermore, besides the foliar treatment, removing wheat bran to reduce Cd contamination in wheat grains is highly recommended to ensure the safe production of wheat.

Screening of Maize Varieties with High Biomass and Low Accumulation of Pb and Cd around Lead and Zinc Smelting Enterprises: Field Experiment

In the long-term production process of lead and zinc smelting enterprises, atmospheric subsidence leads to the accumulation of heavy metals in surrounding farmland, which poses a serious threat to the growth of crops and food safety. Given the knowledge that heavy metal pollution in cultivated land does not support treatment and restoration, determining how to ensure the quality and safety of agricultural products is the main problem facing the agricultural industry at present. Previous studies have mainly focused on the low accumulation of heavy metals in maize varieties, while the removal of heavy metals from soil through high biomass has been studied less. In order to identify the maize varieties that demonstrate high removal and low accumulation of heavy metals, 29 maize varieties were planted in soil contaminated with lead (Pb) and cadmium (Cd), and the growth status of the maize varieties and the absorption and transport of Pb and Cd by different tissues were studied. The results showed that heavy metals had the least effect on the growth of the Longhuangbai3, Jinqiuyu 35, Jinyi 418, and Qiuqing 88 varieties, and the content of Pb and Cd in maize varieties was in the order leaf > stem > root > grain. It was found that soil remediation and safe production can be taken into account in the results of the Qiuqing 88 (Pb, Cd), Fengdeng 2025 (Cd), and Yayu 719 (Pb, Cd) varieties. Moreover, the Xinzhongyu 801 (Cd) and Longdan 1701 (Pb) varieties demonstrated high metal accumulation in the edible part, which poses a potential risk to human health; thus, they are not recommended for local cultivation.

Exceptional anti-toxic growth of water spinach in arsenic and cadmium co-contaminated soil remediated using biochar loaded with Bacillus aryabhattai

Functionalized biochars are crucial for simultaneous soil remediation and safe agricultural production. However, a comprehensive understanding of the remediation mechanism and crop safety is imperative. In this work, the all-in-one biochars loaded with a Bacillus aryabhattai (B10) were developed via physisorption (BBC) and sodium alginate embedding (EBC) for simultaneous toxic As and Cd stabilization in soil. The bacteria-loaded biochar composites significantly decreased exchangeable As and Cd fractions in co-contaminated soil, with enhanced residual fractions. Heavy metal bioavailability analysis showed a maximum CaCl2-As concentration decline of 63.51% and a CaCl2-Cd decline of 50.96%. At a 3% dosage of composite, rhizosphere soil showed improved organic matter, cation exchange capacity, and enzyme activity. The aboveground portion of water spinach grown in pots was edible, with final As and Cd contents (0.347 and 0.075 mg·kg⁻¹, respectively) meeting food safety standards. Microbial analysis revealed the composite's influence on the rhizosphere microbial community, favoring beneficial bacteria and reducing plant pathogenic fungi. Additionally, it increased functional microorganisms with heavy metal-resistant genes, limiting metal migration in plants and favoring its growth. Our research highlights an effective strategy for simultaneous As and Cd immobilization in soil and inhibition of heavy metal accumulation in vegetables.

Plant Biostimulant as an Environmentally Friendly Alternative to Modern Agriculture.

Ensuring the safety of crop production presents a significant challenge to humanity. Pesticides and fertilizers are commonly used to eliminate external interference and provide nutrients, enabling crops to sustain growth and defense. However, the addition of chemical substances does not meet the environmental standards required for agricultural production. Recently, natural sources such as biostimulants have been found to help plants with growth and defense. The development of biostimulants provides new solutions for agricultural product safety and has become a widely utilized tool in agricultural. The review summarizes the classification of biostimulants, including humic-based biostimulant, protein-based biostimulant, oligosaccharide-based biostimulant, metabolites-based biostimulants, inorganic substance, and microbial inoculant. This review attempts to summarize suitable alternative technology that can address the problems and analyze the current state of biostimulants, summarizes the research mechanisms, and anticipates future technological developments and market trends, which provides comprehensive information for researchers to develop biostimulants.