Main Production Regions Research Articles

Spatial-Temporal Evolution of Carbon Sequestration and Emission Reduction Capacity of Agricultural Cultivation in Different Functional Grain Areas

The capacity of agricultural cultivation for carbon sequestration and emission reduction plays a key role in advancing the green and sustainable development of agriculture and achieving both emission reduction and carbon sink objectives. This study constructs an index for agricultural carbon sequestration and emission reduction capacity using spatial autocorrelation analysis, Theil index, and kernel density estimation, to thoroughly explore spatiotemporal changes and regional differences in carbon sequestration and emission reduction capacity across China and its different grain functional areas. Major findings include: (1) From 2000 to 2020, China’s agricultural carbon sequestration and emission reduction capacity exhibited a fluctuating upward trend, consistent in grain main production areas, while production–consumption and main marketing areas showed a fluctuating decline. (2) From 2000 to 2020, positive spatial correlation in carbon sequestration and emission reduction capacity increased at both national and regional levels, particularly in the main production and production–consumption areas, while the main consumption areas demonstrated a trend toward negative correlation. (3) The Theil index of China’s agricultural carbon sequestration and emission reduction capacity showed an overall fluctuating increase, with interregional differences contributing over 54%, significantly surpassing intra-regional contributions. Moreover, the main producing regions are the main source of the overall national variance. (4) Kernel density analysis highlights that there are significant differences in the distribution and evolution of carbon sequestration and emission reduction capacity among different grain functional areas, and the overall development is unbalanced. Therefore, each region needs to formulate tailored measures to improve agricultural carbon sequestration efficiency, promote regional sustainable development, and achieve carbon reduction goals.

Equationing Arabica coffee: Adaptation, calibration, and application of an agrometeorological model for yield estimation

CONTEXTCoffee cultivation is important to Brazil's economy, positioning the country as a global leader in production and export. Given the complex environmental and management factors affecting yields, particularly due to climate change, there is a pressing need from farmers and dealers for more precise crop estimation models. OBJECTIVEThis study aimed to refine and calibrate an agrometeorological model, originally developed by Santos and Camargo (2006) and later adapted by Verhage et al. (2017a), to estimate Arabica coffee yield in the main producing regions of Minas Gerais and São Paulo. Additionally, sensitivity analysis was also performed to identify the most influential model parameters and variables. METHODSYield data from 28 coffee-producing locations (2003−2020) and meteorological data alongside irrigation use were employed. Following calibration and adaptation, a sensitivity analysis was conducted to determine the model's response to variations in coffee plant parameters and environmental conditions. Local sensitivity analysis (LSA) focused on meteorological variables, while global sensitivity analysis (GSA) addressed coffee-related parameters. RESULTS AND CONCLUSIONSThe adaptations proposed to the original model led to a significant refinement in the yield estimates, emphasizing the complex interactions between climatic variables and agricultural management practices. Key adaptations include the estimation of potential yield (Yp), the incorporation of temporal curves for root growth, leaf area index, available water capacity, and crop coefficient, as well as a water balance that accounts for irrigation and its effect on attenuating high canopy temperatures. Calibration improved the model's accuracy and precision, with the RMSE decreasing from 13.66 (819.6 kg ha−1; 1 bag ha−1 = 60 kg ha−1) to 8.65 (519.0 kg ha−1) bags ha−1, R2 improving from 0.62 to 0.65, d-index from 0.79 to 0.88, and NSE from 0.09 to 0.64. During the evaluation phase, with independent data, RMSE was 7.76 bags ha−1 (465.6 kg ha−1), d-index 0.85, and R2 0.55. Sensitivity analysis emphasized the importance of mean temperature and solar radiation on Yp, as well as the impact of irrigation practices and water deficit management under rainfed conditions. Additionally, factors specific to the coffee plant itself directly affect its yield. SIGNIFICANCEThe findings underscore the importance of a multifactorial and adaptive approach to coffee cultivation, addressing the complexities and challenges posed by varying climatic conditions. This work offers valuable insights into optimizing coffee production, presenting the model as a tool for developing more resilient cultivation strategies and enhancing the sustainability of Brazilian Arabica coffee in future climate change scenarios.

Predicting Wheat Potential Yield in China Based on Eco-Evolutionary Optimality Principles

Accurately predicting the wheat potential yield (PY) is crucial for enhancing agricultural management and improving resilience to climate change. However, most existing crop models for wheat PY rely on type-specific parameters that describe wheat traits, which often require calibration and, in turn, reduce prediction confidence when applied across different spatial or temporal scales. In this study, we integrated eco-evolutionary optimality (EEO) principles with a universal productivity model, the Pmodel, to propose a comprehensive full-chain method for predicting wheat PY. Using this approach, we forecasted wheat PY across China under typical shared socioeconomic pathways (SSPs). Our findings highlight the following: (1) Incorporating EEO theory improves PY prediction performance compared to current parameter-based crop models. (2) In the absence of phenological responses, rising atmospheric CO2 concentrations universally benefit wheat growth and PY, while increasing temperatures have predominantly negative effects across most regions. (3) Warmer temperatures expand the window for selecting sowing dates, leading to a national trend toward earlier sowing. (4) By simultaneously considering climate impacts on wheat growth and sowing dates, we predict that PY in China’s main producing regions will significantly increase from 2020 to 2060 and remain stable under SSP126. However, under SSP370, while there is no significant trend in PY during 2020–2060, increases are expected thereafter. These results provide valuable insights for policymakers navigating the complexities of climate change and optimizing wheat production to ensure food security.

Satellite Imagery, Big Data, IoT and Deep Learning Techniques for Wheat Yield Prediction in Morocco

In the domain of efficient management of resources and ensuring nutritional consistency, accuracy in predicting crop yields becomes crucial. The advancement of artificial intelligence techniques, synchronized with satellite imagery, has emerged as a potent approach for forecasting crop yields in modern times. We used two types of data: spatial data and temporal data. Spatial data are gathered from satellite imagery and processed using ArcGIS to extract data about crops based on several indices like NDVI and NWDI. Temporal data are gathered from agricultural sensors such as temperature sensors, rainfall sensor, precipitation sensor and soil moisture sensor. In our case we used Sentinel 2 satellite to extract vegetation indices. We have used IoT systems, especially Raspberry Pi B+ to collect and process data coming from sensors. All data collected are then stored into a NoSQL server to be analysed and processed. Several machine learning and deep learning algorithms have been used for the processing of crop recommendation system, such as logistic regression, KNN, decision tree, support vector machine, LSTM, and Bi-LSTM through the collected dataset. We used GRU deep learning model for the best performance, the RMSE and R2 for this model was 0,00036 and 0,99 respectively.The main contribution of our paper is the development of a new system that can predict several crop yields, such as wheat, maize, etc, using IoT, satellite imagery for spatial data and the use of sensors for temporal data. We are the first paper that has combined spatial data and temporal data to predict crop yield based on deep learning algorithms, unlike other works that uses only remote sensing data or temporal data. We created an E-monitoring crop yield prediction system that helps farmers track all information about crops and show the result of prediction in a mobile application. This system helps farmers with more efficient decision making to enhance crop production. The main production regions of wheat in Morocco are in the rainfed areas of the plains and plateaus of Chaouia, Abda, Haouz, Tadla, Gharb and Saïs. We studied three main regions well known for wheat production which are Rabat-Salé, Fez-Meknes, Casablanca-Settat.

Winter wheat identification in China through FY-3D NDVI&EVI satellite data and DNN Fusion

ABSTRACT Medium Resolution Spectral Imager-II (MERSI-II) on FengYun 3D (FY-3D) meteorological satellite is a visible and infrared spectral imaging instrument comparable with Moderate Resolution Imaging Spectroradiometer (MODIS), which provides ample opportunities for regional crop mapping. However, current research may have overlooked the potential of combining FY-3D MERSI-II data with deep neural network (DNN) algorithms for winter wheat identification. This research utilizes a DNN method to train a nonlinear model with the synthetic normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) at a 250 m resolution from FY-3D MERSI-II as the eigenvalues, enabling to accurately identify spatial distribution of winter wheat in China’s main production regions and filling the gap in the application of FY-3D MERSI-II data. The results indicate that the winter wheat identification with the combination of NDVI and EVI achieved an accuracy of 95.32%, with a Kappa coefficient of 0.87 and a determination coefficient (R 2 ) of 0.73, improving the overall accuracy by 1.27% and 1.07% compared to models trained solely using NDVI or EVI, respectively. In terms of spatial distribution, the overlap rate with the winter wheat dataset of China from MODIS at a 500 m resolution exceeded 95%, with an area bias of 2.67%. This study indicates that the medium resolution FY-3D MERSI-II remote sensing dataset combined with the DNN algorithm can accurately identify crop information over large areas.

Artificial intelligence tools and concepts give access to authenticity and quality information in Brazilian olive oil volatilome

Brazil is the world's second largest importer of olive oil and stared to produce its own oil quite recently in two distinct geographic areas. The production is still very small, and studies regarding oil composition are scarce. Determination of discriminant differences are useful to support appeals regarding origin indication and to detect fraud and adulteration with oils from other countries. In this work the Brazilian extra virgin olive oils (EVOOs) volatilome is explored by Artificial Intelligence (AI) tools developed on multiple headspace solid-phase microextraction (MHS-SPME) combined with comprehensive 2D gas chromatography-mass spectrometry and flame ionization detection (GC×GC-MS/FID) data. Using MHS-SPME, external standard calibration, and FID predicted relative response factors (RRF), the accurate quantification of 51 informative volatile compounds was carried out and the odorants responsible for key-positive sensory attributes were used to generate distinctive aroma blueprints aligned with the AI smelling based on sensomics. As authenticity and origin assessment decision-making tool, augmented visualization by computer vision was applied on volatilome 2D fingerprints. By this approach, extra-virgin olive oil samples (n=35), from two olive cultivars (Arbequina and Koroneiki) harvested in the main producing regions in Brazil (Rio Grande do Sul and Serra da Mantiqueira) in 2021 and 2022, were effectively discriminated and mislabeled products regarding their geographical origin were, for the first time, promptly identified.

Quantitative assessment of the effects of rising temperature on the grain protein of winter wheat in china and its adaptive strategies

With advancements in genetics and technology, wheat yields have continuously increased, but grain quality has remained stagnant or even decreased in recent decades. The effects of temperature on the future grain quality of winter wheat in China are unknown. This study used an improved WheatGrow model, along with two future temperature scenarios, to quantify the regional impacts of temperature increases on grain protein in China and aimed to find effective strategies to adapt to the impact of temperature increases on quality. Compared with those in the baseline period, the protein concentration of wheat grains in the main winter wheat production region of China increased by 0.27% and 0.41% under the 1.5 ℃ and 2.0 ℃ temperature increase scenarios, respectively. However, the grain protein concentrations in the Sichuan Basin of Medium and Weak Gluten Wheat Subregion (Ⅴ) decreased. The temperature rise was estimated to increase the proportion of strong gluten-type wheat but reduce the proportion of medium gluten-type wheat, with the proportion of weak gluten-type wheat changing slightly. Advanced planting date was projected to increase the grain protein concentration in the Northern Strong Gluten Wheat Subregion (Ⅰ), the Northern of Huang-Huai Strong and Medium Gluten Wheat Subregion (Ⅱ), the Southern of Huang-Huai Medium Gluten Wheat Subregion (Ⅲ) and the Yangzi River of Medium and Weak Gluten Wheat Subregion (Ⅳ), whereas a delayed planting date reduced the protein concentration in subregions Ⅰ, Ⅱ, and Ⅲ. In addition, the grain protein concentration was not significantly affected in the higher high-temperature threshold varieties in the southern winter wheat production region under the global warming scenarios. However, if more strong gluten-type wheat is required in the northern China in the future, varieties with lower high-temperature thresholds can be selected without considering wheat yields. Moreover, increasing the nitrogen fertilizer application rate could further increase the wheat protein concentration under future warming scenarios, and the protein concentration of wheat was more sensitive to the nitrogen fertilizer application rate in the northern winter wheat production region than in the southern region. These findings are crucial for improving grain quality across different wheat production regions of China in the future.

Botryosphaeriaceae species associated with branch dieback and decline of macadamia trees in South Africa

Botryosphaeriaceae species are important latent pathogens causing diseases on trees utilized in forestry and agriculture. In recent years, there has been an increase in the incidence and severity of branch dieback and decline on macadamia trees in South Africa, and species of Botryosphaeriaceae have been considered as a possible cause. Although botryosphaeria dieback has been well-studied in Australia, there is little information regarding these fungi on Macadamia in South Africa. The aims of this study were consequently to (i) identify species of Botryosphaeriaceae from Macadamia branches from main production regions in South Africa, (ii) compare the diversity of species between symptomatic and asymptomatic branches, as well as between different growing regions, (iii) and to consider their relative importance in causing dieback. Eight species and three putative hybrids of the Botryosphaeriaceae were identified based on a phylogenetic comparison of sequence data from the ITS rDNA, tub2, tef-1α and rpb2 loci. These included an unidentified Diplodia sp., and Lasiodiplodia sp., as well as L. gilanensis, L. theobromae, L. pseudotheobromae, Neofusicoccum kwambonambiense, N. luteum, N. parvum and three hybrid species. The unidentified species of Diplodia., Lasiodiplodia sp., L. gilanensis, and N. kwambonambiense are reported for the first time on Macadamia in South Africa. All species showed a potential to cause branch dieback symptoms, with species of Neofusicoccum identified as the most aggressive species. This study revealed a high level of diversity of Botryosphaeriaceae species and illustrates their potential as causal agents of dieback on Macadamia in South Africa.

Study of NO and CO Formation Pathways in Jet Flames with CH4/H2 Fuel Blends

The existing natural gas transportation pipelines can withstand a hydrogen content of 0 to 50%, but further research is still needed on the pathways of NO and CO production under moderate or intense low oxygen dilution (MILD) combustion within this range of hydrogen blending. In this paper, we present a computational fluid dynamics (CFD) simulation of hydrogen-doped jet flame combustion in a jet in a hot coflow (JHC) burner. We conducted an in-depth study of the mechanisms by which NO and CO are produced at different locations within hydrogen-doped flames. Additionally, we established a chemical reaction network (CRN) model specifically for the JHC burner and calculated the detailed influence of hydrogen content on the mechanisms of NO and CO formation. The findings indicate that an increase in hydrogen content leads to an expansion of the main NO production region and a contraction of the main NO consumption region within the jet flame. This phenomenon is accompanied by a decline in the sub-reaction rates associated with both the prompt route and NO-reburning pathway via CHi=0–3 radicals, alongside an increase in N2O and thermal NO production rates. Consequently, this results in an overall enhancement of NO production and a reduction in NO consumption. In the context of MILD combustion, CO production primarily arises from the reduction of CO2 through the reaction CH2(S) + CO2 ⇔ CO + CH2O, the introduction of hydrogen into the system exerts an inhibitory effect on this reduction reaction while simultaneously enhancing the CO oxidation reaction, OH + CO ⇔ H + CO2, this dual influence ultimately results in a reduction of CO production.

Metals Transfer in Mushroom Tricholoma matsutake from Regional High Geochemical Background Areas: Environmental Influences and Human Health Risk.

Wild-grown edible mushrooms are important in world diets and are also efficient metal accumulators. Yunnan, Southwest China, is the main producing region, with typically high levels of geochemical metals. The environmental factors, bioaccumulation, distribution and human health risks of metals were examined in paired soil and Tricholoma matsutake (n = 54). T. matsutake grows on acidified soils (pH = 3.95-6.56), and metals show a strong heterogeneity, with Fe, Mn, Zn and Cu in the ranges of 16-201, 0.046-8.58 g kg-1, and 22.6-215, 3.7-155 mg kg-1. High soil Fe content led to great accumulation in T. matsutake (0.24-18.8 g kg-1). However, though the soil Mn content was higher than that of Zn and Cu, their concentrations in T. matsutake were comparable (21.1-487 vs. 38.7-329 and 24.9-217 mg kg-1). This suggested that T. matsutake prefers to accumulate Zn and Cu compared to Mn, and this is supported by the bioaccumulation factors (BAFs = 0.32-17.1 vs. 0.006-1.69). Fe was mainly stored in stipes, while Mn, Zn and Cu were stored in caps, and the translocation factors (TFs) were 0.58 vs. 1.28-1.94. Therefore, stipe Fe showed the highest health risk index (HRI) at 1.28-26.9, followed by cap Cu (1.01-2.33), while 98-100% of the Mn and Zn were risk-free. The higher concentration and greater risk of Fe was attributed to the significant effect of soil Fe content (R = 0.34) and soil pH (R = -0.57). This study suggested that Fe, as an essential mineral, may exert toxic effects via the consumption of T. matsutake from high geochemical background areas.

Unveiling the dynamic evolution of comparative advantage: assessing the competitiveness of China’s main winter and spring vegetable production regions

IntroductionChina is the world’s largest producer and consumer of vegetables, with all provinces, cities, and autonomous regions cultivating vegetables. The main production areas for winter and spring vegetables have higher accumulated temperatures, allowing for multiple planting seasons throughout the year. Therefore, this article examines the competitiveness of vegetable production in China’s main winter and spring vegetable producing areas from the perspective of the dynamic evolution of comparative advantages.MethodsDrawing on statistical data from 2000 to 2018 on the development of the vegetable industry in these key provinces, our analysis incorporates methods such as the Resource Endowment Coefficient, the Comprehensive Comparative Advantage Index, the entropy method, and systematic cluster analysis to explore the evolution of competitive advantages in vegetable production across various provinces and cities.Results and discussionThe findings reveal significant regional disparities in resource endowments and overall advantages in vegetable production during the winter and spring seasons. Notably, Chongqing, Hubei Province, and Shanghai exhibit a Resource Endowment Factor (EF) averaging above 1, indicating a relative abundance of production resources and a comparative advantage. Similarly, Hainan Province, the Guangxi Zhuang Autonomous Region, Guangdong Province, Fujian Province, Shanghai, and Zhejiang Province each demonstrate an average Comprehensive Advantage Index (AAI) exceeding 1, affirming their comprehensive comparative strengths. Overall, the competitiveness of vegetable production in these regions exhibits a fluctuating yet ascending trend, with notable disparities in competitive strengths across different areas. The ranking of annual average competitiveness scores in vegetable production is as follows: Shanghai > Zhejiang > Hubei > Hunan > Guangdong > Sichuan > Chongqing > Fujian > Guangxi > Jiangxi > Hainan > Yunnan. Based on these outcomes, the study proposes tailored strategic recommendations for provinces and cities according to their unique resource endowments and comparative advantages, aiming to enhance the sector’s productivity and competitive stance.

Conversion of monocropping to intercropping promotes rhizosphere microbiome functionality and soil nitrogen cycling

Intercropping can increase soil nutrient availability and provide greater crop yields for intensive agroecosystems. Despite its multiple benefits, how intercropping influences rhizosphere microbiome assemblages, functionality, and complex soil nitrogen cycling is not fully understood. Here, a three-year field experiment was carried out on different cropping system with five fertilization treatments at the main soybean production regions. We found that soybean yields in intercropped systems were on average 17 % greater than in monocropping system, regardless of fertilization treatments. We also found that intercropping systems significant increased network modularity (by 46 %) and functional diversity (by 11 %) than monocropping systems. Metagenomics analyses further indicated intercropping promotes microbiome functional adaptation, particularly enriching core functions related to nitrogen metabolism. Cropping patterns had a stronger influence on the functional genes associated with soil nitrogen cycling (R2 = 0.499). Monocropping systems increased the abundance of functional genes related to organic nitrogen ammonification, nitrogen fixation, and denitrification, while functional guilds of nitrate assimilation (by 28 %), nitrification (by 31 %), and dissimilatory nitrate reduction (by 10.1 %) genes were enriched in intercropping systems. Furthermore, we found that abiotic factors (i.e. AP, pH, and Moisture) are important drivers in shaping soil microbial community assemblage and nitrogen cycling. The functional genes include hzsB, and nrfA, and nxrA that affected by these biotic and abiotic variables were strongly related to crop yield (R2 = 0.076 ~ R2 = 0.249), suggesting a key role for maintaining crop production. We demonstrated that land use conversion from maize monocropping to maize-soybean intercropping diversify rhizosphere microbiome and functionality signatures, and intercropping increased key gene abundance related to soil nitrogen cycling to maintain the advantage of crop yield. The results of this study significantly facilitate our understanding of the complex soil nitrogen cycling processes and lay the foundation for manipulating desired specific functional taxa for improved crop productivity under sustainable intensification.

Enhancing epidemiological knowledge of Botryosphaeriaceae in Mexican vineyards

Grapevine cultivation in Mexico is important, especially in the states of Baja California and Coahuila, which are the main wine production regions in the country. Grapevine trunk diseases (GTDs) impact productivity and cause substantial economic losses, with Botryosphaeria dieback being one of the most destructive. This disease is caused by fungi in the Botryosphaeriaceae, including species of Botryosphaeria, Diplodia, Lasiodiplodia, and Neofusicoccum. To date, Lasiodiplodia spp. are the primary Botryosphaeriaceae fungi reported in Mexico. The present study aimed to enhance the epidemiological knowledge of Botryosphaeriaceae in Mexican vineyards. Samples from grapevine plants exhibiting disease symptoms were collected from the states of Baja California and Coahuila. Of a total of 37 Botryosphaeriaceae isolates, six species were identified: Neofusicoccum parvum, N. australe, N. vitifusiforme, Botryosphaeria dothidea, Diplodia corticola, and D. seriata. Neofusicoccum parvum isolates were the most virulent, but were less virulent than previously reported Lasiodiplodia spp. The optimum growth temperatures for N. parvum and B. dothidea were from 28 to 30°C, but 25°C for D. seriata, N. vitifusiforme, and N. australe isolates. Only D. seriata isolates recovered growth when transferred to room temperature after exposure to 37°C or 40°C. This report is the first identification of B. dothidea and N. parvum as causative agents of Botryosphaeria dieback in the vine-growing regions of Mexico.

First report of Fusarium solani causing pink root disease on garlic (Allium sativum L.) in Lombok, Indonesia

Abstract Garlic (Allium sativum L.) is one of the important agricultural commodities in Indonesia, with Nusa Tenggara Barat (NTB) Province among the main producing regions. Several incidences of pink root disease symptoms have been reported in garlic plantations in the Sembalun highlands of Eastern Lombok District, NTB Province, Indonesia. This study aimed to isolate and identify the fungal pathogen responsible for pink root disease in garlic plants in this region. Fungal pathogens isolated from diseased garlic plants were morphologically investigated under a microscope and molecularly identified which includes DNA isolation, PCR, sequencing, processing of sequencing data, and NCBI blasts. The symptoms of the diseased plants in the fields were leaf wilting, stunted growth, and shortened roots which first turn light pink, then darken through red and purple, later turn black, and die. The mycelial colour of the pathogen on Potato Dextrose Agar (PDA) media was growth white. Observation of the pathogen under a microscope indicated that the pathogen produced oval micro-conidia and crescent-shaped macro-conidia. Based on the BLAST results which are based on the NCBI database, it can be concluded that the species of Fusarium solani was responsible for invading the garlic plant. This is the first report of pink root disease on Lombok Island in Indonesia.

A Biological Comparison of Three Colletotrichum Species Associated with Alfalfa Anthracnose in Northern China.

Anthracnose caused by various species of Colletotrichum is one of the most prevalent diseases in alfalfa worldwide that not only reduces forage yields but also severely compromises forage quality. A comprehensive survey was conducted in 2020 in the main production regions of northern China. The survey results showed that alfalfa anthracnose is prevalent in northern China, with the disease incidence ranging from 9% to 45% and the disease index from 5 to 17 (maximum possible score: 100). In total, 24 isolates were collected and identified as three Colletotrichum species (C. trifolii, C. truncatum and C. americae-borealis) based on morphological characteristics and phylogenetic analysis (combined sequences ITS, HIS3, ACT and GAPDH). The three species displayed remarkable environmental adaptability, exhibiting a capacity for growth, sporulation and conidial germination in temperatures ranging from 4 to 35 °C and in different nutrient conditions. Pathogenicity assays showed that C. trifolii was more virulent than the other two species, although the growth vigor (in terms of colony diameter, sporulation and conidial germination) of C. truncatum was the greatest.

Occurrence and characterization of Puccinia triticina in Zimbabwe

Wheat leaf rust caused by Puccinia triticina (Pt) is an important disease of bread wheat in Zimbabwe. This study was carried out to determine the distribution, avirulence/virulence profile(s) and genotypic variation among isolates of the pathogen. In total, 104 single-pustule isolates were established from infected wheat samples that were collected from the main wheat production regions in Zimbabwe in surveys conducted from 2019 to 2021. Phenotyping of the isolates on a set of 46 differential and additional wheat lines revealed a single Pt race, MCDS. For genotypic assessment, 48 Pt isolates, including eight South African races, were genotyped with 19 microsatellite markers. Using the Bayesian model, isolates were subdivided into three clusters in both a DARwin neighbour-joining tree and STRUCTURE analysis. The results confirmed a high genetic similarity between the Zimbabwean Pt isolates and the South African races MCDS, MCPS and MFPS. Furthermore, Pt isolates from Zimbabwe were clonal, which was in accordance with all 104 isolates that typed to race MCDS.

Prevention of Cadmium Contamination by Microbial Inoculant and Its Potential Mechanism

Cadmium is the main heavy metal contaminant of food in the world. The extent of cadmium pollution in peanut in China remains unclear. To determine the cadmium pollution level in peanut, samples from the main producing regions in China were assessed. The findings revealed that the cadmium pollution level in Chinese peanuts was relatively low. Moreover, the Aflatoxin Rhizobia Couple B. amyloliquefaciens, B. laterosporu, B. mucilaginosus, E. ludwiggi (ARC-BBBE) microbial inoculants on cadmium contamination in peanut were evaluated. The fertilization methods were categorized into conventional fertilization and conventional fertilization supplemented with 60 kg/hectare of microbial inoculant ARC-BBBE as the base fertilizer. The cadmium contents in the soil and peanut plant parts were detected and analyzed. The results demonstrated that the microbial inoculant ARC-BBBE significantly reduced the total cadmium content in peanut, as well as the available cadmium and exchangeable cadmium in soil. Furthermore, the pH and urease and alkaline phosphatase activities in soil were significantly enhanced, suggesting that the microbial inoculant ARC-BBBE decreased cadmium content in soil and reduced the cadmium uptake by plants through a combination of the action of the bacteria itself and the secretion of extracellular substances. This ultimately achieves the goal of reducing the cadmium content in peanut seeds.

Identification of Meloidogyne Species on Traditional Chinese Medicine Plants in the Qinling Mountain Area of China and Their Aggressiveness to Different Medicinal Herbs.

Root-knot nematodes (Meloidogyne spp.) are plant-parasitic nematodes that cause serious damage worldwide. There are many species of traditional Chinese medicine (TCM) plants, but only a few have been reported to be infected by Meloidogyne species. From 2020 to 2022, a survey was conducted in the Qinling mountain area, which is the main production region of TCM plants in China. Obvious galling symptoms were observed on the root systems of 15 species of TCM plants. Females were collected from diverse diseased TCM plants and subsequently identified at morphological and molecular levels. Among the 20 diseased root samples collected, Meloidogyne hapla populations were identified in 12 samples (60%), and M. incognita populations were identified in eight samples (40%). Among the 15 species of diseased TCM plants, eight species, namely, Scutellaria baicalensis, Leonurus japonicus, Dioscorea zingiberensis, Cornus officinalis, Viola philippica, Achyranthes bidentata, Senecio scandens, and Plantago depressa, were reported to be infected by Meloidogyne species for the first time. The host status of five species of TCM plants for two M. hapla isolates and one M. incognita isolate from TCM plants in this study was then evaluated. Differences in TCM plants' response to nematode infection were apparent when susceptibility was evaluated by the egg counts per gram of fresh root and the reproduction factor of the nematodes. Among the five species of TCM plants tested, Salvia miltiorrhiza and Gynostemma pentaphyllum were the most susceptible, while S. baicalensis and V. philippica were not considered suitable hosts for M. hapla or M. incognita.

Biological strategies to control caterpillars in soybean, cotton and maize in the main producing regions of Brazil

Soybean, cotton and maize crops represent some of the main agricultural commodities grown during the hot season in Brazil. In order to provide more effective control options to producers, it is essential to deepen studies on management strategies using biological insecticides. This study aims to evaluate the effectiveness of the application of the bioinsecticide Ocimum basilicum, in controlling the caterpillar complex, notably the looper (Chrysodeixis includens) in soybeans, the fall armyworm (Spodoptera frugiperda) in maize and the cotton leafworm caterpillar (Alabama argillacea) in cotton, as a control alternative aimed at mitigating damage and contributing to more sustainable agricultural practices. Experiments were carried out in each culture in various environments in the states of MT, MS, SP and PR in Brazil. A randomized block design with four replications was used in all the experiments. Agronomic traits, insect pest incidence and insecticide efficiency were evaluated for each of the crops. The data were subjected to after being subjected to stratified analysis of variance for each experimental phase. The interaction significant interactions were broken down to simple effects using Tukey's multiple comparison test of means at 5% probability. Subsequently, Scott-Knott mean grouping was used at 5% probability. Management using 1L per hectare of basil extract (Ocimum basilicum L) demonstrated satisfactory efficacy in controlling the caterpillar complex, approaching the results obtained with traditional chemical control.

Multi-Dimensional Impacts of Climate Change on China’s Food Security during 2002–2021

Climate change poses a significant threat to food security, necessitating a thorough examination across multiple dimensions. Establishing appropriate food security evaluation indicators that align with the evolving concept of food security is imperative. This study enhances food security evaluation by designing a multi-dimensional framework and analyzing the impact of climate variations across various regions from 2002 to 2021. By constructing a food security evaluation system that encompasses the production quantity and quality, sustainability, affordability, and resources, and utilizing the entropy method for accurate weighting, the impacts of climate variations on food security are accessed using a climate–economic model. The food security structure in China largely mirrors the regional division of grain, with the production quantity being the primary contributor. Overall, China’s food security has generally demonstrated improvements across various dimensions, with the exception of production quality. Regarding climate change, which encompasses variations in mean states and climate extremes, the panel regression analysis uncovers a negative linear relationship between food security and temperature. Conversely, the impact of precipitation on food security is non-linear, manifesting as inverse U-shaped patterns. In regions with balanced production and consumption, both accumulated temperatures and extreme high temperatures have a negative linear effect on food security. On the other hand, both accumulated and extreme precipitation exhibit inverse U-shaped non-linear impacts on food security in the main production and main consumption regions. These findings highlight the intricate interplay between climate change, regional disparities, and food security in China, emphasizing the need to consider multi-dimensional factors and regional variations in addressing food security challenges. These insights are invaluable for policymaking and planning aimed at enhancing food security in China.