Ten physical and environmental variables collected from an on-the-go soil sensor at two field sites (MF3E and MF11S) in Mississippi, USA, were analyzed to assess soil variability and the interrelationships among the m...Ten physical and environmental variables collected from an on-the-go soil sensor at two field sites (MF3E and MF11S) in Mississippi, USA, were analyzed to assess soil variability and the interrelationships among the measurements. At MF3E, moderate variability was observed in apparent electrical conductivity shallow (ECas), slope, and ECa ratio measurements, with coefficients of variation ranging from 20% to 27%. In contrast, MF11S exhibited higher variability, particularly in ECas and ECad (deep) measurements, which exceeded 30% in their coefficient of variation values, indicating significant differences in soil composition and moisture content. Correlation analysis revealed strong positive relationships between the near-infrared-to-red ratio and red reflectance (r = 0.897***) soil values at MF3E. MF11S demonstrated a strong negative correlation between ECas and ECad readings with the x-coordinate (r ***). Scatter plots and fitted models illustrated the complexity of relationships, with many showing nonlinear trends. These findings emphasize the need for continuous monitoring and advanced modeling to understand the dynamic nature of soil properties and their implications for agricultural practices. Future research should explore the underlying mechanisms driving variability in the soil characteristics to enhance soil management strategies at the study sites.展开更多
Background India harbors the world’s largest cattle population,encompassing over 50 distinct Bos indicus breeds.This rich genetic diversity underscores the inadequacy of a single reference genome to fully capture the...Background India harbors the world’s largest cattle population,encompassing over 50 distinct Bos indicus breeds.This rich genetic diversity underscores the inadequacy of a single reference genome to fully capture the genomic landscape of Indian cattle.To comprehensively characterize the genomic variation within Bos indicus and,specifically,dairy breeds,we aim to identify non-reference sequences and construct a comprehensive pangenome.Results Five representative genomes of prominent dairy breeds,including Gir,Kankrej,Tharparkar,Sahiwal,and Red Sindhi,were sequenced using 10X Genomics‘linked-read’technology.Assemblies generated from these linked-reads ranged from 2.70 Gb to 2.77 Gb,comparable to the Bos indicus Brahman reference genome.A pangenome of Bos indicus cattle was constructed by comparing the newly assembled genomes with the reference using alignment and graph-based methods,revealing 8 Mb and 17.7 Mb of novel sequence respectively.A confident set of 6,844 Non-reference Unique Insertions(NUIs)spanning 7.57 Mb was identified through both methods,representing the pange-nome of Indian Bos indicus breeds.Comparative analysis with previously published pangenomes unveiled 2.8 Mb(37%)commonality with the Chinese indicine pangenome and only 1%commonality with the Bos taurus pange-nome.Among these,2,312 NUIs encompassing~2 Mb,were commonly found in 98 samples of the 5 breeds and des-ignated as Bos indicus Common Insertions(BICIs)in the population.Furthermore,926 BICIs were identified within 682 protein-coding genes,54 long non-coding RNAs(lncRNA),and 18 pseudogenes.These protein-coding genes were enriched for functions such as chemical synaptic transmission,cell junction organization,cell-cell adhesion,and cell morphogenesis.The protein-coding genes were found in various prominent quantitative trait locus(QTL)regions,suggesting potential roles of BICIs in traits related to milk production,reproduction,exterior,health,meat,and carcass.Notably,63.21%of the bases within the BICIs call set contained interspersed repeats,predominantly Long Inter-spersed Nuclear Elements(LINEs).Additionally,70.28%of BICIs are shared with other domesticated and wild species,highlighting their evolutionary significance.Conclusions This is the first report unveiling a robust set of NUIs defining the pangenome of Bos indicus breeds of India.The analyses contribute valuable insights into the genomic landscape of desi cattle breeds.展开更多
Emerging new races of wheat stem rust(Puccinia graminis f.sp.tritici)are threatening global wheat(Triticum aestivum L.)production.Host resistance is the most effective and environmentally friendly method of controllin...Emerging new races of wheat stem rust(Puccinia graminis f.sp.tritici)are threatening global wheat(Triticum aestivum L.)production.Host resistance is the most effective and environmentally friendly method of controlling stem rust.The stem rust resistance gene Sr59 was previously identified within a T2DS 2RL wheat-rye whole arm translocation,providing broad-spectrum resistance to various stem rust races.Seedling evaluation,molecular marker analysis,and cytogenetic studies identified wheat-rye introgression line#284 containing a new translocation chromosome T2BL 2BS-2RL.This line has demonstrated broad-spectrum resistance to stem rust at the seedling stage.Seedling evaluation and cytogenetic analysis of three backcross populations between the line#284 and the adapted cultivars SLU-Elite,Navruz,and Linkert confirmed that Sr59 is located within the short distal 2RL translocation.This study aimed physical mapping of Sr59 in the 2RL introgression segment and develop a robust molecular marker for marker-assisted selection.Using genotyping-by-sequencing(GBS),GBS-derived SNPs were aligned with full-length annotated rye nucleotide-binding leucine-rich repeat(NLR)genes in the parental lines CS ph1b,SLU238,SLU-Elite,Navruz,and Linkert,as well as in 33 BC4F5progeny.Four NLR genes were identified on the 2R chromosome,with Chr2R_NLR_60 being tightly linked to the Sr59resistance gene.In-silico functional enrichment analysis of the translocated 2RL region(25,681,915 bp)identified 223 genes,with seven candidate genes associated with plant disease resistance and three linked to agronomic performance,contributing to oxidative stress response,protein kinase activity,and cellular homeostasis.These findings facilitate a better understanding of the genetic basis of stem rust resistance provided by Sr59.展开更多
Ultrahigh-resolution Fourier transform ion cyclotron resonance mass spectrometry(FT-ICR MS)is an advanced instrument capable of separating and determining molecular mass-to-charge ratios with sub-ppm level accuracy.A ...Ultrahigh-resolution Fourier transform ion cyclotron resonance mass spectrometry(FT-ICR MS)is an advanced instrument capable of separating and determining molecular mass-to-charge ratios with sub-ppm level accuracy.A typical FT-ICR MS spectrogram can identify hundreds to thousands of formulas in a complex sample.This perspective briefly examines the application of FT-ICR MS to soil organic matter and plant biomass studies,highlighting their significant contributions to sustainable agriculture and environment.展开更多
Remote sensing technologies play a vital role in our understanding of earthquakes and their impact on the Earth's surface.These technologies,including satellite imagery,aerial surveys,and advanced sensors,contribu...Remote sensing technologies play a vital role in our understanding of earthquakes and their impact on the Earth's surface.These technologies,including satellite imagery,aerial surveys,and advanced sensors,contribute significantly to our understanding of the complex nature of earthquakes.This review highlights the advancements in the integration of remote sensing technologies into earthquake studies.The combined use of satellite imagery and aerial photography in conjunction with geographic information systems(GIS)has been instrumental in showcasing the significance of fusing various types of satеllitеdata sourcеs for comprеhеnsivееarthquakеdamagеassеssmеnts.However,remote sensing encounters challenges due to limited pre-event imagery and restricted postearthquake site access.Furthеrmorе,thеapplication of dееp-lеarning mеthods in assеssingеarthquakе-damagеd buildings dеmonstratеs potеntial for furthеr progrеss in this fiеld.Overall,the utilization of remote sensing technologies has greatly enhanced our comprehension of earthquakes and their effects on the Earth's surface.The fusion of remote sensing technology with advanced data analysis methods holds tremendous potential for driving progress in earthquake studies and damage assessment.展开更多
In response to the effectiveness of reforestation in controlling soil erosion,there has been a dramatic increase in forest coverage in the hilly red soil region of southern China.Aggregate stability and soil shear str...In response to the effectiveness of reforestation in controlling soil erosion,there has been a dramatic increase in forest coverage in the hilly red soil region of southern China.Aggregate stability and soil shear strength are indicators that reflect soil resistance to erosion and its ability to prevent shallow landslides,respectively.However,limited research has focused on the response of soil aggregate stability and shear strength to reforestation.We selected three types of reforestations(Phyllostachys edulis forest,Cunninghamia lanceolata(Lamb.)Hook.forest,Citrus sinensis(L.)Osbeck.orchard),a natural forest(mixed coniferous and broadleaf forests),and a fallow land as study plots,and measured root traits,and soil physicochemical traits,i.e.,pH,soil organic matter(SOC),Soil water content(SWC),soil bulk density(BD),soil cohesion(c),soil internal friction angle(φ)and analyzed their multiple interactions.The soil aggregate stability traits,refer to the mean weight diameter(MWD)and geometric mean diameter(GMD),exhibited a significant increase in reforested plots,approximately 200%compared to fallow land and 50%compared to natural forests.For soil shear strength the values were approximately 20%higher than in fallow land and approximately 10%lower than in natural forests.Soil aggregate stability and soil shear strength did not exhibit a significant positive correlation across all plots,and the underlying drivers of these traits were variable.For instance,in natural forest and timber stands,soil aggregate stability was mainly influenced by soil organic carbon,while soil shear strength was primarily affected by root length density.In economic forest,aggregate stability and shear strength are mainly affected by organic carbon.Overall,we found that vegetation restoration enhances soil erosion resistance,however,the primary drivers for the improvement of aggregate stability(soil organic carbon)and shear strength(root length density)are different.Therefore,in future benefit assessments of vegetation restoration projects aimed at soil erosion control,different indicators should be considered based on specific conditions.展开更多
Oxalate content in spinach is a key trait of interest due to its relevance to human health.Understanding the genetic basis of it can facilitate the development of spinach varieties with reduced oxalate levels.In pursu...Oxalate content in spinach is a key trait of interest due to its relevance to human health.Understanding the genetic basis of it can facilitate the development of spinach varieties with reduced oxalate levels.In pursuit of understanding the genetic determinants,a diverse panel comprising 288 spinach accessions underwent thorough phenotyping of oxalate content and were subjected to whole-genome resequencing,resulting in a comprehensive dataset encompassing 14386 single-nucleotide polymorphisms(SNPs).Leveraging this dataset,we conducted a genome-wide association study(GWAS)to identify noteworthy SNPs associated with oxalate content.Furthermore,we employed genomic prediction(GP)via cross-prediction,utilizing five GP models,to assess genomic estimated breeding values(GEBVs)for oxalate content.The observed normal distribution and the wide range of oxalate content,exceeding 600.0 mg$100 g^(-1),underscore the complex and quantitative nature of this trait,likely influenced by multiple genes.Additionally,our analysis revealed distinct stratification,delineating the population into four discernible subpopulations.Furthermore,GWAS analysis employing five models in GAPIT 3 and TASSEL 5 unveiled nine significant SNPs(four SNPs on chromosome 1 and five on chromosome 5)associated with oxalate content.These loci exhibited associations with six candidate genes,which might have potential contribution to oxalate content regulation.Remarkably,our GP models exhibited notable predictive abilities,yielding average accuracies of up to 0.51 for GEBV estimation.The integration of GWAS and GP approaches offers a holistic comprehension of the genetic underpinnings of oxalate content in spinach.These findings offered a promising avenue for the development of spinach cultivars and hybrids optimized for oxalate levels,promoting consumer health.展开更多
Crop leaf area index(LAI)and biomass are two major biophysical parameters to measure crop growth and health condition.Measuring LAI and biomass in field experiments is a destructive method.Therefore,we focused on the ...Crop leaf area index(LAI)and biomass are two major biophysical parameters to measure crop growth and health condition.Measuring LAI and biomass in field experiments is a destructive method.Therefore,we focused on the application of unmanned aerial vehicles(UAVs)in agriculture,which is a cost and labor-efficientmethod.Hence,UAV-captured multispectral images were applied to monitor crop growth,identify plant bio-physical conditions,and so on.In this study,we monitored soybean crops using UAV and field experiments.This experiment was conducted at theMAFES(Mississippi Agricultural and Forestry Experiment Station)Pontotoc Ridge-Flatwoods Branch Experiment Station.It followed a randomized block design with five cover crops:Cereal Rye,Vetch,Wheat,MC:mixed Mustard and Cereal Rye,and native vegetation.Planting was made in the fall,and three fertilizer treatments were applied:Synthetic Fertilizer,Poultry Litter,and none,applied before planting the soybean,in a full factorial combination.We monitored soybean reproductive phases at R3(initial pod development),R5(initial seed development),R6(full seed development),and R7(initial maturity)and used UAV multispectral remote sensing for soybean LAI and biomass estimations.The major goal of this study was to assess LAI and biomass estimations from UAV multispectral images in the reproductive stages when the development of leaves and biomass was stabilized.Wemade about fourteen vegetation indices(VIs)fromUAVmultispectral images at these stages to estimate LAI and biomass.Wemodeled LAI and biomass based on these remotely sensed VIs and ground-truth measurements usingmachine learning methods,including linear regression,Random Forest(RF),and support vector regression(SVR).Thereafter,the models were applied to estimate LAI and biomass.According to the model results,LAI was better estimated at the R6 stage and biomass at the R3 stage.Compared to the other models,the RF models showed better estimation,i.e.,an R^(2) of about 0.58–0.68 with an RMSE(rootmean square error)of 0.52–0.60(m^(2)/m^(2))for the LAI and about 0.44–0.64 for R^(2) and 21–26(g dry weight/5 plants)for RMSE of biomass estimation.We performed a leave-one-out cross-validation.Based on cross-validatedmodels with field experiments,we also found that the R6 stage was the best for estimating LAI,and the R3 stage for estimating crop biomass.The cross-validated RF model showed the estimation ability with an R^(2) about 0.25–0.44 and RMSE of 0.65–0.85(m^(2)/m^(2))for LAI estimation;and R^(2) about 0.1–0.31 and an RMSE of about 28–35(g dry weight/5 plants)for crop biomass estimation.This result will be helpful to promote the use of non-destructive remote sensing methods to determine the crop LAI and biomass status,which may bring more efficient crop production and management.展开更多
Background GOSSYM is a mechanistic,process-based cotton model that can simulate cotton crop growth and development,yield,and fiber quality.Its fiber quality module was developed based on controlled experiments explici...Background GOSSYM is a mechanistic,process-based cotton model that can simulate cotton crop growth and development,yield,and fiber quality.Its fiber quality module was developed based on controlled experiments explicitly conducted on the Texas Marker^(-1)(TM1)variety,potentially making its functional equations more aligned with this cultivar.To assess the model’s broader applicability,this study analyzed fiber quality data from 40 upland cotton cultivars,including TM1.The measured fiber quality from all cultivars was then compared with the modelsimulated fiber quality.Results Among the 40 upland cultivars,fiber strength varied from 28.4 cN·tex^(-1) to 34.6 cN·tex^(-1),fiber length ranged from 27.1 mm to 33.3 mm,micronaire value ranged from 2.7 to 4.6,and length uniformity index varied from 82.3%to 85.5%.The model simulated fiber quality closely matched the measured values for TM1,with the absolute percentage error(APE)being less than 0.92%for fiber strength,fiber length,and length uniformity index and 4.7%for micronaire.However,significant differences were observed for the other cultivars.The Pearson correlation coefficient(r)between the measured and simulated values was negative for all fiber quality traits,and Wilmotts’s index of agreement(WIA)was below 0.45,indicating a strong model bias toward TM1 without incorporating cultivar-specific parameters.After incorporating cultivar-specific parameters,the model’s performance improved significantly,with an average r-value of 0.84 and WIA of 0.88.Conclusions The adopted methodology and estimated cultivar-specific parameters improved the model’s simulation accuracy.This approach can be applied to newer cotton cultivars,enhancing the GOSSYM model’s utility and its applicability for agricultural management and policy decisions.展开更多
Sugarcane is recognized as the fifth largest crop globally,supplying 80%of sugar and 40%of bioenergy production.However,sugarcane genetic research has significantly lagged behind other crops due to its complex genetic...Sugarcane is recognized as the fifth largest crop globally,supplying 80%of sugar and 40%of bioenergy production.However,sugarcane genetic research has significantly lagged behind other crops due to its complex genetic background,high ploidy(8-13×),aneuploidy,limited flowering,and a long growth cycle(more than one year).Cross breeding began in 1887 following the discovery that sugarcane seeds could germinate.Both self-and cross-pollination and selection were conducted by sugarcane breeders,but new cultivars were often eliminated due to disease susceptibility.Within the Saccharum genus,different species possess variable numbers of chromosomes.Wild sugarcane species intercrossed with each other,leading to development of the‘Nobilization’breeding strategy,which significantly improved yield,sucrose,fiber content,and disease resistance,and accelerated genetic improvement of cultivars.In recent years,scientific achievements have also been made in sugarcane genome sequencing,molecular marker development,genetic linkage map construction,localization of quantitative trait locus(QTL),and trait-associated gene identification.This review focuses on the progress in sugarcane genetic research,analyzes the technical difficulties faced,presents opportunities and challenges,and provides guidance and references for future sugarcane genetics research and cultivar breeding.Finally,it offers directions for future on sugarcane genetics.展开更多
Non-destructive time-series assessment of chlorophyll content in flag-leaf(FLC)accurately mimics the senescence rate and the identification of genetic loci associated with senescence provides valuable knowledge to imp...Non-destructive time-series assessment of chlorophyll content in flag-leaf(FLC)accurately mimics the senescence rate and the identification of genetic loci associated with senescence provides valuable knowledge to improve yield stability under stressed environments.In this study,we employed both unmanned aerial vehicles(UAVs)equipped with red–green–blue(RGB)camera and ground-based SPAD-502 instrument to conduct temporal phenotyping of senescence.A total of 262 recombinant inbred lines derived from the cross of Zhongmai 578/Jimai 22 were evaluated for senescence-related traits across three environments,spanning from heading to 35 d post-anthesis.The manual senescence rate(MSR)was quantified using the FLC and the active accumulated temperature,and UAV derived vegetation index were utilized to assess the stay-green rate(USG)facilitating the identification of senescent and stay-green lines.Results indicated that higher senescence rates significantly impacted grain yield,primarily by influencing thousand-kernel weight,and plant height.Quantitative trait loci(QTL)mapping for FLC,USG,and MSR using the 50K SNP array identified 38 stable loci associated with RGB-based vegetation indices and senescence-related traits:among which 19 loci related to senescence traits from UAV and FLC were consistently detected across at least two growth stages,with nine loci likely representing novel QTL.This study highlights the potential of UAV-based high-throughput phenotyping and phenology in identifying critical loci associated with senescence rates in wheat,validating the relationship between senescence rates and yield-related traits in wheat,offering valuable opportunities for gene discovery and significant applications in breeding programs.展开更多
黄曲霉毒素是广泛存在于玉米中且具有剧毒的一种代谢产物，以美国农业部农业研究署（USDA-ARS） Toxicology and Mycotoxin Research Unit提供的2010年先锋玉米为研究对象，验证了高光谱成像技术对玉米中黄曲霉毒素检测的可行性。以甲...黄曲霉毒素是广泛存在于玉米中且具有剧毒的一种代谢产物，以美国农业部农业研究署（USDA-ARS） Toxicology and Mycotoxin Research Unit提供的2010年先锋玉米为研究对象，验证了高光谱成像技术对玉米中黄曲霉毒素检测的可行性。以甲醇为溶剂制备四种不同浓度的黄曲霉毒素溶液，并将其逐一滴在等量的4组共120粒玉米颗粒表面，以未处理的30粒洁净玉米作为一组对照样本，将大小、形状相似的150个样品随机分为训练集103个，验证集47个；对获取的400～1000 nm波段范围内的高光谱图像，先进行标准正态变量变换（standard normal variate transformation ，SNV）预处理，然后引入基于 Fisher判别最小误判率的方法选择最优波长，并以所选波长作为Fisher判别分析法的输入建立判别模型，对玉米颗粒表面不同浓度的黄曲霉毒素进行识别，最后对模型判别正确率进行了验证。结果表明，选取四个最优波长（812.42，873.00，900.36和965.00 nm ）时Fisher判别分析模型对训练集与验证集的准确率分别为87.4％和80.9％。该方法为含黄曲霉毒素玉米颗粒便携式检测仪器的开发，以及对田间霉变玉米自然代谢产生毒素的检测奠定了技术基础。展开更多
文摘Ten physical and environmental variables collected from an on-the-go soil sensor at two field sites (MF3E and MF11S) in Mississippi, USA, were analyzed to assess soil variability and the interrelationships among the measurements. At MF3E, moderate variability was observed in apparent electrical conductivity shallow (ECas), slope, and ECa ratio measurements, with coefficients of variation ranging from 20% to 27%. In contrast, MF11S exhibited higher variability, particularly in ECas and ECad (deep) measurements, which exceeded 30% in their coefficient of variation values, indicating significant differences in soil composition and moisture content. Correlation analysis revealed strong positive relationships between the near-infrared-to-red ratio and red reflectance (r = 0.897***) soil values at MF3E. MF11S demonstrated a strong negative correlation between ECas and ECad readings with the x-coordinate (r ***). Scatter plots and fitted models illustrated the complexity of relationships, with many showing nonlinear trends. These findings emphasize the need for continuous monitoring and advanced modeling to understand the dynamic nature of soil properties and their implications for agricultural practices. Future research should explore the underlying mechanisms driving variability in the soil characteristics to enhance soil management strategies at the study sites.
基金the project “Genomics for Conservation of Indigenous Cattle Breeds and for Enhancing Milk Yield, Phase-I” [BT/ PR26466/AAQ/1/704/2017], funded by the Department of Biotechnology (DBT ), Indiathe project “Identification of key molecular factors involved in resistance/susceptibility to paratuberculosis infection in indigenous breeds of cows” [BT/PR32758/AAQ/1/760/2019], which was also funded by Department of Biotechnology (DBT ), India。
文摘Background India harbors the world’s largest cattle population,encompassing over 50 distinct Bos indicus breeds.This rich genetic diversity underscores the inadequacy of a single reference genome to fully capture the genomic landscape of Indian cattle.To comprehensively characterize the genomic variation within Bos indicus and,specifically,dairy breeds,we aim to identify non-reference sequences and construct a comprehensive pangenome.Results Five representative genomes of prominent dairy breeds,including Gir,Kankrej,Tharparkar,Sahiwal,and Red Sindhi,were sequenced using 10X Genomics‘linked-read’technology.Assemblies generated from these linked-reads ranged from 2.70 Gb to 2.77 Gb,comparable to the Bos indicus Brahman reference genome.A pangenome of Bos indicus cattle was constructed by comparing the newly assembled genomes with the reference using alignment and graph-based methods,revealing 8 Mb and 17.7 Mb of novel sequence respectively.A confident set of 6,844 Non-reference Unique Insertions(NUIs)spanning 7.57 Mb was identified through both methods,representing the pange-nome of Indian Bos indicus breeds.Comparative analysis with previously published pangenomes unveiled 2.8 Mb(37%)commonality with the Chinese indicine pangenome and only 1%commonality with the Bos taurus pange-nome.Among these,2,312 NUIs encompassing~2 Mb,were commonly found in 98 samples of the 5 breeds and des-ignated as Bos indicus Common Insertions(BICIs)in the population.Furthermore,926 BICIs were identified within 682 protein-coding genes,54 long non-coding RNAs(lncRNA),and 18 pseudogenes.These protein-coding genes were enriched for functions such as chemical synaptic transmission,cell junction organization,cell-cell adhesion,and cell morphogenesis.The protein-coding genes were found in various prominent quantitative trait locus(QTL)regions,suggesting potential roles of BICIs in traits related to milk production,reproduction,exterior,health,meat,and carcass.Notably,63.21%of the bases within the BICIs call set contained interspersed repeats,predominantly Long Inter-spersed Nuclear Elements(LINEs).Additionally,70.28%of BICIs are shared with other domesticated and wild species,highlighting their evolutionary significance.Conclusions This is the first report unveiling a robust set of NUIs defining the pangenome of Bos indicus breeds of India.The analyses contribute valuable insights into the genomic landscape of desi cattle breeds.
基金the financial support from FORMAS(2018-01029)the Swedish Institute(01132-2022)for supporting Ivan Motsnyi’s visit and research at Swedish University of Agricultural Sciences。
文摘Emerging new races of wheat stem rust(Puccinia graminis f.sp.tritici)are threatening global wheat(Triticum aestivum L.)production.Host resistance is the most effective and environmentally friendly method of controlling stem rust.The stem rust resistance gene Sr59 was previously identified within a T2DS 2RL wheat-rye whole arm translocation,providing broad-spectrum resistance to various stem rust races.Seedling evaluation,molecular marker analysis,and cytogenetic studies identified wheat-rye introgression line#284 containing a new translocation chromosome T2BL 2BS-2RL.This line has demonstrated broad-spectrum resistance to stem rust at the seedling stage.Seedling evaluation and cytogenetic analysis of three backcross populations between the line#284 and the adapted cultivars SLU-Elite,Navruz,and Linkert confirmed that Sr59 is located within the short distal 2RL translocation.This study aimed physical mapping of Sr59 in the 2RL introgression segment and develop a robust molecular marker for marker-assisted selection.Using genotyping-by-sequencing(GBS),GBS-derived SNPs were aligned with full-length annotated rye nucleotide-binding leucine-rich repeat(NLR)genes in the parental lines CS ph1b,SLU238,SLU-Elite,Navruz,and Linkert,as well as in 33 BC4F5progeny.Four NLR genes were identified on the 2R chromosome,with Chr2R_NLR_60 being tightly linked to the Sr59resistance gene.In-silico functional enrichment analysis of the translocated 2RL region(25,681,915 bp)identified 223 genes,with seven candidate genes associated with plant disease resistance and three linked to agronomic performance,contributing to oxidative stress response,protein kinase activity,and cellular homeostasis.These findings facilitate a better understanding of the genetic basis of stem rust resistance provided by Sr59.
文摘Ultrahigh-resolution Fourier transform ion cyclotron resonance mass spectrometry(FT-ICR MS)is an advanced instrument capable of separating and determining molecular mass-to-charge ratios with sub-ppm level accuracy.A typical FT-ICR MS spectrogram can identify hundreds to thousands of formulas in a complex sample.This perspective briefly examines the application of FT-ICR MS to soil organic matter and plant biomass studies,highlighting their significant contributions to sustainable agriculture and environment.
基金funded through an appointment with the Agricultural Research Service,managed by the Oak Ridge Institute for Science and Education。
文摘Remote sensing technologies play a vital role in our understanding of earthquakes and their impact on the Earth's surface.These technologies,including satellite imagery,aerial surveys,and advanced sensors,contribute significantly to our understanding of the complex nature of earthquakes.This review highlights the advancements in the integration of remote sensing technologies into earthquake studies.The combined use of satellite imagery and aerial photography in conjunction with geographic information systems(GIS)has been instrumental in showcasing the significance of fusing various types of satеllitеdata sourcеs for comprеhеnsivееarthquakеdamagеassеssmеnts.However,remote sensing encounters challenges due to limited pre-event imagery and restricted postearthquake site access.Furthеrmorе,thеapplication of dееp-lеarning mеthods in assеssingеarthquakе-damagеd buildings dеmonstratеs potеntial for furthеr progrеss in this fiеld.Overall,the utilization of remote sensing technologies has greatly enhanced our comprehension of earthquakes and their effects on the Earth's surface.The fusion of remote sensing technology with advanced data analysis methods holds tremendous potential for driving progress in earthquake studies and damage assessment.
基金supported by the National Natural Science Foundation of China(NO.32201626)the Key Research and Development Program of Jiangxi Province(20223BBG74S01,20223BBG71013).
文摘In response to the effectiveness of reforestation in controlling soil erosion,there has been a dramatic increase in forest coverage in the hilly red soil region of southern China.Aggregate stability and soil shear strength are indicators that reflect soil resistance to erosion and its ability to prevent shallow landslides,respectively.However,limited research has focused on the response of soil aggregate stability and shear strength to reforestation.We selected three types of reforestations(Phyllostachys edulis forest,Cunninghamia lanceolata(Lamb.)Hook.forest,Citrus sinensis(L.)Osbeck.orchard),a natural forest(mixed coniferous and broadleaf forests),and a fallow land as study plots,and measured root traits,and soil physicochemical traits,i.e.,pH,soil organic matter(SOC),Soil water content(SWC),soil bulk density(BD),soil cohesion(c),soil internal friction angle(φ)and analyzed their multiple interactions.The soil aggregate stability traits,refer to the mean weight diameter(MWD)and geometric mean diameter(GMD),exhibited a significant increase in reforested plots,approximately 200%compared to fallow land and 50%compared to natural forests.For soil shear strength the values were approximately 20%higher than in fallow land and approximately 10%lower than in natural forests.Soil aggregate stability and soil shear strength did not exhibit a significant positive correlation across all plots,and the underlying drivers of these traits were variable.For instance,in natural forest and timber stands,soil aggregate stability was mainly influenced by soil organic carbon,while soil shear strength was primarily affected by root length density.In economic forest,aggregate stability and shear strength are mainly affected by organic carbon.Overall,we found that vegetation restoration enhances soil erosion resistance,however,the primary drivers for the improvement of aggregate stability(soil organic carbon)and shear strength(root length density)are different.Therefore,in future benefit assessments of vegetation restoration projects aimed at soil erosion control,different indicators should be considered based on specific conditions.
基金supported by USDA-SCRI(Grant Nos.2017-51181-26830 and 2023-51181-41321)USDA-AMS SCMP(Grant No.16SCCMAR0001)+1 种基金Arkansas Department of Agriculture SCBGP(Grant No.AM22SCBGPAR1130-00)USDA NIFA Hatch project(Grant Nos.ARK0VG2018 and ARK02440).
文摘Oxalate content in spinach is a key trait of interest due to its relevance to human health.Understanding the genetic basis of it can facilitate the development of spinach varieties with reduced oxalate levels.In pursuit of understanding the genetic determinants,a diverse panel comprising 288 spinach accessions underwent thorough phenotyping of oxalate content and were subjected to whole-genome resequencing,resulting in a comprehensive dataset encompassing 14386 single-nucleotide polymorphisms(SNPs).Leveraging this dataset,we conducted a genome-wide association study(GWAS)to identify noteworthy SNPs associated with oxalate content.Furthermore,we employed genomic prediction(GP)via cross-prediction,utilizing five GP models,to assess genomic estimated breeding values(GEBVs)for oxalate content.The observed normal distribution and the wide range of oxalate content,exceeding 600.0 mg$100 g^(-1),underscore the complex and quantitative nature of this trait,likely influenced by multiple genes.Additionally,our analysis revealed distinct stratification,delineating the population into four discernible subpopulations.Furthermore,GWAS analysis employing five models in GAPIT 3 and TASSEL 5 unveiled nine significant SNPs(four SNPs on chromosome 1 and five on chromosome 5)associated with oxalate content.These loci exhibited associations with six candidate genes,which might have potential contribution to oxalate content regulation.Remarkably,our GP models exhibited notable predictive abilities,yielding average accuracies of up to 0.51 for GEBV estimation.The integration of GWAS and GP approaches offers a holistic comprehension of the genetic underpinnings of oxalate content in spinach.These findings offered a promising avenue for the development of spinach cultivars and hybrids optimized for oxalate levels,promoting consumer health.
基金This research was supported in part by a postdoctoral research fellow appointment to the Agricultural Research Service(ARS)Research Participation Program administered by the Oak Ridge Institute for Science and Education(ORISE)through an interagency agreement between the U.S.Department of Energy(DOE)and the U.S.Department of Agriculture(USDA).
文摘Crop leaf area index(LAI)and biomass are two major biophysical parameters to measure crop growth and health condition.Measuring LAI and biomass in field experiments is a destructive method.Therefore,we focused on the application of unmanned aerial vehicles(UAVs)in agriculture,which is a cost and labor-efficientmethod.Hence,UAV-captured multispectral images were applied to monitor crop growth,identify plant bio-physical conditions,and so on.In this study,we monitored soybean crops using UAV and field experiments.This experiment was conducted at theMAFES(Mississippi Agricultural and Forestry Experiment Station)Pontotoc Ridge-Flatwoods Branch Experiment Station.It followed a randomized block design with five cover crops:Cereal Rye,Vetch,Wheat,MC:mixed Mustard and Cereal Rye,and native vegetation.Planting was made in the fall,and three fertilizer treatments were applied:Synthetic Fertilizer,Poultry Litter,and none,applied before planting the soybean,in a full factorial combination.We monitored soybean reproductive phases at R3(initial pod development),R5(initial seed development),R6(full seed development),and R7(initial maturity)and used UAV multispectral remote sensing for soybean LAI and biomass estimations.The major goal of this study was to assess LAI and biomass estimations from UAV multispectral images in the reproductive stages when the development of leaves and biomass was stabilized.Wemade about fourteen vegetation indices(VIs)fromUAVmultispectral images at these stages to estimate LAI and biomass.Wemodeled LAI and biomass based on these remotely sensed VIs and ground-truth measurements usingmachine learning methods,including linear regression,Random Forest(RF),and support vector regression(SVR).Thereafter,the models were applied to estimate LAI and biomass.According to the model results,LAI was better estimated at the R6 stage and biomass at the R3 stage.Compared to the other models,the RF models showed better estimation,i.e.,an R^(2) of about 0.58–0.68 with an RMSE(rootmean square error)of 0.52–0.60(m^(2)/m^(2))for the LAI and about 0.44–0.64 for R^(2) and 21–26(g dry weight/5 plants)for RMSE of biomass estimation.We performed a leave-one-out cross-validation.Based on cross-validatedmodels with field experiments,we also found that the R6 stage was the best for estimating LAI,and the R3 stage for estimating crop biomass.The cross-validated RF model showed the estimation ability with an R^(2) about 0.25–0.44 and RMSE of 0.65–0.85(m^(2)/m^(2))for LAI estimation;and R^(2) about 0.1–0.31 and an RMSE of about 28–35(g dry weight/5 plants)for crop biomass estimation.This result will be helpful to promote the use of non-destructive remote sensing methods to determine the crop LAI and biomass status,which may bring more efficient crop production and management.
基金supported by United States Department of Agriculture,Agricultural Research Service(No.58-8042-9-072)United States Department of Agriculture-National Institute of Food and Agriculture(No.2019-34263-30552)+1 种基金Management Information System(No.043050)United States Department of Agriculture-Agricultural Research Service-Non-Assistance Cooperative Agreement(No.58-6066-2-030).
文摘Background GOSSYM is a mechanistic,process-based cotton model that can simulate cotton crop growth and development,yield,and fiber quality.Its fiber quality module was developed based on controlled experiments explicitly conducted on the Texas Marker^(-1)(TM1)variety,potentially making its functional equations more aligned with this cultivar.To assess the model’s broader applicability,this study analyzed fiber quality data from 40 upland cotton cultivars,including TM1.The measured fiber quality from all cultivars was then compared with the modelsimulated fiber quality.Results Among the 40 upland cultivars,fiber strength varied from 28.4 cN·tex^(-1) to 34.6 cN·tex^(-1),fiber length ranged from 27.1 mm to 33.3 mm,micronaire value ranged from 2.7 to 4.6,and length uniformity index varied from 82.3%to 85.5%.The model simulated fiber quality closely matched the measured values for TM1,with the absolute percentage error(APE)being less than 0.92%for fiber strength,fiber length,and length uniformity index and 4.7%for micronaire.However,significant differences were observed for the other cultivars.The Pearson correlation coefficient(r)between the measured and simulated values was negative for all fiber quality traits,and Wilmotts’s index of agreement(WIA)was below 0.45,indicating a strong model bias toward TM1 without incorporating cultivar-specific parameters.After incorporating cultivar-specific parameters,the model’s performance improved significantly,with an average r-value of 0.84 and WIA of 0.88.Conclusions The adopted methodology and estimated cultivar-specific parameters improved the model’s simulation accuracy.This approach can be applied to newer cotton cultivars,enhancing the GOSSYM model’s utility and its applicability for agricultural management and policy decisions.
基金supported by the National Key Research and Development Program of China(2022YFD2301100)National Natural Science Foundation of China(32272156)+3 种基金Natural Science Foundation of Fujian Province,China(2022J01160)Central Publicinterest Scientific Institution Basal Research Fund(1630052024003,1630052024020)Chinese Academy of Tropical Agricultural Sciences for Science and Technology Innovation Team of National Tropical Agricultural Science Center(CATASCXTD202402)China Agriculture Research System of MOF and MARA(CARS-17).
文摘Sugarcane is recognized as the fifth largest crop globally,supplying 80%of sugar and 40%of bioenergy production.However,sugarcane genetic research has significantly lagged behind other crops due to its complex genetic background,high ploidy(8-13×),aneuploidy,limited flowering,and a long growth cycle(more than one year).Cross breeding began in 1887 following the discovery that sugarcane seeds could germinate.Both self-and cross-pollination and selection were conducted by sugarcane breeders,but new cultivars were often eliminated due to disease susceptibility.Within the Saccharum genus,different species possess variable numbers of chromosomes.Wild sugarcane species intercrossed with each other,leading to development of the‘Nobilization’breeding strategy,which significantly improved yield,sucrose,fiber content,and disease resistance,and accelerated genetic improvement of cultivars.In recent years,scientific achievements have also been made in sugarcane genome sequencing,molecular marker development,genetic linkage map construction,localization of quantitative trait locus(QTL),and trait-associated gene identification.This review focuses on the progress in sugarcane genetic research,analyzes the technical difficulties faced,presents opportunities and challenges,and provides guidance and references for future sugarcane genetics research and cultivar breeding.Finally,it offers directions for future on sugarcane genetics.
基金funded by the National Key Research and Development Program of China(2022ZD0115703)the National Natural Science Foundation of China(32372196)+1 种基金the Beijing Joint Research Program for Germplasm Innovation and New Variety Breeding(G20220628002)National Natural Science Foundation of China(32250410307)。
文摘Non-destructive time-series assessment of chlorophyll content in flag-leaf(FLC)accurately mimics the senescence rate and the identification of genetic loci associated with senescence provides valuable knowledge to improve yield stability under stressed environments.In this study,we employed both unmanned aerial vehicles(UAVs)equipped with red–green–blue(RGB)camera and ground-based SPAD-502 instrument to conduct temporal phenotyping of senescence.A total of 262 recombinant inbred lines derived from the cross of Zhongmai 578/Jimai 22 were evaluated for senescence-related traits across three environments,spanning from heading to 35 d post-anthesis.The manual senescence rate(MSR)was quantified using the FLC and the active accumulated temperature,and UAV derived vegetation index were utilized to assess the stay-green rate(USG)facilitating the identification of senescent and stay-green lines.Results indicated that higher senescence rates significantly impacted grain yield,primarily by influencing thousand-kernel weight,and plant height.Quantitative trait loci(QTL)mapping for FLC,USG,and MSR using the 50K SNP array identified 38 stable loci associated with RGB-based vegetation indices and senescence-related traits:among which 19 loci related to senescence traits from UAV and FLC were consistently detected across at least two growth stages,with nine loci likely representing novel QTL.This study highlights the potential of UAV-based high-throughput phenotyping and phenology in identifying critical loci associated with senescence rates in wheat,validating the relationship between senescence rates and yield-related traits in wheat,offering valuable opportunities for gene discovery and significant applications in breeding programs.
基金Supported by 863 Program of Chinese Science & Technology Ministry(2004AA2Z4120)and.by Scientific Research Foundation for Doctoral Discipline of Chinese Ministry(20040712018)
文摘结合中国现实国情,应用先进的单片机、G IS、GPS以及变量控制技术,设计了一种经济实用、先进的变量灌溉控制系统。系统设计采用AT 89C 51单片机作为系统微处理器,Jup iter GPS OEM二次开发作为GPS接收机,IC卡作为G IS数据传递媒体,并根据矢量法提出一种简单快速的田间定位和数据查询算法。灌溉机在田间工作时,系统可从GPS OEM得到位置信息,进行地块识别和位置判断;然后根据位置信息从IC卡的G IS信息中查询土壤属性数据或处方数据,结合机械行走速度、施水幅宽等进行运算,得出某一时刻的灌水量;最后向控制器发出指令,实现变量节水灌溉。田间和实验室模拟试验结果表明,自行研制的低成本GPS OEM接收机,可获得与A g GPS132相当的定位精度,控制系统能根据处方图的不同需水量,通过电磁阀驱动电路得到变化的输出,实现自动变量施水,系统也能根据需水量进行手动灌溉。
文摘黄曲霉毒素是广泛存在于玉米中且具有剧毒的一种代谢产物，以美国农业部农业研究署（USDA-ARS） Toxicology and Mycotoxin Research Unit提供的2010年先锋玉米为研究对象，验证了高光谱成像技术对玉米中黄曲霉毒素检测的可行性。以甲醇为溶剂制备四种不同浓度的黄曲霉毒素溶液，并将其逐一滴在等量的4组共120粒玉米颗粒表面，以未处理的30粒洁净玉米作为一组对照样本，将大小、形状相似的150个样品随机分为训练集103个，验证集47个；对获取的400～1000 nm波段范围内的高光谱图像，先进行标准正态变量变换（standard normal variate transformation ，SNV）预处理，然后引入基于 Fisher判别最小误判率的方法选择最优波长，并以所选波长作为Fisher判别分析法的输入建立判别模型，对玉米颗粒表面不同浓度的黄曲霉毒素进行识别，最后对模型判别正确率进行了验证。结果表明，选取四个最优波长（812.42，873.00，900.36和965.00 nm ）时Fisher判别分析模型对训练集与验证集的准确率分别为87.4％和80.9％。该方法为含黄曲霉毒素玉米颗粒便携式检测仪器的开发，以及对田间霉变玉米自然代谢产生毒素的检测奠定了技术基础。
