Accurate nitrogen(N)nutrition diagnosis is essential for improving N use efficiency in crop production.The widely used critical N(Nc)dilution curve traditionally depends solely on agronomic variables,neglecting crop w...Accurate nitrogen(N)nutrition diagnosis is essential for improving N use efficiency in crop production.The widely used critical N(Nc)dilution curve traditionally depends solely on agronomic variables,neglecting crop water status.With three-year field experiments with winter wheat,encompassing two irrigation levels(rainfed and irrigation at jointing and anthesis)and three N levels(0,180,and 270 kg ha1),this study aims to establish a novel approach for determining the Nc dilution curve based on crop cumulative transpiration(T),providing a comprehensive analysis of the interaction between N and water availability.The Nc curves derived from both crop dry matter(DM)and T demonstrated N concentration dilution under different conditions with different parameters.The equation Nc=6.43T0.24 established a consistent relationship across varying irrigation regimes.Independent test results indicated that the nitrogen nutrition index(NNI),calculated from this curve,effectively identifies and quantifies the two sources of N deficiency:insufficient N supply in the soil and insufficient soil water concentration leading to decreased N availability for root absorption.Additionally,the NNI calculated from the Nc-DM and Nc-T curves exhibited a strong negative correlation with accumulated N deficit(Nand)and a positive correlation with relative grain yield(RGY).The NNI derived from the Nc-T curve outperformed the NNI derived from the Nc-DM curve concerning its relationship with Nand and RGY,as indicated by larger R2 values and smaller AIC.The novel Nc curve based on T serves as an effective diagnostic tool for assessing winter wheat N status,predicting grain yield,and optimizing N fertilizer management across varying irrigation conditions.These findings would provide new insights and methods to improve the simulations of water-N interaction relationship in crop growth models.展开更多
Genotype-environment interaction(G×E)models have potential in digital breeding and crop phenotype pre-diction.Using genotype-specific parameters(GSPs)as a bridge,crop growth models can capture G×E and simula...Genotype-environment interaction(G×E)models have potential in digital breeding and crop phenotype pre-diction.Using genotype-specific parameters(GSPs)as a bridge,crop growth models can capture G×E and simulate plant growth and development processes.In this study,a dataset containing multi-environmental planting and flowering data for 169 genotypes,each with 700K single nucleotide polymorphism(SNP)markers was used.Three rice growth models(ORYZA,CERES-Rice,and RiceGrow),SNPs,and climatic indices were in-tegrated for flowering time prediction.Significant associations between GSPs and quantitative trait nucleotides(QTNs)were investigated using genome-wide association study(GWAS)methods.Several GSPs were associated with previously reported rice flowering genes,including DTH2,DTH3 and OsCOL15,demonstrating the genetic interpretability of the models.The rice models driven by SNPs-based GSPs showed a decrease in goodness of fit as reflected by increased root mean square errors(RMSE),compared to the traditional model calibration.The predictions of crop model were further modified using the machine learning(ML)methods and climate indicators.The accuracy of the modified predictions were comparable to what was achieved using the traditional calibration approach.In addition,the Multi-model ensemble(MME)was comparable to that of the best individual model.Implications of our findings can potentially facilitate molecular breeding and phenotypic prediction of rice.展开更多
Soil,as the largest terrestrial organic carbon reservoir,contains approximately 1500 petagrams of organic carbon in its top meter.This amount is roughly twice the carbon stored in the atmosphere and three times that i...Soil,as the largest terrestrial organic carbon reservoir,contains approximately 1500 petagrams of organic carbon in its top meter.This amount is roughly twice the carbon stored in the atmosphere and three times that in vegetation[1].Enhancing soil organic carbon(SOC)storage is an effective strategy for mitigating climate change.Moreover,SOC is a key indicator of soil quality and ecological health and plays a central role in the global carbon cycle.Given increasing concerns over global warming,food security,and environmental sustainability,accurately quantifying SOC at large scales with high spatial resolution and precision is essential.展开更多
Plant root-derived carbon(C)inputs(I_(root))are the primary source of C in mineral bulk soil.However,a fraction of I_(root)may lose quickly(I_(loss),e.g.,via rhizosphere microbial respiration,leaching and fauna feedin...Plant root-derived carbon(C)inputs(I_(root))are the primary source of C in mineral bulk soil.However,a fraction of I_(root)may lose quickly(I_(loss),e.g.,via rhizosphere microbial respiration,leaching and fauna feeding)without contributing to long-term bulk soil C storage,yet this loss has never been quantified,particularly on a global scale.In this study we integrated three observational global data sets including soil radiocarbon content,allocation of photo synthetically assimilated C,and root biomass distribution in 2,034 soil profiles to quantify I_(root)and its contribution to the bulk soil C pool.We show that global average I_(root)in the 0-200 cm soil profile is 3.5 Mg ha^(-1)yr^(-1),~80%of which(i.e.,I_(loss))is lost rather than co ntributing to long-term bulk soil C storage.I_(root)decreases exponentially with soil depth,and the top 20 cm soil contains>60%of total I_(root).Actual C input contributing to long-term bulk soil storage(i.e.,I_(root)-I_(loss))shows a similar depth distribution to I_(root).We also map I_(loss)and its depth distribution across the globe.Our results demonstrate the global significance of direct C losses which limit the contribution of I_(root)to bulk soil C storage;and provide spatially explicit data to facilitate reliable soil C predictions via separating direct C losses from total root-derived C inputs.展开更多
基金supported by the National Key Research and Development Program of China(2022YFD2001005)the Key Research&Development Program of Jiangsu province(BE2021358)+2 种基金the National Natural Science Foundation of China(32271989)the Natural Science Foundation of Jiangsu province(BK20220146)the Jiangsu Independent Innovation Fund Project of Agricultural Science and Technology[CX(23)3121].
文摘Accurate nitrogen(N)nutrition diagnosis is essential for improving N use efficiency in crop production.The widely used critical N(Nc)dilution curve traditionally depends solely on agronomic variables,neglecting crop water status.With three-year field experiments with winter wheat,encompassing two irrigation levels(rainfed and irrigation at jointing and anthesis)and three N levels(0,180,and 270 kg ha1),this study aims to establish a novel approach for determining the Nc dilution curve based on crop cumulative transpiration(T),providing a comprehensive analysis of the interaction between N and water availability.The Nc curves derived from both crop dry matter(DM)and T demonstrated N concentration dilution under different conditions with different parameters.The equation Nc=6.43T0.24 established a consistent relationship across varying irrigation regimes.Independent test results indicated that the nitrogen nutrition index(NNI),calculated from this curve,effectively identifies and quantifies the two sources of N deficiency:insufficient N supply in the soil and insufficient soil water concentration leading to decreased N availability for root absorption.Additionally,the NNI calculated from the Nc-DM and Nc-T curves exhibited a strong negative correlation with accumulated N deficit(Nand)and a positive correlation with relative grain yield(RGY).The NNI derived from the Nc-T curve outperformed the NNI derived from the Nc-DM curve concerning its relationship with Nand and RGY,as indicated by larger R2 values and smaller AIC.The novel Nc curve based on T serves as an effective diagnostic tool for assessing winter wheat N status,predicting grain yield,and optimizing N fertilizer management across varying irrigation conditions.These findings would provide new insights and methods to improve the simulations of water-N interaction relationship in crop growth models.
基金supported by the National Key Research and Development Program of China(2022YFD2001001)the Jiangsu Independent Innovation Fund Project of Agricultural Science and Technology[CX(21)1006]+1 种基金the Jiangsu Collaborative Innovation Center for Modern Crop Production(JCICMCP)the 111 Project.
文摘Genotype-environment interaction(G×E)models have potential in digital breeding and crop phenotype pre-diction.Using genotype-specific parameters(GSPs)as a bridge,crop growth models can capture G×E and simulate plant growth and development processes.In this study,a dataset containing multi-environmental planting and flowering data for 169 genotypes,each with 700K single nucleotide polymorphism(SNP)markers was used.Three rice growth models(ORYZA,CERES-Rice,and RiceGrow),SNPs,and climatic indices were in-tegrated for flowering time prediction.Significant associations between GSPs and quantitative trait nucleotides(QTNs)were investigated using genome-wide association study(GWAS)methods.Several GSPs were associated with previously reported rice flowering genes,including DTH2,DTH3 and OsCOL15,demonstrating the genetic interpretability of the models.The rice models driven by SNPs-based GSPs showed a decrease in goodness of fit as reflected by increased root mean square errors(RMSE),compared to the traditional model calibration.The predictions of crop model were further modified using the machine learning(ML)methods and climate indicators.The accuracy of the modified predictions were comparable to what was achieved using the traditional calibration approach.In addition,the Multi-model ensemble(MME)was comparable to that of the best individual model.Implications of our findings can potentially facilitate molecular breeding and phenotypic prediction of rice.
基金supported by the National Natural Science Foundation of China(42375116)the Scientific Innovation Projects of Meteorological Bureau in Nei Mongol Autonomous Region(nmqxkjcx202422)the Fundamental Research Funds for the Central Universities.
文摘Soil,as the largest terrestrial organic carbon reservoir,contains approximately 1500 petagrams of organic carbon in its top meter.This amount is roughly twice the carbon stored in the atmosphere and three times that in vegetation[1].Enhancing soil organic carbon(SOC)storage is an effective strategy for mitigating climate change.Moreover,SOC is a key indicator of soil quality and ecological health and plays a central role in the global carbon cycle.Given increasing concerns over global warming,food security,and environmental sustainability,accurately quantifying SOC at large scales with high spatial resolution and precision is essential.
基金supported by the National Key Research and Development Program(Grant No.2021YFE0114500)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDA26010103)the Major Program for Basic Research Project of Yunnan Province(Grant No.202101BC070002)。
文摘Plant root-derived carbon(C)inputs(I_(root))are the primary source of C in mineral bulk soil.However,a fraction of I_(root)may lose quickly(I_(loss),e.g.,via rhizosphere microbial respiration,leaching and fauna feeding)without contributing to long-term bulk soil C storage,yet this loss has never been quantified,particularly on a global scale.In this study we integrated three observational global data sets including soil radiocarbon content,allocation of photo synthetically assimilated C,and root biomass distribution in 2,034 soil profiles to quantify I_(root)and its contribution to the bulk soil C pool.We show that global average I_(root)in the 0-200 cm soil profile is 3.5 Mg ha^(-1)yr^(-1),~80%of which(i.e.,I_(loss))is lost rather than co ntributing to long-term bulk soil C storage.I_(root)decreases exponentially with soil depth,and the top 20 cm soil contains>60%of total I_(root).Actual C input contributing to long-term bulk soil storage(i.e.,I_(root)-I_(loss))shows a similar depth distribution to I_(root).We also map I_(loss)and its depth distribution across the globe.Our results demonstrate the global significance of direct C losses which limit the contribution of I_(root)to bulk soil C storage;and provide spatially explicit data to facilitate reliable soil C predictions via separating direct C losses from total root-derived C inputs.
