摘要
Inefficient nitrogen(N)utilization in agricultural production has led to many negative impacts such as excessive use of N fertilizers,redundant plant growth,greenhouse gases,long-lasting toxicity in ecosystem,and even effect on human health,indicating the importance to optimize N applications in cropping systems.Here,we present a multiseasonal study that focused on measuring phenotypic changes in wheat plants when they were responding to different N treatments under field conditions.Powered by drone-based aerial phenotyping and the AirMeasurer platform,we first quantified 6 N response-related traits as targets using plot-based morphological,spectral,and textural signals collected from 54 winter wheat varieties.Then,we developed dynamic phenotypic analysis using curve fitting to establish profile curves of the traits during the season,which enabled us to compute static phenotypes at key growth stages and dynamic phenotypes(i.e.,phenotypic changes)during N response.After that,we combine 12 yield production and N-utilization indices manually measured to produce N efficiency comprehensive scores(NECS),based on which we classified the varieties into 4 N responsiveness(i.e.,N-dependent yield increase)groups.The NECS ranking facilitated us to establish a tailored machine learning model for N responsiveness-related varietal classification just using N-response phenotypes with high accuracies.Finally,we employed the Wheat55K SNP Array to map single-nucleotide polymorphisms using N response-related static and dynamic phenotypes,helping us explore genetic components underlying N responsiveness in wheat.In summary,we believe that our work demonstrates valuable advances in N response-related plant research,which could have major implications for improving N sustainability in wheat breeding and production.
基金
supported by the National Natural Science Foundation of China(32070400 to J.Z.)
The drone-based phenotyping,field experiments,yield,and N measures were also supported by the Key Project of Modern Agriculture of Jiangsu Province(BE2019383)
J.Z.,R.J.,and G.Deakin were partially supported by the Allan&Gill Gray Philanthropies'sustainable productivity for crops improvement(G118688 to the University of Cambridge and Q-20-0370 to NIAB)
J.Z.and R.J.were supported by the One CGIAR's Seed Equal Initiative(5507-CGIA-07 to J.Z.),as well as the United Kingdom Research and Innovation's(UKRI)Biotechnology and Biological Sciences Research Council's(BBSRC)International Partnership Grant(BB/X511882/1).
作者简介
Guohui Ding,contributed equally to this work;Liyan Shen,contributed equally to this work;Jie Dai,contributed equally to this work;Address correspondence to:Ji Zhou,ji.zhou@njau.edu.cn,ji.zhou@niab.co(J.Z.);Address correspondence to:Xiu-e Wang,xiuew@njau.edu.cn(X.-e.W.)
