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第二十届中国作物学会学术年会

作者：陈文康 Xuan Zhang Jianghua Guo Zhenyuan Chen Xiangyu Zhao Xiuyi Fu Min Wang Shenghui Ji Pengfei Yin Ning Yang Lu Chen Lichun Cai Jing Xu Lili Zhang Yingjia Han Yingni Xiao Gen Xu Yuebin Wang Shuhui Wang Fang Yang David Jackson Jinkui Cheng Feng Qin Feng Tian Jianbing Yan Jiansheng Li 杨小红 state key laboratory of plant environmental resilience and national maize improvement center of china China Agricultural University national key laboratory of Crop Genetic improvement Huazhong Agricultural University Hubei Hongshan laboratory Cold Spring Harbor laboratory Cold Spring Harbor center for Crop Functional Genomics and Molecular Breeding China Agricultural University

【Objective】 Increasing maize yield per unit area plays an important role in food demands and security. As a key component of maize yield, kernel row number(KRN) has a direct effect on improving maize yield per unit area. Therefore, understanding the genetic basis of KRN and cloning the gene controlling KRN are of great significance for molecular breeding of new maize varieties with high yield. 【Method】Following a recombinantderived progeny testing strategy, we performed positional cloning of q KRN2 for KRN identified in a maize recombinant inbred line(RIL) population developed from a cross between an inbred line, B73, and an introgression line, MT-6, of which ～25% of its genome is derived from that of teosinte, the wild ancestor of maize. 【Result】We delimited qKRN2 to a 5799–base pair(bp) region that contained only one candidate gene, which we named KRN2(Kernel Row Number2). Selection in the noncoding upstream regions resulted in a reduction of KRN2 expression and an increased grain number through an increase in kernel rows. KRN2 encodes a WD40 protein and function synergistically with a gene of unknown function, DUF1644, by protein-protein interaction. Field tests show that knockout of KRN2 in maize increased grain yield by ～10%, with no apparent trade-off in other agronomic traits. To further test whether KRN2 has the same effects in the elite inbred lines and hybrids used in modern maize breeding, we used two strategies—CRISPR/Cas9-mediated gene editing and marker assisted selection(MAS)—to improve a set of maize elite inbred lines. Compared with the corresponding wild-types, most of the improved lines with null allele of KRN2 showed an increased KRN by ～0.8 to 2.1 rows. Subsequently, we developed two improved hybrids, Zhengdan958 and Nongda788, for yield testing and investigated their KRN and other agronomic traits. Remarkably, both improved Zhengdan958 and improved Nongda788 derived from crossing improved male parent and female parent could increase KRN by ～1.5

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Quantitative analysis of irrigation water productivity in the middle reaches of Heihe River Basin, Northwest china

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International Journal of Agricultural and Biological Engineering 2019年第5期12卷 119-125页

作者： Donghao Li Taisheng Du Yue Cao Manoj Kumar Shukla Di Wu Xiuwei Guo Shichao Chen center for Agricultural Water Research in china China Agricultural UniversityBeijing 100083China Department of plant and environmental Sciences New Mexico State UniversityNM 88003USA

With the growing shortage of surface water resources,it is of great significance for improving the irrigation water productivity(IWP)to ensure the water and food *** contribution of the driving factors of the IWP and the rational regulation of the input factors of agricultural production is *** this paper,118 and 80 sampling points were selected in Pingchuan and Liaoquan irrigation districts(PLID,the spacing of sampling point is approximately 1 km)and the middle reaches of the Heihe River basin(MHRB,the spacing of sampling point is approximately 10 km),*** characteristics and management measures near the sampling points were *** showed that the average value of the IWP in MHRB was 1.67 kg/m3,with a moderate heterogeneity in the *** main driving factors of IWP were irrigation,fertilization and planting *** the PLID,the contribution rates of soil factors and management measures to IWP were 20.6%and 35.2%,respectively,and the contribution of soil factors to IWP increased to 43.8%in the MHRB,while the contribution rate of management measures decreased to 24.8%.It shows that in a small irrigation districts,from the perspective of farmers,the improvement of IWP should be mainly controlled by management measures,while in the large area of watershed scale,the spatial differences in soil factors also need to be considered by the government management departments,when they want to increase IWP through regulating management measures.

关键词： irrigation water productivity driving factors quantitative analysis partial least squares maize

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Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction

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Molecular plant 2022年第11期15卷 1664-1695页

作者： Yunbi Xu Xingping Zhang Huihui Li Hongjian Zheng Jianan Zhang Michael S.Olsen Rajeev K.Varshney Boddupalli M.Prasanna Qian Qian Institute of Crop Sciences CIMMYT-ChinaChinese Academy of Agricultural SciencesBeijing 100081China CIMMYT-china Tropical maize Research center School of Food Science and EngineeringFoshan UniversityFoshanGuangdong 528231China Peking University Institute of Advanced Agricultural Sciences WeifangShandong 261325China CIMMYT-china Specialty maize Research center Shanghai Academy of Agricultural SciencesShanghai 201400China MolBreeding Biotechnology Co. Ltd.ShijiazhuangHebei 050035China national Nanfan Research Institute(Sanya) Chinese Academy of Agricultural SciencesSanyaHainan 572024China CIMMYT(International maize and Wheat improvement center) ICRAF CampusUnited Nations AvenueNairobiKenya state Agricultural Biotechnology Centre Centre for Crop and Food InnovationFood Futures InstituteMurdoch UniversityMurdochAustralia

The first paradigm of plant breeding involves direct selection-based phenotypic observation,followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and,more recently,by incorporation of molecular marker ***,plant performance or phenotype(P)is determined by the combined effects of genotype(G),envirotype(E),and genotype by environment interaction(GEI).Phenotypes can be predicted more precisely by training a model using data collected from multiple sources,including spatiotemporal omics(genomics,phenomics,and enviromics across time and space).Integration of 3D information profiles(G-P-E),each with multidimensionality,provides predictive breeding with both tremendous opportunities and great ***,we first review innovative technologies for predictive *** then evaluate multidimensional information profiles that can be integrated with a predictive breeding strategy,particularly envirotypic data,which have largely been neglected in data collection and are nearly untouched in model *** propose a smart breeding scheme,integrated genomic-enviromic prediction(iGEP),as an extension of genomic prediction,using integrated multiomics information,big data technology,and artificial intelligence(mainly focused on machine and deep learning).We discuss how to implement iGEP,including spatiotemporal models,environmental indices,factorial and spatiotemporal structure of plant breeding data,and cross-species prediction.A strategy is then proposed for prediction-based crop redesign at both the macro(individual,population,and species)and micro(gene,metabolism,and network)***,we provide perspectives on translating smart breeding into genetic gain through integrative breeding platforms and open-source breeding *** call for coordinated efforts in smart breeding through iGEP,institutional partnerships,and innovative technological support.

关键词： smart breeding genomic selection integrated genomic-enviromic selection spatiotemporal omics crop design machine and deep learning big data artificial intelligence

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A new phase of treasure hunting in plant genebanks

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Molecular plant 2023年第3期16卷 503-505页

作者： Guangwei Li Zhiyong Wang Yuxuan Meng Zheng Qing Fu Daowen Wang Kunpu Zhang state key laboratory of Wheat and maize Crop Science Center for Crop Genome Engineeringand College of AgronomyHenan Agricultural UniversityZhengzhouHenan 450002China The Shennong laboratory ZhengzhouHenan 450002China Department of Biological Sciences University of South CarolinaColumbiaSC 29208USA

The United Nations has estimated that the world population will surpass 8 billion on Nov.15,2022,and will continue to rise to 11.2 billion by *** that agricultural resources are limited,it will be a huge challenge to produce sufficient food to feed such a rapidly rising global ***,the ongoing climate changes are adding more pressures on worldwide crop *** cope with these problems,it is both imperative and urgent to develop the crop cultivars with higher yield potential,improved nutritional quality,and better resilience to environmental stresses.

关键词： pass phase adding

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