QTL Analysis of Some Agronomic Traits in Rice Under Different Growing Environments

The culm length, panicle length, spikelets per panicle and panicle exsertion were evaluated by using F2：3 population including 200 lines derived from the cross of indica and japonica Milyang 23/Jileng 1 under five different environments of Beijing （natural normal growing environment）, Kunming （natural cold environment）, Gongzhuling of China （cold water irrigation） and Chuncheon of Korea （natural normal growing environment and cold water irrigation）, and QTLs of these traits were analyzed by using SSR markers. The results showed that 44 QTLs related to these agronomic traits were detected under five different growing environments, and these QTLs have been located on 11 chromosomes except chromosome 9. The QTLs for qCLla, qCL1b, qCL5a, qCL6b, qPLla, qPL3a, qPL6b, qPL6c, qPL7b, qSP8b, qSPlc, qSP11a, qSP12, and qPE1 have been detected under more than two growing environments, and those that were little affected by environments, were stable QTLs. Among them, qCLla, qCLlb, qPLla, qSPlc, and qPE1 explained 24.2-55.2%, 22.7-39.9%, 12.5-27.7%, 14.4-33.5%, and 26.6-28.7% of observed phynotypic variation, respectively, which were major genes mainly appearing as overdominance. These QTLs cause the increase in action to culm length, panicle length, spikelets per panicle, and panicle exsertion under cold environment, showing that these QTLs were correlated with cold tolerance.

The environment,especially the minimum temperature,affects summer maize grain yield by regulating ear differentiation and grain development

Ear differentiation,grain development and their interaction with factors in the growing environment,such as temperature,solar radiation and precipitation,greatly influence grain number and grain weight,and ultimately affect summer maize production.In this study,field experiments involving different sowing dates were conducted over three years to evaluate the effects of temperature factors,average solar radiation and total precipitation on the growth process,ear differentiation,fertilization characteristics,grain filling and yield of summer maize varieties with different growth durations.Four hybrids were evaluated in Huang-Huai-Hai Plain(HHHP),China from 2018 to 2020 with five different sowing dates.The results showed that the grain yield formation of summer maize was strongly impacted by the environment from the silking(R1)to milking(R3)stage.Average minimum temperature(AT_(min))was the key environmental factor that determined yield.Reductions in the length of the growing season(r=–0.556,P<0.01)and the total floret number on ear(R^(2)=0.200,P<0.001)were found when AT_(min) was elevated from the emerging(VE)to R1 stage.Both grain-filling rate(R^(2)=0.520,P<0.001)and the floret abortion rate on ear(R^(2)=0.437,P<0.001)showed quadratic relationships with AT_(min) from the R1 to physiological maturity(R6)stage,while the number of days after the R1 stage(r=–0.756,P<0.01)was negatively correlated with AT_(min).An increase in AT_(min) was beneficial for the promotion of yield when it did not exceeded a certain level(above 23°C during the R1–R3 stage and 20–21°C during the R1-R6 stage).Enhanced solar radiation and precipitation during R1–R6 increased the grain-filling rate(R^(2)=0.562,P<0.001 and R^(2)=0.229,P<0.05,respectively).Compared with short-season hybrids,full-season hybrids showed much greater suitability for a critical environment.The coordinated regulation of AT_(min),ear differentiation and grain development at the pre-and post-silking stages improved maize yield by increasing total floret number and grain-filling rate,and by reducing the floret abortion rate on ear.

GIS-based evaluation of maize cultivar lodging resistance performance in target growing environments

Lodging in maize is one of the major problems in maize production worldwide,which causes serious yield and economic losses annually.By evaluating cultivar lodging resistance performance in target growing environments before cultivar extension and application,the risks and losses can be significantly reduced.In this study,a GIS-based quantitative method for evaluating maize cultivar lodging resistance performance in target growing environments was established based on full cognition of environment stress,cultivar resistance,and the interaction between them.At first,comprehensive environment lodging stress is measured by three factors:1)extreme wind event in maize vegetative stage which is the direct factor,2)soil potassium content in target growing environment which is an indirect factor affecting corn stem sturdiness,and 3)planting density which is a human influence factor.Quantification methods of extreme probability analysis,spatial interpolation,normalization,and so on were used.Then,maize cultivar lodging resistance was determined using cumulative frequency distribution analysis of tested lodging data.At last,an evaluation matrix was established combining environment lodging stress and cultivar lodging resistance together,which was very simple and easy to understand method and the result is promising providing good direct support in practical cultivar application.The method used in this study,at county-level,cultivar-level and stress-level with GIS,can facilitate the identification of better-adapted growing environments for a specific maize cultivar,and provide direct support for maize cultivar recommendation and extension,so as to reduce the risk and loss of lodging in maize.It is more easy-operational and feasible than traditional surveying approach,especially for large-scale spatial trend analysis.So it is of both academic significance in accelerating precision agriculture development and practical significance in improving maize cultivar application.

New Stably Expressed Loci Responsible for Panicle Angle Trait in Japonica Rice in Four Environments

Panicle angle （PA） of 254 recombinant inbred lines derived from a cross between two japonica varieties Xiushui 79 and C Bao was investigated under four environments,and a genetic linkage map including 111 SSR markers was constructed.Genetic analysis was conducted by mixed major gene plus polygene inheritance models,and quantitative trait loci （QTLs） identification by the QTLNetwork 2.0 and the composite interval mapping approach of WinQTLCart 2.5 software.Results showed that the PA trait was controlled by two major genes plus polygenes,mainly by major genes.Eight QTLs for PA were detected by the QTLNetwork 2.0 software,and each locus explained 0.01% to 39.89% of the phenotypic variation.Twelve QTLs for PA were detected by the WinQTLCart 2.5 software,with each locus explaining 2.83% to 30.60% of the phenotypic variation.Two major QTLs （qPA9.2 and qPA9.5） distributed between RM3700 and RM3600 and between RM5652 and RM410,respectively,and a moderate QTL （qPA9.7） distributed between RM257 and OSR28,were both detected by the two methods in all of the four environments.The negative effect alleles of the three QTLs were from Xiushui 79.In addition,eight pairs of epistatic QTLs with minor effects were also detected.QTL × environment interactions were not significant for additive QTLs and epistatic QTL pairs.