Crop diagnosis and probe genotypes for interpreting genotype environment interaction in winter wheat trials

Genotype*environment interaction has been analyzed with 12 genotypes and four probe genotypes in French wheat trials. An integrated approach was developed which combined crop diagnosis with the analysis of interaction by factorial regression. Crop diagnosis was helpful to characterize the environments and to select environmental variables. Such an approach succeeded in providing an agronomic explanation of genotype*environment interaction and in defining the responses or parameters for each genotype and each environment. Earliness at heading, susceptibility to powdery mildew and susceptibility to lodging were the three major genotypic covariates. Interaction could also be related to environment features, measured indirectly by the behavior of the four probe genotypes during the formation of yield, what we called the outputs of a simplified crop diagnosis, or described directly by indicators of yield-limiting factors. Two important crop diagnosis covariates were analyzed in order to characterize interaction during the formation of yield: the reduction in kernel number, which described the time-period until flowering, and the reduction in thousand kernel weight, which corresponded to the period after flowering. These variates were estimated for each probe genotype and allowed us to compare the behavior of the 12 genotypes to that of the probe genotypes. Both periods of the formation of yield contributed to the interaction, and ’Camp-Rémy’ was the probe of particular interest for the comparisons. When true environmental variates were used, factorial regression revealed that water deficits during the formation of grain number and level of nitrogen were predominant. Such an integrated approach could be exploited when varieties are tested in a network where numerous and diverse yield-limiting factors may occur. Received: 3 August 1998 / Accepted: 16 March 1999     

Factor regression for interpreting genotype-environment interaction in bread-wheat trials

Summary The French INRA wheat (Triticum aestivum L. em Thell.) breeding program is based on multilocation trials to produce high-yielding, adapted lines for a wide range of environments. Differential genotypic responses to variable environment conditions limit the accuracy of yield estimations. Factor regression was used to partition the genotype-environment (GE) interaction into four biologically interpretable terms. Yield data were analyzed from 34 wheat genotypes grown in four environments using 12 auxiliary agronomic traits as genotypic and environmental covariates. Most of the GE interaction (91%) was explained by the combination of only three traits: 1,000-kernel weight, lodging susceptibility and spike length. These traits are easily measured in breeding programs, therefore factor regression model can provide a convenient and useful prediction method of yield.     

Determining environmental covariates which explain genotype environment interaction in winter wheat through probe genotypes and biadditive factorial regression

Genotype-environment interaction has been analyzed in a winter-wheat breeding network using bi-additive factorial regression models. This family of models generalizes both factorial regression and biadditive (or AMMI) models; it fits especially well when abundant external information is available on genotypes and/or environments. Our approach, focused on environmental characterization, was performed with two kinds of covariates: (1) deviations of yield components measured on four probe genotypes and (2) usual indicators of yield-limiting factors. The first step was based on the analysis of a crop diagnosis on four probe genotypes. Difference of kernel number to a threshold number (DKN) and reduction of thousand-kernel weight from a potential value (RTKW) were used to characterize the grain-number formation and the grain-filling periods, respectively. Grain yield was analyzed according to a biadditive factorial regression model using eight environmental covariates (DKN and RTKW measured on each of four probe genotypes). In the second step, the usual indicators of yield-limiting factors were too numerous for the analysis of grain yield. Thus a selection of a subset of environmental covariates was performed on the analysis of DKN and RTKW for the four probe genotypes. Biadditive factorial regression models involved environmental covariates related to each deviation and included environmental main effect, sum of water deficits, an indicator of nitrogen stress, sum of daily radiation, high temperature, pressure of powdery mildew and lodging. The correlations of each environmental covariate to the synthetic variates helped to discard those poorly involved in interaction (with | correlation | <0.3). The grain yield of 12 genotypes was interpreted with the retained covariates using biadditive factorial regression. The models explained about 75% of the interaction sums of squares. In addition, the biadditive factorial regression biplot gave relevant information about the interaction of the genotypes (interaction pattern and sensitivities to environmental covariates) with respect to the environmental covariates and proved to be interesting for such an approach. Received: 8 March 1999 / Accepted: 29 July 1999     

Grouping genotypes by a cluster method directly related to genotype-environment interaction mean square

Summary To facilitate the interpretation of data from a genotype by environment (GE) experiment when the GE interaction is large, a cluster method is proposed to group genotypics according to their response to the environments. The dissimilarity index between a pair of genotypes is defined in terms of distance adjusted for the average effects of genotypes, and Sokal and Michener's (1958) unweighted pair-group method is used in the clustering algorithm. The new index, constructed in each cluster cycle for any group, is shown to be equivalent to within group GE interaction mean square under 2-way ANOVA. Thus, if the F-value is used as an empirical stopping criterion for clustering, there will be no significant GE interaction within groups and the genotypes within the groups can be compared by their average effects. The method of analysis is illustrated by an example.Contribution no. I-348 from the Engineering and Statistical Research Institute     

Use of AMMI and linear regression models to analyze genotype-environment interaction in durum wheat

Summary The joint durum wheat (Triticum turgidum L var durum) breeding program of the International Maize and Wheat Improvement Center (CIMMYT) and the International Center for Agricultural Research in the Dry Areas (ICARDA) for the Mediterranean region employs extensive multilocation testing. Multilocation testing produces significant genotype-environment (GE) interaction that reduces the accuracy for estimating yield and selecting appropriate germ plasm. The sum of squares (SS) of GE interaction was partitioned by linear regression techniques into joint, genotypic, and environmental regressions, and by Additive Main effects and the Multiplicative Interactions (AMMI) model into five significant Interaction Principal Component Axes (IPCA). The AMMI model was more effective in partitioning the interaction SS than the linear regression technique. The SS contained in the AMMI model was 6 times higher than the SS for all three regressions. Postdictive assessment recommended the use of the first five IPCA axes, while predictive assessment AMMI1 (main effects plus IPCA1). After elimination of random variation, AMMI1 estimates for genotypic yields within sites were more precise than unadjusted means. This increased precision was equivalent to increasing the number of replications by a factor of 3.7.     

Multi-criteria clustering in genotype-environment interaction problems

Summary Significant genotype-environment interactions in an ANOVA can be found for a number of reasons: one is the differences in the among-environments variances for each genotype, another is the differences in the ordering of the environments by each genotype. Using conditional clustering, groups may be formed in which the means, variances and patterns are used simultaneously but separately to decide on group homogeneity. Contribution No. I-685 from the Engineering and Statistical Research Institute     

Variation in nitrogen use efficiency among soft red winter wheat genotypes

Summary Nitrogen use efficiency (NUE), defined as grain dry weight or grain nitrogen as a function of N supply, was evaluated in 25 soft red winter wheat genotypes for two years at one location. Significant genotypic variation was observed for NUE, nitrogen harvest index, and grain yield. Genotype x environment interaction for these traits was not significant. Several variables including N uptake efficiency (total plant N as a function of N supply), grain harvest index, and N concentration at maturity were evaluated for their role in determining differences in NUE. Nitrogen uptake efficiency accounted for 54% of the genotypic variation in NUE for yield and 72% of the genotypic variation in NUE for protein. A path coefficient analysis revealed that the direct effect of uptake efficiency on NUE was high relative to indirect effects.The investigation reported in this paper (No. 85-3-122) is in connection with a project of the Kentucky Agricultural Experiment Station and is published with approval of the Director     

Metabolic profiling of benzoxazinoids in the roots and rhizosphere of commercial winter wheat genotypes

Plant and Soil - Integrated weed management in commercial wheat production is urgently needed due to increasing herbicide resistance and production costs. Benzoxazinoids (BXs), which include...     

Grain aphid infestations of winter wheat variety trials, 1984

Aphid infestations of wheat yield trials near Cambridge were assessed by shoot sampling and by breeder's scores. Both resistant and extremely susceptible cultivars were identified. Results from the two methods of assessing infestation were well correlated, indicating that the simpler scores could be used effectively for making selections.     

基因型和地域分布对小麦籽粒氨基酸含量影响的研究

根据河南省7个试验基点的10个小麦基因型籽粒中17种氨基酸含量的测定结果,对不同基因型氨基酸含量差异及其地域分布进行了研究,结果表明,小麦籽粒氨基酸含量不仅存在基因型的差异,而且在很大程度上受生态环境条件的影响,环境间氨基酸含量的变异明显大于基因型,约为基因型间变异的1．5倍。不同试点所对应的气候分布、土壤类型与氨基酸含量变异有较大的吻合程度,表现出随湿润条件增加,氨基酸含量和必需氨基酸、非必需氨基酸总量逐渐下降,而非必需氨基酸占氨基酸总量的比例呈逐渐上升趋势,与人体代谢关系密切的赖氨酸化学记分较 低,且变异较小。     

Quantitative characteristics of the conducting system in stem of winter wheat genotypes with spike fertility genes

Three groups of winter wheat (Triticum aestivum L.) genotypes having spike fertility genes (SFG) were used in field trials: (1) Tetrastichon sessile spikelets (TSS), (2) Normal spikelets (NS), (3) Indeterminate rachilla spikelets (IRS). The capacity of conducting system of the peduncle and the ear sink capacity of the main stem have been measured. There was a highly significant positive correlation (r = 0.899 and higher) between peduncle diameter and parameters quantifying peduncle vascular system. Compared with the control cultivar Hana, the TSS and NSS genotypes had higher both the number of vascular bundles, phloem and bundle cross section area and kernel number per ear. However, the highest kernel number per ear was found in the IRS genotypes although their bundle and phloem area was only equal or even lower then that of the variety Hana. Further studies are needed in developmental anatomy of spikes and stems to elucidate also differences in the relationships between the conducting capacity and kernel number per spikes in the TSS, NS and IRS genotypes.     

Relationships between grain yield and morphological characters in the winter wheat genotypes of the Belgian national list

Yield per shoot and to a much lesser extend yield per unit area were related to morphological characters. The flag sheath was better related to shoot yield than were any of the three uppermost leaf laminae. Among these the areas of the two lower leaves showed a better relationship to the yield than did the flag leaf lamina. Variation in main shoot yield was associated mainly with variation in grain number. More attention should be given to morphological character related to spike development before anthesis.     

The use of environmental variables in the interpretation of genotype-environment interaction.

The method proposed by Hardwick and Wood (1972) for relating genotype-environment interactions to measures of environmental variables is extended and two examples are discussed.     

有限灌溉条件下冬小麦水分亏缺的灵敏度分析

水分亏缺是干旱、半干旱区作物生产的最主要限制因子。在我国西北半干旱区,随着集水生态农业的发展．实施在作物生育期对其进行关键性的有限灌溉．这就使得更多的土地具有种植价值了。用一个度量作物不同生长阶段水分亏缺的灵敏度指标值λ来确定灌溉关键期。对一个给定的作物．在不同的生长阶段．λ的值是不同的,λ的值越大．生长阶段对水分越灵敏,水分亏缺对产量造成的危害越大,从而越需要保持水分平衡。在西北半干旱区甘肃省定西农业试验站,经过两年冬小麦种植试验,建立了冬小麦产量-耗水量Jensen模型,该模型表明拔节期以后．λ值明显增大．至抽穗期达最大,这说明从拔节期开始．该地冬小麦进入灌溉关键期．抽穗期是灌溉的最关键时期。一个理论方法应用到有限灌溉试验田．确定了灌溉水减少与经济效益的关系,结果表明在作物生育期,一些水分减少是可能的。用Nairizi和Rydzewski的方法计算出的λ值确定出的最大允许水分亏缺是不合理的高,它们计算λ值的方法在半干旱区是不适用的,用我们建立的模型计算出的最大允许水分亏缺比较合理,在效益成本比为1．5时,冬小麦允许的水分亏缺是12．4％．这与大田试验较为接近。     

三种栽培模式下不同基因型冬小麦旗叶衰老代谢比较

为了探索冬小麦在不同栽培模式下功能叶片衰老代谢的生理机制,以cp02(213)、cp99(1)和陕农512为材料,比较研究了常规栽培、覆草栽培、地膜覆盖3种栽培模式下小麦旗叶衰老代谢特性.结果表明,覆草栽培叶面积、旗叶功能期、叶绿素含量、叶片保护酶活性(SOD、POD、CAT)显著高于常规栽培,膜脂过氧化程度较低,叶片衰老速度缓慢,代谢强度旺盛,有利于籽粒灌浆和光合产物的积累.灌浆前期,地膜覆盖叶面积、旗叶功能期、叶绿素含量、叶片保护性酶活性(SOD、POD、CAT)显著高于常规栽培,膜脂过氧化程度低于常规栽培;灌浆后期,叶绿素含量急剧下降,叶片衰老速度加快,膜脂过氧化程度加剧.参试品种(系)中陕农512叶片衰老速度缓慢,保绿性好.