William Berzonsky

Effect of pre-harvest sprouting on physicochemical changes of proteins in wheat.

BACKGROUND High moisture before harvest can cause sprouting of the wheat kernel, which is termed pre-harvest sprouting (PHS). The aim of this study was to examine the variation in physicochemical properties of proteins in PHS-damaged (sprouted) hard red and white spring wheat genotypes. Specifically, protein content, enzyme activity and degradation of proteins were evaluated in sound and PHS-damaged wheat. RESULTS Protein contents of sprouted wheat samples were lower than that of non-sprouted samples; however, their differences were not significantly (P > 0.05) correlated with sprouting score. Sodium dodecyl sulfate (SDS) buffer extractable proteins (EXP) and unextractable proteins (UNP) were analyzed by high-performance size exclusion chromatography. PHS damage elevated endoprotease activity and consequently increased the degradation of polymeric UNP and free asparagine concentration in wheat samples. Free asparagine is known to be a precursor for formation of carcinogenic acrylamide during high heat treatment, such as baking bread. Free asparagine content had significant correlations (P < 0.01) with sprouting score, endoprotease activity and protein degradation. CONCLUSIONS Genotypes with higher endoprotease activity tend to exhibit a larger degree of degradation of UNP and higher free asparagine concentration in sprouted wheat samples.

Effect of Pre-Harvest Sprouting on Physicochemical Properties of Starch in Wheat

Pre-harvest sprouting (PHS) in wheat (Triticum aestivum L.) occurs when physiologically mature kernels begin germinating in the spike. The objective of this study was to provide fundamental information on physicochemical changes of starch due to PHS in Hard Red Spring (HRS) and Hard White Spring (HWS) wheat. The mean values of α-amylase activity of non-sprouted and sprouted wheat samples were 0.12 CU/g and 2.00 CU/g, respectively. Sprouted samples exhibited very low peak and final viscosities compared to non-sprouted wheat samples. Scanning electron microscopy (SEM) images showed that starch granules in sprouted samples were partially hydrolyzed. Based on High Performance Size Exclusion Chromatography (HPSEC) profiles, the starch from sprouted samples had relatively lower molecular weight than that of non-sprouted samples. Overall, high α-amylase activity caused changes to the physicochemical properties of the PHS damaged wheat.

Genetic analysis without replications: model evaluation and application in spring wheat

Genetic data collected from various plant breeding and genetic studies may not be replicated in field designs although field variation is always present. In this study, we addressed this problem using spring wheat (Triticum aestivum L.) trial data collected from two locations. There were no intralocation replications and an extended additive-dominance (AD) model was used to account for field variation. We numerically evaluated the data from simulations and estimated the variance components. For demonstration purposes we also analyzed three agronomic traits from the actual spring wheat data set. Results showed that these data could be effectively analyzed using an extended AD model, which was more comparable to a conventional AD model. Actual data analysis revealed that grain yield was significantly influenced by systematic field variation. Additive effects were significant for all traits and dominance effects were significant for plant height and time-to-flowering. Genetic effects were predicted and used to demonstrate that most spring wheat lines developed by the South Dakota State University breeding program (SD lines) exhibited good general combining ability effects for yield improvement. Thus, this study provides a general framework to appropriately analyze data in situations where field crop data are collected from non-replicated designs.

Impact of Solid and Hollow Varieties of Winter and Spring Wheat on Severity of Wheat Stem Sawfly (Hymenoptera: Cephidae) Infestations and Yield and Quality of Grain

ABSTRACT Wheat stem sawfly (WSS), Cephus cinctus Norton (Hymenoptera: Cephidae), has recently emerged as a key pest of wheat (Triticum aestivum L.) in the Great Plains and Canadian provinces. The expanding impact of WSS has caused considerable economic losses to wheat production. Solid-stem varieties of wheat remain the only effective measure of suppression of WSS, and the goal of this research was to test whether five solid- and hollow-stem varieties of winter and spring wheat reduce survival of WSS in South Dakota.We reported that solid-stem varieties had significantly lower numbers of WSS larvae, and this effect was especially evident when WSS infestation rates exceeded 15%. We also observed that the yield of solid-stem varieties was significantly lower than hollow-stem varieties when the abundance of WSS was low, but not when populations of WSS were relatively high. We did not observe consistent differences in grain quality between solid- and hollow-stem varieties, however, and in case of protein levels of grain, solid-stem wheat varieties performed better than hollow-stem wheat. We conclude that solidstem varieties of wheat appear to effectively suppress WSS survival, and reduced yield of these varieties is less apparent when populations of C. cinctus are high enough to affect the yield of hollow-stem wheat. This is the first report to describe the effectiveness of solid-stem varieties of wheat on WSS in South Dakota. More research in the state is necessary before more robust conclusions can be drawn.

STATISTICAL TESTS FOR STABILITY ANALYSIS WITH RESAMPLING TECHNIQUES

Crop trials or crop performance trials (CPT), which are among the most important activities associated with plant breeding programs, are commonly used to measure the performance stability of genotypes. Several methods which include variation, regression, and cluster analyses for determination of crop stability have been proposed and are commonly used. However, many of these approaches require the use of normally distributed data. Thus, commonly used statistical tests, like the tor F-test may not be appropriate when the assumptions of data are violated. In this study, two resampling techniques (jackknife and bootstrapping) were integrated into several crop stability analyses. An upland cotton data set from China was analyzed to demonstrate the utility of these methods in measuring performance stability.

Flanking SSR markers for alleles involved in the necrosis of hybrids between hexaploid bread wheat and synthetic hexaploid wheat

ABSTRACT Hybrid necrosis in wheat (Triticum aestivum L.) is the premature death of leaves or plants caused by the interaction of two dominant complementary genes, Ne1 and Ne2, located on chromosomes 5B and 2B, respectively. We examined allelic interaction effects of necrosis alleles using simple sequence repeat (SSR) markers in F2 populations derived from crossing the cultivar “Alsen” with a synthetic hexaploid “TA4152-37”. The SSR marker Xbarc7 was linked at a distance of 3 cM to the quantitative trait loci (QTL) located on chromosome 2B, and Xgwm639 was 11 cM from the 5B QTL. A significant additive by additive epistatic interaction was detected between Ne1 and Ne2 QTL, and the results suggest that Alsen possesses a moderate necrosis allele, Ne2m; whereas, TA4152-37 possesses a moderate necrosis allele, Ne1m. The Ne2m allele had a stronger effect than the Ne1m allele, and a total of 94.6% of phenotypic variance was explained by these genes and their interactions. This demonstrates the strong phenotypic effect due to even moderate necrosis alleles, and emphasizes the need for breeders to accurately predict and identify hybrids that will result in necrosis.

Genetic analyses using GGE model and a mixed linear model approach, and stability analyses using AMMI bi-plot for late-maturity alpha-amylase activity in bread wheat genotypes

Low falling number and discounting grain when it is downgraded in class are the consequences of excessive late-maturity α-amylase activity (LMAA) in bread wheat (Triticum aestivum L.). Grain expressing high LMAA produces poorer quality bread products. To effectively breed for low LMAA, it is necessary to understand what genes control it and how they are expressed, particularly when genotypes are grown in different environments. In this study, an International Collection (IC) of 18 spring wheat genotypes and another set of 15 spring wheat cultivars adapted to South Dakota (SD), USA were assessed to characterize the genetic component of LMAA over 5 and 13 environments, respectively. The data were analysed using a GGE model with a mixed linear model approach and stability analysis was presented using an AMMI bi-plot on R software. All estimated variance components and their proportions to the total phenotypic variance were highly significant for both sets of genotypes, which were validated by the AMMI model analysis. Broad-sense heritability for LMAA was higher in SD adapted cultivars (53%) compared to that in IC (49%). Significant genetic effects and stability analyses showed some genotypes, e.g. ‘Lancer’, ‘Chester’ and ‘LoSprout’ from IC, and ‘Alsen’, ‘Traverse’ and ‘Forefront’ from SD cultivars could be used as parents to develop new cultivars expressing low levels of LMAA. Stability analysis using an AMMI bi-plot revealed that ‘Chester’, ‘Lancer’ and ‘Advance’ were the most stable across environments, while in contrast, ‘Kinsman’, ‘Lerma52’ and ‘Traverse’ exhibited the lowest stability for LMAA across environments.

DETECTING FACTORS ASSOCIATED WITH SPRINGWHEAT YIELD STABILITY IN SOUTH DAKOTA ENVIRONMENTS

Conventional yield stability analyses are focused on yield stability itself by using single linear regression method and/or additive main effect and multiplicative interaction (AMMI) analysis. It is likely that yield stability for a genotype is associated with many factors such as fertilizer level, soil types, weather conditions, and/or yield components. Detection of factors highly associated with yield stability, therefore, will help breeders develop cultivars adapted to diverse environments or to specific environments. In this study, we conducted correlation analysis based on both environments and genotypes for a data set with 22 spring wheat genotypes, which were evaluated in 18 environments (combinations of years and locations) in South Dakota from 2009 to 2011. In addition, a multiple linear regression method was used to detect the associations of three agronomic traits with yield stability. The results showed that yield had diverse correlations each of three traits among different environments, indicating the importance of these three traits varied among environments. Our results also showed that plant height played a consistent important role on spring wheat yield production while the other two traits played less frequent role on yield production based on multiple linear regression analyses.