minque: An R Package for Analyzing Various Linear Mixed Models

Linear mixed model (LMM) approaches offer much more flexibility comparing ANOVA (analysis of variance) based methods. There are three commonly used LMM approaches: maximum likelihood, restricted maximum likelihood, and minimum norm quadratic unbiased estimation. These three approaches, however, sometimes could also lead low testing power compared to ANOVA methods. Integration of resampling techniques like jackknife could help improve testing power based on both our simulation studies. In this presentation, I will introduce a R package, minque, which integrates LMM approaches and resampling techniques and demonstrate the use of this packages in various linear mixed model analyses

STABILITY ANALYSIS FOR YIELD AND SEED QUALITY OF SOYBEAN [Glycine max (L.) Merril] ACROSS DIFFERENT ENVIRONMENTS IN EASTERN SOUTH DAKOTA

Genotype-environment interaction has always been an important and challenging issue for plant breeders in developing desirable varieties. Determination of genotype and environment is common in breeding program as it helps to find out the genotypes that have wide or specific adaptability across various environmental conditions. In this study, fifteen varieties of soybean were evaluated for stability of grain yield (ton/ha), protein content (%), and oil content (%) at six different locations of Eastern South Dakota in 2011. Mixed linear model and Additive main effects and multiplicative interactions (AMMI) were applied to detect genotype-by-environment (G*E) interactions and stability of each variety regarding these three traits. Variance components for genotypic and G*E interaction effects were significant for all these three traits, indicating that the tested genotypes ranked differently at these locations. Based on AMMI analysis, genotypes HEFTY H15Y12 and HEFTY H19Y12 for grain yield, genotypes HEFTY H12Y12, SD 2172, NORTHSTAR 1325R2, and NORTHSTAR 1726NR2 for protein content, and genotypes HEFTY H12Y12 and NUTECH 6145 for oil content had general adaptability under the conditions of Eastern South Dakota

Evaluating Adaptions of Soft Red Winter Wheat in Eastern Region of USA

Identification of winter wheat cultivars that are highly adapted to a wide range of environmental conditions is one of the most important wheat research objectives. Multi-environment crop trials under diverse environments is a commonly used practice to evaluate yield stability. For example, uniform eastern and southern red soft winter wheat nursery trials are conducted annually. However, locations and cultivars may vary from year to year and may cause yield stability analysis to be statistically challenging. In this study, we evaluated cultivars that were widely adapted to eastern production areas and those that were specifically adapted to other environments. We used linear mixed approaches to detect genotype-by-environment interaction effects for yield and heading date based on uniform eastern soft red winter wheat yield trial data from four growing seasons (2012/2013 to 2015/2016). Differences in yield responses and cultivar adaptation will be reported

Evaluating Adaptions of Soft Red Winter Wheat in Eastern Region of USA

Identification of winter wheat genotypes that are highly adapted to a wide range of environmental conditions is one of the most important wheat research objectives. Multienvironment trials (METs) under diverse environments is a commonly used practice to evaluate mean performance and yield stability. However, locations used and genotypes planted may vary from year to year which may cause yield stability analysis to be statistically challenging. In this study, we evaluated yield trial data containing 117 eastern soft red winter wheat genotypes that were grown in 35 locations in eastern production areas and four growing seasons (2012/2013 to 2015/2016). We used linear mixed model (LMM) and additive main effect and multiplicative interaction (AMMI) approaches to evaluate the mean performance and yield stability for each season. Genotype and location effects were highly significant at α = 0.001 for all four seasons and location effects had higher variation compared to genotypic effects. For example, the proportional variance components for location and genotype effects varied from 58-78% and 4-11% among seasons. The first two PC score contribution ranged from 40.7 to 67.3 % to the total genotypeenvironment variation for all seasons. Both LMM and AMMI approaches detected that Branson, and MO080108-4 were better performers, thus these two methods were consistent

Research of Security Routing Technology in Wireless Sensor Network

Wireless Sensor Networks WSN (Wireless Sensor Network) as a new access to information technology and network technology, it has a wide range of applications in many important areas for the performances with low-energy, little-cost, distribution and self-organization, such as military, environmental science, health care, etc., is currently study abroad hot spot. In this paper, along with the extensive application of wireless sensor networks, ZigBee wireless sensor network as a typical protocol, widely used in the actual system design. The importance and current situation of the development of security routing technology were especially introduced in this paper. And we have given the design scheme for the wireless router system based on ZigBee protocol and verified the feasibility of the system in order to provide some useful information for the related techniques

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 t- or 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

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

Exploring multi-year soybean yield trial data in South Dakota environments

Crop performance test (CPT) is a common practice to evaluate yield performance and adaptability of each cultivar. In this study, we combined 16 years of soybean CPT data, which included six representative locations, three major maturity groups, and over 1000 cultivars, to determine some patterns associated with yield production. As expected, the repeatability for these cultivars in trial over years was very low. Thus, the data processing in this study was focused on descriptive statistics regarding time, location, and seed supplier and several linear model analyses. The results will be presented during the conference