Plant Variety Selection Using Interaction Classes Derived From Factor Analytic Linear Mixed Models: Models With Independent Variety Effects

A major challenge in the analysis of plant breeding multi-environment datasets is the provision of meaningful and concise information for variety selection in the presence of variety by environment interaction (VEI). This is addressed in the current paper by fitting a factor analytic linear mixed model (FALMM) then using the fundamental factor analytic parameters to define groups of environments in the dataset within which there is minimal crossover VEI, but between which there may be substantial crossover VEI. These groups are consequently called interaction classes (iClasses). Given that the environments within an iClass exhibit minimal crossover VEI, it is then valid to obtain predictions of overall variety performance (across environments) for each iClass. These predictions can then be used not only to select the best varieties within each iClass but also to match varieties in terms of their patterns of VEI across iClasses. The latter is aided with the use of a new graphical tool called an iClass Interaction Plot. The ideas are introduced in this paper within the framework of FALMMs in which the genetic effects for different varieties are assumed independent. The application to FALMMs which include information on genetic relatedness is the subject of a subsequent paper.

Classical crossover in the modern musical space of Ukraine

The purpose of the article is to consider the genesis of the classical crossover and traces its path of development in the Ukrainian cultural and artistic space. The methodology involves a combination of historical and cultural, analytical, and comparative methods, which clarified the place of the classical crossover in modern music culture. The scientific novelty of the study is that for the first time in Ukrainian science, Ukrainian representatives of the classical crossover are considered. Conclusions. At the turn of the 20th – 21st centuries one of the areas that synthesize the achievements of academic and variety (rock, pop, electronic music) art has become a classic crossover. The origins of this trend are more ancient and go back to the 1960s when the first attempts were made to synthesize academic and variety performance. Famous Ukrainian singers of the academic school D. Hnatiuk, Yu. Gulyaev, A. Mokrenko, D. Petrinenko, L. Ostapenko performed both classical and variety works. Today, a striking example of the synthesis of academic and variety vocal art is the duets of F. Mustafayev – T. Samaya and V. Stepova – V. Sinchuk. Ukrainian singer, soprano of the new generation Arina Domski is the only representative of the Ukrainian scene, which consistently turns to the direction of the classical crossover, synthesizing in its performance variety and academic vocal repertoire.

Muslim Magomayev’s variety performance features

A significant part of the work of the outstanding singer, People's artist of the USSR Muslim Magomayev is his work in the variety genre. M. Magomayev made a significant contribution to the development of variety music not only in Azerbaijan, but also in the USSR, enriching this genre with newer traditions. So, concerning the variety music of the 1960–1980 years of the last century, first of all, M.Magomayev is remembered. While on the subject of M. Magomayev's variety creative work from this point of view, it is not enough to mention only his song performance. It is also necessary to emphasize the trends introduced by him in this genre. The legendary musician raised Azerbaijan’s variety performance to an academic level. At the same time, M. Magomayev successfully created his own performing line in the song genre. He managed to expand the boundaries of the song genre and to introduce contrasting tricks.

Peanut Variety Performance in Florida 2002-2005

Variety choice is a critical management decision for peanut production. There are several good peanut varieties to choose from today. This document is SS-AGR-13, one of a series of the Agronomy Department, UF/IFAS Extension. Originally published as Marianna NFREC Research Report 06-1. Publication date January 2006.

Crop variety management for climate adaptation supported by citizen science

Crop adaptation to climate change requires accelerated crop variety introduction accompanied by recommendations to help farmers match the best variety with their field contexts. Existing approaches to generate these recommendations lack scalability and predictivity in marginal production environments. We tested if crowdsourced citizen science can address this challenge, producing empirical data across geographic space that, in aggregate, can characterize varietal climatic responses. We present the results of 12,409 farmer-managed experimental plots of common bean (Phaseolus vulgarisL.) in Nicaragua, durum wheat (Triticum durumDesf.) in Ethiopia, and bread wheat (Triticum aestivumL.) in India. Farmers collaborated as citizen scientists, each ranking the performance of three varieties randomly assigned from a larger set. We show that the approach can register known specific effects of climate variation on varietal performance. The prediction of variety performance from seasonal climatic variables was generalizable across growing seasons. We show that these analyses can improve variety recommendations in four aspects: reduction of climate bias, incorporation of seasonal climate forecasts, risk analysis, and geographic extrapolation. Variety recommendations derived from the citizen science trials led to important differences with previous recommendations.

Adaptability analysis in a participatory variety trial of organic vegetable crops

Successful organic farming requires crop varieties that are resilient to environmental variability. Assessing variety performance across the range of conditions represented on working farms is vital to developing such varieties; however, data collected from on-farm, participatory trials can be difficult to both collect and interpret. To assess the utility of data arising from participatory trialing efforts, we examined the performance of butternut squash (Cucurbita moschata L.), broccoli (Brassica oleracea L.) and carrot (Daucus carota L.) varieties grown in diverse organic production environments in participatory trials in Oregon, Washington, Wisconsin and New York using adaptability analysis (regression of variety means on environmental index). Patterns of adaptation varied across varieties, with some demonstrating broad adaptation and others showing specific adaptation to low- or high-yielding environments. Selection of varieties with broad vs specific adaptation should be guided by farmers’ risk tolerance and on-farm environmental variation. Adaptability analysis was appropriate for continuous variables (e.g., yield traits), but less so for ordinal variables and quality traits such as flavor and appearance, which can be vitally important in organic vegetable crop variety selection. The relative advantages of adaptability analysis and additive main effects and multiplicative interactions are also discussed in relation to on-farm trial networks. This work demonstrated the unique challenges presented by extensive participatory vegetable trialing efforts, which, as compared to grain crops, require novel approaches to facilitating farmer participation as well as data collection and analysis. Efficient, precise and reliable methods for evaluating quality related traits in these crops would allow researchers to assess stability and adaptation across a wider range of traits, providing advantages for effective plant breeding and trialing activities within the organic sector.

Optimal models in the yield analysis of new flax cultivars

Multi-environment trials are conducted to evaluate the performance of cultivars. In a combined analysis, the mixed model is superior to an analysis of variance for evaluating and comparing cultivars and dealing with an unbalanced data structure. This study seeks to identify the optimal models using the Saskatchewan Variety Performance Group post-registration regional trial data for flax. Yield data were collected for 15 entries in post-registration tests conducted in Saskatchewan from 2007 to 2016 (except 2011) and 16 mixed models with homogeneous or heterogeneous residual errors were compared. A compound symmetry model with heterogeneous residual error (CSR) had the best fit, with a normal distribution of residuals and a mean of zero fitted to the trial data for each year. The compound symmetry model with homogeneous residual error (CS) and a model extending the CSR to higher dimensions (DIAGR) were the next best models in most cases. Five hundred random samples from a two-stage sampling method were produced to determine the optimal models suitable for various environments. The CSR model was superior to other models for 396 out of 500 samples (79.2%). The top three models, CSR, CS, and DIAGR, had higher statistical power and could be used to access the yield stability of the new flax cultivars. Optimal mixed models are recommended for future data analysis of new flax cultivars in regional tests.