A multi-part strategy for introgression of exotic germplasm into elite plant breeding programs using genomic selection

Some of the most economically important traits in plant breeding show highly polygenic inheritance. Genetic variation is a key determinant of the rates of genetic improvement in selective breeding programs. Rapid progress in genetic improvement comes at the cost of a rapid loss of genetic variation. Germplasm available through expired Plant Variety Protection (exPVP) lines is a potential resource of variation previously lost in elite breeding programs. Introgression for polygenic traits is challenging, as many genes have a small effect on the trait of interest. Here we propose a way to overcome these challenges with a multi-part pre-breeding program that has feedback pathways to optimise recurrent genomic selection. The multi-part breeding program consists of three components, namely a bridging component, population improvement, and product development. Parameters influencing the multi-part program were optimised with the use of a grid search. Haploblock effect and origin were investigated. Results showed that the introgression of exPVP germplasm using an optimised multi-part breeding strategy resulted in 1.53 times higher genetic gain compared to a two-part breeding program. Higher gain was achieved through reducing the performance gap between exPVP and elite germplasm and breaking down linkage drag. Both first and subsequent introgression events showed to be successful. In conclusion, the multi-part breeding strategy has a potential to improve long-term genetic gain for polygenic traits and therefore, potential to contribute to global food security.

An overview of genotype x environment interaction and yield stability analysis in applied plant breeding: great emphasis given to coffee (Coffea arabica L.)

The inconsistence of genotypes across location during plant breeding is the major challenges to the breeder. That is the differential response of genotypes to different environment. Meanwhile stability is the ability of a genotype to withstand stressful conditions and yet be able to produce yield. Thus, stability is an absolute and relative measure. Arabica coffee has location specific adaptation nature and that leads to highly significant instability in its breeding program. In the study of coffee bean yield stability cultivars tested at multi- locations within the domain of coffee growing ecologies of Ethiopia, showed a significant genotype x environment interaction. The review of previous research also indicated inconsistent effects of genotype x environment interaction on cup quality. Yield-stability analysis is very important in measuring cultivar stability and suitability for growing crops across seasons and agro-ecological region to identify stable genotype. The yield stability have been challenge to the plant breeders and biometricians, it complicates the selection of superior genotypes. It is important to minimize the usefulness of the genotype across environments for selecting. Since approach of plant breeding is to develop genotypes that are, optimum for the condition under which they will be grown breeders have to manage yield instability throughout formalized procedures of plant breeding. During stability measurement if the variance is found to be significant, various methods of measuring the stability of genotypes can be used to identify the stable genotype(s). Most of stability analysis parameters are briefly discussed in this review. Int. J. Agril. Res. Innov. Tech. 11(2): 117-123, Dec 2021

Random Forest for Genomic Prediction

We give a detailed description of random forest and exemplify its use with data from plant breeding and genomic selection. The motivations for using random forest in genomic-enabled prediction are explained. Then we describe the process of building decision trees, which are a key component for building random forest models. We give (1) the random forest algorithm, (2) the main hyperparameters that need to be tuned, and (3) different splitting rules that are key for implementing random forest models for continuous, binary, categorical, and count response variables. In addition, many examples are provided for training random forest models with different types of response variables with plant breeding data. The random forest algorithm for multivariate outcomes is provided and its most popular splitting rules are also explained. In this case, some examples are provided for illustrating its implementation even with mixed outcomes (continuous, binary, and categorical). Final comments about the pros and cons of random forest are provided.

Support Vector Machines and Support Vector Regression

In this chapter, the support vector machines (svm) methods are studied. We first point out the origin and popularity of these methods and then we define the hyperplane concept which is the key for building these methods. We derive methods related to svm: the maximum margin classifier and the support vector classifier. We describe the derivation of the svm along with some kernel functions that are fundamental for building the different kernels methods that are allowed in svm. We explain how the svm for binary response variables can be expanded for categorical response variables and give examples of svm for binary and categorical response variables with plant breeding data for genomic selection. Finally, general issues for adopting the svm methodology for continuous response variables are provided, and some examples of svm for continuous response variables for genomic prediction are described.

Convolutional Neural Networks

We provide the fundamentals of convolutional neural networks (CNNs) and include several examples using the Keras library. We give a formal motivation for using CNN that clearly shows the advantages of this topology compared to feedforward networks for processing images. Several practical examples with plant breeding data are provided using CNNs under two scenarios: (a) one-dimensional input data and (b) two-dimensional input data. The examples also illustrate how to tune the hyperparameters to be able to increase the probability of a successful application. Finally, we give comments on the advantages and disadvantages of deep neural networks in general as compared with many other statistical machine learning methodologies.

Introduction of didactic principles in the “Floriculture and ornamental gardening” academic subject

The urgency of the topic is due to the search for new didactic principles and their introduction into the teaching of the discipline “Floriculture and ornamental gardening” at the Department of Plant Breeding PSAU. The study was conducted in 2019–2021 by the method of included observation and formative experiment among applicants for higher education 2nd year of the Faculty of Agrotechnology and Ecology, specialty 201 Agronomy. The introduction of four didactic principles, namely responsibility, patriotism, environmental and aesthetic principles, in the teaching of the discipline “Floriculture and Ornamental Horticulture” promotes the education of spiritually mature, intelligent and hard-working professionals in the agricultural sector of Ukraine.