Incorporating spatial and genetic competition into breeding pipelines with the R package gencomp.

Genetic competition can obscure the true merit of selection candidates, potentially leading to altered genotype rankings and a divergence between expected and actual genetic gains. Despite a wealth of literature on genetic competition in plant and animal breeding, the separation of genetic values into direct genetic effects (DGE, related to a genotype's merit) and indirect genetic effects (IGE, related to the effects of a genotype's alleles on its neighbor's phenotype) in linear mixed models is often overlooked, likely due to the complexity involved. To address this, we introduce gencomp, a new R package designed to simplify the use of (spatial-) genetic competition models in crop and tree breeding routines.

Mr.Bean: a comprehensive statistical and visualization application for modeling agricultural field trials data.

Crop improvement efforts have exploited new methods for modeling spatial trends using the arrangement of the experimental units in the field. These methods have shown improvement in predicting the genetic potential of evaluated genotypes. However, the use of these tools may be limited by the exposure and accessibility to these products.

Differences in excito-repellent behavior between susceptible and permethrin-resistant strains of Aedes aegypti upon exposure to insecticide-treated fabric.

Background: An important component of the biological activity of pyrethroids, when used in disease vector control, is excito-repellency. In this study, behavioral differences between insecticide susceptible (Orlando) and pyrethroid resistant (Puerto Rican) strains of Aedes aegypti were explored in a round glass arena using fabrics treated with permethrin, etofenprox, deltamethrin, or DDT. Repellency was evaluated across several variables, including the time to first flight (TFF), number of landings (NOL), total flight time (TFT), and maximum surface contact (MSC), all by video analysis.

Optimizing Sparse Testing for Genomic Prediction of Plant Breeding Crops.

While sparse testing methods have been proposed by researchers to improve the efficiency of genomic selection (GS) in breeding programs, there are several factors that can hinder this. In this research, we evaluated four methods (M1-M4) for sparse testing allocation of lines to environments under multi-environmental trails for genomic prediction of unobserved lines. The sparse testing methods described in this study are applied in a two-stage analysis to build the genomic training and testing sets in a strategy that allows each location or environment to evaluate only a subset of all genotypes rather than all of them.

Increasing genomic prediction accuracy for unphenotyped full-sib families by modeling additive and dominance effects with large datasets in white spruce.

Introduction: Genomic selection is becoming a standard technique in plant breeding and is now being introduced into forest tree breeding. Despite promising results to predict the genetic merit of superior material based on their additive breeding values, many studies and operational programs still neglect non-additive effects and their potential for enhancing genetic gains.

Methods: Using two large comprehensive datasets totaling 4,066 trees from 146 full-sib families of white spruce (Picea glauca (Moench) Voss), we evaluated the effect of the inclusion of dominance on the precision of genetic parameter estimates and on the accuracy of conventional pedigree-based (ABLUP-AD) and genomic-based (GBLUP-AD) models.