Results 91 to 100 of about 10,959 (264)
Genomic selection is a technology that allows for the determination of the genetic value of varieties of agricultural plants and animal breeds, based on information about genotypes and phenotypes. The measured breeding value (BV) for varieties and breeds
N. A. Potapova +4 more
doaj +1 more source
DAIRRy-BLUP: a high-performance computing approach to genomic prediction
In genomic prediction, common analysis methods rely on a linear mixed-model framework to estimate SNP marker effects and breeding values of animals or plants. Ridge regression–best linear unbiased prediction (RR-BLUP) is based on the assumptions that SNP
De Coninck, Arne +3 more
core +1 more source
Abstract High‐throughput phenotyping (HTP) techniques have brought new opportunities to understand and evaluate key traits in plant breeding programs. Combining multiple measures through time and random regression models permits a more comprehensive understanding of the genetic and environmental effects on trait expression over time. This study aims to
Felipe Sabadin +16 more
wiley +1 more source
Evaluation of Seed Yield Stability of Lentil Genotypes Based on REML/BLUP and Multi-Trait Stability Index (MTSI) [PDF]
Extended Abstract Background: Lentil is a popular legume crop in the Mediterranean region, widely grown for its nutritious seeds and improving soil fertility.
Payam Pezeshkpour +3 more
doaj
Single-step SNP-BLUP with on-the-fly imputed genotypes and residual polygenic effects
Background Single-step genomic best linear unbiased prediction (BLUP) evaluation combines relationship information from pedigree and genomic marker data.
Matti Taskinen +2 more
doaj +1 more source
Lineer karma model altında blue ve blup [PDF]
06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması ...
Yiğit, Melike
core
Drone‐based phenotyping of maize for multiple disease resistance and yield in breeding field trials
Abstract Improving selection for multiple disease resistance (MDR) and yield in maize (Zea mays L.) requires high‐throughput, objective phenotyping tools, particularly under field conditions where several foliar diseases co‐occur. We evaluated drone‐based multispectral vegetation indices (VIs) for predicting resistance to northern leaf blight (NLB ...
Danilo E. Moreta +7 more
wiley +1 more source
Breeding new maize varieties that take up more N from the soil and increase N fixation is a crucial source of combined nitrogen in agricultural systems.
Rodolfo Buzinaro +4 more
doaj
Comparison of collinearity mitigation techniques used in predicting BLUP breeding values and genetic gains over generations [PDF]
Collinearity potentially has a negative impact on the prediction of genetic gains in tree breeding programs. This study investigated the reliability and impact of best linear unbiased prediction (BLUP) using various collinearity mitigation techniques ...
K A Eatwell +7 more
core +1 more source
Abstract Data from high‐throughput phenotyping (HTP) could be used for phenotype imputation to enhance genomic selection (GS) or gene discovery, but this has not been explored in crop species. Three machine learning models: multiple linear regression (MLR), missForest, and k‐nearest neighbors, were evaluated for grain yield (GY) phenotype imputation in
Raysa Gevartosky +2 more
wiley +1 more source

