Person: Poland, J.
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Poland
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J.
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Poland, J.
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0000-0002-7856-1399 8 results
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- Improving wheat yield prediction using secondary traits and high-density phenotyping under heat-stressed environments(Frontiers, 2021) Rahman, M.M.; Crain, J.; Haghighattalab, A.; Singh, R.P.; Poland, J.
Publication - Aerial high‐throughput phenotyping enabling indirect selection for grain yield at the early‐generation seed‐limited stages in breeding programs(CSSA, 2020) Krause, M.; Mondal, S.; Crossa, J.; Singh, R.P.; Pinto Espinosa, F.; Haghighattalab, A.; Shrestha, S.; Rutkoski, J.; Gore, M.A.; Sorrells, M.E.; Poland, J.
Publication - Multitrait, random regression, or simple repeatability model in high-throughput phenotyping data improve genomic prediction for wheat grain yield(CSSA :, 2017) Jin Sun; Rutkoski, J.; Poland, J.; Crossa, J.; Jannink, J.L.; Sorrells, M.E.
Publication - Aerial high‐throughput phenotyping enables indirect selection for grain yield at the early generation, seed‐limited stages in breeding programs(Crop Science Society of America (CSSA), 2020) Krause, M.; Mondal, S.; Crossa, J.; Singh, R.P.; Pinto Espinosa, F.; Haghighattalab, A.; Shrestha, S.; Rutkoski, J.; Gore, M.A.; Sorrells, M.E.; Poland, J.
Publication - Breeder friendly phenotyping(Elsevier, 2020) Reynolds, M.P.; Chapman, S.; Crespo Herrera, L.A.; Molero, G.; Mondal, S.; Pequeno, D.N.L.; Pinto Espinosa, F.; Piñera Chavez, F.J; Poland, J.; Rivera-Amado, C.; Saint Pierre, C.; Sukumaran, S.
Publication - High-throughput phenotyping enabled genetic dissection of crop lodging in wheat(Frontiers, 2019) Singh, D.; Xu Wang; Kumar, U.; Liangliang Gao; Muhammad Noor; Imtiaz, M.; Singh, R.P.; Poland, J.Novel high-throughput phenotyping (HTP) approaches are needed to advance the understanding of genotype-to-phenotype and accelerate plant breeding. The first generation of HTP has examined simple spectral reflectance traits from images and sensors but is limited in advancing our understanding of crop development and architecture. Lodging is a complex trait that significantly impacts yield and quality in many crops including wheat. Conventional visual assessment methods for lodging are time-consuming, relatively low-throughput, and subjective, limiting phenotyping accuracy and population sizes in breeding and genetics studies. Here, we demonstrate the considerable power of unmanned aerial systems (UAS) or drone-based phenotyping as a high-throughput alternative to visual assessments for the complex phenological trait of lodging, which significantly impacts yield and quality in many crops including wheat. We tested and validated quantitative assessment of lodging on 2,640 wheat breeding plots over the course of 2 years using differential digital elevation models from UAS. High correlations of digital measures of lodging to visual estimates and equivalent broadsense heritability demonstrate this approach is amenable for reproducible assessment of lodging in large breeding nurseries. Using these high-throughput measures to assess the underlying genetic architecture of lodging in wheat, we applied genome-wide association analysis and identified a key genomic region on chromosome 2A, consistent across digital and visual scores of lodging. However, these associations accounted for a very minor portion of the total phenotypic variance. We therefore investigated whole genome prediction models and found high prediction accuracies across populations and environments. This adequately accounted for the highly polygenic genetic architecture of numerous small effect loci, consistent with the previously described complex genetic architecture of lodging in wheat. Our study provides a proof-of-concept application of UAS-based phenomics that is scalable to tens-of-thousands of plots in breeding and genetic studies as will be needed to uncover the genetic factors and increase the rate of gain for complex traits in crop breeding.
Publication - Utilizing genomics and phenomics in CIMMYT wheat breeding(CIMMYT, 2016) Mondal, S.; Poland, J.; Haghighattalab, A.; Singh, D.; Rahmanm, M.; Sorrells, M.E.; Jin Sun; Singh, R.P.; Crossa, J.; Dreisigacker, S.; Kumar, U.; Imtiaz, M.; Juliana, P.
Publication - Canopy temperature and vegetation indices from high-throughput phenotyping improve accuracy of pedigree and genomic selection for grain yield in wheat(Genetics Society of America, 2016) Rutkoski, J.; Poland, J.; Mondal, S.; Autrique, E.; González Pérez, L.; Crossa, J.; Reynolds, M.P.; Singh, R.G.Genomic selection can be applied prior to phenotyping, enabling shorter breeding cycles and greater rates of genetic gain relative to phenotypic selection. Traits measured using high-throughput phenotyping based on proximal or remote sensing could be useful for improving pedigree and genomic prediction model accuracies for traits not yet possible to phenotype directly. We tested if using aerial measurements of canopy temperature, and green and red normalized difference vegetation index as secondary traits in pedigree and genomic best linear unbiased prediction models could increase accuracy for grain yield in wheat, Triticum aestivum L., using 557 lines in five environments. Secondary traits on training and test sets, and grain yield on the training set were modeled as multivariate, and compared to univariate models with grain yield on the training set only. Cross validation accuracies were estimated within and across-environment, with and without replication, and with and without correcting for days to heading. We observed that, within environment, with unreplicated secondary trait data, and without correcting for days to heading, secondary traits increased accuracies for grain yield by 56% in pedigree, and 70% in genomic prediction models, on average. Secondary traits increased accuracy slightly more when replicated, and considerably less when models corrected for days to heading. In across-environment prediction, trends were similar but less consistent. These results show that secondary traits measured in high-throughput could be used in pedigree and genomic prediction to improve accuracy. This approach could improve selection in wheat during early stages if validated in early-generation breeding plots.
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