Improvement in Genetic Yield Potential of Semi-dwarf Wheat in the Great Plains of the USA

Recently, private companies and public entities have made significant investments in and improvements to their wheat (Triticum aestivum L.) breeding programs. Because of this increased interest, recent genetic improvements made in wheat through traditional plant breeding need to be analyzed. Many studies have noted the significant yield improvement from tall cultivars to semi-dwarf cultivars, but no studies have documented improvements made from the earliest semi-dwarfs to present-day cultivars. Thirty cultivars were tested including 2 tall varieties (Kharkof, 1921 and Triumph 64, 1964), and 28 semi-dwarf cultivars spanning the period from 1971 (TAM 101) to 2008 (Jackpot and TAM 401). Cultivars were tested in 2010 and 2011 at eleven locations across Oklahoma, Kansas, and Texas with adequate disease protection and fertilizer. Experimental design was a split-plot design with fungicide treatment as the main plot and cultivar as the sub-plot with three replications per location. Yields for cultivars protected by fungicide treatment were higher than those without fungicide at most locations. A significant yield increase of 13.68 kg ha-1 yr-1 or 0.93% per year of Kharkof yield was obtained across all locations with the tall cultivars included. When gain was restricted to only semi-dwarf cultivars (1971 to 2008), yield gain was reduced to 11.65 kg ha-1 yr-1 or 0.46% per year of TAM 101 yield. Yield gain among semi-dwarf cultivars in locations with significant fungicide effect was only 10.51 kg ha-1 yr-1 or 0.37% per year of TAM 101 yield, which more accurately represents gain in genetic yield potential made excluding defensive breeding efforts. No evidence of a yield plateau was found.Department of Plant and Soil Science

Panduan Kewirausahaan Bagi Perupa

Program kewirausahaan, sebagai bagian dari kegiatan kemahasiswaan yang telah dilakukan di ISI Yogyakarta sejak tahun 2009 semakin menunjukkan semangat kewirausahaan yang memang sudah melekat dengan mahasiswa. Kewirausahaan yang akhirnya tidak hanya menjadi kegiatan kemahasiswaan tapi juga menjadi mata kuliah wajib di semua jurusan. Pada jurusan seni murni dimana banyak mahasiswa melanjutkan karir sebagai perupa atau seniman, kewirausahaan khusus bidang seni rupa murni butuh dipelajari. Profesi perupa bisa dikategorikan sebagai kegiatan kewirausahaan. Untuk itu pemahaman kerja profesional serta pengembangan profesi dibutuhkan untuk diberikan dalam kelas kewirausahaan. Perubahan dunia seni rupa menuntut profesi perupa memahami kerja profesional diluar ketrampilan menghasilkan karya seni yang bagus dan menarik. Profesi perupa bukan lagi sebagai profesi yang tidak terencana dan musiman. Perencanaa untuk langkah yang strategis dibutuhkan dalam semua jenis kewirausahaan termasuk perupa. Berangkat dari fakta diatas maka buku dengan judul The Artist’s Career Guide : How To Make A Living Doing What You Love karya Jackie Battenfield, diterbitkan oleh Da Capo Press, tahun 2009 dianggap butuh untuk diterjemahkan. Pnterjemahan buku ini sebagai salah satu materi bahan ajar atau bacaan untuk mata kuliah Haki dan Kewirausahaan kuliah di jurusan Seni Murni, Fakultas Seni Rupa ISI Yogyakarta. Penterjemahan buku ini memiliki arti penting untuk meningkatkan kualitas mahasiswa yang ingin mengembangkan karir profesi sebagai perupa. Penulis buku ini Jackie Battenfield, membagikan pengalaman dari dua sisi sebagai perupa profesional dan pengelola galeri seni. Hasil dari pengalamannya yang dirangkum dalam buku ini meliputi berbagai aspek mulai dari membuat resume, membangun komunitas sampai mengelola jaringan kerja

Genomic selection and association mapping for wheat processing and end-use quality

Doctor of PhilosophyGenetics Interdepartmental ProgramAllan K. FritzGlobally, wheat (Triticum aestivum L.) is the second most widely grown cereal grain and is primarily used as a food crop. To meet the demands for human consumption, cultivars must possess suitable end-use quality for release and acceptability. However, breeding for quality traits is often considered a secondary goal, largely due to amount of seed needed and overall expense of such testing. Without testing and selection, many undesirable materials tend to be advanced. Here we demonstrate two methods, mega-genome-wide association mapping and genomic selection, to enhance selection accuracy for quality traits in the CIMMYT bread wheat breeding program. The methods were developed using high-density SNPs detected from genotyping-by-sequencing and processing and end-use quality evaluations from unbalanced yield trial entries (n = 4,095) during 2009 to 2014, at Ciudad Obregon, Sonora, Mexico. Genome-wide association mapping, with covariates for population structure and kinship, was applied for each trait to each site-year individually and results were combined across years in a mega-analysis using an inverse variance, fixed effect model in JMP-Genomics. This method presents a new way to detect genes of interest within a breeding program and develop markers for selection of these traits, which can then be used in earlier generations. Genomic selection prediction models were developed using ridge regression, Gaussian kernel, partial least squares, elastic net, and random forest models in R. With these predictions genomic selection (GS) can be applied at earlier stages and undesirable materials culled before implementing expensive yield and quality screenings. In general, prediction accuracy increased over time as more data was available to train the model. Based on these prediction accuracies, we conclude that genomic selection can be a useful tool to facilitate earlier generation selection for end-use quality in CIMMYT bread wheat breeding. Genomic selection was conducted for processing and end-use quality traits in the Kansas hard red winter wheat breeding unit. Genomic predictions demonstrate increases in accuracy with added data over time. These data demonstrate that current genomic selection models will need more data to continue improvement in prediction accuracy

Genomic Selection for Processing and End-Use Quality Traits in the CIMMYT Spring Bread Wheat Breeding Program

Citation: Battenfield, S. D., Guzman, C., Gaynor, R. C., Singh, R. P., Pena, R. J., Dreisigacker, S., . . . Poland, J. A. (2016). Genomic Selection for Processing and End-Use Quality Traits in the CIMMYT Spring Bread Wheat Breeding Program. Plant Genome, 9(2), 12. doi:10.3835/plantgenome2016.01.0005Wheat (Triticum aestivum L.) cultivars must possess suitable end-use quality for release and consumer acceptability. However, breeding for quality traits is often considered a secondary target relative to yield largely because of amount of seed needed and expense. Without testing and selection, many undesirable materials are advanced, expending additional resources. Here, we develop and validate whole-genome prediction models for end-use quality phenotypes in the CIMMYT bread wheat breeding program. Model accuracy was tested using forward prediction on breeding lines (n = 5520) tested in unbalanced yield trials from 2009 to 2015 at Ciudad Obregon, Sonora, Mexico. Quality parameters included test weight, 1000-kernel weight, hardness, grain and flour protein, flour yield, sodium dodecyl sulfate sedimentation, Mixograph and Alveograph performance, and loaf volume. In general, prediction accuracy substantially increased over time as more data was available to train the model. Reflecting practical implementation of genomic selection (GS) in the breeding program, forward prediction accuracies (r) for quality parameters were assessed in 2015 and ranged from 0.32 (grain hardness) to 0.62 (mixing time). Increased selection intensity was possible with GS since more entries can be genotyped than phenotyped and expected genetic gain was 1.4 to 2.7 times higher across all traits than phenotypic selection. Given the limitations in measuring many lines for quality, we conclude that GS is a powerful tool to facilitate early generation selection for end-use quality in wheat, leaving larger populations for selection on yield during advanced testing and leading to better gain for both quality and yield in bread wheat breeding programs

Wheat quality improvement at CIMMYT and the use of genomic selection on it

Citation: Guzman, C., Pena, R. J., Singh, R., Autrique, E., Dreisigacker, S., Crossa, J., . . . Battenfield, S. (2016). Wheat quality improvement at CIMMYT and the use of genomic selection on it. Applied and Translational Genomics, 11, 3-8. https://doi.org/10.1016/j.atg.2016.10.004The International Center for Maize and Wheat Improvement (CIMMYT) leads the Global Wheat Program, whose main objective is to increase the productivity of wheat cropping systems to reduce poverty in developing countries. The priorities of the program are high grain yield, disease resistance, tolerance to abiotic stresses (drought and heat), and desirable quality. The Wheat Chemistry and Quality Laboratory has been continuously evolving to be able to analyze the largest number of samples possible, in the shortest time, at lowest cost, in order to deliver data on diverse quality traits on time to the breeders formaking selections for advancement in the breeding pipeline. The participation of wheat quality analysis/selection is carried out in two stages of the breeding process: evaluation of the parental lines for new crosses and advanced lines in preliminary and elite yield trials. Thousands of lines are analyzed which requires a big investment in resources. Genomic selection has been proposed to assist in selecting for quality and other traits in breeding programs. Genomic selection can predict quantitative traits and is applicable to multiple quantitative traits in a breeding pipeline by attaining historical phenotypes and adding high-density genotypic information. Due to advances in sequencing technology, genome-wide single nucleotide polymorphism markers are available through genotyping-by-sequencing at a cost conducive to application for genomic selection. At CIMMYT, genomic selection has been applied to predict all of the processing and end-use quality traits regularly tested in the spring wheat breeding program. These traits have variable levels of prediction accuracy, however, they demonstrated that most expensive traits, dough rheology and baking final product, can be predicted with a high degree of confidence. Currently it is being explored how to combine both phenotypic and genomic selection to make more efficient the genetic improvement for quality traits at CIMMYT spring wheat breeding program. (C) 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license

Increasing genomic-enabled prediction accuracy by modeling genotype × environment interactions in kansas wheat

Citation: Jarquín, Diego, Cristiano Lemes da Silva, R. Chris Gaynor, Jesse Poland, Allan Fritz, Reka Howard, Sarah Battenfield, and Jose Crossa. “Increasing Genomic-Enabled Prediction Accuracy by Modeling Genotype × Environment Interactions in Kansas Wheat.” The Plant Genome 10, no. 2 (July 2017): plantgenome2016.12.0130. https://doi.org/10.3835/plantgenome2016.12.0130.Wheat (Triticum aestivum L.) breeding programs test experimental lines in multiple locations over multiple years to get an accurate assessment of grain yield and yield stability. Selections in early generations of the breeding pipeline are based on information from only one or few locations and thus materials are advanced with little knowledge of the genotype × environment interaction (G × E) effects. Later, large trials are conducted in several locations to assess the performance of more advanced lines across environments. Genomic selection (GS) models that include G × E covariates allow us to borrow information not only from related materials, but also from historical and correlated environments to better predict performance within and across specific environments. We used reaction norm models with several cross-validation schemes to demonstrate the increased breeding efficiency of Kansas State University’s hard red winter wheat breeding program. The GS reaction norm models line effect (L) + environment effect (E), L + E + genotype environment (G), and L + E + G + (G × E) effects) showed high accuracy values (>0.4) when predicting the yield performance in untested environments, sites or both. The GS model L + E + G + (G × E) presented the highest prediction ability (r = 0.54) when predicting yield in incomplete field trials for locations with a moderate number of lines. The difficulty of predicting future years (forward prediction) is indicated by the relatively low accuracy (r = 0.171) seen even when environments with 300+ lines were included

Haplotype based genotyping-by-sequencing in oat genome research

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