Sowing the wheat seeds of Afghanistan's future

Nigel Poole Rajiv Sharma Orzala Nemat Jason Donovan Alison Bentley (2022, [Artículo])

Humanitarian Intervention CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA FOOD SECURITY IRRIGATION NUTRITION PLANT BREEDING SEED SYSTEMS

The generation challenge programme platform: Semantic standards and workbench for crop science

Richard Bruskiewich Guy Davenport Mathieu Rouard Reinhard Simon Samart Wanchana Trushar Shah Victor Jun Ulat Andrew Farmer Pankaj Jaiswal Mark Wilkinson David Marshall Alyssa Collins (2008, [Artículo])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CROP IMPROVEMENT GENETIC RESOURCES PLANT BREEDING BIODIVERSITY COMPUTER APPLICATIONS DIGITAL TECHNOLOGY DATA PROCESSING

Enhancement of plant variety protection and regulation using molecular marker technology

Yunbi Xu Jian Zhang Jiansheng LI (2022, [Artículo])

Plant Variety Protection Distinctness-Uniformity-Stability Essentially Derived Variety Molecular Markers Molecular Diagnostics Genetic Similarity CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GENETICS GENETIC MARKERS PLANT BREEDING VARIETIES

Performance evaluation and identification of highland quality protein maize hybrids in Ethiopia

Adefris Teklewold (2022, [Artículo])

Quality Protein Conventional Maize CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE PROTEIN QUALITY CROSS-BREEDING HYBRIDS

Selection indices for identifying heat tolerant of maize (Zea mays)

Pervez Zaidi (2023, [Artículo])

Stress Tolerance Indices Geometric Mean Productivity Stress Susceptibility Index Statistical Correlation CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CORRELATION HEAT STRESS ZEA MAYS DROUGHT STRESS BREEDING PROGRAMMES

Editorial: Model organisms in plant science: Maize

Manje Gowda (2023, [Artículo])

Model Organism Genomic Selection CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE PLANT SCIENCES RESEARCH CROP IMPROVEMENT PLANT PHYSIOLOGY PLANT BREEDING

Editorial: Genomic selection: Lessons learned and perspectives

Johannes Martini Sarah Hearne Valentin Wimmer Fernando Henrique Toledo (2022, [Artículo])

Genomic Selection Selection Gain Breeding Schemes CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA BREEDING PROGRAMMES MARKER-ASSISTED SELECTION GENOTYPE ENVIRONMENT INTERACTION PLANT BREEDING

Exploring GWAS and genomic prediction to improve Septoria tritici blotch resistance in wheat

Admas Alemu Abebe Pawan Singh Aakash Chawade (2023, [Artículo])

Septoria Tritici Blotch Wheat Breeding Genomic Prediction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GENOME-WIDE ASSOCIATION STUDIES MYCOSPHAERELLA GRAMINICOLA DISEASE RESISTANCE WHEAT PLANT GROWTH

AGG-maize year 3 major achievements and next steps

Yoseph Beyene (2023, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE BREEDING PROGRAMMES INNOVATION HYBRIDS GERMPLASM

Using an incomplete block design to allocate lines to environments improves sparse genome-based prediction in plant breeding

Osval Antonio Montesinos-Lopez ABELARDO MONTESINOS LOPEZ RICARDO ACOSTA DIAZ Rajeev Varshney Jose Crossa ALISON BENTLEY (2022, [Artículo])

Genomic selection (GS) is a predictive methodology that trains statistical machine-learning models with a reference population that is used to perform genome-enabled predictions of new lines. In plant breeding, it has the potential to increase the speed and reduce the cost of selection. However, to optimize resources, sparse testing methods have been proposed. A common approach is to guarantee a proportion of nonoverlapping and overlapping lines allocated randomly in locations, that is, lines appearing in some locations but not in all. In this study we propose using incomplete block designs (IBD), principally, for the allocation of lines to locations in such a way that not all lines are observed in all locations. We compare this allocation with a random allocation of lines to locations guaranteeing that the lines are allocated to

the same number of locations as under the IBD design. We implemented this benchmarking on several crop data sets under the Bayesian genomic best linear unbiased predictor (GBLUP) model, finding that allocation under the principle of IBD outperformed random allocation by between 1.4% and 26.5% across locations, traits, and data sets in terms of mean square error. Although a wide range of performance improvements were observed, our results provide evidence that using IBD for the allocation of lines to locations can help improve predictive performance compared with random allocation. This has the potential to be applied to large-scale plant breeding programs.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA Bayes Theorem Genome Inflammatory Bowel Diseases Models, Genetic Plant Breeding