QTL mapping and genomic prediction of resistance to wheat head blight caused by Fusarium verticillioides

XUECAI ZHANG Yong Zhang (2022, [Artículo])

Fusarium Head Blight Resistance Fusarium verticillioides QTL Mapping Genomic Prediction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA FUSARIUM QUANTITATIVE TRAIT LOCI MAPPING TRITICUM AESTIVUM

Improving hybrid rice breeding programs via stochastic simulations: number of parents, number of hybrids, tester update, and genomic prediction of hybrid performance

Roberto Fritsche-Neto Marlee Labroo (2024, [Artículo])

Genomic Prediction Reciprocal Recurrent Selection Heterotic Pools CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA STOCHASTIC MODELS RICE HYBRIDS GENETIC IMPROVEMENT GENETIC GAIN BREEDING PROGRAMMES

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

Maintenance of UK bread baking quality: Trends in wheat quality traits over 50 years of breeding and potential for future application of genomic-assisted selection

Nick Fradgley Alison Bentley Keith Gardner Stéphanie M. Swarbreck (2023, [Artículo])

Sustainable Food Systems Genomic Prediction Genome-Wide Association Analysis CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT BREEDING MARKER-ASSISTED SELECTION VARIETIES FOOD SYSTEMS QUALITY

Genome-wide association study and genomic prediction on plant architecture traits in sweet corn and waxy corn

XUECAI ZHANG Ao Zhang (2023, [Artículo])

Genome-Wide Association Study Genomic Prediction Ear Height Tassel Branch Number Waxy Corn CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GENOMICS PLANT HEIGHT SWEET CORN WAXY MAIZE

Genomic prediction of yield performance among single-cross maize hybrids using a partial diallel cross design

Junjie Fu XUECAI ZHANG (2023, [Artículo])

Genomic Prediction Prediction Model Genetic Effects Hybrid Performance CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE GENETICS HYBRIDS PERFORMANCE ASSESSMENT

Efficacy of plant breeding using genomic information

Osval Antonio Montesinos-Lopez Alison Bentley Carolina Saint Pierre Leonardo Abdiel Crespo Herrera Morten Lillemo Jose Crossa (2023, [Artículo])

Genomic Selection Genomic Prediction Genomic Best Linear Unbiased Predictor CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA PLANT BREEDING GENOMICS MARKER-ASSISTED SELECTION ENVIRONMENT

Results from rapid-cycle recurrent genomic selection in spring bread wheat

Susanne Dreisigacker Paulino Pérez-Rodríguez Leonardo Abdiel Crespo Herrera Alison Bentley Jose Crossa (2023, [Artículo])

Genomic-Assisted Breeding Molecular Markers Pedigree Information Genomic Prediction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GENOMICS GENETIC MARKERS WHEAT BREEDING PROGRAMMES

Multi-environment genomic prediction of plant traits using deep learners with dense architecture

Osval Antonio Montesinos-Lopez Jose Crossa (2018, [Artículo])

Shared Data Resources Deep Learning Genomic Prediction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA ACCURACY GENOMICS NEURAL NETWORKS FORECASTING DATA MARKER-ASSISTED SELECTION

Smallholder maize yield estimation using satellite data and machine learning in Ethiopia

Zhe Guo Jordan Chamberlin Liangzhi You (2023, [Artículo])

The lack of timely, high-resolution data on agricultural production is a major challenge in developing countries where such information can guide the allocation of scarce resources for food security, agricultural investment, and other objectives. While much research has suggested that remote sensing can potentially help address these gaps, few studies have indicated the immediate potential for large-scale estimations over both time and space. In this study we described a machine learning approach to estimate smallholder maize yield in Ethiopia, using well-measured and broadly distributed ground truth data and freely available spatiotemporal covariates from remote sensing. A neural networks model outperformed other algorithms in our study. Importantly, our work indicates that a model developed and calibrated on a previous year's data could be used to reasonably estimate maize yield in the subsequent year. Our study suggests the feasibility of developing national programs for the routine generation of broad-scale and high-resolution estimates of smallholder maize yield, including seasonal forecasts, on the basis of machine learning algorithms, well-measured ground control data, and currently existing time series satellite data.

Sentinel-2 Smallholder Agriculture Yield Prediction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA INTENSIFICATION SMALLHOLDERS AGRICULTURE YIELD FORECASTING