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1114 research outputs found
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Replication Data for: Multi-trait genome prediction of new environments with partial least squares
No full textThe genomic selection (GS) methodology has revolutionized plant breeding. This methodology makes predictions for genotyped candidate lines based on statistical machine learning algorithms that are trained with phenotypic and genotypic data of a reference population. GS can save significant resources in the selection of candidate individuals. However, plant breeders can face challenges when trying to implement it practically to make predictions for future seasons or new locations and/or environments. To help address this challenge, this study seeks to explore the use of the multi-trait partial least square (MT-PLS) regression methodology and to compare its performance with the Bayesian Multi-trait Genomic Best Linear Unbiased Predictor (MT-GBLUP) method. A benchmarking process was performed with five actual data sets contained in this study. The results of the analysis are reported in the accompanying article
Maize experiment with increasing rates of nitrogen to develop a calibration for the GreenSeeker in San Luis Potosí.
No full textThis experiments were established with different rates of nitrogen in order to generate a wide range of values for NDVI and grain yield in order to develop a calibration model for the GreenSeeker in San Luis Potosí
Fertility Maps of "Jonacatepec, Morelos".
No full textSoil sampling with 1x1 km grid in the agricultural area of the Jonacatepec Municipality at at 0 to 30 cm depth
Replication Data for: Modeling genotype × environment interaction for single and multi-trait genomic prediction in potato (Solanum tuberosum L.)
No full textGenomic prediction (GP) can be used in the breeding of polysomic polyploid plant species. Different models can be used to generate the genomic prediction including single trait and multi-trait models. The data provided in this dataset were used to investigate the accuracy of four different genomic prediction models use for several traits in potato. The results of the analysis are reported in the accompanying journal article
8th Stress Adapted Trait Yield Nurseries
No full textWithin the framework of SATYN, two types of nurseries are produced: SATYN series with odd numbers are lines for drought-stressed areas, and SATYN series with even numbers are lines for heat stress conditions. These nurseries have been phenotyped in the major wheat-growing mega environments through the International Wheat Improvement Network (IWIN) and the Cereal System Initiative for South Asia (CSISA) network, which included a total of 136 environments (site-year combinations) in major spring wheat-growing countries such as Bangladesh, China, Egypt, India, Iran, Mexico, Nepal, and Pakistan
20th Karnal Bunt Screening Nursery
No full textThe Karnal Bunt Screening Nursery is a single replicate nursery that contains diverse spring bread wheat (Triticum aestivum) germplasm adapted to ME1 (Optimally irrigated, low rainfall environment) with total 50-100 entries and white/red grain color
12th Helminthium Leaf Blight Screening Nursery
No full textThe Helminthium Leaf Blight Screening Nursery is a single replicate nursery that contains diverse spring bread wheat (Triticum aestivum) germplasm with total 50-100 entries and 2 REPs
Fertility Maps of Yautepec-Morelos. 2020
No full textSoil sampling with 1x1 km grid in the agricultural area of the Yautepec Municipality at at 0 to 30 cm depth
Evaluation of international Low-ODAP grasspea lines for highlands in El Batán, México (2016)
No full textSeveral international varieties of Low-ODAP grass pea were evaluated. Some of the variables measured were Total biomass weight (g), Fresh weigth of the subsample (g), Dry weight of the subsample (g) and Harvest area (m²). The experiment was conducted in El Batán, México
54th International Bread Wheat Screening Nursery
No full textThe International Bread Wheat Screening Nursery (IBWSN) is designed to rapidly assess a large number of advanced generation (F3-F7) lines of spring bread wheat under Mega-environment 1 (ME1) which represents diversity for a wide range of latitudes, climates, daylengths, fertility conditions, water management, and (most importantly) disease conditions. The distribution of these nurseries is deliberately biased toward the major spring wheat regions of the world where the diseases of wheat are of high incidence. It is distributed to 180 locations and contains 300-450 entries