CIMMYT Research Data

Free, open access repository of research studies developed by CIMMYT scientists.

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7,931 to 7,940 of 10,613 Results

Data_Set_6.RData.gz Oct 22, 2018 - New deep learning genomic prediction model for multi-traits with mixed binary, ordinal, and continuous phenotypes Gzip Archive - 153.6 KB - MD5: d891bbd1db0577097ef9ce267581237f
Data_Set_7.RData.gz Oct 22, 2018 - New deep learning genomic prediction model for multi-traits with mixed binary, ordinal, and continuous phenotypes Gzip Archive - 261.2 KB - MD5: f0396ede52b49e1a91090a95e299bc31
SUPPLEMENTARY MATERIAL.docx Oct 22, 2018 - New deep learning genomic prediction model for multi-traits with mixed binary, ordinal, and continuous phenotypes MS Word - 358.9 KB - MD5: 6bfa2583115c2816b2945150fb882e77
Evaluating Radar Satellite Based Remote Sensing for Conservation Agriculture Adoption Detection in North and Central Mexico Oct 8, 2018 Sonder, Kai; Schulthess, Urs; Chomé, Guillaume, 2018, "Evaluating Radar Satellite Based Remote Sensing for Conservation Agriculture Adoption Detection in North and Central Mexico", https://hdl.handle.net/11529/10548137, CIMMYT Research Data & Software Repository Network, V1 Conservation agriculture has been tested and out scaled for over 60 years in rural areas of Mexico, however the rate of adoption and dis adoption, as well as the current area under CA is unknown. Estimates range between 50,000 ha and over 800,000 ha depending on the source. Studi...
Technical Report-FAO-SIAC-Estimating CA adoption in Mexico-03Jun16 to 30....pdf Oct 8, 2018 - Evaluating Radar Satellite Based Remote Sensing for Conservation Agriculture Adoption Detection in North and Central Mexico Adobe PDF - 1.9 MB - MD5: 37f4bdf8baee6dec17d3ca1678950a9c
Supplemental Materials for The Relative Efficiency of Three Constrained Multistage Linear Phenotypic Selection Indices Oct 8, 2018 Cerón-Rojas, J.Jesús; Toledo, Fernando; Crossa, Jose, 2018, "Supplemental Materials for The Relative Efficiency of Three Constrained Multistage Linear Phenotypic Selection Indices", https://hdl.handle.net/11529/10548136, CIMMYT Research Data & Software Repository Network, V1 This dataset provides supplemental information related to an investigation of constrained multistage linear phenotypic selection indices.
Supplementary-Materials-final.docx Oct 8, 2018 - Supplemental Materials for The Relative Efficiency of Three Constrained Multistage Linear Phenotypic Selection Indices MS Word - 358.0 KB - MD5: 02e60fff9e7c5c038887aa8a2dde15c8 This file has supplementary material of the article "The Relative Efficiency of Three Constrained Multistage Linear Phenotypic Selection Indices"
Supplemental data for multi-trait, multi-environment deep learning modeling for genomic-enabled prediction of plant traits Sep 28, 2018 Montesinos-López, Osval A.; Montesinos-López, Abelardo; Crossa, Jose; Gianola, Daniel; Hernández-Suarez, Carlos Moisés; Martín-Vallejo, Javier, 2018, "Supplemental data for multi-trait, multi-environment deep learning modeling for genomic-enabled prediction of plant traits", https://hdl.handle.net/11529/10548134, CIMMYT Research Data & Software Repository Network, V1 This study provides supplemental data to support an investigation of the power of multi-trait deep learning (MTDL) models in terms of genomic-enabled prediction accuracy.
DataSets_Multi_trait.rar Sep 28, 2018 - Supplemental data for multi-trait, multi-environment deep learning modeling for genomic-enabled prediction of plant traits RAR Archive - 41.9 MB - MD5: cdd5ff94aff34916a943fe27c4bb20a6 Supporting data
Supplemental data for hybrid wheat prediction using genomic, pedigree and environmental covariables interaction models Sep 21, 2018 Basnet, Bhoja Raj; Crossa, Jose; Pérez-Rodríguez, Paulino; Manes, Yann; Singh, Ravi; Rosyara, Umesh; Camarillo-Castillo, Fatima; Murua, Mercedes, 2018, "Supplemental data for hybrid wheat prediction using genomic, pedigree and environmental covariables interaction models", https://hdl.handle.net/11529/10548129, CIMMYT Research Data & Software Repository Network, V1 Genomic prediction of hybrids unobserved in field evaluations is crucial. In this study, we used genomic G×E models for hybrid prediction, where similarity between lines was assessed by pedigree and molecular markers, and similarity between environments was accounted for by envir...

Data_Set_6.RData.gz

Oct 22, 2018 - New deep learning genomic prediction model for multi-traits with mixed binary, ordinal, and continuous phenotypes

Gzip Archive - 153.6 KB -

Data_Set_7.RData.gz

Oct 22, 2018 - New deep learning genomic prediction model for multi-traits with mixed binary, ordinal, and continuous phenotypes

Gzip Archive - 261.2 KB -

SUPPLEMENTARY MATERIAL.docx

Oct 22, 2018 - New deep learning genomic prediction model for multi-traits with mixed binary, ordinal, and continuous phenotypes

MS Word - 358.9 KB -

Evaluating Radar Satellite Based Remote Sensing for Conservation Agriculture Adoption Detection in North and Central Mexico

Oct 8, 2018

Sonder, Kai; Schulthess, Urs; Chomé, Guillaume, 2018, "Evaluating Radar Satellite Based Remote Sensing for Conservation Agriculture Adoption Detection in North and Central Mexico", https://hdl.handle.net/11529/10548137, CIMMYT Research Data & Software Repository Network, V1

Conservation agriculture has been tested and out scaled for over 60 years in rural areas of Mexico, however the rate of adoption and dis adoption, as well as the current area under CA is unknown. Estimates range between 50,000 ha and over 800,000 ha depending on the source. Studi...

Technical Report-FAO-SIAC-Estimating CA adoption in Mexico-03Jun16 to 30....pdf

Oct 8, 2018 - Evaluating Radar Satellite Based Remote Sensing for Conservation Agriculture Adoption Detection in North and Central Mexico

Adobe PDF - 1.9 MB -

Supplemental Materials for The Relative Efficiency of Three Constrained Multistage Linear Phenotypic Selection Indices

Oct 8, 2018

Cerón-Rojas, J.Jesús; Toledo, Fernando; Crossa, Jose, 2018, "Supplemental Materials for The Relative Efficiency of Three Constrained Multistage Linear Phenotypic Selection Indices", https://hdl.handle.net/11529/10548136, CIMMYT Research Data & Software Repository Network, V1

This dataset provides supplemental information related to an investigation of constrained multistage linear phenotypic selection indices.

Supplementary-Materials-final.docx

Oct 8, 2018 - Supplemental Materials for The Relative Efficiency of Three Constrained Multistage Linear Phenotypic Selection Indices

MS Word - 358.0 KB -

This file has supplementary material of the article "The Relative Efficiency of Three Constrained Multistage Linear Phenotypic Selection Indices"

Supplemental data for multi-trait, multi-environment deep learning modeling for genomic-enabled prediction of plant traits

Sep 28, 2018

Montesinos-López, Osval A.; Montesinos-López, Abelardo; Crossa, Jose; Gianola, Daniel; Hernández-Suarez, Carlos Moisés; Martín-Vallejo, Javier, 2018, "Supplemental data for multi-trait, multi-environment deep learning modeling for genomic-enabled prediction of plant traits", https://hdl.handle.net/11529/10548134, CIMMYT Research Data & Software Repository Network, V1

This study provides supplemental data to support an investigation of the power of multi-trait deep learning (MTDL) models in terms of genomic-enabled prediction accuracy.

DataSets_Multi_trait.rar

Sep 28, 2018 - Supplemental data for multi-trait, multi-environment deep learning modeling for genomic-enabled prediction of plant traits

RAR Archive - 41.9 MB -

Supporting data

Supplemental data for hybrid wheat prediction using genomic, pedigree and environmental covariables interaction models

Sep 21, 2018

Basnet, Bhoja Raj; Crossa, Jose; Pérez-Rodríguez, Paulino; Manes, Yann; Singh, Ravi; Rosyara, Umesh; Camarillo-Castillo, Fatima; Murua, Mercedes, 2018, "Supplemental data for hybrid wheat prediction using genomic, pedigree and environmental covariables interaction models", https://hdl.handle.net/11529/10548129, CIMMYT Research Data & Software Repository Network, V1

Genomic prediction of hybrids unobserved in field evaluations is crucial. In this study, we used genomic G×E models for hybrid prediction, where similarity between lines was assessed by pedigree and molecular markers, and similarity between environments was accounted for by envir...

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