CIMMYT Research Data

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

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7,831 to 7,840 of 10,528 Results

A Bayesian genomic multi-output regressor stacking model for predicting multi-trait multi-environment plant breeding data Oct 26, 2018 Montesinos-López, Osval A.; Montesinos-López, Abelardo; Crossa, Jose; Cuevas, Jaime; Montesinos-López, José Cricelio; Gutiérrez, Zitlalli Salas; Lillemo, Morten; Juliana, Philomin; Singh, Ravi, 2018, "A Bayesian genomic multi-output regressor stacking model for predicting multi-trait multi-environment plant breeding data", https://hdl.handle.net/11529/10548141, CIMMYT Research Data & Software Repository Network, V1 A new statistical model is presented for genomic prediction on maize and wheat data comprising multi-trait, multi-environment data.
Data_EYT_Set_4.RData.gz Oct 26, 2018 - A Bayesian genomic multi-output regressor stacking model for predicting multi-trait multi-environment plant breeding data Gzip Archive - 4.3 MB - MD5: d89c19aa2d574666d7e60b00be99d867
Data_EYT_Set_5.RData.gz Oct 26, 2018 - A Bayesian genomic multi-output regressor stacking model for predicting multi-trait multi-environment plant breeding data Gzip Archive - 4.4 MB - MD5: 648b8504a29690109dbbb2c93c4adbcd
Data_EYT_Set_6.RData.gz Oct 26, 2018 - A Bayesian genomic multi-output regressor stacking model for predicting multi-trait multi-environment plant breeding data Gzip Archive - 6.8 MB - MD5: 170f30875373b5db186a0a854ab30d2a
Data_EYT_Set_7.RData.gz Oct 26, 2018 - A Bayesian genomic multi-output regressor stacking model for predicting multi-trait multi-environment plant breeding data Gzip Archive - 7.0 MB - MD5: 7eadf14b110d8863226ec817e7c0c577
Data_Maize_Set_1.RData.gz Oct 26, 2018 - A Bayesian genomic multi-output regressor stacking model for predicting multi-trait multi-environment plant breeding data Gzip Archive - 374.3 KB - MD5: e3954a629bc843561362f80d8e09a90c
Data_Wheat_Iranian_Set_3.RData.gz Oct 26, 2018 - A Bayesian genomic multi-output regressor stacking model for predicting multi-trait multi-environment plant breeding data Gzip Archive - 41.1 MB - MD5: fd6c1e6305f656eb8476209bba629a03
Data_Wheat_Set_2.RData.gz Oct 26, 2018 - A Bayesian genomic multi-output regressor stacking model for predicting multi-trait multi-environment plant breeding data Gzip Archive - 458.8 KB - MD5: bdebf18268314e8e14812abb2b1751d0
Zero-Tillage adoption and its welfare impacts at the farm household level Oct 24, 2018 Keil, Alwin; Michalski, Joel, 2016, "Zero-Tillage adoption and its welfare impacts at the farm household level", https://hdl.handle.net/11529/10840, CIMMYT Research Data & Software Repository Network, V3 The purpose of the study was (1) to assess the performance of ZT wheat as compared to conventional-tillage wheat in farmers' fields in six CSISA target districts in Bihar; (2) to assess farmers’ resource endowment, risk exposure, risk preferences, and risk management practices; (...
New deep learning genomic prediction model for multi-traits with mixed binary, ordinal, and continuous phenotypes Oct 22, 2018 Montesinos-López, Osval A.; Martín-Vallejo, Javier; Crossa, Jose; Gianola, Daniel; Hernández-Suarez, Carlos Moisés; Montesinos-López, Abelardo; Juliana, Philomin; Singh, Ravi, 2018, "New deep learning genomic prediction model for multi-traits with mixed binary, ordinal, and continuous phenotypes", https://hdl.handle.net/11529/10548140, CIMMYT Research Data & Software Repository Network, V1 The seven data sets are wheat data from CIMMYT Global Wheat Breeding program. They comprise different traits, like days to heading, days to maturity, grain yield, grain color, different type of leaf and stripe rust in wheat. Also the trials were run in different environments.

A Bayesian genomic multi-output regressor stacking model for predicting multi-trait multi-environment plant breeding data

Oct 26, 2018

Montesinos-López, Osval A.; Montesinos-López, Abelardo; Crossa, Jose; Cuevas, Jaime; Montesinos-López, José Cricelio; Gutiérrez, Zitlalli Salas; Lillemo, Morten; Juliana, Philomin; Singh, Ravi, 2018, "A Bayesian genomic multi-output regressor stacking model for predicting multi-trait multi-environment plant breeding data", https://hdl.handle.net/11529/10548141, CIMMYT Research Data & Software Repository Network, V1

A new statistical model is presented for genomic prediction on maize and wheat data comprising multi-trait, multi-environment data.

Data_EYT_Set_4.RData.gz

Oct 26, 2018 - A Bayesian genomic multi-output regressor stacking model for predicting multi-trait multi-environment plant breeding data

Gzip Archive - 4.3 MB -

Data_EYT_Set_5.RData.gz

Oct 26, 2018 - A Bayesian genomic multi-output regressor stacking model for predicting multi-trait multi-environment plant breeding data

Gzip Archive - 4.4 MB -

Data_EYT_Set_6.RData.gz

Oct 26, 2018 - A Bayesian genomic multi-output regressor stacking model for predicting multi-trait multi-environment plant breeding data

Gzip Archive - 6.8 MB -

Data_EYT_Set_7.RData.gz

Oct 26, 2018 - A Bayesian genomic multi-output regressor stacking model for predicting multi-trait multi-environment plant breeding data

Gzip Archive - 7.0 MB -

Data_Maize_Set_1.RData.gz

Oct 26, 2018 - A Bayesian genomic multi-output regressor stacking model for predicting multi-trait multi-environment plant breeding data

Gzip Archive - 374.3 KB -

Data_Wheat_Iranian_Set_3.RData.gz

Oct 26, 2018 - A Bayesian genomic multi-output regressor stacking model for predicting multi-trait multi-environment plant breeding data

Gzip Archive - 41.1 MB -

Data_Wheat_Set_2.RData.gz

Oct 26, 2018 - A Bayesian genomic multi-output regressor stacking model for predicting multi-trait multi-environment plant breeding data

Gzip Archive - 458.8 KB -

Zero-Tillage adoption and its welfare impacts at the farm household level

Oct 24, 2018

Keil, Alwin; Michalski, Joel, 2016, "Zero-Tillage adoption and its welfare impacts at the farm household level", https://hdl.handle.net/11529/10840, CIMMYT Research Data & Software Repository Network, V3

The purpose of the study was (1) to assess the performance of ZT wheat as compared to conventional-tillage wheat in farmers' fields in six CSISA target districts in Bihar; (2) to assess farmers’ resource endowment, risk exposure, risk preferences, and risk management practices; (...

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

Oct 22, 2018

Montesinos-López, Osval A.; Martín-Vallejo, Javier; Crossa, Jose; Gianola, Daniel; Hernández-Suarez, Carlos Moisés; Montesinos-López, Abelardo; Juliana, Philomin; Singh, Ravi, 2018, "New deep learning genomic prediction model for multi-traits with mixed binary, ordinal, and continuous phenotypes", https://hdl.handle.net/11529/10548140, CIMMYT Research Data & Software Repository Network, V1

The seven data sets are wheat data from CIMMYT Global Wheat Breeding program. They comprise different traits, like days to heading, days to maturity, grain yield, grain color, different type of leaf and stripe rust in wheat. Also the trials were run in different environments.

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