Replication Data for: Bayesian linear regression near infrared spectroscopy (NIR) to predict provitamin A carotenoids content in maize breeding programs

Jose Crossa Thanda Dhliwayo THOKOZILE NDHLELA natalia palacios rojas (2021, [Dataset])

Vitamin A deficiency (VAD) is a public health problem worldwide. For countries with a high per capita consumption of maize, breeding varieties with higher provitamin A carotenoid content than normal yellow maize — biofortification — can be a viable strategy to reduce VAD. Selection for provitamin A carotenoid content uses molecular markers and phenotypic data generated using expensive and laborious wet lab analyses. Near-infrared spectroscopy (NIRS) could be a fast and cheap method to measure carotenoids. This dataset contains carotenoid and NIRS data from 1857 tropical maize samples used as a training set to predict provitamin A carotenoid content of an independent set of 650 tropical maize samples using Bayesian linear regression models. The datasets contain information about specific carotenoids measured and the NIRS values measured at different wavelengths. The results of the analysis are described in the accompanying article.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

9th Wheat Yield Collaboration Yield Trial

Matthew Paul Reynolds Carolina Saint Pierre (2022, [Dataset])

The WYCYT international nurseries are the result of research conducted to raise the yield potential of spring wheat through the strategic crossing of physiological traits related to source and sink potential in wheat. These trials 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.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Daily wheat canopy temperature and meteorological data for IWIN locations

Carlo Montes Urs Schulthess Azam lashkari (2021, [Dataset])

Dataset of daily canopy temperature and meteorological data from the ECMWF’s AgERA5 product for the period 1979 though 2020, and for 785 points belonging to the International Wheat Improvement Network (IWIN). Wheat canopy temperature was estimated from a linear model using maximum air temperature, vapor pressure deficit, and solar radiation as inputs. The model was calibrated using multiple measurements of wheat canopy temperature.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

53rd International Bread Wheat Screening Nursery

Ravi Singh Thomas Payne (2021, [Dataset])

The 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.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Wheat Blast Data for the 8th to 12th Helminthosporium Leaf Blight Screening Nursery by the scientists

Pawan Singh Xinyao He (2022, [Dataset])

Wheat head blast index (%) data for the 8th to 12th HLBSN is presented. Field trials took place in Quirusillas and Okinawa (Bolivia) and Jashore (Bangladesh) during the 2018 to 2021 cycles. Two sowings were made in each location/cycle.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

22nd Karnal Bunt Screening Nursery

Ravi Singh Thomas Payne (2022, [Dataset])

The 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.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

54IBWSN, 39SAWSN, and 32HRWSN - Gene-based marker data for marker-assisted selection

Susanne Dreisigacker (2021, [Dataset])

Gene-based marker data from international screening nurseries for marker-assisted selection.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Genotypic data for a spring wheat panel

Xinyao He Pawan Singh (2021, [Dataset])

GBS genotypic data for a panel of 266 spring wheat lines from China, CIMMYT and other countries.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Projecting Food Demand in 2030 and 2050: Can Uganda Attain the Zero Hunger Goal?

Khondoker Mottaleb (2021, [Dataset])

LSMS-ISA 2010-11, LSMS-ISA 2013-14, and LSMS-ISA 2015-16 of Uganda

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Replication Data for: A Bayesian Linear Phenotypic Selection Index to Predict the Net Genetic Merit

J. Jesús Cerón Rojas Sergio Pérez-Elizalde Jose Crossa Johannes Martini (2021, [Dataset])

In breeding, the plant net genetic merit may be predicted through the linear phenotypic selection index (LPSI). This paper associated with this dataset proposes a Bayesian LPSI (BLPSI). The supplemental files provided in this dataset include data that were used to compare the two indices as well as figures showing the results from these comparisons. The analysis revealed that the BLPSI is a good option when carrying out phenotypic selections in breeding programs.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA