Repositorio Nacional

The study was conducted as part of International Treaty on Plant Genetic Resources for Food and Agriculture (ITPGRFA) Project titled: “Improving food security by enhancing wheat production and its resilience to climate change through maintaining the diversity of currently grown landraces”. The project was successfully conducted in Afghanistan, Iran and Turkey in 2015-2019 and had the following objectives: 1. Participatory selection of drought and heat tolerant wheat landraces among the set of the germplasm recently collected from the farming communities in the target countries using modern phenotyping and genotyping tools in collaboration with farming communities, research institutions, NGOs and extension services. 2. Development of germplasm combining drought and heat tolerance with disease resistance (primarily yellow rust and common as well as leaf and stem rust) through crosses, marker assisted selection and backcrossing to the landraces. 3. Promotion of selected drought and heat tolerant landraces in the targeted regions through enhanced on-farm seed production and bulk selection, improved agronomic practices and large scale awareness campaign. 4. Training of farmers, extension services and local administration, policy-makers, NGOs and researchers on sustainable cultivation of wheat landraces and role of biodiversity in mitigation of adverse effects of climate change. Important part of the project activities was characterization of wheat 85 wheat landraces currently collected from Afghanistan, Iran and Turkey along with modern winter wheat germplasm adapted to irrigated and rainfed conditions and checks making the total 158 entries. The sets was thoroughly phenotyped for agronomic and physiological traits in Turkey (Konya, Ankara and Sakarya provinces) in 2018 and 2019, in Afghanistan (Kabul) in 2019 and in Iran (Maragheh) in 2019. The ITPGR requirement to the project was to make the data freely available through the Multilateral System. The phenotyping of the trial was supported by ITPGRFA Project No: W2B-PR-41-Turkey with funding from the European Union. CIMMYT-Turkey is supported by Ministry of Agriculture and Forestry of the Turkish Republic and CRP WHEAT. The file contained in this study provides both phenotypic and genotypic data for the landraces.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

26th Elite Selection Wheat Yield Trial

Ravi Singh Thomas Payne (2019, [Dataset])

The Elite Selection Wheat Yield Trial (ESWYT) is a replicated yield trial that contains spring bread wheat (Triticum aestivum) germplasm adapted to Mega-environment 1 (ME1) which represents the optimally irrigated, low rainfall areas. Major stresses include leaf, stem and yellow rusts, Karnal bunt, and lodging. Representative areas include the Gangetic Valley (India), the Indus Valley (Pakistan), the Nile Valley (Egypt), irrigated river valleys in parts of China (e.g. Chengdu), and the Yaqui Valley (Mexico). This ME encompasses 36 million hectares spread primarily over Asia and Africa between 350S -350N latitudes. White (amber)-grained types are preferred by consumers of wheat in the vast majority of the areas. It is distributed to upto 200 locations and contains 50 entries.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Prediction models for canopy hyperspectral reflectance in wheat breeding data

Osval Antonio Montesinos-Lopez Jose Crossa Gustavo de los Campos Gregorio Alvarado Suchismita Mondal Jessica Rutkoski Lorena González Pérez Juan Burgueño (2016, [Dataset])

Vegetation indices (VI) generated by using some bands from hyperspectral cameras are used as predictors of primary traits. This study proposes models that use all available bands as predictors of primary traits. The proposed models were ordinal least square (OLS), Bayes B, principal components with Bayes B, functional B-spline, functional Fourier and functional partial least square (PLS). The results were compared with the OLS performed using as predictors each of the eight VIs individually and combined. The data set comes from CIMMYT’s Global Wheat Program and comprises 1170 genotypes evaluated for grain yield in five environments with the reflectance data measured in 250 discrete narrow bands ranging between 492 and 851 nm. in 9 time-points of the crop cycle. Results show that using all the bands simultaneously produced better predictions than using one VI alone or all the VI together, but when used only the bands with heritabilities > 0.5 in Drought environment, the predictions improved, while in the rest of the environments, using all the bands simultaneously produced slightly better prediction accuracies. The models with highest prediction when using all bands were functional B-spline and Fourier. Time-point 6 gives gave promising prediction accuracies for wheat lines before harvesting.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Replication Data for: Approximate kernels for large data sets In genome-based prediction

Osval Antonio Montesinos-Lopez Johannes Martini Paulino Pérez-Rodríguez Jose Crossa (2020, [Dataset])

The rapid development of molecular markers and sequencing technologies has made it possible to use genomic selection (GS) and genomic prediction (GP) in animal and plant breeding. However, computational difficulties arise when the number of observations is large. This five datasets provided here were used to support a comparative analysis of two genomic-enabled prediction models: the full genomic method single environment (FGSE) and the approximate kernel method for a single environment model (APSE). The data were also used to compare the full genomic method with genotype × environment model (FGGE) to the approximate kernel method with genotype × environment interaction (APGE). The results of the analyses are described in the related publication.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA