TAMASA Ethiopia. Yield, soil and agronomy data from farmers’ maize fields collected by EIAR, 2015 season

Peter Craufurd (2017, [Dataset])

This dataset was obtained from maize Crop cut survey conducted in 2015 by EIAR and CIMMYT. Replicated crop cuts of 16m2 in farmers fields along with addition data on nutrient use and variety, and soil sample (0-20, 20-50 cm). Note that not all soil samples have been analysed yet.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

CIMMYT Maize Regional Trial Data for Eastern Africa 2017

MacDonald Jumbo Yoseph Beyene Dan Makumbi Lewis Machida Suresh L.M. Amsal Tarekegne Manje Gowda Vijay Chaikam Prasanna Boddupalli (2020, [Dataset])

The summary results of the Regional Trials for CIMMYT Maize Hybrids in Eastern Africa for 2017. The trials include: EHYB17-Set I – Early/extra-early maturing elite pre-commercial hybrids regional trials (including external and internal checks); IHYB17-Set I – Intermediate maturing elite pre-commercial hybrids regional trial (including external and internal checks); ILHYB17 – Intermediate-Late maturing elite pre-released and released hybrids regional trials (including external and internal checks); EHYB17-Set II – Early maturing elite pre-commercial hybrids regional trials; ILHYB17 Set II – Intermediate/late maturing elite pre-commercial hybrids regional trials.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Molecular characterization of modern durum wheat Mediterranean landraces and modern cultivars with molecular markers associated to vernalization response (Vrn-1) and photoperiod sensitivity (Ppd-1) genes

Susanne Dreisigacker Karim Ammar (2020, [Dataset])

We characterized a panel of 151 durum wheat Mediterranean landraces and 20 modern cultivars via a series of molecular markers associated with Vrn-1 and Ppd-1 genes. The molecular data were used estimate the effects of the observed alleles on the time needed to reach six different growth stages under field conditions. Field experiments were carried out over six years in Lleida, northeastern Spain.

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Genomic and pedigree prediction with genotype × environment interaction in spring wheat grown in South and Western Asia, North Africa, and Mexico

Sivakumar Sukumaran Jose Crossa Carlos Jara Marta Lopes Matthew Paul Reynolds (2016, [Dataset])

Increases in genetic gains in grain yield can be accelerated through genomic selection (GS). In the present study seven genomic prediction models under two cross validation scenarios were evaluated on the Wheat Association Mapping Initiative population of 287 advanced elite lines phenotyped for grain yield (GY), thousand grain weight (GW), grain number (GN), and thermal time for flowering (TTF) in 18 environments (year location combinations) in major wheat producing countries in 2010 and 2011. The seven genomic prediction models tested herein: four of them (model 1 (L+E), model 2 (L+E+G), model 3 (L+E+A) , and model 4 (L+E+A+G )) with main effects (lines (L), environme nts (E), genetic relationship matrix (G), and pedigree derived matrix (A) and three of them (model 5 (L+E+A+AE), model 6 (L+E+G+GE), and model 7 (L+E+G+A+AE+GE)) with interaction effects between A×E, G×E, and both together with main effects. Moreover, two cross validation (CV) schemes were applied: (1) predicting lines’ performance at untested sites (CV1) and (2) predicting the lines’ performance at some sites with the performance from other sites (CV2). The genomic prediction models with interaction terms, models 6 and 7 had the highest prediction accuracy on average for CV1 for GY (0.31), GN (0.30), and model 5 for TTF (0.26). Models 3 and 7 2, were the best model for GW (0.45 each) under CV1 scenario. For CV2, the prediction accuracy was generally high for the model with interaction terms models 5, 6, and 7 for GY (0.39), model 5 and 7 for GN (0.43. For GW and TTF models prediction accuracy were similar. Results indicated genomic selection can be used to predict genotype by environment (G×E) interaction in multi environment trials to select varieties for release as well as for accelerated breeding.

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Replication Data for: Genomic Prediction with Genotype by Environment Interaction Analysis for Kernel Zinc Concentration in Tropical Maize Germplasm

Edna Mageto Jose Crossa Paulino Pérez-Rodríguez Thanda Dhliwayo natalia palacios rojas XUECAI ZHANG (2020, [Dataset])

The Zinc association mapping (ZAM) panel is a set of 923 elite inbred lines from the International Maize and Wheat Improvement Center (CIMMYT) biofortification breeding program. The panel represented wide genetic diversity for kernel Zn and is comprised of several lines with tolerance/resistance to an array of abiotic and biotic stresses commonly affecting maize production in the tropics, improved nitrogen use efficiency, and grain nutritional quality. The ZAM panel_923_LINES_GENO and Zinc association mapping (ZAM) panel_phenotypic data are two files with GBS and phenotypic data for zinc (Zn) from this population. From the ZAM panel, four inbred lines (two with high-Zn and two with low-Zn) were selected and used to form the bi-parental populations, namely DH population1 and DH population2. Genotypic and phenotypic data corresponding to these populations are DH populations1&2_255_LINES_GENO and DH population1_phenotypic data and DH population2_phenotypic data

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Blue tortilla preference in Mexico

Trent Blare (2020, [Dataset])

The study shows the database made from interviews conducted in Texcoco, Mexico during April, May and June 2019. The objective of the study was to understand consumer preferences and consumption and purchasing habits for white and blue maize tortillas.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

6th Stress Adapted Trait Yield Nurseries

Matthew Paul Reynolds Thomas Payne (2020, [Dataset])

Within 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.

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Replication Data for: Genomic prediction of kernel zinc content in multiple maize populations using genotyping-by-sequencing and repeat amplification sequencing markers

Thanda Dhliwayo Edna Mageto Michael Olsen Jose Crossa Prasanna Boddupalli XUECAI ZHANG (2020, [Dataset])

An association-mapping panel (DTMA) and two DH populations (DH1 and DH2) were used in the current study, which in total includes 487 materials. The dataset includes three types of files. One is the genotype of 487 lines sequenced by GbS, named DTMA_DH2_DH3-955690.hmp.txt; one is the genotype of 487 lines sequenced by rAmpSeq named genotype-rAmpSeq.csv; and the third type of files are the phenotypic data files named DH1-phenotype.csv, DH2-phenotype.csv and DTMA-phenotype.csv.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

6th Wheat Yield Collaboration Yield Trial

Matthew Paul Reynolds Thomas Payne (2020, [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.

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47th International Bread Wheat Screening Nursery MAS data

Susanne Dreisigacker (2017, [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.

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