Dataset on rhizosphere microbiota and yield of maize and barley in a field experiment with six seed treatments in the Central Highlands of Mexico

Pablo Jaramillo-López Marcela Sarabia Simon Fonteyne Abel Saldivia Tejeda Nele Verhulst Mette Vestergård John Larsen (2023, [Dataset])

Six seed treatments were tested for 2 growing cycles (summer of 2021 and 2022) in a field experiment with maize (Zea mays L.) and barley (Hordeum vulgare L.) under conservation agriculture in the Mexican highlands, at CIMMYT’s experiment station of El Batán Texcoco, the State of Mexico, Mexico. The experiment was a randomized complete block design with 3 replicates, with separate areas for maize and barley. The six seed treatments included a negative control, a chemical seed treatment (different for maize and barley, depending on common practices in the area), Trichoderma, Metarhizium, a commercial mixture of plant growth promoting rhizobacteria, and a combination of Trichoderma and Metarhizium. Soil and root samples were taken at two and three sampling times during the 2021 crop cycle for barley and maize, respectively. Yield and yield components were determined at the end of the crop cycle in 2021 and 2022. The soil and root samples were used to measure root growth (root biomass per core), root colonization with mycorrhizal fungi, root infection with pathogens (Polymyxa, Pythium, Microdochium), soil microbial communities in terms of biomarker fatty acids, and ecological guilds of soil nematodes (Bacterivores, fungivores, plant parasitic and predators).

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Genotypic data for the IND296 panel

Xinyao He Pawan Singh arun joshi Gyanendra Singh (2023, [Dataset])

GBS genotypic data for an Indian panel with 296 common wheat accessions.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Genotypic data (DArTAG panel 2) for the IBWSN and SAWSN

Susanne Dreisigacker (2023, [Dataset])

DArTAG panel 2 validation data on 1454 CIMMYT spring bread wheat elite lines included in the 53, 54 and 55IWBWSN and 38, 39 and 40 SAWSN. DArTAG panel 2 consists of 3897 selected SNPs.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Replication Data for: Optimizing Genomic-Enabled Prediction: A Feature Weighting Approach for Enhancing within Family Accuracy

Guillermo Gerard Paolo Vitale Susanne Dreisigacker Morten Lillemo Jose Crossa (2023, [Dataset])

This study provides supplemental data to support the study on Optimizing Genomic-Enabled Prediction: A Feature Weighting Approach for Enhancing within Family Accuracy.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

21st High Temperature Wheat Yield Trial

Ravi Singh Carolina Saint Pierre (2023, [Dataset])

CIMMYT annually distributes improved germplasm developed by its researchers and partners in international nurseries trials and experiments. The High Temperature Wheat Yield Trial (HTWYT) is a replicated yield trial that contains spring bread wheat (Triticum aestivum) germplasm adapted to Mega-environment 1 (ME1) which represents high temperature areas.

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Genealogical information for maize pre-breeding materials in the Seeds of Discovery-MasAgro Biodiversidad project: 2013-2015

Terence Molnar (2015, [Dataset])

This dataset contains pedigree information for the maize breeding materials generated through the Seeds of Discovery-Ma sAgro Biodiversidad project from 2013 through 2015. These tropical, subtropical and highland materials are being developed to introduce novel alleles for tar spot resistance, drought tolerance, and high-anthocyanin content into elite maize lines.

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Replication data for: Increased ranking change in wheat breeding under climate change

Wei Xiong Matthew Paul Reynolds Jose Crossa Urs Schulthess Kai Sonder Carlo Montes Nicoletta Addimando Ravi Singh Karim Ammar Bruno Gerard Thomas Payne (2022, [Dataset])

A standard quantitative genetic model was used to examine how genotype-environment interactions have changed over the past decades from four spring wheat trial data sets. The variability of cross interactions for yield from one year to another is explained in more than 70% by climatic factors.

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Toward greater sustainability: how investing in soil health may enhance maize productivity in Southern Africa

Christian Thierfelder Eric Paterson Lumbani Mwafulirwa Tim Daniell Jill Cairns Blessing Mhlanga (2022, [Dataset])

Climate change and soil fertility decline are major threats to smallholder farmers' food and nutrition security in southern Africa, and cropping systems that improve soil health are needed to address these challenges. Cropping systems that invest in soil organic matter, such as no-tillage (NT) with crop residue retention, have been proposed as potential solutions. However, a key challenge for assessing the sustainability of NT systems is that soil carbon (C) stocks develop over long timescales, and there is an urgent need to identify trajectory indicators of sustainability and crop productivity. Here we examined the effects of NT as compared with conventional tillage without residue retention on relationships between soil characteristics and maize (Zea mays L.) productivity in long-term on-farm and on-station trials in Zimbabwe. Our results show that relationships between soil characteristics and maize productivity, and the effects of management on these relationships, varied with soil type. Total soil nitrogen (N) and C were strong predictors of maize grain yield and above-ground biomass (i.e., stover) in the clayey soils, but not in the sandy soils, under both managements. This highlights context-specific benefits of management that fosters the accumulation of soil C and N stocks. Despite a strong effect of NT management on soil C and N in sandy soils, this accrual was not sufficient to support increased crop productivity in these soils. We suggest that sandy soils should be the priority target of NT with organic resource inputs interventions in southern Africa, as mineral fertilizer inputs alone will not halt the soil fertility decline. This will require a holistic management approach and input of C in various forms (e.g., biomass from cover crops and tree components, crop residues, in combination with mineral fertilizers). Clayey soils on the other hand have greater buffering capacity against detrimental effects of soil tillage and low C input.

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Consumer study on acceptance and willingness to pay for instant cereal products with food-to-food fortification, using affective tests and experimental auctions, in Eldoret, Kenya

Hugo De Groote Violet Mugalavai Mario Ferruzzi Augustino Onkware emmanuel ayua Kwaku Duodu Michael Ndegwa Bruce Hamaker (2022, [Dataset])

In this study 220 urban consumers of Eldoret were asked i) about socioeconomic characteristics and nutritional knowledge, ii) evaluate five flours with two preparations using affective tests, iii) to provide willingness-to-pay for the five flours using with experimental auctions. The data contain a set for each of the three objectives. The first data set contains ID number and socioeconomic information of the participants, with one line per person (N = 220). The second data set contains the results of the affective tests. Five flours were used in the affected test: sifted conventional maize flour, sifted mixed (maize and sorghum flour) conventional flour, instant sifted mixed flour, instant whole mixed flour, and instant sifted mixed flour with food-to-food fortification. From each of the flours, two products were prepared: ugali (stiff porridge) or uji (liquid porridge). All participants were asked to evaluate the 5 x 2 = 10 preparations on five traits: appearance, texture in hand, aroma, texture in mouth, taste, and overall. The results are presented in one line per participant per product (220 x 5 = 1100 lines), with the results for ugali and uji in different columns. The third data set contains the WTP, elicited through an experimental auction using BDM, for the five flours. Participants were split into three information treatments: A1: WTP without information, A2: repeat WTP now with information, B: information from the start, each with 110 participants, so in total 3 treatments x 110 x 5 products = 1650 observations.

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Replication Data for: Multi-trait genome prediction of new environments with partial least squares

Osval Antonio Montesinos-Lopez Brandon Alejandro Mosqueda González Marco Alberto Valenzo-Jimenez Jose Crossa (2022, [Dataset])

The genomic selection (GS) methodology has revolutionized plant breeding. This methodology makes predictions for genotyped candidate lines based on statistical machine learning algorithms that are trained with phenotypic and genotypic data of a reference population. GS can save significant resources in the selection of candidate individuals. However, plant breeders can face challenges when trying to implement it practically to make predictions for future seasons or new locations and/or environments. To help address this challenge, this study seeks to explore the use of the multi-trait partial least square (MT-PLS) regression methodology and to compare its performance with the Bayesian Multi-trait Genomic Best Linear Unbiased Predictor (MT-GBLUP) method. A benchmarking process was performed with five actual data sets contained in this study. The results of the analysis are reported in the accompanying article.

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