This dataset contains the results from three regional trials carried out in Eastern Africa in 2019. Each report focuses on a different product profile. Two reports present data for product profile EA-PP1, It focuses on includes early/intermediate-maturing white maize with multiple stress tolerance (drought, low N, MLN, MSV, TLB, GLS, ear rots) for the Eastern African rainfed mid-altitude dry/wet agro-ecologies. The third report presents data for product profile EA-PP2. It focuses on late-maturing, white maize varieties with multiple stress tolerance (drought, low N, GLS, TLB, MSV, ear rots, Striga) for the Eastern African rainfed upper mid-altitude region.
Several different genome-based prediction models are available for the analysis of multi-trait data in genomic selection. The supplemental files included in this dataset provide six extensive multi-trait wheat datasets (quality and grain yield) that enable the comparison of performance of genomic-enabled-prediction when calculating the prediction accuracy using different methods. The related article describes the results of the analysis and reports that trait grain yield prediction performance is better under a multi-trait model as compared with the single-trait model.
Odjo et al. (2020) reported results on the effect of different storage technologies on postharvest losses of maize. CIMMYT and its network of collaborators implemented a second part of these experiments in 2017 and 2018 to evaluate the effect of different storage storages technologies on grain and seed quality under “controlled” (i.e., managed by researchers) conditions and to assess the relationships between storage conditions, grain composition, grain damage, and grain and seed quality parameters (Odjo et al., 2021). Each experiment compared the conventional storage technology (polypropylene bag with and/or aluminum phosphide) commonly used by farmers in the area to other storage technologies (selected from hermetic metal silos, hermetic bags, recycled plastic bottle, silage plastic bags, polypropylene bag with standard lime-calcium hydroxide- polypropylene bag with micronized lime). The dataset contains data about the experiments: (1) characteristics of the site of experiments (elevation, municipality, state, type of climate); (2) storage technologies evaluated; (3) grain characteristics (type of variety-hybrid or native-, color of the maize variety evaluated) moisture content, temperature); Additionally, it consists in data collected at the beginning and the end of each experiment: (1) grain damage and number of live insects per 500 g of sample: percentage of insect-damaged grain, percentage of fungi-damaged grain, percentage of total damage, weight loss; number of live Sitophilus zeamais Motschulsky, number of live Prostephanus truncatus Horn, number of live Sitotroga cerealella Olivier); These data were published by Odjo et al. (2020), but are paired here with new data; (5) storage time (in days and months) and average climatic data during storage period (minimum and maximum temperature, minimum and maximum relative humidity already published by Odjo et al. (2020); (6) seed quality parameters: percentages of normal seedlings, abnormal seedlings, germination, non-germinated viable seeds, non-germinated non-viable seeds; (7) grain chemical and physical composition: percentages of starch, protein and oil contents; hundred kernel weight; flotation index; ether extract; fat acidity; p-coumaric acid and total ferulic acids contents; color parameters L*, a*, b* and total color difference ΔE*.
The experiment was conducted in 2016 and 2017 in three regions in Oaxaca, Mexico. The design of the study was to evaluate fifteen combinations of tillage and weed management practices at each of the three sites, with each trial consisting of three blocks with a different type of tillage: zero tillage (ZT), minimum tillage (MT) and conventional tillage (CT). The first site is in the “Sitio Experimental Mixteca” research station in Santo Domingo Yanhuitlán, the Mixteca Region, located at 2195 m above sea level (masl), it has Vertisol soils, a temperate subhumid climate. The second site is in the town of San Felipe Zihualtepec in the municipality of San Juan Cotzocón, Papaloapan Region, located at 60 masl and has Luvisol soils, a hot humid climate. The third location was in the town of Ciénega de Zimatlán in the municipality with the same name, Valles Centrales Region. The original site was changed in 2017 to a nearby field, because the collaborating farmer did not want to continue the trial. Both fields are located at 1552 masl, have Vertisol soils, a hot semi-arid climate. Weed management treatments were conducted similarly in all tillage treatments and reflected common local practices and available herbicides or equipment, as well as weather, soil moisture and weed species and comprised combinations of the following: 1) MEC, mechanical control. Weeds were mechanically controlled after reaching 20 cm in height approximately 20-25 days after sowing (DAS), as per the common practice. Weeding was carried out with a hand hoe by 4 to 10 workers per hectare, depending on the quantity of weeds, or using a tractor-drawn cultivator. 2)PRE, pre-emergent herbicide. Only a pre-emergent herbicide with residual effect was applied before sowing. 3) POST, post-emergent herbicide. When weeds reached 5-10 cm in height and based on soil moisture conditions and the types of weeds present, a selective herbicide or direct contact herbicide was applied. 4) PRE+POST, integrated weed management. A pre-emergent herbicide was applied, followed by post-emergence control as necessary, using either selective herbicides or manual controls. 5) CONT, control: No weed management was practiced. The dataset contains the data on maize yield, weed density, weed species (broadleaf or narrowleaf) and weed biomass from the experiment.
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.
In breeding, multi-trait data can be used with different models for genomic prediction analyses. The data files associated with this dataset were used to explore Bayesian multi-trait kernel methods for genomic prediction and to compare the performance of the different analyses.
This dataset contains information about the performance of maize inbred lines in Eastern Africa following inoculation with aflatoxin. The aflatoxin-related traits described in the dataset are: AFLTXB1 = Aflatoxin B1; AFLTXB2 = Aflatoxin B2; AFLTXG1 = Aflatoxin G1; AFLTXG2 = Aflatoxin G2, and AFLTXPPB = Aflatoxin Total.
