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Data for: Hermetic storage technologies preserve maize seed quality and minimize grain quality loss in smallholder farming systems in Mexico
No full textOdjo 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*
Genotypic data for a spring wheat panel
No full textGBS genotypic data for a panel of 266 spring wheat lines from China, CIMMYT and other countries
Replication Data for: Maize dispersal patterns associated with different types of endosperm and migration of indigenous groups in lowland South America
No full textThis dataset contains the genotypic data used identify dispersal patterns of maize genetic diversity in the lowlands of South America. In the study, 184 maize accessions were characterized with 5,313 single nucleotide polymorphisms (SNPs)
Replication Data for: Identification and validation of genomic regions associated with charcoal rot resistance in tropical maize by genome-wide association and linkage mapping
No full textCharcoal rot, caused by the fungal pathogen, Macrophomina phaseolina, is a serious concern for small holder maize cultivation. It can cause significant yield loss and plant lodging at harvest. A genome wide association study (GWAS) was conducted using the CIMMYT Asia panel of 396 tropical-adapted lines to identify and validate genomic variants associated with charcoal rot resistance. In addition, two F2:3 populations were used in a QTL mapping exercise. This dataset contains the genotypic data underlying both types of analyses. Results of the analysis are presented in the related journal article
Performance of maize inbred lines under artificial inoculation with aflatoxin
No full textThis 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
28th Semi-Arid Wheat Yield Trial
No full textThe Semi-Arid Wheat Yield Trial (SAWYT) is a replicated yield trial that contains spring bread wheat (Triticum aestivum) germplasm adapted to low rainfall, drought prone environments typically receiving less than 500 mm of water available during the cropping cycle. The combination of water-use efficiency and water responsive broad adaptation plus yield potential is important in drought environments where rainfall is frequently erratic across and within years. Stripe rust, leaf rust and stem rust, root rots, nematodes, and bunts are the key biotic constraints. Typical target environments include winter rain or Mediterranean-type drought associated with post-flowering moisture stress and heat stress such as those found at Aleppo (Syria), Settat (Morocco) and Marcos Juarez (Argentina), all classified by CIMMYT within Wheat Mega Environment 4 (Low rainfall, semi-arid environment; ME4: SA). It is distributed to 150 locations, and contains 50 entries
Replication Data for: Genomic insights on global journeys of adaptive wheat genes that brought us to modern wheat (table 1)
No full textCompilation of information of all flowering QTL reported in 18 publications. This information has been used in meta-analysis conducted in the referred study to identify meta-QTL of flowering time
52nd International Durum Yield Nursery
No full textInternational Durum Yield Nurseries (IDYN) are replicated yield trials designed to measure the yield potential and adaptation of superior CIMMYT-bred spring durum wheat germplasm that have been developed from tests conducted under irrigation and induced stressed cropping conditions in northwest Mexico. These materials have been subjected to numerous diseases (leaf, stem and yellow rust; Septoria tritici blotch) and varied growing environments. It is distributed to 70 locations, and contains 50 entries
Data on identifying sustainable wheat productivity drivers in Nepal’s Terai
No full textThe objective of this survey was to identify the major wheat productivity and profitability drivers in Nepal Terai. Nepal Terai is considered as the part of Indo-Gangetic Basin and its the major cereal production domain in Nepal. In year 2016, immediately following the wheat harvest, this survey was conducted. A total of 10 districts were purposively selected for the survey based on the highest wheat acreage. In each district, a total of five sub-districts were further selected purposively based on the highest wheat acreage. In each sub-districts (or village development committees: VDCs) a total of 10 wheat producing farms were selected randomly from the farmers name list provided by the village level administrative authorities. The overall sampling frame consists of 10 samples from each VDC × 5 sub-districts (VDCs) in each district × 10 districts in Nepal Terai = 500 samples. Moreover, in order to check the farmers self reported yield, 50% farmers largest plots were selected for the crop cuts and the crop cuts data are also available with this dataset. These crop cuts were conducted prior to the survey – during the time when farmers harvest their wheat crop. Farmers may have multiple plots and asking the data from each plot may reduce the data quality. Therefore, to increase the precision and collect the quality data, farmers inputs (e.g., seed, fertilizer, weed management practices, varieties, sowing time, harvesting time, and other crop management practices), and outputs were asked only for largest plot. Data were collected through a direct farm visit and paper-based survey was deployed. The details of the questions asked, codebook, their description, meta-data, and data can be found in this dataset
Crop types of the Yaqui Valley during the 2016-17 winter growing season
No full textOur study region is located in the northwest of Mexico, in the Yaqui Valley, where most farmers predominantly grow crops under irrigated conditions during the winter months. Sowing typically starts in late October. Dry bean is one of the first crops to be sown (Table 1). Wheat, the dominant crop, is usually sown between mid-November and mid-December, however, some fields are sown as late as early January. Among the other eight crops that will be referred to as minority crops in this study, maize and chickpea were the most important ones. The last crop to be sown during the winter months is safflower. It is typically sown in March or April, after field pea or fallow. Sen2-Agri allows for the identification of only one crop per field and season, we therefore did not include it in the study. The Yaqui Valley also is an important producer of various types of vegetables. Their production is quite dynamic. The growth cycle of vegetables tends to be quite short and often, they do not have a distinct seasonality. Broccoli and different types of tomatoes are the most important ones. We also included some permanent crops such as asparagus, alfalfa and pasture (grassland), as well as tree fruit and nuts, categorized as orchard. Alfalfa and pasture were categorized as forage crop.
The planners of the Yaqui Valley irrigation scheme had divided the land into blocks, measuring 2 by 2 km. The blocks were then further subdivided into 40 lots, each measuring 10 ha. The blocks and lots were numbered consecutively. At the beginning of the winter growing season, the irrigation district, called Distrito del Riego del Rio Yaqui, requires each farmer to declare the type of crop they plan to grow on each irrigated lot. The irrigation district kindly shared those data with us. Most farmers do not follow the initial lot boundaries anymore. Some lots got split up, whereas in the majority of cases, lots were merged. If farmers had merged several lots, they would use the number of their first lot as an anchor and also report the area of the entire field, i.e., the merged lots, that was planted with the same crop. This then allowed us to visually match the reported data with the crop fields. Based on the farmer's declarations, which include the crop type, block, lot and field size, the crop types were then assigned to the field boundaries which had been manually drawn beforehand, using a Sentinel-2 image from March 13, 2017 as a background. This resulted in 6048 labeled fields. The average area of a field was 11.5 ha