Adoption of agronomic practices based on zero tillage and residue retention is increasing, due to their potential for climate change adaptation and mitigation. Genotype by tillage interactions for yield are not well understood. Fourteen CIMMYT bread (Triticum aestivum) and durum (Triticum turgidum) wheat genotypes, created between 1964 and 2011, were tested for yield and agronomic performance at CIMMYT’s experimental station near Ciudad Obregon, Mexico, over nine years (harvest years 2010 to 2018). Treatments included conventional and permanent raised beds with full and reduced irrigation. The dataset contains data on wheat performance (days to emergence, flowering and maturity, plant height, harvest index, grain yield, thousand grain weight, test weight, NDVI), performance traits calculated from these data (e.g. number of grains per m2, number of grains per spike, grain production rate from flowering to maturity) and daily weather data during the study period (precipitation, maximum and minimum temperature). This is an updated version of a dataset published in Honsdorf et al. (2018), where we have added three more years of data (harvest years 2016, 2017 and 2018) and one additional bread wheat genotype, Borlaug 100 (created in 2011) that was tested only in the six last years of the experiment (harvest years 2013 to 2018).
When genomic selection (GS) is used in breeding schemes, data from multiple generations can provide opportunities to increase sample size and thus the likelihood of extracting useful information from the training data. The Sparse Selection Index (SSI), is is a method for optimizing training data selection. The data files provided with this study include a large multigeneration wheat dataset of grain yield for 68,836 lines generated across eight cycles (years) as well as genotypic data that were analyzed to test this method. The results of the analysis are published in the corresponding journal article.
When multi-trait data are available and the degree of correlation between phenotypic traits is moderate or large, genomic prediction accuracy can increase when models are used that account for correlations between the phenotypic traits. The data files associated with this dataset were used to explore Bayesian multi-trait kernel methods for genomic prediction. Linear, Gaussian, polynomial and sigmoid kernels were studied and compared with the GBLUP multi-trait models. The results of the analysis are reported in the accompanying article.
Compilation 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.
The 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.
(prepared R.A.(Tony) Fischer 14 August 2021) This dataset contains three data files that were obtained and/or constructed an analysis of 60 years’ yield progress with irrigated wheat yield in northwest Mexico: 1. Wheat harvested area and average yield each harvest year for the irrigated Cajeme District of Sonora State, Mexico, from 1960 to 2019 (file name = 1. Annual wheat average yield and harvest area 1960 to 2019, Yaqui Valley (Cajeme District) 2. Monthly average Tmax, Tmin, precipitation total and frost days from November 1959 to April 2019 (only for November to April each year, which corresponds to the irrigated wheat season) for a location at or near the centrally-located CIANO (now CENEB) agricultural research station of the Yaqui Valley. Monthly mean solar radiation (Rs) was added from November 1967 (unavailable beforehand) (file name = 2. Mean monthly wheat weather 1960 to 2019 Yaqui Valley). 3. Daily solar radiation (Rs), Tmax and Tmin from November 1967 to April 2019 (again only for November to April each year) (file name = 3. Daily wheat weather 1967 to 2019 Yaqui Valley). Locating and acknowledging the exact original sources of these data is not easy, but is explained at the beginning of each file and within the files if the source varies. The wheat area and yield data is publicly available soon after harvest, having been collated by Cajeme District grain receival authority in Cd Obregon, who also regularly provide updated multiyear records of the finalized annual numbers. This District comprises the bulk of the irrigated Yaqui Valley and has been synonymous with the Yaqui Valley for outsiders. The weather data comes from the International Maize and Wheat Improvement Center (CIMMYT) headquarters, which has operated a weather station at CIANO/CEBEB experiment station, located centrally in the Yaqui Valley since 1968. However, for most numbers before 1968 the State Hydraulic Resources (CONAGUA in Cd Obregon), using data held by CIMMYT was used, and a few numbers after 2000 were obtained from the Hydraulic Resources in Cd Obregon and the Sonora State meteorological service (REMAS in Hermosillo). A large part of the daily Tmax and Tmin data (1968 to 1999), although originally from CIMMYT, was collated by Professor L.A. (Tony) Hunt of Guelph University, Canada, as was the daily and (monthly) Rs data from 1968 to 1983. From 1984 the NASA web site has continuous daily Rs data closely matching the Hunt numbers, so this source was used thereafter, while Hunt and Fisiologia data (adjusted to match NASA data) was used from 1968 to 1983. Tmax and Tmin data are also available on the NASA site but did not match CIMMYT data well and was never used. So the weather data has several sources, which increases the risk of error. Only the very strong relationship right across the 60 year period between totally independent variables yield and Tmin reassures us that the weather data is sound. A follow-up study (and associated database) will disaggregate the causes of the spectacular progress recorded.
We investigated the effect of bed width on grain yield under irrigated and rainfed conditions, for crops grown on permanent beds, where the top of the raised beds is not tilled. The study included nine sites in Central Mexico, where wide and narrow permanent beds were compared at the same site for at least three consecutive crop cycles. Six trials were selected under rainfed conditions in the states of Queretaro, Guanajuato, Michoacan, and State of Mexico, and three more with irrigation, which were located in Guanajuato and Queretaro. The data were collected in different periods, from 2007 to 2019. The database contains yield data for maize (Zea mays L.), wheat (Trititcum aestivum L.) and barley (Hordeum vulgare L.) planted on wide and narrow permanent beds.
