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40th International Bread Wheat Screening Nursery
Ravi Singh Thomas Payne (2019, [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.
Thomas Payne Carolina Sansaloni Ravi Singh Hans-Joachim Braun Susanne Dreisigacker (2019, [Dataset])
A total of 359 genotypes used in this study included three Ae. tauschii lines, 30 durum wheat lines, eight synthetic hexaploid wheat, 253 synthetic derivative lines, and 63 bread wheat lines. All entries were genotyped with the DArTseq® technology at the Genetic Analysis Service for Agriculture (SAGA) laboratory at CIMMYT in Mexico. The two types of markers are presented in two different files: rosyara_et_al_data_set_PAV.zip include data for Presence -Absence variation (PAV) and rosyara_et_al_data_set_SNP.zip includes data for Single nucleotide polymorphism (SNPs).
Carolina Sansaloni Jorge Franco Bruno Santos Lawrence Percival-Alwyn Cesar Petroli Jaime Campos Kate Dreher Thomas Payne David Marshall Benjamin Kilian Iain Milne Sebastian Raubach Paul Shaw Gordon Stephen Carolina Saint Pierre Juan Burgueño Jose Crossa Huihui Li Andrzej Kilian Peter Wenzl Ahmed Amri Cristobal Uauy Marianne Bänziger Mario Caccamo Kevin Pixley (2020, [Dataset])
A diverse panel of domesticated hexaploid and tetraploid wheat lines and their tetraploid and diploid wild relatives were genotyped using the DArtSeq technology and characterized in a global wheat diversity analysis.
Jonas Aguirre Brandon Gaut Juan P. Jaramillo_Correa Maud Tenaillon Felipe García Oliva Sarah Hearne Luis Eguiarte (2019, [Dataset])
Dartseq data were used to analyze the demographic history of teosintes, and also identify SNPs under selection to bioclimatic and soil variables (pH, phosphorus and nitrogen concentration in the soil).
Prediction of multiple-trait and multiple-environment genomic data using recommender systems
Osval Antonio Montesinos-Lopez Jose Crossa Ravi Singh Suchismita Mondal Philomin Juliana (2017, [Dataset])
In genomic-enabled prediction, the task of improving the accuracy of the prediction of lines in environments is difficult because the available information is generally sparse and usually has low correlations between traits. In current genomic selection, while researchers have a large amount of information and appropriate statistical models to process it, there is still limited computing efficiency to do so. Although statistical models are usually mathematically elegant, they are also computatio nally inefficient, and they are impractical for many traits, lines, environments, and years because they need to sample from huge normal multivariate distributions. For these reasons, this study explores two recommender systems: a) item-based collaborative filtering (IBCF; method M1) and b) the matrix factorization algorithm (method M2) in the context of multiple traits and multiple environments. The IBCF and matrix factorization methods were compared with two conventional methods on simulated and real data. Results of the simulated and real data sets show that the IBCF technique (method M1) was slightly better in terms of prediction accuracy than the two conventional methods and the matrix factorization method when the correlation was moderately high. The IBCF technique is very attractive because it produces good predictions when there is high correlation between items (environment-trait combinations) and its implementation is computationally feasible, which can be useful for plant breeders who deal with very large data sets.
MESFIN KEBEDE DESTA Tolera Abera Goshu (2017, [Dataset])
Performance trials (N=52) in two zones (West Shewa and Jimma) in Ethiopia. Trials comprise four nutrient management treatments, namely control with zero fertilizer ; and three fertilizer recommendations to achieve the same target yield based on regional fertilizer recommendation, a Nutrient Expert (IPNI software) based recommendation and a soil-test NE based recommendation. Trials were conducted on-farm with four plots per farm. Observations include biomass and grain yields, as well as pre-sowing pH, nitrogen and phosphorus levels. Some N & K data are missing.
47th International Durum Screening Nursery
Karim Ammar Thomas Payne (2020, [Dataset])
International Durum Screening Nursery (IDSN) distributes diverse CIMMYT-bred spring durum wheat germplasm adapted to irrigated and variable moisture stressed environments. Disease resistance and high industrial pasta quality are essential traits possessed in this germplasm. It is distributed to 100 locations, and contains 150 entries.
Thomas Payne Sarah Hearne Michael Abberton Peter Wenzl (2017, [Dataset])
Genotyping and re-sequencing are among a suite of tools used to enable rapid and cost-effective tool to study genetic diversity. This workshop will explore its use in the genetic curation of accessi ons within and between collection(s). With such information across global collections, it becomes possible identify the truly unique accessions across all of our gene banks, identify possible gaps in the global collection and enable more targeted access to genetic diversity. The workshop will discuss presentations from each participant on use cases. Lessons learned from initiatives such as CIMMYT’s Seeds for Discovery for maize and wheat, and CIP’s investigations on sweet potato and at IITA and CIAT for cassava will be considered.
Zero-Tillage adoption and its welfare impacts at the farm household level
Alwin Keil (2016, [Dataset])
The purpose of the study was (1) to assess the performance of ZT wheat as compared to conventional-tillage wheat in farmers' fields in six CSISA target districts in Bihar; (2) to assess farmers’ resource endowment, risk exposure, risk preferences, and risk management practices; (3) based on (2), to identify influencing factors of farmers' awareness and adoption of ZT in wheat, including social network effects.
FAO-SIAC Estimating CA adoption in Sinaloa, Mexico (calibration sites)
Kai Sonder Guillaume Chomé (2017, [Dataset])
Use of remote sensing based radar images for zero tillage detection in Sinaloa, Mexico.