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Performance and grain yield stability of maize populations developed using marker-assisted recurrent selection and pedigree selection procedures

Beyene, Y.; Gakunga, J.; Sehabiague, P.; Meisel, B.; Oikeh, S.O.; Crossa, J.; Mugo, S.N.; Fentaye Kassa Semagn; Amsal Tesfaye Tarekegne; Prasanna, B.M.; Olsen, M. (Springer, 2016)

A marker-assisted recurrent selection (MARS) program was undertaken in sub-Saharan Africa to improve grain yield under drought-stress in 10 biparental tropical maize populations. The objectives of the present study were ...

Factors affecting the accuracy of genotype imputation in populations from several maize breeding programs

Hickey, J.M.; Crossa, J.; Babu, R.; Campos, G. de los (2012)

Genomic selection and association mapping offer great potential to increase rates of genetic progress in plants. The prediction of genomic breeding values usually requires that missing genotypes be imputed because a ...

A comparison of results obtained with two methods for assessing yield stability

Crossa, J. (1988)

Genome-enabled prediction of genetic values using radial basis function neural networks

Gonzalez-Camacho, J.M.; Campos, G. de los; Perez, P.; Gianola, D.; Cairns, J.E.; Mahuku, G.; Babu, R.; Crossa, J. (2012)

The availability of high density panels of molecular markers has prompted the adoption of genomic selection (GS) methods in animal and plant breeding. In GS, parametric, semi-parametric and non-parametric regressions models ...

Genomic prediction in maize breeding populations with genotyping-by sequencing

Crossa, J.; Beyene, Y.; Semagn, K.; Perez, P.; Hickey, J.M.; Chen Charles; Campos, G. de los; Burgueño, J.; Windhausen, V.S.; Buckler, E.S.; Jannink, J.L.; Lopez Cruz, M.A.; Babu, R. (2013)

Genotyping-by-sequencing (GBS) technologies have proven capacity for delivering large numbers of marker genotypes with potentially less ascertainment bias than standard single nucleotide polymorphism (SNP) arrays. Therefore, ...

Identification of drought, heat, and combined drought and heat tolerant donors in maize

Cairns, J.E.; Crossa, J.; Zaidi, P.H.; Grudloyma, P.; Sanchez, C.; Araus, J.L.; Thaitad, S.; Makumbi, D.; Magorokosho, C.; Banziger, M.; Menkir, A; Hearne, S.; Atlin, G.N. (2013)

Low maize (Zea maysL.) yields and the impacts of climate change on maize production highlight the need to improve yields in eastern and southern Africa. Climate projections suggest higher temperatures within drought-prone ...

Large-scale screening for maize drought resistance using multiple selection criteria evaluated under water-stressed and well-watered environments

Yanli Lu; Zhuanfang Hao; Chuanxiao Xie; Crossa, J.; Araus, J.L.; Shibin Gao; Vivek, B.; Magorokosho, C.; Mugo, S.; Makumbi, D.; Taba, S.; Guangtang Pan; Xinhai Li; Tingzhao Rong; Shihuang Zhang; Yunbi Xu (2011)

A total of 550 maize inbred lines collected from global breeding programs were evaluated for drought resistance under both well-watered and water-stressed environments. The evaluation was based on multiple measurements of ...

Effectiveness of Genomic Prediction of Maize Hybrid Performance in Different Breeding Populations and Environments

Windhausen, V.S.; Atlin, G.N.; Hickey, J.M.; Crossa, J.; Jannink, J-L; Sorrells, M.E.; Babu, R.; Cairns, J.E.; Amsal Tesfaye Tarekegne; Fentaye Kassa Semagn; Beyene, Y.; Grudloyma, P.; Technow, F.; Riedelsheimer, C.; Melchinger, A.E. (2012)

Genomic prediction is expected to considerably increase genetic gains by increasing selection intensity and accelerating the breeding cycle. In this study, marker effects estimated in 255 diverse maize (Zea mays L.) hybrids ...

Gains in maize genetic improvement in Eastern and Southern Africa : II. CIMMYT open-pollinated variety breeding pipeline

Masuka, B.; Magorokosho, C.; Olsen, M.; Atlin, G.N.; Banziger, M.; Pixley, K.V.; Vivek, B.; Labuschagne, M.; Matemba-Mutasa, R.; Burgueño, J.; MacRobert, J.F.; Prasanna, B.M.; Das, B.; Makumbi, D.; Amsal Tesfaye Tarekegne; Crossa, J.; Zaman-Allah, Mainassara; Biljon, A.; Cairns, J.E. (CSSA, 2017)

Open-pollinated varieties (OP Vs) still represent a significant proportion of the maize (Zea mays L.) seed system in many countries of sub- Saharan Africa. The International Maize and Wheat Improvement Centre (CIMMYT) has ...

Genomic-enabled prediction in maize using kernel models with genotype x environment interaction

Bandeira e Sousa, M.; Cuevas, J.; De Oliveira Couto, E.G.; Pérez-Rodríguez, P.; Jarquin, D.; Fritsche-Neto, R.; Burgueño, J.; Crossa, J. (Genetics Society of America, 2017)

Multi-environment trials are routinely conducted in plant breeding to select candidates for the next selection cycle. In this study, we compare the prediction accuracy of four developed genomic-enabled prediction models: ...

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