Person: Piepho, H.P.
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Piepho
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H.P.
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Piepho, H.P.
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0000-0001-7813-2992 4 results
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- Complex traits and candidate genes: estimation of genetic variance components across multiple genetic architectures(Genetics Society of America, 2023) Feldmann, M.J.; Covarrubias, E.; Piepho, H.P.
Publication - Genome-enabled prediction for sparse testing in multi-environmental wheat trials(CSSA, 2021) Crespo Herrera, L.A.; Howard, R.; Piepho, H.P.; Pérez-Rodríguez, P.; Montesinos-Lopez, O.A.; Burgueño, J.; Singh, R.P.; Mondal, S.; Jarquin, D.; Crossa, J.
Publication - Strategies to subdivide a target population of environments: Results from the CIMMYT-led maize hybrid testing programs in Africa(Crop Science Society of America (CSSA), 2012) Windhausen, V.S.; Wagener, S.; Magorokosho, C.; Makumbi, D.; Vivek, B.; Piepho, H.P.; Melchinger, A.E.; Atlin, G.To develop stable and high-yielding maize (Zea mays L.) hybrids for a diverse target population of environments (TPE), breeders have to decide whether greater gains result from selection across the undivided TPE or within more homogeneous subregions. Currently, CIMMYT subdivides the TPE in eastern and southern Africa into climatic and geographic subregions. To study the extent of specific adaptation to these subregions and to determine whether selection within subregions results in greater gains than selection across the undivided TPE, yield data of 448 maize hybrids evaluated in 513 trials across 17 countries from 2001 to 2009 were used. The trials were grouped according to five subdivision systems into climate, altitude, geographic, country, and yield-level subregions. For the first four subdivision systems, genotype × subregion interaction was low, suggesting broad adaptation of maize hybrids across eastern and southern Africa. In contrast, genotype × yield-level interactions and moderate genotypic correlations between low- and high-yielding subregions were observed. Therefore, hybrid means should be estimated by stratifying the TPE considering the yield-level effect as fixed and appropriately weighting information from both subregions. This strategy was at least 10% better in terms of predicted gains than direct selection using only data from the low- or high-yielding subregion and should facilitate the identification of hybrids that perform well in both subregions.
Publication - Managing genotype x environment interaction in plant breeding programs: A selection theory approach(Indian Society of Agricultural Statistics, 2011) Atlin, G.; Kleinknecht, K.; Singh, K.P.; Piepho, H.P.Two forms of genotype - environment interaction (GEI) are of concern to plant breeders. One consists of fixed GEI associated with predictable environmental, geographical, or management factors that can be used to delineate a target population of environments (TPE) for cultivar development and testing. The other consists of random and unexplained rank changes among trials within the TPE which are not associated with any known factor. These two types of GEI must be managed differently by plant breeding programs; fixed GEI is managed by developing or identifying cultivars with adaptation to the specific fixed factor causing the interaction, while random GEI is a noise stratum that is managed through wide-scale testing that adequately samples environmental variation in the TPE, and through the use of best linear unbiased prediction (BLUP). There is substantial evidence that fixed GEI is of limited importance within well-designed TPE. Management of GEI in cultivar development programs, and the estimation of means from multi-environment trials with appropriate measures of precision (METs) has been hampered by the widespread use of inappropriate models that designate trials or trial locations as fixed effects in the combined analysis of cultivar testing data, resulting in unnecessary division of TPEs, identification of putative patterns of adaptation that are not repeated in subsequent testing, and over-estimation of the precision of entry means in multi-environment trials. Mixed model approaches to testing the relative importance of fixed and random GEI in METs are presented.
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