Browsing by Person "Windhausen, Sandra Vanessa"

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Strategies for selecting high-yielding and broadly adapted maize hybrids for the target environment in Eastern and Southern Africa
(2012) Windhausen, Sandra Vanessa; Melchinger, Albrecht E.
Maize is a major food crop in Africa and primarily grown by small-holder farmers under rain-fed conditions with low fertilizer input. Projections of decreasing precipitation and increasing fertilizer prices accentuate the need to provide farmers with maize varieties tolerant to random abiotic stress, especially drought and N deficiency. Genetic improvement for the target environment in Eastern and Southern Africa can be achieved by: (i) direct selection of grain yield in random abiotic stress environments, (ii) indirect selection for a secondary trait or grain yield in optimal, low-N and/or managed stress environments, or (iii) index selection using information from all test environments. At present, the maize hybrid testing programs of the International Maize and Wheat Improvement Center (CIMMYT) select primarily for grain yield under managed stress and optimal environments and subdivide the target environment according to geographic and climatic differences. It is not known to what extend the current strategy contributes to selection gains. The same holds true for genomic prediction, a strategy that is not yet implemented into the CIMMYT maize breeding program but that may accelerate breeding progress and reduce cycle length by predicting genotype performance based on molecular markers. Regarding the different strategies mentioned for selecting high-yielding and broadly adapted maize hybrids, the breeder needs to decide which of them are most promising to increase genetic gains. Consequently, the objectives of my thesis were to (1) evaluate the potential of leaf and canopy spectral reflectance as novel secondary traits to predict grain yield across different environments, (2) estimate to what extent indirect selection in managed drought and low-N stress environments is predictive of grain yield in random abiotic stress environments, (3) investigate whether subdividing the target environment into climate, altitude, geographic, yield level or country subregions is likely to increase rates of genetic gain, and (4) evaluate the prospects of genomic prediction in the presence of population structure. The measurement of spectral reflectance (495 ? 1853 nm) of both leaves and canopy at anthesis and milk grain stage explained less than 40% of the genetic variation in grain yield after validation. Consequently, selection based on predicted grain yield is only suitable for pre-screening, while final yield evaluation will still be necessary. Nevertheless, the prospect of developing inexpensive and easy to handle devices that can provide, at anthesis, precise estimates of final grain yield warrants further research. Based on a retrospective analysis across 9 years, more than 600 trials and 448 maize hybrids, it was shown that maize hybrids were broadly adapted to climate, altitude, geographic and country subregions in Eastern and Southern Africa. Consequently, I recommend that the maize breeding programs of CIMMYT in the region should be consolidated. Within the consolidated breeding programs, genotypes should be selected for performance in low- and high yielding environments as the genotype-by-yield level interaction variance was high relative to the genetic variance and genetic correlations between low- and high-yielding environments were moderate. Genetic gains were maximized by index selection, considering the yield-level effect as fixed and appropriately weighting information from all trials. To allow better allocation of resources, locations with high occurrence of random abiotic stress need to be identified. Heritability in trials conducted at these locations may be increased by the use of row- and column designs and/or spatial adjustment. Furthermore, resources invested into managed drought trials should be maintained during early breeding stages but shifted to the conduct of low-N trials at later breeding stages. Investments in a larger number of low-N trials may increase selection gain, because performance under low-N and random abiotic stress was highly correlated and genotypes can be easily selected under different levels of soil N. Prospects are promising to accelerate breeding cycles by the use of genomic prediction. Based on two large data sets on the performance of eight breeding populations, it was shown that prediction accuracy resulted primarily from differences in mean performance of these populations. Genomic prediction may be implemented into the CIMMYT maize breeding program to predict the performance of lines from a diversity panel, segregating lines from the same or related crosses, and progenies from closed populations within a recurrent selection program. The breeding scenarios in which genomic prediction is most promising still need to be defined. Generally, the construction of larger training sets with strong relationship to the validation set and a detailed analysis of the population structure within the training and validation sets are required. In conclusion, combining index and genomic selection is the most promising strategy for providing high-yielding and broadly adapted maize genotypes for the target environments in Eastern and Southern Africa.