No abstract

Inability to efficiently implement high-throughput field phenotyping is increasingly perceived as a key component that limits genetic gain in breeding programs. Field phenotyping must be integrated into a wider context than just choosing the correct selection traits, deployment tools, evaluation platforms, or basic data-management methods. Phenotyping means more than conducting such activities in a resource-efficient manner; it also requires appropriate trial management and spatial variability handling, definition of key constraining conditions prevalent in the target population of environments, and the development of more comprehensive data management, including crop modeling. This review will provide a wide perspective on how field phenotyping is best implemented. It will also outline how to bridge the gap between breeders and ‘phenotypers’ in an effective manner.

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 were used to predict grain yield, anthesis date, and anthesis-silking interval within the diversity panel and testcross progenies of 30 F2-derived lines from each of five populations. Although up to 25% of the genetic variance could be explained by cross validation within the diversity panel, the prediction of testcross performance of F2-derived lines using marker effects estimated in the diversity panel was on average zero. Hybrids in the diversity panel could be grouped into eight breeding populations differing in mean performance. When performance was predicted separately for each breeding population on the basis of marker effects estimated in the other populations, predictive ability was low (i.e., 0.12 for grain yield). These results suggest that prediction resulted mostly from differences in mean performance of the breeding populations and less from the relationship between the training and validation sets or linkage disequilibrium with causal variants underlying the predicted traits. Potential uses for genomic prediction in maize hybrid breeding are discussed emphasizing the need of (1) a clear definition of the breeding scenario in which genomic prediction should be applied (i.e., prediction among or within populations), (2) a detailed analysis of the population structure before performing cross validation, and (3) larger training sets with strong genetic relationship to the validation set.

Given the accumulating evidence of climate change in sub-Saharan Africa, there is an urgent need to develop more climate resilient maize systems. Adaptation strategies to climate change in maize systems in subSaharan Africa are likely to include improved germplasm with tolerance to drought and heat stress and improved management practices. Adapting maize systems to future climates requires the ability to accurately predict future climate scenarios in order to determine agricultural responses to climate change and set priorities for adaptation strategies. Here we review the projected climate change scenarios for Africa's maize growing regions using the outputs of 19 global climate models. By 2050, air temperatures are expected to increase throughout maize mega-environments within sub-Saharan Africa by an average of 2.1°C. Rainfall changes during the maize growing season varied with location. Given the time lag between the development of improved cultivars until the seed is in the hands of farmers and adoption of new management practices, there is an urgent need to prioritise research strategies on climate change resilient germplasm development to offset the predicted yield declines.

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 areas. Research in model species suggests that tolerance to combined drought and heat stress is genetically distinct from tolerance to either stress alone, but this has not been confirmed in maize. In this study we evaluated 300 maize inbred lines testcrossed to CML539. Experiments were conducted under optimal conditions, reproductive stage drought stress, heat stress, and combined drought and heat stress. Lines with high levels of tolerance to drought and combined drought and heat stress were identified. Significant genotype × trial interaction and very large plot residuals were observed; consequently, the repeatability of individual managed stress trials was low. Tolerance to combined drought and heat stress in maize was genetically distinct from tolerance to individual stresses, and tolerance to either stress alone did not confer tolerance to combined drought and heat stress. This finding has major implications for maize drought breeding. Many current drought donors and key inbreds used in widely grown African hybrids were susceptible to drought stress at elevated temperatures. Several donors tolerant to drought and combined drought and heat stress, notably La Posta Sequia C7‐F64‐2‐6‐2‐2 and DTPYC9‐F46‐1‐2‐1‐2, need to be incorporated into maize breeding pipelines.