Jill E. Cairns

Field high-throughput phenotyping: the new crop breeding frontier

Constraints in field phenotyping capability limit our ability to dissect the genetics of quantitative traits, particularly those related to yield and stress tolerance (e.g., yield potential as well as increased drought, heat tolerance, and nutrient efficiency, etc.). The development of effective field-based high-throughput phenotyping platforms (HTPPs) remains a bottleneck for future breeding advances. However, progress in sensors, aeronautics, and high-performance computing are paving the way. Here, we review recent advances in field HTPPs, which should combine at an affordable cost, high capacity for data recording, scoring and processing, and non-invasive remote sensing methods, together with automated environmental data collection. Laboratory analyses of key plant parts may complement direct phenotyping under field conditions. Improvements in user-friendly data management together with a more powerful interpretation of results should increase the use of field HTPPs, therefore increasing the efficiency of crop genetic improvement to meet the needs of future generations.

Metabolic and Phenotypic Responses of Greenhouse-Grown Maize Hybrids to Experimentally Controlled Drought Stress

Adaptation to abiotic stresses like drought is an important acquirement of agriculturally relevant crops like maize. Development of enhanced drought tolerance in crops grown in climatic zones where drought is a very dominant stress factor therefore plays an essential role in plant breeding. Previous studies demonstrated that corn yield potential and enhanced stress tolerance are associated traits. In this study, we analyzed six different maize hybrids for their ability to deal with drought stress in a greenhouse experiment. We were able to combine data from morphophysiological parameters measured under well-watered conditions and under water restriction with metabolic data from different organs. These different organs possessed distinct metabolite compositions, with the leaf blade displaying the most considerable metabolome changes following water deficiency. Whilst we could show a general increase in metabolite levels under drought stress, including changes in amino acids, sugars, sugar alcohols, and intermediates of the TCA cycle, these changes were not differential between maize hybrids that had previously been designated based on field trial data as either drought-tolerant or susceptible. The fact that data described here resulted from a greenhouse experiment with rather different growth conditions compared to natural ones in the field may explain why tolerance groups could not be confirmed in this study. We were, however, able to highlight several metabolites that displayed conserved responses to drought as well as metabolites whose levels correlated well with certain physiological traits.

Effectiveness of genomic prediction of maize hybrid performance in different breeding populations and environments.

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.

Adapting maize production to climate change in sub-Saharan Africa

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 sub-Saharan 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.

Maize Production in a Changing Climate: Impacts, Adaptation, and Mitigation Strategies

Abstract Plant breeding and improved management options have made remarkable progress in increasing crop yields during the past century. However, climate change projections suggest that large yield losses will be occurring in many regions, particularly within sub-Saharan Africa. The development of climate-ready germplasm to offset these losses is of the upmost importance. Given the time lag between the development of improved germplasm and adoption in farmers’ fields, the development of improved breeding pipelines needs to be a high priority. Recent advances in molecular breeding provide powerful tools to accelerate breeding gains and dissect stress adaptation. This review focuses on achievements in stress tolerance breeding and physiology and presents future tools for quick and efficient germplasm development. Sustainable agronomic and resource management practices can effectively contribute to climate change mitigation. Management options to increase maize system resilience to climate-related stresses and mitigate the effects of future climate change are also discussed.

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

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 are used for predicting quantitative traits. This article shows how to use neural networks with radial basis functions (RBFs) for prediction with dense molecular markers. We illustrate the use of the linear Bayesian LASSO regression model and of two non-linear regression models, reproducing kernel Hilbert spaces (RKHS) regression and radial basis function neural networks (RBFNN) on simulated data and real maize lines genotyped with 55,000 markers and evaluated for several trait–environment combinations. The empirical results of this study indicated that the three models showed similar overall prediction accuracy, with a slight and consistent superiority of RKHS and RBFNN over the additive Bayesian LASSO model. Results from the simulated data indicate that RKHS and RBFNN models captured epistatic effects; however, adding non-signal (redundant) predictors (interaction between markers) can adversely affect the predictive accuracy of the non-linear regression models.

Phenotyping for Abiotic Stress Tolerance in Maize

The ability to quickly develop germplasm having tolerance to several complex polygenic inherited abiotic and biotic stresses combined is critical to the resilience of cropping systems in the face of climate change. Molecular breeding offers the tools to accelerate cereal breeding; however, suitable phenotyping protocols are essential to ensure that the much-anticipated benefits of molecular breeding can be realized. To facilitate the full potential of molecular tools, greater emphasis needs to be given to reducing the within-experimental site variability, application of stress and characterization of the environment and appropriate phenotyping tools. Yield is a function of many processes throughout the plant cycle, and thus integrative traits that encompass crop performance over time or organization level (i.e. canopy level) will provide a better alternative to instantaneous measurements which provide only a snapshot of a given plant process. Many new phenotyping tools based on remote sensing are now available including non-destructive measurements of growth-related parameters based on spectral reflectance and infrared thermometry to estimate plant water status. Here we describe key field phenotyping protocols for maize with emphasis on tolerance to drought and low nitrogen.

Maize Production in a Changing Climate

Abstract Plant breeding and improved management options have made remarkable progress in increasing crop yields during the past century. However, climate change projections suggest that large yield losses will be occurring in many regions, particularly within sub-Saharan Africa. The development of climate-ready germplasm to offset these losses is of the upmost importance. Given the time lag between the development of improved germplasm and adoption in farmers’ fields, the development of improved breeding pipelines needs to be a high priority. Recent advances in molecular breeding provide powerful tools to accelerate breeding gains and dissect stress adaptation. This review focuses on achievements in stress tolerance breeding and physiology and presents future tools for quick and efficient germplasm development. Sustainable agronomic and resource management practices can effectively contribute to climate change mitigation. Management options to increase maize system resilience to climate-related stresses and mitigate the effects of future climate change are also discussed.

Conservation agriculture in Southern Africa: Advances in knowledge

The increasing demand for food from limited available land, in light of declining soil fertility and future threats of climate variability and change have increased the need for more sustainable crop management systems. Conservation agriculture (CA) is based on the three principles of minimum soil disturbance, surface crop residue retention and crop rotations, and is one of the available options. In Southern Africa, CA has been intensively promoted for more than a decade to combat declining soil fertility and to stabilize crop yields. The objective of this review is to summarize recent advances in knowledge about the benefits of CA and highlight constraints to its widespread adoption within Southern Africa. Research results from Southern Africa showed that CA generally increased water infiltration, reduced soil erosion and run-off, thereby increasing available soil moisture and deeper drainage. Physical, chemical and biological soil parameters were also improved under CA in the medium to long term. CA increased crop productivity and also reduced on-farm labor, especially when direct seeding techniques and herbicides were used. As with other cropping systems, CA has constraints at both the field and farm level. Challenges to adoption in Southern Africa include the retention of sufficient crop residues, crop rotations, weed control, pest and diseases, farmer perception and economic limitations, including poorly developed markets. It was concluded that CA is not a ‘one-size-fits-all’ solution and often needs significant adaptation and flexibility when implementing it across farming systems. However, CA may potentially reduce future soil fertility decline, the effects of seasonal dry-spells and may have a large impact on food security and farmers’ livelihoods if the challenges can be overcome.

Metabolite Profiles of Maize Leaves in Drought, Heat, and Combined Stress Field Trials Reveal the Relationship between Metabolism and Grain Yield

Foliar metabolite levels, including myoinositol, show correlation with grain yield in tropical maize field trials during drought, heat, and simultaneous drought/heat stresses. The development of abiotic stress-resistant cultivars is of premium importance for the agriculture of developing countries. Further progress in maize (Zea mays) performance under stresses is expected by combining marker-assisted breeding with metabolite markers. In order to dissect metabolic responses and to identify promising metabolite marker candidates, metabolite profiles of maize leaves were analyzed and compared with grain yield in field trials. Plants were grown under well-watered conditions (control) or exposed to drought, heat, and both stresses simultaneously. Trials were conducted in 2010 and 2011 using 10 tropical hybrids selected to exhibit diverse abiotic stress tolerance. Drought stress evoked the accumulation of many amino acids, including isoleucine, valine, threonine, and 4-aminobutanoate, which has been commonly reported in both field and greenhouse experiments in many plant species. Two photorespiratory amino acids, glycine and serine, and myoinositol also accumulated under drought. The combination of drought and heat evoked relatively few specific responses, and most of the metabolic changes were predictable from the sum of the responses to individual stresses. Statistical analysis revealed significant correlation between levels of glycine and myoinositol and grain yield under drought. Levels of myoinositol in control conditions were also related to grain yield under drought. Furthermore, multiple linear regression models very well explained the variation of grain yield via the combination of several metabolites. These results indicate the importance of photorespiration and raffinose family oligosaccharide metabolism in grain yield under drought and suggest single or multiple metabolites as potential metabolic markers for the breeding of abiotic stress-tolerant maize.