New Tools for Enhancing Crop Adaptation to Climate Change

The final section of the book presents tools at the 'cutting edge' of agricultural technology. Increased integration of these approaches into breeding programmes is inevitable, at least for those providing unequivocal benefits. Recent advances in genomics research address the multigenic nature of plant abiotic stress adaptation, including the potential of genetic engineering of new traits which are not amenable to conventional breeding (Ortiz, 2008; Federoff et al., 2010). The marriage of geographic information systems (GIS) with sophisticated statistical and modelling tools is also addressed as a means to better target breeding efforts through enhanced understanding of the interaction of complex and changing environments with genes and genomes.

As pointed out by Whitford et al. in Chapter 12, important new tools are becoming available to assist with breeding for climate change. Chapter 12 is also helpful in introducing some of the basic concepts of biotechnology. The authors provide details of induced genetic variation in crops, such as introgression through backcrossing, amphi-diploidy, mutagenesis, in vitro culture and transgenics. Recent advances in genomics are highlighted as tools to dissect stress adaptive mechanisms both metabolically and genetically. The authors also indicate the use of model plant systems and their ability for predicting, through modelling, traits in other crops. Molecular breeding tools such as marker-aided backcrossing (MABC) or

MAS are presented as the promising new additions to the breeder toolkit. Other methods such as early generation MAS, in silico breeding and metabolite-assisted breeding are also described. The analysis of diversity and population dynamics are other important uses of DNA markers for designing knowledge-led plant breeding approaches and managing genebank collections for further use in crop improvement. High-throughput genotyping and phenotyping are also important tools for accelerating both population improvement and cultivar development. The authors explain in detail the steps of transgenic approaches as well as the advances in gene discovery technology that can assist plant-breeding endeavours to address climate change. The chapter ends by discussing investments on capacity building by both private and public sectors, and access to technology, whose deployment may be affected by intellectual property issues and regulatory systems.

While GIS and crop modelling are essential tools in predicting climate change, the same tools have a variety of other applications that can assist with many of the research areas discussed in previous chapters. Chapter 13 by Hodson and White demonstrates a central role for these technologies, including: (i) interpolating meteorological data to define climatic zones; (ii) estimating spatial variation in soils to infer agronomic potential; (iii) defining climatic suitability zones of pests and diseases to predict the likelihood of their incidence; and (iv) identification of potential collection sites of crop wild relatives in terms of likely genetic potential based on environmental selection pressures. One of the major benefits of improved characterization of target environments is that resources for crop improvement can be deployed more effectively. Crop simulation models simulate the key physiological processes believed to determine crop performance so as to predict crop development, adaptation and performance. Therefore, in combination with GIS databases, which capture the heterogeneity of environments in both space and time, crop modelling permits a more systematic approach to understanding how genotypes interact with environmental factors and are likely to perform in response to climatic as well as other environmental variables. Given the considerable challenges facing crop scientists to maintain food security, it can be expected that application of these tools will soon become routine in crop research. A recent application has been to monitor shifting abiotic and biotic stress distributions for major cereal crops, indicating likely changes in the size and distribution of target environments in the near future; this has important implications for how breeding resources must be redeployed to meet demands 10-20 years from now as outlined by Braun et al. (Chapter 7).

As climate changes and becomes less predictable, the use of statistical tools to achieve a better understanding of how culti-vars interact with environment will become invaluable both in deploying genes and germ-plasm and in defining 'weak links' as targets for research investment. Chapter 14 by Crossa et al. provides an overview of several statistical models and their application for explaining the climatic and genetic causes of genotype x environment (GE) interaction. Their advantages and shortcomings are also highlighted by the authors, who claim that multi-environment trials are very important for breeding cultivars with general or specific adaptation and yield stability, studying GE interactions, and predicting the performance of new cultivars in future years and new locations. They indicate that data ensuing from such trials should include not only pheno-typic measurements of cultivars across environments but also climatic and soil data as well as molecular markers representing genetic data. Some examples are given to illustrate the use of appropriate statistical models for gaining better insights about the GE interaction in multi-environment trials.

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