Applied Mathematics and Omics to Assess Crop Genetic Resources for Climate Change Adaptive Traits

Abdallah Bari, Ardeshir B. Damania, Michael Mackay, Selvadurai Dayanandan, Abdallah Bari, Ardeshir B. Damania, Michael Mackay, Selvadurai Dayanandan

• Éditeur: CRC Press
• Année: 2018
• ISBN:
  9781315359991
• Édition: 1
• Sujet: Sciences biologiques
• Sous-sujet: Botanique

Applied Mathematics and Omics to Assess Crop Genetic Resources for Climate Change Adaptive Traits focuses on practical means and approaches to further the use of genetic resources for mitigating the effects of climate change and improving crop production. Genetic diversity in crop plants is being further explored to increase yield, disease resistance, and nutritional value by employing recent advances in mathematics and omics technologies to promote the adaptation of crops to changing climatic conditions.

This book presents a broad view of biodiversity and genetic resources in agriculture and provides answers to some current problems. It also highlights ways to provide much-needed information to practitioners and innovators engaged in addressing the effects of global climate change on agriculture. The book is divided into sections that cover:

• The implications of climate change for drylands and farming communities
• The potential of genetic resources and biodiversity to adapt to and mitigate climate change effects
• Applications of mathematics and omics technologies
• Genomics and gene identification

We are in the midst of significant changes in global climates, and its effects are already being felt throughout the world. The increasing frequency of droughts and heat waves has had negative impacts on agricultural production, especially in the drylands of the world. This book shares the collective knowledge of leading scientists and practitioners, giving readers a broader appreciation and heightened awareness of the stakes involved in improving and sustaining agricultural production systems in the face of climate change.

Section III

Applied Mathematics (Unlocking the Potential of Mathematical Conceptual Frameworks)

10 Applied Mathematics in Genetic Resources

Toward a Synergistic Approach Combining Innovations with Theoretical Aspects

A. Bari, Y.P. Chaubey, M.J. Sillanpää, F.L. Stoddard, A.B. Damania, S.B. Alaoui, and M.C. Mackay

CONTENTS

Why a Synergistic Approach?

Data—Large Datasets

Concepts and Analytical Conceptual Frameworks

Bayes–Laplace

Neural Networks

Support Vector Machines

Random Forest

Accuracy Metrics

Conclusion

References

Instead of allowing the flexibility of our computer tools to continue to overwhelm us with a surplus of riches, we should make use of theory to help focus these powerful resources upon the task at hand.

Nilsson, N
The Interplay Between Experimental and Theoretical Methods in Artificial Intelligence, Cognition and Brain Theory, January 1981

Genetic resources consist of genes and genotypes with frequencies and patterns generated over space and time that may significantly enhance their potential for adaptive evolution to changing conditions (Darwin 1859, Harlan 1992). Capturing these patterns requires digging into large and complex datasets, including people’s knowledge associated with the resources. These data consist mostly of nonreplicated records or observations with limited information on a number of variables. Analyzing such complex and large datasets with limited information requires the elaboration of new mathematical conceptual frameworks and new approaches for a cost-effective and timely utilization of these resources. The lack of availability of ex ante evaluation of genetic resources for indicative traits has also been highlighted as the most prevalent and long-standing impediment to their effective use in plant improvement (Koo and Wright 2000, FAO 2010). There is also a lack of methodologies or more elaborated approaches specific to mining genetic resources data, restricting their effective deployment to enhance farm productivity, sustainability, and livelihoods.

The synergistic approach involving innovative strategies for utilizing plant genetic resources (PGR), farmers’ innovations, expert knowledge, applied mathematics, and omics is intended to address the complexity inherent in genetic resources with the aim to contribute to the needed increase in agricultural production in a time of rapidly changing conditions. Such synergistic approaches between innovations and theories, between practice and models, have helped enormously in accelerating progress made in a number of other disciplines such as physics, medicine, and information technology. This is even more important in agriculture, in order to offset the effects of climate change while achieving the dual goals of increased production coupled with sustainable intensification to meet the global increase in food demands (FAO 2011).

Genetic resources and genetic improvement have provided 50% of the increase in yields achieved over the past few decades in major global crops such as wheat, rice, and maize, with the other 50% coming from improved management and use of inputs (Byerlee et al. 1999, FAO 2011, Smith et al. 2014). They also helped in closing the yield gaps by generating cultivars adapted to local conditions and by making them more resilient to biotic (e.g., insects, diseases, and viruses) and abiotic stresses such as droughts and floods (FAO 2009).

The challenge of climate change emphasizes the value of these resources for farming in the future, as stated at the 2014 Lillehammer International Conference on Genetic Resources for Food and Agriculture in a Changing Climate. This conference asserted that genetic resources are more important for the future of farming than any other factor, because they contain the genes that will facilitate adaptation (Præbel and Groeneveld 2014).

To effectively contribute to the sustainable crop production and intensification process (FAO 2011, CGIAR 2015), farmers will also need a new and genetically diverse portfolio of improved cultivars with adaptive traits that allow the crops to provide higher yields under changing conditions of drought, heat, and increasingly virulent pests and diseases (FAO 2011). In addition, there is an urgency to accelerate the delivery of new cultivars by strengthening the connections among PGR, plant breeding, and seed delivery (FAO 2011). The timing issue addressed in early findings suggested that the speed by which novel trait variation is found is as important as the process of incorporating such novel variation into an improved genetic background. The need to shorten the time to deliver these improved cultivars, while also involving farming communities, is as crucial as the development of improved cultivars. Recent studies have shown that farmers are active in responding to changing climate conditions by applying a systems approach combining agronomic practices with the use of alternative species or cultivars (GFA 2014, Dawson et al. 2015).

The application of mathematics to capture genetic patterns, such as those present in genetic resources, was recognized in 1948 by Gustave Malécot (in English: Malécot 1969) under his probabilistic theory underlying genetic differentiation and spatial genetic structuring as a result of stochastic processes, following Sewall Wright’s statistical theory (Heywood 1991, Ishida 2009). Recent studies on marine species have also revealed the presence of such patterns because of ecological and oceanographic factors. These patterns were previously dismissed as chaotic (Selkoe et al. 2010).

The synergistic approach aims thus to explore genetic resources and exploit the patterns of adaptation displayed by these resources, including patterns induced by climate change, while involving the perspective of farmers. Such approaches and patterns have helped in tracing the origin and diversity of crops and in locating new and agronomically important trait variation (Bari et al. 2012). The presence of such patterns and a priori information implies the possibility of prediction to locate and identify adaptive and rare traits, with the aim to achieve the dual goals of crop production and intensification.

This chapter introduces and presents current progress made by combining practical innovations, including farmers’ innovations, with applied mathematics to accelerate the process in identifying traits related to climate change. The chapter presents a synergistic approach to explore patterns and a priori information, combining mathematics with omics to value genetic resources. It also discusses future prospects in addressing uncertainties in the shifts in phenological as well as physiological traits and changes in plant responses induced by climate change.

Evaluating genetic resources with mathematical models, while involving farmers, could be of tremendous relevance to decision making (Kotschi 2007). A tight and remarkable interdependency between advances made in medicine and those made in mathematics has been reported and demonstrated to be beneficial to both of these disciplines (Glamore et al. 2013). In terms of farmers’ perspectives, Soleri and Cleveland (2001) included social science, as well as a new method using hypothetical scenarios based on the biological model and the farmers’ own experiences to explore their perceptions of genetics, with particular regard to their knowledge of genetic variation and its relation to environmental variation and heritability. Their hypotheses in terms of heritability and genetic variation of two traits of maize provided insights into the nature of farmer knowledge that is of particular relevance to the theoretical basis for exploring genetic variation (Soleri and Cleveland 2001).

WHY A SYNERGISTIC APPROACH?

The objective of this chapter is to demonstrate the importance of a synergistic approach in utilizing PGR through comb...

Table des matières

• Cover
• Half Title
• Title Page
• Copyright Page
• Table of Contents
• Foreword
• Preface
• Editors
• Contributors
• Section I Climate-Change Implications for Drylands and Farming Communities
• Section II Potential of Using Genetic Resources and Biodiversity to Adapt to and Mitigate Climate Change
• Section III Applied Mathematics (Unlocking the Potential of Mathematical Conceptual Frameworks)
• Section IV Applied Omics Technologies
• Index