On to the next chapter for crop breeding: Convergence with data science

Abstract

Crop breeding is as ancient as the invention of cultivation. In essence, the objective of crop breeding is to improve plant fitness under human cultivation conditions, making crops more productive while maintaining consistency in life cycle and quality. Predictive breeding has been demonstrated in the agricultural industry and in public breeding programs for over a decade. The massive stores of data that have been generated by industry, farmers, and scholars through several decades have finally been recognized as a potential asset that can be brought to bear on specific breeding decisions. A wide range of analytical methods that were initially developed for various other quantitative disciplines, such as machine learning, deep learning, and artificial intelligence, are now being adapted for application in crop breeding to support analytics and decision making processes. This convergence between data science and crop breeding analytics is expected to address long-standing gaps in crop breeding analytics, and realize the potential of applying advanced analytics to multidimensional data such as geospatial variables, a multitude of phenotypic responses, and genetic information. Here, we summarize the few existing examples followed by perspectives on where else these technologies would have applications to accelerate operational aspects of crop breeding and agricultural product development efforts.

Data synthesis for crop variety evaluation. A review

“…These revolutions are driven by increased data storage and computing capacity, the availability of sensors, improved DNA sequencing technologies and new field data collection approaches, such as high-throughput and high-precision field phenotyping and crowdsourcing (Tardieu et al 2017;Esposito et al 2020;Chawade et al 2019;Reynolds et al 2020;Van Etten et al 2016). This has caused not only a quantitative leap in data volumes but also a shift to 'big data' approaches that move beyond small-sample statistics to data analysis based on machine learning (Breiman 2001;Thessen 2016;van Etten et al 2017;Ersoz et al 2020). While there are multiple examples of useful applications of big data analysis in agriculture (Kamilaris et al 2017;Liakos et al 2018), such cases are still few compared to other industries (Kamilaris et al 2017).…”

Crop science: A foundation for advancing predictive agriculture

“…In this special issue, Casadebaig, Debaeke, and Wallach (2020) explore the opportunity to leverage concepts from meteorology and climatology, known as postprocessing, to improve prediction skill to levels adequate Crop Science for target applications. Washburn, Burch, and Valdez Franco (2020) review methods for integrating statistical prediction with mechanistic biological models as a means to accelerate genetic gain in plant breeding. Recent advances in information technologies, statistical learning algorithms, and the availability of large datasets for analysis motivated studies to replace science-based models with data-driven models to enable prediction and classification in breeding (Ersoz, Martin, & Stapleton, 2020;Washburn et al, 2020) and agronomy (Schwalbert et al, 2020).…”

“…Washburn, Burch, and Valdez Franco (2020) review methods for integrating statistical prediction with mechanistic biological models as a means to accelerate genetic gain in plant breeding. Recent advances in information technologies, statistical learning algorithms, and the availability of large datasets for analysis motivated studies to replace science-based models with data-driven models to enable prediction and classification in breeding (Ersoz, Martin, & Stapleton, 2020;Washburn et al, 2020) and agronomy (Schwalbert et al, 2020). While these studies have produced useful solutions in agriculture, it is opportune to ask how this encapsulated knowledge will contribute to advancing plant science, and in turn to improving the robustness of prediction methodologies (Mitchell, 2019).…”

“…The mathematical framework applied by Ersoz et al (2020) to demonstrate prediction methodologies illustrates the possibility of advancing understanding of the genetic and phenotypic architecture of integrated traits (e.g., kernel numbers). In turn, this knowledge can help inform efforts to improve prediction based on advanced genomic prediction approaches (Amadeu et al, 2020;Ferrão, Marinho, Muñoz, & Resende Jr, 2020).…”

“…However, using suitable crop models (Archontoulis et al, 2020;Hammer et al, 2020) it is feasible to predict important, fundamental properties of the performance landscapes (Cooper et al, 2020;Messina et al, 2019). In combination with statistical learning methods, this enables applications to advance predictive breeding (Bogard et al, 2020;Washburn et al, 2020), predictive breeding and agronomy (Cooper et al, 2020;Messina et al, 2018), forecasting (Archontoulis et al, 2020), and crop design (Cooper et al, 2020;Hammer et al, 2020) for a wide range of agricultural production systems. These principles have the potential to move the problem from that of attempting to independently predict genetic (breeding) and management (agronomy) solutions to poorly defined GxExM problem spaces, to that of predicting integrated genetic-management (GxM) solutions for the challenges inherent in both current and future target populations of environments (TPEs) of agricultural systems.…”