Data Analytical Skills in Agricultural Science: Growing Knowledge, Training in Emerging High‐Throughput Techniques and Machine‐Learning Applications.

A United Nations report predicts that the world population will reach 10 billion by 2050 and will require 70% more food than is currently produced. To meet this demand, crop performance and productivity need to be improved through crop improvement and breeding programs. High-throughput phenotyping and sequencing techniques have been adopted to screen crop phenotypes and genetic makeup, allowing for the optimization of crop yield and adaptation to different environments. Data analytical skills are crucial for effectively harnessing the potential of large datasets generated by high-throughput techniques. Machine learning approaches, such as neural networks, support vector machines, regression, clustering, and random forest, are used for data analysis and visualization. However, it is important to understand the issues of correlation and causation when working with big data. Resources and training programs are available to develop data analytical skills, and several programming languages and online learning platforms offer courses in data analytics. The integration of high-throughput methods with machine learning has revolutionized agricultural research and provides opportunities for advancements in agricultural science. [Extracted from the article]