Dr. Takashi Tanaka
Laboratory of Crop Science
Faculty of Applied Biological Sciences
Gifu University, Japan
After graduating from the Department of Bioresource Sciences, Faculty of Agriculture, Kyoto University in 2012, Dr. Tanaka received his M.S. and Ph.D. degrees from the Department of Agricultural Sciences, Graduate School of Agriculture, Kyoto University. He then worked as a Research Assistant at the Graduate School of Agriculture, Kyoto University (2016-2017) and Assistant Professor at the Faculty of Applied Biological Sciences, Gifu University (2017- 2022) before assuming his current position in April 2022. He has also served as a director of Sagri Corporation since 2021.
Data analytics for on-farm experimentation using precision agricultural technology
His current work focuses on developing data analytics for agronomy and crop science using statistical modelling, crop simulation model, and machine learning techniques. The outcomes of agronomic field experiments can be easily affected by spatial and temporal variations of environmental factors such as soil and weather. Since Sir R.A. Fisher introduced the theory of experimental design in the early 20th century, small-plot randomized experiments followed by analysis of variance (ANOVA) became a standard statistical approach for agronomic research. However, this methodology had limitations and challenges with regard to scalability of outcomes to real large farmers’ fields due to the impact of uncontrollable underlying environmental effects on crop. Meanwhile, the adoption of precision agriculture such as yield monitor and satellite/UAV remote sensing is increasingly enabling farmers to collect data in their own fields. Variable-rate application technology enables farmers to implement on-farm experimentations to understand crop responses to agronomic input treatments (e.g., fertilizers, seeds, and herbicides). However, observation derived from on-farm experimentation usually violates a conventional statistical assumption. He will talk about possibilities and issues in on-farm experimentations in regard to data analytics.
Zhou, X., Heuvelink, G., Kono, Y., Matsui, T.,Tanaka, T.S.T. (2022) Using linear mixed-effects modeling to evaluate the impact of edaphic factors on spatial variation in winter wheat grain yield in Japanese consolidated paddy fields. European Journal of Agronomy 133: 126447. doi:10.1016/j.eja.2021.126447
Kakimoto, S., Mieno, T., Tanaka, T.S.T., Bullock, D.S. (2022) Causal forest approach for site-specific input management via on-farm precision experimentation. Computers and Electronics in Agriculture 199: 107164
Tanaka, T.S.T. (2021) Assessment of design and analysis frameworks for on-farm experimentation through a simulation study of wheat yield in Japan. Precision Agriculture 22: 1601–1616 doi:10.1007/s11119-021-09802-1
Zhou, X., Kono, Y., Win, A., Matsui, T.,Tanaka, T.S.T. (2021) Predicting within-field variability in grain yield and protein content of winter wheat using UAV-based multispectral imagery and machine learning approaches. Plant Production Science24(2):137–151. 10.1080/1343943X.2020.1819165
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