University of Oulu

Ruusunen, O.; Jalli, M.; Jauhiainen, L.; Ruusunen, M.; Leiviskä, K. Identification of Optimal Starting Time Instance to Forecast Net Blotch Density in Spring Barley with Meteorological Data in Finland. Agriculture 2022, 12, 1939.

Identification of optimal starting time instance to forecast net blotch density in spring barley with meteorological data in Finland

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Author: Ruusunen, Outi1; Jalli, Marja2; Jauhiainen, Lauri2;
Organizations: 1Control Engineering Research Group in Environmental and Chemical Engineering Research Unit, Faculty of Technology, University of Oulu, FI-90014 Oulu, Finland
2Natural Resources Institute Finland, Tietotie 4, 31600 Jokioinen, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 2.3 MB)
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Language: English
Published: Multidisciplinary Digital Publishing Institute, 2022
Publish Date: 2023-03-31


The performance of meteorological data-based methods to forecast plant diseases strongly depends on temporal weather information. In this paper, a data analysis procedure is presented for finding the optimal starting time for forecasting net blotch density in spring barley based on meteorological data. For this purpose, changes in the information content of typically measured weather variables were systemically quantified in sliding time windows and with additionally generated mathematical transformations, namely with features. Signal-to-noise statistics were applied in a novel way as a metric for identifying the optimal starting time instance and the most important features to successfully distinguish between two net blotch densities during springtime itself. According to the results, the information content of meteorological data used in classifying between nine years with and four years without net blotch reached its maximum in Finnish weather conditions on the 41st day from the beginning of the growing season. Specifically, utilising weather data at 41–55 days from the beginning of the growing season maximises successful forecasting potential of net blotch density. It also seems that this time instance enables a linear classification task with a selected feature subset, since the averages of the metrics in two data groups differ statistically with a minimum 68% confidence level for nine days in a 14-day time window.

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Series: Agriculture
ISSN: 2077-0472
ISSN-E: 2077-0472
ISSN-L: 2077-0472
Volume: 12
Issue: 11
Article number: 1939
DOI: 10.3390/agriculture12111939
Type of Publication: A1 Journal article – refereed
Field of Science: 1172 Environmental sciences
215 Chemical engineering
Funding: This research was funded by the Ministry of Agriculture and Forestry of Finland, Document number 632/03.01.02/2017.
Copyright information: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (