Prediction of municipality-level winter wheat yield based on meteorological data using machine learning in Hokkaido, Japan.

This study analyzed meteorological constraints on winter wheat yield in the northern Japanese island, Hokkaido, and developed a machine learning model to predict municipality-level yields from meteorological data. Compared to most wheat producing areas, this island is characterized by wet climate ow...

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Autores principales:	Keach Murakami, Seiji Shimoda, Yasuhiro Kominami, Manabu Nemoto, Satoshi Inoue
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/153cb3ff3c33401ab65957d9f4b67f8e
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spelling	oai:doaj.org-article:153cb3ff3c33401ab65957d9f4b67f8e2021-12-02T20:16:49ZPrediction of municipality-level winter wheat yield based on meteorological data using machine learning in Hokkaido, Japan.1932-620310.1371/journal.pone.0258677https://doaj.org/article/153cb3ff3c33401ab65957d9f4b67f8e2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0258677https://doaj.org/toc/1932-6203This study analyzed meteorological constraints on winter wheat yield in the northern Japanese island, Hokkaido, and developed a machine learning model to predict municipality-level yields from meteorological data. Compared to most wheat producing areas, this island is characterized by wet climate owing to greater annual precipitation and abundant snowmelt water supply in spring. Based on yield statistics collected from 119 municipalities for 14 years (N = 1,516) and high-resolution surface meteorological data, correlation analyses showed that precipitation, daily minimum air temperature, and irradiance during the grain-filling period had significant effects on the yield throughout the island while the effect of snow depth in early winter and spring was dependent on sites. Using 10-d mean meteorological data within a certain period between seeding and harvest as predictor variables and one-year-leave-out cross-validation procedure, performance of machine learning models based on neural network (NN), random forest (RF), support vector machine regression (SVR), partial least squares regression (PLS), and cubist regression (CB) were compared to a multiple linear regression model (MLR) and a null model that returns an average yield of the municipality. The root mean square errors of PLS, SVR, and RF were 872, 982, and 1,024 kg ha-1 and were smaller than those of MLR (1,068 kg ha-1) and null model (1,035 kg ha-1). These models outperformed the controls in other metrics including Pearson's correlation coefficient and Nash-Sutcliffe efficiency. Variable importance analysis on PLS indicated that minimum air temperature and precipitation during the grain-filling period had major roles in the prediction and excluding predictors in this period (i.e. yield forecast with a longer lead-time) decreased forecast performance of the models. These results were consistent with our understanding of meteorological impacts on wheat yield, suggesting usefulness of explainable machine learning in meteorological crop yield prediction under wet climate.Keach MurakamiSeiji ShimodaYasuhiro KominamiManabu NemotoSatoshi InouePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10, p e0258677 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Keach Murakami Seiji Shimoda Yasuhiro Kominami Manabu Nemoto Satoshi Inoue Prediction of municipality-level winter wheat yield based on meteorological data using machine learning in Hokkaido, Japan.
description	This study analyzed meteorological constraints on winter wheat yield in the northern Japanese island, Hokkaido, and developed a machine learning model to predict municipality-level yields from meteorological data. Compared to most wheat producing areas, this island is characterized by wet climate owing to greater annual precipitation and abundant snowmelt water supply in spring. Based on yield statistics collected from 119 municipalities for 14 years (N = 1,516) and high-resolution surface meteorological data, correlation analyses showed that precipitation, daily minimum air temperature, and irradiance during the grain-filling period had significant effects on the yield throughout the island while the effect of snow depth in early winter and spring was dependent on sites. Using 10-d mean meteorological data within a certain period between seeding and harvest as predictor variables and one-year-leave-out cross-validation procedure, performance of machine learning models based on neural network (NN), random forest (RF), support vector machine regression (SVR), partial least squares regression (PLS), and cubist regression (CB) were compared to a multiple linear regression model (MLR) and a null model that returns an average yield of the municipality. The root mean square errors of PLS, SVR, and RF were 872, 982, and 1,024 kg ha-1 and were smaller than those of MLR (1,068 kg ha-1) and null model (1,035 kg ha-1). These models outperformed the controls in other metrics including Pearson's correlation coefficient and Nash-Sutcliffe efficiency. Variable importance analysis on PLS indicated that minimum air temperature and precipitation during the grain-filling period had major roles in the prediction and excluding predictors in this period (i.e. yield forecast with a longer lead-time) decreased forecast performance of the models. These results were consistent with our understanding of meteorological impacts on wheat yield, suggesting usefulness of explainable machine learning in meteorological crop yield prediction under wet climate.
format	article
author	Keach Murakami Seiji Shimoda Yasuhiro Kominami Manabu Nemoto Satoshi Inoue
author_facet	Keach Murakami Seiji Shimoda Yasuhiro Kominami Manabu Nemoto Satoshi Inoue
author_sort	Keach Murakami
title	Prediction of municipality-level winter wheat yield based on meteorological data using machine learning in Hokkaido, Japan.
title_short	Prediction of municipality-level winter wheat yield based on meteorological data using machine learning in Hokkaido, Japan.
title_full	Prediction of municipality-level winter wheat yield based on meteorological data using machine learning in Hokkaido, Japan.
title_fullStr	Prediction of municipality-level winter wheat yield based on meteorological data using machine learning in Hokkaido, Japan.
title_full_unstemmed	Prediction of municipality-level winter wheat yield based on meteorological data using machine learning in Hokkaido, Japan.
title_sort	prediction of municipality-level winter wheat yield based on meteorological data using machine learning in hokkaido, japan.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/153cb3ff3c33401ab65957d9f4b67f8e
work_keys_str_mv	AT keachmurakami predictionofmunicipalitylevelwinterwheatyieldbasedonmeteorologicaldatausingmachinelearninginhokkaidojapan AT seijishimoda predictionofmunicipalitylevelwinterwheatyieldbasedonmeteorologicaldatausingmachinelearninginhokkaidojapan AT yasuhirokominami predictionofmunicipalitylevelwinterwheatyieldbasedonmeteorologicaldatausingmachinelearninginhokkaidojapan AT manabunemoto predictionofmunicipalitylevelwinterwheatyieldbasedonmeteorologicaldatausingmachinelearninginhokkaidojapan AT satoshiinoue predictionofmunicipalitylevelwinterwheatyieldbasedonmeteorologicaldatausingmachinelearninginhokkaidojapan
_version_	1718374451491373056

Prediction of municipality-level winter wheat yield based on meteorological data using machine learning in Hokkaido, Japan.

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