Pub Date : 2023-01-01DOI: 10.2480/agrmet.d-23-00004
Akira Mori, Y. Doi, T. Iizumi
{"title":"GCPE: The global dataset of crop phenological events for agricultural and earth system modelling","authors":"Akira Mori, Y. Doi, T. Iizumi","doi":"10.2480/agrmet.d-23-00004","DOIUrl":"https://doi.org/10.2480/agrmet.d-23-00004","url":null,"abstract":"","PeriodicalId":56074,"journal":{"name":"Journal of Agricultural Meteorology","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69170711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.2480/agrmet.d-22-00016
A. Maruyama, T. Kuwagata, Tsutomu Watanabe
{"title":"Observations on dew formation in the rice canopy and its simulation using a multilayer microclimate model","authors":"A. Maruyama, T. Kuwagata, Tsutomu Watanabe","doi":"10.2480/agrmet.d-22-00016","DOIUrl":"https://doi.org/10.2480/agrmet.d-22-00016","url":null,"abstract":"","PeriodicalId":56074,"journal":{"name":"Journal of Agricultural Meteorology","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69170628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.2480/agrmet.d-22-00021
Md Rakibul HASSAN, D. Ito
{"title":"Down-regulation of photosynthesis in apple leaves under elevated CO2 concentration: a long-term field study with different fruit load","authors":"Md Rakibul HASSAN, D. Ito","doi":"10.2480/agrmet.d-22-00021","DOIUrl":"https://doi.org/10.2480/agrmet.d-22-00021","url":null,"abstract":"","PeriodicalId":56074,"journal":{"name":"Journal of Agricultural Meteorology","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69170650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.2480/agrmet.d-22-00026
Nobuyuki Kayaba, R. Sameshima
{"title":"Improved methods for estimating effective accumulated temperature for pest forecasting","authors":"Nobuyuki Kayaba, R. Sameshima","doi":"10.2480/agrmet.d-22-00026","DOIUrl":"https://doi.org/10.2480/agrmet.d-22-00026","url":null,"abstract":"","PeriodicalId":56074,"journal":{"name":"Journal of Agricultural Meteorology","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69170682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.2480/agrmet.d-23-00101
K. Ono
{"title":"Special Collection: Gas fluxes and micrometeorology of agricultural ecosystems in Japan","authors":"K. Ono","doi":"10.2480/agrmet.d-23-00101","DOIUrl":"https://doi.org/10.2480/agrmet.d-23-00101","url":null,"abstract":"","PeriodicalId":56074,"journal":{"name":"Journal of Agricultural Meteorology","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69170752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.2480/agrmet.d-22-00031
S. Shimoda, Maki Kanaya, Y. Kominami, H. Tsuji
{"title":"Decline in tuber number and weight of potato (Solanum tuberosum L) associated with drought in 2020 and 2021 in a humid region of eastern Hokkaido","authors":"S. Shimoda, Maki Kanaya, Y. Kominami, H. Tsuji","doi":"10.2480/agrmet.d-22-00031","DOIUrl":"https://doi.org/10.2480/agrmet.d-22-00031","url":null,"abstract":"","PeriodicalId":56074,"journal":{"name":"Journal of Agricultural Meteorology","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69170691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.2480/agrmet.d-23-00005
K. Fujiwara, T. Jishi, Kyohei Nishino, A. Yano
{"title":"Net photosynthetic rate of cos lettuce under pulsed light generated with full-wave rectification of 50 Hz sine-wave alternating-current power","authors":"K. Fujiwara, T. Jishi, Kyohei Nishino, A. Yano","doi":"10.2480/agrmet.d-23-00005","DOIUrl":"https://doi.org/10.2480/agrmet.d-23-00005","url":null,"abstract":"","PeriodicalId":56074,"journal":{"name":"Journal of Agricultural Meteorology","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69170721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.2480/agrmet.d-23-00019
R. Matsuda, Soichiro Shiba, Y. Chen, Shunsuke Kubo, Liyao Yu, Ji-Yoon Lee, K. Fujiwara
{"title":"Growth of cucumber seedlings under emulated sunlight with artificially reproduced fluctuations in photosynthetic photon flux density","authors":"R. Matsuda, Soichiro Shiba, Y. Chen, Shunsuke Kubo, Liyao Yu, Ji-Yoon Lee, K. Fujiwara","doi":"10.2480/agrmet.d-23-00019","DOIUrl":"https://doi.org/10.2480/agrmet.d-23-00019","url":null,"abstract":"","PeriodicalId":56074,"journal":{"name":"Journal of Agricultural Meteorology","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69170731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.2480/agrmet.d-22-00024
Sunchai PHUNGERN, Yuji GOTO, Liya DING, Iain MCTAGGART, Kosuke NOBORIO
Measurements of greenhouse gas (GHG) emissions from paddy fields can often include flux measurement errors due to either instrument errors or unfavorable weather. Therefore, data post-processing, including the gap-filling process, is required to improve data quality and quantify the GHG flux budget. This study applied machine learning (ML) techniques with polynomial and multivariate polynomial regression models for gap-filling methane (CH4) and carbon dioxide (CO2) fluxes from closed chamber (CC) method measurements and compared results with mean diurnal variation (MDV) and look-up table (LUT) techniques. The most influential factors affecting methane emissions in the paddy field were used for input variables in the models: air temperature, soil temperature, soil redox potential, soil water content, solar radiation, and days after transplanting. The models' performances were compared using mean absolute error (MAE) and root mean square error (RMSE). The results showed that MAE and RMSE for gap-filling CH4 fluxes were 1.299-2.984 and 2.499-4.981 mg CH4 m-2 h-1, respectively. Also, the multivariate polynomial regression models performed better for gap-filling CH4 fluxes (RMSE = 2.499 mg CH4 m-2 h-1) than the polynomial regression models, MDV (RMSE = 3.210 mg CH4 m-2 h-1), and LUT (RMSE = 3.339 mg CH4 m-2 h-1) techniques. The MAE and RMSE for gap-filling CO2 fluxes were 0.282-0.949 and 0.435-1.078 g CO2 m-2 h-1, respectively. The ML techniques with polynomial regression using solar radiation (RMSE = 0.435 g CO2 m-2 h-1) and multivariate models (RMSE = 0.445 g CO2 m-2 h-1) perform better on gap-filling CO2 fluxes than MDV (RMSE = 0.544 g CO2 m-2 h-1), and LUT (RMSE = 0.553 g CO2 m-2 h-1) techniques. The gap-filling using the multivariate polynomial regression models used in this study improved the reliability of the diurnal variation in GHG fluxes. Therefore, ML techniques could be a proper alternative for gap-filling GHG fluxes.
稻田温室气体(GHG)排放的测量常常包括由于仪器误差或不利天气造成的通量测量误差。因此，需要对数据进行后处理，包括填补空白过程，以提高数据质量并量化温室气体通量预算。本研究将机器学习(ML)技术与多项式和多元多项式回归模型应用于封闭室(CC)方法测量的空隙填充甲烷(CH4)和二氧化碳(CO2)通量，并将结果与平均日变化(MDV)和查找表(LUT)技术进行比较。模型输入变量采用对稻田甲烷排放影响最大的因子:气温、土壤温度、土壤氧化还原电位、土壤含水量、太阳辐射和定植后天数。使用平均绝对误差(MAE)和均方根误差(RMSE)比较模型的性能。结果表明，填隙CH4通量的MAE和RMSE分别为1.299 ~ 2.984和2.499 ~ 4.981 mg CH4 m-2 h-1。此外，多元多项式回归模型(RMSE = 2.499 mg CH4 m-2 h-1)比多项式回归模型、MDV (RMSE = 3.210 mg CH4 m-2 h-1)和LUT (RMSE = 3.339 mg CH4 m-2 h-1)技术具有更好的空隙填充CH4通量(RMSE = 2.499 mg CH4 m-2 h-1)。填隙CO2通量的MAE和RMSE分别为0.282 ~ 0.949和0.435 ~ 1.078 g CO2 m-2 h-1。利用太阳辐射(RMSE = 0.435 g CO2 m-2 h-1)和多变量模型(RMSE = 0.445 g CO2 m-2 h-1)进行多项式回归的ML技术在填补空隙的CO2通量方面优于MDV (RMSE = 0.544 g CO2 m-2 h-1)和LUT (RMSE = 0.553 g CO2 m-2 h-1)技术。本研究采用多元多项式回归模型进行补空，提高了温室气体通量日变化的可靠性。因此，ML技术可能是填补间隙的温室气体通量的适当替代方法。
{"title":"Gap-filling greenhouse gas fluxes for the closed chamber method at paddy fields using machine learning techniques","authors":"Sunchai PHUNGERN, Yuji GOTO, Liya DING, Iain MCTAGGART, Kosuke NOBORIO","doi":"10.2480/agrmet.d-22-00024","DOIUrl":"https://doi.org/10.2480/agrmet.d-22-00024","url":null,"abstract":"Measurements of greenhouse gas (GHG) emissions from paddy fields can often include flux measurement errors due to either instrument errors or unfavorable weather. Therefore, data post-processing, including the gap-filling process, is required to improve data quality and quantify the GHG flux budget. This study applied machine learning (ML) techniques with polynomial and multivariate polynomial regression models for gap-filling methane (CH4) and carbon dioxide (CO2) fluxes from closed chamber (CC) method measurements and compared results with mean diurnal variation (MDV) and look-up table (LUT) techniques. The most influential factors affecting methane emissions in the paddy field were used for input variables in the models: air temperature, soil temperature, soil redox potential, soil water content, solar radiation, and days after transplanting. The models' performances were compared using mean absolute error (MAE) and root mean square error (RMSE). The results showed that MAE and RMSE for gap-filling CH4 fluxes were 1.299-2.984 and 2.499-4.981 mg CH4 m-2 h-1, respectively. Also, the multivariate polynomial regression models performed better for gap-filling CH4 fluxes (RMSE = 2.499 mg CH4 m-2 h-1) than the polynomial regression models, MDV (RMSE = 3.210 mg CH4 m-2 h-1), and LUT (RMSE = 3.339 mg CH4 m-2 h-1) techniques. The MAE and RMSE for gap-filling CO2 fluxes were 0.282-0.949 and 0.435-1.078 g CO2 m-2 h-1, respectively. The ML techniques with polynomial regression using solar radiation (RMSE = 0.435 g CO2 m-2 h-1) and multivariate models (RMSE = 0.445 g CO2 m-2 h-1) perform better on gap-filling CO2 fluxes than MDV (RMSE = 0.544 g CO2 m-2 h-1), and LUT (RMSE = 0.553 g CO2 m-2 h-1) techniques. The gap-filling using the multivariate polynomial regression models used in this study improved the reliability of the diurnal variation in GHG fluxes. Therefore, ML techniques could be a proper alternative for gap-filling GHG fluxes.","PeriodicalId":56074,"journal":{"name":"Journal of Agricultural Meteorology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136053524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.2480/agrmet.d-22-00025
H. Oue
{"title":"Comparisons of the stomatal conductance and electron transport rate of three Japanese rice cultivars including Himenorin in Ehime Prefecture","authors":"H. Oue","doi":"10.2480/agrmet.d-22-00025","DOIUrl":"https://doi.org/10.2480/agrmet.d-22-00025","url":null,"abstract":"","PeriodicalId":56074,"journal":{"name":"Journal of Agricultural Meteorology","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69170670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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