Articles | Volume 18, issue 22
https://doi.org/10.5194/gmd-18-8927-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-8927-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development of the global maize yield model MATCRO-Maize version 1.0
Marin Nagata
Graduate School of Global Food Resources, Hokkaido University, Sapporo, Hokkaido 060-0809, Japan
Astrid Yusara
Graduate School of Agriculture, Hokkaido University, Sapporo, Hokkaido 060-0808, Japan
Center for Climate Change Adaptation, National Institute for Environmental Studies, Tsukuba, Ibaraki 305-8506, Japan
Research Faculty of Agriculture, Hokkaido University, Sapporo, Hokkaido 060-8589, Japan
Yuji Masutomi
Center for Climate Change Adaptation, National Institute for Environmental Studies, Tsukuba, Ibaraki 305-8506, Japan
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Astrid Yusara, Tomomichi Kato, Elizabeth A. Ainsworth, Rafael Battisti, Etsushi Kumagai, Satoshi Nakano, Yushan Wu, Yutaka Tsutsumi-Morita, Kazuhiko Kobayashi, and Yuji Masutomi
Geosci. Model Dev., 18, 8801–8826, https://doi.org/10.5194/gmd-18-8801-2025, https://doi.org/10.5194/gmd-18-8801-2025, 2025
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We developed a soybean model, an ecosystem model for crop yield (namely MATCRO-Soy), integrating crop response toward climate variables. It offers a detailed yield estimation. Parameter tuning in the model used literature and field experiments. The model shows a moderate correlation with observed yields at the global, national, and grid-cell levels. Development of this model enhances crop modeling diversity approaches, particularly in climate change impact studies.
Masahito Ueyama, Yuta Takao, Hiromi Yazawa, Makiko Tanaka, Hironori Yabuki, Tomo'omi Kumagai, Hiroki Iwata, Md. Abdul Awal, Mingyuan Du, Yoshinobu Harazono, Yoshiaki Hata, Takashi Hirano, Tsutom Hiura, Reiko Ide, Sachinobu Ishida, Mamoru Ishikawa, Kenzo Kitamura, Yuji Kominami, Shujiro Komiya, Ayumi Kotani, Yuta Inoue, Takashi Machimura, Kazuho Matsumoto, Yojiro Matsuura, Yasuko Mizoguchi, Shohei Murayama, Hirohiko Nagano, Taro Nakai, Tatsuro Nakaji, Ko Nakaya, Shinjiro Ohkubo, Takeshi Ohta, Keisuke Ono, Taku M. Saitoh, Ayaka Sakabe, Takanori Shimizu, Seiji Shimoda, Michiaki Sugita, Kentaro Takagi, Yoshiyuki Takahashi, Naoya Takamura, Satoru Takanashi, Takahiro Takimoto, Yukio Yasuda, Qinxue Wang, Jun Asanuma, Hideo Hasegawa, Tetsuya Hiyama, Yoshihiro Iijima, Shigeyuki Ishidoya, Masayuki Itoh, Tomomichi Kato, Hiroaki Kondo, Yoshiko Kosugi, Tomonori Kume, Takahisa Maeda, Shoji Matsuura, Trofim Maximov, Takafumi Miyama, Ryo Moriwaki, Hiroyuki Muraoka, Roman Petrov, Jun Suzuki, Shingo Taniguchi, and Kazuhito Ichii
Earth Syst. Sci. Data, 17, 3807–3833, https://doi.org/10.5194/essd-17-3807-2025, https://doi.org/10.5194/essd-17-3807-2025, 2025
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The JapanFlux2024 dataset, created through collaboration across Japan and East Asia, includes eddy covariance data from 83 sites spanning 683 site-years (1990–2023). This comprehensive dataset offers valuable insights into energy, water, and CO2 fluxes, supporting research on land–atmosphere interactions and process models; fosters global collaboration; and advances research in environmental science and regional climate dynamics.
Tatsuya Miyauchi, Makoto Saito, Hibiki M. Noda, Akihiko Ito, Tomomichi Kato, and Tsuneo Matsunaga
Geosci. Model Dev., 18, 2329–2347, https://doi.org/10.5194/gmd-18-2329-2025, https://doi.org/10.5194/gmd-18-2329-2025, 2025
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Solar-induced chlorophyll fluorescence (SIF) is an effective indicator for monitoring photosynthetic activity. This paper introduces VISIT-SIF, a biogeochemical model developed based on the Vegetation Integrative Simulator for Trace gases (VISIT) to represent satellite-observed SIF. Our simulations reproduced the global distribution and seasonal variations in observed SIF. VISIT-SIF helps to improve photosynthetic processes through a combination of biogeochemical modeling and observed SIF.
Reza Kusuma Nurrohman, Tomomichi Kato, Hideki Ninomiya, Lea Végh, Nicolas Delbart, Tatsuya Miyauchi, Hisashi Sato, Tomohiro Shiraishi, and Ryuichi Hirata
Biogeosciences, 21, 4195–4227, https://doi.org/10.5194/bg-21-4195-2024, https://doi.org/10.5194/bg-21-4195-2024, 2024
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SPITFIRE (SPread and InTensity of FIRE) was integrated into a spatially explicit individual-based dynamic global vegetation model to improve the accuracy of depicting Siberian forest fire frequency, intensity, and extent. Fires showed increased greenhouse gas and aerosol emissions in 2006–2100 for Representative Concentration Pathways. This study contributes to understanding fire dynamics, land ecosystem–climate interactions, and global material cycles under the threat of escalating fires.
Xin Zhao, Kazuya Nishina, Haruka Izumisawa, Yuji Masutomi, Seima Osako, and Shuhei Yamamoto
Earth Syst. Sci. Data, 16, 3893–3911, https://doi.org/10.5194/essd-16-3893-2024, https://doi.org/10.5194/essd-16-3893-2024, 2024
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Mapping a rice calendar in a spatially explicit manner with a consistent framework remains challenging at a global or continental scale. We successfully developed a new gridded rice calendar for monsoon Asia based on Sentinel-1 and Sentinel-2 images, which characterize transplanting and harvesting dates and the number of rice croppings in a comprehensive framework. Our rice calendar will be beneficial for rice management, production prediction, and the estimation of greenhouse gas emissions.
Hideki Ninomiya, Tomomichi Kato, Lea Végh, and Lan Wu
Geosci. Model Dev., 16, 4155–4170, https://doi.org/10.5194/gmd-16-4155-2023, https://doi.org/10.5194/gmd-16-4155-2023, 2023
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Non-structural carbohydrates (NSCs) play a crucial role in plants to counteract the effects of climate change. We added a new NSC module into the SEIB-DGVM, an individual-based ecosystem model. The simulated NSC levels and their seasonal patterns show a strong agreement with observed NSC data at both point and global scales. The model can be used to simulate the biotic effects resulting from insufficient NSCs, which are otherwise difficult to measure in terrestrial ecosystems globally.
Yuji Masutomi, Toshichika Iizumi, Key Oyoshi, Nobuyuki Kayaba, Wonsik Kim, Takahiro Takimoto, and Yoshimitsu Masaki
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-131, https://doi.org/10.5194/gmd-2021-131, 2021
Revised manuscript not accepted
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The accuracy of seasonal climate forecasts for monthly precipitation of JMA/MRI-CPS2, a dynamical seasonal climate forecast (SCF) system, is higher than that of statistical SCF (St-SCF) system using climate indices around the equator (10° S–10° N) even for six-month lead forecasts. On a global scale, the forecast accuracy of JMA/MRI-CPS2 is higher for one-month lead forecasts; however, St-SCFs were more accurate for forecasts more than two months in advance.
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Short summary
We developed a maize version of a process-based crop model coupled to a land-surface model by incorporating photosynthesis for C4 plants and maize-specific parameters. The model was calibrated with field data and literature, and it was extensively validated with global reference yields. The model effectively captured interannual yield variability in global and county-level yield data, demonstrating its potential for assessing the climate impacts on maize production.
We developed a maize version of a process-based crop model coupled to a land-surface model by...