Developing an eco-physiological process-based model of soybean growth and yield (MATCRO-Soy v.1): model calibration  and evaluation

Yusara, Astrid; Kato, Tomomichi; Ainsworth, Elizabeth A.; Battisti, Rafael; Kumagai, Etsushi; Nakano, Satoshi; Wu, Yushan; Tsutsumi-Morita, Yutaka; Kobayashi, Kazuhiko; Masutomi, Yuji

doi:10.5194/gmd-18-8801-2025

Articles | Volume 18, issue 22

https://doi.org/10.5194/gmd-18-8801-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Agricultural Model Intercomparison and Improvement Project...

https://doi.org/10.5194/gmd-18-8801-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 18, issue 22

Development and technical paper

|

20 Nov 2025

Development and technical paper |

| 20 Nov 2025

Developing an eco-physiological process-based model of soybean growth and yield (MATCRO-Soy v.1): model calibration and evaluation

Astrid Yusara, Tomomichi Kato, Elizabeth A. Ainsworth, Rafael Battisti, Etsushi Kumagai, Satoshi Nakano, Yushan Wu, Yutaka Tsutsumi-Morita, Kazuhiko Kobayashi, and Yuji Masutomi

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May 2025	122	68	4	194
Jun 2025	118	38	12	168
Jul 2025	78	14	2	94
Aug 2025	844	24	2	870
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Feb 2026	173	62	17	252
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May 2026	84	46	5	135
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Cumulative views and downloads (calculated since 21 Mar 2025)

Month	HTML	PDF	XML	Total
Mar 2025	160	30	8	198
Apr 2025	60	42	8	110
May 2025	122	68	4	194
Jun 2025	118	38	12	168
Jul 2025	78	14	2	94
Aug 2025	844	24	2	870
Sep 2025	2,290	16	18	2,324
Oct 2025	70	32	4	106
Nov 2025	295	56	25	376
Dec 2025	199	101	20	320
Jan 2026	138	126	28	292
Feb 2026	173	62	17	252
Mar 2026	158	80	13	251
Apr 2026	110	58	5	173
May 2026	84	46	5	135
Jun 2026	13	4	2	19

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Short summary

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.