Citation: Pham, Q.V.; Nguyen, T.T.N.; Vo, T.T.X.; Le, P.H.; Nguyen, X.T.T.; Duong, N.V.; Le, C.T.S. Applying the SIMPLE Crop Model to Assess Soybean (Glicine max. (L.) Merr.) Biomass and Yield in Tropical Climate Variation. Agronomy 2023, 13, 1180. https://doi.org/10.3390/ agronomy13041180 Academic Editors: Nguyenthanh Son, Chien-Hui Syu, Cheng-Ru Chen and Gniewko Niedbala Received: 20 December 2022 Revised: 16 April 2023 Accepted: 19 April 2023 Published: 21 April 2023 Copyright: © 2023 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 (https:// creativecommons.org/licenses/by/ 4.0/). agronomy Article Applying the SIMPLE Crop Model to Assess Soybean (Glicine max. (L.) Merr.) Biomass and Yield in Tropical Climate Variation Quang V. Pham 1,2 , Tanh T. N. Nguyen 2,3,4, * , Tuyen T. X. Vo 1,2 , Phuoc H. Le 1,2 , Xuan T. T. Nguyen 1,2 , Nha V. Duong 5 and Ca T. S. Le 2,3 1 Faculty of Agriculture and Natural Resources, An Giang University, 18 Ung Van Khiem St., Long Xuyen City 90000, An Giang Province, Vietnam; pvquang@agu.edu.vn (Q.V.P.) 2 Vietnam National University Ho Chi Minh City, Linh Trung Ward, Thu Duc District, Ho Chi Minh City 71300, Vietnam 3 Faculty of Engineering, Technology and Environment, An Giang University, 18 Ung Van Khiem St., Long Xuyen City 90000, An Giang Province, Vietnam 4 Climate Change Institute, An Giang University, 18 Ung Van Khiem St., Long Xuyen City 90000, An Giang Province, Vietnam 5 Faculty of Agriculture and Rural Development, Kien Giang University, 320A, 61 National Highway, Minh Luong Town, Chau Thanh District 91700, Kien Giang Province, Vietnam * Correspondence: ntntanh@agu.edu.vn Abstract: Soybean Glicine max. (L.) Merr. is one of the most major food crops. In some areas, its responses to different climates have not been well studied, particularly in tropical countries where other crops are more dominant. Accordingly, we adopted the SIMPLE crop model to investigate the responses of soybeans to the climate. We conducted two experiments on crop growth in the Summer–Autumn season of 2020, and Winter–Spring 2021 in the Hoa Binh Commune, in the Mekong Delta, Vietnam, which is an area that is vulnerable to climate change impacts, to obtain data for our model input and assessment. The assessment was concerned with the effects of climate variables (temperature and CO 2 ) on soybean biomass and yield. The results indicated that the SIMPLE model performed well in simulating soybean yields, with an RRMSE of 9–10% overall. The drought stress results showed a negative impact on the growth and development of soybeans, although drought stress due to less rainfall seemed more serious in Spring–Winter 2021 than in Summer–Autumn 2020. This study figured out the trend that higher temperatures can shorten biomass development and lead to yield reduction. In addition, soybeans grown under high CO 2 concentrations of 600 ppm gave a higher biomass and a greater yield than in the case with 350 ppm. In conclusion, climate variance can affect the soybean yield, which can be well investigated using the SIMPLE model. Keywords: SIMPLE; soybean biomass and yield; climate change; modeling 1. Introduction Climate varies over time and can cause problems for food crops. For this reason, an assessment of the responses of crops to the climate is needed, particularly for major food crops. Soybean, a leguminous annual C3 plant in the Fabaceae family [1], is a food crop that has caused much concern due to its nutritional values for animals and humans. Soybean responses to the climate have been widely studied, but the investigation of the responses in tropical areas is uncommon. For countries where soybean is not a major crop, knowledge of soybean responses is rarely known. Previous studies have reported the key effects of climatic factors on soybeans. For instance, the temperature can affect the soybean yield [2]; for example, an increase of 1 ◦ C in summer led to a decrease in the yield by 16% in Wisconsin, USA [3]. For Rio Grande do Sul (southern Brazil), a subtropical area, water supply and photothermal quotient are the factors that cause yield variation [4]. As reported in the study in Matopiba, Brazil, the Agronomy 2023, 13, 1180. https://doi.org/10.3390/agronomy13041180 https://www.mdpi.com/journal/agronomy