Soybean Yield Simulation and Sustainability Assessment Based on the DSSAT-CROPGRO-Soybean Model

被引:0
|
作者
Zhang, Lei [1 ]
Cao, Zhenxi [1 ,2 ,3 ]
Gao, Yang [1 ,3 ,4 ]
Huang, Weixiong [5 ]
Si, Zhuanyun [4 ]
Guo, Yuanhang [1 ]
Wang, Hongbo [1 ,2 ,3 ]
Wang, Xingpeng [1 ,2 ,3 ,6 ]
机构
[1] Tarim Univ, Modern Agr Engn Key Lab Univ, Coll Water Hydraul & Architectural Engn, Educ Dept Xinjiang Uygur Autonomous Reg, Alar 843300, Peoples R China
[2] Tarim Univ, Minist Educ, Key Lab Tarim Oasis Agr, Alar 843300, Peoples R China
[3] Chinese Acad Agr Sci, Western Agr Res Ctr, Changji 831100, Peoples R China
[4] Chinese Acad Agr Sci, Inst Farmland Irrigat, Xinxiang 453002, Peoples R China
[5] China Univ Geosci, Sch Environm Studies, Hubei Key Lab Yangtze Catchment Environm Aquat Sci, Wuhan 430078, Peoples R China
[6] Minist Agr & Rural Affairs, Key Lab Northwest Oasis Water Saving Agr, Shihezi 832000, Peoples R China
来源
PLANTS-BASEL | 2024年 / 13卷 / 17期
关键词
soybean; DSSAT model; yield; biomass; irrigation water use efficiency; DRIP IRRIGATION; SOUTHERN XINJIANG; WATER; GROWTH;
D O I
10.3390/plants13172525
中图分类号
Q94 [植物学];
学科分类号
071001 ;
摘要
In order to ensure national grain and oil security, it is imperative to expand the soybean planting area in the Xinjiang region. However, the scarcity of water resources in southern Xinjiang, the relatively backward soybean planting technology, and the lack of a supporting irrigation system have negatively impacted soybean planting and yield. In 2022 and 2023, we conducted an experiment which included three irrigation amounts of 27 mm, 36 mm, and 45 mm and analyzed the changes in dry mass and yield. Additionally, we simulated the potential yield using the corrected DSSAT-CROPGRO-Soybean model and biomass based on the meteorological data from 1994 to 2023. The results demonstrated that the model was capable of accurately predicting soybean emergence (the relative root mean square error (nRMSE) = 0, the absolute relative error (ARE) = 0), flowering (nRMSE = 0, ARE = 2.78%), maturity (nRMSE = 0, ARE = 3.21%). The model demonstrated high levels of accuracy in predicting soybean biomass (R-2 = 0.98, nRMSE = 20.50%, ARE = 20.63%), 0-80 cm soil water storage (R-2 = 0.64, nRMSE = 7.78%, ARE = 3.24%), and yield (R-2 = 0.81, nRMSE = 10.83%, ARE = 8.79%). The biomass of soybean plants increases with the increase in irrigation amount. The highest biomass of 63 mm is 9379.19 kg.hm(-2). When the irrigation yield is 36-45 mm (p < 0.05), the maximum yield can reach 4984.73 kg.hm(-2); the maximum efficiency of soybean irrigation water was 33-36 mm. In light of the impact of soybean yield and irrigation water use efficiency, the optimal irrigation amount for soybean cultivation in southern Xinjiang is estimated to be between 36 and 42 mm. The simulation results provide a theoretical foundation for soybean cultivation in southern Xinjiang.
引用
收藏
页数:14
相关论文
共 50 条
  • [1] Simplifying the prediction of phenology with the DSSAT-CROPGRO-soybean model based on relative maturity group and determinacy
    Salmeron, Montserrat
    Purcell, Larry C.
    AGRICULTURAL SYSTEMS, 2016, 148 : 178 - 187
  • [2] Impact of soil compaction on 30-year soybean yield simulated with CROPGRO-DSSAT
    Mulazzani, Rodrigo Pivoto
    Gubiani, Paulo Ivonir
    Zanon, Alencar Junior
    Drescher, Marta Sandra
    Schenato, Ricardo Bergamo
    Girardello, Vitor Cauduro
    AGRICULTURAL SYSTEMS, 2022, 203
  • [3] Modelling the effect of soybean rust on soybean yield using the CSM CROPGRO: Soybean
    Rodrigues, R. A.
    Pedrini, J.
    Fraisse, C. W.
    Fernandes, J. C.
    Justino, F. B.
    Heinemann, A.
    Vale, F. X.
    Costa, L.
    PHYTOPATHOLOGY, 2012, 102 (07) : 101 - 102
  • [4] Uncertainty assessment of soya bean yield gaps using DSSAT-CSM-CROPGRO-Soybean calibrated by cultivar maturity groups
    Ribeiro Teixeira, Wilson Wagner
    Battisti, Rafael
    Sentelhas, Paulo Cesar
    de Moraes, Milton Ferreira
    de Oliveira Junior, Adilson
    JOURNAL OF AGRONOMY AND CROP SCIENCE, 2019, 205 (05) : 533 - 544
  • [5] Impact of projected climate on yield of soybean using CROPGRO-Soybean model in Madhya Pradesh
    Walikar, L. D.
    Bhan, Manish
    Giri, A. K.
    Dubey, A. K.
    Agrawal, K. K.
    JOURNAL OF AGROMETEOROLOGY, 2018, 20 (03): : 211 - 215
  • [6] Quantifying Potential Yield and Yield Gaps of Soybean Using CROPGRO-Soybean Model in the Humid Tropics of Southwestern Ethiopia
    Mekonnen, Ashebir
    Getnet, Mezegebu
    Nebiyu, Amsalu
    Abebe, Abush Tesfaye
    INTERNATIONAL JOURNAL OF PLANT PRODUCTION, 2022, 16 (04) : 653 - 667
  • [7] Analysis of potential yields and yield gaps of rainfed soybean in India using CROPGRO-Soybean model
    Bhatia, V. S.
    Singh, Piara
    Wani, S. P.
    Chauhan, G. S.
    Rao, A. V. R. Kesava
    Mishra, A. K.
    Sriniuas, K.
    AGRICULTURAL AND FOREST METEOROLOGY, 2008, 148 (8-9) : 1252 - 1265
  • [8] Quantifying Potential Yield and Yield Gaps of Soybean Using CROPGRO-Soybean Model in the Humid Tropics of Southwestern Ethiopia
    Ashebir Mekonnen
    Mezegebu Getnet
    Amsalu Nebiyu
    Abush Tesfaye Abebe
    International Journal of Plant Production, 2022, 16 : 653 - 667
  • [9] Evaluation and application of the CROPGRO-Soybean simulation model in Vertic Inceptisol
    Alagarswamy, G
    Singh, P
    Hoogenboom, G
    Wani, SP
    Pathak, P
    Virmani, SM
    AGRICULTURAL SYSTEMS, 2000, 63 (01) : 19 - 32
  • [10] Impact Assessment of Climate Change on Soybean Crop Using CROPGRO-Soybean Model in Central India
    Bhan, Manish
    Patel, Deshraj
    Bal, Santanu K.
    Kumar, Puppala V.
    AGRICULTURAL RESEARCH, 2025,