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Effects of Projected Climate Change on Quantity and Quality of Soybean Yield under Different Emission Scenarios


Affiliations
1 Department of Biological and Agricultural Engineering, Universiti Putra Malaysia, 43400, Serdang, Selangor Darul Ehsan, Malaysia
2 Seed and Plant Improvement Institute, Agricultural Research, Education and Extension Organization (AREEO) Karaj, Iran, Islamic Republic of
3 Department of Civil Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
4 Department of Irrigation and Drainage Engineering, Aburaihan Campus, University of Tehran, Iran, Islamic Republic of
5 Department of Land Management, Universiti Putra Malaysia, 43400, Serdang, Selangor Darul Ehsan, Malaysia
6 Institute of Graduate Studies Building, Institute of Ocean and Earth Science (IOES), University of Malaya, 50603 Kuala Lumpur, Malaysia
 

Soybean is one of the most important oilseed crops in the world. Its economic value is based on the concentration of protein and oil produced in the seeds. However, in climate change studies, a crop simulation model like AquaCrop is unable to predict the qualitative yield of crops. Therefore, this study aims to simulate qualitative soybean yield based on regression models between observed dry grain yield (Yd) from 12 treatments as independent variables with their corresponding observed values for oil and protein contents as dependent variables. The P-value (<0.05) and R2 value of the linear regression model showed that oil content was positively regressed with yield, whereas protein content was negatively regressed with yield. On the other hand, predicted values of Yd from the calibrated AquaCrop model over general circulation models based on weighted multi-model ensemble means of five emission scenarios have been used for simulation of soybean oil and protein contents in the future. The results obtained by comparing historical period (1985–2010) to the future period (2020–2039) centred on the 2030s, showed that soybean oil content increased similarly as yield increased in the future period while protein content decreased inversely with yield. Overall, statistical indicators showed that the linear regression model performed well to predict the soybean oil and protein content when AquaCrop model not able to simulate the qualitative yield.

Keywords

Dry Grain Yield, Linear Regression, Oil Contents, Protein Contents, Soybean.
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  • Effects of Projected Climate Change on Quantity and Quality of Soybean Yield under Different Emission Scenarios

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Authors

Hamidreza Ahmadzadeh Araji
Department of Biological and Agricultural Engineering, Universiti Putra Malaysia, 43400, Serdang, Selangor Darul Ehsan, Malaysia
Aimrun Wayayok
Department of Biological and Agricultural Engineering, Universiti Putra Malaysia, 43400, Serdang, Selangor Darul Ehsan, Malaysia
Jahanfar Daneshian
Seed and Plant Improvement Institute, Agricultural Research, Education and Extension Organization (AREEO) Karaj, Iran, Islamic Republic of
Majid Mirzaei
Department of Civil Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
Ali Reza Massah Bavani
Department of Irrigation and Drainage Engineering, Aburaihan Campus, University of Tehran, Iran, Islamic Republic of
C. B. S. Teh
Department of Land Management, Universiti Putra Malaysia, 43400, Serdang, Selangor Darul Ehsan, Malaysia
Ahmad Fikri Abdullah
Department of Biological and Agricultural Engineering, Universiti Putra Malaysia, 43400, Serdang, Selangor Darul Ehsan, Malaysia
Parisa Ahmadi
Institute of Graduate Studies Building, Institute of Ocean and Earth Science (IOES), University of Malaya, 50603 Kuala Lumpur, Malaysia

Abstract


Soybean is one of the most important oilseed crops in the world. Its economic value is based on the concentration of protein and oil produced in the seeds. However, in climate change studies, a crop simulation model like AquaCrop is unable to predict the qualitative yield of crops. Therefore, this study aims to simulate qualitative soybean yield based on regression models between observed dry grain yield (Yd) from 12 treatments as independent variables with their corresponding observed values for oil and protein contents as dependent variables. The P-value (<0.05) and R2 value of the linear regression model showed that oil content was positively regressed with yield, whereas protein content was negatively regressed with yield. On the other hand, predicted values of Yd from the calibrated AquaCrop model over general circulation models based on weighted multi-model ensemble means of five emission scenarios have been used for simulation of soybean oil and protein contents in the future. The results obtained by comparing historical period (1985–2010) to the future period (2020–2039) centred on the 2030s, showed that soybean oil content increased similarly as yield increased in the future period while protein content decreased inversely with yield. Overall, statistical indicators showed that the linear regression model performed well to predict the soybean oil and protein content when AquaCrop model not able to simulate the qualitative yield.

Keywords


Dry Grain Yield, Linear Regression, Oil Contents, Protein Contents, Soybean.

References





DOI: https://doi.org/10.18520/cs%2Fv118%2Fi1%2F103-107