Simulation of Staple Crop Yields for Determination of Regional Impacts of Climate Change: A Case Study in Chonnam Province, Republic of Korea
Abstract
:1. Introduction
2. Materials and Methods
2.1. Experimental Field Data for Rice, Barley, and Soybean
2.2. Crop Models
2.3. Geospatial Simulation of the Climate Change Impacts
2.4. Data for the Crop Simulation
2.5. Climate Projections
2.6. Statistical Analysis
3. Results
3.1. Simulation of Rice, Barley, and Soybean Yields
3.2. Geographical Simulation of the Climate Change Impacts
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
Appendix A
ID | Texture | Depth (cm) | Soil Water † (cm3 cm−3) | |
---|---|---|---|---|
CLL | DUL | |||
IB00000002 | Medium silty clay | 150 | 0.228 | 0.385 |
IB00000003 | Shallow silty clay | 60 | 0.228 | 0.385 |
IB00000005 | Medium silty loam | 150 | 0.108 | 0.218 |
IB00000006 | Shallow silty loam | 60 | 0.108 | 0.218 |
IB00000008 | Medium sandy loam | 150 | 0.052 | 0.176 |
IB00000009 | Shallow sandy loam | 60 | 0.052 | 0.176 |
IB00000011 | Medium sand | 150 | 0.024 | 0.096 |
IB00000012 | Shallow sand | 60 | 0.024 | 0.096 |
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Crop | Cultivar and Cultivation Year | Planting Date in DOY ♩ |
---|---|---|
Rice | Chopyong (2013–2015), Chonnam-3 (2013–2015), Ilmi (2016–2018), Nampyong (2016–2018), Saemnuri (2018), Hwangkeum (2016–2018) | DOY 156–166 |
Barley | Doosan 29 (2000–2018), Heenchal-naked (2012–2018), Hopum (2005–2018), Jinyang (2000–2018), Saechal (2000–2018), Saechal-naked (2000–2018) | DOY 271–296 |
Soybean | Daewon (2011–2018), Daweon (2006–2014), Daepung (2013–2014), Haepoom (2014–2018), Pungsannamul (2003–2018), Pungwon (2011–2014), SeoNam (2003–2005, 2008–2010), SoWeon (2003–2006), Tawkwang (2011–2018) | DOY 161–176 |
Coeff. † | Default ‡ | Ilmi | Nampyeong | Saenuri | Hwangkeum | Chopyong | Chonnam-3 |
---|---|---|---|---|---|---|---|
P1 | 220.0 | 320.0 | 300.0 | 200.0 | 400.0 | 320.0 | 390.0 |
P2O | 12.0 | 12.8 | 12.8 | 12.8 | 12.8 | 12.8 | 12.8 |
P2R | 35.0 | 20.0 | 90.0 | 10.0 | 45.0 | 35.0 | 20.0 |
P5 | 510.0 | 530.0 | 550.0 | 530.0 | 670.0 | 500.0 | 530.0 |
G1 | 55.0 | 65.0 | 65.0 | 65.0 | 65.0 | 65.0 | 65.0 |
G2 | 0.025 | 0.022 | 0.022 | 0.021 | 0.024 | 0.021 | 0.021 |
G3 | 1.0 | 1.2 | 1.2 | 1.3 | 1.2 | 1.2 | 1.2 |
G4 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
Generic Coefficient † | Default ‡ | SaeChal | SaeChal-Naked | Doosan 29 | Jinyang | Hopum | HeenChal-Naked |
---|---|---|---|---|---|---|---|
P1V | 5 | 10 | 10 | 10 | 10 | 10 | 10 |
P1D | 75 | 23 | 23 | 20 | 20 | 20 | 20 |
P5 | 450 | 200 | 200 | 180 | 180 | 200 | 100 |
G1 | 30 | 22 | 22 | 22 | 20 | 22 | 22 |
G2 | 35 | 55 | 49 | 35 | 40 | 35 | 45 |
G3 | 1.0 | 1.5 | 1.5 | 1.5 | 1.5 | 1.5 | 1.5 |
PHINT | 60 | 90 | 90 | 93 | 83 | 80 | 80 |
Coeff. † | Default ‡ | Daewon | Taekwang | Pungsan | Haepoom | Daepung | Pungwon | Dawon |
---|---|---|---|---|---|---|---|---|
CSDL | 14.60 | 14.80 | 14.61 | 13.92 | 13.58 | 12.04 | 12.57 | 12.02 |
PPSEN | 0.129 | 0.345 | 0.32 | 0.344 | 0.287 | 0.266 | 0.249 | 0.241 |
EM-FL | 15.5 | 21.0 | 21.0 | 22.5 | 19.2 | 18.0 | 18.0 | 16.5 |
FL-SH | 5.0 | 6.0 | 6.0 | 6.0 | 9.0 | 8.0 | 9.0 | 9.0 |
FL-SD | 12.0 | 13.0 | 13.0 | 12.0 | 13.0 | 14.0 | 16.0 | 16.0 |
SD-PM | 29.5 | 34.5 | 34.0 | 34.0 | 35.5 | 32.0 | 32.0 | 32.6 |
FL-LF | 26.0 | 18.0 | 18.0 | 18.0 | 18.0 | 26.0 | 26.0 | 26.0 |
LFMAX | 1.03 | 1.03 | 1.03 | 1.03 | 1.03 | 1.03 | 1.02 | 1.02 |
SLAVR | 375 | 375 | 375 | 400 | 375 | 385 | 375 | 390 |
SIZLF | 180 | 180 | 180 | 150 | 180 | 180 | 170 | 180 |
WTPSD | 0.19 | 0.19 | 0.19 | 0.19 | 0.15 | 0.19 | 0.18 | 0.17 |
SFPDV | 23.0 | 23.0 | 22.0 | 24.0 | 23.0 | 21.0 | 21.0 | 22.0 |
RCP Scenario | Year | CO2 (ppm) | T Change (°C) | P Change (%) |
---|---|---|---|---|
4.5 | 2050 | 480 | +0.9 | +3 |
2070 | 520 | +1.9 | +6 | |
2100 | 540 | +2.3 | +7 | |
8.5 | 2050 | 530 | +1.5 | +3 |
2070 | 680 | +2.7 | +7 | |
2100 | 940 | +4.3 | +10 |
Crop | Evaluation | Sim | Meas | MAE | RMSD | p (a = 0.05) | NSE |
---|---|---|---|---|---|---|---|
ton ha−1 | |||||||
Rice | Val (2017–18) | 7.00 ± 0.615 | 7.00 ± 0.535 | 0.319 | 0.455 | 0.99 | 0.24 |
Cal (2013–16) | 6.29 ± 1.712 | 6.41 ± 1.071 | 0.171 | 0.209 | 0.66 | 0.96 | |
Soybean | Val (2014–18) | 4.12 ± 1.034 | 4.00 ± 0.974 | 0.312 | 0.424 | 0.51 | 0.81 |
Cal (2003–13) | 2.69 ± 0.868 | 2.66 ± 0.846 | 0.126 | 0.141 | 0.80 | 0.97 | |
Barley | Val (2010–18) | 3.89 ± 0.697 | 3.75 ± 0.729 | 0.213 | 0.453 | 0.48 | 0.60 |
Cal (2000–09) | 3.85 ± 0.502 | 3.81 ± 0.552 | 0.112 | 0.132 | 0.69 | 0.94 |
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Choi, J.; Ko, J.; An, K.-N.; Qaisrani, S.A.; Ban, J.-O.; Kim, D.-K. Simulation of Staple Crop Yields for Determination of Regional Impacts of Climate Change: A Case Study in Chonnam Province, Republic of Korea. Agronomy 2021, 11, 2544. https://doi.org/10.3390/agronomy11122544
Choi J, Ko J, An K-N, Qaisrani SA, Ban J-O, Kim D-K. Simulation of Staple Crop Yields for Determination of Regional Impacts of Climate Change: A Case Study in Chonnam Province, Republic of Korea. Agronomy. 2021; 11(12):2544. https://doi.org/10.3390/agronomy11122544
Chicago/Turabian StyleChoi, Jinsil, Jonghan Ko, Kyu-Nam An, Saeed A. Qaisrani, Jong-Oh Ban, and Dong-Kwan Kim. 2021. "Simulation of Staple Crop Yields for Determination of Regional Impacts of Climate Change: A Case Study in Chonnam Province, Republic of Korea" Agronomy 11, no. 12: 2544. https://doi.org/10.3390/agronomy11122544