The Chinese Academy of Agricultural Sciences (CAAS) and the International Food Policy Research Institute (IFPRI) jointly hosted the International Conference on Climate Change and Food Security (ICCCFS) November 6-8, 2011 in Beijing, China. This conference provided a forum for leading international scientists and young researchers to present their latest research findings, exchange their research ideas, and share their experiences in the field of climate change and food security. The event included technical sessions, poster sessions, and social events. The conference results and recommendations were presented at the global climate talks in Durban, South Africa during an official side event on December 1.

1. Climate conditions and
their impacts on soybean crop yield
Kan-ichiro Matsumura Kwansei Gakuin University
School of Policy Studies, Department of Applied Informatics
International Conference on Climate Change and Food Security (ICCCFS)
Beijing, China, November the 6th to 8th

2. Participating this conference after lecture@JILIN
University is my pleasure.
University is my pleasure
I am appreciated for
I am appreciated for
Dr. Wu Wenbin and Dr Dawen Yang
D W W bi dD D Y
Institute of Agricultural Resources and Regional Planning,
Chinese Academy of Agricultural Sciences, Beijing

3. Participating this conference after lecture@JILIN
Single cropping maize.
University is my pleasure.
University is my pleasure
60 000 元
I am appreciated for/year (Sales)
I am appreciated for/year (Sales)
60,000 元
Dr. Wu Wenbin and Dr Dawen Yang
D W W bi dD D Y
10,000元/buying seeds and
fertilizer
f tili
Institute of Agricultural Resources and Regional Planning,
Chinese Academy of Agricultural Sciences, Beijing
50,000元 /Net Income

4. Participating this conference after lecture@JILIN
University is my pleasure.
University is my pleasure
I am appreciated for
I am appreciated for
Dr. Wu Wenbin and Dr Dawen Yang
D W W bi dD D Y
Institute of Agricultural Resources and Regional Planning,
Chinese Academy of Agricultural Sciences, Beijing
By Matsumura 2011 Oct

5. Participating this conference after lecture@JILIN
University is my pleasure.
University is my pleasure
I am appreciated for
I am appreciated for
Dr. Wu Wenbin and Dr Dawen Yang
D W W bi dD D Y
Institute of Agricultural Resources and Regional Planning,
Chinese Academy of Agricultural Sciences, Beijing

6. 関西学院大学総合政策学部メディア情報学科准教授
独立行政法人国立環境研究所客員研究員
東京大学空間情報科学研究センター客員研究員
北海道大学環境科学院非常勤講師
北里大学獣医学部非常勤講師
立命館大学テクノロジーマネジメント研究科非常勤講師
立命館大学テクノロジ マネジメント研究科非常勤講師
客員研究員：Wangari Maathai Institute for Peace and
客員研究員 W iM h iI i f P d
Environmental Studies The University of Nairobi
客員准教授
Visiting Associate Professor, Earth and Ocean
y p
Science, University of British Columbia (2012Apr)

7. Back Ground
Mumbai, India, 2010
By Matsumura 2010 Aug

8. By Matsumura 2011 Sep

9. Topics
1.DATASETS USED
2.Relationships among temperature,
2 Relationships among temperature
precipitation, and fertilizer for major crop
yield such as Maize, Rice, Soybean and Wheat
3.Future prospect for major crop yield
3 Future prospect for major crop yield
4.CAIFA concept (Climate, Agriculture, Impacts,
p ( , g , p ,
Fertilizer, Adaptation)

10. FAO-STAT 国連食糧農業機関

11. Top5 Major crop producing country
Year 2009 M aize Production (t
P d ti (tonnes)
) Soybeans
USA 333,010,910 USA 91,417,300
China 163,118,097 Brazil 56,960,732
Brazil 51,232,447 Argentina 30,993,379
M exico 20,202,600 China 14,500,141
Indonesia 17,629,740 India 10,217,000
Rice, paddy Wheat
China 197,257,175
197 257 175 China 114,950,296
114 950 296
India 131,274,000 India 80,680,000
Indonesia 64,398,890 Russian Federation 61,739,750
Bangladesh 45,075,000 USA 60,314,290
Viet Nam 38,895,500 France 38,324,700

12. Monthly
Crop Land
p Vegetation
g
Temperature
T t
Paddy Field Mosaic
Precipitation
Monthly Temperature & Precipitation Cropping
pp g
On Cropland and Paddy Field Calendar
Country Based Monthly Temperature & Precipitation Country
Country
On Cropland and Paddy Field Boarder
Country Based
Country Based
Generated Database By Country
Yield & Fertilizer

13. Datasets provide by “CRU TS3.0”
1961 January to 2009 December, Monthly Data
Monthly 720 × 360 Resolution
Temperature
T t
Precipitation
label Variable units
cld cloud cover percentage
dtr diurnal temperature range degrees Celsius
frs frost day frequency days
pre Precipitation millimeters
tmp daily mean temperature degrees Celsius
tmn
t monthly
thl average daily
d il degrees C l i
d Celsius
minimum temperature
tmx monthly average daily degrees Celsius
maximum temperature
vap vapour pressure hecta-Pascals
wet wet day frequency Days
CRU TS3.0&3.1, 2010, Climatic Research Unit, University of East Anglia,
In http://www.cru.uea.ac.uk/cru/data/

14. Monthly
Crop Land
p Vegetation
g
Temperature
T t
Paddy Field Mosaic
Precipitation
GLCNMO, 2008, ©GSI Chiba University, Collaborating
Organizations, In http://www.iscgm.org

15. 解析方法
Climate Conditions on Crop Producing Area
p g

16. Monthly
Crop Land
p Vegetation
g
Temperature
T t
Paddy Field Mosaic
Precipitation
Monthly Temperature & Precipitation Cropping
pp g
On Cropland and Paddy Field Calendar
Country Based Monthly Temperature & Precipitation Country
Country
On Cropland and Paddy Field Boarder
Country Based
Country Based
Generated Database By Country
Yield & Fertilizer

17. Country Based Monthly Temperature & Precipitation
On Cropland and Paddy Field FAOSTAT,2010,In
http://faostat.fao.org/site/567/
default.aspx#ancor
Country Based
Country Based
Generated Database By Country
Yield & Fertilizer

18. Country Based Monthly Temperature & Precipitation
On Cropland and Paddy Field FAOSTAT,2010,In
http://faostat.fao.org/site/567/
default.aspx#ancor
Country Based
Country Based
Generated Database By Country
Yield & Fertilizer

19. Monthly
Crop Land
p Vegetation
g
Temperature
T t
Paddy Field Mosaic
Precipitation
Cropping
pp g
Calendar
Country
Country
Boarder
Cropping Calendar, 2010, University of
Wisconsin, In
http://www.sage.wisc.edu/download/sacks/
http://www sage wisc edu/download/sacks/
crop_calendar.html

20. Cropping Calendar, University of Wisconsin
栽培歴（米の収穫開始時期）

21. Cropping Calendar in India
Plant_Avg M onth Harvest_Avg M onth
M aize 172 6 324 11
Rice 179 6 304 10
Soybean 182 6 308 10
Wheat
Wh 172 6 254 8

22. Y F
Fertilizer
Tsum Psum
Generated Database By Country

23. Topics
1.DATASETS USED
2.Relationships among temperature,
2 Relationships among temperature
precipitation, and fertilizer for major crop
yield such as Maize, Rice, Soybean and Wheat
3.Future prospect for major crop yield
3 Future prospect for major crop yield
4.CAIFA concept (Climate, Agriculture, Impacts,
p ( , g , p ,
Fertilizer, Adaptation)

24. Y F
Fertilizer
Tsum Psum
Generated Database By Country

25. Yield in each country is explained by......
CASEA
(
(Temperature, Precipitation)
p p )
CASEB
(Temperature, Precipitation ,
(T t P i it ti
fertilizer)
f tili )

26. Case A and Case B of Maize yield in USA

27. Actual, Case A and Case B of Soybean yield in USA

28. Actual, Case A and Case B of Wheat yield in USA

29. Actual, Case A and Case B of Rice yield in China

30. Fertilizer input and rice yield in China
70,000
60,000
50,000
ce(Hg/Ha)
40,000
Yield of Ric
30,000
20,000
10,000
0
0 50 100 150 200 250 300 350 400 450 500
Fertilizer (Kg/Ha)

31. Global Soybean Yield : Case A&B
Table 1 Results of Multiple Regression Analysis: Case A Table 2 Results of Multiple Regression Analysis: Case B
Regression Coefficient Regression Coefficient Multiple Correlation Regression Coefficient Regression Coefficient Multiple Correlation
Constant Constant Regression Coefficient (Fertilizer)
(Temperature) (Precipitation) Coefficient (Temperature) (Precipitation) Coefficient
13 Brazil -274,029.2462 490.0380 3.5708 0.7037 13 Brazil -53,408.7291 112.3607 0.0210 75.0728 0.91143426
t-value -5.9870 6.2121 1.1253 t-value -1.7510 2.1343 0.0129 9.2484
14 Chile -102,340.9609 1,746.3907 48.6952 0.1724 14 Chile 21,893.0791 -66.9644 32.2336 82.3271 0.926402115
t-value -0.6246
-0 6246 0.9105
0 9105 0.6285
0 6285 t-value 0.3669 -0.0955 1.1349 15.0363
15 Ecuador 18,130.7322 -32.8178 0.9737 0.2706 15 Ecuador -6,829.4975 43.0996 0.6811 13.6743 0.599063447
t-value -0.4831 0.9094 1.9203 3.1479
t-value 0.8583 -0.4630 1.7673
16 Paraguay 10,230.9934 -1.4764 1.2295 198.6756 0.687192458
16 Paraguay -37,884.0323 81.7823 3.0066 0.3779
t-value 0.3831 -0.0315 1.2266 4.9040
t-value -1.1099 1.3596 2.4085
17 Peru -5,797.8262 643.6998 -13.1356 28.7266 0.637497917
17 Peru -32,724.9938 1,650.2488 -15.5178 0.3905
t-value -0.3552 1.2933 -1.3828 4.3686
t-value -1.5121 2.5533 -1.1855
g y
18 Uruguay 18,001.4423
, -33.4248 0.8391 90.0260 0.791623108
18 Uruguay -154,384.5465
1 4 384 46 255.6830
2 6830 4.0210
4 0210 0.3252
0 32 2
t-value 0.3660 -0.4253 0.6595 6.6890
t-value -1.6109 1.6723 1.6645
19 Canada 31,508.4236 458.9164 178.4005 131.0546 0.813722804
19 Canada 53,679.0027 1,328.1467 318.3086 0.5317
t-value 3.8250 2.0284 2.0150 7.1480
t-value 4.1987 4.0639 2.2594
20 Guatemala -1,356.3159 40.2235 -0.6351 27.5754 0.772951103
20 Guatemala -71,358.2413 259.2435 0.7145 0.5047
t-value -0.0552 0.5341 -0.5368 5.3762
t-value -3.1298 3.7525 0.4846
21 Mexico -86,431.7263 334.9184 9.6027 52.3990 0.8944216
21 Mexico -172,916.2538 669.8765 16.0370 0.6746
t-value -4.3581 4.6710 2.4578 8.9839
t-value -5.3789 5.8472 2.2759
24 Dominican Republic 72,210.8963 -109.0459 0.3796 0.1248 0.486306755
24 Dominican Republic 70,974.4002 -106.3652 0.3152 0.4864
t-value 4.3112 -3.3858 0.4990 0.0833
t-value 4.5688 -3.5858 0.4656
25 Haiti 15,165.3643 -7.6073 -0.3482 -48.6625 0.656746599
25 Haiti 25,812.6641 -24.5915 -0.6392 0.4845
t-value 1.8964 -0.5726 -1.1729 -3.6078
t-value 3.1287 -1.7985 -2.1648
29 Colombia 16,795.5634 -16.6030 -0.7430 15.0891 0.896058756
29 Colombia -84,476.2842 255.2843 5.0145 0.4197
t-value 1.1544 -0.4136 -1.0766 11.9271
t-value
t value -2.0954
2 0954 2.2949
2 2949 2.7191
2 7191
31 Cuba -15,037.2388 35.8998 3.2124 -1.2530 0.420222547
31 Cuba -33,931.9344 67.9663 1.7597 0.2065
t-value -0.4917 0.7583 2.3398 -1.7221
t-value -0.8083 1.0424 0.9368
32 El Salvador -67,521.8684 190.7505 0.1448 9.0920 0.438349496
32 El Salvador -97,037.7591 268.9645 -1.4351 0.5060
t-value -2.2244 2.7429 0.0457 1.2342
t-value -3.1122 3.7594 -0.4269
33 Honduras -21,614.7797 84.5559 -1.1130 4.3079 0.633072265
33 Honduras -30,698.8853 106.8013 -0.8713 0.6063
t-value -2.1759 3.5639 -1.5222 1.9482
t-value -3.2865 4.8171 -1.1733 34 Nicaragua -40,708.7061 79.1031 0.8325 8.0967 0.627362613
34 Nicaragua -47,097.0292 93.0445 0.5918 0.5833 t-value -3.4433 4.1401 1.8013 2.4740
t-value -3.7781 4.6266 1.2537 36 Puerto Rico No Fertilizer Data
36 Puerto Rico -79,022.4147 154.3287 1.7554 0.1617 t-value
t-value -0.6577 0.7530 0.9901 45 Ghana -25,954.3978 65.4180 2.0894 -25.5619 0.322901723
45 Ghana -53,527.6385 111.6345 3.7078 0.3785 t-value -0.8513 1.2113 0.8200 -1.2962
t-value -1.9498 2.2882 1.5095 47 Morocco 9,301.5888 -10.7045 0.9454 -7.0852 0.185393078
47 Morocco 11,393.6002 -21.4534 0.8474 0.1274 t-value 1.0466 -0.2754 0.5410 -0.4979
t-value 1.3570 -0.6418 0.5464 48 Portugal -47,110.7576 114.6155 1.3881 73.3492 0.831890856
48 Portugal -164,950.3647 558.6887 -4.4567 0.5104 t-value -1.2564 1.0068 0.6666 6.6593
t-value -3.1988 3.8300 -1.4232 49 Spain -26,871.8590 104.2399 -0.3448 85.9495 0.931790535
49 Spain -130,809.2550 643.9765 -18.4494 0.6622 t-value -1.1981 1.3443 -0.1062 10.0183
t-value -3.1318 5.3708 -3.1213 52 Guinea -29,040.8764 133.7282 -2.5790 47.5045 0.513945367
52 Guinea -40,083.7137 170.5657 -2.0134 0.5731 t-value -2.4050 3.4086 -1.4352 0.9948
t-value -3.2824 4.3073 -1.0979

32. Global Soybean Yield : Case A
Temp(+) & Yield (‐)
Ecuador Dominican Republic Haiti
Dominican Republic
Morocco Iraq Russia Cameroon
Chad Croatia Serbia & Montenegro
Georgia G
G i Greece C h R
Czech Republic
bli
Slovakia Belarus Romania Ukraine
Yemen Botswana Zimbabwe
Namibia

33. Global Soybean Yield : Case A
Precip(+) & Yield (‐) Peru Haiti El
Salvador Honduras Portugal Spain
Guinea Mali Senegal Ethiopia Uganda Iraq
Israel Central African Republic Albania
Croatia Italy Georgia Greece Turkey Austria
Croatia Italy Georgia Greece Turkey Austria
Hungary Poland Belgium France Germany
g y g y
Netherlands Switzerland Romania Somalia
urkmenistan Saudi Arabia Nepal China
k d b l h
South Korea Cambodia Vietnam Zimbabwe
South Korea Cambodia Vietnam Zimbabwe
New Zealand

34. Topics
1.DATASETS USED
2.Relationships among temperature,
2 Relationships among temperature
precipitation, and fertilizer for major crop
yield such as Maize, Rice, Soybean and Wheat
3.Future prospect for soybean yield
3 Future prospect for soybean yield
4.CAIFA concept (Climate, Agriculture, Impacts,
p ( , g , p ,
Fertilizer, Adaptation)

35. Back Ground economic development
SRES concept
A1b A2
• rapid economic growth • low economic growth
• low population growth • high population growth
• efficient technology • low technological change
global local
B1 B2
• sustainable development • low economic growth
• high economic growth • medium population growth
• low population growth
• slow technological change
environmental protection

36. A1b Scenario
Source: CIESIN, Columbia University
http://beta.ciesin.columbia.edu/datasets/do
wnscaled/
l d/

37. A2 Scenario
Source: CIESIN, Columbia University
http://beta.ciesin.columbia.edu/datasets/do
wnscaled/
l d/

38. B1 Scenario
Source: CIESIN, Columbia University
http://beta.ciesin.columbia.edu/datasets/do
wnscaled/
l d/

39. B2 Scenario
Source: CIESIN, Columbia University
http://beta.ciesin.columbia.edu/datasets/do
wnscaled/
l d/

40. Future Prospect for crop yield in China
The GCM output’s average from 1971 to
2000 is calculated and imposed in 0.5
2000 i l l t d di di 05
degree spatial dataset.
g p
The GCM outputs based on SRES scenarios
in 2010, 2020, 2030, 2040 and 2050 are
in 2010 2020 2030 2040 and 2050 are
obtained and imposed in 0.5 degree spatial
dataset.
Datasets are provided by Kenji Sugimoto(2011)

41. Unit:H
Hg/Ha
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
1961
1962
1963
1964
1965
1966
1967
1968
1969
Maize_Yield_Actual
1970
Maize_Yield_Calculated
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
Future Prospect for crop yield in China

42. Future Prospect for crop yield in China
200,000
180,000
Maize_Yield_Calculated
M i Yi ld C l l d Maize_Yield_Actual
M i Yi ld A l
160,000
140,000
120,000
Ha
Unit:Hg/H
100,000
80,000
60,000
40,000
20,000
0
1961 1970 1980 1990 2000 2010 2020 2030 2040 2050

43. Unit:H
Hg/Ha
40 000
80,000
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
Rice_Yield_Actual
1972
Rice_Yield_Calculated
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
Future Prospect for crop yield in China

44. Future Prospect for crop yield in China
250,000
Rice_Yield_Calculated
Rice_Yield_Actual
200,000
150,000
Ha
Unit:Hg/H
100,000
50,000
0
1961 1970 1980 1990 2000 2010 2020 2030 2040 2050

45. Unit:H
Hg/Ha
10 000
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
20,000
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
Soybean_Yield_Actual
1972
1973
Soybean_Yield_Calculated
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
Future Prospect for crop yield in China
2008

46. Future Prospect for crop yield in China
20,000
Soybean_Yield_Calculated
Soybean_Yield_Actual
18,000
16,000
14,000
12,000
Ha
Unit:Hg/H
10,000
8,000
6,000
4,000
2,000
0
1961 1970 1980 1990 2000 2010 2020 2030 2040 2050

47. Unit:H
Hg/Ha
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
Maize_Yield_Actual
1971
Maize_Yield_Calculated
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
Future Prospect for crop yield in China

48. Future Prospect for crop yield in China
180,000
Wheat_Yield_Calculated
Wheat_Yield_Actual
160,000
140,000
120,000
100,000
Ha
Unit:Hg/H
80,000
60,000
40,000
40 000
20,000
0
1961 1970 1980 1990 2000 2010 2020 2030 2040 2050

49. Topics
1.Back Ground and Advantage of using
Geographical Information System
Geographical Information System
2.DATASETS USED
3.Relationships among temperature,
precipitation, and fertilizer for major crop
precipitation and fertilizer for major crop
yield ( Maize, Rice, Soybean, Wheat)
4.Future prospect for major crop yield
5.CAIFA concept (Climate, Agriculture, Impacts,
Fertilizer, Adaptation)
Fertilizer, Adaptation)

50. Future work

51. Future work

52. linear to Non linear regression Analysis
Earth and Ocean Science
The University of British Columbia
Th U i i f B i i h C l bi

53. Conclusion
Relationships among temperature,
precipitation, and fertilizer for major crop
precipitation, and fertilizer for major crop
yield ( Maize, Rice, Soybean, Wheat) were
calculated.
l l t d
Future prospect for major crop yield is obtained
If yield information at targeted area can be
obtained, relationships with temperature and
obtained, relationships with temperature and
precipitation can be obtained.

54. 謝謝