Climate conditions and their impacts on soybean crop yield
Kan-ichiro Matsumura Kwansei Gakuin UniversitySchool of Policy Studies, Department of Applied Informatics
International Conference on Climate Change and Food Security (ICCCFS)Beijing, China, November the 6th to 8th
Participating this conference after lecture@JILINUniversity is my pleasureUniversity is my pleasure.
I am appreciated forI am appreciated for
D W W bi d D D YDr. Wu Wenbin and Dr Dawen Yang
Institute of Agricultural Resources and Regional Planning,Chinese Academy of Agricultural Sciences, Beijing
Participating this conference after lecture@JILIN University is my pleasure
Single cropping maize.University is my pleasure.
I am appreciated for60 000 元 /year (Sales)I am appreciated for
D W W bi d D D Y
60,000 元 /year (Sales)
Dr. Wu Wenbin and Dr Dawen Yang 10,000元/buying seeds and f tiliInstitute of Agricultural Resources and Regional Planning,
Chinese Academy of Agricultural Sciences, Beijingfertilizer
50,000元 /Net Income
Participating this conference after lecture@JILIN University is my pleasureUniversity is my pleasure.
I am appreciated forI am appreciated for
D W W bi d D D YDr. Wu Wenbin and Dr Dawen Yang
Institute of Agricultural Resources and Regional Planning,Chinese Academy of Agricultural Sciences, Beijing
By Matsumura 2011 Oct
Participating this conference after lecture@JILIN University is my pleasureUniversity is my pleasure.
I am appreciated forI am appreciated for
D W W bi d D D YDr. Wu Wenbin and Dr Dawen Yang
Institute of Agricultural Resources and Regional Planning,Chinese Academy of Agricultural Sciences, Beijing
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 マネジメント研究科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
客員研究員 W i M h i I i f P d客員研究員:Wangari Maathai Institute for Peace and Environmental Studies The University of Nairobi
客員准教授Visiting Associate Professor, Earth and Ocean Science, University of British Columbia (2012Apr)y p
Back GroundMumbai, India, 2010
By Matsumura 2010 Aug
By Matsumura 2011 Sep
Topics1.DATASETS USED
2 Relationships among temperature2.Relationships among temperature, precipitation, and fertilizer for major crop yield such as Maize, Rice, Soybean and Wheat
3 Future prospect for major crop yield3.Future prospect for major crop yield
4.CAIFA concept (Climate, Agriculture, Impacts, p ( , g , p ,Fertilizer, Adaptation)
FAO-STAT 国連食糧農業機関
Top5 Major crop producing country Top5 Major crop producing country Year 2009 Maize SoybeansP d ti (t )Year 2009 Maize SoybeansUSA 333,010,910 USA 91,417,300
Production (tonnes)
China 163,118,097 Brazil 56,960,732Brazil 51,232,447 Argentina 30,993,379Mexico 20,202,600 China 14,500,141Indonesia 17,629,740 India 10,217,000Indonesia 17,629,740 India 10,217,000
Rice, paddy WheatChina 197 257 175 China 114 950 296China 197,257,175 China 114,950,296India 131,274,000 India 80,680,000Indonesia 64,398,890 Russian Federation 61,739,750Bangladesh 45,075,000 USA 60,314,290Viet Nam 38,895,500 France 38,324,700
Monthly T t
Crop Land Vegetation TemperaturePrecipitation
pPaddy Field
gMosaic
Monthly Temperature & Precipitation Cropping On Cropland and Paddy Field
pp gCalendar
CountryCountry Boarder
Country Based Monthly Temperature & Precipitation On Cropland and Paddy Field
Country BasedCountry Based Yield & Fertilizer
Generated Database By Country
Monthly T t
Datasets provide by “CRU TS3.0”1961 January to 2009 December, Monthly Data
720 × 360 ResolutionTemperaturePrecipitation
label Variable unitscld cloud cover percentagedtr diurnal temperature range degrees Celsiusfrs frost day frequency daysfrs frost day frequency dayspre Precipitation millimeterstmp daily mean temperature degrees Celsiust thl d il d C l itmn monthly average daily
minimum temperaturedegrees Celsius
tmx monthly average daily degrees Celsiusmaximum temperature
vap vapour pressure hecta-Pascalswet wet day frequency Dayswet 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/
Monthly T t
Crop Land Vegetation TemperaturePrecipitation
pPaddy Field
gMosaic
GLCNMO, 2008, ©GSI Chiba University, Collaborating Organizations, In http://www.iscgm.org
解析方法解析方法
Climate Conditions on Crop Producing Areap g
Monthly T t
Crop Land Vegetation TemperaturePrecipitation
pPaddy Field
gMosaic
Monthly Temperature & Precipitation Cropping On Cropland and Paddy Field
pp gCalendar
CountryCountry Boarder
Country Based Monthly Temperature & Precipitation On Cropland and Paddy Field
Country BasedCountry Based Yield & Fertilizer
Generated Database By Country
Country Based Monthly Temperature & Precipitation On Cropland and Paddy Field FAOSTAT,2010,In
http://faostat.fao.org/site/567/
Country Based
default.aspx#ancor
Country Based Yield & Fertilizer
Generated Database By Country
Country Based Monthly Temperature & Precipitation On Cropland and Paddy Field FAOSTAT,2010,In
http://faostat.fao.org/site/567/
Country Based
default.aspx#ancor
Country Based Yield & Fertilizer
Generated Database By Country
Monthly T t
Crop Land Vegetation TemperaturePrecipitation
pPaddy Field
gMosaic
Cropping pp gCalendar
CountryCountry 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
Cropping Calendar, University of Wisconsin
栽培歴(米の収穫開始時期)栽培歴(米の収穫開始時期)
Cropping Calendar in IndiaCropping Calendar in India
Plant_Avg Month Harvest_Avg MonthMaize 172 6 324 11Rice 179 6 304 10Rice 179 6 304 10Soybean 182 6 308 10Wh 172 6 254 8Wheat 172 6 254 8
Y FY FFertilizer
T PTsum Psum
Generated Database By Country
Topics1.DATASETS USED
2 Relationships among temperature2.Relationships among temperature, precipitation, and fertilizer for major crop yield such as Maize, Rice, Soybean and Wheat
3 Future prospect for major crop yield3.Future prospect for major crop yield
4.CAIFA concept (Climate, Agriculture, Impacts, p ( , g , p ,Fertilizer, Adaptation)
Y FY FFertilizer
T PTsum Psum
Generated Database By Country
Yield in each country is explained by......Yield in each country is explained by......
CASEA
(Temperature, Precipitation)( p p )
CASEBCASEB
(T t P i it ti(Temperature, Precipitation , f tili )fertilizer)
Case A and Case B of Maize yield in USA
Actual, Case A and Case B of Soybean yield in USA
Actual, Case A and Case B of Wheat yield in USA
Actual, Case A and Case B of Rice yield in China
70,000
Fertilizer input and rice yield in ChinaFertilizer input and rice yield in China
60,000
50,000
40,000
ce(H
g/H
a)
30,000
Yie
ld o
f Ric
20,000
10,000
0
0 50 100 150 200 250 300 350 400 450 500
Fertilizer (Kg/Ha)
Global Soybean Yield : Case A&BGlobal Soybean Yield : Case A&BTable 1 Results of Multiple Regression Analysis: Case A
Constant Regression Coefficient (Temperature)
Regression Coefficient (Precipitation)
Multiple Correlation Coefficient
13 Brazil -274,029.2462 490.0380 3.5708 0.7037
t-value -5.9870 6.2121 1.1253
14 Chile -102,340.9609 1,746.3907 48.6952 0.1724
t-value -0 6246 0 9105 0 6285
Table 2 Results of Multiple Regression Analysis: Case B
Constant Regression Coefficient (Temperature)
Regression Coefficient (Precipitation) Regression Coefficient (Fertilizer) Multiple Correlation
Coefficient
13 Brazil -53,408.7291 112.3607 0.0210 75.0728 0.91143426
t-value -1.7510 2.1343 0.0129 9.2484
14 Chile 21,893.0791 -66.9644 32.2336 82.3271 0.926402115
t-value 0.3669 -0.0955 1.1349 15.0363 t-value -0.6246 0.9105 0.6285
15 Ecuador 18,130.7322 -32.8178 0.9737 0.2706
t-value 0.8583 -0.4630 1.7673
16 Paraguay -37,884.0323 81.7823 3.0066 0.3779
t-value -1.1099 1.3596 2.4085
17 Peru -32,724.9938 1,650.2488 -15.5178 0.3905
t-value -1.5121 2.5533 -1.1855
18 1 4 384 46 2 6830 4 0210 0 32 2
15 Ecuador -6,829.4975 43.0996 0.6811 13.6743 0.599063447
t-value -0.4831 0.9094 1.9203 3.1479
16 Paraguay 10,230.9934 -1.4764 1.2295 198.6756 0.687192458
t-value 0.3831 -0.0315 1.2266 4.9040
17 Peru -5,797.8262 643.6998 -13.1356 28.7266 0.637497917
t-value -0.3552 1.2933 -1.3828 4.3686
18 Uruguay 18,001.4423 -33.4248 0.8391 90.0260 0.79162310818 Uruguay -154,384.5465 255.6830 4.0210 0.3252
t-value -1.6109 1.6723 1.6645
19 Canada 53,679.0027 1,328.1467 318.3086 0.5317
t-value 4.1987 4.0639 2.2594
20 Guatemala -71,358.2413 259.2435 0.7145 0.5047
t-value -3.1298 3.7525 0.4846
21 Mexico -172,916.2538 669.8765 16.0370 0.6746
g y ,
t-value 0.3660 -0.4253 0.6595 6.6890
19 Canada 31,508.4236 458.9164 178.4005 131.0546 0.813722804
t-value 3.8250 2.0284 2.0150 7.1480
20 Guatemala -1,356.3159 40.2235 -0.6351 27.5754 0.772951103
t-value -0.0552 0.5341 -0.5368 5.3762
21 Mexico -86,431.7263 334.9184 9.6027 52.3990 0.8944216
t-value -4.3581 4.6710 2.4578 8.9839 t-value -5.3789 5.8472 2.2759
24 Dominican Republic 70,974.4002 -106.3652 0.3152 0.4864
t-value 4.5688 -3.5858 0.4656
25 Haiti 25,812.6641 -24.5915 -0.6392 0.4845
t-value 3.1287 -1.7985 -2.1648
29 Colombia -84,476.2842 255.2843 5.0145 0.4197
t value 2 0954 2 2949 2 7191
24 Dominican Republic 72,210.8963 -109.0459 0.3796 0.1248 0.486306755
t-value 4.3112 -3.3858 0.4990 0.0833
25 Haiti 15,165.3643 -7.6073 -0.3482 -48.6625 0.656746599
t-value 1.8964 -0.5726 -1.1729 -3.6078
29 Colombia 16,795.5634 -16.6030 -0.7430 15.0891 0.896058756
t-value 1.1544 -0.4136 -1.0766 11.9271 t-value -2.0954 2.2949 2.7191
31 Cuba -33,931.9344 67.9663 1.7597 0.2065
t-value -0.8083 1.0424 0.9368
32 El Salvador -97,037.7591 268.9645 -1.4351 0.5060
t-value -3.1122 3.7594 -0.4269
33 Honduras -30,698.8853 106.8013 -0.8713 0.6063
t-value -3.2865 4.8171 -1.1733
31 Cuba -15,037.2388 35.8998 3.2124 -1.2530 0.420222547
t-value -0.4917 0.7583 2.3398 -1.7221
32 El Salvador -67,521.8684 190.7505 0.1448 9.0920 0.438349496
t-value -2.2244 2.7429 0.0457 1.2342
33 Honduras -21,614.7797 84.5559 -1.1130 4.3079 0.633072265
t-value -2.1759 3.5639 -1.5222 1.9482
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 -3.7781 4.6266 1.2537
36 Puerto Rico -79,022.4147 154.3287 1.7554 0.1617
t-value -0.6577 0.7530 0.9901
45 Ghana -53,527.6385 111.6345 3.7078 0.3785
t-value -1.9498 2.2882 1.5095
47 Morocco 11,393.6002 -21.4534 0.8474 0.1274
t-value -3.4433 4.1401 1.8013 2.4740
36 Puerto Rico No Fertilizer Data
t-value
45 Ghana -25,954.3978 65.4180 2.0894 -25.5619 0.322901723
t-value -0.8513 1.2113 0.8200 -1.2962
47 Morocco 9,301.5888 -10.7045 0.9454 -7.0852 0.185393078
t-value 1.0466 -0.2754 0.5410 -0.4979
t-value 1.3570 -0.6418 0.5464
48 Portugal -164,950.3647 558.6887 -4.4567 0.5104
t-value -3.1988 3.8300 -1.4232
49 Spain -130,809.2550 643.9765 -18.4494 0.6622
t-value -3.1318 5.3708 -3.1213
52 Guinea -40,083.7137 170.5657 -2.0134 0.5731
t-value -3.2824 4.3073 -1.0979
48 Portugal -47,110.7576 114.6155 1.3881 73.3492 0.831890856
t-value -1.2564 1.0068 0.6666 6.6593
49 Spain -26,871.8590 104.2399 -0.3448 85.9495 0.931790535
t-value -1.1981 1.3443 -0.1062 10.0183
52 Guinea -29,040.8764 133.7282 -2.5790 47.5045 0.513945367
t-value -2.4050 3.4086 -1.4352 0.9948
Global Soybean Yield : Case AGlobal Soybean Yield : Case A
Temp(+) & Yield (‐)
Ecuador Dominican Republic HaitiEcuador Dominican Republic HaitiMorocco Iraq Russia CameroonChad Croatia Serbia & MontenegroG i G C h R bliGeorgia Greece Czech RepublicSlovakia Belarus Romania UkraineSlovakia Belarus Romania UkraineYemen Botswana ZimbabweNamibia
Global Soybean Yield : Case AGlobal Soybean Yield : Case A
Precip(+) & Yield (‐) Peru Haiti El Salvador Honduras Portugal SpainSalvador Honduras Portugal SpainGuinea Mali Senegal Ethiopia Uganda Iraq Israel Central African Republic Albania Croatia Italy Georgia Greece Turkey AustriaCroatia Italy Georgia Greece Turkey Austria Hungary Poland Belgium France Germany g y g yNetherlands Switzerland Romania Somalia k d b l hurkmenistan Saudi Arabia Nepal China
South Korea Cambodia Vietnam ZimbabweSouth Korea Cambodia Vietnam Zimbabwe New Zealand
Topics1.DATASETS USED
2 Relationships among temperature2.Relationships among temperature, precipitation, and fertilizer for major crop yield such as Maize, Rice, Soybean and Wheat
3 Future prospect for soybean yield3.Future prospect for soybean yield
4.CAIFA concept (Climate, Agriculture, Impacts, p ( , g , p ,Fertilizer, Adaptation)
economic development
A2
Back GroundSRES concept
• rapid economic growth
A1b A2
• low economic growth• low population growth• efficient technology
• high population growth• low technological change
global local
B1 B2
• sustainable development• high economic growth• low population growth
• low economic growth• medium population growth
slow technological changelow population growth • slow technological change
environmental protection
A1b Scenario
Source: CIESIN, Columbia University
http://beta.ciesin.columbia.edu/datasets/dol d/wnscaled/
A2 Scenario
Source: CIESIN, Columbia University
http://beta.ciesin.columbia.edu/datasets/dol d/wnscaled/
B1 Scenario
Source: CIESIN, Columbia University
http://beta.ciesin.columbia.edu/datasets/dol d/wnscaled/
B2 Scenario
Source: CIESIN, Columbia University
http://beta.ciesin.columbia.edu/datasets/dol d/wnscaled/
Future Prospect for crop yield in ChinaFuture Prospect for crop yield in China
The GCM output’s average from 1971 to 2000 i l l t d d i d i 0 52000 is calculated and imposed in 0.5 degree spatial dataset. g p
The GCM outputs based on SRES scenarios in 2010 2020 2030 2040 and 2050 arein 2010, 2020, 2030, 2040 and 2050 are obtained and imposed in 0.5 degree spatial dataset.
Datasets are provided by Kenji Sugimoto(2011)
Future Prospect for crop yield in ChinaFuture Prospect for crop yield in China70,000
60,000
Maize_Yield_Calculated
Maize_Yield_Actual
50,000
40,000
Hg/Ha
30,000
Unit:H
20,000
10,000
0
1961
19
62
1963
19
64
1965
19
66
1967
19
68
1969
19
70
1971
19
72
1973
19
74
1975
19
76
1977
19
78
1979
19
80
1981
19
82
1983
19
84
1985
19
86
1987
19
88
1989
19
90
1991
19
92
1993
19
94
1995
19
96
1997
19
98
1999
20
00
2001
20
02
2003
20
04
2005
20
06
2007
20
08
Future Prospect for crop yield in ChinaFuture Prospect for crop yield in China200,000
180,000
M i Yi ld C l l d M i Yi ld A l
140,000
160,000 Maize_Yield_Calculated Maize_Yield_Actual
120,000
Ha
80,000
100,000
Unit:Hg/H
60,000
20,000
40,000
0
1961 1970 1980 1990 2000 2010 2020 2030 2040 2050
Future Prospect for crop yield in ChinaFuture Prospect for crop yield in China80,000
70,000
80,000
Rice_Yield_Calculated
Rice_Yield_Actual
60,000
40 000
50,000
Hg/
Ha
30,000
40,000
Uni
t:H
20,000
10,000
0
1961
19
62
1963
19
64
1965
19
66
1967
19
68
1969
19
70
1971
19
72
1973
19
74
1975
19
76
1977
19
78
1979
19
80
1981
19
82
1983
19
84
1985
19
86
1987
19
88
1989
19
90
1991
19
92
1993
19
94
1995
19
96
1997
19
98
1999
20
00
2001
20
02
2003
20
04
2005
20
06
2007
20
08
Future Prospect for crop yield in ChinaFuture Prospect for crop yield in China250,000
Rice_Yield_Calculated
Rice_Yield_Actual
200,000
150,000
Ha
100,000
Unit:Hg/H
50,000
0
1961 1970 1980 1990 2000 2010 2020 2030 2040 2050
Future Prospect for crop yield in ChinaFuture Prospect for crop yield in China20,000
18,000
Soybean_Yield_Calculated
14,000
16,000 Soybean_Yield_Actual
10 000
12,000
Hg/Ha
8,000
10,000
Unit:H
4,000
6,000
2,000
0
1961
19
62
1963
19
64
1965
19
66
1967
19
68
1969
19
70
1971
19
72
1973
19
74
1975
19
76
1977
19
78
1979
19
80
1981
19
82
1983
19
84
1985
19
86
1987
19
88
1989
19
90
1991
19
92
1993
19
94
1995
19
96
1997
19
98
1999
20
00
2001
20
02
2003
20
04
2005
20
06
2007
20
08
Future Prospect for crop yield in ChinaFuture Prospect for crop yield in China20,000
18,000
Soybean_Yield_Calculated
Soybean_Yield_Actual
14,000
16,000
12,000
Ha
8,000
10,000
Unit:Hg/H
6,000
2,000
4,000
0
1961 1970 1980 1990 2000 2010 2020 2030 2040 2050
Future Prospect for crop yield in ChinaFuture Prospect for crop yield in China70,000
60,000
Maize_Yield_Calculated
Maize_Yield_Actual
50,000
40,000
Hg/
Ha
30,000
Uni
t:H
20,000
10,000
0
1961
19
62
1963
19
64
1965
19
66
1967
19
68
1969
19
70
1971
19
72
1973
19
74
1975
19
76
1977
19
78
1979
19
80
1981
19
82
1983
19
84
1985
19
86
1987
19
88
1989
19
90
1991
19
92
1993
19
94
1995
19
96
1997
19
98
1999
20
00
2001
20
02
2003
20
04
2005
20
06
2007
20
08
Future Prospect for crop yield in ChinaFuture Prospect for crop yield in China180,000
160,000
Wheat_Yield_Calculated
Wheat_Yield_Actual
140,000
100,000
120,000
Ha
80,000 Unit:Hg/H
40 000
60,000
20,000
40,000
0
1961 1970 1980 1990 2000 2010 2020 2030 2040 2050
Topics1.Back Ground and Advantage of using Geographical Information SystemGeographical Information System
2.DATASETS USED
3.Relationships among temperature, precipitation and fertilizer for major cropprecipitation, 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)
Future work
Future work
linear to Non linear regression Analysis linear to Non linear regression Analysis
Earth and Ocean ScienceTh U i i f B i i h C l biThe University of British Columbia
ConclusionRelationships among temperature, precipitation, and fertilizer for major cropprecipitation, and fertilizer for major crop yield ( Maize, Rice, Soybean, Wheat) were l l t dcalculated.
Future prospect for major crop yield is obtained
If yield information at targeted area can be obtained, relationships with temperature andobtained, relationships with temperature and precipitation can be obtained.
謝謝謝謝謝謝謝謝謝謝謝謝
Top Related