Kan Ichiro Matsumura — Climate conditions and their impacts on soybean crop yield

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.




By Matsumura 2011 Oct

Participating this conference after lecture@JILIN University is my pleasureUniversity is my pleasure.




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


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


pPaddy Field

gMosaic

Monthly Temperature & Precipitation Cropping On Cropland and Paddy Field

pp gCalendar


Country Based Monthly Temperature & Precipitation On Cropland and Paddy Field

Country BasedCountry Based Yield & Fertilizer


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


Monthly T t


pPaddy Field

gMosaic

Cropping pp gCalendar


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




3 Future prospect for major crop yield3.Future prospect for major crop yield


Y FY FFertilizer

T PTsum Psum


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



3 Future prospect for soybean yield3.Future prospect for soybean yield


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



B1 Scenario



B2 Scenario



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


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


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


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


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


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


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


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

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

Kan Ichiro Matsumura — Climate conditions and their impacts on soybean crop yield

Documents

Transcript of Kan Ichiro Matsumura — Climate conditions and their impacts on soybean crop yield