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

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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.

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

  1. 1. Climate conditions and their impacts on soybean crop yieldKan-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
  2. 2. Participating this conference after lecture@JILINUniversity is my pleasure.University is my pleasureI am appreciated forI am appreciated forDr. Wu Wenbin  and Dr Dawen Yang D W W bi dD D YInstitute of Agricultural Resources and Regional Planning,Chinese Academy of Agricultural Sciences, Beijing
  3. 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 tiliInstitute of Agricultural Resources and Regional Planning,Chinese Academy of Agricultural Sciences, Beijing 50,000元 /Net Income
  4. 4. Participating this conference after lecture@JILIN University is my pleasure.University is my pleasureI am appreciated forI am appreciated forDr. Wu Wenbin  and Dr Dawen Yang D W W bi dD D YInstitute of Agricultural Resources and Regional Planning,Chinese Academy of Agricultural Sciences, Beijing By Matsumura 2011 Oct
  5. 5. Participating this conference after lecture@JILIN University is my pleasure.University is my pleasureI am appreciated forI am appreciated forDr. Wu Wenbin  and Dr Dawen Yang D W W bi dD D YInstitute of Agricultural Resources and Regional Planning,Chinese Academy of Agricultural Sciences, Beijing
  6. 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 dEnvironmental Studies The University of Nairobi客員准教授Visiting Associate Professor, Earth and Ocean  y pScience, University of British Columbia (2012Apr)
  7. 7. Back GroundMumbai, India, 2010 By Matsumura 2010 Aug
  8. 8. By Matsumura 2011 Sep
  9. 9. Topics1.DATASETS USED2.Relationships among temperature, 2 Relationships among temperature precipitation, and fertilizer for major crop  yield such as Maize, Rice, Soybean and Wheat3.Future prospect for major crop yield3 Future prospect for major crop yield4.CAIFA concept (Climate, Agriculture, Impacts,  p ( , g , p , Fertilizer, Adaptation)
  10. 10. FAO-STAT 国連食糧農業機関
  11. 11. Top5 Major crop producing countryYear 2009 M aize Production (t P d ti (tonnes) ) SoybeansUSA 333,010,910 USA 91,417,300China 163,118,097 Brazil 56,960,732Brazil 51,232,447 Argentina 30,993,379M exico 20,202,600 China 14,500,141Indonesia 17,629,740 India 10,217,000 Rice, paddy WheatChina 197,257,175 197 257 175 China 114,950,296 114 950 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
  12. 12. Monthly  Crop Land p Vegetation  gTemperatureT t Paddy Field MosaicPrecipitation 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. 13. Datasets provide by “CRU TS3.0” 1961 January to 2009 December, Monthly Data Monthly  720 × 360 ResolutionTemperatureT tPrecipitation 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. 14. Monthly  Crop Land p Vegetation  gTemperatureT t Paddy Field MosaicPrecipitation GLCNMO, 2008, ©GSI Chiba University, Collaborating Organizations, In http://www.iscgm.org
  15. 15. 解析方法 Climate Conditions on Crop Producing Area p g
  16. 16. Monthly  Crop Land p Vegetation  gTemperatureT t Paddy Field MosaicPrecipitation 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. 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. 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. 19. Monthly  Crop Land p Vegetation  gTemperatureT t Paddy Field MosaicPrecipitation Cropping   pp g Calendar Country   Country BoarderCropping Calendar, 2010, University ofWisconsin, Inhttp://www.sage.wisc.edu/download/sacks/http://www sage wisc edu/download/sacks/crop_calendar.html
  20. 20. Cropping Calendar, University of Wisconsin 栽培歴(米の収穫開始時期)
  21. 21. Cropping Calendar in India Plant_Avg M onth Harvest_Avg M onthM aize 172 6 324 11Rice 179 6 304 10Soybean 182 6 308 10WheatWh 172 6 254 8
  22. 22. Y F Fertilizer Tsum PsumGenerated Database By Country
  23. 23. Topics1.DATASETS USED2.Relationships among temperature, 2 Relationships among temperature precipitation, and fertilizer for major crop  yield such as Maize, Rice, Soybean and Wheat3.Future prospect for major crop yield3 Future prospect for major crop yield4.CAIFA concept (Climate, Agriculture, Impacts,  p ( , g , p , Fertilizer, Adaptation)
  24. 24. Y F Fertilizer Tsum PsumGenerated Database By Country
  25. 25. Yield in each country is explained by......CASEA((Temperature, Precipitation) p p )CASEB(Temperature, Precipitation , (T t P i it tifertilizer)f tili )
  26. 26. Case A and Case B of Maize yield in USA
  27. 27. Actual, Case A and Case B of Soybean yield in USA
  28. 28. Actual, Case A and Case B of Wheat yield in USA
  29. 29. Actual, Case A and Case B of Rice yield in China
  30. 30. Fertilizer input and rice yield in China 70,000 60,000 50,000 ce(Hg/Ha) 40,000Yield of Ric 30,000 20,000 10,000 0 0 50 100 150 200 250 300 350 400 450 500 Fertilizer (Kg/Ha)
  31. 31. Global Soybean Yield : Case A&BTable 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. 32. Global Soybean Yield : Case ATemp(+) & Yield (‐) Ecuador Dominican Republic Haiti Dominican RepublicMorocco Iraq Russia CameroonChad Croatia Serbia & MontenegroGeorgia GG i Greece C h R Czech Republic bliSlovakia Belarus Romania UkraineYemen Botswana ZimbabweNamibia
  33. 33. Global Soybean Yield : Case APrecip(+) & Yield (‐) Peru Haiti El Salvador Honduras Portugal SpainGuinea Mali Senegal Ethiopia Uganda Iraq Israel Central African Republic Albania  Croatia Italy Georgia Greece Turkey Austria Croatia Italy Georgia Greece Turkey AustriaHungary Poland Belgium France Germany  g y g yNetherlands Switzerland Romania Somalia urkmenistan Saudi Arabia Nepal China  k d b l hSouth Korea Cambodia Vietnam Zimbabwe South Korea Cambodia Vietnam ZimbabweNew Zealand 
  34. 34. Topics1.DATASETS USED2.Relationships among temperature, 2 Relationships among temperature precipitation, and fertilizer for major crop  yield such as Maize, Rice, Soybean and Wheat3.Future prospect for soybean yield3 Future prospect for soybean yield4.CAIFA concept (Climate, Agriculture, Impacts,  p ( , g , p , Fertilizer, Adaptation)
  35. 35. Back Ground economic developmentSRES concept A1b A2 • rapid economic growth • low economic growth • low population growth • high population growth • efficient technology • low technological changeglobal local B1 B2 • sustainable development • low economic growth • high economic growth • medium population growth • low population growth • slow technological change environmental protection
  36. 36. A1b Scenario Source: CIESIN, Columbia University http://beta.ciesin.columbia.edu/datasets/do wnscaled/ l d/
  37. 37. A2 Scenario Source: CIESIN, Columbia University http://beta.ciesin.columbia.edu/datasets/do wnscaled/ l d/
  38. 38. B1 Scenario Source: CIESIN, Columbia University http://beta.ciesin.columbia.edu/datasets/do wnscaled/ l d/
  39. 39. B2 Scenario Source: CIESIN, Columbia University http://beta.ciesin.columbia.edu/datasets/do wnscaled/ l d/
  40. 40. Future Prospect for crop yield in ChinaThe GCM output’s average from 1971 to 2000 is calculated and imposed in 0.5 2000 i l l t d di di 05degree spatial dataset.  g pThe GCM outputs based on SRES scenarios in 2010, 2020, 2030, 2040 and 2050 are in 2010 2020 2030 2040 and 2050 areobtained and imposed in 0.5 degree spatial dataset. Datasets are provided by Kenji Sugimoto(2011)
  41. 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_Actual1970  Maize_Yield_Calculated1971 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. 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  HaUnit:Hg/H 100,000  80,000  60,000  40,000  20,000  0  1961  1970  1980  1990  2000  2010  2020  2030  2040  2050 
  43. 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_Actual1972  Rice_Yield_Calculated1973 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. 44. Future Prospect for crop yield in China 250,000  Rice_Yield_Calculated Rice_Yield_Actual 200,000  150,000  HaUnit:Hg/H 100,000  50,000  0  1961  1970  1980  1990  2000  2010  2020  2030  2040  2050 
  45. 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_Actual1972 1973  Soybean_Yield_Calculated1974 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 China2008 
  46. 46. Future Prospect for crop yield in China 20,000  Soybean_Yield_Calculated Soybean_Yield_Actual 18,000  16,000  14,000  12,000  HaUnit:Hg/H 10,000  8,000  6,000  4,000  2,000  0  1961  1970  1980  1990  2000  2010  2020  2030  2040  2050 
  47. 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_Actual1971  Maize_Yield_Calculated1972 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. 48. Future Prospect for crop yield in China 180,000  Wheat_Yield_Calculated Wheat_Yield_Actual 160,000  140,000  120,000  100,000  HaUnit:Hg/H 80,000  60,000  40,000  40 000 20,000  0  1961  1970  1980  1990  2000  2010  2020  2030  2040  2050 
  49. 49. Topics1.Back Ground and Advantage of using  Geographical Information System Geographical Information System2.DATASETS USED3.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 yield5.CAIFA concept (Climate, Agriculture, Impacts,  Fertilizer, Adaptation) Fertilizer, Adaptation)
  50. 50. Future work
  51. 51. Future work
  52. 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. 53. ConclusionRelationships among temperature,  precipitation, and fertilizer for major crop  precipitation, and fertilizer for major crop yield ( Maize, Rice, Soybean, Wheat) were  calculated. l l t dFuture prospect for major crop yield is obtainedIf yield information at targeted area can be  obtained,  relationships with temperature and  obtained, relationships with temperature and precipitation can be obtained.
  54. 54. 謝謝

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