Korean Journal of Agricultural and Forest Meteorology (한국농림기상학회지)

Korean Society of Agricultural and Forest Meteorology (한국농림기상학회)
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Application of a Climate Suitability Model to Assess Spatial Variability in Acreage and Yield of Wheat in Ukraine

우크라이나 밀 재배 면적 및 수량의 공간적 변이 평가를 위한 기후적합도 모델의 활용

  • Jin Yeong Oh (Department of Plant Science, Seoul National University) ;
  • Shinwoo Hyun (Department of Agriculture, Forestry and Bioresources, Seoul National University) ;
  • Seungmin Hyun (Department of Agriculture, Forestry and Bioresources, Seoul National University) ;
  • Kwang Soo Kim (Department of Plant Science, Seoul National University)
  • 오진영 (서울대학교 식물생산과학부) ;
  • 현신우 (서울대학교 농림생물자원학부) ;
  • 현승민 (서울대학교 농림생물자원학부) ;
  • 김광수 (서울대학교 식물생산과학부)
  • Received : 2023.11.08
  • Accepted : 2024.03.20
  • Published : 2024.03.30
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Abstract

It would be advantageous to predict acreage and yield of crops in major grain-exporting countries, which would improve decisions on policy making and grain trade in Korea. A climate suitability model can be used to assess crop acreage and yield in a region where the availability of observation data is limited for the use of process-based crop models. The objective of this study was to determine the climate suitability index of wheat by province in Ukraine, which would allow for the spatial assessment of acreage and yield for the given crop. In the present study, the official data of wheat acreage and yield were collected from the State Statistics Service of Ukraine. The EarthStat data, which is a data product derived from satellite data and official crop reports, were also gathered for the comparison with the map of climate suitability index. The Fuzzy Union model was used to create the climate suitability maps under the historical climate conditions for the period from 1970 to 2000. These maps were compared against actual acreage and yield by province. It was found that the EarthStat data for acreage and yield of wheat differed from the corresponding official data in several provinces. On the other hand, the climate suitability index obtained using the Fuzzy Union model explained the variation in acreage and yield at a reasonable degree. For example, the correlation coefficient between the climate suitability index and yield was 0.647. Our results suggested that the climate suitability index could be used to indicate the spatial distribution of acreage and yield within a region of interest.

우리나라는 수요 곡물의 대부분을 수입으로 의존하기 때문에 주요 곡물 수출 국가의 재배 면적과 생산량 예측을 통해 식량안보를 증진시킬 수 있다. 특히, 밀 주요 수출국인 우크라이나를 대상으로 재배지역의 변화 전망을 파악하는 것이 장기적인 밀 수급에 대한 미래 정책 결정에 도움을 줄 수 있다. 본 연구에서는 작물의 기후적합도를 예측하는 Fuzzy Union 모델을 사용하여 과거 기후조건(1970~2000)에서 우크라이나 지역의 밀의 기후적합도를 평가하고자 하였다. 우크라이나 통계청으로부터 밀 생산량과 재배면적 통계자료를 수집하였다. 또한, 위성영상을 활용하여 작물의 재배면적과 수량에 대한 공간자료인 EarthStat 자료를 수집하였다. 모델로 계산된 기후적합도와 밀 관측지점과 비교하여 임계값을 설정하였다. EarthStat 자료와 실제 관측자료를 비교한 결과 일정 지역에서 재배면적과 수량이 일치하지 않는 지역들이 존재하였다. 과거 기후 조건에서 산출된 기후적합도의 경우에도 지역적인 차이를 보였다. 예를 들어, 우크라이나의 서북부 지역에서 0.8 이상의 높은 기후적합도의 분포를 보였으나 동남부 지역에서는 0.15 수준의 낮은 기후적합도를 보였다. 그러나, 기후적합도의 행정구역별 통계량과 실제 밀 재배면적과 생산량을 비교한 결과 일정 수준의 상관관계를 가졌다. 특히, 단위면적당 생산량과 기후적합도의 상관계수는 0.647로 중위 정상관을 나타냈다. 이러한 결과는 기후적합도를 활용하여 재배면적 추정 및 단위면적당 수량 예측이 가능함을 시사하였다.

Keywords

Acknowledgement

본 연구는 농업 빅데이터 및 활용모델 통합 연계체계 개발(과제번호: RS-2022-RD010426)의 지원에 의해 수행되었습니다.

References

  1. Ajadi, O. A., J. Barr, S. Z. Liang, R. Ferreira, S. P. Kumpatla, R. Patel, and A. Swatantran, 2021: Large-scale crop type and crop area mapping across Brazil using synthetic aperture radar and optical imagery. International Journal of Applied Earth Observation and Geoinformation 97, 102294. DOI: 10.1016/j.jag.2020.102294
  2. Ban, H. Y., K. S. Kim, N. W. Park, and B. W. Lee, 2016: Using MODIS data to predict regional corn yields. Remote Sensing 9(1), 16. DOI: 10.3390/rs9010016
  3. Bhadra, M., M. J. Gul, and G. S. Choi, 2023: Implications of war on the food, beverage, and tobacco industry in South Korea. Humanities and Social Sciences Communications 10(1), 1-8. DOI: 10.1057/s41599-023-01659-1
  4. CLIP (Customs Law Information Portal), 2023: https://unipass.customs.go.kr/ets/index.do/, accessed on 2023.07.07.
  5. Cord, A. F., D. Klein, D. S. Gernandt, J. A. P. de la Rosa, and S. Dech, 2014: Remote sensing data can improve predictions of species richness by stacked species distribution models: a case study for Mexican pines. Journal of biogeography 41(4), 736-748. DOI: 10.1111/jbi.12225
  6. Dwivedi, A. K., S. Roy and D. Singh, 2020: An adaptive neuro-fuzzy approach for decomposition of mixed pixels to improve crop area estimation using satellite images. In IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium, IEEE. 4191-4194. DOI: 10.1109/IGARSS39084.2020.9323128
  7. Erenstein, O., M. Jaleta, K. A. Mottaleb, K. Sonder, J. Donovan and H.-J. Braun, 2022: Global trends in wheat production, consumption and trade. Wheat improvement: food security in a changing climate. Springer International Publishing Cham, 47-66. DOI: 10.1007/978-3-030-90673-3_4
  8. FAOSTAT, 2023: https://www.fao.org/faostat/en/#home/, accessed on 2023.07.07.
  9. Fick, S. E., and R. J. Hijmans, 2017: WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. International journal of climatology 37(12), 4302-4315. DOI: 10.1002/joc.5086
  10. Friedl, M. A., D. K. McIver, J. C. Hodges, X. Y. Zhang, D. Muchoney, A. H. Strahler, C. E. Woodcock, S. Gopal, A. Schneider, A. Cooper, A. Baccini, F. Gao, and C. Schaaf, 2002: Global land cover mapping from MODIS: algorithms and early results. Remote sensing of Environment 83(1-2), 287-302. DOI: 10.1016/S0034-4257(02)00078-0
  11. GBIF.org, 2023: https://doi.org/10.15468/dl.aaf4m9, accessed on 2023-07-11.
  12. Gonzalez-Alonso, F., J. M. Cuevas, R. Arbiol, and X. Baulies, 1997: Remote sensing and agricultural statistics: crop area estimation in north-eastern Spain through diachronic Landsat TM and ground sample data. International Journal of Remote Sensing 18(2), 467-470. DOI: 10.1080/014311697219213
  13. Hunter, R., and O. Crespo, 2019: Large scale crop suitability assessment under future climate using the Ecocrop model: the case of six provinces in Angola's Planalto region. The Climate-Smart Agriculture Papers: Investigating the Business of a Productive, Resilient and Low Emission Future, 39-48. DOI: 10.1007/978-3-319-92798-5_4
  14. Hyun, S. and K. S. Kim, 2018: Evaluation of fuzzy logic systems to assess climate suitability of italian ryegrass. Climate Smart Agriculture for the Small-Scale Farmers in the Asian and Pacific Region, 157.
  15. Jeong, W. C., and S. B. Kim, 2020: Utilization of UAV and GIS for Efficient Agricultural Area Surve. Journal of Convergence for Information Technology 10(12), 201-207. DOI: 10.22156/CS4SMB.2020.10.12.201
  16. Joshi, A., B. Pradhan, S. Gite, and S. Chakraborty, 2023: Remote-Sensing Data and Deep-Learning Techniques in Crop Mapping and Yield Prediction: A Systematic Review. Remote Sensing 15(8), 2014. DOI: 10.3390/rs15082014
  17. Kang, M., S. Hyun, and K. S. Kim, 2022: Spatial Assessment of Climate Suitability for Summer Cultivation of Potato in North Korea. Korean Journal of Agricultural and Forest Meteorology 24(1), 35-47. DOI: 10.5532/KJAFM.2022.24.1.35
  18. Khanal, S., K. Kc, J. P. Fulton, S. Shearer, and E. Ozkan, 2020: Remote sensing in agriculture-accomplishments, limitations, and opportunities. Remote Sensing 12(22), 3783. DOI: 10.3390/rs12223783
  19. Kim, H., S. W. Hyun, G. Hoogenboom, C. H. Porter, and K. S. Kim, 2018: Fuzzy union to assess climate suitability of annual ryegrass (Lolium multiflorum), alfalfa (Medicago sativa) and sorghum (Sorghum bicolor). Scientific reports 8(1), 10220. DOI: 10.1038/s41598-018-28291-3
  20. Kim, H., S. Hyun, and K. S. Kim, 2014: A study on the prediction of suitability change of forage crop Italian Ryegrass (Lolium multiflorum L.) using spatial distribution model. Korean Journal of Agricultural and Forest Meteorology 16(2), 103-113. DOI: 10.5532/KJAFM.2014.16.2.103
  21. Kim, Y. H., and B. O. Lee, 2002: Cultivator's crop selection and uncertainty. Journal of Social Science 41, 41-54.
  22. Kim, Y. S., H. Y. Yoo, N. W. Park, and K. D. Lee, 2015: Classification of crop cultivation areas using active learning and temporal contextual information. Journal of the Korean Association of Geographic Information Studies 18(3), 76-88. DOI: 10.11108/kagis.2015.18.3.076
  23. Lee, J. H., 2019: Flowering-time Genes and Flowering-time Pathways in Wheat (Triticum aestivum L.). Korean Society of Breeding Science 51(2), 65-72. DOI: 10.9787/KJBS.2019.51.2.65
  24. Lee, K. D., C. W. Park, S. I. Na, M. P. Jung, and J. Kim, 2017: Monitoring on crop condition using remote sensing and model. Korean Journal of Remote Sensing 33(5_2), 617-620. DOI: 10.7780/kjrs.2017.33.5.2.1
  25. Lee, Y., J. J. Kim, S. H. Kim, and J. H. Choi, 2022: Analysis of the Price Impact and Supply Shortage Effects of Imported Grains. Journal of Rural Development/Nongchon-Gyeongje 45(2), 1-20. DOI: 10.36464/jrd.2022.45.2.001
  26. Li, Z., H. Shen, Q. Cheng, W. Li, and L. Zhang, 2019: Thick cloud removal in high-resolution satellite images using stepwise radiometric adjustment and residual correction. Remote Sensing 11(16), 1925. DOI: 10.3390/rs11161925
  27. Liu, Y., J. Kim, D. H. Fleisher, and K. S. Kim, 2021: Analogy-Based Crop Yield Forecasts Based on Temporal Similarity of Leaf Area Index. Remote Sensing 13(16), 3069. DOI: 10.3390/rs13163069
  28. Meraj, G., S. Kanga, A. Ambadkar, P. Kumar, S. K. Singh, M. Farooq, B. A. Johnson, A. Rai, and N. Sahu, 2022: Assessing the yield of wheat using satellite remote sensing-based machine learning algorithms and simulation modeling. Remote Sensing 14(13), 3005. DOI: 10.3390/rs14133005
  29. Ministry of Agriculture, Food and Rural Affairs, 2023: Yearly Grain Self-Sufficiency Rate (up to the 2021 grain year), Food Policy Division.
  30. Monfreda, C., N. Ramankutty, and J. A. Foley, 2008: Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Global biogeochemical cycles 22(1). DOI: 10.1029/2007GB002947
  31. Muller, D., A. Jungandreas, F. Koch, and F. Schierhorn, 2016: Impact of climate change on wheat production in Ukraine. Kyiv: Institute for Economic Research and Policy Consulting 41.
  32. Phillips, S. J., R. P. Anderson, and R. E. Schapire, 2006: Maximum entropy modeling of species geographic distributions. Ecological modelling 190(3-4), 231-259. DOI: 10.1016/j.ecolmodel.2005.03.026
  33. Potgieter, A. B., K. Lawson, and A. R. Huete, 2013: Determining crop acreage estimates for specific winter crops using shape attributes from sequential MODIS imagery. International Journal of Applied Earth Observation and Geoinformation 23, 254-263. DOI: 10.1016/j.jag.2012.09.009
  34. Pozniak, S., 2019: Chernozems of Ukraine: past, present and future perspectives. Soil Science Annual 70(3). DOI: 10.2478/ssa-2019-0017
  35. Prudente, V. H. R., V. S. Martins, D. C. Vieira, N. R. D. F. e Silva, M. Adami, and I. D. A. Sanches, 2020: Limitations of cloud cover for optical remote sensing of agricultural areas across South America. Remote Sensing Applications: Society and Environment 20, 100414. DOI: 10.1016/j.rsase.2020.100414
  36. Ramankutty, N., A. T. Evan, C. Monfreda, and J. A. Foley, 2008: Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000. Global biogeochemical cycles 22(1). DOI: 10.1029/2007GB002952
  37. Ramirez-Villegas, J., A. Jarvis, and P. Laderach, 2013: Empirical approaches for assessing impacts of climate change on agriculture: The EcoCrop model and a case study with grain sorghum. Agricultural and Forest Meteorology 170, 67-78. DOI: 10.1016/j.agrformet.2011.09.005
  38. Som-Ard, J., C. Atzberger, E. Izquierdo-Verdiguier, F. Vuolo, and M. Immitzer, 2021: Remote sensing applications in sugarcane cultivation: A review. Remote sensing 13(20), 4040. DOI: 10.3390/rs13204040
  39. Son, M. B., J. H. Chung, Y. G., Lee, and S. J. Kim, 2021: A comparative analysis of vegetation and agricultural monitoring of Terra MODIS and Sentinel-2 NDVIs. Journal of The Korean Society of Agricultural Engineers 63(6), 101-115. DOI: 10.5389/KSAE.2021.63.6.101
  40. Song, X. P., P. V. Potapov, A. Krylov, L. King, C. M. Di Bella, A. Hudson, A. Hudson, A. Khan, B. Adusei, S. V. stehman and M. C. Hansen, 2017: National-scale soybean mapping and area estimation in the United States using medium resolution satellite imagery and field survey. Remote sensing of environment 190, 383-395. DOI: 10.1016/j.rse.2017.01.008
  41. Udgata, A. R., P. M. Sahoo, T. Ahmad, A. Rai, and G. Krishna, 2020: Remote Sensing and Machine Learning techniques for acreage estimation of mango (Mangifera indica). Indian Journal of Agricultural Sciences 90(3), 551-555. DOI: 10.56093/ijas.v90i3.101473
  42. Vila-Vicosa, C., S. Arenas-Castro, B. Marcos, J. Honrado, C. Garcia, F. M. Vazquez, R. Almeida, and J. Goncalves, 2020: Combining satellite remote sensing and climate data in species distribution models to improve the conservation of iberian white oaks (Quercus L.). ISPRS International Journal of Geo-Information 9(12), 735. DOI: 10.3390/ijgi9120735
  43. Vyshkvarkova, E., and E. Voskresenskaya, 2012: Precipitation inequality over Ukraine. Methodology 2013. DOI: 10.9734/JSRR/2014/5711
  44. Whitcraft, A. K., E. F. Vermote, I. Becker-Reshef, and C. O. Justice, 2015: Cloud cover throughout the agricultural growing season: Impacts on passive optical earth observations. Remote sensing of Environment 156, 438-447. DOI: 10.1016/j.rse.2014.10.009
  45. Yoo, S. H., S. H. Lee, J. Y. Choi, and J. B. Im, 2016: Estimation of potential water requirements using water footprint for the target of food self-sufficiency in South Korea. Paddy and water environment 14, 259-269. DOI: 10.1007/s10333-015-0495-x
  46. Yoon, J. S., E. K. Lee, Y. S. Park, and S. R. Kim, 2014: A Study on Economy of Agricultural Investment Business to the Ukraine. Journal of Agricultural Extension and Community Development 21(1), 117-142. DOI: 10.12653/jecd.2014.21.1.0117
  47. Zhang, H., Q. Li, J. Liu, J. Shang, X. Du, L. Zhao, N. Wang, and T. Dong, 2017: Crop classification and acreage estimation in North Korea using phenology features. GIScience and Remote Sensing 54(3), 381-406. DOI: 10.1080/15481603.2016.