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International Journal of Biometeorology

, Volume 56, Issue 4, pp 749–763 | Cite as

Investigating the impact of climate change on crop phenological events in Europe with a phenology model

  • Shaoxiu Ma
  • Galina Churkina
  • Kristina Trusilova
Original Paper

Abstract

Predicting regional and global carbon and water dynamics requires a realistic representation of vegetation phenology. Vegetation models including cropland models exist (e.g. LPJmL, Daycent, SIBcrop, ORCHIDEE-STICS, PIXGRO) but they have various limitations in predicting cropland phenological events and their responses to climate change. Here, we investigate how leaf onset and offset days of major European croplands responded to changes in climate from 1971 to 2000 using a newly developed phenological model, which solely relies on climate data. Net ecosystem exchange (NEE) data measured with eddy covariance technique at seven sites in Europe were used to adjust model parameters for wheat, barley, and rapeseed. Observational data from the International Phenology Gardens were used to corroborate modeled phenological responses to changes in climate. Enhanced vegetation index (EVI) and a crop calendar were explored as alternative predictors of leaf onset and harvest days, respectively, over a large spatial scale. In each spatial model simulation, we assumed that all European croplands were covered by only one crop type. Given this assumption, the model estimated that the leaf onset days for wheat, barley, and rapeseed in Germany advanced by 1.6, 3.4, and 3.4 days per decade, respectively, during 1961–2000. The majority of European croplands (71.4%) had an advanced mean leaf onset day for wheat, barley, and rapeseed (7.0% significant), whereas 28.6% of European croplands had a delayed leaf onset day (0.9% significant) during 1971–2000. The trend of advanced onset days estimated by the model is similar to observations from the International Phenology Gardens in Europe. The developed phenological model can be integrated into a large-scale ecosystem model to simulate the dynamics of phenological events at different temporal and spatial scales. Crop calendars and enhanced vegetation index have substantial uncertainties in predicting phenological events of croplands. Caution should be exercised when using these data.

Keywords

Phenology model International phenology gardens Crop calendar Remote sensing 

Notes

Acknowledgements

We thank Bernard Heinesch, Corinna Rebmann, Eric Ceschia, Christian Bernhofer, Quentin Laffineur, Enzo Magliulo, Marc Aubinet, Nina Buchmann, Olivier Zurfluh, Pierre Béziat, Pierre Cellier, Paul di Tommasi, Werner Eugster, Werner Kutsch, Thomas Grünwald, and Eric Larmanou, for sharing their measurement data. We also thank Christian Kersebaum for helpful comments on an earlier version of this manuscript. We thank two anonymous reviewers for constructive comments. We thank Arthur Gessler for improving the grammar and style of the manuscript.

Financial support

A Ph.D. scholarship is provided to Shaoxiu Ma by the Max-Planck Society (MPG) and the Chinese Academy of Sciences (CAS) through a joint doctoral program and the Leibniz-Centre for Agricultural Landscape Research (ZALF).

References

  1. Adiku SGK, Reichstein M, Lohila A, Dinh NQ, Aurela M, Laurila T, Lueers J, Tenhunen JD (2006) PIXGRO: A model for simulating the ecosystem CO2 exchange and growth of spring barley. Ecol Model 190:260–276CrossRefGoogle Scholar
  2. Anthoni PM, Freibauer A, Kolle O, Schulze E-D (2004) Winter wheat carbon exchange in Thuringia, Germany. Agric For Meteorol 121:55–67CrossRefGoogle Scholar
  3. Aubinet M, Moureaux C, Bodson B, Dufranne D, Heinesch B, Suleau M, Vancutsem F, Vilret A (2009) Carbon sequestration by a crop over a 4-year sugar beet/winter wheat/seed potato/winter wheat rotation cycle. Agric For Meteorol 149:407–418CrossRefGoogle Scholar
  4. Baldocchi DD (2003) Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: past, present and future. Glob Chang Biol 9:479–492CrossRefGoogle Scholar
  5. Baldocchi DD, Black TA, Curtis PS, Falge E, Fuentes JD, Granier A, Gu L, Knohl A, Pilegaard K, Schmid HP, Valentini R, Wilson K, Wofsy S, Xu L, Yamamoto S (2005) Predicting the onset of net carbon uptake by deciduous forests with soil temperature and climate data: a synthesis of FLUXNET data. Int J Biometeorol 49:377–387CrossRefGoogle Scholar
  6. Béziat P, Ceschia E, Dedieu G (2009) Carbon balance of a three crop succession over two cropland sites in South West France. Agric For Meteorol 149:1628–1645CrossRefGoogle Scholar
  7. Birch CJ, Vos J, van der Putten PEL (2003) Plant development and leaf area production in contrasting cultivars of maize grown in a cool temperate environment in the field. Eur J Agron 19:173–188CrossRefGoogle Scholar
  8. Bondeau A, Smith PC, Zaehle S, Schaphoff S, Lucht W, Cramer W, Gerten D (2007) Modelling the role of agriculture for the 20th century global terrestrial carbon balance. Glob Chang Biol 13:679–706CrossRefGoogle Scholar
  9. Boonjung H, Fukai S (1996) Effects of soil water deficit at different growth stages on rice growth and yield under upland conditions. 2. Phenology, biomass production and yield. Field Crops Res 48:47–55CrossRefGoogle Scholar
  10. Botta A, Viovy N, Ciais P, Friedlingstein P, Monfray P (2000) A global prognostic scheme of leaf onset using satellite data. Glob Chang Biol 6:709–725CrossRefGoogle Scholar
  11. Brisson N, Gary C, Justes E, Roche R, Mary B, Ripoche D, Zimmer D, Sierra J, Bertuzzi P, Burger P, Bussiere F, Cabidoche YM, Cellier P, Debaeke P, Gaudillere JP, Heault C, Maraux F, Seguin B, Sinoquet H (2003) An overview of the crop model Stics. Eur J Agron 18:309–332CrossRefGoogle Scholar
  12. Carberry PS, Muchow RC, McCown RL (1989) Testing the CERES-Maize simulation model in a semi-arid tropical environment. Field Crops Res 20:297–315CrossRefGoogle Scholar
  13. Chen Y, Galina C, Martin H (2009) Constructing a consistent historical climate data set for the European domain. Tech report, Max-plank institute for Biogeochemistry, 15, p 30Google Scholar
  14. Chmielewski FM, Koen W (2000) Impact of weather on yield components of winter rye over 30 years. Agric For Meteorol 102:253–261CrossRefGoogle Scholar
  15. Chmielewski F-M, Rozer T (2001) Response of tree phenology to climate change across Europe. Agric For Meteorol 108:101–112CrossRefGoogle Scholar
  16. Chmielewski F-M, Mueler A, Bruns E (2004) Climate changes and trends in phenology of fruit trees and field crops in Germany, 1961–2000. Agric For Meteorol 121:69–78CrossRefGoogle Scholar
  17. Chuine I, Cour P (1999) Climatic determinants of Budburst seasonality in four temperate-zone tree species. New Phytol 143:339–349CrossRefGoogle Scholar
  18. Churkina G, Schimel D, Braswell BH, Xiao X (2005) Spatial analysis of growing season length control over net ecosystem exchange. Glob Chang Biol 11:1777–1787CrossRefGoogle Scholar
  19. Dietiker D, Buchmann N, Eugster W (2010) Testing the ability of the DNDC model to predict CO2 and water vapour fluxes of a Swiss cropland site. Agric Ecosyst Environ 139:396–401CrossRefGoogle Scholar
  20. Fitter AH, Fitter RS (2002) Rapid changes in flowering time in British plants. Science 296:1689–1691CrossRefGoogle Scholar
  21. Foley JA, Prentice IC, Ramankutty N, Levis S, Pollard D, Sitch S, Haxeltine A (1996) An integrated biosphere model of land surface processes, terrestrial carbon balance, and vegetation dynamics. Glob Biogeochem Cycles 10:603–628CrossRefGoogle Scholar
  22. Goulden ML, Munger JW, Fan SM, Daube BC, Wofsy SC (1996) Exchange of carbon dioxide by a deciduous forest: Response to interannual climate variability. Science 271:1576–1578CrossRefGoogle Scholar
  23. Gungula DT, Kling JG, Togun AO (2003) CERES-maize predictions of maize phenology under nitrogen-stressed conditions in Nigeria. Agron J 95:892–899CrossRefGoogle Scholar
  24. Hollinger DY, Aber J, Dail B, Davidson EA, Goltz SM, Hughes H, Leclerc MY, Lee JT, Richardson AD, Rodrigues C, Scott NA, Achuatavarier D, Walsh J (2004) Spatial and temporal variability in forest-atmosphere CO2 exchange. Glob Chang Biol 10:1689–1706CrossRefGoogle Scholar
  25. Huld TA, Suri M, Dunlop ED, Micale F (2006) Estimating average daytime and daily temperature profiles within Europe. Environ Model Softw 21:1650–1661CrossRefGoogle Scholar
  26. Hungerford RD, Nemani RR, Running SW, Coughlan JC (1989) MTCLIM: a mountain microclimate simulation model. USDA Forest Service, Ogden, p 52Google Scholar
  27. Jame YW, Cutforth HW, Ritchie JT (1998) Interaction of temperature and daylength on leaf appearance rate in wheat and barley. Agric For Meteorol 92:241–249CrossRefGoogle Scholar
  28. Jamieson PD, Brooking IR, Semenov MA, Porter JR (1998) Making sense of wheat development: a critique of methodology. Field Crops Res 55:117–127CrossRefGoogle Scholar
  29. Jolly WM, Nemani R, Running SW (2005) A generalized, bioclimatic index to predict foliar phenology in response to climate. Glob Chang Biol 11:619–632CrossRefGoogle Scholar
  30. Keatinge JDH, Qi A, Wheeler TR, Ellis RH, Summerfield RJ (1998) Effects of temperature and photoperiod on phenology as a guide to the selection of annual legume cover and green manure crops for hillside farming systems. Field Crops Res 57:139–152CrossRefGoogle Scholar
  31. Keeling CD, Chin JFS, Whorf TP (1996) Increased activity of northern vegetation inferred from atmospheric CO2 measurements. Nature 382:146–149CrossRefGoogle Scholar
  32. Kucharik CJ (2006) A multidecadal trend of earlier corn planting in the central USA. American Society of Agronomy, Madison, WI, USA, p 7Google Scholar
  33. Kucharik CJ, Foley JA, Delire C, Fisher VA, Coe MT, Lenters JD, Young-Molling C, Ramankutty N, Norman JM, Gower ST (2000) Testing the performance of a dynamic global ecosystem model: water balance, carbon balance, and vegetation structure. Glob Biogeochem Cycles 14:795–825CrossRefGoogle Scholar
  34. Lamaud E, Loubet B, Irvine M, Stella P, Personne E, Cellier P (2009) Partitioning of ozone deposition over a developed maize crop between stomatal and non-stomatal uptakes, using eddy-covariance flux measurements and modelling. Agric For Meteorol 149:1385–1396CrossRefGoogle Scholar
  35. Lawless C, Semenov MA, Jamieson PD (2005) A wheat canopy model linking leaf area and phenology. Eur J Agron 22:19–32CrossRefGoogle Scholar
  36. Linderholm H, Walther A, Chen D (2008) Twentieth-century trends in the thermal growing season in the greater Baltic area. Clim Chang 87:405–419CrossRefGoogle Scholar
  37. Lindquist JL, Mortensen DA (1999) Ecophysiological characteristics of four maize hybrids and Abutilon theophrasti. Weed Res 39:271–285CrossRefGoogle Scholar
  38. Lokupitiya E, Denning S, Paustian K, Baker I, Schaefer K, Verma S, Meyers T, Bernacchi CJ, Suyker A, Fischer M (2009) Incorporation of crop phenology in Simple Biosphere Model (SiBcrop) to improve land-atmosphere carbon exchanges from croplands. Biogeosciences 6:1103–1103CrossRefGoogle Scholar
  39. Mauder M, Foken T, Clement R, Elbers JA, Eugster W, Gruenwald T, Heusinkveld B, Kolle O (2008) Quality control of CarboEurope flux data. Part 2: Inter-comparison of eddy-covariance software. Biogeosciences 5:451–462CrossRefGoogle Scholar
  40. McMaster GS, Wilhelm WW (1997) Growing degree-days: one equation, two interpretations. Agric For Meteorol 87:291–300CrossRefGoogle Scholar
  41. Menzel A (2000) Trends in phenological phases in Europe between 1951 and 1996. Int J Biometeorol 44:76–81CrossRefGoogle Scholar
  42. Menzel A (2002) Phenology: its importance to the global change community. Clim Chang 54:379–385CrossRefGoogle Scholar
  43. Menzel A (2006) European phenological response to climate change matches the warming pattern. Glob Chang Biol 12:1969–1976CrossRefGoogle Scholar
  44. Mitchell TD, Hulme M (2002) Length of the growing season. Weather 57:196–198Google Scholar
  45. Mitchell TD, Jones PD (2005) An improved method of constructing a database of monthly climate observations and associated high-resolution grids. Int J Climatol 25:693–712CrossRefGoogle Scholar
  46. Moore KE, Fitzjarrald DR, Sakai RK, Goulden ML, Munger JW, Wofsy SC (1996) Seasonal variation in radiative and turbulent exchange at a deciduous forest in central Massachusetts. J Appl Meteorol 35:122–134CrossRefGoogle Scholar
  47. Moors E, Jacobs C, Jans W, Supit I, Kutsch W, Bernhofer C, Béziat P, et al. (2010). Variability in carbon exchange of European croplands. Agriculture, Ecosystems & Environment, 139(3):325–335Google Scholar
  48. Moureaux C, Debacq A, Hoyaux J, Suleau M, Tourneur D, Vancutsem F, Bodson B, Aubinet M (2008) Carbon balance assessment of a Belgian winter wheat crop (Triticum aestivum L.). Glob Chang Biol 14:1353–1366CrossRefGoogle Scholar
  49. Muchow RC, Carberry PS (1989) Environmental control of phenology and leaf growth in a tropically adapted maize. Field Crops Res 20:221–236CrossRefGoogle Scholar
  50. Myneni RB, Keeling CD, Tucker CJ, Asrar G, Nemani RR (1997) Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 386:698–702CrossRefGoogle Scholar
  51. Parmesan C, Yohe G (2003) A globally coherent fingerprint of climate change impacts across natural systems. Nature 421:37–42CrossRefGoogle Scholar
  52. Piao S, Friedlingstein P, Ciais P, Viovy N, Demarty J (2007) Growing season extension and its impact on terrestrial carbon cycle in the Northern Hemisphere over the past 2 decades. Glob Biogeochem Cycles 21:GB3018CrossRefGoogle Scholar
  53. Prescher A-K, Gruewald T, Bernhofer C (2010) Land use regulates carbon budgets in eastern Germany: from NEE to NBP. Agric For Meteorol 150:1016–1025CrossRefGoogle Scholar
  54. Richardson A, Jenkins J, Braswell B, Hollinger D, Ollinger S, Smith M-L (2007) Use of digital webcam images to track spring green-up in a deciduous broadleaf forest. Oecologia 152:323–334CrossRefGoogle Scholar
  55. Rötzer T, Chmielewski F-M (2001) Phenological maps of Europe. Clim Res 18:249–257CrossRefGoogle Scholar
  56. Sacks WJ, Deryng D, Foley JA, Ramankutty N (2010) Crop planting dates: an analysis of global patterns. Glob Ecol Biogeogr 19:607–620Google Scholar
  57. Sakai RK, Fitzjarrald DR, Moore KE (1997) Detecting leaf area and surface resistance during transition seasons. Agric For Meteorol 84:273–284CrossRefGoogle Scholar
  58. Schwartz MD (1999) Advancing to full bloom: planning phenological research for the 21st century. Int J Biometeorol 42:113–118CrossRefGoogle Scholar
  59. Schwartz MD, Reiter BE (2000) Changes in North American spring. Int J Climatol 20:929–932CrossRefGoogle Scholar
  60. Sidaway-Lee K, Josse E-M, Brown A, Gan Y, Halliday KJ, Graham IA, Penfield S (2010) SPATULA links daytime temperature and plant growth rate. Curr Biol 20:1493–1497CrossRefGoogle Scholar
  61. Smith PC, De Noblet-Ducoudr N, Ciais P, Peylin P, Viovy N, Meurdesoif Y, Bondeau A (2010) European-wide simulations of croplands using an improved terrestrial biosphere model: phenology and productivity. J Geophys Res 115:G01014CrossRefGoogle Scholar
  62. Solantie R (2004) Daytime temperature sum: a new thermal variable describing growing season characteristics and explaining evapotranspiration. Boreal Environment Res 9:319–333Google Scholar
  63. Sparks TH, Menzel A (2002) Observed changes in seasons: an overview. Int J Climatol 22:1715–1725CrossRefGoogle Scholar
  64. Stehfest E, Heistermann M, Priess JA, Ojima DS, Alcamo J (2007) Simulation of global crop production with the ecosystem model DayCent. Ecol Model 209:203–219CrossRefGoogle Scholar
  65. Thornton PE (1998) Regional ecosystem simulation: combining surface- and satellite-based observations to study linkages between terrestrial energy and mass budgets. Ph.D. Dissertation, University of Montana, pp 56–80Google Scholar
  66. Thum T, Aalto T, Laurila T, Aurela M, Hatakka J, Lindroth A, Vesala T (2009) Spring initiation and autumn cessation of boreal coniferous forest CO2 exchange assessed by meteorological and biological variables. Tellus B 61:701–717CrossRefGoogle Scholar
  67. Travis KZ, Day W, Porter JR (1988) Modelling the timing of the early development of winter wheat. Agric For Meteorol 44:67–79CrossRefGoogle Scholar
  68. Tucker CJ, Slayback DA, Pinzon JE, Los SO, Myneni RB, Taylor MG (2001) Higher northern latitude normalized difference vegetation index and growing season trends from 1982 to 1999. Int J Biometeorol 45:184–190CrossRefGoogle Scholar
  69. Vetter M, Churkina G, Jung M, Reichstein M, Zaehle S, Bondeau A, Chen Y, Ciais P, Feser F, Freibauer A, Geyer R, Jones C, Papale D, Tenhunen J, Tomelleri E, Trusilova K, Viovy N, Heimann M (2008) Analyzing the causes and spatial pattern of the European 2003 carbon flux anomaly using seven models. Biogeosciences 5:561–583CrossRefGoogle Scholar
  70. White MA, Thornton PE, Running SW (1997) A continental phenology model for monitoring vegetation responses to interannual climatic variability. Glob Biogeochemical Cycles 11:217–234CrossRefGoogle Scholar
  71. White MA, Running SW, Thornton PE (1999) The impact of growing-season length variability on carbon assimilation and evapotranspiration over 88 years in the eastern US deciduous forest. Int J Biometeorol 42:139–145CrossRefGoogle Scholar
  72. White MA, de Beurs KM, Didan K, Inouye DW, Richardson AD, Jensen OP, O'Keefe J, Zhang G, Nemani RR, van Leeuwen WJD, Brown JF, de Wit A, Schaepman M, Lin XM, Dettinger M, Bailey AS, Kimball J, Schwartz MD, Baldocchi DD, Lee JT, Lauenroth WK (2009) Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982–2006. Glob Chang Biol 15:2335–2359CrossRefGoogle Scholar

Copyright information

© ISB 2011

Authors and Affiliations

  • Shaoxiu Ma
    • 1
    • 2
    • 3
  • Galina Churkina
    • 1
    • 4
  • Kristina Trusilova
    • 1
  1. 1.Leibniz Center for Agricultural Landscape ResearchMünchebergGermany
  2. 2.Max-Planck Institute for BiogeochemistryJenaGermany
  3. 3.Key Laboratory of Desert and DesertificationCold and Arid Region Environmental and Engineering Research Institute, CASLanzhouChina
  4. 4.Institute of GeographyHumboldt University of BerlinBerlinGermany

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