Effect of weather data aggregation on regional crop simulation for different crops, production conditions, and response variables

Climate Research

Volumn 65, Issue , 2015, Pages 141-157

  Zhao, Gang ^a   Hoffmann, Holger ^a   Van Bussel, Lenny G J ^a,b   Enders, Andreas ^a   Specka, Xenia ^c   Sosa, Carmen ^d   Yeluripati, Jagadeesh ^e,q   Tao, Fulu ^f   Constantin, Julie ^g   Raynal, Helene ^g   Teixeira, Edmar ^h   Grosz, Balázs ⁱ   Doro, Luca ^j   Zhao, Zhigan ^k   Nendel, Claas ^c   Kiese, Ralf ^l   Eckersten, Henrik ^d   Haas, Edwin ^l   Vanuytrecht, Eline ^m   Wang, Enli ^k   Kuhnert, Matthias ^e   Trombi, Giacomo ^p   Moriondo, Marco ^o   Bindi, Marco ^p   Lewan, Elisabet ^d   Bach, Michaela ⁱ   Kersebaum, Kurt Christian ^c   Rötter, Reimund ^f   Roggero, Pier Paolo ^j   Wallach, Daniel ^g   Cammarano, Davide ⁿ   Asseng, Senthold ⁿ   Krauss, Gunther ^a   Siebert, Stefan ^a   Gaiser, Thomas ^a   Ewert, Frank ^a   more..

^a UNIVERSITY OF BONN   (Germany)

^b WAGENINGEN UNIVERSITY   (Netherlands)

^c LEIBNIZ CENTRE FOR AGRICULTURAL LANDSCAPE RESEARCH ZALF   (Germany)

^d SWEDISH UNIVERSITY OF AGRICULTURAL SCIENCES   (Sweden)

^e UNIVERSITY OF ABERDEEN   (United Kingdom)

^f FINNISH FOREST RESEARCH INSTITUTE   (Finland)

^g UNIVERSITÉ DE TOULOUSE   (France)

^h THE NEW ZEALAND INSTITUTE FOR PLANT AND FOOD RESEARCH LIMITED   (New Zealand)

ⁱ THÜNEN INSTITUTE OF CLIMATE SMART AGRICULTURE   (Germany)

^j UNIVERSITY OF SASSARI   (Italy)

^k CSIRO   (Australia)

^l KARLSRUHE INSTITUTE OF TECHNOLOGY   (Germany)

^m UNIVERSITY OF LEUVEN   (Belgium)

ⁿ University of Florida   (United States)

^o CNR Ibimet   (Italy)

^p UNIVERSITY OF FLORENCE   (Italy)

^q SCOTTISH CROP RESEARCH INSTITUTE   (United Kingdom)

more..

Author keywords

Crop model; Data aggregation; Model comparison; Scaling; Spatial heterogeneity; Spatial resolution

Indexed keywords

CROPPING PRACTICE; DATA ASSIMILATION; ENVIRONMENTAL CONDITIONS; EVAPOTRANSPIRATION; GROWING SEASON; SPATIAL RESOLUTION; SPATIAL VARIATION;

GERMANY; NORTH RHINE-WESTPHALIA;

TRITICUM AESTIVUM; ZEA MAYS;

EID: 84940513077     PISSN: 0936577X     EISSN: 16161572     Source Type: Journal    
DOI: 10.3354/cr01301     Document Type: Article

References (84)

1. Adam M, Van Bussel LGJ, Leffelaar PA, Van Keulen H, Ewert F (2011) Effects of modelling detail on simulated potential crop yields under a wide range of climatic conditions. Ecol Model 222: 131-143
2. Angulo C, Rötter R, Trnka M, Pirttioja N, Gaiser T, Hlavinka P, Ewert F (2013) Characteristic 'fingerprints' of crop model responses to weather input data at different spatial resolutions. Eur J Agron 49: 104-114
3. Angulo C, Gaiser T, Rötter RP, Børgesen CD, Hlavinka P, Trnka M, Ewert F (2014) 'Fingerprints' of four crop models as affected by soil input data aggregation. Eur J Agron 61: 35-48
4. Asseng S, Keating B, Fillery I, Gregory P and others (1998) Performance of the APSIM-wheat model in Western Australia. Field Crops Res 57: 163-179
5. Asseng S, Jamieson PD, Kimball B, Pinter P, Sayre K, Bowden JW, Howden SM (2004) Simulated wheat growth affected by rising temperature, increased water deficit and elevated atmospheric CO2. Field Crops Res 85: 85-102
6. Asseng S, Ewert F, Rosenzweig C, Jones JW and others (2013) Uncertainty in simulating wheat yields under climate change. Nat Clim Change 3: 827-832
7. Asseng S, Ewert F, Martre P, Rotter RP and others (2015) Rising temperatures reduce global wheat production. Nat Clim Change 5: 143-147
8. Atkinson PM, Tate NJ (2000) Spatial scale problems and geostatistical solutions: a review. Prof Geogr 52: 607-623
9. Bergez JE, Chabrier P, Gary C, Jeuffroy MH and others (2013) An open platform to build, evaluate and simulate integrated models of farming and agro-ecosystems. Environ Model Softw 39: 39-49
10. Bian L, Butler R (1999) Comparing effects of aggregation methods on statistical and spatial properties of simulated spatial data. Photogramm Eng Remote Sensing 65: 73-84
11. Bormann H, Breuer L, Gräff T, Huisman J, Croke B (2009) Assessing the impact of land use change on hydrology by ensemble modelling (LUCHEM). IV. Model sensitivity to data aggregation and spatial (re-) distribution. Adv Water Resour 32: 171-192
12. Brisson N, Mary B, Ripoche D, Jeuffroy MH and others (1998) STICS: a generic model for the simulation of crops and their water and nitrogen balances. I. Theory and parameterization applied to wheat and corn. Agronomie 18: 311-346
13. Carter TR, Parry ML, Porter JH (1991) Climatic change and future agroclimatic potential in Europe. Int J Climatol 11: 251-269
14. Chambers R, Dunstan R (1986) Estimating distribution functions from survey data. Biometrika 73: 597-604
15. Conrad Y, Fohrer N (2009) Modelling of nitrogen leaching under a complex winter wheat and red clover crop rotation in a drained agricultural field. Phys Chem Earth Parts ABC 34: 530-540
16. Crossman ND, Bryan BA (2009) Identifying cost-effective hotspots for restoring natural capital and enhancing landscape multifunctionality. Ecol Econ 68: 654-668
17. Datensammlung KTBL (Kuratorium für Technik und Bau wesen in der Landwirtschaft) (2008) Betriebsplanung Landwirtschaft 2008/09-Daten für die Betriebsplanung in der Landwirtschaft. KTBL, Darmstadt
18. Davis LL, Dodd BG (2003) Item exposure constraints for testlets in the verbal reasoning section of the MCAT. Appl Psychol Meas 27: 335-356
19. Davis FW, Quattrochi DA, Ridd MK, Lam NN and others (1991) Environmental analysis using integrated GIS and remotely sensed data: some research needs and priorities. Photogramm Eng Remote Sensing 57: 689-697
20. de Wit AJW, Boogaard HL, van Diepen CA (2005) Spatial resolution of precipitation and radiation: the effect on regional crop yield forecasts. Agric For Meteorol 135: 156-168
21. Del Grosso SJ, Parton WJ, Mosier AR, Walsh MK, Ojima DS, Thornton P (2006) DAYCENT national-scale simulations of nitrous oxide emissions from cropped soils in the United States. J Environ Qual 35: 1451-1460
22. Di Vittorio AV, Miller NL (2014) Reducing the impact of model scale on simulated, gridded switchgrass yields. Environ Model Softw 51: 70-83
23. Doherty RM, Mearns LO, Reddy KR, Downton MW, McDaniel L (2003) Spatial scale effects of climate scenarios on simulated cotton production in the southeastern USA. Clim Change 60: 99-129
24. Durman C, Gregory J, Hassell D, Jones R, Murphy J (2001) A comparison of extreme European daily precipitation simulated by a global and a regional climate model for present and future climates. QJR Meteorol Soc 127: 1005-1015
25. Easterling WE, Weiss A, Hays CJ, Mearns LO (1998) Spatial scales of climate information for simulating wheat and maize productivity: the case of the US Great Plains. Agric For Meteorol 90: 51-63
26. Eitzinger J, Thaler S, Schmid E, Strauss F and others (2013) Sensitivities of crop models to extreme weather conditions during flowering period demonstrated for maize and winter wheat in Austria. J Agric Sci 151: 813-835
27. Ewert F, van Ittersum MK, Heckelei T, Therond O, Bezlepkina I, Andersen E (2011) Scale changes and model linking methods for integrated assessment of agrienvironmental systems. Agric Ecosyst Environ 142: 6-17
28. Eyshi Rezaei E, Siebert S, Ewert F (2015) Impact of data resolution on heat and drought stress simulated for winter wheat in Germany. Eur J Agron 65: 69-82
29. Falloon P, Challinor A, Dessai S, Hoang L, Johnson J, Koehler AK (2014) Ensembles and uncertainty in climate change impacts. Front Environ Sci 2: 33
30. Folberth C, Yang H, Wang X, Abbaspour KC (2012) Impact of input data resolution and extent of harvested areas on crop yield estimates in large-scale agricultural modeling for maize in the USA. Ecol Model 235-236: 8-18
31. Gaiser T, Perkons U, Küpper PM, Kautz T and others (2013) Modeling biopore effects on root growth and biomass production on soils with pronounced sub-soil clay accumulation. Ecol Model 256: 6-15
32. GDNRW (Geologischer Dienst Nordrhein-Westfalen) (2001) AGBK50-Allgemeine Informationen zur Bodenkarte 1: 50 000. Arbeitsgruppe BK50, Geologischer Dienst Nord rhein-Westfalen, Krefeld
33. Gimona A, Birnie R, Sibbald A (2006) Scaling up of a mechanistic dynamic model in a GIS environment to model temperate grassland production at the regional scale. Grass Forage Sci 61: 315-331
34. Glemnitz M, Wurbs A, Roth R (2011) Derivation of regional crop sequences as an indicator for potential GMO dis persal on large spatial scales. Ecol Indic 11: 964-973
35. Haas E, Klatt S, Fröhlich A, Kraft P and others (2013) LandscapeDNDC: a process model for simulation of biosphere-atmosphere-hydrosphere exchange processes at site and regional scale. Landsc Ecol 28: 615-636
36. Hansen JW, Jones JW (2000) Scaling-up crop models for climate variability applications. Agric Syst 65: 43-72
37. Hoffmann H, Zhao G , van Bussel LGJ, Enders A and others (2015) Variability of spatial aggregation effects of climate data on regional yield simulation by crop models for a selected region in Germany. Clim Res 65:53-69
38. Holzworth DP, Huth NI, deVoil PG, Zurcher EJ and others (2014) APSIM-evolution towards a new generation of agricultural systems simulation. Environ Model Softw 62: 327-350
39. Jansson PE, Karlberg L (2001) Coupled heat and mass transfer model for soil-plant-atmosphere systems. Royal Institute of Technology, Department of Civil and Environmental Engineering, Stockholm
40. Jones J (1993) Decision support systems for agricultural development. In: Penning de Vries F, Teng P, Metselaar K (eds) Systems approaches for agricultural development. Springer, Dordrecht, p 459-471
41. Jones JW, Hoogenboom G, Porter C, Boote K and others (2003) The DSSAT cropping system model. Eur J Agron 18: 235-265
42. Kaspar F, Müller-Westermeier G, Penda E, Mächel H, Zimmermann K, Kaiser-Weiss A, Deutschländer T (2013) Monitoring of climate change in Germany-data, products and services of Germany's National Climate Data Centre. Adv Sci Res 10: 99-106
43. Keating BA, Carberry PS, Hammer GL, ProbertME and others (2003) An overview of APSIM, a model designed for farming systems simulation. Eur J Agron 18: 267-288
44. Kersebaum KC (2007) Modelling nitrogen dynamics in soil-crop systems with HERMES. In: Kersebaum KC, Hecker JM, Mirschel W, Wegehenkel M (eds) Modelling water and nutrient dynamics in soil-crop systems. Springer, p 147-160
45. Kersebaum K (2011) Special features of the HERMES model and additional procedures for parameterization, calibration, validation, and applications. In: Ahuja LR, Ma L (eds) Methods of introducing system models into agri cultural research. Advances in agricultural systems modeling 2. ASA, CSSA, SSSA, Madison, WI, p 65-94
46. Kraus D, Weller S, Klatt S, Haas E, Wassmann R, Kiese R, Butterbach-Bahl K (2015) A new LandscapeDNDC biogeochemical module to predict CH4 and N2O emissions from lowland rice and upland cropping systems. Plant Soil 386: 125-149
47. Mearns LO, Mavromatis T, Tsvetsinskaya E, Hays C, Easterling W (1999) Comparative responses of EPIC and CERES crop models to high and low spatial resolution climate change scenarios. J Geophys Res Atmos 104: 6623-6646
48. Mearns LO, Easterling W, Hays C, Marx D (2001) Comparison of agricultural impacts of climate change calculated from high and low resolution climate change scenarios. I. The uncertainty due to spatial scale. Clim Change 51: 131-172
49. Mearns LO, Carbone G, Doherty RM, Tsvetsinskaya E, McCarl BA, Adams RM, McDaniel L (2004) The uncertainty due to spatial scale of climate scenarios in integrated assessments: an example from U.S. agriculture. Integr Assess 4: 225-235
50. Meentemeyer V (1989) Geographical perspectives of space, time, and scale. Landsc Ecol 3: 163-173
51. Mo X, Liu S, Chen DAN, Lin Z, Guo R, Wang KUN (2009) Grid-size effects on estimation of evapotranspiration and gross primary production over a large Loess Plateau basin, China. Hydrol Sci J 54: 160-173
52. Müller C, Waha K, Bondeau A, Heinke J (2014) Hotspots of climate change impacts in sub-Saharan Africa and implications for adaptation and development. Glob Change Biol 20: 2505-2517
53. Mummery D, Battaglia M (2002) Data input quality and resolution effects on regional and local scale Eucalyptus globulus productivity predictions in north-east Tasmania. Ecol Model 156: 13-25
54. Nendel C, Wieland R, Mirschel W, Specka X, Guddat C, Kersebaum K (2013) Simulating regional winter wheat yields using input data of different spatial resolution. Field Crops Res 145: 67-77
55. Olesen JE, Bøcher PK, Jensen T (2000) Comparison of scales of climate and soil data for aggregating simulated yields of winter wheat in Denmark. Agric Ecosyst Environ 82: 213-228
56. Palosuo T, Kersebaum KC, Angulo C, Hlavinka P and others (2011) Simulation of winter wheat yield and its variability in different climates of Europe: a comparison of eight crop growth models. Eur J Agron 35: 103-114
57. Porter JR, Semenov MA (2005) Crop responses to climatic variation. Philos Trans R Soc Lond B Biol Sci 360: 2021-2035
58. Posselt R, Müller R, Stöckli R, Trentmann J (2011) CM SAF surface radiation MVIRI Data Set 1.0-Monthly means/daily means/hourly means. Satellite application facility on climate monitoring. Available at http://dx.doi. org/10.5676/ EUM-SAF-CM/RAD-MVIRI/V001
59. Raes D, Steduto P, Hsiao TC, Fereres E (2009) AquaCrop- the FAO crop model to simulate yield response to water. II. Main algorithms and software description. Agron J 101: 438-447
60. Rauthe M, Steiner H, Riediger U, Mazurkiewicz A, Gratzki A (2013) A Central European precipitation climatology. I. Generation and validation of a high-resolution gridded daily data set (HYRAS). Meteorol Z 22: 235-256
61. Raymond CM, Bryan BA, MacDonald DH, Cast A, Strathearn S, Grandgirard A, Kalivas T (2009) Mapping community values for natural capital and ecosystem services. Ecol Econ 68: 1301-1315
62. Resop JP, Fleisher DH, Timlin DJ, Reddy VR (2014) Biophysical constraints to potential production capacity of potato across the U.S. eastern seaboard region. Agron J 106: 43-56
63. Rötter RP, Carter TR, Olesen JE, Porter JR (2011) Cropclimate models need an overhaul. Nat Clim Change 1: 175-177
64. Schulze R (2000) Transcending scales of space and time in impact studies of climate and climate change on agrohydrological responses. Agric Ecosyst Environ 82: 185-212
65. Siebert S, Ewert F (2012) Spatio-temporal patterns of phenological development in Germany in relation to temperature and day length. Agric For Meteorol 152: 44-57
66. Steduto P, Hsiao TC, Raes D, Fereres E (2009) AquaCrop The FAO crop model to simulate yield response to water. I. Concepts and underlying principles. Agron J 101: 426-437
67. Tao F, Zhang Z (2013) Climate change, wheat productivity and water use in the North China Plain: a new superensemble based probabilistic projection. Agric For Meteorol 170: 146-165
68. Tao F, Yokozawa M, Zhang Z (2009) Modelling the impacts of weather and climate variability on crop productivity over a large area: a new process-based model development, optimization, and uncertainties analysis. Agric For Meteorol 149: 831-850
69. Teixeira EI, Fischer G, van Velthuizen H, Walter C, Ewert F (2013) Global hot-spots of heat stress on agricultural crops due to climate change. Agric For Meteorol 170: 206-215
70. Tsvetsinskaya EA, Mearns LO, Mavromatis T, Gao W, McDaniel L, Downton MW (2003) The effect of spatial scale of climatic change scenarios on simulated maize, winter wheat, and rice production in the southeastern United States. In: Mearns L (ed) Issues in the impacts of climate variability and change on agriculture. Springer, Netherlands, p 37-71
71. Van Bussel LGJ, Ewert F, Leffelaar PA (2011) Effects of data aggregation on simulations of crop phenology. Agric Ecosyst Environ 142: 75-84
72. van Ittersum MK, Leffelaar P, Van Keulen H, Kropff M, Bastiaans L, Goudriaan J (2003) On approaches and applications of the Wageningen crop models. Eur J Agron 18: 201-234
73. Vanuytrecht E, Raes D, Steduto P, Hsiao TC and others (2014) AquaCrop: FAO's crop water productivity and yield response model. Environ Model Softw 62: 351-360
74. Wang E, Robertson MJ, Hammer GL, Carberry PS and others (2002) Development of a generic crop model template in the cropping system model APSIM. Eur J Agron 18: 121-140
75. Watson J, Challinor A (2013) The relative importance of rainfall, temperature and yield data for a regional-scale crop model. Agric For Meteorol 170: 47-57
76. Webber H, Gaiser T, Ewert F (2014) What role can crop models play in supporting climate change adaptation decisions to enhance food security in Sub-Saharan Africa? Agric Syst 127: 161-177
77. Williams JR (1995) The EPIC model. In: Singh V (ed) Computer models of watershed hydrology. Water Resources Publications, Highlands Ranch, CO, p 909-1000
78. Williams J, Renard K, Dyke P (1983) EPIC: a new method for assessing erosion's effect on soil productivity. J Soil Water Conserv 38: 381-383
79. Willmott CJ, Matsuura K (2005) Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Clim Res 30: 79-82
80. Wu J (2004) Effects of changing scale on landscape pattern analysis: scaling relations. Landscape Ecol 19: 125-138
81. Yeluripati JB, Van Oijen M, Wattenbach M, Neftel A, Ammann A, Parton W, Smith P (2009) Bayesian calibration as a tool for initialising the carbon pools of dynamic soil models. Soil Biol Biochem 41: 2579-2583
82. Zhang X, Sahajpal R, Manowitz DH, Zhao K and others (2014) Multi-scale geospatial agroecosystem modeling: a case study on the influence of soil data resolution on carbon budget estimates. Sci Total Environ 479-480: 138-150
83. Zhao G, Bryan BA, King D, Luo Z and others (2013) Largescale, high-resolution agricultural systems modeling using a hybrid approach combining grid computing and parallel processing. Environ Model Softw 41: 231-238
84. Zhao G, Siebert S, Enders A, Eyshi Rezaei E, Yan C, Ewert F (2015) Demand for multi-scale weather data for regional crop modelling. Agric For Meteorol 200: 156-171