Effects of data aggregation on simulations of crop phenology

Abstract Policy decisions are often taken at the regional scale, while crop models, supporting these decisions, have been developed for individual locations, expecting location-specific, spatially homogeneous input data. Crop models are able to account for the variation in climatic conditions and management activities and their effects on crop productivity. However, regional applications require consideration of spatial variability in these factors. Several studies have analyzed effects of using spatially aggregated climate data on model outcomes. The effects of spatially aggregated sowing dates on simulations of crop phenological development have not been studied, however. We investigated the impact of spatial aggregation of sowing dates and temperatures on the simulated occurrence of ear emergence and physiological maturity of winter wheat in Germany, using the phenological model of AFRCWHEAT2. We observed time ranges for crop emergence exceeding 90 d, whereas for harvesting this was more than 75 d. Spatial aggregation to 100 km × 100 km reduced this range to less than 30 and 20 d for emergence and harvest, respectively. Differences among selected regions were relatively small, suggesting that non-climatic factors largely determined the spatial variability in sowing dates and consecutive phenological stages. Application of the AFRCWHEAT2 phenology model using location-specific weather data and emergence dates, and an identical crop parameter set across Germany gave similar deviations in all studied regions, suggesting that varietal differences were less important among regions than within regions. Importantly, bias in model outcomes as a result of using aggregated input data was small. Increase in resolution from 100 km to 50 km resulted in slight improvements in the simulations. We conclude that using spatially aggregated weather data and emergence dates to simulate the length of the growing season for winter wheat in Germany is justified for grid cells with a maximum area of 100 km × 100 km and for the model considered here. As spatial variability of sowing dates within a region or country can be large, it is important to obtain information about the representative sowing date for the region.

[1]  C. Hays,et al.  Comparison of Agricultural Impacts of Climate Change Calculated from High and Low Resolution Climate Change Scenarios: Part I. The Uncertainty Due to Spatial Scale , 2001 .

[2]  Mikhail A. Semenov,et al.  Reconciling alternative models of phenological development in winter wheat , 2007 .

[3]  F. Tubiello,et al.  Simulating the effects of elevated CO2 on crops: approaches and applications for climate change , 2002 .

[4]  J. Porter,et al.  Comparison of the wheat simulation models Afrcwheat2, Ceres-wheat and Swheat for non-limiting conditions of crop growth , 1993 .

[5]  James W. Jones,et al.  POTENTIAL USES AND LIMITATIONS OF CROP MODELS , 1996 .

[6]  Carlo Giupponi,et al.  Socio-economic scenario development for the assessment of climate change impacts on agricultural land use : a pairwise comparison approach , 2006 .

[7]  Louise E. Jackson,et al.  Ecology in agriculture , 1997 .

[8]  S. Nonhebel.  Effects of temperature rise and increase in CO2 concentration on simulated wheat yields in Europe , 1996 .

[9]  James W. Jones,et al.  Short survey Scaling-up crop models for climate variability applications $ , 2000 .

[10]  J. Goudriaan,et al.  ON APPROACHES AND APPLICATIONS OF THE WAGENINGEN CROP MODELS , 2003 .

[11]  Jørgen E. Olesen,et al.  Comparison of scales of climate and soil data for aggregating simulated yields of winter wheat in Denmark. , 2000 .

[12]  Philippe Lagacherie,et al.  Modelling wheat yield responses to soil and climate variability at the regional scale , 1999 .

[13]  J. Porter,et al.  Simulation of growth and development processes of spring wheat in response to CO2 and ozone for different sites and years in Europe using mechanistic crop simulation models , 1999 .

[14]  C. A. van Diepen,et al.  Spatial resolution of precipitation and radiation: The effect on regional crop yield forecasts , 2005 .

[15]  W. J. Thompson,et al.  An analysis of morphological development stages in avalon winter wheat crops with different sowing dates and at ten sites in England and Scotland , 1987, The Journal of Agricultural Science.

[16]  W. J. Thompson,et al.  An analysis of primordium initiation in Avalon winter wheat crops with different sowing dates and at nine sites in England and Scotland , 1987, The Journal of Agricultural Science.

[17]  John R. Porter,et al.  A winter wheat crop simulation model without water or nutrient limitations , 1984, The Journal of Agricultural Science.

[18]  Antje Müller,et al.  Climate changes and trends in phenology of fruit trees and field crops in Germany, 1961-2000 , 2004 .

[19]  J. Porter,et al.  Use of AFRCWHEAT2 to predict the development of main stem and tillers in winter triticale and winter wheat in North East Germany , 1996 .

[20]  Frank Ewert,et al.  Crops and climate change: progress, trends, and challenges in simulating impacts and informing adaptation. , 2009, Journal of experimental botany.

[21]  C. Hays,et al.  Comparison of Agricultural Impacts of Climate Change Calculated from High and Low Resolution Climate Change Scenarios: Part II. Accounting for Adaptation and CO2 Direct Effects , 2001 .

[22]  J. Porter,et al.  Making sense of wheat development: a critique of methodology , 1998 .

[23]  W. Anderson,et al.  Variability of optimum sowing time for wheat yield in Western Australia , 2008 .

[24]  R. Shibasaki,et al.  Global estimation of crop productivity and the impacts of global warming by GIS and EPIC integration , 2003 .

[25]  H. Gómez-Macpherson,et al.  Effect of sowing time on yield and agronomic characteristics of wheat in south-eastern Australia , 1995 .

[26]  T. Lyons,et al.  Variability and trends in sowing dates across the Australian wheatbelt , 1998 .

[27]  Gustavo A. Slafer,et al.  Wheat: Ecology and Physiology of Yield Determination , 1999 .

[28]  C. Müller,et al.  Modelling the role of agriculture for the 20th century global terrestrial carbon balance , 2007 .

[29]  J. Monteith,et al.  Agricultural Meteorology: evolution and application , 2000 .

[30]  R. Reese.  Geostatistics for Environmental Scientists , 2001 .

[31]  A. Challinor,et al.  Toward a combined seasonal weather and crop productivity forecasting system: determination of the working spatial scale , 2003 .

[32]  Christian Baron,et al.  From GCM grid cell to agricultural plot: scale issues affecting modelling of climate impact , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[33]  A. Challinor,et al.  Design and optimisation of a large-area process-based model for annual crops , 2004 .

[34]  Annette Menzel,et al.  Trends and temperature response in the phenology of crops in Germany , 2007 .

[35]  Caspar A. Mücher,et al.  A climatic stratification of the environment of Europe , 2005 .

[36]  R. Leemans,et al.  12 – Effects of Global Change on Agricultural Land Use: Scaling Up from Physiological Processes to Ecosystem Dynamics , 1997 .

[37]  A. Worland,et al.  The distribution, in European winter wheats, of genes that influence ecoclimatic adaptability whilst determining photoperiodic insensitivity and plant height , 2004, Euphytica.

[38]  J. Porter,et al.  Scaling-up the AFRCWHEAT2 model to assess phenological development for wheat in Europe , 2000 .

[39]  M. Yokozawa,et al.  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 , 2009 .

[40]  Linda O. Mearns,et al.  Spatial scales of climate information for simulating wheat and maize productivity: The case of the US Great Plains , 1998 .