Skip to main content
×
Home
    • Aa
    • Aa

Simulating regional climate-adaptive field cropping with fuzzy logic management rules and genetic advance

  • P. PARKER (a1) (a2), J. INGWERSEN (a3), P. HÖGY (a4), E. PRIESACK (a5) and J. AURBACHER (a1)...
Abstract
SUMMARY

Agriculture is a largely technical endeavour involving complicated managerial decision-making that affects crop performance. Farm-level modelling integrates crop models with agent behaviour to account for farmer decision-making and complete the representation of agricultural systems. To replicate an important part of agriculture in Central Europe a crop model was calibrated for a unique region's predominant crops: winter wheat, winter and spring barley, silage maize and winter rapeseed. Their cultivation was then simulated over multiple decades at daily resolution to test validity and stability, while adding the dimension of agent behaviour in relation to environmental and economic conditions. After validation against regional statistics, simulated future weather scenarios were used to forecast crop management and performance under anticipated global change. Farm management and crop genetics were treated as adaptive variables in the milieu of shifting climatic conditions to allow projections of agriculture in the study region into the coming decades.

Copyright
Corresponding author
*To whom all correspondence should be addressed. Email: phillip.parker@zalf.de
Linked references
Hide All

This list contains references from the content that can be linked to their source. For a full set of references and notes please see the PDF or HTML where available.

C. Angulo , R. Rötter , R. Lock , A. Enders , S. Fronzek & F. Ewert (2013). Implication of crop model calibration strategies for assessing regional impacts of climate change in Europe. Agricultural and Forest Meteorology 170, 3246.

J. Aurbacher & S. Dabbert (2011). Generating crop sequences in land-use models using maximum entropy and Markov chains. Agricultural Systems 104, 470479.

J. Aurbacher , P. S. Parker , G. A. Calberto Sánchez , J. Steinbach , E. Reinmuth , J. Ingwersen & S. Dabbert (2013). Influence of climate change on short term management of field crops – A modelling approach. Agricultural Systems 119, 4457.

S. Bassu , N. Brisson , J.-L. Durand , K. Boote , J. Lizaso , J. W. Jones , C. Rosenzweig , A. C. Ruane , M. Adam , C. Baron , B. Basso , C. Biernath , H. Boogaard , S. Conijn , M. Corbeels , D. Deryng , G. De Sanctis , S. Gayler , P. Grassini , J. Hatfield , S. Hoek , C. Izaurralde , R. Jongschaap , A. R. Kemanian , K. C. Kersebaum , S.-H. Kim , N. S. Kumar , D. Makowski , C. Müller , C. Nendel , E. Priesack , M. V. Pravia , F. Sau , I. Shcherbak , F. Tao , E. Teixeira , D. Timlin & K. Waha (2014). How do various maize crop models vary in their responses to climate change factors? Global Change Biology 20, 23012320.

L. Bizikova , E. Crawford , M. Nijnik & R. Swart (2014). Climate change adaptation planning in agriculture: processes, experiences and lessons learned from early adapters. Mitigation and Adaptation Strategies for Global Change 19, 411430.

D. Deryng , W. J. Sacks , C. C. Barford & N. Ramankutty (2011). Simulating the effects of climate and agricultural management practices on global crop yield. Global Biogeochemical Cycles 25, GB2006. doi: 10.1029/2009GB003765.

N. Estrella , T. H. Sparks & A. Menzel (2007). Trends and temperature response in the phenology of crops in Germany. Global Change Biology 13, 17371747.

F. Ewert , M. D. A. Rounsevell , I. Reginster , M. J. Metzger & R. Leemans (2005). Future scenarios of European agricultural land use: I. Estimating changes in crop productivity. Agriculture, Ecosystems and Environment 107, 101116.

S. Gayler , J. Ingwersen , E. Priesack , T. Wöhling , V. Wulfmeyer & T. Streck (2013). Assessing the relevance of subsurface processes for the simulation of evapotranspiration and soil moisture dynamics with CLM3. 5: comparison with field data and crop model simulations. Environmental Earth Sciences 69, 415427.

P. Högy , C. Zörb , G. Langenkämper , T. Betsche & A. Fangmeier (2009). Atmospheric CO2 enrichment changes the wheat grain proteome. Journal of Cereal Science 50, 248254.

J. Ingwersen , K. Steffens , P. Högy , K. Warrach-Sagi , D. Zhunusbayeva , M. Poltoradnev , R. Gäbler , H.-D. Wizemann , A. Fangmeier , V. Wulfmeyer & T. Streck (2011). Comparison of Noah simulations with eddy covariance and soil water measurements at a winter wheat stand. Agricultural and Forest Meteorology 151, 345355.

M.-H. Jeuffroy , P. Casadebaig , P. Debaeke , C. Loyce & J.-M. Meynard (2014). Agronomic model uses to predict cultivar performance in various environments and cropping systems. A review. Agronomy for Sustainable Development 34, 121137.

D. Leclère , P.-A. Jayet & N. de Noblet-Ducoudré (2013). Farm-level autonomous adaptation of European agricultural supply to climate change. Ecological Economics 87, 114.

D. Leenhardt & P. Lemaire (2002). Estimating the spatial and temporal distribution of sowing dates for regional water management. Agricultural Water Management 55, 3752.

D. B. Lobell , W. Schlenker & J. Costa-Roberts (2011). Climate trends and global crop production since 1980. Science 333, 616620.

A. Marjanović-Jeromela , R. Marinković , S. Ivanovska , M. Jankulovska , A. Mijić & N. Hristov (2011). Variability of yield determining components in winter rapeseed (Brassica napus L.) and their correlation with seed yield. Genetika 43, 5166.

B. Mast , W. Claupein & S. Graeff-Hönninger (2014). Using a crop growth model to quantify regional biogas potentials: an example of the model region Biberach (South-West Germany). BioEnergy Research 7, 10141025.

R. B. Matthews , M. Rivington , S. Muhammed , A. C. Newton & P. D. Hallett (2013). Adapting crops and cropping systems to future climates to ensure food security: The role of crop modelling. Global Food Security 2, 2428.

J. E. Nash & J. V. Sutcliffe (1970). River flow forecasting through conceptual models part I — A discussion of principles. Journal of Hydrology 10, 282290.

J. E. Olesen , C. D. Børgesen , L. Elsgaard , T. Palosuo , R. Rötter , A. Skjelvåg , P. Peltonen-Sainio , T. Börjesson , M. Trnka , F. Ewert , S. Siebert , N. Brisson , J. Eitzinger , H. J. van der Fels-Klerx & E. van Asselt (2012). Changes in time of sowing, flowering and maturity of cereals in Europe under climate change. Food Additives and Contaminants: Part A 29, 15271542.

T. Palosuo , K. C. Kersebaum , C. Angulo , P. Hlavinka , M. Moriondo , J. E. Olesen , R. H. Patil , F. Ruget , C. Rumbaur , J. Takáč , M. Trnka , M. Bindi , B. Çaldağ , F. Ewert , R. Ferrise , W. Mirschel , L. Şaylan , B. Šiška & R. Rötter (2011). Simulation of winter wheat yield and its variability in different climates of Europe: A comparison of eight crop growth models. European Journal of Agronomy 35, 103114.

R. P. Rötter , T. Palosuo , K. C. Kersebaum , C. Angulo , M. Bindi , F. Ewert , R. Ferrise , P. Hlavinka , M. Moriondo , C. Nendel , J. E. Olesen , R. H. Patil , F. Ruget , J. Takáč & M. Trnka (2012). Simulation of spring barley yield in different climatic zones of Northern and Central Europe: A comparison of nine crop models. Field Crops Research 133, 2336.

C. A. Rotz & T. M. Harrigan (2005). Predicting suitable days for field machinery operations in a whole farm simulation. Applied Engineering in Agriculture 21, 563571.

W. J. Sacks & C. J. Kucharik (2011). Crop management and phenology trends in the U.S. Corn Belt: Impacts on yields, evapotranspiration and energy balance. Agricultural and Forest Meteorology 151, 882894.

M. K. Van Ittersum , K. G. Cassman , P. Grassini , J. Wolf , P. Tittonell & Z. Hochman (2013). Yield gap analysis with local to global relevance – A review. Field Crops Research 143, 417.

K. Waha , L. G. J. van Bussel , C. Müller & A. Bondeau (2012). Climate-driven simulation of global crop sowing dates. Global Ecology and Biogeography 21, 247259.

K. Waha , C. Müller , A. Bondeau , J. Dietrich , P. Kurukulasuriya , J. Heinke & H. Lotze-Campen (2013). Adaptation to climate change through the choice of cropping system and sowing date in sub-Saharan Africa. Global Environmental Change 23, 130143.

R. Wieland , W. Mirschel , C. Nendel & X. Specka (2013). Dynamic fuzzy models in agroecosystem modelling. Environmental Modelling and Software 46, 4449.

X. Yin & H. Van Laar (2005). Crop Systems Dynamics: an Ecophysiological Simulation Model for Genotype-by-Environment Interactions. Wageningen, The Netherlands: Wageningen Academic Pub.

Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

The Journal of Agricultural Science
  • ISSN: 0021-8596
  • EISSN: 1469-5146
  • URL: /core/journals/journal-of-agricultural-science
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×