Mining temperature profile data for shire-level crop yield prediction

This paper is a continuation of the series of qualitative and quantitative investigations carried out for the processing and analysis of geographic land-use data in an agricultural context. The geographic data was made up of crop and cereal production land use profiles. These were linked to previously recorded climatic data from fixed weather stations in Australia that was interpolated using ordinary krigeing to fit a surface grid. In this investigation, the stochastic average monthly temperature profiles for a selected study area were used to determine the effects on crop production. The areas within the study area were spatially scaled to correspond to individual shires within the South West Agricultural region of Western Australia. The temperature was sampled for three selected years of crop production for 2002, 2003 and 2005. The evaluation was carried out using graphical, correlation and data mining regression techniques in order to detect the patterns of crop production. The patterns suggested that crop production can generally be expected to increase with an increase in temperature during the wheat growing season for some shires.

[1]  Y. Vagh.  An Investigation into the Effect Of Stochastic Annual Rainfall on Crop Yields in South Western Australia , 2012 .

[2]  J. Morison,et al.  Effects of CO2 and temperature on growth and yield of crops of winter wheat over four seasons , 1997 .

[3]  J. Hansen,et al.  Bias correction of daily GCM rainfall for crop simulation studies , 2006 .

[4]  Roberta A. Brown,et al.  Sensitivity of crop yield and water use to change in a range of climatic factors and CO2 concentrations: a simulation study applying EPIC to the central USA , 1997 .

[5]  P. V. Vara Prasad,et al.  Temperature variability and the yield of annual crops , 2000 .

[6]  P. Jamieson,et al.  Simplifying Sirius: sensitivity analysis and development of a meta-model for wheat yield prediction , 2001 .

[7]  Miroslav Trnka,et al.  Projections of uncertainties in climate change scenarios into expected winter wheat yields , 2004 .

[8]  R. Martin,et al.  Seasonal maize forecasting for South Africa and Zimbabwe derived from an agroclimatological model , 2000 .

[9]  M. Buchanan-Smith,et al.  Famine early warning and response: the missing link. , 1995 .

[10]  James W. Jones,et al.  Spatial validation of crop models for precision agriculture , 2001 .

[11]  M. Sivakumar,et al.  Climate prediction and agriculture: current status and future challenges , 2006 .

[12]  R. Shibasaki,et al.  National spatial crop yield simulation using GIS-based crop production model , 2001 .

[13]  P. Cantelaube,et al.  Seasonal weather forecasts for crop yield modelling in Europe , 2005 .

[14]  Aggregate food supply and famine early warning , 1991 .

[15]  H. Eakin.  SEASONAL CLIMATE FORECASTING AND THE RELEVANCE OF LOCAL KNOWLEDGE , 1999 .

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