Spring wheat (Triticum aestivum L.) ideotype responses to elevated CO2 and temperature levels : A cereal yield modeling study using satellite information

5 CONTRIBUTIONS . 7 LIST OF DEFINITIONS AND ABBREVIATIONS . 7 INTRODUCTION . 10 The wheat ideotype, ideotype profile (ItPrf) and Cultivation value (Cval) concepts . 10 Global trends and adaptation strategies in cereal production under projected climate change . 12 Climate change scenarios 2050-2100 . 12 High-latitude SILMU climate change scenario for Finland . 12 High-latitude FINSKEN climate change scenario for Finland . 14 CO2 and temperature effects in crop physiology experiments . 15 Modeling CO2 and temperature effects . 16 Implications from previous remote sensing experiments . 18 OBJECTIVES OF THE STUDY . 19 MATERIAL AND METHODS. 21 System analysis and modeling process . 21 Soil types and growing zones in the experimental locations . 26 The electromagnetic spectrum . 27 Theoretical background for crop modeling . 27 Potential (ypot) and non-potential (yb) yield modeling using the CERES-Wheat crop model . 29 Genetic coefficients for spring wheat genotypes . 29 Crop model internal error estimation with the sensitivity analysis . 30 Theoretical background for satellite measurements . 30 Satellite measurement locations and technical configuration of sensors . 31 SatPhenClass phenological classification algorithm for satellite data . 33 Cereal identification using the Phenological Spectral Signature Profiles (SSPph) . 33 Vegetation Indices (VGI) and Composite Multispectral (CMM) models using minimum datasets . 34 Calibration and description of VGI and CMM models . 35 Validation of VGI and CMM models . 35 RESULTS . 36 Spring wheat crop physiological results from the OTC experiments with elevated CO2 and temperature growing conditions . 36 Yield component and vegetation parameter modeling results for high yielding HiL and MidE ideotypes . 36 CERES-Wheat calibration results for genetic coefficients with generic HiL and MidE genotypes . 37 The CERES-Wheat crop potential (ypot) and non-potential (yb) yield modeling results for HiL and MidE genotypes . 38 The CERES-Wheat internal error estimation with the sensitivity analysis . 39 Ideotype profile (ItPrf) modeling results for generic HiL and MidE genotypes . 39 The Mixed Structural Covariance error variance in non-potential yield (yb) estimations . 40 Non-potential baseline yield (yb(Sat)) modeling results for large cultivation area estimations with VGI and CMM models . 41 Optical Phenological Spectral Signature Profile (SSPphOpt) and Vegetation Indices variation . 41 SAR Spectral Signature profiles (SSPphSAR) and canopy×soil backscattering covariance . 45 Historical trends in spring wheat yield levels, cultivation area and total production in Finland . 47 DISCUSSION . 49 Implications from the CERES-Wheat modeling results vs. OTC crop physiological results . 49 The SatPhenClass classification accuracy results . 51 The Optical and SAR Phenological Spectral Signature profile (SSPph) results . 51 The optical VGI modeling results for non-potential baseline yield (yb(Sat)) estimations in large area conditions . 52 The Composite Multispectral ASAR/SAR & NDVI (CMM) modeling results for non-potential baseline yield (yb(Sat)) estimations in large area conditions . 53 Soil×cereal canopy covariances modeling results using optical VGI and multispectral CMM models . 54 Implications from the non-limited, potential (ypot) and non-potential baseline (yb) yield modeling results with HiL and MidE Ideotype profiles . 56 Strategies for future spring wheat breeding programs and cultivation practices . 56 Future adaptation strategies using high yielding spring wheat ideotypes . 58