• Energy Research

We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.

Atmospheric carbon dioxide concentrations ([CO₂]) are increasing, but little is known about how this will affect macronutrient (nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg)) accumulation and partitioning in the aboveground biomass (AGB) for different hard spring wheat genotypes. We examined the responses of six spring wheat genotypes (‘Discovery’, ‘Duchess’, ‘Reliance’, PFR-3026, PFR-3019, PFR-2021) to two CO₂ levels (ambient [aCO₂] and elevated [eCO₂]) and six nitrogen rates (N; 1–10 mM), at the stem elongation growth stage of wheat grown in controlled environment chambers. The AGB yield increased by 35.2% with increasing [CO₂] when N rate was >2 mM. Increasing N supply also increased AGB by up to 3.2-fold over the entire N range applied. The AGB responses to N differed among the genotypes, being lowest for PFR-3019 (7.71 ± 0.11 g/pot) and highest for PFR-2021, PFR-3026 and Duchess at 8.84 ± 0.11 g/pot at both CO₂ levels. Macronutrient concentrations decreased with eCO₂ by 28.0% for Ca to 17.4% for P and K. Nevertheless, absolute nutrient uptake was higher for eCO₂ treatments, because the AGB increase (20.0–52.0%) was proportionally higher than the 4.0–28.0% increase in nutrient uptake. The AGB non-response to [CO₂] at N rates <2mM indicates that this nutrient deficiency was more limiting than the effects of CO₂ level. Therefore, the impact of eCO₂ in the future will depend on N fertilizer management. These results suggest that critical nutrient concentrations used to diagnose the nutrient status of wheat crops will need to be reassessed for eCO₂ conditions.

AbstractPotential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly −0.5 Mg ha−1 per °C. Doubling [CO2] from 360 to 720 μmol mol−1 increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2] among models. Model responses to temperature and [CO2] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.