The response of crop yields to elevated [CO2] is a key parameter in projections of the effects of climate change on global crop yields, world food supply and risk of hunger in the future (e.g., Parry et al. 1999, 2004). Inclusion of the direct effects of elevated [CO2] in a recent assessment substantially improved estimated world cereal prices and reduced the risk of hunger for 500 million people by 2080 (Parry et al. 2005). Process-based crop models with deterministic equations of underlying physiological processes compute crop growth and development, biomass partitioning and economic yield in response to environmental inputs (see Chapter 4). A CO2-response factor can be applied to different physiological processes in the models in order to reflect the direct or indirect effects of elevated [CO2] (reviewed by Tubiello and Ewert 2002). While the direct, instantaneous effects of elevated [CO2] on photosynthesis and stomatal conductance can be accurately modeled (Farquhar et al. 1980; Ball et al. 1987), scaling these direct effects into long-term crop growth and ultimately seed yield is much more challenging.
Furthermore, mechanistic equations such as the Farquhar et al. (1980) model of C3 photosynthesis are only occasionally used to calculate a CO2 response (e.g., in the DEMETER model, Kartschall et al. 1995); more often, simple linear or curvi-linear multipliers are used to model the effects of [CO2] on photosynthesis, stomatal conductance, carbon partitioning, plant water relations and/or yield (Tubiello and Ewert 2002; Parry et al. 2004). Literature reviews from the 1980s are reportedly used as the basis for these linear or curvi-linear multipliers (e.g., Kimball 1983; Rogers et al. 1983; Cure and Acock 1986; Allen et al. 1987; Peart et al. 1989), which has raised the concern that estimates of future food supply may be overly optimistic (Long et al. 2005, 2006; Ainsworth et al. 2008b). While this issue has been the subject of debate, it is clear that before incorporating any crop model into an assessment of climate change impacts on global crop production and food supply, it is critical to evaluate the crop model's performance against field data (Tubiello and Ewert 2002).
A number of process-based crop models have been evaluated against data from FACE experiments (Tubiello et al. 1999; Ewert et al. 2002; Kartschall et al. 1995; Grossman-Clarke et al. 2001; Grant et al. 1999; Jamieson et al. 2000; Bannayan et al. 2005; Asseng et al. 2004). In addition to CO2 treatments, these models have tested the interaction of CO2 with drought stress and N supply. A comparison of effects of elevated [CO2] on wheat and rice grain yield from two FACE experiments and five crop model simulations is shown in Fig. 7.3. LINTULCC2 and AFRCWHEAT2 were able to capture the stronger effect of elevated [CO2] on wheat yields under water-stressed conditions compared to well-watered conditions; however, the magnitude of the stimulation in the model was greater than the stimulation in the field (Fig. 7.3a; Ewert et al. 2002). APSIM-N also captured a positive effect of elevated [CO2] on wheat yield under high N fertilization, but again the magnitude of the modeled response was greater than the FACE result (Fig. 7.3b; Asseng et al. 2004). Finally, the Oryza2000 model matched very well with the FACE results,
LINTULCC2 AFRCWHEAT2 Sirius
High N Low N
High N Medium N Low N
Fig. 7.3 A comparison of stimulation of crop yields from crop models and FACE experiments. (a) A comparison of wheat yields from three crop models (LINTULCC2, AFRCWHEAT2, Sirius) with the Maricopa Free-Air CO2 Enrichment (FACE) experiment (Ewert et al. 2002). (b) A comparison of the APSIM-N crop model with wheat yield results from the Maricopa FACE experiment (Asseng et al. 2004). (c) A comparison of the 0ryza2000 crop model with rice yield results from a FACE experiment in northern Japan (Bannayan et al. 2005)
b c except for under low N conditions (Fig. 7.3c; Bannayan et al. 2005). Overall, the models successfully captured the direction of the response of wheat and rice yields to elevated [CO2], but the magnitude of the modeled output was often significantly different from the experimental results (Fig. 7.3). More often than not, the crop models overestimated the actual yield stimulation measured in the field.
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Renewable energy is energy that is generated from sunlight, rain, tides, geothermal heat and wind. These sources are naturally and constantly replenished, which is why they are deemed as renewable. The usage of renewable energy sources is very important when considering the sustainability of the existing energy usage of the world. While there is currently an abundance of non-renewable energy sources, such as nuclear fuels, these energy sources are depleting. In addition to being a non-renewable supply, the non-renewable energy sources release emissions into the air, which has an adverse effect on the environment.