The task of predicting crop responses to climate would be easy if crop yield were determined by a single and simple biological process. The reality, of course, is more complex. Crop growth and reproduction are governed by many interacting processes that present an enormous challenge to efforts at prediction. Many of the most relevant processes have been outlined in Chapter 4, which describes efforts to develop models that capture the essence of each process without being too complex to prevent reliable model calibration and applications.

An alternative to this process-based approach is to rely on the statistical relationships that emerge between historical records of crop production and weather variations. In short, we observe the past and use it to build models to inform the future. From the outset, it should be clear that purely statistical approaches, whether based on time series as discussed in this chapter or cross-sectional data as discussed in the next, are not inherently better or worse than more process-based approaches. There are some disadvantages, such as difficulty in extrapolating beyond historical extremes, as well as some advantages, such as limited data requirements and the potential to capture effects of processes that are relatively poorly understood, such as pest dynamics.

It should also be clear that statistical approaches cannot proceed successfully without some consideration of the underlying processes. For example, the choice of which months of weather to consider will depend on the growing season of the

D. Lobell

Stanford University, CA, USA

D. Lobell and M. Burke (eds.), Climate Change and Food Security, Advances in Global Change Research 37, DOI 10.1007/978-90-481-2953-9_5, © Springer Science + Business Media, B.V. 2010

crop, and the choice of what climate variables to use will depend on the processes thought to be most important. Such considerations will be explained in more detail below. The general point, and one that is often confused in the existing literature, is that the distinction between "process-based" and "statistical" models is somewhat arbitrary. All process-based models have some level of empiricism, and all statistical models have some underlying assumptions about processes.

This chapter seeks to describe time series based approaches to crop modeling, highlighting the important decisions that can affect the outcomes. Time series models have been widely used to evaluate the impacts of climate variability and change on crop production. They are particularly useful in situations where there is insufficient data to calibrate more process-based models, and where detailed spatial datasets are not available, both of which are accurate descriptions of the situation in many developing countries. Their main requirement is the availability of sufficiently long time series (at least 20 years) of both weather and crop harvests.

Renewable Energy 101

Renewable Energy 101

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.

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