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Improved soil and water quality, and carbon sequestration are notable benefits of agroforestry practices compared to row-crop agriculture. Over an agricultural watershed with two buffer cropping systems (agroforestry buffers and grass buffers) soybean crop evapotranspiration was calculated from the Penman-Monteith equation using 10-min averages of meteorological measurements within crop alleys for 54 days in summer 2007. Wind speeds were consistently lower over the agroforestry buffer portion of the watershed by an average of 0.42 m s-1. For calculated evapotranspiration assuming water-stressed conditions, this decrease in wind speed from the presence of agroforestry buffers was offset almost entirely by an increase in net radiation. Net radiation differences between the two systems were highest during the morning (~40 W m-2) and were likely the result of solar radiation scattered from the agroforestry buffers. Wind speed reduction over the crop portion surrounded by agroforestry buffers varied by wind direction with daytime winds ≥ 0.6 m s-1 greater over the grass buffer portion of the crop for northerly and southerly winds (nearly perpendicular to the agroforestry buffers). Therefore, buffer orientation relative to the prevailing wind is important for reducing evapotranspiration. Changes in crop alley width would be expected to impact the portion of the crop within windsheltered zones and the portion receiving scattered radiation from trees. The sensitivity of evapotranspiration to agroforestry buffer orientation and crop alley width should be a focus of future investigations.

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The focus of this dissertation was on the role played by weather classification in regression-based downscaling of daily temperature extrema. Three closely related studies were conducted, each using a different criterion for weather classification. The primary objective of all these studies was to evaluate changes in downscaling model performance as meteorological properties of the training periods were varied. This objective was of interest due to potential improvements in downscaling performance when accounting for non-static relationships between predictors and predictands. The first study used the time of day of the temperature extremum as the weather classification, while the third study used the direction of the wind as the weather classification. The second study used temperature as the weather classification, with a focus on possible consequences for downscaling in warmer conditions that were not present in the training conditions. Results from all three studies indicated that downscaling performance had the potential to be affected by the weather conditions seen in the training periods.

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