Statistics Seminar: Nathaniel Newlands (AAFC)

Natural resource problems typically must be modeled using data that is often incomplete, asynchronous and collected at different spatial and temporal scales with differing levels of measurement uncertainty. Both deterministic and stochastic models are widely applied in assessing environmental impacts, identifying risks and informing resource management decision-making for agricultural systems. However, existing models are high-dimensional, requiring extensive site-specific calibration, thereby limiting their spatial application. Likewise, simpler models, inevitably, must be combined to aid in more robust, integrative regional management or national policy-relevant decision-support. Using variable- and model-selection statistical techniques, one can identify models of intermediate complexity that can achieve appreciable reductions in parameter and structural uncertainty. In this way, such models may offer more reliable support to address a range of applications/problems and to identify critical thresholds and allocation trade-offs.

My talk will discuss several collaborative, inter-disciplinary projects that are investigating ways to improve the prediction and forecasting of crop production for food and energy in relation to water-use efficiency and climate variability across Canada.

I will highlight the use of wireless sensor monitoring network and satellite remote-sensing data. The talk will also showcase several national-scale, web-based decision-support systems currently in development. Here, the ability to refine and adapt models to take into account spatial and temporal-type operational constraints is of vital importance.