Abstract
Abstract. Climate change will impact agricultural production both directly and indirectly, but uncertainties related to likely impacts constrain current political decision making on adaptation. This analysis focuses on a methodology for applying probabilistic climate change projections to assess modelled wheat yields and nitrate leaching from arable land in Denmark. The probabilistic projections describe a range of possible changes in temperature and precipitation. Two methodologies to apply climate projections in impact models were tested. Method A was a straightforward correction of temperature and precipitation, where the same correction was applied to the baseline weather data for all days in the year, and method B used seasonal changes in precipitation and temperature to correct the baseline weather data. Based on climate change projections for the time span 2000 to 2100 and two soil types, the mean impact and the uncertainty of the climate change projections were analysed. Combining probability density functions of climate change projections with crop model simulations, the uncertainty and trends in nitrogen (N) leaching and grain yields with climate change were quantified. The uncertainty of climate change projections was the dominating source of uncertainty in the projections of yield and N leaching, whereas the methodology to seasonally apply climate change projections had a minor effect. For most conditions, the probability of large yield reductions and large N leaching losses tracked trends in mean yields and mean N leaching. The impacts of the uncertainty in climate change were higher for loamy sandy soil than for sandy soils due to generally higher yield levels for loamy sandy soils. There were large differences between soil types in response to climate change, illustrating the importance of including soil information for regional studies of climate change impacts on cropping systems.
Highlights
Biophysical processes of agroecosystems are strongly affected by environmental conditions
The temperature rises at nearly the same rate for all four seasons, there is a tendency for the largest increases to occur in the summer period (JJA) and the smallest in spring (MAM)
A new method for obtaining probabilistic CC projections of N leaching and grain yield due to climate change was applied for winter wheat in Denmark
Summary
Biophysical processes of agroecosystems are strongly affected by environmental conditions. The projected increases in greenhouse gases will affect agroecosystems either directly (primarily by increasing photosynthesis at higher CO2 concentrations; Long et al, 2006) or indirectly via effects on climate (e.g., temperature and precipitation affecting several aspects of ecosystem functioning, Olesen and Bindi, 2002). Many studies have assessed effects of climate change (CC) on agricultural productivity in Europe (e.g., Harrison et al, 2000; Maracchi et al, 2005; Olesen et al, 2007, 2011; Challinor et al, 2009). Much fewer studies have attempted to quantify the effect of the uncertainty of the climate change projections on crop production.
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