ALBERT

Description: 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.

Description: SUMMARYThere is a very significant, cost effective greenhouse gas (GHG) mitigation potential in agriculture. The annual mitigation potential in agriculture is estimated to be 4200, 2600 and 1600 Mt CO2 equiv/yr at C prices of 100, 50 and 20 US$/t CO2 equiv, respectively. The value of GHG mitigated each year is equivalent to 420 000, 130 000 and 32 000 million US$/yr for C prices of 100, 50 and 20 US$/t CO2 equiv, respectively. From both the mitigation and economic perspectives, we cannot afford to miss out on this mitigation potential.The challenge of agriculture within the climate change context is two-fold, both to reduce emissions and to adapt to a changing and more variable climate. The primary aim of the mitigation options is to reduce emissions of methane or nitrous oxide or to increase soil carbon storage. All the mitigation options, therefore, affect the carbon and/or nitrogen cycle of the agroecosystem in some way. This often not only affects the GHG emissions but also the soil properties and nutrient cycling. Adaptation to increased variability of temperature and rainfall involves increasing the resilience of the production systems. This may be done by improving soil water holding capacities through adding crop residues and manure to arable soils or by adding diversity to the crop rotations.Though some mitigation measures may have negative impacts on the adaptive capacity of farming systems, most categories of adaptation options for climate change have positive impacts on mitigation. These include: (1) measures that reduce soil erosion, (2) measures that reduce leaching of nitrogen and phosphorus, (3) measures for conserving soil moisture, (4) increasing the diversity of crop rotations by choices of species or varieties, (5) modification of microclimate to reduce temperature extremes and provide shelter, (6) land use change involving abandonment or extensification of existing agricultural land, or avoidance of the cultivation of new land. These adaptation measures will in general, if properly applied, reduce GHG emissions, by improving nitrogen use efficiencies and improving soil carbon storage.There appears to be a large potential for synergies between mitigation and adaptation within agriculture. This needs to be incorporated into economic analyses of the mitigation costs. The inter-linkages between mitigation and adaptation are, however, not very well explored and further studies are warranted to better quantify short- and long-term effects on suitability for mitigation and adaptation to climate change. In order to realize the full potential for agriculture in a climate change context, new agricultural production systems need to be developed that integrate bioenergy and food and feed production systems. This may possibly be obtained with perennial crops having low-environmental impacts, and deliver feedstocks for biorefineries for the production of biofuels, biomaterials and feed for livestock.

Description: SUMMARYIt is predicted that climate change will increase not only seasonal air and soil temperatures in northern Europe but also the variability of rainfall patterns. This may influence temporal soil moisture regimes and the growth and yield of winter wheat. A lysimeter experiment was carried out in 2008/09 with three factors: rainfall amount, rainfall frequency and soil warming (two levels in each factor), on sandy loam soil in Denmark. The soil warming treatment included non-heated as the control and an increase in soil temperature by 5°C at 100 mm depth as heated. The rainfall treatment included the site mean for 1961–90 as the control and the projected monthly mean change for 2071–2100 under the International Panel on Climate Change (IPCC) A2 scenario for the climate change treatment. Projected monthly mean changes in rainfall compared to the reference period 1961–90 show, on average, 31% increase during winter (November–March) and 24% decrease during summer (July–September) with no changes during spring (April–June). The rainfall frequency treatment included mean monthly rainy days for 1961–90 as the control and a reduced frequency treatment with only half the number of rainy days of the control treatment, without altering the monthly mean rainfall amount. Mobile rain-out shelters, automated irrigation system and insulated heating cables were used to impose the treatments.Soil warming hastened crop development during early stages (until stem elongation) and shortened the total crop growing season by 12 days without reducing the period taken for later development stages. Soil warming increased green leaf area index (GLAI) and above-ground biomass during early growth, which was accompanied by an increased amount of nitrogen (N) in plants. However, the plant N concentration and its dilution pattern during later developmental stages followed the same pattern in both heated and control plots. Increased soil moisture deficit was observed only during the period when crop growth was significantly enhanced by soil warming. However, soil warming reduced N concentration in above-ground biomass during the entire growing period, except at harvest, by advancing crop development. Soil warming had no effect on the number of tillers, but reduced ear number and increased 1000 grain weight. This did not affect grain yield and total above-ground biomass compared with control. This suggests that genotypes with a longer vegetative period would probably be better adapted to future warmer conditions. The rainfall pattern treatments imposed in the present study did not influence either soil moisture regimes or performance of winter wheat, though the crop receiving future rainfall amount tended to retain more green leaf area. There was no significant interaction between the soil warming and rainfall treatments on crop growth.

Description: SUMMARYThe effects of projected changes in climate and atmospheric CO2 concentration on productivity and nitrogen (N) leaching of characteristic arable and pig farming rotations in Denmark were investigated with the FASSET simulation model. The LARS weather generator was used to provide climatic data for the baseline period (1961–90) and in combination with two regional circulation models (RCM) to generate climatic data under the Intergovernmental Panel on Climate Change (IPCC) A1B emission scenario for four different 20-year time slices (denoted by midpoints 2020, 2040, 2060 and 2080) for two locations in Denmark, differing in soil and climate, and representative of the selected production systems. The CO2 effects were modelled using projected CO2 concentrations for the A1B emission scenario. Crop rotations were irrigated (sandy soil) and unirrigated (sandy loam soil), and all included systems with and without catch crops, with field operation dates adapted to baseline and future climate change. Model projections showed an increase in the productivity and N leaching in the future that would be dependent on crop rotation and crop management, highlighting the importance of considering the whole rotation rather than single crops for impact assessments. Potato and sugar beet in arable farming and grain maize in pig farming contributed most to the productivity increase in the future scenarios. The highest productivity was obtained in the arable system on the sandy loam soil, with an increase of 20% on average in 2080 with respect to the baseline. Irrigation and fertilization rates would need to be increased in the future to achieve optimum yields. Growing catch crops reduces N leaching, but current catch crop management might not be sufficient to control the potential increase of leaching and more efficient strategies are required in the future. The uncertainty of climate change scenarios was assessed by using two different climate projections for predicting crop productivity and N leaching in Danish crop rotations, and this showed the consistency of the projected trends when used with the same crop model.

Description: SUMMARYData on grain yield from field trials on winter wheat under conventional farming, harvested between 1992 and 2008, were combined with daily weather data available for 44 grids covering Denmark. Nine agroclimatic indices were calculated and used for describing the relation between weather data and grain yield. These indices were calculated as average temperature, radiation and precipitation during winter (1 October–31 March), spring (1 April–15 June) and summer (16 June–31 July), and they were included as linear and quadratic covariates in a mixed regression model. The model also included an effect of year to describe the change in yield caused by unrecorded variables such as management changes. The final model included all effects that were significant for at least one of the two soil types (sandy and loamy soils). Seven of the nine agroclimatic indices were included in the final model that was used to predict the wheat grain yield under five climate scenarios (a baseline for 1985 and two climate change projections for 2020 and 2040) for two soil types and two locations in Denmark.The agroclimatic index for summer temperature showed the strongest effect causing lower yields with increasing temperature, whereas yield increased with increasing radiation during summer and spring. Winter precipitation and spring temperature did not affect grain yield significantly. Grain yield responded non-linearly to mean winter temperature with the highest yield at 4·4°C and lower yields both below and above this inflection point.The application of the model predicted that the average yield would decrease under projected climate change. The average decrease varied between 0·1 and 0·8 t/ha (comparable to a relative reduction of 1·6–12.3%) depending on the climate projection, location and soil type. On average, the grain yield decreased by about 0·25 t/ha (c. 3.6%) from 1985 to 2020 and by about 0·55 t/ha (c. 8·0%) from 1985 to 2040. The predicted yield decrease depended on climate projection and was larger for wheat grown in West Zealand than in Central Jutland and in most cases also larger for loamy soils than for sandy soils.The inter-annual variation in grain yield varied greatly between climate projections. The coefficient of variation (CV) varied between 0·16 and 0·46 and was smallest for wheat grown on loamy soils in Central Jutland in the baseline climate and largest for winter wheat grown under one of the 2040 climate projections. The increase in CV is not so much an effect of increased climatic variability under the climate change projections, but more an effect of increased winter temperature, where more extreme winter temperatures (lower or higher than the inflection point at 4·4°C) increased the effect of winter temperatures.

Description: SUMMARYThe objective of the present study was to compare the performance of seven different, widely applied crop models in predicting heat and drought stress effects. The study was part of a recent suite of model inter-comparisons initiated at European level and constitutes a component that has been lacking in the analysis of sources of uncertainties in crop models used to study the impacts of climate change. There was a specific focus on the sensitivity of models for winter wheat and maize to extreme weather conditions (heat and drought) during the short but critical period of 2 weeks after the start of flowering. Two locations in Austria, representing different agro-climatic zones and soil conditions, were included in the simulations over 2 years, 2003 and 2004, exhibiting contrasting weather conditions. In addition, soil management was modified at both sites by following either ploughing or minimum tillage. Since no comprehensive field experimental data sets were available, a relative comparison of simulated grain yields and soil moisture contents under defined weather scenarios with modified temperatures and precipitation was performed for a 2-week period after flowering. The results may help to reduce the uncertainty of simulated crop yields to extreme weather conditions through better understanding of the models’ behaviour. Although the crop models considered (DSSAT, EPIC, WOFOST, AQUACROP, FASSET, HERMES and CROPSYST) mostly showed similar trends in simulated grain yields for the different weather scenarios, it was obvious that heat and drought stress caused by changes in temperature and/or precipitation for a short period of 2 weeks resulted in different grain yields simulated by different models. The present study also revealed that the models responded differently to changes in soil tillage practices, which affected soil water storage capacity.