Sigrun Hjalmarsdottir Kværnø

Research Scientist

(+47) 918 89 028
sigrun.kvaerno@nibio.no

Place
Ås O43

Visiting address
Oluf Thesens vei 43, 1433 Ås

Abstract

A large proportion of the soils in Norway require artificial drainage to improve the conditions for crop growth and field operations, but also to reduce the risk of soil compaction, surface runoff and erosion. The need for artificial drainage depends on climate, topography, soil type, groundwater conditions, and also the crop. At present, about 60-70 % of the agricultural land in Norway is artificially drained. Future climate change is expected to lead to higher temperatures, more precipitation and more frequent extreme events in Norway. This poses a challenge with respect to the drainage systems as more intensive drainage than present today may be required in some areas, although it is unclear whether this will be an efficient solution. In this study we aimed to evaluate the possible future changes in subsurface runoff and water balance elements at the Kvithamar experimental site. We set up the and calibrated the DrainMod model for the experimental data from poorly and optimally drained experimental fields. The calibrated model was further used to evaluate changes in subsurface runoff and the water cycle as a whole under changing conditions. We tested the effect of different drainage system designs (drain depth and spacing) on water regime under present and future climate conditions. It was quite difficult to calibrate the DrainMod model for surface runoff and drain flow measured from the Kvithamar lysimeter plots and to find a parameter set that could give a reasonable partitioning of the water. We concluded that due to the complexity of the hydrological regime of a drained field the effect of drains can be masked by other factors, like land use and spatio-temporal variability of soil properties. Our simulation results indicate that drainage system design has a big effect on surface and subsurface runoff as well as on evapotranspiration. Concerning future changes in the hydrological regime, the results varied depending on the future climate scenarios selected.

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Abstract

Measures designed to control erosion serve two purposes: on site (reduce soil loss) and off site (reduce sediment delivery to streams and lakes). While these objectives often coincide or at least are complementary, they could result in different priority areas when spatial planning is concerned. Prioritising for soil loss reduction at the field level will single out areas with high erosion risk. When sediment flux at the catchment scale is concerned, sediment pathways need to be identified in ex ante analyses of soil conservation plans. In Norway, different subsidy schemes are in place to reduce the influx of solutes and sediments to the freshwater system. Financial support is given to agronomic measures, the most important of which is reduced autumn tillage where areas with higher erosion risk receive higher subsidies. The objectives of this study are (1) to assess the use of an index of connectivity to estimate specific sediment yields, and (2) to test whether conservation measures taken in critical source areas are more effective than those taken at where erosion risk levels are the highest. Different modelling approaches are combined to assess soil loss at catchment level from sheet and gully erosion and soil losses through the drainage system. A calibration on two parameters gave reasonable results for annual soil loss. This model calibration was then used to quantify the effectiveness of three strategies for spatial prioritisation: according to hydrological connectivity, sheet erosion risk level and estimated specific sediment yield. The latter two strategies resulted in a maximum reduction in total soil loss due to reduced autumn tillage of 10%. Both model performance and the effectiveness of the different prioritisation strategies varied between the study catchments.

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Abstract

Soils are vital for supporting food security and other ecosystem services. Climate change can affect soil functions both directly and indirectly. Direct effects include temperature, precipitation, and moisture regime changes. Indirect effects include those that are induced by adaptations such as irrigation, crop rotation changes, and tillage practices. Although extensive knowledge is available on the direct effects, an understanding of the indirect effects of agricultural adaptation options is less complete. A review of 20 agricultural adaptation case‐studies across Europe was conducted to assess implications to soil threats and soil functions and the link to the Sustainable Development Goals (SDGs). The major findings are as follows: (a) adaptation options reflect local conditions; (b) reduced soil erosion threats and increased soil organic carbon are expected, although compaction may increase in some areas; (c) most adaptation options are anticipated to improve the soil functions of food and biomass production, soil organic carbon storage, and storing, filtering, transforming, and recycling capacities, whereas possible implications for soil biodiversity are largely unknown; and (d) the linkage between soil functions and the SDGs implies improvements to SDG 2 (achieving food security and promoting sustainable agriculture) and SDG 13 (taking action on climate change), whereas the relationship to SDG 15 (using terrestrial ecosystems sustainably) is largely unknown. The conclusion is drawn that agricultural adaptation options, even when focused on increasing yields, have the potential to outweigh the negative direct effects of climate change on soil degradation in many European regions.

Abstract

Farmers are exposed to climate change and uncertainty about how that change will develop. As farm incomes, in Norway and elsewhere, greatly depend on government subsidies, the risk of a policy change constitutes an additional uncertainty source. Hence, climate and policy uncertainty could substantially impact agricultural production and farm income. However, these sources of uncertainty have, so far, rarely been combined in food production analyses. The aim of this study was to determine the effects of a combination of policy and climate uncertainty on agricultural production, land use, and social welfare in Norway. Output yield distributions of spring wheat and timothy, a major forage grass, from simulations with the weatherdriven crop models, CSM-CERES-Wheat and, LINGRA, were processed in the a stochastic version Jordmod, a price-endogenous spatial economic sector model of the Norwegian agriculture. To account for potential effects of climate uncertainty within a given future greenhouse gas emission scenario on farm profitability, effects on conditions that represented the projected climate for 2050 under the emission scenario A1B from the 4th assessment report of the Intergovernmental Panel on Climate Change and four Global Climate Models (GCM) was investigated. The uncertainty about the level of payment rates at the time farmers make their management decisions was handled by varying the distribution of payment rates applied in the Jordmod model. These changes were based on the change in the overall level of agricultural support in the past. Three uncertainty scenarios were developed and tested: one with climate change uncertainty, another with payment rate uncertainty, and a third where both types of uncertainty were combined. The three scenarios were compared with results from a deterministic scenario where crop yields and payment rates were constant. Climate change resulted in on average 9% lower cereal production, unchanged grass production and more volatile crop yield as well as 4% higher farm incomes on average compared to the deterministic scenario. The scenario with a combination of climate change and policy uncertainty increased the mean farm income more than a scenario with only one source of uncertainty. On the other hand, land use and farm labour were negatively affected under these conditions compared to the deterministic case. Highlighting the potential influence of climate change and policy uncertainty on the performance of the farm sector our results underline the potential error in neglecting either of these two uncertainties in studies of agricultural production, land use and welfare.

Abstract

Knowledge of hydrological processes and water balance elements are important for climate adaptive water management as well as for introducing mitigation measures aiming to improve surface water quality. Mathematical models have the potential to estimate changes in hydrological processes under changing climatic or land use conditions. These models, indeed, need careful calibration and testing before being applied in decision making. The aim of this study was to compare the capability of five different hydrological models to predict the runoff and the soil water balance elements of a small catchment in Norway. The models were harmonised and calibrated against the same data set. In overall, a good agreement between the measured and simulated runoff was obtained for the different models when integrating the results over a week or longer periods. Model simulations indicate that forest appears to be very important for the water balance in the catchment, and that there is a lack of information on land use specific water balance elements. We concluded that joint application of hydrological models serves as a good background for ensemble modelling of water transport processes within a catchment and can highlight the uncertainty of models forecast.

Abstract

The effects of soil variability on regional crop yield under projected climate change are largely unknown. In Southeastern Norway, increased temperature and precipitation are projected for the mid-21st century. Crop simulation models in combination with scaling techniques can be used to determine the regional pattern of crop yield. In the present paper, the CSM-CROPSIM-CERES-Wheat model was applied to simulate regional spring wheat yield for Akershus and Østfold counties in Southeastern Norway. Prior to the simulations, parameters in the CSM-CROPSIM-CERES-Wheat model were calibrated for the spring wheat cvars Zebra, Demonstrant and Bjarne, using cultivar trial data from Southeastern Norway and site-specific weather and soil information.Weather input data for regional yield simulations represented the climate in 1961–1990 and projections of the climate in 2046–2065. The latter were based on four Global Climate Models and greenhouse gas emission scenario A1B in the IPCC 4th Assessment Report. Data on regional soil particle size distribution, water-holding characteristics and organic matter data were obtained from a database. To determine the simulated grain yield sensitivity to soil input, the number of soil profiles used to describe the soilscape in the region varied from 76 to 16, 5 and 1. The soils in the different descriptions were selected by arranging them into groups according to similarities in physical characteristics and taking the soil in each group occupying the largest area in the region to represent other soils in that group. The simulated grain yields were higher under all four projected future climate scenarios than the corresponding average yields in the baseline conditions. On average across the region, there were mostly non-significant differences in grain yield between the soil extrapolations for all cultivars and climate projections. However, for sub-regions grain yield varied by up to 20% between soil extrapolations. These results indicate how projected climate change could affect spring wheat yield given the assumed simulated conditions for a region with similar climate and soil conditions to many other cereal production regions in Northern Europe. The results also provide useful information about how soil input data could be handled in regional crop yield determinations under these conditions.

Abstract

Interactions between soil properties and climate affect forage grass productivity. Dynamic models, simulating crop performance as a function of environmental conditions, are valid for a specific location with given soil and weather conditions. Extrapolations of local soil properties to larger regions can help assess the requirement for soil input in regional yield estimations. Using the LINGRA model, we simulated the regional yield level and variability of timothy, a forage grass, in Akershus and Østfold counties, Norway. Soils were grouped according to physical similarities according to 4 sets of criteria. This resulted in 66, 15, 5 and 1 groups of soils. The properties of the soil with the largest area was extrapolated to the other soils within each group and input to the simulations. All analyses were conducted for 100 yr of generated weather representing the period 1961-1990, and climate projections for the period 2046-2065, the Intergovernmental Panel on Climate Change greenhouse gas emission scenario A1B, and 4 global climate models. The simulated regional seasonal timothy yields were 5-13% lower on average and had higher inter-annual variability for the least detailed soil extrapolation than for the other soil extrapolations, across climates. There were up to 20% spatial intra-regional differences in simulated yield between soil extrapolations. The results indicate that, for conditions similar to these studied here, a few representative profiles are sufficient for simulations of average regional seasonal timothy yield. More spatially detailed yield analyses would benefit from more detailed soil input.

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Abstract

There is a common need for reliable hydropedological information in Europe. In the last decades research institutes, universities and government agencies have developed local, regional and national datasets containing soil physical, chemical, hydrological and taxonomic information often combined with land use and landform data. A hydrological database for western European soils was also created in the mid-1990s. However, a comprehensive European hydropedological database, with possible additional information on chemical parameters and land use is still missing. A comprehensive joint European hydropedological inventory can serve multiple purposes, including scientific research, modelling and application of models on different geographical scales. The objective of the joint effort of the participants is to establish the European Hydropedological Data Inventory (EU-HYDI). This database holds data from European soils focusing on soil physical, chemical and hydrological properties. It also contains information on geographical location, soil classification and land use/cover at the time of sampling. It was assembled with the aim of encompassing the soil variability in Europe. It contains data from 18 countries with contributions from 29 institutions. This report presents an overview of the database, details the individual contributed datasets and explains the quality assurance and harmonization process that lead to the final database.

Abstract

The source of input data for soil physical properties may contribute to uncertainty in simulated catchment response. The objective of this study was to quantify the uncertainty in catchment surface runoff and erosion predicted by the physically based model LISEM, as influenced by uncertainty in soil texture and SOM content, and the pedotransfer function derived soil water retention curve, hydraulic conductivity, aggregate stability and cohesion. LISEM was first calibrated using measured data in a sub-catchment, and then run for the whole catchment for a summer storm event with basic input data from two data sources: soil series specific generic data from the national soil survey database, and measured data collected in a grid within the catchment. The measured data were assigned in two ways: mean values per map unit, or random distribution (50 realizations) per map unit. The model was run both for a low risk situation (crop covered surface) and a high risk situation (without crop cover and with reduced aggregate stability and cohesion). The main results were that 1) using non-local database data yielded much higher peak discharge and five to six times higher soil loss than using locally measured data, 2) there was little difference in simulated runoff and soil loss between the two approaches (mean value versus randomdistribution) to assign locally measured data, 3) differences between the 50 random realizationswere insignificant, for both low-risk and high-risk situations, and 4) uncertainty related to input data could result in larger differences between runswith different input data source than between runswith the same input data source but extreme differences in erosion risk. The main conclusion was that inadequate choice of input data source can significantly affect general soil loss and the effect of measures.