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

There is a scientific consensus that the future climate change will affect grass and crop dry matter (DM) yields. Such yield changes may entail alterations to farm management practices to fulfill the feed requirements and reduce the farm greenhouse gas (GHG) emissions from dairy farms. While a large number of studies have focused on the impacts of projected climate change on a single farm output (e.g. GHG emissions or economic performance), several attempts have been made to combine bio-economic systems models with GHG accounting frameworks. In this study, we aimed to determine the physical impacts of future climate scenarios on grass and wheat DM yields, and demonstrate the effects such changes in future feed supply may have on farm GHG emissions and decision-making processes. For this purpose, we combined four models: BASGRA and CSM-CERESWheat models for simulating forage grass DM and wheat DM grain yields respectively; HolosNor for estimating the farm GHG emissions; and JORDMOD for calculating the impacts of changes in the climate and management on land use and farm economics. Four locations, with varying climate and soil conditions were included in the study: south-east Norway, south-west Norway, central Norway and northern Norway. Simulations were carried out for baseline (1961–1990) and future (2046–2065) climate conditions (projections based on two global climate models and the Special Report on Emissions Scenarios (SRES) A1B GHG emission scenario), and for production conditions with and without a milk quota. The GHG emissions intensities (kilogram carbon dioxide equivalent: kgCO2e emissions per kg fat and protein corrected milk: FPCM) varied between 0.8 kg and 1.23 kg CO2e (kg FPCM)−1 , with the lowest and highest emissions found in central Norway and south-east Norway, respectively. Emission intensities were generally lower under future compared to baseline conditions due mainly to higher future milk yields and to some extent to higher crop yields. The median seasonal aboveground timothy grass yield varied between 11,000 kg and 16,000 kg DM ha−1 and was higher in all projected future climate conditions than in the baseline. The spring wheat grain DM yields simulated for the same weather conditions within each climate projection varied between 2200 kg and 6800 kg DM ha−1 . Similarly, the farm profitability as expressed by total national land rents varied between 1900 million Norwegian krone (NOK) for median yields under baseline climate conditions up to 3900 million NOK for median yield under future projected climate conditions.

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Abstract

Based on soil temperature, snow depth and the grown cultivar's maximum attainable level of frost tolerance (LT50c), the FROSTOL model simulates development of frost tolerance (LT50) and winter damage, thereby enabling risk calculations for winter wheat survival. To explore the accuracy of this model, four winter wheat cultivars were sown in a field experiment in Uppsala, Sweden in 2013 and 2014. The LT50 was determined by tests of frost tolerance in November, and the cultivars’ LT50c was estimated. Further, recorded winter survival from 20 winter wheat field variety trials in Sweden and Norway was collected from two winter seasons with substantial winter damages. FROSTOL simulations were run for selected cultivars at each location. According to percentage of winter damage, the cultivar survival was classified as “survived,” “intermediate” or “killed.” Mean correspondence between recorded and simulated class of winter survival was 75% and 37% for the locations in Sweden and Norway, respectively. Stress factors that were not accounted for in FROSTOL might explain the poorer accuracy at the Norwegian locations. The accuracy was poorest for cultivars with intermediate LT50c levels. When low temperature was the main cause of damage, as at the Swedish locations, the model accuracy was satisfying.

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Abstract

The rapid increase of the world population constantly demands more food production from agricultural soils. This causes conflicts, since at the same time strong interest arises on novel bio-based products from agriculture, and new perspectives for rural landscapes with their valuable ecosystem services. Agriculture is in transition to fulfill these demands. In many countries, conventional farming, influenced by post-war food requirements, has largely been transformed into integrated and sustainable farming. However, since it is estimated that agricultural production systems will have to produce food for a global population that might amount to 9.1 billion by 2050 and over 10 billion by the end of the century, we will require an even smarter use of the available land, including fallow and derelict sites. One of the biggest challenges is to reverse non-sustainable management and land degradation. Innovative technologies and principles have to be applied to characterize marginal lands, explore options for remediation and re-establish productivity. With view to the heterogeneity of agricultural lands, it is more than logical to apply specific crop management and production practices according to soil conditions. Cross-fertilizing with conservation agriculture, such a novel approach will provide (1) increased resource use efficiency by producing more with less (ensuring food security), (2) improved product quality, (3) ameliorated nutritional status in food and feed products, (4) increased sustainability, (5) product traceability and (6) minimized negative environmental impacts notably on biodiversity and ecological functions. A sustainable strategy for future agriculture should concentrate on production of food and fodder, before utilizing bulk fractions for emerging bio-based products and convert residual stage products to compost, biochar and bioenergy. The present position paper discusses recent developments to indicate how to unlock the potentials of marginal land.

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Abstract

Deoxynivalenol (DON) in cereals, produced by Fusarium fungi, cause poisoning in humans and animals. Fusarium infections in cereals are favoured by humid conditions. Host species are susceptible mainly during the anthesis stage. Infections are also positively correlated with a regional history of Fusarium infections, frequent cereal production and non-tillage field management practices. Here, previously developed process-based models based on relative air humidity, rain and temperature conditions, Fusarium sporulation, host phenology and mycelium growth in host tissue were adapted and tested on oats. Model outputs were used to calculate risk indices. Statistical multivariate models, where independent variables were constructed from weather data, were also developed. Regressions of the risk indices obtained against DON concentrations in field experiments on oats in Sweden and Norway 2012–14 had coefficient of determination values (R2) between 0.84 and 0.88. Regressions of the same indices against DON concentrations in oat samples averaged for 11 × 11 km grids in farmers’ fields in Sweden 2012–14 resulted in R2 values between 0.27 and 0.41 for randomly selected grids and between 0.31 and 0.62 for grids with average DON concentration above 1000 μg kg–1 grain in the previous year. When data from all three years were evaluated together, a cross-validated statistical partial least squares model resulted in R2 = 0.70 and a standard error of cross-validation (SECV) = 522 μg kg–1 grain for the period 1 April–28 August in the construction of independent variables and R2 = 0.54 and SECV = 647 μg kg–1 grain for 1 April–23 June. Factors that were not accounted for in this study probably explain large parts of the variation in DON among samples and make further model development necessary before these models can be used practically. DON prediction in oats could potentially be improved by combining weather-based risk index outputs with agronomic factors.

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Abstract

Accurate estimation of winter wheat frost kill in cold-temperate agricultural regions is limited by lack of data on soil temperature at wheat crown depth, which determines winter survival. We compared the ability of four models of differing complexity to predict observed soil temperature at 2 cm depth during two winter seasons (2013-14 and 2014-15) at Ultuna, Sweden, and at 1 cm depth at Ilseng and Ås, Norway. Predicted and observed soil temperature at 2 cm depth was then used in FROSTOL model simulations of the frost tolerance of winter wheat at Ultuna. Compared with the observed soil temperature at 2 cm depth, soil temperature was better predicted by detailed models than simpler models for both seasons at Ultuna. The LT50 (temperature at which 50 % of plants die) predictions from FROSTOL model simulations using input from the most detailed soil temperature model agreed better with LT50 FROSTOL outputs from observed soil temperature than what LT50 FROSTOL predictions using temperature from simpler models did. These results highlight the need for simpler temperature prediction tools to be further improved when used to evaluate winter wheat frost kill.

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

Simulation models are widely used to assess the impact of climate change on crop production and adaptation options, but few model comparisons have been done to assess uncertainties in the simulation results of forage grass models. The aim of this study was to compare the performance of three models (BASGRA, CATIMO, and STICS) to simulate the dry matter yield of the first and second cut of timothy (Phleum pratense L.) using observed field data from a wide range of climatic conditions, cultivars, soil types and crop management practices that are associated with timothy production in its main production regions in Canada and Northern Europe. The performance of the models was assessed with both cultivarspecific and non-cultivar-specific (generic) calibrations. The results showed the strengths and weaknesses of different modelling approaches and the magnitude of uncertainty related to simulated timothy grass yield. Model results were sensitive to calibrations applied.

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Abstract

Grassland-based ruminant production systems are integral to sustainable food production in Europe, converting plant materials indigestible to humans into nutritious food, while providing a range of environmental and cultural benefits. Climate change poses significant challenges for such systems, their productivity and the wider benefits they supply. In this context, grassland models have an important role in predicting and understanding the impacts of climate change on grassland systems, and assessing the efficacy of potential adaptation and mitigation strategies. In order to identify the key challenges for European grassland modelling under climate change, modellers and researchers from across Europe were consulted via workshop and questionnaire. Participants identified fifteen challenges and considered the current state of modelling and priorities for future research in relation to each. A review of literature was undertaken to corroborate and enrich the information provided during the horizon scanning activities. Challenges were in four categories relating to: 1) the direct and indirect effects of climate change on the sward 2) climate change effects on grassland systems outputs 3) mediation of climate change impacts by site, system and management and 4) cross-cutting methodological issues. While research priorities differed between challenges, an underlying theme was the need for accessible, shared inventories of models, approaches and data, as a resource for stakeholders and to stimulate new research. Developing grassland models to effectively support efforts to tackle climate change impacts, while increasing productivity and enhancing ecosystem services, will require engagement with stakeholders and policy-makers, as well as modellers and experimental researchers across many disciplines. The challenges and priorities identified are intended to be a resource 1) for grassland modellers and experimental researchers, to stimulate the development of new research directions and collaborative opportunities, and 2) for policy-makers involved in shaping the research agenda for European grassland modelling under climate change.

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Abstract

Process-based models (PBM) for simulation of weather dependent grass growth can assist farmers andplant breeders in addressing the challenges of climate change by simulating alternative roads of adap-tation. They can also provide management decision support under current conditions. A drawback ofexisting grass models is that they do not take into account the effect of winter stresses, limiting theiruse for full-year simulations in areas where winter survival is a key factor for yield security. Here, wepresent a novel full-year PBM for grassland named BASGRA. It was developed by combining the LIN-GRA grassland model (Van Oijen et al., 2005a) with models for cold hardening and soil physical winterprocesses. We present the model and show how it was parameterized for timothy (Phleum pratense L.),the most important forage grass in Scandinavia and parts of North America and Asia. Uniquely, BASGRAsimulates the processes taking place in the sward during the transition from summer to winter, includ-ing growth cessation and gradual cold hardening, and functions for simulating plant injury due to lowtemperatures, snow and ice affecting regrowth in spring. For the calibration, we used detailed data fromfive different locations in Norway, covering a wide range of agroclimatic regions, day lengths (latitudesfrom 59◦to 70◦N) and soil conditions. The total dataset included 11 variables, notably above-ground drymatter, leaf area index, tiller density, content of C reserves, and frost tolerance. All data were used inthe calibration. When BASGRA was run with the maximum a-posteriori (MAP) parameter vector fromthe single, Bayesian calibration, nearly all measured variables were simulated to an overall normalizedroot mean squared error (NRMSE) < 0.5. For many site × experiment combinations, NRMSE was <0.3. Thetemporal dynamics were captured well for most variables, as evaluated by comparing simulated timecourses versus data for the individual sites. The results may suggest that BASGRA is a reasonably robustmodel, allowing for simulation of growth and several important underlying processes with acceptableaccuracy for a range of agroclimatic conditions. However, the robustness of the model needs to be testedfurther using independent data from a wide range of growing conditions. Finally we show an exampleof application of the model, comparing overwintering risks in two climatically different sites, and dis-cuss future model applications. Further development work should include improved simulation of thedynamics of C reserves, and validation of winter tiller dynamics against independent data.

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.