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Mäkinen, H., Kaseva, J., Trnka, M., Balek, J., Kersebaum, K. C., Nendel, C., et al. (2018). Sensitivity of European wheat to extreme weather. Field Crops Research, 222, 209–217.
Abstract: The frequency and intensity of extreme weather is increasing concomitant with changes in the global climate change. Although wheat is the most important food crop in Europe, there is currently no comprehensive empirical information available regarding the sensitivity of European wheat to extreme weather. In this study, we assessed the sensitivity of European wheat yields to extreme weather related to phenology (sowing, heading) in cultivar trials across Europe (latitudes 37.21 degrees to 61.34 degrees and longitudes- 6.02 degrees to 26.24 degrees) during the period 1991-2014. All the observed agro-climatic extremes (>= 31 degrees C, >= 35 degrees C, or drought around heading; >= 35 degrees C from heading to maturity; excessive rainfall; heavy rainfall and low global radiation) led to marked yield penalties in a selected set of European cultivars, whereas few cultivars were found to with no yield penalty in such conditions. There were no European wheat cultivars that responded positively (+ 10%) to drought after sowing, or frost during winter (- 15 degrees C and – 20 degrees C). Positive responses to extremes were often shown by cultivars associated with specific regions, such as good performance under high temperatures by southern-origin cultivars. Consequently, a major future breeding challenge will be to evaluate the potential of combining such cultivar properties with other properties required under different growing conditions with, for example, long day conditions at higher latitudes, when the intensity and frequency of extremes rapidly increase.
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Korhonen, P., Palosuo, T., Persson, T., Höglind, M., Jego, G., Van Oijen, M., et al. (2018). Modelling grass yields in northern climates – a comparison of three growth models for timothy. Field Crops Research, 224, 37–47.
Abstract: During the past few years, several studies have compared the performance of crop simulation models to assess the uncertainties in model-based climate change impact assessments and other modelling studies. Many of these studies have concentrated on cereal crops, while fewer model comparisons have been conducted for grasses. We compared the predictions for timothy grass (Phleum pratertse L.) yields for first and second cuts along with the dynamics of above-ground biomass for the grass simulation models BASGRA and CATIMO, and the soil -crop model STICS. The models were calibrated and evaluated using field data from seven sites across Northern Europe and Canada with different climates, soil conditions and management practices. Altogether the models were compared using data on timothy grass from 33 combinations of sites, cultivars and management regimes. Model performances with two calibration approaches, cultivar-specific and generic calibrations, were compared. All the models studied estimated the dynamics of above-ground biomass and the leaf area index satisfactorily, but tended to underestimate the first cut yield. Cultivar-specific calibration resulted in more accurate first cut yield predictions than the generic calibration achieving root mean square errors approximately one third lower for the cultivar-specific calibration. For the second cut, the difference between the calibration methods was small. The results indicate that detailed soil process descriptions improved the overall model performance and the model responses to management, such as nitrogen applications. The results also suggest that taking the genetic variability into account between cultivars of timothy grass also improves the yield estimates. Calibrations using both spring and summer growth data simultaneously revealed that processes determining the growth in these two periods require further attention in model development.
Keywords: Forage grass; Model comparison; Timothy; Uncertainty; Yield; Nutritive-Value; Catimo Model; Nitrogen Balances; Simulation; Regrowth; Wheat; Stics; Dynamics; Harvest; Water
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Lai, R., Arca, P., Lagomarsino, A., Cappai, C., Seddaiu, G., Demurtas, C. E., et al. (2017). Manure fertilization increases soil respiration and creates a negative carbon budget in a Mediterranean maize (Zea mays L.)-based cropping system. Catena, 151, 202–212.
Abstract: Agronomic research is important to identify suitable options for improving soil carbon (C) sequestration and reducing soil CO2 emissions. Therefore, the objectives of this study were i) to analyse the on-farm effects of different nitrogen fertilization sources on soil respiration, ii) to explore the effect of fertilization on soil respiration sensitivity to soil temperature (T) and iii) to assess the effect of the different fertilization regimes on the soil C balance. We hypothesized that i) the soil CO2 emission dynamics in Mediterranean irrigated cropping systems were mainly affected by fertilization management and T and ii) fertilization affected the soil C budget via different C inputs and CO2 efflux. Four fertilization systems (farmyard manure, cattle slurry, cattle slurry + mineral, and mineral) were compared in a double-crop rotation based on silage maize (Zea mays L) and a mixture of Italian ryegrass (Lolium multiflorum Lam.) and oats (Avena sativa L). The research was performed in the dairy district of Arborea, in the coastal zone of Sardinia (Italy), from May 2011 to May 2012. The soil was a Psammentic Palexeralfs with a sandy texture (940 g sand kg(-1)). The soil total respiration (SR), heterotrophic respiration (Rh), T and soil water content (SWC) were simultaneously measured in situ. The soil C balance was computed considering the Rh C losses and the soil C inputs from fertilizer and crop residues. The results showed that the maximum soil CO2 emission rates soon after the application of organic fertilizer reached values up to 121,1111 1 111(-2) s(-1). On average, the manure fertilizer showed significantly higher CO2 emissions, which resulted in a negative annual C balance (-2.9 t ha(-1)). T also affected the soil respiration temporal dynamics during the summer, consistently with results obtained in other temperate climatic regions that are characterized by wet summers and contrary to results from rainfed Mediterranean systems where the summer SR and Rh are constrained by the low SWC. The sensitivity of soil respiration to temperature significantly increased with C input from fertilizer. In conclusion, this research supported the hypotheses tested. Furthermore, the results indicated that i) soil CO2 efflux was significantly affected by fertilization management and T, and ii) fertilization with manure increased the soil respiration and resulted in a significantly negative soil C budget. This latter finding could be primarily explained by a reduction in productivity and, consequently, in crop residue with organic fertilization alone as compared to mineral, by the favourable SWC and T for mineralization, and by the sandy soil texture, which hindered the formation of macroaggregates and hence soil C stabilization, making fertilizer organic inputs highly susceptible to mineralization. (C) 2016 Elsevier B.V. All rights reserved.
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Mittenzwei, K., Persson, T., Höglind, M., & Kværnø, S. (2017). Combined effects of climate change and policy uncertainty on the agricultural sector in Norway. Agric. Syst., 153, 118–126.
Abstract: Highlights • A framework to study climate and policy uncertainty in agriculture is presented. • Combining both sources of uncertainty has ambiguous effects on agriculture. • Uncertainty needs to be highlighted in modelling tools for policy analysis. 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 weather-driven 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.
Keywords: Climate change; Norway; Agriculture; Policy uncertainty; Modelling; LINGRA; CSM-CERES-Wheat; DSSAT
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Balkovič, J., van der Velde, M., Schmid, E., Skalský, R., Khabarov, N., Obersteiner, M., et al. (2013). Pan-European crop modelling with EPIC: Implementation, up-scaling and regional crop yield validation. Agricultural Systems, 120, 61–75.
Abstract: Justifiable usage of large-scale crop model simulations requires transparent, comprehensive and spatially extensive evaluations of their performance and associated accuracy. Simulated crop yields of a Pan-European implementation of the Environmental Policy Integrated Climate (EPIC) crop model were satisfactorily evaluated with reported regional yield data from EUROSTAT for four major crops, including winter wheat, rainfed and irrigated maize, spring barley and winter rye. European-wide land use, elevation, soil and daily meteorological gridded data were integrated in GIS and coupled with EPIC. Default EPIC crop and biophysical process parameter values were used with some minor adjustments according to suggestions from scientific literature. The model performance was improved by spatial calculations of crop sowing densities, potential heat units, operation schedules, and nutrient application rates. EPIC performed reasonable in the simulation of regional crop yields, with long-term averages predicted better than inter-annual variability: linear regression R-2 ranged from 0.58 (maize) to 0.91 (spring barley) and relative estimation errors were between +/- 30% for most of the European regions. The modelled and reported crop yields demonstrated similar responses to driving meteorological variables. However, EPIC performed better in dry compared to wet years. A yield sensitivity analysis of crop nutrient and irrigation management factors and cultivar specific characteristics for contrasting regions in Europe revealed a range in model response and attainable yields. We also show that modelled crop yield is strongly dependent on the chosen PET method. The simulated crop yield variability was lower compared to reported crop yields. This assessment should contribute to the availability of harmonised and transparently evaluated agricultural modelling tools in the EU as well as the establishment of modelling benchmarks as a requirement for sound and ongoing policy evaluations in the agricultural and environmental domains. (C) 2013 The Authors. Published by Elsevier Ltd. All rights reserved.
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