Taru Palosuo

Multimodel ensembles of wheat growth: many models are better than one.

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.

Changes in time of sowing, flowering and maturity of cereals in Europe under climate change

The phenological development of cereal crops from emergence through flowering to maturity is largely controlled by temperature, but also affected by day length and potential physiological stresses. Responses may vary between species and varieties. Climate change will affect the timing of cereal crop development, but exact changes will also depend on changes in varieties as affected by plant breeding and variety choices. This study aimed to assess changes in timing of major phenological stages of cereal crops in Northern and Central Europe under climate change. Records on dates of sowing, flowering, and maturity of wheat, oats and maize were collected from field experiments conducted during the period 1985–2009. Data for spring wheat and spring oats covered latitudes from 46 to 64°N, winter wheat from 46 to 61°N, and maize from 47 to 58°N. The number of observations (site–year–variety combinations) varied with phenological phase, but exceeded 2190, 227, 2076 and 1506 for winter wheat, spring wheat, spring oats and maize, respectively. The data were used to fit simple crop development models, assuming that the duration of the period until flowering depends on temperature and day length for wheat and oats, and on temperature for maize, and that the duration of the period from flowering to maturity in all species depends on temperature only. Species-specific base temperatures were used. Sowing date of spring cereals was estimated using a threshold temperature for the mean air temperature during 10 days prior to sowing. The mean estimated temperature thresholds for sowing were 6.1, 7.1 and 10.1°C for oats, wheat and maize, respectively. For spring oats and wheat the temperature threshold increased with latitude. The effective temperature sums required for both flowering and maturity increased with increasing mean annual temperature of the location, indicating that varieties are well adapted to given conditions. The responses of wheat and oats were largest for the period from flowering to maturity. Changes in timing of cereal phenology by 2040 were assessed for two climate model projections according to the observed dependencies on temperature and day length. The results showed advancements of sowing date of spring cereals by 1–3 weeks depending on climate model and region within Europe. The changes were largest in Northern Europe. Timing of flowering and maturity were projected to advance by 1–3 weeks. The changes were largest for grain maize and smallest for winter wheat, and they were generally largest in the western and northern part of the domain. There were considerable differences in predicted timing of sowing, flowering and maturity between the two climate model projections applied.

Sensitivities of crop models to extreme weather conditions during flowering period demonstrated for maize and winter wheat in Austria

The 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.

Greenhouse Impact Due to the Use of Combustible Fuels: Life Cycle Viewpoint and Relative Radiative Forcing Commitment

Extensive information on the greenhouse impacts of various human actions is important in developing effective climate change mitigation strategies. The greenhouse impacts of combustible fuels consist not only of combustion emissions but also of emissions from the fuel production chain and possible effects on the ecosystem carbon storages. It is important to be able to assess the combined, total effect of these different emissions and to express the results in a comprehensive way. In this study, a new concept called relative radiative forcing commitment (RRFC) is presented and applied to depict the greenhouse impact of some combustible fuels currently used in Finland. RRFC is a ratio that accounts for the energy absorbed in the Earth system due to changes in greenhouse gas concentrations (production and combustion of fuel) compared to the energy released in the combustion of fuel. RRFC can also be expressed as a function of time in order to give a dynamic cumulative picture on the caused effect. Varying time horizons can be studied separately, as is the case when studying the effects of different climate policies on varying time scales. The RRFC for coal for 100 years is about 170, which means that in 100 years 170 times more energy is absorbed in the atmosphere due to the emissions of coal combustion activity than is released in combustion itself. RRFC values of the other studied fuel production chains varied from about 30 (forest residues fuel) to 190 (peat fuel) for the 100-year study period. The length of the studied time horizon had an impact on the RRFC values and, to some extent, on the relative positions of various fuels.

A multi-model comparison of soil carbon assessment of a coniferous forest stand

We simulated soil carbon stock dynamics of an Austrian coniferous forest stand with five soil-only models (Q, ROMUL, RothC, SoilCO2/RothC and Yasso07) and three plant-soil models (CENTURY, CoupModel and Forest-DNDC) for an 18-year period and the decomposition of a litter pulse over a 100-year period. The objectives of the study were to assess the consistency in soil carbon estimates applying a multi-model comparison and to present and discuss the sources of uncertainties that create the differences in model results. Additionally, we discuss the applicability of different modelling approaches from the view point of large-scale carbon assessments.Our simulation results showed a wide range in soil carbon stocks and stock change estimates reflecting substantial uncertainties in model estimates. The measured stock change estimate decreased much more than the model predictions. Model results varied not only due to the model structure and applied parameters, but also due to different input information and assumptions applied during the modelling processes. Initialization procedures applied with the models induced large differences among the modelled soil carbon stocks and stock change estimates. Decomposition estimates of the litter pulse driven by model structures and parameters also varied considerably.Our results support the use of relatively simple soil-only models with low data requirements in inventory type of large-scale carbon assessments. It is important that the modelling processes within the national inventories are transparently reported and special emphasis is put on how the models are used, which assumptions are applied and what is the quality of data used both as input and to calibrate the models.

The uncertainty of crop yield projections is reduced by improved temperature response functions

Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections.

Comparing the performance of 11 crop simulation models in predicting yield response to nitrogen fertilization

Eleven widely used crop simulation models (APSIM, CERES, CROPSYST, COUP, DAISY, EPIC, FASSET, HERMES, MONICA, STICS and WOFOST) were tested using spring barley ( Hordeum vulgare L.) data set under varying nitrogen (N) fertilizer rates from three experimental years in the boreal climate of Jokioinen, Finland. This is the largest standardized crop model inter-comparison under different levels of N supply to date. The models were calibrated using data from 2002 and 2008, of which 2008 included six N rates ranging from 0 to 150 kg N/ha. Calibration data consisted of weather, soil, phenology, leaf area index (LAI) and yield observations. The models were then tested against new data for 2009 and their performance was assessed and compared with both the two calibration years and the test year. For the calibration period, root mean square error between measurements and simulated grain dry matter yields ranged from 170 to 870 kg/ha. During the test year 2009, most models failed to accurately reproduce the observed low yield without N fertilizer as well as the steep yield response to N applications. The multi-model predictions were closer to observations than most single-model predictions, but multi-model mean could not correct systematic errors in model simulations. Variation in soil N mineralization and LAI development due to differences in weather not captured by the models most likely was the main reason for their unsatisfactory performance. This suggests the need for model improvement in soil N mineralization as a function of soil temperature and moisture. Furthermore, specific weather event impacts such as low temperatures after emergence in 2009, tending to enhance tillering, and a high precipitation event just before harvest in 2008, causing possible yield penalties, were not captured by any of the models compared in the current study.

Sustainability impact assessment of increasing resource use intensity in forest bioenergy production chains.

Changing forest management practices towards more intensive biomass utilization for energy purposes will affect the sustainability of resource management. The Tool for Sustainability Impact Assessment was applied to evaluate the environmental, social, and economic sustainability impacts of the stepwise increased extraction of forest biomass of three typical Scandinavian Scots pine bioenergy production chains (BPCs). The assessed sources of the woody biomass were pellets as a by‐product of the sawmilling industry, wood chips deriving from early whole‐tree harvesting, and residues from final cuttings. Three commercially practiced BPCs were compared. By the additional extraction of biomass for heat production, the employment increased by 0.6 person‐years 1000 m−3 solid wood chips, while there was a decrease in the costs and greenhouse gases emitted per unit of heat consumed. Furthermore this practice did not only add positive socio‐economic but also positive environmental impacts on sustainability, particularly on the greenhouse gas balance and the energy efficiency ratio (input to output ratio along the BPC), which was determined to be 1–24. Potential drawbacks, on the other hand, include decreasing nutrient returns to the soil and the associated potential reduction in future stand productivity. Fertilization might be needed to maintain sustainable forest growth on poor sites.

Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments

Climate change impact assessments are plagued with uncertainties from many sources, such as climate projections or the inadequacies in structure and parameters of the impact model. Previous studies tried to account for the uncertainty from one or two of these. Here, we developed a triple-ensemble probabilistic assessment using seven crop models, multiple sets of model parameters and eight contrasting climate projections together to comprehensively account for uncertainties from these three important sources. We demonstrated the approach in assessing climate change impact on barley growth and yield at Jokioinen, Finland in the Boreal climatic zone and Lleida, Spain in the Mediterranean climatic zone, for the 2050s. We further quantified and compared the contribution of crop model structure, crop model parameters and climate projections to the total variance of ensemble output using Analysis of Variance (ANOVA). Based on the triple-ensemble probabilistic assessment, the median of simulated yield change was -4% and +16%, and the probability of decreasing yield was 63% and 31% in the 2050s, at Jokioinen and Lleida, respectively, relative to 1981-2010. The contribution of crop model structure to the total variance of ensemble output was larger than that from downscaled climate projections and model parameters. The relative contribution of crop model parameters and downscaled climate projections to the total variance of ensemble output varied greatly among the seven crop models and between the two sites. The contribution of downscaled climate projections was on average larger than that of crop model parameters. This information on the uncertainty from different sources can be quite useful for model users to decide where to put the most effort when preparing or choosing models or parameters for impact analyses. We concluded that the triple-ensemble probabilistic approach that accounts for the uncertainties from multiple important sources provide more comprehensive information for quantifying uncertainties in climate change impact assessments as compared to the conventional approaches that are deterministic or only account for the uncertainties from one or two of the uncertainty sources.

Method for estimating soil carbon stock changes in Finnish mineral cropland and grassland soils

ABSTRACT This study presents a method for estimating the soil organic carbon (SOC) stock changes in mineral agricultural soils developed for the Finnish GHG inventory. SOC stock changes in mineral cropland and grassland soils from 1990 to 2013 were calculated by combining agricultural statistics and national conversion factors to estimate the organic inputs to soil, along with the Yasso07 soil carbon model. The effects of selected key assumptions on the simulation results were studied. The method yielded SOC change estimates closer to the observed SOC change than were the results of the previously used Tier 1 method. The SOC stocks of croplands in 1-m soil profiles were slightly decreasing in most regions of the country. At the national level, the decrease was on average 0.05 Mg C ha–1 year–1 (0.01%). Selection of climate data (annual vs. long-term mean) and the initialization procedure had large impacts on the simulated SOC stock and change results, whereas the simulations at regional and subnational levels provided similar results. The method was found to be suitable for the GHG inventory and preferable to the Tier 1 method. The modular structure of the system allows for continuous improvements when more information and data are gathered.