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Tao, F., Roetter, R. P., Palosuo, T., Hernandez Diaz-Ambrona, C. G., Ines Minguez, M., Semenov, M. A., et al. (2018). Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments. Glob. Chang. Biol., 24(3), 1291–1307.
Abstract: 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.
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Tao, F., Roetter, R. P., Palosuo, T., Diaz-Ambrona, C. G. H., Ines Minguez, M., Semenov, M. A., et al. (2017). Designing future barley ideotypes using a crop model ensemble. Europ. J. Agron., 82, 144–162.
Abstract: Climate change and its associated higher frequency and severity of adverse weather events require genotypic adaptation. Process-based ecophysiological modelling offers a powerful means to better target and accelerate development of new crop cultivars. Barley (Hordeum vulgare L) is an important crop throughout the world, and a good model for study of the genetics of stress adaptation because many quantitative trait loci and candidate genes for biotic and abiotic stress tolerance have been identified in it. Here, we developed a new approach to design future crop ideotypes using an ensemble of eight barley simulation models (i.e. APSIM, CropSyst, HERMES, MCWLA, MONICA, SIMPLACE, Sirius Quality, and WOFOST), and applied it to design climate-resilient barley ideotypes for Boreal and Mediterranean climatic zones in Europe. The results showed that specific barley genotypes, represented by sets of cultivar parameters in the crop models, could be promising under future climate change conditions, resulting in increased yields and low inter-annual yield variability. In contrast, other genotypes could result in substantial yield declines. The most favorable climate-zone-specific barley ideotypes were further proposed, having combinations of several key genetic traits in terms of phenology, leaf growth, photosynthesis, drought tolerance, and grain formation. For both Boreal and Mediterranean climatic zones, barley ideotypes under future climatic conditions should have a longer reproductive growing period, lower leaf senescence rate, larger radiation use efficiency or maximum assimilation rate, and higher drought tolerance. Such characteristics can produce substantial positive impacts on yields under contrasting conditions. Moreover, barley ideotypes should have a low photoperiod and high vernalization sensitivity for the Boreal climatic zone; for the Mediterranean, in contrast, it should have a low photoperiod and low vernalization sensitivity. The drought-tolerance trait is more beneficial for the Mediterranean than for the Boreal climatic zone. Our study demonstrates a sound approach to design future barley ideotypes based on an ensemble of well-tested, diverse crop models and on integration of knowledge from multiple disciplines. The robustness of model-aided ideotypes design can be further enhanced by continuously improving crop models and enhancing information exchange between modellers, agro-meteorologists, geneticists, physiologists, and plant breeders. (C) 2016 Elsevier B.V. All rights reserved.
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Dumont, B., Basso, B., Leemans, V., Bodson, B., Destain, J. - P., & Destain, M. - F. (2015). Systematic analysis of site-specific yield distributions resulting from nitrogen management and climatic variability interactions. Precision Agric., 16(4), 361–384.
Abstract: At the plot level, crop simulation models such as STICS have the potential to evaluate risk associated with management practices. In nitrogen (N) management, however, the decision-making process is complex because the decision has to be taken without any knowledge of future weather conditions. The objective of this paper is to present a general methodology for assessing yield variability linked to climatic uncertainty and variable N rate strategies. The STICS model was coupled with the LARS-Weather Generator. The Pearson system and coefficients were used to characterise the shape of yield distribution. Alternatives to classical statistical tests were proposed for assessing the normality of distributions and conducting comparisons (namely, the Jarque-Bera and Wilcoxon tests, respectively). Finally, the focus was put on the probability risk assessment, which remains a key point within the decision process. The simulation results showed that, based on current N application practice among Belgian farmers (60-60-60 kgN ha(-1)), yield distribution was very highly significantly non-normal, with the highest degree of asymmetry characterised by a skewness value of -1.02. They showed that this strategy gave the greatest probability (60 %) of achieving yields that were superior to the mean (10.5 t ha(-1)) of the distribution.
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Fan, F., Henriksen, C. B., & Porter, J. (2016). Valuation of ecosystem services in organic cereal crop production systems with different management practices in relation to organic matter input. Ecosystem Services, 22, 117–127.
Abstract: As the degradation of global ecosystem services (ES) continues in the last five decades, maintaining or even enhancing the ES of agro-ecosystem is one of the approaches to mitigate the global ES loss. This study provides the first estimate of an economic valuation of ES provided by organic cereal crop production systems with different management practices in relation to organic matter input (low, medium and high). Our results show that organic matter inputs significantly affect the total ES value on organic cereal crop production systems. The system with high organic matter input has the highest gross total ES value (US$ 1969 ha(-1) yr(-1)), followed by the low organic matter input system (US$ 1688 ha(-1) yr(-1)), and the lowest ES value are found in the medium organic matter input system (US$ 1492 ha(-1) yr(-1)). Organic matter inputs have strong positive relationship with non-marketable ES values, while this relationship was not found in marketable ES values. Monetizing the ES can be used by land managers and policy makers to adjust management practices in terms of organic matter input in cereal production system with a long term goal for sustainable agriculture.
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Perego, A., Giussani, A., Sanna, M., Fumagalli, M., Carozzi, M., Alfieri, L., et al. (2013). The ARMOSA simulation crop model: overall features, calibration and validation results. Italian Journal of Agrometeorology, 3, 23–38.
Abstract: ARMOSA is a dynamic simulation model which was developed to simulate crop growth and development, water and nitrogen dynamics under different pedoclimatic conditions and cropping systems in the arable land. The model is meant to be a tool for the evaluation of the impact of different crop management practices on soil nitrogen and carbon cycles and groundwater nitrate pollution. A large data set collected over three to six years from six monitoring sites in Lombardia plain was used to calibrate and validate the model parameters. Measured meteorological data, soil chemical and physical characterizations, crop-related data of different cropping systems allowed for a proper parameterization. Fit indexes showed the reliability of the model in adequately predicting crop-related variables, such as above ground biomass (RRMSE=11.18, EF=0.94, r=0.97), Leaf Area Index maximum value (RRMSE=8.24, EF=0.37, r=0.72), harvest index (RRMSE=19.4, EF=0.32, r=0.74), and crop N uptake (RRMSE=20.25, EF=0.69, r=0.85). Using two different one-year data set from each monitoring site, the model was calibrated and validated, getting to encouraging results: RRMSE=6.28, EF=0.52, r=0.68 for soil water content at different depths, and RRMSE=34.89, EF=0.59, r=0.75 for soil NO3-N content along soil profile. The simulated N leaching was in full agreement with measured data (RRMSE=26.62, EF=0.88, r=0.98).
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