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Klosterhalfen, A., Herbst, M., Weihermueller, L., Graf, A., Schmidt, M., Stadler, A., et al. (2017). Multi-site calibration and validation of a net ecosystem carbon exchange model for croplands. Ecol. Model., 363, 137–156.
Abstract: Croplands play an important role in the carbon budget of many regions. However, the estimation of their carbon balance remains difficult due to diversity and complexity of the processes involved. We report the coupling of a one-dimensional soil water, heat, and CO2 flux model (SOILCO2), a pool concept of soil carbon turnover (RothC), and a crop growth module (SUCROS) to predict the net ecosystem exchange (NEE) of carbon. The coupled model, further referred to as AgroC, was extended with routines for managed grassland as well as for root exudation and root decay. In a first step, the coupled model was applied to two winter wheat sites and one upland grassland site in Germany. The model was calibrated based on soil water content, soil temperature, biometric, and soil respiration measurements for each site, and validated in terms of hourly NEE measured with the eddy covariance technique. The overall model performance of AgroC was sufficient with a model efficiency above 0.78 and a correlation coefficient above 0.91 for NEE. In a second step, AgroC was optimized with eddy covariance NEE measurements to examine the effect of different objective functions, constraints, and data-transformations on estimated NEE. It was found that NEE showed a distinct sensitivity to the choice of objective function and the inclusion of soil respiration data in the optimization process. In particular, both positive and negative day- and nighttime fluxes were found to be sensitive to the selected optimization strategy. Additional consideration of soil respiration measurements improved the simulation of small positive fluxes remarkably. Even though the model performance of the selected optimization strategies did not diverge substantially, the resulting cumulative NEE over simulation time period differed substantially. Therefore, it is concluded that data transformations, definitions of objective functions, and data sources have to be considered cautiously when a terrestrial ecosystem model is used to determine NEE by means of eddy covariance measurements. (C) 2017 Elsevier B.V. All rights reserved.
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Yin, X. G., Kersebaum, K. C., Kollas, C., Manevski, K., Baby, S., Beaudoin, N., et al. (2017). Performance of process-based models for simulation of grain N in crop rotations across Europe. Agric. Syst., 154, 63–77.
Abstract: The accurate estimation of crop grain nitrogen (N; N in grain yield) is crucial for optimizing agricultural N management, especially in crop rotations. In the present study, 12 process-based models were applied to simulate the grain N of i) seven crops in rotations, ii) across various pedo-climatic and agro-management conditions in Europe, under both continuous simulation and single year simulation, and for iv) two calibration levels, namely minimal and detailed calibration. Generally, the results showed that the accuracy of the simulations in predicting grain N increased under detailed calibration. The models performed better in predicting the grain N of winter wheat (Triticum aestivum L.), winter barley (Hordewn vulgare L.) and spring barley (Hordeum vulgare L.) compared to spring oat (Avena saliva L.), winter rye (Secale cereale L.), pea (Piswn sativum L.) and winter oilseed rape (Brassica napus L.). These differences are linked to the intensity of parameterization with better parameterized crops showing lower prediction errors. The model performance was influenced by N fertilization and irrigation treatments, and a majority of the predictions were more accurate under low N and rainfed treatments. Moreover, the multi-model mean provided better predictions of grain N compared to any individual model. In regard to the Individual models, DAISY, FASSET, HERMES, MONICA and STICS are suitable for predicting grain N of the main crops in typical European crop rotations, which all performed well in both continuous simulation and single year simulation. Our results show that both the model initialization and the cover crop effects in crop rotations should be considered in order to achieve good performance of continuous simulation. Furthermore, the choice of either continuous simulation or single year simulation should be guided by the simulation objectives (e.g. grain yield, grain N content or N dynamics), the crop sequence (inclusion of legumes) and treatments (rate and type of N fertilizer) included in crop rotations and the model formalism.
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Lotze-Campen, H., von Witzke, H., Noleppa, S., & Schwarz, G. (2015). Science for food, climate protection and welfare: An economic analysis of plant breeding research in Germany. Agric. Syst., 136, 79–84.
Abstract: Highlights • We analyze the economic effects of plant breeding research in Germany. • Effects of reduced CO2 emissions due to productivity increases are being quantified. • Expansion of global agricultural area has been reduced by 1–1.5 million ha. • CO2 emissions have been reduced by 160–235 million tons. • German plant breeding research has an economic value of 10.8–15.6 billion EUR. Abstract We analyze the economic effects of plant breeding research in Germany. In addition to market effects, for the first time also effects of reduced CO2 emissions due to productivity increases are being quantified. The analysis shows that investments in German plant breeding research in the period 1991–2010 have reduced the global expansion of agricultural area by 1–1.5 million hectares. This has led to reduced CO2 emissions of 160–235 million tons. The economic value generated by plant breeding research, through increased production and reduced greenhouse gas emissions, is estimated at 10.8–15.6 billion EUR in the same period. This can be translated into a social rate of return on research investment in the range of 40–80% per year. Projections for the period 2011–2030 generate a return rate in the range of 65–140% per year. Investments into plant breeding research in Germany are highly profitable from a societal point of view. At the same time, our results show significant under-investments in agricultural research in Germany. These results provide a good justification for policy-makers to reverse funding cuts for public agricultural research over the last decades and to improve institutional conditions for private research, e.g. through better protection of intellectual property rights.
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Lorite, I. J., Gabaldon-Leal, C., Ruiz-Ramos, M., Belaj, A., de la Rosa, R., Leon, L., et al. (2018). Evaluation of olive response and adaptation strategies to climate change under semi-arid conditions. Agric. Water Manage., 204, 247–261.
Abstract: AdaptaOlive is a simplified physically-based model that has been developed to assess the behavior of olive under future climate conditions in Andalusia, southern Spain. The integration of different approaches based on experimental data from previous studies, combined with weather data from 11 climate models, is aimed at overcoming the high degree of uncertainty in the simulation of the response of agricultural systems under predicted climate conditions. The AdaptaOlive model was applied in a representative olive orchard in the Baeza area, one of the main producer zone in Spain, with the cultivar ‘Picual’. Simulations for the end of the 21st century showed olive oil yield increases of 7.1 and 28.9% under rainfed and full irrigated conditions, respectively, while irrigation requirements decreased between 0.5 and 6.2% for full irrigation and regulated deficit irrigation, respectively. These effects were caused by the positive impact of the increase in atmospheric CO2 that counterbalanced the negative impacts of the reduction in rainfall. The high degree of uncertainty associated with climate projections translated into a high range of yield and irrigation requirement projections, confirming the need for an ensemble of climate models in climate change impact assessment. The AdaptaOlive model also was applied for evaluating adaptation strategies related to cultivars, irrigation strategies and locations. The best performance was registered for cultivars with early flowering dates and regulated deficit irrigation. Thus, in the Baeza area full irrigation requirements were reduced by 12% and the yield in rainfed conditions increased by 7% compared with late flowering cultivars. Similarly, regulated deficit irrigation requirements and yield were reduced by 46% and 18%, respectively, compared with full irrigation. The results confirm the promise offered by these strategies as adaptation measures for managing an olive crop under semi-arid conditions in a changing climate.
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Webber, H., Ewert, F., Olesen, J. E., Müller, C., Fronzek, S., Ruane, A. C., et al. (2018). Diverging importance of drought stress for maize and winter wheat in Europe. Nat. Comm., 9, 4249.
Abstract: Understanding the drivers of yield levels under climate change is required to support adaptation planning and respond to changing production risks. This study uses an ensemble of crop models applied on a spatial grid to quantify the contributions of various climatic drivers to past yield variability in grain maize and winter wheat of European cropping systems (1984-2009) and drivers of climate change impacts to 2050. Results reveal that for the current genotypes and mix of irrigated and rainfed production, climate change would lead to yield losses for grain maize and gains for winter wheat. Across Europe, on average heat stress does not increase for either crop in rainfed systems, while drought stress intensifies for maize only. In low-yielding years, drought stress persists as the main driver of losses for both crops, with elevated CO2 offering no yield benefit in these years.
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