Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Implications of crop model ensemble size and composition for estimates of adaptation effects and agreement of recommendations
Rodríguez, A. ; Ruiz-Ramos, M. ; Palosuo, T. ; Carter, T.R. ; Fronzek, S. ; Lorite, I.J. ; Ferrise, R. ; Pirttioja, N. ; Bindi, M. ; Baranowski, P. ; Buis, S. ; Cammarano, D. ; Chen, Y. ; Dumont, B. ; Ewert, F. ; Gaiser, T. ; Hlavinka, P. ; Hoffmann, H. ; Höhn, J.G. ; Jurecka, F. ; Kersebaum, K.C. ; Krzyszczak, J. ; Lana, M. ; Mechiche-Alami, A. ; Minet, J. ; Montesino, M. ; Nendel, C. ; Porter, J.R. ; Ruget, F. ; Semenov, M.A. ; Steinmetz, Z. ; Stratonovitch, P. ; Supit, I. ; Tao, F. ; Trnka, M. ; Wit, A. de; Rötter, R.P. - \ 2018
Agricultural and Forest Meteorology (2018). - ISSN 0168-1923 - 12 p.
Climate change - Decision support - Outcome confidence - Response surface - Uncertainty - Wheat adaptation

Climate change is expected to severely affect cropping systems and food production in many parts of the world unless local adaptation can ameliorate these impacts. Ensembles of crop simulation models can be useful tools for assessing if proposed adaptation options are capable of achieving target yields, whilst also quantifying the share of uncertainty in the simulated crop impact resulting from the crop models themselves. Although some studies have analysed the influence of ensemble size on model outcomes, the effect of ensemble composition has not yet been properly appraised. Moreover, results and derived recommendations typically rely on averaged ensemble simulation results without accounting sufficiently for the spread of model outcomes. Therefore, we developed an Ensemble Outcome Agreement (EOA) index, which analyses the effect of changes in composition and size of a multi-model ensemble (MME) to evaluate the level of agreement between MME outcomes with respect to a given hypothesis (e.g. that adaptation measures result in positive crop responses). We analysed the recommendations of a previous study performed with an ensemble of 17 crop models and testing 54 adaptation options for rainfed winter wheat (Triticum aestivum L.) at Lleida (NE Spain) under perturbed conditions of temperature, precipitation and atmospheric CO2 concentration. Our results confirmed that most adaptations recommended in the previous study have a positive effect. However, we also showed that some options did not remain recommendable in specific conditions if different ensembles were considered. Using EOA, we were able to identify the adaptation options for which there is high confidence in their effectiveness at enhancing yields, even under severe climate perturbations. These include substituting spring wheat for winter wheat combined with earlier sowing dates and standard or longer duration cultivars, or introducing supplementary irrigation, the latter increasing EOA values in all cases. There is low confidence in recovering yields to baseline levels, although this target could be attained for some adaptation options under moderate climate perturbations. Recommendations derived from such robust results may provide crucial information for stakeholders seeking to implement adaptation measures.

Multimodel ensembles improve predictions of crop–environment–management interactions
Wallach, Daniel ; Martre, Pierre ; Liu, Bing ; Asseng, Senthold ; Ewert, Frank ; Thorburn, Peter J. ; Ittersum, Martin van; Aggarwal, Pramod K. ; Ahmed, Mukhtar ; Basso, Bruno ; Biernath, Christian ; Cammarano, Davide ; Challinor, Andrew J. ; Sanctis, Giacomo De; Dumont, Benjamin ; Eyshi Rezaei, Ehsan ; Fereres, Elias ; Fitzgerald, Glenn J. ; Gao, Y. ; Garcia-Vila, Margarita ; Gayler, Sebastian ; Girousse, Christine ; Hoogenboom, Gerrit ; Horan, Heidi ; Izaurralde, Roberto C. ; Jones, Curtis D. ; Kassie, Belay T. ; Kersebaum, Christian C. ; Klein, Christian ; Koehler, Ann Kristin ; Maiorano, Andrea ; Minoli, Sara ; Müller, Christoph ; Naresh Kumar, Soora ; Nendel, Claas ; O'Leary, Garry J. ; Palosuo, Taru ; Priesack, Eckart ; Ripoche, Dominique ; Rötter, Reimund P. ; Semenov, Mikhail A. ; Stöckle, Claudio ; Stratonovitch, Pierre ; Streck, Thilo ; Supit, Iwan ; Tao, Fulu ; Wolf, Joost ; Zhang, Zhao - \ 2018
Global Change Biology 24 (2018)11. - ISSN 1354-1013 - p. 5072 - 5083.
climate change impact - crop models - ensemble mean - ensemble median - multimodel ensemble - prediction

A recent innovation in assessment of climate change impact on agricultural production has been to use crop multimodel ensembles (MMEs). These studies usually find large variability between individual models but that the ensemble mean (e-mean) and median (e-median) often seem to predict quite well. However, few studies have specifically been concerned with the predictive quality of those ensemble predictors. We ask what is the predictive quality of e-mean and e-median, and how does that depend on the ensemble characteristics. Our empirical results are based on five MME studies applied to wheat, using different data sets but the same 25 crop models. We show that the ensemble predictors have quite high skill and are better than most and sometimes all individual models for most groups of environments and most response variables. Mean squared error of e-mean decreases monotonically with the size of the ensemble if models are added at random, but has a minimum at usually 2–6 models if best-fit models are added first. Our theoretical results describe the ensemble using four parameters: average bias, model effect variance, environment effect variance, and interaction variance. We show analytically that mean squared error of prediction (MSEP) of e-mean will always be smaller than MSEP averaged over models and will be less than MSEP of the best model if squared bias is less than the interaction variance. If models are added to the ensemble at random, MSEP of e-mean will decrease as the inverse of ensemble size, with a minimum equal to squared bias plus interaction variance. This minimum value is not necessarily small, and so it is important to evaluate the predictive quality of e-mean for each target population of environments. These results provide new information on the advantages of ensemble predictors, but also show their limitations.

The Hot Serial Cereal Experiment for modeling wheat response to temperature: field experiments and AgMIP-Wheat multi-model simulations
Martre, Pierre ; Kimball, Bruce A. ; Ottman, Michael J. ; Wall, Gerard W. ; White, Jeffrey W. ; Asseng, Senthold ; Ewert, Frank ; Cammarano, Davide ; Maiorano, Andrea ; Aggarwal, Pramod K. ; Anothai, Jakarat ; Basso, Bruno ; Biernath, Christian ; Challinor, Andrew J. ; Sanctis, Giacomo De; Doltra, Jordi ; Dumont, Benjamin ; Fereres, Elias ; Garcia-Vila, Margarita ; Gayler, Sebastian ; Hoogenboom, Gerrit ; Hunt, Leslie A. ; Izaurralde, Roberto C. ; Jabloun, Mohamed ; Jones, Curtis D. ; Kassie, Belay T. ; Kersebaum, Kurt C. ; Koehler, Ann-Kristin ; Müller, Christoph ; Kumar, Soora Naresh ; Liu, Bing ; Lobell, David B. ; Nendel, Claas ; O'Leary, Garry ; Olesen, Jørgen E. ; Palosuo, Taru ; Priesack, Eckart ; Rezaei, Ehsan Eyshi ; Ripoche, Dominique ; Rötter, Reimund P. ; Semenov, Mikhail A. ; Stöckle, Claudio ; Stratonovitch, Pierre ; Streck, Thilo ; Supit, Iwan ; Tao, Fulu ; Thorburn, Peter ; Waha, Katharina ; Wang, Enli ; Wolf, Joost ; Zhao, Zhigan ; Zhu, Yan - \ 2018
ODjAR : open data journal for agricultural research 4 (2018). - ISSN 2352-6378 - p. 28 - 34.
The data set reported here includes the part of a Hot Serial Cereal Experiment (HSC) experiment recently used in the AgMIP-Wheat project to analyze the uncertainty of 30 wheat models and quantify their response to temperature. The HSC experiment was conducted in an open-field in a semiarid environment in the southwest USA. The data reported herewith include one hard red spring wheat cultivar (Yecora Rojo) sown approximately every six weeks from December to August for a two-year period for a total of 11 planting dates out of the 15 of the entire HSC experiment. The treatments were chosen to avoid any effect of frost on grain yields. On late fall, winter and early spring plantings temperature free-air controlled enhancement (T-FACE) apparatus utilizing infrared heaters with supplemental irrigation were used to increase air temperature by 1.3°C/2.7°C (day/night) with conditions equivalent to raising air temperature at constant relative humidity (i.e. as expected with global warming) during the whole crop growth cycle. Experimental data include local daily weather data, soil characteristics and initial conditions, detailed crop measurements taken at three growth stages during the growth cycle, and cultivar information. Simulations include both daily in-season and end-of-season results from 30 wheat models.
Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change
Fronzek, Stefan ; Pirttioja, Nina ; Carter, Timothy R. ; Bindi, Marco ; Hoffmann, Holger ; Palosuo, Taru ; Ruiz-Ramos, Margarita ; Tao, Fulu ; Trnka, Miroslav ; Acutis, Marco ; Asseng, Senthold ; Baranowski, Piotr ; Basso, Bruno ; Bodin, Per ; Buis, Samuel ; Cammarano, Davide ; Deligios, Paola ; Destain, Marie France ; Dumont, Benjamin ; Ewert, Frank ; Ferrise, Roberto ; François, Louis ; Gaiser, Thomas ; Hlavinka, Petr ; Jacquemin, Ingrid ; Kersebaum, Kurt Christian ; Kollas, Chris ; Krzyszczak, Jaromir ; Lorite, Ignacio J. ; Minet, Julien ; Minguez, M.I. ; Montesino, Manuel ; Moriondo, Marco ; Müller, Christoph ; Nendel, Claas ; Öztürk, Isik ; Perego, Alessia ; Rodríguez, Alfredo ; Ruane, Alex C. ; Ruget, Françoise ; Sanna, Mattia ; Semenov, Mikhail A. ; Slawinski, Cezary ; Stratonovitch, Pierre ; Supit, Iwan ; Waha, Katharina ; Wang, Enli ; Wu, Lianhai ; Zhao, Zhigan ; Rötter, Reimund P. - \ 2018
Agricultural Systems 159 (2018). - ISSN 0308-521X - p. 209 - 224.
Classification - Climate change - Crop model - Ensemble - Sensitivity analysis - Wheat

Crop growth simulation models can differ greatly in their treatment of key processes and hence in their response to environmental conditions. Here, we used an ensemble of 26 process-based wheat models applied at sites across a European transect to compare their sensitivity to changes in temperature (-2 to +9°C) and precipitation (-50 to +50%). Model results were analysed by plotting them as impact response surfaces (IRSs), classifying the IRS patterns of individual model simulations, describing these classes and analysing factors that may explain the major differences in model responses.The model ensemble was used to simulate yields of winter and spring wheat at four sites in Finland, Germany and Spain. Results were plotted as IRSs that show changes in yields relative to the baseline with respect to temperature and precipitation. IRSs of 30-year means and selected extreme years were classified using two approaches describing their pattern.The expert diagnostic approach (EDA) combines two aspects of IRS patterns: location of the maximum yield (nine classes) and strength of the yield response with respect to climate (four classes), resulting in a total of 36 combined classes defined using criteria pre-specified by experts. The statistical diagnostic approach (SDA) groups IRSs by comparing their pattern and magnitude, without attempting to interpret these features. It applies a hierarchical clustering method, grouping response patterns using a distance metric that combines the spatial correlation and Euclidian distance between IRS pairs. The two approaches were used to investigate whether different patterns of yield response could be related to different properties of the crop models, specifically their genealogy, calibration and process description.Although no single model property across a large model ensemble was found to explain the integrated yield response to temperature and precipitation perturbations, the application of the EDA and SDA approaches revealed their capability to distinguish: (i) stronger yield responses to precipitation for winter wheat than spring wheat; (ii) differing strengths of response to climate changes for years with anomalous weather conditions compared to period-average conditions; (iii) the influence of site conditions on yield patterns; (iv) similarities in IRS patterns among models with related genealogy; (v) similarities in IRS patterns for models with simpler process descriptions of root growth and water uptake compared to those with more complex descriptions; and (vi) a closer correspondence of IRS patterns in models using partitioning schemes to represent yield formation than in those using a harvest index.Such results can inform future crop modelling studies that seek to exploit the diversity of multi-model ensembles, by distinguishing ensemble members that span a wide range of responses as well as those that display implausible behaviour or strong mutual similarities.

Adaptation response surfaces for managing wheat under perturbed climate and CO2 in a Mediterranean environment
Ruiz-Ramos, M. ; Ferrise, R. ; Rodríguez, A. ; Lorite, I.J. ; Bindi, M. ; Carter, T.R. ; Fronzek, S. ; Palosuo, T. ; Pirttioja, N. ; Baranowski, P. ; Buis, S. ; Cammarano, D. ; Chen, Y. ; Dumont, B. ; Ewert, F. ; Gaiser, T. ; Hlavinka, P. ; Hoffmann, H. ; Höhn, J.G. ; Jurecka, F. ; Kersebaum, K.C. ; Krzyszczak, J. ; Lana, M. ; Mechiche-Alami, A. ; Minet, J. ; Montesino, M. ; Nendel, C. ; Porter, J.R. ; Ruget, F. ; Semenov, M.A. ; Steinmetz, Z. ; Stratonovitch, P. ; Supit, I. ; Tao, F. ; Trnka, M. ; Wit, A. De; Rötter, R.P. - \ 2018
Agricultural Systems 159 (2018). - ISSN 0308-521X - p. 260 - 274.
Adaptation of crops to climate change has to be addressed locally due to the variability of soil, climate and the specific socio-economic settings influencing farm management decisions. Adaptation of rainfed cropping systems in the Mediterranean is especially challenging due to the projected decline in precipitation in the coming decades, which will increase the risk of droughts. Methods that can help explore uncertainties in climate projections and crop modelling, such as impact response surfaces (IRSs) and ensemble modelling, can then be valuable for identifying effective adaptations. Here, an ensemble of 17 crop models was used to simulate a total of 54 adaptation options for rainfed winter wheat (Triticum aestivum) at Lleida (NE Spain). To support the ensemble building, an ex post quality check of model simulations based on several criteria was performed. Those criteria were based on the “According to Our Current Knowledge” (AOCK) concept, which has been formalized here. Adaptations were based on changes in cultivars and management regarding phenology, vernalization, sowing date and irrigation. The effects of adaptation options under changed precipitation (P), temperature (T), [CO2] and soil type were analysed by constructing response surfaces, which we termed, in accordance with their specific purpose, adaptation response surfaces (ARSs). These were created to assess the effect of adaptations through a range of plausible P, T and [CO2] perturbations. The results indicated that impacts of altered climate were predominantly negative. No single adaptation was capable of overcoming the detrimental effect of the complex interactions imposed by the P, T and [CO2] perturbations except for supplementary irrigation (sI), which reduced the potential impacts under most of the perturbations. Yet, a combination of adaptations for dealing with climate change demonstrated that effective adaptation is possible at Lleida. Combinations based on a cultivar without vernalization requirements showed good and wide adaptation potential. Few combined adaptation options performed well under rainfed conditions. However, a single sI was sufficient to develop a high adaptation potential, including options mainly based on spring wheat, current cycle duration and early sowing date. Depending on local environment (e.g. soil type), many of these adaptations can maintain current yield levels under moderate changes in T and P, and some also under strong changes. We conclude that ARSs can offer a useful tool for supporting planning of field level adaptation under conditions of high uncertainty.
Author Correction: The uncertainty of crop yield projections is reduced by improved temperature response functions
Wang, Enli ; Martre, Pierre ; Zhao, Zhigan ; Ewert, Frank ; Maiorano, Andrea ; Rötter, Reimund P. ; Kimball, Bruce A. ; Ottman, Michael J. ; Wall, Gerard W. ; White, Jeffrey W. ; Reynolds, Matthew P. ; Alderman, Phillip D. ; Aggarwal, Pramod K. ; Anothai, Jakarat ; Basso, Bruno ; Biernath, Christian ; Cammarano, Davide ; Challinor, Andrew J. ; Sanctis, Giacomo De; Doltra, Jordi ; Dumont, Benjamin ; Fereres, Elias ; Garcia-Vila, Margarita ; Gayler, Sebastian ; Hoogenboom, Gerrit ; Hunt, Leslie A. ; Izaurralde, Roberto C. ; Jabloun, Mohamed ; Jones, Curtis D. ; Kersebaum, Kurt C. ; Koehler, Ann-Kristin ; Liu, Leilei ; Müller, Christoph ; Kumar, Soora Naresh ; Nendel, Claas ; O’Leary, Garry ; Olesen, Jørgen E. ; Palosuo, Taru ; Priesack, Eckart ; Rezaei, Ehsan Eyshi ; Ripoche, Dominique ; Ruane, Alex C. ; Semenov, Mikhail A. ; Shcherbak, Iurii ; Stöckle, Claudio ; Stratonovitch, Pierre ; Streck, Thilo ; Supit, Iwan ; Tao, Fulu ; Thorburn, Peter ; Waha, Katharina ; Wallach, Daniel ; Wang, Zhimin ; Wolf, Joost ; Zhu, Yan ; Asseng, Senthold - \ 2017
Nature Plants 3 (2017)10. - ISSN 2055-026X - p. 833 - 833.
Relationships between greenhouse gas emissions and cultivable bacterial populations in conventional, organic and long-term grass plots as affected by environmental variables and disturbances
Bruggen, A.H.C. van; He, M. ; Zelenev, V.V. ; Semenov, V.M. ; Semenov, A.M. ; Kuznetsova, T.V. ; Khodzaeva, Anna K. ; Kuznetsov, A.M. ; Semenov, M.V. - \ 2017
Soil Biology and Biochemistry 114 (2017). - ISSN 0038-0717 - p. 145 - 159.
Daily dynamics of greenhouse gas (GHG) emissions and cultivable bacterial populations have rarely been examined. The objectives were: (1) to investigate if dynamics of GHG emissions can be described by harmonics and are related to those of cultivable bacteria after soil disturbances in three grassland management systems; (2) to determine to which extent daily GHG emissions are related to environmental variables rather than disturbance events in two climate zones; and (3) to investigate differences in GHG emissions between organic and conventional tilled grassland versus no-till long-term grassland systems (OG, CG and LG, respectively). In replicated field experiments with OG, CG, and LG plots in the Netherlands and Russia, GHG (CO2, N2O and CH4) emissions and cultivable bacterial populations were measured daily during two one-month periods at each location. Tillage, fertilization, biomass incorporation and irrigation were considered disturbances. The dynamics were subjected to harmonics, cross-correlation, and canonical correspondence analyses (CCA). The dynamics of cultivable bacterial populations and GHG fluxes rarely reflected autonomous growth and death cycles of bacteria after a disturbance due to the overarching influences of environmental conditions, especially in spring. Thus, GHG emissions were influenced more by weather variables than by agronomic disturbances. This was confirmed by CCA. Cultivable bacterial populations were cross correlated with CO2 fluxes and sometimes N2O emissions, but generally not with CH4 fluxes. Average cultivable bacterial populations and CO2 emissions were highest in OG and lowest in LG; N2O emissions were mostly highest in CG and lowest in LG; and CH4 fluxes were frequently highest in OG and lowest in LG. Thus, although bacteria and GHG peaks were induced by disturbances, sometimes followed by autonomous oscillations due to growth and death cycles and associated cycles in nutrient and oxygen availability, the dynamics were mainly affected by environmental variables and long-term management, with the smallest GHG emissions from LG plots.
Short-term dynamics of greenhouse gas emissions and cultivable bacterial populations in response to induced and natural disturbances in organically and conventionally managed soils
He, Miaomiao ; Ma, Wenjun ; Zelenev, Vladimir V. ; Khodzaeva, Anna K. ; Kuznetsov, Alexander M. ; Semenov, Alexander M. ; Semenov, Vyacheslav M. ; Blok, Wim ; Bruggen, Ariena H.C. Van - \ 2017
Applied Soil Ecology 119 (2017). - ISSN 0929-1393 - p. 294 - 306.
Organically managed (ORG) soil is often considered healthier than conventionally managed (CONV) soil, with greater resistance and resilience to disturbances, as evidenced by reduced oscillations in bacterial populations and activities. Greenhouse gas (GHG) fluxes are mediated by bacterial processes, but variations in GHG emissions have not been related to bacterial oscillations in soil. Two environmentally controlled and two field experiments were set up to compare oscillations in bacterial colony-forming-units (CFUs) and GHG (nitrous oxide (N2O), carbon dioxide (CO2) and methane (CH4)) fluxes after disturbances in ORG and CONV soils. Soil amendment with grass-clover (GC) or cattle manure (CM) resulted in peaks in N2O and CO2 emission, followed by CFUs. CH4 temporarily increased in GC- but decreased in CM-amended soil. Ratios of CFUs and GHGs in amended over nonamended soils oscillated during three weeks, mostly with lower frequencies and amplitudes in ORG soils. Fluctuations were more irregular in field soils, but significant oscillations were detected after irrigation or intensive rain in summer. Cross correlations between variables showed several significant sequences of microbial processes under controlled conditions but not in the field. Average GHG emissions were higher from ORG soil than CONV soil under these conditions, indicating that these have to be taken into consideration when estimating soil health.
Mode of action based risk assessment of the botanical food-borne alkenylbenzenesapiol and myristicin
Alajlouni, Abdul - \ 2017
University. Promotor(en): Ivonne Rietjens, co-promotor(en): Jacques Vervoort; Sebas Wesseling. - Wageningen : Wageningen University - ISBN 9789463434584 - 212

Alkenylbenzenes including estragole, methyleugenol, safrole, elemicin, apiol, and myristicin are naturally occurring in many herbs such as parsley, dill, basil, tarragon, fennel and nutmeg (Kreydiyyeh and Usta, 2002, Smith et al., 2002, Semenov et al., 2007). Estragole, methyleugenol and safrole are genotoxic and carcinogenic in rodent bioassays inducing liver tumors (Boberg et al., 1986, Boberg et al., 1983, Drinkwater et al., 1976, Miller et al., 1983, Swanson et al., 1981, Wiseman et al., 1985, Wiseman et al., 1987, Wislocki et al., 1977). Because of that, the use of methyleugenol, safrole and estragole as pure substances in foodstuff has been prohibited in the EU from September 2008 onwards (European Commission (EC), 2008). For apiol and myristicin data for their risk assessment are limited and more research is needed to support the evaluation of the risk resulting from consumption of products containing these compounds (WHO, 2009). The aim of the current thesis was to perform a mode of action based risk assessment of exposure to low doses of apiol and myristicin by using physiologically based kinetic (PBK) modelling based read-across from other alkenylbenzenes and to use the results obtained for risk assessment of consumption of plant food supplements (PFS) and other botanical products containing parsley and dill.

Chapter 1 provides general background information to alkenylbenzenes especially apiol and myristicin, a description of the chemical, metabolic and toxicity characteristics of apiol and myristicin and other structurally related alkenylbenzenes, a brief outline of the method used for their risk assessment and a short introduction to PBK modelling. Besides that, Chapter 1 include the aim of the current thesis. In Chapter 2 and Chapter 3, PBK models for respectively apiol and myristicin in male rat and human were defined, enabling prediction of dose-dependent effects in bioactivation and detoxification of these alkenylbenzenes. The PBK model based predictions were subsequently compared to those for safrole enabling estimation of a BMDL10 for apiol and myristicin from read-across from the BMDL10 available for safrole, thereby enabling risk assessment of current dietary exposure to apiol. In Chapter 4 and 5, the risk assessment of exposure to apiol and related alkenylbenzenes through drinking of parsley and dill based herbal teas and consumption of parsley and dill containing PFS was performed using the BMDL10 values derived in Chapter 2 and 3. The results showed that consumption of parsley and dill based herbal teas and PFS would be a priority for risk management if consumed for longer periods of time. Chapter 6 includes a general discussion of the thesis results obtained and the future perspectives that describe the needs to further research, based on alternatives for animals testing, to improve the risk assessment approaches for different botanical preparations.

Altogether, the results obtained through different thesis chapters show that integration of different approaches provides the basis for a mode of action and PBK modelling based read-across from compounds for which tumor data are available to related compounds for which such data are lacking. This can contribute to the development of alternatives for animal testing and will facilitate the risk assessment of compounds for which in vivo toxicity studies on tumor formation data are unavailable.

The uncertainty of crop yield projections is reduced by improved temperature response functions
Wang, Enli ; Martre, Pierre ; Zhao, Zhigan ; Ewert, Frank ; Maiorano, Andrea ; Rötter, Reimund P. ; Kimball, Bruce A. ; Ottman, Michael J. ; Wall, Gerard W. ; White, Jeffrey W. ; Reynolds, Matthew P. ; Alderman, Phillip D. ; Aggarwal, Pramod K. ; Anothai, Jakarat ; Basso, Bruno ; Biernath, Christian ; Cammarano, Davide ; Challinor, Andrew J. ; Sanctis, Giacomo De; Doltra, Jordi ; Fereres, Elias ; Garcia-Vila, Margarita ; Gayler, Sebastian ; Hoogenboom, Gerrit ; Hunt, Leslie A. ; Izaurralde, Roberto C. ; Jabloun, Mohamed ; Jones, Curtis D. ; Kersebaum, Kurt Christian ; Koehler, Ann Kristin ; Liu, Leilei ; Müller, Christoph ; Naresh Kumar, Soora ; Nendel, Claas ; O'Leary, Garry ; Olesen, Jørgen E. ; Palosuo, Taru ; Priesack, Eckart ; Eyshi Rezaei, Ehsan ; Ripoche, Dominique ; Ruane, Alex C. ; Semenov, Mikhail A. ; Shcherbak, Iurii ; Stöckle, Claudio O. ; Stratonovitch, Pierre ; Streck, Thilo ; Supit, Iwan ; Tao, Fulu ; Thorburn, Peter J. ; Waha, Katharina ; Wallach, Daniel ; Wang, Zhimin ; Wolf, Joost ; Zhu, Yan ; Asseng, Senthold ; Dumont, Benjamin - \ 2017
Nature Plants 3 (2017). - ISSN 2055-026X
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.
Multi-wheat-model ensemble responses to interannual climate variability
Ruane, Alex C. ; Hudson, Nicholas I. ; Asseng, Senthold ; Camarrano, Davide ; Ewert, Frank ; Martre, Pierre ; Boote, Kenneth J. ; Thorburn, Peter J. ; Aggarwal, Pramod K. ; Angulo, Carlos ; Basso, Bruno ; Bertuzzi, Patrick ; Biernath, Christian ; Brisson, Nadine ; Challinor, Andrew J. ; Doltra, Jordi ; Gayler, Sebastian ; Goldberg, Richard ; Grant, Robert F. ; Heng, Lee ; Hooker, Josh ; Hunt, Leslie A. ; Ingwersen, Joachim ; Izaurralde, Roberto C. ; Kersebaum, Kurt Christian ; Kumar, Soora Naresh ; Müller, Christoph ; Nendel, Claas ; O'Leary, Garry ; Olesen, Jørgen E. ; Osborne, Tom M. ; Palosuo, Taru ; Priesack, Eckart ; Ripoche, Dominique ; Rötter, Reimund P. ; Semenov, Mikhail A. ; Shcherbak, Iurii ; Steduto, Pasquale ; Stöckle, Claudio O. ; Stratonovitch, Pierre ; Streck, Thilo ; Supit, Iwan ; Tao, Fulu ; Travasso, Maria ; Waha, Katharina ; Wallach, Daniel ; White, Jeffrey W. ; Wolf, Joost - \ 2016
Environmental Modelling & Software 81 (2016). - ISSN 1364-8152 - p. 86 - 101.
AgMIP - Climate impacts - Crop modeling - Interannual variability - Multi-model ensemble - Precipitation - Temperature - Uncertainty - Wheat

We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981-2010 grain yield, and we evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models' climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal common characteristics of yield response to climate; however models rarely share the same cluster at all four sites indicating substantial independence. Only a weak relationship (R2 ≤ 0.24) was found between the models' sensitivities to interannual temperature variability and their response to long-term warming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts.

A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration
Makowski, D. ; Asseng, S. ; Ewert, F. ; Bassu, S. ; Durand, J.L. ; Li, G. ; Martre, P. ; Adam, M.Y.O. ; Aggarwal, P.K. ; Angulo, C. ; Baron, C. ; Basso, B. ; Bertuzzi, P. ; Biernath, C. ; Boogaard, H.L. ; Boote, K.J. ; Bouman, B. ; Bregaglio, S. ; Brisson, N. ; Buis, S. ; Cammarano, D. ; Challinor, A.J. ; Confalonieri, R. ; Conijn, J.G. ; Corbeels, M. ; Deryng, D. ; Sanctis, G. De; Doltra, J. ; Fumoto, T. ; Gayler, S. ; Gaydon, D. ; Goldberg, R. ; Grant, R.F. ; Grassini, P. ; Hatfield, J.L. ; Hasegawa, T. ; Heng, L. ; Hoek, S.B. ; Hooker, J. ; Hunt, L.A. ; Ingwersen, J. ; Izaurralde, C. ; Jongschaap, R.E.E. ; Jones, J.W. ; Kemanian, R.A. ; Kersebaum, K.C. ; Kim, S.H. ; Lizaso, J. ; Marcaida III, M. ; Müller, C. ; Nakagawa, H. ; Naresh Kumar, S. ; Nendel, C. ; O'Leary, G.J. ; Olesen, J.E. ; Oriol, P. ; Osborne, T.M. ; Palosuo, T. ; Pravia, M.V. ; Priesack, E. ; Ripoche, D. ; Rosenzweig, C. ; Ruane, A.C. ; Ruget, F. ; Sau, F. ; Semenov, M.A. ; Shcherbak, I. ; Singh, B. ; Soo, A.K. ; Steduto, P. ; Stöckle, C.O. ; Stratonovitch, P. ; Streck, T. ; Supit, I. ; Tang, L. ; Tao, F. ; Teixeira, E. ; Thorburn, P. ; Timlin, D. ; Travasso, M. ; Rötter, R.P. ; Waha, K. ; Wallach, D. ; White, J.W. ; Wilkens, P. ; Williams, J.R. ; Wolf, J. ; Ying, X. ; Yoshida, H. ; Zhang, Z. ; Zhu, Y. - \ 2015
Agricultural and Forest Meteorology 214-215 (2015). - ISSN 0168-1923 - p. 483 - 493.
Ensembles of process-based crop models are increasingly used to simulate crop growth for scenarios of temperature and/or precipitation changes corresponding to different projections of atmospheric CO2 concentrations. This approach generates large datasets with thousands of simulated crop yield data. Such datasets potentially provide new information but it is difficult to summarize them in a useful way due to their structural complexities. An associated issue is that it is not straightforward to compare crops and to interpolate the results to alternative climate scenarios not initially included in the simulation protocols. Here we demonstrate that statistical models based on random-coefficient regressions are able to emulate ensembles of process-based crop models. An important advantage of the proposed statistical models is that they can interpolate between temperature levels and between CO2 concentration levels, and can thus be used to calculate temperature and [CO2] thresholds leading to yield loss or yield gain, without re-running the original complex crop models. Our approach is illustrated with three yield datasets simulated by 19 maize models, 26 wheat models, and 13 rice models. Several statistical models are fitted to these datasets, and are then used to analyze the variability of the yield response to [CO2] and temperature. Based on our results, we show that, for wheat, a [CO2] increase is likely to outweigh the negative effect of a temperature increase of +2°C in the considered sites. Compared to wheat, required levels of [CO2] increase are much higher for maize, and intermediate for rice. For all crops, uncertainties in simulating climate change impacts increase more with temperature than with elevated [CO2].
Letter : Rising temperatures reduce global wheat production
Asseng, S. ; Ewert, F. ; Martre, P. ; Rötter, R.P. ; Cammarano, D. ; Kimball, B.A. ; Ottman, M.J. ; Wall, G.W. ; White, J.W. ; Reynolds, M.P. ; Alderman, P.D. ; Prasad, P.V.V. ; Lobell, D.B. ; Aggarwal, P.K. ; Anothai, J. ; Basso, B. ; Biernath, C. ; Challinor, A.J. ; Sanctis, G. De; Doltra, J. ; Fereres, E. ; Garcia-Vila, M. ; Gayler, S. ; Hoogenboom, G. ; Hunt, L.A. ; Izaurralde, C. ; Jabloun, M. ; Jones, C.D. ; Kersebaum, K.C. ; Koehler, A.K. ; Müller, C. ; Naresh Kumar, S. ; Nendel, C. ; O’Leary, G. ; Olesen, J.E. ; Palosuo, T. ; Priesack, E. ; Eyshi Rezae, E. ; Ruane, A.C. ; Semenov, M.A. ; Shcherbak, I. ; Stöckle, C.O. ; Stratonovitch, P. ; Streck, T. ; Supit, I. ; Tao, T. ; Thorburn, P. ; Waha, K. ; Wang, E. ; Wallach, D. ; Wolf, J. ; Zhao, Z. ; Zhu, Y. - \ 2015
Nature Climate Change 5 (2015). - ISSN 1758-678X - p. 143 - 147.
climate-change - spring wheat - dryland wheat - yield - growth - drought - heat - co2 - agriculture - adaptation
Crop models are essential tools for assessing the threat of climate change to local and global food production(1). Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature(2). Here we systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 degrees C to 32 degrees C, including experiments with artificial heating. Many models simulated yields well, but were less accurate at higher temperatures. The model ensemble median was consistently more accurate in simulating the crop temperature response than any single model, regardless of the input information used. Extrapolating the model ensemble temperature response indicates that warming is already slowing yield gains at a majority of wheat-growing locations. Global wheat production is estimated to fall by 6% for each degrees C of further temperature increase and become more variable over space and time.
Crop modelling for integrated assessment of risk to food production from climate change
Ewert, F. ; Rötter, R.P. ; Bindi, M. ; Webber, Heidi ; Trnka, M. ; Kersebaum, K.C. ; Olesen, J.E. ; Ittersum, M.K. van; Janssen, S.J.C. ; Rivington, M. ; Semenov, M.A. ; Wallach, D. ; Porter, J.R. ; Stewart, D. ; Verhagen, J. ; Gaiser, T. ; Palosuo, T. ; Tao, F. ; Nendel, C. ; Roggero, P.P. ; Bartosová, L. ; Asseng, S. - \ 2015
Environmental Modelling & Software 72 (2015). - ISSN 1364-8152 - p. 287 - 303.
The complexity of risks posed by climate change and possible adaptations for crop production has called for integrated assessment and modelling (IAM) approaches linking biophysical and economic models. This paper attempts to provide an overview of the present state of crop modelling to assess climate change risks to food production and to which extent crop models comply with IAM demands. Considerable progress has been made in modelling effects of climate variables, where crop models best satisfy IAM demands. Demands are partly satisfied for simulating commonly required assessment variables. However, progress on the number of simulated crops, uncertainty propagation related to model parameters and structure, adaptations and scaling are less advanced and lagging behind IAM demands. The limitations are considered substantial and apply to a different extent to all crop models. Overcoming these limitations will require joint efforts, and consideration of novel modelling approaches.
Statistical Analysis of Large Simulated Yield Datasets for Studying Climate Effects
Makowski, D. ; Asseng, S. ; Ewert, F. ; Bassu, S. ; Durand, J.L. ; Martre, P. ; Adam, M. ; Aggarwal, P.K. ; Angulo, C. ; Baron, C. ; Basso, B. ; Bertuzzi, P. ; Biernath, C. ; Boogaard, H. ; Boote, K.J. ; Brisson, N. ; Cammarano, D. ; Challinor, A.J. ; Conijn, J.G. ; Corbeels, M. ; Deryng, D. ; Sanctis, G. De; Doltra, J. ; Gayler, S. ; Goldberg, R. ; Grassini, P. ; Hatfield, J.L. ; Heng, L. ; Hoek, S.B. ; Hooker, J. ; Hunt, L.A. ; Ingwersen, J. ; Izaurralde, C. ; Jongschaap, R.E.E. ; Jones, J.W. ; Kemanian, R.A. ; Kersebaum, K.C. ; Kim, S.H. ; Lizaso, J. ; Müller, C. ; Naresh Kumar, S. ; Nendel, C. ; O'Leary, G.J. ; Olesen, J.E. ; Osborne, T.M. ; Palosuo, T. ; Pravia, M.V. ; Priesack, E. ; Ripoche, D. ; Rosenzweig, C. ; Ruane, A.C. ; Sau, F. ; Semenov, M.A. ; Shcherbak, I. ; Steduto, P. ; Stöckle, C.O. ; Stratonovitch, P. ; Streck, T. ; Supit, I. ; Tao, F. ; Teixeira, E. ; Thorburn, P. ; Timlin, D. ; Travasso, M. ; Roetter, R.P. ; Waha, K. ; Wallach, D. ; White, J.W. ; Williams, J.R. ; Wolf, J. - \ 2015
In: Handbook of Climate Change and Agroecosystems: The Agricultural Model Intercomparison and Improvement Project (AgMIP) / Hillel, D., Rosenzweig, C., - 1100 p.
Many simulation studies have been carried out to predict the effect of climate change on crop yield. Typically, in such study, one or several crop models are used to simulate series of crop yield values for different climate scenarios corresponding to different hypotheses of temperature, CO2 concentration, and rainfall changes. These studies usually generate large datasets including thousands of simulated yield data. The structure of these datasets is complex because they include series of yield values obtained with different mechanistic crop models for different climate scenarios defined from several climatic variables (temperature, CO2 etc.). Statistical methods can play a big part for analyzing large simulated crop yield datasets, especially when yields are simulated using an ensemble of crop models. A formal statistical analysis is then needed in order to estimate the effects of different climatic variables on yield, and to describe the variability of these effects across crop models. Statistical methods are also useful to develop meta-models i.e., statistical models summarizing complex mechanistic models. The objective of this paper is to present a random-coefficient statistical model (mixed-effects model) for analyzing large simulated crop yield datasets produced by the international project AgMip for several major crops. The proposed statistical model shows several interesting features; i) it can be used to estimate the effects of several climate variables on yield using crop model simulations, ii) it quantities the variability of the estimated climate change effects across crop models, ii) it quantifies the between-year yield variability, iv) it can be used as a meta-model in order to estimate effects of new climate change scenarios without running again the mechanistic crop models. The statistical model is first presented in details, and its value is then illustrated in a case study where the effects of climate change scenarios on different crops are compared. See more from this Division: Special Sessions See more from this Session: Symposium--Perspectives on Climate Effects on Agriculture: The International Efforts of AgMIP
Multimodel ensembles of wheat growth: Many models are better than one
Martre, P. ; Wallach, D. ; Asseng, S. ; Ewert, F. ; Jones, J.W. ; Rötter, R.P. ; Boote, K.J. ; Ruane, A.C. ; Thorburn, P. ; Cammarano, D. ; Hatfield, J.L. ; Rosenzweig, C. ; Aggarwal, P.K. ; Angula, C. ; Basso, B. ; Bertuzzi, P. ; Biernath, C. ; Brisson, N. ; Challinor, A. ; Doltra, J. ; Gayler, S. ; Goldberg, R.A. ; Grant, R.F. ; Heng, L. ; Hooker, J. ; Hunt, L.A. ; Ingwersen, J. ; Izaurralde, C. ; Kersebaum, K.C. ; Mueller, C. ; Kumar, S. ; Nendel, C. ; O'Leary, G.J. ; Olesen, J.E. ; Osborne, T.M. ; Palosuo, T. ; Priesack, E. ; Ripoche, D. ; Semenov, M.A. ; Shcherbak, I. ; Steduto, P. ; Stöckle, C.O. ; Stratonovitch, P. ; Streck, T. ; Supit, I. ; Tao, Fulu ; Travasso, M. ; Waha, K. ; White, J.W. ; Wolf, J. - \ 2015
Global Change Biology 21 (2015)2. - ISSN 1354-1013 - p. 911 - 925.
climate-change - crop production - impacts - yield - simulations - calibration - australia - billion - europe - grain
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.
. Challenges for Agro-Ecosystem Modelling in Climate Change Risk Assessment for major European Crops and Farming systems
Rotter, R.P. ; Ewert, F. ; Palosuo, T. ; Bindi, M. ; Kersebaum, K.C. ; Olesen, J.E. ; Trnka, M. ; Ittersum, M.K. van; Janssen, S.J.C. ; Rivington, M. ; Semenov, M. ; Wallach, D. ; Porter, J.R. ; Stewart, D. ; Verhagen, J. ; Angulo, C. ; Gaiser, T. ; Nendel, C. ; Martre, P. ; Wit, A. de - \ 2013
The use of statistical tools in field testing of putative effects of genetically modified plants on nontarget organisms
Semenov, A.V. ; Elsas, J.D. van; Glandorf, D.C.M. ; Schilthuizen, M. ; Boer, W.F. de - \ 2013
Ecology and Evolution 3 (2013)8. - ISSN 2045-7758 - p. 2739 - 2750.
herbicide-tolerant crops - farm-scale evaluations - gene flow - population-structure - habitat preference - land snail - dispersal - design - power - invertebrates
To fulfill existing guidelines, applicants that aim to place their genetically modified (GM) insect-resistant crop plants on the market are required to provide data from field experiments that address the potential impacts of the GM plants on nontarget organisms (NTO's). Such data may be based on varied experimental designs. The recent EFSA guidance document for environmental risk assessment (2010) does not provide clear and structured suggestions that address the statistics of field trials on effects on NTO's. This review examines existing practices in GM plant field testing such as the way of randomization, replication, and pseudoreplication. Emphasis is placed on the importance of design features used for the field trials in which effects on NTO's are assessed. The importance of statistical power and the positive and negative aspects of various statistical models are discussed. Equivalence and difference testing are compared, and the importance of checking the distribution of experimental data is stressed to decide on the selection of the proper statistical model. While for continuous data (e.g., pH and temperature) classical statistical approaches - for example, analysis of variance (ANOVA) - are appropriate, for discontinuous data (counts) only generalized linear models (GLM) are shown to be efficient. There is no golden rule as to which statistical test is the most appropriate for any experimental situation. In particular, in experiments in which block designs are used and covariates play a role GLMs should be used. Generic advice is offered that will help in both the setting up of field testing and the interpretation and data analysis of the data obtained in this testing. The combination of decision trees and a checklist for field trials, which are provided, will help in the interpretation of the statistical analyses of field trials and to assess whether such analyses were correctly applied.
Short-term fluctuations of sugar-beet damping-off by Pythium ultimum in relation to changes in bacterial communities after organic amendments to two soils
He, M. ; Tian, G. ; Semenov, A.M. ; Bruggen, A.H.C. van - \ 2012
Phytopathology 102 (2012)4. - ISSN 0031-949X - p. 413 - 420.
gradient gel-electrophoresis - 16s ribosomal-rna - microbial activity - pseudomonas-fluorescens - fertility amendments - rhizoctonia-solani - fungal antagonists - southern blight - wheat roots - populations
Previously, oscillations in beet seedling damping-off by Pythium ultimum, measured as area under the disease progress curve (AUDPC), were demonstrated after incorporation of organic materials into organic and conventional soils. These periodic fluctuations of P. ultimum infections were cross-correlated with oscillations of copiotrophic CFU at lags of 2 to 4 days. For this article, we investigated whether bacterial communities and microbial activities fluctuated after a disturbance from incorporation of organic materials, and whether these fluctuations were linked to the short-term oscillations in AUDPC of beet seedling damping-off and bacterial populations (CFU) in soil. Soil microbial communities studied by polymerase chain reaction-DGGE analysis of 16S DNA after isolation of total DNA from soil and microbial activities measured as CO2 emission rates were monitored daily for 14 days after addition of grass-clover (GC) or composted manure (CM) into organic versus conventional soils. Similar to our previous findings, AUDPC and density of copiotrophic bacteria oscillated with time. Fluctuations in species richness (S), Shannon diversity index (H), and individual amplicons on DGGE gels were also detected. Oscillations in AUDPC were positively cross-correlated with copiotrophic CFU in all soils. Oscillations in AUDPC were also positively cross-correlated with 19 to 35% of the high-intensity DNA fragments in soils amended with GC but only 2 to 3% of these fragments in CM-amended soils. AUDPC values were negatively cross-correlated with 13 to 17% of the amplicons with low average intensities in CM-amended soils, which were not correlated with densities of copiotrophic CFU. CO2 emission rates had remarkable variations in the initial 7 days after either of the soil amendments but were not associated with daily changes in AUDPC. The results suggest that infection by P. ultimum is hampered by competition from culturable copiotrophic bacteria and some high-intensity DGGE amplicons, because AUDPC is cross-correlated with these variables at lags of 1 to 4 days. However, negative cross-correlations with low-intensity DNA fragments indicate that P. ultimum infection could also be suppressed by antagonistic bacteria with low densities that may be nonculturable species, especially in CM amended soil. The organic soil generally had lower AUDPC values, higher bacterial diversity, and negative cross-correlations between AUDPC and low-intensity DNA fragments (after CM amendment), indicating that specific bacteria that do not attain high densities may contribute to P. ultimum suppression in organic soils
Influence of aerobic and anaerobic conditions on survival of Escherichia coli O157:H7 and Salmonella enterica serovar Typhimurium in Luria-Bertani broth, farm-yard manure and slurry
Semenov, A.V. ; Overbeek, L.S. van; Termorshuizen, A.J. ; Bruggen, A.H.C. van - \ 2011
Journal of Environmental Management 92 (2011)3. - ISSN 0301-4797 - p. 780 - 787.
gradient gel-electrophoresis - amended soil - cow manure - united-states - aggregative behavior - o157-h7 survival - fresh produce - ribosomal-rna - dairy manure - swine manure
The influence of aerobic and anaerobic conditions on the survival of the enteropathogens Escherichia coli O157:H7 and Salmonella serovar Typhimurium was investigated in microcosms with broth, cattle manure or slurry. These substrates were inoculated with a green fluorescent protein transformed strain of the enteropathogens at 107 cells g-1 dry weight. Survival data was fitted to the Weibull model. The survival curves in aerobic conditions generally showed a concave curvature, while the curvature was convex in anaerobic conditions. The estimated survival times showed that E. coli O157:H7 survived significantly longer under anaerobic than under aerobic conditions. Survival ranged from approximately. 2 weeks for aerobic manure and slurry to more than six months for anaerobic manure at 16 °C. On average, in 56.3% of the samplings, the number of recovered E. coli O157:H7 cells by anaerobic incubation of Petri plates was significantly (p <0.05) higher in comparison with aerobic incubation. Survival of Salmonella serovar Typhimurium was not different between aerobic and anaerobic storage of LB broth or manure as well as between aerobic and anaerobic incubation of Petri dishes. The importance of changes in microbial community and chemical composition of manure and slurry was distinguished for the survival of E. coli O157:H7 in different oxygen conditions.
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