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Savary, S., Jouanin, C., Félix, I., Gourdain, E., Piraux, F., Brun, F., et al. (2016). Assessing plant health in a network of experiments on hardy winter wheat varieties in France: patterns of disease-climate associations. Eur. J. Plant Pathol., 146, 741–755.
Abstract: A data set generated by a multi-year (2003–2010) and multi-site network of experiments on winter wheat varieties grown at different levels of crop management is analysed in order to assess the importance of climate on the variability of wheat health. Wheat health is represented by the multiple pathosystem involving five components: leaf rust, yellow rust, fusarium head blight, powdery mildew, and septoria tritici blotch. An overall framework of associations between multiple diseases and climate variables is developed. This framework involves disease levels in a binary form (i.e. epidemic vs. non-epidemic) and synthesis variables accounting for climate over spring and early summer. The multiple disease-climate pattern of associations of this framework conforms to disease-specific knowledge of climate effects on the components of the pathosystem. It also concurs with a (climate-based) risk factor approach to wheat diseases. This report emphasizes the value of large scale data in crop health assessment and the usefulness of a risk factor approach for both tactical and strategic decisions for crop health management.
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Stefańczyk, E., Sobkowiak, S., Brylińska, M., & Śliwka, J. (2016). Diversity of Fusarium spp. associated with dry rot of potato tubers in Poland. Eur. J. Plant Pathol., .
Abstract: Fusarium spp. belong to the division Ascomycota and cause important plant diseases; these fungi may contaminate food products with mycotoxins, endangering human and animal health. Several Fusarium spp. have been associated with potato dry rot. The most frequent and devastating of these species are F. sambucinum, F. solani and F. oxysporum, depending on the geographic location and the season. Samples of potato tubers with dry rot symptoms were collected, and their putative fungal isolates were identified as Fusarium species using partial nucleotide sequences of the internal transcribed spacer, translation elongation factor 1-α and β-tubulin genes. Among 149 isolates, 12 species were identified. F. oxysporum was the most frequent (45 % of the isolates), followed by F. avenaceum (12.1 %), F. solani (10.7 %) and F. sambucinum (7.4 %). Phylogenetic analyses confirmed the species identifications and revealed a high diversity of F. solani and a low diversity of F. oxysporum. Potential producers of zearalenone and trichothecenes were identified within the obtained isolates using PCR markers. Isolates that were pathogenic to potatoes in laboratory tests were found in four species: F. sambucinum, F. avenaceum, F. culmorum, and F. graminearum. The effects of increased temperature and mixed inoculum on the pathogenicities of chosen species were evaluated. This study adds 434 potato-derived Fusarium sequences to the NCBI GenBank database and demonstrates that the list of Fusarium species and mycotoxins present in potato tubers may be richer than previously believed, regardless of whether these species cause dry rot or live as saprophytes.
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Wallach, D., Mearns, L. O., Ruane, A. C., Rötter, R. P., & Asseng, S. (2016). Lessons from climate modeling on the design and use of ensembles for crop modeling. Clim. Change, .
Abstract: Working with ensembles of crop models is a recent but important development in crop modeling which promises to lead to better uncertainty estimates for model projections and predictions, better predictions using the ensemble mean or median, and closer collaboration within the modeling community. There are numerous open questions about the best way to create and analyze such ensembles. Much can be learned from the field of climate modeling, given its much longer experience with ensembles. We draw on that experience to identify questions and make propositions that should help make ensemble modeling with crop models more rigorous and informative. The propositions include defining criteria for acceptance of models in a crop MME, exploring criteria for evaluating the degree of relatedness of models in a MME, studying the effect of number of models in the ensemble, development of a statistical model of model sampling, creation of a repository for MME results, studies of possible differential weighting of models in an ensemble, creation of single model ensembles based on sampling from the uncertainty distribution of parameter values or inputs specifically oriented toward uncertainty estimation, the creation of super ensembles that sample more than one source of uncertainty, the analysis of super ensemble results to obtain information on total uncertainty and the separate contributions of different sources of uncertainty and finally further investigation of the use of the multi-model mean or median as a predictor.
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Bai, H., Tao, F., Xiao, D., Liu, F., & Zhang, H. (2016). Attribution of yield change for rice-wheat rotation system in China to climate change, cultivars and agronomic management in the past three decades. Clim. Change, 135(3-4), 539–553.
Abstract: Using the detailed field experiment data from 1981 to 2009 at four representative agro-meteorological experiment stations in China, along with the Agricultural Production System Simulator (APSIM) rice-wheat model, we evaluated the impact of sowing/transplanting date on phenology and yield of rice-wheat rotation system (RWRS). We also disentangled the contributions of climate change, modern cultivars, sowing/transplanting density and fertilization management, as well as changes in each climate variables, to yield change in RWRS, in the past three decades. We found that change in sowing/transplanting date did not significantly affect rice and wheat yield in RWRS, although alleviated the negative impact of climate change to some extent. From 1981 to 2009, climate change jointly caused rice and wheat yield change by -17.4 to 1.5 %, of which increase in temperature reduced yield by 0.0-5.8 % and decrease in solar radiation reduced it by 1.5-8.7 %. Cultivars renewal, modern sowing/transplanting density and fertilization management contributed to yield change by 14.4-27.2, -4.7- -0.1 and 2.3-22.2 %, respectively. Our findings highlight that modern cultivars and agronomic management compensated the negative impacts of climate change and played key roles in yield increase in the past three decades.
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Hoffmann, H., Zhao, G., Asseng, S., Bindi, M., Biernath, C., Constantin, J., et al. (2016). Impact of spatial soil and climate input data aggregation on regional yield simulations. PLoS One, 11(4), e0151782.
Abstract: We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.
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