Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2018
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Anett Kristin Larsen Ingebjørg Helena Nymo Karen Kristine Sørensen Marit Seppola Rolf Rødven María Pilar Jiménez de Bagüés Sascha Al Dahouk Jacques GodfroidSammendrag
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Abdelhameed ElameenSammendrag
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Isabella Børja Kjell Andreassen Jan Čermák Lise Dalsgaard Arthur Gessler Douglas Lawrence Godbold Rainer Hentschel Zachary E. Kayler Paal Krokene Nadezhda Nadezhdina Sabine Rosner Halvor Solheim Jan Svetlik Mari Mette Tollefsrud Ole Einar TveitoSammendrag
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Xiao Huang Chaoqing Yu Jiarui Fang Guorui Huang Shaoqiang Ni Jim Hall Conrad Zorn Xiaomeng Huang Wenyuan ZhangSammendrag
Crop models are widely used to evaluate the response of crop growth to drought. However, over large geographic regions, the most advanced models are often restricted by available computing resource. This limits capacity to undertake uncertainty analysis and prohibits the use of models in real-time ensemble forecasting systems. This study addresses these concerns by presenting an integrated system for the dynamic prediction and assessment of agricultural yield using the top-ranked Sunway TaihuLight supercomputer platform. This system enables parallelization and acceleration for the existing AquaCrop, DNDC (DeNitrification and DeComposition) and SWAP (Soil Water Atmosphere Plant) models, thus facilitating multi-model ensemble and parameter optimization and subsequent drought risk analysis in multiple regions and at multiple scales. The high computing capability also opens up the possibility of real-time simulation during droughts, providing the basis for more effective drought management. Initial testing with varying core group numbers shows that computation time can be reduced by between 2.6 and 3.6 times. Based on the powerful computing capacity, a county-level model parameter optimization (2043 counties for 1996–2007) by Bayesian inference and multi-model ensemble using BMA (Bayesian Model Average) method were performed, demonstrating the enhancements in predictive accuracy that can be achieved. An application of this system is presented predicting the impacts of the drought of May–July 2017 on maize yield in North and Northeast China. The spatial variability in yield losses is presented demonstrating new capability to provide high resolution information with associated uncertainty estimates.
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Xiao Huang Shaoqiang Ni Chaoqing Yu Jim Hall Conrad Zorn Xiaomeng HuangSammendrag
Precipitation is an important source of soil water, which is critical to crop growth, and is therefore an important input when modelling crop growth. Although advances are continually being made in predicting and recording precipitation, input uncertainty of precipitation data is likely to influence the robustness of parameter estimate and thus the predictive accuracy in soil water and crop modelling. In this study, we use the Bayesian total error analysis (BATEA) method for the water-oriented crop model AquaCrop to identify the input uncertainty from multiple precipitation products respectively, including gauge-corrected grid dataset CPC, remote sensing based TRMM and reanalysis based ERA-Interim. This methodology uses latent variables to correct the input data errors. Adopting a single-multiplier method for precipitation correction, we simulate maize growth in both field and regional levels in China for a range of different possible climatic scenarios. Meanwhile, we use the average of multiple products for model driving in comparison. The results show that the BATEA method can consistently reduce uncertainty for crop growth prediction among different precipitation products. In regional simulation, the improvements for the three products are 1%, 7.3% and 2.8% on average in drought scenarios. These results imply the BATEA approach can be of great assistance for crop modeling studies and agricultural assessments under future changing climates.
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The ericaceous shrub bilberry (Vaccinium myrtillus L.) is a keystone species of the Eurasian boreal forest. The most optimal light condition for this plant is partial shading. Shade from the forest canopy depends on the stand density, a forest attribute that can be manipulated by forest managers. Most previous studies of the relationship between bilberry abundance and forest density have not explored the potentially modifying impacts of factors like stand age, tree species composition, and the solar irradiation at the site, as determined by location and topography. Using data from the Norwegian National Forest Inventory, we developed a generalized linear model applicable to estimate local bilberry cover across a wide range of environmental conditions in Norway. The explanatory terms in the final model were stand density (basal area per ha), solar irradiation, stand age, percentages of deciduous, pine, and spruce trees, summer (June-August) mean temperature and precipitation sum, mean temperature in January, site index, and soil category, in addition to the two-way interactions between stand density and the following: solar irradiation, stand age, percentage of deciduous trees, and percentage of Norway spruce (Picea abies). The final model explained ca. 21% of the total variation in bilberry cover. We conclude that a stand density of c. 30 m2 ha−1 in general will create favourable conditions for bilberry. If the forest is younger than 80 years old, or dominated by Norway spruce or deciduous trees, the optimal stand density is reduced to around 20 m2 ha−1. In a forest dominated by Scots pine (Pinus sylvestris), basal areas up to 40 m2 ha−1 would be beneficial to bilberry abundance. Our results demonstrate the importance of considering interactions between stand density and other stand and site characteristics.