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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Abdelhameed ElameenAbstract
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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 TveitoAbstract
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Xiao Huang Chaoqing Yu Jiarui Fang Guorui Huang Shaoqiang Ni Jim Hall Conrad Zorn Xiaomeng Huang Wenyuan ZhangAbstract
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.
Authors
Xiao Huang Shaoqiang Ni Chaoqing Yu Jim Hall Conrad Zorn Xiaomeng HuangAbstract
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.
Abstract
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.
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Arne StensvandAbstract
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Many nonlinear methods of time series analysis require a minimal number of observations in the hundreds to thousands, which is not always easy to achieve for observations of environmental systems. As a result, finite size effects often hamper proper interpretation of the results; the estimation of the correlation dimension, Lyapunov exponents or KolmogorovSinai entropies, to name a few, is plagued by huge uncertainties. Eddy Covariance (EC) measurements of the carbon exchange between the atmosphere and vegetation provide a noticeable exception. The turbulent wind fields transporting carbon dioxide to the surface layer show variability over a large range of spatiotemporal scales, and their quantification demand a high temporal resolution, typically at 20 Hz. This generates very long time series even for short measurement periods; usually, the raw data are aggregated to carbon cycle observables, like Gross Primary Productivity (GPP) or Net Ecosystem Exchange (NEE) at half-hourly time steps. In this presentation, we investigate the high-resolution raw data of 3D wind speed and CO2 concentrations measured at a young forest plantation in Southeast Norway since July 2018. After introducing the EC technique and the Integrated Carbon Observation System (ICOS), we present results of complexity analysis, Tarnopolski diagrams, q-Entropy and Hurst analysis, and Empirical Mode Decomposition. This provides insights into not only whether the young forest stand is actually a source or sink of carbon, but also when, how and how strong carbon uptake and release are taking place at the site, and the nature of dynamics of carbon fluxes across this system boundary in general.
Conference lecture – Land lease at the extensive and intensive margins
Eirik Romstad, Grete Stokstad
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The extent of land lease is increasing in many countries, including Norway. This paper develops a von Thünen type model of optimal land plots to lease from a farm’s center. For a single farm setting the optimality principle is that land is leased as long as the expected marginal value of leasing the land is greater than or equal to the expected marginal costs of leasing the land. The single farm model setting captures land lease at the extensive margin, i.e., under absence of competition for leasing land. Land lease at the intensive margin, i.e., when there is competition for leasing farm fields, is more interesting. We distinguish between two cases. In the first case, continued farm operations do not depend on being able to lease more land. Then we show that optimal land lease results when the expected profits for each farm of leasing its least profitable field is equal among farms competing for the same farm field. This also corresponds to an economically efficient allocation of leased land. Our second case at the intensive margin is more complicated. Here, farm survival depends on attracting acreage of leased land to allow for investment in cost saving technology. We show that the resulting allocation of leased land corresponds to the solution of a game involving bidding for land to prevent other farmers from getting land, which in turn leads to farmer exit and therefore increases the future supply of land available at the land lease market. In the first round of the game, winners of the land lease auction pays more for the leased land than they would have done in absence preventive bidding. The model framework is applicable for other settings where locking out competitors are parts of agents’ strategy space. Key words: von Thünen, non-cooperative game theory, auctions with preventive bidding. JEL classification: C72, D44, L13