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Author Jing, Q.; Bélanger, G.; Baron, V.; Bonesmo, H.; Virkajärvi, P.; Young, D. url  doi
openurl 
  Title Regrowth simulation of the perennial grass timothy Type Journal Article
  Year 2012 Publication Ecological Modelling Abbreviated Journal Ecol. Model.  
  Volume 232 Issue Pages 64-77  
  Keywords biomass; carbohydrate; leaf area index; n uptake; reserve-dependent growth; temperature; nutritive-value; carbohydrate reserves; phleum-pratense; catimo model; leaf-area; nitrogen-fertilization; spring harvest; meadow fescue; tall fescue; growth  
  Abstract Several process-based models for simulating the growth of perennial grasses have been developed but few include the simulation of regrowth. The model CATIMO simulates the primary growth of timothy (Phleum pratense L), an important perennial forage grass species in northern regions of Europe and North America. Our objective was to further develop the model CATIMO to simulate timothy regrowth using the concept of reserve-dependent growth. The performance of this modified CATIMO model in simulating leaf area index (LAI), biomass dry matter (DM) yield, and N uptake of regrowth was assessed with data from four independent field experiments in Norway, Finland, and western and eastern Canada using an approach that combines graphical comparison and statistical analysis. Biomass DM yield and N uptake of regrowth were predicted at the same accuracy as primary growth with linear regression coefficients of determination between measured and simulated values greater than 0.79, model simulation efficiencies greater than 0.78, and normalized root mean square errors (14-30% for biomass and 24-34% for N uptake) comparable with the coefficients of variation of measured data (1-21% for biomass and 1-25% for N uptake). The model satisfactorily simulated the regrowth LAI but only up to a value of about 4.0. The modified CATIMO model with its capacity to simulate regrowth provides a framework to simulate perennial grasses with multiple harvests, and to explore management options for sustainable grass production under different environmental conditions. Crown Copyright (C) 2012 Published by Elsevier B.V. All rights reserved.  
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  Corporate Author Thesis  
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  Language English Summary Language Original Title  
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  ISSN (down) 0304-3800 ISBN Medium Article  
  Area Expedition Conference  
  Notes CropM, LiveM Approved no  
  Call Number MA @ admin @ Serial 4473  
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Author Hidy, D.; Barcza, Z.; Haszpra, L.; Churkina, G.; Pintér, K.; Nagy, Z. url  doi
openurl 
  Title Development of the Biome-BGC model for simulation of managed herbaceous ecosystems Type Journal Article
  Year 2012 Publication Ecological Modelling Abbreviated Journal Ecol. Model.  
  Volume 226 Issue Pages 99-119  
  Keywords biogeochemical model; biome-bgc; grassland; management; soil moisture; bayesian calibration; carbon flux model; regional applications; bayesian calibration; use efficiency; general-model; exchange; balance; climate; grassland; variability  
  Abstract Apart from measurements, numerical models are the most convenient instruments to analyze the carbon and water balance of terrestrial ecosystems and their interactions with changing environmental conditions. The process-based Biome-BGC model is widely used to simulate the storage and flux of water, carbon, and nitrogen within the vegetation, litter, and soil of unmanaged terrestrial ecosystems. Considering herbaceous vegetation related simulations with Biome-BGC, soil moisture and growing season control on ecosystem functioning is inaccurate due to the simple soil hydrology and plant phenology representation within the model. Consequently, Biome-BGC has limited applicability in herbaceous ecosystems because (1) they are usually managed; (2) they are sensitive to soil processes, most of all hydrology; and (3) their carbon balance is closely connected with the growing season length. Our aim was to improve the applicability of Biome-BGC for managed herbaceous ecosystems by implementing several new modules, including management. A new index (heatsum growing season index) was defined to accurately estimate the first and the final days of the growing season. Instead of a simple bucket soil sub-model, a multilayer soil sub-model was implemented, which can handle the processes of runoff, diffusion and percolation. A new module was implemented to simulate the ecophysiological effect of drought stress on plant mortality. Mowing and grazing modules were integrated in order to quantify the functioning of managed ecosystems. After modifications, the Biome-BGC model was calibrated and validated using eddy covariance-based measurement data collected in Hungarian managed grassland ecosystems. Model calibration was performed based on the Bayes theorem. As a result of these developments and calibration, the performance of the model was substantially improved. Comparison with measurement-based estimate showed that the start and the end of the growing season are now predicted with an average accuracy of 5 and 4 days instead of 46 and 85 days as in the original model. Regarding the different sites and modeled fluxes (gross primary production, total ecosystem respiration, evapotranspiration), relative errors were between 18-60% using the original model and 10-18% using the developed model; squares of the correlation coefficients were between 0.02-0.49 using the original model and 0.50-0.81 using the developed model. (c) 2011 Elsevier B.V. All rights reserved.  
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  Corporate Author Thesis  
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  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN (down) 0304-3800 ISBN Medium Article  
  Area Expedition Conference  
  Notes LiveM Approved no  
  Call Number MA @ admin @ Serial 4472  
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Author Cortignani, R.; Dono, G. url  doi
openurl 
  Title Simulation of the impact of greening measures in an agricultural area of the southern Italy Type Journal Article
  Year 2015 Publication Land Use Policy Abbreviated Journal Land Use Policy  
  Volume 48 Issue Pages 525-533  
  Keywords agricultural policy; supply analysis; mathematical programming; maximum-entropy; level; models  
  Abstract Together, sustainable management of natural resources and climate action form one of the three objectives of the 2014-2020 Common Agricultural Policy. This objective is being addressed by replacing the existing direct payments under Pillar 1 with a basic payment, combined with an additional payment conditional on farmers undertaking agricultural practices beneficial for the climate and the environment, a policy referred to as greening. In this study, the impact of greening was assessed using a hybrid model calibrated using positive mathematical programming. The model describes the macro-types of farm production in a Mediterranean agricultural area. The results show that greening was not beneficial throughout the study area and only some farm types have been particularly affected. However, greening appears to have a positive impact on curtailing the use of chemicals, particularly nitrogen, and on crop diversity. (C) 2015 Elsevier Ltd. All rights reserved.  
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  Language English Summary Language Original Title  
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  Series Volume Series Issue Edition  
  ISSN (down) 0264-8377 ISBN Medium Article  
  Area Expedition Conference  
  Notes TradeM, ft_macsur Approved no  
  Call Number MA @ admin @ Serial 4746  
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Author Sieber, S.; Amjath-Babu, T.S.; Jansson, T.; Müller, K.; Tscherning, K.; Graef, F.; Pohle, D.; Helming, K.; Rudloff, B.; Saravia-Matus, B.S.; Gomez y Paloma, S. url  doi
openurl 
  Title Sustainability impact assessment using integrated meta-modelling: Simulating the reduction of direct support under the EU common agricultural policy (CAP) Type Journal Article
  Year 2013 Publication Land Use Policy Abbreviated Journal Land Use Policy  
  Volume 33 Issue Pages 235-245  
  Keywords SIAT; CAP; sustainability; impact assessment; land use change; trade off analysis; model; Netherlands; systems  
  Abstract Assessing the impact of macro-level policy driven land use changes on regional sustainability is an important task that can facilitate complex decision making processes of introducing reforms. The research work demonstrates the ability of Sustainability Impact Assessment Tool (SIAT), a meta-model, in conducting ex ante spatially explicit cross sectoral impact assessments of changes in common agricultural policy (CAP). The meta-model is able to appraise impacts of CAP amendments on land use and their repercussions on multiple indicators of sustainability. The presented study comprehensively analyses the possible impacts of discontinuing direct financial support to farmers under CAP. The simulations of the meta-model are able to reveal the land use changes both at EU and regional levels as well as to bring forth the subsequent changes in a number of indicators representing the regional sustainability (for five case study regions). In a nutshell, the simulations indicate that a reduction in direct support brings in general, a decrease in farmed area, an increase in forested land, less fluctuation in natural vegetation coverage, increase in abandoned arable land area and negligible changes in built-up area despite regionally diverging land use trends. The simulated changes in sustainability indicators for the study regions in consequence to these land use changes show that the discontinuation of subsidies evokes responses that are in general climate friendly (reduction in methane and N2O emissions, diminishing energy use and reduction in global warming potential), economically beneficial (increase in gross value of agriculture) and socially desired (decrease in unemployment rate) as well as environmentally harmful (increase in pesticide use). Even though the appraisals of diversity indicators such as forest deadwood and farmland birds are not conclusive for all regions, the changes are positive for the former indicator and slightly negative for the latter in general. The trade-offs among these regional sustainability indicators using their directional associations are also presented for a comprehensive assessment of the impacts. (C) 2013 Elsevier Ltd. All rights reserved.  
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  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN (down) 0264-8377 ISBN Medium Article  
  Area Expedition Conference  
  Notes TradeM Approved no  
  Call Number MA @ admin @ Serial 4479  
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Author Murat, M.; Malinowska, I.; Gos, M.; Krzyszczak, J. doi  openurl
  Title Forecasting daily meteorological time series using ARIMA and regression models Type Journal Article
  Year 2018 Publication International Agrophysics Abbreviated Journal Int. Agrophys.  
  Volume 32 Issue 2 Pages 253-264  
  Keywords regression models; forecast; time series; meteorological quantities; Response Surfaces; Extreme Heat; Wheat; Climate  
  Abstract The daily air temperature and precipitation time series recorded between January 1, 1980 and December 31, 2010 in four European sites (Jokioinen, Dikopshof, Lleida and Lublin) from different climatic zones were modeled and forecasted. In our forecasting we used the methods of the Box-Jenkins and Holt-Winters seasonal auto regressive integrated moving-average, the autoregressive integrated moving-average with external regressors in the form of Fourier terms and the time series regression, including trend and seasonality components methodology with R software. It was demonstrated that obtained models are able to capture the dynamics of the time series data and to produce sensible forecasts.  
  Address 2018-06-14  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN (down) 0236-8722 ISBN Medium  
  Area Expedition Conference  
  Notes CropM, ft_macsur Approved no  
  Call Number MA @ admin @ Serial 5202  
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