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Author Kopacz, M.; Twardy, S. url  openurl
  Title Water and sewage management in the upper Dunajec river basin compared to the socio-structural transformations and surface water quality Type (down) Report
  Year 2012 Publication Woda Srodowisko Obszary Wiejskie Abbreviated Journal  
  Volume 123 Issue Pages 103-122  
  Keywords CropM; LiveM  
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  Language Summary Language Original Title  
  Series Editor ITP Falenty Series Title Abbreviated Series Title  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number MA @ admin @ Serial 2067  
Permanent link to this record
 
 
Author Hutchings, N.; Kipling, R. url  openurl
  Title Inventory of farm-scale models within LiveM Type (down) Report
  Year 2014 Publication FACCE MACSUR Reports Abbreviated Journal  
  Volume 3 Issue Pages D-L3.1  
  Keywords LiveM;  
  Abstract The aim of WP3 is to improve the assessment of the impact of climate change on livestock and grassland systems at the farm-scale. The first step in this process is to understand the current state of the art in farm-scale modelling, and the resources available within the MACSUR knowledge hub. Here, an inventory of the farm-scale models available within LiveM is presented, along with a summary of the types of model represented. Thirteen farm-scale models were identified, three of which focus on environmental aspects of farm systems (GHG emissions etc.) and ten of which focus on management strategies (productivity, economics etc.).  
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  Area Expedition Conference  
  Notes Approved no  
  Call Number MA @ admin @ Serial 2064  
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Author Kowalczyk, A.; Twardy, S. url  openurl
  Title Comparison of the water erosion magnitude estimated by the modified USLE methods Type (down) Report
  Year 2012 Publication Woda Srodowisko Obszary Wiejskie Abbreviated Journal  
  Volume 121 Issue Pages 83-92  
  Keywords CropM  
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  Language Summary Language Original Title  
  Series Editor ITP Falenty Series Title Abbreviated Series Title  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number MA @ admin @ Serial 2068  
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Author Kirchner, M.; Schmid, E.; Mitter, H.; Schönhart, M. openurl 
  Title Modeling the Impacts of Climate Change and Market Integration on Agricultural Production and Land Use Management in Austria Type (down) Report
  Year 2015 Publication IIASA Interim Report Young Scientists Summer Program Abbreviated Journal  
  Volume Issue Pages  
  Keywords TradeM C6 -  
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  Series Editor Series Title Abbreviated Series Title  
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  Notes Approved no  
  Call Number MA @ admin @ Serial 2065  
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Author Wallach, D.; Thorburn, P.; Asseng, S.; Challinor, A.J.; Ewert, F.; Jones, J.W.; Rötter, R.; Ruane, A. url  openurl
  Title Overview paper on comprehensive framework for assessment of error and uncertainty in crop model predictions Type (down) Report
  Year 2016 Publication FACCE MACSUR Reports Abbreviated Journal  
  Volume 8 Issue Pages C4.1-D  
  Keywords MACSUR_ACK; CropM  
  Abstract Crop models are important tools for impact assessment of climate change, as well as for  exploring management options under current climate. It is essential to evaluate the  uncertainty associated with predictions of these models. Several ways of quantifying  prediction uncertainty have been explored in the literature, but there have been no  studies of how the different approaches are related to one another, and how they are  related to some overall measure of prediction uncertainty. Here we show that all the  different approaches can be related to two different viewpoints about the model; either  the model is treated as a fixed predictor with some average error, or the model can be  treated as a random variable with uncertainty in one or more of model structure, model  inputs and model parameters. We discuss the differences, and show how mean squared  error of prediction can be estimated in both cases. The results can be used to put  uncertainty estimates into a more general framework and to relate different uncertainty  estimates to one another and to overall prediction uncertainty. This should lead to a  better understanding of crop model prediction uncertainty and the underlying causes of  that uncertainty. This study was published as (Wallach et al. 2016)  
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  Notes Approved no  
  Call Number MA @ office @ Serial 2954  
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