toggle visibility Search & Display Options

Select All    Deselect All
 |   | 
Details
   print
  Records Links
Author Bodirsky, B.L.; Popp, A.; Lotze-Campen, H.; Dietrich, J.P.; Rolinski, S.; Weindl, I.; Schmitz, C.; Müller, C.; Bonsch, M.; Humpenöder, F.; Biewald, A.; Stevanovic, M. url  doi
openurl 
  Title Reactive nitrogen requirements to feed the world in 2050 and potential to mitigate nitrogen pollution Type Journal Article
  Year 2014 Publication Nature Communications Abbreviated Journal Nat. Comm.  
  Volume 5 Issue Pages 3858  
  Keywords Animals; Crops, Agricultural/metabolism/*supply & distribution; Environmental Pollution/*prevention & control; *Food Supply; Humans; Models, Theoretical; Nitrogen Fixation; *Population Growth; Reactive Nitrogen Species/*supply & distribution  
  Abstract Reactive nitrogen (Nr) is an indispensable nutrient for agricultural production and human alimentation. Simultaneously, agriculture is the largest contributor to Nr pollution, causing severe damages to human health and ecosystem services. The trade-off between food availability and Nr pollution can be attenuated by several key mitigation options, including Nr efficiency improvements in crop and animal production systems, food waste reduction in households and lower consumption of Nr-intensive animal products. However, their quantitative mitigation potential remains unclear, especially under the added pressure of population growth and changes in food consumption. Here we show by model simulations, that under baseline conditions, Nr pollution in 2050 can be expected to rise to 102-156% of the 2010 value. Only under ambitious mitigation, does pollution possibly decrease to 36-76% of the 2010 value. Air, water and atmospheric Nr pollution go far beyond critical environmental thresholds without mitigation actions. Even under ambitious mitigation, the risk remains that thresholds are exceeded.  
  Address  
  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 2041-1723 ISBN Medium Article  
  Area Expedition Conference  
  Notes CropM Approved no  
  Call Number MA @ admin @ Serial (up) 4513  
Permanent link to this record
 

 
Author Challinor, A.; Martre, P.; Asseng, S.; Thornton, P.; Ewert, F. url  doi
openurl 
  Title Making the most of climate impacts ensembles Type Journal Article
  Year 2014 Publication Nature Climate Change Abbreviated Journal Nat. Clim. Change  
  Volume 4 Issue 2 Pages 77-80  
  Keywords uncertainty; model; adaptation  
  Abstract Increasing use of regionally and globally oriented impacts studies, coordinated across international modelling groups, promises to bring about a new era in climate impacts research. Coordinated cycles of model improvement and projection are needed to make the most of this potential.  
  Address  
  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 1758-678x 1758-6798 ISBN Medium Commentary  
  Area Expedition Conference  
  Notes CropM Approved no  
  Call Number MA @ admin @ Serial (up) 4516  
Permanent link to this record
 

 
Author Dumont, B.; Basso, B.; Leemans, V.; Bodson, B.; Destain, J.-P.; Destain, M.-F. url  doi
openurl 
  Title Systematic analysis of site-specific yield distributions resulting from nitrogen management and climatic variability interactions Type Journal Article
  Year 2015 Publication Precision Agriculture Abbreviated Journal Precision Agric.  
  Volume 16 Issue 4 Pages 361-384  
  Keywords nitrogen management; climatic variability; lars-wg weather generator; stics soil-crop model; pearson system; probability risk assessment; crop model stics; fertilizer nitrogen; generic model; wheat yield; maize; simulation; skewness; field; agriculture; scenarios  
  Abstract At the plot level, crop simulation models such as STICS have the potential to evaluate risk associated with management practices. In nitrogen (N) management, however, the decision-making process is complex because the decision has to be taken without any knowledge of future weather conditions. The objective of this paper is to present a general methodology for assessing yield variability linked to climatic uncertainty and variable N rate strategies. The STICS model was coupled with the LARS-Weather Generator. The Pearson system and coefficients were used to characterise the shape of yield distribution. Alternatives to classical statistical tests were proposed for assessing the normality of distributions and conducting comparisons (namely, the Jarque-Bera and Wilcoxon tests, respectively). Finally, the focus was put on the probability risk assessment, which remains a key point within the decision process. The simulation results showed that, based on current N application practice among Belgian farmers (60-60-60 kgN ha(-1)), yield distribution was very highly significantly non-normal, with the highest degree of asymmetry characterised by a skewness value of -1.02. They showed that this strategy gave the greatest probability (60 %) of achieving yields that were superior to the mean (10.5 t ha(-1)) of the distribution.  
  Address  
  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 1385-2256 ISBN Medium Article  
  Area Expedition Conference  
  Notes CropM, ft_macsur Approved no  
  Call Number MA @ admin @ Serial (up) 4519  
Permanent link to this record
 

 
Author Dumont, B.; Leemans, V.; Mansouri, M.; Bodson, B.; Destain, J.-P.; Destain, M.-F. url  doi
openurl 
  Title Parameter identification of the STICS crop model, using an accelerated formal MCMC approach Type Journal Article
  Year 2014 Publication Environmental Modelling & Software Abbreviated Journal Env. Model. Softw.  
  Volume 52 Issue Pages 121-135  
  Keywords crop model; parameter estimation; bayes; stics; dream; global sensitivity-analysis; simulation-model; nitrogen balances; bayesian-approach; generic model; wheat; prediction; water; optimization; algorithm  
  Abstract This study presents a Bayesian approach for the parameters’ identification of the STICS crop model based on the recently developed Differential Evolution Adaptive Metropolis (DREAM) algorithm. The posterior distributions of nine specific crop parameters of the STICS model were sampled with the aim to improve the growth simulations of a winter wheat (Triticum aestivum L) culture. The results obtained with the DREAM algorithm were initially compared to those obtained with a Nelder-Mead Simplex algorithm embedded within the OptimiSTICS package. Then, three types of likelihood functions implemented within the DREAM algorithm were compared, namely the standard least square, the weighted least square, and a transformed likelihood function that makes explicit use of the coefficient of variation (CV). The results showed that the proposed CV likelihood function allowed taking into account both noise on measurements and heteroscedasticity which are regularly encountered in crop modelling. (C) 2013 Elsevier Ltd. All rights reserved.  
  Address  
  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 1364-8152 ISBN Medium Article  
  Area Expedition Conference  
  Notes CropM Approved no  
  Call Number MA @ admin @ Serial (up) 4520  
Permanent link to this record
 

 
Author Ewert, F.; Rötter, R.P.; Bindi, M.; Webber, H.; Trnka, M.; Kersebaum, K.C.; Olesen, J.E.; van Ittersum, M.K.; Janssen, S.; Rivington, M.; Semenov, M.A.; Wallach, D.; Porter, J.R.; Stewart, D.; Verhagen, J.; Gaiser, T.; Palosuo, T.; Tao, F.; Nendel, C.; Roggero, P.P.; Bartošová, L.; Asseng, S. url  doi
openurl 
  Title Crop modelling for integrated assessment of risk to food production from climate change Type Journal Article
  Year 2015 Publication Environmental Modelling & Software Abbreviated Journal Env. Model. Softw.  
  Volume 72 Issue Pages 287-303  
  Keywords uncertainty; scaling; integrated assessment; risk assessment; adaptation; crop models; agricultural land-use; change adaptation strategies; farming systems simulation; agri-environmental systems; enrichment face experiment; high-temperature stress; change impacts; nitrogen dynamics; atmospheric co2; spring wheat  
  Abstract The complexity of risks posed by climate change and possible adaptations for crop production has called for integrated assessment and modelling (IAM) approaches linking biophysical and economic models. This paper attempts to provide an overview of the present state of crop modelling to assess climate change risks to food production and to which extent crop models comply with IAM demands. Considerable progress has been made in modelling effects of climate variables, where crop models best satisfy IAM demands. Demands are partly satisfied for simulating commonly required assessment variables. However, progress on the number of simulated crops, uncertainty propagation related to model parameters and structure, adaptations and scaling are less advanced and lagging behind IAM demands. The limitations are considered substantial and apply to a different extent to all crop models. Overcoming these limitations will require joint efforts, and consideration of novel modelling approaches.  
  Address  
  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 1364-8152 ISBN Medium Article  
  Area Expedition Conference  
  Notes CropM, ft_macsur Approved no  
  Call Number MA @ admin @ Serial (up) 4521  
Permanent link to this record
Select All    Deselect All
 |   | 
Details
   print

Save Citations:
Export Records: