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Author Dumont, B.; Basso, B.; Leemans, V.; Bodson, B.; Destain, J.-P.; Destain, M.-F. url  doi
openurl 
  Title A comparison of within-season yield prediction algorithms based on crop model behaviour analysis Type Journal Article
  Year 2015 Publication Agricultural and Forest Meteorology Abbreviated Journal Agricultural and Forest Meteorology  
  Volume 204 Issue Pages 10-21  
  Keywords stics crop model; climate variability; lars-wg; yield prediction; log-normal distribution; convergence in law theorem; central limit theorem; weather generator; nitrogen balances; generic model; wheat; simulation; climate; stics; variability; skewness; efficiency  
  Abstract The development of methodologies for predicting crop yield, in real-time and in response to different agro-climatic conditions, could help to improve the farm management decision process by providing an analysis of expected yields in relation to the costs of investment in particular practices. Based on the use of crop models, this paper compares the ability of two methodologies to predict wheat yield (Triticum aestivum L.), one based on stochastically generated climatic data and the other on mean climate data. It was shown that the numerical experimental yield distribution could be considered as a log-normal distribution. This function is representative of the overall model behaviour. The lack of statistical differences between the numerical realisations and the logistic curve showed in turn that the Generalised Central Limit Theorem (GCLT) was applicable to our case study. In addition, the predictions obtained using both climatic inputs were found to be similar at the inter and intra-annual time-steps, with the root mean square and normalised deviation values below an acceptable level of 10% in 90% of the climatic situations. The predictive observed lead-times were also similar for both approaches. Given (i) the mathematical formulation of crop models, (ii) the applicability of the CLT and GLTC to the climatic inputs and model outputs, respectively, and (iii) the equivalence of the predictive abilities, it could be concluded that the two methodologies were equally valid in terms of yield prediction. These observations indicated that the Convergence in Law Theorem was applicable in this case study. For purely predictive purposes, the findings favoured an algorithm based on a mean climate approach, which needed far less time (by 300-fold) to run and converge on same predictive lead time than the stochastic approach. (C) 2015 Elsevier B.V. All rights reserved.  
  Address  
  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 0168-1923 ISBN Medium Article  
  Area Expedition Conference  
  Notes CropM Approved no  
  Call Number (up) MA @ admin @ Serial 4647  
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.  
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  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 (up) MA @ admin @ Serial 4520  
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Author Lindeskog, M.; Arneth, A.; Bondeau, A.; Waha, K.; Seaquist, J.; Olin, S.; Smith, B. url  doi
openurl 
  Title Implications of accounting for land use in simulations of ecosystem carbon cycling in Africa Type Journal Article
  Year 2013 Publication Earth System Dynamics Abbreviated Journal Earth System Dynamics  
  Volume 4 Issue 2 Pages 385-407  
  Keywords global vegetation model; sub-saharan africa; climate-change; yield gaps; co2; balance; dynamics; atmosphere; cover; variability  
  Abstract Dynamic global vegetation models (DGVMs) are important tools for modelling impacts of global change on ecosystem services. However, most models do not take full account of human land management and land use and land cover changes (LULCCs). We integrated croplands and pasture and their management and natural vegetation recovery and succession following cropland abandonment into the LPJ-GUESS DGVM. The revised model was applied to Africa as a case study to investigate the implications of accounting for land use on net ecosystem carbon balance (NECB) and the skill of the model in describing agricultural production and reproducing trends and patterns in vegetation structure and function. The seasonality of modelled monthly fraction of absorbed photosynthetically active radiation (FPAR) was shown to agree well with satellite-inferred normalised difference vegetation index (NDVI). In regions with a large proportion of cropland, the managed land addition improved the FPAR vs. NDVI fit significantly. Modelled 1991-1995 average yields for the seven most important African crops, representing potential optimal yields limited only by climate forcings, were generally higher than reported FAO yields by a factor of 2-6, similar to previous yield gap estimates. Modelled inter-annual yield variations during 1971-2005 generally agreed well with FAO statistics, especially in regions with pronounced climate seasonality. Modelled land-atmosphere carbon fluxes for Africa associated with land use change (0.07 PgC yr(-1) release to the atmosphere for the 1980s) agreed well with previous estimates. Cropland management options (residue removal, grass as cover crop) were shown to be important to the land-atmosphere carbon flux for the 20th century.  
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  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2190-4979 ISBN Medium Article  
  Area Expedition Conference  
  Notes CropM Approved no  
  Call Number (up) MA @ admin @ Serial 4494  
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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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  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0304-3800 ISBN Medium Article  
  Area Expedition Conference  
  Notes LiveM Approved no  
  Call Number (up) MA @ admin @ Serial 4472  
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Author Francone, C.; Cassardo, C.; Richiardone, R.; Confalonieri, R. url  doi
openurl 
  Title Sensitivity Analysis and Investigation of the Behaviour of the UTOPIA Land-Surface Process Model: A Case Study for Vineyards in Northern Italy Type Journal Article
  Year 2012 Publication Boundary-Layer Meteorology Abbreviated Journal Boundary-Layer Meteorology  
  Volume 144 Issue 3 Pages 419-430  
  Keywords energy balance; hydrological balance; land-surface model; morris method; vegetation cover; vitis vinifera l.; atmosphere transfer scheme; environmental-models; energy-balance; uncertainty; simulation; canopy  
  Abstract We used sensitivity-analysis techniques to investigate the behaviour of the land-surface model UTOPIA while simulating the micrometeorology of a typical northern Italy vineyard (Vitis vinifera L.) under average climatic conditions. Sensitivity-analysis experiments were performed by sampling the vegetation parameter hyperspace using the Morris method and quantifying the parameter relevance across a wide range of soil conditions. This method was used since it proved its suitability for models with high computational time or with a large number of parameters, in a variety of studies performed on different types of biophysical models. The impact of input variability was estimated on reference model variables selected among energy (e.g. net radiation, sensible and latent heat fluxes) and hydrological (e.g. soilmoisture, surface runoff, drainage) budget components. Maximum vegetation cover and maximum leaf area index were ranked as the most relevant parameters, with sensitivity indices exceeding the remaining parameters by about one order of magnitude. Soil variability had a high impact on the relevance of most of the vegetation parameters: coefficients of variation calculated on the sensitivity indices estimated for the different soils often exceeded 100 %. The only exceptions were represented by maximum vegetation cover and maximum leaf area index, which showed a low variability in sensitivity indices while changing soil type, and confirmed their key role in affecting model results.  
  Address  
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  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0006-8314 1573-1472 ISBN Medium Article  
  Area Expedition Conference  
  Notes CropM Approved no  
  Call Number (up) MA @ admin @ Serial 4470  
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