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Berlin Brandenburg

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  • 1
    In: Risk Analysis, June 2010, Vol.30(6), pp.906-915
    Description: Different international plant protection organisations advocate different schemes for conducting pest risk assessments. Most of these schemes use structured questionnaire in which experts are asked to score several items using an ordinal scale. The scores are then combined using a range of procedures, such as simple arithmetic mean, weighted averages, multiplication of scores, and cumulative sums. The most useful schemes will correctly identify harmful pests and identify ones that are not. As the quality of a pest risk assessment can depend on the characteristics of the scoring system used by the risk assessors (i.e., on the number of points of the scale and on the method used for combining the component scores), it is important to assess and compare the performance of different scoring systems. In this article, we proposed a new method for assessing scoring systems. Its principle is to simulate virtual data using a stochastic model and, then, to estimate sensitivity and specificity values from these data for different scoring systems. The interest of our approach was illustrated in a case study where several scoring systems were compared. Data for this analysis were generated using a probabilistic model describing the pest introduction process. The generated data were then used to simulate the outcome of scoring systems and to assess the accuracy of the decisions about positive and negative introduction. The results showed that ordinal scales with at most 5 or 6 points were sufficient and that the multiplication‐based scoring systems performed better than their sum‐based counterparts. The proposed method could be used in the future to assess a great diversity of scoring systems.
    Keywords: Invasive Species ; Pest Risk Assessment ; Roc ; Scoring Systems ; Sensitivity ; Specificity ; Stochastic Model
    ISSN: 0272-4332
    E-ISSN: 1539-6924
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  • 2
    Language: English
    In: Plos One, 8, 2013
    Description: The world's population is predicted to exceed nine billion by 2050 and there is increasing concern about the capability of agriculture to feed such a large population. Foresight studies on food security are frequently based on crop yield trends estimated from yield time series provided by national and regional statistical agencies. Various types of statistical models have been proposed for the analysis of yield time series, but the predictive performances of these models have not yet been evaluated in detail. In this study, we present eight statistical models for analyzing yield time series and compare their ability to predict wheat yield at the national and regional scales, using data provided by the Food and Agriculture Organization of the United Nations and by the French Ministry of Agriculture. The Holt-Winters and dynamic linear models performed equally well, giving the most accurate predictions of wheat yield. However, dynamic linear models have two advantages over Holt-Winters models: they can be used to reconstruct past yield trends retrospectively and to analyze uncertainty. The results obtained with dynamic linear models indicated a stagnation of wheat yields in many countries, but the estimated rate of increase of wheat yield remained above 0.06 t ha(-1) year(-1) in several countries in Europe, Asia, Africa and America, and the estimated values were highly uncertain for several major wheat producing countries. The rate of yield increase differed considerably between French regions, suggesting that efforts to identify the main causes of yield stagnation should focus on a subnational scale.
    Keywords: Croissance ; Sélection ; France ; Rice ; Trend ; Stability ; France ; Selection ; Growth
    ISSN: 19326203
    E-ISSN: 19326203
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  • 3
    Language: English
    In: 2012, Vol.7(11), p.e50950
    Description: Nitrous oxide (N 2 O) is a greenhouse gas with a global warming potential approximately 298 times greater than that of CO 2 . In 2006, the Intergovernmental Panel on Climate Change (IPCC) estimated N 2 O emission due to synthetic and organic nitrogen (N) fertilization at 1% of applied N. We investigated the uncertainty on this estimated value, by fitting 13 different models to a published dataset including 985 N 2 O measurements. These models were characterized by (i) the presence or absence of the explanatory variable “applied N”, (ii) the function relating N 2 O emission to applied N (exponential or linear function), (iii) fixed or random background (i.e. in the absence of N application) N 2 O emission and (iv) fixed or random applied N effect. We calculated ranges of uncertainty on N 2 O emissions from a subset of these models, and compared them with the uncertainty ranges currently used in the IPCC-Tier 1 method. The exponential models outperformed the linear models, and models including one or two random effects outperformed those including fixed effects only. The use of an exponential function rather than a linear function has an important practical consequence: the emission factor is not constant and increases as a function of applied N. Emission factors estimated using the exponential function were lower than 1% when the amount of N applied was below 160 kg N ha −1 . Our uncertainty analysis shows that the uncertainty range currently used by the IPCC-Tier 1 method could be reduced.
    Keywords: Research Article ; Agriculture ; Biology ; Mathematics ; Plant Biology ; Biotechnology ; Mathematics
    E-ISSN: 1932-6203
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  • 4
    In: Risk Analysis, September 2017, Vol.37(9), pp.1693-1705
    Description: According to E.U. regulations, the maximum allowable rate of adventitious transgene presence in non‐genetically modified (GM) crops is 0.9%. We compared four sampling methods for the detection of transgenic material in agricultural non‐GM maize fields: random sampling, stratified sampling, random sampling + ratio reweighting, random sampling + regression reweighting. Random sampling involves simply sampling maize grains from different locations selected at random from the field concerned. The stratified and reweighting sampling methods make use of an auxiliary variable corresponding to the output of a gene‐flow model (a zero‐inflated Poisson model) simulating cross‐pollination as a function of wind speed, wind direction, and distance to the closest GM maize field. With the stratified sampling method, an auxiliary variable is used to define several strata with contrasting transgene presence rates, and grains are then sampled at random from each stratum. With the two methods involving reweighting, grains are first sampled at random from various locations within the field, and the observations are then reweighted according to the auxiliary variable. Data collected from three maize fields were used to compare the four sampling methods, and the results were used to determine the extent to which transgene presence rate estimation was improved by the use of stratified and reweighting sampling methods. We found that transgene rate estimates were more accurate and that substantially smaller samples could be used with sampling strategies based on an auxiliary variable derived from a gene‐flow model.
    Keywords: Gene‐Flow Model ; Genetically Modified Crop ; Sampling ; Stratification
    ISSN: 0272-4332
    E-ISSN: 1539-6924
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  • 5
    Language: English
    In: Environmental Pollution, June, 2013, Vol.177, p.156(8)
    Description: To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.envpol.2013.02.019 Byline: Aurore Philibert (b)(a), Chantal Loyce (b)(a), David Makowski (a)(b) Abstract: Nitrous oxide is a potent greenhouse gas, with a global warming potential 298 times greater than that of CO.sub.2. In agricultural soils, N.sub.2O emissions are influenced by a large number of environmental characteristics and crop management techniques that are not systematically reported in experiments. Random Forest (RF) is a machine learning method that can handle missing data and ranks input variables on the basis of their importance. We aimed to predict N.sub.2O emission on the basis of local information, to rank environmental and crop management variables according to their influence on N.sub.2O emission, and to compare the performances of RF with several regression models. RF outperformed the regression models for predictive purposes, and this approach led to the identification of three important input variables: N fertilization, type of crop, and experiment duration. This method could be used in the future for prediction of N.sub.2O emissions from local information. Author Affiliation: (a) INRA, UMR 211 Agronomie, F-78000 Thiverval Grignon, France (b) AgroParisTech, UMR 211 Agronomie, F-78000 Thiverval Grignon, France Article History: Received 23 November 2012; Revised 25 January 2013; Accepted 8 February 2013
    Keywords: Atmospheric Carbon Dioxide -- Analysis ; Global Warming Potential -- Analysis ; Greenhouse Gases -- Analysis ; Air Pollution -- Analysis ; Nitrous Oxide -- Analysis ; Global Warming -- Analysis
    ISSN: 0269-7491
    Source: Cengage Learning, Inc.
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  • 6
    Language: English
    In: Agriculture, Ecosystems and Environment, Feb 15, 2012, Vol.148, p.72(11)
    Description: To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.agee.2011.12.003 Byline: Aurore Philibert (a), Chantal Loyce (b), David Makowski (a) Keywords: Meta-analysis; Nitrous oxide; Mixed model; Bayesian statistics; Sensitivity analysis; Legume Abstract: a* Eight criteria were defined for assessing the quality of meta-analysis carried out in agronomy. a* These criteria were used to assess 73 meta-analyses. a* No meta-analysis satisfied all the quality criteria. a* Recommendations were formulated and illustrated in a case study on nitrous oxide emission in legume crops. a* The proposed list of quality criteria can serve as a guide for future studies in this area. Author Affiliation: (a) INRA, UMR 211 Agronomie, F-78000 Thiverval Grignon, France (b) AgroParisTech, UMR 211 Agronomie, F-78000 Thiverval Grignon, France Article History: Received 3 June 2011; Revised 28 November 2011; Accepted 1 December 2011
    Keywords: Nitrous Oxide -- Analysis
    ISSN: 0167-8809
    Source: Cengage Learning, Inc.
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  • 7
    Language: English
    In: European Journal of Agronomy, August 2017, Vol.88, pp.76-83
    Description: Multi-model forecasting has drawn some attention in crop science for evaluating effect of climate change on crop yields. The principle is to run several individual process-based crop models under several climate scenarios in order to generate ensembles of output values. This paper describes a simple Bayesian method – called Bayes linear method – for updating ensemble of crop model outputs using yield observations. The principle is to summarize the ensemble of crop model outputs by its mean and variance, and then to adjust these two quantities to yield observations in order to reduce uncertainty. The adjusted mean and variance combine two sources of information, i.e., the ensemble of crop model outputs and the observations. Interestingly, with this method, observations collected under a given climate scenario can be used to adjust mean and variance of the model ensemble under a different scenario. Another advantage of the proposed method is that it does not rely on a separate calibration of each individual crop model. The uncertainty reduction resulting from the adjustment of an ensemble of crop models to observations was assessed in a numerical application. The implementation of the Bayes linear method systematically reduced uncertainty, but the results showed the effectiveness of this method varied in function of several factors, especially the accuracy of the yield observation, and the covariance between the crop model output and the observation.
    Keywords: Bayesian Method ; Climate Change ; Ensemble Modelling ; Uncertainty ; Yield ; Agriculture
    ISSN: 1161-0301
    E-ISSN: 1873-7331
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  • 8
    Language: English
    In: NeoBiota, 01 September 2013, Vol.18, pp.157-171
    Description: Quantitative models have several advantages compared to qualitative methods for pest risk assessments (PRA). Quantitative models do not require the definition of categorical ratings and can be used to compute numerical probabilities of entry and establishment, and to quantify spread and impact. These models are powerful tools, but they include several sources of uncertainty that need to be taken into account by risk assessors and communicated to decision makers. Uncertainty analysis (UA) and sensitivity analysis (SA) are useful for analyzing uncertainty in models used in PRA, and are becoming more popular. However, these techniques should be applied with caution because several factors may influence their results. In this paper, a brief overview of methods of UA and SA are given. As well, a series of practical rules are defined that can be followed by risk assessors to improve the reliability of UA and SA results. These rules are illustrated in a case study based on the infection model of Magarey et al. (2005) where the results of UA and SA are shown to be highly dependent on the assumptions made on the probability distribution of the model inputs.
    Keywords: Biology
    ISSN: 1619-0033
    E-ISSN: 1314-2488
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  • 9
    Language: English
    In: Environmental pollution, 2013, Vol.177, pp.156-163
    Description: Nitrous oxide is a potent greenhouse gas, with a global warming potential 298 times greater than that of CO₂. In agricultural soils, N₂O emissions are influenced by a large number of environmental characteristics and crop management techniques that are not systematically reported in experiments. Random Forest (RF) is a machine learning method that can handle missing data and ranks input variables on the basis of their importance. We aimed to predict N₂O emission on the basis of local information, to rank environmental and crop management variables according to their influence on N₂O emission, and to compare the performances of RF with several regression models. RF outperformed the regression models for predictive purposes, and this approach led to the identification of three important input variables: N fertilization, type of crop, and experiment duration. This method could be used in the future for prediction of N₂O emissions from local information. ; p. 156-163.
    Keywords: Models ; Artificial Intelligence ; Emissions ; Agricultural Soils ; Nitrous Oxide ; Crop Management ; Prediction ; Nitrogen Fertilizers ; Greenhouse Gases ; Regression Analysis ; Global Warming ; Carbon Dioxide
    ISSN: 0269-7491
    Source: AGRIS (Food and Agriculture Organization of the United Nations)
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  • 10
    Language: English
    In: Environmental Pollution, June 2013, Vol.177, pp.156-163
    Description: Nitrous oxide is a potent greenhouse gas, with a global warming potential 298 times greater than that of CO . In agricultural soils, N O emissions are influenced by a large number of environmental characteristics and crop management techniques that are not systematically reported in experiments. Random Forest (RF) is a machine learning method that can handle missing data and ranks input variables on the basis of their importance. We aimed to predict N O emission on the basis of local information, to rank environmental and crop management variables according to their influence on N O emission, and to compare the performances of RF with several regression models. RF outperformed the regression models for predictive purposes, and this approach led to the identification of three important input variables: N fertilization, type of crop, and experiment duration. This method could be used in the future for prediction of N O emissions from local information. ► Random Forest gave more accurate N O predictions than regression. ► Missing data were well handled by Random Forest. ► The most important factors were nitrogen rate, type of crop and experiment duration. Random Forest, a machine learning method, outperformed the regression models for predicting N O emissions and led to the identification of three important input variables.
    Keywords: Agriculture ; Climate Change ; Machine Learning ; Nitrous Oxide ; Random Forest ; Agriculture ; Engineering ; Environmental Sciences ; Anatomy & Physiology
    ISSN: 0269-7491
    E-ISSN: 1873-6424
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