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A methodological approach to identify cheap and accurate indicators for biodiversity assessment: application to grazing management and two grassland bird species

Published online by Cambridge University Press:  19 January 2010

M. Tichit ,
A. Barbottin  and
D. Makowski
M. Tichit*
Affiliation:
INRA, UMR 1048 SAD-APT, F-75231 Paris, France AgroParisTech, UMR 1048 SAD-APT, F-75231 Paris, France
A. Barbottin
Affiliation:
INRA, UMR 1048 SAD-APT, F-75231 Paris, France AgroParisTech, UMR 1048 SAD-APT, F-75231 Paris, France
D. Makowski
Affiliation:
INRA, UMR 211 Agronomie, F-78850 Thiverval-Grignon, France AgroParisTech, UMR 211 Agronomie, F-78850 Thiverval-Grignon, France
*
†E-mail: muriel.tichit@agroparistech.fr
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Abstract

In response to environmental threats, numerous indicators have been developed to assess the impact of livestock farming systems on the environment. Some of them, notably those based on management practices have been reported to have low accuracy. This paper reports the results of a study aimed at assessing whether accuracy can be increased at a reasonable cost by mixing individual indicators into models. We focused on proxy indicators representing an alternative to the direct impact measurement on two grassland bird species, the lapwing Vanellus vanellus and the redshank Tringa totanus. Models were developed using stepwise selection procedures or Bayesian model averaging (BMA). Sensitivity, specificity, and probability of correctly ranking fields (area under the curve, AUC) were estimated for each individual indicator or model from observational data measured on 252 grazed plots during 2 years. The cost of implementation of each model was computed as a function of the number and types of input variables. Among all management indicators, 50% had an AUC lower than or equal to 0.50 and thus were not better than a random decision. Independently of the statistical procedure, models combining management indicators were always more accurate than individual indicators for lapwings only. In redshanks, models based either on BMA or some selection procedures were non-informative. Higher accuracy could be reached, for both species, with model mixing management and habitat indicators. However, this increase in accuracy was also associated with an increase in model cost. Models derived by BMA were more expensive and slightly less accurate than those derived with selection procedures. Analysing trade-offs between accuracy and cost of indicators opens promising application perspectives as time consuming and expensive indicators are likely to be of low practical utility.

Keywords

livestock farming system, Bayesian model averaging, model selection, sensitivity, specificity
Type
Full Paper
Information
animal , Volume 4 , Issue 6 , June 2010 , pp. 819 - 826
Copyright
Copyright © The Animal Consortium 2010

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References

Akaike, H 1974. A new look at statistical model identification. IEEE Transactions on Automatic Control 19, 716–722.CrossRefGoogle Scholar
Barbottin, A, Makowski, D, Le Bail, M, Jeuffroy, MH, Bouchard, C, Barrier, C 2008. Comparison of models and indicators for categorizing soft wheat fields according to their grain protein contents. European Journal of Agronomy 29, 175–183.CrossRefGoogle Scholar
Bockstaller, C, Guichard, L, Makowski, D, Aveline, A, Girardin, P, Plantureux, S 2008. Agri-environmental indicators to assess cropping and farming systems. A Review. Agronomy for Sustainable Development 28, 139–149.CrossRefGoogle Scholar
Donald, PF, Green, RE, Heath, MF 2001. Agricultural intensification and the collapse of Europe’s farmland bird populations. Proceedings of the Royal Society of London Series B 268, 25–29.CrossRefGoogle Scholar
Donald, PF, Sanderson, FJ, Burfield, IJ, van Bommel, FPJ 2006. Further evidence of continent-wide impacts of agricultural intensification on European farmland birds, 1990–2000. Agriculture Ecosystems and Environment 116, 189–196.CrossRefGoogle Scholar
Dumont, B, Meuret, M, Boissy, A, Petit, M 2001. Le pâturage vu par l’animal: mécanismes comportementaux et applications en élevage. Fourrages 166, 213–238.Google Scholar
Durant, D, Tichit, M, Fritz, H, Kernéïs, E 2008a. Field occupancy by breeding lapwings Vanellus vanellus and redshanks Tringa totanus in agricultural wet grasslands. Agriculture Ecosystems and Environment 128, 146–150.CrossRefGoogle Scholar
Durant, D, Tichit, M, Kerneis, E, Fritz, H 2008b. Management of agricultural grasslands for breeding waders: integrating ecological and livestock system perspectives – a review. Biodiversity and Conservation 17, 2275–2295.CrossRefGoogle Scholar
Flint, VE 1998. Waders as indicators of biological diversity. International Wader Studies 10, 23.Google Scholar
Grafen, A, Hails, R 2004. Modern statistics for the life sciences. Oxford University Press, Oxford, UK.Google Scholar
Halberg, N, van der Werf, HMG, Basset-Mens, C, Dalgaard, R, de Boer, IJM 2005. Environmental assessment tools for the evaluation and improvement of European livestock production systems. Livestock Production Science 96, 33–50.CrossRefGoogle Scholar
Henkens, P, Van Keulen, H 2001. Mineral policy in the Netherlands and nitrate policy within the European Community. Netherlands Journal of Agricultural Science 49, 117–134.Google Scholar
Hughes, G, McRoberts, N, Burnett, FJ 1999. Decision-making and diagnosis in disease management. Plant Pathology 48, 147–153.CrossRefGoogle Scholar
Langeveld, JWA, Verhagen, A, Neeteson, JJ, Van Keulen, H, Conijn, JG, Schils, RLM, Oenema, J 2007. Evaluating farm performance using agri-environmental indicators: recent experiences for nitrogen management in the Netherlands. Journal of Environmental Management 82, 363–376.CrossRefGoogle ScholarPubMed
Makowski, D, Taverne, M, Bolomier, J, Ducarne, M 2005. Comparison of risk indicators for sclerotinia control in oilseed rape. Crop Protection 24, 527–531.CrossRefGoogle Scholar
Makowski, D, Tichit, M, Guichard, L, Van Keulen, H, Beaudoin, N 2009. Measuring the accuracy of agro-environmental indicators. Journal of Environmental Management 90 (Suppl. 2), S139–S146.CrossRefGoogle ScholarPubMed
Manel, S, Williams, HC, Ormerod, SJ 2001. Evaluating presence-absence models in ecology: the need to account for prevalence. Journal of Applied Ecology 38, 921–931.CrossRefGoogle Scholar
McPherson, JM, Jetz, W, Rogers, D 2004. The effects of species’ range sizes on the accuracy of distribution models: ecological phenomenon or statistical artefact? Journal of Applied Ecology 41, 811–823.CrossRefGoogle Scholar
Milsom, TP, Langton, SD, Parkin, WK, Peel, S, Bishop, JD, Hart, JD, Moore, NP 2000. Habitat models of bird species’ distribution: an aid to the management of coastal grazing marshes. Journal of Applied Ecology 37, 706–727.CrossRefGoogle Scholar
Murtaugh, PA 1996. The statistical evaluation of ecological indicators. Ecological Applications 6, 132–139.CrossRefGoogle Scholar
Organisation for Economic Co-operation and Development (OECD) 2003. OECD environmental indicators – development, measurement and use – Reference paper. OECD Environment Directorate, Environmental Performance and Information Division, Paris, France.Google Scholar
Primot, S, Valantin-Morison, M, Makowski, D 2006. Predicting the risk of weed infestation in winter oilseed rape crops. Weed Research (Oxford) 46, 22–33.CrossRefGoogle Scholar
Prost, L, Makowski, D, Jeuffroy, MH 2008. Comparison of stepwise selection and Bayesian model averaging for yield gap analysis. Ecological Modelling 219, 66–76.CrossRefGoogle Scholar
Raftery, AE, Zheng, Y 2003. Discussion: Performance of Bayesian Model Averaging. Journal of the American Statistical Association 98, 931–937.CrossRefGoogle Scholar
Schwarz, G 1978. Estimating the dimension of a model. Annals of Statistics 6, 461–464.CrossRefGoogle Scholar
Sing, T, Sander, O, Beerenwinkel, N, Lengauer, T 2005. ROCR: visualizing classifier performance in R. Bioinformatics 21, 3940–3941.CrossRefGoogle ScholarPubMed
Swets, JA 1988. Measuring the accuracy of diagnostic systems. Science 240, 1285–1293.CrossRefGoogle ScholarPubMed
Thomassen, MA, de Boer, IJM 2005. Evaluation of indicators to assess the environmental impact of dairy production systems. Agriculture Ecosystems and Environment 111, 185–199.CrossRefGoogle Scholar
Tichit, M, Renault, O, Potter, T 2005. Grazing regime as a tool to assess positive side effects of livestock farming systems on wading birds. Livestock Production Science 96, 109–117.CrossRefGoogle Scholar
Van der Werf, HMG, Petit, J 2002. Evaluation of the environmental impact of agriculture at the farm level: a comparison and analysis of 12 indicator-based methods. Agriculture Ecosystems and Environment 93, 131–145.CrossRefGoogle Scholar
Viallefont, V, Raftery, AE, Richardson, S 2001. Variable selection and Bayesian model averaging in case-control studies. Statistics in Medicine 20, 3215–3230.CrossRefGoogle ScholarPubMed
Wallach, D 2006. Evaluating crop models. In Working with dynamic crop models (ed. D Wallach, D Makowski and JW Jones), pp. 11–53. Elsevier, Amsterdam, The Netherlands.Google Scholar
Whittingham, MJ, Stephens, PA, Bradburry, RB, Freckleton, RP 2006. Why do we still use stepwise modelling in ecology and behaviour? Journal of Animal Ecology 75, 1182–1189.CrossRefGoogle ScholarPubMed