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The relation between input-output transformation and gastrointestinal nematode infections on dairy farms

Published online by Cambridge University Press:  26 October 2015

M. van der Voort ,
J. Van Meensel ,
L. Lauwers ,
G. Van Huylenbroeck  and
J. Charlier
M. van der Voort*
Affiliation:
Social Sciences Unit, Institute for Agricultural and Fisheries Research (ILVO), Burg. Van Gansberghelaan 115, 9820 Merelbeke, Belgium
J. Van Meensel
Affiliation:
Social Sciences Unit, Institute for Agricultural and Fisheries Research (ILVO), Burg. Van Gansberghelaan 115, 9820 Merelbeke, Belgium
L. Lauwers
Affiliation:
Social Sciences Unit, Institute for Agricultural and Fisheries Research (ILVO), Burg. Van Gansberghelaan 115, 9820 Merelbeke, Belgium Department of Agricultural Economics, Faculty of Bio-Engineering, Ghent University, Coupure Links 653, 9000 Gent, Belgium
G. Van Huylenbroeck
Affiliation:
Department of Agricultural Economics, Faculty of Bio-Engineering, Ghent University, Coupure Links 653, 9000 Gent, Belgium
J. Charlier
Affiliation:
Department of Virology, Parasitology and Immunology, Faculty of Veterinary Medicine, Ghent University, Salisburylaan 133, 9820 Merelbeke, Belgium Avia-GIS, Risschotlei 33, 2980 Zoersel, Belgium
*
†E-mail: [email protected]
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Abstract

Efficiency analysis is used for assessing links between technical efficiency (TE) of livestock farms and animal diseases. However, previous studies often do not make the link with the allocation of inputs and mainly present average effects that ignore the often huge differences among farms. In this paper, we studied the relationship between exposure to gastrointestinal (GI) nematode infections, the TE and the input allocation on dairy farms. Although the traditional cost allocative efficiency (CAE) indicator adequately measures how a given input allocation differs from the cost-minimising input allocation, they do not represent the unique input allocation of farms. Similar CAE scores may be obtained for farms with different input allocations. Therefore, we propose an adjusted allocative efficiency index (AAEI) to measure the unique input allocation of farms. Combining this AAEI with the TE score allows determining the unique input-output position of each farm. The method is illustrated by estimating efficiency scores using data envelopment analysis (DEA) on a sample of 152 dairy farms in Flanders for which both accountancy and parasitic monitoring data were available. Three groups of farms with a different input-output position can be distinguished based on cluster analysis: (1) technically inefficient farms, with a relatively low use of concentrates per 100 l milk and a high exposure to infection, (2) farms with an intermediate TE, relatively high use of concentrates per 100 l milk and a low exposure to infection, (3) farms with the highest TE, relatively low roughage use per 100 l milk and a relatively high exposure to infection. Correlation analysis indicates for each group how the level of exposure to GI nematodes is associated or not with improved economic performance. The results suggest that improving both the economic performance and exposure to infection seems only of interest for highly TE farms. The findings indicate that current farm recommendations regarding GI nematode infections could be improved by also accounting for the allocation of inputs on the farm.

Keywords

data envelopment analysiscluster analysisOstertagia ostertagiinput allocationtechnical efficiency
Type
Research Article
Information
animal , Volume 10 , Issue 2 , February 2016 , pp. 274 - 282
Copyright
© The Animal Consortium 2015 

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References

Barnes, AP, Rutherford, KMD, Langford, FM and Haskell, MJ 2011. The effect of lameness prevalence on technical efficiency at the dairy farm level: an adjusted data envelopment analysis approach. Journal of Dairy Science 94, 5449–5457.Google Scholar
Bennema, SC, Ducheyne, E, Vercruysse, J, Hendrickx, G, Claerebout, E and Charlier, J 2011. Relative importance of management, meteorological and environmental factors in the spatial distribution of Fasciola hepatica in dairy cattle in a temperate climate zone. International Journal for Parasitology 41, 225–233.CrossRefGoogle Scholar
Bennema, SC, Vercruysse, J, Morgan, E, Stafford, K, Höglund, J, Demeler, J, von Samson-Himmelstjerna, G and Charlier, J 2010. Epidemiology and risk factors for exposure to gastrointestinal nematodes in dairy herds in north western Europe. Veterinary Parasitology 173, 247–254.Google Scholar
Blanco-Penedo, I, Högland, J, Fall, N and Emanuelson, U 2012. Exposure to pasture borne nematodes affect individual milk yield in Swedish dairy herds. Veterinary Parasitology 188, 93–98.CrossRefGoogle ScholarPubMed
Charlier, J, Claerebout, E, Duchateau, L and Vercruysse, J 2005. Assessment of the repeatability of a milk Ostertagia ostertagi ELISA and effects of sample preparation. Veterinary Parasitology 68, 277–288.Google ScholarPubMed
Charlier, J, Duchateau, L, Claerebout, E, Williams, D and Vercruysse, J 2007. Associations between anti-Fasciola hepatica antibody levels in bulk-tank milk samples and production parameters in dairy herds. Preventive Veterinary Medicine 78, 57–66.Google Scholar
Charlier, J, Höglund, J, von Samson-Himmelstjerna, G, Dorny, P and Vercruysse, J 2009. Gastrointestinal nematode infections in adult dairy cattle: impact on production, diagnosis and control. Veterinary Parasitology 164, 70–79.Google Scholar
Coelli, TJ, Rao, DSP, O’Donnell, CJ and Battese, GE 2005. An introduction to efficiency and productive analysis. Springer, New York, NY.Google Scholar
Dimander, SO, Höglund, J, Uggla, A, Spörndly, E and Waller, PJ 2003. Evaluation of gastrointestinal nematode parasite control strategies for first-season grazing cattle in Sweden. Veterinary Parasitology 111, 193–209.CrossRefGoogle ScholarPubMed
Farrell, MJ 1957. The measurement of productive efficiency. Journal of the Royal Statistical Society: Series A 120, 253–290.CrossRefGoogle Scholar
Gelasakis, AI, Valergakis, GE, Arsenos, G and Banos, G 2012. Description and typology of intensive Chios dairy sheep farms in Greece. Journal of Dairy Science 95, 3070–3079.Google Scholar
Hansson, H and Öhlmér, B 2008. The effect of operational managerial practices on economic, technical and allocative efficiency at Swedish dairy farms. Livestock Science 118, 34–43.Google Scholar
Hogeveen, H, Huijps, K and Lam, TJGM 2011. Economic aspects of mastitis: new developments. New Zealand Veterinary Journal 59, 16–23.CrossRefGoogle ScholarPubMed
Köbrich, C, Rehman, T and Khan, M 2003. Typification of farming systems for constructing representative farm models: two illustrations of the application of multi-variate analyses in Chile and Pakistan. Agricultural Systems 76, 141–157.Google Scholar
Lawson, LG, Agger, JF, Lund, M and Coelli, T 2004. Lameness, metabolic and digestive disorders, and technical efficiency in Danish dairy herds: a stochastic frontier production function approach. Livestock Production Science 91, 157–172.Google Scholar
Morgan, ER, Charlier, J, Hendrickx, G, Biggeri, A, Catalan, D, von Samson-Himmelstjerna, G, Demeler, J, Müller, E, van Dijk, J, Kenyon, F, Skuce, P, Höglund, J, O’Kiely, P, van Ranst, B, de Waal, T, Rinaldi, L, Cringoli, G, Hertzberg, H, Torgerson, P, Wolstenholme, A and Vercruysse, J 2013. Global change and helminth infection in grazing ruminants in Europe: impact, trends and sustainable solutions. Agriculture 3, 484–502.Google Scholar
Sanchez, J and Dohoo, I 2002. A bulk tank milk survey of Ostertagia ostertagi antibodies in dairy herds in Prince Edward Island and their relationship with herd management factors and milk yield. The Canadian Veterinary Journal 4, 454–459.Google Scholar
Sekiya, M, Zintl, A and Doherty, M 2013. Bulk milk ELISA and the diagnosis of parasite infections in dairy herds: a review. Irish Veterinary Journal 66, 14.Google Scholar
Sithole, F, Dohoo, I, Leslie, K, DesCoteaux, L, Godden, S, Campbell, J, Keefe, G and Sanchez, J 2006. Effect of eprinomectin pour-on treatment around calving on reproduction parameters in adult dairy cows with limited outdoor exposure. Preventive Veterinary Medicine 75, 267–279.Google Scholar
Usai, MG, Casu, S, Molle, G, Decandia, M, Ligios, S and Carta, A 2006. Using cluster analysis to characterize the goat farming system in Sardinia. Livestock Science 104, 63–76.Google Scholar
Vanderstichel, R, Dohoo, I, Sanchez, J and Conboy, G 2012. Effects of farm management practices and environmental factors on bulk tank milk antibodies against gastrointestinal nematodes in dairy farms across Canada. Preventive Veterinary Medicine 104, 53–64.Google Scholar
van der Voort, M, Charlier, J, Lauwers, L, Vercruysse, J, Van Huylenbroeck, G and Van Meensel, J 2013. Conceptual framework for analysing farm-specific economic effects of helminth infections in ruminants and control strategies. Preventive Veterinary Medicine 109, 228–235.Google Scholar
van der Voort, M, Van Meensel, J, Lauwers, L, Vercruysse, J, Van Huylenbroeck, G and Charlier, J 2014. A stochastic frontier approach to study the relationship between gastrointestinal nematode infections and technical efficiency of dairy farms. Journal of Dairy Science 97, 3498–3508.CrossRefGoogle Scholar
Van Meensel, J, Lauwers, L, Kempen, I, Dessein, J and Van Huylenbroeck, G 2012. Effect of a participatory approach on the successful development of agricultural decision support systems: the case of Pigs2win. Decision Support Systems 54, 164–172.CrossRefGoogle Scholar