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MELODIE: a whole-farm model to study the dynamics of nutrients in dairy and pig farms with crops

Published online by Cambridge University Press:  03 April 2012

X. Chardon ,
C. Rigolot ,
C. Baratte ,
S. Espagnol ,
C. Raison ,
R. Martin-Clouaire ,
J.-P. Rellier ,
A. Le Gall ,
J. Y. Dourmad  and
B. Piquemal
...Show all authors
X. Chardon
Affiliation:
INRA, UMR1080, Production du Lait, F-35590 St-Gilles, France Agrocampus Ouest, UMR1080, Production du Lait, F-35000 Rennes, France Institut de l'Elevage, Monvoisin, F-35650 Le Rheu, France
C. Rigolot
Affiliation:
INRA, UMR1080, Production du Lait, F-35590 St-Gilles, France Agrocampus Ouest, UMR1080, Production du Lait, F-35000 Rennes, France INRA, UMR1079, Système d’élevage nutrition animale et humaine, F-35590 St-Gilles, France
C. Baratte
Affiliation:
INRA, UMR1080, Production du Lait, F-35590 St-Gilles, France Agrocampus Ouest, UMR1080, Production du Lait, F-35000 Rennes, France
S. Espagnol
Affiliation:
IFIP –Institut du porc, 35650 Le Rheu, France
C. Raison
Affiliation:
Institut de l'Elevage, Monvoisin, F-35650 Le Rheu, France
R. Martin-Clouaire
Affiliation:
INRA, UR875, Biométrie et Intelligence Artificielle, 31326 Castanet-Tolosan, France
J.-P. Rellier
Affiliation:
INRA, UR875, Biométrie et Intelligence Artificielle, 31326 Castanet-Tolosan, France
A. Le Gall
Affiliation:
Institut de l'Elevage, Monvoisin, F-35650 Le Rheu, France
J. Y. Dourmad
Affiliation:
INRA, UMR1079, Système d’élevage nutrition animale et humaine, F-35590 St-Gilles, France Agrocampus Ouest, UMR1079, Système d’élevage nutrition animale et humaine, F-35590 St-Gilles, France
B. Piquemal
Affiliation:
INRA, UMR1080, Production du Lait, F-35590 St-Gilles, France Agrocampus Ouest, UMR1080, Production du Lait, F-35000 Rennes, France
P. Leterme
Affiliation:
INRA, UMR1069, Sol Agro et hydrosystème Spatialisation, F-35000 Rennes, France Agrocampus Ouest, UMR1069, Sol Agro et hydrosystème Spatialisation, F-35000 Rennes, France
J. M. Paillat
Affiliation:
INRA, UMR1069, Sol Agro et hydrosystème Spatialisation, F-35000 Rennes, France Agrocampus Ouest, UMR1069, Sol Agro et hydrosystème Spatialisation, F-35000 Rennes, France
L. Delaby
Affiliation:
INRA, UMR1080, Production du Lait, F-35590 St-Gilles, France Agrocampus Ouest, UMR1080, Production du Lait, F-35000 Rennes, France
F. Garcia
Affiliation:
INRA, UMR1080, Production du Lait, F-35590 St-Gilles, France Agrocampus Ouest, UMR1080, Production du Lait, F-35000 Rennes, France
J. L. Peyraud
Affiliation:
INRA, UMR1080, Production du Lait, F-35590 St-Gilles, France Agrocampus Ouest, UMR1080, Production du Lait, F-35000 Rennes, France
J. C. Poupa
Affiliation:
INRA, UMR1302, Sciences sociales, agriculture et alimentation, espace et environnement, F-35000 Rennes, France Agrocampus Ouest, UMR1302, Sciences sociales, agriculture et alimentation, espace et environnement, F-35000 Rennes, France
T. Morvan
Affiliation:
INRA, UMR1069, Sol Agro et hydrosystème Spatialisation, F-35000 Rennes, France Agrocampus Ouest, UMR1069, Sol Agro et hydrosystème Spatialisation, F-35000 Rennes, France
P. Faverdin*
Affiliation:
INRA, UMR1080, Production du Lait, F-35590 St-Gilles, France Agrocampus Ouest, UMR1080, Production du Lait, F-35000 Rennes, France
*
†E-mail: philippe.faverdin@rennes.inra.fr
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Abstract

In regions of intensive pig and dairy farming, nutrient losses to the environment at farm level are a source of concern for water and air quality. Dynamic models are useful tools to evaluate the effects of production strategies on nutrient flows and losses to the environment. This paper presents the development of a new whole-farm model upscaling dynamic models developed at the field or animal scale. The model, called MELODIE, is based on an original structure with interacting biotechnical and decisional modules. Indeed, it is supported by an ontology of production systems and the associated programming platform DIESE. The biotechnical module simulates the nutrient flows in the different animal, soil and crops and manure sub-models. The decision module relies on an annual optimization of cropping and spreading allocation plans, and on the flexible execution of activity plans for each simulated year. These plans are examined every day by an operational management sub-model and their application is context dependent. As a result, MELODIE dynamically simulates the flows of carbon, nitrogen, phosphorus, copper, zinc and water within the whole farm over the short and long-term considering both the farming system and its adaptation to climatic conditions. Therefore, it is possible to study both the spatial and temporal heterogeneity of the environmental risks, and to test changes of practices and innovative scenarios. This is illustrated with one example of simulation plan on dairy farms to interpret the Nitrogen farm-gate budget indicator. It shows that this indicator is able to reflect small differences in Nitrogen losses between different systems, but it can only be interpreted using a mobile average, not on a yearly basis. This example illustrates how MELODIE could be used to study the dynamic behaviour of the system and the dynamic of nutrient flows. Finally, MELODIE can also be used for comprehensive multi-criterion assessments, and it also constitutes a generic and evolving framework for virtual experimentation on animal farming systems.

Keywords

farming systemswhole-farm modelenvironmental impactdecision-making
Type
Full Paper
Information
animal , Volume 6 , Issue 10 , October 2012 , pp. 1711 - 1721
Copyright
Copyright © The Animal Consortium 2012

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References

Beaujouan, V, Durand, P, Ruiz, L 2001. Modelling the effect of the spatial distribution of agricultural practices on nitrogen fluxes in rural catchments. Ecological Economics 137, 93–105.Google Scholar
Berntsen, J, Petersen, BM, Olesen, JE, Eriksen, J, Sregaard, K 2005. Simulation of residual effects and nitrate leaching after incorporation of different ley types. European Journal of Agronomy 23, 290–304.CrossRefGoogle Scholar
Brisson, N, Gary, C, Justes, E, Roche, R, Mary, B, Ripoche, D, Zimmer, D, Sierra, J, Bertuzzi, P, Burger, P 2003. An overview of the crop model STICS. European Journal of Agronomy 18, 309–332.CrossRefGoogle Scholar
Chardon, X 2008. Evaluation environnementale des exploitations laitières par modélisation dynamique de leur fonctionnement et des flux de matière: développement et application du simulateur Mélodie. PhD thesis, AgroParisTech, Paris, France.Google Scholar
Chardon, X, Le Gall, A, Raison, C, Morvan, T, Faverdin, P 2008. FUMIGENE: a model to plan the allocation of agricultural wastes at the farm level. Journal of Agricultural Science Cambridge 146, 521–539.CrossRefGoogle Scholar
Coquil, X, Faverdin, P, Garcia, F 2005. Dynamic modelling of dairy herd demography. Rencontres autour des Recherches sur les Ruminants 12, 213.Google Scholar
Cros, MJ, Duru, M, Garcia, F, Martin-Clouaire, R 2004. Simulating management strategies: the rotational grazing example. Agricultural Systems 80, 23–42.CrossRefGoogle Scholar
Delagarde, R, Faverdin, P, Baratte, C, Peyraud, JL 2011. GrazeIn: a model of herbage intake and milk production for grazing dairy cows. 2. Prediction of intake under rotational and continuously stocked grazing management. Grass and Forage Science 66, 45–60.CrossRefGoogle Scholar
Faverdin, P, Baratte, C, Delagarde, R, Peyraud, JL 2011. GrazeIn: a model of herbage intake and milk production for grazing dairy cows. 1. Prediction of intake capacity, voluntary intake and milk production during lactation. Grass and Forage Science 66, 29–44.CrossRefGoogle Scholar
Faverdin, P, Maxin, G, Chardon, X, Brunschwig, P, Vermorel, M 2007. A model to predict the carbon balance of a dairy cow. Rencontres autour des Recherches sur les Ruminants 14, 66.Google Scholar
Garcia, F, Faverdin, P, Delaby, L, Peyraud, JL 2005. Tournesol: a model to simulate cropping plans in dairy production systems. Rencontres autour des Recherches sur les Ruminants 12, 195–198.Google Scholar
INRA 2007. Alimentation des bovins, ovins et caprins: besoins des animaux – valeurs des aliments. Tables Inra 2007. Editions Quae, Paris, France.Google Scholar
Jacobsen, BH, Petersen, BM, Berntsen, J, Boye, C, Sørensen, CG, Søgaard, HT, Hansen, JP 1998. An integrated economic and environmental farm simulation model (FASSET). Danish Institute of Agricultural and Fisheries Economics, Copenhagen, Denmark.Google Scholar
Kächele, H, Dabbert, T 2003. An economic approach for a better understanding of conflicts between farmers and nature conservationists – an application of the decision support system ODAM to the Lower Odra Valley National Park. Agricultural Systems 74, 241–255.CrossRefGoogle Scholar
Martin, G, Martin-Clouaire, R, Rellier, JP, Duru, M 2011. A simulation framework for the grassland-based beef-cattle farms. Environmental Modelling and Software 26, 371–385.CrossRefGoogle Scholar
Martin-Clouaire, R, Rellier, JP 2003. A conceptualization of farm management strategies. Proceedings of the EFITA-03 Conference, 5–9 July 2003, Debrecen, Hungary, pp. 719–726.Google Scholar
Martin-Clouaire, R, Rellier, JP 2009. Modelling and simulating work practices in agriculture. International Journal of Metadata, Semantics and Ontologies 4, 42–53.CrossRefGoogle Scholar
Maxin, G 2006. Modélisation des bilans Entrée/Sortie des éléments carbone, azote, eau et minéraux chez la vache laitière. Master thesis, ESITPA, Rouen, France.Google Scholar
Oenema, O, Kros, H, de Wris, W 2003. Approaches and uncertainties in nutrient budgets: implications for nutrient management and environmental policies. European Journal of Agronomy 20, 3–16.CrossRefGoogle Scholar
Payraudeau, S, van der Werf, HMG 2005. Environmental impact assessment for a farming region: a review of methods. Agriculture, Ecosystems & Environment 107, 1–19.CrossRefGoogle Scholar
PostgreSQL Global Development Group 2008. PostgreSQL 8.3 Documentation. Retrieved January 10, 2012, from http://www.postgresql.org/docs/8.3/static/index.html Google Scholar
R Development Core Team 2008. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.Google Scholar
Rigolot, C 2009. Modélisation de l'impact environnemental des pratiques en élevage porcin à l’échelle sectorielle (animal et effluent), de l'exploitation, et du cycle de vie (ACV). Incidence du niveau d'approche sur la perception des pratiques. PhD thesis, Agrocampus Ouest- Université de Rennes 1, Rennes, France.Google Scholar
Rigolot, C, Espagnol, S, Pomar, C, Dourmad, JY 2010a. Modelling of manure production by pigs and NH3, N2O and CH4 emissions. Part I: Animal excretion and enteric CH4, effect of feeding and performance. Animal 4, 1401–1412.CrossRefGoogle ScholarPubMed
Rigolot, C, Espagnol, S, Robin, P, Hassouna, M, Béline, F, Paillat, JM, Dourmad, JY 2010b. Modelling of manure production by pigs and NH3, N2O and CH4 emissions. Part II: Effect of animal housing, manure storage and treatment practices. Animal 4, 1413–1424.CrossRefGoogle ScholarPubMed
Rotz, CA, Corson, MS, Chianese, DS, Montes, F, Hafner, SD, Jarvis, R, Coiner, CU 2011. The Integrated Farm System Model: Reference Manual, Version 3.4. Retrieved January 10, 2012, from http://www.ars.usda.gov/Main/docs.htm?docid=8519 Google Scholar
Schils, RLM, de Haan, MHA, Hemmer, JGA, van den Pol-van Dasselaar, A, de Boer, JA, Evers, AG, Holshof, G, van Middelkoop, JC, Zom, RLG 2007. Dairywise: a whole-farm dairy model. Journal of Dairy Science 90, 5334–5346.CrossRefGoogle ScholarPubMed
Steinfeld, H, Gerber, P, Wassenaar, T, Castel, V, Rosales, M, Haan, C 2006. Livestock long shadow. Environmental issues and options. Animal production and health division. FAO, Rome, Italy.Google Scholar
Swensson, C 2003. Analyses of mineral element balances between 1997 and 1999 from dairy farms in the south of Sweden. European Journal of Agronomy 20, 63–69.CrossRefGoogle Scholar
Wastney, ME, Palliser, CC, Lile, JA, McDonald, KA, Penno, JW, Bright, KP 2002. A whole-farm model applied to a dairy system. Proceedings of the New Zealand Society of Animal Production 62, 120–123.Google Scholar