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Links between functional composition, biomass production and forage quality in permanent grasslands over a broad gradient of conditions

Published online by Cambridge University Press:  14 July 2014

A. MICHAUD ,
S. PLANTUREUX ,
E. POTTIER  and
R. BAUMONT
A. MICHAUD*
Affiliation:
Clermont Université, VetAgro Sup, UMR1213 Herbivores, 89 avenue de l'Europe, BP 35, 63370 Lempdes, France INRA UMR1213 Herbivores, 63122 Saint-Genès-Champanelle, France
S. PLANTUREUX
Affiliation:
Laboratoire Agronomie et Environnement, Université de Lorraine, UMR 1121, Vandoeuvre, F-54500, France Inra, Laboratoire Agronomie et Environnement, UMR 1121, Vandoeuvre, F-54500, France
E. POTTIER
Affiliation:
Institut de l'Elevage, Le Mourier, 87800 Saint-Priest-Ligoure, France
R. BAUMONT
Affiliation:
Clermont Université, VetAgro Sup, UMR1213 Herbivores, 89 avenue de l'Europe, BP 35, 63370 Lempdes, France INRA UMR1213 Herbivores, 63122 Saint-Genès-Champanelle, France
*
*To whom all correspondence should be addressed. Email: [email protected]
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Summary

To upgrade the use of permanent grasslands in livestock farming systems for their economic and environmental utility, their value needs better assessment in terms of both quantity (biomass production) and quality (nutritive value: organic matter digestibility (OMD) and crude protein content (CP)). The wide variability in permanent grassland botanical composition makes it important to understand the links between vegetation characteristics and permanent grassland value, and how far environmental factors influence this value. The current work investigated how vegetation characteristics and weather explained the variability of the biomass production and nutritive value of permanent grasslands. Two models were used to determine the best vegetation characteristics for the prediction: (i) plant functional types (PFT), proportions of grasses, legumes and forbs and weather, and (ii) two proxies for PFT (dry matter content (DMC) and phenological development at medium plant stage (MPS)), proportion of grasses, legumes and forbs, and weather. The study was conducted on a set of 190 permanent grasslands distributed over a wide range of soil, climatic and management conditions, and lasted 2 years (2009/10). For each of the permanent grasslands, climatic data, values of vegetation characteristics, biomass production and nutritive value were collected at the beginning and end of spring, and during summer and autumn regrowths. Contribution of weather was important and particularly for regrowths. Composition in terms of botanical families, plant stage and sward DMC was the common variables that explained both biomass production and nutritive value during the growing season. Biomass production was mainly explained by the proportion of legumes and forbs, MPS and DMC considering both models. Grass nutritive value was linked to the same factors, including PFT. However, the contribution of grass PFTs was lower in models. Both models could be used to predict biomass production and nutritive value: R2 of the two models are quite similar. Over a wide range of environmental and management conditions, vegetation characteristics and climatic data explained almost half of the variance of forage quality and 20–40% of the variance of biomass production.

Type
Crops and Soils Research Papers
Information
The Journal of Agricultural Science , Volume 153 , Issue 5 , July 2015 , pp. 891 - 906
Copyright
Copyright © Cambridge University Press 2014 

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References

REFERENCES

Andueza, D., Cruz, P., Farruggia, A., Baumont, R., Picard, F. & Michalet-Doreau, B. (2010). Nutritive value of two meadows and relationships with some vegetation traits. Grass and Forage Science 65, 325–334.CrossRefGoogle Scholar
Ansquer, P., Duru, M., Theau, J. P. & Cruz, P. (2009). Convergence in plant traits between species within grassland communities simplifies their monitoring. Ecological Indicators 9, 1020–1029.CrossRefGoogle Scholar
Arzani, H., Zohdi, M., Fish, E., Zahedi Amiri, G. H., Nikkhah, A. & Wester, D. (2004). Phenological effects on forage quality of five grass species. Journal of Range Management 57, 624–629.CrossRefGoogle Scholar
Aufrère, J. & Demarquilly, C. (1989). Predicting organic matter digestibility of forage by two pepsin–cellulase methods. In Proceedings of the XVI International Grassland Congress (Ed. Desroches, R.), pp. 877–878. Nice, France: Association Française pour la Production Fourragère.Google Scholar
Aufrère, J., Baumont, R., Delaby, L., Peccatte, J-R., Andrieu, J., Andrieu, J-P. & Dulphy, J-P. (2007). Laboratory prediction of forage digestibility by the pepsin–cellulase method. The renewed equations. Productions Animales 20, 129–135.CrossRefGoogle Scholar
Baumont, R., Pelletier, P., Peccatte, J. R., Aufrère, J., Delaby, L., Surault, F., Niderkorn, V. & Andueza, D. (2008). Specific diversity in forages: its consequences on the feeding value. Fourrages 194, 189–206.Google Scholar
Bornard, A. & Dubost, M. (1992). Agro-ecological diagnosis of the vegetation on dairy (cow mountain pastures in the French northern Alps-development and utilization of a simplified typology. Agronomie 12, 581–599.CrossRefGoogle Scholar
Bruinenberg, M. H., Valk, H., Korevaar, H. & Stuik, P. C. (2002). Factors affecting digestibility of temperate forages from seminatural grasslands: a review. Grass and Forage Science 57, 292–301.CrossRefGoogle Scholar
Buxton, D. R. (1996). Quality-related characteristics of forages as influenced by plant environment and agronomic factors. Animal Feed Science Technology 59, 37–49.CrossRefGoogle Scholar
Cordlandwehr, V., Meredith, R. L., Ozinga, W. A., Bekker, R. M., Van Groenendael, J. M. & Bakker, J. P. (2013). Do plant traits retrieved from database accurately predict on-site measurements? Journal of Ecology 101, 662–670.CrossRefGoogle Scholar
Corral, A. J. & Fenlon, J. S. (1977). A comparative method for describing the seasonal distribution of production from grasses. Journal of Agricultural Science, Cambridge 91, 61–67.CrossRefGoogle Scholar
Cruz, P., Duru, M., Therond, O., Theau, J. P., Ducourtieux, C., Jouany, C., Al Haj Khaled, R. & Ansquer, P. (2002). Une nouvelle approche pour caractériser les prairies naturelles et leur valeur d'usage. Fourrages 172, 335–354.Google Scholar
Cruz, P., Theau, J.-P., Lecloux, E., Jouany, C. & Duru, M. (2010). Typologie fonctionnelle de graminées fourragères pérennes: une classification multitraits. Fourrages 201, 11–17.Google Scholar
Daccord, R., Wyss, U., Kessler, J., Arrigo, Y., Rouel, M., Lehmann, J. & Jeangros, B. (2006). Estimation de la Valeur du Fourrage des Prairies. Lausanne, Switzerland: Agridea.Google Scholar
Demarquilly, C. & Andrieu, J. (1988). Les fourrages. In Alimentation des Bovins, Ovins et Caprins (Ed. Agabriel, J.), pp. 315–337. Paris, France: INRA.Google Scholar
Demarquilly, C. & Jarrige, R. (1981). Panorama des méthodes de prévision de la digestibilité et de la valeur énergétique des fourrages. In Prévision de la Valeur Nutritive des Aliments des Ruminants (Ed. Demarquilly, C.), pp. 41–59. Paris, France: INRA Publ.Google Scholar
De Montard, F. X. (1981). L'action des facteurs climatiques sur la croissance de l'herbe. Fourrages 85, 39–52.Google Scholar
Dirienzo, D. B., Webb, K. E. & Brann, D. E. (1991). Spring nitrogen application-effects on yield and quality of barley silage. Journal of Production Agriculture 4, 45–50.CrossRefGoogle Scholar
Duru, M., Theau, J.-P., Cruz, P., Jouany, C., Therond, O., Al Haj Khaled, R. & Ansquer, P. (2007). Typologies des prairies riches en espèces en vue d’évaluer leur valeur d'usage: bases agro-écologiques et exemple d'application. Fourrages 192, 453–475.Google Scholar
Duru, M., Cruz, P., Al Haj Khaled, R., Ducourtieux, C. & Theau, J-P. (2008). Relevance of plant functional types based on leaf dry matter content for assessing digestibility of native grass species and species-rich grassland communities in spring. Agronomy Journal 100, 1622–1630.CrossRefGoogle Scholar
Duru, M., Al Haj Khaled, R., Ducourtieux, C., Theau, J-P., De Quadros, F. L. F. & Cruz, P. (2009). Do plant functional types based on leaf dry matter content allow characterizing native grass species and grasslands for herbage growth pattern? Plant Ecology 201, 421–433.CrossRefGoogle Scholar
Duru, M., Cruz, P. & Theau, J. P. (2010). A simplified method for characterising agronomic services provided by species-rich grasslands. Crop & Pasture Science 61, 420–433.CrossRefGoogle Scholar
EC Eurostat (2012). Agriculture, Fishery and Forestry Statistics. Main Results 2010–11. Eurostat Pocketbooks. Luxembourg, Belgium: Publications Office of the European Union.Google Scholar
Gierus, M., Kleen, J., Loges, R. & Taube, F. (2012). Forage legume species determine the nutritional quality of binary mixtures with perennial ryegrass in the first production year. Animal Feed and Science Technology 172, 150–161.CrossRefGoogle Scholar
Grime, J. P., Hodgson, J. G. & Hunt, R. (1979). Comparative Plant Ecology: a Functional Approach to Common British Species. London: Unwin Hyman.Google Scholar
Hermann, A., Kelm, M., Kornher, A. & Taube, F. (2005). Performance of grassland under different cutting regimes as affected by sward composition, nitrogen input, soil conditions and weather – a simulation study. European Journal of Agronomy 22, 141–158.CrossRefGoogle Scholar
Huygue, C. (2008). Multi-function grasslands in France. I. Production functions. Cahiers Agricultures 17, 427–435.Google Scholar
Huygue, C., Baumont, R. & Isselstein, J. (2008). Plant diversity in grasslands and feed quality. Grassland Science in Europe 13, 375–386.Google Scholar
Isselstein, J. (1993). Influence of slight shading, sward density and nitrogen fertilization on yield and nutritive value of Lolium multiflorum LAM. Journal of Agronomy and Crop Science 170, 341–347.CrossRefGoogle Scholar
Jeangros, B. & Amaudruz, M. (2005). Dix ans d'observations sur la phénologie des prairies permanentes en Suisse Romande. Revue Suisse Agriculture 37, 201–209.Google Scholar
Jeangros, B. & Schmid, W. (1991). Production et valeur nutritive des prairies permanentes riches en espèces. Fourrages 126, 131–136.Google Scholar
Jing, Q., Bélanger, G., Baron, V., Bonesmo, H., Virkajarvi, P. & Young, D. (2012). Regrowth simulation of the perennial grass timothy. Ecological Modelling 232, 64–77.CrossRefGoogle Scholar
Jouven, M., Carrère, P. & Baumont, R. (2006). Model predicting dynamics of biomass, structure and digestibility of herbage in managed permanent pastures. 1. Model description. Grass and Forage Science 61, 112–124.CrossRefGoogle Scholar
Kleyer, M., Bekker, R. M., Knevel, I. C., Bakker, J. P., Thomson, K., Sonnenschein, M., Poschold, P., Van Groenendael, J. M., Klime, L., Klimesova, J., Klotz, S., Rusch, G. M., Hermy, M., Adriaens, D., Boedeltje, G., Bossuyt, B., Dannemann, A., Endels, P., Götzenberger, L., Hodgson, J. G., Jackel, A-K., Khün, I., Kunzmann, D., Ozinga, W. A., Römermann, C., Stadler, M., Schlegelmilch, J., Steendam, H. J., Tackenberg, O., Wilmann, B., Cornelissen, J. H. C., Eriksson, O., Garnier, E. & Peco, B. (2008). The LEDA traitbase: a database of life history traits of the NorthWest European flora. Journal of Ecology 96, 1266–1274.CrossRefGoogle Scholar
Kowalenko, C. G. & Bittman, S. (2000). Within season grass yield and nitrogen uptake and soil nitrogen as affected by nitrogen applied at various rates and distributions in a high rainfall environment. Canadian Journal of Plant Science 80, 287–301.CrossRefGoogle Scholar
Louault, F., Pillar, V. D., Aufrère, J., Garnier, E. & Soussana, J-F. (2005). Plant traits and functional types in response to reduced disturbance in a semi-natural grassland. Journal of Vegetation Science 16, 151–160.CrossRefGoogle Scholar
Maire, V. (2009). Des traits des graminées au fonctionnement de l’écosystème prairial: une approche de modélisation mécaniste. Ph.D. Thesis, Université Blaise Pascal, Clermont Ferrand, France.Google Scholar
Michaud, A., Andueza, D., Picard, F., Plantureux, S. & Baumont, R. (2012 a). The seasonal dynamics of biomass production and herbage quality of three grasslands with contrasting functional compositions. Grass and Forage Science 67, 64–76.CrossRefGoogle Scholar
Michaud, A., Plantureux, S., Amiaud, B., Carrère, P., Cruz, P., Duru, M., Dury, B., Farruggia, A., Fiorelli, J. L., Kerneis, E. & Baumont, R. (2012 b). Environmental factors influencing the botanical and functional composition of permanent grasslands. Journal of Agricultural Science, Cambridge 150, 219–236.CrossRefGoogle Scholar
Moore, K. J. & Moser, L. E. (1995). Quantifying developmental morphology of perennial grasses. Crop Science 35, 37–43.CrossRefGoogle Scholar
Moore, K. J., Moser, L. E., Vogel, K. P., Waller, S. S., Johnson, B. E. & Pedersen, J. F. (1991). Describing and quantifying growth stages of perennial forage grasses. Agronomie 83, 1073–1077.CrossRefGoogle Scholar
Mosimann, E. (2001). Croissance des herbages. Méthodes de mesures et applications pratiques. Revue Suisse d'Agriculture 33, 163–167.Google Scholar
Niqueux, M. & Arnaud, R. (1981). Peut-on prévoir la date d’épiaison des variétés de graminées? Fourrages 88, 39–56.Google Scholar
Pavlu, V., Hejcman, M., Pavlanduring, L. & Gaisler, J. (2007). Restoration of grazing management and its effect on vegetation in an upland grassland. Applied Vegetation Science 10, 375–382.CrossRefGoogle Scholar
Pontes, L., Soussana, J-F., Louault, F., Andueza, D. & Carrère, P. (2007). Leaf traits affect the above-ground productivity and quality of pasture grasses. Functional Ecology 21, 844–853.CrossRefGoogle Scholar
R Development Core Team (2013). R: A Language and Environment for Statistical Computing. Reference Index. Vienna, Austria: R foundation for Statistical Computing.Google Scholar
Rabaut, V. (2000). Les Prairies en 1998. Agreste Chiffres et Données Agriculture no. 128. Toulouse, France: Ministère de l'agriculture et de la pêche, Bureau des Statistique Végétales et Forestières.Google Scholar
Rose, L., Rubarth, M. C., Hertel, D. & Leuschner, C. (2013). Management alters interspecific leaf trait relationships and trait-based species rankings in permanent meadows. Journal of Vegetation Science 24, 239–250.CrossRefGoogle Scholar
Rossignol, N., Andueza, D., Carrère, P., Cruz, P., Duru, M., Fiorelli, J.-L., Michaud, A., Plantureux, S., Pottier, E. & Baumont, R. (2013). Assessing population maturity of three perennial grass species: influence of phenology and tiller demography along latitudinal and altitudinal gradients. Grass and Forage Science DOI: 10.1111/gfs.12067.CrossRefGoogle Scholar
Shenk, J. S. & Westerhaus, M. O. (1991). New standardization and calibration procedures for Nirs analytical systems. Crop Science 31, 1694–1696.CrossRefGoogle Scholar
Skinner, R. H., Gustine, D. L. & Sandersson, M. A. (2004). Growth, water relations and nutritive value of pasture species mixtures under moisture stress. Crop Science 44, 1361–1369.CrossRefGoogle Scholar
Theau, J-P. & Zerourou, A. (2008). Herb’âge, une méthode de calcul des sommes de température pour la gestion des prairies. In Les cahiers d'Orphée. Outils pour la Gestion des Prairies Permanentes, Vol. 1 (Eds Cruz, P., Jouany, C. & Theau, J.-P.), pp. 91–102. Paris, France: INRA.Google Scholar
Topp, C. F. E. & Doyle, C. J. (1996). Simulating the impact of global warming on milk and forage production in Scotland: 1. The effects on dry-matter yield of grass and grass-white clover swards. Agricultural Systems 52, 213–242.CrossRefGoogle Scholar
Van Soest, P. J. (1994). Nutritional Ecology of the Ruminant, 2nd edn. Ithaca, NY, USA: Cornell University Press.CrossRefGoogle Scholar
Violle, C., Navas, M. L., Vile, D., Kazakou, E., Fortunel, C., Hummel, I. & Garnier, E. (2007). Let the concept of trait be functional! OIKOS 116, 882–892.CrossRefGoogle Scholar