Herb'sim : un modèle pour raisonner la production et l'utilisation de l'herbe

SUMMARY Herb’sim, a model for a rational management of grass production and grass utilization A computerized simulation constitutes an efficient tool for the forecast of the effects of pasture management methods on the availability of grass. In this paper, a simulation model is presented that will be available soon on the Web. This model for a rational production and utilization of grass has two main objects : i) to represent in an integrated and dynamic way the growth of grass in leys (including leys with legumes) or in multi-specific permanent pastures, the model being thus a training basis, and ii) to assist in the setting-up of regional data bases. The inputs concern the mineral nutrition, the type of vegetation, the water reserve, and the daily weather data. The model gives the amount of standing grass with its digestibility and protein content. Illustrations are given to show the performances of the model, especially as regards the differences among types of vegetation, and the effects of the management methods on growth and on the harvested bio-mass.

[1]  M. Duru,et al.  Effect of nitrogen fertiliser supply and winter cutting on morphological composition and herbage digestibility of a Dactylis glomerata L sward in spring , 2000 .

[2]  M. Duru,et al.  A nitrogen and phosphorus herbage nutrient index as a tool for assessing the effect of N and P supply on the dry matter yield of permanent pastures , 1996, Nutrient Cycling in Agroecosystems.

[3]  Marie-Josée Cros,et al.  A biophysical dairy farm model to evaluate rotational grazing management strategies , 2003 .

[4]  E. Justes,et al.  A spreadsheet model for developing field indicators and grazing management tools to meet environmental and production targets for dairy farms. , 2007, Journal of environmental management.

[5]  P. Choler,et al.  Relationship between the Al resistance of grasses and their adaptation to an infertile habitat. , 2007, Annals of botany.

[6]  M. Duru,et al.  Un modèle générique de digestibilité des graminées des prairies semées et permanentes pour raisonner les pratiques agricoles , 2008 .

[7]  G. Lemaire,et al.  The effect of nitrogen fertilization upon the herbage production of tall fescue swards continuously grazed with sheep. 1. Herbage growth dynamics , 1994 .

[8]  M. Duru,et al.  Modeling growth of cocksfoot (Dactylis glomerata L.) and tall fescue (Festuca arundinacea schreb.) at the end of spring in relation to herbage nitrogen status , 1995 .

[9]  M. Duru,et al.  Convergence in plant traits between species within grassland communities simplifies their monitoring , 2009 .

[10]  Pablo Cruz,et al.  Do plant functional types based on leaf dry matter content allow characterizing native grass species and grasslands for herbage growth pattern? , 2009, Plant Ecology.

[11]  C. Topp,et al.  Simulating the impact of global warming on milk and forage production in Scotland: 2. The effects on milk yields and grazing management of dairy herds , 1996 .

[12]  M. Duru Leaf and stem in vitro digestibility for grasses and dicotyledons of meadow plant communities in spring , 1997 .

[13]  Derrick J. Moot,et al.  Radiation use efficiency and biomass partitioning of lucerne (Medicago sativa) in a temperate climate , 2006 .

[14]  R. Rabbinge,et al.  Concepts in production ecology for analysis and quantification of agricultural input-output combinations , 1997 .

[15]  Claude Varlet-Grancher,et al.  Mise au point: Rayonnement solaire absorbé ou intercepté par un couvert végétal , 1989 .

[16]  Derrick J. Moot,et al.  How does defoliation management impact on yield, canopy forming processes and light interception of lucerne (Medicago sativa L.) crops? , 2007 .

[17]  F. Ruget,et al.  Du modèle STICS au système ISOP pour estimer la production fourragère. Adaptation à la prairie, application spatialisée , 2006 .

[18]  M. Duru,et al.  Effets des niveaux de nutrition en phosphore et en azote et de la composition botanique de communautés prairiales sur l'accumulation de biomasse au printemps , 1996 .

[19]  Gilles Lemaire,et al.  Production maximale de matière sèche et rayonnement solaire intercepté par un couvert végétal , 1986 .

[20]  Duru,et al.  The effect of N and P fertilizer application and botanical composition on the leaf/stem ratio patterns in spring in Pyrenean meadows , 1999 .

[21]  M. Williamson,et al.  Relationships between first flowering date and temperature in the flora of a locality in central England , 1995 .

[22]  D. Moot,et al.  The components of lucerne (Medicago sativa) leaf area index respond to temperature and photoperiod in a temperate environment , 2005 .

[23]  G. Lemaire,et al.  N Uptake and Distribution in Plant Canopies , 1997 .

[24]  G. Bélanger,et al.  Carbon Balance of Tall Fescue (Festuca arundinacea Schreb.): Effects of Nitrogen Fertilization and the Growing Season , 1994 .

[25]  C. Alan Rotz,et al.  Modification of the SPUR rangeland model to simulate species composition and pasture productivity in humid temperate regions , 2006 .

[26]  Jacques Wery,et al.  Response of a plurispecific permanent grassland to border irrigation regulated by tensiometers , 2008 .

[27]  Modeling Net Herbage Accumulation of an Orchardgrass Sward , 2002 .

[28]  J. Raven,et al.  Use of white clover as an alternative to nitrogen fertiliser for dairy pastures in nitrate vulnerable zones in the UK: productivity, environmental impact and economic considerations , 2007 .

[29]  Luca Bechini,et al.  A preliminary evaluation of the simulation model CropSyst for alfalfa , 2004 .

[30]  J. Dulphy,et al.  Les tables de la valeur des aliments , 2007 .

[31]  G. Martin,et al.  Modelling above-ground herbage mass for a wide range of grassland community types , 2009 .

[32]  D. Moot,et al.  The dynamics of lucerne (Medicago sativa L.) yield components in response to defoliation frequency , 2007 .