GrazeGro: a European herbage growth model to predict pasture production in perennial ryegrass swards for decision support
GrazeGro: a European herbage growth model to predict pasture production in perennial ryegrass swards for decision support
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DOI：
10.1016/j.eja.2004.09.006
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发表时间：
2005-07-01
影响因子：
5.2
通讯作者：
Mayne, CS
中科院分区：
文献类型：
--
作者：
Barrett, PD;Laidlaw, AS;Mayne, CS
Mayne C.S, Rook AJ.。牧草生长模型GrazeGro的构建及其在决策支持系统（DSS; GrazeMore）中的应用[j]。，陈建军，陈建军，陈建军，2004。通过增加对放牧牧场的依赖，提高欧洲牛奶生产系统的可持续性。参见：欧洲草原联合会第20届大会论文集，Luzern，第584-586页]关于整个西北欧乳制品生产系统中的牧场管理。该模型基于现有的LINGRA （LINtul-GRAss）模型[Schapendonk， a.h.m.c., Stol， W., Van Kraalingen, d.w.g., Bournan, b.a.m., 1998]。LINGRA是一个源/汇模型，用于模拟欧洲草原生产力。欧元。J. Agron. 9, 87-100]，重新开发和校准以满足GrazeMore项目的规范要求。通过独立的欧洲历史数据对模型的验证表明，模型预测足够准确，可以为农场决策过程提供有用的帮助。然而，进一步的参数化和验证对于在实际参数下使用良好监测的swsws操作模型的位置将是有用的。在生长数据监测良好的局部环境中，模型精度很高。虽然模型的进一步发展是可能的，但目前对有效的草生长预测的信心是好的，GrazeGro被认为适合于决策支持应用。(C) 2004 Elsevier B.V.版权所有
GrazeGro, a herbage growth model, was constructed for use in a decision support system (DSS; GrazeMore) [Mayne C.S., Rook AJ., Peyraud J.L., Cone J., Martinsson K., Gonzalez A., 2004. Improving sustainability of milk production systems in Europe through increasing reliance on grazed pasture. In: Proceedings of the 20th General Meeting of the European Grassland Federation, Luzern, pp. 584-586] for pasture management in dairy production systems throughout northwest Europe. The model was based on the existing LINGRA (LINtul-GRAss) model [Schapendonk, A.H.M.C., Stol, W., Van Kraalingen, D.W.G., Bournan, B.A.M., 1998. LINGRA, a source/sink model to simulate grassland productivity in Europe. Eur. J. Agron. 9, 87-100], redeveloped and calibrated to meet the specification requirements of the GrazeMore project. Validation of the model from independent historical European data showed that model predictions were sufficiently accurate to make it a useful aid for on-farm decision-making processes. However, further parameterisation and validation would be useful for locations where the model will be operated using well-monitored swards under realistic parameters. In a local setting where growth data were monitored well, model precision was high. Although further development of the model is possible, currently confidence in effective grass growth predictions is good and GrazeGro is considered suitable for a decision support application. (C) 2004 Elsevier B.V. All rights reserved.