Empirical and dynamic approaches for modelling the yield and N content of European grasslands. (December 2019)

• Record Type: Journal Article
• Authors: Dellar, Martha; Topp, Cairistiona; Pardo, Guillermo; del Prado, Agustin; Fitton, Nuala; Holmes, David; Banos, Georgios; Wall, Eileen
• Is Part Of: Environmental modelling & software. Volume 122(2019)
• Publication Date: 2019-12
• Subjects: Grasslands -- Yield -- Nitrogen -- Modelling; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2019.104562
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: We applied two approaches to model grassland yield and nitrogen (N) content. The first was a series of regression equations; the second was the Century dynamic model. The regression model was generated from data from eighty-nine experimental sites across Europe, distinguishing between five climatic regions. The Century model was applied to six sites across these regions. Both approaches estimated mean grassland yields and N content reasonably well, though the root mean squared error tended to be lower for the dynamic model. The regression model achieved better correlations between observed and predicted values. Both models were more sensitive to uncertainties in weather than in soil properties, with precipitation often accounting for the majority of model uncertainty. The regression approach is applicable over large spatial scales but lacks precision, making it suitable for considering general trends. Century is better applied at a local level where more detailed and specific analysis is required. Highlights: Regression and Century models give good estimates of mean yield and N content. Both models are more sensitive to uncertainties in weather than in soil properties. Regression approach lacks precision but is good for identifying general trends. Century is best in the Atlantic region and less good in Alpine and northern areas.