Using site-specific data to estimate energy crop yield. (December 2015)

• Record Type: Journal Article
• Authors: Laurent, A.; Loyce, C.; Makowski, D.; Pelzer, E.
• Is Part Of: Environmental modelling & software. Volume 74(2015:Dec.)
• Page Start: 104
• Page End: 113
• Publication Date: 2015-12
• Subjects: Bayesian model -- Energy crops -- Site-specific data -- Yield estimation -- Miscanthus × giganteus; 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.2015.09.008
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: The estimation of energy crop yields is important, to help the firms responsible for collecting them to estimate biomass production in a given area, for example. A Bayesian modelling framework for site-specific yield estimation is presented in this paper. The proposed approach is based on a hierarchical model describing between-site and within-site yield variability. Probability distributions are used to describe the uncertainty of model estimations. The model can be fitted to site-specific yield data, to obtain both average and site-specific yield estimates. Site-specific yield data may be obtained from measurements for crop species other than those for which estimations are required, or from past measurements on perennial crop species grown over a period of several years at a given site. These two options were illustrated in two case studies, in which our model was used to estimate the yields of several energy crops. In most situations, site-specific yield estimations were more accurate than average estimations. Highlights: A Bayesian modelling framework was developed for site-specific yield estimation. The proposed models can be fitted to site-specific yield data. Site-specific estimations were frequently more accurate than average estimations. Our approach could be used to select the most appropriate crop species for a given area.