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dc.contributor.authorRichter, Goetz M.
dc.contributor.authorAgostini, Francesco
dc.contributor.authorBarler, Alexandra
dc.contributor.authorCostomiris, Delphine
dc.contributor.authorQi, Aiming
dc.date.accessioned2018-01-30T22:32:23Z
dc.date.available2018-01-30T22:32:23Z
dc.date.issued2016-02-29
dc.identifier.citationRichter , G M , Agostini , F , Barler , A , Costomiris , D & Qi , A 2016 , ' Assessing on-farm productivity of Miscanthus crops by combining soil mapping, yield modelling and remote sensing ' , Biomass and Bioenergy , vol. 85 , pp. 252-261 . https://doi.org/10.1016/j.biombioe.2015.12.024
dc.identifier.otherPURE: 13073574
dc.identifier.otherPURE UUID: 90ca0f7c-fe5e-47dd-bbe3-a88035e0f846
dc.identifier.otherScopus: 84951959183
dc.identifier.urihttp://hdl.handle.net/2299/19676
dc.descriptionCrown Copyright © 2015 Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
dc.description.abstractBiomass from agricultural land is a key component of any sustainable bioenergy strategy, and 2nd generation, ligno-cellulosic feedstocks are part of the UK government policy to meet the target of reduced CO2 emission. Pre-harvest estimates of the biomass supply potential are usually based on experimental evidence and little is known about the yield gap between biologically obtainable and actual achievable on-farm biomass yields. We propose a systematic integration of mapped information fit for estimating obtainable yields using an empirical model, observed on-farm yields and remote sensing. Thereby, one can identify the sources of yield variation and supply uncertainty. Spatially explicit Miscanthus potential yields are compared with delivered on-farm yields from established crops ≥5 years after planting, surveyed among participants in the Energy Crop Scheme. Actual on-farm yield averaged at 8.94 Mg ha−1 and it varied greatly (coefficient of variation 34%), largely irrespective of soil type. The average yield gap on clay soils was much larger than that on sandy or loamy soils (37% vs 10%). Miscanthus is noticeably slower to establish on clay soils as shown by fitting a logistic Gompertz equation to yield time series. However, gaps in crop cover as identified by density counts, visual inspection (Google Earth) and remote sensing (Landsat-5) correlated with observed on-farm yields suggesting patchiness as causal for reduced yields. The analysis shows ways to improve the agronomy for these new crops to increase economic returns within the supply chain and the environmental benefits (reduced GHG emission, greater carbon sequestration) and reduce the land demand of bio-energy production.en
dc.format.extent10
dc.language.isoeng
dc.relation.ispartofBiomass and Bioenergy
dc.subjectAgronomyCrop establishmentLand useOptical remote sensingSoil mapsYield gap
dc.subjectAgronomy and Crop Science
dc.subjectPlant Science
dc.subjectAgricultural and Biological Sciences (miscellaneous)
dc.titleAssessing on-farm productivity of Miscanthus crops by combining soil mapping, yield modelling and remote sensingen
dc.contributor.institutionAgriculture, Food and Veterinary Sciences
dc.contributor.institutionGeography, Environment and Agriculture
dc.contributor.institutionCrop Protection and Climate Change
dc.contributor.institutionDepartment of Biological and Environmental Sciences
dc.contributor.institutionSchool of Life and Medical Sciences
dc.description.statusPeer reviewed
rioxxterms.versionVoR
rioxxterms.versionofrecordhttps://doi.org/10.1016/j.biombioe.2015.12.024
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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