Assessing on-farm productivity of Miscanthus crops by combining soil mapping, yield modelling and remote sensing. (February 2016)

• Record Type: Journal Article
• Authors: Richter, Goetz M.; Agostini, Francesco; Barker, Alexandra; Costomiris, Delphine; Qi, Aiming
• Is Part Of: Biomass and bioenergy. Volume 85(2016:Feb.)
• Page Start: 252
• Page End: 261
• Publication Date: 2016-02
• Subjects: Agronomy -- Crop establishment -- Land use -- Optical remote sensing -- Soil maps -- Yield gap; Biomass energy -- Periodicals; Biomass -- Periodicals; Energy-Generating Resources -- Periodicals; Bioénergie -- Périodiques; 333.9539
• DOI: 10.1016/j.biombioe.2015.12.024
• Languages: English
• ISSNs: 0961-9534

Abstract

Abstract: Biomass from agricultural land is a key component of any sustainable bioenergy strategy, and 2 nd 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. Highlights: Yield maps based on soil water and climate data over-estimate on-farm yields. The yield gap was greater for clay (37%) than on light/medium textured soils (10%). Crop patchiness was reflected in the Normalised Difference Vegetation Index (NDVI). Remote sensing could help to improve on-farm Miscanthus productivity predictions.