Abstract
Biomass is a promising renewable energy option that provides a more environmentally sustainable alternative to fossil resources by reducing the net flux of greenhouse gasses to the atmosphere. Yet, allometric models that allow the prediction of aboveground biomass (AGB), biomass carbon (C) stock non-destructively have not yet been developed for tropical perennial C4 grasses currently under consideration as potential bioenergy feedstock in Hawaii and other subtropical and tropical locations. The objectives of this study were to develop optimal allometric relationships and site-specific models to predict AGB, biomass C stock of napiergrass, energycane, and sugarcane under cultivation practices for renewable energy and validate these site-specific models against independent data sets generated from sites with widely different environments. Several allometric models were developed for each species from data at a low elevation field on the island of Maui, Hawaii. A simple power model with stalk diameter (D) was best related to AGB and biomass C stock for napiergrass, energycane, and sugarcane, (R2 = 0.98, 0.96, and 0.97, respectively). The models were then tested against data collected from independent fields across an environmental gradient. For all crops, the models over-predicted AGB in plants with lower stalk D, but AGB was under-predicted in plants with higher stalk D. The models using stalk D were better for biomass prediction compared to dewlap H (Height from the base cut to most recently exposed leaf dewlap) models, which showed weak validation performance. Although stalk D model performed better, however, the mean square error (MSE)-systematic was ranged from 23 to 43 % of MSE for all crops. A strong relationship between model coefficient and rainfall was existed, although these were irrigated systems; suggesting a simple site-specific coefficient modulator for rainfall to reduce systematic errors in water-limited areas. These allometric equations provide a tool for farmers in the tropics to estimate perennial C4 grass biomass and C stock during decision-making for land management and as an environmental sustainability indicator within a renewable energy system.
Highlights
The most effective biofuel feedstocks offer potential renewable energy to reduce our dependence on fossil fuels, and minimization of net greenhouse gas flux during production (Lynd et al, 2008; DeLucia, 2016)
The objectives of the study were to: (1) develop allometric relationships and site-specific models to predict aboveground biomass (AGB) and biomass C stock from measurements of stalk D or dewlap H of individual energycane, napiergrass and sugarcane stalks, (2) select best model based on goodness of fit indices, (3) test selected model against data sets generated from independent sites with different environments, and (4) assess the effect of environmental factors on model accuracy
The study was conducted at the Hawaiian Commercial and Sugar (HC&S) plantation and Maui Agricultural Research Center (MARC) on the Island of Maui, Hawaii
Summary
The most effective biofuel feedstocks offer potential renewable energy to reduce our dependence on fossil fuels, and minimization of net greenhouse gas flux during production (Lynd et al, 2008; DeLucia, 2016). Managed bioenergy cropping systems, including tropical perennial C4 grasses, can produce large amounts of biomass and increase soil sequestration (Matsuoka et al, 2014; Meki et al, 2014, 2015; Stokes et al, 2016). Many generalized models predict biomass and C stock in forestry and agroforestry systems (Nair et al, 2009; Chave et al, 2014; Vahedi et al, 2014; Ali et al, 2015; Kuyah and Rosenstock, 2015; Kuyah et al, 2016; Mganga, 2016), but only a few equations have been developed for non-forest crops (Navar et al, 2004; Nafus et al, 2009; Martin et al, 2013; Fard and Heshmati, 2014; Oliveras et al, 2014). Yield and growth rate data exist for multiple bioenergy crops across a range of environments in Hawaii and the tropics (Meki et al, 2014), yet to our knowledge there are no fully developed allometric models to predict AGB and C stock non-destructively for bioenergy crops
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