Spatially-explicit projection of future microbial protein from lignocellulosic waste

Liwei  Chen; Thomas Upcraft; Ellen Piercy; Miao Guo

doi:10.1016/j.crbiot.2022.10.008

Spatially-explicit projection of future microbial protein from lignocellulosic waste

Liwei Chen, Thomas Upcraft, Ellen Piercy, Miao Guo^*

^*Corresponding author for this work

Engineering

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

Abstract

Plant- and animal-sourced proteins are carbon-intensive and vulnerable to extreme events. This combined with increasing protein demands highlight the challenge on providing sustainable protein derived from alternative sources. Microbial protein derived from microorganisms offers potential solutions. Notably, some microbial strains e.g. Fusarium venenatum could efficiently convert carbon sources from food-safe agricultural lignocellulosic ‘waste’ (e.g. food crop residues such as wheat straw) to microbial protein. Our study is underpinned by data-driven approach and presents a modelling framework and analyses to predict spatially-explicit yields of staple crop lignocellulosic residues in response to spatial variation and climate change and highlight their potential for microbial protein production. Five food crops residues have been modelled including barley, wheat, maize, sorghum and rice straws worldwide (around 227 countries) to show future potential for microbial protein production. This study predicts crop residue yields using the data set collection and data pre-processing of crop residues and other environmental factors like temperature. Then, global autocorrelation is used to identify crop residues’ worldwide distribution patterns, and local autocorrelation is used to identify hot and cold spots. Feature selection and four models including ordinary least squares (OLS), multilayer perceptron (MLP), lasso regression and spatial error model are applied in prediction. With the updated independent factor ‘future temperature’ obtained from the autoregressive integrated moving average (ARIMA) model, the selected model is used to predict crop residues in 2030, 2040 and visualizations are created to show the projection outcomes. Our quantitative projection suggests that the future lignocellulosic microbial protein supply in different scenarios would sufficiently satisfy the global protein demands considering the average adult daily protein recommendation (50 g protein per capita 70 kg per day).

Original language	English
Pages (from-to)	544-563
Number of pages	20
Journal	Current Research in Biotechnology
Volume	4
DOIs	https://doi.org/10.1016/j.crbiot.2022.10.008
Publication status	Published - 3 Nov 2022

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title = "Spatially-explicit projection of future microbial protein from lignocellulosic waste",

abstract = "Plant- and animal-sourced proteins are carbon-intensive and vulnerable to extreme events. This combined with increasing protein demands highlight the challenge on providing sustainable protein derived from alternative sources. Microbial protein derived from microorganisms offers potential solutions. Notably, some microbial strains e.g. Fusarium venenatum could efficiently convert carbon sources from food-safe agricultural lignocellulosic {\textquoteleft}waste{\textquoteright} (e.g. food crop residues such as wheat straw) to microbial protein. Our study is underpinned by data-driven approach and presents a modelling framework and analyses to predict spatially-explicit yields of staple crop lignocellulosic residues in response to spatial variation and climate change and highlight their potential for microbial protein production. Five food crops residues have been modelled including barley, wheat, maize, sorghum and rice straws worldwide (around 227 countries) to show future potential for microbial protein production. This study predicts crop residue yields using the data set collection and data pre-processing of crop residues and other environmental factors like temperature. Then, global autocorrelation is used to identify crop residues{\textquoteright} worldwide distribution patterns, and local autocorrelation is used to identify hot and cold spots. Feature selection and four models including ordinary least squares (OLS), multilayer perceptron (MLP), lasso regression and spatial error model are applied in prediction. With the updated independent factor {\textquoteleft}future temperature{\textquoteright} obtained from the autoregressive integrated moving average (ARIMA) model, the selected model is used to predict crop residues in 2030, 2040 and visualizations are created to show the projection outcomes. Our quantitative projection suggests that the future lignocellulosic microbial protein supply in different scenarios would sufficiently satisfy the global protein demands considering the average adult daily protein recommendation (50 g protein per capita 70 kg per day).",

author = "Liwei Chen and Thomas Upcraft and Ellen Piercy and Miao Guo",

note = "Funding Information: We would also like to acknowledge the UK Engineering and Physical Sciences Research Council (EPSRC) for providing financial support for research under the DTP programmes [EP/N034740/1] and [EP/R513064/1/2381897]. Publisher Copyright: {\textcopyright} 2022 The Author(s)",

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doi = "10.1016/j.crbiot.2022.10.008",

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T1 - Spatially-explicit projection of future microbial protein from lignocellulosic waste

AU - Chen, Liwei

AU - Upcraft, Thomas

AU - Piercy, Ellen

AU - Guo, Miao

N1 - Funding Information: We would also like to acknowledge the UK Engineering and Physical Sciences Research Council (EPSRC) for providing financial support for research under the DTP programmes [EP/N034740/1] and [EP/R513064/1/2381897]. Publisher Copyright: © 2022 The Author(s)

PY - 2022/11/3

Y1 - 2022/11/3

N2 - Plant- and animal-sourced proteins are carbon-intensive and vulnerable to extreme events. This combined with increasing protein demands highlight the challenge on providing sustainable protein derived from alternative sources. Microbial protein derived from microorganisms offers potential solutions. Notably, some microbial strains e.g. Fusarium venenatum could efficiently convert carbon sources from food-safe agricultural lignocellulosic ‘waste’ (e.g. food crop residues such as wheat straw) to microbial protein. Our study is underpinned by data-driven approach and presents a modelling framework and analyses to predict spatially-explicit yields of staple crop lignocellulosic residues in response to spatial variation and climate change and highlight their potential for microbial protein production. Five food crops residues have been modelled including barley, wheat, maize, sorghum and rice straws worldwide (around 227 countries) to show future potential for microbial protein production. This study predicts crop residue yields using the data set collection and data pre-processing of crop residues and other environmental factors like temperature. Then, global autocorrelation is used to identify crop residues’ worldwide distribution patterns, and local autocorrelation is used to identify hot and cold spots. Feature selection and four models including ordinary least squares (OLS), multilayer perceptron (MLP), lasso regression and spatial error model are applied in prediction. With the updated independent factor ‘future temperature’ obtained from the autoregressive integrated moving average (ARIMA) model, the selected model is used to predict crop residues in 2030, 2040 and visualizations are created to show the projection outcomes. Our quantitative projection suggests that the future lignocellulosic microbial protein supply in different scenarios would sufficiently satisfy the global protein demands considering the average adult daily protein recommendation (50 g protein per capita 70 kg per day).

AB - Plant- and animal-sourced proteins are carbon-intensive and vulnerable to extreme events. This combined with increasing protein demands highlight the challenge on providing sustainable protein derived from alternative sources. Microbial protein derived from microorganisms offers potential solutions. Notably, some microbial strains e.g. Fusarium venenatum could efficiently convert carbon sources from food-safe agricultural lignocellulosic ‘waste’ (e.g. food crop residues such as wheat straw) to microbial protein. Our study is underpinned by data-driven approach and presents a modelling framework and analyses to predict spatially-explicit yields of staple crop lignocellulosic residues in response to spatial variation and climate change and highlight their potential for microbial protein production. Five food crops residues have been modelled including barley, wheat, maize, sorghum and rice straws worldwide (around 227 countries) to show future potential for microbial protein production. This study predicts crop residue yields using the data set collection and data pre-processing of crop residues and other environmental factors like temperature. Then, global autocorrelation is used to identify crop residues’ worldwide distribution patterns, and local autocorrelation is used to identify hot and cold spots. Feature selection and four models including ordinary least squares (OLS), multilayer perceptron (MLP), lasso regression and spatial error model are applied in prediction. With the updated independent factor ‘future temperature’ obtained from the autoregressive integrated moving average (ARIMA) model, the selected model is used to predict crop residues in 2030, 2040 and visualizations are created to show the projection outcomes. Our quantitative projection suggests that the future lignocellulosic microbial protein supply in different scenarios would sufficiently satisfy the global protein demands considering the average adult daily protein recommendation (50 g protein per capita 70 kg per day).

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U2 - 10.1016/j.crbiot.2022.10.008

DO - 10.1016/j.crbiot.2022.10.008

M3 - Article

SN - 2590-2628

VL - 4

SP - 544

EP - 563

JO - Current Research in Biotechnology

JF - Current Research in Biotechnology

ER -

Spatially-explicit projection of future microbial protein from lignocellulosic waste

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