The development of a new crop growth model SwitchFor for yield mapping of switchgrass

Yanmei Liu; Astley Hastings; Shaolin Chen; Andre Faaij

doi:10.1111/gcbb.12998

The development of a new crop growth model SwitchFor for yield mapping of switchgrass

Yanmei Liu^* (Corresponding Author), Astley Hastings, Shaolin Chen, Andre Faaij

^*Corresponding author for this work

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

4 Citations (Scopus)

Abstract

Switchgrass is a promising energy crop has the potential to mitigate global warming and energy security, improve local ecology and generate profit. Its quantitative traits, such as biomass productivity and environmental adaptability, are determined by genotype-by-environment interaction (GEI) or response of genotypes grown across different target environments. To simulate the yield of switchgrass outside its original habitat, a genotype-specific growth model, SwitchFor that captures GEI was developed by parameterising the MiscanFor model. Input parameters were used to describe genotype-specific characteristics under different soil and climate conditions, which enables the model to predict the yield in a wide range of environmental and climate conditions. The model was validated using global field trail data and applied to estimate the switchgrass yield potentials on the marginal land of the Loess Plateau in China. The results suggest that upland and lowland switchgrass have significant differences in the spatial distribution of the adaptation zone and site-specific biomass yield. The area of the adaption zone of upland switchgrass was 4.5 times of the lowland ecotype's. The yield difference between upland and lowland ecotypes ranges from 0 to 34 Mg ha−1. The weighted average yield of the lowland ecotype (20 Mg ha−1) is significantly higher than the upland type (5 Mg ha−1). The optimal yield map, generated by comparing the yield of upland and lowland ecotypes based on 1 km2 grid locations, illustrates that the total yield potential of the optimal switchgrass is 61.6–106.4 Tg on the marginal land of the Loess Plateau, which is approximately twice that of the individual ecotypes. Compared with the existing models, the accuracy of the yield prediction of switchgrass is significantly improved by using the SwitchFor model. This spatially explicit and cultivar-specific model provides valuable information on land management and crop breeding and a robust and extendable framework for yield mapping of other cultivars.

Original language	English
Pages (from-to)	1281-1302
Number of pages	22
Journal	Global Change Biology. Bioenergy
Volume	14
Issue number	12
Early online date	3 Oct 2022
DOIs	https://doi.org/10.1111/gcbb.12998
Publication status	Published - 1 Dec 2022

Bibliographical note

ACKNOWLEDGMENTS
This study was supported by the Chinese Scholarship Council (CSC) and partially supported by the National Key Project of Intergovernmental Cooperation in International Scientific and Technological Innovation (2018YFE0112400 to S.C.). A.H. was funded by was funded by the ADVENT project funded by the UK Natural Environment Research Council (NE/M019691/1) and ADVANCES funded by the UK Natural Environment Research Council (NE/M019691/1), EPSRC funded UKERC-4 and the BBSRC funded PCB4GGR project (BB/V011553/1). We would like to Bingchen Xu, from Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, China, for providing field trail data of the switchgrass on the Loess Plateau. We also acknowledge the data support from the Loess Plateau Data Center, National Earth System Science Data Sharing Infrastructure, and National Science and Technology Infrastructure of China (http://loess.geodata.cn).

Data Availability Statement

Data Availability
The climate data of China was obtained from the National Scientific Meteorological Centre (http://data.cma.cn/). The climate data of the US, Canada, and European was obtained from public database. The daily temperature and precipitation data was obtained from national centers for environmental information (NOAA), the evapotranspiration data were collected from the National Aeronautics and Space Administration (NASA), and the solar radiation data were collected from Application for Extracting and Exploring Analysis Ready Samples (APPEEARS). The key output of Switch For model in this study are the spatial distribution biomass yield maps of switchgrass (upland switchgrass, lowland switchgrass, and optimal switchgrass) on the marginal land of the Loess Plateau in the land use scenario1 and scenario2. The data supporting the findings of this study are openly available in the file name of “Spatial distribution of the yield biomass of switchgrass on the marginal land o fthe Loess Plateau” at https://doi.org/10.6084/m9.figsh are.21047 224.v1. The data are raster maps in Tiffformation with resolution of1 × 1 km2.

Supporting Information
Additional supporting information can be found online in the Supporting Information section at the end of this article

Keywords

bio-energy
biomass production
genotype-by-environment interaction
genotype-specific plant growth model
marginal land
SwitchFor model

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Cite this

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title = "The development of a new crop growth model SwitchFor for yield mapping of switchgrass",

abstract = "Switchgrass is a promising energy crop has the potential to mitigate global warming and energy security, improve local ecology and generate profit. Its quantitative traits, such as biomass productivity and environmental adaptability, are determined by genotype-by-environment interaction (GEI) or response of genotypes grown across different target environments. To simulate the yield of switchgrass outside its original habitat, a genotype-specific growth model, SwitchFor that captures GEI was developed by parameterising the MiscanFor model. Input parameters were used to describe genotype-specific characteristics under different soil and climate conditions, which enables the model to predict the yield in a wide range of environmental and climate conditions. The model was validated using global field trail data and applied to estimate the switchgrass yield potentials on the marginal land of the Loess Plateau in China. The results suggest that upland and lowland switchgrass have significant differences in the spatial distribution of the adaptation zone and site-specific biomass yield. The area of the adaption zone of upland switchgrass was 4.5 times of the lowland ecotype's. The yield difference between upland and lowland ecotypes ranges from 0 to 34 Mg ha−1. The weighted average yield of the lowland ecotype (20 Mg ha−1) is significantly higher than the upland type (5 Mg ha−1). The optimal yield map, generated by comparing the yield of upland and lowland ecotypes based on 1 km2 grid locations, illustrates that the total yield potential of the optimal switchgrass is 61.6–106.4 Tg on the marginal land of the Loess Plateau, which is approximately twice that of the individual ecotypes. Compared with the existing models, the accuracy of the yield prediction of switchgrass is significantly improved by using the SwitchFor model. This spatially explicit and cultivar-specific model provides valuable information on land management and crop breeding and a robust and extendable framework for yield mapping of other cultivars.",

keywords = "bio-energy, biomass production, genotype-by-environment interaction, genotype-specific plant growth model, marginal land, SwitchFor model",

author = "Yanmei Liu and Astley Hastings and Shaolin Chen and Andre Faaij",

note = "ACKNOWLEDGMENTS This study was supported by the Chinese Scholarship Council (CSC) and partially supported by the National Key Project of Intergovernmental Cooperation in International Scientific and Technological Innovation (2018YFE0112400 to S.C.). A.H. was funded by was funded by the ADVENT project funded by the UK Natural Environment Research Council (NE/M019691/1) and ADVANCES funded by the UK Natural Environment Research Council (NE/M019691/1), EPSRC funded UKERC-4 and the BBSRC funded PCB4GGR project (BB/V011553/1). We would like to Bingchen Xu, from Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, China, for providing field trail data of the switchgrass on the Loess Plateau. We also acknowledge the data support from the Loess Plateau Data Center, National Earth System Science Data Sharing Infrastructure, and National Science and Technology Infrastructure of China (http://loess.geodata.cn).",

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AU - Liu, Yanmei

AU - Hastings, Astley

AU - Chen, Shaolin

AU - Faaij, Andre

N1 - ACKNOWLEDGMENTS This study was supported by the Chinese Scholarship Council (CSC) and partially supported by the National Key Project of Intergovernmental Cooperation in International Scientific and Technological Innovation (2018YFE0112400 to S.C.). A.H. was funded by was funded by the ADVENT project funded by the UK Natural Environment Research Council (NE/M019691/1) and ADVANCES funded by the UK Natural Environment Research Council (NE/M019691/1), EPSRC funded UKERC-4 and the BBSRC funded PCB4GGR project (BB/V011553/1). We would like to Bingchen Xu, from Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, China, for providing field trail data of the switchgrass on the Loess Plateau. We also acknowledge the data support from the Loess Plateau Data Center, National Earth System Science Data Sharing Infrastructure, and National Science and Technology Infrastructure of China (http://loess.geodata.cn).

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N2 - Switchgrass is a promising energy crop has the potential to mitigate global warming and energy security, improve local ecology and generate profit. Its quantitative traits, such as biomass productivity and environmental adaptability, are determined by genotype-by-environment interaction (GEI) or response of genotypes grown across different target environments. To simulate the yield of switchgrass outside its original habitat, a genotype-specific growth model, SwitchFor that captures GEI was developed by parameterising the MiscanFor model. Input parameters were used to describe genotype-specific characteristics under different soil and climate conditions, which enables the model to predict the yield in a wide range of environmental and climate conditions. The model was validated using global field trail data and applied to estimate the switchgrass yield potentials on the marginal land of the Loess Plateau in China. The results suggest that upland and lowland switchgrass have significant differences in the spatial distribution of the adaptation zone and site-specific biomass yield. The area of the adaption zone of upland switchgrass was 4.5 times of the lowland ecotype's. The yield difference between upland and lowland ecotypes ranges from 0 to 34 Mg ha−1. The weighted average yield of the lowland ecotype (20 Mg ha−1) is significantly higher than the upland type (5 Mg ha−1). The optimal yield map, generated by comparing the yield of upland and lowland ecotypes based on 1 km2 grid locations, illustrates that the total yield potential of the optimal switchgrass is 61.6–106.4 Tg on the marginal land of the Loess Plateau, which is approximately twice that of the individual ecotypes. Compared with the existing models, the accuracy of the yield prediction of switchgrass is significantly improved by using the SwitchFor model. This spatially explicit and cultivar-specific model provides valuable information on land management and crop breeding and a robust and extendable framework for yield mapping of other cultivars.

AB - Switchgrass is a promising energy crop has the potential to mitigate global warming and energy security, improve local ecology and generate profit. Its quantitative traits, such as biomass productivity and environmental adaptability, are determined by genotype-by-environment interaction (GEI) or response of genotypes grown across different target environments. To simulate the yield of switchgrass outside its original habitat, a genotype-specific growth model, SwitchFor that captures GEI was developed by parameterising the MiscanFor model. Input parameters were used to describe genotype-specific characteristics under different soil and climate conditions, which enables the model to predict the yield in a wide range of environmental and climate conditions. The model was validated using global field trail data and applied to estimate the switchgrass yield potentials on the marginal land of the Loess Plateau in China. The results suggest that upland and lowland switchgrass have significant differences in the spatial distribution of the adaptation zone and site-specific biomass yield. The area of the adaption zone of upland switchgrass was 4.5 times of the lowland ecotype's. The yield difference between upland and lowland ecotypes ranges from 0 to 34 Mg ha−1. The weighted average yield of the lowland ecotype (20 Mg ha−1) is significantly higher than the upland type (5 Mg ha−1). The optimal yield map, generated by comparing the yield of upland and lowland ecotypes based on 1 km2 grid locations, illustrates that the total yield potential of the optimal switchgrass is 61.6–106.4 Tg on the marginal land of the Loess Plateau, which is approximately twice that of the individual ecotypes. Compared with the existing models, the accuracy of the yield prediction of switchgrass is significantly improved by using the SwitchFor model. This spatially explicit and cultivar-specific model provides valuable information on land management and crop breeding and a robust and extendable framework for yield mapping of other cultivars.

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KW - biomass production

KW - genotype-by-environment interaction

KW - genotype-specific plant growth model

KW - marginal land

KW - SwitchFor model

U2 - 10.1111/gcbb.12998

DO - 10.1111/gcbb.12998

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SN - 1757-1693

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JO - Global Change Biology. Bioenergy

JF - Global Change Biology. Bioenergy

IS - 12

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The development of a new crop growth model SwitchFor for yield mapping of switchgrass

Abstract

Bibliographical note

Data Availability Statement

Keywords

UN SDGs

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