Soybean-BioCro: A semi-mechanistic model of soybean growth

IF 2.6 Q1 AGRONOMY in silico Plants Pub Date : 2021-12-05 DOI:10.1093/insilicoplants/diab032
Megan L. Matthews, Amy Marshall-Colón, J. McGrath, E. Lochocki, S. Long
{"title":"Soybean-BioCro: A semi-mechanistic model of soybean growth","authors":"Megan L. Matthews, Amy Marshall-Colón, J. McGrath, E. Lochocki, S. Long","doi":"10.1093/insilicoplants/diab032","DOIUrl":null,"url":null,"abstract":"\n Soybean is a major global source of protein and oil. Understanding how soybean crops will respond to the changing climate and identifying the responsible molecular machinery, are important for facilitating bioengineering and breeding to meet the growing global food demand. The BioCro family of crop models are semi-mechanistic models scaling from biochemistry to whole crop growth and yield. BioCro was previously parameterized and proved effective for the biomass crops miscanthus, coppice willow, and Brazilian sugarcane. Here, we present Soybean-BioCro, the first food crop to be parameterized for BioCro. Two new module sets were incorporated into the BioCro framework describing the rate of soybean development and carbon partitioning and senescence. The model was parameterized using field measurements collected over the 2002 and 2005 growing seasons at the open air [CO2] enrichment (SoyFACE) facility under ambient atmospheric [CO2]. We demonstrate that Soybean-BioCro successfully predicted how elevated [CO2] impacted field-grown soybean growth without a need for re-parameterization, by predicting soybean growth under elevated atmospheric [CO2] during the 2002 and 2005 growing seasons, and under both ambient and elevated [CO2] for the 2004 and 2006 growing seasons. Soybean-BioCro provides a useful foundational framework for incorporating additional primary and secondary metabolic processes or gene regulatory mechanisms that can further aid our understanding of how future soybean growth will be impacted by climate change.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"in silico Plants","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/insilicoplants/diab032","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRONOMY","Score":null,"Total":0}
引用次数: 4

Abstract

Soybean is a major global source of protein and oil. Understanding how soybean crops will respond to the changing climate and identifying the responsible molecular machinery, are important for facilitating bioengineering and breeding to meet the growing global food demand. The BioCro family of crop models are semi-mechanistic models scaling from biochemistry to whole crop growth and yield. BioCro was previously parameterized and proved effective for the biomass crops miscanthus, coppice willow, and Brazilian sugarcane. Here, we present Soybean-BioCro, the first food crop to be parameterized for BioCro. Two new module sets were incorporated into the BioCro framework describing the rate of soybean development and carbon partitioning and senescence. The model was parameterized using field measurements collected over the 2002 and 2005 growing seasons at the open air [CO2] enrichment (SoyFACE) facility under ambient atmospheric [CO2]. We demonstrate that Soybean-BioCro successfully predicted how elevated [CO2] impacted field-grown soybean growth without a need for re-parameterization, by predicting soybean growth under elevated atmospheric [CO2] during the 2002 and 2005 growing seasons, and under both ambient and elevated [CO2] for the 2004 and 2006 growing seasons. Soybean-BioCro provides a useful foundational framework for incorporating additional primary and secondary metabolic processes or gene regulatory mechanisms that can further aid our understanding of how future soybean growth will be impacted by climate change.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
大豆BioCro:大豆生长的半机械模型
大豆是全球蛋白质和油脂的主要来源。了解大豆作物将如何应对气候变化,并确定负责任的分子机制,对于促进生物工程和育种以满足日益增长的全球粮食需求至关重要。BioCro作物模型家族是从生物化学到整个作物生长和产量的半机械模型。BioCro之前被参数化,并被证明对生物量作物芒草、矮林柳和巴西甘蔗有效。在这里,我们介绍大豆BioCro,这是第一种为BioCro参数化的粮食作物。两个新的模块集被纳入BioCro框架,描述大豆的发育速率、碳分配和衰老。该模型使用2002年和2005年生长季节在环境大气[CO2]下露天[CO2]富集(SoyFACE)设施收集的现场测量值进行参数化。我们证明,大豆BioCro在不需要重新参数化的情况下,通过预测2002年和2005年生长季节大气[CO2]升高以及2004年和2006年生长季节环境和二氧化碳升高的情况下的大豆生长,成功地预测了[CO2]增高对田间大豆生长的影响。大豆BioCro为结合额外的初级和次级代谢过程或基因调控机制提供了一个有用的基础框架,可以进一步帮助我们了解未来大豆生长将如何受到气候变化的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
in silico Plants
in silico Plants Agricultural and Biological Sciences-Agronomy and Crop Science
CiteScore
4.70
自引率
9.70%
发文量
21
审稿时长
10 weeks
期刊最新文献
Model-based inference of a dual role for HOPS in regulating guard cell vacuole fusion. Playing a crop simulation model using symbols and sounds: the ‘mandala’ A Scalable Pipeline to Create Synthetic Datasets from Functional-Structural Plant Models for Deep Learning In a PICKLE: A gold standard entity and relation corpus for the molecular plant sciences A comparison of empirical and mechanistic models for wheat yield prediction at field level in Moroccan rainfed areas
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1