BR112021020760A2 - Método de predição de produtividade de soja - Google Patents

Método de predição de produtividade de soja

Info

Publication number
BR112021020760A2
BR112021020760A2 BR112021020760A BR112021020760A BR112021020760A2 BR 112021020760 A2 BR112021020760 A2 BR 112021020760A2 BR 112021020760 A BR112021020760 A BR 112021020760A BR 112021020760 A BR112021020760 A BR 112021020760A BR 112021020760 A2 BR112021020760 A2 BR 112021020760A2
Authority
BR
Brazil
Prior art keywords
soybean
prediction method
productivity prediction
yield
soybean productivity
Prior art date
Application number
BR112021020760A
Other languages
English (en)
Inventor
Jun Deguchi
Keiji Endo
Mai Suetsugu
Teruhisa Fujimatsu
Original Assignee
Kao Corp
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from JP2019078180A external-priority patent/JP7244338B2/ja
Application filed by Kao Corp filed Critical Kao Corp
Publication of BR112021020760A2 publication Critical patent/BR112021020760A2/pt

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
    • G06Q10/06375—Prediction of business process outcome or impact based on a proposed change
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/02—Agriculture; Fishing; Forestry; Mining
    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01G—HORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING
    • A01G7/00—Botany in general
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06395—Quality analysis or management

Landscapes

  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Engineering & Computer Science (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Educational Administration (AREA)
  • General Business, Economics & Management (AREA)
  • Theoretical Computer Science (AREA)
  • Marketing (AREA)
  • Development Economics (AREA)
  • Tourism & Hospitality (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Quality & Reliability (AREA)
  • Operations Research (AREA)
  • Game Theory and Decision Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Animal Husbandry (AREA)
  • Marine Sciences & Fisheries (AREA)
  • Health & Medical Sciences (AREA)
  • Agronomy & Crop Science (AREA)
  • Primary Health Care (AREA)
  • Mining & Mineral Resources (AREA)
  • General Health & Medical Sciences (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Botany (AREA)
  • Ecology (AREA)
  • Forests & Forestry (AREA)
  • Environmental Sciences (AREA)
  • Other Investigation Or Analysis Of Materials By Electrical Means (AREA)
  • Beans For Foods Or Fodder (AREA)

Abstract

método de predição de produtividade de soja. a presente invenção fornece um método de predição de uma produtividade de soja em estágio inicial com alta precisão. o método de predição de uma produtividade de soja compreende: adquirir dados analíticos de um ou mais componentes de uma amostra foliar coletada da soja; e predizer uma produtividade de soja usando uma correlação entre os dados e uma produtividade de soja.
BR112021020760A 2019-04-16 2020-04-16 Método de predição de produtividade de soja BR112021020760A2 (pt)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
JP2019078179 2019-04-16
JP2019078180A JP7244338B2 (ja) 2019-04-16 2019-04-16 ダイズの収量予測方法
PCT/JP2020/016687 WO2020213672A1 (ja) 2019-04-16 2020-04-16 ダイズの収量予測方法

Publications (1)

Publication Number Publication Date
BR112021020760A2 true BR112021020760A2 (pt) 2021-12-14

Family

ID=72837187

Family Applications (1)

Application Number Title Priority Date Filing Date
BR112021020760A BR112021020760A2 (pt) 2019-04-16 2020-04-16 Método de predição de produtividade de soja

Country Status (4)

Country Link
US (1) US20220198360A1 (pt)
CN (1) CN113748337A (pt)
BR (1) BR112021020760A2 (pt)
WO (1) WO2020213672A1 (pt)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113748337A (zh) * 2019-04-16 2021-12-03 花王株式会社 大豆的产量预测方法
CN114946485B (zh) * 2022-05-13 2023-07-07 广西壮族自治区林业科学研究院 一种基于vnir和opls-da预判桉树缺铁性黄化病的方法
CN116990409A (zh) * 2023-07-17 2023-11-03 中国科学院兰州化学物理研究所 一种基于角鲨烯和甾醇组成的特级初榨橄榄油鉴别方法

Family Cites Families (26)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5516528A (en) * 1995-01-13 1996-05-14 Wake Forest University Dietary phytoestrogen in estrogen replacement therapy
US5821361A (en) * 1996-06-11 1998-10-13 Protein Technologies International, Inc. Recovery of isoflavones from soy molasses
HUP0001798A3 (en) * 1997-03-27 2002-03-28 Du Pont A method for indentifying genetic marker loci associated with trait loci
US7702597B2 (en) * 2004-04-20 2010-04-20 George Mason Intellectual Properties, Inc. Crop yield prediction using piecewise linear regression with a break point and weather and agricultural parameters
US7294766B2 (en) * 2005-01-31 2007-11-13 Pioneer Hi-Bred International, Inc. Soybean variety XB28F05
US7968770B2 (en) * 2006-06-28 2011-06-28 Pioneer Hi-Bred International, Inc. Methods for improving yield using soybean event 3560.4.3.5
CA2959417C (en) * 2007-01-09 2019-10-01 Novozymes Biologicals Holding A/S Lipo-chitooligosaccharide and flavonoid combination for enhanced plant growth and yield
WO2010080829A1 (en) * 2009-01-07 2010-07-15 Basf Agrochemical Products B.V. Soybean event 127 and methods related thereto
CN101891796A (zh) * 2010-08-04 2010-11-24 上海交通大学 高纯度大豆皂苷单体的制备方法
AR083897A1 (es) * 2010-11-17 2013-04-10 Pioneer Hi Bred Int Prediccion de fenotipos y rasgos en base al metaboloma
AR085702A1 (es) * 2011-03-07 2013-10-23 Syngenta Participations Ag Metodos para predecir el rendimiento en plantas y aplicaciones de uso de los mismos
CN102768246B (zh) * 2011-05-06 2013-12-04 中国科学院大连化学物理研究所 基于芯片纳流液相色谱/质谱的类黄酮代谢轮廓分析方法
TWI597365B (zh) * 2012-06-25 2017-09-01 陶氏農業科學公司 大豆品件ｐＤＡＢ９５８２.８１６.１５.１檢測方法
CA2912489C (en) * 2013-05-17 2021-11-23 Bayer Cropscience Nv Methods and means for determining plant characteristics
UA124407C2 (uk) * 2013-06-14 2021-09-15 Монсанто Текнолоджі Елелсі Рекомбінантна молекула днк, яка надає рослині стійкості до лускокрилої комахи-шкідника, і спосіб її виявлення та використання
CN103323557A (zh) * 2013-06-16 2013-09-25 中国科学院成都生物研究所 一种大豆及其制品中异黄酮的分析方法
MY189656A (en) * 2013-09-04 2022-02-23 Sime Darby Plantation Berhad Methods for predicting oil yield of a test oil palm plant
CA2988354A1 (en) * 2015-05-20 2016-11-24 Syngenta Participations Ag Plant with increased silicon uptake
CN106706820B (zh) * 2015-11-13 2018-05-25 中国科学院大连化学物理研究所 一种通用的大规模代谢组学数据的校正方法
JP6786262B2 (ja) * 2016-05-23 2020-11-18 キリンホールディングス株式会社 ビールテイスト飲料およびその製造方法
JP2018062492A (ja) * 2016-10-14 2018-04-19 株式会社Kri リグニン含有バイオマスからフェノール類の製造方法
GB201701349D0 (en) * 2017-01-27 2017-03-15 Univ Newcastle Non-target site resistance
CN109509112A (zh) * 2018-10-31 2019-03-22 武汉珈和科技有限公司 基于modis ndvi的全球大豆和玉米主产区产量评估方法及系统
CN109444314A (zh) * 2018-11-28 2019-03-08 中国农业科学院作物科学研究所 利用gc-ms法快速分析大豆香气特征化合物2-乙酰基-1吡咯啉含量的方法及应用
JP7244338B2 (ja) * 2019-04-16 2023-03-22 花王株式会社 ダイズの収量予測方法
CN113748337A (zh) * 2019-04-16 2021-12-03 花王株式会社 大豆的产量预测方法

Also Published As

Publication number Publication date
US20220198360A1 (en) 2022-06-23
WO2020213672A1 (ja) 2020-10-22
CN113748337A (zh) 2021-12-03

Similar Documents

Publication Publication Date Title
BR112021020760A2 (pt) Método de predição de produtividade de soja
Drissi et al. Proteomics methods for subcellular proteome analysis
Povey et al. Rapid high-throughput characterisation, classification and selection of recombinant mammalian cell line phenotypes using intact cell MALDI-ToF mass spectrometry fingerprinting and PLS-DA modelling
Tezel A proteomics view of the molecular mechanisms and biomarkers of glaucomatous neurodegeneration
Coombs Quantitative proteomics of complex mixtures
EA201400824A1 (ru) Спектроскопические устройства и способы идентификации микроорганизмов в культуре
Cohen Freue et al. Multiple reaction monitoring (MRM) principles and application to coronary artery disease
Pino et al. Improved SILAC quantification with data-independent acquisition to investigate bortezomib-induced protein degradation
McDonnell et al. Mass spectrometry image correlation: quantifying colocalization
BRPI0515581A (pt) métodos e produtos de programa de computador para análise e otimização de candidatos marcadores para prognóstico de cáncer
Han et al. Proteomics: from hypothesis to quantitative assay on a single platform. Guidelines for developing MRM assays using ion trap mass spectrometers
BR112019000742A2 (pt) método e kit para analisar uma amostra
EA200800901A1 (ru) Способ и устройство элементного анализа флюида в скважине
WO2008100582A3 (en) Spectroscopic system and method for predicting outcome of disease
ATE505785T1 (de) System zur identifikation gesprochener sprache und verfahren zum training und betrieb dazu
NZ629074A (en) Biomarkers for gastric cancer and uses thereof
WO2014065889A3 (en) Multiplexed method for diagnosing classical hodgkin lymphoma
BR112022011676A2 (pt) Kit para detectar um analito de interesse em uma pluralidade de amostras líquidas biológicas, método para detectá-lo e /ou quantificá-lo e cartucho para realizar tal método
BR112022019764A2 (pt) Ensaios para detecção de sars-cov-2
Kreshuk et al. Automated detection and analysis of bimodal isotope peak distributions in H/D exchange mass spectrometry using HeXicon
BR112023015956A2 (pt) Dispositivo para examinar um alvo, sistema para examinar um alvo, dispositivo para treinar um modelo de análise para analisar imagens de alvos com base em fluorescência, método para examinar um alvo e método para treinar um modelo de análise para analisar imagens com base em fluorescência de alvos
BR112022006800A2 (pt) Método para predizer um valor de propriedade de interesse de um material
Chen et al. Mass spectrometry-based protein quantification
EA201990098A1 (ru) Способ неинвазивного пренатального выявления эмбриональной хромосомной анеуплоидии с использованием материнской крови
Quandt et al. Using synthetic peptides to benchmark peptide identification software and search parameters for MS/MS data analysis