CN109616159A - A phenotypic distance-based screening method for soybean specificity testing similar varieties - Google Patents
A phenotypic distance-based screening method for soybean specificity testing similar varieties Download PDFInfo
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- 238000012216 screening Methods 0.000 title claims abstract description 36
- 244000068988 Glycine max Species 0.000 title claims abstract description 34
- 235000010469 Glycine max Nutrition 0.000 title claims abstract description 34
- 238000000034 method Methods 0.000 title claims abstract description 30
- 238000012360 testing method Methods 0.000 title claims abstract description 26
- 230000014509 gene expression Effects 0.000 claims abstract description 94
- 230000007613 environmental effect Effects 0.000 claims abstract description 10
- 244000046052 Phaseolus vulgaris Species 0.000 claims 1
- 235000010627 Phaseolus vulgaris Nutrition 0.000 claims 1
- 230000003993 interaction Effects 0.000 abstract description 5
- 238000013479 data entry Methods 0.000 abstract 1
- 241000196324 Embryophyta Species 0.000 description 8
- 229930002877 anthocyanin Natural products 0.000 description 2
- 235000010208 anthocyanin Nutrition 0.000 description 2
- 239000004410 anthocyanin Substances 0.000 description 2
- 150000004636 anthocyanins Chemical class 0.000 description 2
- 150000001875 compounds Chemical class 0.000 description 2
- 238000012812 general test Methods 0.000 description 2
- 241001573881 Corolla Species 0.000 description 1
- 238000005452 bending Methods 0.000 description 1
- 238000009395 breeding Methods 0.000 description 1
- 230000001488 breeding effect Effects 0.000 description 1
- 238000010835 comparative analysis Methods 0.000 description 1
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- 230000007812 deficiency Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
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Abstract
本发明公开了一种基于表型距离的大豆特异性测试近似品种筛选方法。该方法根据各个表型性状的表达状态观测值受环境条件影响的程度和人员观测偏差的大小，对各个表达状态的代码差值设置不同的权重(如表1)，所有表型性状上的权重相加得出两品种间的表型距离；将大量表型距离与品种间差异的明显程度进行比较，确定一个表型距离阈值：计算申请品种与每个已知品种的表型距离，表型距离小于阈值的已知品种作为申请品种的近似品种。采用这种方法，较好排除了人员观测偏差、互作和数据录入错误和对近似品种筛选的影响，提高了近似品种筛选的严谨性；该方法还便于田间试验时将最近似的品种与申请品种相邻种植，提高了特异性测试的针对性。
The invention discloses a soybean specificity test approximate variety screening method based on phenotypic distance. This method sets different weights for the code differences of each expression state according to the degree to which the observed value of the expression state of each phenotypic trait is affected by environmental conditions and the magnitude of the deviation of personnel observations (see Table 1). The weights on all phenotypic traits Add the phenotypic distance between the two varieties; compare a large number of phenotypic distances with the obvious degree of differences between varieties, and determine a phenotypic distance threshold: calculate the phenotypic distance between the applied variety and each known variety, and the phenotype The known varieties whose distance is less than the threshold are regarded as the approximate varieties of the applied varieties. By adopting this method, personnel observation bias, interaction and data entry errors and the influence on the screening of similar varieties are better excluded, and the rigor of screening of similar varieties is improved; this method is also convenient for comparing the most similar varieties with application Varieties are planted next to each other, improving the pertinence of specific tests.
Description
Technical field
The present invention relates to a kind of soybean specific test approximation method for screening varieties based on phenotype distance, it is new to belong to plant
Kind test (or DUS test) technical field.
Background technique
Specificity, consistency and stability are the basic demands to plant variety, are that new variety of plant obtains kind power guarantor
The precondition of shield, staple crops kind variety certification and the registration of non-principal variety of crops.Specificity refers to application kind
All before the applying date known belong to or kind of the same race should be clearly distinguishable from.Have specificity for one kind of identification, needs
Prove that the kind is different from other any kinds of known similar crop on phenotypic character.Kind known to chief crop is often
It is ten hundreds of, by the kind (" application kind ") of each application DUS test compared with these known kinds carry out field planting,
It is infeasible in practice.For this reason, it may be necessary to which those, which are not needed plantation, relatively can determine and apply by some Filtering systems
The visibly different kind of kind phenotypic character excludes, and only planting those and not passing through field comparative test then not can determine that and application product
Whether kind has the kind (i.e. approximate kind) of obvious phenotypic difference.It is tested by field planting, compares application kind and approximate product
Kind whether there is notable difference, show whether application kind has the conclusion of specificity accordingly.Therefore, the selection of approximate kind is
The key link of specific test.
Although kind is large number of known to chief crop, for most of known kinds, the especially product of breeding morning
Kind, because the reasons such as yielding ability have been unable to meet current production needs, can not considered in approximate screening varieties.Approximate kind
Screening generally have by comparing application kind and still the known kind (such as the kind being bred as nearly ten years) of application potential
Trait expression state carries out.If can determine a known kind and apply kind expression status difference be big enough in
Notable difference is inherently shown when field planting between the two, then can be excluded the known kind, remaining kind is as close
Like kind.For quantitative character and false qualitative character (expression status in the consecutive variations region of especially false qualitative character),
Following characteristics can be presented because of the influence of environmental condition and human factor in expression status observation: (1) same by environmental influence
The same character of a kind, when different year and different regions are planted, expression status observation (expression status code) meeting
Certain fluctuation occurs；(2) different trait expressions degree affected by environment is different, show as expression status degree of fluctuation because
Character and it is different.(3) different testers are easy to produce deviation to the observed result of trait expression state.The size of deviation, with property
Shape and personnel have relationship.How to determine approximate screening varieties index, is the difficult point of approximate screening varieties.A kind of way is basis
The expression way of character, trait expression are influenced degree of fluctuation by environment degree and are influenced size by human error, by applying for product
Kind screens approximate kind: for false qualitative character and quantitative character, using therewith compared with each expression status of known kind
Differ the expression status range of 1-3 code.It is very high for the accuracy requirement of data using above-mentioned way.In practice, exist
Individual character expression status data it is excessive or due to data inputting due to being influenced to deviate normal value by personnel's observed deviation
Mistake, or due to the interaction (under rare occasion) of character and environmental condition, will lead to should be as the known product of approximate kind
Kind fails screening and comes in, and causes the omission of approximate kind, directly affects the accuracy of specific test.In addition, to each kind
Screening conditions be configured, process is cumbersome, and screening efficiency is lower.
Applicant develops the approximate method for screening varieties in a kind of soybean specific test based on phenotypic character before
(application number of invention patent 201610061379.6).This method is the approximate method for screening varieties based on single phenotypic character, is
Degree of fluctuation is influenced by environment degree according to the expression way of character, trait expression and is influenced size by human error, for Shen
Please kind each false qualitative character and quantitative character, it is known that kind is using the expression status model for differing 1-3 code therewith
It encloses, through application kind compared with each expression status of known kind, screens approximate kind.Applicant sends out in follow-up study
Existing, this method has still had following deficiency, needs further to improve: (1) poor fault tolerance, very to the accuracy requirements of data
It is high.In practice, there is the data of individual character expression status due to by personnel's observed deviation influenced deviate normal value it is excessive,
Or due to data inputting mistake, or due to the interaction (under rare occasion) of character and environmental condition, will lead to should be as close
Fail to screen like the known kind of kind, causes the omission of approximate kind.(2) this method is sieved based on the difference of single character
Approximate kind is selected, interracial difference cannot be added up, be selected into less approximate kind；(3) selected approximation cannot be shown
The degree of approximation of kind and application kind is not easy to design field specific test test.
Summary of the invention
Aiming at the problem that the above-mentioned approximate method for screening varieties based on single character is easy to cause omission approximation kind, this hair
It is bright to provide a kind of soybean specific test approximation method for screening varieties based on phenotype distance.This method is directed to kind dissimilarity
Shape expression status observation is observed this different feature of influence degree by environment and personnel, between application kind and known kind
Selected phenotype trait expression state code difference gives certain weight, by application kind and known kind in one group of character
Weight is added, and obtains interracial phenotype distance.The size of weight depends on the trait expression State Viewpoint measured value by environment and people
The size of member observation influence degree and code difference.Trait expression is more stable, observed deviation is smaller, and weight is bigger；Code difference
Bigger, weight is bigger.By, there are the statistics of a large amount of test article inter-species phenotype distances of notable difference, finding out one to character
The safe distance of approximate screening varieties, guarantee be higher than the safe distance kind there are apparent differences.Screening approximate kind
When, using the safe distance as the threshold value of approximate screening varieties: the known kind higher than the distance exists obviously with approximate kind
Difference, do not need to plant；Less than the known kind of the distance as the plantation adjacent with application kind progress field of approximate kind
And Evaluation on specificity.In this way, personnel's observed deviation, interaction and data inputting mistake and pairing approximation product are preferably eliminated
The influence of kind screening, improves the preciseness of approximate screening varieties.
The technical scheme is that a kind of soybean specific test approximation method for screening varieties based on phenotype distance,
It is characterized in that
(1) weight of each soybean phenotypic character code difference is set
The phenotypic character number of the current still soybean varieties (such as the kind being bred as nearly ten years) with application potential of acquisition
According to (the high character of Variety identification power in DUS Testing Guideline, two growth cycles of each kind general test), to the table of acquisition
Type trait data is arranged, and soybean varieties phenotype trait data library is established；It is observed according to the expression status of each phenotypic character
Value is by the degree of environmental influence and the size of personnel's observed deviation, to all expression status of each soybean phenotypic character
(such as code is 1-9 to code difference, and difference 0,1,2 ... 8) is arranged different weights, obtains data as shown in Table 1, is inputted
Soybean varieties phenotype trait data library；
For qualitative character, when code difference is more than or equal to 1, weight 4-6, for quantitative character and false qualitative character,
It is poor to different codes according to trait expression State Viewpoint measured value by the degree of environmental influence and the size of personnel's observed deviation
Value, gives different weights.
The weight of 1 soybean difference DUS test character expression status difference of table
Note: 1.QN: quantitative character, PQ: false qualitative character；QL: qualitative character
2. "-" indicates do not have (weight of corresponding respective code difference)
(2) setting of phenotype distance threshold
To the phenotypic character expression status code difference of pairs of soybean varieties, weight is assigned by the method for step (1), is owned
Weight addition on phenotypic character obtains two interracial phenotype distances；By the obvious journey of a large amount of phenotype distances and Differences
Degree is compared, and obtains relationship between the two, determines a phenotype distance threshold on this basis: when phenotype distance between kind
When greater than the threshold value, difference compares it is obvious that not needing field planting between kind；And by phenotype distance threshold input database
System.
The present invention after years of research and found that, suitable phenotype distance threshold be 2, i.e., when between kind phenotype distance >=2 when, product
Difference is apparent between kind；It is determined as approximate kind when two interracial phenotype distance < 2.
(3) approximate screening varieties
The approximate screening varieties software based on phenotype distance is developed in soybean varieties phenotype trait data library based on foundation.
The phenotype trait data of application kind to be measured is inputted into soybean varieties phenotype trait data library, which can be according to step
Suddenly the method for (2) calculates the phenotype distance of application kind to be measured and each known kind, and is compared with phenotype distance threshold
Compared with phenotype distance is less than approximate kind of the known kind of threshold value as the application kind to be measured.
Method of the invention is different with influence degree of the personnel to various trait expression status observation according to environmental condition
This feature gives a weight, the sum of all differences character weight is used as product between calculating kind to the difference of code kind
The phenotype distance of inter-species.By the comparative analysis between phenotype distance and difference obvious degree a large amount of true kinds, one is determined
The phenotype distance threshold of safety when guaranty inter-species phenotype distance is higher than the threshold value, certainly exists apparent difference between kind.
When carrying out approximate screening varieties, lower than the kind of the phenotype distance threshold as approximate kind.Method of the invention has following excellent
Point: (1) fault-tolerance is strong, and preciseness is good.Even if individual character expression status observations are (observed deviation, individual due to artificial origin
Error in data) or kind and environment interaction, deviate normal value, approximate kind, the i.e. fault-tolerance of this method will not be omitted more
By force, the preciseness of approximate screening varieties is improved.(2) with strong points.This method by Database Systems calculate application kind with
The phenotype distance of approximate kind, convenient for, by the plantation adjacent with application kind of closest kind, being improved special when field trial
Property test specific aim；(3) high-efficient.Using this method, do not need to be separately provided according to the expression status of application kind each
The expression status range of character shortens the approximate screening varieties time.
Detailed description of the invention
Fig. 1 is soybean application kind 2014-0698A (the approximate screening varieties result of peaceful 17) in mountain.
Specific embodiment
Embodiment 1: kind phenotype trait data library known to soybean is established
2004 to 2017, applicant carried out DUS survey to more than 700 part of Huang-Huai-Hai time soybean application kind altogether
Examination.Two growth cycles of each kind general test acquire number to all 31 characters in Testing Guideline in each period
According to acquisition phenotype trait data more than 20,000 is a altogether.To the phenotype trait data of acquisition, according to test character expression way, data class
Type is arranged.On this basis, Main Characters of Soybean Cultivars In The Huang-huai-hai Valley phenotype trait data library is established, stores 550 altogether
The DUS test character data of kind.The screening of this example approximation kind relies on above-mentioned phenotype trait data library to carry out.
According to the expression status observation of each phenotypic character by the degree and personnel's observed deviation of environmental influence
Size, to all expression status code differences of selected soybean phenotypic character, (such as code is 1-9, and difference 0,1,2 ... 8)
Different weights is set, and the weight of each character is arranged as shown in table 2.1-23.2, is further arranged based on the table
To one group of statistical data (table 1), above-mentioned phenotype trait data library is inputted.
1 hypocotyl of character: anthocyanin colour developing；
Trait expression mode: QL；
Stability grade: 1.
2.1 expression status of table and code
| Expression status | Nothing | Have |
| Code | 1 | 2 |
2.2 code difference of table and weight
| Code difference | 0 | 1 |
| Weight | 0 | 6 |
2 hypocotyl of character: anthocyanin colored intensity；
Trait expression mode: QN；
Stability grade: 4 (generally 4 codes of fluctuation).
3.1 expression status of table and code
3.2 code difference of table and weight
| Code difference | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
| Weight | 0 | 0 | 0 | 1 | 2 | 3 | 4 | 5 | 6 |
3 stem of character: fine hair color；
Trait expression mode: QL；
Stability grade: 2.
4.1 expression status of table and code
| Expression status | Grey | Brown |
| Code | 1 | 2 |
4.2 code difference of table and weight
| Code difference | 0 | 1 |
| Weight | 0 | 4 |
4 stem of character: density of pubescent；
Trait expression mode: QN；
Stability grade: 4 (generally 4 codes of fluctuation).
5.1 expression status of table and code
5.2 code difference of table and weight
| Code difference | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
| Weight | 0 | 0 | 0 | 1 | 2 | 3 | 4 | 5 | 6 |
5 compound leaf of character: leaflet shape；
Trait expression mode: PQ；
Stability grade: 2 (generally 2 codes of fluctuation).
6.1 expression status of table and code
| Expression status | Lanceolar | Triangle | Point is oval | Circle is oval |
| Code | 1 | 2 | 3 | 4 |
6.2 code difference of table and weight
| Code difference | 0 | 1 | 2 | 3 |
| Weight | 0 | 0 | 2 | 4 |
6 compound leaf of character: leaflet quantity；
Trait expression mode: QN；
Stability grade: 3 (generally 3 codes of fluctuation).
7.1 expression status of table and code
| Expression status | Three leaflets | Five leaflets | More leaflets |
| Code | 1 | 2 | 3 |
7.2 code difference of table and weight
| Code difference | 0 | 1 | 2 |
| Weight | 0 | 1 | 4 |
7 blade of character: green intensity；
Trait expression mode: QN；
Stability grade * *: 4 (generally 4 codes of fluctuation).
8.1 expression status of table and code
8.2 code difference of table and weight
| Code difference | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
| Weight | 0 | 0 | 0 | 1 | 2 | 3 | 4 | 5 | 6 |
8 florescence of character；
Trait expression mode: QN；
Stability grade: 4 (generally 3-4 codes of fluctuation).
9.1 expression status of table and code
9.2 code difference of table and weight
| Code difference | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
| Weight | 0 | 0 | 0 | 1 | 2 | 3 | 4 | 5 | 6 |
The flower of character 9: corolla color；
Trait expression mode: QL；
Stability grade: 1.
101 expression status of table and code
| Expression status | White | Purple |
| Code | 1 | 2 |
10.2 code difference of table and weight
| Code difference | 0 | 1 |
| Weight | 0 | 6 |
12 plant of character: height；
Trait expression mode: QN；
Stability grade: 3 (generally 3 codes of fluctuation).
11.1 expression status of table and code
11.2 code difference of table and weight
13 plant of character: pod bearing habit；
Trait expression mode: PQ；
Stability grade * *: 2.
12.1 expression status of table and code
| Expression status | It is limited | It is sub- limited | Infinitely |
| Code | 1 | 2 | 3 |
12.2 code difference of table and weight
| Code difference | 0 | 1 | 2 |
| Weight | 0 | 1 | 4 |
15 maturity period of character；
Trait expression mode: QN；
Stability grade: 3 (generally 3 codes of fluctuation).
13.1 expression status of table and code
13.2 code difference of table and weight
| Code difference | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
| Weight | 0 | 0 | 0 | 1 | 2 | 3 | 4 | 5 | 6 |
16 stem of character: joint number amount；
Trait expression mode: QN；
Stability grade: 3 (generally 3 codes of fluctuation).
14.1 expression status of table and code
14.2 code difference of table and weight
| Code difference | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
| Weight | 0 | 0 | 0 | 1 | 2 | 3 | 4 | 5 | 6 |
18 plant of character: fallen leaves property；
Trait expression mode: QN；
Stability grade: 2.
15.1 expression status of table and code
| Expression status | It does not fall leaves | Half falls leaves | Fallen leaves |
| Code | 1 | 2 | 3 |
15.2 code difference of table and weight
| Code difference | 0 | 1 | 2 |
| Weight | 0 | 0 | 2 |
19 plant of character: pod quantity；
Trait expression mode: QN；
Stability grade: 4 (can up to 4 codes).
16.1 expression status of table and code
16.2 code difference of table and weight
| Code difference | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
| Weight | 0 | 0 | 0 | 1 | 2 | 3 | 4 | 5 | 6 |
21 pod of character: bending degree；
Trait expression mode: PQ；
Stability grade: 2 (generally 2 codes of fluctuation).
17.1 expression status of table and code
17.2 code difference of table and weight
| Code difference | 0 | 1 | 2 | 3 |
| Weight | 0 | 0 | 2 | 4 |
23 pod of character: color；
Trait expression mode: PQ；
Stability grade: 2 (generally 2 codes of fluctuation).
18.1 expression status of table and code
18.2 code difference of table and weight
| Code difference | 0 | 1 | 2 | 3 | 4 |
| Weight | 0 | 0 | 1 | 2 | 3 |
Character 24:* 100-grain weight；
Trait expression mode: QN；
Stability grade: 3 (generally 3 codes of fluctuation).
19.1 expression status of table and code
19.2 code difference of table and weight
| Code difference | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
| Weight | 0 | 0 | 0 | 1 | 2 | 3 | 4 | 5 | 6 |
Character 26: seed: kernel seed coat colour quantity；
Trait expression mode: QL；
Stability grade: 1.
20.1 expression status of table and code
| Expression status | It is monochromatic | It is double-colored |
| Code | 1 | 2 |
20.2 code difference of table and weight
| Code difference | 0 | 1 |
| Weight | 0 | 6 |
Character 27: it is only applicable to monochromatic kind of skin kind: seed: kernel seed coat colour；
Trait expression mode: PQ；
Stability grade: 2 (generally 2 codes of fluctuation).
21.1 expression status of table and code
21.2 code difference of table and weight
| Code difference | 0 | 1 | 2 | 3 | 4 | 5 | 6 |
| Weight | 0 | 0 | 4 | 4 | 4 | 4 | 4 |
Character 29:* is only applicable to monochromatic kind of skin kind: seed: Cotyledon color；
Trait expression mode: PQ；
Stability grade: 2 (generally 2 codes of fluctuation).
22.1 expression status of table and code
| Expression status | Yellow | Yellow green | Green |
| Code | 1 | 2 | 3 |
22.2 code difference of table and weight
| Code difference | 0 | 1 | 2 |
| Weight | 0 | 0 | 4 |
Character 30: hilum: color；
Trait expression mode: PQ；
Stability grade: 2 (generally 2 codes of fluctuation).
23.1 expression status of table and code
23.2 code difference of table and weight
| Code difference | 0 | 1 | 2 | 3 | 4 | 5 |
| Weight | 0 | 0 | 1 | 4 | 4 | 4 |
Embodiment 2: the screening of soybean approximation kind
1. soybean application kind totally 6, code name and title are as shown in table 24；
2. application kind is passed through each trait data (table 25) of the acquisition of plantation in 1 year, importing soybean phenotype trait data library；
3. setting 2 for safe distance, " screening " button is clicked, Database Systems list the close of corresponding each application kind
Like kind inventory (table 26, Fig. 1).Table 26 only lists the approximate kind that phenotype distance is 0.
Table 24 applies for kind
Table 25 applies for the data (expression status of each character, i.e. code value) of kind First Year acquisition
The part approximation kind that table 36 filters out
This method automatically can be lined up selected approximate kind according to phenotype by Database Systems apart from size, be convenient for
By the plantation adjacent with application kind of closest kind when field trial, the specific aim of specific test is improved.
Claims (2)
1. a kind of soybean specific test approximation method for screening varieties based on phenotype distance, characterized in that
(1) weight of each soybean phenotypic character code difference is set
The phenotype trait data of the current still soybean varieties with application potential of acquisition, carries out the phenotype trait data of acquisition whole
Reason, establishes soybean varieties phenotype trait data library；According to the expression status observation of each phenotypic character by environmental influence
Degree and personnel's observed deviation size, different power is arranged to the code difference of the expression status of each soybean phenotypic character
Weight obtains data as shown in Table 1, inputs soybean varieties phenotype trait data library；
Table 1
(2) setting of phenotype distance threshold
To the phenotypic character expression status code difference of pairs of soybean varieties, weight, all phenotypes are assigned by the method for step (1)
Weight addition in character obtains two interracial phenotype distances；By the obvious degree of a large amount of phenotype distances and Differences into
Row compares, and determines a phenotype distance threshold: and it is inputted soybean varieties phenotype trait data library；
(3) approximate screening varieties
The approximate screening varieties software based on phenotype distance is developed in soybean varieties phenotype trait data library based on foundation；Xiang great
The phenotype trait data of application kind to be measured is inputted in beans kind phenotype trait data library, which can be according to step
(2) method calculates the phenotype distance of application kind to be measured and each known kind, and is compared with phenotype distance threshold,
Phenotype distance is less than approximate kind of the known kind of threshold value as the application kind to be measured.
2. a kind of soybean specific test approximation method for screening varieties based on phenotype distance as described in claim 1, special
Sign is, step (2) the phenotype distance threshold is 2, and whens two interracial phenotypes distance < 2 is determined as approximate kind.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| WO2014003530A4 (en) * | 2012-06-28 | 2014-03-13 | Moroccan Foundation For Advanced Science, Innovation & Research (Mascir) | Method for increasing the potential for biofuel production from microalgae by using bio-modulators |
| CN105132551A (en) * | 2015-09-01 | 2015-12-09 | 山东省农业科学院作物研究所 | Method for screening wheat approximate variety by virtue of SSR molecular marker |
| CN105740655A (en) * | 2016-01-29 | 2016-07-06 | 山东省农业科学院作物研究所 | Approximate variety screening method in soybean specificity test on the basis of phenotypic character |
| CN108796121A (en) * | 2018-07-04 | 2018-11-13 | 山东省农业科学院作物研究所 | A kind of specific test approximation kind high-efficiency screening method based on genetic similarty |
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2018
- 2018-12-03 CN CN201811462618.4A patent/CN109616159A/en not_active Withdrawn
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014003530A4 (en) * | 2012-06-28 | 2014-03-13 | Moroccan Foundation For Advanced Science, Innovation & Research (Mascir) | Method for increasing the potential for biofuel production from microalgae by using bio-modulators |
| CN105132551A (en) * | 2015-09-01 | 2015-12-09 | 山东省农业科学院作物研究所 | Method for screening wheat approximate variety by virtue of SSR molecular marker |
| CN105740655A (en) * | 2016-01-29 | 2016-07-06 | 山东省农业科学院作物研究所 | Approximate variety screening method in soybean specificity test on the basis of phenotypic character |
| CN108796121A (en) * | 2018-07-04 | 2018-11-13 | 山东省农业科学院作物研究所 | A kind of specific test approximation kind high-efficiency screening method based on genetic similarty |
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| Title |
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| GENEVA: "TRIAL DESIGN AND TECHNIQUES USED IN THE EXAMINATION OF DISTINCTNESS, UNIFORMITY AND STABILITY", 《INTERNATIONAL UNION FOR THE PROTECTION OF NEW VARIETIES OF PLANTS》 * |
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Application publication date: 20190412 |