CN108564200A - A kind of soil fertility prediction technique building geographical MDS minimum data set based on yield - Google Patents
A kind of soil fertility prediction technique building geographical MDS minimum data set based on yield Download PDFInfo
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- 230000035558 fertility Effects 0.000 title claims abstract description 118
- 239000002689 soil Substances 0.000 title claims abstract description 85
- 238000000034 method Methods 0.000 title claims abstract description 23
- 238000011068 loading method Methods 0.000 claims abstract description 47
- 241001122767 Theaceae Species 0.000 claims abstract description 30
- 238000000513 principal component analysis Methods 0.000 claims abstract description 13
- 238000004458 analytical method Methods 0.000 claims abstract description 8
- 238000012545 processing Methods 0.000 claims abstract description 3
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 claims description 36
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 claims description 22
- 229910052757 nitrogen Inorganic materials 0.000 claims description 18
- OAICVXFJPJFONN-UHFFFAOYSA-N Phosphorus Chemical compound [P] OAICVXFJPJFONN-UHFFFAOYSA-N 0.000 claims description 15
- 239000011574 phosphorus Substances 0.000 claims description 15
- 229910052698 phosphorus Inorganic materials 0.000 claims description 15
- OYPRJOBELJOOCE-UHFFFAOYSA-N Calcium Chemical compound [Ca] OYPRJOBELJOOCE-UHFFFAOYSA-N 0.000 claims description 14
- 239000011575 calcium Substances 0.000 claims description 14
- 229910052791 calcium Inorganic materials 0.000 claims description 14
- 238000005070 sampling Methods 0.000 claims description 14
- 238000010219 correlation analysis Methods 0.000 claims description 11
- 229910052742 iron Inorganic materials 0.000 claims description 11
- FYYHWMGAXLPEAU-UHFFFAOYSA-N Magnesium Chemical compound [Mg] FYYHWMGAXLPEAU-UHFFFAOYSA-N 0.000 claims description 10
- 239000002131 composite material Substances 0.000 claims description 10
- 239000011777 magnesium Substances 0.000 claims description 10
- 229910052749 magnesium Inorganic materials 0.000 claims description 10
- ZLMJMSJWJFRBEC-UHFFFAOYSA-N Potassium Chemical compound [K] ZLMJMSJWJFRBEC-UHFFFAOYSA-N 0.000 claims description 9
- 239000011591 potassium Substances 0.000 claims description 9
- 229910052700 potassium Inorganic materials 0.000 claims description 9
- 239000005416 organic matter Substances 0.000 claims description 8
- 239000011573 trace mineral Substances 0.000 claims description 7
- 235000013619 trace mineral Nutrition 0.000 claims description 7
- 238000013523 data management Methods 0.000 claims description 4
- 239000000284 extract Substances 0.000 claims description 4
- HCHKCACWOHOZIP-UHFFFAOYSA-N Zinc Chemical compound [Zn] HCHKCACWOHOZIP-UHFFFAOYSA-N 0.000 claims description 3
- 238000001514 detection method Methods 0.000 claims description 3
- 239000003337 fertilizer Substances 0.000 claims description 3
- WPBNNNQJVZRUHP-UHFFFAOYSA-L manganese(2+);methyl n-[[2-(methoxycarbonylcarbamothioylamino)phenyl]carbamothioyl]carbamate;n-[2-(sulfidocarbothioylamino)ethyl]carbamodithioate Chemical compound [Mn+2].[S-]C(=S)NCCNC([S-])=S.COC(=O)NC(=S)NC1=CC=CC=C1NC(=S)NC(=O)OC WPBNNNQJVZRUHP-UHFFFAOYSA-L 0.000 claims description 3
- 239000011701 zinc Substances 0.000 claims description 3
- 229910052725 zinc Inorganic materials 0.000 claims description 3
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 claims description 2
- 229910052802 copper Inorganic materials 0.000 claims description 2
- 239000010949 copper Substances 0.000 claims description 2
- 238000007405 data analysis Methods 0.000 claims description 2
- 238000001179 sorption measurement Methods 0.000 abstract description 2
- 239000003921 oil Substances 0.000 description 32
- 235000019198 oils Nutrition 0.000 description 32
- 238000011156 evaluation Methods 0.000 description 13
- 238000004519 manufacturing process Methods 0.000 description 9
- 235000015097 nutrients Nutrition 0.000 description 8
- 230000012010 growth Effects 0.000 description 6
- 239000010495 camellia oil Substances 0.000 description 5
- 238000012360 testing method Methods 0.000 description 5
- 241000526900 Camellia oleifera Species 0.000 description 4
- 230000002146 bilateral effect Effects 0.000 description 4
- 230000002596 correlated effect Effects 0.000 description 4
- 238000011161 development Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 4
- 235000018597 common camellia Nutrition 0.000 description 3
- 239000004615 ingredient Substances 0.000 description 3
- 230000001737 promoting effect Effects 0.000 description 3
- 240000001548 Camellia japonica Species 0.000 description 2
- 241000196324 Embryophyta Species 0.000 description 2
- 244000269722 Thea sinensis Species 0.000 description 2
- 235000006468 Thea sinensis Nutrition 0.000 description 2
- OWXLRKWPEIAGAT-UHFFFAOYSA-N [Mg].[Cu] Chemical compound [Mg].[Cu] OWXLRKWPEIAGAT-UHFFFAOYSA-N 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 238000013507 mapping Methods 0.000 description 2
- 230000008635 plant growth Effects 0.000 description 2
- WRIDQFICGBMAFQ-UHFFFAOYSA-N (E)-8-Octadecenoic acid Natural products CCCCCCCCCC=CCCCCCCC(O)=O WRIDQFICGBMAFQ-UHFFFAOYSA-N 0.000 description 1
- LQJBNNIYVWPHFW-UHFFFAOYSA-N 20:1omega9c fatty acid Natural products CCCCCCCCCCC=CCCCCCCCC(O)=O LQJBNNIYVWPHFW-UHFFFAOYSA-N 0.000 description 1
- QSBYPNXLFMSGKH-UHFFFAOYSA-N 9-Heptadecensaeure Natural products CCCCCCCC=CCCCCCCCC(O)=O QSBYPNXLFMSGKH-UHFFFAOYSA-N 0.000 description 1
- 241000209507 Camellia Species 0.000 description 1
- OYHQOLUKZRVURQ-HZJYTTRNSA-N Linoleic acid Chemical compound CCCCC\C=C/C\C=C/CCCCCCCC(O)=O OYHQOLUKZRVURQ-HZJYTTRNSA-N 0.000 description 1
- PWHULOQIROXLJO-UHFFFAOYSA-N Manganese Chemical compound [Mn] PWHULOQIROXLJO-UHFFFAOYSA-N 0.000 description 1
- ZQPPMHVWECSIRJ-UHFFFAOYSA-N Oleic acid Natural products CCCCCCCCC=CCCCCCCCC(O)=O ZQPPMHVWECSIRJ-UHFFFAOYSA-N 0.000 description 1
- 239000005642 Oleic acid Substances 0.000 description 1
- 239000002253 acid Substances 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- ZFXVRMSLJDYJCH-UHFFFAOYSA-N calcium magnesium Chemical compound [Mg].[Ca] ZFXVRMSLJDYJCH-UHFFFAOYSA-N 0.000 description 1
- 230000000875 corresponding effect Effects 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 239000008157 edible vegetable oil Substances 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- QXJSBBXBKPUZAA-UHFFFAOYSA-N isooleic acid Natural products CCCCCCCC=CCCCCCCCCC(O)=O QXJSBBXBKPUZAA-UHFFFAOYSA-N 0.000 description 1
- 235000020778 linoleic acid Nutrition 0.000 description 1
- OYHQOLUKZRVURQ-IXWMQOLASA-N linoleic acid Natural products CCCCC\C=C/C\C=C\CCCCCCCC(O)=O OYHQOLUKZRVURQ-IXWMQOLASA-N 0.000 description 1
- 229910052748 manganese Inorganic materials 0.000 description 1
- 239000011572 manganese Substances 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- ZQPPMHVWECSIRJ-KTKRTIGZSA-N oleic acid Chemical compound CCCCCCCC\C=C/CCCCCCCC(O)=O ZQPPMHVWECSIRJ-KTKRTIGZSA-N 0.000 description 1
- 238000001303 quality assessment method Methods 0.000 description 1
- 239000002881 soil fertilizer Substances 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 235000021122 unsaturated fatty acids Nutrition 0.000 description 1
- 150000004670 unsaturated fatty acids Chemical class 0.000 description 1
- 235000015112 vegetable and seed oil Nutrition 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
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Abstract
The invention belongs to Soil K+adsorption technical fields, and in particular to a kind of soil fertility prediction technique being built geographical MDS minimum data set based on yield is included the following steps：(1) pedotheque is acquired；(2) every fertility index content of sample is detected；(3) analysis that Nei Meiluo individual event fertility indexes and comprehensive fertility index are carried out to the surveyed index content of step (2) calculates；(4) principal component analysis is carried out using SPSS data processing softwares to the surveyed index content of step (2), high factor loading index in Selective principal component analysis, into MDS minimum data set；(5) calculating is standardized to the data set of step (4)；(6) MDS minimum data set after being standardized to step (5) calculates MDS minimum data set fertility index.By MDS minimum data set fertility index and correlation with yield analysis result, soil fertility can be predicted in this fertility index；Soil fertility prediction technique of the present invention can be unified to evaluate for the oil tea soil fertility that spatial diversity is larger, applied widely.
Description
Technical field
The invention belongs to Soil K+adsorption technical fields, and in particular to a kind of soil building geographical MDS minimum data set based on yield
Earth fertility prediction technique.
Background technology
Oil tea (Camellia oleifera Abel.), Theaceae oil tea category evergreen dungarunga, seed oil rich in oleic acid,
The unsaturated fatty acids such as linoleic acid are the important woody edible oil seeds in south.The existing 200,000 hectares of camellia oleifera lams in Zhejiang Province, position
Occupy the whole nation the 4th, wherein 70% be distributed in Lishui and Quzhou area, be hill farmer increase wealth valuable source advantage and
Where potentiality.Currently, the old camellia oleifera lam that the camellia oleifera lam in 60% or more Zhejiang Province is extensive management, low yield is inefficient, such standing forest are deposited
Growth status slowly, bad phenomenons, the serious raising for restricting tea-oil tree yield and quality such as hip number declines, biennial bearing is apparent.
《National camellia oleiferaindustry development plan (2009-2020)》It is to improve tea-oil tree yield in a short time to have an efficacious prescriptions to specify low production forest improvement
Formula, and propose that Zhejiang Province low production forest is transformed plan.In recent years Zhejiang Province's Transformation of Oiltea Camellia Low-yield Forest every year with nearly ten thousand hectares gradually
It promotes.As the important measures of low production forest improvement, accurate water and fertilizer management increasingly draws attention.But oil tea fertilising at this stage exists
Ignore oil tea land occupation condition to the demand difference of fertility, ignores the effect of trace element, oil tea fertility evaluation is immature etc.
Problem, there is an urgent need to establish the fertility evaluation system of suitable oil tea.
The foundation of soil fertility MDS minimum data set is fertility evaluation and determines the basis of the crucial fertilising factor and important
Link.There are commonly Principal Component Analysis, correlation analysises for the construction of MDS minimum data set, also have many scholars to use norm values
Extract index.Selective principal component analysis of the present invention is main approaches, to establish Zhejiang Province's oil tea soil fertility minimum data
Collection, oil tea main producing region soil fertility is evaluated, and using Nemerow Index method combination sample yield to MDS minimum data set into
Row verification, result of study to promoting Zhejiang Province's oil tea plant production development, improvement, blindly with important guiding anticipate by fertilising, environmental protection
Justice.
Soil fertility is the ability of nutrient necessary to soil supply plant, and related with indigenous nutrient supply capacity various
Soil property and state.Fertility evaluation is then to select most reflect the index of soil quality situation and production capacity and to soil
Loamy texture amount makes judge, therefore the structure of MDS minimum data set is the key link of fertility evaluation.Have not yet to see document pair
Oil tea soil fertility carries out the relevant reports such as MDS minimum data set structure.
Invention content
It is an object of the invention to overcome the deficiencies of the prior art and provide one kind building geographical MDS minimum data set based on yield
Soil fertility prediction technique, in the case that various nutrient component differences are larger in the soil, which can be suitably used for respectively
The fertility index evaluation of the soil of kind of property and state, and can accurate judgement oil tea soil productivity the case where.
The technical solution adopted by the present invention to solve the technical problems is：
A kind of soil fertility prediction technique being built geographical MDS minimum data set based on yield, is included the following steps：
(1) it samples：Sampling position is set according to yield, acquires oil tea pedotheque；
(2) it detects：Every fertility index content of the oil tea pedotheque of detecting step (1) acquisition；
(3) full dose data set Data Management Analysis：
Nei Meiluo individual event fertility index assessments：Pi=Ci/Si
Pi：The individual event fertility index of certain index i in soil；
Ci：The measured data of certain index i in soil；
Si：The standard value of certain index i in soil.
Nei Meiluo comprehensive fertility index assessments
P is comprehensive：Soil fertility composite index；(Ci/Si)2min：Individual event fertility Index Min square (Ci/Si)2ave：Soil
The mean square of all fertility indexes in earth；n：Soil fertility index number (4) MDS minimum data set Data Management Analysis：
S1：Principal component analysis is carried out using SPSS data processing softwares to the surveyed index content of step (2), according to characteristic value
More than 1 and contribution rate of accumulative total is more than 70% principle, extracts several principal components；
S2：By certain standard, the high factor loading of several principal components obtained by selecting step S1；
S3：By certain standard, the high factor loading of several principal components obtained by selecting step S2 enters MDS minimum data set：
(5) MDS minimum data set fertility index standardizes：
Since the soil organism, full nitrogen, alkali-hydrolyzable nitrogen etc. are generally S-shaped with plant growth effect curve, it is subordinate to using S types
Membership fuction carries out the standardization of fertility index.
Wherein x1 is the low critical value of fertility index；X2 is the high critical value of fertility index；X is the measurement of fertility index
Value.
(6) foundation of MDS minimum data set fertility index
Communality is acquired using Principal Component Analysis, determines therefrom that weight coefficient, soil is calculated according to following formula
Fertility index NFI.
Wi：Fertility index weight coefficient；Fi：Fertility index standard value
(7) MDS minimum data set fertility index and correlation with yield
MDS minimum data set fertility index and full dose data set composite index and yield are done into correlation data analysis respectively.
Preferably, in the step (1), the method for sampling is：Sample it is divided into upper, middle and lower slope, using S-shaped sampling method, adopts
Collect 0~40cm layers of soil, wherein each gradient randomly selects 15~20 sampling points, 100g is no less than per sampling point example weight.
Preferably, the sample of each gradient takes quartering to leave and take 1kg after mixing well, it is packed into polybag, carries out volume
Number, sample message is registered, laboratory is taken back and air-dries.
It is fertility index pH, full nitrogen, full phosphorus, organic matter, fast nitrogen, available phosphorus, quick-acting preferably, in the step (2)
Potassium, the soil texture, effective trace elements iron, copper, zinc, manganese, exchangeable calcium, exchangeable magnesium.
Preferably, in the step (4), the selection principle of high factor loading is in principal component：The high factor loading
Absolute value is more than 90% of maximum factor loading in the principal component.
Preferably, in the step (4), the high factor loading for choosing principal component enters the standard of MDS minimum data set and is：
(a) when a principal component high factor loading index only there are one when, then the index enters MDS minimum data set；
(b) when the high factor loading index of a principal component is two, correlation analysis is done to two indices, if phase relation
When number absolute value r ＜ 0.4, then two indices all enter MDS minimum data set；If related coefficient absolute value r >=0.4, factor loading
High index enters data set；
(c) when the high factor loading index of a principal component is three or three or more, correlation analysis is done respectively two-by-two,
(1) all indexs two-by-two related coefficient absolute value r ＜ 0.4 when, then all indexs all enter MDS minimum data set；(2) all indexs
When two-by-two the case where related coefficient absolute value r presence >=0.4：If the index quantity of related coefficient absolute value r >=0.4 accounts for always two-by-two
70% or more of index quantity then enters data with the maximum high factor loading index of the sum of remaining index related coefficient absolute value
Collection；Otherwise all indexs enter data set.
The advantageous effect that the present invention obtains is：
(a) Selective principal component analysis organon of the present invention determines oil tea soil fertility MDS minimum data set, to oil tea soil fertilizer
Power is evaluated, and using Nemerow Index method combination sample yield verifies MDS minimum data set；The present invention based on it is geographical most
The prediction technique of the soil fertility of small data set structure, can reflect the relationship between tea-oil tree yield and soil fertility well, this
Result of study is to promoting oil tea plant production development, improving blindly fertilising, environmental protection with great importance；
(b) the soil fertility prediction technique that geographical MDS minimum data set is built based on yield of the invention, to the fertility of soil
Metrics evaluation is more comprehensive, includes that the detection to trace element is evaluated, can largely reflect that tea-oil tree yield and soil are each
Relationship between kind fertility index, and for the larger oil tea soil fertility of spatial diversity, can unify to evaluate, the scope of application
Extensively.
Description of the drawings
The Zhejiang Province Fig. 1 oil tea soil Nei Meiluo composite indexes, MDS minimum data set fertility index and Yield mapping.
In figure：a：MDS minimum data set fertility index；b：Nei Meiluo composite indexes c：Yield.
Specific implementation mode
The present invention and its specific implementation mode are described in further detail with reference to embodiment, but are not intended to limit
The scope of the present invention.
Embodiment 1：
Oil tea pedotheque is taken within the scope of Zhejiang Province, according to sample and sample ground where county average product, according to sample
Oil yield is more than 150kgha in ground-1, 75-150kgha-1, it is less than 75kgha-1Three ranks, to soil test result
It is divided.
When sampling, sample it is divided into upper, middle and lower slope, using S-shaped sampling method, 0~40cm layers of soil is acquired, wherein each slope
Degree randomly selects 15~20 sampling points, is no less than 100g per sampling point example weight, is adopted after the sample of each gradient is mixed well
It takes quartering to leave and take 1kg, is packed into polybag, carry out number, register sample message, take back after laboratory air-dries, detect therein
Various fertility index contents.It detects specific division result and the soil fertility index content in each county, detection level is shown in Table 1, table 2
With table 3.
1 sample of table yield be more than 150kgha-1Soil test result
2 sample of table ground yield 75-150kgha-1Soil test result
3 sample of table yield be less than 75kgha-1Soil test result
By in table 2, sample oil yield be in 75-150kgha-1The soil fertility testing result average value conduct in each county
Fertility index standard value Si (being shown in Table 4).With southern area arable soil fertility index Si specified in NY/T 1749-20091Phase
Than relative standard deviation is effective iron, effective manganese, full nitrogen, fast nitrogen, fast potassium less than 10%.Deviation it is larger for fast phosphorus, it is complete
Phosphorus, Available cupper and organic matter, remaining belongs to medium deviation, this easily lacks characteristic with oil tea characteristic of acid red soil phosphorus, organic matter and is consistent,
Also illustrate that these indexs may be the restriction factor of oil tea production simultaneously.
The Si standard values of 4 each index of table
Soil fertility index in table 1, table 2 and table 3 is evaluated, the Nei Meiluo individual event fertility for calculating each area refers to
Number PiWith Nei Meiluo comprehensive fertility indices PsIt is comprehensive, calculating formula is such as shown in (I) and (II)：
Nei Meiluo individual event fertility index assessments：Pi=Ci/Si(I)
In formula (I), Pi：The individual event fertility index of certain index i in soil；Ci：The measured data of certain index i in soil；Si：
The standard value of certain index i in soil；
Comprehensive fertility evaluation number：
In formula (II), (Ci/Si)2min：Individual event fertility Index Min square；(Ci/Si)2ave：All fertility in soil
The mean square of index；n：Soil fertility index number；
The Nei Meiluo individual event fertility indices Ps in each areaiWith Nei Meiluo comprehensive fertility indices PsIt is comprehensiveResult of calculation such as 5 institute of table
Show；
5 each county's soil assessment result of table
For the Ci/Si indexs of the PH in each area, when PH≤5, Ci/Si=1；PH ranges are in 5.0-5.5, Ci/Si
=1.5；
To in table 5, Nei Meiluo composite indexes are analyzed with correlation with yield, as a result show that the two related coefficient is
0.348, the two correlation is poor, and reason may be that part fertility index has no directly related property with yield, therefore Nei Meiluo is comprehensive
Hop index can not evaluate the fertility index of soil oil production, and structure can reflect the MDS minimum data set of oil tea output level
The promotion of fertility exponent pair camellia oleiferaindustry is of great significance.
Embodiment 2：
MDS minimum data set fertility index construction：
Principal component analysis is carried out using SPSS softwares to surveyed index content in the table 1, table 2 and table 3 of embodiment 1, as a result
As shown in table 6；
The characteristic value and contribution rate of 6 each factor principal component of table
According to table 6,1 and principle of the contribution rate of accumulative total more than 70% are more than according to characteristic value, extract 5 principal components；According to
Principal component calculation formula, the results are shown in Table 7 for principal component analysis；
Table 7 rotates component matrix
Predominantly exchangeable calcium, magnesium, fast potassium and the pH contents for determining 1 size of principal component as shown in Table 7, can be classified as exchange
Property nutrient.Determine 2 size of principal component mainly has full nitrogen, organic matter and full phosphorus, can be classified as full effect nutrient.Determine principal component 3
Predominantly Available cupper, zinc, the manganese of size, can be classified as trace element.Predominantly fast nitrogen, the fast phosphorus for determining principal component 4, can sort out
For available nutrient.Determine predominantly effective iron of principal component 5.It can be seen that 5 principal components of Principal Component Analysis extraction have agronomy
Most number variation of full dose can be explained in meaning.
The high factor loading of above-mentioned 5 principal components is chosen, the principle chosen is：The absolute value of the high factor loading is more than
The 90% of maximum factor loading in the principal component.
Maximum factor loading is exchangeable calcium 0.879 in principal component 1 as shown in Table 7, and load absolute value is more than the maximum factor
The index of load 90% is exchangeable calcium, magnesium, pH.In view of the important indicator that fast potassium is soil nutrient, factor loading is smaller
In the 90% of maximum factor loading, to ensure the comprehensive and accuracy of evaluation, fast potassium is also included in high factor loading, i.e. principal component
1 high factor loading exchangeable calcium, magnesium, pH and fast potassium.The high factor loading of principal component 2 be full nitrogen, organic matter, principal component 3
High factor loading is Available cupper, and the high factor loading of principal component 4 is fast phosphorus, and the high factor loading of principal component 5 is effective iron.Because of master
Ingredient 1 and principal component 2 have multiple high factor loadings, divide respectively high factor loading correlation in principal component 1 and principal component 2
Analysis, the results are shown in Table 8.
8 high factor loading correlation of table
In table 8, * * indicate that significantly correlated * indicates the notable phase on 0.05 horizontal (bilateral) on 0.01 horizontal (bilateral)
It closes.
The high factor loading for choosing principal component enters the standard of MDS minimum data set and is：
(a) when a principal component high factor loading index only there are one when, then the index enters MDS minimum data set；
(b) when the high factor loading index of a principal component is two, correlation analysis is done to two indices, if phase relation
When number absolute value r ＜ 0.4, then two indices all enter MDS minimum data set；If related coefficient absolute value r >=0.4, factor loading
High index enters data set；
(c) when the high factor loading index of a principal component is three or three or more, correlation analysis is done respectively two-by-two,
(1) all indexs two-by-two related coefficient absolute value r ＜ 0.4 when, then all indexs all enter MDS minimum data set；(2) all indexs
When two-by-two the case where related coefficient absolute value r presence >=0.4：If the index quantity of related coefficient absolute value r >=0.4 accounts for always two-by-two
70% or more of index quantity then enters data with the maximum high factor loading index of the sum of remaining index related coefficient absolute value
Collection；Otherwise all indexs enter data set.
Show that exchangeable calcium and exchangeable magnesium and the related coefficient of other indexs are exhausted in principal component 1 by 8 correlation analysis of table
To value both greater than 0.4, the sum of exchangeable magnesium and remaining index related coefficient absolute value are 2.228, and numerical value is maximum, is selected into minimum number
According to collection.But the sum of exchangeable calcium and the related coefficient absolute value of remaining index are 2.178, and difference of them is especially small, and due to handing over
Transsexual calcium, magnesium are all the crucial fertility indexs of comparison to Zhejiang oil tea soil, therefore exchangeable calcium is also selected into MDS minimum data set.It is main
High factor loading has full nitrogen and organic matter in ingredient 2；Show that the two related coefficient is 0.926 by 8 correlation analysis of table, it is extremely aobvious
Correlation is write, and the factor loading of full nitrogen is more than organic matter, therefore full nitrogen is selected in MDS minimum data set.Principal component 3, principal component 4 and master
Only there are one high factor loadings for ingredient 5, therefore are selected in Available cupper, fast phosphorus and effective iron into MDS minimum data set.Thus Zhejiang is obtained
Province's oil tea fertility evaluation MDS minimum data set is 6 full nitrogen, fast phosphorus, exchangeable calcium, exchangeable magnesium, Available cupper, effective iron fingers
Mark.
Data set does not include fast potassium, and it is related that influence that may be with the potassium of document report to yield is not up to the level of signifiance.
In recent years the application of a great number of elements such as nitrogen, phosphorus is commonly available attention in low production forest improvement.And the application of middle trace element is not yet
Cause enough attention.Calcium and magnesium copper and iron is the necessary nutrient of growth and development of plants, these elements of calcium and magnesium copper and iron are to promoting oil tea
Growth plays an important roll, therefore the MDS minimum data set for containing a great number of elements, moderate-element and trace element is to meet oil tea
Growth characteristics.
The MDS minimum data set of the present embodiment structure is full nitrogen, fast phosphorus, exchangeable calcium, exchangeable magnesium, Available cupper, effective iron.
Embodiment 3：
The foundation of MDS minimum data set fertility index：
The standardization of soil fertility index is the important link of soil quality assessment, total soil nitrogen, fast phosphorus, exchangeable calcium
Magnesium, Available cupper and effective iron and plant growth parabolically shape curved line relation, i.e., there are one most suitable to crop growth for index
Suitable growth scope determines that critical value can be converted into corresponding curve in polygronal function, Chinese document：Soil Quality Indexes
This is described with evaluation (Xu Jianming, Zhang Ganlin thank positive seedling etc., Science Press, 2010).The present invention is according to document：Zhejiang
The data and this data of forestry soil (Ye Zhongjie, Chai Xizhou, Zhejiang science tech publishing house, 1986) are drafted critical
Value, the results are shown in Table 9 for critical value；
9 oil tea soil membership function critical value of table
According to table 9, the MDS minimum data set index that each department are collected by embodiment 2 is standardized, standardization formula is such as
Under：
MDS minimum data set after standardization, by the method for principal component analysis in embodiment 2, obtain each index it is public because
Sub- variance accounts for population variance ratio using communality and obtains each index weights value coefficient, and then calculates the fertilizer of MDS minimum data set
Power index, calculating formula are as follows：
In above-mentioned formula, Wi：Fertility index weight coefficient；Fi：Fertility index standard value.
The concrete numerical value of the NFI in each county of Zhejiang Province that the present embodiment is calculated：
The NFI values in each county of 10 Zhejiang Province of table
Embodiment 4：
The fertility index NFI that the MDS minimum data set of Zhejiang Province each department is calculated by embodiment 3, in the table 5 of embodiment 1
The oil yield and NeiMeiLuo Index evaluation number of each department and embodiment 3 calculated NFI mappings, the results are shown in Figure 1；
As shown in Figure 1, Nei Meiluo composite indexes are consistent on MDS minimum data set fertility index and yield general trend,
Correlation analysis is carried out to three, analysis result is as shown in table 10；
The relative coefficient of 10 Nei Meiluo composite indexes of table, MDS minimum data set fertility index and yield
In table 10, * * significantly correlated * on 0.01 horizontal (bilateral) are significantly correlated on 0.05 flat (bilateral).
As shown in Table 10, MDS minimum data set fertility index is in significantly correlated with yield, with Nei Meiluo composite indexes in extremely aobvious
Positive correlation is write, explanation can evaluate oil tea soil fertility with MDS minimum data set fertility index, and the present invention solves complicated soil
The more difficult problem of evaluation of fertility quality.The MDS minimum data set fertility index of the soil of each department is calculated as a result, you can prediction
The tea-oil tree yield of this area.
Above-mentioned embodiment is only a preferred solution of the present invention, not the present invention is made in any form
Limitation, for the those familiar of this field, can readily realize other modification, without prejudice to claim and equivalency range
Defined by the case of universal, the present invention is not limited to specific details.
Claims (6)
1. a kind of soil fertility prediction technique building geographical MDS minimum data set based on yield, which is characterized in that including following step
Suddenly：
(1) it samples：According to sample yield sets sampling position, acquires oil tea pedotheque；
(2) it detects：Every fertility index content of the oil tea pedotheque of detecting step (1) acquisition；
(3) to the numerical value of step (2) detection, full dose data set Data Management Analysis is carried out：
Nei Meiluo individual event fertility index assessments：Pi=Ci/Si
Pi：The individual event fertility index of certain index i in soil；
Ci：The measured data of certain index i in soil；
Si：The standard value of certain index i in soil.
Nei Meiluo comprehensive fertility index assessments：
P is comprehensive：Soil fertility composite index； (Ci/Si)2min：Individual event fertility Index Min square
(Ci/Si)2ave：The mean square of all fertility indexes in soil；n：Soil fertility index number
(4) MDS minimum data set Data Management Analysis：
S1：Principal component analysis is carried out using SPSS data processing softwares to the surveyed index content of step (2), is more than according to characteristic value
1 and contribution rate of accumulative total be more than 70% principle, extract several principal components；
S2：By certain standard, the high factor loading of several principal components is chosen；
S3：By certain standard, the high factor loading for choosing above-mentioned several principal components enters MDS minimum data set；
(5) MDS minimum data set fertility index standardizes：
MDS minimum data set obtained by step (4) carries out the standardization of fertility index using S π membership functions.
Wherein x1 is the low critical value of fertility index；X2 is the high critical value of fertility index；X is the measured value of fertility index.
(6) foundation of MDS minimum data set fertility index
MDS minimum data set after step (5) standardization, acquires communality using Principal Component Analysis, determines therefrom that weight system
Number calculates soil index of fertilizer NFI according to following formula.
Wi：Fertility index weight coefficient；Fi：Fertility index standard value
(7) MDS minimum data set fertility index does correlation analysis with yield.
MDS minimum data set fertility index and full dose data set composite index and yield are done into correlation data analysis respectively, as a result most
Small data set fertility index and correlation with yield are preferable, and the MDS minimum data set fertility index can predict soil fertility.
2. a kind of prediction technique of soil fertility building geographical MDS minimum data set based on yield according to claim 1,
It is characterized in that, in the step (1), the method for sampling is：Sample it is divided into upper, middle and lower slope, using S-shaped sampling method, acquisition 0~
40cm layers of soil.
3. a kind of prediction technique of soil fertility building geographical MDS minimum data set based on yield according to claim 2,
It is characterized in that, each gradient takes 15~20 sampling points at random, the example weight per sampling point is not less than 100g, the sample of each gradient
Product take quartering to leave and take 1kg after mixing well, and are air-dried.
4. a kind of prediction technique of soil fertility building geographical MDS minimum data set based on yield according to claim 1,
It is characterized in that, in the step (2), fertility index pH, full nitrogen, full phosphorus, organic matter, fast nitrogen, available phosphorus, available potassium, soil
Loamy texture, effective trace elements iron, copper, zinc, manganese, exchangeable calcium, exchangeable magnesium.
5. a kind of prediction technique of soil fertility building geographical MDS minimum data set based on yield according to claim 1,
It is characterized in that, in the step (4), the selection standard of high factor loading is in principal component：The absolute value of the high factor loading
More than 90% of maximum factor loading in the principal component.
6. a kind of prediction technique of soil fertility building geographical MDS minimum data set based on yield according to claim 1,
It is characterized in that, in the step (4), the high factor loading for choosing principal component enters the standard of MDS minimum data set and is：
(a) when a principal component high factor loading index only there are one when, then the index enters MDS minimum data set；
(b) when the high factor loading index of a principal component is two, correlation analysis is done to two indices, if related coefficient is exhausted
When to value r ＜ 0.4, then two indices all enter MDS minimum data set；If related coefficient absolute value r >=0.4, factor loading are high
Index enters data set；
(c) when the high factor loading index of a principal component is three or three or more, correlation analysis is done respectively two-by-two, (1)
All indexs two-by-two related coefficient absolute value r ＜ 0.4 when, then all indexs all enter MDS minimum data set；(2) all indexs are two-by-two
When the case where related coefficient absolute value r presence >=0.4：If the index quantity of related coefficient absolute value r >=0.4 accounts for overall performane two-by-two
70% or more of quantity then enters data set with the maximum high factor loading index of the sum of remaining index related coefficient absolute value；
Otherwise all indexs enter data set.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109358178A (en) * | 2018-11-01 | 2019-02-19 | 四川省农业科学院土壤肥料研究所 | A kind of purple soil soil fertility of paddy field evaluation method |
| CN110007060A (en) * | 2019-02-14 | 2019-07-12 | 河南省农业科学院植物营养与资源环境研究所 | A kind of detection method for the prediction model that soil available phosphorus element is horizontal |
| CN110261272A (en) * | 2019-07-05 | 2019-09-20 | 西南交通大学 | Based on geographical detection with PCA to the Key Influential Factors screening technique of PM2.5 concentration distribution |
| CN110378623A (en) * | 2019-08-22 | 2019-10-25 | 新疆农业科学院土壤肥料与农业节水研究所(新疆维吾尔自治区新型肥料研究中心) | A kind of orchard soil fertility characterizing method and system |
| CN112836912A (en) * | 2019-11-25 | 2021-05-25 | 天津大学 | GIS-based soil pollution control partition defining method |
| CN113344409A (en) * | 2021-06-22 | 2021-09-03 | 山东农业大学 | Evaluation method and system for facility continuous cropping soil quality |
| CN116894514A (en) * | 2023-07-13 | 2023-10-17 | 中国农业科学院农业环境与可持续发展研究所 | Crop yield prediction method and system based on soil quality index |
| CN117010587A (en) * | 2023-06-03 | 2023-11-07 | 中国农业科学院农业环境与可持续发展研究所 | Integrated learning optimization evaluation method for soil quality improvement effect of organic materials |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120129706A1 (en) * | 2010-11-22 | 2012-05-24 | Ashvini Chauhan | Method of Assessing Soil Quality and Health |
| CN107561242A (en) * | 2017-08-29 | 2018-01-09 | 山西省环境科学研究院 | A kind of evaluation method of combined pollution agricultural land soil quality |
-
2018
- 2018-03-08 CN CN201810189545.XA patent/CN108564200A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120129706A1 (en) * | 2010-11-22 | 2012-05-24 | Ashvini Chauhan | Method of Assessing Soil Quality and Health |
| CN107561242A (en) * | 2017-08-29 | 2018-01-09 | 山西省环境科学研究院 | A kind of evaluation method of combined pollution agricultural land soil quality |
Non-Patent Citations (2)
| Title |
|---|
| 杨皓等: "喀斯特地区金刺梨种植基地土壤肥力研究", 《水土保持研究》 * |
| 樊亚男等: "基于产量的稻田肥力质量评价及障碍因子区划--以进贤县为例", 《土壤学报》 * |
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| CN110007060A (en) * | 2019-02-14 | 2019-07-12 | 河南省农业科学院植物营养与资源环境研究所 | A kind of detection method for the prediction model that soil available phosphorus element is horizontal |
| CN110261272A (en) * | 2019-07-05 | 2019-09-20 | 西南交通大学 | Based on geographical detection with PCA to the Key Influential Factors screening technique of PM2.5 concentration distribution |
| CN110261272B (en) * | 2019-07-05 | 2020-08-18 | 西南交通大学 | Method for screening key influence factors on PM2.5 concentration distribution based on geographic detection and PCA (principal component analysis) |
| CN110378623A (en) * | 2019-08-22 | 2019-10-25 | 新疆农业科学院土壤肥料与农业节水研究所(新疆维吾尔自治区新型肥料研究中心) | A kind of orchard soil fertility characterizing method and system |
| CN110378623B (en) * | 2019-08-22 | 2021-02-09 | 新疆农业科学院土壤肥料与农业节水研究所(新疆维吾尔自治区新型肥料研究中心) | Representation method and system for orchard soil fertility |
| CN112836912A (en) * | 2019-11-25 | 2021-05-25 | 天津大学 | GIS-based soil pollution control partition defining method |
| CN113344409A (en) * | 2021-06-22 | 2021-09-03 | 山东农业大学 | Evaluation method and system for facility continuous cropping soil quality |
| CN117010587A (en) * | 2023-06-03 | 2023-11-07 | 中国农业科学院农业环境与可持续发展研究所 | Integrated learning optimization evaluation method for soil quality improvement effect of organic materials |
| CN116894514A (en) * | 2023-07-13 | 2023-10-17 | 中国农业科学院农业环境与可持续发展研究所 | Crop yield prediction method and system based on soil quality index |
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