JP6755474B1 - Cation exchange capacity calculation method, cation exchange capacity calculation program and cation exchange capacity calculation system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a cation exchange capacity value calculation method, a cation exchange capacity calculation program and a cation exchange capacity calculation system capable of obtaining a simple measurement value close to a more accurate value more simply and easily. SOLUTION: This is a simple calculation method of cation exchange capacity in soil, in which a step of measuring calcium equivalent, magnesium equivalent and potassium equivalent, a step of measuring humus rate, and each measured value obtained are as follows. A cation exchange capacity value calculation method including a step of substituting into an equation to obtain a cation exchange capacity correlation value. Formula: Cation exchange capacity correlation value = partial regression coefficient Pc x rot rate (measured value) + partial regression coefficient Pca x Ca equivalent (measured value) + partial regression coefficient Pmg x Mg equivalent (measured value) + partial regression coefficient Pk x K equivalent (measured value) + partial regression coefficient Pct of constant term [selection diagram] None

Description

The present invention relates to a cation exchange capacity value calculation method, a cation exchange capacity calculation program, and a cation exchange capacity calculation system. More specifically, it is possible to obtain a simple measurement value closer to a more accurate value in a simpler and simpler manner. It relates to a cation exchange capacity value calculation method, a cation exchange capacity calculation program, and a cation exchange capacity calculation system.

The cation exchange capacity (hereinafter sometimes referred to as "CEC") is an index showing the ability of soil to retain fertilizer, and is usually expressed in milligram equivalent (meq) of cations per 100 g of dry soil (1 meq = atomic weight). (Mg) / charge number), the smaller the value, the lower the fertilizer holding capacity, and conversely, the higher the value, the higher the fertilizer holding power. Understanding this CEC is necessary for the efficient operation of the farm, but the current situation is that CEC measurement is rarely measured because it takes time and effort.
Therefore, various methods for simply measuring CEC have been proposed, and for example, the following proposals have been made in Patent Documents 1 and 2.
Patent Document 1 proposes a method for measuring a cation exchange capacity in soil that can easily and quickly determine the cation exchange capacity in soil, and a soil analyzer suitable for measuring the cation exchange capacity in soil. ing. Specifically, the index value corresponding to the content of rot plant matter in the soil is measured, and the measured value is converted into the cation exchange capacity. The measurement method of the cation exchange capacity in the soil and the data on the measured soil are input. Data input means to measure, absorbance measuring means to measure the absorbance of the rot plant-containing extract, show the correlation between the absorbance of the rot plant-containing solution and the rot plant content or cation exchange capacity, and select according to the pH of the soil to be measured. A storage means for storing the relational expression to be measured, a selection means for selecting the relational expression to be used according to the pH of the soil to be measured, a calculation means for obtaining the cation exchange capacity based on the measured value of the absorbance and the weighed value of the soil, and the calculated cation. A soil analyzer equipped with a display means for displaying the value of the ion exchange capacity has been proposed.
Further, Patent Document 2 proposes a soil analyzer and a soil analysis method for which CEC as an index for evaluating soil fertility is accurately required. Specifically, the soil analyzer generates an excitation signal input to the excitation coil for each frequency in order to apply an alternating magnetic field to the soil to be analyzed and a sensor having an excitation coil and a detection coil. A measuring unit that processes the detection signal output from the detection coil by applying an alternating magnetic field to the soil to be analyzed, and a quantitative value, sensor, and measuring unit of soil geological traits including CEC for multiple soils with different components. An alternating magnetic field is applied to the soil to be analyzed by a sensor and a storage unit that stores data related to the correlation with the estimated value of soil geological traits including CEC obtained from the detected signal after processing measured using. A device including an estimation unit that estimates soil geological traits including CEC of the soil to be analyzed using the data stored in the storage unit based on the detection signal processed by the measurement unit has been proposed. ing.

Japanese Unexamined Patent Publication No. 2012-26803 International Publication WO2017 / 221901

However, the conventionally proposed cation exchange capacity calculation method still has a problem of lacking in convenience and accuracy.

Therefore, an object of the present invention is to provide a cation exchange capacity value calculation method, a cation exchange capacity calculation program, and a cation exchange capacity calculation system that can obtain a simple measurement value closer to an accurate value more simply and easily. To provide.

As a result of diligent studies to solve the above problems, the present inventors have found that there is a correlation between a specific component and a cation exchange capacity, and by performing multiple regression analysis of this correlation, the above object We have found that the above can be achieved, and have completed the present invention.
That is, the present invention provides the following inventions.
1. 1. It is a simple calculation method of the cation exchange capacity in soil.
Steps to measure calcium equivalent, magnesium equivalent and potassium equivalent, and steps to measure humus rate,
A cation exchange capacity value calculation method including a step of substituting each obtained measured value into the following formula to obtain a cation exchange capacity correlation value.
Formula: Cation exchange capacity correlation value = partial regression coefficient Pc x rot rate (measured value) + partial regression coefficient Pca x Ca equivalent (measured value) + partial regression coefficient Pmg x Mg equivalent (measured value) + partial regression coefficient Pk x K equivalent (measured value) + partial regression coefficient Pct of constant term
(In the formula, the partial regression coefficient indicates the numerical value calculated by the statistical analysis data (multiple regression analysis) of the measured value for each measurement object, and the constant term is the statistical analysis data of the cation exchange capacity analysis value measured in advance ( Shows the cation exchange capacity partial regression coefficient calculated by multiple regression analysis)
2. 2. A computer program stored in a computer storage device that calculates a cation exchange capacity value by the cation exchange capacity value calculation method described in 1.
A computer program that activates the step of substituting the entered calcium equivalents, magnesium equivalents and potassium equivalents, and humus rate into the above equation to obtain a cation exchange capacity correlation value.
The computer in which the computer program described in 3.2 is stored and the computer
A display means for displaying the multiple regression analysis result and the cation exchange capacity correlation value calculated by the above computer, and
It comprises a measuring system for performing a step of measuring calcium equivalent, magnesium equivalent and potassium equivalent and a step of measuring humus rate.
Cation exchange capacity calculation system

The cation exchange capacity calculation method, the cation exchange capacity calculation program, and the cation exchange capacity calculation system of the present invention can obtain a simple measurement value of a cation exchange capacity value close to a more accurate value more simply and easily. Can be done.

Hereinafter, the present invention will be described in more detail.
[Calculation method]
The calculation method of the present invention is a simple calculation method of the CEC value in soil.
A step of measuring calcium equivalent, magnesium equivalent and potassium equivalent (hereinafter, this step is referred to as step A), a step of measuring humus rate (hereinafter, this step is referred to as step B), and
A step (hereinafter, this step is referred to as step C) of substituting each of the obtained actually measured values into a predetermined equation to obtain a CEC correlation value is provided.

<Step A>
Step A is a step of measuring calcium equivalent, magnesium equivalent and potassium equivalent.
This step A, for example determine the amount of ions by analytical methods that are described in Japanese Patent No. 5379622 obtains nutrients of the entire field from the ion amount, if the nutrient content amount of substance (K 2 _O if 47, CaO 28, for MgO, divide by 20) to calculate the equivalent.
Specifically, it can be performed as follows.
First, a predetermined soil is cut to a predetermined depth, and a predetermined amount of sample is collected.
Here, the predetermined soil means a soil on which a desired crop grows and a place where analysis sampling is performed. Sampling may be done at multiple locations, appropriately distributed so that the required data can be collected from the entire farm so that accurate analysis results can be obtained, but the sum of the four corners of the entire farm and any two points diagonally. It is preferable to set 6 points as sampling points. The farm may be divided into a plurality of rectangular sections and sampling may be performed for each section. Here, the diagonal line may be on the same diagonal line or on a different diagonal line, but as shown in FIG. 1, on one diagonal line, the center point (intersection of the diagonal lines) is sandwiched between the center points and the most diagonal lines. It is preferable to set two points (a total of four points) when the end points are divided into three equal parts, and two points on the other diagonal line on the most end point side.
The predetermined depth varies depending on the purpose, but refers to the depth of the soil portion where the desired crop is growing. For example, the depth of the main root group area (soil layer where most of the root group (about 90% or more) is distributed) and the effective root group area (soil layer where roots can grow), which are important parts for crop growth. ) Can be set to a depth for collecting the soil, and specifically, it is preferably 10 to 30 cm.
For collection, set the target location, for example, collect the soil part to be collected in a cylindrical shape with a cylindrical soil collector with a diameter of 6 cm and a length of the soil area of about 30 cm, or under the ground surface with a shovel or the like. It can be carried out by making the soil surface of the above-mentioned soil appear, cutting out the side soil (side soil located on the bottom surface from the ground surface) of a predetermined depth, and collecting the cut-out soil. When digging up the soil, it can be collected using a soil sampling device used in a normal geological survey without any particular limitation.

Then, the collected sample is treated with a treatment solution containing a strong acid to obtain an extract, and the obtained extract is filtered through a 0.2 to 0.45 μm membrane filter and chemically analyzed by an ion chromatograph.
The strong acid used in the treatment liquid is composed of an inorganic acid mixture of hydrochloric acid and sulfuric acid, and has high soil component extraction efficiency. The concentration of the strong acid used is preferably 0.001 to 0.010 mol / L from the viewpoint of extraction effect and measurement efficiency.
In addition to the strong acid, other components may be mixed with the treatment liquid as long as the desired effect of the present invention is not impaired. Examples of other components include salts such as sodium chloride.
When the above inorganic acid mixture is used as the treatment liquid, the mixing ratio of hydrochloric acid and sulfuric acid is 50 to 150 sulfuric acid with respect to 100 hydrochloric acid in terms of volume ratio (when both hydrochloric acid and sulfuric acid have the same molar concentration). From the viewpoint of efficiency, it is preferably 60 to 120, more preferably 60 to 100, and most preferably 60 to 100.
The amount of the treatment liquid used depends on the type of soil and the amount to be measured, but the reason why the amount is 50 to 150 parts by weight with respect to 1 part by weight of the sample is that the extraction efficiency is stabilized. Is preferable.
The extraction process can be performed by adding the treatment solution to the sample, placing it in a beaker made of glass or fiber reinforced plastic (FRP), and shaking it for 30 to 60 minutes, thereby extracting the components. , Use as a sample for ion chromatographic analysis.

The extract is analyzed by removing the insoluble substances contained therein with a centrifuge, a filter, or the like and subjecting it to an ion chromatograph.
As the ion chromatograph, those usually used for this kind of chemical analysis can be used without particular limitation. Specifically, although not particularly shown, a pump unit, an injection unit, a column unit, and a detection unit, and a suppressor provided if necessary, can be used. Further, as the detector, an electric conductivity detector is usually used, but in the case of anion analysis, an absorbance detector may be used, and a post-column derivatization / absorbance detector may be used if necessary. As the column used for ion analysis and the carrier used for chromatography, those used in ordinary analysis can be used without particular limitation.
The analysis conditions for performing ion chromatographic analysis can be as shown below.
Measurement conditions for anion analysis:
Ion chromatograph: (manufactured by Toa DDK, device name: IA-300), etc. Column: (manufactured by Toa DDK, trade name: PCI-211, length: 100 mm, inner diameter: 4.6 mm), etc. Sample injection amount: 10 ~ 30 μL
Column oven temperature: 20-60 ° C
Eluent: 2.3 mM phthalic acid / 2.8 mM, 6-amino-n-hexane acid / 200 mM, boric acid mixed solution, etc. (The concentration of the eluent is from the concentration of this example to 1000 times the concentration of this example. It can be any concentration)
Flow velocity: 0.8-1.5 mL / min
Detector: Electrical conductivity detector measurement Anion:
PO ₄ ^3-3 , Cl ⁻ , Br ⁻ , SO ₄ ^2- , F ⁻ , NO ₂ ⁻ , NO ₃ ⁻
Measurement conditions for cation analysis:
Ion chromatograph: (manufactured by Toa DDK, device name: IA-300), etc. Column: (manufactured by Toa DDK, trade name: PCI-322, length: 250 mm, inner diameter: 4.6 mm), etc. Sample injection amount: 10 ~ 30 μL
Column oven temperature: 20-60 ° C
Eluent: 6M methanesulfonic acid equal flow rate: 0.3-1.5mL / min
Detector: Electrical conductivity detector Measurement cations: K ⁺ , Ca ²⁺ , Mg ²⁺

Each ion amount can be obtained by a known method such as using a method usually used for this type of ion amount calculation or calculating from the peak area by using commercially available software.
The amount of ions obtained is calculated in units of mg / 100 g, and the amount of nutrients (kg) can be converted into the known formula mg / 100 g = kg / 10a.
Further, each equivalent can be obtained by dividing the obtained amount of nutrients by the amount of substance.

Further, when the fertilizer application design is performed using the chemical analysis value obtained in step A, it is preferable to further perform the physical analysis.
The physical analysis is preferably carried out by analyzing the measured bulk density of the soil.
The measured bulk density is the value obtained by dividing the mass of dry soil by the volume of raw soil.
The present inventors have found that the more the value of the measured bulk density deviates from 1.0, the more the amount of fertilizing component required by the cultivated plant becomes excessive or insufficient.
The measured bulk density can be calculated by the following formula.
Measured bulk density (grams / milliliter) = dry soil mass (grams) / raw soil volume (milliliters)
(In the formula, the dry soil mass is the mass when 10 grams of the soil sample is constant at 105 ° C. (dried under normal pressure for 30 to 50 minutes), and the raw soil volume is the predetermined amount of the soil sample 10 grams in advance. (It is an increase volume (milliliter) from the predetermined amount by putting it in the female cylinder in which the water was put.)
That is, in the present invention, when measuring the physical properties, the dry soil mass obtained by measuring the mass of 10 grams of soil sample at a constant volume of 105 ° C. (dried under normal pressure for 30 to 50 minutes). And 10 grams of the soil sample is put into a measuring cylinder into which a predetermined amount of water has been charged in advance, and the volume of raw soil obtained by measuring the increased volume (milliliter) from the predetermined amount is obtained.
True specific gravity refers to the density of soil during drying.
The true specific gravity can be calculated by the following formula.
True specific gravity (grams / milliliter) = dry soil mass (grams) / dry soil volume (milliliters)
(In the formula, the dry soil mass is the mass when 10 grams of soil sample is constant at 105 ° C. (in a state of being dried under normal pressure for 30 to 50 minutes), and the dry soil volume is 105 ° C. for 10 grams of soil sample. A constant amount of dry soil is obtained, and the dry soil is put into a measuring cylinder into which a predetermined amount of water has been charged in advance to increase the volume (milliliter) from the predetermined amount).
In the physical analysis, in addition to the above analysis of the bulk density of the soil, a three-phase analysis of the solid phase ratio, the liquid phase ratio, and the gas phase ratio may be added as appropriate.

<Step B>
Step B is a step of measuring the humus rate, and specifically, it can be measured as follows.
First, 2 g of the collected soil is weighed in a 100 ml container, and 20 ml of the following extract is added thereto. After adding the extract, shake for 3 minutes and filter. 1 ml of the filtrate is placed in a 25 ml test tube, 11 ml of water is added, and colorimetry is performed using a spectrophotometer (530 nm). At this time, water is colorimetrically used as a blank, the absorbance of water is set to 0, and the absorbance of the filtrate is measured.
At this time, under the conditions of calculation SPAD method, when humus rate (%) Y, the absorbance and X, the regression equation ^{Y = (162.4 × X + 24.97} ) is 0.5 -4.19.
-Extract: The following solution A and the following solution B are mixed and water is added to adjust to 1 liter.
A solution: Sodium grade pyrophosphate _{(Na 4 P 2 O 7 ·} 10H 2 O) 22.3g those were dissolved in water at about 400 ml.
Solution B: 10 g of special grade NaOH dissolved in about 400 ml of water.

<Step C>
Step C is a step of substituting each of the obtained measured values into the following equation to obtain a CEC correlation value, and is a step of performing multiple regression analysis to obtain a CEC correlation value.
As a result of various studies on what should be emphasized among various measured values in order to obtain a CEC correlation value, the present inventor found that the measured values having a high correlation with the CEC measured values are the humus rate, Ca equivalent, Mg equivalent and K equivalent. It was found that other measured values can be ignored. Then, they have found that a CEC correlation value, which is a value close to the true CEC value, can be obtained by substituting these data into the regression equation, and have completed the present invention.
Here, each partial regression coefficient is obtained by taking a plurality of actually measured values of CEC value, rot rate, Ca equivalent, Mg equivalent, and K equivalent, and performing multiple regression analysis on them. Obtain the numerical values shown in 3 and substitute them into the following equations to determine the coefficients. In this embodiment, each partial regression coefficient is determined with 134 cases. In addition, when determining these coefficients, it is also possible to calculate using commercially available multiple regression analysis software. Examples of such multiple regression analysis software include the registered trademark "BellCurve" manufactured by Social Information Service Co., Ltd.
The CEC correlation value can be obtained by substituting the measured values of humus rate, Ca equivalent, Mg equivalent and K equivalent into the formula completed by determining the coefficient.
Further, in the present invention, the obtained data can be added to further increase the number of observations to perform multiple regression analysis, and the coefficient can be updated every time the measurement is performed to improve the reliability.
Formula: Cation exchange capacity correlation value = partial regression coefficient Pc x rot rate (measured value) + partial regression coefficient Pca x Ca equivalent (measured value) + partial regression coefficient Pmg x Mg equivalent (measured value) + partial regression coefficient Pk x K equivalent (measured value) + partial regression coefficient Pct of constant term
Each partial regression coefficient = partial regression coefficient Pct of numerical constant term calculated by statistical analysis data (multiple regression analysis) of measured values for each measurement object = statistical analysis data of CEC analysis values measured in advance (multiple regression analysis) Calculated CEC partial regression coefficient

<Other steps>
In the present invention, in addition to the above-mentioned steps A to C, other steps that can be usually used in the calculation of CEC may be performed as long as the desired effect of the present invention is not impaired.

[Computer program]
The program of the present invention can also be configured as a computer program in which the above-mentioned simple calculation method of the CEC value in the soil of the present invention is stored in a computer and the computer operates the step C.
That is, the computer program of the present invention is stored in a computer storage device and causes the computer to operate a simple calculation method of the CEC value in soil, and the input calcium equivalent, magnesium equivalent and potassium equivalent, In addition, the step of obtaining the CEC correlation value by substituting the rot planting rate into the above equation (step C above) is operated.
In addition, the computer program of the present invention can be configured to further perform the following steps.
The input calcium equivalent, magnesium equivalent, potassium equivalent, and humus rate are stored in the storage medium as measured values, and the multiple regression analysis results obtained in step C above are also stored to update the preset database. Database update step (step D).
An expression update step (step E) in which the above equation is newly updated with each partial regression coefficient obtained as the multiple regression analysis result when the measured value, the multiple regression analysis result and the CEC correlation value are newly stored.
The steps C, D and E will be described in detail below.
Here, as the computer, a keyboard and a mouse as an input device, a monitor as an output device, a CPU as an arithmetic processing device, and various other data and programs for system execution are stored to construct a database. A hard disk as a storage device and a device having a memory as a temporary storage device for temporarily storing data for arithmetic processing can be used.

(Step C)
When performing the step C, the above formula and the above software are stored in the storage device of the computer in advance. Separately, the above storage device stores the CEC value, calcium equivalent, magnesium equivalent and potassium equivalent, and the corrosion rate measured by the conventional method (Schollenberger method) in association with each sample and stores them in a database. To build. Based on this database, the above-mentioned software is used to obtain each partial regression coefficient to complete the above equation, and each measured value is substituted into the above equation. This completes the formula for calculation (the above formula in which each partial regression coefficient is specifically determined below is called a "calculation formula"). After performing these pre-steps, the above step C is performed.
Further, the calcium equivalent, magnesium equivalent and potassium equivalent, and the corrosion rate input to the computer are set to be automatically substituted in the corresponding parts of the above calculation formula. This setting may be set so as to be substituted into the formula by inputting it into a specific cell of the spreadsheet software by a conventional method.
Then, when the calcium equivalent, magnesium equivalent and potassium equivalent, and the corrosion rate obtained in steps A and B in the farm where the CEC value is specifically desired to be obtained are input to the computer in which the program set in this way is stored. , Each data is substituted into the above calculation formula in which each partial regression coefficient is determined, and a CEC calculated value is obtained.

(Step D)
Step D is a step of updating the above-mentioned data, in which the calcium equivalent, the magnesium equivalent and the potassium equivalent, and the rot planting rate entered in order to obtain the CEC calculated value are stored in the above database stored in the storage device of the computer. , Each is stored as a measured value. At the same time, each partial regression coefficient obtained as a calculation of the result of multiple regression analysis is updated and stored in the storage medium to update the database.

(Step E)
Step E is a step of updating the above calculation formula as the database is updated.
As a result of multiple regression analysis of the above measured values, each partial regression coefficient is updated. With this, each partial regression coefficient in the above equation is changed and updated to an equation that can obtain a CEC calculated value more accurately.

〔system〕
The CEC value calculation system of the present invention
The computer in which the above computer program of the present invention is stored, and
Display means for displaying the multiple regression analysis results and CEC correlation values shown in Tables 1 to 3 above calculated by the computer, and
A measurement system for performing the above steps A and B is provided.
Here, as the display means, a normal monitor can be used without limitation. Further, as the above-mentioned measuring system, a generally known equivalent measuring system and a corrosion rate measuring system can be used without particular limitation.

The present invention is not limited to the above-described embodiment, and various modifications can be made without departing from the spirit of the present invention.

Hereinafter, the present invention will be described in more detail with reference to Examples and Comparative Examples, but the present invention is not limited thereto.

[Example 1]
<Sampling>
The soil of a predetermined farm was analyzed using the soil analysis method of the present invention.
Soil samples are collected by dividing into predetermined rectangular sections and collecting about 420 g of a predetermined depth (soil-growing part, depth of about 10 cm) at 6 points on the diagonal of the rectangular section. It was.
The collected soil samples were placed in a plastic bag with a chuck, sealed, and stored in a cool and dark place until chemical analysis and physical analysis were performed.
<Chemical analysis>
(Pretreatment of chemical analysis sample)
Visible foreign matter was removed from the collected soil samples.
Next, it was dried in an incubator at 50 ° C. for 24 to 30 hours, and foreign matter was removed using a 1 mm mesh sieve.
(Extraction process)
Chemical analysis sample 0.5 g of the sample after pretreatment was measured in a test tube with a stopper, 50 mL of the treatment solution was added, and the mixture was shaken for 30 minutes with a shaker to perform an extraction treatment.
Next, the suspension after the extraction treatment was centrifuged at 3500 rpm at room temperature for 6 minutes, and the obtained supernatant was separated by filtering using a membrane filter having a pore size of 0.45 μm for extraction. Obtained liquid.
As the treatment liquid, hydrochloric acid was adjusted to a concentration of 0.004 mol / L with pure water, sulfuric acid was adjusted to a concentration of 0.003 mol / L with pure water, and the above 0.004 mol / L concentration hydrochloric acid aqueous solution and the above 0 were used. It was prepared by mixing with a sulfuric acid aqueous solution having a concentration of .003 mol / L at a ratio of (1: 1, volume ratio).
(Analysis using an ion chromatograph)
The obtained extract was cationized using an ion chromatograph.
The analysis was performed under the following conditions.
·Measurement condition:
Ion chromatograph: (manufactured by Toa DDK, device name: IA-300)
Column: Made by Toa DDK Ltd., Product name: PCI-322, Length: 250 mm, Inner diameter: 4.6 mm
Sample injection volume: 20 μL
Column oven temperature: 39.7 ° C
Eluent: 6M methanesulfonic acid Flow rate: 0.8mL / min
Detector: (Electrical conductivity detector)
Measurement ions: K ⁺ , Ca ²⁺ , Mg ²⁺
Each ion amount is calculated by by calculating the peak area, further, it calculates a Nutrient held in soil under field across Conversion area 10a, material amounts Nutrient final (K 2 _O, if 47, The equivalent was calculated by dividing by 28 for CaO and 20) for MgO. The obtained equivalents are shown below.
Equivalent (K ₂ O): 2.46
Equivalent (CaO): 35.18
Equivalent (MgO): 3.16
It became.

<Measurement of humus rate>
The humus rate was measured according to the above method for measuring the humus rate.
Specifically, first, 100 ml of the collected soil (2 g) is collected, and 20 ml of the following extract is added thereto. After adding the extract, shake for 3 minutes and filter. 1 ml of the filtrate was placed in a 25 ml test tube, 11 ml of water was added, and colorimetry was performed using a spectrophotometer (530 nm). At this time, water was colorimetrically used as a blank, and water had an absorbance of 0.
At this time, under the conditions of calculation SPAD method, humus rate (%) Y, the absorbance and X, since the regression equation ^{Y = (162.4 × X + 24.97} ) is 0.5 -4.19, the The humus rate was calculated by substituting the absorbance into the formula.
-Extract: The following solution A and the following station B are mixed and water is added to adjust to 1 liter.
A solution: Sodium grade pyrophosphate _{(Na 4 P 2 O 7 ·} 10H 2 O) 22.3g those were dissolved in water at about 400 ml.
Solution B: 10 g of special grade NaOH dissolved in about 400 ml of water.
The resulting rot planting rate was 3.30. The pH was 7.

<Calculation of CEC value>
The CEC correlation value (CEC value) was obtained by substituting each of the above equivalents and the humus rate into the above formula. At this time, the partial regression coefficients shown in Table 3 were used.
As a result, the CEC correlation value was 21.405.
Separately, the CEC value measured by the Schorenberger method is 21.40, and the CEC value calculation method of the present invention is 0.005 compared to the measurement method which is considered to have the highest system. It can be seen that the simple value of CEC can be calculated accurately.

[Comparative Example 1]
The CEC correlation value was calculated in the same manner as in Example 1 except that the equivalents were only Ca and K.
As a result, it can be seen that the CEC correlation value is 31.692, and the difference from the measured value is 10.292, which is different from that of Example 1.
[Comparative Example 2]
The CEC correlation value was calculated in the same manner as in Example 1 except that the equivalents were only Ca and Mg.
As a result, the CEC correlation value is 20.686, and the difference from the measured value is 0.714, which shows that there is a difference as compared with Example 1.
[Comparative Example 3]
The CEC correlation value was calculated in the same manner as in Example 1 except that the equivalents were only Mg and K.
As a result, the CEC correlation value is 21.992, and the difference from the measured value is 0.592, which shows that there is a difference as compared with Example 1.
[Comparative Example 4]
The CEC correlation value was calculated in the same manner as in Example 1 except that the corrosion rate was excluded.
As a result, the CEC correlation value is 31.837, and the difference from the measured value is 10.437, which shows that there is a difference as compared with Example 1.
[Comparative Example 5]
The CEC correlation value was calculated in the same manner as in Example 1 except that the CEC correlation value was calculated using the following formula as the partial regression coefficient of Ca, Mg and K as the partial regression coefficient for all these equivalents. Calculated.

Formula: Cation exchange capacity correlation value = partial regression coefficient x rot rate (actual measurement value) + partial regression coefficient x (Ca equivalent (actual measurement value) + Mg equivalent (actual measurement value) + K equivalent (actual measurement value)) + partial regression of the constant term Coefficient As a result, the CEC correlation value is 24.983, and the difference from the measured value is 3.583, which shows that there is a difference as compared with Example 1.

[Example 2]
As shown in Table 4, the farms were changed, and for the samples 2 to 24, each equivalent and the corrosion rate were obtained in the same manner as in Example 1, and the obtained values were substituted into the above formula to calculate the CEC. Asked.
In order to confirm the correctness of the obtained CEC calculated value, the CEC measured value was separately obtained for each farm sample in the same manner as in Example 1.
The results are shown in Table 4.
As is clear from the results shown in Table 4, the difference from the measured value is small in any case, and according to the calculation method of the present invention, it is possible to obtain a characteristic CEC calculated value by a simple and simple method. Recognize.

Claims (3)

It is a simple calculation method of the cation exchange capacity in soil.
Steps to measure calcium equivalent, magnesium equivalent and potassium equivalent, and steps to measure humus rate,
A cation exchange capacity value calculation method including a step of substituting each obtained measured value into the following formula to obtain a cation exchange capacity correlation value.
Formula: Cation exchange capacity correlation value = partial regression coefficient Pc x rot rate (measured value) + partial regression coefficient Pca x Ca equivalent (measured value) + partial regression coefficient Pmg x Mg equivalent (measured value) + partial regression coefficient Pk x K equivalent (measured value) + partial regression coefficient Pct of constant term
(In the formula, the partial regression coefficient indicates the numerical value calculated by the statistical analysis data (multiple regression analysis) of the measured value for each measurement object, and the constant term is the statistical analysis data of the cation exchange capacity analysis value measured in advance ( Shows the cation exchange capacity partial regression coefficient calculated by multiple regression analysis) A computer program stored in a storage device of a computer and calculating a cation exchange capacity value by the cation exchange capacity value calculation method according to claim 1.
A computer program that activates the step of substituting the entered calcium equivalents, magnesium equivalents and potassium equivalents, and humus rate into the above equation to obtain a cation exchange capacity correlation value. A computer in which the computer program according to claim 2 is stored, and
A display means for displaying the multiple regression analysis result and the cation exchange capacity correlation value calculated by the above computer, and
It comprises a measuring system for performing a step of measuring calcium equivalent, magnesium equivalent and potassium equivalent and a step of measuring humus rate.
Cation exchange capacity calculation system