CN112417655A - Method for establishing farmland soil organic matter prediction model - Google Patents
Method for establishing farmland soil organic matter prediction model Download PDFInfo
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- CN112417655A CN112417655A CN202011222922.9A CN202011222922A CN112417655A CN 112417655 A CN112417655 A CN 112417655A CN 202011222922 A CN202011222922 A CN 202011222922A CN 112417655 A CN112417655 A CN 112417655A
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Abstract
The invention relates to a method for establishing a farmland soil organic matter prediction model, which comprises the following steps: the method comprises the steps of collecting a farmland planting soil sample, and measuring nutrients such as organic matters, total nitrogen, quick-acting nitrogen, pH value and the like; performing correlation analysis on all nutrient data by using SPSS software; and thirdly, establishing a prediction model of the soil organic matter. According to the method, a prediction model of the soil organic matter is established, the organic matter content can be calculated through the total nitrogen content and the pH value, the working process of measuring the organic matter is greatly simplified, the application of dangerous chemicals such as concentrated sulfuric acid and potassium dichromate is reduced, and the method is economical, environment-friendly, safe and efficient.
Description
Technical Field
The invention belongs to the technical field of soil nutrient prediction, and relates to a method for establishing a farmland soil organic matter prediction model, in particular to a method for predicting farmland soil organic matters in Tianjin areas, and particularly relates to a method for establishing a farmland soil organic matter prediction model.
Background
Soil organic matters are important indexes for evaluating soil fertility and contain various nutrient components required by crop growth. The soil organic matter has the effects of promoting the growth and development of crops, improving the soil structure, improving the water and fertilizer retention capacity of soil, promoting the activity of soil microorganisms, promoting the physiological activity of plants and the like. Before crops are planted, the nutrient content, particularly the content of organic matters, of planting soil is known, so that the growth vigor and the yield of the crops can be evaluated, and theoretical support is provided for more reasonable fertilizer application.
At present, the determination of soil organic matters is mainly based on a detection method of related industrial standards, the detection period is long, the efficiency is low, and dangerous chemicals such as concentrated sulfuric acid, potassium dichromate and the like are used in key detection steps, so that potential safety hazards exist, and environmental pollution is easily caused. A prediction model between total nitrogen and pH and organic matters is established, the correlation among key nutrient indexes of the soil is disclosed, the fertility condition of the soil is reflected visually, the use of dangerous chemicals is reduced, and the method is green, efficient, safe and environment-friendly. In China, a lot of reports about soil nutrient index prediction exist, and partial documents research on the correlation between organic matters and total nitrogen, so that the established linear models are mostly unary, and the research of establishing a multiple regression model by taking other soil nutrient parameters into consideration is rare. And the soil in other areas is taken as a sample in related researches, and a prediction model aiming at Tianjin local planting soil is not reported.
Through searching, no patent publication related to the present patent application has been found.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides a method for establishing a farmland soil organic matter prediction model.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a method for establishing a farmland soil organic matter prediction model comprises the following steps:
the method comprises the steps of collecting a plurality of farmland planting soil samples in the same area, and measuring key nutrient indexes according to a national standard method;
secondly, performing relevance analysis on all data by using SPSS software, and displaying that 3 indexes have significant relevance according to results;
establishing a prediction model of the soil organic matter by a multivariate linear regression analysis method of SPSS software, wherein the relation model is as follows: y ═ a + bx1+cx2；
Wherein y is the organic matter content of the soil, and the unit is g/kg; x is the number of1Is total nitrogen content, in%; x is the number of2The pH value of the soil; a. b and c are constants;
according to the prediction model, determining the total nitrogen content and the pH value of the soil sample to be detected in the area, and substituting the total nitrogen content and the pH value into the prediction model to calculate the organic matter content; the calculated organic matter content is compared with the organic matter actually measured by detecting the soil sample, and the accuracy and the reliability of the model can be verified.
Moreover, soil samples in the step are all farmland planting soil in Tianjin Ninghe area, and the soil sampling depth is 0-15 cm.
In addition, key nutrient indexes in the steps comprise organic matters, total nitrogen and a pH value.
And, the step two in 3 indexes are total nitrogen (%), pH and organic matter.
And performing multiple regression analysis on the experimental data by using SPSS software to obtain the prediction model, and predicting that the prediction deviation is less than 5% when the organic matter content of the selected soil sample is compared with the actual measurement result.
The invention has the advantages and positive effects that:
1. the method analyzes the correlation among the main nutrients of the soil by combining experimental data with mathematical model statistics, establishes a prediction model and discloses the relationship among various nutrient indexes and the mode of mutual influence. The establishment of the prediction model simplifies the working process of organic matter determination, reduces workload, simplifies the working process, is safe, environment-friendly and efficient, and improves the scientific research efficiency.
2. The method reduces the application of dangerous chemicals such as concentrated sulfuric acid, potassium dichromate and the like and the generation amount of high-pollution waste liquid, reduces the safety risk of analysis and test personnel, and is economical, environment-friendly, safe and efficient.
3. The method provides a theoretical basis for researching soil nutrient prediction models for planting in other provinces and cities in the future, can be popularized to soil nutrient index prediction in various regions according to the method, establishes a data management system according to the method, and provides powerful data support for crop planting management and control.
Detailed Description
The present invention is further illustrated by the following examples, which are intended to be illustrative, not limiting and are not intended to limit the scope of the invention.
The raw materials used in the invention are all conventional commercial products if no special description is provided, the method used in the invention is all conventional methods in the field if no special description is provided, and the mass of all the materials used in the invention is the conventional use mass.
A method for establishing a farmland soil organic matter prediction model comprises the following steps:
the method comprises the steps of collecting a plurality of farmland planting soil samples in the same area, and measuring key nutrient indexes according to a national standard method;
secondly, performing relevance analysis on all data by using SPSS software, and displaying that 3 indexes have significant relevance according to results;
establishing a prediction model of the soil organic matter by a multivariate linear regression analysis method of SPSS software, wherein the relation model is as follows: y ═ a + bx1+cx2；
Wherein y is the organic matter content of the soil, and the unit is g/kg; x is the number of1Is total nitrogen content, in%; x is the number of2The pH value of the soil; a. b and c are constants;
according to the prediction model, determining the total nitrogen content and the pH value of the soil sample to be detected in the area, and substituting the total nitrogen content and the pH value into the prediction model to calculate the organic matter content; the calculated organic matter content is compared with the organic matter actually measured by detecting the soil sample, and the accuracy and the reliability of the model can be verified.
Preferably, the soil samples in the step are all farmland planting soil in Tianjin Ninghe area, and the soil sampling depth is 0-15 cm.
Preferably, the key nutrient indexes in the step are organic matters, total nitrogen and pH values.
Preferably, the 3 indexes in the step II are total nitrogen (%), pH and organic matters.
Preferably, the experimental data are subjected to multiple regression analysis by using SPSS software to obtain the prediction model, and the prediction deviation is less than 5% when the organic matter content of the selected soil sample is predicted to be compared with the actual measurement result.
Specifically, the preparation and detection are as follows:
a method for establishing a farmland soil organic matter prediction model comprises the following steps:
(1) establishment of prediction model
Collecting 100 farmland soil samples in different areas respectively, and determining key nutrient indexes including organic matters, total nitrogen, pH value and other parameters according to a national standard method, see table 1;
table 13 area soil sample nutrient index content (n ═ 100)
(2) Performing correlation analysis on all nutrient data by using SPSS software, wherein the result shows that 3 indexes have significant correlation, as shown in Table 2;
correlation between the 23 nutrient indices in Table
Note: significant correlation at the 0.01 level; *. were significantly related at the 0.05 level.
(3) A prediction model of soil organic matters is established by a multivariate linear regression analysis method of SPSS software, and the relation model is as follows:
y＝a+bx1+cx2
wherein y is the organic matter content of the soil, and the unit is g/kg; x is the number of1Is total nitrogen content, in%; x is the number of2The pH value of the soil; a to c are constants;
according to the method and regression analysis of the soil sample nutrient data of 3 regions, 3 region soil organic matter prediction model equations are respectively established, and are shown in table 3. After the model is established, the content of organic matters can be predicted through the relevant nutrient parameters of the soil sample to be detected.
Table 33 area organic matter prediction model equation
(4) Inspection of predictive models
Taking 3 areas of soil samples respectively, determining total nitrogen, pH value and organic matter content according to a national standard method, substituting the total nitrogen and the pH value into a prediction model respectively to obtain a predicted value of the organic matter content of each soil sample, and comparing the detected value with the predicted value to check the reliability of the prediction model.
Table 4 comparison between organic matter detection value and prediction value
As can be seen from Table 4, the errors between the detection value and the prediction value are less than 5%, which shows that the prediction model established by the invention has good reliability and can be applied to the prediction of the organic matter content of the soil.
Although the embodiments of the present invention have been disclosed for illustrative purposes, those skilled in the art will appreciate that: various substitutions, changes and modifications are possible without departing from the spirit and scope of the invention and the appended claims, and therefore the scope of the invention is not limited to the embodiments disclosed.
Claims (5)
1. A method for establishing a farmland soil organic matter prediction model is characterized by comprising the following steps: the method comprises the following steps:
the method comprises the steps of collecting a plurality of farmland planting soil samples in the same area, and measuring key nutrient indexes according to a national standard method;
secondly, performing relevance analysis on all data by using SPSS software, and displaying that 3 indexes have significant relevance according to results;
establishing a prediction model of the soil organic matter by a multivariate linear regression analysis method of SPSS software, wherein the relation model is as follows: y ═ a + bx1+cx2；
Wherein y is the organic matter content of the soil, and the unit is g/kg; x is the number of1Is total nitrogen content, in%; x is the number of2The pH value of the soil; a. b and c are constants;
according to the prediction model, determining the total nitrogen content and the pH value of the soil sample to be detected in the area, and substituting the total nitrogen content and the pH value into the prediction model to calculate the organic matter content; the calculated organic matter content is compared with the organic matter actually measured by detecting the soil sample, and the accuracy and the reliability of the model can be verified.
2. The method for establishing the farmland organic matter index prediction model according to claim 1, which is characterized in that: the method comprises the steps of enabling soil samples to be all Tianjin Ninghe area farmland planting soil, and enabling the soil sampling depth to be 0-15 cm.
3. The method for establishing the farmland organic matter index prediction model according to claim 1, which is characterized in that: key nutrient indexes in the steps comprise organic matters, total nitrogen and a pH value.
4. The method for establishing the farmland organic matter index prediction model according to claim 1, which is characterized in that: the method comprises the following steps that 3 indexes are total nitrogen (%), pH (potential of hydrogen) and organic matters.
5. The method for establishing the farmland organic matter index prediction model according to any one of claims 1 to 4, wherein the method comprises the following steps: and performing multiple regression analysis on the experimental data by using SPSS software to obtain the prediction model, and predicting that the prediction deviation is less than 5% when the organic matter content of the selected soil sample is compared with the actual measurement result.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113484384A (en) * | 2021-07-07 | 2021-10-08 | 中国科学院东北地理与农业生态研究所 | Method for predicting in-situ pH values of soils in different plough layers in rice growth period in soda saline-alkali soil |
| CN114048897A (en) * | 2021-10-29 | 2022-02-15 | 西藏电建成勘院工程有限公司 | Method for constructing straw compost organic matter content prediction model based on temperature and humidity conditions |
| CN116384601A (en) * | 2023-06-07 | 2023-07-04 | 吉林农业大学 | Soil organic matter accumulation prediction method in cattle-corn circulating agriculture |
| CN117808173A (en) * | 2024-02-29 | 2024-04-02 | 四川省水利科学研究院 | Paddy field fertility detection method, related product and planting method based on related product |
| CN117808173B (en) * | 2024-02-29 | 2024-04-30 | 四川省水利科学研究院 | Paddy field fertility detection method, related product and planting method based on related product |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107860889A (en) * | 2017-09-22 | 2018-03-30 | 华南农业大学 | The Forecasting Methodology and equipment of the soil organism |
| US20180249648A1 (en) * | 2015-11-28 | 2018-09-06 | China Institute Of Water Resources And Hydropower Research | Surface water depth information based ground irrigation control method |
| CN108801934A (en) * | 2018-04-10 | 2018-11-13 | 安徽师范大学 | A kind of modeling method of soil organic carbon EO-1 hyperion prediction model |
| CN108828016A (en) * | 2018-05-23 | 2018-11-16 | 北京农业智能装备技术研究中心 | A kind of self-operated measuring unit and method of the soil organism |
| CN111047223A (en) * | 2019-12-31 | 2020-04-21 | 黑龙江八一农垦大学 | Risk assessment method for predicting arsenic content in rice |
-
2020
- 2020-11-05 CN CN202011222922.9A patent/CN112417655A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180249648A1 (en) * | 2015-11-28 | 2018-09-06 | China Institute Of Water Resources And Hydropower Research | Surface water depth information based ground irrigation control method |
| CN107860889A (en) * | 2017-09-22 | 2018-03-30 | 华南农业大学 | The Forecasting Methodology and equipment of the soil organism |
| CN108801934A (en) * | 2018-04-10 | 2018-11-13 | 安徽师范大学 | A kind of modeling method of soil organic carbon EO-1 hyperion prediction model |
| CN108828016A (en) * | 2018-05-23 | 2018-11-16 | 北京农业智能装备技术研究中心 | A kind of self-operated measuring unit and method of the soil organism |
| CN111047223A (en) * | 2019-12-31 | 2020-04-21 | 黑龙江八一农垦大学 | Risk assessment method for predicting arsenic content in rice |
Non-Patent Citations (1)
| Title |
|---|
| 黄兴成等: "低山丘陵区农田土壤有机质预测性制图", 《西南师范大学学报（自然科学版）》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113484384A (en) * | 2021-07-07 | 2021-10-08 | 中国科学院东北地理与农业生态研究所 | Method for predicting in-situ pH values of soils in different plough layers in rice growth period in soda saline-alkali soil |
| CN114048897A (en) * | 2021-10-29 | 2022-02-15 | 西藏电建成勘院工程有限公司 | Method for constructing straw compost organic matter content prediction model based on temperature and humidity conditions |
| CN116384601A (en) * | 2023-06-07 | 2023-07-04 | 吉林农业大学 | Soil organic matter accumulation prediction method in cattle-corn circulating agriculture |
| CN116384601B (en) * | 2023-06-07 | 2023-08-11 | 吉林农业大学 | Soil organic matter accumulation prediction method in cattle-corn circulating agriculture |
| CN117808173A (en) * | 2024-02-29 | 2024-04-02 | 四川省水利科学研究院 | Paddy field fertility detection method, related product and planting method based on related product |
| CN117808173B (en) * | 2024-02-29 | 2024-04-30 | 四川省水利科学研究院 | Paddy field fertility detection method, related product and planting method based on related product |
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Application publication date: 20210226 |