Simple method for measuring carbon reserve of alfalfa

Abstract

The invention relates to the technical field of crop planting, and discloses a simple method for measuring alfalfa carbon reserve, which comprises the following steps: S1, acquiring state data of alfalfa in a to-be-measured area, the state data comprising overground part dry matter biomass and underground part dry matter biomass; s2, judging the phenological period of the alfalfa while obtaining the state data, and determining a corresponding phenological period correction factor according to the phenological period; and S3, based on the state data, calculating living biomass carbon reserves corresponding to the overground part dry matter biomass and the underground part dry matter biomass by adopting a preset alfalfa biomass core carbon content coefficient and combining the phenological period correction factor. According to the method, the correlation prediction model is constructed, the phenological period correction factor is introduced, and layered calculation is performed on the litter, so that lossless, dynamic and refined metering of the alfalfa carbon reserve is realized, and the operation convenience and the result accuracy are improved.

Description

Technical Field

[0001] This invention relates to the field of crop cultivation technology, specifically a simple method for measuring carbon storage in alfalfa. Background Technology

[0002] Alfalfa, as an important legume forage crop, occupies a vital position in global agricultural ecosystems, playing a significant role in biological carbon sequestration. Accurate measurement of carbon storage in alfalfa grassland ecosystems is fundamental to assessing their ecological functions, developing scientific grassland management strategies, and participating in carbon trading.

[0003] Methods for measuring alfalfa carbon storage primarily rely on biomass measurement and carbon content analysis. In practice, obtaining the aboveground biomass of alfalfa is relatively direct, but measuring its underground root biomass presents inherent technical difficulties. While the traditional whole-excavation method can obtain relatively accurate underground biomass data, this method is destructive, consuming significant manpower and time, and cannot conduct continuous observations at the same location, limiting its application in large-scale, high-frequency monitoring.

[0004] Existing metrology methods typically employ a fixed carbon content coefficient when converting biomass into carbon storage. However, alfalfa, as a living organism, exhibits dynamic changes in its carbon content throughout its growth and development stages (i.e., phenological stages). For example, physiological metabolic activities differ between the rapid growth and maturity stages, leading to variations in carbon distribution and accumulation across different plant organs. Using a single, static carbon content coefficient ignores these physiological differences caused by phenological changes, introducing systematic bias into the metrology results and reducing measurement accuracy.

[0005] A complete alfalfa ecosystem carbon pool includes not only living plants but also the litter layer on the ground. During litter decomposition, the carbon content is not constant but gradually decreases as decomposition progresses. Existing metrology methods often treat all litter as a whole, using uniform parameters for estimation, failing to distinguish the significant differences in carbon residue rates at different decomposition stages. This results in insufficient precision in the accounting of the non-living carbon pool, affecting the completeness and accuracy of the overall ecosystem carbon storage assessment. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a simplified method for measuring alfalfa carbon storage, which solves the problems of complex and destructive underground biomass acquisition operations and low accuracy and application efficiency of existing alfalfa carbon storage measurement methods due to neglecting the dynamic changes of vegetation phenology and the differences in litter decomposition stages.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a simple method for measuring alfalfa carbon storage, comprising the following steps:

[0008] S1. Obtain the status data of alfalfa in the area to be tested, the status data including aboveground dry matter biomass and underground dry matter biomass;

[0009] S2. While acquiring the status data, determine the phenological stage of the alfalfa and determine a corresponding phenological stage correction factor based on the phenological stage;

[0010] S3. Based on the state data, a preset alfalfa biomass core carbon content coefficient is used, and combined with the phenological correction factor, the living biomass carbon storage corresponding to the aboveground dry matter biomass and the underground dry matter biomass is calculated.

[0011] S4. Obtain the dry matter biomass of surface litter in the area to be tested, and determine the corresponding carbon residue rate coefficient based on the decomposition stage of dry matter biomass to calculate the carbon storage of litter.

[0012] S5. Add the carbon storage of the living biomass to the carbon storage of the litter to obtain the carbon storage of alfalfa per unit area.

[0013] Preferably, in step S1, acquiring the state data of alfalfa within the test area includes:

[0014] Record the growth years of alfalfa in the area to be tested;

[0015] The underground dry matter biomass is calculated based on the correlation prediction model between the aboveground dry matter biomass and the growth years.

[0016] The correlation prediction model has one input variable, which includes a regression model of the aboveground dry matter biomass and the growth years.

[0017] Preferably, the calculated underground dry matter biomass includes:

[0018] Multiply the value of the aboveground dry matter biomass by a preset first regression coefficient to obtain the first product;

[0019] The value of the alfalfa's growth years is multiplied by a preset second regression coefficient to obtain a second product;

[0020] The first product, the second product, and a preset regression constant term are added together to obtain the calculated result of the underground dry matter biomass. The calculation process includes the following formula:

[0021]

[0022] In the formula, B represents the calculated underground dry matter biomass. ag β1 represents the aboveground dry matter biomass; N represents the growth years; β1, β2, and β0 represent the first regression coefficient, the second regression coefficient, and the regression constant, respectively.

[0023] Preferably, in step S2, determining the phenological stage of the alfalfa includes:

[0024] Through instrumental analysis, the overall growth and development status of the alfalfa population was identified, and the development status was divided into the greening stage, budding stage, full bloom stage, and pod formation stage.

[0025] Retrieve the phenological correction factor corresponding to the determined phenological period from a lookup table that stores the correspondence between different phenological periods and phenological correction factors.

[0026] Preferably, the step of identifying the overall growth and development status of the alfalfa population through instrumental analysis specifically includes:

[0027] Portable spectrometers or drones equipped with multispectral sensors were used to acquire spectral reflectance data of alfalfa canopies in the area to be tested.

[0028] Based on the acquired spectral reflectance data, a preset vegetation index is calculated, wherein the vegetation index is the normalized differential vegetation index.

[0029] The calculated vegetation index value is compared with a threshold range that includes different value intervals and the corresponding phenological periods to determine the current phenological period of the alfalfa population.

[0030] Preferably, in step S3, the living biomass carbon storage includes:

[0031] The total living biomass is obtained by adding the aboveground dry matter biomass to the underground dry matter biomass.

[0032] Multiply the total living biomass by the alfalfa biomass core carbon content coefficient to obtain the preliminary carbon storage value;

[0033] The preliminary carbon storage value is then multiplied by the phenological correction factor to obtain the final living biomass carbon storage.

[0034] Preferably, the process of multiplying the total living biomass by the alfalfa biomass core carbon content coefficient specifically includes:

[0035] The alfalfa biomass core carbon content coefficient is a preset fixed value;

[0036] The preset fixed value is 0.30.

[0037] Preferably, in step S4, the carbon residue rate coefficient includes:

[0038] The dry matter biomass of the surface litter was sorted into three categories based on its morphology and texture: fresh litter, semi-decomposed litter, and highly decomposed litter.

[0039] A different carbon residue coefficient is assigned to each of the fresh litter, semi-decomposed litter, and highly decomposed litter.

[0040] The carbon residue coefficient of the fresh litter is greater than that of the semi-decomposed litter, and the carbon residue coefficient of the semi-decomposed litter is greater than that of the highly decomposed litter.

[0041] Preferably, assigning different carbon residue coefficients to the fresh litter, semi-decomposed litter, and highly decomposed litter specifically includes:

[0042] The value of the carbon residue rate coefficient of the fresh litter is set to be equal to the value of the carbon content coefficient of the alfalfa biomass core.

[0043] The carbon residue coefficients of the semi-decomposed litter and the highly decomposed litter are respectively set to values ​​smaller than the carbon residue coefficient of the fresh litter.

[0044] Preferably, in step S5, the alfalfa carbon storage per unit area includes:

[0045] Before adding the living biomass carbon storage to the litter carbon storage, the litter carbon storage at different decomposition stages is summed to obtain a total litter carbon storage.

[0046] The carbon storage of litter at different decomposition stages is the product of the dry matter biomass of fresh litter, semi-decomposed litter, and highly decomposed litter and their respective carbon residue coefficients.

[0047] The carbon storage per unit area of ​​alfalfa is obtained by adding the carbon storage of the living biomass to the carbon storage of the total litter.

[0048] This invention provides a simple method for measuring alfalfa carbon reserves. It has the following beneficial effects:

[0049] 1. This invention constructs a correlation prediction model based on the aboveground dry matter biomass and growth years to calculate the underground dry matter biomass, thereby avoiding the destructive root digging and sampling operations required in traditional methods, simplifying the on-site data collection process, and reducing the operational difficulty and time cost.

[0050] 2. This invention introduces a correction factor corresponding to the phenological stages of alfalfa to dynamically correct the calculation of carbon storage in living biomass. This method enables the measurement results to reflect the physiological differences of alfalfa at different growth and development stages, transforming the static average value calculation into a dynamic assessment related to growth rhythms, thus improving the systematic nature of the measurement method.

[0051] 3. This invention divides the decomposition stages of surface litter and assigns differentiated carbon residue coefficients to fresh, semi-decomposed, and highly decomposed litter. This refined processing method enables differentiated measurement of non-living carbon pools, constructs a more complete carbon storage accounting system, and makes the final results closer to the actual process of material cycling in ecosystems. Attached Figure Description

[0052] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0053] Figure 2 A schematic diagram of a correlation prediction model for underground dry matter biomass.

[0054] Figure 3 A schematic diagram of the sub-process for analyzing and determining the phenological stage of alfalfa;

[0055] Figure 4 This is a schematic diagram of the litter layering calculation method of the present invention. Detailed Implementation

[0056] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] See attached document Figures 1-4 , Figure 1 This is a flowchart illustrating a method for measuring alfalfa carbon reserves according to an embodiment of the present invention. The present invention provides a simplified method for measuring alfalfa carbon reserves, which may include the following steps:

[0058] First, in step S1, the state data of alfalfa within the test area is acquired. This state data includes aboveground dry matter biomass and belowground dry matter biomass. In one specific embodiment, the process of acquiring the belowground dry matter biomass is calculated using an association prediction model. This process includes: first, recording the growth years of alfalfa within the test area; then, using the measured aboveground dry matter biomass and the recorded growth years as input variables, and substituting them into a preset regression model for calculation.

[0059] In step S2, the phenological stage of the alfalfa at the time the state data was acquired is determined, and a corresponding phenological stage correction factor is determined based on this phenological stage. This phenological stage can be divided into different growth and development stages such as the greening stage, budding stage, full bloom stage, and pod formation stage.

[0060] In step S3, based on the state data obtained in step S1 and combined with the phenological correction factor determined in step S2, a preset alfalfa biomass core carbon content coefficient is used to calculate the living biomass carbon storage. In one embodiment, the preset value of this core carbon content coefficient is 0.30. The calculation process is as follows: the aboveground dry matter biomass and the underground dry matter biomass are added to obtain the total living dry matter biomass, and then this sum is multiplied by the core carbon content coefficient and the phenological correction factor.

[0061] In step S4, the dry matter biomass of litter on the surface of the area to be tested is obtained, and the corresponding carbon residue coefficient is determined according to its decomposition stage to calculate the litter carbon storage. This step includes sorting the litter into three categories based on its physical form and texture: fresh litter, semi-decomposed litter, and highly decomposed litter, and assigning a different carbon residue coefficient to each category, with values ​​decreasing sequentially. For example, the carbon residue coefficient of fresh litter can be set to be equal to the value of the core carbon content coefficient of alfalfa biomass.

[0062] In step S5, the carbon storage of living biomass calculated in step S3 is summed with the carbon storage of litter calculated in step S4. The litter carbon storage is the sum of the products of the dry matter biomass of litter at each decomposition stage and its corresponding carbon residue coefficient. The sum of the two carbon storage values ​​is the alfalfa carbon storage per unit area.

[0063] See attached document Figures 1-4 In this embodiment, the specific execution process of step S1 begins with the determination of aboveground dry matter biomass. Several 1m × 1m standard quadrats are set up within the testing area. To ensure the representativeness of the sampling, the location of the quadrats is determined using a random number method. Within each quadrat, all alfalfa plants are cut at ground level using a sickle or scissors, and all aboveground parts (including stems and leaves) are collected into pre-numbered sample bags.

[0064] The collected aboveground samples were brought back to the laboratory, where their fresh weight was first measured and recorded using an electronic balance. The samples were then placed in a constant-temperature drying oven at 65°C for drying. During this process, the sample weight was measured every 12 hours. A constant weight was considered reached when the difference between two consecutive weighings was less than 0.5%. This weight is the total dry matter weight of the aboveground alfalfa within the plot. Dividing this total dry matter weight by the plot area (1 square meter) yields the aboveground dry matter biomass per unit area (B0.05). ag ).

[0065] Next, the underground dry matter biomass is obtained. This embodiment uses an association prediction model for calculation, a method that does not require destructive root excavation. First, the actual growth years (N) of alfalfa in the area to be tested are accurately recorded by consulting the planting records of the plot or inquiring with field managers.

[0066] Then, the measured aboveground dry matter biomass (B) ag The data, including the number of years of growth recorded (N), were used as input variables and substituted into a pre-defined linear regression model. The coefficients of this model (β1, β2, and β0) were empirical constants obtained by fitting a large amount of previous experimental data, which included simultaneous measurement of aboveground biomass and complete excavation and measurement of underground root systems.

[0067] The calculation process of this correlation prediction model is defined by the following formula:

[0068]

[0069] In the formula, B represents the calculated underground dry matter biomass. ag β1 represents the aboveground dry matter biomass; N represents the growth years; β1, β2, and β0 represent the first regression coefficient, the second regression coefficient, and the regression constant, respectively.

[0070] See attached document Figures 1-4 , Figure 1 This is a flowchart illustrating a method for measuring alfalfa carbon reserves according to an embodiment of the present invention. In this embodiment, the specific execution process of step S2 is as follows.

[0071] The purpose of this step is to objectively determine the growth and development status of alfalfa and obtain a phenological correction factor (k) for dynamically correcting carbon storage calculations. This step is performed within the same period as the status data acquisition in step S1 to ensure consistency between phenological and biomass data.

[0072] In this embodiment, the determination of phenological periods is accomplished through instrumental analysis. Specifically, a drone equipped with a multispectral sensor is used to collect data on the area to be measured in step S1. The multispectral sensor includes channels capable of receiving spectral information in the red (R) band and near-infrared (NIR) band. The drone flies over the area to be measured at a constant speed at a flight altitude of 30 meters, with 80% forward overlap and 70% lateral overlap, acquiring multispectral image data of the area. The acquired raw image data is then radiometrically calibrated and image stitched to generate a complete orthophoto map of the area to be measured with accurate spectral reflectance information.

[0073] Based on this orthophoto map, a preset vegetation index, namely the Normalized Differential Vegetation Index (NDVI), is calculated. The calculation formula is as follows:

[0074]

[0075] In the formula, NIR is the spectral reflectance in the near-infrared band; R is the spectral reflectance in the red band; and NDVI is the normalized differential vegetation index. The NDVI values ​​of all pixels within the core sampling area are extracted, and their average value is calculated.

[0076] The calculated average NDVI value is compared with a preset threshold table that includes the correspondence between different NDVI value ranges and phenological stages. For example, this threshold table can be preset as follows: when the NDVI value is less than 0.45, it is determined to be the greening stage; when the NDVI value is between 0.45 (inclusive) and 0.65 (exclusive), it is determined to be the budding stage; when the NDVI value is between 0.65 (inclusive) and 0.80 (exclusive), it is determined to be the full bloom stage; and when the NDVI value is greater than or equal to 0.80, it is determined to be the pod-setting stage. Through this comparison, the current phenological stage of the alfalfa population can be determined.

[0077] After determining the phenological period, the corresponding phenological period correction factor (k) is retrieved from a pre-defined lookup table that stores the correspondence between different phenological periods and phenological period correction factors. An example of this lookup table is as follows:

[0078] The phenological correction factor (k) for the greening period is 0.95.

[0079] The phenological correction factor (k) for the budding stage is 1.05.

[0080] During the peak flowering period, the corresponding phenological correction factor (k) is 1.10;

[0081] The phenological correction factor (k) for the pod-setting stage is 1.08.

[0082] By performing the above operations, step S2 will finally output a definite phenological correction factor (k), which will serve as a key correction parameter for calculating the carbon storage of living biomass in step S3.

[0083] See attached document Figures 1-4 , Figure 1 This is a flowchart illustrating a method for measuring alfalfa carbon reserves according to an embodiment of the present invention. In this embodiment, the specific execution process of step S3 is as follows.

[0084] The purpose of this step is to calculate the living biomass carbon storage per unit area (C) using the biomass data obtained in step S1 and the dynamic correction parameters determined in step S2. L The input data for this calculation process is: aboveground dry matter biomass per unit area (B). ag ), and underground dry matter biomass per unit area (B bg ) and phenological period correction factor (k).

[0085] This embodiment introduces a preset alfalfa biomass core carbon content coefficient (C). c This coefficient serves as the basis for calculations. It represents the relatively stable proportion of basic carbon content in alfalfa plants under different growth environments. In this embodiment, the alfalfa biomass core carbon content coefficient (C0.05) is used as the basis for these calculations. c It is set to a fixed value, namely 0.30.

[0086] The specific calculation process is broken down into the following consecutive operations. First, the aboveground dry matter biomass per unit area (B) obtained in step S1 is... ag ) and the dry matter biomass per unit area of ​​underground part (B bg Summing these values ​​yields the total living dry matter biomass per unit area (B). t ).

[0087] Subsequently, the total living dry matter biomass (B) obtained will be... t ) and the alfalfa biomass core carbon content coefficient (C c Multiplying these values ​​yields a preliminary carbon storage value (C) that is not corrected for phenological periods. p This value reflects the basic carbon reserves without taking into account differences in growth stages.

[0088] Finally, this preliminary carbon storage value (C p This is multiplied by the phenological correction factor (k) determined in step S2. The purpose of this operation is to dynamically adjust the baseline carbon storage using the correction factor to reflect the fluctuations in carbon content caused by changes in physiological activity during specific phenological stages (such as the flowering period). The result of the multiplication is the final, dynamically corrected biomass carbon storage (Cb). L ).

[0089] The entire calculation process of step S3 can be defined by the following comprehensive formula:

[0090] C L = (B ag +B bg )·C c ·k;

[0091] In the formula, C L B represents the final calculated carbon storage per unit area of ​​living biomass. ag B is the aboveground dry matter biomass per unit area measured in step S1; bg C is the dry matter biomass per unit area of ​​underground material calculated by the model in step S1; c is the preset alfalfa biomass core carbon content coefficient, with a value of 0.30; k is the correction factor determined in step S2 based on phenological stages.

[0092] See attached document Figures 1-4 , Figure 1 This is a flowchart illustrating a method for measuring alfalfa carbon reserves according to an embodiment of the present invention. In this embodiment, the specific execution process of step S4 is as follows.

[0093] The purpose of this step is to perform a refined measurement of the non-living carbon pool, i.e., surface litter, to obtain the total litter carbon storage per unit area (C). D This step first requires obtaining the total dry matter biomass of litter, and this is done simultaneously with the collection of aboveground samples in step S1. Specifically, within the core sampling area, all litter on the ground surface, including fallen leaves and stems, is manually collected. The collected litter samples are cleaned to remove attached soil particles, then dried at 65°C to constant weight. The total dry matter biomass per unit area is then obtained by weighing.

[0094] Next, the dried litter samples were sorted into three different decomposition stage categories based on their physical morphology and structural integrity: fresh litter, semi-decomposed litter, and highly decomposed litter. The specific criteria for this sorting process were as follows:

[0095] Fresh litter: refers to recently shed plant debris that has largely retained its original color (such as yellow or light brown) and intact tissue structure, making it easy to identify as leaves or stems, and its texture still retains a certain degree of flexibility.

[0096] Semi-decomposed litter: refers to plant remains that have decomposed over a period of time. Their color has turned dark brown, their tissue structure has been partially broken, and their texture has become brittle, but the original organ morphology can still be roughly identified.

[0097] Highly decomposed litter: refers to plant remains that have undergone long-term decomposition. It is dark brown in color, has completely lost its original structure, and is in the form of fragments or powder. Its original organs cannot be identified, and it is brittle.

[0098] After sorting, the dry matter biomass of litter belonging to these three categories was weighed and denoted as B. D,fresh (Fresh), B D,semi (Semi-decomposition), and B D,high (Highly decomposed)

[0099] Subsequently, different preset carbon residue coefficients were assigned to the litter at each of the three decomposition stages. These coefficients were set according to a basic principle: as decomposition deepens, carbon is lost due to microbial respiration, thus the carbon residue coefficient decreases sequentially. That is, the carbon residue coefficient of fresh litter is greater than that of semi-decomposed litter, and the carbon residue coefficient of semi-decomposed litter is greater than that of highly decomposed litter.

[0100] In this embodiment, the specific values ​​of these coefficients are set as follows:

[0101] Carbon residue coefficient of fresh litter (k) D,fresh The value of ) is set to be the same as the alfalfa biomass core carbon content coefficient (C) described in step S3. c The values ​​are equal, i.e., 0.30.

[0102] Carbon residue coefficient of semi-decomposed litter (k) D,semi The value of ) is set to 0.20.

[0103] Carbon residue coefficient (k) of highly decomposed litter D,high The value of ) is set to 0.10.

[0104] Finally, the litter dry matter biomass at each decomposition stage is multiplied by its corresponding carbon residue coefficient to calculate the carbon storage at that stage. The carbon storage at all three stages is then summed to obtain the total litter carbon storage (C0). D The calculation process can be defined by the following formula:

[0105] C D = (B D,fresh ·k D,fresh )+(B D,semi ·k D,semi )+(B D,high ·k D,high );

[0106] In the formula, C D B represents the total litter carbon storage; D,fresh B is the dry matter biomass of fresh litter; D,semiB is the dry matter biomass of semi-decomposed litter; D,high The dry matter biomass of highly decomposed litter; k D,fresh k is the carbon residue coefficient of fresh litter; D,semi k is the carbon residue coefficient of semi-decomposed litter; D,high The carbon residue coefficient of highly decomposed litter.

[0107] See attached document Figures 1-4 , Figure 1 This is a flowchart illustrating a method for measuring alfalfa carbon reserves according to an embodiment of the present invention. In this embodiment, the specific execution process of step S5 is as follows.

[0108] The purpose of this step is to integrate the various carbon pool components calculated in the preceding steps, ultimately obtaining a comprehensive value representing the total carbon storage per unit area of ​​the alfalfa ecosystem. The input data for this step is: the carbon storage per unit area of ​​living biomass (C) calculated in step S3. L ), and the total litter carbon storage per unit area (C) calculated in step S4. D ).

[0109] Before performing the calculations in this step, confirm the total litter carbon storage (C) output in step S4. D This already includes carbon reserves at all stages of decomposition. That is, the value is the sum of the product of the dry matter biomass of fresh litter, semi-decomposed litter, and highly decomposed litter and their corresponding carbon residue coefficients.

[0110] The core operation of this step is to measure the carbon storage per unit area of ​​living biomass (C). L ) and total litter carbon storage per unit area (C D The arithmetic summation operation combines the two main carbon pools in the alfalfa ecosystem—the living plant carbon pool and the surface non-living organic matter carbon pool—into a single total.

[0111] The final result obtained by adding them together is the alfalfa carbon storage per unit area (C) measured by the method of this invention. total The entire calculation process of step S5 can be defined by the following formula:

[0112] C total =C L +C D C total =C L +C D ;

[0113] In the formula, C total This represents the total carbon storage per unit area of ​​alfalfa, calculated in the final step; C LThis represents the carbon storage per unit area of ​​living biomass calculated in step S3; C D This represents the total litter carbon storage per unit area calculated in step S4.

[0114] By performing the above operations, a definite numerical result is finally output: alfalfa carbon storage per unit area (C). total This result can be used to assess the carbon sequestration capacity of a specific site. To obtain the total carbon storage at the regional scale, the carbon storage value per unit area can be multiplied by the total area of ​​the target region.

Claims

1. A simple method for measuring the carbon stock of alfalfa, characterized by, The method comprises the following steps: S1, obtaining state data of alfalfa in a to-be-measured area, the state data comprising aboveground dry matter biomass and underground dry matter biomass; S2, determining a phenological phase in which the alfalfa is located while obtaining the state data, and determining a corresponding phenological phase correction factor according to the phenological phase; S3, calculating living biomass carbon storage corresponding to the aboveground dry matter biomass and the underground dry matter biomass based on the state data, a preset alfalfa biomass core carbon content coefficient, and the phenological phase correction factor; S4, obtaining dry matter biomass of surface litter in the to-be-measured area, and calculating litter carbon storage according to a carbon residual rate coefficient corresponding to a dry matter biomass decomposition stage; S5, adding the living biomass carbon storage and the litter carbon storage to obtain alfalfa carbon storage per unit area.

2. A simple method of measuring carbon stock of alfalfa as claimed in claim 1, wherein, In step S1, the obtaining of the state data of alfalfa in the to-be-measured area comprises: recording the growth age of alfalfa in the to-be-measured area; calculating the underground dry matter biomass based on a correlation prediction model of the aboveground dry matter biomass and the growth age; the correlation prediction model is a regression model of an input variable, and the input variable comprises the aboveground dry matter biomass and the growth age.

3. A simple method of measuring carbon stock of alfalfa as claimed in claim 2, wherein, The calculation of the underground dry matter biomass comprises: multiplying the numerical value of the aboveground dry matter biomass by a preset first regression coefficient to obtain a first product; multiplying the numerical value of the growth age of the alfalfa by a preset second regression coefficient to obtain a second product; adding the first product, the second product, and a preset regression constant term, and the sum is the calculation result of the underground dry matter biomass, and the calculation process comprises the following formula: wherein B is the calculated belowground dry matter biomass; B ag B is the aboveground dry matter biomass; N is the number of growing years; β1, β2, and β0are the first regression coefficient, the second regression coefficient, and the regression constant term, respectively.

4. A simple method of measuring carbon stock of alfalfa as claimed in claim 1, wherein, In step S2, the determination of the phenological phase in which the alfalfa is located comprises: identifying the overall growth and development status of the alfalfa population through instrument analysis, and dividing the development status into a green-up period, a budding period, a full-bloom period, and a podding period; obtaining a phenological phase correction factor corresponding to the determined phenological phase from a lookup table in which different phenological phases and phenological phase correction factors are stored in a corresponding relationship.

5. A simple method of measuring carbon stock of alfalfa as claimed in claim 4, wherein, The identification of the overall growth and development status of the alfalfa population through instrument analysis specifically comprises: obtaining spectral reflectance data of the alfalfa canopy in the to-be-measured area by using a portable spectrometer or a drone equipped with a multispectral sensor; calculating a preset vegetation index based on the obtained spectral reflectance data, the vegetation index being a normalized difference vegetation index; comparing the numerical value of the calculated vegetation index with a threshold range comprising different numerical value intervals and corresponding relationships of phenological phases, to determine the current phenological phase of the alfalfa population.

6. A simple method of measuring carbon stock of alfalfa as claimed in claim 1, wherein, In step S3, the living biomass carbon storage comprises: adding the aboveground dry matter biomass and the underground dry matter biomass to obtain total living biomass; multiplying the total living biomass and the alfalfa biomass core carbon content coefficient to obtain a preliminary carbon storage value; The preliminary carbon storage value is multiplied by the phenological correction factor to obtain the live biomass carbon storage.

7. A simple method of measuring carbon stock of alfalfa as claimed in claim 6 wherein, The process of multiplying the total live biomass by the alfalfa biomass core carbon content coefficient specifically includes: The alfalfa biomass core carbon content coefficient is a preset fixed value; The preset fixed value is 0.

30.

8. A simple method of measuring carbon stock of alfalfa as claimed in claim 1, wherein, The carbon residual rate coefficient in step S4 includes: The dry matter biomass of the surface litter is sorted into three categories of fresh litter, semi-decomposed litter and highly decomposed litter according to the form and texture; The fresh litter, semi-decomposed litter and highly decomposed litter are respectively assigned a different carbon residual rate coefficient; The carbon residual rate coefficient of the fresh litter is greater than that of the semi-decomposed litter, and the carbon residual rate coefficient of the semi-decomposed litter is greater than that of the highly decomposed litter.

9. A simple method of measuring carbon stock in alfalfa as claimed in claim 8, wherein, The process of respectively assigning a different carbon residual rate coefficient to the fresh litter, semi-decomposed litter and highly decomposed litter specifically includes: The value of the carbon residual rate coefficient of the fresh litter is set to be equal to the value of the alfalfa biomass core carbon content coefficient; The carbon residual rate coefficients of the semi-decomposed litter and the highly decomposed litter are respectively set to be less than the value of the carbon residual rate coefficient of the fresh litter.

10. A simple method of measuring carbon stock of alfalfa as claimed in claim 1, wherein, The alfalfa carbon storage per unit area in step S5 includes: Before the live biomass carbon storage is added to the litter carbon storage, the litter carbon storages at different decomposition stages are summed to obtain a total litter carbon storage; The litter carbon storages at different decomposition stages are respectively the products of the dry matter biomasses of the fresh litter, semi-decomposed litter and highly decomposed litter and their respective carbon residual rate coefficients; The live biomass carbon storage is added to the total litter carbon storage to obtain the alfalfa carbon storage per unit area.

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