Method for more accurately and simply measuring carbon reserve of celery

Abstract

The invention discloses a method for measuring carbon reserve of celery more accurately and simply, and belongs to the technical field of agricultural ecological carbon sink measurement. The method comprises the following steps: firstly, collecting a celery sample in a quadrat, measuring plant height, stem diameter and relative chlorophyll content (SPAD value), then obtaining the fresh weight of an overground part through destructive sampling, and constructing a three-parameter fresh weight prediction model containing H, Dand SPAD by utilizing multiple regression; secondly, performing non-destructive field measurement in a to-be-measured area to obtain plant heights, stem diameters and SPAD values of a large number of samples, and substituting the plant heights, the stem diameters and the SPAD values into the model to calculate the predicted fresh weight of a single plant; and finally, combining the planting density and the carbon content conversion coefficient to calculate the carbon reserve of the celery in unit area. According to the method, the defects that a traditional drying method is high in destructiveness and long in period are overcome, the model prediction precision is remarkably improved by introducing SPAD physiological indexes, rapid, in-situ and low-cost measurement of the celery carbon reserves is achieved, the relative error can be controlled within 5%, and the method is suitable for accurate accounting of farmland carbon sequestration.

Description

Technical Field

[0001] This invention relates to the fields of agricultural ecology and carbon sequestration measurement technology, specifically to a method for rapidly determining the carbon storage of the aboveground parts of celery based on morphological and physiological indicators. Background Technology

[0002] Agro-ecosystem carbon sequestration is a crucial aspect of global climate change research. Celery, a widely cultivated leafy vegetable, has a short growing cycle and rapid biomass accumulation; therefore, accurately measuring its carbon storage is essential for assessing the carbon budget of farmland ecosystems. Currently, testing and inspection institutions typically use the traditional "whole-plant harvest and drying method" to determine the carbon storage of celery and other vegetable crops. This method involves the following steps: harvesting all celery in a sample plot, washing off the soil, blanching at 105℃ for 30 minutes, drying at 70–80℃ to constant weight, measuring the dry matter weight, and then determining the carbon content of the dry matter using an elemental analyzer to calculate the carbon storage. While this method offers high accuracy, it has significant drawbacks: firstly, it is highly destructive, as the crop cannot continue to grow or be sold after measurement; secondly, the process is cumbersome and time-consuming (drying typically requires more than 48 hours), failing to meet the needs for rapid, large-scale carbon storage assessment; and thirdly, it is lagging, as measurement results are often only available after harvest, making real-time estimation of carbon storage before harvest impossible. Existing improved technologies, such as those based on remote sensing imagery or hyperspectral analysis, while achieving non-destructive monitoring, involve expensive equipment (such as hyperspectral cameras and lidar) and complex data processing, making them unsuitable for rapid celery measurement in individual plots or small-scale farming operations. Therefore, there is an urgent need for a simple, low-cost, and highly accurate in-situ rapid method for measuring celery carbon reserves. Summary of the Invention

[0003] The purpose of this invention is to provide a more accurate and simpler method for measuring carbon storage in celery, solving the problems of existing technologies being highly destructive, time-consuming, and complex. This invention couples morphological indicators (plant height, stem diameter) and physiological indicators (SPAD value, relative chlorophyll content) of celery to construct a high-precision fresh weight biomass prediction model. Combined with planting density and carbon conversion coefficient, this achieves non-destructive, rapid, and accurate measurement of celery carbon storage. To achieve the above objective, this invention provides the following technical solution: A more accurate and simpler method for measuring carbon storage in celery, specifically including the following steps: Step 1: Constructing a regionalized fresh weight prediction model (model training phase). In the target planting area, representative sample plots are selected. Five quadrats are selected using a five-point sampling method, with 10 celery plants consecutively selected in each quadrat as modeling samples. Plant height (H, from the ground to the highest growth point of the plant) is measured using a ruler, and stem base diameter (D, stem diameter at 1 cm above the ground) is measured using a vernier caliper. Simultaneously, the SPAD value of the main functional leaves of the plant is measured using a SPAD-502 chlorophyll meter. After measuring the above indicators, the celery samples were cut at ground level, yellow leaves and roots were removed, and the fresh weight (FW) was immediately measured using an electronic balance. Statistical software (such as SPSS) was used to perform multiple regression analysis on the acquired H, D, SPAD, and FW data to establish a fresh weight biomass prediction model applicable to celery varieties in this region: FW = a × H² + b × D² + c × SPAD + d Where H² (plant height squared) reflects the nonlinear relationship between plant volume and biomass, D² (stem diameter squared) is related to the plant cross-sectional area, and the SPAD value is directly related to the nitrogen nutrition and photosynthetic capacity of the leaves, thus indirectly reflecting the dry matter accumulation rate. Step 2: Non-destructive field measurement and carbon storage estimation (application stage) In the celery plots requiring measurement, 3–5 days before harvest, at least 30 measurement points were randomly selected using an "S"-shaped or checkerboard sampling route. At each measurement point, select a celery plant of moderate growth and measure its height (H), stem diameter (D), and SPAD value according to the method in step 1, without destructively harvesting the plant. Substitute the measured H, D, and SPAD values ​​into the prediction model established in step 1 to calculate the predicted fresh weight of the celery plant. Calculate the average predicted fresh weight (FW_pred_avg) for all measurement points and investigate the actual planting density (ρ) of the plot. Calculate the carbon storage of the celery using the following core algorithm: C = (FW_pred_avg × ρ × f) / 1000, where C is the carbon storage (unit: kg C / m²), f is the carbon content conversion factor, and f = dry-to-fresh ratio × carbon content. The dry-to-fresh ratio can be determined by collecting a small number of representative plants (e.g., 5 plants) and drying them. If no actual measurement conditions are available, the carbon content is usually taken as the IPCC recommended value of 0.45.Preferably, the fitting coefficients a, b, c, and d of the model in step 1 need to be dynamically corrected for different celery varieties (such as native celery and Western celery) and different growth stages to ensure the transfer accuracy of the model. Preferably, if the area to be tested is large (more than 10 mu), sampling units should be divided according to the differences in soil fertility, and a prediction model should be independently established for each unit or the number of sampling points should be increased. Compared with the prior art, the present invention has the following beneficial effects: Non-destructive and simple: The entire measurement process does not require cutting down celery, only contact measurement of plant height, stem diameter, and chlorophyll content is required, the equipment cost is low (only a measuring tape, vernier caliper, and handheld SPAD meter are required), the operation is simple, and ordinary agricultural technicians can master it. Accuracy and timeliness: By introducing the SPAD value, a dynamic physiological indicator, the deficiency of relying solely on morphological indicators (H and D) cannot reflect the current nutritional status and water content of the plant. Actual verification shows that the prediction accuracy of this method (R² can reach more than 0.85) is significantly better than the regression model based solely on morphological parameters, and carbon storage data can be generated in real time before harvest, avoiding the lag of the drying method. High sample representativeness: This method first establishes a model using a small number of destructive samples (50 plants), and then uses a large number of non-destructive samples (more than 30 points) for calculation. This ensures the accuracy of model construction and eliminates the error caused by uneven growth in the field by using a large sample size. It is more representative than the traditional method of directly harvesting a few small plots. Detailed Implementation

[0004] The technical solution of the present invention will be described in detail below with reference to specific embodiments.

[0005] Example 1: Taking "Lunan Shiqin No. 1" celery from a vegetable planting base in Shandong Province as an example, the carbon storage measurement method of the present invention was implemented. Step 1: Model Establishment. A uniformly growing area was selected at the base, and five 1m x 1m quadrats were set up, with 10 plants taken from each quadrat, for a total of 50 plants. Measurement parameters: Plant height (H): average value 55.3 cm; Stem base diameter (D): average value 1.85 cm; SPAD value: average value 42.5; Fresh weight per plant (FW): measured average value 450.2 g / plant. Data Fitting: The 50 sets of data were entered into SPSS software for nonlinear regression analysis, yielding the following fitting equation: FW = 0.035 × H² + 2.86 × D² + 0.57 × SPAD - 32.4. The model's coefficient of determination R² = 0.89, passing the significance test (p < 0.01). Step 2: Carbon Storage Calculation. Three days before celery harvest, 50 non-destructive measurement points were randomly selected from the same plot (avoiding overlap with modeling points). Data Collection: The H, D, and SPAD of 50 celery plants were measured and substituted into the model above. The calculated average predicted fresh weight per plant (FW_pred_avg) for this plot was 460.5 g / plant. Density Survey: The measured planting density (ρ) was 12 plants / m². Conversion Coefficient Determination: Another 5 celery plants were taken, weighed, and dried. The average dry-to-fresh ratio was calculated to be 0.062. The carbon content of the dry matter was determined using an elemental analyzer to be 43.5%. Therefore, the carbon content conversion coefficient f = 0.062 × 0.435 = 0.02697. Carbon Storage Calculation: C = (460.5 g / plant × 12 plants / m² × 0.02697) / 1000 = 0.149 kg C / m². Step 3: Accuracy Verification. To verify the accuracy of this method, after the calculation was completed, all the celery in the five sample plots used for verification was harvested, and the carbon storage was measured using the traditional drying method. The measured carbon storage using the drying method was 0.155 kg C / m². The result calculated by this method was 0.149 kg C / m². The relative error = (0.149 - 0.155) / 0.155 × 100% = -3.87%. The results show that this method, while ensuring non-destructive testing, has a relative error of less than 5%, fully meeting the accuracy requirements for agricultural carbon sequestration. Comparative Example 1: A traditional binary equation based solely on morphological indicators (H and D) was used for prediction, ignoring the SPAD value. Prediction model: FW = 0.042 × H² + 3.12 × D² - 15.6. The calculated carbon storage for the same plot was 0.128 kg C / m². Compared with the measured value (0.155 kg C / m²), the relative error reached -17.4%. The comparison shows that after introducing the SPAD value in this invention, the model error was significantly reduced and the accuracy was greatly improved.In summary, this invention, by constructing a "morphological-physiological" coupled model, achieves a simple, accurate, and non-destructive measurement of carbon storage in celery, providing strong technical support for agricultural carbon trading and farmland carbon sequestration capacity assessment.

Claims

1. A more accurate and simple method for measuring the carbon storage of celery, characterized in that, Includes the following steps: S1. Sample Collection and Basic Parameter Measurement: Within the celery planting area to be tested, at least 5 quadrats were determined using the five-point sampling method or diagonal sampling method. Ten consecutive celery plants with uniform growth were selected from each quadrat as samples. The plant height (H, unit: cm) and stem base diameter (D, unit: cm) of each celery plant were measured on-site, and the actual planting density (ρ, unit: plants / m²) within the quadrat was recorded. S2. Chlorophyll Relative Content Measurement: Using a handheld chlorophyll meter (SPAD-502 or equivalent precision device), measurements were taken at three locations on the main functional leaves (the 3rd to 4th unfolded leaves from the top) of each celery sample plant: the leaf tip, leaf middle, and leaf base. The average value was taken as the relative chlorophyll content (SPAD value) of that celery plant. S3. Fresh weight biomass model fitting: A fresh weight prediction model was established based on destructive sampling. After collecting SPAD values, the aboveground parts of the celery samples were harvested at ground level, and the roots and obviously withered yellow leaves were removed. The fresh weight (FW, unit: g) of the aboveground parts was immediately weighed using a 1% precision electronic balance. The plant height (H), stem base diameter (D) measured in step S1 and the SPAD value measured in step S2 were used as independent variables, and the fresh weight (FW) of the aboveground parts was used as the dependent variable. A fresh weight biomass fitting equation was constructed through multiple regression analysis: FW = a × H² + b × D² + c × SPAD + d where a, b, c, and d are the fitting coefficients of the equation, which are obtained by nonlinear regression analysis of the sample data using SPSS or equivalent statistical software. S4. Regional carbon storage calculation: S41. No less than 30 nondestructive measurement points were randomly selected in the area to be measured. Steps S1 and S2 were repeated, and the H, D, and SPAD values ​​of each measurement point were recorded. S42. Substitute the parameters measured in S41 into the fresh weight biomass fitting equation established in step S3 to calculate the predicted fresh weight (FW_pred) of a single celery plant at each measurement point; S43. Calculate the carbon storage (C, unit: kgC / m²) of the celery in the area to be measured according to the following formula: C = (FW_pred_avg × ρ × f) / 1000 Where, FW_pred_avg is the average predicted fresh weight per plant (unit: g / plant) calculated in step S42, ρ is the planting density (unit: plant / m²), and f is the biomass carbon content conversion coefficient of celery, which is 0.089 or corrected by the measured dry-wet ratio and carbon content.

2. The method for more accurately and easily measuring the carbon storage of celery according to claim 1, characterized in that, The size of the quadrat mentioned in step S1 is set according to the width of the planting ridge. The length of the quadrat is not less than 5 meters and the width covers the entire planting ridge.

3. The method for more accurately and easily measuring the carbon storage of celery according to claim 1, characterized in that, The multiple regression analysis described in step S3 has a fitting coefficient a ranging from 0.02 to 0.06, a b ranging from 1.5 to 4.5, a c ranging from 0.3 to 0.9, and a constant term ranging from -50 to 10. The specific values ​​vary depending on the celery variety and growth stage.

4. A more accurate and simple method for measuring the carbon storage of celery according to claim 1, characterized in that, The biomass carbon content conversion factor f mentioned in step S43 is determined by the following formula: f = (FW_dry / FW_fresh) ×C_content, where FW_dry is the weight of the dry matter after drying to constant weight, FW_fresh is the corresponding fresh weight, and C_content is the carbon content in the dry matter, which is determined by an elemental analyzer. If no actual measurement conditions are available, C_content is taken as the standard value of 0.

45.

5. A more accurate and simple method for measuring the carbon storage of celery according to claim 1, characterized in that, The method involves data collection and calculation 3 to 5 days before celery harvest to avoid measurement errors caused by water loss after harvest.