A method and system for estimating the spatio-temporal distribution of natural rubber production

Abstract

The application provides a method and system for estimating the spatio-temporal distribution of natural rubber yield, and belongs to the technical field of forest growth simulation and physiological and ecological model application; the method comprises the following steps: acquiring multi-source spatio-temporal driving data of a target region, converting the multi-source spatio-temporal driving data into a spatial grid format, and calculating the current net primary productivity of a stand at a preset time scale based on a 3-PG model; setting latex as an independent secondary metabolic carbon sink, reconstructing the carbon distribution mechanism of the 3-PG model and obtaining an improved 3-PG model; performing parameter sensitivity analysis and parameter calibration on the improved 3-PG model; inputting the multi-source spatio-temporal driving data of each pixel into the 3-PG model after parameter calibration, performing iterative operation with a preset time scale as a step, using a rubber forest spatial distribution map to perform mask processing on the operation result, removing the values in the non-rubber forest region, and generating a spatial distribution grid map of the natural rubber yield at the preset time scale. The application effectively improves the problems of large prediction errors of stem biomass and latex yield.

Description

Technical Field

[0001] This invention belongs to the technical field of forest growth simulation and physiological and ecological model application, specifically involving a method and system for estimating the spatiotemporal distribution of natural rubber production. Background Technology

[0002] The 3-PG (Physiological Principles Predicting Growth) ecophysiological model mainly comprises three core components: biomass production, biomass allocation, and water-carbon balance. Model parameters are calibrated and estimated using field quadrat data and least squares methods. This model can fully utilize available environmental information, fitting long-term meteorological, soil, and stand age-related data into the physiological process model to coordinate continuous tree growth and carbon assimilation. By simulating the internal carbon transfer and allometric growth distribution patterns within plants, it can reflect the cumulative changes in stand biomass over many years. This model is currently widely used for estimating forest productivity. As natural rubber is an important strategic resource, estimating rubber production from rubber plantations has always been a hot topic, providing a certain reference for the sustainable development of rubber plantations.

[0003] Currently, existing literature primarily employs two methods for estimating the yield of economic crops such as rubber plantations. One method utilizes multi-source remote sensing vegetation indices combined with environmental factors for statistical and machine learning estimation, as described in the paper "Enhanced Rubber Yield Prediction in High-Density Plantation Areas Using a GIS and Machine Learning-Based Forest Classification and Regression Model." The other method uses process models based on physiological mechanisms for dynamic simulation to estimate stand yield. Currently, the mainstream forestry process simulation models include 3-PG, as described in the paper "Application of the 3-PG model to predict growth and yield of plantations," and the LUCIA model, as described in the paper "Climbing the mountain fast but smart: Modelling rubber tree growth and latex yield under climate change." However, the remote sensing and machine learning methods, due to the significant biological differences between vegetation greenness information and latex synthesis mechanisms, are prone to generalization bottlenecks under complex climatic conditions when using indirect features for yield estimation, leading to large errors in the final estimation results. In comparison, estimation methods using process models have clearer physical and physiological mechanisms and improved accuracy. However, complex models such as LUCIA, due to their large parameter systems, easily overlook the difficulty of obtaining parameters for regional application, especially since rubber plantations often experience high-intensity human tapping and latex removal. Compared to LUCIA, 3-PG is a simplified, more accessible, and general mechanistic model, making it more suitable for dynamic estimation of rubber plantation yields on a large scale. Current technologies using ecophysiological models like 3-PG to estimate the yield of economic forests such as natural rubber suffer from several drawbacks. The underlying carbon allocation logic of these models is primarily designed for the vegetative organs of ordinary timber forests, neglecting the secondary metabolic carbon consumption caused by tapping. Typically, attempts are made to improve accuracy by directly adjusting conventional growth parameters. While simple, this method fails to address the mechanistic issue of abnormal dry matter accumulation due to the failure to deduct latex carbon, and the competition mechanism between carbon sources and sinks among the parameters remains unclear.

[0004] Therefore, how to more effectively improve the performance of the 3-PG model in terms of carbon allocation mechanisms and secondary metabolic consumption, and achieve more accurate estimation of natural rubber production, is an urgent problem to be solved. Summary of the Invention

[0005] The purpose of this invention is to propose a method and system for estimating the spatiotemporal distribution of natural rubber production, based on carbon balance mechanism reconstruction to improve the problem of large prediction errors in stem biomass and rubber production.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows:

[0007] A method for estimating the spatiotemporal distribution of natural rubber production includes the following steps:

[0008] S1. Acquire multi-source spatiotemporal driving data of the target area, including remote sensing data, meteorological data, site condition data, and sample plot biomass data; convert the multi-source spatiotemporal driving data into a spatial raster format, and calculate the current net primary productivity of the forest stand at a preset time scale based on the 3-PG model.

[0009] S2. By setting latex as an independent secondary metabolic carbon sink, the carbon allocation mechanism of the 3-PG model is reconstructed, and an improved 3-PG model is obtained:

[0010] Based on the latex conversion of the basic latex yield and the current net primary productivity, the potential latex yield under a preset time scale is dynamically estimated by combining multiple constraint factors affecting latex yield. The multiple constraint factors include tapping ratio constraint factor, latex duct physiological maturity factor, aging and dermal disease factor, rest tapping factor, water turgor pressure factor and precipitation interference factor.

[0011] A metabolic conversion coefficient is introduced to convert the potential latex production into the actual carbon consumption required for latex synthesis; and the actual carbon allocation ratio of latex is determined based on the ratio of actual carbon consumption to current net primary productivity.

[0012] The carbon allocation ratio of the root system is determined by deducting the carbon allocation ratio corresponding to the root system and latex from the total allocation share of the net primary productivity in the current period, and then allocating the remaining carbon source to the trunk and leaves of the tree to obtain the carbon allocation ratio of the trunk and leaves.

[0013] S3. Perform parameter sensitivity analysis and parameter calibration on the improved 3-PG model;

[0014] S4. Input the multi-source spatiotemporal driving data of each pixel into the 3-PG model after parameter calibration, perform iterative calculations with a preset time scale as the step size, use the rubber forest spatial distribution map to mask the calculation results, remove the values ​​of non-rubber forest areas, and generate a spatial distribution raster map of natural rubber production at the preset time scale.

[0015] Preferably, the preset time scale is a monthly scale.

[0016] Preferred potential rubber production on a monthly scale The calculation is as follows:

[0017]

[0018] in, Characterizes the basic latex yield that depends on the tree's historical reserves; Characterized by current net primary productivity; The allocation multiplier representing the conversion of net primary productivity into latex in the current period; This is the cutting ratio constraint factor. It is a factor for the physiological maturity of the mammary ducts. As a factor contributing to aging and degeneration, The rest factor, It is the water turgor pressure factor. It is a precipitation disturbance factor.

[0019] Preferably, the multiple constraint factors affecting rubber production are calculated as follows:

[0020]

[0021] In the formula, The initial incision diameter at breast height. The average diameter at breast height (DBH) of the forest stand for that month. This represents the span of the diameter at breast height increment range corresponding to the increase in the rubber plantation tapping rate from 0 to 1.

[0022]

[0023] In the formula, The median breast diameter when the glue removal machine reaches a semi-saturated state. This is the slope parameter of the lactiferous duct maturation curve;

[0024]

[0025] In the formula, Given the current age of the forest, The initial standing age threshold at which yield begins to decline; This represents the average annual rate of capacity decline.

[0026]

[0027] In the formula, Soil moisture modification factors for the 3-PG model. To preset the power exponent, This represents the lower threshold of soil moisture modification factors;

[0028]

[0029] In the formula, This represents the total rainfall for the month. To trigger the rainfall threshold that would halt harvesting, For precipitation penalty sensitivity parameters, This is the adjustment parameter for the maximum precipitation threshold.

[0030] Preferably, the introduction of a metabolic conversion coefficient converts the potential latex yield into the actual carbon consumption required for latex synthesis, as specifically calculated below:

[0031]

[0032] in, This is the metabolic conversion coefficient. Let be the carbon content constant of dry matter. This represents the actual carbon consumption required for latex synthesis.

[0033] Preferably, the actual carbon allocation ratio of latex is determined based on the ratio of actual carbon consumption to current net primary productivity, and by introducing a maximum carbon allocation threshold for latex:

[0034]

[0035] in, This refers to the actual carbon distribution ratio of the latex. The maximum carbon allocation threshold that allows flow to secondary metabolism.

[0036] Preferably, the carbon distribution ratio of the stem and leaves is calculated as follows:

[0037]

[0038]

[0039] in, , These represent the carbon allocation ratio of the stem. This refers to the actual carbon distribution ratio of the latex. The carbon allocation ratio of the root system. The leaf-to-stem ratio, , The proportion constant of the leaf-to-stem distribution ratio. The power exponent of the leaf-stem allocation ratio.

[0040] Preferably, the Morris basic effects method is used to perform parameter sensitivity analysis on the improved 3-PG model to screen out the key parameters that play a dominant role in the model.

[0041] Preferably, a single-parameter dynamic sensitivity analysis is performed on the selected key parameters. The nonlinear response characteristics of the key parameters are identified by calculating the sensitivity coefficient (SSC), and the sensitive value range of each key parameter is determined. Based on the identification results of the nonlinear response characteristics, the selected key parameters are calibrated within the sensitive value range using the measured dry rubber yield and forest biomass data of the sample plot, and the optimal parameter combination of the model after calibration and verification is obtained.

[0042] A system for estimating the spatiotemporal distribution of natural rubber production includes a processor, a memory, and a computer program stored in the memory. When the processor executes the computer program, it specifically performs any of the steps in the above-described method for estimating the spatiotemporal distribution of natural rubber production.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] (1) This invention eliminates the technical defect of severely overestimating the biomass of rubber plantation trunks from the perspective of carbon allocation mechanisms. Existing technologies correct estimation errors by adjusting growth parameters, but do not address the problem of abnormal dry matter accumulation caused by latex carbon consumption at the mechanism level. This invention sets latex as an independent secondary metabolic carbon sink, introduces a metabolic conversion coefficient to quantify the energy consumption of latex synthesis, and preferentially deducts latex carbon consumption from net primary productivity under carbon balance constraints, with the remaining carbon source redistributed to the stems and leaves. Verification by examples shows that the improved 3-PG model not only effectively solves the problem of inaccurate dry matter simulation, but also has excellent predictive ability for the vegetative growth and secondary metabolism (latex production) dynamics of the rubber plantation.

[0045] (2) For the first time, the predictability of rubber plantation latex yield has been achieved under the 3-PG framework. This invention integrates multiple nonlinear constraint factors such as tapping ratio, latex maturity, aging and decline, water turgor pressure and precipitation interference by constructing a dedicated latex production module, which expands the 3-PG model from a single volume model to an economic yield prediction model, providing technical support for the precise cultivation management and regional yield monitoring of rubber plantations. Attached Figure Description

[0046] Figure 1 This is a schematic diagram of the steps of the present invention;

[0047] Figure 2 This is a flowchart illustrating the implementation of the present invention. Detailed Implementation

[0048] The following is in conjunction with the appendix Figure 1-2 The technical solution of the present invention will be described in detail below.

[0049] Traditional 3-PG models are primarily used to simulate photosynthesis, carbon accumulation and distribution, and hydrological balance in forest stands to predict dry matter growth. However, rubber trees, as an important economic forest species, have their main economic product—latex—continuously depleting tree nutrients through synthesis and harvesting, thus altering the natural growth patterns of the trees. To accurately simulate the growth and latex production dynamics of rubber plantations, this invention has undergone in-depth development based on the underlying architecture of the 3-PG model, constructing a dedicated latex production module and mechanistically reconstructing the logic of biomass allocation, mortality, and carbon balance in forests. Figure 1 As shown, in a preferred embodiment of the present invention, a method and system for estimating the spatiotemporal distribution of natural rubber production on a monthly scale are provided (wherein, using a monthly step size is only a preferred embodiment and not a limitation of the present invention; the preset time scale can be adjusted according to actual application conditions), the steps of which include:

[0050] S1, Multi-source data-driven and net primary productivity calculation;

[0051] S2, Dynamic estimation of monthly potential rubber production, and reconstruction of carbon allocation and mechanism in rubber plantations;

[0052] S3, parameter sensitivity analysis and parameter calibration;

[0053] S4. Spatiotemporal dynamic simulation and result output;

[0054] As an implementation method, S1 acquires spatiotemporal driving data including multi-source remote sensing, meteorological, site conditions, and plot biomass. Based on the basic photosynthesis and productivity module of the 3-PG model, combined with environmental and physiological constraints, the photosynthetically active radiation intercepted by the canopy and the initial net primary productivity of the current stand are calculated.

[0055] As an implementation method, in S2, since rubber tree latex production is a complex process influenced by both physiological rhythms and external disturbances in reality, this invention treats latex as an independent secondary metabolic carbon sink. It combines multiple nonlinear physical and physiological factors, including the tapping ratio controlled by stand diameter at breast height (DBH), latex duct physiological maturity, aging and necrosis, and latex discharge interference limited by water and rainfall, to jointly regulate and calculate the potential latex production for the month. Because latex synthesis requires energy, simple material deduction cannot reflect its metabolic cost. This invention introduces a metabolic conversion coefficient to quantify this energy consumption. Under the premise of maintaining the model's total carbon balance, the portion used for latex synthesis and metabolic consumption is deducted from the initial net primary productivity. The remaining carbon source is then redistributed to the tree's stems and leaves according to allometric growth patterns. Simultaneously, a maximum carbon allocation protection threshold is set to prevent tree death under extreme environmental simulations.

[0056] As an implementation method, in S3, to ensure the model can accurately predict under different environmental and management scenarios, the improved model is evaluated and calibrated. First, a global sensitivity analysis method (such as the Morris method) is used to identify the core parameters that significantly affect the output results such as stem biomass and gum yield, and local dynamic sensitivity analysis is combined to reveal their nonlinear response characteristics. Subsequently, using the measured dry gum yield and forest biomass data from sample plots, the selected core parameters are calibrated and validated to obtain the optimal parameter combination for the model, thereby improving the reliability of the model simulation.

[0057] As an implementation method, in S4, the driving data of each pixel is input into the system model after parameter calibration for iterative calculation with a monthly time step. Finally, the results are masked using the spatial distribution map of rubber forests to remove values ​​from non-rubber forest areas and generate a corrected raster map of the spatial distribution of natural rubber monthly production.

[0058] The following example, based on data from a rubber farm in a certain province, will be used to estimate the spatiotemporal distribution of monthly natural rubber production, providing a clear and complete description of the technical solution of this invention. The described embodiments are only a part of the embodiments of this invention, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without inventive effort are within the scope of protection of this invention.

[0059] S1. Acquire meteorological data (including precipitation, temperature, solar radiation, etc.), site conditions (altitude, latitude, soil fertility, etc.), and plot biomass (as basic biomass data for driving data and for comparative verification) for the target study area. It should be noted that the meteorological data collection methods and temporal and spatial scales differ in different regions, and the meteorological data needs to be converted into monthly data based on the simulation area.

[0060] Specifically, the hourly precipitation (mm), temperature (°C), and solar radiation (J / m²) data from ERA5 meteorological data were first analyzed. 2 The data (such as samples) are converted into monthly scale data, and then the site conditions of the sample plots are input into the system in raster data format according to the format.

[0061] The formulas for calculating reconstructed biomass in the S2 and 3-PG models are as follows:

[0062] (1) Production

[0063] In the 3-PG model, total primary productivity equals canopy quantum efficiency multiplied by the energy of effective photosynthetic radiation absorbed by the plant.

[0064]

[0065] Where, α cIt is the canopy quantum efficiency. It is the absorbed effective photosynthetic radiation, P G It is the total primary productive forces.

[0066] The formula for calculating the canopy quantum efficiency is as follows:

[0067]

[0068] Where, α cx This is the canopy quantum efficiency at the point of maximum total primary productivity. These are physiological modifiers that comprehensively reflect the limitations of atmospheric vapor pressure deficit (VPD), soil moisture, and forest age on photosynthesis. As a temperature regulation factor, As a frost regulator, As a soil fertility regulator, It is a regulator of atmospheric CO2 concentration.

[0069] Therefore, total primary productivity is equal to the following formula:

[0070]

[0071]

[0072] Among them, P N is net primary productivity, and c is the proportion of net primary productivity to total primary productivity.

[0073] (2) Allocation

[0074] To maintain the overall carbon source conservation in the model, the total net primary productivity (NPP) of the stand was calculated. Afterwards, the model needs to follow the allometric growth pattern of trees and environmental constraints to rationally distribute the accumulated carbon source to various organs:

[0075] 1) Dynamic Simulation of Latex Synthesis and Excretion: Latex synthesis and excretion is a complex physiological process influenced by multiple factors, including tree age, diameter at breast height (DBH), meteorological conditions, and carbon metabolism. The latex production module constructed in this invention is nested within the monthly cycle of the model, with the monthly potential latex production (…) The comprehensive calculation formula for () is:

[0076]

[0077] In the formula, Characterizes the basic latex yield that depends on the tree's historical reserves; Characterized by current net primary productivity ( ) is converted into the distribution multiplier of latex. The series of variables are multiple nonlinear dimensionless modifiers between [0,1]. The specific mathematical expressions and physiological mechanisms of the rest factor and other modifying factors are as follows:

[0078] Cutting ratio constraint factor ( According to conventional agronomic standards, rubber plantations have strict diameter at breast height (DBH) thresholds for production (typically around 13-16 cm). The model sets a basic tapping threshold and simulates the process of the proportion of trees ready for tapping gradually increasing to 100% after the average DBH of the stand exceeds this threshold.

[0079]

[0080] In the formula, The average diameter at breast height (DBH) of the stand for the month is (cm). The initial incision diameter at breast height; The value represents the span of the diameter at breast height increment range corresponding to the increase in the tapping ratio of rubber plantations from 0 to 1, which characterizes the transition diameter range when the plantations are fully operational (set to 2.0 cm in this embodiment).

[0081] Milk duct physiological maturation factor ( Latex is primarily synthesized in secondary laticifers. This example uses a logistic curve to simulate the biological process of the laticifer system gradually maturing as the trunk thickens radially.

[0082]

[0083] In the formula, The median breast diameter when the glue removal machine reaches a semi-saturated state; The slope parameter of the maturity curve reflects the process by which the glue removal function gradually approaches physiological saturation from initial activation.

[0084] Aging and degeneration factors ( Field measurements show that the latex production potential of rubber plantations exhibits a typical non-linear dynamic characteristic with stand age, typically reaching peak production 7-8 years after tapping (tree age approximately 13-15 years). Subsequently, with long-term mechanical stress from tapping and natural aging of the trees, excessive consumption of bark on the tapped surface makes it highly susceptible to tapping panel dryness (TPD). The model assumes that after the stand age crosses the inflection point, production capacity decreases linearly at a set rate.

[0085]

[0086] In the formula, The current forest age (in years) The initial standing age threshold at which yield begins to decline; This represents the average annual rate of capacity decline.

[0087] Water turgor pressure factor ( ) and precipitation disturbance factors ( Latex flow rate is physically controlled by turgor pressure within latex cells, which is directly influenced by soil moisture. The model extracts soil moisture modifiers from 3-PG. Set a lower limit and introduce a power exponent. :

[0088]

[0089] This setting effectively simulates the nonlinear characteristics of how, under moderate drought stress, photosynthesis is limited but the bark can still maintain a certain level of latex production. Furthermore, extreme rainfall not only disrupts tapping but also easily induces canker disease. When monthly rainfall exceeds a set threshold, latex production is forcibly reduced.

[0090]

[0091] In the formula, This represents the total rainfall for the month (mm). To trigger the rainfall threshold that would halt harvesting, This is a parameter related to precipitation penalty sensitivity.

[0092] 2) Reconstruction of the carbon allocation mechanism of secondary metabolic costs

[0093] The main active ingredient in latex is cis-1,4-polyisoprene, whose biosynthesis depends on the mevalonate pathway (MVA pathway). Converting photosynthetic products (glucose) into highly reduced isoprene requires the consumption of ATP and NADPH. Theoretically, synthesizing one unit of dry weight latex requires approximately one unit of glucose equivalent. To maintain the mass and carbon source conservation of the 3-PG model, this invention introduces a metabolic conversion coefficient (MCC). The preset value is 3.0), and the carbon allocation logic of allometric growth has been reconstructed. Its actual carbon allocation ratio ( The calculation is as follows:

[0094]

[0095]

[0096] In the formula, This represents the actual carbon consumption required for latex synthesis. Let be the carbon content constant of dry matter. To set a maximum carbon allocation threshold that allows flow to secondary metabolism, preventing the model from crashing under extremely low NPP conditions.

[0097] Root system allocation ratio ( Plants possess compensatory mechanisms for resource acquisition under adversity. When faced with water shortages or insufficient soil fertility (FR), trees spontaneously transfer more carbon sources to their roots. This proportion is determined by the upper and lower limits of root allocation (…). Effective photosynthetic radiation ( ) and fertility correction factor Joint decision:

[0098]

[0099] stem ( ) and blades ( ) Allocation ratio: NPP satisfies the root system ( ) and latex ( After the initial demand is met, the remaining portion is allocated to the stems and leaves. Following the principle of allometric growth, the leaf-to-stem allocation ratio is calculated first. , The proportion constant of the leaf-to-stem distribution ratio. The power exponent of the leaf-stem allocation ratio is then considered, followed by the overall physiological depletion effect of continuous tapping (i.e., ), and derive the final allocation coefficients:

[0100]

[0101]

[0102] S3. Based on the improved 3-PG model, parameter sensitivity analysis and calibration of the model are performed, as follows:

[0103] This invention employs the Morris Elementary Effects Method for global sensitivity analysis. This method is a screening algorithm based on One-at-a-Time (OAT) design, capable of effectively identifying key parameters that significantly influence model output in a high-dimensional parameter space while maintaining low computational cost. The core of the Morris method lies in calculating each parameter... Elementary Effect For a given parameter vector ,in If the k-th parameter is represented, then the i-th parameter is represented. The basic effect is defined as:

[0104]

[0105] in, To improve the output variables of the 3-PG model, This represents the step size for parameter variation. To eliminate errors caused by the cancellation of positive and negative parameter effects (non-monotonicity effects), this invention employs a corrected average basic effect. (Mean of the absolute Elementary Effects) serves as a metric for measuring the overall importance of parameters:

[0106]

[0107] in, This represents the total number of randomly sampled trajectories in the Morris method. Let be the basic effect of the i-th parameter on the j-th sampling trajectory, where j is the trajectory number;

[0108] At the same time, the standard deviation of the basic effect is calculated. To assess the nonlinearity of parameter effects or their interactions with other parameters.

[0109] After completing the global parameter sensitivity analysis and identifying the key parameters that play a dominant role in the model, this invention further conducts single-parameter dynamic sensitivity analysis to reveal the nonlinear influence of these core parameters on the model output at different value levels. While the Morris method effectively identifies the global importance of parameters, dynamic sensitivity analysis focuses on characterizing the evolution of parameter sensitivity as its value changes. This method calculates the dimensionless sensitivity coefficient... Sensitivity coefficient quantifies the local dynamic sensitivity of a specific parameter to the model's output variable over a continuous range of values. The calculation formula is defined as follows:

[0110]

[0111] Where Y is a specific output variable of the improved 3-PG model (such as biomass, diameter at breast height, etc.), and X is a given physiological or structural parameter value. SSC is the partial derivative of the model output variable with respect to the input parameter. The absolute value of SSC reflects the sensitivity of the model output to changes in the current value of the parameter. Its positive or negative sign indicates the positive or negative promoting relationship between the parameter and the output variable, respectively.

[0112] The model validation results for this rubber farm also showed extremely high simulation accuracy, with the simulation results closely distributed around the 1:1 line. Referring to Table 1, during the model calibration and validation phases, the model correlation coefficients (r) for each key indicator ranged from 0.62 to 0.91. The relative root mean square error (rRMSE) ranged from 13.90% to 35.80%. Particularly noteworthy was the model validation phase, where the correlation coefficient for stem biomass reached 0.89, and the correlation coefficient for diameter at breast height (DBH) was even higher at 0.91 (rRMSE = 13.90%). Furthermore, the model successfully predicted monthly latex production (r = 0.63, rRMSE = 24.89%), indicating that the improved 3-PG model not only effectively solved the problem of inaccurate dry matter simulation but also demonstrated excellent predictive ability for the vegetative growth and secondary metabolism (latex production) dynamics of this rubber plantation.

[0113] Table 1. Validation results based on data from a rubber farm.

[0114]

[0115] The parameters for simulating natural rubber production using the improved 3-PG model are shown in Table 2:

[0116] Table 2 Model parameters related to rubber production

[0117]

[0118] S4. Spatiotemporal Dynamic Simulation and Result Output. This invention simulates the growth and production dynamics of natural rubber. The scenarios are set based on actual meteorological observation data and conventional forestry harvesting records. Users can design specific parameter values ​​for the simulation scenarios according to their needs. Finally, the masked output shows the corresponding monthly yield and biomass spatial distribution raster results under different scenarios.

[0119] The above are preferred embodiments of the present invention. Any changes made to the technical solution of the present invention that do not exceed the scope of the technical solution of the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for estimating the spatiotemporal distribution of natural rubber production, characterized in that, Includes the following steps: S1. Acquire multi-source spatiotemporal driving data of the target area, including remote sensing data, meteorological data, site condition data, and sample plot biomass data; convert the multi-source spatiotemporal driving data into a spatial raster format, and calculate the current net primary productivity of the forest stand at a preset time scale based on the 3-PG model. S2. By setting latex as an independent secondary metabolic carbon sink, the carbon allocation mechanism of the 3-PG model is reconstructed, and an improved 3-PG model is obtained: Based on the latex conversion of the basic latex yield and the current net primary productivity, the potential latex yield under a preset time scale is dynamically estimated by combining multiple constraint factors affecting latex yield. The multiple constraint factors include tapping ratio constraint factor, latex duct physiological maturity factor, aging and dermal disease factor, rest tapping factor, water turgor pressure factor and precipitation interference factor. A metabolic conversion coefficient is introduced to convert the potential latex production into the actual carbon consumption required for latex synthesis; and the actual carbon allocation ratio of latex is determined based on the ratio of actual carbon consumption to current net primary productivity. The carbon allocation ratio of the root system is determined by deducting the carbon allocation ratio corresponding to the root system and latex from the total allocation share of the net primary productivity in the current period, and then allocating the remaining carbon source to the trunk and leaves of the tree to obtain the carbon allocation ratio of the trunk and leaves. S3. Perform parameter sensitivity analysis and parameter calibration on the improved 3-PG model; S4. Input the multi-source spatiotemporal driving data of each pixel into the 3-PG model after parameter calibration, perform iterative calculations with a preset time scale as the step size, use the rubber forest spatial distribution map to mask the calculation results, remove the values ​​of non-rubber forest areas, and generate a spatial distribution raster map of natural rubber production at the preset time scale.

2. The method for estimating the spatiotemporal distribution of natural rubber production according to claim 1, characterized in that, The preset time scale is a monthly scale.

3. The method for estimating the spatiotemporal distribution of natural rubber production according to claim 2, characterized in that, Potential rubber production on a monthly scale The calculation is as follows: in, Characterizes the basic latex yield that depends on the tree's historical reserves; Characterized by current net primary productivity; The allocation multiplier representing the conversion of net primary productivity into latex in the current period; This is the cutting ratio constraint factor. It is a factor for the physiological maturity of the mammary ducts. As a factor contributing to aging and degeneration, The rest factor, It is the water turgor pressure factor. It is a precipitation disturbance factor.

4. The method for estimating the spatiotemporal distribution of natural rubber production according to claim 3, characterized in that, The specific calculations of the multiple constraint factors affecting rubber production are as follows: In the formula, The initial incision diameter at breast height. The average diameter at breast height (DBH) of the forest stand for that month. This represents the span of the diameter at breast height increment range corresponding to the increase in the rubber plantation tapping rate from 0 to 1. In the formula, The median breast diameter when the glue removal machine reaches a semi-saturated state. This is the slope parameter of the lactiferous duct maturation curve; In the formula, Given the current age of the forest, The initial standing age threshold at which yield begins to decline; This represents the average annual rate of capacity decline. In the formula, Soil moisture modification factors for the 3-PG model. To preset the power exponent, This represents the lower threshold of soil moisture modification factors; In the formula, This represents the total rainfall for the month. To trigger the rainfall threshold that would halt harvesting, For precipitation penalty sensitivity parameters, This is the adjustment parameter for the maximum precipitation threshold.

5. The method for estimating the spatiotemporal distribution of natural rubber production according to claim 3, characterized in that, The introduction of a metabolic conversion coefficient converts the potential latex yield into the actual carbon consumption required for latex synthesis. The specific calculation is as follows: in, This is the metabolic conversion coefficient. Let be the carbon content constant of dry matter. This represents the actual carbon consumption required for latex synthesis.

6. The method for estimating the spatiotemporal distribution of natural rubber production according to claim 5, characterized in that, The actual carbon allocation ratio of latex is determined based on the ratio of actual carbon consumption to current net primary productivity, and by introducing a maximum carbon allocation threshold for latex: in, This refers to the actual carbon distribution ratio of the latex. The maximum carbon allocation threshold that allows flow to secondary metabolism.

7. The method for estimating the spatiotemporal distribution of natural rubber production according to claim 1, characterized in that, The carbon distribution ratio between the stem and leaves is calculated as follows: in, , These represent the carbon allocation ratio of the stem. This refers to the actual carbon distribution ratio of the latex. The carbon allocation ratio of the root system. The leaf-to-stem ratio, , The proportion constant of the leaf-to-stem distribution ratio. The power exponent of the leaf-stem allocation ratio.

8. The method for estimating the spatiotemporal distribution of natural rubber production according to claim 1, characterized in that, The Morris basic effects method was used to perform parameter sensitivity analysis on the improved 3-PG model to identify the key parameters that play a dominant role in the model.

9. The method for estimating the spatiotemporal distribution of natural rubber production according to claim 8, characterized in that, Single-parameter dynamic sensitivity analysis was performed on the selected key parameters. The nonlinear response characteristics of the key parameters were identified by calculating the sensitivity coefficient SSC, and the sensitive value range of each key parameter was determined. Based on the identification results of the nonlinear response characteristics, the key parameters selected were calibrated within the sensitive value range using the measured dry rubber yield and forest biomass data from the sample plots, and the optimal parameter combination of the model after calibration and verification was obtained.

10. A system for estimating the spatiotemporal distribution of natural rubber production, characterized in that, It includes a processor, a memory, and a computer program stored in the memory. When the processor executes the computer program, it specifically performs the steps in the method for estimating the spatiotemporal distribution of natural rubber production as described in any one of claims 1-9.

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