Method and system for simulating growth of highland barley based on wofost crop model

Abstract

This application relates to the field of dynamic modeling technology, specifically to a method and system for simulating barley growth based on the WOFOST crop model. The method includes: acquiring the dry matter accumulation of barley under different oxygen partial pressures at each growth stage, and the dry matter distribution ratio of each organ; measuring the degree of adaptation to different hypoxia partial pressures at each growth stage, and acquiring the respiration retention rate under different hypoxia partial pressures at each growth stage; measuring the sensitivity of each organ to hypoxia at each growth stage, measuring the severity of the impact of different hypoxia partial pressures on each organ at each growth stage, and optimizing the parameters of the WOFOST crop model. This application aims to improve the accuracy of WOFOST crop model in simulating barley growth under hypoxia conditions by comprehensively evaluating the impact of hypoxia on various organs of barley and optimizing the WOFOST crop model.

Description

Technical Field

[0001] This application relates to the field of dynamic modeling technology, specifically to a method and system for simulating barley growth based on the WOFOST crop model. Background Technology

[0002] Highland barley is a specialty grain crop in the Qinghai-Tibet Plateau and surrounding high-altitude areas, exhibiting ecological adaptability such as cold resistance, drought resistance, and tolerance to poor soil conditions. The WOFOST crop model, a classic dynamic crop growth simulation model, has been widely used in yield simulation, regional suitability evaluation, and resource utilization efficiency analysis of temperate plain crops such as wheat, maize, and rice, providing technical support for production management.

[0003] However, the original parameter library and algorithm of the WOFOST crop model are mainly designed for temperate plain crops, and their direct application to barley on the Qinghai-Tibet Plateau exhibits significant biases. Barley's energy requirements and metabolic characteristics differ at different growth stages, and the impact of hypoxia on each stage varies. Furthermore, under hypoxia, the distribution process of barley changes, leading to alterations in the distribution of dry matter among different organs. Existing WOFOST crop models do not consider these factors, resulting in insufficient characterization of key physiological processes in barley under hypoxia conditions, thus affecting the accuracy of barley growth simulation in the plateau region. Summary of the Invention

[0004] In light of the above, it is necessary to provide a method and system for simulating barley growth based on the WOFOST crop model. Compared with traditional methods for simulating barley growth based on the WOFOST crop model, this method comprehensively evaluates the impact of hypoxia on various organs of barley and optimizes the WOFOST crop model, thereby improving the accuracy of simulating barley growth under hypoxic conditions. In a first aspect, embodiments of this application provide a method for simulating barley growth based on the WOFOST crop model, the method comprising the following steps: The dry matter accumulation and dry matter increment of barley under different oxygen partial pressures at different growth stages, as well as the dry matter distribution ratio of different organs of barley, were obtained to analyze the effect of hypoxia on barley growth, including normoxic partial pressure and different hypoxic partial pressures. By comparing the changes in dry matter accumulation, the growth similarity of barley under each hypoxia partial pressure condition and normoxic partial pressure condition is obtained. Combined with the similarity of dry matter increment at each growth stage under different oxygen partial pressure conditions, the degree of adaptation of each growth stage to each hypoxia partial pressure condition is measured. Then, combined with the dispersion of the degree of adaptation of each growth stage to all hypoxia partial pressure conditions, the respiration retention rate of each growth stage under each hypoxia partial pressure condition is obtained. By comparing the dry matter distribution ratio of each organ at each growth stage under different oxygen partial pressure conditions, the influence of each low oxygen partial pressure condition on the distribution ratio of each organ at each growth stage is measured. By the dispersion of the influence of different low oxygen partial pressure conditions and the respiration retention, the sensitivity of each organ to low oxygen at each growth stage is measured. Then, by combining the influence and the respiration retention, the severity of the influence of each organ on each low oxygen partial pressure condition at each growth stage is measured, thereby optimizing the parameters of the WOFOST crop model.

[0005] In one embodiment, the process of obtaining the barley growth similarity is as follows: Calculate the mean dry matter increment for all growth stages under each oxygen partial pressure condition; Calculate the difference between the dry matter increment at each growth stage under each oxygen partial pressure condition and the mean value; Calculate the difference values ​​of the aforementioned differences between each growth stage under each low oxygen partial pressure condition and a normal oxygen partial pressure condition; The similarity of barley growth was negatively correlated with all the differences corresponding to each hypoxia partial pressure condition.

[0006] In one embodiment, the process of measuring the degree of adaptation is as follows: Obtain the deviation of dry matter increment at each growth stage between low oxygen partial pressure conditions and normal oxygen partial pressure conditions. The degree of adaptation is measured by multiplying the deviation by the similarity of barley growth.

[0007] In one embodiment, the process of obtaining the respiratory maintenance degree is as follows: Obtain the sum of the differences between the measures of each growth stage's adaptation to all low oxygen partial pressure conditions and the minimum values ​​among them; The respiratory retention rate is positively correlated with the measurement of the degree of adaptation, and negatively correlated with the summation result.

[0008] In one embodiment, the measure of influence is the difference in the proportion of dry matter distribution in each organ at each growth stage between hypoxia partial pressure conditions and normoxic partial pressure conditions.

[0009] In one embodiment, the sensitivity measurement process is as follows: The average value of the measures of the influence of all hypoxia partial pressure conditions on the distribution ratio of each organ at each growth stage is calculated. The summation of the differences between the measures of the influence of all hypoxia partial pressure conditions on the distribution ratio of each organ at each growth stage and the average value; The sensitivity measurement result is positively correlated with the cumulative result and negatively correlated with the respiratory retention rate.

[0010] In one embodiment, the process for measuring the severity of the impact is as follows: By combining the results of the sensitivity measurement with the results of the influence measurement, the sensitivity of each organ to each hypoxia partial pressure condition at each growth stage is evaluated. The measurement of the severity of the impact is positively correlated with the assessment of the sensitivity, and negatively correlated with the respiratory retention.

[0011] In one embodiment, the sensitivity assessment result is the product of the sensitivity measurement result and the influence measurement result.

[0012] In one embodiment, the method for optimizing the parameters of the WOFOST crop model is as follows: During the training of the WOFOST crop model, the severity of the impact is used to assign importance to the experimental data of each organ at each growth stage and under each hypoxia partial pressure condition, so as to optimize the loss function and thus optimize the parameters of the WOFOST crop model.

[0013] Secondly, embodiments of this application also provide a barley growth simulation system based on the WOFOST crop model, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any of the above-described barley growth simulation methods based on the WOFOST crop model.

[0014] This application has at least the following beneficial effects: By calculating growth similarity, the overall impact of hypoxia on barley growth can be quantified, clarifying the differences between barley growth status under different hypoxic conditions and normoxic conditions, thus providing a basis for subsequent assessment of barley's adaptability under different hypoxic conditions. Furthermore, by combining the similarity of dry matter increment at each growth stage under different oxygen partial pressure conditions, the degree of adaptation of each growth stage to each hypoxic partial pressure condition can be measured. Finally, the respiration retention rate can be calculated, reflecting the ability of barley to maintain normal energy metabolism and dry matter accumulation under hypoxic conditions, which helps to assess the hypoxia tolerance of barley at each growth stage. Furthermore, this study clarifies the sensitivity of different organs to hypoxia at different growth stages, thereby measuring the severity of the impact of various hypoxia partial pressures on each organ at each growth stage. This comprehensive assessment of the impact of hypoxia on various organs of highland barley provides a more accurate basis for optimizing the WOFOST crop model. The optimized WOFOST crop model can better adapt to the high-altitude hypoxia environment, thus improving the applicability of the WOFOST crop model for simulating highland barley growth and enhancing the accuracy of the WOFOST crop model in simulating highland barley growth under hypoxia conditions. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart illustrating the steps of a barley growth simulation method based on the WOFOST crop model, provided in one embodiment of this application; Figure 2 This is a schematic diagram illustrating the process of obtaining the environmental impact coefficient. Figure 3 This is a diagram illustrating the process of measuring the severity of an impact. Detailed Implementation

[0017] In the description of the embodiments in this application, the words "exemplary," "or," and "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary," "or," and "for example" is intended to present the relevant concepts in a specific manner.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. It should be understood that, unless otherwise stated, " / " in this application means "or".

[0019] It should also be noted that the terms "first" and "second" in this application are used to distinguish similar objects, rather than to describe a specific order or sequence.

[0020] The following, in conjunction with the accompanying drawings, details the specific scheme of the barley growth simulation method and system based on the WOFOST crop model provided in this application.

[0021] Please see Figure 1 The diagram illustrates a flowchart of a method for simulating barley growth based on the WOFOST crop model, according to an embodiment of this application. The method includes the following steps: Step 1: Obtain multi-source experimental data.

[0022] To simulate barley growth based on the WOFOST crop model, multi-source experimental data, including meteorological, soil, crop physiological, and field management data, is required. Specifically, data is collected using artificial climate chambers to simulate different oxygen partial pressure environments, in order to correct the WOFOST crop model for hypoxia adaptation. The specific information of the multi-source experimental data is as follows: (1) Obtain the daily average temperature, maximum temperature, minimum temperature, solar radiation, precipitation, relative humidity, and wind speed in the artificial climate chamber. (2) For the low-oxygen environment of the plateau, use the artificial climate chamber to simulate different oxygen partial pressure environments and obtain oxygen partial pressure data. (3) Obtain soil data, including soil saturated water content, field water holding capacity, wilting coefficient, and hydraulic conductivity. (4) Measure the fresh weight of the aboveground and underground parts of the plant, and measure the dry weight after blanching at 105℃ and drying at 80℃ to constant weight to obtain the total dry matter accumulation. Decompose the dried plant into four organs: spike, stem, root, and leaf, and measure the dry weight of each organ. (5) Field management data include crop variety, fertilization records, and irrigation records.

[0023] Step 2: Obtain the dry matter accumulation and dry matter increment of barley under different oxygen partial pressures at each growth stage, as well as the dry matter distribution ratio of each organ of barley, in order to analyze the effect of hypoxia on barley growth, including normoxic partial pressure and different hypoxic partial pressures; and measure the severity of the effect of different hypoxic partial pressures on each organ at each growth stage.

[0024] Step 2.1: By comparing the changes in dry matter accumulation, obtain the similarity of barley growth between each hypoxia partial pressure condition and normoxic partial pressure condition. Combine the similarity of dry matter increment at each growth stage under different oxygen partial pressure conditions to measure the degree of adaptation of each growth stage to each hypoxia partial pressure condition. Then, combine the dispersion of the degree of adaptation of each growth stage to all hypoxia partial pressure conditions to obtain the respiration retention rate of each growth stage under each hypoxia partial pressure condition.

[0025] The energy requirements and metabolic characteristics of barley vary at different growth stages, resulting in differences in dry matter production under hypoxic conditions. In other words, the degree of change in barley growth at different stages differs between hypoxic and normoxic conditions. The greater this difference, the less similar the growth status of barley under hypoxic conditions is to that under normoxic conditions, and the greater the impact of hypoxia on barley growth. Furthermore, at the same growth stage, the smaller the change in dry matter accumulation with oxygen partial pressure, the less impact hypoxia has on barley growth, and the stronger the barley's tolerance to hypoxia.

[0026] Because the physiological metabolic centers, organ development priorities, and energy requirements of barley differ at different growth stages, the impact of hypoxia on barley varies at different growth stages. The growth period of barley is divided into I growth stages. In this embodiment, the number of growth stages is 5: seedling stage, jointing stage, heading stage, grain-filling stage, and maturity stage. Based on the normoxic partial pressure conditions in plains areas, there are K hypoxic partial pressures. In this embodiment, the number of hypoxic partial pressures is 5: 80% plains oxygen partial pressure, 75% plains oxygen partial pressure, 70% plains oxygen partial pressure, 65% plains oxygen partial pressure, and 60% plains oxygen partial pressure. The number of growth stages and the number of hypoxic partial pressures are preset and can be set by the implementer according to actual conditions; this application does not impose any special restrictions.

[0027] Because the energy requirements and metabolic characteristics of barley differ at different growth stages, the amount of dry matter produced also varies at different growth stages. Therefore, the increase in dry matter at each growth stage is calculated to characterize the accumulation of net photosynthetic products at each growth stage. The expression is as follows: In the formula, This represents the increase in dry matter during the i-th growth stage under the j-th oxygen partial pressure condition; , These represent the dry matter accumulation at the i-th and (i-1)-th growth stages under the j-th oxygen partial pressure condition, respectively; that is, the dry matter accumulation at the end of the i-th and (i-1)-th growth stages under the j-th oxygen partial pressure condition. It should be noted that when i is 1, the dry matter accumulation at the end of the first growth stage under the j-th oxygen partial pressure condition is taken as the dry matter increment of the first growth stage under the j-th oxygen partial pressure condition.

[0028] Furthermore, to avoid differences in dimensions, the calculated dry matter increment was normalized.

[0029] In this embodiment, the Min-Max normalization method is used to normalize the dry matter increment. The Min-Max normalization method is a well-known technique and will not be described further in this application. Unless otherwise specified, the Min-Max normalization method is used to normalize the data in this application.

[0030] Furthermore, by comparing the changes in dry matter accumulation, the similarity of barley growth between various hypoxic partial pressure conditions and normoxic partial pressure conditions was obtained. The specific process is as follows: Calculate the mean of dry matter increment for all growth stages under each oxygen partial pressure condition; calculate the difference between the dry matter increment for each growth stage under each oxygen partial pressure condition and the mean; calculate the difference between the difference for each growth stage under each low oxygen partial pressure condition and the normal oxygen partial pressure condition; the similarity of barley growth is negatively correlated with all the difference values ​​corresponding to each low oxygen partial pressure condition.

[0031] It should be noted that: difference refers to the degree of distinction between data, which can be achieved by calculating the absolute value of the difference, the square of the difference, the ratio, etc. This application does not impose any special restrictions on this.

[0032] It should be noted that negative correlation means that the variables change in opposite directions; when one variable increases, the other decreases, and vice versa.

[0033] In this embodiment, the expression for the similarity of barley growth between each hypoxia partial pressure condition and a normoxic partial pressure condition is as follows: In the formula, represents the similarity of barley growth between the k-th hypoxia partial pressure condition and the normoxic partial pressure condition; exp() represents an exponential function with the natural constant as the base, used to inversely map the data to the interval (0,1]; I represents the total number of growth stages; This represents the increase in dry matter during the i-th growth stage under the k-th low oxygen partial pressure condition; This represents the mean of the dry matter increment during all growth stages under the k-th low oxygen partial pressure condition; This represents the increase in dry matter during the i-th growth stage under normal oxygen partial pressure conditions; This represents the average increase in dry matter at all growth stages under normal oxygen partial pressure conditions. This indicates the operation of taking the absolute value. In the process of calculating the similarity of barley growth, the difference quantity and difference value involved are all absolute values ​​of the difference.

[0034] In another embodiment, the expression for the similarity of barley growth between each hypoxia partial pressure condition and a normoxic partial pressure condition is as follows: In the formula, This represents the similarity of barley growth between the k-th hypoxia partial pressure condition and the normoxic partial pressure condition; I represents the total number of growth stages. This represents the increase in dry matter during the i-th growth stage under the k-th low oxygen partial pressure condition; This represents the mean of the dry matter increment during all growth stages under the k-th low oxygen partial pressure condition; This represents the increase in dry matter during the i-th growth stage under normal oxygen partial pressure conditions; This represents the average increase in dry matter at all growth stages under normal oxygen partial pressure conditions. This indicates the operation of taking the absolute value. In calculating the similarity of barley growth, all differences and their values ​​are absolute values. Adding 1 to the denominator is to avoid a denominator of 0.

[0035] It should be noted that: under hypoxia partial pressure conditions, the difference between the increase in dry matter and its mean reflects the stability of dry matter accumulation; under normoxic partial pressure conditions, the difference between the increase in dry matter and its mean reflects the baseline value of the stability of dry matter accumulation. By comparing the stability of dry matter accumulation under hypoxia partial pressure conditions with that under normoxic partial pressure conditions, the similarity between the growth status of barley under hypoxia partial pressure conditions and the growth status of barley under normoxic conditions can be measured. The greater the calculated similarity of barley growth, the higher the degree of similarity, and the less the growth status of barley is affected by hypoxia.

[0036] Furthermore, the closer the increase in dry matter produced under hypoxic and normoxic conditions, and the higher the overall similarity of barley growth, the stronger the overall adaptability of barley's respiratory metabolism to hypoxic conditions. Therefore, by measuring the similarity of barley growth between hypoxic and normoxic partial pressure conditions, and the similarity of dry matter increments at each growth stage under different oxygen partial pressure conditions, the respiratory metabolic adaptation coefficients of each growth stage under each hypoxic partial pressure condition are obtained. These coefficients are used to measure the degree of adaptation of each growth stage to each hypoxic partial pressure condition. Specifically: Obtain the deviation of dry matter increment at each growth stage between each hypoxia partial pressure condition and normoxic partial pressure condition; multiply the deviation by the growth similarity of barley as the respiratory metabolic adaptation coefficient at each growth stage under each hypoxia partial pressure condition.

[0037] In this embodiment, the expression for the respiratory metabolic adaptation coefficient of each growth stage under each hypoxia partial pressure condition is as follows: In the formula, denoted as the respiratory-metabolic adaptation coefficient for the i-th growth stage under the k-th hypoxia partial pressure condition; This represents the similarity of barley growth between the k-th hypoxia partial pressure condition and the normoxic partial pressure condition. This represents the increase in dry matter during the i-th growth stage under the k-th low oxygen partial pressure condition; This represents the increase in dry matter during the i-th growth stage under normal oxygen partial pressure conditions; min() represents the minimum value operation; max() represents the maximum value operation.

[0038] It should be noted that the closer the increase in dry matter produced under hypoxic and normoxic conditions is, the higher the overall similarity of barley growth, the higher the degree of adaptation of each growth stage to each hypoxic partial pressure condition, and the larger the calculated respiratory metabolic adaptation coefficient.

[0039] Furthermore, the better the respiratory metabolic stability of barley under different hypoxic conditions at the same growth stage, that is, the smaller the change in hypoxic partial pressure at the same growth stage, the less impact hypoxia has on barley, and the stronger its hypoxia tolerance. Therefore, the hypoxia stability at each growth stage is obtained by analyzing the dispersion of the respiratory metabolic adaptation coefficients under all hypoxic partial pressure conditions. The specific process is as follows: The summation of the differences between the respiratory and metabolic adaptation coefficients of each growth stage and the minimum value under all hypoxia partial pressure conditions is obtained; the hypoxia stability of each growth stage is negatively correlated with the summation result.

[0040] In this embodiment, the expression for hypoxia stability at each growth stage is as follows: In the formula, This represents the hypoxia stability during the i-th growth stage; denoted as the respiratory-metabolic adaptation coefficient for the i-th growth stage under the k-th hypoxia partial pressure condition; denoted as the minimum respiratory metabolic adaptation coefficient of the i-th growth stage under all hypoxia partial pressure conditions; K represents the total number of hypoxia partial pressures.

[0041] In another embodiment, the expression for hypoxia stability at each growth stage is: In the formula, This represents the hypoxia stability during the i-th growth stage; denoted as the respiratory-metabolic adaptation coefficient for the i-th growth stage under the k-th hypoxia partial pressure condition; represents the minimum respiratory metabolic adaptation coefficient of the i-th growth stage under all hypoxia partial pressure conditions; exp() represents an exponential function with the natural constant as the base.

[0042] It should be noted that the higher the calculated hypoxia stability, the more stable the respiratory metabolism at each growth stage under different hypoxia conditions, and the more stable the barley's adaptation to the hypoxia environment at each growth stage.

[0043] Furthermore, by combining the respiratory metabolic adaptation coefficients of each growth stage under various hypoxia partial pressure conditions, and the hypoxia stability of each growth stage, the respiratory retention rate of each growth stage under various hypoxia partial pressure conditions is obtained, expressed as: In the formula, This represents the respiration retention rate of the i-th growth stage under the k-th low oxygen partial pressure condition; This represents the hypoxia stability during the i-th growth stage; represents the respiratory and metabolic adaptation coefficient of the i-th growth stage under the k-th hypoxia partial pressure condition; norm[ ] represents the normalization operation.

[0044] It should be noted that the higher the calculated respiratory retention rate, the less the respiratory metabolism of barley is affected by hypoxia at each growth stage and under each hypoxia partial pressure condition, and the stronger its ability to maintain normal energy metabolism and dry matter accumulation.

[0045] Step 2.2: By comparing the dry matter distribution ratio of each organ at each growth stage under different oxygen partial pressure conditions, the influence of each hypoxia partial pressure condition on the distribution ratio of each organ at each growth stage is measured; by the dispersion of the influence of different hypoxia partial pressure conditions and the respiratory retention rate, the sensitivity of each organ to hypoxia at each growth stage is measured; and by combining the influence, the hypoxia sensitivity factor of each organ at each growth stage under each hypoxia partial pressure condition is obtained, which is used to evaluate the sensitivity of each organ to each hypoxia partial pressure condition at each growth stage.

[0046] Step 2.1 analyzed the degree to which barley was affected by hypoxia at each growth stage. Further, it analyzed the changes in the dry matter distribution process of barley under hypoxia. Specifically, under hypoxia, barley tends to allocate more photosynthetic products to the roots to promote root growth and enhance oxygen uptake, thereby adapting to the hypoxic environment. The greater the impact of hypoxia, i.e., the lower the respiration retention, the more obvious the changes in the distribution process. The greater the deviation of the distribution process from normoxic conditions, the more significant the impact of hypoxia on the organ distribution ratio. The greater the fluctuation in the material distribution ratio of each organ under different hypoxic conditions, the higher the correlation between organ distribution and oxygen concentration, and the more sensitive each organ is to hypoxia.

[0047] Based on the multi-source experimental data obtained in step 1, the dry matter increment of each organ of barley at each growth stage under various oxygen partial pressure conditions is extracted. In this embodiment, the number of organs of barley is 4, including: ear, stem, root and leaf.

[0048] Furthermore, the dry matter distribution ratio of each organ at each growth stage under various oxygen partial pressure conditions was obtained, expressed as: In the formula, Let represent the dry matter distribution ratio of organ a in the i-th growth stage under the j-th oxygen partial pressure condition; Q represents the total number of organs. , Let represent the increase in dry matter of organ a during the i-th growth stage under the j-th oxygen partial pressure condition.

[0049] In hypoxic environments, to adapt to the lack of oxygen, barley tends to allocate more photosynthetic products to its roots to promote root growth and enhance oxygen uptake, thus altering the dry matter distribution process. Therefore, by comparing the dry matter distribution ratios of various organs at different growth stages under different oxygen partial pressures, we obtain the organ distribution influence coefficients for each organ at each growth stage under different hypoxic partial pressures. This coefficient is used to measure the influence of each hypoxic partial pressure on the distribution ratio of each organ at each growth stage. Specifically: The organ allocation influence coefficient represents the difference in the proportion of dry matter allocated to each organ at each growth stage between hypoxia partial pressure conditions and normoxic partial pressure conditions.

[0050] In this embodiment, the expression for the organ allocation influence coefficient of each organ at each growth stage under each hypoxia partial pressure condition is as follows: In the formula, This represents the organ allocation influence coefficient of the a-th organ at the i-th growth stage under the k-th hypoxia partial pressure condition; This represents the proportion of dry matter allocated to organ a during the i-th growth stage under the k-th hypoxia partial pressure condition. This represents the dry matter distribution ratio of the a-th organ in the i-th growth stage under normoxic partial pressure conditions; min() represents the minimum value operation; max() represents the maximum value operation.

[0051] It should be noted that the greater the deviation from normoxic conditions in organ distribution, the greater the influence of hypoxic partial pressures on the distribution ratio of each organ at each growth stage, and the larger the calculated organ distribution influence coefficient. It should also be added that if an organ has not yet developed at a certain growth stage, the organ distribution influence coefficient is assigned a value of 1.

[0052] Furthermore, when the partial pressure of oxygen changes, the greater the fluctuation in the material distribution ratio of each organ under different hypoxic conditions, and the higher the correlation between the distribution ratio of each organ and the oxygen concentration, the more sensitive each organ is to hypoxia. Therefore, by measuring the dispersion of the organ distribution influence coefficient of each organ at each growth stage under all hypoxia partial pressure conditions, and the respiratory retention rate at each growth stage under each hypoxia partial pressure condition, the sensitivity coefficient of each organ to hypoxia at each growth stage is obtained. This coefficient is used to measure the sensitivity of each organ to hypoxia at each growth stage. The specific process is as follows: Calculate the average value of the organ allocation influence coefficient of each organ at each growth stage under all hypoxia partial pressure conditions; calculate the summation of the differences between the organ allocation influence coefficient of each organ at each growth stage under all hypoxia partial pressure conditions and the average value. The sensitivity coefficients of each organ to hypoxia at each growth stage are positively correlated with the cumulative results and negatively correlated with the respiratory retention rate.

[0053] It should be noted that positive correlation means that the variables change in the same direction; when one variable increases, the other variable also increases, and when one variable decreases, the other variable also decreases.

[0054] In this embodiment, the expression for the sensitivity coefficient of each organ to hypoxia at each growth stage is as follows: In the formula, denoted by , where represents the sensitivity coefficient of organ a to hypoxia at the i-th growth stage; K represents the total partial pressure of hypoxia. This represents the organ allocation influence coefficient of the a-th organ at the i-th growth stage under the k-th hypoxia partial pressure condition; This represents the average value of the organ allocation influence coefficient of the a-th organ at the i-th growth stage under all hypoxia partial pressure conditions; represents the respiration retention rate of the i-th growth stage under the k-th hypoxia partial pressure condition; norm[ ] represents the normalization operation; This indicates the absolute value operation.

[0055] In another embodiment, the expression for the sensitivity coefficient of each organ to hypoxia at each growth stage is as follows: In the formula, denoted by , where represents the sensitivity coefficient of organ a to hypoxia at the i-th growth stage; K represents the total partial pressure of hypoxia. This represents the organ allocation influence coefficient of the a-th organ at the i-th growth stage under the k-th hypoxia partial pressure condition; This represents the average value of the organ allocation influence coefficient of the a-th organ at the i-th growth stage under all hypoxic partial pressure conditions; represents the respiration retention rate of the i-th growth stage under the k-th hypoxia partial pressure condition; norm[ ] represents the normalization operation; The expression represents the absolute value operation; exp() represents the exponential function with the natural constant as the base.

[0056] It should be noted that the greater the fluctuation in the distribution ratio of each organ under different hypoxic conditions, the more sensitive it is to hypoxia; the lower the respiratory retention, the greater the impact of hypoxia; and the larger the calculated sensitivity coefficient, the higher the sensitivity of each organ to hypoxia at each growth stage.

[0057] Furthermore, by combining the sensitivity coefficients of each organ to hypoxia at each growth stage and the organ allocation influence coefficients of each organ at each growth stage under each hypoxia partial pressure condition, hypoxia sensitivity factors of each organ at each growth stage under each hypoxia partial pressure condition are obtained. These factors are used to assess the sensitivity of each organ to each hypoxia partial pressure condition at each growth stage. The expression is as follows: In the formula, This represents the hypoxia-sensitive factor of the a-th organ at the i-th growth stage under the k-th hypoxia partial pressure condition; This represents the sensitivity coefficient of organ a to hypoxia during the i-th growth stage; This represents the organ allocation influence coefficient of the a-th organ at the i-th growth stage under the k-th hypoxia partial pressure condition.

[0058] It should be noted that the higher the calculated hypoxia sensitivity factor, the greater the sensitivity of each organ to different hypoxia partial pressure conditions at each growth stage.

[0059] Step 2.3: Obtain the environmental impact coefficient of each organ at each growth stage under each hypoxia partial pressure condition, which is used to measure the severity of the impact of each organ on each hypoxia partial pressure condition at each growth stage.

[0060] Steps 2.1 and 2.2 analyzed the effects of hypoxia on dry matter production and material distribution in barley. Furthermore, by comprehensively considering respiration retention at each growth stage under different hypoxia partial pressures, and the hypoxia sensitivity factors of each organ at each growth stage under different hypoxia partial pressures, environmental impact coefficients for each organ at each growth stage under different hypoxia partial pressures were obtained. These coefficients are used to measure the severity of the impact of different hypoxia partial pressures on each organ at each growth stage. Specifically: The environmental impact coefficients of each organ at each growth stage under various hypoxia partial pressure conditions are positively correlated with the hypoxia-sensitive factor and negatively correlated with the respiratory retention rate.

[0061] In this embodiment, the expression for the environmental influence coefficient of each organ at each growth stage under each hypoxia partial pressure condition is as follows: In the formula, This represents the environmental impact coefficient of the a-th organ at the i-th growth stage under the k-th hypoxia partial pressure condition. This represents the hypoxia-sensitive factor of the a-th organ at the i-th growth stage under the k-th hypoxia partial pressure condition; represents the respiration retention rate of the i-th growth stage under the k-th low oxygen partial pressure condition; norm[ ] represents the normalization operation.

[0062] In another embodiment, the expression for the environmental influence coefficient of each organ at each growth stage under each hypoxia partial pressure condition is as follows: In the formula, This represents the environmental impact coefficient of the a-th organ at the i-th growth stage under the k-th hypoxia partial pressure condition. This represents the hypoxia-sensitive factor of the a-th organ at the i-th growth stage under the k-th hypoxia partial pressure condition; represents the respiration retention rate of the i-th growth stage under the k-th hypoxia partial pressure condition; norm[ ] represents the normalization operation; exp() represents the exponential function with the natural constant as the base.

[0063] It should be noted that a higher calculated environmental impact coefficient indicates a greater overall impact of hypoxia on various organs at different growth stages under various hypoxia partial pressures. This means that energy metabolism and nutrient distribution are more severely affected by hypoxia, and the accuracy of WOFOST crop model simulations of barley growth is lower. A schematic diagram of the process for obtaining the environmental impact coefficient is shown below. Figure 2 As shown in the diagram. The process for measuring the severity of an impact is illustrated below. Figure 3 As shown.

[0064] Step 3: Optimize the parameters of the WOFOST crop model by assessing the severity of the impact of low oxygen partial pressure conditions on each organ at each growth stage.

[0065] Furthermore, the calculated environmental impact coefficient is used as a weight and introduced into the training process of the WOFOST crop model, thereby improving the accuracy of the WOFOST crop model in simulating barley growth under high-altitude and low-oxygen conditions. The specific process is as follows: The environmental impact coefficients of all organs under all hypoxia partial pressure conditions at all growth stages were normalized.

[0066] By utilizing the dry matter accumulation and distribution ratio of each organ at each growth stage under various hypoxia partial pressure conditions, and combining this with the environmental impact coefficient, a weighted loss function is constructed, the expression of which is: + In the formula, L represents the weighted loss value; This represents the environmental impact coefficient of the a-th organ at the i-th growth stage under the k-th hypoxia partial pressure condition. and All represent simulated values ​​from the model; and All represent observed values; specifically: The WOFOST crop model predicts the dry matter accumulation of the a-th organ at the i-th growth stage under the k-th hypoxia partial pressure condition. The WOFOST crop model predicts the dry matter distribution ratio of the a-th organ at the i-th growth stage under the k-th hypoxia partial pressure condition. Let be the actual value of the dry matter accumulation of the a-th organ at the i-th growth stage under the k-th hypoxia partial pressure condition. This represents the actual value of the dry matter distribution ratio of the a-th organ at the i-th growth stage under the k-th hypoxia partial pressure condition.

[0067] The WOFOST crop model was constructed and trained using multi-source data obtained in steps 1 and 2. Key parameters of the WOFOST crop model were selected for optimization, including: specific leaf area, maximum CO2 assimilation rate, and dry matter partition coefficient. A weighted loss function was used to optimize these key parameters, ensuring that the WOFOST crop model parameters reflect the physiological changes of barley growing under hypoxic conditions.

[0068] The optimized hypoxia-adaptive WOFOST crop model was used to dynamically simulate the growth process and predict the yield of barley.

[0069] Based on the same inventive concept as the above methods, this application also provides a barley growth simulation system based on the WOFOST crop model, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described barley growth simulation methods based on the WOFOST crop model.

[0070] In summary, by calculating growth similarity, the overall impact of hypoxia on barley growth can be quantified, clarifying the differences between barley growth status under different hypoxic conditions and normoxic conditions, thus providing a basis for subsequent assessment of barley's adaptability under different hypoxic conditions. Furthermore, by combining the similarity of dry matter increment at each growth stage under different oxygen partial pressure conditions, the degree of adaptation to each hypoxic partial pressure condition at each growth stage can be measured. Finally, the respiration retention rate can be calculated, reflecting barley's ability to maintain normal energy metabolism and dry matter accumulation under hypoxic conditions, which helps to assess barley's hypoxia tolerance at each growth stage. Furthermore, this study clarifies the sensitivity of different organs to hypoxia at different growth stages, thereby measuring the severity of the impact of various hypoxia partial pressures on each organ at each growth stage. This comprehensive assessment of the impact of hypoxia on various organs of highland barley provides a more accurate basis for optimizing the WOFOST crop model. The optimized WOFOST crop model can better adapt to the high-altitude hypoxia environment, thus improving the applicability of the WOFOST crop model for simulating highland barley growth and enhancing the accuracy of the WOFOST crop model in simulating highland barley growth under hypoxia conditions.

[0071] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0072] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from its essential characteristics. Therefore, the embodiments described above should be considered exemplary and non-limiting in all respects.

Claims

1. A method for simulating barley growth based on the WOFOST crop model, characterized in that, The method includes the following steps: The dry matter accumulation and dry matter increment of barley under different oxygen partial pressures at different growth stages, as well as the dry matter distribution ratio of different organs of barley, were obtained to analyze the effect of hypoxia on barley growth, including normoxic partial pressure and different hypoxic partial pressures. By comparing the changes in dry matter accumulation, the growth similarity of barley under each hypoxia partial pressure condition and normoxic partial pressure condition is obtained. Combined with the similarity of dry matter increment at each growth stage under different oxygen partial pressure conditions, the degree of adaptation of each growth stage to each hypoxia partial pressure condition is measured. Then, combined with the dispersion of the degree of adaptation of each growth stage to all hypoxia partial pressure conditions, the respiration retention rate of each growth stage under each hypoxia partial pressure condition is obtained. By comparing the dry matter distribution ratio of each organ at each growth stage under different oxygen partial pressure conditions, the influence of each low oxygen partial pressure condition on the distribution ratio of each organ at each growth stage is measured. By the dispersion of the influence of different low oxygen partial pressure conditions and the respiration retention, the sensitivity of each organ to low oxygen at each growth stage is measured. Then, by combining the influence and the respiration retention, the severity of the influence of each organ on each low oxygen partial pressure condition at each growth stage is measured, thereby optimizing the parameters of the WOFOST crop model.

2. The method for simulating barley growth based on the WOFOST crop model as described in claim 1, characterized in that, The process for obtaining the similarity of barley growth is as follows: Calculate the mean dry matter increment for all growth stages under each oxygen partial pressure condition; Calculate the difference between the dry matter increment at each growth stage under each oxygen partial pressure condition and the mean value; Calculate the difference values ​​of the aforementioned differences between each growth stage under each low oxygen partial pressure condition and a normal oxygen partial pressure condition; The similarity of barley growth was negatively correlated with all the differences corresponding to each hypoxia partial pressure condition.

3. The method for simulating barley growth based on the WOFOST crop model as described in claim 1, characterized in that, The process for measuring the degree of adaptation is as follows: Obtain the deviation of dry matter increment at each growth stage between low oxygen partial pressure conditions and normal oxygen partial pressure conditions. The degree of adaptation is measured by multiplying the deviation by the similarity of barley growth.

4. The method for simulating barley growth based on the WOFOST crop model as described in claim 1, characterized in that, The process for obtaining the respiratory maintenance rate is as follows: Obtain the sum of the differences between the measures of each growth stage's adaptation to all low oxygen partial pressure conditions and the minimum values ​​among them; The respiratory retention rate is positively correlated with the measurement of the degree of adaptation, and negatively correlated with the summation result.

5. The method for simulating barley growth based on the WOFOST crop model as described in claim 1, characterized in that, The measure of influence is the difference in the proportion of dry matter distribution in each organ at each growth stage between hypoxia partial pressure conditions and normoxic partial pressure conditions.

6. The method for simulating barley growth based on the WOFOST crop model as described in claim 1, characterized in that, The process of measuring the sensitivity is as follows: The average value of the measures of the influence of all hypoxia partial pressure conditions on the distribution ratio of each organ at each growth stage is calculated. The summation of the differences between the measures of the influence of all hypoxia partial pressure conditions on the distribution ratio of each organ at each growth stage and the average value; The sensitivity measurement result is positively correlated with the cumulative result and negatively correlated with the respiratory retention rate.

7. The method for simulating barley growth based on the WOFOST crop model as described in claim 1, characterized in that, The process for measuring the severity of the impact is as follows: By combining the results of the sensitivity measurement with the results of the influence measurement, the sensitivity of each organ to each hypoxia partial pressure condition at each growth stage is evaluated. The measurement of the severity of the impact is positively correlated with the assessment of the sensitivity, and negatively correlated with the respiratory retention.

8. The method for simulating barley growth based on the WOFOST crop model as described in claim 7, characterized in that, The sensitivity assessment result is the product of the sensitivity measurement result and the influence measurement result.

9. The method for simulating barley growth based on the WOFOST crop model as described in claim 1, characterized in that, The method for optimizing the parameters of the WOFOST crop model is as follows: During the training of the WOFOST crop model, the severity of the impact is used to assign importance to the experimental data of each organ at each growth stage and under each hypoxia partial pressure condition, so as to optimize the loss function and thus optimize the parameters of the WOFOST crop model.

10. A barley growth simulation system based on the WOFOST crop model, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the barley growth simulation method based on the WOFOST crop model as described in any one of claims 1-9.

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