A dynamic simulation method of rice dry matter

Abstract

The application provides a kind of dynamic simulation method of rice dry matter, including constructing the relationship between rice leaf area and dry matter, based on observed net radiation, air temperature and soil moisture content and calculated rice leaf area, using dynamic Priestley-Taylor model to estimate rice daily transpiration, based on the obtained rice daily transpiration and observed water vapor pressure difference data to calculate gross primary productivity, based on observed air temperature data to calculate daily actual maintenance respiration rate, based on the gross primary productivity calculated in step 3 and the daily actual maintenance respiration rate calculated in step 4 to dynamically simulate dry matter growth rate, the application provides a kind of dynamic simulation method of rice dry matter, which can predict the dry matter production of rice on any day after planting with limited data, and the model parameters are less, only need net radiation, temperature and humidity data to predict, in addition, the influence of CO2 and temperature rise on rice dry matter production can be evaluated.

Description

Technical Field

[0001] This invention belongs to the field of agricultural information processing technology, and specifically relates to a dynamic simulation method for rice dry matter. Background Technology

[0002] Rice is one of the world's major food crops, and the staple food for 50% of the global population, and it is widely cultivated worldwide. According to FAO statistics, in 2020, the global rice planting area reached 164 million hectares, with a yield of 757 million tons. Dry matter production is a crucial material basis for rice yield and quality. During the vegetative growth stage, dry matter allocated to leaves mainly affects leaf area, thus influencing photosynthetic and transpiration rates; dry matter allocated to roots mainly affects root water and nutrient absorption; and during the reproductive growth stage, dry matter allocated to panicles directly impacts rice yield and quality. Therefore, the dynamic simulation of rice dry matter has always been a focus of many crop models, and it is crucial for estimating the final crop yield.

[0003] Currently, there are three main methods for dry matter simulation: effective accumulated temperature method, radiative heat accumulation method, and photosynthesis-driven model. The effective accumulated temperature method and radiative heat accumulation method primarily simulate dry matter by establishing the relationship between aboveground dry matter and the crop's effective accumulated temperature or radiative heat accumulation. The photosynthesis-driven model obtains the daily total dry matter mass by separately describing single-leaf and canopy photosynthesis and then integrating the data. However, all three methods for estimating dry matter have certain limitations. The effective accumulated temperature method does not consider the lag effect of high temperatures on crop growth; the radiative heat accumulation method does not separately consider temperature and light effects, resulting in significant deviations in estimated dry matter under low light intensity and high temperature conditions; and the photosynthesis-driven model is computationally complex due to the need to measure photosynthetic parameters. Furthermore, these models cannot simulate the effects of environmental changes such as CO2 and warming on rice dry matter. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the purpose of this invention is to provide a dynamic simulation method for rice dry matter. Based on limited data, this method can quickly and dynamically simulate changes in rice dry matter and assess the impact of future CO2 and temperature increases on rice dry matter production.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0006] A dynamic simulation method for rice dry matter includes the following steps:

[0007] Step 1: Construct the relationship between rice leaf area and dry matter;

[0008] Step 2: Based on the observed net radiation, air temperature, and soil moisture content, and the rice leaf area calculated in Step 1, the daily average transpiration of rice is estimated using a dynamic Priestley-Taylor model.

[0009] Step 3: Calculate the total primary productivity (GPP) based on the average daily transpiration of rice obtained in Step 2 and the observed water vapor pressure difference data;

[0010] Step 4: Calculate the daily actual maintenance respiratory rate Rm based on the observed temperature data;

[0011] Step 5: Based on the total primary productivity (GPP) calculated in Step 3 and the actual daily maintenance respiration rate (Rm) calculated in Step 4, establish a dynamic daily dry matter growth rate model to obtain the impact of CO2 and temperature rise on rice dry matter production.

[0012] Furthermore, in step 1, the rice leaf area LAI and dry matter B exhibit a parabolic relationship, calculated using the following formula:

[0013]

[0014] Where LAI is the leaf area of ​​rice, in m². 2 ·m -2 Dry matter B, unit is g·m -2 a and b are empirical parameters. Based on observational data, the parameters for rice are a = -6.21 × 10⁻⁶. -6 b = 1.22 × 10 -2 .

[0015] Furthermore, in step 2, the rice transpiration rate T is calculated using a dynamic Priestley-Taylor model. r :

[0016]

[0017] Among them, T r This refers to rice transpiration, expressed in mm·d. -1 λ is the latent heat of vaporization, λ = 44 kJ·mol⁻¹ -1 γ is the hygrometer constant; Δ is the slope of the vapor pressure versus temperature curve; Rn is the net radiation, in W·m. -2 LAI is the leaf area of ​​rice, in m²·m⁻²; k is the canopy extinction coefficient, which is 0.45 for rice; f t It is the plant temperature constraint coefficient; α c0 This is the PT correction factor for the canopy under energy-limited conditions.

[0018] Furthermore, the plant temperature constraint coefficient f t The calculation formula is as follows:

[0019]

[0020] Among them, T aα represents air temperature, expressed in °C. c0 and α s0 The PT correction factors for the canopy and soil under energy-limited conditions are as follows:

[0021]

[0022]

[0023] τ=exp(-kLAI) (6)

[0024] Where α0 is the standard PT coefficient, α0 = 1.26; τ c The critical value of τ is τ = 0.55.

[0025] Furthermore, in step 3, the total primary productivity (GPP) is calculated using a formula based on water use efficiency:

[0026] GPP = 12 × WUE e ×λT r (7)

[0027] WUE e ∝WUE l (8)

[0028] GPP stands for Gross Primary Productivity, measured in g·cm³. -2 ·d -1 ;12 represents the total molar mass of C; WUE e and WUE l Water use efficiency at the canopy and leaf scales, respectively.

[0029] Furthermore, WUE e and WUE l The calculation formula is as follows:

[0030]

[0031] E = 1.6g c (e i -e a )≈1.6g c D (10)

[0032]

[0033]

[0034] Among them, A n The photosynthetic rate of the leaf is expressed in μmol·m⁻¹. -2 ·s -1 E represents the leaf transpiration rate, in mol / m³. -2 s-1 g c Stomatal conductance of the blade, in mol·m -2 ·s -1 c a and c i These represent atmospheric and intercellular CO2 concentrations, respectively, in μmol·mol⁻¹. -1 D is the water vapor pressure difference, in kPa; f(D) is the function of the ratio of intercellular CO2 concentration at the leaf scale to atmospheric CO2 concentration as a function of water vapor pressure.

[0035] Furthermore, in step 4, the actual daily maintenance respiration rate (Rm) can be calculated as follows:

[0036]

[0037] R m (T0)=εB (14)

[0038] Among them, R m (T0) is the sustaining respiration rate at a reference temperature T0 of 25℃, expressed in g·C·m³. -2 ·d -1 Q 10 To maintain the temperature sensitivity of respiration, a value of 2 is usually used; ε is the maintenance coefficient, taken as 0.0055g. -1 dry mass d -1 .

[0039] Furthermore, in step 5, the daily growth rate of the dry matter is:

[0040]

[0041] Where B represents dry matter, with units of g·m³. -2 t represents time, in days; C f Carbon content in dry matter, expressed in g·C·m -2 ·d -1 This invention C f The value is 0.446;

[0042] Finally, the dynamic daily dry matter growth rate model is expressed as:

[0043]

[0044] Among them, c a This refers to the atmospheric CO2 concentration, expressed in μmol·mol⁻¹. -1 D represents the vapor pressure difference, in kPa; f(D) is the function representing the ratio of intercellular CO2 concentration at the leaf scale to atmospheric CO2 concentration as a function of vapor pressure; tγ is the plant temperature constraint coefficient; Δ is the slope of the water vapor pressure versus temperature curve; γ is the hygrometer constant; a and b are empirical parameters, and the parameters for rice obtained based on observation data are a = -6.21 × 10⁻⁶. -6 b = 1.22 × 10 -2 k is the canopy extinction coefficient; α c0 R is the PT correction factor for the canopy under energy-limited conditions. n Net radiation, measured in W·m -2 B represents dry matter, with units of g·m³. -2 Q 10 To maintain the temperature sensitivity of respiration, a value of 2 is usually used; ε is the maintenance coefficient, taken as 0.0055g. -1 dry·mass·d -1 T0 is the reference temperature; T a This refers to the air temperature.

[0045] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0046] This invention provides a dynamic simulation method for rice dry matter. By obtaining the quantitative relationship between rice leaf area and dry matter, and using observed net radiation, air temperature, and soil moisture content data, along with calculated leaf area values, a dynamic Priestley-Taylor model is employed to calculate the average daily transpiration of rice. Based on the calculated average daily transpiration and observed air-water pressure difference data, total primary productivity is calculated, and the actual daily sustaining respiration rate is calculated based on observed air temperature data. Finally, a dynamic daily dry matter growth rate model is established. The dynamic daily dry matter growth rate model established using this invention can predict rice dry matter production on any day after planting using limited data, and the model requires few parameters, only needing net radiation, temperature, and humidity data for forecasting. Furthermore, some parameters in the dynamic simulation model of rice dry matter change with CO2 and air temperature, enabling the assessment of the impact of rising CO2 and air temperature on rice dry matter production. Attached Figure Description

[0047] Figure 1 This is a flowchart of the implementation steps of the present invention.

[0048] Figure 2 This is a graph showing the relationship between rice leaf area and dry matter.

[0049] Figure 3(a) shows the dynamic simulation effect of the constructed dry matter model in Lagula.

[0050] Figure 3(b) shows the dynamic simulation effect of the constructed dry matter model in Kunshan.

[0051] Figure 3(c) shows the dynamic simulation effect of the constructed dry matter model in Nanjing.

[0052] Figure 4(a) Dynamic changes of dry matter in Laguna under conditions of increased CO2 concentration.

[0053] Figure 4(b) shows the dynamic changes of dry matter in Kunshan under conditions of increased CO2 concentration.

[0054] Figure 4(c) shows the dynamic changes of dry matter in Nanjing under conditions of increased CO2 concentration.

[0055] Figure 5(a1) Dynamic changes of dry matter in Laguna under daytime warming scenario.

[0056] Figure 5(b1) Dynamic changes of dry matter in Laguna under a nighttime warming scenario.

[0057] Figure 5(c1) shows the dynamic changes of dry matter in Laguna under a constant warming scenario.

[0058] Figure 5(a2) shows the dynamic changes of dry matter in Kunshan under a daytime warming scenario.

[0059] Figure 5(b2) Dynamic changes of dry matter in Kunshan under nighttime warming scenario.

[0060] Figure 5(c2) shows the dynamic changes of dry matter in Kunshan under a constant warming scenario.

[0061] Figure 5(a3) shows the dynamic changes in dry matter in Nanjing under a daytime warming scenario.

[0062] Figure 5(b3) shows the dynamic changes in dry matter in Nanjing under a nighttime warming scenario.

[0063] Figure 5(c3) shows the dynamic changes of dry matter in Nanjing under a constant warming scenario. Detailed Implementation

[0064] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0065] This invention provides a dynamic simulation method for rice dry matter, which solves the problems of existing technologies having many parameters, complex technology, and inability to simulate the effects of CO2 and warming on rice dry matter.

[0066] This invention provides a dynamic simulation method for rice dry matter, such as... Figure 1 As shown, it includes the following steps:

[0067] Step 1: Construct the relationship between rice leaf area and dry matter;

[0068] Step 2: Based on the observed net radiation, air temperature, and soil moisture content, and the rice leaf area calculated in Step 1, the daily average transpiration of rice is estimated using a dynamic Priestley-Taylor model.

[0069] Step 3: Calculate the total primary productivity (GPP) based on the average daily transpiration of rice obtained in Step 2 and the observed water vapor pressure difference data;

[0070] Step 4: Calculate the daily actual maintenance respiratory rate Rm based on the observed temperature data;

[0071] Step 5: Based on the total primary productivity (GPP) calculated in Step 3 and the daily actual maintenance respiration rate (Rm) calculated in Step 4, establish a dynamic daily growth rate model of total dry biomass to obtain the impact of CO2 and temperature rise on rice dry matter production.

[0072] This invention provides a dynamic simulation method for rice dry matter. By obtaining the quantitative relationship between rice leaf area and dry matter, and using observed net radiation, air temperature, and soil moisture content data, along with calculated leaf area values, a dynamic Priestley-Taylor model is employed to calculate the average daily transpiration of rice. Based on the calculated average daily transpiration and observed air-water pressure difference data, total primary productivity is calculated, and the actual daily sustaining respiration rate is calculated based on observed air temperature data. Finally, a dynamic daily dry matter growth rate model is established. The dynamic daily dry matter growth rate model established using this invention can predict rice dry matter production on any day after planting using limited data, and the model requires few parameters, only needing net radiation, temperature, and humidity data for forecasting. Furthermore, some parameters in the dynamic simulation model of rice dry matter change with CO2 and air temperature, enabling the assessment of the impact of rising CO2 and air temperature on rice dry matter production.

[0073] In step 1, based on literature and measured data, it was found that there is a parabolic relationship between rice leaf area (LAI) and dry matter (B), and the calculation formula is as follows:

[0074]

[0075] Where LAI is the leaf area of ​​rice, in m². 2 ·m -2 B represents dry matter, with units of g·m³. -2 a and b are empirical parameters. Based on observational data, the parameters for rice are a = -6.21 × 10⁻⁶. -6 b = 1.22 × 10 -2 .

[0076] In step 2, the dynamic Priestley-Taylor model is used to calculate the rice transpiration rate T. r :

[0077]

[0078] Among them, T r This refers to rice transpiration, expressed in mm·d. -1λ is the latent heat of vaporization, λ = 44 kJ·mol⁻¹ -1 γ is the hygrometer constant; Δ is the slope of the vapor pressure versus temperature curve; Rn is the net radiation, in W·m. -2 LAI is the leaf area of ​​rice, in m²·m⁻²; k is the canopy extinction coefficient, which is 0.45 for rice; f t It is the plant temperature constraint coefficient; α c0 This is the PT correction factor for the canopy under energy-limited conditions.

[0079] Specifically, the plant temperature constraint coefficient f t The calculation formula is as follows:

[0080]

[0081] Among them, T a α represents air temperature, expressed in °C. c0 and α s0 The PT correction factors for the canopy and soil under energy-limited conditions are as follows:

[0082]

[0083]

[0084] τ=exp(-kLAI) (6)

[0085] Where α0 is the standard PT coefficient, α0 = 1.26; τ c The critical value of τ is τ = 0.55.

[0086] In step 3, the gross primary productivity (GPP) is calculated using a formula based on water use efficiency:

[0087] GPP = 12 × WUE e ×λT r (7)

[0088] WUE e ∝WUE l (8)

[0089] GPP stands for Gross Primary Productivity, measured in g·cm³. -2 ·d -1 ;12 represents the total molar mass of C; WUE e and WUE l Water use efficiency at the canopy and leaf scales, respectively.

[0090] WUE e and WUE l The calculation formula is as follows:

[0091]

[0092] E = 1.6g c (e i -e a )≈1.6g c D (10)

[0093]

[0094]

[0095] Among them, A n The photosynthetic rate of the leaf is expressed in μmol·m⁻¹. -2 ·s -1 E represents the leaf transpiration rate, in mol / m³. -2 s -1 g c Stomatal conductance of the blade, in mol·m -2 ·s -1 c a and c i These represent atmospheric and intercellular CO2 concentrations, respectively, in μmol·mol⁻¹. -1 D is the water vapor pressure difference, in kPa; f(D) is the function of the ratio of intercellular CO2 concentration at the leaf scale to atmospheric CO2 concentration as a function of water vapor pressure.

[0096] In step 4, the actual daily maintenance respiratory rate (Rm) can be calculated as follows:

[0097]

[0098] R m (T0)=εB (14)

[0099] Among them, R m (T0) is the sustaining respiration rate at a reference temperature T0 of 25℃, expressed in g·C·m³. -2 ·d -1 Q 10 To maintain the temperature sensitivity of respiration, a value of 2 is usually used; ε is the maintenance coefficient, taken as 0.0055g. -1 dry mass d -1 .

[0100] In step 5, the daily growth rate of dry matter is:

[0101]

[0102] Where B represents dry matter, with units of g·m³. -2 t represents time, in days; C f Carbon content in dry matter, expressed in g·C·m-2 ·d -1 This invention C f The value is 0.446;

[0103] Finally, the dynamic daily dry matter growth rate model is expressed as:

[0104]

[0105] Among them, c a This refers to the atmospheric CO2 concentration, expressed in μmol·mol⁻¹. -1 D represents the vapor pressure difference, in kPa; f(D) is the function representing the ratio of intercellular CO2 concentration at the leaf scale to atmospheric CO2 concentration as a function of vapor pressure; t γ is the plant temperature constraint coefficient; Δ is the slope of the water vapor pressure versus temperature curve; γ is the hygrometer constant; a and b are empirical parameters, and the parameters for rice obtained based on observation data are a = -6.21 × 10⁻⁶. -6 b = 1.22 × 10 -2 k is the canopy extinction coefficient; α c0 R is the PT correction factor for the canopy under energy-limited conditions. n Net radiation, measured in W·m -2 B represents dry matter, with units of g·m³. -2 Q 10 To maintain the temperature sensitivity of respiration, a value of 2 is usually used; ε is the maintenance coefficient, taken as 0.0055g. -1 dry·mass·d -1 T0 is the reference temperature; T a This refers to the air temperature.

[0106] In this embodiment of the invention, taking Laguna, Kunshan, and Nanjing as examples, a dynamic daily dry matter growth rate model obtained using a dynamic simulation method for rice dry matter provided by the present invention is used. According to the present invention... Figures 3(a)-3(c) As shown, the dynamic daily dry matter growth rate model provided by this invention can effectively capture the dynamic changes in the actual total dry matter of rice in different locations, and the simulation accuracy is high.

[0107] like Figures 4(a)-4(c) As shown, the dynamic daily dry matter growth rate model provided by this invention can assess the impact of CO2 on rice dry matter production.

[0108] like Figures 5(a1)-5(c3) As shown, the dynamic daily dry matter growth rate model provided by this invention can assess the impact of rising temperatures on rice dry matter production.

[0109] In summary, the rice dry matter model established using this invention can predict the aboveground dry matter production of crops on any day after transplanting, and the model has relatively few parameters. This model can also assess the impact of CO2 and rising temperatures on rice dry matter production. The above embodiments are merely specific implementation examples of this invention, and all instances that conform to the inventive spirit of this patent are within the protection scope of this invention.

[0110] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for dynamic simulation of rice dry matter, characterized in that, Includes the following steps: Step 1: Construct the relationship between rice leaf area and dry matter; Step 2: Based on the observed net radiation, air temperature, and soil moisture content, and the rice leaf area calculated in Step 1, the daily average transpiration of rice is estimated using a dynamic Priestley-Taylor model; the Priestley-Taylor model is expressed as: （1） in, This refers to the transpiration rate of rice, measured in units of... ; It is the latent heat of vaporization. ; This is the hygrometer constant; The slope of the vapor pressure versus temperature curve; Net radiation, in units of LAI stands for rice leaf area, in m². 2 m -2 ; k The extinction coefficient of the canopy is 0.45 for rice; It is the plant temperature constraint coefficient; The PT correction factor for the canopy under energy-limited conditions; Step 3: Calculate the total primary productivity based on the average daily transpiration of rice obtained in Step 2 and the observed water vapor pressure difference data. GPP ; Step 4: Calculate the daily actual maintenance respiratory rate based on observed temperature data. Rm ; Step 5: Total primary productivity calculated in Step 3 GPP and the actual daily maintenance respiratory rate calculated in step 4 Rm Establish a dynamic daily dry matter growth rate model to obtain... The impact of rising temperatures on rice dry matter production; the daily growth rate of dry matter is: （2） in, B It is dry matter, and the unit is 1. ; t Time, in days (d). Carbon content in dry matter, in units of , The value is 0.446; Finally, the dynamic daily dry matter growth rate model is expressed as: （3） in, For the atmosphere Concentration, in units of ; D This represents the water vapor pressure difference, expressed in kPa. For leaf-scale intercellular and atmospheric The concentration ratio is a function of water vapor pressure. It is the plant temperature constraint coefficient; The slope of the vapor pressure versus temperature curve; This is the hygrometer constant; , These are empirical parameters; the parameters for rice obtained based on observational data are as follows: , ; The extinction coefficient of the canopy; The PT correction factor for the canopy under energy-limited conditions; Net radiation, in units of ; B It is dry matter, and the unit is 1. ; To maintain temperature sensitivity during respiration, a value of 2 is typically used; To maintain the coefficient, take ; For reference temperature; Air temperature.

2. The dynamic simulation method for rice dry matter according to claim 1, characterized in that: In step 1, the rice leaf area LAI and dry matter B There is a parabolic relationship, and the calculation formula is as follows: （4） in, LAI Rice leaf area, unit: Dry matter B The unit is ; , These are empirical parameters; the parameters for rice obtained based on observational data are as follows: , .

3. The dynamic simulation method for rice dry matter according to claim 1, characterized in that: Plant temperature constraint coefficient The calculation formula is as follows: （5） in, Air temperature, unit: ; and The PT correction factors for the canopy and soil under energy-limited conditions are as follows: （6） （7） （8） in, The standard PT coefficient, ; yes critical value .

4. The dynamic simulation method for rice dry matter according to claim 1, characterized in that: In step 3, the gross primary productivity (GPP) is calculated using a formula based on water use efficiency: （9） （10） GPP stands for Total Primary Productivity, measured in units of... ;12 represents the total molar mass of C; and Water use efficiency at the canopy and leaf scales, respectively; This refers to the transpiration rate of rice, measured in units of... ; It is the latent heat of vaporization. .

5. The dynamic simulation method for rice dry matter according to claim 4, characterized in that: and The calculation formula is as follows: （11） （12） （13） （14） in, The photosynthetic rate of the leaf, in units of ; E Leaf transpiration rate, in units of ; Stomatal conductance of the blade, in units of , and They are respectively atmospheric and intercellular. Concentration, in units of ; D This represents the water vapor pressure difference, expressed in kPa. For leaf-scale intercellular and atmospheric The concentration ratio is a function of water vapor pressure.

6. The dynamic simulation method for rice dry matter according to claim 1, characterized in that: In step 4, the actual daily maintenance of respiration Rm The following can be calculated: （15） （16） in, It is 25 Reference temperature The unit is to maintain breathing. ; To maintain temperature sensitivity during respiration, a value of 2 is typically used; To maintain the coefficient, take ; B It is dry matter, and the unit is 1. .

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