Method for quantifying physical stress of field soil based on field in-situ monitoring and model simulation

Abstract

The invention provides a method for simulating and quantifying physical stress of field soil based on field in-situ monitoring and a model. The method comprises the following steps: step 1, acquiring soil bulk density, water content and penetration resistance values under different water potentials; step 2, fitting the transfer equation of each limiting condition by using each measured data in the step 1, and establishing soil transfer equations of four limiting conditions of LLWR; 3, field soil bulk density dynamic measured data are obtained; 4, constructing a field soil bulk density dynamic prediction model in the implementation stage and the expansion and shrinkage stage under the influence of cultivation and rainfall; step 5, acquiring actually measured data of field rainfall and soil water content of the research area; and step 6, obtaining the LLWR range of the research area and the dynamic change of each limiting condition. The method solves the technical problems that in the prior art, seasonal dynamic of soil bulk density, which is a key transmission variable influencing LLWR, is ignored, and it is difficult to accurately estimate dynamic changes of LLWR in seasons and quantify physical stress caused by the LLWR on crop growth.

Description

Technical Field

[0001] The present invention relates to the technical field of methods for simulating quantitative field soil physical stress, and in particular to a method for simulating quantitative field soil physical stress based on field in-situ monitoring and model simulation. Background Art

[0002] The least limit water range (LLWR) is a key indicator of the combined effects of the soil physical environment on crop growth. It integrates the triple constraints of water availability, aeration, and mechanical strength to define the critical soil moisture range suitable for crop growth. However, LLWR is highly sensitive to soil physical properties such as soil moisture and bulk density, resulting in highly dynamic temporal and spatial LLWR, particularly in non-rigid soils with strong expansion and contraction properties. Under the influence of complex environmental factors such as tillage and rainfall, the continuous dynamics of LLWR in the field and the resulting physical stress on crop growth remain difficult to quantify. Therefore, accurately predicting the dynamics of LLWR and quantifying the physical stress it imposes on crop growth are of great significance for evaluating soil physical quality and guiding farmland water and structure management.

[0003] Existing techniques typically use soil moisture and the LLWR boundary condition to evaluate the soil water stress index. Based on the relative relationship between soil moisture and the LLWR boundary water content, the difference between the effective water storage capacity of the soil layer in the absence of water stress and the initial effective water storage capacity is used to assess soil drought severity. However, this method ignores the seasonal dynamics of soil bulk density, a key transfer variable influencing the magnitude of the LLWR. This makes it difficult to accurately estimate the dynamic changes of the LLWR within a season and hinders its application to other soil types.

[0004] Therefore, how to accurately quantify the linked changes in soil moisture and bulk density within a season is of great significance for evaluating soil physical quality and guiding farmland structure and water management. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for quantifying field soil physical stress based on in-situ field monitoring and model simulation. This method solves the technical problem that the existing technology ignores the seasonal dynamics of soil bulk density, a key transfer variable that affects the size of LLWR, making it difficult to accurately estimate the dynamic changes of LLWR within the season and quantify the physical stress it causes to crop growth, and is unable to meet the needs of evaluating soil physical quality and guiding farmland water and structure management.

[0006] In order to solve the above technical problems, the technical solutions of the present invention are as follows:

[0007] The present invention provides a method for quantifying field soil physical stress based on field in-situ monitoring and model simulation, comprising the following steps:

[0008] Step 1: Obtain soil bulk density, water content, and penetration resistance values under different water potentials;

[0009] Step 2: Use the measured data in step 1 to fit the transfer equations of each constraint condition and establish the soil transfer equations of the four constraint conditions of LLWR;

[0010] Step 3: Obtain dynamic measured data of field soil bulk density;

[0011] Step 4: Construct a dynamic prediction model of field soil bulk density in the implementation stage and expansion and contraction stage under the influence of tillage and rainfall;

[0012] Step 5: Obtain measured data of field rainfall and soil moisture in the study area;

[0013] Step 6: By substituting the measured data obtained in step 5 into the soil bulk density dynamic prediction model constructed in step 4, the continuous dynamic changes of soil bulk density are obtained. Then, the dynamic data of soil moisture and bulk density are input into the LLWR soil transfer equation established in step 2 to obtain the continuous changes of each LLWR boundary condition. Finally, the seasonal dynamic change range of LLWR is determined.

[0014] Furthermore, the step 3 specifically includes:

[0015] During the monitoring process, the errors caused by daytime solar radiation and ambient background temperature drift on soil thermal characteristics were corrected by daytime data elimination and linear extrapolation, and the dynamic changes in soil bulk density were calculated using the soil thermal conductivity index model.

[0016] Furthermore, the step 4 specifically includes:

[0017] The dynamics of soil bulk density during soil compaction is expressed by the measured soil bulk density (ρ b ) and cumulative rainfall (P t ) are fitted with the empirical exponential equation:

[0018] ρ b =ρ bi +(ρ bm -ρ bi )[1-exp(-P t -P m )] P t <P m (13)

[0019] Where, ρ bi is the initial bulk density of soil after tillage (g·cm -3 ), P m The bulk density of the soil reaches a stable value after it is solidified (ρ bm ) cumulative rainfall at ;

[0020] When the soil gradually settles and reaches stability (P t >P m ), soil structure changes began to be dominated by the hydraulically driven soil expansion and contraction process; the changes in soil bulk density during the expansion and contraction process were calculated using the soil bulk density curve model considering shrinkage (SSC ρb ) predicts:

[0021]

[0022] Where χ, p, and q are dimensionless soil shrinkage curve fitting parameters, and ρ s is the soil particle density (g·cm -3 ), e s , e r They are soil moisture ratio, saturated porosity and residual porosity (cm 3 cm -3 ).

[0023] Furthermore, the method further includes step 7: according to the change of LLWR under different bulk density conditions, a curve of LLWR range versus soil bulk density is obtained, and the soil bulk density range suitable for crop growth is determined when LLWR>0; according to the linkage change relationship between soil moisture and structure, the soil bulk density and LLWR boundary conditions are determined from the soil water content, and finally θ is determined. v Soil moisture range that consistently falls within the LLWR range.

[0024] Furthermore, step 8 is included: using the measured soil moisture value and the LLWR boundary condition to determine the soil physical stress type and time proportion.

[0025] Furthermore, step 8 specifically includes: based on the relative dynamics of soil moisture and LLWR boundary conditions within the season, soil physical stress can be subdivided into soil aeration stress, soil waterlogging stress, soil drought stress and soil strength stress, and the seasonal time proportion of each physical stress type can be further calculated.

[0026] Furthermore, step 9 is included: determining key soil physical stress factors that affect crop growth within the season.

[0027] Furthermore, the step 9 specifically includes: for crops planted in the field, correlation analysis is performed between the soil physical stress time during each key growth period of the crop and the crop growth traits to determine the key soil physical stress factors that affect crop growth.

[0028] Furthermore, the crop growth traits include aboveground / underground biomass and crop yield.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] Through in-situ field monitoring and model simulation, this paper establishes a hydraulically driven soil bulk density prediction model. By leveraging the linkage between soil moisture and structure and the soil transfer function for the minimum limiting moisture range, this method provides dynamic predictions of soil physical stress in the field at high temporal resolution and defines the soil moisture and bulk density ranges suitable for crop growth. This method not only enables dynamic prediction of field soil physical conditions but also, combined with crop growth traits, identifies key growth periods and key physical limiting factors affecting crop growth. This method can provide a decision-making basis for farmland physical quality assessment and soil structure / water management. Specifically, by incorporating the seasonal dynamics of soil bulk density, a key transfer variable influencing LLWR, this method accurately estimates the dynamics of LLWR within a season and can be applied to other soil types. Furthermore, in addition to soil moisture stress, this method also considers the important impact of physical stresses such as soil aeration and soil strength on crop growth, thereby prioritizing soil physical stress. Finally, by considering the seasonal matching between soil physical stress and crop stress tolerance, farmland management can be rationally planned based on crop growth needs and intraseasonal climate variations. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0032] Figure 1 It is a schematic diagram of the process of the present invention;

[0033] Figure 2 Soil shrinkage curves under different tillage treatments;

[0034] Figure 3 The minimum limiting soil moisture range under different tillage treatments;

[0035] Figure 4 is the volumetric water content of soil under different tillage treatments (θ v ) and bulk density (ρ b )dynamic;

[0036] Figure 5 The dynamic changes of predicted and measured soil bulk density under different tillage treatments from 2017 to 2022;

[0037] Figure 6 The relationship between the estimated and measured soil bulk density under different tillage treatments from 2017 to 2022;

[0038] Figure 7 The dynamic changes of soil water content and LLWR boundary conditions under different tillage treatments;

[0039] Figure 8 The LLWR curves of soil bulk density under different tillage treatments are shown;

[0040] Figure 9 It is the soil moisture range suitable for crop growth under different tillage treatments. DETAILED DESCRIPTION

[0041] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0042] This example takes the sandy black soil of Longkang Farm in Anhui Province under different tillage treatments as an example. Figure 1 As shown, the method for quantifying field soil physical stress based on in-situ field monitoring and model simulation includes the following steps:

[0043] Step 1: Obtain soil bulk density, water content, and penetration resistance values under different water potentials;

[0044] Step 1.1: Collect undisturbed ring cutter samples (5 cm diameter × 5 cm height, 100 cm volume) from the 0-10 cm soil layer under three different tillage treatments: no-tillage, rotary tillage, and deep plowing. 3 ); After indoor saturation, the sand box moisture balance and pressure membrane instrument method were used to perform moisture balance and weighing at 0hPa, -1kPa, -3kPa, -6kPa, -10kPa, -33kPa, -100kPa, -500kPa, and -1500kPa water suction;

[0045] Step 1.2: During the water characteristic curve determination process, soil height was measured at each water potential using a depth gauge with an accuracy of 0.01 mm. Five points on each soil sample were measured at a time. Under the assumption of isotropic shrinkage (longitudinal and horizontal shrinkage were the same), the change in soil volume during dehydration was calculated.

[0046] Step 1.3: Simultaneously, use the penetration resistance meter provided with the universal material testing machine to measure the soil penetration resistance. The probe diameter is 2 mm, the needle cone angle is 30°, the penetration height is 45 mm, and the penetration speed is 10 mm / min. -1 , return speed 140mm·min -1The penetrometer records a data point every 0.5 mm. The average penetration resistance between 0 and 45 mm is used as the penetration resistance value of the soil ring knife sample under the water potential, and the soil penetration resistance curve under different bulk density and moisture conditions is obtained.

[0047] Step 1.4: After the above steps are completed, dry the soil sample at 105℃ to constant weight, and calculate the dry soil mass and the soil moisture ratio after each pressure balance ( cm 3 cm -3 ) and porosity (e, cm 3 cm -3 ), fitting soil shrinkage curve ( Figure 2 ), and the soil shrinkage curve parameters under different tillage treatments were obtained (Table 1); the soil bulk density (ρ b , g cm -3 ) and soil volumetric water content (θ v , cm 3 cm -3 ).

[0048] Table 1. Soil shrinkage curve parameters of the 0-10 cm soil layer under different tillage treatments

[0049]

[0050] Step 2: Establish the soil minimum limiting water range (LLWR) transfer equation;

[0051] The four limiting conditions of LLWR are soil field water holding capacity (θ FC , Field capacity), soil wilting coefficient (θ WP , Wilting point), 3MPa soil penetration resistance curve (θ PR＝3MPa , Penetration resistance), 10% soil ventilation porosity (θ AFP＝10％ , Air-filled porosity). The soil transfer equations for each LLWR constraint are:

[0052] lnθ=a1+a2ρ b +a3 lnΨ (1)

[0053] Where a1, a2, and a3 are the regression coefficients of the soil moisture characteristic curve transfer equation; θ is the soil volume water content (cm 3 cm -3 ), ρ b is the soil bulk density (g·cm -3), ψ is the soil water suction value (kPa). When ψ is equal to 33kPa and 1500kPa respectively, the soil field water holding capacity (θ FC ) and soil wilting coefficient (θ WP ).

[0054] The soil penetration resistance curve equation is used to calculate the soil volumetric water content when PR is equal to 3 MPa:

[0055] lnPR=b1+b2 lnθ+b3 lnρ b (2)

[0056] Where b1, b2, and b3 are the regression coefficients of the soil anti-penetration curve, and PR is the soil penetration resistance under various moisture conditions.

[0057] The soil volumetric water content when the soil aeration porosity is 10% is calculated using the soil bulk density (i.e., total soil porosity), ρ s is the soil particle density (g·cm -3 ):

[0058]

[0059] Using the measured data in step 1 to fit the LLWR equation, the soil transfer equations (Table 2) and corresponding curves ( Figure 3 ).

[0060] Table 2 Soil moisture characteristic curve and penetration resistance curve under different tillage methods Soil transfer equation

[0061]

[0062] Step 3: Obtain dynamic measured data of field soil bulk density;

[0063] Step 3.1: Soil moisture and thermal pulse sensors were embedded in layers at 5 cm and 15 cm depths in the three tillage treatments: no-till, rotary tillage, and deep plowing. Soil moisture and thermal properties in the 0-10 cm and 10-20 cm soil layers were continuously monitored. The soil moisture and thermal pulse sensors used were 315H soil moisture sensors (Acclima, Inc., USA) and TP01 soil thermal pulse sensors (Huksfulx, Inc., The Netherlands), respectively. Before installing the probes, the TP01 foil was coated with a highly conductive thermal paste (TFX, Thermalright Inc., λ = 14 W m -1 K -1) to improve the thermal contact between the probe and the soil. The two sensors were connected to a CR300 data logger (Campbell, USA) through a specific control program to achieve real-time synchronous monitoring of soil water and heat properties. The monitoring frequency was set to 3 hours / time. Before the field measurement, the known ρ b and θ v The PVC ring knife (15 cm in diameter and 19 cm in height) was used to calibrate the 315H sensor.

[0064] Step 3.2: To correct for errors in the determination of soil thermal properties caused by solar radiation and changes in ambient background temperature, the field monitoring data were processed as follows: first, the daytime monitoring data (6:00-18:00) were eliminated. Then, the linear extrapolation method was used to correct the impact of ambient temperature changes on the nighttime monitoring data (21:00-3:00). The soil thermal conductivity index model (Equations 4-6) was used to calculate the dynamic changes in soil bulk density.

[0065] λ=λ dry +exp(β-θ -α ) (4)

[0066] α=0.67f clay +0.24 (5)

[0067] β=1.97f clay +1.87ρ b -1.36f sand ρ b -0.95 (6)

[0068] λ dry =-0.56Φ+0.51 (7)

[0069] Where α and β are the shape factors of the thermal conductivity curve, f sand , f silt and f clay are the percentages (%) of sand, silt and clay in mineral solids respectively. dry and Φ are the dry soil thermal conductivity and total soil porosity, respectively.

[0070] Finally, the daily nighttime monitoring data were averaged to obtain the daily measured data of soil bulk density. The average value was used to predict the soil bulk density and obtain the daily variation curve of soil bulk density ( Figure 4 );

[0071] The specific steps are as follows: assuming that the changing trend of the soil background temperature during and after heating is consistent with the change of the soil background temperature before heating, the soil background temperature during the entire heat pulse measurement process is obtained by linear extrapolation using the change of the soil background temperature before heating; then, the change of the soil background temperature is subtracted from the heat pulse sensing needle temperature to obtain the sensing needle temperature change under the single action of the heat released by the heat pulse, and then the soil thermal characteristics are obtained by fitting.

[0072] Soil background temperature before heating (T v ) changes with time (t):

[0073] T v =at+b (8)

[0074] Where a is the slope of the linear fitting formula, and b is the intercept;

[0075] The needle temperature (T o ) minus the change in soil background temperature to obtain the temperature of the sensing needle under the action of a single heat pulse, that is, the heat pulse sensing temperature after calibrating the change in soil background temperature (T c ):

[0076] T c =T0-at (9)

[0077] Since the TP01 heat pulse sensor converts soil temperature changes into small voltage output values, T v The changing trend equation of (t) is transformed into the U0(t) equation, T c (t) The equation is transformed into U 180 (t) equation, that is:

[0078] U0(t)=at+b (10)

[0079] U 180c =U 180 -at (11)

[0080]

[0081] Where S is the probe sensitivity (VK -1 );U 0c and U 180c is the output voltage (mV) corrected for ambient temperature before and after heating, R is the resistance of the heating wire (Ω), and L is the length of the heating wire (m).

[0082] Step 4: Construct a two-stage field soil bulk density dynamic prediction model under the influence of tillage and rainfall;

[0083] According to the in situ dynamics of soil bulk density, the soil structure of the no-tillage treatment showed a "wet-swell-dry-shrink" phenomenon.b Dynamic. Under the influence of rainfall and field dry-wet cycle, rotary tillage and deep plowing treatments showed that the soil first settled (landed) and then expanded and contracted. b Based on this, a two-stage model for predicting soil bulk density under different tillage treatments was established.

[0084] The dynamics of soil bulk density during soil solidification are expressed by comparing the measured soil bulk density with the cumulative rainfall (P t ) are fitted with the empirical exponential equation:

[0085] ρ b =ρ bi +(ρ bm -ρ bi )[1-exp(-P t -P m )] P t <P m (13)

[0086] Where, ρ bi is the initial bulk density of soil after tillage (g cm -3 ), P m The bulk density of the soil reaches a stable value after it is solidified (ρ bm ) cumulative rainfall at .

[0087] When the soil gradually settles and reaches stability (P t >P m ), soil structure changes began to be dominated by the soil expansion and contraction process driven by water. The changes in bulk density during the soil expansion and contraction process were analyzed using the soil bulk density curve model considering shrinkage (SSC ρb ) predicts:

[0088]

[0089] Where χ, p, and q are dimensionless soil shrinkage curve fitting parameters, and ρ s is the soil particle density (g·cm -3 ), e s , e r They are soil moisture ratio, saturated porosity and residual porosity (cm 3 cm -3 ).

[0090] Field soil moisture (θ v ) is usually continuously monitored by a soil moisture sensor, which is related to the soil moisture ratio The relationship is usually expressed as:

[0091]

[0092] Using the soil shrinkage parameter obtained by fitting in step 1, the soil ρ under a specific water content can be calculated by combining equations (14) and (15). b value.

[0093] The parameters of the bulk density prediction equation for different tillage treatments are shown in Table 3.

[0094] Table 3 Parameters of soil bulk density prediction equation under different tillage methods

[0095]

[0096] Step 5: Obtain daily measured meteorological and hydrological data for the study area;

[0097] Standard weather stations and soil moisture sensors were deployed throughout the fields to collect hourly rainfall and soil moisture data. Hourly rainfall was summed to obtain daily cumulative rainfall data, and daily soil moisture data was averaged to obtain daily soil moisture data.

[0098] Step 6: Obtain the LLWR range and dynamic changes of each restriction condition in the study area;

[0099] Step 6.1: Further combine the field precipitation and soil moisture monitoring data, and use the soil bulk density prediction model in step 4 to further extrapolate to obtain ρ for 2017-2022 b dynamic( Figure 5 The predicted bulk density values are in good agreement with the measured values ( Figure 6 , R 2 =0.589, RMSE <0.10 g cm -3 );

[0100] Step 6.2: Combine the predicted soil bulk density with the soil water dynamic data obtained from long-term monitoring, and use the soil transfer equation of LLWR established in step 2 to obtain the continuous dynamic changes of each boundary condition of LLWR under different tillage treatments ( Figure 7 );

[0101] Step 6.3: The size of LLWR is calculated by the difference between the upper minimum and the lower maximum, i.e., LLWR = min(θ AFP＝10％ ,θ FC )-max(θ PR＝3MPa ,θ WP The seasonal dynamics of LLWR are calculated based on the dynamic changes of various boundary conditions.

[0102] Step 7: Determine the soil moisture and bulk density range suitable for crop growth based on the linkage relationship between soil moisture and LLWR boundary conditions;

[0103] Step 7.1: Set the upper boundary min(θAFP＝10％ ,θ FC ) and LLWR lower boundary max(θ PR＝3MPa ,θ WP ) is subtracted to obtain the LLWR range versus soil bulk density curve. When LLWR>0, the soil bulk density range suitable for crop growth is determined ( Figure 8 );

[0104] Step 7.2: Based on the relative dynamics of soil moisture within the season and the LLWR boundary conditions predicted in Step 6, use θ v =θ v -min(θ AFP＝10％ ,θ FC )<0 and θ v -max(θ PR＝3MPa ,θ WP )>0, determine θ v Soil moisture content that always falls within the LLWR range is the soil moisture range suitable for crop growth ( Figure 9 ).

[0105] Step 8: Using the measured soil moisture values and LLWR boundary conditions, determine the soil physical stress type and time proportion;

[0106] According to the relative dynamics of soil moisture and LLWR boundary conditions within the season, soil physical stress can be subdivided into soil aeration stress (θ v >θ AFP＝10％ ), soil waterlogging stress (θ v >θ FC ), soil drought stress (θ v <θ WP ) and soil strength stress (θ v <θ PR＝3MPa ). The seasonal time proportion of each physical stress type was further calculated:

[0107] Aeration stress percentage (ASP):

[0108]

[0109] Waterlogging stress percentage (WLP):

[0110]

[0111] Soil drought stress percentage (DSP):

[0112]

[0113] Soil strength stress percentage (SSP):

[0114]

[0115] Soil physical stress percentage (PSP):

[0116]

[0117] Wheat and corn were planted continuously in the field from 2017 to 2022. Based on the continuous relative dynamics of soil moisture and LLWR boundary conditions within the season, the soil θ of different crop growing seasons was determined. v The proportion of time falling outside the LLWR range is the proportion of time under soil physical stress (Table 4).

[0118] Table 4 Wheat and corn yield and the proportion of soil physical stress time in each key growth period

[0119]

[0120] Step 9: Identify key soil physical stress factors that affect crop growth within the season;

[0121] Plant crops in the field and conduct correlation analysis between soil physical stress time and crop growth traits (such as aboveground / underground biomass, crop yield, etc.) during each key growth period of crops to determine the key soil physical stress factors affecting crop growth

[0122] Correlation analysis between crop yield and soil physical stress duration was conducted, and it was determined that the key soil physical stress factors affecting wheat and corn yields were soil strength during the seedling-grain filling period and soil aeration stress during the jointing-heading period (Table 6).

[0123] Table 6 Relationship between wheat and corn yield and the proportion of soil physical stress time in each key growth period

[0124]

Claims

1. A method for quantifying field soil physical stress based on in-situ field monitoring and model simulation, characterized in that: The following steps are involved: Step 1: Obtain soil bulk density, water content, and penetration resistance values under different water potentials; Step 2: Use the measured data in step 1 to fit the transfer equations of each constraint condition and establish the soil transfer equations of the four constraint conditions of LLWR; Step 3: Obtain dynamic measured data of field soil bulk density; Step 4: Construct a dynamic prediction model of field soil bulk density in the implementation stage and expansion and contraction stage under the influence of tillage and rainfall; Step 5: Obtain measured data of field rainfall and soil moisture in the study area; Step 6: By substituting the measured data obtained in step 5 into the soil bulk density dynamic prediction model constructed in step 4, the continuous dynamic changes of soil bulk density are obtained. Then, the dynamic data of soil moisture and bulk density are input into the LLWR soil transfer equation established in step 2 to obtain the continuous changes of each LLWR boundary condition. Finally, the seasonal dynamic change range of LLWR is determined.

2. The method for quantifying field soil physical stress based on field in-situ monitoring and model simulation according to claim 1, characterized in that: The step 3 specifically also includes: during the monitoring process, after correcting the errors caused by daytime solar radiation and ambient background temperature drift on soil thermal characteristics using daytime data elimination and linear extrapolation, the dynamic changes in soil bulk density are calculated using the soil thermal conductivity index model.

3. The method for quantifying field soil physical stress based on field in-situ monitoring and model simulation according to claim 1, characterized in that: The step 4 specifically further includes: The dynamics of soil bulk density during soil compaction is expressed by the measured soil bulk density (ρ b ) and cumulative rainfall (P t ) are fitted with the empirical exponential equation: r b =ρ bi +(r bm -r bi )[1-exp(-P t -P m )] P t <P m (13) Where, ρ bi is the initial bulk density of soil after tillage (g·cm -3 ), P m The bulk density of the soil reaches a stable value after it is solidified (ρ bm ) cumulative rainfall at ; When the soil gradually settles and reaches stability (P t >P m ), soil structure changes began to be dominated by the hydraulically driven soil expansion and contraction process; the changes in soil bulk density during the expansion and contraction process were calculated using the soil bulk density curve model considering shrinkage (SSC ρb ) predicts: Where χ, p, and q are dimensionless soil shrinkage curve fitting parameters, and ρ s is the soil particle density (g·cm -3 ), e s , e r They are soil moisture ratio, saturated porosity and residual porosity (cm 3 cm -3 ).

4. The method for quantifying field soil physical stress based on field in-situ monitoring and model simulation according to claim 3, characterized in that: The method also includes step 7: according to the change of LLWR under different bulk density conditions, obtain the change curve of LLWR range with soil bulk density, and determine the soil bulk density range suitable for crop growth when LLWR>0; according to the linkage change relationship between soil moisture and structure, determine the soil bulk density and LLWR boundary conditions from the soil water content, and finally determine θ v Soil moisture range that consistently falls within the LLWR range.

5. The method for quantifying field soil physical stress based on field in-situ monitoring and model simulation according to claim 4, characterized in that: It also includes step 8: using the measured soil moisture value and LLWR boundary conditions to determine the soil physical stress type and time proportion.

6. The method for quantifying field soil physical stress based on field in-situ monitoring and model simulation according to claim 5, characterized in that: The step 8 specifically also includes: based on the relative dynamics of soil moisture and LLWR boundary conditions within the season, soil physical stress can be subdivided into soil aeration stress, soil waterlogging stress, soil drought stress and soil strength stress, and the seasonal time proportion of each physical stress type is further calculated.

7. The method for quantifying field soil physical stress based on field in-situ monitoring and model simulation according to claim 5, characterized in that: It also includes step 9: identifying key soil physical stress factors that affect crop growth within the season.

8. The method for quantifying field soil physical stress based on field in-situ monitoring and model simulation according to claim 7, characterized in that: The step 9 specifically further includes: for crops planted in the field, correlation analysis is performed between the soil physical stress time during each key growth period of the crop and the crop growth traits to determine the key soil physical stress factors that affect crop growth.

9. The method for quantifying field soil physical stress based on field in-situ monitoring and model simulation according to claim 8, characterized in that: The crop growth traits include above-ground / underground biomass and crop yield.

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