CN116796790A - Yield prediction and irrigation system optimization method based on crop water deficiency degree - Google Patents

Yield prediction and irrigation system optimization method based on crop water deficiency degree Download PDF

Info

Publication number
CN116796790A
CN116796790A CN202310739378.2A CN202310739378A CN116796790A CN 116796790 A CN116796790 A CN 116796790A CN 202310739378 A CN202310739378 A CN 202310739378A CN 116796790 A CN116796790 A CN 116796790A
Authority
CN
China
Prior art keywords
water
soil
pwdi
irrigation
crop
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202310739378.2A
Other languages
Chinese (zh)
Inventor
吴训
蔡滢銮
左强
石建初
郝军帅
许艳奇
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
China Agricultural University
Original Assignee
China Agricultural University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by China Agricultural University filed Critical China Agricultural University
Priority to CN202310739378.2A priority Critical patent/CN116796790A/en
Publication of CN116796790A publication Critical patent/CN116796790A/en
Pending legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/12—Computing arrangements based on biological models using genetic models
    • G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/02—Agriculture; Fishing; Forestry; Mining
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Theoretical Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Business, Economics & Management (AREA)
  • General Health & Medical Sciences (AREA)
  • Biophysics (AREA)
  • General Physics & Mathematics (AREA)
  • Economics (AREA)
  • Evolutionary Computation (AREA)
  • Human Resources & Organizations (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Computational Linguistics (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • General Business, Economics & Management (AREA)
  • Tourism & Hospitality (AREA)
  • Strategic Management (AREA)
  • Primary Health Care (AREA)
  • Marketing (AREA)
  • Molecular Biology (AREA)
  • Biomedical Technology (AREA)
  • Artificial Intelligence (AREA)
  • Mining & Mineral Resources (AREA)
  • Marine Sciences & Fisheries (AREA)
  • Animal Husbandry (AREA)
  • Agronomy & Crop Science (AREA)
  • Genetics & Genomics (AREA)
  • Physiology (AREA)
  • Public Health (AREA)
  • Water Supply & Treatment (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The application belongs to the field of agricultural crop yield prediction and irrigation technical optimization, and discloses a crop water deficiency degree-based yield prediction and irrigation system optimization method, wherein the yield prediction method comprises the following steps of S1: simulating the dynamic distribution of soil moisture and salt in the root zone of the crop; s2: assessing a plant water deficit index PWDI; s3: and constructing a water salt production function continuous multiplication model for predicting crop yield. The irrigation system optimizing method comprises the following steps: and combining soil water and salt migration simulation, PWDI, a water and salt production function and a genetic algorithm to construct an optimization model of the saline-alkali farmland crop irrigation system for optimizing the crop irrigation system. The application estimates PWDI based on the dynamic simulation of soil water and salt migration, thereby constructing a water and salt production function and an irrigation system optimization model, simultaneously achieving the purposes of diagnosis of crop water deficiency degree, yield prediction and irrigation system optimization, and providing an effective tool for promoting the yield increase and the water efficient utilization of crops in saline-alkali farmlands.

Description

Yield prediction and irrigation system optimization method based on crop water deficiency degree
Technical Field
The application relates to the technical field of agricultural crop yield prediction and agricultural irrigation, in particular to a crop water deficiency degree-based yield prediction and irrigation system optimization method.
Background
In saline-alkali soil fields, crop water deficiency is normal, and a series of crop physiological growth disorders are extremely easy to cause, so that the final yield is reduced and the economic benefit is reduced. In order to alleviate the influence of water deficit, scientific and reasonable irrigation measures are indispensable, and are based on reasonable knowledge and quantification of the relationship between crop yield and water deficit. In the existing numerous yield prediction or evaluation methods, the crop water-salt production function quantitatively describes the relationship between crop yield and water availability by means of a relatively simple mathematical model, has the advantages of simple principle, strong operability and the like, and has been widely applied to the agricultural water resource optimization management in arid and saline-alkali areas. The indexes used for representing the water effectiveness in the crop water salt production function mainly comprise irrigation quantity, soil water storage quantity, crop transpiration or transpiration and the like, and obviously, the physiological activities such as photosynthesis of the transpiration and yield formation of leaves are more direct and close, so that the crop water salt production function is more suitable for constructing the crop water salt production function.
Crop transpiration is affected by water and salt stress to a degree that is practically representative of the degree of water deficit suffered by the crop, as indicated by the commonly used plant water deficit index (Plant Water Deficit Index, PWDI). Unlike the traditional method of evaluating PWDI by considering only root zone water and salt quantity, the latest research weights root zone soil water and salt by introducing relative root length density, comprehensively considers the influence of root zone soil water and salt quantity and the relative water-salt-root distribution relation on crop transpiration, thereby improving the PWDI evaluation process under the condition of water-salt stress and leading the diagnosis result of crop water deficiency degree to be more reasonable and accurate.
In addition, the water sensitivity index is an important parameter used for representing the water stress sensitivity degree of different growth stages of crops in a crop water salt production function, and the determination process is easily influenced by the number and the length of the division of the growth stages of the crops: too few stage divisions (long intervals) are not beneficial to capturing moisture sensitivity dynamic of crops, so that moisture sensitivity information in the stages is lost, and the determination of irrigation time is not timely and accurate; too many phase divisions (short intervals) can easily lead to unstable parameter optimization results, and even situations beyond reasonable ranges, such as negative values, can occur. A large number of researches show that the crop moisture sensitivity indexes are in low-high-low bell-shaped distribution in the whole growth period, the accumulation process along with time approximately accords with an S-shaped curve, and the S-shaped curve can be used for describing, so that the problems of sensitive information loss or unstable parameters and the like caused by stage division randomness are effectively avoided. Therefore, the application combines the latest improved PWDI and S-type accumulated moisture sensitivity index, can provide a new thought for reasonable construction of the crop water salt production function, and is beneficial to accurately predicting the yield of saline-alkali farmland crops.
Crop irrigation system optimization is an important content of efficient utilization of saline-alkali farmland crop water and research of root zone salinity control theory and method, but the existing research is mostly focused on determining irrigation quota, namely mainly discussing how limited water resources are distributed in different growing periods of crops, and it is difficult to reasonably give specific irrigation time, and the method is mainly determined by subjective experience, so that great randomness and uncertainty exist. Of course, the crop water deficit (PWDI) is an important basis for determining the time of irrigation, and most critical is how to reasonably determine the PWDI threshold for starting irrigation. Studies have shown that by using a soil moisture migration model to simulate soil moisture distribution dynamics, simulating crop yield based on a moisture production function, and combining with a global optimization algorithm genetic algorithm to iterate optimization, an optimal PWDI threshold combination can be optimized for guiding irrigation practice. However, the existing optimization thinking only considers the situation of soil water deficiency and ignores the influence of soil salinity stress. Therefore, the application provides a method for constructing an optimized model of the irrigation system of the saline-alkali farmland crops by comprehensively considering the influence of soil water and salt stress, and provides theoretical support for reasonably determining the optimal PWDI irrigation threshold (namely the irrigation time).
Disclosure of Invention
The application aims to provide a yield prediction and irrigation system optimization method based on crop water deficiency degree, which is characterized in that soil water salt distribution conditions are obtained by dynamically simulating saline-alkali farmland soil water salt distribution and used for evaluating crop water deficiency degree, namely plant water deficiency index PWDI, and then a crop water salt production function is established by combining the PWDI and S-shaped accumulated water sensitivity index for crop yield prediction; and then, by fusing soil water and salt migration simulation, PWDI, water and salt production functions and a genetic algorithm, constructing an optimization model of the irrigation system of the saline-alkali farmland crops, and taking the relative yield maximization as an objective function, the optimal PWDI irrigation threshold combination can be automatically optimized and obtained, so that the optimization of the irrigation strategy of the saline-alkali farmland crops is realized.
In order to achieve the above object, the present application provides the following technical solutions:
the yield prediction method based on the water deficiency degree of crops comprises the following steps:
s1: the method comprises the steps of taking the water content and the salt concentration of soil during crop sowing as initial conditions, and simulating the dynamic distribution of the water content and the salt in the root zone of the crops in the whole growth period;
s2: based on the soil moisture and salinity distribution dynamic data obtained by simulation in the step S1, weighting the effective moisture and salinity of the soil in the root zone by adopting the relative root length density so as to evaluate a plant water deficit index PWDI so as to reflect the water and salinity stress degrees suffered by crops in different periods;
s3: and (3) constructing a water-salt production function continuous multiplication model by combining the PWDI and the S-type accumulated moisture sensitivity index obtained in the step (S2) so as to realize the prediction of the crop yield of the saline-alkali farmland.
Further, in S1, a coupled model based on richard equation and convection-dispersion equation simulates the dynamics of root zone soil moisture and salinity distribution during the whole growth period of crops:
the formula (1) is a Richards equation, and the formula (2) is a convection-dispersion equation; wherein: z is soil depth, cm; t is time, d; c (h) is soil water content, cm -1 The method comprises the steps of carrying out a first treatment on the surface of the S (z, t) is the root system water absorption rate, cm 3 cm -3 d -1 The method comprises the steps of carrying out a first treatment on the surface of the θ is the water content of the soil, cm 3 cm -3 The method comprises the steps of carrying out a first treatment on the surface of the h is the soil water matrix potential, cm; k (h) is the unsaturated water conductivity of the soil, cm d -1 The method comprises the steps of carrying out a first treatment on the surface of the v is soil water pore speed, cm d -1 ；c s Mg cm is the concentration of solute in the soil profile -3 The method comprises the steps of carrying out a first treatment on the surface of the q is soil water flux, cm d -1 ；D sh For effective dispersion coefficient, cm 2 d -1 。
Further, in S2, the expression for evaluating the plant water deficit index PWDI is:
wherein ,
L nrd (z r )＝a(1-z r ) a-1 (6)
wherein: gamma (h) is a soil water stress correction coefficient calculated based on the soil water matric potential h;is based on the osmotic potential of soil water>Calculating a soil salinity stress correction coefficient; z r For the relative depth of soil (=z/L) r )；L r Is the maximum root taking depth, cm; l (L) nrd (z r ) Is z r Relative root length density at; h is a H 、h L and hW The upper limit and the lower limit of the soil water matrix potential and the wilting coefficient are respectively suitable for crop growth, and cm; />Is a soil salinity stress response threshold, which represents +.>The corresponding critical soil water penetration potential is cm; ρ, τ and a are empirical parameters.
Further, in S3, the water salt production function continuous multiplication model for predicting crop yield is expressed as:
in the formula ：Ya and Yp The actual and potential yields, kg ha respectively -1 The method comprises the steps of carrying out a first treatment on the surface of the Pi is a continuous multiplication symbol; t is time, d; t is the number of days from planting to maturation, d; lambda (lambda) t For daily moisture sensitivity index, the function C is accumulated by the S-shaped moisture sensitivity index t And (3) calculating:
λ t ＝C t -C t-1 (8)
wherein: m, k, b are fitting parameters; h t To normalize the thermal unit index, 0<H t <1, calculated on the basis of the growth day GDD as:
wherein: i is the number of days from sowing to the t-th day; h m Effective heat accumulation required for planting to maturity, DEG C; t (T) ave Is the average temperature of the day, the temperature is DEG C; t (T) u Is a temperature threshold value suitable for crop growth, DEG C; t (T) b Is the basic temperature required by the growth of crops, and the temperature is DEG C.
The yield prediction method based on the water deficiency degree of crops establishes an irrigation system optimization method, combines a soil water salt migration model of formulas (1) and (2) in step S1, PWDI of formula (3) in step S2 and a water salt production function of formula (7) in step S3 with a genetic algorithm to construct an alkaline farmland crop irrigation system optimization model, and is used for determining optimal PWDI thresholds under different irrigation scene modes so as to optimize farmland irrigation management; the genetic algorithm is used for automatically generating a large number of irrigation scenes, and the crop yield is obtained through a series of genetic operations such as selection, crossing, variation and the like and repeated iterative simulation calculation, and PWDI threshold combinations corresponding to the maximum relative yield are automatically screened out, so that the optimization of an irrigation system is completed.
Further, the flow for optimizing the irrigation system comprises:
a1: determining an objective function:
at a relative yield of Y r (＝Y a /Y p ) Maximization is an optimization objective, namely:
maxY r ＝f(PWDI v )(12)
0≤PWDI v ≤1
wherein: PWDI v Is a vector formed by PWDI threshold, wherein the number of contained elements is the same as the number of times of irrigation; in each irrigation simulation scenario, the irrigation is started when the PWDI calculated in real time reaches a corresponding threshold value, and the irrigation quota is determined by the difference between the soil water content simulated in real time and the irrigation target soil water content, namely:
wherein: i is irrigation quota, cm; z is soil depth, cm; θ (z) is the real-time simulated soil moisture content, cm 3 cm -3 ；θ g For irrigation target water content, cm 3 cm -3 The method comprises the steps of carrying out a first treatment on the surface of the Beta is the soil irrigation wetting ratio,%; r is the soil salinity leaching coefficient; d (D) w To plan wetting layer depth, cm;
a2: initializing genetic algorithm parameters:
initializing genetic algorithm parameters including population scale, maximum iteration times, crossover probability and variation probability; PWDI v The element initial value of each individual in the population is automatically generated by using a rand function;
a3: calculating an individual fitness initial value:
simulating soil water salt distribution by using the soil water salt migration models of the formulas (1) and (2), further calculating daily PWDI by using the formulas (3) to (6), starting irrigation if the PWDI reaches a corresponding threshold value, and finally calculating the relative yield Y according to the water salt production function of the formula (7) r As an individual fitness initial value;
a4: iterative calculation:
determining optimal individuals according to individual fitness values obtained by calculation in the previous generation population, reserving the optimal individuals in the next generation population, and performing operations such as selection, crossover, mutation and the like on other individuals to generate the next generation PWDI v The population continues to simulate and calculate individual fitness values of all individuals, and the population circulates in sequence;
a5: judging termination conditions:
if the iteration number reaches the maximum value, the highest fitness value Y in the evolution process is used r Corresponding individual PWDI v As the optimal solution output, terminating the calculation; otherwise, go to step A4 to continue the simulation calculation.
The technical proposal has the beneficial effects that:
by combining the plant water deficit index PWDI and the S-type water sensitivity index cumulative function, the crop water salt production function is constructed, and the crop yield can be predicted more stably and accurately. By combining the soil water and salt migration simulation, PWDI, water and salt production function and genetic algorithm, an optimization model of the saline-alkali farmland crop irrigation system is constructed, is used for optimizing the crop irrigation system, and provides a basis for the efficient management of the saline-alkali farmland moisture.
Drawings
FIG. 1 is a flow chart of a crop water deficit level based yield prediction and irrigation regime optimization method of the present application;
FIG. 2 is a schematic diagram of a "one-film three-tube six-row" planting mode used in a cotton under-film drip irrigation field test in accordance with an embodiment of the present application;
FIG. 3 shows soil water stress correction coefficient gamma (h) and soil salinity stress correction coefficient in an embodiment of the present applicationS-shaped moisture sensitivity index cumulative function C t Is based on C t Estimated daily moisture sensitivity index lambda t Dynamic;
FIG. 4 is a graph showing the relative cotton yield versus actual measurement obtained by 15 different test treatments based on the yield estimation method according to the present application.
Detailed Description
The application is described in further detail below with reference to the attached drawings and to specific examples:
as shown in fig. 1, the yield prediction method based on the water deficit degree of crops comprises the following steps:
s1: the method is characterized in that the soil moisture content and the salt concentration during crop sowing are used as initial conditions, and the dynamic state of the soil moisture and the salt distribution in the root zone in the whole growth period of crops is simulated based on a coupled model of a Richards equation and a convection-dispersion equation:
the formula (1) is a Richards equation, and the formula (2) is a convection-dispersion equation; wherein: z is soil depth, cm; t is time, d; c (h) is soil water content, cm -1 The method comprises the steps of carrying out a first treatment on the surface of the S (z, t) is the root system water absorption rate, cm 3 cm -3 d -1 The method comprises the steps of carrying out a first treatment on the surface of the θ is the water content of the soil, cm 3 cm -3 The method comprises the steps of carrying out a first treatment on the surface of the h is the soil water matrix potential, cm; k (h) is the unsaturated water conductivity of the soil, cm d -1 The method comprises the steps of carrying out a first treatment on the surface of the v is soil water pore speed, cm d -1 ；c s Mg cm is the concentration of solute in the soil profile -3 The method comprises the steps of carrying out a first treatment on the surface of the q is soil water flux, cm d -1 ；D sh For effective dispersion coefficient, cm 2 d -1 ；
S2: based on the soil moisture and salinity distribution dynamic data obtained by simulation in the step S1, weighting the effective moisture and salinity of the soil in the root zone by adopting the relative root length density so as to evaluate a plant water deficit index PWDI so as to reflect the water and salinity stress degrees suffered by crops in different periods; wherein, the expression for evaluating the plant water deficit index PWDI is:
wherein ,
L nrd (z r )＝a(1-z r ) a-1 (6)
wherein: gamma (h) is a soil water stress correction coefficient calculated based on the soil water matric potential h;is based on the osmotic potential of soil water>Calculating a soil salinity stress correction coefficient; z r For the relative depth of soil (=z/L) r )；L r Is the maximum root taking depth, cm; l (L) nrd (z r ) Is z r Relative root length density at; h is a H 、h L and hW The upper limit and the lower limit of the soil water matrix potential and the wilting coefficient are respectively suitable for crop growth, and cm; />Is a soil salinity stress response threshold, which represents +.>The corresponding critical soil water penetration potential is cm; ρ, τ and a are empirical parameters;
s3: constructing a water salt production function continuous multiplication model by combining the PWDI and the S-type accumulated moisture sensitivity index obtained in the step S2 so as to realize the prediction of the crop yield of the saline-alkali farmland; wherein, the water salt production function continuous multiplication model for predicting crop yield is expressed as:
in the formula ：Ya and Yp The actual and potential yields, kg ha respectively -1 The method comprises the steps of carrying out a first treatment on the surface of the Pi is a continuous multiplication symbol; t is time, d; t is the number of days from planting to maturation, d; lambda (lambda) t For daily moisture sensitivity index, the function C is accumulated by the S-shaped moisture sensitivity index t And (3) calculating:
λ t ＝C t -C t-1 (8)
wherein: m, k, b are fitting parameters; h t To normalize the thermal unit index, 0<H t <1, calculated on the basis of the growth day GDD as:
wherein: i is the number of days from sowing to t days; h m Effective heat accumulation required for planting to maturity, DEG C; t (T) ave Is the average temperature of the day, the temperature is DEG C; t (T) u Is a temperature threshold value suitable for crop growth, DEG C; t (T) b Is the basic temperature required by the growth of crops, and the temperature is DEG C.
Establishing an irrigation system optimization method based on a yield prediction method of the water deficiency degree of crops, combining a soil water salt migration model of formulas (1) and (2) in the step S1, PWDI of formula (3) in the step S2, a water salt production function of formula (7) in the step S3 and a genetic algorithm, and constructing an alkaline farmland crop irrigation system optimization model for determining optimal PWDI thresholds under different irrigation scene modes so as to optimize farmland irrigation management; the genetic algorithm is used for automatically generating a large number of irrigation scenes, crop yield is obtained through a series of genetic operations and repeated iterative simulation calculation, and PWDI threshold combination corresponding to the maximum relative yield is automatically screened out. The flow for optimizing the irrigation system based on the genetic algorithm comprises the following steps:
a1: determining an objective function:
generally at relative yield Y r (＝Y a /Y p ) Maximization is an optimization objective, namely:
maxY r ＝f(PWDI v )(12)
0≤PWDI≤1
v
wherein: PWDI v Is a vector formed by PWDI threshold, wherein the number of contained elements is the same as the number of times of irrigation; in each irrigation simulation scenario, the irrigation is started when the PWDI calculated in real time reaches a corresponding threshold value, and the irrigation quota is determined by the difference between the simulated soil water content and the irrigation target soil water content, namely:
wherein I is irrigation quota, cm; z is soil depth, cm; θ (z) is the real-time simulated soil moisture content, cm 3 cm -3 ；θ g For irrigation target water content, cm 3 cm -3 The method comprises the steps of carrying out a first treatment on the surface of the Beta is the soil irrigation wetting ratio,%; r is the salt leaching coefficient of soil salt; d (D) w To plan wetting layer depth, cm;
a2: initializing genetic algorithm parameters:
initializing genetic algorithm parameters including population scale, maximum iteration times, crossover probability and variation probability; PWDI v The initial values of elements in each individual of the population are automatically generated by using a rand function;
a3: calculating an individual fitness initial value:
simulating soil water salt distribution by using the soil water salt migration models of the formulas (1) and (2), further calculating daily PWDI by using the formulas (3) to (6), starting irrigation if the PWDI reaches a corresponding threshold value, and finally calculating the relative yield Y according to the water salt production function of the formula (7) r As an individual fitness initial value;
a4: iterative calculation:
determining optimal individuals according to individual fitness values obtained by calculation in the previous generation population, reserving the optimal individuals in the next generation population, and performing operations such as selection, crossover, mutation and the like on other individuals to generate the next generation PWDI v The population continues to simulate and calculate individual fitness values of all individuals, and the population circulates in sequence;
a5: judging termination conditions:
if the iteration number reaches the maximum value, the highest fitness value Y in the evolution process is used r Corresponding individual PWDI v As the optimal solution output, terminating the calculation; otherwise, go to step A4 to continue the simulation calculation.
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
In order to verify the key technology of the application, a Xinjiang saline-alkali mulch drip irrigation cotton field test is specially used: the field test is carried out in 2017, 4 months and 2020The 10 month development, including 4 cotton growing seasons, involved 15 test treatments, and the information of irrigation times, irrigation quota and the like of each treatment are shown in table 1. The cotton variety to be tested in the field test is new Nongda No. 4, and is planted in a mode of ' dry sowing and wet sowing ' and ' one-film three-tube six-row ' wide and narrow row ', as shown in FIG. 2, the planting density is about 22 ten thousand plants/hm 2 Each treatment contained 3 duplicate test cells (6.9 m x 7.5 m).
TABLE 1 field test conditions of drip irrigation cotton under film
For this test, a crop water deficit degree based yield prediction and irrigation regime optimization method comprising the following steps:
simulating soil water salt distribution dynamic state
The soil water content and the soil salt concentration distribution during sowing are taken as initial conditions, and the root area soil water salt distribution dynamics are simulated by combining a Richards equation (formula (1)) and a convection-dispersion equation (formula (2)):
wherein: z is soil depth, cm; t is time, d; c (h) is soil water content, cm -1 The method comprises the steps of carrying out a first treatment on the surface of the S (z, t) is the root system water absorption rate, cm 3 cm -3 d -1 The method comprises the steps of carrying out a first treatment on the surface of the θ is the water content of the soil, cm 3 cm -3 The method comprises the steps of carrying out a first treatment on the surface of the h is the soil water matrix potential, cm and theta are converted through a soil water characteristic curve to obtain h; k (h) is the unsaturated water conductivity of the soil, cm d -1 The method comprises the steps of carrying out a first treatment on the surface of the v isSoil water pore speed, cm d -1 ；c s Mg cm is the concentration of solute in the soil profile -3 The method comprises the steps of carrying out a first treatment on the surface of the q is soil water flux, cm d -1 ；D sh For effective dispersion coefficient, cm 2 d -1 . In the soil moisture migration simulation process, the upper boundary condition is an evaporation (which can be approximately 0 because of a coating) boundary, and the lower boundary condition is a soil moisture content boundary; in the soil salinity migration simulation process, the upper boundary condition is a second type flux boundary, the lower boundary condition is a salinity concentration boundary, and the model is solved by adopting an implicit differential format.
(II) evaluation of plant Water deficiency index PWDI
On the basis of (one) obtaining soil water and salt distribution through simulation, weighting soil effective moisture and salt by introducing relative root length density so as to evaluate PWDI:
wherein ,
L nrd (z r )＝a(1-z r ) a-1 (6)
wherein: gamma (h) is the soil water stress correction coefficient, as shown in fig. 3 a;the soil salinity stress correction coefficient is shown as b in fig. 3; z r For the relative depth of soil (=z/L) r )；L r Is the maximum root taking depth, cm; l (L) nrd (z r ) Is z r Relative root length density at; h is a H 、h L and hW Soil water matrix suitable for crop growthThe upper and lower potential limits and wilting coefficients are generally recommended to be-50 cm, -400cm and-15000 cm; />Is a soil salinity stress response threshold, which represents +.>The corresponding critical soil water penetration potential is cm,; a is the relative root length density of the ground surface, and cotton is recommended to be 1.96; ρ and τ are empirical parameters; />ρ and τ are optimized by a nonlinear least squares method.
(III) simulation prediction of Cotton yield
Combining PWDI and S-type cumulative moisture sensitivity index, a cotton water salt production function was constructed for estimating production:
in the formula ：Ya and Yp The actual and potential yields, kg ha respectively -1 The method comprises the steps of carrying out a first treatment on the surface of the t is time, d; t is the number of days from planting to maturation, d; lambda (lambda) t For daily moisture sensitivity index, the moisture sensitivity index cumulative function C by S-shape t Calculated (as shown in fig. 3 c) as (as shown in fig. 3 d):
λ t ＝C t -C t-1 (8)
wherein: m, k and b are fitting parameters, and optimization determination is carried out through a nonlinear least square method; h t To normalize the thermal unit index, 0<H t <1, calculated on the basis of growth days (GDD), as:
wherein: i is the number of days from sowing to t days; h m Effective heat accumulation required for planting to maturity, DEG C; t (T) ave Is the average temperature of the day, the temperature is DEG C; t (T) u Is a temperature threshold value suitable for crop growth, DEG C; t (T) b Is the basic temperature required by the growth of crops, and the temperature is DEG C.
The relative yields of all treated cotton in this test were simulated through three steps (one), (two) and (three), and the results of the comparison of simulated values with measured values are shown in FIG. 4. The figure shows that: the method provided by the application predicts that the relative yield of cotton is well matched with the measured value, and determines the coefficient R between the relative yield and the measured value 2 The root mean square error RMSE of 0.84 and the relative root mean square error NRMSE of 0.17 are all within acceptable ranges, indicating that the constructed cotton water salt production function can be used to accurately predict cotton yield.
(IV) optimizing cotton irrigation system
And constructing a cotton irrigation system optimization model for optimally determining an optimal PWDI irrigation threshold by coupling the obtained soil water salt migration model, the PWDI, the cotton water salt production function and the genetic function. The application takes 2018 cotton growing season as a simulation period, optimizes the optimal PWDI threshold combination corresponding to the local conventional irrigation frequency (generally about 9 times) based on the optimization model, and comprises the following specific operation processes:
(1) determining an objective function:
with relative cotton yield Y r (＝Y a /Y p ) Maximization is aimed at, namely:
maxY r ＝f(PWDI v )(12)
0≤PWDI≤1
v
wherein: PWDI v Is a vector formed by PWDI threshold value, wherein the number of contained elements is equal to the number of times of water filling, according to the local traditional settingOptimizing 9 PWDI thresholds for 9 times, i.e., total; starting irrigation when PWDI reaches a corresponding threshold in the simulation process, and setting the irrigation quota to be 40mm in order to conform to the local tradition and simplify the calculation process;
(2) initializing genetic algorithm parameters:
initializing genetic algorithm parameters, including population scale (set to 120), maximum iteration number (100), crossover probability (0.85) and variation probability (0.15); PWDI v The initial values of elements in each individual of the population are automatically generated by using a rand function;
(3) calculating an individual fitness initial value:
simulating soil water salt distribution by using the soil water salt migration models of the formulas (1) and (2), further calculating daily PWDI by using the formulas (3) to (6), starting irrigation if the PWDI reaches a corresponding threshold value, and finally simulating the cotton relative yield Y according to the cotton water salt production function of the formula (7) r As an individual fitness initial value;
(4) iterative calculation:
determining optimal individuals according to individual fitness values obtained by calculation in the previous generation population, reserving the optimal individuals in the next generation population, and performing operations such as selection, crossover, mutation and the like on other individuals to generate the next generation PWDI v Population, and continuously calculating individual fitness value (Y r ) Sequentially circulating;
(5) judging termination conditions:
if the iteration number reaches the maximum value, taking an individual corresponding to the highest fitness value in the evolution process as an optimal solution to output, and terminating calculation; otherwise, the step (4) is carried out to continue the simulation calculation.
Under the condition of 9 times of irrigation, the PWDI threshold value corresponding to the traditional irrigation system adopted locally is 0.35, 0.37, 0.45, 0.41, 0.42, 0.36 and 0.34 respectively, and the corresponding simulated yield is 322.21kg ha -1 While the PWDI threshold values obtained by optimizing the model were 0.50, 0.46, 0.41, 0.46, 0.44, 0.41, 0.43, 0.45, 0.50, respectively, and the simulated yield was 381.66kg ha -1 Compared with the traditional irrigation system, the yield is improved by 18.45 percent, which indicates that the irrigation system established by the method for optimizing the irrigation system provided by the application,is more favorable for increasing the yield of cotton and has important reference value for the optimized management of the water content in the saline-alkali cotton fields.
The foregoing is merely exemplary embodiments of the present application, and detailed technical solutions or features that are well known in the art have not been described in detail herein. It should be noted that, for those skilled in the art, several variations and modifications can be made without departing from the technical solution of the present application, and these should also be regarded as the protection scope of the present application, which does not affect the effect of the implementation of the present application and the practical applicability of the patent. The protection scope of the present application is subject to the content of the claims, and the description of the specific embodiments and the like in the specification can be used for explaining the content of the claims.

Claims (6)

1. The yield prediction method based on the water deficiency degree of crops is characterized by comprising the following steps:
s1: the method comprises the steps of taking the water content and the salt concentration of soil during crop sowing as initial conditions, and simulating the dynamic distribution of the water content and the salt in the root zone of the crops in the whole growth period;
s2: based on the soil moisture and salinity distribution dynamic data obtained by simulation in the step S1, weighting the effective moisture and salinity of the soil in the root zone by adopting the relative root length density so as to evaluate a plant water deficit index PWDI so as to reflect the water and salinity stress degrees suffered by crops in different periods;
s3: and (3) constructing a water-salt production function continuous multiplication model by combining the PWDI and the S-type accumulated moisture sensitivity index obtained in the step (S2) so as to realize the prediction of the crop yield of the saline-alkali farmland.
2. The crop water deficit degree based yield prediction method according to claim 1, wherein in S1, a coupled model based on richard' S equation and convection-dispersion equation simulates the dynamics of root zone soil moisture and salinity distribution during the whole growth period of the crop:
the formula (1) is a Richards equation, and the formula (2) is a convection-dispersion equation; wherein: z is soil depth, cm; t is time, d; c (h) is soil water content, cm -1 The method comprises the steps of carrying out a first treatment on the surface of the S (z, t) is the root system water absorption rate, cm 3 cm -3 d -1 The method comprises the steps of carrying out a first treatment on the surface of the θ is the water content of the soil, cm 3 cm -3 The method comprises the steps of carrying out a first treatment on the surface of the h is the soil water matrix potential, cm; k (h) is the unsaturated water conductivity of the soil, cm d -1 The method comprises the steps of carrying out a first treatment on the surface of the v is soil water pore speed, cm d -1 ；c s Mg cm is the concentration of solute in the soil profile -3 The method comprises the steps of carrying out a first treatment on the surface of the q is soil water flux, cm d -1 ；D sh For effective dispersion coefficient, cm 2 d -1 。
3. The crop water deficiency degree-based yield prediction method according to claim 2, wherein in S2, the expression for evaluating the plant water deficiency index PWDI is:
wherein ,
L nrd (z r ) ＝a(1-z r ) a-1 (6)
wherein: gamma (h) is a soil water stress correction coefficient calculated based on the soil water matric potential h;is based on the osmotic potential of soil water>Calculating a soil salinity stress correction coefficient; z r For the relative depth of soil (=z/L) r )；L r Is the maximum root taking depth, cm; l (L) nrd (z r ) Is z r Relative root length density at; h is a H 、h L and hW The upper limit and the lower limit of the soil water matrix potential and the wilting coefficient are respectively suitable for crop growth, and cm; />Is a soil salinity stress response threshold, which represents +.>The corresponding critical soil water penetration potential is cm; ρ, τ and a are empirical parameters.
4. A method of predicting crop water deficit degree based yield as claimed in claim 3, wherein in S3, the water salt production function continuous multiplication model predicting crop yield is expressed as:
in the formula ：Ya and Yp The actual and potential yields, kg ha respectively -1 The method comprises the steps of carrying out a first treatment on the surface of the Pi is a continuous multiplication symbol; t is time, d; t is the number of days from planting to maturation, d; lambda (lambda) t For daily moisture sensitivity index, the function C is accumulated by the S-shaped moisture sensitivity index t And (3) calculating:
λ t ＝C t -C t-1 (8)
wherein: m, k, b are fitting parameters; h t To normalize the thermal unit index, 0<H t <1, calculated on the basis of the growth day GDD as:
wherein: i is the number of days from sowing to the t-th day; h m Effective heat accumulation required for planting to maturity, DEG C; t (T) ave Is the average temperature of the day, the temperature is DEG C; t (T) u Is a temperature threshold value suitable for crop growth, DEG C; t (T) b Is the basic temperature required by the growth of crops, and the temperature is DEG C.
5. The method for optimizing irrigation system based on yield prediction of crop water deficiency degree according to claim 4, wherein the method comprises the following steps: combining the soil water salt migration models of the formulas (1) and (2) in the step S1, the PWDI of the formula (3) in the step S2 and the water salt production function of the formula (7) in the step S3 with a genetic algorithm to construct an optimization model of the irrigation system of the saline-alkali farmland crops, and determining the optimal PWDI threshold values under different irrigation contextual models so as to optimize the farmland irrigation management; the genetic algorithm is used for automatically generating a large number of irrigation scenes, and the crop yield is obtained through repeated iterative simulation calculation through a series of genetic operations such as selection, crossing and mutation, and PWDI threshold combination corresponding to the maximum relative yield is automatically screened out.
6. The method for optimizing an irrigation regimen based on a yield prediction method of a crop water deficit degree according to claim 5, wherein the flow of optimizing the irrigation regimen comprises:
a1: determining an objective function:
at a relative yield of Y r (＝Y a /Y p ) Maximization is an optimization objective, namely:
wherein: PWDI v Is a vector formed by PWDI threshold, wherein the number of contained elements is the same as the number of times of irrigation; in each irrigation simulation scenario, the irrigation is started when the PWDI calculated in real time reaches a corresponding threshold value, and the irrigation quota is determined by the difference between the soil water content simulated in real time and the irrigation target soil water content, namely:
wherein: i is irrigation quota, cm; z is soil depth, cm; θ (z) is the real-time simulated soil moisture content, cm 3 cm -3 ；θ g For irrigation target water content, cm 3 cm -3 The method comprises the steps of carrying out a first treatment on the surface of the Beta is the soil irrigation wetting ratio,%; r is the soil salinity leaching coefficient; d (D) w To plan wetting layer depth, cm;
a2: initializing genetic algorithm parameters:
initializing genetic algorithm parameters including population scale, maximum iteration times, crossover probability and variation probability; PWDI v The element initial value of each individual in the population is automatically generated by using a rand function;
a3: calculating an individual fitness initial value:
simulating soil water salt distribution by using the soil water salt migration models of the formulas (1) and (2), further calculating daily PWDI by using the formulas (3) to (6), starting irrigation if the PWDI reaches a corresponding threshold value, and finally calculating the relative yield Y according to the water salt production function of the formula (7) r As an individual fitness initial value;
a4: iterative calculation:
determining optimal individuals according to individual fitness values obtained by calculation in the previous generation population, reserving the optimal individuals in the next generation population, and selecting, crossing and changing other individualsExclusive-or operation to generate next generation PWDI v The population continues to simulate and calculate individual fitness values of all individuals, and the population circulates in sequence;
a5: judging termination conditions:
if the iteration number reaches the maximum value, the highest fitness value Y in the evolution process is used r Corresponding individual PWDI v As the optimal solution output, terminating the calculation; otherwise, go to step A4 to continue the simulation calculation.
CN202310739378.2A 2023-06-21 2023-06-21 Yield prediction and irrigation system optimization method based on crop water deficiency degree Pending CN116796790A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310739378.2A CN116796790A (en) 2023-06-21 2023-06-21 Yield prediction and irrigation system optimization method based on crop water deficiency degree

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310739378.2A CN116796790A (en) 2023-06-21 2023-06-21 Yield prediction and irrigation system optimization method based on crop water deficiency degree

Publications (1)

Publication Number Publication Date
CN116796790A true CN116796790A (en) 2023-09-22

Family

ID=88043257

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310739378.2A Pending CN116796790A (en) 2023-06-21 2023-06-21 Yield prediction and irrigation system optimization method based on crop water deficiency degree

Country Status (1)

Country Link
CN (1) CN116796790A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117575094A (en) * 2023-11-27 2024-02-20 浪潮智慧科技有限公司 Crop yield prediction and optimization method and device based on digital twin

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110288169A (en) * 2019-07-24 2019-09-27 中国水利水电科学研究院 Water in Shallow Groundwater Areas deficit irrigation schedule partition zone optimizing method

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110288169A (en) * 2019-07-24 2019-09-27 中国水利水电科学研究院 Water in Shallow Groundwater Areas deficit irrigation schedule partition zone optimizing method

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
LINING LIU ET AL.: "《Plant water deficit index-based irrigation under conditions of salinity》", 《AGRICULTURAL WATER MANAGEMENT》, 1 July 2022 (2022-07-01), pages 1 - 13 *
XUN WU ET AL.: "《Crop yield estimation and irrigation scheduling optimization using a root-weighted soil water availability based water production function》", 《FIELD CROPS RESEARCH》, 1 August 2022 (2022-08-01), pages 1 - 14 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117575094A (en) * 2023-11-27 2024-02-20 浪潮智慧科技有限公司 Crop yield prediction and optimization method and device based on digital twin

Similar Documents

Publication Publication Date Title
Doré et al. A diagnostic method for assessing regional variations in crop yield
WO2023179167A1 (en) Crop irrigation water demand prediction method based on aquacrop model and svr
Saggi et al. A survey towards decision support system on smart irrigation scheduling using machine learning approaches
Zhang et al. Simulation and prediction of soybean growth and development under field conditions
Abd El Baki et al. A new scheme to optimize irrigation depth using a numerical model of crop response to irrigation and quantitative weather forecasts
CN116796790A (en) Yield prediction and irrigation system optimization method based on crop water deficiency degree
Uniyal et al. Modifying automatic irrigation in swat for plant water stress scheduling
CN116307191B (en) Water resource configuration method, device and equipment based on artificial intelligence algorithm
CN114662742A (en) Dynamic decision method and device for field irrigation and fertilization system
Linker et al. Model-based deficit irrigation of maize in Kansas
Heermann et al. Interdisciplinary irrigated precision farming research
Kothari et al. Assessing the climate change impacts on grain sorghum yield and irrigation water use under full and deficit irrigation strategies
CN114818324A (en) Method, device, medium and equipment for automatically regulating and controlling water quantity and water quality of basin scale
Liu et al. Introduce canopy temperature to evaluate actual evapotranspiration of green peppers using optimized ENN models
Tollenaar et al. Modeling the effects of genotypic and environmental variation on maize phenology: the phenology subroutine of the AgMaize crop model
CN112801364A (en) Reference crop evapotranspiration amount prediction method based on MIV and improved bat algorithm
CN117575094A (en) Crop yield prediction and optimization method and device based on digital twin
CN108122137A (en) A kind of method and system of intelligent equalization agricultural product supply and demand
Ahmed et al. Shifting from extensive to intensive agricultural systems: A case study in the Sudan
Wu et al. Crop yield estimation and irrigation scheduling optimization using a root-weighted soil water availability based water production function
CN116090842A (en) Farmland irrigation decision-making method, device, equipment and storage medium
De Koeijer et al. Annual variation in weather: its implications for sustainability in the case of optimising nitrogen input in sugar beet
CN114936765A (en) Agricultural drought index construction method considering spatial heterogeneity
Nyariki et al. Technical innovation and farm productivity growth in dryland Africa: The effects of structural adjustment on smallholders in Kenya
Conrad et al. Simulating impacts of silage maize (Zea mays) in monoculture and undersown with annual grass (Lolium perenne L.) on the soil water balance in a sandy-humic soil in Northwest Germany

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination