CN110162830A - A kind of variable based on blade tensity on-line monitoring is poured water node prediction technique - Google Patents
A kind of variable based on blade tensity on-line monitoring is poured water node prediction technique Download PDFInfo
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Abstract
The present invention discloses a kind of variable based on blade tensity on-line monitoring and pours water node prediction technique, belongs to water-saving irrigation field.Leaf area, biomass estimation model are established, equilateral hyperbola equation building biomass and blade tensity relational model are utilized.Based on maximum leaf, long, leaf width and plant height nondestructive measurement, monitors biomass on-line.Using Logistic equation, to biomass, curve is fitted over time, to fit equation derivation, calculates biomass rate of rise.Using the rate of rise of biomass under control level as reference, growth time corresponding when biomass rate of rise is reference point fixed proportion under different drought level, node of as pouring water are calculated.According to the value of Logistic equation and biomass blade tensity corresponding with blade tensity relational model calculating, to be predicted by the on-line monitoring of blade tensity variable node of pouring water.The present invention overcomes the prior arts cannot predict that plant physiology needs the deficiency of water node in time, provides foundation for variable irrigation.
Description
Technical field
The present invention relates to a kind of variables based on blade tensity on-line monitoring to pour water node prediction technique, belongs to farming
Object infomation detection and water-saving irrigation technique field.
Background technique
Traditionally, the information such as crop water demand critical period and critical period, Soil Water consumption or canopy surface temperature
It is normally used as formulating the foundation of irrigation period and irrigation quantity.But limitation of these methods vulnerable to factors such as environment, geography, or
Experience is relied primarily on, is unable to fully be easy to cause in view of crop itself water utilization situation and irrigate excess or insufficient water.It plants
Object is not always to need sufficient moisture in growth period, and drought and water shortage does not also always reduce yield.Water-saving irrigation is to ensure work
Highest economic well-being of workers and staff is obtained using least water consumption on the basis of produce amount.Crop exists to the physiological responses information of arid
The research applied in precision irrigation gradually draws attention, and the smooth implementation of precision irrigation is to need water node to plant under arid
Real-time monitoring.However, the prior art still cannot achieve the on-line prediction of the node of pouring water based on plant physiology response.
Crop yield and biomass are positively correlated, and biological quantifier elimination plays a significant role the prediction of crop yield, raw
The parsing of the estimation on line and its changing rule of object amount facilitates plant and pours water the look-ahead of node.In most of researchs,
The measurement of biomass is built upon on the basis of plant partly or wholly wrecks, phytomass to directly obtain comparison tired
Difficulty needs to express estimation using some indexs directly easily surveyed for the non-destructive on-line determination method for establishing biomass.
In addition, the direct and quick determination of water physiological mechanism and its between biomass the foundation of relationship then can be section of pouring water
The on-line prediction of point provides a strong guarantee.In the case where crop is by drought stress, internal carbonic anhydrase is stimulated and active
It increases, is catalyzed HCO intracellular3 -It is converted into H2O changes cell water regime and photosynthetic variation tendency, delays crop to water
The urgent need divided.The moisture regulation process can change the variation of the indexs such as leaf water potential or stomatal conductance to a certain extent
The effect of rule, the process has retardance, it is not easy to be come out by instrument at-once monitor, influence people according to leaf water potential, gas
Accurate Analysis and judgement of the traditional index such as hole degree of leading to Crop water deficits situation.Blade tensity can indicate cell liquid concentration
With the variation of cell volume, it more preferably can more directly reflect the water regime of plant, blade tensity and biomass direct relation
Building then facilitate the timely implementation that will be poured water establish on the on-line monitoring of blade tensity.
Summary of the invention
The technical problem to be solved by the present invention is to provide a kind of variable based on blade tensity on-line monitoring and pour water node
Prediction technique, can by electric-physiology parameter and the on-line monitoring of biomass, in conjunction with biomass estimation model and biomass with
Relational model between blade tensity realizes that plant variable is poured water the timely prediction of node, and simple and efficient, accuracy is high, is
Plant physiology infomation detection and water-saving irrigation technique provide science support.
A kind of variable based on blade tensity on-line monitoring is poured water node prediction technique, comprising the following steps:
Step 1, laboratory cultures Model Plants seedling, chooses the Model Plants of investigated plant；
Step 2, setting different drought stress level cultivate Model Plants；
Step 3 carries out index determining to Model Plants；
Step 4 is fitted leaf area and the product of the long maximum width of blade of maximum leaf using leaf area regression equation, obtains
The appraising model of leaf area out；Relationship between biomass and plant height, leaf area is fitted using Biomass Models, must be born
The appraising model of object amount；Utilize the relational model of equilateral hyperbola equation building biomass and blade tensity；
Step 5 chooses the investigated plant under different drought level, is spaced same time identical period in step 3
Measure its long maximum leaf, maximum width of blade and plant height；
Step 6, long, maximum width of blade and plant height according to maximum leaf, using leaf area estimation model, biomass estimation model,
Calculate biomass curve over time；
Step 7, to biomass, curve is fitted over time, then to fit equation derivation, calculates different drought
The rate of rise of the lower biomass of level；
Step 8 calculates biomass under different drought level and increases using the rate of rise of biomass under control level as reference
Long rate growth time corresponding when being reference point P%, node of as pouring water；
Step 9, according to fit equation and biomass and blade tensity relational model, according to node of pouring water, calculating pair
The value of blade tensity is answered, so that on-line monitoring predicts variable node of pouring water.
Further, leaf area regression equation is A=u × (X in the step 41×X2)v, wherein u, v are constant, X1Table
Show that maximum leaf is long, X2Indicate that maximum width of blade, A indicate leaf area.
Further, Biomass Models are logDW=r+q × log (A in the step 42× H), wherein DW is biomass,
A is leaf area, and H is plant height, and r, q are constant.
Further, equilateral hyperbola equation is in the step 4Wherein DW is biology
Amount, TdFor blade tensity, m, n are constant, and wherein m indicates that when biomass be maximum biomass DWmaxT when halfdValue.
Further, fit equation uses 4 parameter Logistic equations in the step 9:Its
Middle Y0For logarithmic growth phase initial amount, a is the upper limit of the increment of entire growth course growth indexes, X0Increase to reach logarithm
Time required for the 50% of phase maximum growth, X are processing number of days, and Y is biomass dry weight, and b is fitting coefficient.
Further, to 4 parameter Logistic equation derivations, biomass rate of rise is obtained
Further, node calculation method of pouring water in the step 8 is as follows: with the biomass rate of rise under control level
GRfFor reference, the horizontal lower biomass rate of rise GR of each arid is calculatedeCorresponding growth time, is defined as when for reference point P%
It pours water node；That is GRe=Ye'=P% × Yf'=P% × GRf, wherein Ye' leading for the horizontal lower biomass growth curve of each arid
Number, Yf' it is the derivative for compareing lower biomass growth curve, P is integer.
Advantages of the present invention is as follows:
1) nondestructive measurement of this method based on maximum leaf length, maximum width of blade and plant height, according to leaf area and biomass estimation
Model, it can be achieved that biomass online non-destructive monitoring, it is simple and efficient.
2) on-line monitoring of this method based on biomass parses its growth rate, determines node of pouring water accordingly, water can be improved
Divide utilization efficiency, realizes that economic well-being of workers and staff maximizes.
3) this method is tight by blade according to 4 parameter Logistic equations and biomass and blade tensity relational model
The monitoring of tonicity realizes that variable is poured water the prediction of node, is applied to water-saving irrigation for plant physiology response message, as a result accuracy
It is high.
Detailed description of the invention
Linear relationship chart between Fig. 1 leaf area and the long maximum width of blade product of blade maximum leaf；
The log-log linear relationship chart of Fig. 2 biomass dry weight and leaf area and plant height；
The matched curve figure of Fig. 3 blade tensity and biomass dry weight relationship；
Biomass changes with time figure under Fig. 4 different drought level.
Specific embodiment
Concrete scheme of the invention is described further below in conjunction with attached drawing.
Basic principle of the invention are as follows:
Indicate the starting velocity of enzymatic reaction and the Michaelis-Menten equation of concentration of substrate relationship are as follows:
In formula: I is absorption rate of the plant to nutrient；ImaxIt is plant to the absorption maximum rate of nutrient；KmFor Michaelis
Constant, i.e., when absorption rate is absorption maximum rate ImaxExtraneous nutrient density when half；C is extraneous nutrient density.
Equally, Michaelis-Menten equation can also be used for description Net Photosynthetic Rate and photosynthetically active radiation intensity or CO2Between relationship,
They can equally be indicated with equilateral hyperbola equation, such as formula (2):
In formula: PNFor Net Photosynthetic Rate；I is photosynthetically active radiation intensity or intercellular CO2Concentration；PN maxTo be saturated light intensity
Or CO2Net Photosynthetic Rate when saturation, i.e. maximum net photosynthetic rate；R is respiratory rate；K is Michaelis constant.
Photosynthesis net reaction is CO2+H2O=(CH2O)+O2, wherein (CH2O carbohydrate) is indicated.CO2And H2O is all light
The reaction substrate of cooperation, and reaction ratio is 1:1, i.e., the net CO in photosynthesis2Assimilation rate is equal to net H2O is same
Change rate.Plant leaf blade is made of a large amount of cells, and the variation of cell liquid concentration and volume can accurately reflect plant leaf blade
Water regime, and the variation of cell liquid concentration and volume can be reflected with blade tensity.Biomass depends primarily on plant
The size of object net photosynthetic rate.So Photosynthetic is strong or CO2The equilateral hyperbola model of response is equally available
In the curve matching that biomass responds blade tensity, such as formula (3):
In formula: DW is biomass；TdFor blade tensity；M, n is constant, and wherein m indicates that when biomass be maximum biology
Measure DWmaxT when halfdValue.
In addition, 4 parameter Logistic equations are as follows:
In formula: Y0For logarithmic growth phase initial amount；A is the upper limit of the increment of entire growth course growth indexes；X0For up to
To the time (number of days) required for the 50% of increased logarithmic phase maximum growth；X is processing number of days；Y is biomass；B is fitting system
Number.
To equation (4) derivation, the rate of rise of biomass can be obtained, as shown in equation (5):
Wherein, GR indicates the rate of rise of biomass, using the rate of rise of biomass under control level as reference, calculates each
The rate of rise of arid horizontal lower biomass corresponding growth time when being reference point P%, is defined as node of pouring water；Different drought
The calculation formula of the rate of rise of the lower biomass of level is as follows:
GRe=Ye'=P% × Yf'=P% × GRf (6)
Wherein, GRfIndicate the rate of rise of biomass under control level, GReIndicate the growth of the horizontal lower biomass of each arid
Rate, Ye' it is the derivative that each arid level descends biomass growth curve, Yf' led for biomass growth curve under control level
Number, P is integer, and value is between 0~100.
Pour water node of each arid under horizontal, which is corresponded to time (corresponding to X) substitution equation (4), can be obtained corresponding biology
Amount further can calculate the corresponding blade anxiety angle value of node of respectively pouring water by equation (3).It is tight based on plant leaf blade under arid
The monitoring of tonicity can pour water, when its value is down to the corresponding blade anxiety angle value of node of pouring water from there through blade tensity
On-line monitoring grasp variable in advance and pour water node, realize prediction.
Specific implementation process of the invention is as follows:
Step 1 sprouts vegetable seeds using the hole tray of same specification in laboratory, prepares culture solution culture model plant
Seedling, until 3 leaves more than the phase, select to grow Model Plants of the more consistent plant as investigated plant；
Step 2, setting different drought stress level cultivate Model Plants；
Step 3, to Model Plants culture to 1 week or so, the Yu Tongyi period is referred to using the first expansion leaf as investigation object
Mapping is fixed；It randomly selects plant and measures the long X of maximum leaf1, maximum width of blade X2With leaf area A；Randomly select other plant measurements blade face
Product, plant height H and biomass DW；Plant under different drought level is randomly selected, leaf water potential W and physiology capacitor CP is measured, is calculated
Blade tensity Td, while measuring corresponding biomass；
The calculation formula of blade tensity are as follows:
Wherein: TdFor blade tensity, CP is physiology capacitor, and W is leaf water potential, and coefficient dissociates in i system, and R is gas constant,
T is thermodynamic temperature, ε0For permittivity of vacuum, a is the relative dielectric constant of cell liquid solute, and M is the phase of cell liquid solute
To molecular mass.
Step 4 is fitted leaf area and maximum leaf length, the product of maximum width of blade using leaf area regression equation, obtains
The appraising model of leaf area out；Relationship between biomass and plant height, leaf area is fitted using Biomass Models, must be born
The appraising model of object amount；Utilize the relational model of equilateral hyperbola equation building biomass and blade tensity；
The appraising model of leaf area are as follows:
A=u × (X1×X2)v (8)
Wherein: u, v are constant, X1Indicate that maximum leaf is long, X2Indicate that maximum width of blade, A indicate leaf area；
The appraising model of biomass are as follows:
LogDW=r+q × log (A2×H) (9)
Wherein: DW is biomass, and H is plant height, and r, q are constant；
The relational model of biomass and blade tensity are as follows:
Wherein: m, n are constant, and wherein m indicates that when biomass be maximum biomass DWmaxT when halfdValue；
Step 5 chooses the investigated plant grown under different drought level to be measured, is investigation pair with the first expansion leaf
As, from different drought level handles investigated plant the 1st day, every 2 days in step 3 the identical period measure its maximum
Leaf length, maximum width of blade and plant height, persistently measure 2 weeks or more；
Step 6, long, maximum width of blade and plant height according to maximum leaf, using leaf area estimation model, biomass estimation model,
Calculate biomass curve over time；
Step 7, using 4 parameter Logistic equations, to biomass, curve is fitted over time, is fitted
Equation
Wherein: Y0For logarithmic growth phase initial amount, a is the upper limit of the increment of entire growth course growth indexes, X0For up to
To the time (number of days) required for the 50% of increased logarithmic phase maximum growth, X is processing number of days, and Y is biomass dry weight, and b is quasi-
Collaboration number
Again to fit equation derivation, obtain:
Wherein biomass rate of rise GR=Y' calculates the rate of rise of biomass under different drought level；
Step 8 calculates biomass under different drought level and increases using the rate of rise of biomass under control level as reference
Long rate growth time corresponding when being reference point P%, node of as pouring water；Node calculation method of pouring water is as follows: with control
Under biomass rate of rise (GRf) it is reference, calculate the horizontal lower biomass rate of rise (GR of each aride, wherein e is arid water
It is flat) corresponding growth time when being the P% of reference point, it is defined as node of pouring water；That is GRe=Ye'=P% × Yf'=P% ×
GRf, wherein Ye' it is the derivative that each arid level descends biomass growth curve, Yf' leading for the lower biomass growth curve of control
Number, P is integer, and value is between 0~100；
Step 9, according to 4 parameter Logistic equations and biomass and blade tensity relational model (equation (3),
(4)), according to the node of pouring water in step 8, the value of corresponding blade tensity is calculated, to pass through the online prison of blade tensity
It surveys and variable node of pouring water is predicted.
Based on the monitoring of plant leaf blade tensity under arid, when its value is down to the corresponding blade anxiety angle value of node of pouring water
When, it can pour water, grasp variable in advance from there through the on-line monitoring of blade tensity and pour water node, realize prediction.
Embodiment:
Orychophiagmus violaceus seed is sprouted using the hole tray of same specification in laboratory, prepares culture solution culture model plant seedlings,
To 3 leaves more than the phase, select to grow Model Plants of the more consistent Orychophragmus violaceus plant as investigated plant, by adding poly- second
Glycol 6000 simulates different drought stress level (0,10,20,40,80gL-1, with 0gL-1For control) to Model Plants into
Row culture.To Model Plants culture to 1 week or so, during Yu Shangwu 9:00~11:00 using the first expansion leaf as investigation object into
Row index measurement.The long maximum leaf of 15 plants of measurements, maximum width of blade and leaf area (being shown in Table 1) are randomly selected, 5 plants of measurement leaves are randomly selected
Area, plant height and biomass (being shown in Table 2) randomly select horizontal 15 plants lower (each horizontal lower 3 plants of selection) the measurement leaf of different drought
The piece flow of water and physiology capacitor calculate blade tensity, while measuring corresponding biomass (being shown in Table 3).
1 Orychophragmus violaceus Model Plants maximum leaf length of table, maximum width of blade and leaf area
2 Orychophragmus violaceus Model Plants leaf area of table, plant height and biomass
Orychophragmus violaceus Model Plants leaf water potential, physiology capacitor, blade tensity and biomass under 3 different drought level of table
Leaf area and the product of the long maximum width of blade of maximum leaf are fitted using leaf area regression equation, matched curve is such as
Fig. 1, obtaining leaf area estimation model is A=0.93 × (X1×X2)1.03, wherein R2=0.973, P < 0.0001, n=15.It utilizes
Biomass Models are fitted relationship between biomass and plant height, leaf area, and matched curve such as Fig. 2 obtains estimating for biomass
Calculation model is logDW=-2.75+0.86 × log (A2× H), wherein R2=0.985, P < 0.001, n=5.Utilize right angle hyperbolic
Line equation is fitted relationship between biomass and blade tensity, and matched curve such as Fig. 3 show that fit equation isWherein R2=0.809, P < 0.0001, n=15.
Choose grown under different drought level to be measured by investigation Orychophragmus violaceus, using the first expansion leaf as investigation object, from
From different drought level is to handling the 1st day by investigation Orychophragmus violaceus, its maximum was measured during morning 9:00~11:00 every 2 days
Leaf length, maximum width of blade and plant height, persistently measure 2 weeks or more.According to maximum leaf, long, maximum width of blade and plant height, are estimated using leaf area
Calculate model A=0.93 × (X1×X2)1.03With biomass estimation model logDW=-2.75+0.86 × log (A2× H), it calculates each
Arid level descends biomass curve (such as Fig. 4) over time.Utilize 4 parameter Logistic equations
To biomass under different drought level, curve is fitted over time, obtains corresponding fit equation, then to fit equation
Derivation, such as table 4, the rate of rise of biomass as under different drought level.
The 4 parameter Logistic equations of table 4 estimate biomass and fit equation derivation under Orychophragmus violaceus different drought level
Using the rate of rise of biomass under control level as reference, calculating biomass rate of rise under different drought level is
Corresponding growth time when reference point P%, node of as pouring water.By taking P=70 and 50 as an example, then calculates corresponding node of pouring water and see
Table 5.Meanwhile according to 4 parameter Logistic equations and biomass and blade tensity relational model, according to above-mentioned node of pouring water,
Calculate the value of corresponding blade tensity.
The node of pouring water of Orychophragmus violaceus under 5 different drought level of table
*Indicate invalid number of days and TdValue.
X in table 40For reach increased logarithmic phase maximum growth 50% required for number of days, biomass rate of rise is in X0It
When highest, be hereafter gradually reduced.Therefore, the biomass rate of rise under each stress level is in respective corresponding X0After it is final under
It is reduced to 0, X0Biomass rate of rise before it can more preferably represent vegetation growth state.This example is with X under each stress level0
Biomass rate of rise before it compared with the control, 10,20,40 and 80gL-1X under horizontal0Value respectively 6.28,
1.47,3.53 and 3.85, therefore, 20gL-1Horizontal lower 1.77 days, 40gL-120.10 and 15.60 days under horizontal and
80g·L-18.08 and 7.06 days under horizontal are invalid value.
Appropriate water deficit can reduce plant growth rate, influence yield, however can also promote the suitable of yield and quality simultaneously
Degree is promoted, and improves water use efficiency.It as known from Table 5, is respectively 70% or 50% compareed for control Orychophragmus violaceus growth rate,
10g·L-1The node of pouring water of Orychophragmus violaceus should be respectively 4.90 or 2.66 days under PEG level, 20gL-1Orychophragmus violaceus under PEG level
Node of pouring water should be 1.41 days.10g·L-1The corresponding T of node of pouring water of Orychophragmus violaceus under PEG leveldValue be respectively 0.99 or
0.48,20gL-1The corresponding T of node of pouring water of Orychophragmus violaceus under PEG leveldValue is 0.69.For 10gL-1Under PEG level
Orychophragmus violaceus can establish variable irrigation scheme, implement to pour water when the 4th~5 day growth rate is reduced to the 70% of control, later wait fill
The 2nd~3 day growth rate after water is again carried out when being reduced to the 50% of control and pours water.And pass through the online prison of blade tensity
It surveys, it is i.e. implementable when it is reduced to above-mentioned respective value to pour water.The variable based on blade tensity on-line monitoring is achieved in pour water
The prediction of node.
The above results also indicate that 10gL-1Orychophragmus violaceus and 20gL under PEG level-1Plant phase under PEG level
Than longer time stress, 40,80gL can be endured-1Orychophragmus violaceus growth under PEG level is heavily suppressed.
Described above is only that presently preferred embodiments of the present invention should be said the present invention is not limited to enumerate above-described embodiment
Bright, under the introduction of this specification, all equivalent substitutes for being made obvious become anyone skilled in the art
Shape form, all falls within the essential scope of this specification, ought to be protected by the present invention.
Claims (7)
- The node prediction technique 1. a kind of variable based on blade tensity on-line monitoring is poured water, which is characterized in that including following step It is rapid:Step 1, laboratory cultures Model Plants seedling, chooses the Model Plants of investigated plant；Step 2, setting different drought stress level cultivate Model Plants；Step 3 carries out index determining to Model Plants；Step 4 is fitted leaf area and the product of the long maximum width of blade of maximum leaf using leaf area regression equation, obtains leaf The appraising model of area；Relationship between biomass and plant height, leaf area is fitted using Biomass Models, obtains biomass Appraising model；Utilize the relational model of equilateral hyperbola equation building biomass and blade tensity；Step 5 chooses the investigated plant under different drought level, is spaced same time identical period measurement in step 3 Its maximum leaf length, maximum width of blade and plant height；Step 6, according to maximum leaf, long, maximum width of blade and plant height are calculated using leaf area estimation model, biomass estimation model Biomass curve over time；Step 7, to biomass, curve is fitted over time, then to fit equation derivation, it is horizontal to calculate different drought The rate of rise of lower biomass；Step 8 calculates biomass under different drought level and increases speed using the rate of rise of biomass under control level as reference Rate growth time corresponding when being reference point P%, node of as pouring water；Step 9 calculates corresponding leaf according to node of pouring water according to fit equation and biomass and blade tensity relational model The value of piece tensity, so that on-line monitoring predicts variable node of pouring water.
- The node prediction technique 2. a kind of variable based on blade tensity on-line monitoring according to claim 1 is poured water, It is characterized in that, leaf area regression equation is A=u × (X in the step 41×X2)v, wherein u, v are constant, X1Indicate maximum Leaf is long, X2Indicate that maximum width of blade, A indicate leaf area.
- The node prediction technique 3. a kind of variable based on blade tensity on-line monitoring according to claim 1 is poured water, It is characterized in that, Biomass Models are logDW=r+q × log (A in the step 42× H), wherein DW is biomass, and A is leaf Area, H are plant height, and r, q are constant.
- The node prediction technique 4. a kind of variable based on blade tensity on-line monitoring according to claim 1 is poured water, It is characterized in that, equilateral hyperbola equation is in the step 4Wherein DW is biomass, Td For blade tensity, m, n are constant, and wherein m indicates that when biomass be maximum biomass DWmaxT when halfdValue.
- The node prediction technique 5. a kind of variable based on blade tensity on-line monitoring according to claim 1 is poured water, It is characterized in that, fit equation uses 4 parameter Logistic equations in the step 9:Wherein Y0It is right Number growth period initial amount, a are the upper limit of the increment of entire growth course growth indexes, X0It most increases to reach increased logarithmic phase Time required for long 50%, X are processing number of days, and Y is biomass dry weight, and b is fitting coefficient.
- The node prediction technique 6. a kind of variable based on blade tensity on-line monitoring according to claim 5 is poured water, It is characterized in that, to 4 parameter Logistic equation derivations, obtains biomass rate of rise
- The node prediction technique 7. a kind of variable based on blade tensity on-line monitoring according to claim 1 is poured water, It is characterized in that, node calculation method of pouring water in the step 8 is as follows: with the biomass rate of rise GR under control levelfFor ginseng According to the horizontal lower biomass rate of rise GR of each arid of calculatingeCorresponding growth time, is defined as section of pouring water when for reference point P% Point；That is GRe=Ye'=P% × Yf'=P% × GRf, wherein Ye' it is the derivative that each arid level descends biomass growth curve, Yf′ For the derivative for compareing lower biomass growth curve, P is integer.
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| CN114414429A (en) * | 2022-01-26 | 2022-04-29 | 中国农业科学院农田灌溉研究所 | Construction method and application of corn water critical model based on biomass accumulation |
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| CN107085643A (en) * | 2017-04-24 | 2017-08-22 | 江苏大学 | A kind of acquisition methods of saline Irrigation node |
| CN107155665A (en) * | 2017-05-16 | 2017-09-15 | 江苏大学 | A kind of method for strategy of being poured water based on leaf area model of growth formulation crop |
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| CN107085643A (en) * | 2017-04-24 | 2017-08-22 | 江苏大学 | A kind of acquisition methods of saline Irrigation node |
| CN107155665A (en) * | 2017-05-16 | 2017-09-15 | 江苏大学 | A kind of method for strategy of being poured water based on leaf area model of growth formulation crop |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114414429A (en) * | 2022-01-26 | 2022-04-29 | 中国农业科学院农田灌溉研究所 | Construction method and application of corn water critical model based on biomass accumulation |
| CN114414429B (en) * | 2022-01-26 | 2023-08-08 | 中国农业科学院农田灌溉研究所 | Building method and application of corn moisture critical model based on biomass accumulation |
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