CN102608155B - Portable tree transpiration measuring device and tree transpiration measuring method - Google Patents

Abstract

The invention relates to the field of measurement of plant transpiration water consumption, and discloses a portable tree transpiration measuring device and a tree transpiration measuring method. The portable tree transpiration measuring device comprises a transparent horizontal cylindrical leaf chamber box, wherein an air-tight door is arranged on one side of the leaf chamber box, a horizontal isolation net is arranged in the leaf chamber box, a humidity sensor and a temperature sensor which extend into the box are arranged above the leaf chamber box, the humidity sensor and the temperature sensor are respectively connected with a data processor through data wires, and the data processor is used for acquiring humidity and temperature data in the box in real time and calculating and outputting tree transpiration measuring data. The measuring principle is scientific, the measuring method is simple and the equipment investment is low.

Description

The rising measurement mechanism of a kind of Portable tree and measuring method thereof

Technical field

The present invention relates to plant transpiration water consumption and measure field, particularly the rising measurement mechanism of a kind of Portable tree and measuring method thereof.

Background technology

The shortage of water resources problem that become international.It is the important means of research plant drought characteristic, water-saving agriculture, Water Saving Forestry and ecologic environment that plant transpiration water consumption is measured.The instrument of plant transpiration water consumption test mainly adopts the multiple photosynthetic determining instrument of external import, and instrument is expensive, experimental procedure complexity, and testing cost is high.

Summary of the invention

The object of the present invention is to provide the rising measurement mechanism of a kind of cheap, simple and practical Portable tree, and measuring method based on this measurement mechanism.

In order to achieve the above object, the present invention is achieved by the following technical solutions.

(1) the rising measurement mechanism of a kind of Portable tree, it is characterized in that, comprise a transparent horizontal circle column leaf chamber box, one side of leaf chamber box is provided with hermatic door, in the box of leaf chamber, horizontal separation net is set, the top of leaf chamber box is provided with the humidity sensor and the temperature sensor that stretch in box, and humidity sensor is connected a data processor by data line respectively with temperature sensor, humidity and temperature data in described data processor Real-time Collection box, calculate and export tree transpiration measurement data.

The technical characterstic of technique scheme and further improvement are:

The diameter of described leaf chamber box is 10cm-20cm, and length is 25cm-35cm.

Described leaf chamber box adopts transparent organic glass to make.

Described hermatic door is card slot type sliding door, is provided with rubber or silica gel sealing bar on sliding door.

Described horizontal separation net adopts entanglement.

Described data processor is notebook computer.

The measuring accuracy of described humidity sensor is controlled at ± 2% in, measure response time≤5s.

(2) a tree transpiration measuring method, based on the rising measurement mechanism of above-mentioned Portable tree, is characterized in that, comprises the following steps:

First, branches and leaves are stretched in the box of leaf chamber, close hermatic door;

Secondly, relative humidity and temperature in the box of data processor Real-time Collection leaf chamber, data acquisition time is 30-60 second, by formula U=D ₁/ D ₂× 100%, reverse goes out absolute humidity value, is the accumulation transpiration rate of unit volume, wherein, and U-relative humidity (RH%); D ₁-absolute humidity (gL); D ₂the maximum absolute humidity (gL) that may reach under-relevant temperature; Adopt power function model to carry out linear fit to accumulation transpiration rate data, calculate linear fit coefficient E, be the transpiration rate E (gLs of unit volume ^-1),

Then, ignore the impact on transpiration rate of air pressure and wind, blade face transpiration rate

P _r＝E*V/S

P _r-blade face transpiration rate, (gm ^-2s ^-1); Transpiration rate (the gLs of E-unit volume ^-1); V-leaf chamber box body amasss (L); Leaf area (m in the box of S-leaf chamber ²);

Finally, measure branches and leaves leaf area and whole leaf area by leaf area instrument, can instead push away whole tree transpiration rate

P _r/S＝T _Pr/T _S

P _rblade face transpiration rate (gm ^-2s ^-1); Leaf area (m in the box of S-leaf chamber ²); T _pr-whole tree transpiration rate, (gm ^-2s ^-1); T _s-whole leaf area (m ²).

In technique scheme, preferential data acquisition time is 30-60 second.

(3) a tree transpiration measuring method, based on the rising measurement mechanism of above-mentioned Portable tree, is characterized in that, comprises the following steps:

First, branches and leaves are stretched in the box of leaf chamber, close hermatic door;

Secondly, relative humidity and temperature in the box of data processor Real-time Collection leaf chamber, data acquisition time is not little 120 seconds, by formula U=D ₁/ D ₂× 100%, reverse goes out absolute humidity value, is the accumulation transpiration rate of unit volume, wherein, and U-relative humidity (RH%); D ₁-absolute humidity (gL); D ₂the maximum absolute humidity (gL) that may reach under-relevant temperature; Adopt cube function model to carry out linear fit to accumulation transpiration rate data, calculate linear fit coefficient E, be the transpiration rate E (gLs of unit volume ^-1);

Then, ignore the impact on transpiration rate of air pressure and wind, blade face transpiration rate

P _r＝E*V/S

P _r-blade face transpiration rate, (gm ^-2s ^-1); Transpiration rate (the gLs of E-unit volume ^-1); V-leaf chamber box body amasss (L); Leaf area (m in the box of S-leaf chamber ²);

Finally, measure branches and leaves leaf area and whole leaf area by leaf area instrument, can instead push away whole tree transpiration rate

P _r/S＝T _Pr/T _S

P _r-blade face transpiration rate (gm ^-2s ^-1); Leaf area (m in the box of S-leaf chamber ²); T _pr-whole tree transpiration rate, (gm ^-2s ^-1); T _s-whole leaf area (m ²).

In technique scheme, adopt cube function model to carry out sectional linear fitting to accumulation transpiration rate data, find best-fit point, then get the 30-60 accumulation transpiration rate data of second in the front and back of best-fit point, calculate linear fit coefficient E, be the transpiration rate E (gLs of unit volume ^-1).

The rising measurement mechanism of Portable tree of the present invention, mainly comprises leaf chamber box, temperature sensor, humidity sensor and data processor, and data processor is notebook computer, and leaf chamber box is diameter 10cm-20cm, the transparent column shape of length 25cm-35cm.Whole device is simply small and exquisite, easily portable, cheap, simple and practical.Measuring principle science of the present invention, measuring method is simple, and equipment investment is low.

Accompanying drawing explanation

Below in conjunction with the drawings and specific embodiments, the present invention is described in further details.

Fig. 1 is the structural representation of the rising measurement mechanism of a kind of Portable tree of the present invention;

Fig. 2 is the structural representation of leaf chamber box;

Fig. 3 is the structural representation of draw-in groove;

Fig. 4 is the structural representation of sliding door;

In Fig. 1-Fig. 4: 1, leaf chamber box; 101, box body; 102, horizontal separation net; 103, hermatic door; 104, draw-in groove; 105, sliding door; 2, temperature sensor; 3, humidity sensor; 4, data line; 5, data processor.

Fig. 5 is the power function model curve map of closed container blade accumulation transpiration rate;

Fig. 6 is the cube function model curve map of closed container blade accumulation transpiration rate;

Fig. 7 is the power function curve figure of 8 points, 9 points, 10 points, 11 points, 12 mensuration accumulation transpiration rates;

Fig. 8 is the cube function curve diagram of 8 points, 9 points, 10 points, 11 points, 12 mensuration accumulation transpiration rates;

In Fig. 5-Fig. 8: horizontal ordinate is the time, unit is second (s); Ordinate is accumulation transpiration rate, and unit is (gL).

The front 30s that Fig. 9-Figure 13 is respectively lower 8 points of power function model, 9 points, 10 points, 11 points, 12 mensuration carries out the curve map of data linear fit to front 60s different time sections, wherein: horizontal ordinate is the time, unit is second (s); Ordinate is accumulation transpiration rate, and unit is (gL).Figure 14-Figure 18 is respectively lower 8 points of cube function model, 9 points, 10 points, 11 points, the transpiration rate function of 12 mensuration and the curve map of transpiration rate rate of change function, and wherein: horizontal ordinate is the time, unit is second (s); Main ordinate is transpiration rate, and unit is (gLs ^-1), secondary ordinate is transpiration rate rate of change, without unit.

Figure 19-Figure 23 is respectively lower 8 points of cube function model, 9 points, 10 points, 11 points, at 12 and measures the different stable state time periods and carry out the curve map of data linear fit, and horizontal ordinate is the time, and unit be second (s); Ordinate is accumulation transpiration rate, and unit is (gL).

Figure 24 is the measurement data comparison diagram of the rising measurement mechanism of Portable tree of the present invention (having another name called DMT-D dynamic detector) and photosynthetic instrument, and wherein: horizontal ordinate is the time, unit is hour (h); Ordinate is transpiration rate, and unit is (gm ^-2s ^-1).

Embodiment

With reference to Fig. 1, for the rising measurement mechanism of a kind of Portable tree of the present invention, mainly comprise leaf chamber box 1, temperature sensor 2, humidity sensor 3 and data processor 5.Wherein.Leaf chamber box 1 is transparent horizontal circle column, and a side of leaf chamber box 1 is provided with hermatic door, for stretching into the branch with leaf.The top of leaf chamber box 1 is provided with the humidity sensor 3 and the temperature sensor 2 that stretch in box, humidity sensor 3 and temperature sensor 2 are respectively by data line connection data processor 5, data processor 5 adopts notebook computer to realize its function, humidity and temperature data in Real-time Collection box, calculate and export tree transpiration measurement data.Wherein, the measuring accuracy of humidity sensor 3 is controlled at ± 2% in, the precision of temperature sensor is controlled at ± 1%.Measure response time≤5s, to guarantee measuring accuracy; The present embodiment adopts Temperature Humidity Sensor, its moisture measurement scope: 0-100%rh, and temperature measurement range: 0-100 ℃, moisture measurement precision: ± 2%rh, temperature measurement accuracy is: ± 1%K, the measurement response time is 5s.

With reference to Fig. 2, be the concrete structure of leaf chamber box, this box body 101 adopts transparent organic glass to make, and to guarantee that measured blade can continue to produce photosynthesis, its diameter is 15cm, and length is 30cm.Hermatic door 103 is card slot type sliding door, and wherein as shown in Figure 3, as shown in Figure 4, and upper sliding door 105 is provided with rubber or silica gel sealing bar, sealing strip thickness 2mm-3mm to sliding door 105 to draw-in groove 104.Sealing strip mainly prevents the steam leakage leafing chamber of blade evaporation, 1.Box body 101 is interior arranges horizontal separation net 102, horizontal separation net 102 is entanglement, entanglement is 5cm apart from box top, leaf chamber, determined leaf or the small stems with leaf are in the time putting into leaf chamber box, be positioned at horizontal separation net below, prevent direct feeler, and play fixing effect to measuring branches and leaves.

The measuring method of the rising measurement mechanism of Portable tree of the present invention is based on following measuring principle.Transpiration is aqueous vapor molecule from mesophyll cell gap by pore to the process spreading air, and its result increases blade ambient air humidity; Inventor thinks, in certain space, as long as record the blade variation of relative humidity within a certain period of time in the air of certain volume around, can try to achieve Transpiration.But, in measuring process, need to overcome following problem: (1) increases because blade transpiration makes air humidity in confined space, suppress transpiration rate, need to determine that in the box of leaf chamber, humidity increases the marginal time of causing transpiration rate to decline, find humidity to increase the rising minimum period of impact of blade, this period is called stable state.In other words, stable state is the more stable or substantially constant state of transpiration rate.(2) measurement data while utilizing stable state, sets up the transformational relation of humidity and transpiration rate.

The present invention, measuring in the process of plant transpiration speed, directly obtains the relative humidity data of confined space, by formula U=D by existing humidity sensor ₁/ D ₂× 100% (U-relative humidity (RH%); D ₁-absolute humidity (gL); D ₂the maximum absolute humidity (gL) that may reach under-relevant temperature), reverse goes out absolute humidity value, i.e. and the weight of the gas contained humidity of per unit volume also represents the accumulation transpiration rate of unit volume.The instantaneous rate of change of the accumulation transpiration rate of unit volume, is the transpiration rate E (gLs of unit volume ^-1).

Ignore the impact on transpiration rate of air pressure and wind, blade face transpiration rate

P _r＝E*V/S

P _r-blade face transpiration rate, (gm ^-2s ^-1); Transpiration rate (the gLs of E-unit volume ^-1); V-leaf chamber box body amasss (L); Leaf area (m in the box of S-leaf chamber ²).

(3) mensuration of blade face transpiration rate belongs to local mensuration, measures branches and leaves leaf area and whole leaf area by leaf area instrument, can instead push away whole tree transpiration rate

P _r/S＝T _Pr/T _s

P _r-blade face transpiration rate (gm ^-2s ^-1); Leaf area (m in the box of S-leaf chamber ²); T _pr-whole tree transpiration rate, (gm ^-2s ^-1); T _s-whole leaf area (m ²).

In the present invention, box body long-pending V (L) in leaf chamber is fixed value, leaf area S (m in the box of leaf chamber ²) adopt leaf area instrument to measure, its key is to determine the transpiration rate (gLs of E-unit volume ^-1).

Inventor is through a large amount of data acquisition and analyze and find, in the constant situation of assumed temperature and pressure, in the box of leaf chamber, accumulation transpiration rate and minute are nonlinear function model, and have searched out two kinds of nonlinear function model, are respectively power function y=x ^a(y-accumulation transpiration rate (gL); X-minute (s)) and cube function y=ax ³+ bx ²+ cx+d, (y-accumulation transpiration rate (gL); X-minute (s); A, b, c, d are coefficient).As shown in Fig. 5 power function model curve and Fig. 6 cube function model curve.Observe knownly according to Fig. 5, Fig. 6, transpiration rate reaches the time period of stable state, and general persistence is that 30s is to 60s.

Transpiration rate E (the gLs of unit volume is described with power function model and cube function model respectively below ^-1) computing method.

One, power function model method

Observed from Fig. 5, in power function model, when stable state, main position of time period of living in is in the forward period of minute.It is fixed that stable position starts to get from 0s, and the duration is got 30s to 60s, is also the stable state time period that power function model curve is corresponding, and in this stable state time period, the data of collection are valid data, according to above-mentioned valid data to power function y=x ^acarry out linear fit.

Take August 23 Mizhi potted plant experiment as example, weather is Clear Day, carries out local branches and leaves hygrometer and measures in whole 8 points, 9 points, 10 points, 11 points, 12 times.Fig. 7 is the power function fitting curve of 8 points, 9 points, 10 points, 11 points, 12 mensuration accumulation transpiration rates, is carrying out power function y=x ^aleast-squares algorithm linear fitting in, its coefficient of determination R ²value is all greater than 0.94, and meets power function model in Fig. 5.

To different minute points, each front 30s measuring carries out linear fit (shown in Fig. 8-Figure 12) to front 60s different time segment data, its maximum coefficient of determination R ²corresponding duration section is the best-fit time period.The best-fit time be the stable state time period that will find, as shown in table 1.

The stable state time period of table 1 different measuring time

The linear fit coefficient E that calculates the best-fit time period, wherein the physical significance of linear fit coefficient E is exactly the transpiration rate E (gLs of unit volume ^-1).

In order to make measurement more accurate, we consider the factor of temperature variation, ignore the factor that pressure changes.By formula U=D ₁/ D ₂× 100% (U-relative humidity (RH%); D ₁-absolute humidity (gL); D ₂the maximum absolute humidity (gL) that may reach under-uniform temp) known, the temperature during according to the local air pressure of measurement and measurement, accurately searches corresponding absolute humidity D ₁(gL).

Introduce the linear fit principle that the present invention adopts below.

First, ask regression straight line.

If the expression formula of straight-line equation is:

Y=a+bx (2-1) will obtain best a and b according to measurement data.To meeting one group of equal precision measurement data (x of linear relationship _i, y _i), suppose independent variable x _ierror can ignore, at same x _iunder, measurement point y _iwith the some a+bx on straight line _ideviation d _ias follows:

d ₁＝y ₁-a-bx ₁

d ₂＝y ₂-a-bx ₂

d _n＝y _n-a-bx _n

Obviously preferably measurement point (is all d on straight line ₁=d ₂=...=d _n=0), a obtaining and b are optimal, but measurement point can not, all on straight line, only have consideration d like this ₁, d ₂..., d _nfor minimum, namely consider d ₁+ d ₂+ ... + d _nfor minimum, but because of d ₁, d ₂..., d _nhaving just has negatively, adds up and may cancel out each other, therefore inadvisable; And d ₁+ d ₂+ ... + d _nbad solving an equation again, thus infeasible.Take now a kind of equivalent method: work as d ₁ ²+ d ₂ ²+ ... + d _n ²to a and b for hour, d ₁, d ₂..., d _nalso be minimum.Get (d ₁ ²+ d ₂ ²+ ... + d _n ²) be minimum value, ask the method for a and b least square method.

Order $D=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{d}_i^2=D=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{d}_i^2=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{[y_i-a-b_i]}^2---(2-2)$

D asks respectively single order partial derivative to be to a and b:

$\frac{\∂D}{\∂a}=-a[\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{y}_i-\mathrm{na}-b\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{x}_i]$

$\frac{\∂D}{\∂b}=-2[\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{x}_i{y}_i-a\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{x}_i-b\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{x_i}^2]$

Ask again second-order partial differential coefficient to be:

$\frac{{\∂}^{2}D}{{\∂a}^2}=2n;$ $\frac{{\∂}^{2}D}{{\∂b}^2}=2\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{x}_i^2$

Obvious: $\frac{{\∂}^{2}D}{{\∂a}^2}=2n\≥0;$ $\frac{{\∂}^{2}D}{{\∂b}^2}=2\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{x}_i^2\≥0$

Meet minimum conditions, making single order partial derivative is zero:

$\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{y}_i-\mathrm{na}-b\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{x}_i=0---(2-3)$

$\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{x}_i{y}_i-a\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{x}_i-b\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{x_i}^2=0---(2-4)$

Introduce mean value: $\stackrel{\‾}{x}=\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{x}_i;$ $\stackrel{\‾}{y}=\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{y}_i;$

$\stackrel{\‾}{x^2}=\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{x}_i^2;$ $\stackrel{\‾}{\mathrm{xy}}=\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{x}_i{y}_i$

: $\stackrel{\‾}{y}-a-b\stackrel{\‾}{x}=0$

$\stackrel{\‾}{\mathrm{xy}}-a\stackrel{\‾}{x}-b\stackrel{\‾}{x^2}=0---(2-5)$

Solve: $a=\stackrel{\‾}{y}-b\stackrel{\‾}{x}---(2-6)$

$b=\frac{\stackrel{\‾}{\mathrm{xy}}-\stackrel{\‾}{x}\stackrel{\‾}{y}}{\stackrel{\‾}{x^2}-{\stackrel{\‾}{x}}^2}---(2-7)$

Bring a, b value into linear equation y=a+bx, obtain regression beeline equation.

Then, calculate the standard deviation of y, a, b.

In least square method, suppose that independent variable error is negligible, be in order to facilitate derivation regression equation.The error that in operation, the error of function is greater than independent variable can be thought and meets supposition.In fact both are all variablees, have error, thereby cause the standard deviation (n >=6) of result y, a, b as follows:

${\mathrm{\σ}}_y=\sqrt{\frac{\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{d}_i^2}{n-2}}=\sqrt{\frac{\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{(y_1-{\mathrm{bx}}_i-a)}^2}{n-2}}---(2-8)$

(denominator of radical is n-2, is because there are two variablees)

${\mathrm{\σ}}_a=\sqrt{\frac{\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{x}_i^2}{n\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{x}_i^2-{\left(\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{x}_i\right)}^2}}{\mathrm{\σ}}_y=\sqrt{\frac{\stackrel{\‾}{x^2}}{n(\stackrel{\‾}{x^2}-{\stackrel{\‾}{x}}^2)}}{\mathrm{\σ}}_y---(2-9)$

${\mathrm{\σ}}_b=\sqrt{\frac{n}{n\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{x}_i^2-{\left(\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{x}_i\right)}^2}}{\mathrm{\σ}}_y=\sqrt{\frac{1}{n(\stackrel{\‾}{x^2}-{\stackrel{\‾}{x}}^2)}}{\mathrm{\σ}}_y---(2-10)$

Finally, calculate related coefficient.

Related coefficient is to weigh one group of measurement data x _i, y _ithe parameter of linear dependence degree, it is defined as:

$r=\frac{\stackrel{\‾}{\mathrm{xy}}-\stackrel{\‾}{x}\stackrel{\‾}{y}}{\sqrt{(\stackrel{\‾}{x^2}-{\stackrel{\‾}{x}}^2)(\stackrel{\‾}{y^2}-{\stackrel{\‾}{y}}^2)}}---(2-11)$

R value is in 0 < r≤1.A kind of index that is measurement regression equation matching Optimality is multiple correlation coefficient, | r|≤1, the absolute value of r is larger, and more approaching 1 explanation matching must be better.Square R of multiple correlation coefficient r ₂be called the coefficient of determination.

Two, cube function model method

Observe Fig. 5, Fig. 6 known, in power function model, the stable state time period is mainly in front end period of minute.Different from power function model, in cube function model, the stable state time period is mainly in middle front end period of minute, stable position starts to get that fix time can not be from 0s, but by cube function being carried out once and secondary differentiate, determine that second derivative is that 0 time point is best-fit time point (best stable state time point), near of best-fit time point get 30s to the duration section of 60s as the best-fit time period, the best-fit time period is the stable state time period that will find; The data that gather in this stable state time period are valid data, according to above-mentioned valid data according to cube function y=ax ³+ bx ²+ cx+d carries out linear fit.

Take September 2 Mizhi potted plant experiment as example, weather is typical case cloudy day.Carry out the mensuration of local branches and leaves in whole 8 points, 9 points, 10 points, 11 points, 12 times.Fig. 8 is the cube Function Fitting curve map that 8 points, 9 points, 10 points, 11 points, 12 are measured accumulation transpiration rates, and carrying out in cube Function Fitting, coefficient of determination R2 value is all greater than 0.99, and meets the model of the cube function of Fig. 6.

By Figure 14-Figure 18 by (being cube function y=ax to transpiration rate rate of change ³+ bx ²the second derivative of+cx+d) analysis, can obtain the roughly particular location in Measuring Time section of best stable state time point, as shown in table 3.Due to particular location the astable Origin And Destination of the best stable state time point of determining, just a time point in the middle of the stable state time period, as shown in table 2.

The stable state time point of table 2 different measuring time

The stable state time period is to extend value from best stable state time point toward both sides, and shown in Figure 19-Figure 23, this extension value is respectively 20s, 25s, 35s, 45s, 55s.Maximum coefficient of determination R ²the instant best-fit time period of corresponding duration section, the best-fit time period is as shown in table 3.

The stable state time of table 3 different measuring time

Then, calculate the linear fit coefficient E of best-fit time period, wherein the physical significance of linear fit coefficient E is exactly the transpiration rate E (gLs of unit volume ^-1).

Experimental example: make the rising measurement mechanism of Portable tree (having another name called DMT-D dynamic detector), format diameter is 15cm, length is the centric leaf chamber (attached autcad figure) of 30cm, with notebook computer, data line, hygrometer (having temp sensing function) composition measuring apparatus simultaneously.The band leaf sprig of an about 10cm length is put into by sliding door, then close sliding door, read the humidity changing value of the front 60s of the front 30s-of this device, carry software by hygrometer and be converted to absolute humidity value, and as basis, measurement data is carried out to linear fit take power function model by notebook computer, obtain linear fit coefficient E, then bring formula P into _r=E*V/S, wherein, P _r-blade face transpiration rate, (gm ^-2s ^-1); Transpiration rate (the gLs of E-unit volume ^-1); V-leaf chamber box body amasss (L); Leaf area (m in the box of S-leaf chamber ²), then measure branches and leaves leaf area and whole leaf area by leaf area instrument, can instead push away whole tree transpiration rate P _r/ S=T _pr/ T _s, wherein P _r-blade face transpiration rate (gm ^-2s ^-1); Leaf area (m in the box of S-leaf chamber ²); T _pr-whole tree transpiration rate, (gm ^-2s ^-1); T _s-whole leaf area (m ²).Above-mentioned measurement data with similarity condition under photosynthetic instrument measured value more as shown in figure 24, its result reaches significant correlation.

Claims (3)

1. a tree transpiration measuring method, based on the rising measurement mechanism of Portable tree, the rising measurement mechanism of described Portable tree, comprise a transparent horizontal circle column leaf chamber box, one side of leaf chamber box is provided with hermatic door, in the box of leaf chamber, horizontal separation net is set, the top of leaf chamber box is provided with the humidity sensor and the temperature sensor that stretch in box, humidity sensor is connected a data processor by data line respectively with temperature sensor, humidity and temperature data in described data processor Real-time Collection box, calculate and export tree transpiration measurement data, it is characterized in that, described tree transpiration measuring method comprises the following steps:

First, branches and leaves are stretched in the box of leaf chamber, close hermatic door;

Secondly, relative humidity and temperature in the box of data processor Real-time Collection leaf chamber, data acquisition time is 30-60 second, by formula U=D ₁/ D ₂× 100%, reverse goes out absolute humidity value, is the accumulation transpiration rate of unit volume, wherein, and U-relative humidity (RH%); D ₁-absolute humidity (gL ^-1); D ₂maximum absolute humidity (the gL that may reach under-relevant temperature ^-1); Adopt power function model to carry out linear fit to accumulation transpiration rate data, calculate linear fit coefficient E, be the transpiration rate E(gL of unit volume ^-1s ^-1),

Then, ignore the impact on transpiration rate of air pressure and wind, blade face transpiration rate

P _r=E*V/S

P _r-blade face transpiration rate (gm ^-2s ^-1); Transpiration rate (the gL of E-unit volume ^-1s ^-1); V-leaf chamber box body amasss (L); Leaf area (m in the box of S-leaf chamber ²);

Finally, measure branches and leaves leaf area and whole leaf area by leaf area instrument, can instead push away whole tree transpiration rate

P _r/S=T _Pr/T _S

P _r-blade face transpiration rate (gm ^-2s ^-1); Leaf area (m in the box of S-leaf chamber ²); T _pr-whole tree transpiration rate (gm ^-2s ^-1); T _s-whole leaf area (m ²).

2. a tree transpiration measuring method, based on the rising measurement mechanism of Portable tree, the rising measurement mechanism of described Portable tree, comprise a transparent horizontal circle column leaf chamber box, one side of leaf chamber box is provided with hermatic door, in the box of leaf chamber, horizontal separation net is set, the top of leaf chamber box is provided with the humidity sensor and the temperature sensor that stretch in box, humidity sensor is connected a data processor by data line respectively with temperature sensor, humidity and temperature data in described data processor Real-time Collection box, calculate and export tree transpiration measurement data, it is characterized in that, described tree transpiration measuring method comprises the following steps:

First, branches and leaves are stretched in the box of leaf chamber, close hermatic door;

Secondly, relative humidity and temperature in the box of data processor Real-time Collection leaf chamber, data acquisition time is not less than 120 seconds, by formula U=D ₁/ D ₂× 100%, reverse goes out absolute humidity value, is the accumulation transpiration rate of unit volume, wherein, and U-relative humidity (RH%); D ₁-absolute humidity (gL ^-1); D ₂maximum absolute humidity (the gL that may reach under-relevant temperature ^-1); Adopt cube function model to carry out linear fit to accumulation transpiration rate data, calculate linear fit coefficient E, be the transpiration rate E(gL of unit volume ^-1s ^-1);

Then, ignore the impact on transpiration rate of air pressure and wind, blade face transpiration rate

P _r=E*V/S

P _r-blade face transpiration rate (gm ^-2s ^-1); Transpiration rate (the gL of E-unit volume ^-1s ^-1); V-leaf chamber box body amasss (L); Leaf area (m in the box of S-leaf chamber ²);

Finally, measure branches and leaves leaf area and whole leaf area by leaf area instrument, can instead push away whole tree transpiration rate

P _r/S=T _Pr/T _S

P _r-blade face transpiration rate (gm ^-2s ^-1); Leaf area (m in the box of S-leaf chamber ²); T _pr-whole tree transpiration rate (gm ^-2s ^-1); T _s-whole leaf area (m ²).

3. tree transpiration measuring method according to claim 2, it is characterized in that, adopt cube function model to carry out sectional linear fitting to accumulation transpiration rate data, find best-fit point, then get the 30-60 accumulation transpiration rate data of second in the front and back of best-fit point, calculate linear fit coefficient E, be the transpiration rate E(gL of unit volume ^-1s ^-1).

Images