CN102608155A - 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

A kind of portable tree transpiration measurement mechanism and measuring method thereof

Technical field

The present invention relates to the plant transpiration water consumption and measure field, particularly a kind of portable tree transpiration measurement mechanism 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 the 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 costs an arm and a leg, and experimental procedure is complicated, and testing cost is high.

Summary of the invention

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

In order to achieve the above object, the present invention adopts following technical scheme to be achieved.

(1) a kind of portable tree transpiration measurement mechanism is characterized in that, comprises 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 humidity sensor and the temperature sensor that stretches in the box, and humidity sensor is connected a data processor through data line respectively with temperature sensor; Humidity and temperature data in the real-time collecting cassette of said data processor calculate and output tree transpiration measurement data.

The technical characterstic of technique scheme is with further improving:

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

Said leaf chamber box adopts transparent organic glass to process.

Said hermatic door is the card slot type sliding door, and sliding door is provided with rubber or silica gel sealing bar.

Said horizontal separation net adopts entanglement.

Said data processor is a notebook computer.

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

(2) a kind of tree transpiration measuring method based on above-mentioned portable tree transpiration measurement mechanism, is characterized in that, may further comprise the steps:

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

Secondly, data processor is gathered relative humidity and the temperature in the box of leaf chamber in real time, and data acquisition time is 30-60 second, through formula U=D ₁/ D ₂* 100%, the anti-absolute humidity value of obtaining 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 possibly reach under-the relevant temperature; Adopt power function model that accumulation transpiration rate data are carried out linear fit, calculate linear fit coefficient E, be the transpiration rate E (gLs of unit volume ^-1),

Then, ignore the influence of air pressure and wind, the blade face transpiration rate to 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);

At last, measure branches and leaves leaf area and whole leaf area, can instead push away whole tree transpiration rate through the leaf area appearance

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-tree transpiration rate, (gm ^-2S ^-1); T _s-whole leaf area (m ²).

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

(3) a kind of tree transpiration measuring method based on above-mentioned portable tree transpiration measurement mechanism, is characterized in that, may further comprise the steps:

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

Secondly, data processor is gathered relative humidity and the temperature in the box of leaf chamber in real time, and data acquisition time is not little 120 seconds, through formula U=D ₁/ D ₂* 100%, the anti-absolute humidity value of obtaining 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 possibly reach under-the relevant temperature; Adopt the cube function model that accumulation transpiration rate data are carried out linear fit, calculate linear fit coefficient E, be the transpiration rate E (gLs of unit volume ^-1);

Then, ignore the influence of air pressure and wind, the blade face transpiration rate to 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);

At last, measure branches and leaves leaf area and whole leaf area, can instead push away whole tree transpiration rate through the leaf area appearance

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-tree transpiration rate, (gm ^-2S ^-1); T _s-whole leaf area (m ²).

In the technique scheme; Adopt the cube function model that accumulation transpiration rate data are carried out sectional linear fitting, seek the best-fit point, get the 30-60 accumulation transpiration rate data of second then in the front and back of best-fit point; Calculate linear fit coefficient E, be the transpiration rate E (gLs of unit volume ^-1).

Portable tree transpiration measurement mechanism of the present invention mainly comprises leaf chamber box, temperature sensor, humidity sensor and data processor, and data processor is a 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, and is portable easily, cheap, simple and practical.Measuring principle science of the present invention, measuring method is simple, and equipment drops into low.

Description of drawings

Below in conjunction with accompanying drawing and embodiment the present invention is explained further details.

Fig. 1 is the structural representation of a kind of portable tree transpiration measurement mechanism 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;

Among 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 8 points, 9 points, 10 points, 11 points, 12 power function curve figure that measure the accumulation transpiration rate;

Fig. 8 is 8 points, 9 points, 10 points, 11 points, 12 cube function curve diagrams of measuring the accumulation transpiration rate;

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

The preceding 30s that Fig. 9-Figure 13 is respectively following 8 points of power function model, 9 points, 10 points, 11 points, 12 mensuration carries out the curve map of data linear fit to preceding 60s different time sections, and wherein: horizontal ordinate is the time, and unit is second (s); Ordinate is the accumulation transpiration rate, and unit is (gL).Figure 14-Figure 18 is respectively the transpiration rate function of following 8 points of cube function model, 9 points, 10 points, 11 points, 12 mensuration and the curve map of transpiration rate rate of change function, and wherein: horizontal ordinate is the time, and unit is second (s); Main ordinate is a transpiration rate, and unit is (gLs ^-1), secondary ordinate is the transpiration rate rate of change, no unit.

Figure 19-Figure 23 is respectively following 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 the accumulation transpiration rate, and unit is (gL).

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

Embodiment

With reference to Fig. 1,, mainly comprise leaf chamber box 1, temperature sensor 2, humidity sensor 3 and data processor 5 for a kind of portable tree transpiration measurement mechanism of the present invention.Wherein.Leaf chamber box 1 is transparent horizontal circle column, and a side of leaf chamber box 1 is provided with hermatic door, is used to stretch into the branch of band leaf.The top of leaf chamber box 1 is provided with the humidity sensor 3 and temperature sensor 2 that stretches in the box; Humidity sensor 3 is connected data processor 5 through data line respectively with temperature sensor 2; Data processor 5 adopts notebook computer to realize its function; Humidity and temperature data in the collecting cassette calculate and output tree transpiration measurement data in real time.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; 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 process, and can continue to produce photosynthesis to guarantee measured blade, and its diameter is 15cm, and length is 30cm.Hermatic door 103 is the card slot type sliding door, and wherein draw-in groove 104 is as shown in Figure 3, and sliding door 105 is as shown in Figure 4, and upward sliding door 105 is provided with rubber or silica gel sealing bar, and sealing strip thickness 2mm-3mm gets final product.Sealing strip mainly prevents the steam leakage leafing chamber of blade evaporation, 1.Horizontal separation net 102 is set in the box body 101; Horizontal separation net 102 is an entanglement; Entanglement is 5cm apart from box top, leaf chamber, determined leaf or with the small stems of leaf when putting into leaf chamber box, be positioned at horizontal separation net below; Prevent direct feeler, and play fixing effect measuring branches and leaves.

The measuring method of portable tree transpiration measurement mechanism of the present invention is based on following measuring principle.Transpiration is the process that the aqueous vapor molecule spreads to air through pore from the mesophyll cell gap, and its result increases blade ambient air humidity; The inventor thinks, in certain space, as long as record the variation of relative humidity within a certain period of time in the air of certain volume around the blade, can try to achieve the blade transpiration rate.But; In measuring process, need overcome following problem: (1) is because the blade transpiration makes air humidity increase in the confined space; Suppress transpiration rate; Need to confirm that humidity increases the marginal time of causing transpiration rate to descend in the box of leaf chamber, promptly find humidity to increase the rising minimum period of influence of blade, this period is called stable state.In other words, stable state is the more stable or constant basically state of transpiration rate.Measurement data when (2) utilizing stable state is set up the transformational relation of humidity and transpiration rate.

The present invention directly obtains the relative humidity data of confined space through existing humidity sensor, through formula U=D in the process of measuring plant transpiration speed ₁/ D ₂* 100% (U-relative humidity (RH%); D ₁-absolute humidity (gL); D ₂The maximum absolute humidity (gL) that possibly reach under-the relevant temperature), the anti-absolute humidity value of obtaining, i.e. the weight of the gas contained humidity of per unit volume is also represented 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 influence of air pressure and wind, the blade face transpiration rate to 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 through the leaf area appearance, 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-tree transpiration rate, (gm ^-2S ^-1); T _s-whole leaf area (m ²).

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

The inventor is through the lot of data collection and analyze to find, under the constant situation of assumed temperature and pressure, accumulates transpiration rate in the box of leaf chamber and minute is a nonlinear function model, and has searched out two kinds of nonlinear function model, is respectively power function y=x ^a(y-accumulates transpiration rate (gL); X-minute (s)) and cube function y=ax ³+ bx ²+ cx+d, (y-accumulates transpiration rate (gL); X-minute (s); A, b, c, d are coefficient).Shown in Fig. 5 power function model curve and Fig. 6 cube function model curve.Can know that according to Fig. 5, Fig. 6 observation transpiration rate reaches the time period of stable state, 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 visiblely by Fig. 5, in the power function model, main position of time period of living in is in the forward period of minute during stable state.It is fixed that stable position begins to get from 0s, and the duration is got 30s to 60s, also is the corresponding stable state time period of power function model curve, and the data of collection are valid data in this stable state time period, according to above-mentioned valid data to power function y=x ^aCarry out linear fit.

With the potted plant experiment in Mizhi on August 23 is example, and weather is that the typical case is fine, carries out local branches and leaves hygrometer and measures in whole 8 points, 9 points, 10 points, 11 points, 12 times.Fig. 7 is 8 points, 9 points, 10 points, 11 points, 12 power function fitting curves of measuring the accumulation transpiration rate, is carrying out power function y=x ^aLeast-squares algorithm linear fitting in, its coefficient of determination R ²Be worth all greater than 0.94, and meet power function model among Fig. 5.

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

The stable state time period of table 1 different measuring time

The linear fit coefficient E of calculating optimum match 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, the factor that our account temperature changes is ignored the factor that pressure changes.Through formula U=D ₁/ D ₂* 100% (U-relative humidity (RH%); D ₁-absolute humidity (gL); D ₂Can know that the maximum absolute humidity (gL) that-uniform temp possibly reach down) temperature during according to the air pressure of measuring the locality and measurement is accurately searched corresponding absolute humidity D ₁(gL) get final product.

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

At 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 satisfying one group of equal precision measurement data (x of linear relationship _i, y _i), suppose independent variable x _iError can ignore, then at same x _iDown, measurement point y _iWith the some a+bx on the 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 d on straight line all ₁=d ₂=...=d _n=0), a that obtains and b are optimal, but measurement point can not have only consideration d so all on straight line ₁, d ₂..., d _nBe minimum, just consider d ₁+ d ₂+ ... + d _nBe minimum, but because of d ₁, d ₂..., d _nHaving just has negatively, adds up and possibly cancel out each other, therefore inadvisable; And d ₁+ d ₂+ ... + d _nBad again solving an equation, thus infeasible.Take a kind of equivalent method now: 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, the method for asking a and b is least square method.

Order $D=\underset{i=1}{\overset{\mathrm{<figure-callout\; id="2"\; label="n\; d\; i"\; filenames="HDA0000142663830000091.png"\; state="\{\{state\}\}">n</figure-callout>}}{\mathrm{\&Sigma;}}}{d}_i^{}{}_2^{}=D=\underset{i=1}{\overset{\mathrm{<figure-callout\; id="2"\; label="n\; d\; i"\; filenames="HDA0000142663830000091.png"\; state="\{\{state\}\}">n</figure-callout>}}{\mathrm{\&Sigma;}}}{d}_i^{}{}_2^{}=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{[y_i-a-b_i]}^2---(2-2)$

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

$\frac{\&PartialD;D}{\&PartialD;a}=-a[\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{y}_i-\mathrm{na}-b\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{x}_i]$

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

Ask second-order partial differential coefficient to be again:

$\frac{{\&PartialD;}^{2}D}{{\&PartialD;a}^2}=2n;$ $\frac{{\&PartialD;}^{2}D}{{\&PartialD;b}^2}=2\underset{i=1}{\overset{\mathrm{<figure-callout\; id="2"\; label="n\; x\; i"\; filenames="HDA0000142663830000091.png"\; state="\{\{state\}\}">n</figure-callout>}}{\mathrm{\&Sigma;}}}{x}_i^{}{}_2^{}$

Obviously: $\frac{{\&PartialD;}^{2}D}{{\&PartialD;a}^2}=2n\&GreaterEqual;0;$ $\frac{{\&PartialD;}^{2}D}{{\&PartialD;b}^2}=2\underset{i=1}{\overset{\mathrm{<figure-callout\; id="2"\; label="n\; x\; i"\; filenames="HDA0000142663830000091.png"\; state="\{\{state\}\}">n</figure-callout>}}{\mathrm{\&Sigma;}}}{x}_i^{}{}_2^{}\&GreaterEqual;0$

Satisfy minimum conditions, make that the single order partial derivative is zero:

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

$\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{x}_i{y}_i-a\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{x}_i-b\underset{i=1}{\overset{\mathrm{<figure-callout\; id="2"\; label="n\; x\; i"\; filenames="HDA0000142663830000091.png"\; state="\{\{state\}\}">n</figure-callout>}}{\mathrm{\&Sigma;}}}{x_i}^{}{{}_{}}^2=0---(2-4)$

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

$\stackrel{\&OverBar;}{x^2}=\frac{1}{n}\underset{i=1}{\overset{\mathrm{<figure-callout\; id="2"\; label="n\; x\; i"\; filenames="HDA0000142663830000091.png"\; state="\{\{state\}\}">n</figure-callout>}}{\mathrm{\&Sigma;}}}{x}_i^{}{}_2^{};$ $\stackrel{\&OverBar;}{\mathrm{xy}}=\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{x}_i{y}_i$

Then: $\stackrel{\&OverBar;}{y}-a-b\stackrel{\&OverBar;}{x}=0$

$\stackrel{\&OverBar;}{\mathrm{xy}}-a\stackrel{\&OverBar;}{x}-\mathrm{<figure-callout\; id="2"\; label="b\; x"\; filenames="HDA0000142663830000091.png"\; state="\{\{state\}\}">b</figure-callout>}\stackrel{}{x^{}}\stackrel{\&OverBar;}{{}^2}=0---(2-5)$

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

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

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

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

In least square method, suppose that the independent variable error can ignore, be the regression equation of deriving for ease.The error of function can be thought greater than the error of independent variable and satisfies supposition in the operation.In fact both all are variablees, and error is all arranged, thereby cause as a result the standard deviation of y, a, b (n >=6) as follows:

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

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

${\mathrm{\&sigma;}}_a=\sqrt{\frac{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{x}_i^2}{n\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{x}_i^2-{\left(\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{x}_i\right)}^2}}{\mathrm{\&sigma;}}_y=\sqrt{\frac{\stackrel{\&OverBar;}{x^2}}{n(\stackrel{\&OverBar;}{x^2}-{\stackrel{\&OverBar;}{x}}^2)}}{\mathrm{\&sigma;}}_y---(2-9)$

${\mathrm{\&sigma;}}_b=\sqrt{\frac{n}{n\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{x}_i^2-{\left(\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{x}_i\right)}^2}}{\mathrm{\&sigma;}}_y=\sqrt{\frac{1}{n(\stackrel{\&OverBar;}{x^2}-{\stackrel{\&OverBar;}{x}}^2)}}{\mathrm{\&sigma;}}_y---(2-10)$

At last, 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:

$r=\frac{\stackrel{\&OverBar;}{\mathrm{xy}}-\stackrel{\&OverBar;}{x}\stackrel{\&OverBar;}{y}}{\sqrt{(\stackrel{\&OverBar;}{x^2}-{\stackrel{\&OverBar;}{x}}^2)(\stackrel{\&OverBar;}{{\mathrm{<figure-callout\; id="2"\; label="y"\; filenames="HDA0000142663830000091.png"\; state="\{\{state\}\}">y</figure-callout>}}^2}-{\stackrel{\&OverBar;}{y}}^2)}}---(2-11)$

The r value is in 0＜r≤1.A kind of index that is the good property of measurement regression equation match is a multiple correlation coefficient, | r|≤1, the absolute value of r is big more, more must be good more near 1 explanation match.Square R of multiple correlation coefficient r ₂Be called the coefficient of determination.

Two, cube function model method

Observe Fig. 5, Fig. 6 can know, in the power function model, the stable state time period is mainly in front end period of minute.Different with power function model; The stable state time period is mainly in middle front end period of minute in the cube function model; Stable position begins to get fixes time and can not begin from 0s, but through the cube function being carried out once and the secondary differentiate, confirms that second derivative is that 0 time point is best-fit time point (a best stable state time point); Get near the best-fit time point 30s to the duration section of 60s as the best-fit time period, the best-fit time period promptly is the stable state time period that will seek; The data of gathering 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.

With the potted plant experiment in Mizhi on September 2 is example, and weather is typical case's 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 matched curve figure that 8 points, 9 points, 10 points, 11 points, 12 are measured the accumulation transpiration rates, and in carrying out the match of cube function, 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 through (being cube function y=ax to the transpiration rate rate of change ³+ bx ²The second derivative of+cx+d) analysis can obtain the particular location that best stable state time point roughly is in the Measuring Time section, and is as shown in table 3.Because the particular location of the best stable state time point of confirming and astable Origin And Destination, just a time point in the middle of the stable state time period is 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 toward both sides from best stable state time point, and in conjunction with Figure 19-shown in Figure 23, this extension value is respectively 20s, 25s, 35s, 45s, 55s.Maximum coefficient of determination R ²The instant best-fit time period of pairing duration section, the best-fit time period is as shown in table 3.

The stable state time of table 3 different measuring time

Then, the linear fit coefficient E of calculating optimum match 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 portable tree transpiration measurement mechanism (having another name called the DMT-D dynamic detector); Format diameter is 15cm; Length is the centric leaf chamber (attaching autcad figure) of 30cm, with notebook computer, data line, hygrometer (having temp sensing function simultaneously) composition measuring apparatus.The band leaf sprig of an about 10cm length is put into by sliding door; Close sliding door then; Read before this device the humidity changing value of 60s before the 30s-, carry software through hygrometer and convert the absolute humidity value into, and be that linear fit is carried out to measurement data in the basis with the power function model through notebook computer; Obtain linear fit coefficient E, bring formula P then 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), measure branches and leaves leaf area and whole leaf area through the leaf area appearance again, can instead push away whole tree transpiration rate P _r/ S=T _Pr/ T _S, P wherein _r-blade face transpiration rate (gm ^-2S ^-1); Leaf area (m in the box of S-leaf chamber ²); T _Pr-imperial decree tree transpiration rate, (gm ^-2S ^-1); T _s-whole leaf area (m ²).Above-mentioned measurement data and more shown in figure 24 with the photosynthetic appearance measured value under the similarity condition, its result reaches significant correlation.

Claims (10)

1. a portable tree transpiration measurement mechanism is characterized in that, comprises 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 humidity sensor and the temperature sensor that stretches in the box, and humidity sensor is connected a data processor through data line respectively with temperature sensor; Humidity and temperature data in the real-time collecting cassette of said data processor calculate and output tree transpiration measurement data.

2. portable tree transpiration measurement mechanism according to claim 1 is characterized in that, the diameter of said leaf chamber box is 10cm-20cm, and length is 25cm-35cm.

3. portable tree transpiration measurement mechanism according to claim 1 is characterized in that, said leaf chamber box adopts transparent organic glass to process.

4. portable tree transpiration measurement mechanism according to claim 1 is characterized in that said hermatic door is the card slot type sliding door, and sliding door is provided with rubber or silica gel sealing bar.

5. portable tree transpiration measurement mechanism according to claim 1 is characterized in that, said horizontal separation net adopts entanglement.

6. portable tree transpiration measurement mechanism according to claim 1 is characterized in that said data processor is a notebook computer.

7. portable tree transpiration measurement mechanism according to claim 1 is characterized in that, the measuring accuracy of said humidity sensor is controlled at ± 2% in, measure response time≤5s.

8. a tree transpiration measuring method based on the described portable tree transpiration measurement mechanism of claim 1, is characterized in that, may further comprise the steps:

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

Secondly, data processor is gathered relative humidity and the temperature in the box of leaf chamber in real time, and data acquisition time is 30-60 second, through formula U=D ₁/ D ₂* 100%, the anti-absolute humidity value of obtaining 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 possibly reach under-the relevant temperature; Adopt power function model that accumulation transpiration rate data are carried out linear fit, calculate linear fit coefficient E, be the transpiration rate E (gLs of unit volume ^-1),

Then, ignore the influence of air pressure and wind, the blade face transpiration rate to 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);

At last, measure branches and leaves leaf area and whole leaf area, can instead push away whole tree transpiration rate through the leaf area appearance

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-tree transpiration rate, (gm ^-2S ^-1); T _s-whole leaf area (m ²).

9. a tree transpiration measuring method based on the described portable tree transpiration measurement mechanism of claim 1, is characterized in that, may further comprise the steps:

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

Secondly, data processor is gathered relative humidity and the temperature in the box of leaf chamber in real time, and data acquisition time is not little 120 seconds, through formula U=D ₁/ D ₂* 100%, the anti-absolute humidity value of obtaining 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 possibly reach under-the relevant temperature; Adopt the cube function model that accumulation transpiration rate data are carried out linear fit, calculate linear fit coefficient E, be the transpiration rate E (gLs of unit volume ^-1);

Then, ignore the influence of air pressure and wind, the blade face transpiration rate to 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);

At last, measure branches and leaves leaf area and whole leaf area, can instead push away whole tree transpiration rate through the leaf area appearance

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-tree transpiration rate, (gm ^-2S ^-1); T _s-whole leaf area (m ²).

10. tree transpiration measuring method according to claim 9; It is characterized in that; Adopt the cube function model that accumulation transpiration rate data are carried out sectional linear fitting, seek the best-fit point, get the 30-60 accumulation transpiration rate data of second then in the front and back of best-fit point; Calculate linear fit coefficient E, be the transpiration rate E (gLs of unit volume ^-1).

Images