CN105447317A - Analysis method for crop climate yield potential - Google Patents

Abstract

The present invention provides an analysis method of crop climatic yield potential, comprising: constructing a crop climatic yield potential analysis model; when it is necessary to analyze the total climatic yield potential corresponding to the specified length of time in the cotton growth period, constructing the climatic yield obtained in step 1 The starting time data and ending time data are input into the potential analysis model; the total climate yield potential in the analyzed time period is output. The advantages are: (1) The daily water demand, daily water supply and demand balance, and daily precipitation correction coefficient calculated by using function approximation are more in line with the actual situation of daily water demand in the process of crop growth; (2) Through the accumulation of daily climate yield potential Obtaining the total climate yield potential can reflect the formation process of crop climate yield potential, accurately reflect the accumulation state of crop dry matter, and improve the accuracy and objectivity of climate yield potential.

Description

Analytical method of crop climate yield potential

technical field

The invention belongs to the technical field of agricultural resource research, and in particular relates to an analysis method for crop climate yield potential.

Background technique

Crop climate yield potential refers to the highest biological yield or agricultural yield per unit area of land when the local light, heat, and water climate resources are fully and reasonably utilized, and other conditions, such as soil and nutrients, are in the most suitable state. Therefore, solar radiation energy, crop photosynthetic characteristics, and temperature conditions have become the basic factors for the formation of crop yield. Climatic yield potential reflects the basic potential of agricultural production in a certain region under a specific climate background. It can quantitatively characterize the regional climate resource status and the allocation of climate elements. It is an important indicator for scientifically measuring regional food productivity, agricultural development and population carrying capacity. one.

The traditional climate yield potential is calculated as:

Y _w =Y _T *f(W);

Among them, Y _w is the total climate yield potential during the crop growth period; Y _T is the total light-temperature yield potential during the crop growth period; f(W) is the water correction coefficient.

It can be seen that the determination of the water correction coefficient is the key when calculating the climate yield potential. The traditional moisture correction factor f(W) is determined by the following method:

$\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; W</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}\mathrm{<span\; class="notranslate">\; R</span>}<span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; E.</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}& \mathrm{<span\; class="notranslate">\; R</span>}<span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; E.</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}\\ \mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; R</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; E.</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 3</span>}{\mathrm{<span\; class="notranslate">\; E.</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}& {\mathrm{<span\; class="notranslate">\; E.</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; <</span>\mathrm{<span\; class="notranslate">\; R</span>}<span\; class="notranslate">\; <</span>\mathrm{<span\; class="notranslate">\; 4</span>}{\mathrm{<span\; class="notranslate">\; E.</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}\\ \mathrm{<span\; class="notranslate">\; 0</span>}& \mathrm{<span\; class="notranslate">\; R</span>}<span\; class="notranslate">\; \&Greater\; Equal;</span>\mathrm{<span\; class="notranslate">\; 4</span>}{\mathrm{<span\; class="notranslate">\; E.</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}\end{array}\right.$

Among them, R is the total precipitation during the crop growth period, in mm, and E ₀ is the total evaporation during the crop growth period, in mm.

It can be seen that in the calculation of the traditional moisture correction coefficient, since the parameters considered are the total precipitation during the crop growth period and the total evaporation during the crop growth period, the calculated moisture correction coefficient is actually the total correction coefficient of the crop growth period , cannot reflect the gradual change process of climate yield potential due to the evolution of daily water supply status, and is inconsistent with the accumulation process of crop dry matter production. Therefore, the accuracy of climate yield potential obtained by traditional methods is very limited, and often deviates seriously from the actual situation .

Contents of the invention

Aiming at the defects existing in the prior art, the present invention provides an analysis method of crop climatic yield potential, which can effectively improve the accuracy and objectivity of the analyzed climatic yield potential.

The technical scheme that the present invention adopts is as follows:

The present invention provides a kind of analysis method of crop climate yield potential, comprises the following steps:

Step 1, constructing a crop climate yield potential analysis model, the crop climate yield potential analysis model is expressed as:

Y _w ＝ΣY _wi (1)

in:

Y _wi =r _i ·y _i (2)

${\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; log</span>}}_{\mathrm{<span\; class="notranslate">\; 25</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; |</span><span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; )</span>$

p _i =0.6p _i-1 -w _i +d _i (4)

${\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Center\; Dot;</span>{\mathrm{<span\; class="notranslate">\; log</span>}}_{\mathrm{<span\; class="notranslate">\; 31</span>}}^{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 5</span>}<span\; class="notranslate">\; )</span>$

where: Y _w - the total climate yield potential over the length of the analysis period;

Y _wi - the daily climate yield potential over the length of the analysis period;

r _i - daily precipitation correction coefficient;

y _i - solar temperature yield potential;

p _i - balance of daily water supply and demand;

p _i-1 - yesterday's water supply and demand balance;

w _i - daily water demand;

w _m - water demand on the first day of the month;

w _m+1 - water demand on the first day of the next month;

n- the date sequence within the month, the maximum value is not more than 31;

d _i - the actual precipitation of the day;

Step 2, when it is necessary to analyze the total climate yield potential corresponding to the specified length of time in the cotton growth period, input the start time data and the end time data into the climate yield potential analysis model constructed in step 1;

Step 3: After the climate yield potential analysis model receives the starting time data and the ending time data, record any day in the time period from the starting time to the ending time as the i-th day of month j, and then read the pre-stored first day of the month The daily water demand w _m and the value of the water demand w _m+1 on the first day of the next month are substituted into the formula (5) to calculate the daily water demand w _i ; where, n=i;

Step 4, read the actual precipitation d _i of the day and the balance of water supply and demand p _i-1 yesterday, and substitute the daily water demand w _i , the actual precipitation d _i of the day and the balance of water supply and demand p _i-1 of yesterday into the formula (4 ), calculate the daily water supply and demand balance p _i ; here, for the first day of the analyzed time period, let yesterday’s water supply and demand balance be 0;

Step 5, substituting the daily water supply and demand balance p _i into the formula (3), and calculating the daily precipitation correction coefficient r _i ;

Step 6, read the pre-stored solar temperature yield potential y _i , substitute the solar temperature yield potential y _i and the daily precipitation correction coefficient r _i into formula (2), and calculate the daily climate yield potential Y _wi ;

Step 7, repeat step 3-step 6, calculate the daily climate yield potential of each day in the analyzed time period, and then, based on the formula (1), sum the calculated daily climate yield potential of each day, that is, get Total climate yield potential for the time period analyzed;

Step 8, output the total climate yield potential in the analyzed time period obtained in step 7.

Preferably, in step 3, the pre-stored values of water demand on the first day of each month are shown in Table 1:

Table 1 Cotton water demand on the first day of each month

Among them, since the growth period of cotton is from May to September, Table 1 only stores the water demand on the first day of each month from May to September.

Preferably, in step 3, the read solar temperature yield potential _yi is obtained by the following method:

Step 3.1, read the pre-stored temperature of the three base points of the month to be analyzed, which are the suitable temperature T ₁ , the lowest temperature T ₂ that can be tolerated, and the highest temperature T ₃ that can be tolerated in the current period of cotton; then read the actual daily average air temperature T _i ;

Step 3.2, substituting T _1, T ₂ , T ₃ and T _i into the formula (6) to calculate the daily temperature correction factor F _i ;

${\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>\frac{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span>{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; B</span>}}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span>{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; B</span>}}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 6</span>}<span\; class="notranslate">\; )</span>$

Wherein, B=(T ₃ -T ₁ )/(T ₁ -T ₂ );

Step 3.3, read the pre-stored daily maximum actual temperature T _u , substitute the daily maximum actual temperature T _u into the formula (7), and calculate the daily maximum temperature correction coefficient V _i ;

V _i ＝(1-l _i ² ) ²⁵ -29l _i ²

in:

${\mathrm{<span\; class="notranslate">\; l</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; log</span>}}_{\mathrm{<span\; class="notranslate">\; 26</span>}}^{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; u</span>}}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; log</span>}}_{\mathrm{<span\; class="notranslate">\; 26</span>}}^{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; u</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 7</span>}<span\; class="notranslate">\; )</span>$

In step 3.4, the daily maximum temperature correction coefficient V _i and the daily temperature correction coefficient F _i are substituted into the formula (8), and the daily correction coefficient t _i of the light-temperature yield potential is calculated;

t _i =0.3V _i +0.7F _i (8)

Step 3.5, read the pre-stored daily photosynthetic yield potential h _i , substitute t _i and h _i into formula (9), and calculate the solar temperature yield potential y _i ;

y _i =t _i ·h _i (9).

Preferably, in step 3.5, the read daily photosynthetic yield potential _hi is obtained by the following method:

Step 3.5.1, establishing the weight coefficient table for the first day of each month during the growth period;

Step 3.5.2, read the weight coefficient table of the first day of each month, obtain the weight v _m of the first day of the current month and the weight v _m+1 of the first day of the next month, and then calculate the weight v _m of the first day of the current month and the weight of the first day of the next month v Substituting _m+1 into formula (10), the daily weight v _i is calculated:

${\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&CenterDot;</span>{\mathrm{<span\; class="notranslate">\; log</span>}}_{\mathrm{<span\; class="notranslate">\; 31</span>}}^{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 10</span>}<span\; class="notranslate">\; )</span>$

Among them, n is the order of the day within the month, n=i;

Step 3.5.3, read the pre-stored solar radiation G, substitute the solar radiation G and the daily weight v _i into formula (11), and calculate the solar photosynthetic yield potential h _i :

h _i =v _i ·G(11).

Preferably, in step 3.5.1, see Table 2 for the established weight coefficient table for the first day of each month:

Table 2 Cotton photosynthetic potential month first day weight

Among them, v ₄ , v ₅ , v ₆ , v ₇ , v ₈ , v ₉ and v ₁₀ are respectively the weight of the first day of April, the weight of the first day of May, the weight of the first day of June, the weight of the first day of July, and the weight of the first day of August. First day weight, September first day weight and October first day weight.

The analysis method of crop climatic yield potential provided by the invention has the following advantages:

(1) The daily water demand, daily water supply and demand balance and daily precipitation correction coefficient calculated by using function approximation are more in line with the actual situation of daily water demand in the process of crop growth;

(2) The total climate yield potential obtained through the accumulation of daily climate yield potential can reflect the formation process of crop climate yield potential, accurately reflect the accumulation state of crop dry matter, and improve the accuracy and objectivity of climate yield potential.

Description of drawings

Fig. 1 is the comparative graph of the moisture correction system that traditional method and the inventive method form;

Fig. 2 is the zoning map of cotton climate yield potential provided by the present invention;

Fig. 3 is the zoning map of the cotton light-temperature yield potential provided by the present invention;

Fig. 4 is the cotton photosynthetic yield potential zoning map provided by the present invention;

Fig. 5 is the changing figure of 1971-1975 cotton climate yield potential provided by the present invention;

Fig. 6 is a change map of the cotton climate yield potential in 2002-2006 provided by the present invention.

detailed description

In order to make the technical problems, technical solutions and beneficial effects solved by the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

The present invention provides a kind of analysis method of crop climate yield potential, comprises the following steps:

Step 1, constructing a crop climate yield potential analysis model, the crop climate yield potential analysis model is expressed as:

Y _w ＝ΣY _wi (1)

in:

Y _wi =r _i ·y _i (2)

${\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; log</span>}}_{\mathrm{<span\; class="notranslate">\; 25</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; |</span><span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; )</span>$

p _i =0.6p _i-1 -w _i +d _i (4)

${\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&CenterDot;</span>{\mathrm{<span\; class="notranslate">\; log</span>}}_{\mathrm{<span\; class="notranslate">\; 31</span>}}^{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 5</span>}<span\; class="notranslate">\; )</span>$

where: Y _w - the total climate yield potential over the length of the analysis period;

Y _wi - the daily climate yield potential over the length of the analysis period;

r _i - daily precipitation correction coefficient;

y _i - solar temperature yield potential;

p _i - balance of daily water supply and demand;

p _i-1 - yesterday's water supply and demand balance;

w _i - daily water demand;

w _m - water demand on the first day of the month;

w _m+1 - water demand on the first day of the next month;

n- the date sequence within the month, the maximum value shall not exceed 31;

d _i - the actual precipitation of the day;

Step 2, when it is necessary to analyze the total climate yield potential corresponding to the specified length of time in the cotton growth period, input the start time data and the end time data into the climate yield potential analysis model constructed in step 1;

Step 3: After the climate yield potential analysis model receives the starting time data and the ending time data, record any day in the time period from the starting time to the ending time as the i-th day of month j, and then read the pre-stored first day of the month The daily water demand w _m and the value of the water demand w _m+1 on the first day of the next month are substituted into the formula (5) to calculate the daily water demand w _i ; where, n=i;

In this step, the pre-stored water demand values on the first day of each month are shown in Table 1:

Table 1 Cotton water demand on the first day of each month

Among them, since the growth period of cotton is from May to September, Table 1 only stores the water demand on the first day of each month from May to September.

Step 4, read the actual precipitation d _i of the day and the balance of water supply and demand p _i-1 yesterday, and substitute the daily water demand w _i , the actual precipitation d _i of the day and the balance of water supply and demand p _i-1 of yesterday into the formula (4 ), calculate the daily water supply and demand balance p _i ; here, for the first day of the analyzed time period, let yesterday’s water supply and demand balance be 0;

Step 5, substituting the daily water supply and demand balance p _i into the formula (3), and calculating the daily precipitation correction coefficient r _i ;

Step 6, read the pre-stored solar temperature yield potential y _i , substitute the solar temperature yield potential y _i and the daily precipitation correction coefficient r _i into formula (2), and calculate the daily climate yield potential Y _wi ;

Step 7, repeat step 3-step 6, calculate the daily climate yield potential of each day in the analyzed time period, and then, based on the formula (1), sum the calculated daily climate yield potential of each day, that is, get Total climate yield potential for the time period analyzed;

Step 8, output the total climate yield potential in the analyzed time period obtained in step 7.

For example, when it is necessary to calculate the total climate yield potential of a certain region during the period from May 1, 2015 to September 1, 2015, it is only necessary to input the start time of May 1, 2015, and the end time of September 2015 into the crop climate yield potential analysis model. 1 These two data, the crop climate yield potential analysis model can automatically calculate the total climate yield potential in this interval. The specific calculation method is:

(1) For any day, such as 2015.8.3, first calculate the daily water demand through formula 5.

8.3 Daily water demand on this day = 2015.8.1 water demand + (2015.9.1 water demand - 2015.8.1 water demand)

It can be seen from the above formula that when formula 5 is used to calculate the daily water demand, for the daily water demand of each day in the same month, starting from the first day of the current month, the daily water demand gradually increases in the form of a function parabola, and continues to approach the first day of the next month daily water demand. This calculation method is more in line with the actual demand for water demand in the cotton growth stage.

(2) Then, calculate the daily water supply and demand balance on August 3, 2015, namely:

The daily water supply and demand balance on 2015.8.3 = 0.6 (the daily water supply and demand balance on 2015.8.2) - the daily water demand on 2015.8.3 + the actual daily precipitation on 2015.8.3.

(3) Use Formula 3 to calculate the daily precipitation correction coefficient on August 3, 2015.

(4) Substituting the daily sunlight temperature yield potential and daily precipitation correction coefficient of 2015.8.3 into formula 2, the daily climate yield potential of 2015.8.3 was calculated.

(5) For the time period from 2015.5.1 to 2015.9.1, the above method is used to calculate the daily climate yield potential of each day, and then summed to obtain the total climate yield potential of this time period.

Under the same water supply situation, if the calculation method of the traditional moisture correction coefficient is adopted, the broken line curve in Fig. 1 can be obtained; and if the method of the present invention is adopted, the daily precipitation correction coefficient is in the form of a parabola in Fig. 1 . Observing Figure 1, it can be seen that the traditional method has three disadvantages: first, it uses large-scale precipitation, such as monthly and ten-day precipitation instead of daily precipitation, which blocks the possibility of real-time tracking and calculation of yield potential; 2. The accumulation of precipitation in large stages is equivalent to using the average precipitation, which does not reflect the evolution of the water supply state at all, which is inconsistent with the environment of crop dry matter production and accumulation; 3. Use threshold intervals to divide fixed correction coefficients, such as fixing a value interval within the interval The external uniformity is 0, which does not conform to the actual accumulation of crop dry matter production. However, the improved method of the present invention completely overcomes the above three defects, and can truly reflect the change of yield potential brought about by the change of water supply environment.

The analysis method of crop climate yield potentiality provided by the present invention, innovation point is as follows:

(1) The daily water demand calculated by using the function approximation is more in line with the actual situation of the daily water demand in the process of crop growth;

(2) Approximate calculation of the daily water supply and demand balance, and the calculation results are more in line with the actual situation of the daily water supply and demand balance in the process of crop growth;

(3) Approximate calculation of the damage caused by moisture deviation from the optimal state, that is, the actual calculation of daily precipitation correction coefficient;

(4) The total climate yield potential obtained through the accumulation of daily climate yield potential can reflect the formation process of crop climate yield potential, accurately reflect the accumulation state of crop dry matter, and improve the accuracy and objectivity of climate yield potential.

(5) Since the daily climate yield potential can be calculated, it is convenient for automatic applications such as daily real-time calculation and monitoring;

(6) Applicable to fields such as crop yield monitoring, agricultural production resource assessment, resource evolution and early warning.

In addition, the present invention has the following two major innovations:

Innovation 1: Sunlight temperature yield potential

In the above step 3, the read solar temperature yield potential _yi is obtained by the following method:

Step 3.1, read the pre-stored temperature of the three base points of the month to be analyzed, which are the suitable temperature T ₁ , the lowest temperature T ₂ that can be tolerated, and the highest temperature T ₃ that can be tolerated in the current period of cotton; then read the actual daily average air temperature T _i ;

Step 3.2, substituting T _1, T ₂ , T ₃ and T _i into the formula (6) to calculate the daily temperature correction factor F _i ;

${\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>\frac{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span>{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; B</span>}}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span>{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; B</span>}}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 6</span>}<span\; class="notranslate">\; )</span>$

Wherein, B=(T ₃ -T ₁ )/(T ₁ -T ₂ );

Step 3.3, read the pre-stored daily maximum actual temperature T _u , substitute the daily maximum actual temperature T _u into the formula (7), and calculate the daily maximum temperature correction coefficient V _i ;

V _i ＝(1-l _i ² ) ²⁵ -29l _i ²

in:

${\mathrm{<span\; class="notranslate">\; l</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; log</span>}}_{\mathrm{<span\; class="notranslate">\; 26</span>}}^{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; u</span>}}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; log</span>}}_{\mathrm{<span\; class="notranslate">\; 26</span>}}^{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; u</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 7</span>}<span\; class="notranslate">\; )</span>$

In step 3.4, the daily maximum temperature correction coefficient V _i and the daily temperature correction coefficient F _i are substituted into the formula (8), and the daily correction coefficient t _i of the light-temperature yield potential is calculated;

t _i =0.3V _i +0.7F _i (8)

Step 3.5, read the pre-stored daily photosynthetic yield potential h _i , substitute t _i and h _i into formula (9), and calculate the solar temperature yield potential y _i ;

y _i =t _i ·h _i (9).

Innovation 2: Sunsynthetic Yield Potential

In step 3.5, the read potential photosynthetic yield h _i is obtained by the following method:

Step 3.5.1, establishing the weight coefficient table for the first day of each month during the growth period;

In step 3.5.1, see Table 2 for the established weight coefficient table for the first day of each month:

Table 2 Cotton photosynthetic potential month first day weight

Among them, v ₄ , v ₅ , v ₆ , v ₇ , v ₈ , v ₉ and v ₁₀ are respectively the weight of the first day of April, the weight of the first day of May, the weight of the first day of June, the weight of the first day of July, and the weight of the first day of August. First day weight, September first day weight and October first day weight.

Step 3.5.2, read the weight coefficient table of the first day of each month, obtain the weight v _m of the first day of the current month and the weight v _m+1 of the first day of the next month, and then calculate the weight v _m of the first day of the current month and the weight of the first day of the next month v Substituting _m+1 into formula (10), the daily weight v _i is calculated:

${\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Center\; Dot;</span>{\mathrm{<span\; class="notranslate">\; log</span>}}_{\mathrm{<span\; class="notranslate">\; 31</span>}}^{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 10</span>}<span\; class="notranslate">\; )</span>$

Among them, n is the order of the day within the month, n=i;

Step 3.5.3, read the pre-stored solar radiation G, substitute the solar radiation G and the daily weight v _i into formula (11), and calculate the solar photosynthetic yield potential h _i :

h _i =v _i ·G(11).

Test example 1

For the Huang-Huai-Hai area, the daily climate data of 51 stations from 1971 to 2006 were collected, and the light-temperature yield potential, photosynthetic yield potential and climate yield potential of each station were calculated, and then the remaining 383 were calculated based on the reciprocal distance and the principle of similarity. The light-temperature yield potential, photosynthetic yield potential and climate yield potential of county-level administrative regions. The calculation results were plotted with ArcMap to form the corresponding zoning map. The results are shown in Fig. 2, Fig. 3 and Fig. 4, which are respectively the zoning map of cotton climate yield potential, the zoning map of cotton light-temperature yield potential, and the zoning map of cotton photosynthetic yield potential.

Observing Figure 2, Figure 3 and Figure 4, it can be seen that the minimum value of climate yield potential in the region is 1506kg·hm ^-2 , and the maximum value is 1874kg·hm ^-2 , with little difference. The overall trend of the climate yield potential value is gradually increasing from southwest to northeast except for the Jiaodong Peninsula, which is similar to the change trend of the photothermal yield potential zoning map and the photosynthetic yield potential zoning map, and is consistent with the actual climate yield change trend, thus verifying that The accuracy of the climate yield potential analysis method provided by the present invention is guaranteed.

Test example 2

According to the daily meteorological data of the Huang-Huai-Hai area in the period of 1971-2006, the climate yield potential in the two time periods of 1971-1975 and 2002-2006 was calculated respectively, and the evolution of the climate yield potential in these two time periods was compared and analyzed , as shown in Figure 5, is the change map of cotton climate yield potential from 1971 to 1975; as shown in Figure 6, it is the change map of cotton climate yield potential from 2002 to 2006.

Comparing Figure 5 and Figure 6, it can be seen that the highest climatic yield potential area in the Huang-Huai-Hai region shifts to the northeast. The best climate yield potential value dropped from 2042kg·hm ^-2 in 1971-1975 to 1930kg·hm ^-2 in 2002-2006, and the minimum climate yield potential dropped from 1479kg·hm ^-2 in 1971-1975 to 2002 -1328kg·hm ^-2 in 2006, down 5.5% and 10.2% respectively. Among them, the maximum value of ground radiation decreased by 8.6%, and the minimum value decreased by 12.8%. The maximum value of photosynthetic yield potential decreased by 7.6%, and the minimum value decreased by 17.2%. The maximum value of photothermal yield potential decreased by 10.4%, and the minimum value decreased by 13.4%. Judging from the regional changes in production potential, it is basically consistent with the evolution trend of the actual cotton production layout.

The research on the climate yield potential of cotton in the Huang-Huai-Hai region can not only effectively evaluate the regional climate and ecological suitability, but also deeply reveal the degree of influence of different climatic factors on production in the region, so as to make a contribution to the development of regional cotton production and the evolution of production layout. Forward-looking assessment plays an important guiding role in national agriculture and cotton production.

The above is only a preferred embodiment of the present invention, it should be pointed out that for those skilled in the art, without departing from the principle of the present invention, some improvements and modifications can also be made, and these improvements and modifications are also It should be regarded as the protection scope of the present invention.

Claims (5)

1. an analytical method of crop climate yield potential, is characterized in that, comprises the following steps: Step 1, constructing a crop climate yield potential analysis model, the crop climate yield potential analysis model is expressed as: Y _w ＝ΣY _wi (1) in: Y _wi =r _i ·y _i (2) ${\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; log</span>}}_{\mathrm{<span\; class="notranslate">\; 25</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; |</span><span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; )</span>$ p _i =0.6p _i-1 -w _i +d _i (4) ${\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&CenterDot;</span>{\mathrm{<span\; class="notranslate">\; log</span>}}_{\mathrm{<span\; class="notranslate">\; 31</span>}}^{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 5</span>}<span\; class="notranslate">\; )</span>$ where: Y _w - the total climate yield potential over the length of the analysis period; Y _wi - the daily climate yield potential over the length of the analysis period; r _i - daily precipitation correction coefficient; y _i - solar temperature yield potential; p _i - balance of daily water supply and demand; p _i-1 - yesterday's water supply and demand balance; w _i - daily water demand; w _m - water demand on the first day of the month; w _m+1 - water demand on the first day of the next month; n- the date sequence within the month, the maximum value shall not exceed 31; d _i - the actual precipitation of the day; Step 2, when it is necessary to analyze the total climate yield potential corresponding to the specified length of time in the cotton growth period, input the start time data and the end time data into the climate yield potential analysis model constructed in step 1; Step 3: After the climate yield potential analysis model receives the starting time data and the ending time data, record any day in the time period from the starting time to the ending time as the i-th day of month j, and then read the pre-stored first day of the month The daily water demand w _m and the value of the water demand w _m+1 on the first day of the next month are substituted into the formula (5) to calculate the daily water demand w _i ; where, n=i; Step 4, read the actual precipitation d _i of the day and the balance of water supply and demand p _i-1 yesterday, and substitute the daily water demand w _i , the actual precipitation d _i of the day and the balance of water supply and demand p _i-1 of yesterday into the formula (4 ), calculate the daily water supply and demand balance p _i ; here, for the first day of the analyzed time period, let yesterday’s water supply and demand balance be 0; Step 5, substituting the daily water supply and demand balance p _i into the formula (3), and calculating the daily precipitation correction coefficient r _i ; Step 6, read the pre-stored solar temperature yield potential y _i , substitute the solar temperature yield potential y _i and the daily precipitation correction coefficient r _i into formula (2), and calculate the daily climate yield potential Y _wi ; Step 7, repeat step 3-step 6, calculate the daily climate yield potential of each day in the analyzed time period, and then, based on the formula (1), sum the calculated daily climate yield potential of each day, that is, get Total climate yield potential for the time period analyzed; Step 8, output the total climate yield potential in the analyzed time period obtained in step 7. 2. the analysis method of crop climatic yield potential according to claim 1, is characterized in that, in step 3, the value of the first day water demand of each month of pre-stored is shown in Table 1: Table 1 Cotton water demand on the first day of each month Among them, since the growth period of cotton is from May to September, Table 1 only stores the water demand on the first day of each month from May to September. 3. the analysis method of crop climatic yield potential according to claim 1, is characterized in that, in step 3, the daylight temperature yield potential y that _reads is obtained by the following method: Step 3.1, read the pre-stored temperature of the three base points of the month to be analyzed, which are the suitable temperature T ₁ , the lowest temperature T ₂ that can be tolerated, and the highest temperature T ₃ that can be tolerated in the current period of cotton; then read the actual daily average air temperature T _i ; Step 3.2, substituting T _1, T ₂ , T ₃ and T _i into the formula (6) to calculate the daily temperature correction factor F _i ; ${\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>\frac{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span>{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; B</span>}}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span>{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; B</span>}}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 6</span>}<span\; class="notranslate">\; )</span>$ Wherein, B=(T ₃ -T ₁ )/(T ₁ -T ₂ ); Step 3.3, read the pre-stored daily maximum actual temperature T _u, substitute the daily maximum actual temperature T _u into the formula (7), and calculate the daily maximum temperature correction coefficient V _i; V _i ＝(1-l _i ² ) ²⁵ -29l _i ² in: ${\mathrm{<span\; class="notranslate">\; l</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; log</span>}}_{\mathrm{<span\; class="notranslate">\; 26</span>}}^{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; u</span>}}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; log</span>}}_{\mathrm{<span\; class="notranslate">\; 26</span>}}^{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; u</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 7</span>}<span\; class="notranslate">\; )</span>$ In step 3.4, the daily maximum temperature correction coefficient V _i and the daily temperature correction coefficient F _i are substituted into the formula (8), and the daily correction coefficient t _i of the light-temperature yield potential is calculated; t _i =0.3V _i +0.7F _i (8) Step 3.5, read the pre-stored daily photosynthetic yield potential h _i , substitute t _i and h _i into formula (9), and calculate the solar temperature yield potential y _i ; y _i =t _i ·h _i (9). 4. the analytical method of crop climatic yield potential according to claim 3, is characterized in that, in step 3.5, read the day photosynthetic yield potential hi _obtained by following method: Step 3.5.1, establishing the weight coefficient table for the first day of each month during the growth period; Step 3.5.2, read the weight coefficient table of the first day of each month, obtain the weight v _m of the first day of the current month and the weight v _m+1 of the first day of the next month, and then calculate the weight v _m of the first day of the current month and the weight of the first day of the next month v Substituting _m+1 into formula (10), the daily weight v _i is calculated: ${\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&CenterDot;</span>{\mathrm{<span\; class="notranslate">\; log</span>}}_{\mathrm{<span\; class="notranslate">\; 31</span>}}^{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 10</span>}<span\; class="notranslate">\; )</span>$ Among them, n is the order of the day within the month, n=i; Step 3.5.3, read the pre-stored solar radiation G, substitute the solar radiation G and the daily weight v _i into formula (11), and calculate the solar photosynthetic yield potential h _i : h _i =v _i ·G(11). 5. the analysis method of crop climatic yield potential according to claim 4, is characterized in that, step 3.5.1, the weight coefficient table of the first day of each month that establishes is shown in Table 2: Table 2 Cotton photosynthetic potential month first day weight Among them, v ₄ , v ₅ , v ₆ , v ₇ , v ₈ , v ₉ and v ₁₀ are respectively the weight of the first day of April, the weight of the first day of May, the weight of the first day of June, the weight of the first day of July, and the weight of the first day of August. First day weight, September first day weight and October first day weight.