CN111681122A - Construction and application of summer corn drought influence evaluation model based on soil humidity - Google Patents
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Abstract
The invention discloses construction and application of a summer corn drought influence assessment model based on soil humidity. The method aims to solve the technical problem that the influence of drought on the yield of summer corns cannot be quickly and accurately evaluated at low cost by the conventional method. The method utilizes the separated summer corn meteorological output and the relative soil humidity from the first 6 to the last 9 months of the corresponding year to carry out statistical analysis, and establishes a quantitative relation model between the corn yield reduction rate and the automatic soil moisture data. Aiming at the summer corn growth key period, the method can realize the drought influence assessment by ten days, is convenient to apply in agricultural meteorological service, can provide a new thought for the intelligent decision-making diagnosis of corn production in the summer corn growing area in Henan, provides guidance and suggestion for agricultural production decision-making, can promote the efficiency of agricultural production, and avoids unnecessary loss.
Description
Technical Field
The invention relates to the technical field of agricultural disaster assessment, in particular to construction and application of a summer corn drought influence assessment model based on soil humidity.
Background
Corn is one of the main grain crops in China, plays a very important role in national economic development, and Henan is the province where summer corn is planted, the seeding area is 330 more than ten thousand hectares, and the total yield is nearly 1760 thousand tons.
In China, meteorological disasters are important factors causing the reduction of the yield of corns. Among them, drought is one of the meteorological disasters that have a great influence on agricultural production, and drought disaster assessment is also one of the difficult problems in the field of agricultural meteorology. At present, methods for evaluating agricultural meteorological disasters at home and abroad mainly comprise a field test method, a mathematical statistics model method, a crop simulation model method and the like.
The field test method is that in the growth period of crops, environmental factors such as light, temperature, water and the like are manually changed to enable the crops to experience different disaster degrees, and then growth and development indexes and yield changes after the crops experience different disaster degrees are analyzed, so that losses caused by different disaster degrees are determined; however, the method has a long test period, and weather conditions fluctuate greatly every year, so that it is difficult to accurately control the test level to a designed disaster test level, and the test conditions usually have a certain difference from the actual crop field growth environment, and the related correction is required during application.
The mathematical statistical model method is generally used for evaluating the influence by analyzing the relationship between disaster factors, disaster degrees and the like and crop yield or growth and development factors, and an agricultural meteorological disaster mathematical statistical evaluation model is established by utilizing multiple mathematical methods such as regression analysis, a fuzzy mathematical method, gray cluster analysis, an analytic hierarchy process, a BP neural network and the like to qualitatively or quantitatively evaluate the agricultural meteorological disasters; however, the method is difficult to accurately and quantitatively separate the influence of a single disaster, and has certain limitations.
With the rapid development of computers and information technologies and further deep theoretical understanding of crop disaster mechanisms, crop models make great progress in quantitative assessment of agricultural meteorological disasters. The crop growth model has the advantages of strong mechanicalness in agricultural meteorological disaster assessment, can better reflect the relation between the crop growth process and yield and the temperature, precipitation and soil moisture dynamic state of each growth stage, and realizes dynamic decision and climate strain management under different ages. Before application, the model needs to be subjected to parameter localization and verification by using fine field test data.
Disclosure of Invention
The invention aims to solve the technical problem that the influence of drought on the yield of summer corn cannot be quickly, accurately and inexpensively evaluated by the conventional method.
In order to solve the technical problems, the invention adopts the following technical scheme:
a construction method of a summer corn drought influence assessment model based on soil humidity is designed, and comprises the following steps:
(1) arranging a real-time soil moisture monitor in a test field or region, and acquiring average soil relative humidity data in each ten days from 7 months to 8 months during the growth and development period of summer corns for a plurality of years;
(2) simultaneously measuring or acquiring the data of the yield per unit area of the summer corn of the corresponding year in the test place or area, separating the corn yield into a trend yield and a meteorological yield by using a moving average method, and comparing the meteorological yield with the trend yield to obtain a relative meteorological yield;
(3) forming corresponding data samples by the average soil relative humidity data obtained in the step (1) and the relative meteorological output obtained in the step (2), and screening out the data samples with the average soil humidity of less than 75% in ten days to form a new sample sequence;
(4) and (4) performing linear regression on the sample sequence obtained in the step (3) to establish a quantitative relation model between the average soil relative humidity and the relative meteorological output of summer corn in each ten days of 7-8 months.
After the step (4), further comprising: and respectively solving corresponding soil humidity critical values when the yield is reduced by 10% and 20% according to the quantitative relation model so as to guide the water management of the summer corns or evaluate the influence of drought on the yield of the summer corns.
Based on sample data accumulated in the Henan province from 2011 to 2017, the following quantitative relation model is established:
wherein Y is relative meteorological output, and X is the average soil relative humidity in 0-50cm of soil layer.
Determining the following drought and disaster critical indexes of summer corn in the key period according to the quantitative relation model:
in the step (1), the average relative humidity of the soil is the average in the soil layer of 0-50 cm.
A summer corn drought influence assessment method based on soil humidity comprises the following steps:
(1) arranging a real-time soil moisture monitor on a to-be-evaluated or monitored ground, acquiring relative humidity data of 0-50cm of soil day by day, and calculating an average value every ten days in 7-8 months according to the development period of corn;
(2) substituting the obtained value into the summer corn drought influence evaluation model established in claim 1 when the obtained ten-day average soil relative humidity is less than or equal to 75% to obtain the meteorological yield, and if the value is a negative value, determining that the yield is reduced due to drought meteorological factors; and when the average soil relative humidity in ten days is more than 75%, determining that drought disaster does not occur.
Aiming at the Henan summer corn planting area, the yield reduction range or the yield reduction range of the summer corn is judged according to the following drought and disaster critical indexes of the summer corn in the key period:
compared with the prior art, the invention has the main beneficial technical effects that:
1. the method can quickly and accurately evaluate the influence of drought on the corn yield in the Henan Xia corn planting area, can provide a new idea for intelligent decision-making diagnosis of corn production in the Henan Xia corn planting area, provides guidance and suggestions for agricultural production decisions, can promote the efficiency of agricultural production, and avoids unnecessary loss.
2. According to the quantitative drought influence assessment method established by the invention, the required data is only real-time automatic soil moisture observation data, and nearly 500 sets of automatic soil moisture meters are installed in Henan province at present, so that the data is convenient and easy to obtain; therefore, the method is not limited by data in popularization and application range.
3. Aiming at the summer corn growth key period, the method can realize drought influence assessment by ten days, is convenient to apply to agricultural meteorological service, and can provide powerful technical support for developing summer corn drought influence quantitative assessment business service.
Drawings
Fig. 1 is a schematic diagram showing the distribution of automatic soil moisture meters installed in cities and places in the south of the river.
Fig. 2 is a graph showing the average soil humidity change trend in ten days of the whole province in Henan in 2011 + 2018.
Fig. 3 is a graph showing the average soil humidity change trend in ten days of the whole province in Henan in 2011 + 2018.
FIG. 4 is a spatial distribution diagram of the average soil moisture in 7-8 Yue in 2011 of Henan province.
FIG. 5 is a spatial distribution diagram of the average soil moisture in each of 7-8 months in 2014 of Henan province.
FIG. 6 is a diagram of annual variation trend of summer corn yield of a representative agricultural weather observation station in Henan province.
FIG. 7 is a graph of relative meteorological yield versus soil relative humidity (all samples).
FIG. 8 is a graph of relative meteorological production versus soil relative humidity (negative sample of relative meteorological production).
FIG. 9 is a graph of relative meteorological output versus relative soil humidity for each ten days (samples with soil humidity below 75%).
FIG. 10 is a graph of relative meteorological output plotted against average soil relative humidity in 7-8 months (samples with soil humidity below 75%).
Detailed Description
The following examples are intended to illustrate the present invention in detail and should not be construed as limiting the scope of the present invention in any way.
The test apparatus and equipment referred to in the following examples are conventional in the art unless otherwise specified; the test method and the statistical method are conventional methods unless otherwise specified.
The first embodiment is as follows: technology and data base for early stage test research
1. Data source and data processing
Automatic soil moisture monitors (see figure 1) are installed and distributed in stations and places such as county-level meteorological stations, agricultural meteorological stations, high-standard grain fields and the like of the whole province from 2009 in Henan province, so that real-time dynamic monitoring of soil moisture of farmlands is realized. Nearly 300 sets of instruments are put into service application through service test collection in 2011, and a large amount of farmland soil moisture observation data is accumulated through operation for over a decade. Therefore, soil moisture data are further mined and analyzed, the application value of the soil moisture data in agricultural meteorological service in Henan province can be fully exerted, and the effectiveness and pertinence of service products can be improved. The research project of the invention aims at the accumulated data of the agricultural gas observation station, in which the automatic soil moisture data correspond to the corn yield of the observation area one by one, and establishes the corn drought influence assessment method based on the automatic soil moisture by analyzing the correlation between the soil moisture and the yield of the corn in different growth stages, thereby providing powerful scientific and technological support for developing the summer corn drought influence quantitative assessment.
Extracting the day-by-day 0-50cm relative humidity data of each station from 2011 to 2017 from an automatic soil moisture database, and calculating the ten-day average value of 6-9 months according to the development period of the corns. Meanwhile, the yield per unit area of the summer corn corresponding to the summer corn agricultural meteorological observation station is selected as analysis data, and the consistency of automatic soil moisture data and yield data is guaranteed. The corn yield was separated into trend and meteorological yields using a sliding average method. And carrying out statistical analysis by using the separated meteorological yield of the 2011-plus 2017 summer corn and the relative humidity of the soil from the first 6 to the last 9 of the corresponding year, and establishing a quantitative relation between the corn yield reduction rate and the automatic soil moisture data.
2.2011-2018 year corn season farmland soil moisture space-time change
Fig. 2 shows the dynamic time variation of the average soil humidity in the late days of 2011 + 2018 to the full-province of 8 months in the Henan province, which can clearly show the annual time variation of the soil humidity in each ten days, severe summer drought occurs in the Henan province in 2014, the soil humidity in each 8 month in 7 months is the lowest annual time value, the average soil humidity in each 7 month in 2012, 2013 and 2016 is higher, the average soil humidity in each 8 month in 2011 is the highest, the rainfall in the summer and autumn of the Henan province in 2011 is more in the same period than that in the same period in the same year, and the soil humidity is also significantly higher than that in other years.
According to the automatic soil moisture monitoring results in recent years, the relative humidity of the soil is relatively close to each other in every ten days, the variation range is 76.3% -78.7%, the relative humidity is higher in the middle of 7 months and 8 months, and the average value is 78.7% and 78.5%. The fluctuation of the average soil humidity in each ten days is larger between the annual periods, particularly in the middle 7 th month, and in the third 7 th and 8 th months, the fluctuation is larger than that in the other ten days, the fluctuation in the last 7 th month is the largest, the minimum value is 67.6%, and the maximum value is 84.4%; the fluctuation ranges of the last 7 th and the middle 8 th of the month are relatively small; see figure 3 for details.
FIGS. 4 and 5 show spatial distribution diagrams of average soil moisture in two typical year's ten days, which can reflect the time-space dynamic change of soil moisture. 2011 is a year with relatively sufficient soil moisture, especially in 8 months, because of much rainfall, the soil humidity in most regions of the whole province is more than 80%, wherein the range of the soil humidity is widest with more than 80% in the last ten days of 8 months. 2014 is a typical drought year, the distribution area between 60% and 80% is the largest, but the relative humidity of the soil in the middle and western regions is below 50%, and the duration is slightly relieved from 7 months to 8 months.
3. Summer maize yield variation
The change in summer corn yield per unit of time since 1980 is given in fig. 6 for several typical agricultural gas observatories in the south of Henan province. The 5 year moving average trend yields all appear as a trend that rises in the fluctuation. But the actual yield fluctuates greatly between years.
4. Decomposition of yield trend of summer corn
The factors influencing the final yield of the corn are many, and the restriction relationship among the factors is also complicated. These influencing factors can be divided into three main categories, meteorological conditions, agricultural measures and random "noise". The agricultural measures include farming, fertilizing, pest control, variety characteristics and yield increasing measures, and reflect the social production development level in a certain historical period. The corresponding yield component is referred to as the time-technical trend yield, shortly called the trend yield (y)t). In addition to the random error generated in the general statistics, the random "noise" term (Δ y) also includes the influence of other accidental factors that are not considered by the first two types of factors in the specific operation mode, and its general formula can be expressed as follows:
y＝yt+yw+Δy；
in the above formula, y is the summer corn yield; y iswMeteorological component, also known as meteorological yield, which is the yield; the term "noise" Δ y is usually ignored in the actual calculation and is not considered because it is small. Thus, equation (1) is often simplified to:
y＝yt+yw；
the trend yield is simulated by a linear moving average method. The linear moving average method is a very common method for simulating the yield, and the change of the time series of the yield in a certain stage is regarded as a linear function and is in a straight line. With the continuous sliding of the stages, the straight line continuously changes the position and slides backwards to reflect the continuous change of the historical evolution trend of the yield. And sequentially calculating regression models in each stage, wherein the mean value of the linear sliding regression analog values at each time point is the trend yield value. If the linear trend equation at a certain stage is:
yi(t)＝ai+bi(t)；
in the formula, i is n-k +1 and is the equation number, and k is the sliding step length; n is the number of sample sequences; t is a time sequence number, when i is 1, t is 1, 2, 3, …, k; when i is 2, t is 2, 3, 4, …, k + 1; … …, respectively; when i is n-k +1, t is n-k +1, n-k +2, n-k + 3, …, n. Calculating the function value y of each equation at the point ti(t) so that at each point t there are q function values, the number of q being related to n, q. When k is less than or equal to n/2, q is 1, 2, 3, …, k, … k, …, 3, 2, 1; the number of q is k in succession is equal to n-2 (k-1); when k > n/2, then q is 1, 2, 3, …, n-1+1, …, n-k +1, …, 3, 2, 1; the number of q successively being n-k +1 is equal to 2 k-n. Then, the average value of each function value at each point is calculated:
connecting points
I.e. the historical evolution trend of the production. The method is characterized in that the method depends on the value of k, and the influence of short-period fluctuation can be eliminated only when k is large enough. In general k can take 10 years or more.
After the trend yield is found, the meteorological yield is:
yw＝y-yt；
further relative transformation is carried out on the meteorological output:
the meteorological output is changed into a relative ratio, x is called relative meteorological output, is not influenced by the difference of different agricultural technical levels in historical periods, and has a physical meaning that the amplitude of the output fluctuation is comparable without being influenced by time and space.
Example II drought impact assessment model construction based on automatic soil moisture data
1. Analysis of all sample data
Firstly, the correlation between the relative humidity of the average soil of 0-50cm in each ten days from 7 months to 8 months and the meteorological output of the corn is analyzed by using all sample data, namely the meteorological output and the automatic soil moisture data of 17 corn agricultural gas observation stations 2011-2017, which is shown in figure 7. As can be seen from FIG. 7, the relative humidity of the soil in all samples ranges from 40% to 100%, and the relative meteorological output ranges from-90% to 51%, and although both have large variation ranges, there is no obvious correlation between the two in all the ten days. Therefore, the relationship between soil moisture and corn yield cannot be shown using all the sample data.
2. Negative relative weather yield sample analysis
For further analysis, samples with negative meteorological yields (i.e., yield reduction) were screened from all the sample data to form a new sample sequence, and the relationship between yield in the reduced year and automatic soil moisture was analyzed, see fig. 8. As can be seen from fig. 8, the correlation of the sample with a negative relative weather yield is slightly better than that of the whole sample, but the data points are still relatively dispersed. From the data distribution, it can be seen that although the meteorological output is negative, the relative humidity of the soil still has a wide variation range, which indicates that the factors causing the yield reduction are not all caused by water deficit, namely drought. As can be seen from the significance test results, the correlations in late 7 and early 8 months passed the significance test at a level of 0.05. Indicating a better correlation between soil moisture and yield in late 7 and early 8 months.
TABLE 1 correlation analysis results between negative relative meteorological yield samples and automated soil moisture
3. Sample analysis of soil moisture below 75%
The method is characterized in that the method comprises the steps of screening data samples with the average soil humidity lower than 75% in each day from all samples so as to eliminate the yield reduction caused by the overhigh soil relative humidity, wherein the data samples basically comprise samples with the soil relative humidity from proper to different drought degrees, and the influence of yield reduction caused by other adverse factors can be reduced as much as possible. The results of the analysis are shown in FIGS. 9-10 and Table 2 below. According to analysis results, the correlation between the relative humidity of the soil and the relative meteorological yield is remarkably improved after screening, and the correlations in other ten days pass significance tests except for the slightly higher significance test P values in the last 8 th month and the middle 8 th month. The method shows that the relative meteorological yield of the corn and the automatic soil moisture monitoring result have obvious linear correlation under the condition that the relative humidity of the soil is lower than 75%. Accordingly, a quantitative relationship model between the average soil relative humidity and the relative meteorological output of summer corn in each ten days of 7-8 months was established (see Table 2 below).
TABLE 2 correlation analysis results between relative meteorological yields and samples with soil moisture below 75%
Example three: summer corn critical period drought disaster critical index analysis
According to the evaluation model established in the second embodiment (the quantitative relationship between the average soil humidity and the yield reduction rate of summer corn in each ten days during the key growth period of summer corn in table 2), the critical soil humidity index causing the yield reduction of summer corn in each ten days can be further determined, as shown in table 3 below.
The growth period from 7 months to 8 months is the key growth period of jointing, emasculation and grouting of summer corn in Henan province, drought occurrence development monitoring is carried out in real time according to automatic soil moisture data, and the method has important production guidance value for effectively defending drought. The change of the critical value of drought disaster and yield reduction determined by the automatic soil moisture data in every 7-8 months is about 60 percent, and the result is consistent with the common consensus of the predecessors. But also reflects the difference of the critical indexes among different ten days. In addition, the present example provides corresponding soil humidity critical values when yield is reduced by 10% and 20% respectively according to the yield reduction rate relation model, and the service can preliminarily judge the drought influence condition according to the index value so as to grasp the influence of drought on yield in time.
TABLE 3 average soil humidity (%) -in the critical period of drought disaster in summer maize
While the present invention has been described in detail with reference to the drawings and the embodiments, those skilled in the art will understand that various specific parameters in the embodiments can be changed without departing from the technical concept of the present invention, and a plurality of specific embodiments are formed, which are common variation ranges of the present invention, and thus, detailed description thereof is omitted.
Claims (7)
1. A method for constructing a summer corn drought influence assessment model based on soil humidity is characterized by comprising the following steps:
(1) arranging a real-time soil moisture monitor in a test field or region, and acquiring average soil relative humidity data in each ten days from 7 months to 8 months during the growth and development period of summer corns for a plurality of years;
(2) simultaneously measuring or acquiring the data of the yield per unit area of the summer corn of the corresponding year in the test place or area, separating the corn yield into a trend yield and a meteorological yield by using a moving average method, and comparing the meteorological yield with the trend yield to obtain a relative meteorological yield;
(3) forming corresponding data samples by the average soil relative humidity data obtained in the step (1) and the relative meteorological output obtained in the step (2), and screening out the data samples with the average soil humidity of less than 75% in ten days to form a new sample sequence;
(4) and (4) performing linear regression on the sample sequence obtained in the step (3) to establish a quantitative relation model between the average soil relative humidity and the summer jade relative rice meteorological output in each ten days of the 7-8 months.
2. The method for constructing a soil moisture-based summer corn drought influence assessment model according to claim 1, further comprising after step (4):
and respectively solving corresponding soil humidity critical values when the yield is reduced by 10% and 20% according to the quantitative relation model so as to guide the water management of the summer corns or evaluate the influence of drought on the yield of the summer corns.
3. The method for constructing the summer corn drought influence assessment model based on soil humidity according to claim 1, wherein the following quantitative relationship model is established based on sample data accumulated in the Henan province from 2011 to 2017:
Wherein Y is relative meteorological output, and X is the average soil relative humidity in 0-50cm of soil layer.
4. The method for constructing a summer maize drought impact assessment model based on soil humidity according to claim 3, characterized in that the following summer maize critical drought disaster-causing indicators in the key period are determined according to the quantitative relationship model:
。
5. The method for constructing a soil humidity-based summer corn drought influence assessment model according to claim 1, wherein in step (1), the average soil relative humidity is the average within a soil horizon of 0-50 cm.
6. A summer corn drought influence assessment method based on soil humidity is characterized by comprising the following steps:
(1) arranging a real-time soil moisture monitor on a to-be-evaluated or monitored ground, acquiring relative humidity data of 0-50cm of soil day by day, and calculating an average value every ten days in 7-8 months according to the development period of corn;
(2) substituting the obtained value into the summer corn drought influence evaluation model established in claim 1 when the obtained ten-day average soil relative humidity is less than or equal to 75% to obtain the meteorological yield, and if the value is a negative value, determining that the yield is reduced due to drought meteorological factors; and when the average soil relative humidity in ten days is more than 75%, determining that drought disaster does not occur.
7. The method for assessing drought impact of summer maize based on soil humidity according to claim 6, wherein for the south of the Henan summer maize growing area, the summer maize yield reduction amplitude or range is determined according to the following critical drought-causing indicators for the key period of summer maize:
。
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Application publication date: 20200918 |