CN113935542A - Method for predicting cotton yield per unit based on climate suitability - Google Patents

Method for predicting cotton yield per unit based on climate suitability Download PDF

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CN113935542A
CN113935542A CN202111284532.9A CN202111284532A CN113935542A CN 113935542 A CN113935542 A CN 113935542A CN 202111284532 A CN202111284532 A CN 202111284532A CN 113935542 A CN113935542 A CN 113935542A
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suitability
cotton
days
kth
yield
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张立祯
高新程
陈泳帆
张泽山
张长波
罗艳
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Beijing Feihua Technology Co ltd
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Abstract

The invention provides a method for predicting cotton yield per unit based on climate suitability, and belongs to the field of agricultural weather. The method aims at the calculation of the climate suitability of county scale and the regression modeling of the climate suitability and the meteorological output, comprehensively considers the biological characteristics of the cotton, such as upper limit temperature, lower limit temperature, optimal temperature, light demand characteristic, water demand and the like, required by the growth and development of the cotton, quantifies the temperature, sunshine, moisture and climate suitability indexes of the cotton in different growth stages, and finally realizes the fine prediction of the cotton per unit yield. The invention can provide a cotton yield forecasting tool for relevant government departments and agricultural service enterprises, support agricultural meteorological disaster early warning and risk assessment, and enrich the yield forecasting research method for scientific researchers in the agricultural and meteorological fields.

Description

Method for predicting cotton yield per unit based on climate suitability
Technical Field
The invention belongs to the field of agricultural weather, and particularly relates to a method for predicting cotton yield per unit based on climate suitability.
Background
Xinjiang cotton production is concerned with the safety of the Chinese cotton industry. Xinjiang has been the main growing area and the important supply pillar of cotton in China. In the field of cotton planting, varieties, environments and management measures are closely related to cotton yield, but along with climate change and variety updating, the meteorological disasters in Xinjiang cotton areas are increasingly prominent. The low-temperature cold damage causes a great amount of bud bolls to fall off, so that the cotton yield in Xinjiang is greatly reduced. Therefore, the accurate prediction of the cotton yield is realized, and the contribution of each meteorological element to the cotton yield is determined, so that the method has important significance for breeding suitable varieties, adjusting crop layout and defending meteorological disasters.
The method for forecasting the cotton yield at home and abroad comprises an agronomic forecasting method and a statistical forecasting method. The agronomic forecasting method adopts a field sample investigation means to obtain the number of plants per unit area, the number of bolls formed by a single plant and the weight of bolls of cotton, and has the advantages of intuition and accuracy, but the method depends heavily on manual observation, and has high cost, few observation points and poor forecasting time efficiency. The statistical forecasting method carries out yield forecasting by establishing a statistical model of the relation between meteorological conditions and meteorological yields, and although the method is simple and practical, has longer forecasting time effect and can realize dynamic forecasting, the related relation model is directly established without considering the biological characteristics of cotton, which often causes the forecasting result to have limitation and instability.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a method for predicting the unit yield of cotton based on climate suitability.
In order to achieve the above purpose, the invention provides the following technical scheme:
a cotton yield prediction method based on climate suitability comprises the following steps:
establishing a plurality of sites, and collecting meteorological data, cotton yield data, cotton growth period data and current-year meteorological data of regions to be predicted in the past year;
decomposing the unit yield of the cotton of the year to be predicted into a trend yield and a meteorological yield; simulating the trend yield by using the cotton yield data of the area to be predicted over the years and adopting a linear moving average simulation method;
calculating the temperature suitability F (t) of the kth ten days in the cotton development period according to the meteorological data and the cotton growth period data of the area to be predictedk) Suitability for ten days F(s)k) Water suitability in Hex ten days F (w)k) (ii) a Temperature suitability F (t) in k ten daysk) Suitability for ten days F(s)k) Water suitability in Hex ten days F (w)k) Calculating the weather suitability F (c) of the kth ten daysk) (ii) a Using the weather suitability F (c) in the k ten daysk) Calculating to obtain the climate suitability index CSI of the kthk；
Performing linear regression analysis on meteorological data of a region to be predicted over the years and corresponding climate suitability indexes by taking ten days as a time unit, establishing a dynamic forecasting model of the meteorological output of cotton, and calculating the meteorological output;
and predicting the unit yield of the cotton according to the sum of the meteorological yield and the trend yield.
Preferably, the temperature suitability F (t) in the k-th day of the cotton development period is calculated by meteorological data of the area to be predictedk) The method comprises the following steps:
calculating the ith day average temperature t in the kth ten day by the following formulai，
Figure BDA0003332356310000021
Wherein, tlThe lowest temperature at the ith day in the kth dayhThe highest temperature of the ith day;
average temperature t of ith day in kthiSubstituting the formula to calculate the temperature suitability F (t) of the ith day in the kth dayi)，
Figure BDA0003332356310000022
Wherein, taThe highest temperature of the cotton in the development period of the kth day; t is tbThe lowest temperature of the cotton in the development period of the kth day; t is tcThe optimum temperature of cotton development in the development period of the cotton in the kth place;
calculating the arithmetic mean value of the daily temperature suitability in the kth day as the temperature suitability F (t) in the kth dayk)，
Figure BDA0003332356310000023
Wherein m is the total day number of the kth ten days.
Preferably, the calculation of the lighting suitability F(s) of the kth day in the cotton development period through the meteorological data of the area to be predictedk) Comprises the following steps:
calculating the day preference F(s) of the k-th day by the following formulak)：
Figure BDA0003332356310000031
Wherein s is the cumulative sunshine duration in the kth ten year to be predicted; h is the calendar year average value of the daily hours in the kth ten days.
Preferably, the calculation of the water fitness F (w) in the k-th ten days of the cotton development period through the meteorological data of the area to be predictedk) Comprises the following steps:
calculating the soil moisture content suitability F (w) of the cotton field in the ith day of the kth day by the following formulai)，
Figure BDA0003332356310000032
Wherein, wiIs an observed value of the relative humidity of the soil at a position of 20cm of the station, woIs a stand forThe relative humidity of soil is suitable for the k th development stage of the cotton in the station;
calculating the arithmetic mean value of the soil moisture content suitability degree in the k th ten days as the water suitability degree in the k th ten days,
Figure BDA0003332356310000033
wherein m is the total day number of the kth ten days.
Preferably, the temperature suitability F (t) in ten days of the kth dayk) Suitability for ten days F(s)k) Water suitability in Hex ten days F (w)k) Calculating to obtain the weather suitability F (c) of the k ten daysk) Comprises the following steps:
calculating the weather suitability F (c) of the k-th ten days by the following formulak)
Figure BDA0003332356310000034
Preferably, the climate suitability F (c) in the k-th ten days is usedk) Calculating to obtain the climate suitability index CSI of the kthkComprises the following steps:
the climate suitability F (c) in the k ten daysk) Normalizing the correlation coefficient with the meteorological influence index to obtain a standard value R of the correlation coefficient in the kth ten dayssk，
Figure BDA0003332356310000041
Wherein R iskWeather suitability in K ten days F (c)k) Correlation coefficient with weather influence index; rmaxThe climatic suitability F (c) in ten days of the cotton development periodk) A maximum value of the correlation coefficient with the weather influence index; rminThe climatic suitability F (c) in ten days of the cotton development periodk) A minimum value of the correlation coefficient with the weather influence index;
using the standard value R of the k-th coefficientskCalculating to obtain the firstWeight coefficient k of k ten daysk，
Figure BDA0003332356310000042
Using the weighting coefficient k passing through the kthkObtaining the climate suitability index CSI of the kthk，
CSIk＝∑[kk·F(ck)]。
Preferably, the trending yield is a contribution of agricultural production skill level to cotton yield, and the meteorological yield is a contribution of meteorological conditions to cotton yield.
Preferably, the method further comprises the following steps:
and carrying out correlation analysis on different weather suitability indexes of the area to be detected and the corresponding weather yield, and determining the correlation between the weather suitability indexes and the annual change of the weather yield.
Preferably, the meteorological data includes:
weather data of a national station of 30 years in history, including daily maximum air temperature, daily minimum air temperature, daily precipitation and sunshine hours;
meteorological data of the national station of the current year, including daily maximum air temperature, daily minimum air temperature, daily precipitation and sunshine hours;
the live meteorological data of the automatic station comprises daily maximum air temperature, daily minimum air temperature, daily precipitation and sunshine hours.
Preferably, the method is characterized by further comprising the step of performing missing processing on the meteorological data of the historical 30-year national station, and specifically comprising the following steps:
when the meteorological data of the national station has data loss below 5 continuous days, taking the average value of all the meteorological data within 2 days before and 2 days after the loss date as the meteorological data of the loss date;
when the meteorological data of the national station has data loss for more than 5 continuous days, the meteorological data of the loss date is taken as the average value of the historical synchronization;
and when the live meteorological data of the automatic station is deficient, adopting the synchronous data of the national station corresponding to the region to be supplemented.
The method for predicting the cotton yield per unit based on the climate suitability provided by the invention has the following beneficial effects: the method aims at the calculation of the climate suitability of county scale and the regression modeling of the climate suitability and the meteorological output, comprehensively considers the biological characteristics of the cotton, such as upper limit temperature, lower limit temperature, optimal temperature, light demand characteristic, water demand and the like, required by the growth and development of the cotton, quantifies the temperature, sunshine, moisture and climate suitability indexes of the cotton in different growth stages, and finally realizes the fine prediction of the cotton per unit yield. The invention can provide a cotton yield forecasting tool for relevant government departments and agricultural service enterprises, support agricultural meteorological disaster early warning and risk assessment, and enrich the yield forecasting research method for scientific researchers in the agricultural and meteorological fields.
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In order to more clearly illustrate the embodiments of the present invention and the design thereof, the drawings required for the embodiments will be briefly described below. The drawings in the following description are only some embodiments of the invention and it will be clear to a person skilled in the art that other drawings can be derived from them without inventive effort.
Fig. 1 is a schematic flow chart of a method for predicting cotton yield based on climate suitability in embodiment 1 of the present invention.
Detailed Description
In order that those skilled in the art will better understand the technical solutions of the present invention and can practice the same, the present invention will be described in detail with reference to the accompanying drawings and specific examples. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
Example 1
The invention provides a method for predicting cotton yield per unit based on climate suitability, which comprises the following steps of: establishing a plurality of stations, and collecting meteorological data, cotton yield data and cotton growth period data of the area to be predicted in the past year; decomposing the unit yield of the cotton of the year to be predicted into a trend yield and a meteorological yield; wherein the trend yieldThe method is a contribution of agricultural production technology level to cotton yield, and the meteorological yield is a contribution of meteorological conditions to cotton yield; simulating the trend yield by using the cotton yield data of the area to be predicted over the years and adopting a linear moving average simulation method; calculating the temperature suitability F (t) of the kth place in the cotton development period according to the meteorological data and cotton growth period data of the area to be predictedk) Suitability for ten days F(s)k) Water suitability in Hex ten days F (w)k) (ii) a Temperature suitability F (t) in k ten daysk) Suitability for ten days F(s)k) Water suitability in Hex ten days F (w)k) Calculating to obtain the weather suitability F (c) of the k ten daysk) (ii) a Using the weather suitability F (c) in the k ten daysk) Calculating to obtain the climate suitability index CSI of the kthk(ii) a Performing linear regression analysis on meteorological data of a region to be predicted in the past year and corresponding climate suitability index CSI by taking ten days as a time unit, and establishing a dynamic forecasting model of the meteorological output of cotton; and calculating the sum of the result output by the cotton meteorological output dynamic forecasting model and the trend output, and forecasting the cotton yield per unit.
Wherein, meteorological data includes: weather data of a national station of 30 years in history, including daily maximum air temperature, daily minimum air temperature, daily precipitation and sunshine hours; meteorological data of the national station of the current year, including daily maximum air temperature, daily minimum air temperature, daily precipitation and sunshine hours; the live meteorological data of the automatic station comprises daily maximum air temperature, daily minimum air temperature, daily precipitation and sunshine hours.
And cleaning the meteorological data, including processing abnormal values and missing values, screening sites, matching meteorological sites with administrative regions and the like.
In the present embodiment, the meteorological data missing value processing includes: and (3) missing meteorological data of national stations and missing live meteorological data of automatic stations in 30 years.
The method specifically comprises the following steps of missing meteorological data of a national station in the history of 30 years: when the meteorological data of the national station has data loss below 5 continuous days, taking the average value of all the meteorological data within 2 days before and 2 days after the loss date as the meteorological data of the loss date; when the weather data of the national station has data missing for more than 5 continuous days, the average value of the historical synchronization is used as the weather data of the missing date.
And when the live meteorological data of the automatic station is deficient, adopting the synchronous data of the national station corresponding to the region to be supplemented.
The cotton yield data is historical unit yield data of each county level in the area to be detected and is derived from national statistical yearbook, statistical yearbook data of each province and agricultural meteorological stations. The cotton yield data of more than 10 years needs to be counted, and the counted cotton yield data is cleaned, wherein the steps comprise mutual verification of data at all levels and processing of abnormal values and missing values.
In this example, cotton growth data includes: main cotton growth period data and physiological index data of crops in each cotton growth period.
In the embodiment, the trend yield is separated by using a linear moving average method to obtain the meteorological yield; the cotton yield per unit Y is decomposed into the trend yield YtAnd meteorological output Yw：
Y＝Yt+Yw+ε
Wherein the trend yield YtThe stability is progressive and relative; meteorological output YwHas short-term volatility; epsilon is random noise, influenced by other factors and ignored. Accordingly, the above formula is simplified as follows,
Y＝Yt+Yw
the linear moving average simulation method is a linear function taking the unit production time sequence change in a certain stage as a linear function and is distributed in a straight line. A linear sliding average simulation method is adopted, the step length is set to be 7 years, then continuous sliding is carried out on each stage, the position of a straight line is continuously changed, and a function of the straight line is arranged at each stage, so that continuous change of the historical evolution trend of the unit production can be reflected, and the trend yield can be further obtained. The meteorological production is represented by the yield per unit and the trending production as follows,
Yw＝Y-Yt
the trend yield is separated by using a linear sliding average method, and the obtained meteorological yield has the following advantages: the subjective assumption of the type of the curve of the unit yield sequence is avoided, and the influence of human factors is reduced to a certain extent; the loss of the number of samples is avoided, and the time series of the meteorological unit can keep better stability along with the increase of years.
Further, calculating the temperature suitability F (t) of the kth day in the cotton development period according to the meteorological data of the area to be predictedk) The method comprises the following steps:
calculating the ith day average temperature t in the kth ten day by the following formulai，
Figure BDA0003332356310000071
Wherein, tlThe lowest temperature at the ith day in the kth dayhThe highest temperature on the ith day.
Average temperature t of ith day in kthiSubstituting the formula to calculate the temperature suitability F (t) of the ith day in the kth dayi)，
Figure BDA0003332356310000081
Wherein, taThe highest temperature of the cotton in the development period of the kth day; t is tbThe lowest temperature of the cotton in the development period of the kth day; t is tcIs the optimum temperature for the cotton development in the development period of the cotton in the kth day.
Calculating the arithmetic mean value of the daily temperature suitability in the kth day as the temperature suitability F (t) in the kth dayk)，
Figure BDA0003332356310000082
Wherein m is the total day number of the kth ten days.
Further, calculating the lighting suitability F(s) of the kth day in the cotton development period according to the meteorological data of the area to be predictedk) Comprises the following steps:
calculating the day preference F(s) of the k-th day by the following formulak)：
Figure BDA0003332356310000083
Wherein s is the cumulative sunshine duration in the kth ten year to be predicted; h is the calendar year average value of the daily hours in the kth ten days.
Further, calculating the water suitability F (w) of the k-th day in the cotton development period according to the meteorological data of the area to be predictedk) Comprises the following steps:
calculating the soil moisture content suitability F (w) of the cotton field in the ith day of the kth day by the following formulai)，
Figure BDA0003332356310000084
Wherein, wiIs a relative humidity observed value, w, of soil at a site 20cmoThe relative humidity of the soil is suitable for the k th development stage of the cotton at the station.
Calculating the arithmetic mean value of the soil moisture content suitability degree in the k th ten days as the water suitability degree in the k th ten days,
Figure BDA0003332356310000091
wherein m is the total day number of the kth ten days.
Temperature suitability F (t) in k ten daysk) Suitability for ten days F(s)k) Water suitability in Hex ten days F (w)k) Calculating to obtain the weather suitability F (c) of the k ten daysk) Comprises the following steps:
calculating the weather suitability F (c) of the k-th ten days by the following formulak)
Figure BDA0003332356310000092
Further, the climate suitability F (c) in the k-th ten days is utilizedk) Calculating to obtain the climate suitability index CSI of the kthkComprises the following steps:
the climate suitability F (c) in the k ten daysk) Normalizing the correlation coefficient with the meteorological influence index to obtain a standard value R of the correlation coefficient in the kth ten dayssk，
Figure BDA0003332356310000093
Wherein R iskWeather suitability in K ten days F (c)k) Correlation coefficient with weather influence index; rmaxThe climatic suitability F (c) in ten days of the cotton development periodk) A maximum value of the correlation coefficient with the weather influence index; rminThe climatic suitability F (c) in ten days of the cotton development periodk) A minimum value of the correlation coefficient with the weather influence index;
using the standard value R of the k-th coefficientskCalculating to obtain the weight coefficient k of the kth ten daysk，
Figure BDA0003332356310000094
Using the weighting coefficient k passing through the kthkObtaining the climate suitability index CSI of the kthk，
CSIk＝∑[kk·F(ck)]。
In this embodiment, the method further includes the following steps: and carrying out correlation analysis on different weather suitability indexes of the area to be detected and corresponding weather output, and determining correlation between the weather suitability indexes and the annual change of the weather output.
Taking Xinjiang area as an example, relevant analysis is carried out on climate suitability indexes and cotton meteorological output at different growth stages of agricultural meteorological sites in the whole Xinjiang area, the results show that the correlation of the climate suitability indexes and the cotton meteorological output in all the areas passes 0.01 significance test, the correlation coefficient is higher than 0.7, the higher consistency of the climate suitability indexes and the annual change of the cotton meteorological output is shown, and the reliability of predicting the cotton meteorological output by using the indexes as driving factors is verified.
In the embodiment, linear regression analysis is carried out by using the meteorological output of cotton in 1991-2017 in Xinjiang and the weather suitability indexes of the cotton planted to different growth stages, and county-area meteorological output dynamic forecasting models in corresponding time periods are respectively established, and the results show that the meteorological output of the cotton is increased along with the increase of the weather suitability indexes in a certain range and passes significance test of 0.05 level.
In Xinjiang cotton district, day-by-day meteorological data of 1991-2020 in each county is used as a model input item, a cotton meteorological yield dynamic forecasting model of sowing to different growth stages in each county is used for carrying out back generation test on the cotton yield in 1991-2020, and compared with the actual yield abundance apology trend, the forecasting accuracy of each year is generally over 95.0%.
The above embodiments are only preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, and any simple changes or equivalent substitutions of the technical solutions that can be obviously obtained by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.

Claims (10)

1. A cotton yield prediction method based on climate suitability is characterized by comprising the following steps:
establishing a plurality of sites, and collecting meteorological data, cotton yield data, cotton growth period data and current-year meteorological data of regions to be predicted in the past year;
decomposing the unit yield of the cotton of the year to be predicted into a trend yield and a meteorological yield; simulating the trend yield by using the cotton yield data of the area to be predicted over the years and adopting a linear moving average simulation method;
calculating the temperature suitability F (t) of the kth ten days in the cotton development period according to the meteorological data and the cotton growth period data of the area to be predictedk) Suitability for ten days F(s)k) Water suitability in Hex ten days F (w)k) (ii) a Temperature suitability F (t) in k ten daysk) Suitability for ten days F(s)k) Water suitability in Hex ten days F (w)k) Calculating the weather suitability F (c) of the kth ten daysk) (ii) a Using the weather suitability F (c) in the k ten daysk) Calculating to obtain the climate suitability index CSI of the kthk；
Performing linear regression analysis on meteorological data of a region to be predicted over the years and corresponding climate suitability indexes by taking ten days as a time unit, establishing a dynamic forecasting model of the meteorological output of cotton, and calculating the meteorological output;
and predicting the unit yield of the cotton according to the sum of the meteorological yield and the trend yield.
2. The method as claimed in claim 1, wherein the temperature suitability in every k days F (t) in the cotton development period is calculated from the meteorological data of the area to be predictedk) The method comprises the following steps:
calculating the ith day average temperature t in the kth ten day by the following formulai，
Figure FDA0003332356300000011
Wherein, tlThe lowest temperature at the ith day in the kth dayhThe highest temperature of the ith day;
average temperature t of ith day in kthiSubstituting the formula to calculate the temperature suitability F (t) of the ith day in the kth dayi)，
Figure FDA0003332356300000012
Wherein, taThe highest temperature of the cotton in the development period of the kth day; t is tbThe lowest temperature of the cotton in the development period of the kth day; t is tcThe optimum temperature of cotton development in the development period of the cotton in the kth place;
calculating the arithmetic mean value of the daily temperature suitability in the kth day as the temperature suitability F (t) in the kth dayk)，
Figure FDA0003332356300000021
Wherein m is the total day number of the kth ten days.
3. The method for predicting cotton yield based on climate suitability according to claim 1, wherein the step of calculating the lighting suitability F (sk) in the kth day of the cotton development period from the meteorological data of the area to be predicted comprises:
the day illumination suitability F (sk) for the k-th day is calculated by the following formula:
Figure FDA0003332356300000022
wherein s is the cumulative sunshine duration in the kth ten year to be predicted; h is the calendar year average value of the daily hours in the kth ten days.
4. The method for predicting cotton yield based on climate suitability according to claim 1, wherein the water suitability in ten days F (w) at kth in the development period of cotton is calculated from the meteorological data of the area to be predictedk) Comprises the following steps:
calculating the soil moisture content suitability F (w) of the cotton field in the ith day of the kth day by the following formulai)，
Figure FDA0003332356300000023
Wherein, wiIs an observed value of the relative humidity of the soil at a position of 20cm of the station, woThe relative humidity of soil is suitable for the k th development stage of the cotton of the station;
calculating the arithmetic mean value of the soil moisture content suitability degree in the k th ten days as the water suitability degree in the k th ten days,
Figure FDA0003332356300000024
wherein m is the total day number of the kth ten days.
5. The method of predicting cotton yield based on climate suitability according to claim 1, wherein the temperature suitability F (t) in ten days through kthk) Suitability for ten days F(s)k) Water suitability in Hex ten days F (w)k) Calculating to obtain the weather suitability F (c) of the k ten daysk) Comprises the following steps:
calculating the weather suitability F (c) of the k-th ten days by the following formulak)
Figure FDA0003332356300000031
6. The method for predicting cotton yield based on climate suitability according to claim 1, wherein the climate suitability F (c) in k-th ten days is utilizedk) Calculating to obtain the climate suitability index CSI of the kthkComprises the following steps:
the climate suitability F (c) in the k ten daysk) Normalizing the correlation coefficient with the meteorological influence index to obtain a standard value R of the correlation coefficient in the kth ten dayssk，
Figure FDA0003332356300000032
Wherein R iskWeather suitability in K ten days F (c)k) Correlation coefficient with weather influence index; rmaxThe climatic suitability F (c) in ten days of the cotton development periodk) A maximum value of the correlation coefficient with the weather influence index; rminThe climatic suitability F (c) in ten days of the cotton development periodk) A minimum value of the correlation coefficient with the weather influence index;
using the standard value R of the k-th coefficientskCalculating to obtain the weight coefficient k of the kth ten daysk，
Figure FDA0003332356300000033
Using the weighting coefficient k passing through the kthkObtaining the climate suitability index CSI of the kthk，
CSIk＝∑[kk·F(ck)]。
7. The method of claim 1, wherein the trending yield is a contribution of agricultural production skill level to cotton yield, and the meteorological yield is a contribution of meteorological conditions to cotton yield.
8. The method of predicting cotton yield based on climate suitability according to claim 1, further comprising the steps of:
and carrying out correlation analysis on different weather suitability indexes of the area to be detected and the corresponding weather yield, and determining the correlation between the weather suitability indexes and the annual change of the weather yield.
9. The method of claim 1, wherein the meteorological data comprises:
weather data of a national station of 30 years in history, including daily maximum air temperature, daily minimum air temperature, daily precipitation and sunshine hours;
meteorological data of the national station of the current year, including daily maximum air temperature, daily minimum air temperature, daily precipitation and sunshine hours;
the live meteorological data of the automatic station comprises daily maximum air temperature, daily minimum air temperature, daily precipitation and sunshine hours.
10. The method for predicting cotton yield based on climate suitability according to claim 9, further comprising the step of performing deletion processing on the meteorological data of the historical 30-year national station, and specifically comprising the following steps:
when the meteorological data of the national station has data loss below 5 continuous days, taking the average value of all the meteorological data within 2 days before and 2 days after the loss date as the meteorological data of the loss date;
when the meteorological data of the national station has data loss for more than 5 continuous days, the meteorological data of the loss date is taken as the average value of the historical synchronization;
and when the live meteorological data of the automatic station is deficient, adopting the synchronous data of the national station corresponding to the region to be supplemented.
CN202111284532.9A 2021-11-01 2021-11-01 Method for predicting cotton yield per unit based on climate suitability Pending CN113935542A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115316214A (en) * 2022-10-12 2022-11-11 山东亿云信息技术有限公司 Rural agricultural information management system and method based on big data

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115316214A (en) * 2022-10-12 2022-11-11 山东亿云信息技术有限公司 Rural agricultural information management system and method based on big data
CN115316214B (en) * 2022-10-12 2023-01-24 山东亿云信息技术有限公司 Rural agricultural information management system and method based on big data

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