CN116362399A

CN116362399A - Climate change-based wheat climatic period and yield prediction method and system

Info

Publication number: CN116362399A
Application number: CN202310334965.3A
Authority: CN
Inventors: 严应存; 高贵生; 何生录; 余迪; 李红梅; 李璠; 赵梦凡
Original assignee: Qinghai Institute Of Meteorology Science
Current assignee: Qinghai Institute Of Meteorology Science
Priority date: 2023-03-31
Filing date: 2023-03-31
Publication date: 2023-06-30

Abstract

The invention discloses a climate change-based wheat climatic period and yield prediction method and system, and relates to the technical field of agriculture, wherein the method comprises the following steps: acquiring climate period data, yield constituent factor data and climate data of a region to be predicted; carrying out statistical calculation on the climatic period data, the yield data and the yield constituent factor data to obtain the climatic period index data and the yield index data, and further carrying out drift diameter analysis to obtain climatic parameters of the climatic period and the yield climate parameters; stepwise regression is carried out to obtain a weathered period prediction model and a yield prediction model; based on the climate data, combining initial climate prediction data corresponding to a plurality of climate prediction models to obtain climate prediction data of a region to be predicted; based on the climate prediction data, a climate period prediction result and a yield prediction result are obtained by combining the climate period prediction model and the yield prediction model. The invention can not only predict the wheat yield, but also predict the wheat waiting period, and greatly improve the prediction precision.

Description

Climate change-based wheat climatic period and yield prediction method and system

Technical Field

The invention relates to the technical field of agriculture, in particular to a climate change-based wheat climatic period and yield prediction method and system.

Background

Wheat is one of main grain crops in China, is also one of important sources for providing daily nutrition for people, has the yield related to the survival problem of people in China, and is particularly important to forecasting research on the yield of wheat in a certain period in order to know the yield of the wheat in advance and adjust and guide the production operation process of the wheat in time. The method for predicting the yield of the wheat by using the scientific method has important significance for guiding the production of the wheat, scientifically and timely regulating and controlling the development of the wheat industry, ensuring the stable yield increase of the wheat, keeping the continuous and stable development of agriculture, promoting the social development and the like.

The yield of wheat is affected by a number of factors, the final yield of which is affected by a combination of factors, wherein some factors have a relatively large effect on the yield and some factors have a relatively small effect, and the extent of influence of these factors on the growth of wheat needs to be scientifically mined. At present, the wheat yield estimation is mainly divided into a crop yield estimation model using meteorological factors and a crop yield estimation model using remote sensing, but the data after the prediction is carried out based on the current data, so that the data size is large, the workload is large, and the prediction precision can not meet the precision requirement of the demand.

Disclosure of Invention

The invention aims to provide a climate change-based wheat climatic period and yield prediction method and system, which not only can predict the yield of wheat, but also can predict the wheat climatic period, and greatly improve the prediction precision.

In order to achieve the above object, the present invention provides the following solutions:

a climate change-based wheat climate period and yield prediction method, comprising:

acquiring climate period data, yield constituent factor data and climate data of a region to be predicted;

carrying out statistical calculation on the physical period data to obtain physical period index data; carrying out statistical calculation on the yield data and the yield constituent factor data to obtain yield index data;

performing drift diameter analysis on the physical climate period index data, the yield index data and the climate data to obtain physical climate period climate parameters and yield climate parameters;

based on the climatic parameters of the physical period, combining the climatic data and the climatic data, and carrying out stepwise regression to obtain a climatic period prediction model;

based on the yield climate parameters, carrying out stepwise regression by combining the yield data and the climate data to obtain a yield prediction model;

Based on the climate data, combining initial climate prediction data of the areas to be predicted corresponding to the climate prediction models to obtain climate prediction data of the areas to be predicted;

based on the weather forecast model, combining the weather forecast data to obtain a weather forecast result of the area to be forecast;

and based on the yield prediction model, combining the climate prediction data to obtain a yield prediction result of the region to be predicted.

Optionally, based on the climate data, predicting the climate of the area to be predicted by combining a plurality of climate prediction models to obtain the climate prediction data, which specifically includes:

acquiring initial climate prediction data of a region to be predicted corresponding to a plurality of climate prediction models;

calculating each initial climate prediction data based on the climate data to obtain prediction precision corresponding to each initial climate prediction data;

and taking the initial climate prediction data corresponding to the maximum value of the prediction precision as the climate prediction data.

Optionally, the expression of the weathered period prediction model is:

wherein: a, a ₁ 、b ₁ 、a ₂ 、b ₂ And c ₁ All are coefficient values, y ₁ For the predicted value of the julian date of sowing, y ₂ Is the predicted value of the number of mature julian days, T _ave4 Average air temperature of 4 months, R ₃ For 3 months precipitation, K0 _3-9 Representing the active accumulation temperature of more than or equal to 0 ℃ in 3 months to 9 months.

Optionally, the expression of the yield prediction model is:

y ₃ ＝a ₃ +b ₃ T _ave3-9 +c ₂ R _3-9 ；

wherein: a, a ₃ 、b ₃ And c ₂ All are coefficient values, y ₃ T as yield predictor _ave3-9 Represents an average air temperature of 3 months to 9 months, R _3-9 Representing 3 months to 9 months of precipitation.

The invention also provides a climate change-based wheat climatic period and yield prediction system, which comprises:

the data acquisition module is used for acquiring the physical period data, the yield constituent factor data and the climate data of the area to be predicted;

the statistical calculation module is used for carrying out statistical calculation on the physical weather period data to obtain physical weather period index data, and carrying out statistical calculation on the yield data and the yield constituent factor data to obtain yield index data;

the drift diameter analysis module is used for carrying out drift diameter analysis on the physical weather period index data, the yield index data and the climate data to obtain physical weather period climate parameters and yield climate parameters;

the weathered period model module is used for carrying out stepwise regression based on the weathered period weather parameters and combining the weathered period data and the weather data to obtain a weathered period prediction model;

The yield model module is used for carrying out stepwise regression based on the yield climate parameters and combining the yield data and the climate data to obtain a yield prediction model;

the climate prediction module is used for obtaining climate prediction data of the area to be predicted by combining initial climate prediction data of the area to be predicted corresponding to the plurality of climate prediction models based on the climate data;

the weather period prediction module is used for obtaining a weather period prediction result of the area to be predicted based on the weather period prediction model and combining the weather prediction data;

and the yield prediction module is used for obtaining a yield prediction result of the area to be predicted based on the yield prediction model and combining the climate prediction data.

Optionally, the climate prediction module specifically includes:

the initial climate prediction data acquisition unit is used for acquiring initial climate prediction data of the areas to be predicted corresponding to the climate prediction models;

the prediction accuracy calculation unit is used for calculating the initial climate prediction data based on the climate data to obtain the prediction accuracy corresponding to the initial climate prediction data;

and the climate prediction data determining unit is used for taking the initial climate prediction data corresponding to the maximum value of the prediction precision as the climate prediction data.

Optionally, the expression of the weathered period prediction model is:

Optionally, the expression of the yield prediction model is:

y ₃ ＝a ₃ +b ₃ T _ave3-9 +c ₂ R _3-9 ；

According to the specific embodiment provided by the invention, the invention discloses the following technical effects:

the invention discloses a climate change-based wheat climatic period and yield prediction method and system, wherein the method comprises the following steps: acquiring climate period data, yield constituent factor data and climate data of a region to be predicted; carrying out statistical calculation on the physical period data to obtain physical period index data; carrying out statistical calculation on the yield data and the yield constituent factor data to obtain yield index data; performing drift diameter analysis on the physical climate period index data, the yield index data and the climate data to obtain physical climate period climate parameters and yield climate parameters; based on the climatic parameters of the physical period, combining the climatic data and the climatic data, and carrying out stepwise regression to obtain a climatic period prediction model; based on the yield climate parameters, carrying out stepwise regression by combining the yield data and the climate data to obtain a yield prediction model; based on the climate data, combining initial climate prediction data of the areas to be predicted corresponding to the climate prediction models to obtain climate prediction data of the areas to be predicted; based on the weather forecast model, combining the weather forecast data to obtain a weather forecast result of the area to be forecast; and based on the yield prediction model, combining the climate prediction data to obtain a yield prediction result of the region to be predicted. The invention can not only predict the wheat yield, but also predict the wheat waiting period, and greatly improve the prediction precision.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of a climate change-based wheat climate period and yield prediction method of the present invention;

FIG. 2 is a block diagram of a climate change-based wheat climate period and yield prediction system of the present invention;

FIG. 3 is a graph showing the correlation coefficients of the yield of Delink Hachun wheat and its constituent factors in 1981-2020;

FIG. 4 is a graph showing the correlation coefficient between yield and climatic period of Denopsis Hachun wheat in 1981-2020;

FIG. 5 is a schematic diagram showing the change of the influence coefficient of light and mild water on yield in each ten days of the growing season of the Dehlawchun wheat;

FIG. 6 is a schematic diagram showing the trend of temperature change in various ten days of the growing season of the wheat in the German Hachun and the influence coefficient on the yield;

FIG. 7 is a schematic diagram showing the precipitation trend and the influence coefficient on yield in each ten days of the growing season of the De-order Hachun wheat;

FIG. 8 is a schematic diagram of the trend of the number of sunshine hours and the influence coefficient on the yield in each ten days of the growing season of the Dehlawchun wheat;

FIG. 9 is a schematic diagram showing the predicted yield change of the Denopsis hachun wheat 2021-2100 based on 1986-2005;

fig. 10 is a schematic diagram showing the predicted yield change of the delharchun wheat 2021-2100 using 2000-2020 as a reference value.

Symbol description: 1. a data acquisition module; 2. a statistics calculation module; 3. the drift diameter analysis module; 4. a weathered period model module; 5. a yield model module; 6. a climate prediction module; 7. a weathered period prediction module; 8. and a yield prediction module.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.

FIG. 1 is a flow chart of the climate change-based wheat climate period and yield prediction method of the invention. As shown in fig. 1, the present invention provides a climate change-based wheat climate period and yield prediction method, comprising:

and S1, acquiring physical period data, yield constituent factor data and climate data of a region to be predicted.

Step S2, carrying out statistical calculation on the physical weather period data to obtain physical weather period index data; and carrying out statistical calculation on the yield data and the yield constituent factor data to obtain yield index data.

And S3, performing path analysis on the weathered period index data, the yield index data and the climate data to obtain weathered period climate parameters and yield climate parameters.

And S4, based on the climatic parameters of the physical period, combining the climatic data and the climatic data, and performing stepwise regression to obtain a climatic period prediction model.

Preferably, the expression of the weathered period prediction model is:

And step S5, carrying out stepwise regression based on the yield climate parameters and combining the yield data and the climate data to obtain a yield prediction model.

Optionally, the expression of the yield prediction model is:

y ₃ ＝a ₃ +b ₃ T _ave3-9 +c ₂ R _3-9 ；

And step S6, based on the climate data, combining initial climate prediction data of the area to be predicted corresponding to the plurality of climate prediction models to obtain the climate prediction data of the area to be predicted.

Specifically, the step S6 specifically includes:

and acquiring initial climate prediction data of the region to be predicted corresponding to the plurality of climate prediction models.

The climate prediction model is a regional climate simulation result business product developed by the national climate center to the Qinghai province climate center and based on 21 CMIP5 global climate modes, and comprises 1901-2005 histocal and 2006-2100 RCP2.6, RCP4.5 and RCP8.5 average month data under the emission conditions. Its horizontal resolution is 50km.

And calculating each initial climate prediction data based on the climate data to obtain the prediction precision corresponding to each initial climate prediction data.

And S7, based on the weather forecast model, combining the weather forecast data to obtain a weather forecast result of the area to be forecast.

And S8, based on the yield prediction model, combining the climate prediction data to obtain a yield prediction result of the region to be predicted.

Specifically, the following description will be given with the fadawood basin as a specific example:

according to the agricultural meteorological observation station network of the Qinghai province meteorological office, three agricultural test stations of gelwood, north wood flood and German hara are arranged on the firewood basin, so that spring wheat is mainly observed, as the 21 st century is early, the gelwood and North wood flood are adjusted to be medlar for pertinence of agricultural service along with adjustment of a planting structure, the German hara spring wheat has long observation time and perfect data, and can represent typical crops of the Qidamu. The weather period data, the yield constituent factor data and the climate data of the DenopahChun wheat in 1980-2020 are obtained, and are particularly obtained through agricultural meteorological observation Specification (winding up) crop booklet.

The spring wheat waiting period comprises sowing, seedling emergence, trefoil, tillering, jointing, booting, heading, flowering, milk ripening and maturing. Sowing 4 months, 21-5 months, 8 days, emergence of seedlings, 5 months, 3-5 months, 25 days, three leaves, 5 months, 10 days, tillering, 29 days, 26 days, knot, 6 months, 16-7 months, 10 days, booting, 27 days, 7 months, 20 days, heading, 28 days, 20 days, ripeness, and 20 days, 8 months, 22 days, 9 months. The development stage is used for sowing, seedling emergence, three leaves 6-20d, three leaves 5-30d, tillering, jointing 4-32d, jointing 6-25d, booting 5-20d, heading-flowering 2-21d, flowering-milk maturation 13-36d, milk maturation 8-36d, seedling emergence-maturation 112-143d, sowing-maturation 134-181d and d representing days. The weather period index data, namely the weather period index of the spring wheat and the characteristic values thereof are shown in table 1.

TABLE 1 spring wheat climatic index and characteristic values thereof

Weather period	Sowing seeds	Emergence of seedlings	Three leaves	Tillering	Jointing part	Booting	Heading	Flowering process	Cooked milk	Maturation of
											Earliest (earlie st)	3/16	4/21	5/3	5/10	5/29	6/16	6/27	7/4	7/28	8/22
Average (Averge)	4/1	4/29	5/11	5/26	6/15	6/27	7/7	7/15	8/8	8/31
											Latest (Latest)	4/16	5/8	5/25	6/10	6/26	7/10	7/20	7/28	8/20	9/20
Standard Deviation (SD)	8.40	4.43	4.96	7.06	5.56	4.90	5.32	6.19	6.04	7.05
											Coefficient of Variation (CV)	2.74	1.10	1.12	1.43	1.00	0.82	0.84	0.94	0.82	0.87
Correlation coefficient (R)	0.70 ^***	0.05	0.09	0.06	-0.17	0.14	-0.03	-0.17	0.13	-0.30 ^*
											Rate of change 10a (t)	4.91	0.18	0.36	0.65	-0.78	0.56	-0.15	-0.89	0.67	-1.78

In table 1, the bolded "×" indicates a significance test for a climate inclination rate passing the 0.1 test level; upper-band subscript "indicates that the trend rate passes the significance test at the 0.05 test level, and upper-band subscript" indicates that the trend rate passes the significance test at the 0.01 test level, as follows.

The weather tendency rate, the variation coefficient and the correlation coefficient with time of each weather period, each growth period duration of the spring wheat of the near 41 years of the de-nopal haws are calculated, and the growth period index and the characteristic value thereof of the spring wheat can be obtained as shown in table 2.

TABLE 2 spring wheat growth period index and characteristic values thereof

In Table 2, a represents the year, 10a represents the year 10, the shortest, average and longest growth period and standard deviation are all d, namely, the number of days, the change rate is d/10a, namely, the number of days/10 years, and the variation coefficient and the correlation coefficient are dimensionless.

As can be seen from tables 1 and 2, sowing, emergence, trefoil, tillering, booting and milk ripening have a delayed trend, wherein the sowing period is extremely significantly delayed with a trend of 4.91d/10 a; the jointing, heading, flowering and maturation all show an advancing trend, wherein the maturation period is weakly advanced by 1.78d/10a, and the remaining waiting period trend is not obvious. The delay of the sowing period can be seen, so that the tillering period is delayed, but the variation trend of the tillering period is repeated after the tillering period, the booting period and the milk ripening period are delayed, and 60% of the tillering period in the whole growing season is delayed. The sowing period is delayed, the maturing period is advanced, so that the whole growth period of spring wheat is extremely obviously shortened by 6.69d/10a. The growth period of each stage is characterized in that the growth period of each stage is prolonged by 0.17d/10a, 1.34d/10a and 1.60d/10a except for seedling emergence-trefoil, jointing-booting and flowering-milk maturity, and the rest 73 percent is shortened, wherein the growth period and the booting-heading change trend in the seedling emergence-jointing period are not obvious, and the rest are weak, obvious or extremely obvious.

From the variation coefficient, the variation coefficient of each period before jointing is large (1.00-2.74), wherein the seeding period is the largest, the period change after jointing tends to be stable, and the variation coefficient is about 0.85. The duration variation coefficient of each growth period is mostly about 8.0. The coefficient of variation of seeding-maturation and seedling-maturation is small (2.07, 1.58), wherein the coefficient of variation of seedling-maturation is minimum and only 1.58. It can be seen that the change of the sowing time causes a large change of the earlier stage climatic period.

The height of the De-Hachun wheat is 59-106cm, and the average height is 86cm. The maximum density jointing period is 478-1630 strain/m ² Between them, average 825 strain/m ² . The weight of the stalk is 535.38-1914.10g/m ² Between, average 1143.82g/m ² . The small infertility spike rate is 2-33%, average 10%, spike number is 19-39 grains/spike, average 30 grains/spike. Thousand grains have a weight of between 36 and 62g, with an average of 45g. The number of the spikes of the plant is 1-3, and the average number of the spikes is 1. Zone yield 217.5-771.4.0g/m ² Average 522.9g/m ² . Average county yield 1965-6750kg/hm ² Average 4405kg/hm ² . Yield index data, namely spring wheat yield index and characteristic values thereof are shown in table 3.

TABLE 3 spring wheat yield index and characteristic values thereof

From the variation coefficient, the spring wheat has a higher variation degree in the high jointing stage than in the milk ripening stage, a higher density variation degree in the jointing stage than in the three-leaf stage and the milk ripening stage, and the variation degree of the small infertility spike rate in spike factors is the largest, and the second is the number of spikes and grains of the plant, and the variation degree of thousand grain weight is the smallest. The variation degree of the yield of the test field and the average yield of the county is equivalent. In the whole, the variation degree of the small infertility spike rate is the largest, and then the small infertility spike rate is sequentially from small to large, such as the milk ripening height, thousand grain weight, three-leaf density, spike grain number, milk ripening density, county average yield, stem weight, jointing density, jointing height, section yield and plant spike number.

From the trend of variation, the three leaf and jointing density, the small spike rate of infertility and the spike number are reduced, wherein the small spike rate of infertility is extremely remarkable (P<0.01 The tendency to decrease (3%/10 a) and the remaining elements all tended to increase, with a weak (or extremely) significant tendency to increase (P) in the height of the breast maturing and jointing stage growth, the yield in the region, and the average yield in the county<0.1 or P<0.01 4.65cm, 2.52cm, 27.84g/m for each 10a ² 、356.41kg/hm ² . It can be seen that the development direction of the production characteristics of spring wheat is strong, high and big since 1981, and each index of the spike is good, so that the yield of the spring wheat is effectively increased.

The correlation between spring wheat yield and its constituent factors was obtained by analyzing the correlation between spring wheat yield and its constituent factors in 1980-2020, as shown in Table 4. As can be seen from table 4, the growth heights in spring wheat jointing stage and in milk ripening stage, the plant density and the yield are obviously or very obviously positively correlated (P <0.05 or P < 0.01), and P represents the correlation, which indicates that the plant is big and thick, the plant is favorable for forming high yield, but the density in three leaf stage is remarkably excessive, the plant nutrition supply is possibly insufficient, and the spring wheat is not long. The higher the plants in the period of milk maturity, the longer the plants are, the less the small sterile spike rate is, the number of spikes and thousand seed weight of the plants are obviously increased, and the high yield (P < 0.05) is obviously promoted. The density in the three-leaf period is high, the infertility small spike rate and the spike number are not obviously increased, the thousand seed weight and the spike number of the plant are obviously reduced, and the yield of spring wheat is possibly reduced. The density of the jointing and the lactation period is high, the infertility small ear rate is low, the thousand grain weight is reduced, the number of the plant ears is obviously increased, and the yield of the spring wheat is obviously or obviously increased. It can be seen that the control of the density in the trefoil period is critical, the density is too high, the later ear index is not facilitated, the proper close planting is beneficial to forming healthy groups in the jointing and lactation period, the light Wen Shuifei is fully utilized, and the high yield is promoted. The small sterile spike rate is extremely obviously and inversely related to the yield, the small sterile spike rate is high, the yield is low, and the yield is high otherwise.

The more the number of grains per ear, the higher the yield, the higher the density and the plant height, and the increase of the number of grains per ear is promoted. Thousand kernel weight showed no significant positive effect on yield formation. The plants are high and robust, promote the increase of thousand grain weight, but increase density and decrease thousand grain weight. The number of ears and the stalk weight of the plant have very obvious positive effects on the yield formation. It can be seen that controlling the density and cultivating a strong and high spring wheat field population is a key to efficiently utilizing solar radiation energy and water and fertilizer nutrition and promoting yield formation, as shown in table 4 and fig. 3.

Table 41980-2020 correlation coefficient between spring wheat yield and its constituent factors

As shown in fig. 3, from the correlation coefficient of the climate period and the yield, the sowing, tillering, jointing, booting and the yield are extremely obviously correlated (P < 0.01), which indicates that the sowing period is late, the temperature is high, 4-5 months late frost is avoided, and the high yield is formed; the tillering, jointing and early and late stage and yield are negative effects, which shows that the tillering stage is properly advanced, the temperature is low, the tillering is promoted, the seeding stage is late, the late frost is effectively avoided, the young spike differentiation is promoted, and the high yield is promoted. The later the jointing period, the higher the temperature, the faster the plant grows, and the formation of strong seedlings is not facilitated.

As shown in fig. 4, the effect on yield in the reproductive period is significant or very significant, being seeding-emergence, trefoil-tillering, jointing-booting, flowering-milk ripening, milk ripening-ripening, seeding-ripening. The duration of the jointing-booting, flowering-breast maturing and the yield are positive effects, and the rest are negative effects, which indicates that the duration of the sowing-maturing is mainly reflected on the sowing-seedling emergence stage and the three-leaf tillering, and the longer the duration is, the poor matching of light and warm water is indicated, and the formation of aligned seedlings, strong seedlings and nutritional leaves is unfavorable.

According to the variation coefficient, the analysis of the significance test combined with the change with time shows that the environmental factors have larger influence on each development period before the jointing, but the analysis of the trend is combined, both the combination is caused by the change of sowing, so that the sowing period and the maturation period which are obviously changed with time are selected from the physical period. The seeding-seedling emergence, three leaves-tillering, milk ripeness-ripeness and seeding-ripeness with extremely obvious trend along with time are selected in the growth period of each stage to analyze the climate influence factors. The influence of environmental factors on the jointing height and jointing density is the greatest, and from the viewpoint of spike factors, the small sterile spike rate and spike grain number are the greatest influenced by the environmental factors, and the average yield in county is the greatest influenced by the environmental factors. Therefore, the jointing height, the density, the infertility spike rate, the spike number and the county average yield are selected for analysis, and the thousand grain weight is considered as a key factor for yield formation and also is taken into consideration in climate influence analysis.

As shown in table 5, the main factors affecting the sowing time are heat factors and sunlight, and are obviously related to the active accumulated temperature at 0 ℃, sunlight hours, average air temperature for 4 months, average minimum air temperature and average maximum air temperature (P < 0.01), the higher the active accumulated temperature, the more sunny days and the later the sowing time, as can be seen from table 5. Factors which extremely significantly affect maturity are mainly active accumulation temperature of more than 0 ℃ for 3-9 months, sunshine hours during sowing-maturity, sunshine hours during flowering-maturity, average minimum air temperature for 6 months and maximum air temperature for 7 months. It can be seen that the climatic period is inversely related to the caloric factor and positively related to the sunlight, indicating that the better the caloric condition is, the earlier the climatic period is, and vice versa. The response to the number of sunshine hours is the opposite.

The climatic factors that significantly (extremely significantly) influence the joint height are temperatures of 3-4 months, 6-7 months and sunlight, the higher the temperature during the period, the higher the joint height, whereas the lower the reaction to sunlight is a negative effect, and the more the number of 6-month sunlight hours, the lower the joint height. The climate factors which obviously (extremely obviously) influence the density of the jointing period are mainly 4 months of average air temperature, average highest air temperature and highest air temperature, and 4 months of the climate factors are in the seedling emergence period of spring wheat, so that the higher temperature is favorable for the emergence of spring wheat, the formation of uniform seedlings and full seedlings, and the increase of the field density is ensured. Weather factors which extremely significantly influence the small spike rate of infertility are 3 months, 1 day, the average air temperature before jointing, the average air temperature for 5 months, the average maximum air temperature, the minimum air temperature for 8 months, the average minimum air temperature for 3-9 months (within the period of growth), the active accumulated temperature of 10 ℃ or more before emergence, the number of sunshine hours within the period of growth and before jointing. The higher the temperature, the lower the sterility spike rate, which is favorable for the formation of yield. The more the sunshine hours, the more the sterile spike rate increases, which is unfavorable for the yield increase. The climate factors which extremely significantly affect the average yield in county are active accumulation temperature of more than 0 ℃ before 3 months and before sowing, sun hours of 3-9 months, sun hours during sowing-maturing, sun hours and average air temperature during sowing-emergence, 5 months precipitation, 8 months hail times and 4 months average air temperature. The weather resistance is positively related to heat factors and precipitation, and negatively related to sunshine hours and hail days, so that the weather resistance is rich, and precipitation and high temperature are beneficial to promoting high yield. The climate factors which significantly affect the grain number of the spike include the average temperature of emergence-jointing, the number of hail days of 9 months, the temperature of 6-8 months and the precipitation of 6-7 months. The spring wheat of 6 months is in the jointing-booting stage, the water demand is large, the precipitation amount is positive, the spring wheat of 7 months enters the heading and flowering stage, the demand for precipitation is weakened, and the precipitation is less favorable for pollination and granulation, so that the negative correlation is realized. Spring wheat in seedling emergence-booting stage is in a parallel period of vegetative growth and reproductive growth, and has high temperature, rapid development stage promotion and no contribution to the progress of young spike differentiation of spring wheat, and the temperature condition and the spike number have negative effects. 7-8 months spring wheat enters the heading-grouting period, and the temperature has positive effect on the number of grains per ear. The spring wheat maturity period is 9 months, the hail days are more, and the spike grains are less. The climate factors which obviously influence thousand grain weight are mainly the lowest temperature of 7-8 months and have positive effect, which indicates that the lowest temperature factors of 7-8 months of the German Ha are key factors which influence the thousand grain weight of spring wheat.

The climate factors which extremely obviously influence the whole growth period of spring wheat are heat factors and sunshine hours, wherein the better the heat condition is, the shorter the growth period is, and the sunshine hours are opposite, the more the sunshine hours are, the longer the growth period is. The climate factors which extremely and obviously influence the sowing-seedling emergence duration are mainly the basic activity accumulated temperature which is larger than 0 ℃ before sowing, the activity accumulated temperature which is larger than or equal to 10 ℃ during sowing-seedling emergence, the precipitation amount, the sunshine hours and the average air temperature, and the average air temperature and the average highest air temperature within 4 months. The precipitation and sunlight in the period show positive effect and the air temperature shows negative effect, which indicates that the seeding stage has high temperature and good precipitation, and the sunlight is abundant, so that the seedling emergence of spring wheat is facilitated, and the duration is shortened. Otherwise, the method is reverse. The climatic factors which obviously influence the three leaf-tillering duration are 3 months of precipitation and 3-4 months of temperature, the 3 months of precipitation is more, the duration of the three leaf-tillering duration is long, and the 3-4 months of temperature is high and the duration is shortened. The duration of milk maturation-maturation is positive with the number of days in the early period and negative with the temperature, indicating that the temperature is higher and promoting the duration to be shortened.

TABLE 5 correlation coefficient of climate elements and spring wheat related parameters

Element(s)	K0 _3/1-sowing	S _3/1-sowing	T _3/1-sowing	T _ave4	T _avemax4	T _avemin4
								Sowing seeds	0.826	0.969	0.725	0.498	0.459	0.464
Element(s)	K0 _3-9	S _{Sowing-maturing}	S _{Flowering-maturation}	T _ave3-9	T _{Flowering-maturation}	T _avemin6	T _max7
								Maturation of	-0.469	0.412	0.449	-0.461	-0.654	-0.435	-0.424
Element(s)	K3 _3/1-sowing	K5 _{Seeding-emergence of seedlings}	S _{Seeding-emergence of seedlings}	T _{ave 3/1-sowing}	T _{ave sowing-emergence of seedlings}	R ₅	S ₅
								Height of the joint	0.602	0.354	-0.468	0.527	0.521	0.331	-0.391
Element(s)	S ₆	T _ave3	T _ave4	T _avemax3	T _avemin4	T _avemin4	T _max3
								Height of the joint	-0.355	0.401	0.343	0.467	0.34	0.329	0.51
Element(s)	T _ave4	T _avemax4	T _max4	Element(s)	K0 _3/1-sowing	S _3-9	S _{Sowing-maturing}
								Density of joints	0.35	0.41	0.515	Average yield in county	0.545	-0.504	-0.576
Element(s)	S _{Seeding-emergence of seedlings}	T _{ave sowing-maturing}	T _{ave 3/1-sowing}	T _{ave sowing-emergence of seedlings}	D _{8 hail}	R ₅	T _ave4
								Average yield in county	-0.566	0.468	0.622	0.578	0.473	0.429	0.43
Element(s)	K10 _3-9	K0 _3/1-sowing	K10 _{Seeding-emergence of seedlings}	S _{Sowing-maturing}	S _{Seeding-emergence of seedlings}	S _{Seedling emergence-jointing}	T _ave3-9
								Small spike rate of infertility	0.683	0.525	-0.492	0.498	0.583	0.443	0.613
Element(s)	T _{ave 3/1-sowing}	T _{ave sowing-emergence of seedlings}	T _{ave emergence-jointing}	T _ave5	T _avemax5	T _avemin5	T _avemin8
								Small spike rate of infertility	-0.608	-0.502	-0.409	-0.488	-0.449	-0.446	-0.414
Element(s)	T _{ave emergence-jointing}	D _{9 hail}	R ₆	R ₇	T _ave7	T _avemax7	T _avemin7
								Spike and grain number	-0.33	-0.325	0.313	-0.342	0.337	0.341	0.324
Element(s)	T _max6	T _max7	T _max8	Element(s)	T _avemin8	T _min7	T _min8
								Spike and grain number	-0.315	0.456	0.425	Thousand grain weight	0.318	0.347	0.416
Element(s)	K5 _3-9	K5 _{Seeding-emergence of seedlings}	K10 _{Seeding-emergence of seedlings}	S _{Sowing-maturing}	S _{Seeding-emergence of seedlings}	S _{Seedling emergence-jointing}	S _{Flowering-maturation}
								Sowing-maturing	-0.8	-0.423	-0.475	0.835	0.771	0.458	0.492
Element(s)	T _ave3-9	T _{ave sowing-maturing}	T _{ave sowing-emergence of seedlings}	T _{ave flowering-maturation}	T _ave4	T _ave8
								Sowing-maturing	-0.78	-0.849	-0.679	-0.581	-0.589	-0.419
Element(s)	K0 _3/1-sowing	K10 _{Seeding-emergence of seedlings}	R _{Seeding-emergence of seedlings}	S _{Seeding-emergence of seedlings}	T _{Seeding-emergence of seedlings}	T ₄	T _avemax4
								Seeding-emergence of seedlings	-0.698	-0.486	0.403	0.961	-0.785	-0.691	-0.652
Element(s)	R ₄	T _ave3	T _ave4	T _avemax4	T _avemin3	T _max4	T _min3
								Trefoil-tillering	0.315	-0.331	-0.393	-0.45	-0.385	-0.387	-0.443
Element(s)	S _{Sowing-maturing}	S _{Flowering-maturation}	T _ave3-9	T _{ave sowing-maturing}	T _{ave flowering-maturation}	S ₆	T _ave4
								Maturation-ripening of milk	0.497	0.438	-0.354	-0.328	-0.551	0.353	-0.325
Element(s)	T _ave8	T _avemax4	T _avemax8	T _avemin8	T _min8
								Maturation-ripening of milk	-0.352	-0.373	-0.384	-0.392	-0.375

In Table 5, K0, K3, K5, K10 are respectively represented by 0 ℃ or higher, 3 ℃, 5 ℃, 10 ℃ active accumulation temperature (DEGC.d), S is represented by sunshine hours (h), R is represented by precipitation (mm), D is represented by days (D), T is represented by air temperature (DEGC), and the air temperature has the highest, lowest, average, highest average and lowest average, and is represented by subscripts, for example: t (T) _ave Mean air temperature (. Degree.C.), T _avemax Represents the average maximum air temperature (DEG C), T _avemin Represents the average minimum air temperature (DEG C), T _max Represents the highest air temperature (DEG C), T _min The minimum air temperature (. Degree. C.) is indicated.

Subscripts

3, 4, 5, 6, 7, 8, 9 denote month, 3/1 denotes 3 months 1 day, e.g. K0 _3/1-sowing The activity accumulated temperature from 3 months and 1 day to the current sowing day is represented as D _{8 hail} The number of hail days of 8 months is represented, S sowing-maturing is represented by the number of sunshine hours from sowing to maturing, R5 is represented by the precipitation of 5 months, and the rest are analogized.

Through drift diameter analysis, the yield of spring wheat is influenced by residual factors such as errors, nutrition levels, field management and the like besides light and warm water, the influence of the residual factors on the yield is 0.422, and the main factor of the change of the yield of the German is light and warm water change. As shown in fig. 5, 6, 7 and 8, the complex correlation coefficient of the light-warm water integral regression influence coefficient and the meteorological yield from seeding-maturing ten-day-by-ten-day in 3-9 months of germanha is 0.907, the regression equation significance test adopts F test, the F value is 5.274, and the significance test of 0.001 test level is passed. It can be seen that the average air temperature in ten days has the greatest effect on yield, precipitation, and the least effect on sunshine hours. FIG. 5 shows the trend of weather conditions in various ten days of the growing season of De-Hanchun wheat Influence coefficient on yield. The influence of the average air temperature in the last ten days of 3 months to 4 months on the yield of spring wheat is a positive effect, and the influence coefficient is 46.315-132.858 kg.hm ^-2 Mainly because the wheat is in sowing-emergence period, the proper temperature is stable at 0 ℃, and the wheat is suitable for seed germination. The average temperature in the period of German has the temperature of-0.5-13.0 ℃, and the temperature is properly higher to promote the germination of spring wheat seeds, so that strong seedlings and seedlings can be formed, and the high yield is promoted. The influence of the average air temperature in the last ten days of 5 months to 7 months on the formation of the yield of spring wheat is a negative effect, and the influence coefficient is-17.523-158.151 kg.hm ^-2 . Because spring wheat undergoes trefoil-tillering-jointing-booting-heading during the period, the trefoil-booting period of spring wheat is a key period for determining the effective spike number and the spike number, and the temperature is properly lower during the period, so that the increase of the spike is facilitated, and the temperature is generally preferably not higher than 15-16 ℃. The average temperature of the Texatrefoil in the tillering stage is between 5.1 and 15.2 ℃, and the proper lower temperature is beneficial to increasing effective tillering and forming strong seedlings. The Texalink, booting and heading are completed in the last ten days of 6-7 months, the Texal air temperature rises quickly during the period, the average air temperature in the ten days is between 10-20.3 ℃, the temperature is high, the plant height is promoted to be higher, the risk of lodging in the later period is increased, and the proper low temperature is beneficial to promoting young ear differentiation and increasing the small ear number and the ear grain number. Spring wheat enters the flowering-maturing stage in the middle 7-8 months, and is most suitable at 20-22 ℃, and the grouting is affected below 12 ℃ or above 24 ℃. The influence of the average air temperature in the period of Dehlha on the yield is positive effect, and the influence coefficient is 39.637-127.118 kg.hm ^-2 . Since the average temperature of the period of the German has reached 23.6 in the next 7 months of the individual year, most of the average temperature is between 13.3 and 22.5 ℃ and 90% of the average temperature is lower than 20 ℃. Thus, a proper higher temperature during the period helps to fully mature the grout. The high temperature tends to cause high temperature to be more numerous during the period, and thousand grain weight is reduced, which is unfavorable for forming high yield. The influence of precipitation on the yield of spring wheat is mainly reflected in negative effects except the water absorption germination and emergence stages of seeds. In the late 3 months, the influence of precipitation on the yield is a negative effect, which indicates that the more precipitation is, the lower the temperature is, and the sowing and seedling emergence of spring wheat are not facilitated. Spring wheat in middle and late 4 months is in water-absorbing germination period, and precipitation is carried out during the periodThe effect on yield is positive, indicating that irrigation water before sowing during the period is not fully satisfied, and proper rainfall supplementation helps promote emergence. The period of obvious negative effect of precipitation on spring wheat is 5 late month to 6 late month, which indicates that irrigation water can be satisfied during the period, and precipitation is unfavorable for the growth and development of spring wheat. The remaining time has less impact. The influence of sunshine duration on yield is mainly reflected in positive effect before tillering and negative effect after tillering, and the influence is smaller, so that the effect is that the weather is rich.

Throughout the growing season, the average air temperature is not obviously reduced from ten days of the last 6 months to ten days of the last 9 months, and the rest of the air temperature is in an increasing trend. The precipitation trend is decreasing in the late 3 months to the late 5 months (except the early 4 months), and the trend is increasing in the late 5 months to the early 7 months, and the trend of combining the temperature, warm and dry in spring, warm and wet in early summer, warm and wet in late summer and cool in early autumn is developed in the growing season. The number of sunshine hours is more and less, and most of the sunshine hours are in a trend of reduction. The effect of the ten-day average air temperature on the yield of spring wheat is positive (seeding-emergence period) -negative (young spike differentiation period) -positive (grouting period). The effect of sunlight and precipitation is similar, the seeding-seedling stage is positive effect, the rest is negative effect, and the relative temperature effect is small. The key period of the temperature affecting the yield is 5 late-6 late, i.e. tillering-booting stage. On the premise of the current irrigation scheme, the key period of influencing the yield by precipitation and sunlight is the next ten days in 4, namely the seedling emergence period. The key limiting factor for the formation of the yield of the local spring wheat under the irrigation premise is temperature. In the whole, the influence of the della light warm water on the yield of the spring wheat is developed to a good degree, the differentiation and grouting of young ears are facilitated, but the precipitation amount in the seeding seedling stage is reduced, the timely irrigation is needed, and the normal emergence and the seedling alignment of the spring wheat are ensured.

Through the above analysis, in this embodiment, the weather parameters of the weather period are selected from the average air temperature of 4 months, the precipitation of 3 months, and the active accumulated temperature of 3 months to 9 months at 0 ℃ or more, and the obtained weather period prediction model has the following formula:

the yield climate parameters are selected from average air temperature of 3 months to 9 months and precipitation of 3 months to 9 months, and the obtained yield prediction model has the following formula:

y ₃ ＝-14806.904+1554.502T _ave3-9 +8.394R _3-9 。

weather prediction data under three emission scenes of RCP2.6, RCP4.5 and RCP8.5 are predicted based on a weather prediction model and substituted into the above prediction model to obtain the yield change process of the De-Hachun wheat under different emission scenes of 2021-2100 years, as shown in FIG. 9 and FIG. 10, the yield of the De-Hachun wheat in the future 2021-2100 years shows an increasing trend, the change trend of the yield of the De-Hachun wheat under the three emission scenes is 0.579%/10a, 4.934%/10a and 15.092%/10a respectively compared with the reference value of 1986-2005, and the change trend of the average value of the yield is 0.729%/10a, 6.211%/10a and 18.998%/10a respectively compared with 2000-2020. The yield amplitude is relatively stable in the RCP2.6 emission scenario, the yield of the Diels Hakka wheat is continuously increased in the RCP4.5 and RCP8.5 emission scenarios, and the amplification in the RCP8.5 emission scenario is far higher than that in the other two emission scenarios.

Table 6 shows the predicted variation trend of the yield of the De-Hanchun wheat, and as shown in Table 6, the variation rate of the yield of the De-Hanchun wheat in the initial 21 st century (2021-2040 years), the middle 21 st century (2041-2070 years) and the final 21 st century (2071-2100 years) in the RCP2.6 emission scenario shows the trend of increasing-decreasing in three different periods; the rate of change of each phase in the RCP4.5 and RCP8.5 emissions scenarios is characterized by gradual slow down in the RCP4.5 emissions scenario and gradual fast up in the RCP8.5 emissions scenario.

TABLE 6 prediction trend of wheat yield of De-order Hachun (unit:%/10 a)

FIG. 2 is a block diagram of a climate change based wheat climate period and yield prediction system of the present invention. As shown in fig. 2, the present invention provides a climate change-based wheat climate period and yield prediction system comprising: the system comprises a data acquisition module 1, a statistics calculation module 2, an drift diameter analysis module 3, a physical period model module 4, a yield model module 5, a climate prediction module 6, a physical period prediction module 7 and a yield prediction module 8.

The data acquisition module 1 is used for acquiring the weather period data, the yield constituent factor data and the weather data of the area to be predicted.

The statistical calculation module 2 is used for performing statistical calculation on the physical period data to obtain physical period index data, and performing statistical calculation on the yield data and the yield constituent factor data to obtain yield index data.

And the drift diameter analysis module 3 is used for carrying out drift diameter analysis on the physical weather period index data, the yield index data and the climate data to obtain physical weather period climate parameters and yield climate parameters.

The weathered period model module 4 is used for carrying out stepwise regression based on the weathered period weather parameters and combining the weathered period data and the weather data to obtain a weathered period prediction model.

The yield model module 5 is configured to perform stepwise regression based on the yield climate parameters and in combination with the yield data and the climate data, to obtain a yield prediction model.

The climate prediction module 6 is configured to obtain climate prediction data of a region to be predicted by combining initial climate prediction data of the region to be predicted corresponding to a plurality of climate prediction models based on the climate data.

The weather period prediction module 7 is used for obtaining a weather period prediction result of the region to be predicted based on the weather period prediction model and combining the weather prediction data.

The yield prediction module 8 is configured to obtain a yield prediction result of the area to be predicted based on the yield prediction model and in combination with the climate prediction data.

Optionally, the climate prediction module 6 specifically includes: an initial climate prediction data acquisition unit, a prediction accuracy calculation unit, and a climate prediction data determination unit. The initial climate prediction data acquisition unit is used for acquiring initial climate prediction data of the areas to be predicted corresponding to the climate prediction models. The prediction accuracy calculation unit is used for calculating the initial climate prediction data based on the climate data to obtain the prediction accuracy corresponding to the initial climate prediction data. And the climate prediction data determining unit is used for taking the initial climate prediction data corresponding to the maximum value of the prediction precision as the climate prediction data.

Optionally, the expression of the weathered period prediction model is:

Optionally, the expression of the yield prediction model is:

y ₃ ＝a ₃ +b ₃ T _ave3-9 +c ₂ R _3-9 ；

In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.

The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.

Claims

1. A climate change-based wheat climate period and yield prediction method, comprising:

2. The climate change-based wheat climate period and yield prediction method according to claim 1, wherein the climate prediction data of the area to be predicted is obtained by combining initial climate prediction data of the area to be predicted corresponding to a plurality of climate prediction models based on the climate data, and the method specifically comprises the following steps:

3. The climate change-based wheat climate period and yield prediction method according to claim 1, wherein the climate period prediction model is expressed as:

4. The climate change-based wheat climate period and yield prediction method according to claim 1, wherein the expression of the yield prediction model is:

y ₃ ＝a ₃ +b ₃ T _ave3-9 +c ₂ R _3-9 ；

5. A climate change-based wheat climate period and yield prediction system, comprising:

6. Climate change based wheat climate period and yield prediction system according to claim 5, wherein the climate prediction module comprises in particular:

7. The climate change based wheat climate period and yield prediction system according to claim 5, wherein the climate period prediction model is expressed as:

8. The climate change based wheat climate period and yield prediction system according to claim 5, wherein the yield prediction model is expressed as:

y ₃ ＝a ₃ +b ₃ T _ave3-9 +c ₂ R _3-9 ；