CN110956322A - Summer corn flowering phase high-temperature disaster risk prediction method under climate warming trend - Google Patents
Summer corn flowering phase high-temperature disaster risk prediction method under climate warming trend Download PDFInfo
- Publication number
- CN110956322A CN110956322A CN201911188898.9A CN201911188898A CN110956322A CN 110956322 A CN110956322 A CN 110956322A CN 201911188898 A CN201911188898 A CN 201911188898A CN 110956322 A CN110956322 A CN 110956322A
- Authority
- CN
- China
- Prior art keywords
- temperature
- flowering
- risk
- equal
- summer
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 240000008042 Zea mays Species 0.000 title claims abstract description 154
- 235000002017 Zea mays subsp mays Nutrition 0.000 title claims abstract description 154
- 230000017260 vegetative to reproductive phase transition of meristem Effects 0.000 title claims abstract description 131
- 235000005824 Zea mays ssp. parviglumis Nutrition 0.000 title claims abstract description 106
- 235000005822 corn Nutrition 0.000 title claims abstract description 106
- 238000000034 method Methods 0.000 title claims abstract description 23
- 238000010792 warming Methods 0.000 title claims abstract description 22
- 230000008859 change Effects 0.000 claims abstract description 20
- 235000016383 Zea mays subsp huehuetenangensis Nutrition 0.000 claims description 48
- 235000009973 maize Nutrition 0.000 claims description 48
- 230000006378 damage Effects 0.000 claims description 34
- 238000011282 treatment Methods 0.000 claims description 13
- 230000004720 fertilization Effects 0.000 claims description 7
- 208000027418 Wounds and injury Diseases 0.000 claims description 2
- 208000014674 injury Diseases 0.000 claims description 2
- 238000013507 mapping Methods 0.000 claims description 2
- 230000007103 stamina Effects 0.000 claims 2
- 239000002131 composite material Substances 0.000 claims 1
- 150000001875 compounds Chemical class 0.000 claims 1
- 238000004519 manufacturing process Methods 0.000 abstract description 10
- 238000012360 testing method Methods 0.000 abstract description 6
- 230000006978 adaptation Effects 0.000 abstract 1
- 238000013517 stratification Methods 0.000 abstract 1
- 238000009826 distribution Methods 0.000 description 19
- 230000000630 rising effect Effects 0.000 description 9
- 208000003643 Callosities Diseases 0.000 description 8
- 206010020649 Hyperkeratosis Diseases 0.000 description 8
- 230000002829 reductive effect Effects 0.000 description 8
- 235000013339 cereals Nutrition 0.000 description 5
- 230000000694 effects Effects 0.000 description 5
- 238000011160 research Methods 0.000 description 5
- 238000004088 simulation Methods 0.000 description 5
- 238000011161 development Methods 0.000 description 4
- 230000018109 developmental process Effects 0.000 description 4
- 239000003337 fertilizer Substances 0.000 description 4
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000002262 irrigation Effects 0.000 description 3
- 238000003973 irrigation Methods 0.000 description 3
- 230000010152 pollination Effects 0.000 description 3
- 239000000843 powder Substances 0.000 description 3
- 239000000126 substance Substances 0.000 description 3
- 230000035899 viability Effects 0.000 description 3
- 240000007594 Oryza sativa Species 0.000 description 2
- 235000007164 Oryza sativa Nutrition 0.000 description 2
- 206010000210 abortion Diseases 0.000 description 2
- 231100000176 abortion Toxicity 0.000 description 2
- 230000033228 biological regulation Effects 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 239000005431 greenhouse gas Substances 0.000 description 2
- 235000009566 rice Nutrition 0.000 description 2
- 238000010998 test method Methods 0.000 description 2
- 241001270131 Agaricus moelleri Species 0.000 description 1
- ZLMJMSJWJFRBEC-UHFFFAOYSA-N Potassium Chemical compound [K] ZLMJMSJWJFRBEC-UHFFFAOYSA-N 0.000 description 1
- HCHKCACWOHOZIP-UHFFFAOYSA-N Zinc Chemical compound [Zn] HCHKCACWOHOZIP-UHFFFAOYSA-N 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 230000002860 competitive effect Effects 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 238000012272 crop production Methods 0.000 description 1
- 238000005520 cutting process Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000007123 defense Effects 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 238000005315 distribution function Methods 0.000 description 1
- 210000005069 ears Anatomy 0.000 description 1
- 238000005485 electric heating Methods 0.000 description 1
- 230000035558 fertility Effects 0.000 description 1
- 230000005094 fruit set Effects 0.000 description 1
- 230000035784 germination Effects 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 230000002401 inhibitory effect Effects 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 230000000670 limiting effect Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000010899 nucleation Methods 0.000 description 1
- 239000003895 organic fertilizer Substances 0.000 description 1
- 229910052700 potassium Inorganic materials 0.000 description 1
- 239000011591 potassium Substances 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000012502 risk assessment Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 230000001502 supplementing effect Effects 0.000 description 1
- 230000002277 temperature effect Effects 0.000 description 1
- 239000011573 trace mineral Substances 0.000 description 1
- 235000013619 trace mineral Nutrition 0.000 description 1
- 230000005068 transpiration Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
- 239000011701 zinc Substances 0.000 description 1
- 229910052725 zinc Inorganic materials 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Forestry; Mining
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Development Economics (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Educational Administration (AREA)
- Marine Sciences & Fisheries (AREA)
- Animal Husbandry (AREA)
- Agronomy & Crop Science (AREA)
- Mining & Mineral Resources (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Life Sciences & Earth Sciences (AREA)
- Cultivation Of Plants (AREA)
Abstract
The invention discloses a summer corn flowering phase high-temperature disaster risk prediction method under a climate warming trend, and aims to solve the technical problem that a future summer corn flowering phase high-temperature disaster risk and evolution method cannot be accurately and precisely predicted in the prior art. Calculating the perennial value of the florescence start-stop date of the land to be predicted to RCP climate grid point data in the latitude and longitude range of the administrative region of the test land; the high-temperature accumulated value of the maximum temperature in the flowering phase is more than or equal to 32 ℃ or more than or equal to 35 ℃ is used for representing the high-temperature accumulated value; calculating the frequency of high temperature disastersP i Flowering phase high temperature risk comprehensive indexIThreshold for flowering time high temperature risk stratificationI a (ii) a According toI a And the size is applied to the RCP scene for grading the light, medium and heavy risks. The invention can accurately predict the futureThe risk and evolution of the high-temperature disasters in the flowering period of summer corn are of great significance to the adjustment of a corn production system, the stress-resistant cultivation and the adaptation to the climate change.
Description
Technical Field
The invention relates to the technical field of agricultural planting, in particular to a summer corn flowering phase high-temperature disaster risk prediction method under the climate warming trend.
Background
Corn is the first large grain crop in China, plays a very important role in national economic development, and Henan is a big province for planting summer corn, the seeding area is 331.7 ten thousand hectares, and the total yield is 1752.1 thousand tons. With the increase of the yield level of the corn, the determinant effect of the grain number of the ear on the yield is more and more obvious.
Pollen viability is one of the important reasons for influencing the number of grains in the ear, and although the heat resistance and the pollen dispersing characteristics of the maize tassel are different between genotypes, high temperature is an important reason for causing the maize pollen viability to decline. Abnormal high temperature affects pollen structure and function, so that the number and activity of pollen are reduced or the pollen does not meet in flowering phase, and finally, the kernel abortion and seed setting rate is reduced and the yield is reduced. For example, Chenghui et al found that corn, when exposed to an extremely high temperature of 38 ℃ for 3 days, would stop pollen shedding, that the pollination rate was inversely related to the temperature, and that the higher the temperature, the lower the pollination rate. The research of Zhao Long Fei and the like shows that the high-temperature treatment of the dredged corn 20 in the flowering period can reduce the number of grains per ear of the corn by 20.4-22.0 percent and the weight of hundred grains by 8.8-10.5 percent. The proper temperature from the corn tasseling to the silking period is 25-28 ℃, but the selfing and powder scattering of summer corn in the south of Henan is generally in the high-temperature period from late 7 months to early 8 months, particularly under the large background of global warming, the summer corn usually encounters extreme weather such as high-temperature drought in the flowering period, the crop yield is rapidly reduced, effective defense or relief measures are lacked in the current production, and the safety production of the corn is seriously threatened. High temperature in the flowering phase has become one of the main meteorological disasters for the growth of summer corn in the area.
A great deal of intensive research is carried out on the damage characteristics and the influence mechanism of the high temperature in the flowering phase of the summer corn, such as that Li De and the like construct the high-temperature heat damage comprehensive climate index of the Huaibei plain summer corn, and the Wang Hai Mei research shows that the high-temperature stress of more than 32 ℃ can influence the physiological index and the yield structure of the corn in the river sleeve irrigation area. The research on the high-temperature risk is carried out on rice, the number of corns is relatively small, for example, the probability and the spatial distribution of the high-temperature heat injury risk of the corns in the flowering phase of each county and district of Huang-Huai-Hai are calculated by utilizing a daily high-temperature time length probability distribution function in Liu-Zheng and the like, and the high-temperature risk of the corns in the Huang-Huai-Hai-Xia in 2011-; the influence of high temperature in northeast region on corn production is analyzed according to accumulated temperature of more than or equal to 30 ℃ and day number by Yixiaogang and the like.
The current fact that the climate is warming is not competitive, the climate warming can be continued in the future, therefore, the summer corn resists high temperature heat damage, and the situation of ensuring safe production is more severe. In the study of the influence of climate change, the existing study points out that the influence of temperature rise on crop production is very significant, but most of the influence of average temperature on the growth and the yield of crops. With the continuous and deep research of high-temperature disasters, the difference of the influence of different high-temperature durations on the activity and the yield of the corn pollen is obvious, and more refined meteorological data requirements are provided for the evaluation of the influence of the high-temperature disasters.
However, there is no method for scientifically and accurately estimating the risk and evolution of the summer corn high-temperature disaster in the flowering period under the climate warming trend.
Disclosure of Invention
The invention aims to provide a summer corn flowering phase high-temperature disaster risk prediction method under the climate warming trend so as to solve the technical problem that a future summer corn flowering phase high-temperature disaster risk and evolution method cannot be scientifically and accurately predicted in the prior art.
In order to solve the technical problems, the invention adopts the following technical scheme:
the summer corn flowering phase high-temperature disaster risk prediction method under the climate warming trend is designed, and comprises the following steps:
(1) determining the perennial value of the flowering date of the to-be-predicted summer maize, and mapping the perennial value of the flowering date to RCP climate grid point data in the longitude and latitude range of the to-be-predicted administrative region to form a corresponding prediction database;
(2) respectively statistically calculating high-temperature damage values TH corresponding to temperatures of more than or equal to 32 ℃ and more than or equal to 35 ℃ in the flowering period of the summer corn by using the highest air temperature and the day number data of the flowering period in the prediction database according to the following formula32 and TH35：
wherein ,THiRepresents a high temperature harmful value T at a temperature of not less than 32 ℃ or not less than 35 DEG ChiIs the current day hazard value, TmaxFor daily maximum temperature, T0The highest temperature critical value of summer corn in the flowering phase is 32 ℃ or 35 ℃; n is the number of days that the highest temperature of the summer maize in the flowering period is more than or equal to 32 ℃ or the highest temperature is more than or equal to 35 ℃;
(3) respectively statistically calculating the frequency P of high temperature disasters at the local history corresponding to the maximum temperature of more than or equal to 32 ℃ and the maximum temperature of more than or equal to 35 ℃ in the flowering period of summer corn32 and P35：
wherein ,niIs divided into high-temperature days with the temperature of more than or equal to 32 ℃ or more than or equal to 35 ℃ in the flowering period of summer corn in local history, NiThe total number of days of the summer corn in the flowering period;
(4) determining the summer corn flowering phase high temperature risk comprehensive index I according to the following formula:
I＝ω1P32×TH32+ω2P35×TH35-formula (IV)
wherein ,ω1 and ω2Respectively are weight coefficients of high temperature influence of different degrees;
(5) the obtained high-temperature risk comprehensive index I is compared with a known or given risk grading threshold IaAnd attributing to its risk level.
Preferably, in the step (1), the perennial value of the flowering start and stop date is a perennial historical average of the flowering start and end dates.
Preferably, the date of the beginning of the flowering phase is the general period of emasculation, and the date of the end of the flowering phase is 7d later than the general period of emasculation.
Preferably, in step (1), the RCP climate grid point data is future climate change data in RCP4.5 or RCP8.5 emission scenarios.
Preferably, in the step (4), the fertilization seed loss rate a after the high temperature treatment at 32 ℃ or more and 35 ℃ or more for 1 hour is calculated by experimental statistics1 and a2Then, ω is calculated according to the following formula1 and ω2：
Preferably, in the step (5), the highest comprehensive index I in the whole region under the RCP rf scene of the ground to be detected is obtained according to the steps (2) to (4)maxAnd determining the high-temperature risk classification threshold I of the summer corn in the flowering period according to the formulaa：
Ia＝ai×Imax-formula (V)
wherein ,ImaxIs the highest comprehensive index of the whole area to be predicted, aiIs a grading coefficient; wherein the grading factor at the mild to moderate risk cut-off is 0.4 and the grading factor at the moderate to severe risk cut-off is 0.7.
Compared with the prior art, the invention has the main beneficial technical effects that:
according to the method, the time-space change characteristics of the flowering phase high-temperature disasters under the climate warming condition can be known and mastered by predicting the flowering phase high-temperature disaster risks of the summer corns under the future climate warming background, and the flowering phase high-temperature disaster risks and evolution of the summer corns in the future can be accurately predicted, so that the method has important guiding significance for adjusting a corn production system, resisting cultivation and adapting to climate changes.
Drawings
Fig. 1 is a schematic diagram of RCP scene data in the south of hewn province;
FIG. 2 is a schematic view of the perennial value of the summer maize staminate period in Henan province;
FIG. 3 is a graph showing the change of high temperature days in the flowering phase of summer maize in Henan province under different RCP situations;
FIG. 4 is a spatial distribution diagram of the number of high-temperature days and the occurrence frequency of summer maize in Henan province at the flowering stage under different RCP scenarios;
FIG. 5 is a graph showing the change of high temperature damage in the flowering phase of summer maize in Henan province under different RCP situations;
FIG. 6 is a spatial distribution diagram of high temperature damage variation in the flowering phase of summer maize in Henan province under different RCP scenarios;
FIG. 7 is a high temperature comprehensive risk profile of the flowering phase of summer corn in Henan province under different RCP scenarios;
FIG. 8 is a high-temperature disaster graph of summer corn in Xiping county of Juma shop, Henan province, 2017;
fig. 9 is a second high-temperature disaster plot of summer corn in west county of showman shop in Henan province in 2017.
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.
For the fifth evaluation report (AR5) of the United nations climate change inter-government expert Committee (IPCC) of climate change in 2014 in the future, 4 kinds of RCPs (concentration path situation data) data are provided and policy factors are merged for the first time, wherein the RCPs data comprise RCP 2.6, RCP4.5, RCP 6 and RCP8.5 data products.
In the following embodiments, RCP rf is the current-generation scene data, and the simulation time series is 1951-2005; RCP4.5 is a greenhouse gas emission and economic balance development mode, and the simulation time sequence is 2006-2050 years; RCP8.5 is the highest path of greenhouse gas emission, and the simulation time sequence is 2006-2050 years; the related meteorological statistical methods are all conventional methods unless otherwise specified.
And (3) adopting Microsoft Excel to process data, and selecting a kriging interpolation method to construct a graph by using a buffer software.
Example (b): prediction of high-temperature disaster risk of summer corn in flowering phase in Henan province under climate warming trend
1. Test site and test data
(1) Selection of test site and climate profile data
The method is implemented by taking a main planting area (excluding Xinyang areas where rice is planted) of summer corn in Henan province as an implementation object, and combining estimated data for estimating future change trend of climate, namely historical climate simulation RCP rf data and future climate change data under two emission situations of RCP4.5 and RCP8.5, wherein the mode horizontal resolution is 50 km. Longitude ranges of administrative regions of Henan province are 110-117 degrees E, latitude ranges are 31.5-36.5 degrees N, 165 grid points in total cover Henan province regions, and the Suffer software is used for drawing according to grid point data distribution to generate the grid points as shown in figure 1.
(2) Summer corn development period data statistics
Derived from 17 summer corn agricultural meteorological stations in Henan province. In the observation period, the total 30 years of the selected record in 1985-2014 are complete.
2. Test method
(1) Selection of summer maize flowering time
The common period of emasculation is taken as the start date of the flowering phase, and the common period of emasculation is delayed backwards by 7d as the end date of the flowering phase. According to 30 years of agricultural meteorological observation data, the multi-year historical average value of the flowering beginning and ending dates of summer corn, namely the perennial value of the flowering period, is calculated. And (4) calculating the perennial value of the flowering date to the RCP climate lattice point data in the Henan province area according to the principle of close distance. The perennial value of the summer maize androgenesis date is shown in figure 2.
(2) High temperature heat damage index of summer corn in flowering period
Under the high temperature condition of 32-35 ℃, the corn pollen loses water and dries up, loses vitality and has inhibiting effect on the further development of the pollen on the stigma (such as the germination of the pollen and the growth of a pollen tube). The temperature reaches 32 ℃ and lasts for 60 minutes, and the fertilization rate and the total fruiting rate of the florets are obviously lower than those of a control; the fertilization rate and the total fruiting rate of the florets are also significantly lower than the 32 ℃ treatment when the temperature is higher than 35 ℃. Therefore, the temperature of more than or equal to 32 ℃ and the temperature of more than or equal to 35 ℃ are used as disaster-causing thresholds of two different degrees of high-temperature thermal hazards, namely, the temperature of more than or equal to 32 ℃ is slightly damaged, and the temperature of more than or equal to 35 ℃ is severely damaged. Comprehensively considering the frequency and the intensity of high-temperature heat damage in the flowering phase of summer corn, determining the high-temperature days and high-temperature accumulated damage at the temperature of more than or equal to 32 ℃ and more than or equal to 35 ℃ as two indexes for evaluating the influence of the high temperature in the flowering phase.
1) High-temperature day index: in the summer maize flowering period 7d determined in the selection of the summer maize flowering period in the test method (1), when the daily maximum temperature is more than or equal to 32 ℃, the summer maize flowering period is taken as a mild high-temperature day, and when the daily maximum temperature is more than or equal to 35 ℃, the summer maize flowering period is taken as a severe high-temperature day, and the total number of days of each young and severe high-temperature disasters is counted respectively.
2) High temperature hazard index: the severity degree of the high-temperature disaster is represented by a high-temperature accumulated value with the highest temperature of the flowering phase being more than or equal to 32 ℃ or more than or equal to 35 ℃, and the unit is ℃. d. The calculation formula is as follows
wherein ,THiRepresenting the high temperature harmful value of more than or equal to 32 ℃ or more than or equal to 35 DEG C，ThiIs the current day hazard value, TmaxFor daily maximum temperature, T0The cut-off value represents the highest temperature at the flowering phase of summer maize, namely 32 ℃ or 35 ℃.
(3) Frequency of high-temperature disasters in flowering period of summer corn
The high-temperature days in the flowering period of summer maize of 30 years are counted and divided by the total days in the flowering period, and the frequency of the high-temperature disasters in history can be calculated according to the following formula
wherein ,PiThe occurrence frequency of high temperature with the flowering phase more than or equal to 32 ℃ or more than or equal to 35 ℃. n isiThe number of high-temperature days is more than or equal to 32 ℃ or more than or equal to 35 ℃ in the flowering period of summer corn, NiThe total number of days of summer maize in flowering phase.
(4) Summer corn flowering phase high-temperature risk comprehensive index and risk grade division
The climate risk index is represented by multiplying probability by intensity, and the summer corn flowering phase high temperature risk comprehensive index is calculated by the following formula:
I＝ω1P32×TH32+ω2P35×TH35-formula (IV)
Wherein I is the summer maize flowering phase high temperature risk comprehensive index omega1 and ω2Respectively, the weight coefficients of the high temperature effects, P, of different degrees32 and P35Respectively the high temperature generation frequency, TH, of not less than 32 ℃ or not less than 35 DEG C32 and TH35The high temperature damage values of the corresponding grades are respectively.
ω1 and ω2Determination method (see Deg Zhi-Ling, et al, influence of high temperature on pollen viability of corn [ J)]The Chinese university of agriculture proceedings 2016,21(3):25-29) is:
a. setting test: after the corn ear silking period, bagging the corn with basically consistent growth vigor, dividing the cells according to the temperature processing requirement and marking. And (3) cutting the filaments and picking the powder in the powder scattering period, and heating the filaments in an electric heating thermostat to perform high-temperature treatment. And (3) carrying out artificial living pollination after high-temperature treatment, measuring the fertility rate of the florets after 3d, counting the number of the corn ears in the middle stage of formation of the seeds after 10d, and calculating the total seed setting rate.
b. High-temperature treatment: a total of 3 temperature treatment levels were set: 32. 35 and 38 ℃; 5 high temperature duration treatments were set at each temperature: 5. 10, 20, 30 and 60min, set as Control (CK) without temperature treatment; each treatment was repeated 5 times.
c. Respectively counting the fertilization seed-setting loss rates after high-temperature treatment for 1 hour at the temperature of more than or equal to 32 ℃ and at the temperature of more than or equal to 35 ℃ to show that the fertilization seed-setting loss rates after the treatment at the temperature of more than or equal to 32 ℃ or more than or equal to 35 ℃ are obviously different from the control, the fertilization seed-setting rates are respectively reduced by 49 percent and 65 percent compared with the control, and the weight coefficient value is the relative proportion of the two loss rates, and the calculation method comprises:
the RCP rf reference condition is used as a reference, and the summer maize flowering phase high temperature occurrence risk index is divided into three stages, namely light, medium and heavy. The threshold calculation formula of the hierarchy is as follows
Ia＝ai×Imax-formula (V)
wherein ,IaTo a graded threshold value, ImaxIs the highest comprehensive index of the whole area, aiIs the grading coefficient.
The practical experience of corn production in Henan of the root is combined with expert opinions, the mild and moderate risk grading coefficient of 0.4 is selected in the regions of Henan province, and the moderate and severe risk grading coefficient of 0.7 is suitable, so that the practical production condition is met. And directly applying the calculated grading threshold to the RCP4.5 and RCP8.5 scenes to grade the light, medium and heavy risks according to the size of the threshold.
3. Analysis of predictive simulation test results
(1) Annual change of high-temperature days and disaster occurrence frequency of summer corn in flowering period
The change of the high-temperature days of summer maize at the flowering phase of more than or equal to 32 ℃ is shown in figures 3(a1) to (a3), the high-temperature days of more than or equal to 32 ℃ under the RCP rf scene is changed within the range of 0.4-6.8 d, the average day is 4.2d for many years, and the change shows a remarkable rising trend (P is less than 0.05); the average time of the RCP is 4.7d in years under the condition of RCP4.5, and the change trend is not obvious; the temperature rise range is larger under the RCP8.5 scene, the average day number of high temperature of not less than 32 ℃ is 4.8d, and the temperature rises remarkably (P is less than 0.05). The change of the high-temperature days of summer corn with the flowering period of more than or equal to 35 ℃ is shown in figures 3(b1) to (b3), the average number of the high-temperature days of more than or equal to 35 ℃ under the RCP rf scene is 2.0d in years, and the high-temperature days show a remarkable rising trend (P is less than 0.05); the average time of the RCP is 2.7d in years under the RCP4.5 scene, and the change trend is not obvious; under the RCP8.5 scene, the average day number of high temperature higher than or equal to 35 ℃ is 2.8d, and the day number shows a remarkable rising trend (P is less than 0.05).
The high-temperature generation frequency of the summer maize in the flowering period is more than or equal to 32 ℃ and more than or equal to 35 ℃ and the variation trend of the high-temperature days is consistent (not shown), the high-temperature generation frequency of the summer maize in the flowering period is more than or equal to 32 ℃ and more than or equal to 35 ℃ in the RCP rf scene, the average generation frequency of the summer maize in the flowering period is 61.3 percent and 28.8 percent for years, the rising trend of the high-temperature generation frequency of the summer maize in the future RCP4.5 scene is not obvious, and the average generation frequency of the summer maize in the flowering period is 69. The occurrence frequency of the high temperature in the summer maize flowering phase in the future RCP8.5 scene is in a remarkable rising trend (P is less than 0.05), and the average temperature is 70.4 percent (not less than 32 ℃) and 40.1 percent (not less than 35 ℃) for many years. The volatility of the high temperature occurrence frequency of more than or equal to 35 ℃ is increased, and the variation coefficients under the future situation are respectively 15.4 percent (RCP4.5) and 13.9 percent (RCP8.5), which are both higher than 12.1 percent of the reference condition.
(2) High-temperature days and disaster occurrence frequency spatial distribution of summer corn in flowering period
The spatial distribution of the high-temperature days of summer maize at the flowering phase of more than or equal to 32 ℃ and the disaster occurrence frequency is shown in FIGS. 4(a1) to (a3), the high-temperature days of summer maize at the flowering phase of more than or equal to 32 ℃ under the RCPrf scene is in the range of 1.7-5.7 d, and the high-temperature occurrence frequency is in the range of 20.5-81.0% in the province. Wherein the high temperature generation frequency of more than or equal to 32 ℃ in most of the east of Luoyang, Hill and Nanyang is more than 70%. Under the RCP4.5 scene, the number of high-temperature days at the flowering phase of summer corn of more than or equal to 32 ℃ is in the range of 2.4-6.3 d, the high-temperature occurrence frequency is in the range of 30.6-89.9% in the whole province, and high-value areas at the high-temperature occurrence frequency of more than or equal to 32 ℃ are mainly distributed in Zhengzhou, flat-topped mountains and most areas in the middle of south-yang and east, and the frequency is more than 80%. Under the RCP8.5 scene, the occurrence frequency of high temperature of summer corn at the flowering phase of more than or equal to 32 ℃ is within the range of 36.1-87.3%, the number of high temperature days is within the range of 2.8-6.1 d, and the distribution of high value areas with the occurrence frequency of more than 80% is similar to the RCP4.5 scene. Compared with the reference condition, the number of days of high-temperature occurrence of summer corn with the flowering phase of more than or equal to 32 ℃ is respectively increased by 0.6d (RCP4.5) and 0.5d (RCP8.5) under the future emission scene, and the occurrence frequency is increased by 9.1% (RCP4.5) and 11.0% (RCP 8.5).
The spatial distribution of the high-temperature days of the summer maize at the flowering time of more than or equal to 35 ℃ and the disaster occurrence frequency is shown in fig. 4(b1) to (b3), the high-temperature days of the summer maize at the flowering time of more than or equal to 35 ℃ under the RCPrf scene is in the range of 0.3-3.6 d, and the high-temperature occurrence frequency is in the range of 3.9-51.9% in the whole province. Wherein the high temperature of more than or equal to 35 ℃ in most areas of Zhengzhou, xuchang and Huima shop in the south of the east is more than 40 percent. Under the RCP4.5 scene, the occurrence frequency of the high temperature of more than or equal to 35 ℃ in the summer maize florescence is within the range of 8.8-59.7 percent in the whole province, the number of high temperature days is within the range of 0.8-4.2 d, high-value areas of the high temperature occurrence frequency of more than or equal to 35 ℃ are mainly distributed in most areas of the North and east of Xinxiang, Zhengzhou, xuchang and Huma shop, and the occurrence frequency of the high temperature is more than 50 percent. Under the RCP8.5 scene, the occurrence frequency of high temperature of summer corn at the flowering phase of more than or equal to 35 ℃ is in the range of 12.7-56.3%, the number of high temperature days is in the range of 0.9-3.9 d, and the high value area with the occurrence frequency of more than 80% is wider than the RCP4.5 scene distribution range. Compared with the reference condition, the number of days of high-temperature occurrence of summer corn with the flowering phase of more than or equal to 35 ℃ is respectively increased by 0.6d (RCP4.5) and 0.7d (RCP8.5) under the future emission scene, and the occurrence frequency is increased by 8.7% (RCP4.5) and 8.3% (RCP 8.5).
(3) Annual change of high-temperature accumulated damage in summer maize flowering phase
Annual variation of high-temperature damage of summer corn at flowering phase of more than or equal to 32 ℃ is shown in fig. 5(a1) to (a3), and the high-temperature damage of more than or equal to 32 ℃ under RCP rf scene is 151.3 ℃ d on average for many years and shows a remarkable rising trend (P < 0.05); the change trend is not obvious under the RCP4.5 scene, and the average temperature is 174.9 ℃ d for many years; under the RCP8.5 scene, the summer corn has a significantly rising trend of high-temperature damage (P <0.05) with the flowering phase of more than or equal to 32 ℃, and the average temperature of 177.9 ℃ d for many years. Annual variation of high-temperature damage of summer corn at flowering phase of more than or equal to 35 ℃ is shown in fig. 5(b1) to (b3), and the high-temperature damage of more than or equal to 35 ℃ under RCP rf scene is 75.5 ℃ d on average for many years and shows a remarkable rising trend (P < 0.05); the change trend is not obvious under the RCP4.5 scene, and the average temperature is 99.5 ℃ d for many years; under the RCP8.5 scene, the summer maize flowering phase is more than or equal to 35 ℃, and the high-temperature damage also shows a remarkable rising trend (P <0.05), and the average temperature is 106.1 ℃ d in many years.
The difference of the high-temperature harmful coefficient of variation at the temperature of more than or equal to 32 ℃ under different scenes is small, but the high-temperature harmful coefficient of variation at the temperature of more than or equal to 35 ℃ is 48.9 percent (RCP4.5) and 46.1 percent (RCP8.5) under the future scenes, and both are higher than 38.6 percent of the reference condition, which indicates that the volatility of severe high-temperature disasters is larger.
(4) Spatial distribution of high-temperature accumulated damage in summer maize flowering phase
The spatial distribution of high-temperature harmful substances of summer corns with the flowering period of more than or equal to 32 ℃ is shown in FIGS. 6(a1) to (a3), and the total province is in the range of 48.5-200.9 ℃ d under the RCP rf scene. Wherein, the high temperature of most areas of the Ma shop, eastern part of the flat mountain, Zhengzhou and Jiaozhongdong are accumulated at more than 180 ℃ and d, and account for about 53 percent of the area of the main planting area of summer corn in the whole province. Under the RCP4.5 scene, the whole province is in the range of 73.4-231.3 ℃ d, the high-value areas with the accumulated damage higher than 180 ℃ d are mainly distributed in most areas of Jiaozhong, Luoyang, south Yang and east, and the distribution area accounts for about 70% of the main planting area of summer maize in the whole province. Under the RCP8.5 scene, the whole province is in the range of 87.3-223.8 ℃ d, the distribution form is similar to the RCP4.5 scene, and the range of the harmful substances above 180 ℃ d accounts for about 71% of the main planting area of summer corn in the whole province. Compared with the reference condition, the high-temperature damage of summer corn with the flowering phase of more than or equal to 32 ℃ under the future emission scene is increased by 25.4 ℃ d (RCP4.5) and 25.6 ℃ d (RCP8.5) respectively.
The spatial distribution of high-temperature harmful substances of summer corns with the flowering period of more than or equal to 35 ℃ is shown in FIGS. 6(b1) to (b3), and the total province is in the range of 9.8-138.5 ℃ d under the RCP rf scene. The high-temperature accumulated damage in the areas of Zhengzhou, xuchang and the north of the Zhou, which are all east and north, is more than 120 ℃ and d, and accounts for about 21 percent of the area of the main planting area of summer corns in the whole province. Under the RCP4.5 scene, the high-temperature damage of more than or equal to 35 ℃ in the province is in the range of 22.5-160.3 ℃ d, the high-value damage area of more than 120 ℃ d is mainly distributed in most areas of the east of the Xizao, Zheng Zhou and flat mountains, and the area is obviously increased by about 51 percent of the area of the main planting area of the summer corn compared with the reference condition. Under the RCP8.5 scene, the total province is in the range of 32.7-154.9 ℃ d, and the harmful accumulation distribution range larger than 120 ℃ d is wider and accounts for 58% of the main planting area of the summer corn. Compared with the reference condition, the high-temperature damage of summer corn with the flowering phase of more than or equal to 35 ℃ under the future emission scene is increased by 25.8 ℃ d (RCP4.5) and 31.4 ℃ d (RCP8.5) respectively.
(5) Summer corn flowering phase high-temperature comprehensive risk analysis
The occurrence frequency and the harmful intensity of the high temperature in the flowering period of the summer maize are integrated, the integrated risk index is calculated according to the formula (V), the spatial distribution grade of the integrated risk index is shown in figure 7, and the integrated risk index is generally in a distribution form of high east and low west. Under the RCP rf scene, the high-value risk areas are mainly distributed in the areas (except for Shangdu) in the north of Xinxiang, Zhengzhou, xuchang, Luwo and the West of the Zhou, account for about 30.1% of the area of the main planting area of summer corn, and the low-value risk areas are mainly distributed in the three gorges in the west of Yuxi, the west of Luoyang and the northwest of the south Yang. Under the RCP4.5 scene, the central areas of the Yangtze province, the Zhengzhou province and the flattop mountain are high-risk areas, the low-value risk areas are mainly distributed in the three-gate gorges in the west of Henan, the west of Luyang and the northwest of Nanyang, and the areas are obviously reduced compared with the areas under the reference condition. Under the RCP8.5 scene, the temperature rise range is larger, the distribution range of high-value risk areas is wider, and the high-value risk areas are all high-value risk areas in the east of Jiyuan, the east of Luyang, the middle east of south yang and other areas. The areas of the high-value risk areas in the future scene approximately account for 63.4 percent (RCP4.5) and 76.3 percent (RCP4.5) of the main planting area of the summer corn, and are respectively increased by 33.3 percent (RCP4.5) and 46.2 percent (RCP4.5) compared with the reference condition, and the high-temperature disaster risk of the summer corn in the flowering phase in the future RCP scene is obviously increased.
(6) Summary of prediction results
Under the RCP rf scene, the flowering period of summer maize in the whole province is more than or equal to 32 ℃, the high-temperature days are within the range of 1.7-5.7 d, and the occurrence frequency is within the range of 20.5-81.0%. Compared with the reference condition, the number of days of high-temperature occurrence of summer corn with the flowering phase of more than or equal to 32 ℃ under the future RCP scene is respectively increased by 0.6d (RCP4.5) and 0.5d (RCP8.5), and the occurrence frequency is increased by 9.1% (RCP4.5) and 11.0% (RCP 8.5). The number of summer corn flowering days at the temperature of more than or equal to 35 ℃ is 0.3-3.6 d under the RCPrf scene, and the occurrence frequency is 3.9-51.9% in the province. Compared with the reference condition, the number of days of high-temperature occurrence of summer corn with the flowering phase of more than or equal to 35 ℃ under the future RCP scene is respectively increased by 0.6d (RCP4.5) and 0.7d (RCP8.5), and the occurrence frequency is increased by 8.7% (RCP4.5) and 8.3% (RCP 8.5). Under the RCP rf scene, the high-temperature damage of summer corn in the whole province is in the range of 48.5-200.9 ℃ d at the flowering phase of more than or equal to 32 ℃, and in the range of 9.8-138.5 ℃ d at the high-temperature damage of more than or equal to 35 ℃. Compared with the reference condition, the summer corn flowering phase is more than or equal to 32 ℃ and the high-temperature damage is respectively increased by 25.4 ℃ d (RCP4.5) and 25.6 ℃ d (RCP8.5) under the future RCP scene; the high-temperature damage of more than or equal to 35 ℃ is respectively increased by 25.8 ℃ d (RCP4.5) and 31.4 ℃ d (RCP 8.5).
The high-temperature comprehensive risk distribution of the summer maize in the flowering phase is known, a high-value risk area under the RCP rf scene is mainly distributed in the areas (except for Shanghai) north east of New county, Zheng Zhou, Chang, Luo river and Zhou, accounts for about 30.1% of the area of the main planting area of the summer maize, the area of the high-value risk distinguishing area under the future scene is expanded to most areas east of Luoyang and Nanyang, accounts for about 63.4% (RCP4.5) and 76.3% (RCP4.5) of the area of the main planting area of the summer maize, increases 33.3% (RCP4.5) and 46.2% (RCP4.5) respectively compared with the reference condition, and increases the risk of the high-temperature disasters in the flowering phase of the summer maize.
4. Effect verification method for predicting high-temperature disaster risk in flowering phase of summer corn under climate warming trend
Taking the occurrence situation of the high-temperature disasters in the flowering period of summer corn in Xiping county of Chengma shop in Henan province in 2017 as an example, statistics show that the number of high-temperature days at a temperature of not less than 32 ℃ is 5 days, and the number of high-temperature days at a temperature of not less than 35 ℃ is 4 days. The high temperature damage of more than or equal to 32 ℃ is 179.1 ℃ and d, and the high temperature damage of more than or equal to 35 ℃ is 146.2 ℃ and d, which are all in a high-value region of high temperature risk. The field disaster investigation result shows that the summer maize has serious high-temperature disaster (as shown in fig. 8 and 9), pollen abortion causes poor fruit set, and the prediction result of the summer maize florescence high-temperature disaster risk under the climate warming trend of the embodiment is consistent with the actual situation.
With the warming of climate, the method of the invention can predict the occurrence trend of high temperature disaster risk in summer maize florescence in some place in the future, and has important practical significance for maize production guidance, for example, for areas with obvious high temperature hazard, the yield can be improved by replanting late-maturing varieties to fully utilize heat resources in growing seasons in production; in addition, high temperature is often accompanied by drought, drought occurs frequently in areas with high-temperature risks, water and fertilizer regulation can be enhanced at the risk degree of the high-temperature disasters in the flowering period of summer corn, the water and fertilizer regulation is a comprehensive effective measure for defending the high-temperature drought, the heat resistance of the corn can be increased by additionally applying an organic fertilizer, applying a trace element zinc fertilizer and supplementing a potassium fertilizer in the later period, the field temperature can be reduced by reasonable irrigation, the corn can obtain sufficient water after the irrigation, the transpiration is promoted, the canopy temperature is reduced, and therefore the influence of the high-temperature disasters.
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 above embodiments can be changed without departing from the spirit of the present invention, and a plurality of specific embodiments are formed, which are common variation ranges of the present invention, and will not be described in detail herein.
Claims (6)
1. A summer corn flowering phase high-temperature disaster risk prediction method under the climate warming trend is characterized by comprising the following steps:
(1) determining the perennial value of the flowering date of the to-be-predicted summer maize, and mapping the perennial value of the flowering date to RCP climate grid point data in the longitude and latitude range of the to-be-predicted administrative region to form a corresponding prediction database;
(2) respectively statistically calculating high-temperature damage values corresponding to temperatures of not less than 32 ℃ and not less than 35 ℃ in the flowering period of summer corn by using the highest air temperature and the day number of the flowering period in the prediction database according to the following formulaTH 32 AndTH 35 ：
wherein ,TH i the high temperature damage value is more than or equal to 32 ℃ or more than or equal to 35 ℃,T hi the value of the daily product injury is,T max in order to achieve the highest temperature day by day,T 0 the highest temperature critical value of summer corn in the flowering phase is 32 ℃ or 35 ℃;nthe number of days that the highest temperature of the summer maize in the flowering period is more than or equal to 32 ℃ or the highest temperature is more than or equal to 35 ℃;
(3) respectively statistically calculating the frequency of high-temperature disasters at local history corresponding to the temperature of more than or equal to 32 ℃ at the highest temperature and more than or equal to 35 ℃ at the flowering phase of summer cornP 32 AndP 35 ：
wherein ,n i divided into high-temperature days which are more than or equal to 32 ℃ or more than or equal to 35 ℃ in the flowering period of summer corn in local history,N i the total number of days of the summer corn in the flowering period;
(4) determining the high-temperature risk comprehensive index of summer corn in the flowering period according to the following formulaI：
wherein ,ω 1 andω 2 respectively are weight coefficients of high temperature influence of different degrees;
(5) the obtained high-temperature risk comprehensive indexIThresholding with known or given risk levelsI a And attributing to its risk level.
2. The method for predicting high-temperature disaster risk in flowering phase of summer corn in the trend of climate warming according to claim 1, wherein in the step (1), the perennial value of the flowering start and end date is a multi-year historical average of the flowering start and end dates.
3. The method for predicting risk of high-temperature disasters during flowering of summer corn with a tendency toward climate warming according to claim 2, wherein the starting date of the flowering is a general stamina stage, and the ending date of the flowering is a 7d later than the general stamina stage.
4. The method for predicting risk of high-temperature disaster in flowering phase of summer corn under climate warming trend according to claim 1, wherein in the step (1), the RCP climate lattice point data adopts future climate change data under RCP4.5 or RCP8.5 emission scenario.
5. The method for predicting risk of high-temperature disaster in flowering phase of summer corn under climate warming trend according to claim 1, wherein in the step (4), fertilization seed setting loss rate a after high-temperature treatment at 32 ℃ or higher and 35 ℃ or higher for 1 hour is calculated by experimental statistics respectively1 and a2Then calculated according to the following formulaω 1 Andω 2 ：
6. the method for predicting risk of high-temperature disaster in flowering phase of summer corn in climate warming tendency according to claim 1,
in the step (5), the highest comprehensive index in the whole region under the RCP rf scene of the ground to be detected is obtained according to the steps (2) to (4)I max And determining the high temperature risk classification threshold value of summer corn in the flowering period according to the following formulaI a ：
wherein ,I max is the highest composite index of the whole area to be predicted,a i is a grading coefficient; wherein the grading factor at the mild to moderate risk cut-off is 0.4 and the grading factor at the moderate to severe risk cut-off is 0.7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911188898.9A CN110956322B (en) | 2019-11-28 | 2019-11-28 | Summer maize flowering phase high-temperature disaster risk prediction method under climate warming trend |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911188898.9A CN110956322B (en) | 2019-11-28 | 2019-11-28 | Summer maize flowering phase high-temperature disaster risk prediction method under climate warming trend |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110956322A true CN110956322A (en) | 2020-04-03 |
| CN110956322B CN110956322B (en) | 2023-06-20 |
Family
ID=69978813
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911188898.9A Active CN110956322B (en) | 2019-11-28 | 2019-11-28 | Summer maize flowering phase high-temperature disaster risk prediction method under climate warming trend |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110956322B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113902215A (en) * | 2021-11-01 | 2022-01-07 | 北京飞花科技有限公司 | Forecasting method for delayed cold damage dynamics of cotton |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7184891B1 (en) * | 2004-06-15 | 2007-02-27 | The Weather Channel, Inc. | System and method for forecasting pollen in accordance with weather conditions |
| CN106952171A (en) * | 2017-03-01 | 2017-07-14 | 中国农业大学 | A kind of corn high temperature risk class computational methods based on temperature record |
| CN106951980A (en) * | 2017-02-21 | 2017-07-14 | 河海大学 | A kind of multi-reservoir adaptability dispatching method based on RCP scenes |
| CN107392503A (en) * | 2017-08-18 | 2017-11-24 | 中国农业大学 | A kind of appraisal procedure of corn Climatic regionalization risk |
-
2019
- 2019-11-28 CN CN201911188898.9A patent/CN110956322B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7184891B1 (en) * | 2004-06-15 | 2007-02-27 | The Weather Channel, Inc. | System and method for forecasting pollen in accordance with weather conditions |
| CN106951980A (en) * | 2017-02-21 | 2017-07-14 | 河海大学 | A kind of multi-reservoir adaptability dispatching method based on RCP scenes |
| CN106952171A (en) * | 2017-03-01 | 2017-07-14 | 中国农业大学 | A kind of corn high temperature risk class computational methods based on temperature record |
| CN107392503A (en) * | 2017-08-18 | 2017-11-24 | 中国农业大学 | A kind of appraisal procedure of corn Climatic regionalization risk |
Non-Patent Citations (5)
| Title |
|---|
| MYUNG-CHUL SEO: "Assessment on Damage Risk of Corn for High Temperature at Reproductive Stage in Summer Season Based on Climate Scenario RCP 8.5 and 4.5" * |
| 宋瑞明;王卫光;张翔宇;丁一民;: "江苏省水稻高温热害发生规律及未来情景预估" * |
| 张琪;步露蕾;胡正华;沈钰琳;张淑娟;殷进;张庄;: "基于NEX-GDDP降尺度数据的山东省夏玉米生育期极端高温时空特征研究" * |
| 熊伟;冯灵芝;居辉;杨笛;: "未来气候变化背景下高温热害对中国水稻产量的可能影响分析" * |
| 罗丽华;刘国华;肖应辉;唐文帮;陈立云;: "高温胁迫对水稻花粉和小穗育性及稻谷粒重的影响" * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113902215A (en) * | 2021-11-01 | 2022-01-07 | 北京飞花科技有限公司 | Forecasting method for delayed cold damage dynamics of cotton |
| CN113902215B (en) * | 2021-11-01 | 2024-05-07 | 北京飞花科技有限公司 | Method for forecasting cotton delay type cold damage dynamic state |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110956322B (en) | 2023-06-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Cakir | Effect of water stress at different development stages on vegetative and reproductive growth of corn | |
| Mishra et al. | Spatial variability of climate change impacts on yield of rice and wheat in the Indian Ganga Basin | |
| Heinemann et al. | Characterization of drought stress environments for upland rice and maize in central Brazil | |
| Abarshahr et al. | Genetic variability, correlation and path analysis in rice under optimum and stress irrigation regimes | |
| Yin et al. | Climate effects on crop yields in the Northeast Farming Region of China during 1961–2010 | |
| Thimme Gowda et al. | Study on water requirement of Maize (Zea mays L.) using CROPWAT model in northern transitional zone of Karnataka | |
| CN112215716A (en) | Crop growth intervention method, device, equipment and storage medium | |
| Marré et al. | Micronutrient accumulation in conilon coffee berries with different maturation cycles | |
| Wilhelmi et al. | Spatial representation of agroclimatology in a study of agricultural drought | |
| Lou et al. | Effects of climate change on the economic output of the Longjing-43 tea tree, 1972–2013 | |
| Yang et al. | Modeling the effects of plastic film mulching on irrigated maize yield and water use efficiency in sub-humid Northeast China | |
| CN112561315A (en) | Quantitative evaluation and estimation method for meteorological disasters of flue-cured tobacco | |
| Wu et al. | Impact of spatial-temporal variations of climatic variables on summer maize yield in North China Plain | |
| CN113229076A (en) | Wheat breeding method for screening excellent salt-tolerant strains by using salt pond | |
| Irmak et al. | Winter wheat (Triticum aestivum L.) evapotranspiration and single (normal) and basal crop coefficients | |
| Chen et al. | Temperature model of litchi flowering—from induction to anthesis | |
| Nawaz et al. | Climate variables effect on fruiting pattern of Kinnow mandarin (Citrus nobilis Lour× C. deliciosa Tenora) grown at different agro-climatic regions | |
| Carvalho et al. | Evapotranspiration and crop coefficient for potato in organic farming | |
| CN110956322A (en) | Summer corn flowering phase high-temperature disaster risk prediction method under climate warming trend | |
| Xiao et al. | Impact of thermal time shift on wheat phenology and yield under warming climate in the Huang-Huai-Hai Plain, China | |
| CN106105927B (en) | Method for determining early rice sowing period | |
| Li-Juan et al. | Yield performance and resource efficiency of double-cropping maize in yellow-huai-hai river valleys region | |
| Ma et al. | Optimizing ET-based irrigation scheduling for wheat and maize with water constraints | |
| CN106034973B (en) | Disaster prevention control method for fir artificial forest | |
| CN116089789A (en) | Algorithm for constructing freeze injury index of winter wheat in northern Xinjiang |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |