CN109033539A - The calculation method influenced based on plant growth mechanism model separation key factor pair crop phenology - Google Patents
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- 239000002689 soil Substances 0.000 claims description 5
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- 238000009395 breeding Methods 0.000 abstract description 2
- 230000001488 breeding effect Effects 0.000 abstract description 2
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Abstract
The invention discloses a kind of calculation methods influenced based on plant growth mechanism model separation key factor pair crop phenology, crop mechanism model kind accumulated temperature parameter is adjusted by crop field experimental observation Phenological data, process of crop growth under different scenes is simulated using crop mechanism model, separate the influences of the key factors to crop phenology such as climate change, date of seeding adjustment and kind transformation, influence of each key factor to crop phenology can be more intuitively separated, for crop reply climate change, breed breeding and improvement sowing system etc. is instructed to be of great significance.
Description
Technical field
The present invention relates to a kind of to separate and evaluate climate change, date of seeding adjustment using plant growth mechanism model and make article
The calculation method that kind transformation influences crop phenology, belongs to agricultural sustainable development adaptability teaching technical field.
Background technique
Plant phenology refers specifically under certain growth environment, the rudiment that occurs with the seasonal variety of weather, go out leaf,
It blooms and the phenomenon that the regularity variation such as maturation, suspend mode, plant growth and development process can be objectively responded to external environmental condition
Response and adaptability.Along with the lasting raising of global seismic temperature, important change is had occurred in the past few decades plant phenology.
Crop phenology has become research climate change to the important content of impact of agricultural production to the response of climate change.
The crop Phenological change of long-term sequence is able to reflect in the period crop growth to climate change to a certain extent
Adaptability.Crop phenology is influenced by human factor to a certain extent, such as the gas that people are suitble to it to grow by selection
Time condition determines the sowing time.Different crop varieties have differences the response of climate change, the life of same crop varieties
The influence of the long main climate condition of growth course, eventually leads to the volume variance between year border.
It is all polyfactorial common that the past few decades crop phenology receives climate change, sowing time change and kind transformation etc.
It influences.Research key factor has statistical regression and crop modeling analogy method to the main method that crop phenology influences at present.System
Meter model analysis is simple and easy to do, and the confidence level for analyzing result is also relatively high, thus is used widely.But influence crop production
Each factor be frequently not between each other it is independent, statistical model cannot disclose the correlation between numerous impact factors, because
This is difficult to be utilized it and analyzes result it is further proposed that the stronger Applicable Countermeasure of specific aim；However the crop mechanism mould of Kernel-based methods
Type can overcome this disadvantage, and crop modeling can simulate the plant growth under different scenes, therefore can abandon between the factor
Interaction, be precisely separating and quantify the influence of each factor pair crop phenology.
APSIM (Agricultural Production Systems Simulator, agricultural production system simulation model)
Agricultural production system study group (APSRU) by being subordinate to section of Australian Union work tissue (CSIR O) and Queensland state government exists
It was developed in more than 20 years of past, is the mechanism model that can simulate each main component of agricultural system.The model can be used for simulating agriculture
Process of crop growth and soil water nitrogen dynamic in industry system, especially suitable for evaluation farming system productive potentialities and tillage control measure
The influence of production benefit climate fluctuation and environmental change.It is a series of that APSIM model can allow user easily to pass through selection
Crop, soil and other submodules configure oneself crop modeling.Logical relation between module can be particularly simple
It is provided by " plug " function of module.Due to its flexibility, operability, APSIM model is considered more should being one
The flexible software environment of a model system, rather than it is directed to the single model of certain specific crop system.
Summary of the invention
The technical problem to be solved in the present invention is to provide one kind to be made based on plant growth mechanism model separation key factor pair
The calculation method that object phenology influences, relatively intuitively to separate the key factors pair such as climate change, date of seeding adjustment and kind transformation
The influence of crop phenology.
In order to solve the above technical problems, the technical solution used in the present invention is:
A kind of calculation method influenced based on plant growth mechanism model separation key factor pair crop phenology, including it is as follows
Step:
(1) for the long-term sequence crop of multiple research websites, the field trial phenological observation of each research website is utilized
Data determines benchmark crop varieties of each research website in the research period；And according to the crop of each website benchmark crop varieties
Growth course records phenology data, determines the accumulated temperature parameter area of each benchmark crop varieties；
(2) by it is each research website correspond to the time day meteorological data, soil data and field management data input crop give birth to
In long mechanism model, the crop phenological period of modeling is compared with the actual measurement crop phenological period that Field observation is tested, is led to
The kind accumulated temperature parameter value for adjusting model repeatedly is crossed, determines the accumulated temperature parameter value of each benchmark crop varieties；
(3) according to it is each research website date of seeding record, be arranged long-term sequence under fix date of seeding and benchmark crop varieties with
And two simulated scenarios of practical date of seeding and benchmark crop varieties；
(4) according to the accumulated temperature parameter value of each benchmark crop varieties determined in step (2), plant growth mechanism model is utilized
The chief crop phenological period under different scenes is simulated, using method separation climate change, date of seeding adjustment and the kind of linear fit
Change the influence to crop phenology.
Further, in the step (4):
(4-1), which calculates climate change according to following equation, influences crop phenology:
P(cl)i=a(cl)X(cl)i+b(cl)
Wherein, P (cl)iIt is the analogue value in chief crop phenological period under fixed date of seeding and benchmark crop varieties accumulated temperature parameter, X
(cl)iIt is climate change to crop phenology influence time sequence time, i=1,2 ... ..., n；a(cl)It is climate change to crop object
The influence of time, day/year；b(cl)It is linear fit intercept；
(4-2), which calculates date of seeding adjustment according to following equation, influences crop phenology:
P(sd)i=a(sd)X(sd)i+b(sd)
P(sd)i=P (so)i-P(cl)i
Wherein, P (sd)iIt is to adjust lower crop Phenological change, X (sd) date of seedingiIt is date of seeding adjustment to crop phenology influence time
Sequence time, i=1,2 ... ..., n；a(sd)It is the influence adjusted date of seeding to crop phenology, day/year；b(sd)It is that linear fit is cut
Away from；P(so)iIt is the analogue value in chief crop phenological period under practical date of seeding and benchmark crop varieties accumulated temperature parameter, P (cl)iIt is fixed
The analogue value in chief crop phenological period under date of seeding and benchmark crop varieties accumulated temperature parameter；
(4-3), which calculates kind transformation according to following equation, influences crop phenology:
P(cs)i=a(cs)X(cs)i+b(cs)
P(cs)i=P (o)i-P(so)i
Wherein, P (cs)iIt is that kind converts lower crop Phenological change, X (cs)iIt is kind transformation to crop phenology influence time
Sequence time, i=1,2 ... ..., n；a(cs)It is influence of the kind transformation to crop phenology, day/year；b(cs)It is that linear fit is cut
Away from；P(o)iIt is actual observation crop phenological period, P (so)iIt is crop phenology under practical date of seeding and benchmark crop varieties accumulated temperature parameter
The phase analogue value.
Further, in the step (1), the research period is Past 30 Years or more.
Further, in the step (1), it is first determined crop varieties transformation of each research website in the research period,
And the crop varieties of beginning period are benchmark crop varieties in the Selecting research period.
Further, in the step (1), the process of crop growth record phenology data of benchmark crop varieties include sowing
Phase, seeding stage, jointing stage, florescence and maturity period determine each benchmark crop varieties according to actual field trial appraising model
Accumulated temperature parameter area.
Further, in the step (2), field management data include seeding method, irrigation, fertilising and harvest.
Further, in the step (3), fixed date of seeding is the sowing time average value studied in the period.
Further, in the step (4), the chief crop phenological period is florescence and maturity period.
The beneficial effects of adopting the technical scheme are that
The present invention relates to a kind of to separate and evaluate climate change (30 years or more), date of seeding using plant growth mechanism model
The calculation method that adjustment and crop varieties transformation influence crop phenology (florescence and maturity period).The present invention passes through crop field
Experimental observation Phenological data adjusts crop mechanism model kind accumulated temperature parameter, is simulated using crop mechanism model and is made under different scenes
Object growth course, the influences of the key factors to crop phenology such as separation climate change, date of seeding adjustment and kind transformation, can be compared with
Influence of each key factor to crop phenology is intuitively separated, for crop reply climate change, breed breeding is instructed and changes
Kind sowing system etc. is of great significance.
Detailed description of the invention
Fig. 1 is that the florescence measured value and the analogue value in the embodiment of the present invention under corn benchmark crop varieties are verified；
Fig. 2 is that the maturity period measured value and the analogue value in the embodiment of the present invention under corn benchmark crop varieties are verified；
Fig. 3 is influence of the key factor to corn phenology in the embodiment of the present invention.
Specific embodiment
The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.
Embodiment: being based on plant growth mechanism model, calculates Past 30 Years (1981-2010) NORTH CHINA local climate
Variation, date of seeding adjustment and kind convert the influence to corn phenology.
1, the benchmark crop varieties and kind accumulated temperature parameter area of research website are determined
(1-1) selects four representative stations to study website: Chifeng, Fengning, Changzhi and Wuwei, according to National agricultural gas
As the test observation data at station Past 30 Years (1981-2010), determine that the crop varieties of four representative stations Past 30 Years become
It changes, and selecting the crop varieties of beginning period in each website research period is benchmark crop varieties, as shown in table 1.
The transformation of 1 representative stations Past 30 Years kind of table and benchmark crop varieties
(1-2) according to the process of crop growth of each website benchmark crop varieties record phenology data (including sowing time, emergence
The phenological periods such as phase, jointing stage, florescence and maturity period), determine the kind accumulated temperature parameter area of each benchmark crop varieties.
2, the kind accumulated temperature parameter value of model is determined
According to model needs, by each research website correspond to the time day meteorological data, soil data and field management data
(seeding method, irrigation, fertilising, harvest etc.) inputs in APSIM model, and the crop phenological period of modeling and Field observation are tried
The actual measurement crop phenological period tested is compared, and is determined using trial-and-error method by adjusting the kind accumulated temperature parameter value of model repeatedly
The accumulated temperature parameter value of each benchmark crop varieties.
As depicted in figs. 1 and 2, modeling phenological period and actual measurement phenological period are compared according to the research each website in area
Analysis, simulation phenological period and actual measurement phenological period difference are within 5 days, and coefficient R2Reach 0.80, illustrates determining product
Kind accumulated temperature parameter value has preferable applicability and representativeness in the research each website in area.
3, the simulated scenario of different times is set
According to the detailed date of seeding record of 4 representative stations, fixed date of seeding and two simulated scenarios of practical date of seeding are set, such as
Shown in table 2.Wherein, fixed date of seeding is sowing time the past few decades average value (average date of seeding).
The average date of seeding and date of seeding of 2 representative stations Past 30 Years of table, adjust
| Website | Average date of seeding (julain day) | Date of seeding, changes (day/10 year) |
| Chifeng | 119 | -3.0 |
| Fengning | 113 | -0.1 |
| Changzhi | 118 | -4.0 |
| Wuwei | 101 | -1.7 |
4, long-term sequence is fixed plant growth mechanism model phenology under date of seeding and practical date of seeding and is simulated
(4-1), which calculates climate change according to following equation, influences crop phenology:
P(cl)i=a(cl)X(cl)i+b(cl)
Wherein, P (cl)iIt is crop phenology (florescence and maturity period) under fixed date of seeding and benchmark crop varieties accumulated temperature parameter
The analogue value, X (cl)iIt is climate change to crop phenology influence time sequence time (i=1,2 ... ..., n), a(cl)It is weather
Change the influence (day/year) to crop phenology, b(cl)It is linear fit intercept；
(4-2), which calculates date of seeding adjustment according to following equation, influences crop phenology:
P(sd)i=a(sd)X(sd)i+b(sd)
P(sd)i=P (so)i-P(cl)i
Wherein, P (sd)iIt is to adjust lower crop Phenological change, X (sd) date of seedingiIt is date of seeding adjustment to crop phenology influence time
Sequence time (i=1,2 ... ..., n), a(sd)It is the influence (day/year) adjusted date of seeding to crop phenology, b(sd)It is that linear fit is cut
Away from P (so)iIt is the analogue value of crop phenology under practical date of seeding and benchmark crop varieties accumulated temperature parameter, P (cl)iBe fixed date of seeding and
The analogue value of crop phenology (florescence and maturity period) under benchmark crop varieties accumulated temperature parameter；
(4-3), which calculates kind transformation according to following equation, influences crop phenology:
P(cs)i=a(cs)X(cs)i+b(cs)
P(cs)i=P (o)i-P(so)i
Wherein, P (cs)iIt is that kind converts lower crop Phenological change, X (cs)iIt is kind transformation to crop phenology influence time
Sequence time (i=1,2 ... ..., n), a(cs)It is influence (day/year) of the kind transformation to crop phenology, b(cs)It is that linear fit is cut
Away from P (o)iIt is actual observation crop phenology value, P (so)iIt is crop phenology under practical date of seeding and benchmark crop varieties accumulated temperature parameter
The analogue value.
It is calculated by simulation, obtains each representative stations Past 30 Years key factor (climate change, date of seeding adjustment and product
Kind of transformation) influence to corn phenology, as shown in table 3.
It is calculated by being averaging, finally obtaining research area's Past 30 Years (1981-2010) key factor, (climate change is broadcast
Phase adjusts the influence converted with kind) to corn phenology, as shown in Figure 3.
Influence of 3 representative stations Past 30 Years (1981-2010) key factor of table to corn phenology
The above embodiments are only used to illustrate the present invention, and not limitation of the present invention, in relation to the common of technical field
Technical staff can also make a variety of changes and modification without departing from the spirit and scope of the present invention, therefore all
Equivalent technical solution also belongs to scope of the invention, and scope of patent protection of the invention should be defined by the claims.
Claims (8)
1. a kind of calculation method influenced based on plant growth mechanism model separation key factor pair crop phenology, feature are existed
In: the calculation method includes the following steps:
(1) for it is multiple research websites long-term sequence crops, using it is each research website field trial phenological observation data,
Determine benchmark crop varieties of each research website in the research period；And according to the plant growth of each website benchmark crop varieties
Cheng Jilu phenology data determine the accumulated temperature parameter area of each benchmark crop varieties；
(2) by it is each research website correspond to the time day meteorological data, soil data and field management data input plant growth machine
It manages in model, the crop phenological period of modeling is compared with the actual measurement crop phenological period that Field observation is tested, by anti-
The kind accumulated temperature parameter value of polyphony integral mould, determines the accumulated temperature parameter value of each benchmark crop varieties；
(3) it is recorded according to the date of seeding of each research website, is arranged under long-term sequence and fixes date of seeding and benchmark crop varieties and reality
Two simulated scenarios of border date of seeding and benchmark crop varieties；
(4) it according to the accumulated temperature parameter value of each benchmark crop varieties determined in step (2), is simulated using plant growth mechanism model
The chief crop phenological period under different scenes, using method separation climate change, date of seeding adjustment and the variety variations of linear fit
Influence to crop phenology.
2. the calculating according to claim 1 influenced based on plant growth mechanism model separation key factor pair crop phenology
Method, it is characterised in that: in the step (4):
(4-1), which calculates climate change according to following equation, influences crop phenology:
P(cl)i=a(cl)X(cl)i+b(cl)
Wherein, P (cl)iIt is the analogue value in chief crop phenological period under fixed date of seeding and benchmark crop varieties accumulated temperature parameter, X (cl)i
It is climate change to crop phenology influence time sequence time, i=1,2 ... ..., n；a(cl)It is climate change to crop phenology
It influences, day/year；b(cl)It is linear fit intercept；
(4-2), which calculates date of seeding adjustment according to following equation, influences crop phenology:
P(sd)i=a(sd)X(sd)i+b(sd)
P(sd)i=P (so)i-P(cl)i
Wherein, P (sd)iIt is to adjust lower crop Phenological change, X (sd) date of seedingiIt is date of seeding adjustment to crop phenology influence time sequence
Time, i=1,2 ... ..., n；a(sd)It is the influence adjusted date of seeding to crop phenology, day/year；b(sd)It is linear fit intercept；P
(so)iIt is the analogue value in chief crop phenological period under practical date of seeding and benchmark crop varieties accumulated temperature parameter, P (cl)iIt is fixed date of seeding
With the analogue value in chief crop phenological period under benchmark crop varieties accumulated temperature parameter；
(4-3), which calculates kind transformation according to following equation, influences crop phenology:
P(cs)i=a(cs)X(cs)i+b(cs)
P(cs)i=P (o)i-P(so)i
Wherein, P (cs)iIt is that kind converts lower crop Phenological change, X (cs)iIt is kind transformation to crop phenology influence time sequence
Time, i=1,2 ... ..., n；a(cs)It is influence of the kind transformation to crop phenology, day/year；b(cs)It is linear fit intercept；P
(o)iIt is actual observation crop phenological period, P (so)iIt is crop phenological period mould under practical date of seeding and benchmark crop varieties accumulated temperature parameter
Analog values.
3. the calculating according to claim 1 influenced based on plant growth mechanism model separation key factor pair crop phenology
Method, it is characterised in that: in the step (1), the research period is Past 30 Years or more.
4. the calculating according to claim 1 influenced based on plant growth mechanism model separation key factor pair crop phenology
Method, it is characterised in that: in the step (1), it is first determined crop varieties transformation of each research website in the research period, and
The crop varieties of beginning period are benchmark crop varieties in the Selecting research period.
5. the calculating according to claim 1 influenced based on plant growth mechanism model separation key factor pair crop phenology
Method, it is characterised in that: in the step (1), the process of crop growth record phenology data of benchmark crop varieties include sowing
Phase, seeding stage, jointing stage, florescence and maturity period determine each benchmark crop varieties according to actual field trial appraising model
Accumulated temperature parameter area.
6. the calculating according to claim 1 influenced based on plant growth mechanism model separation key factor pair crop phenology
Method, it is characterised in that: in the step (2), field management data include seeding method, irrigation, fertilising and harvest.
7. the calculating according to claim 1 influenced based on plant growth mechanism model separation key factor pair crop phenology
Method, it is characterised in that: in the step (3), fixed date of seeding is the sowing time average value studied in the period.
8. the calculating according to claim 1 influenced based on plant growth mechanism model separation key factor pair crop phenology
Method, it is characterised in that: in the step (4), the chief crop phenological period is florescence and maturity period.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109479481A (en) * | 2019-01-07 | 2019-03-19 | 中国农业科学院茶叶研究所 | A method of prediction famous green tea spring tea absorbs this season fertilizer nitrogen |
Citations (3)
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| CN104134095A (en) * | 2014-04-17 | 2014-11-05 | 中国农业大学 | Crop yield estimation method based on scale transformation and data assimilation |
| CN105023011A (en) * | 2015-08-19 | 2015-11-04 | 苏州奥诺遥感科技有限公司 | HMM based crop phenology dynamic estimation method |
| CN105468854A (en) * | 2015-11-27 | 2016-04-06 | 河北省科学院地理科学研究所 | Key factor yield contribution calculation method based on crop growth mechanism |
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2018
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN104134095A (en) * | 2014-04-17 | 2014-11-05 | 中国农业大学 | Crop yield estimation method based on scale transformation and data assimilation |
| CN105023011A (en) * | 2015-08-19 | 2015-11-04 | 苏州奥诺遥感科技有限公司 | HMM based crop phenology dynamic estimation method |
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| Title |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109479481A (en) * | 2019-01-07 | 2019-03-19 | 中国农业科学院茶叶研究所 | A method of prediction famous green tea spring tea absorbs this season fertilizer nitrogen |
| CN109479481B (en) * | 2019-01-07 | 2020-11-24 | 中国农业科学院茶叶研究所 | Method for predicting nitrogen absorption of famous green tea spring tea on current fertilizer |
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Application publication date: 20181218 |