CN106485002B - In the method for complicated landform climatic province estimation sugarcane potential production - Google Patents
In the method for complicated landform climatic province estimation sugarcane potential production Download PDFInfo
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Abstract
In the method for complicated landform climatic province estimation sugarcane potential production, this method is first to carry out the optimization of angstrom this river nurse sunlight model a, b parameter and calculate the solar radiation earning in a day, and APSIM-Sugar model parameter is then arranged and simulates sugarcane potential production.The present invention is based on angstrom this river nurse sunlight model and an APSIM-Sugar sugarcane production models, the methods of compared by representative stations actual-structure measurement, modeling, parameter optimization, the method for having obtained being more suitable the solar radiation and the assessment of sugarcane potential production of complicated landform climatic province, so that solar radiation data can not be directly acquired, and the sugarcane district of topoclimate complicated condition, it can be improved solar radiation estimation precision, to obtain more accurate sugarcane yield prediction result.
Description
Technical field
The present invention relates to sugarcane farmland production technical fields, and in particular to optimization estimation solar radiation and sugarcane potential production
Method.
Background technique
Solar radiation is the basic driver power of crop growth, accurate to estimate that solar radiation is to utilize crop growth model
Carry out the important prerequisite condition of correlative study.Forefathers studies have shown that angstrom this river nurse sunlight model (-
It Prescottmodel) is the effective ways that solar radiation is estimated using sunshine time；Crop growth model is then to assess in recent years
The powerful of crop yield and climatic effect.But topoclimates complicated condition and shortage solar radiation on Yunnan Low Latitude plateau etc.
The sugarcane district of measured data such as directly drives angstrom this river nurse sunlight model using the preset parameter that international agricultural tissue (FAO) is recommended,
It is excessively high (a small number of area relatively low) often to will appear solar radiation estimated value, so as to cause using crop growth model be difficult to obtain compared with
Accurate sugarcane potential production prediction result.
Summary of the invention
It is an object of the invention to solve the deficiencies in the prior art, providing a kind of can more accurately estimate sugarcane potential production
Method, to improve solar radiation sunlight model and sugarcane production model in the applicability of landform and weather complicated condition region.
The purpose of the present invention is achieved through the following technical solutions:
In the method for complicated landform climatic province estimation sugarcane potential production, technology is as follows:
1) angstrom this river nurse sunlight model a, b parameter optimization and local elevation effect correction are carried out to improve the estimation essence of solar radiation
Degree:
Obtain the latitude and longitude coordinates and altitude data on farmland periphery or local meteorological site, first according to formula (1),
(2) a is carried out, then the elevation effect correction of b value calculates solar radiation Rs according to angstrom this river nurse sunlight model formula (3)；
A=1.57 × 10-5H+0.1705 (1)
B=2.01 × 10-5H+0.5416 (2)
In above-mentioned formula, a, b are model empirical coefficient；
H is height above sea level, unit: m；
Rs is solar radiation, unit: MJm-2day-1；
Ra is zenith radiation, unit: MJ m-2day-1；
Number when n is actual sunshine, unit: h；
N is that local day is long, unit: h；
2) kind, soil and the management parameter setting of APSIM-Sugar model are carried out and predicts sugarcane potential production:
The solar radiation R that will be calculatedsAPSIM-Sugar sugarcane production mould is inputted with local daily meteorological data
Type is accordingly arranged kind module, soil module and management module, predicts sugarcane under the simulated conditions that no liquid manure is coerced
Potential production.
Daily meteorological data of the present invention includes but is not limited to the daily highest temperature, the lowest temperature, precipitation.
The present invention is based on angstrom this river nurse sunlight model and an APSIM-Sugar sugarcane production models, pass through representative stations reality
Measured data statistics, Model Parameter Optimization and sunykatuib analysis are compared, and have obtained being more suitable the sun spoke of complicated landform climatic province
Penetrate the method with the assessment of sugarcane potential production.This method can be applied to topoclimate complexity and can not directly acquire solar radiation number
According to sugarcane district, to improve the overall estimation precision of the solar radiation earning in a day, and obtain more accurate sugarcane yield prediction result.
Specific embodiment
Estimate that solar radiation and sugarcane potential production, specific implementation method are as follows in complicated landform climatic province:
1. obtain the latitude and longitude coordinates and altitude data on farmland periphery or local meteorological site, according to formula (1),
(2) a is calculated, b value calculates solar radiation R according to formula (3)s；In formula, a, b are model empirical coefficient, and H is height above sea level (m), RsFor
Solar radiation (MJ m-2day-1), Ra(MJ m is radiated for zenith-2day-1), number (h) when n is actual sunshine, N is that local day is long
(h):
A=1.57 × 10-5H+0.1705 (1)
B=2.01 × 10-5H+0.5416 (2)
2. by the meteorological number such as the solar radiation earning in a day being calculated and local every daily maximum temperature, the lowest temperature, precipitation
According to input APSIM-Sugar sugarcane production model, kind module, soil module and management module are accordingly arranged, in nothing
Moving model under liquid manure stress conditions obtains sugarcane potential production analog result.For example, in the Sugarcane Fields of Yunnan Province of landform and weather complexity
The localization initial parameter in area, the model main modular can be used below with reference to value, such as have ready conditions, please voluntarily according to measured data
It corrects:
2.1 Cultivar parameters (being reference with Q117)
2.2 soil parameters (being reference with red soil)
2.3 management parameters (being reference with Yunnan Sugarcane Area)
In the setting of APSIM-Sugar model, automatic irrigation module switch is opened, setting is when opposite using soil moisture
Content carries out auto-irrigation when being lower than 100%.The plant phase is set in mid-February~mid-March, and the time of infertility is set as 11~13
Month, while guaranteeing 450kg ha-1Nitrogenous fertilizer supply, points of 3~5 times fertilisings.To guarantee to cause in the time of infertility without liquid manure stress
Production loss.
Yuanjiang County of Yunnan sugarcane district calculated examples: the first step, by sugarcane research institute, academy of agricultural sciences, Yunnan Province Yuanjiang River sugarcane trial website
Latitude and longitude coordinates and elevation data, substitute into formula (1) (2) a=0.18, b=0.55 is calculated；Formula (3) are based on again, benefit
With the daily sunshine time data of Yuanjiang River meteorological site 2012-2015, solar radiation earning in a day R is calculateds, average 16.4MJ m-2day-1.Second step, by meteorological datas such as the solar radiation earning in a day of Yuanjiang River website, the daily highest temperature, the lowest temperature, precipitation
Input APSIM-Sugar model meteorology module；Again respectively according to the content of 2.1,2.2 and 2.3 in APSIM-Sugar model pair
It answers and Cultivar parameter, soil parameters and management parameters is set in module, then moving model, obtain Yuanjiang River sugarcane district 2012-2015
Average sugarcane potential production be 137.9t ha-1。
The present invention compares the solar radiation earning in a day of different parameters optimization method estimation and in this, as sugarcane production model
The sugarcane potential production analogue value that input variable is calculated and actual measurement solar radiate the earning in a day and corresponding yield simulation value it
Between difference and its conspicuousness, obtain the Model Parameter Optimization side of the topoclimates complex regions such as a set of suitable Yunnan Low Latitude plateau
Method.Solar radiation sunlight model a, the b parameter that the method for the present invention uses does not use general international agricultural tissue (FAO) and recommends
Value, but corrected according to local height above sea level；The estimation of sugarcane potential production does not use conventional productive potentialities and corrects step by step
Empirical model method carry out, but use Australia exploitation APSIM-Sugar sugarcane production model coerced in no liquid manure
Under conditions of carry out simulation calculating.
On the basis of solar radiation measured value and its corresponding sugarcane potential production analogue value, in Yunnan Low Latitude plateau difference
The solar radiation earning in a day and its corresponding potential production analogue value and FAO that sugarcane district website uses the method for the present invention to be calculated are conventional
The solar radiation value and its corresponding sugarcane potential production analogue value that method obtains are compared, average opposite root-mean-square error
(NRMSE) reduce 4% (0.64MJ m respectively-2day-1) and 3% (3t ha-1), in individual websites such as Yuanjiang River up to 9.5%
(1.5MJ m-2day-1) and 15.2% (20.1t ha-1).Test result shows that the present invention helps to improve local solar radiation and estimates
Precision and sugarcane potential production simulation precision are calculated, is the complicated landform climatic province of representative particularly suitable for Yunnan Low Latitude plateau
Domain.
Claims (2)
1. in the method for complicated landform climatic province estimation sugarcane potential production, which is characterized in that method is as follows:
1) angstrom this river nurse sunlight model a, b parameter optimization and local elevation effect correction are carried out to improve the estimation precision of solar radiation:
Obtain the latitude and longitude coordinates and altitude data on farmland periphery or local meteorological site, first according to formula (1), (2) into
Then the elevation effect correction of row a, b value calculates solar radiation Rs according to angstrom this river nurse sunlight model formula (3)；
A=1.57 × 10-5 H+0.1705 (1)
B=2.01 × 10-5 H+0.5416 (2)
In above-mentioned formula, a, b are model empirical coefficient；
H is height above sea level, unit: m；
Rs is solar radiation, unit: MJm-2day-1；
Ra is zenith radiation, unit: MJ m-2day-1；
Number when n is actual sunshine, unit: h；
N is that local day is long, unit: h；
2) kind, soil and the management parameter setting of APSIM-Sugar model are carried out and predicts sugarcane potential production:
The solar radiation R that will be calculatedsAPSIM-Sugar sugarcane production model is inputted with local daily meteorological data, to product
Kind module, soil module and management module are accordingly arranged, and the potential production of sugarcane is predicted under the simulated conditions that no liquid manure is coerced
Amount.
2. the method according to claim 1 in complicated landform climatic province estimation sugarcane potential production, which is characterized in that
The daily meteorological data includes but is not limited to the daily highest temperature, the lowest temperature, precipitation.
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| CN107423853B (en) * | 2017-07-25 | 2020-07-03 | 中国农业科学院农业信息研究所 | A Method and System for Determining Yield-Meteorological Variation Coefficient |
| CN108633424B (en) * | 2018-03-16 | 2021-03-23 | 中国农业科学院农业资源与农业区划研究所 | Winter wheat fertilizing method |
| CN111582768A (en) * | 2020-06-09 | 2020-08-25 | 云南省农业科学院甘蔗研究所 | Method for evaluating economic weight value of sugarcane character based on manual harvest marginal cost |
| CN112182714A (en) * | 2020-10-09 | 2021-01-05 | 南京大学 | Method for calculating solar energy potential of buildings considering pilot line of sight and weather conditions |
| CN112949179B (en) * | 2021-03-01 | 2023-05-12 | 中国农业科学院农业信息研究所 | Winter wheat growth simulation method and system under application of resin coated nitrogenous fertilizer |
| CN115293410A (en) * | 2022-07-20 | 2022-11-04 | 中科三清科技有限公司 | Air temperature prediction method, air temperature prediction device, storage medium and electronic equipment |
| CN115600760A (en) * | 2022-11-09 | 2023-01-13 | 南宁师范大学（Cn） | Sugarcane region yield per unit prediction method and system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102004848A (en) * | 2010-11-10 | 2011-04-06 | 中国科学院地理科学与资源研究所 | Method and system for improving calculation accuracy of photosynthetically active radiation (PAR) |
| CN104297816A (en) * | 2014-10-27 | 2015-01-21 | 广东省东莞市气象局 | Calibration correcting method for ultraviolet radiation meter |
| CN104880701A (en) * | 2014-09-23 | 2015-09-02 | 航天恒星科技有限公司 | Satellite-borne sensor imaging simulation method and device |
| CN105184445A (en) * | 2015-08-06 | 2015-12-23 | 北京市气候中心 | Calculation method of average corn loss ratio of many years under corn drought meteorological disasters |
-
2016
- 2016-10-13 CN CN201610893807.1A patent/CN106485002B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102004848A (en) * | 2010-11-10 | 2011-04-06 | 中国科学院地理科学与资源研究所 | Method and system for improving calculation accuracy of photosynthetically active radiation (PAR) |
| CN104880701A (en) * | 2014-09-23 | 2015-09-02 | 航天恒星科技有限公司 | Satellite-borne sensor imaging simulation method and device |
| CN104297816A (en) * | 2014-10-27 | 2015-01-21 | 广东省东莞市气象局 | Calibration correcting method for ultraviolet radiation meter |
| CN105184445A (en) * | 2015-08-06 | 2015-12-23 | 北京市气候中心 | Calculation method of average corn loss ratio of many years under corn drought meteorological disasters |
Non-Patent Citations (3)
| Title |
|---|
| Estimation Solar Radiation and Status Solar Energy in Yazd Province - Iran;Fakharpour Mahsa等;《 2012 Asia-Pacific Power and Energy Engineering Conference》;20120920;全文 * |
| 我国亚热带地区甘蔗产量的模型模拟;黄智刚等;《中国糖料》;20070131(第1期);第8-12页 * |
| 甘蔗生长模型研究进展;杨昆等;《湖南农业大学学报(自然科学版)》;20150228;第41卷(第1期);第29-34页 * |
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