CN107273679A - The intensive corn nonoculture area agricultural non -point pollution in northeast is evaluated and monitoring method - Google Patents
The intensive corn nonoculture area agricultural non -point pollution in northeast is evaluated and monitoring method Download PDFInfo
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Abstract
Evaluated and monitoring method the present invention relates to a kind of intensive corn nonoculture area agricultural non -point pollution in northeast, the agricultural non -point pollution evaluation is soil body Nitrate N leaching amount and ammonia volatilization amount after harvest corn with monitoring index, corn nonoculture agricultural land soil Nitrate Nitrogen Residue amount and NO_3-- N Leaching amount or soil ammonia volatilization amount are set up functional relation by the method for first passage modeling of the present invention on the yardstick of northeast intensive agricultural region, the intensive corn nonoculture area agricultural non -point pollution evaluation in northeast that the present invention is provided can synthetically reflect annual precipitation with monitoring method, plant growth, NO_3-- N Leaching, the comprehensive effects such as soil ammonia volatilization, compared with the monitoring technology that soil eluviation and ammonia volatilization are surveyed in existing farmland, the present invention first provide one can the intensive corn NO_3-- N Leaching amount in comprehensive assessment northeast and ammonia volatilization amount it is accurate, it is easy to parameter and the algorithm determined.
Description
Technical field
The present invention relates to agricultural pollution prevention and control field, and in particular to a kind of intensive corn nonoculture area farmland face in northeast source is dirty
Dye is evaluated and monitoring method.
Background technology
The universal application fertilizer in farmland and irrational Agricultural management system, cause the nitrogenous fertilizer of administration with ammonia volatilization in recent years
Lose serious all the more with Nitrate N leaching form.Thus become increasingly conspicuous the problem of the caused water resource pollution to underground.Soil is cutd open
Face remains higher nitrogen and easily forms the risk migrated vertically downward, and underground water pollution is caused with being oozed under precipitation with irrigating.Such as
What, which simply and accurately assesses pollution of area source generating capacity, turns into the hot issue of domestic and foreign scholars research.At present, farmland nitre is calculated
The method of state N leaching is generally takes leaching liquor or the method for determining soil water potential combined soln nutrient density, meter by leaching disk
The method for calculating soil ammonia volatilization is usually gas tank air suction sampling chemical assay, and this two class determines granting and is only capable of number on measuring point
According to regional representativeness and time are representative very poor, and this method complex operation promotes inconvenience.And the advantage of modeling method exists
In its is simple to operate, simulation accurate and it is representative wide the advantages of, as assessing and prediction Nitrate N leaching and soil ammonia volatilization
Novel method, at present using more model have LEACHM (Leaching Estimation and Chemistry Model),
SWAT (Soil Water Assessment Tool) etc., these models are in comprehensive analysis crop production and soil nitrogen migration knot
It is also to be strengthened in terms of conjunction.And the agricultural production management of RZWQM (Root Zone Water Quality Model) Model coupling
And the module of ambient influnence, as the new tool for predicting and assessing Total Nitrogen leaching and soil ammonia volatilization.
RZWQM models are by agriculture system research institute of United States Department of Agriculture (USDA-ARS, Great Plain System
Research Unit) in 1992 release agricultural system crop and environmental management model.Integrate and consider crop root zone institute
There are physics to plant growth, the influence of biological and chemical process.Winter under the conditions of the application such as Wang model optimization sewage irrigation
Wheat-summer corn measures of fertilizer.Fang etc. simulates crop yield and WUEL under different irrigation systems.It is existing to grind
Study carefully lay particular emphasis on irrigation and WUEL research in terms of, and domestic application model to farmland fertilization process Nitrate N leaching and
The assessment of ammonia volatilization is scarcely out of swaddling-clothes.Because the model is higher to field measured data index request and has spreadability, institute
With according to measured data to parameter rating of the model and checking after can be verified in the region of the data deficiencies such as Northeast Agricultural Region of China or
Promote, be that new thinking and evaluation method are opened up in northeast intensive agricultural region farmland Nitrate N leaching and soil ammonia volatilization prediction.
The content of the invention
Evaluated the technical problem to be solved in the present invention is to provide a kind of intensive corn nonoculture area agricultural non -point pollution in northeast
With monitoring method, this method can simply estimate that each approach of the intensive corn nonoculture area agricultural non -point pollution in northeast occurs exactly
Amount, and then according to forecast assessment result, targetedly research and development technology measure or regulation and control agronomic measures cut down agricultural non -point pollution
Generating capacity, promotes the sustainable production of China's agricultural.
In order to solve the above technical problems, the technical solution adopted by the present invention is：
A kind of intensive corn nonoculture area agricultural non -point pollution in northeast is evaluated and monitoring method, and the agricultural non -point pollution is commented
Valency and monitoring index are corn nonoculture agricultural land soil Nitrate N leaching amount and ammonia volatilization amount, the NO_3-- N Leaching amount and ammonia
Computational methods of the volatile quantity on the yardstick of northeast intensive agricultural region are as follows：
Y1=0.7742X-22.438
Y2=0.1705X-10.923
Y1- Nitrate N leaching amount kghm-2, Nitrate Nitrogen Residue amount kghm after X- harvest corns-2；Y2- soil ammonia volatilization
Measure kghm-2。
The invention has the advantages that：
The intensive corn nonoculture area agricultural non -point pollution evaluation in northeast that the present invention is provided and monitoring method can be according to applying
Fertilizer amount or residual quantity calculate farmland Nitrate N leaching amount and ammonia volatilization amount, and the two indexs are to evaluate Northeast plain farmland face source
The core index of pollution situation, and then the rational application of fertilizer per season crop is instructed, effectively cut down North China agriculture district agricultural non -point pollution
Generating capacity.
The method of first passage modeling of the present invention passes through residual nitrate nitrogen in soil in the intensive corn nonoculture area in northeast
Amount is set up correlation with Nitrate N leaching amount and ammonia volatilization amount and contacted, it is found that residual nitrate nitrogen in soil figureofmerit can be integrated anti-
The comprehensive effects such as annual precipitation, plant growth, NO_3-- N Leaching, soil ammonia volatilization are reflected, are drenched with existing farmland actual measurement soil
Molten to be compared with the technology of ammonia volatilization, the present invention first being capable of comprehensive assessment NO_3-- N Leaching amount and ammonia volatilization there is provided one
Amount is accurate, be easy to the parameter and algorithm of measure.
Brief description of the drawings
Fig. 1 is the different disposal soil moisture content analogue value of harvest corn phase in 2009 and measured value.
Fig. 2 is the different disposal soil profile nitrate nitrogen content analogue value of harvest corn phase in 2009 and measured value.
Fig. 3 is the different disposal soil profile nitrate nitrogen analogue value of harvest corn phase in 2014 and measured value.
Fig. 4 is the different disposal corn yield analogue value and measured value.
Fig. 5-1 and Fig. 5-2 is different disposal nitrate nitrogen and ammoniacal nitrogen loss feature.
Embodiment
The present invention is expanded on further below in conjunction with specific embodiment.Involved method in following embodiments, such as without especially
Explanation is conventional method.
A kind of intensive corn nonoculture area agricultural non -point pollution in northeast is evaluated and monitoring method, solves farmland Nitrate N leaching
The problem of amount and soil ammonia volatilization amount are difficult to direct monitoring, is agricultural non -point pollution letter on the intensive corn nonoculture area yardstick in northeast
Single accurately evaluate provides foundation.
The section soil moisture content of 3 groups of amount of nitrogen gradient field tests actual measurements of the intensive corn field test in collection northeast,
The data such as section content of soil nitrate-N, crop yield, the basic physicochemical character data of collection soil, collection Precipitation During Growing amount,
The meteorological datas such as temperature.
RZWQM models are run, by basic soil physical and chemical property data and meteorological data input system；
RZWQM models are run, calibration is carried out to soil profile water content using the measured data of corn season in 2009；
RZWQM models are run, soil profile water content is verified using the measured data of corn season in 2014；
RZWQM models are run, calibration is carried out to soil profile nitrate nitrogen content using the measured data of corn season in 2009；
RZWQM models are run, soil profile nitrate nitrogen content is verified using the measured data of corn season in 2014；
RZWQM models are run, calibration is carried out to crop yield using the corn measured data of corn season in 2009；
RZWQM models are run, crop yield is verified using corn measured data in 2014；
RZWQM models are run, a variety of dose gradient output soil profile nitrate nitrogen contents and Nitrate N leaching amount is inputted；
RZWQM models are run, a variety of dose gradient output soil profile nitrate nitrogen contents and ammonia volatilization amount is inputted；
Set up soil profile nitrate nitrogen content and Nitrate N leaching flow function relation；
Set up soil profile nitrate nitrogen content and soil ammonia volatilization flow function relation；
The intensive corn nonoculture area in northeast, which has published thesis, surveys Nitrate N leaching amount and the function progress accuracy rate of foundation
Analysis；
The intensive corn nonoculture area in northeast, which has published thesis, surveys ammonia volatilization amount and the function progress accuracy rate analysis of foundation.
Inventor carries out in northeast on the basis of corn long term experiment Monitoring Data, completes RZWQM models moisture, supports
The calibration of point Transport And Transformation module and checking, by setting Different Fertilization amount level, output obtain a series of soil eluviation amounts and
Ammonia volatilization amount, and residual nitrate nitrogen in soil amount, set up after recurrence between pollution of area source generating capacity and residual nitrate nitrogen in soil amount
Functional relation, line function accuracy rate of going forward side by side analysis, concrete operations are as follows：
1 materials and methods
1.1 test material
Test and carried out in the Ministry of Agriculture of Liaoning Province Fuxin agricultural environment with arable land child care scientific experiment station.This area's weather belongs to
The middle continental monsoon climate in temperate zone, average annual temperature is 5.3 DEG C.Average annual rainfall is 806.5mm or so, wherein being concentrated mainly on 7
~September part, rainfall accounts for full total rainfall per year 70% or so.
Field test is since 2009 years, and this experiment is using 2009 annual corn season T1, T2, T3 actual measurement soil moisture contents
Carry out model water parameters calibration and checking, and nutrient parameter calibration.2010-2013 measured datas are lacked.And
2014 only T1 processing soil nutrient measured data, so using the data carry out nutrient module verification and simultaneously predict T2 and
T3 handles dynamics of soil nutrients.Field is sandy loam for examination soil types, and section soil weight excursion is 1.38-1.51g/
cm3, full nitrogen 1.23g/kg, rapid available phosphorus (P2O5) 20.3mg/kg, available potassium (K2O) 51mg/kg, organic matter 26.4g/kg.2009
Soil profile nitrate nitrogen and ammonium nitrogen initial content are shown in Table 1 before year corn is broadcast.
Nitrogen application gradient test designs 3 processing, respectively this area's traditional habit amount of nitrogen T1 (N 240kg/hm2,
Base manure and typhon a mouthful phase topdressing amount are 1：2), (the N 216kg/hm of T2 nitrogenous fertilizer decrement 10%2) and (N of T3 nitrogenous fertilizer decrement 20%
192kg/hm2), each 3 repetitions of processing, each plot area is 30.25m2.Corn variety is single from local main breed the Liao Dynasty
28, April 30 sowing date, the harvest date is respectively September in 2009 27 and September in 2014 28 days, the corn growth cycle 125
My god, trial zone field management is field management mode.
Pedotheque is adopted in corn maturity period (September 20 days).Section soil sample is one layer per 20cm, is divided into five layers to underground 1
Rice collection, randomly selects at 3 points per cell and is fetched earth with earth boring auger.Soil sample uses 0.05mol/L CaCl respectively after fetching2Solution is extracted, shake
40min filterings are swung, each soil layer nitrate nitrogen content is determined with Swiss Foss Flow Analyzers, and according to each layer soil weight by nitre
State nitrogen content is converted into 0-100cm soil body Nitrate-nitrogen accumulations.Corn maturity period collection Plant samples dry to constant weight, for calculating
With checking yield.
The basic physicochemical characteristicses of the soil of table 1
1.2 test model brief introductions
RZWQM is being transported to soil root area water quality, soil nutrient for llanura system research institute of United States Department of Agriculture research and development
Move and plant growth carries out comprehensive simulation model.RZWQM models are made up of 6 modules：Physical module, chemical module, nutrient
Module, plant growth module, insecticide module and management module.Including " day " and " when " two time scales, with " day "
For dimension calculation ion, fertilizer, irrigation water, arable land measure, and potential evapotranspiration hair and transpiration rate.Water translocation and and nutrient laden
Process with " when " dimension calculation, include the reallocation of soil moisture, the Transport And Transformation of nutritive salt, diafiltration, runoff, insecticide and drench
Wash, thermal losses, actual evapotranspiration, plant nutrient absorption process etc.；Then insecticide Transport And Transformation process is calculated again (herein not
Be related to), carbon nitrogen Transport And Transformation process and soil material equilibrium process；Finally run plant growth module.RZWQM can simulate soil
The main process of earth nitrogen conversion, including crop nitrogen residual, the mineralising of organic, N immobilization, Nitrogen Leaching, ammonia volatilization, nitrification
With denitrification etc..Model can preferably simulate the Transport And Transformation of nitrogen, more convenient acquisition Nitrogen Leaching numerical value and
Leaching situation is assessed, and pointedly carries out Optimum measure to cut down loss.
It is Physical Processes, Nutrient to collect the module that is related in this research simulation process
Processes、Plant Growth Processes.Model original input data includes meteorological data and soil master data
(such as table 1).Wherein meteorological data includes daily precipitation, the day highest temperature, day lowest temperature, wind speed, relative air humidity etc. during simulating；
Soil master data include 0-1m soil layers per mono- layer of 20cm soil layer unit weight, field capacity, soil pH value, soil moisture content,
Section nitrate nitrogen and ammonium nitrogen initial content.
1.3 model result evaluations
The calibration of model parameter follows first moisture module, then nutrient module, the order of last crop module.Model calibration
The evaluation of effect is the key of critical parameter optimization, and different statistical indicators respectively have advantage and disadvantage, herein in parameter rating of the model process
It is middle that parameter optimization is carried out using normalization error (root-mean-square deviation/average value), i.e., constantly change parameter value to reduce analog result
With the difference of measured result, when normalizing error value minimum, the selected parameter of model is used as final calibration result.From two
The result of individual metrics evaluation model running：(1) mean square error (RMSE), belongs to absolute error index, and the exhausted of effect is simulated in reflection
To unbiasedness and extreme value effect；(2) average relative error (MRE), belongs to relative error index, the relative nothing of reflection simulation effect
Bias.Parameter rating of the model and verify effect is embodied by RMSE and MRE, when RMSE reaches minimum value, and MRE levels off to 0,
Simulate effect more excellent.Calculation formula is as follows：
Wherein, N is the number of observation, QiRepresent i-th of observation, PiRepresent the analogue value of i-th of observation.
2 results and analysis
2.1 simulation precisions are examined
The parameter of influence model moisture output mainly has field moisture, soil evaporation coefficient etc., Nitrogen Transport module
Including soil chemistry amount, microbiologic population etc. is respectively adopted trial-and-error method and debugged repeatedly, the analogue value and measured value is at utmost connect
Closely.Wherein soil moisture content calibration process RMSE maximums are 0.96cm3/cm3, MRE is 9% to the maximum；Nitrate nitrogen calibration process
RMSE maximums are 4.40mg/kg, and MRE is 68.60% to the maximum；Nitrate nitrogen verification process RMSE maximums are within 2014
6.15mg/kg, MRE are 39.47% to the maximum.Analog result is still within acceptable simulation error.
2.2 model water content calibrations and checking
20,40,60,80 and 100cm soil profile moisture content is respectively under the processing of the harvest time T1 of corn season in 2009
10.39%th, 11.16%, 10.89%, 11.63%, 11.81%, each layer of soil profile moisture content is respectively under T2 processing
10.54%th, 11.26%, 10.95%, 11.68%, 11.93%, each layer of soil profile moisture content is respectively under T3 processing
10.63%th, 11.29%, 11.15%, 11.73%, 11.98%.Soil moisture content calibration RMSE value is respectively 0.96,0.51,
0.65th, 0.90 and 0.81, MRE be respectively 9%, -4%, 6%, 8% and 7%.Soil measured section moisture under different disposal
With soil depth increase without significant change trend, statistical analysis shows, model can the preferably local soil profile of calibration
Moisture.
2.3 model content of soil nitrate-N calibrations and checking
20,40,60,80 and 100cm soil profile nitrate nitrogen contents are respectively under the processing of the harvest time T1 of corn season in 2009
15.82mg kg-1、12.11mg kg-1、8.84mg kg-1、4.62mg kg-1With 3.80mg kg-1, each layer under T2 processing
Soil profile nitrate nitrogen content is respectively 13.62mg kg-1、5.69mg kg-1、4.85mg kg-1、3.32mg kg-1、2.51mg
kg-1, each layer of soil profile nitrate nitrogen content is respectively 14.78mg kg under T3 processing-1、4.79mg kg-1、2.01mg
kg-1、2.33mg kg-1、1.54mg kg-1.T2 processing checking amount of nitrogens are in 210mg kg-1Migration of the lower nitrate nitrogen in soil
Situation.Soil nitrate-N calibration RMSE value is respectively 4.40mg kg-1、3.59mg kg-1、3.83mg kg-1、2.37mg kg-1With
1.91mg kg-1, MRE is respectively -29.14%, 35.51%, 68.60%, 12.90% and -20.15%.Soil nitrate-N calibration
As a result it is identical with measured value variation tendency (Fig. 2).Statistical analysis shows that the simulation soil body nitrate nitrogen migration that model can be good is special
Levy.
In order to verify the accuracy and applicability of model, model checking and prediction are carried out using nitrate in 2014.
Transport conditions of the Fig. 3 for the harvest time nitrate nitrogen of corn season in 2014 in soil, and it is pre- using T2 and T3 processing progress models
Survey research.Under T1 processing, the soil profile nitrate nitrogen analogue value is respectively 7.66mg kg-1、6.34mg kg-1、11.40mg kg-1、6.67mg kg-1、1.96mg kg-1.Each soil layer mean square error RMSE is respectively 1.37mg kg-1、2.21mg kg-1、
0.77mg kg-1、0.17mg kg-1With 2.71mg kg-1.Each soil layer average relative error MRE is respectively 5.96%,
11.62%th, -2.25%, -0.84% and 46.08%.Soil nitrate-N verified in processing T1 under acceptable result, to T2 and
T3 is predicted analysis, and interpretation of result shows, 2014, and model is 6.15mg kg relative to the average RMSE value of 2009 annual datas-1、1.36mg kg-1、1.41mg kg-1、0.57mg kg-1With 1.64mg kg-1.MRE is respectively -28.73%, 18.90%,
39.47%th, 5.83% and 24.97%.Can still can be preferably to local each under T2 and T3 processing nitrates
Nitrate nitrogen content is predicted in soil profile.
2.4 crop yield estimating model parameter calibrations and checking
Model is carried out to soil layer under different amount of nitrogens after accurate parameter calibration and checking, and crop yield is verified
And prediction.Influence of three amount of nitrogens to crop yield is as shown in figure 4, production forecast to three processing of the model to 2009
Respectively less than measured value.Actual measurement yield is divided into 8202kg hm between different repetitions under different amount of nitrogen dispositions-2、9151.5kg
hm-2With 7537.5kg hm-2, and T2 processing under corn yield reach highest, respectively more than T1 and T3 processing yield
10.37% and 17.64%.Crop yield situation is not how many and produce result of substantially successively decreasing according to amount of nitrogen, but in model
Yield still has the trend for reducing and reducing with amount of nitrogen in simulation.Model is verified to different Nitrogen applications within 2014
And prediction, under relative to T2 and T3 processing after the checking of nitrate nitrogen, model production forecast has been carried out to T2 and T3 processing, and
And the trend that tapers off, 15.38% and 22.65% are reduced respectively relative to T1 yield.Analog result coverage bias is acceptable
In the range of.
2.5 Model Extension applications
2.5.1 different disposal NO_3-- N Leaching amount and the prediction of ammonia volatilization amount
Model carries out parameter calibration and checking to soil layer under different amount of nitrogens, determines to keep constant after model major parameter,
Nitrate N leaching situation below single 1 meter of corn season soil layer is predicted by different amount of nitrogen situations (Fig. 5-1 and Fig. 5-
2).From Fig. 5-1 and Fig. 5-2 as can be seen that because of rainfall and soil leaching loss relation, causing Nitrate N leaching amount with amount of nitrogen reduction
Reduce.Soil eluviation amount under three Nitrogen Transport processing crop time of infertility is respectively 52.81,46.56 and 40.62kg hm-2, 22.0%, 21.5% and the 21.2% of processing amount of nitrogen is accounted for respectively.The result is within tolerance interval.
Model can simulate the volatile quantity of the ammoniacal nitrogen in soil under the processing of different amount of nitrogens simultaneously, by model to soil
Earth ammonia volatilization situation is predicted (Fig. 5).Under three treatment conditions, ammoniacal nitrogen volatilization is proportionate pass with field amount of nitrogen
System, is reduced with applying nitrogen to reduce.As a result show, the soil ammonia volatilization amount under different disposal accounts for three processing and always applied respectively
2.13%, 1.75% and the 0.49% of amount of nitrogenous fertilizer.Overall volatile quantity is smaller, and partly cause is in weak acid due to the pH value of local soil
Property, analog result relatively meets local soil property and ammoniacal nitrogen volatilization situation, predicted the outcome more accurately and within tolerance interval.
2.5.2 model prediction under the conditions of Different Fertilization amount
(1) pollution of area source prediction of emergence size valuation functions are set up
By the forecast function of model, obtain Different Fertilization amount Soil Under Conditions Nitrate-nitrogen accumulation corresponding leaching and
Ammonia volatilization amount, recurrence is obtained such as minor function：
Y1=0.7742X-22.438 (R2=0.9832, n=10)
Y2=0.1705X-10.923 (R2=0.9433, n=13)
Y1- Nitrate N leaching amount (kghm-2), Nitrate Nitrogen Residue amount (kghm after X- harvest corns-2)；Y2- ammonia volatilization
Measure (kghm-2)。
Nitrate nitrogen and ammoniacal nitrogen loss feature under the predicted condition of table 2
(2) the function accuracy rate analysis set up
By the intensive corn-growing regions in northeast, leaching function is calculated with reference to 18 groups of gradient datas in 8 pertinent literatures accurate
True rate is 67%.Ammonia volatilization accuracy rate 71% is calculated with reference to 5 groups of gradient datas in 3 pertinent literatures.
The Nitrate N leaching flow function relation accuracy rate of table 3 is verified
The ammonia volatilization flow function relation accuracy rate of table 4 is verified
Claims (1)
1. a kind of intensive corn nonoculture area agricultural non -point pollution in northeast is evaluated and monitoring method, it is characterised in that：The farmland
It is corn nonoculture agricultural land soil Nitrate N leaching amount and ammonia volatilization amount, the soil nitrate-N that pollution of area source, which is evaluated with monitoring index,
The computational methods of leaching and ammonia volatilization amount on the yardstick of northeast intensive agricultural region are as follows：
Y1=0.7742X-22.438
Y2=0.1705X-10.923
Y1- Nitrate N leaching amount kghm-2, Nitrate Nitrogen Residue amount kghm after X- harvest corns-2；Y2- soil ammonia volatilization amount
kg·hm-2。
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110909467A (en) * | 2019-11-21 | 2020-03-24 | 沈阳农业大学 | Three-dimensional soil field water capacity prediction method and system |
| CN115758068A (en) * | 2022-11-15 | 2023-03-07 | 东北农业大学 | Non-point source pollution emission estimation method based on migration-diffusion test fitting optimization |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102087681A (en) * | 2010-11-30 | 2011-06-08 | 浙江大学 | Model for forecasting multi-dimensional flux migration and transformation of fertilizer urea nitrogen in paddy field |
| CN104143048A (en) * | 2014-07-23 | 2014-11-12 | 中国水利水电科学研究院 | Method and device for calculating farmland surface source pollutant river pollution load |
-
2017
- 2017-06-09 CN CN201710433544.0A patent/CN107273679A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102087681A (en) * | 2010-11-30 | 2011-06-08 | 浙江大学 | Model for forecasting multi-dimensional flux migration and transformation of fertilizer urea nitrogen in paddy field |
| CN104143048A (en) * | 2014-07-23 | 2014-11-12 | 中国水利水电科学研究院 | Method and device for calculating farmland surface source pollutant river pollution load |
Non-Patent Citations (2)
| Title |
|---|
| 薛长亮 等: "RZWQM模拟小麦-玉米轮作系统氮素运移及损失特征", 《中国生态农业学报》 * |
| 薛长亮 等: "应用RZWQM模型模拟华北玉米土壤剖面水氮迁移及淋溶特征", 《农业环境科学学报》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110909467A (en) * | 2019-11-21 | 2020-03-24 | 沈阳农业大学 | Three-dimensional soil field water capacity prediction method and system |
| CN115758068A (en) * | 2022-11-15 | 2023-03-07 | 东北农业大学 | Non-point source pollution emission estimation method based on migration-diffusion test fitting optimization |
| CN115758068B (en) * | 2022-11-15 | 2024-04-16 | 东北农业大学 | Non-point source pollution emission estimation method based on migration-diffusion test fitting optimization |
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