CN102156128A - Method for remote sensing monitoring and predicting protein content of winter wheat grains - Google Patents
Method for remote sensing monitoring and predicting protein content of winter wheat grains Download PDFInfo
- Publication number
- CN102156128A CN102156128A CN 201010166000 CN201010166000A CN102156128A CN 102156128 A CN102156128 A CN 102156128A CN 201010166000 CN201010166000 CN 201010166000 CN 201010166000 A CN201010166000 A CN 201010166000A CN 102156128 A CN102156128 A CN 102156128A
- Authority
- CN
- China
- Prior art keywords
- protein content
- wheat
- remote sensing
- grain protein
- winter wheat
- 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.)
- Pending
Links
Images
Abstract
The invention provides a method, comprising the steps of: based on the instantaneity and wide regional coverage for obtaining remote sensing information, building a grain protein content prediction model based on nitrogen accumulation procedure via combining with the influencing characteristics of climatic environmental conditions in the flowering duration of wheat on the formation of grain quality; realizing the coupling of the remote sensing information and a grain protein content prediction model, namely using LAI (leaf area index), biomass and plant nitrogen content data inverted by flowering duration remote sensing images to displace the corresponding parameter variables of the wheat grain protein content prediction model, so as to realize the prediction for the protein content of single-point wheat grains, wherein the prediction precision is more than 85%; and drawing a graded monitoring and predicting thematic map for protein content of winter wheat grains by a scaling converting method of 'point' (the protein content of sample point grains) and 'face' (remote sensing images). The method has the characteristics of intuitionism and good timeliness, and has relatively good practicability for obtaining regional wheat quality information or guiding grain collection by department of agriculture.
Description
1, affiliated technical field
The inventive method relates to a kind of crop quality remote-sensing monitoring method, especially can dynamically carry out monitoring and prediction to winter wheat grain protein content in the regional extent, and can make the wheat quality remote sensing monitoring forecasting procedure of protein content thematic information figure.Belong to the agricultural remote sensing technical applications.
2, background technology
Wheat is global important cereal crops, is the second largest cereal crops that are only second to paddy rice in China, the supply degree and the trophic level of its yield and quality direct relation human foods.Wherein the improvement of quality trait improves economic and social benefit and has extremely important function and significance for the food source that enriches people.Protein content is the topmost factor of decision wheat flour purposes, according to the number of protein content, can be divided into strong gluten wheat, middle gluten wheat and weak muscle wheat.Wherein, the strong gluten wheat grain protein is suitable for producing bread flour and other tailored flours of collocation production more than or equal to 14%; Weak muscle Protein Content of Wheat Kernel is equal to or less than 11.5%, is suitable for making biscuit, cake etc.; The middle gluten wheat grain protein content is suitable for making noodles or steamed bun between strong muscle and weak muscle wheat.
Strong gluten wheat and weak muscle wheat slower development as high-quality flour, especially weak muscle wheat, country stopped extensive import wheat since the second half year in 2000, the use amount of homemade weak muscle wheat flour enterprise rises year by year, because supply and demand wretched insufficiency on the market has appearred in domestic famine at high-quality weak muscle wheat flour, along with industrialization and development of urbanization, the people's living standard improves constantly, and the demand that high-quality grain is consumed improves constantly, and market has openings is very big.The reason of high-quality wheat famine, except the wheat breed factor, the influence of cultivation process and field supporting management measure is comparatively obvious, and the important information of the final grain protein contents of influence such as nitrogen in wheat full growth period is lacked effectively monitoring and control.
At present domestic comparatively slow in the research and development aspect the forecast of winter wheat quality monitoring, Wang Jihua etc. studies show that in the winter wheat growth later stage, the extremely significantly positive correlation of the spectral reflectivity of specific band and blade nitrogen content has the correlativity of highly significant simultaneously with final protein output; The field is superfine forever thinks that heading back canopy vegetation index R1500/R610 and wheat seed protein accumulation amount are extremely significant exponential relationship.These researchs mostly are based on the empirical model that remote sensing parameter and the linear relationship of protein content in winter wheat growth period are set up, shortage mechanism and universality.
3, summary of the invention
For strengthening the dynamic and the mechanism of winter wheat grain protein content remote sensing monitoring, the inventive method combines the quantification analogue technique of space remote sensing inversion technique and winter wheat grain protein accumulative process, on the basis of setting up winter wheat grain protein content forecast model, utilize remote-sensing inversion information to correct protein content forecast model running orbit, by " point " (sampling point protein content predicted value) and " face " (remote sensing image) formal transformation, reach purpose again to the monitoring and prediction of Regional Fall Wheat protein content.The winter wheat protein content remote sensing monitoring forecasting procedure that the present invention set up, mechanism and versatility are stronger, can be agricultural sector or professional of agriculture and in time obtain winter wheat zone quality information technical support is provided.Main summary of the invention and technical system are as follows:
1. winter wheat grain protein content forecast model is set up
The forecast model of winter wheat grain protein content GPC (%) makes up as follows:
GPC＝GNC×β (1)
In the formula (1), β is the conversion coefficient between seed nitrogen content and protein content.GNC (%) is the seed nitrogen content, and its algorithm is as follows:
GNC＝GNW/GW (2)
In the formula (2), GW is kernel weight (kgha-1).GNW is the total accumulation (kgha of nitrogen in the seed
-1), store the running of nitrogen before it derives from and spends and spend the again absorption running of back plant to nitrogen, the influence of the accumulation of nitrogen in the seed (being the filling stage of seed) the gentle soil moisture of mainly being bullied.The formula that is calculated as follows of GNW:
GNW＝(GNSW+GNUW)×min(FT，FW)
GNSW＝(PNC-PNMC)×PFW (3)
GNUW＝F(LAI，PFW)
In the formula (3), GNSW for (referring to full heading time) before spending but the volume of traffic (kgha of storage nitrogen in the plant
-1).GNUW spends the again absorption volume of traffic (kgha of back plant to nitrogen
-1), this a part of nitrogen is mainly supplied with the seed synthetic protein, and (LAI PFW) obtains its value by function F.The relation of colony's leaf area index (LAI) and the upperground part biomass (PFW) before analyzing GNUW and spending, set up F (LAI, following regression algorithm PFW):
F(LAI，PFW)＝20.94×ln(LAI)+19.44×ln(PFW)-174.19 (4)
In the formula (3), PNC is plant nitrogen content (%) before spending.PNMC is a ripe back stalk nitrogen content (%), and strong muscle, middle muscle, weak muscle wheat value respectively are 0.55PNC, 0.60PNC, 0.65PNC.The NDVI of PNC and remote sensing image has correlativity preferably, can obtain by the remote sensing image inverting, and algorithm is as follows:
PNC＝D×NDVIF+E×α (5)
In the formula (5), NDVIF is the normalized differential vegetation index before the winter wheat, and D, α are experience factor, respectively value 1.2624 and 2.4728.E is for adjusting function, represent neat fringe before remote sensing image obtain of the influence of the difference of time to the PNC monitor value, the algorithm of E as shown in the formula:
E＝(B
aT-T
mT)/B
aT (6)
In the formula (6), B
aT is the fate of the here fringe of jointing, and unit is day (d).T
mT is the fate that image obtains here fringe of time, and unit is day (d).
Carry out the LAI remote sensing monitoring before spending, can rationally grasp the growing way change dynamics of colony, monitoring effect is rather obvious.LAI before wheat is spent and PFW (the upperground part biomass) have extremely significantly correlationship, and LAI then obtains by the remote-sensing inversion model.Set up the computation model of PFW:
PFW＝6049.2×ln(LAI)+875.35
LAI＝4.4825×exp(0.4905×NDVIF) (7)
In the formula (2), the harvest index that kernel weight GW can be by kind, spend before the plant dry weight and spend before the conversion of photosynthate between the seed volume of traffic that be stored in the plant obtain, specifically be calculated as follows formula:
GW＝(HI×PFW)/(1-HI+HI×β) (8)
In the formula (8), HI is the harvest index of wheat breed, and β is that the photosynthate (spending the back) that is stored in before spending in the plant accounts for the heavy number percent (to the contribution rate of output) of seed to the seed volume of traffic, according to relevant research, β generally between 20%～30%, because of kind different.
In the formula (3), FT is the temperature effect factor, and the temperature Change between the expression pustulation period forms the influence of (or nitrogen accumulation) to grain protein.Its arthmetic statement as shown in the formula:
In the formula (9), T is an average daily temperature between the pustulation period; T
m, T
bBe respectively grain protein synthetic the maximum temperature upper limit and minimum temperature lower limit; T
Oh, T
OlBe protein synthesis optimum ceiling temperature and optimum lower limit temperature.
In the formula (3), FW is the moisture effects factor, and the soil moisture between the expression pustulation period changes the influence to the seed nitrogen accumulation.When soil moisture remained on the 65%-80% of field capacity, seed normally carried out protein synthesis.When soil moisture be lower than field capacity 50% or be higher than field capacity 100% the time, grain protein is synthetic to be suppressed.FW is calculated as follows formula:
In the formula (10), W is a soil moisture content between the pustulation period; W
m, W
bBe respectively the grain protein synthetic the highest soil moisture content upper limit and minimum soil moisture content lower limit, get 90% and 40% of field capacity respectively; W
Oh, W
OlFor protein synthesis optimum upper limit soil moisture content and optimum lower limit soil moisture content, get 80% and 60% of field capacity respectively.
2. the coupling process of remote-sensing inversion information and winter wheat grain protein content forecast model
The present invention carries out component-based development with winter wheat protein content forecast model and sensor information coupling technique, and component object model (COM) carries out the information encapsulation with standardization DLL form, has extensibility.This component system comprises that the photosynthetic module of the canopy of wheat, dispensed materials module, organ build up parts such as module, Nitrogen Absorption module, nitrogen accumulation and distribution module, remote-sensing inversion module.Winter wheat protein content forecast model and sensor information coupled structure are seen shown in the accompanying drawing 1.
Utilize the sensor information inverting, the revision models running orbit is another core technology of this method.At present relevant aleuronat content Study of Monitoring, majority is that crop modeling or remote sensing linear model use separately, the both has drawback and deficiency separately.The present invention is with leaf area index (LAI), biomass, plant nitrogen content (PNC) information of sensor information inverting, be coupled in the protein prediction model and go, by the middle parameter of revision models operation, make the more realistic winter wheat grain protein content information of result of model running.
3. winter wheat grain protein content remote sensing classified Monitoring forecasting procedure
It is the indispensable prerequisite or the important foundation of remote sensing quality monitoring that the wheat planting area extracts.At first, utilizing the ISODATA method to carry out the wheat planting area extracts.Then, utilize the ground GPS reference mark to extract the NDVI and the RVI value of each sampling point of remote sensing image, institute is extracted NDVI and the input of RVI value leaf area index (LAI), biomass and plant nitrogen content inverse model, just can obtain LAI, biomass and the plant nitrogen content prediction data of each sampling point.At last, LAI, biomass and the plant nitrogen content data of each sampling point is input in the winter wheat grain protein content forecast model, just can obtains the grain protein content predicted data information of each sampling point.Adopt the linear transformation method,, form Regional Fall Wheat grain protein content hum pattern based on remote sensing image with each sampling point grain protein content data message and image NDVI coupling.Carry out classification according to regional wheat breed grain protein content performance situation, carry out each grain protein content ranking score cloth area statistical study and grain protein content thematic map at last and make (referring to accompanying drawing 2).
4, beneficial effect
The winter wheat grain protein content is carried out the classified Monitoring forecast, help agricultural sector and grasp local winter wheat quality multidate information, be convenient to formulate different field management measures, reach the purpose that the tuning quality is produced; Also can provide information guiding, promote the development of made-to-order farming for the raw material purchasing plan of grain purchases processing enterprise.
Utilize the inventive method, 2007～2009 continuous 3 years, the winter wheat grain protein content on ground such as Taixing, Jiangsu Province, Jiangyan City, Yizheng, Xinghua, Da Feng is carried out monitoring and prediction, forecast precision reaches more than 85%; Simultaneously, also agricultural management department and the relevant large-scale grain processing enterprise for these cities and counties provides winter wheat grain protein content remote sensing monitoring forecast classification thematic map, be used to instruct the production management of winter wheat and the site-directed quantitative customization purchase of grain enterprise, application surface accumulation meter reaches more than 500 ten thousand mu.
5, description of drawings
Fig. 1 is sensor information and winter wheat grain protein content model coupled structure figure then in advance.Fig. 2 is the winter wheat grain protein content monitoring and prediction process flow diagram in conjunction with forecast model.Fig. 3 is the Jiangsu Province's Protein Content of Wheat Kernel monitoring and prediction classification figure that utilizes the inventive method to make.Fig. 4 is Jiangyan City's winter wheat grain protein content classified Monitoring prog chart.
6, embodiment
1. materials and methods
1.1 material
The image data that adopts is a U.S. road resource satellite Landsat-TM image, and it is on May 2nd, 2008 time of passing by in Jiangyan City.The same day is fine, and cloudless or partly cloudy, image quality is good.When utilizing ERDAS IMAGINE software that image data is carried out geometry correction, geometric accurate correction is carried out at the GPS reference mark of combined ground actual measurement, guarantees to proofread and correct the back error less than 1 pixel.
The foundation of ground control point, adopt the Juno ST hand held GPS receiver of U.S. Trimble company latest version, several bartons in Jiangyan City (consider that area is bigger, crop varieties is comparatively unified) selected 20 to test sample prescription points and 4 experiment bases that area is bigger, gather geographic coordinate, measure actual range and area, and the data such as upgrowth situation such as kind, leaf area, biomass and plant nitrogen content of record winter wheat.
1.2 winter wheat grain protein content classified Monitoring forecast
At first, utilize Jiangsu Province's Administrative boundaries polar plot, the regional extent of Jiangyan City in the cutting TM image is chosen the synthetic interpretation base map of 7,4,2 wave bands.Because this Regional Fall Wheat is in heading stage to blooming stage in May, leaves of winter wheat area during this, coverage rate and green degree index all reach peak value, and the winter wheat blade nitrogen nutrition index that obtain this moment and the correlationship of maturity stage grain protein content are also remarkable.By the false chromatic image visual interpretation of 7,4,2 band combinations, the sampling point data that superpose are simultaneously assisted interpretation, can be relatively easy pick out winter wheat, also can well reflect the growth information of winter wheat.Secondly, classify by the ISODATA method, the sampling point that stack NDVI gray-scale map and GPS gather and the Crop Information data in sample district are carried out the dynamic interpretation and the visual interpretation of man-machine interactive, extract the wheat planting area.
Then, in ERDAS software, utilize 20 GPS reference mark to extract the NDVI value of each sampling point of remote sensing image, institute is extracted the input of NDVI value leaf area index (LAI), biomass and blade nitrogen content inverse model, just can obtain LAI, biomass and the blade nitrogen content predicted data of each sampling point.At last, with the LAI of each sampling point, biomass and and the plant nitrogen content data be input in the winter wheat grain protein content forecast model, just can obtain the grain protein content predicted data information of each sampling point.Adopt the linear transformation method,, form regional grain protein content hum pattern based on remote sensing image with each sampling point grain protein content data message and image NDVI coupling.Carry out classification according to the local wheat breed grain protein content performance situation in Taixing, utilize generalized information system software to carry out grain protein content statistical study and grain protein content classified Monitoring forecast thematic map making (operating process is referring to accompanying drawing 2).
2. interpretation of result
Through the adjustment of data of overtesting sampling point, finally being obtained Jiangyan City's winter wheat cultivated area in 2008 is 30288ha, and this city actual winter wheat cultivated area in 2008 is 28000ha, and degree of accuracy is 92%, and the result is comparatively reliable.
On the basis of understanding " NDVI index → LAI/ biomass/PNC etc. → forecast model → winter wheat protein content " relation, in conjunction with remote-sensing inversion information and grain protein content forecast model, prediction sampling point wheat seed protein content information, pass through linear transformation again, the wheat seed protein content that can obtain whole zone distributes.According to the classification standard of wheat seed protein content, make this regional winter wheat protein content remote sensing monitoring classification forecast thematic map (as accompanying drawing 4).
Table 1 is according to this regional winter wheat protein content classification figure, utilize the area distributions situation of each the content rating winter wheat that draws after the GIS statistical study, as can be seen, the wheat area of muscle on the weak side and weak muscle (being protein content＜12.5%) accounts for the largest percentage, and accounts for 85.5% of the total area; In the above wheat (being protein content＞12.5) of muscle and middle muscle, it is many to account for one one-tenths of the total area, is mainly go to river in being distributed in plains region and other hypsography low-lying areas, based on drab soil, the sticking weight of the big quality of soil moisture content.In actual samples and image analysing computer process, find to exist because the improper quality of wheat that causes in the field management transforms, be muscle on the weak side or middle partially muscle characteristic, quality of wheat also descends thereupon, therefore strengthen the management of these field pieces, adjust the cultivation management mode, can reach the purpose of optimizing wheat quality.
Table 1 Jiangyan City each grade protein content winter wheat area in 2008 distributes
The inventive method, on the basis of setting up winter wheat grain protein content forecast model, parameter in conjunction with the sensor information inverting, through data assimilation, bring forecast model into and obtain simulation grain protein content data, obtain higher degree of fitting with the sampling point data of actual measurement, precision of prediction can reach more than 85%.Further, the method that adopts " point " (sampling point grain protein content) and " face " (remote sensing image) yardstick to change, made winter wheat grain protein content remote sensing monitoring classification prog chart, have directly perceived, specifically, ageing good characteristics, the basic agriculture technician is obtained regional wheat quality information or instructs grain classification purchase to have better practicability.
Claims (1)
1. winter wheat grain protein content forecast model based on seed nitrogen accumulation process; By the Componentized method for designing, realized the coupling of remote-sensing inversion information and winter wheat grain protein content forecast model.That is, utilize LAI, biomass and the plant nitrogen content of remote sensing image inverting in florescence in time to replace winter wheat grain protein content forecasting model corresponding parameters variable, and then realize prediction single-point winter wheat grain protein content; The method that adopts " point " (sampling point grain protein content) and " face " (remote sensing zone) yardstick to change is carried out regional Protein Content of Wheat Kernel remote sensing classified Monitoring forecast, can make Regional Fall Wheat grain protein content classified Monitoring forecast thematic map.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 201010166000 CN102156128A (en) | 2010-04-17 | 2010-04-17 | Method for remote sensing monitoring and predicting protein content of winter wheat grains |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 201010166000 CN102156128A (en) | 2010-04-17 | 2010-04-17 | Method for remote sensing monitoring and predicting protein content of winter wheat grains |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN102156128A true CN102156128A (en) | 2011-08-17 |
Family
ID=44437703
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN 201010166000 Pending CN102156128A (en) | 2010-04-17 | 2010-04-17 | Method for remote sensing monitoring and predicting protein content of winter wheat grains |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102156128A (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103245665A (en) * | 2013-04-18 | 2013-08-14 | 江苏省农业科学院 | Method for quickly screening out high-sugar rice straws suitable for being used as silage |
| CN103868880A (en) * | 2014-01-24 | 2014-06-18 | 河南农业大学 | Wheat leaf nitrogen content monitoring method based on spectrum double-peak index and method for establishing monitoring model |
| CN103913425A (en) * | 2014-04-17 | 2014-07-09 | 河南农业大学 | Method for predicting content of winter wheat grain protein based on coupling of spectral indexes and climatic factors, and method for establishing prediction model |
| CN104429209A (en) * | 2014-11-28 | 2015-03-25 | 广西壮族自治区农业科学院农业资源与环境研究所 | Soil improvement method implemented before sugarcane planting |
| CN109920472A (en) * | 2019-01-07 | 2019-06-21 | 北京农业信息技术研究中心 | A kind of prediction technique and device of grain protein content |
| CN112632796A (en) * | 2020-12-31 | 2021-04-09 | 广州极飞科技有限公司 | Nitrogen content determination method, operation method, device, electronic device and storage medium |
| CN116187100A (en) * | 2023-04-25 | 2023-05-30 | 吉林大学 | Method for estimating corn kernel protein content by combining crop growth model |
| CN116401508A (en) * | 2023-06-08 | 2023-07-07 | 武汉大学 | Planetscope satellite image-based field wheat grain water content monitoring method |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101144778A (en) * | 2007-06-19 | 2008-03-19 | 中国农业大学 | Spectrum reflectivity measurer |
| US20080131974A1 (en) * | 2004-08-30 | 2008-06-05 | Liegh Albert Sullivan | Systems And Methods For Determining Carbon Credits |
-
2010
- 2010-04-17 CN CN 201010166000 patent/CN102156128A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080131974A1 (en) * | 2004-08-30 | 2008-06-05 | Liegh Albert Sullivan | Systems And Methods For Determining Carbon Credits |
| CN101144778A (en) * | 2007-06-19 | 2008-03-19 | 中国农业大学 | Spectrum reflectivity measurer |
Non-Patent Citations (4)
| Title |
|---|
| 《江苏农业学报》 20091231 李卫国等 基于ISODATA的冬小麦籽粒蛋白质含量遥感分级监测 1247-1251 第25卷, 第6期 2 * |
| 《遥感学报》 20080531 李卫国等 基于NDVI和氮素积累的冬小麦籽粒蛋白质含量预测模型 506-514 第12卷, 第3期 2 * |
| 《遥感应用》 20090831 李正金等 基于ISODATA法的冬小麦产量分级监测预报 30-33 , 2 * |
| 《麦类作物学报》 20090228 李卫国等 冬小麦花期生理形态指标与卫星遥感光谱特征的相关性分析 79-82 第29卷, 第1期 2 * |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103245665A (en) * | 2013-04-18 | 2013-08-14 | 江苏省农业科学院 | Method for quickly screening out high-sugar rice straws suitable for being used as silage |
| CN103868880A (en) * | 2014-01-24 | 2014-06-18 | 河南农业大学 | Wheat leaf nitrogen content monitoring method based on spectrum double-peak index and method for establishing monitoring model |
| CN103868880B (en) * | 2014-01-24 | 2016-08-17 | 河南农业大学 | Wheat leaf blade nitrogen content monitoring method based on the bimodal index of spectrum and the construction method of monitoring model thereof |
| CN103913425A (en) * | 2014-04-17 | 2014-07-09 | 河南农业大学 | Method for predicting content of winter wheat grain protein based on coupling of spectral indexes and climatic factors, and method for establishing prediction model |
| CN103913425B (en) * | 2014-04-17 | 2016-04-06 | 河南农业大学 | The Relation To Grain Protein of Winter Wheat content prediction method be coupled based on spectrum index and climatic factor and the construction method of forecast model thereof |
| CN104429209A (en) * | 2014-11-28 | 2015-03-25 | 广西壮族自治区农业科学院农业资源与环境研究所 | Soil improvement method implemented before sugarcane planting |
| CN109920472A (en) * | 2019-01-07 | 2019-06-21 | 北京农业信息技术研究中心 | A kind of prediction technique and device of grain protein content |
| CN112632796A (en) * | 2020-12-31 | 2021-04-09 | 广州极飞科技有限公司 | Nitrogen content determination method, operation method, device, electronic device and storage medium |
| CN116187100A (en) * | 2023-04-25 | 2023-05-30 | 吉林大学 | Method for estimating corn kernel protein content by combining crop growth model |
| CN116401508A (en) * | 2023-06-08 | 2023-07-07 | 武汉大学 | Planetscope satellite image-based field wheat grain water content monitoring method |
| CN116401508B (en) * | 2023-06-08 | 2023-08-01 | 武汉大学 | Planetscope satellite image-based field wheat grain water content monitoring method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102156128A (en) | Method for remote sensing monitoring and predicting protein content of winter wheat grains | |
| Hoffmann et al. | Simulating potential growth and yield of oil palm (Elaeis guineensis) with PALMSIM: Model description, evaluation and application | |
| Van den Hoof et al. | Simulating dynamic crop growth with an adapted land surface model–JULES-SUCROS: Model development and validation | |
| Trnka et al. | Changing regional weather crop yield relationships across Europe between 1901 and 2012 | |
| Jégo et al. | Calibration and performance evaluation of soybean and spring wheat cultivars using the STICS crop model in Eastern Canada | |
| CN102162850A (en) | Wheat yield remote sensing monitoring and forecasting method based on model | |
| CN101595812B (en) | Method for acquiring crop harvest index | |
| Dalla Marta et al. | The influence of climate on durum wheat quality in Tuscany, Central Italy | |
| CN111898922B (en) | Multi-scale crop yield assessment method and system | |
| CN102073869A (en) | Point location data and remote sensing image data-based regional farmland quality monitoring method | |
| CN102592181A (en) | Method for optimizing spatial distribution of statistical data about crop planting area | |
| CN113822479B (en) | Multi-objective optimization method for regional agricultural planting structure considering production, environment and economic benefits | |
| Xu et al. | Regional crop yield assessment by combination of a crop growth model and phenology information derived from MODIS | |
| CN112598277B (en) | Method for evaluating yield difference of trans-regional reduced winter wheat and improving nitrogen fertilizer efficiency | |
| Ilić et al. | Forecasting corn production in Serbia using ARIMA model | |
| CN114254964B (en) | Rice regional climate quality assessment method and system | |
| Long et al. | Impact of climate change on wheat yield and quality in the Yellow River Basin under RCP8. 5 during 2020–2050 | |
| CN107423850A (en) | Region corn maturity period Forecasting Methodology based on time series LAI curve integral areas | |
| Wellens et al. | Calibration and validation of the FAO AquaCrop water productivity model for cassava (Manihot esculenta Crantz) | |
| Turpin et al. | Simulating fababean development, growth, and yield in Australia | |
| Herrmann et al. | A new harvest time prognosis tool for forage maize production in Germany | |
| CN106203673A (en) | Consider the crops Biomass remote sensing estimation method of water stress | |
| CN116662753B (en) | Rice ear fertilizer nitrogen accurate recommendation method based on crop model and remote sensing coupling | |
| CN101464979A (en) | System and method for evaluating crop yield representation | |
| Recha | Adapting Nyando smallholder farming systems to climate change and variability through modelling |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C12 | Rejection of a patent application after its publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20110817 |