142 results about "Winter wheat" patented technology

The invention discloses a winter wheat LAI and SPAD estimation method based on a lightweight semi-supervised model, and relates to the technical field of agricultural remote sensing monitoring, and the method comprises the steps: obtaining multispectral image data of a winter wheat key growth period, and carrying out the preprocessing of the multispectral image data to generate a standardized multichannel vegetation index image; the method comprises the following steps: constructing a lightweight semi-supervised model MCVI-SANet, and carrying out self-supervised training on the MCVI-SANet by adopting a semi-supervised training strategy driven by VICReg; and inputting the multi-channel vegetation index image into the trained MCVI-SANet, and outputting quantitative estimation results of the LAI and SPAD of the winter wheat. Through combination of a saturation perception mechanism and semi-supervised learning, estimation deviation caused by dense canopy vegetation index saturation and data noise is effectively eliminated, and the estimation precision and generalization ability in a complex agricultural scene are improved while the lightweight deployment characteristic of the model is ensured.

The invention discloses a winter wheat field soil fertility evaluation data processing method based on comprehensive weight calculation, and relates to the technical field of data processing, and the method comprises the steps: S1, collecting nutrient content and moisture retention data from field soil through a sensor network, performing grouping processing on the acquired nutrient content and moisture retention data by adopting decision node splitting and entropy calculation to obtain a soil fertility layering result; s2, according to a soil fertility layering result, acquiring root depth and leaf moisture content indexes of the winter wheat in different layers of soil from crop monitoring equipment, analyzing association strength of the root depth and leaf moisture content indexes and fertility layering by adopting leaf node classification and feature selection standards, and determining a fertility contribution coefficient; according to the winter wheat field soil fertility evaluation data processing method based on comprehensive weight calculation, efficient resource allocation and stable yield are realized, and the drought resistance and resource utilization efficiency of winter wheat in a drought environment are remarkably improved.

The invention discloses a winter wheat LAI inversion method based on scenarized parameter calibration and ensemble learning. The method comprises the following steps: fusing a remote sensing image with auxiliary geographic information such as terrain and soil, and performing typical planting scene division on a research area by adopting a spectral clustering method; then scene optimization is carried out on highly sensitive parameters (such as LAI, Cab, Cm and ALA) in the radiation transfer model PROSAIL based on a Markov chain Monte Carlo (MCMC) algorithm, and parameter posterior distribution more consistent with actual observation is obtained; further utilizing optimization parameters to generate a high-quality and homogenized simulation sample set, and mapping the simulation sample set to a Sentinel-2 waveband to construct a training data set; and finally, performing training by adopting various machine learning models (such as LightGBM, XGBoost, RF and KNN), and constructing a fusion model through a stacking integration strategy to realize high-precision inversion of LAI. Experimental results show that the method has higher fitting precision and stronger generalization ability in a plurality of typical planting scenes.

The invention discloses a winter wheat yield forecasting method based on a random forest regression model, and relates to the technical field of winter wheat yield prediction.The winter wheat yield forecasting method comprises the steps of multi-source data integration, dynamic feature screening, random forest model optimization and yield prediction and verified.The multi-source data integration provides input data for dynamic feature screening; high-quality features screened by the dynamic features are used as training features of a random forest model optimization module, and a result of random forest model optimization is used as final yield prediction and verification of yield prediction and verification. According to the method, by designing multi-source data fusion and automatic model optimization, the problems that a traditional method is single in data source, difficult to capture complex agronomic relations and insufficient in model generalization ability are solved, high-precision and high-stability winter wheat yield forecasting is achieved, and powerful support is provided for agricultural production decision making.

The invention discloses a winter wheat multi-target irrigation dynamic optimization method based on a crop model and artificial intelligence, and belongs to the field of agricultural intelligent irrigation optimization and decision, the method comprises the following steps: preprocessing meteorological data, soil data, crop parameter data and irrigation management data to obtain standardized input data; carrying out coupling processing on the AquaCrop model and the PyFAO56 model based on the standardized input data to obtain final output of a coupling model; based on the final output of the coupling model, optimizing decision variables by adopting an NSGA-2 multi-objective optimization algorithm to obtain a Pareto optimal solution set; and sorting the candidate schemes in the Pareto optimal solution set by combining a fusion strategy of an entropy weight method and a random forest algorithm to obtain a final irrigation strategy. According to the invention, through multi-objective optimization, objective weighting and machine learning intelligent evaluation, a novel precise irrigation scheme considering both yield and water efficiency is provided.

The invention discloses a wheat yield remote sensing prediction method combining phenological parameters, which comprises the following steps: S1, performing field observation in a key growth period of winter wheat, synchronously collecting canopy hyperspectral reflectivity data, leaf area index (LAI) and SPAD value of each observation sample point, and recording wheat grain yield of the corresponding sample point; s2, performing noise reduction preprocessing on the acquired canopy hyperspectral data, and extracting sensitive spectral parameters by combining principal component analysis (PCA) and correlation analysis methods; s3, taking the sensitive spectrum parameters, LAI and SPAD values as independent variables, taking the wheat grain yield as a dependent variable, and adopting partial least squares regression (PLSR) to construct a multivariable yield prediction model; and S4, performing wheat yield prediction on an independent test sample or regional scale remote sensing data by using the trained model. The method overcomes the defect that only yield sensitive spectrum parameters are used for predicting the effect, and accurate estimation of the model is achieved.

The invention provides a winter wheat soil moisture inversion method and system based on an improved water cloud model, storage equipment and electronic equipment, and the method comprises the following steps: obtaining total backscattering characteristics VV and VH, an incident angle and a plurality of polarization characteristic parameters through employing a single-view complex image and a geocoding radiation correction image; calculating a vegetation index from the preprocessed optical image by applying a wave band calculation mode; an improved water cloud model method is applied to correct the radar backscattering coefficient affected by the winter wheat plants, the backscattering coefficient of the earth surface is obtained, and data redundancy is reduced; performing hyper-parameter optimization of the CNN network by applying a CPO algorithm; and constructing a CNN-CPO-RF model by using CNN and RF models to carry out soil moisture estimation. According to the invention, a more accurate soil moisture result can be obtained. According to the method, the robustness of farmland surface soil moisture inversion is remarkably improved, and a new technical means is provided for performing soil moisture inversion by using a satellite-borne SAR image.

The invention provides a method for planting winter wheat in saline-alkali soil, which belongs to the technical field of wheat planting, and comprises the following steps: S1, rotary tillage and soil preparation: carrying out rotary tillage at the depth of 25-30cm 30-40 days before sowing, doping foreign soil, and applying straw and gypsum; s2, seed treatment: selecting saline-alkaline tolerant wheat seeds, soaking the wheat seeds in a liquid medicine, and soaking the wheat seeds in the liquid medicine; s3, ridging operation: high ridges with the ridge height of 15-20 cm, the ridge surface width of 40-50 cm and the ridge spacing of 25-30 cm are constructed; s4, sowing in line on ridges: sowing the wheat seeds treated in the step S2 on the ridges in line, wherein the sowing depth is 2-3cm; s5, film mulching operation, wherein the ridges are covered with mulching films of a double-film structure, and the film edges of the mulching films are sealed with humic acid slurry; s6, field management: uncovering the film and supplementing seedlings in the seedling stage, and drip irrigation and topdressing under the film in the growth stage. According to the saline-alkali soil winter wheat planting method provided by the invention, the wheat emergence rate can be remarkably increased by constructing a low-salt micro-domain environment.

The invention discloses a method for preparing a special fertilizer for winter wheat-middle-season rice crop rotation system crops under straw returning, and belongs to the technical field of wheat-rice crop rotation. According to the method, soil nutrient testing and the yield increasing effect of fertilization on crops are combined, and the nutrient supply level of soil is truly reflected; the habitual fertilization of farmers is optimized under the condition of comprehensively considering the nutrients brought by the straws of previous crops, the nutrient release rate and the nutrient effectiveness of the straws, the relatively high carbon-nitrogen ratio under straw returning and the apparent balance of potassium in the soil of a producing area. Compared with traditional farmer habitual fertilization, the optimized recommended fertilization formula provided by the invention not only contains previous crop straw nutrients, but also provides a special fertilizer formula for a main growth stage. The formula of the special fertilizer for winter wheat-middle-season rice crop rotation system crops under straw returning not only can improve the yield of crops, but also can improve the utilization efficiency of nutrients.

The invention relates to the technical field of agricultural intelligent equipment, in particular to intelligent monitoring and weeding equipment for gramineous weeds in a winter wheat field, and aims to solve the technical problem of high missing detection rate and false detection rate of a traditional visual identification method caused by similar forms of winter wheat and gramineous weeds in a seedling stage. The equipment comprises an autonomous mobile platform, a multi-mode sensing assembly, a data processing and decision-making unit and a targeted clearing execution mechanism. The multi-mode sensing assembly integrates a hyperspectral imaging module, a three-dimensional shape measurement module and a polarization characteristic analysis module, and pixel-level data registration is achieved in combination with the cooperative illumination system. The data processing unit performs high-confidence classification decision on the multi-source features through a hierarchical Bayesian fusion model; and the targeted clearing mechanism adopts a laser galvanometer system to carry out non-contact precise clearing on the identified weed growing points. Intelligent identification and efficient physical weeding of weeds in the winter wheat field are achieved, and the method has the advantages of being free of chemical residues and high in operation continuity.

The invention provides a large-scale winter wheat yield remote sensing estimation method based on unmanned aerial vehicle and satellite image cross-scale information fusion, and the method comprises the following steps: obtaining an unmanned aerial vehicle image in a wheat key growth period and a satellite image in the same period through satellite-aircraft-ground synchronous scatter sampling, and constructing an unmanned aerial vehicle platform winter wheat yield estimation model; obtaining a yield sample augmented data set; the method comprises the following steps: constructing a satellite pixel spatial diversity index by using an unmanned aerial vehicle image, realizing cross-scale information fusion, and determining the uncertainty influence of growth difference in a field and mixed pixels on satellite winter wheat yield estimation; and making a sample augmentation optimization strategy based on the optimal spatial resolution index, determining an unmanned aerial vehicle and satellite fused winter wheat yield estimation model, and realizing large-scale field-level winter wheat yield estimation. According to the method, the winter wheat yield can be estimated timely and accurately, and the method has great application potential in the aspects of grain policy making, safety evaluation and the like.

The invention belongs to the field of agricultural computer electronics, and particularly discloses a winter wheat nitrogen nutrition diagnosis and nitrogen demand estimation method based on hyperspectral data, which comprises the following steps: acquiring winter wheat canopy spectral data by adopting an ASD FieldSpec4 hyperspectral spectrometer, and measuring the nitrogen concentration and biomass of winter wheat leaves at the same time; and estimating the nitrogen concentration and biomass in combination with a one-dimensional convolutional neural network model. And based on the estimation result, calculating the nitrogen nutrition index by using a critical nitrogen dilution curve, and establishing a nitrogen demand estimation model comprehensively considering the number of days after sowing, the nitrogen fertilizer recovery efficiency and the nitrogen nutrition index. The method provides powerful support for accurate management of the winter wheat nitrogen fertilizer.

The invention relates to a winter wheat spring low temperature disaster climate risk evaluation method. The method comprises the steps of obtaining multi-source data of a target time period in a target area; according to the multi-source data, the winter wheat spring development period is divided into different development period periods, and according to the meteorological data, the time minimum temperature and the process cold accumulation capacity of the different development period periods are determined; determining the disaster degree of the winter wheat in different developmental periods according to the minimum temperature and the process cold accumulation capacity in different developmental periods, and determining the occurrence probability of the winter wheat spring low-temperature disaster according to the disaster degree; according to the crop data, determining the winter wheat average yield reduction rate and the winter wheat yield reduction rate variation coefficient, and according to the winter wheat spring low-temperature disaster occurrence probability, the winter wheat average yield reduction rate and the winter wheat yield reduction rate variation coefficient, constructing a winter wheat spring low-temperature disaster climate risk index; and carrying out risk grade division according to the winter wheat spring low-temperature disaster climate risk index. The accuracy of winter wheat spring low-temperature disaster climate risk quantitative evaluation is improved.

The invention relates to the technical field of agricultural planting, and discloses a winter wheat rain-suitable planting configuration optimization method and system based on drought year identification, and the method comprises the steps: constructing a drought year identification index system, collecting the data of each drought year identification index in a target planting region in real time, carrying out the preprocessing of the collected data, and carrying out the calculation of the data; pre-processed data is obtained; inputting the preprocessed data into a drought year type identification model, and identifying the current drought year type of the target planting area through the drought year type identification model; determining planting configuration parameters of winter wheat suitable for rain according to the identified drought type in combination with historical rainfall data and soil characteristics of the target planting area; monitoring parameter information in real time through a sensor to obtain monitoring data, and adjusting planting configuration parameters according to the monitoring data so as to enable winter wheat to obtain optimal growth conditions in different drought years; according to the method, different drought years can be accurately identified, and a reliable basis is provided for winter wheat rain-suitable planting configuration optimization.

The invention discloses a synergistic planting method for spring green manure-winter wheat crop rotation synergistic soil improvement, and relates to the technical field of crop planting, and the method comprises the following steps: S1, carrying out soil preparation treatment on three test fields in the first ten days of June in the current year, and then dividing areas in the three test fields to respectively sow six green manure crops; s2, in the first ten days of September, the upper portion of the green manure is cut up and evenly spread on the spot, and then green manure stubbles are turned and pressed through a small machine; s3, applying a ternary compound fertilizer as a base fertilizer on October 20th in the same year, and sowing wheat after 3 days; and S4, harvesting the wheat by adopting a harvester in the next year, and performing threshing and warehousing operation. The method has the advantages that the soil fertility is improved by turning and pressing green manure, the saline-alkali soil structure is effectively improved, the yield and quality of winter wheat are effectively improved, and environment friendliness is achieved.

The invention discloses an environmental factor and growth process coupled winter wheat late frost damage monitoring method, which comprises the following steps: S1, determining main influence factors of winter wheat late frost damage, and constructing a late frost damage index MIFI basic formula; s2, verifying the MIFI based on a linear regression model of the delta MIFI and the delta yield; and S3, monitoring the winter wheat late frost damage degree and spatial distribution based on MIFI. The method provided by the invention can also be used for monitoring winter wheat late frost damage in other regions. By optimizing the model and improving the prediction method of the key input variables, the MIFI can be expanded to real-time monitoring and prediction, active perception of disaster risks is realized, and disaster loss is reduced.

The invention belongs to the technical field of agricultural management, and particularly relates to a winter wheat growth period prediction and intelligent management method and system based on multi-source data, and the method comprises the steps: generating a continuous time sequence through multi-source heterogeneous data, and sequentially carrying out the space-time modeling and dynamic feature interaction processing of continuous time sequence features, so as to predict the growth period of winter wheat; wherein in the spatio-temporal modeling stage, the feature expression ability is enhanced from three aspects of time periodicity, seasonal gating and geospatial dependence; in the dynamic feature interaction stage, the characterization capacity of the time-varying interaction relation between winter wheat time sequence variables is effectively improved through energy dynamic coupling; after refined prediction of the daily growth process of winter wheat is completed, a key growth stage turning point is extracted, a prediction result is explained in a natural language form, and personalized agricultural management suggestions are generated to facilitate guidance of agricultural operation.

The invention provides a winter wheat leaf area index estimation method and system based on unmanned aerial vehicle remote sensing, and the method comprises the following steps: S1, data acquisition: obtaining unmanned aerial vehicle multispectral remote sensing image data of a research region, and collecting corresponding field actual measurement data in different growth periods of winter wheat; according to the winter wheat leaf area index estimation method and system based on unmanned aerial vehicle remote sensing provided by the invention, through the step design of S1 to S7, especially the collaborative cooperation of data preprocessing, data fusion, model construction and optimization, during use, on one hand, the leaf area index of the winter wheat can be estimated; in S2, splicing and geographic coordinate system embedding processing are carried out on unmanned aerial vehicle multispectral remote sensing image data, spatial positions of different wavebands can be automatically aligned, the problem that the number of rows and columns of multiband data is inconsistent is solved fundamentally, and a spatially unified data source is provided for subsequent vegetation index calculation and feature extraction.

The invention discloses a primer combination for water saving and quality screening of winter wheat and application thereof, and relates to the technical field of molecular biology, the primer combination for water saving and quality screening of winter wheat comprises a primer pair P1 and a primer pair P2; according to the primer combination, the water-saving and drought-resistant characters are screened through PCR amplification, and SDS-PAGE electrophoresis is combined to detect grain protein subunit composition so as to screen quality characters. The efficient breeding method suitable for winter wheat is formed by designing a specific primer combination, constructing a molecular marker screening system and innovatively combining a cultivation management mode of water before drought and drought after water before winter wheat. According to the method, dependence on a single known gene is avoided, oriented improvement of water-saving drought resistance and quality is realized through collaborative screening and environment adaptability cultivation of a primer combination, and a new technical path is provided for wheat breeding in the arid region of North China.

The invention discloses a winter wheat quality monitoring method and device for coupling integrated learning rib shape correction and data assimilation, and relates to the field of crop quality monitoring. The method comprises the steps of preprocessing acquired information data, determining a winter wheat planting area in a target monitoring area based on a classification model, and dividing the winter wheat planting area into different rib type variety areas; performing inversion and extraction of quality-related crop parameters by adopting a machine learning regression model to obtain a crop parameter time sequence; determining a simulation state quantity time sequence of the rib type variety area based on a crop growth model; and performing multivariable collaborative data assimilation processing according to the crop parameter time sequence and the simulation state quantity time sequence, driving the crop growth model to the winter wheat mature harvest time according to the assimilated data, and performing GPC correction prediction on the winter wheat in the tendon type variety area based on the correction factor to represent the quality of the winter wheat grains. The invention aims to improve the quality monitoring precision of regional winter wheat.

The disclosure relates to commercially elite hard red winter wheat varieties having increased dough strength. The wheat variety designated OK15MASBx7 ARS 8-20, the plants and seeds and plant parts of wheat variety OK15MASBx7 ARS 8-20, methods for producing a wheat plant produced by crossing the variety OK15MASBx7 ARS 8-20 with another wheat plant, and hybrid wheat seeds and plants produced by crossing the variety OK15MASBx7 ARS 8-20 with another wheat line or plant, and the creation of variants by mutagenesis or transformation of variety OK15MASBx7 ARS 8-20 are also provided. Methods for producing other wheat varieties or breeding lines derived from wheat variety OK15MASBx7 ARS 8-20 and wheat varieties or breeding lines produced by those methods are also provided. Further provided are commodity plant products from wheat varieties having increased dough strength.

The present invention relates to the field of wheat cultivation technology and discloses a high-yield cultivation method for winter wheat planted in spring. The method comprises screening, land preparation, winter irrigation in November, seed dressing, timely sowing in early to mid-March, topdressing with seedling fertilizer and irrigation during the tillering stage, pest and disease control in the middle and late stages of growth, and timely harvesting. The present invention solves the problem of winter wheat being unable to be planted in Qinghai's spring wheat region due to its inability to undergo normal vernalization and earing maturity by sowing winter wheat in spring wheat areas. This method allows wheat to be produced normally without undergoing vernalization, ultimately achieving a high-yield, low-pest cultivation method.

The present application discloses a method for extracting the spatial distribution of lodging winter wheat using dual-modal feature fusion, which relates to the field of agricultural remote sensing monitoring technology. The method comprises the following steps: establishing a dual-stream semantic segmentation network DMFCFNet and training the network using a constructed sample data set; inputting pre-processed remote sensing images and DSM elevation images into the network in parallel to obtain semantic texture feature information of remote sensing images of different scales and spatial structure feature information of DSM elevation images; performing feature correction and fusion on the acquired semantic texture feature information of remote sensing images and spatial structure feature information of DSM elevation images; and performing segmentation prediction on the fused feature information to extract the spatial distribution information of lodging areas of winter wheat. By correcting and fusing the semantic texture features of remote sensing images and DSM elevation features, combined with multi-scale information aggregation processing, the accuracy of extracting lodging areas of winter wheat is effectively improved, and the problems of missed detection of small targets and blurred boundaries are improved.

The invention discloses a winter wheat ridging, sowing and summer crop ridge sealing intercropping mode and a planting technology, and is characterized in that winter wheat and summer corn are intercropped in a one-year double cropping mode to form a strip planting unit; wherein the strip-shaped planting unit is provided with ridges and ditches, and the ridges and the ditches are arranged alternately; the ridges are formed by gathering soil, and sowing is carried out on the winter wheat ridges; ridge sealing, ridge soil and wheat straw stubbles are smashed, subjected to rotary tillage and turned into ditches, ridges form ditches, ditches form ridges, the ridges and the ditches are interchanged, and summer corn ridges in the ditches are sealed; the steps of ridge turning, ridge sealing, summer corn straw smashing and rotary tillage are carried out to change positions of furrows, and winter wheat is sown on ridges. By means of the mode, the problems that traditional two-season flat furrow land planting is low in illumination and temperature utilization rate, high in energy consumption, hardened in soil and serious in water resource and fertilizer waste can be overcome, and meanwhile the problems that summer corn is prone to being affected by disastrous weather, production cost is increased, and the summer corn cannot keep up with national strategic requirements and sustainable development can be solved. The acre yield does not meet the expected value, and diseases and weather disasters are serious.

The invention discloses a winter wheat yield estimation method and system based on mixed deep learning, and belongs to the technical field of crop prediction, and the method comprises the following steps: obtaining multi-modal data of a winter wheat production area, including remote sensing data, meteorological data, soil data and GPP data; extracting spatial features of the multi-modal data by using a convolutional neural network module; extracting time features of the multi-modal data by using an LSTM module; performing normalization processing on the obtained spatial features and the time features of the multi-modal data, and encoding the spatial features and the time features into corresponding embedded vector sequences; the spatial features and the time features of the coded multi-modal data are fused, the weight of each modal data feature is dynamically adjusted by using an attention mechanism in the fusion process, and each modal data feature comprises the spatial features and the time features; and obtaining a yield estimation value of the winter wheat based on the fused multi-modal data features. The method improves the prediction precision of the winter wheat yield.

The present invention discloses a method, device, medium, and equipment for detecting winter wheat under complex terrain conditions, relating to the field of image analysis technology. The method comprises: using digital elevation model data to reflect the terrain information of a preset winter wheat area; constructing a fusion segmentation network for multimodal data; an encoder comprising two residual modules and four multimodal feature fusion modules with a dual-branch structure; the dual-branch structure comprising an optical branch and an elevation branch; a decoder comprising four connected decoding modules, each of which comprises an upsampling module and a distribution feature enhancement module; the inputs of the last three upsampling modules in the decoder are respectively connected to the outputs of the first three optical branches in the encoder; the fusion segmentation network is trained using a multimodal training dataset to obtain a winter wheat detection model; and finally, the winter wheat detection model outputs a detection result. This method can accurately detect the distribution of winter wheat in complex terrain areas.

The invention discloses a Sentinel-1 SAR (Synthetic Aperture Radar) data-based winter wheat late frost damage assessment method, which comprises the following steps of: firstly, carrying out noise reduction processing on vertical emission-vertical reception (VV) and vertical emission-horizontal reception (VH) wavebands by adopting a 51 * 51 pixel window mean value aggregation method, and while satisfying the stability of a variable coefficient (CV) and a signal-to-noise ratio (SNR), carrying out noise reduction processing on the wavebands; and noise suppression and space detail reservation are effectively balanced. The method comprises the following steps of: extracting a time sequence signal sudden drop characteristic caused by frost on the basis of a VH / VV polarization ratio (VH / VV) by combining a Savitzky-Golay (SG) filtering and envelope line fitting method, and constructing an FDSI index according to the time sequence signal sudden drop characteristic. The verification result of a field survey sample point shows that the correlation coefficient r between the FDSI based on VH / VV and the freeze injury grade reaches 0.82 (plt, 0.01) and is obviously superior to RVI (r = 0.73) and DPSVI (r = 0.70). According to the method, an efficient and reliable evaluation means can be provided for regional-scale late frost damage evaluation.

This invention belongs to the field of agricultural planting technology, specifically relating to a method for wide-row precision sowing and shallow-buried drip irrigation for winter wheat. It combines wide-row precision sowing technology with a shallow-buried drip irrigation system, along with precise water and fertilizer management, to achieve high-yield and efficient winter wheat cultivation. By integrating wide-row precision sowing, shallow-buried drip irrigation, and precise water and fertilizer management technologies, and through a system coupling design, it achieves a synergistic improvement in sowing quality, irrigation efficiency, and fertilizer utilization. The spatially coordinated layout of wide-row precision sowing and shallow-buried drip irrigation enables precise matching of the drip irrigation tape and seed rows, significantly improving water and nutrient utilization efficiency, while simultaneously promoting winter wheat root development and yield stability.

The invention discloses a method for preparing a special fertilizer for winter wheat-summer corn crop rotation system crops under straw returning, and belongs to the technical field of winter wheat-summer corn crop rotation. According to the method, the nitrogen, phosphorus and potassium nutrient demand proportion of the special fertilizer for different growth stages of the winter wheat-summer corn rotation system is provided by combining key parameters such as nutrient absorption, soil nutrient supply and fertilizer utilization rate under the condition of considering the straw nutrient introduction amount and release rate of previous crops. The method is a technology for effectively improving the crop yield and soil fertility of the winter wheat-summer corn crop rotation system under straw returning to the field and realizing fertilizer application reduction and efficiency improvement, and can provide technical support for fertilizer manufacturers to provide a special fertilizer formula for the winter wheat-summer corn crop rotation system crops.

The invention relates to a water and fertilizer integrated drip irrigation experiment monitoring method suitable for winter wheat, and relates to the technical field of crop irrigation, and the method comprises the following steps: 1, selecting an experiment address: selecting 15 mu of land with sufficient light and heat and the same period of rain and heat in a plain area of a warm temperature zone; 2, sowing is conducted, mechanical sowing is conducted, the sowing quantity is 30 jin / mu, the line spacing is 15 cm, and a tested wheat variety is Zhongmai 29; 3, quantitatively applying water and fertilizer, setting irrigation quota of 20, 30 and 40 m < 3 > / mu according to test design, setting fertilization amount as a secondary factor, and respectively setting 210 and 240 kgN / hm < 2 >, 4, walking in an experimental field through a plurality of monitoring devices, picking or measuring and monitoring the wheat in each period of wheat growth, and calculating the water utilization efficiency according to the wheat yield; and step 5, recording meteorological data, and recording the data once every 1h. The method has the beneficial effects that the growth conditions and various parameters of the winter wheat under different variable conditions are obtained, and then the drip irrigation water quantity and the water-fertilizer proportion which are most suitable for the winter wheat are obtained.