Method for predicting wheat yield

Abstract

The invention relates to a method for predicting wheat yield, and relates to the technical field of agriculture. According to the main technical scheme, the yield of the wheat is predicted in the period from the blooming period to the milk-ripe period of the wheat, and the method comprises the following steps that the total area of the wheat needing to be predicted is divided into one or more blocks; sampling is conducted on each block, the ear number and the ear grain number in sampling points are investigated, and the unit ear number and the effective ear grain number are calculated; the growth appearance of the wheat plants in each block is observed, and a seedling condition coefficient is determined; investigating and classifying the disease and pest damage condition of each block, and determining a disease and pest damage coefficient; and predicting the meteorological condition of the wheat in each block from the milk-ripe stage to the full-ripe stage, and determining the meteorological coefficient in the late growth stage. The method for predicting the wheat yield is long in yield prediction period, easy to operate and capable of accurately predicting the wheat yield.

Description

Technical Field

[0001] The invention relates to the technical field of agriculture, and in particular to a method for predicting wheat yield. Background Art

[0002] The method of predicting wheat yield plays a vital role in agricultural production. By accurately predicting wheat yield, we can better arrange agricultural production, ensure food security, and improve agricultural efficiency. At the same time, the yield measurement results are also an important basis for evaluating the quality of varieties and the level of planting technology.

[0003] The traditional method for predicting wheat yield is as follows: when wheat enters the late stage of maturity (i.e., the stage where wheat can be harvested mechanically or manually), a yield measuring frame (1 meter × 1 meter) is used to manually pick all the mature wheat ears in the frame according to 3 or more harvesting points, and then threshing is carried out. The threshed wheat is weighed, and then the weighed wheat weight is divided by the number of sampling points in the sampling frame, and the wheat yield per square meter is calculated using a shrinkage coefficient of 85%, and finally the wheat yield per mu or hectare is determined.

[0004] However, this traditional method has at least the following problems: (1) It can only be implemented when wheat is late in maturity, which has strict time limits for wheat yield measurement. The yield measurement period is short and the operation is difficult (regarding the difficulty of operation, it should be noted that the harvested grains need to be dried, or farmers need to rush to harvest wheat due to weather reasons. Therefore, when the yield is predicted, the farmers may have already harvested the wheat. In addition, since the area of ​​wheat harvesting for yield measurement is large, farmers are unwilling to cooperate due to losses); (2) The simple use of an 85% shrinkage coefficient often results in a large error compared with the actual yield.

[0005] In summary, there is an urgent need for a method for predicting wheat yield that has a long yield measurement period, is simple to operate, and can accurately predict wheat yield. Summary of the invention

[0006] In view of this, the present invention provides a method for predicting wheat yield, the main purpose of which is to provide a method for predicting wheat yield with a long yield measurement period, simple operation, and the ability to more accurately predict wheat yield.

[0007] In order to achieve the above object, the present invention mainly provides the following technical solutions:

[0008] In one aspect, an embodiment of the present invention provides a method for predicting wheat yield, wherein the wheat yield is predicted during the period from flowering to milk maturity of wheat; wherein the method for predicting wheat yield comprises the following steps:

[0009] Block division step: divide the total wheat area to be predicted into one or more blocks;

[0010] Steps for investigating the number of ears and grains per ear: sample each block, investigate the number of ears and grains per ear at the sampling point, and calculate the number of ears per unit and the number of effective grains per ear;

[0011] Steps for determining the seedling condition coefficient: observe the growth appearance of the wheat plants in each block and determine the seedling condition coefficient;

[0012] Steps to determine the pest damage coefficient: Investigate and classify the pest damage situation in each block to determine the pest damage coefficient;

[0013] Steps for determining meteorological coefficients: predict the meteorological conditions of wheat in each block from the milky stage to the mature stage, and determine the meteorological coefficients in the late growth period;

[0014] Steps for predicting yield: Calculate the unit yield of wheat in each block based on the number of ears per unit, the number of effective grains per ear, the seedling condition coefficient, the pest and disease damage coefficient, the meteorological coefficient in the late growth period, and the historical thousand-grain weight and the machine harvest loss reduction coefficient.

[0015] Preferably, in the block division step: the total wheat area to be predicted is divided into blocks according to three dimensions: cultivar, soil condition, and management measures; preferably, the management measures include one or more of sowing time, seed amount, planting density, fertilizer application, pesticide application, irrigation method, and irrigation amount; preferably, the fertilizer application includes the nutrient content of the fertilizer and the application amount of the fertilizer; preferably, the pesticide application includes the composition, content, and application amount of the pesticide; preferably, the soil conditions include sandy soil, clay soil, and loam; if at least one dimension of the cultivar, soil condition, and management measures is different, it needs to be divided into different blocks.

[0016] Preferably, in the step of investigating the number of ears and the number of grains per ear: when performing fixed-point sampling on each block, any one of the diagonal 3-point sampling method, the U-shaped 5-point sampling method, and the chessboard 9-point sampling method is adopted; and / or the growth of wheat at the sampling point is consistent with the overall growth of wheat in the block; and / or 1-10 rows of wheat beads are taken at each sampling site, and the length of each row is 1-10m.

[0017] Preferably, in the step of investigating the number of ears and the number of grains per ear: fixed-point sampling is performed on each block, the number of ears in multiple sampling points is investigated, and the number of unit ears in each block is calculated according to the following formula:

[0018] Number of ears per unit = total number of ears surveyed / surveyed area × unit area;

[0019] Wherein, the survey area = the area of ​​each sampling point × the number of sampling points;

[0020] The total number of ears surveyed refers to the total number of ears surveyed in multiple sampling sites in each block.

[0021] Preferably, in the step of investigating the number of ears and the number of grains per ear: fixed-point sampling is performed on each block, and multiple wheat plants are randomly selected at each sampling point to determine the number of effective grains per ear; wherein the calculation formula for the effective number of grains per ear in each block is as follows: effective number of grains per ear = total number of grains investigated / total number of ears investigated; preferably, at least 10 wheat plants are randomly selected at each sampling point.

[0022] Preferably, in the step of determining the seedling condition coefficient: if the wheat in a certain block is of Class I seedlings, the seedling condition coefficient in the block is determined to be 1.06-1.15; if the wheat in a certain block is of Class II seedlings, the seedling condition coefficient in the block is determined to be 0.95-1.05; if the wheat in a certain block is of Class III seedlings, the seedling condition coefficient in the block is determined to be 0.85-0.94;

[0023] Preferably, if the degree of fit between the plant height, stem hardness and leaf color of the wheat in a certain block and the benchmark value are all greater than or equal to 80%, the wheat is considered to be Class I seedlings; if the degree of fit between one of the plant height, stem hardness and leaf color of the wheat in a certain block and the benchmark value is less than 80%, the wheat in the block is considered to be Class II seedlings; if the degree of fit between two or three of the plant height, stem hardness and leaf color of the wheat in a certain block and the benchmark value is less than 80%, the wheat in the block is considered to be Class III seedlings; among them, the plant height, stem hardness and leaf color in the approval announcement of the same variety are used as the benchmark values.

[0024] Preferably, in the step of determining the pest damage coefficient: if the wheat in a certain block is free of diseases, insect bites and eggs, the pest damage coefficient of the wheat in the block is 1; if the disease incidence of wheat in a certain block is ≤10% and / or the incidence of pest infection symptoms is ≤15%, it has been effectively controlled and no further damage is found, then the pest damage coefficient of the wheat in the block is 0.85-0.99; if the disease incidence of wheat in a certain block is >10% and / or the incidence of pest infection symptoms is greater than 15%, and white ears and dead ears are found, then the pest damage coefficient of the wheat in the block is 0.7-0.84; preferably, the diseases include physiological diseases and infectious diseases; preferably, the diseases include one or more of sheath blight, stem base rot, ergot, rust and powdery mildew; preferably, the pests include one or more of wheat spiders, wheat aphids and midges.

[0025] Preferably, in the step of determining the meteorological coefficient of the late growth period: from the milky stage to the mature stage of wheat, the meteorological conditions of a certain block are predicted as follows: the temperature difference between day and night is greater than 15°C, the cumulative rainfall and / or irrigation amount is 50-80mm, and the maximum temperature is less than 30°C, then the meteorological coefficient of the block is considered to be 1.01-1.2; from the milky stage to the mature stage of wheat, the meteorological conditions of a certain block are predicted as follows: the temperature difference between day and night is greater than 10°C, the cumulative rainfall and / or irrigation amount is 50-80mm, and the maximum temperature is less than 30°C. If the number of days with a temperature greater than 30°C is ≤ 5 days, the meteorological coefficient of the block is considered to be 1; during the milky stage to mature stage of wheat, the meteorological conditions of a certain block are predicted as follows: the difference in temperature between day and night is greater than 10°C, the cumulative rainfall and / or irrigation amount is greater than 80 mm or less than 50 mm, and the number of days with a maximum temperature greater than 30°C is greater than 5 days, then the meteorological coefficient of the block is considered to be 0.8-0.99; preferably, the meteorological conditions of wheat from the milky stage to the mature stage are predicted based on historical weather records and / or future long-term weather forecasts.

[0026] Preferably, in the yield prediction step, the calculation formula of the unit yield of wheat in each block is as follows: unit yield = number of unit ears × number of effective ear grains × thousand-grain weight × seedling condition coefficient × disease and insect damage reduction coefficient × late growth period meteorological coefficient × mechanical harvest loss reduction coefficient. Wherein, the grain weight is obtained based on the historical thousand-grain weight.

[0027] Preferably, the mechanical loss reduction coefficient is 0.95-0.97.

[0028] Compared with the prior art, the method for predicting wheat yield of the present invention has at least the following beneficial effects:

[0029] The embodiment of the present invention provides a method for predicting wheat yield, wherein the yield of wheat is predicted during the period from flowering to milk maturity of wheat; wherein the method for predicting wheat yield comprises the following steps: dividing the total area of ​​wheat to be predicted into one or more blocks; sampling each block, investigating the number of spikes and the number of grains per spike within the sampling point, and calculating the number of spikes per unit and the number of effective grains per spike; observing the growth shape of the wheat plants in each block, and determining the emergence coefficient; investigating and classifying the pest and disease damage in each block, and determining the pest and disease damage coefficient; predicting the meteorological conditions of wheat in each block from the milk maturity stage to the full maturity stage, and determining the meteorological coefficient in the late growth stage; according to the number of spikes per unit, the number of effective grains per spike, the seedling condition coefficient, the pest and disease damage coefficient, the meteorological coefficient, and combining the historical thousand-grain weight and the mechanical harvesting loss reduction coefficient, the unit yield of wheat in each block is calculated. Here, it should be noted that the present invention advances the time for prediction to the period from flowering to milk maturity of wheat, which is at least 30-40 days earlier than the traditional method, so that the yield measurement period is extended. Furthermore, the prediction scheme of the present invention does not require actual harvesting. It can predict wheat yield by determining yield factors (number of ears, number of grains) through fixed-point sampling and comprehensively considering the main factors affecting wheat yield. It can also standardize and digitize its utilization coefficients and calculation formulas. It can be seen that the yield prediction method of the present invention is relatively simple and can be implemented by ordinary technicians.

[0030] In summary, the embodiment of the present invention proposes a method for predicting wheat yield, which provides a relatively simple and convenient feasible method for growers, provides a convenient yield measurement tool for government statistics, provides a basis for the refined production management decision-making of large-scale growers, provides practical and convenient yield measurement tools for agricultural scientific researchers, and provides an ideal service tool for seed and agricultural production and sales companies. In addition, since the method of the present invention can predict the number of ears per mu and the number of grains per ear of wheat, it can also determine the occurrence of diseases and insect pests in the whole field. Therefore, it can help farmers to better comprehensively analyze the advantages and disadvantages of the selected wheat varieties, and it can also analyze whether the farmers' existing agricultural techniques, plant protection and other measures are appropriate, helping farmers to better improve agricultural techniques, plant protection measures and variety selection when planting wheat next time.

[0031] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the preferred embodiments of the present invention are described in detail as follows. DETAILED DESCRIPTION

[0032] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects of the present invention application are described in detail below in combination with the preferred embodiments. In the following description, different "one embodiment" or "embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures, or characteristics in one or more embodiments may be combined in any suitable form.

[0033] The embodiment of the present invention provides a method for predicting wheat yield, which mainly predicts wheat yield through yield factors and key growth coefficients. The present invention can accurately predict wheat yield in the middle growth period of wheat, and has the advantages of simple operation and no time limit.

[0034] Specifically, the method for predicting wheat yield proposed by the present invention mainly comprises the following steps:

[0035] The embodiment of the present invention provides a method for predicting wheat yield, wherein the wheat yield is predicted during the period from flowering to milk maturity of wheat; wherein the method for predicting wheat yield comprises the following steps:

[0036] Block division step: Divide the total wheat area to be predicted into one or more blocks.

[0037] The total wheat area required for prediction is divided into blocks based on three dimensions: cultivar, soil conditions, and management measures.

[0038] Among them, the management measures here include: sowing time, seed amount, planting density, fertilizer (nutrients and content, application amount, application method), pesticide (ingredients and content, usage, dosage), irrigation method and irrigation amount, etc.

[0039] Preferably, the soil conditions include sandy soil, clay soil, and loam;

[0040] If at least one of the dimensions of cultivar, soil condition, or management measure is different, the plots need to be divided into different blocks.

[0041] Preferably, the fertilizer application conditions include the nutrient content of the fertilizer and the application amount of the fertilizer; preferably, the pesticide application conditions include the composition, content and application amount of the pesticide.

[0042] Steps for investigating the number of ears and grains per ear: Take samples in each block, investigate the number of ears and grains per ear at the sampling point, and calculate the number of ears per unit and the number of effective grains per ear.

[0043] Here, the term "number of ears per unit" in the present invention refers to: the total number of wheat ears within an area unit (such as "mu" or "hectare"), and the unit is XX ten thousand ears / mu (hectare).

[0044] The term "effective number of grains per ear" in the present invention refers to the number of enlarged grains on each wheat ear, with the unit of xx grains / ear.

[0045] In this step: first, field observation is carried out. According to the field growth situation, 3 diagonal points, 5 points in the shape of a Chinese character or 9 points in the shape of a chessboard are sampled for each block, and the points are determined according to the principle of representativeness. The growth of wheat at the sampling point is consistent with the overall growth of wheat in the block.

[0046] Preferably, 1-10 rows of wheat are taken at each sampling point, and 1-10 meters are measured at each point (it should be noted here that the determination of the specific value should be determined by the consistency of wheat growth in the block. The better the consistency, the smaller the value can be, such as 1 row × 1 meter; conversely, the worse the consistency, the larger the value should be, such as 10 rows × 10 meters)", and the number of ears in the sampling point is investigated. The above data are measured and recorded respectively. Calculation is performed according to the following formula:

[0047] Number of ears per unit = total number of ears surveyed / surveyed area × unit area;

[0048] Among them, the survey area = the area of ​​each sampling point × the number of sampling points; for example, each point takes 4 adjacent rows, determines the row spacing, and measures a length of 1m. Here, the survey area is: row width × 4 × 1m × the number of sampling points.

[0049] The total number of ears surveyed refers to the total number of ears surveyed in multiple sampling sites in each block.

[0050] In addition, the specific area unit shall be based on local customs, generally "mu (667 square meters)" or "hectare (10,000 square meters)". The unit ear number is determined by the unit area. For example: when the unit ear number is mu, the unit area is 667 square meters; when the unit ear number is hectare, the unit area is 10,000 square meters.

[0051] At each sampling point, randomly select no less than 10 wheat plants, measure the effective number of grains per ear (based on effective fruiting), and record the above data. Calculate according to the following formula:

[0052] The number of effective grains per ear = the total number of grains surveyed / the total number of ears surveyed.

[0053] Here, the total number of grains surveyed refers to the total number of valid grains. For example, the first wheat plant has 4 valid ears and 82 grains in total; the second wheat plant has 2 valid ears and 54 grains in total. After the sampled wheat is counted in this way, there are 32 valid ears and 1115 grains in total. That is, the total number of grains surveyed is 1115 grains and the total number of ears surveyed is 32 ears.

[0054] Steps for determining the seedling condition coefficient: observe the growth appearance of the wheat plants in each block (, and determine the seedling condition coefficient.

[0055] Take the block as the unit and determine the emergence coefficient according to Table 1.

[0056] Table 1

[0057]

[0058] Among them, the three main characteristic indicators (plant height, stem hardness, and leaf color) in the approval announcement of the same variety are used as benchmark values.

[0059] The degree of fit in Table 1 refers to the degree of conformity between the various traits of this variety of wheat in the predicted yield field and the traits described in the wheat certification announcement for this variety.

[0060] It should be noted here that the approval announcement is derived from the requirements of laws and regulations such as the Seed Law of the People's Republic of China and the Measures for the Approval of Major Crop Varieties of the Ministry of Agriculture. Variety approval (including field trials, inoculation trials, quality testing, etc.) must be carried out for major crop varieties including wheat before they are sold and promoted. An announcement (referred to as the "approval announcement") shall be issued for varieties that have passed the approval. The approval announcement shall describe the variety name, breeder, variety source, morphological characteristics, yield, quality, stress resistance, suitable cultivation areas, etc. Among them, the morphological characteristics shall include a description of the basic agronomic traits of the variety, such as plant height, stem hardness, leaf color, etc.

[0061] Steps to determine the pest damage coefficient: Investigate and classify the pest damage situation in each block to determine the pest damage coefficient.

[0062] Here, the investigation of pests and diseases mainly focuses on the investigation and classification of diseases such as sheath blight, stem base rot, ergot, rust, powdery mildew, and insect pests such as wheat spiders, wheat aphids, and midges.

[0063] Among them, the determination of pest damage coefficient is shown in Table 2:

[0064] Table 2

[0065]

[0066] Steps to determine the meteorological coefficient in the late growth period: predict the meteorological conditions of wheat in each block from the milky stage to the mature stage, and determine the meteorological coefficient in the late growth period.

[0067] It should be noted that wheat maturity is divided into three stages, namely milky stage, waxy stage and full maturity. The milky stage is about 15 days, which is the first stage of wheat grain filling. At this time, the grains and the stems, leaves and ears in the middle and upper parts of the wheat are all green. The contents of the grains are milky white slurry with a water content of more than 50%. The volume and fresh weight of the grains reach the maximum at the end. The filling rate is the most vigorous in the milky stage, which has the greatest impact on the fullness of the grains.

[0068] Based on historical weather records and future long-term weather forecasts, forecast the meteorological conditions from the milky stage to the mature stage of wheat;

[0069] See Table 3 for determination of meteorological coefficients:

[0070] Table 3

[0071]

[0072] Here, the accumulated rainfall and / or irrigation during this period is 50-80 mm, which is defined as moderate rainfall and / or irrigation. This data is based on the general characteristics of wheat during the grain filling period. If the rainfall and / or irrigation during the grain filling period (i.e. milk maturity) is insufficient (less than 50 mm) or excessive (greater than 80 mm), and the temperature is too high, the wheat plants will fail or die prematurely, resulting in insufficient grain filling and low grain weight, which will affect the yield.

[0073] Steps for predicting yield: Calculate the unit yield of wheat in each block based on the number of ears per unit, the number of effective grains per ear, the seedling condition coefficient, the pest and disease damage coefficient, the meteorological coefficient, and the historical thousand-grain weight and the machine harvest loss reduction coefficient.

[0074] In the yield prediction step, the calculation formula for the unit yield of wheat in each block is as follows:

[0075] Unit yield = number of unit ears × number of effective grains per ear × grain weight × seedling condition coefficient × disease and insect damage reduction coefficient × meteorological coefficient in the late growth period × machine harvest loss reduction coefficient.

[0076] Grain weight can be calculated based on historical thousand-grain weight. Determine the thousand-grain weight of the wheat variety to be predicted (which can be obtained through the "Variety Approval Announcement") as the benchmark value. Regarding the thousand-grain weight, it should be noted that: since wheat grains are relatively small, one thousand grains are used for weighing in agricultural production for ease of weighing. The thousand-grain weight data will be reflected in the "Approval Announcement" of the variety. In actual calculations, it is necessary to divide the approved thousand-grain weight by 1000 to obtain the single-grain weight for calculation.

[0077] In addition, the loss reduction coefficient for machine harvesting is generally calculated as 0.95-0.97 (0.97 for self-harvesting / newer machinery, 0.95 for hired harvesting / old machinery).

[0078] Here, it should be noted that:

[0079] The traditional method of predicting wheat yield is mainly to do it after wheat matures (at the mature stage), specifically: collect wheat from a certain area, weigh it, and calculate the yield after calculating the shrinkage coefficient of 85%. However, this prediction method is almost simultaneous with harvesting, so it is impossible to make a prediction; the simple use of the shrinkage coefficient of 85% often results in a large error with the actual yield.

[0080] Other existing technologies also mention methods for predicting wheat yield during the critical growth period of wheat. However, the inventors believe that this technology has at least the following problems: it requires a large amount of accumulated historical data, does not take into account the interspecific differences between different varieties, and simply uses a formula to evaluate the growth, which is bound to have large errors and the accuracy of the results cannot be guaranteed; it does not take into account the adverse effects of diseases and pests on wheat yield during the growth period; it does not take into account the impact of meteorological factors in the late growth period on wheat yield; it does not take into account the impact of different harvesting methods on the actual wheat yield; it only states that sampling is needed, but there is no specific method for how to determine the location and take samples, so the results are bound to be inaccurate; the method is too complicated for ordinary technicians to master.

[0081] Compared with the above-mentioned technologies, the scheme of the present invention advances the prediction time to the period from flowering to milky stage of wheat, which is at least 30-40 days faster than the traditional method; there is no need for actual harvesting, and the wheat yield can be determined only by yield factors (number of ears, number of grains, and grain weight); a specific fixed-point sampling method is determined; the main factors affecting wheat yield, including variety, climate, disease, insect pests, etc., are comprehensively considered and standardized and digitized; the method of the present invention is relatively simple and can be implemented by ordinary technicians.

[0082] In summary, the method for predicting wheat yield proposed in the embodiment of the present invention provides farmers with a relatively simple, convenient and feasible method, provides a convenient yield measurement tool for government statistics, provides a basis for the refined production management decisions of large-scale farmers, provides agricultural researchers with practical and convenient yield measurement tools, and provides an ideal service tool for seed and agricultural material production and sales companies.

[0083] The present invention is further described below by specific experimental examples:

[0084] Example 1

[0085] This embodiment mainly adopts a method for predicting wheat yield proposed by the present invention to predict wheat yield, which is specifically as follows:

[0086] In 2021, Li, a large-scale wheat farmer in Jinpu Town, Runan County, Henan Province, planted 185 acres of winter-sown wheat and predicted its yield on April 25, 2022.

[0087] After investigation and understanding, the total wheat area to be predicted is divided into blocks according to the three dimensions of cultivated varieties, soil conditions and management measures. The basic situation is shown in Table 4:

[0088] Table 4 Basic information of each block

[0089]

[0090] Because the plots are generally large, each block is sampled at 9 points in a chessboard pattern (that is, the relevant plots are divided into 16 rectangular areas in a chessboard shape, and the 9 intersecting points inside are the sampling points) to determine the number of ears per mu.

[0091] Table 5 Survey methods and original data of ear number per mu in each block

[0092]

[0093] Based on the above results, the number of ears per mu is determined as follows:

[0094] Table 6 Measured values ​​of ear number per mu in each plot

[0095]

[0096]

[0097] When investigating the number of ears per mu, four whole wheat plants were randomly selected at the starting position of each row (i.e., 9 points per plot × 3 rows per point × 4 plants per row, a total of 108 plants) to investigate the total number of grains and the total number of ears. The data and results are as follows:

[0098] Table 7 Measured values ​​of effective grain number per ear in each plot

[0099] Plots Total number of particles surveyed Total number of ears surveyed Number of effective grains per ear Area A 6439 tablets 185 spike 6439 / 185=34.81 pills Zone B 6027 tablets 177 spike 6027 / 177＝34.05 Zone C 6401 tablets 201 spike 6401 / 201=31.85 grains Area D 6465 tablets 179 spike 6465 / 179=36.12 pills

[0100] Combining field observations and sample surveys, and consulting historical meteorological records and future long-term weather forecasts, the coefficients are determined as follows:

[0101] Table 8 Seedling conditions, pests and diseases survey and coefficient values ​​for each plot

[0102]

[0103]

[0104] Table 9 Meteorological coefficient and mechanical harvesting loss coefficient fixed value

[0105]

[0106] After consulting the approval announcements of cultivars in various plots, we learned that the historical thousand-grain weights of the relevant varieties are as follows:

[0107] Table 10 Thousand-grain weight of planted varieties in each plot

[0108]

[0109]

[0110] Based on the above values, the yield per mu was predicted, and the grower was required to compare the single harvest and single calculation for each plot. The following table lists the predicted yield and the actual harvested yield respectively, and the error values are all within 5%, reflecting the full advantages of the present invention.

[0111] Table 11 Yield prediction methods and results for each plot

[0112]

[0113] Table 12 Comparison of predicted yield and actual harvested yield for each plot

[0114] Plots Forecast total production Total output received Error value Area A 231657 catties 239420 catties -3.2％ Zone B 129490 catties 135338 catties -4.3％ Zone C 172431 catties 169137 catties 1.9％ Area D 137359 catties 140834 catties -2.5％

[0115] Example 2

[0116] In this example, a method for predicting wheat yield proposed by the present invention was mainly used to predict the wheat yield, which is specifically as follows:

[0117] In 2022, grower Liu某某 in Hongshan Town, Shenqiu County, Henan Province, planted 14 mu of winter sown wheat, and its yield was predicted on May 2, 2023 (the prediction implementation was 25 days).

[0118] After investigation, it was found that the grower planted in an intensive and contiguous manner. The basic situation was as follows: loam soil, medium soil fertility, the variety was Zhengmai 139, the seeding rate per mu was 15 kg, and 60 kg / mu of compound fertilizer (NPK15-15-15) was applied as base fertilizer. In February 2023, 10 kg / mu of urea (N 46%) was topdressed, and the growth period was irrigated by flooding twice. Chemical weeding was carried out in the first ten days of December 2022, and chemical pest control and disease prevention were carried out in late February and early April 2023. Here, since the production factors were the same, there was no need to divide the plots.

[0119] 1. Determination of ears per mu

[0120] Sampling was carried out at 5 points in the shape of an instrument (that is, the plot was divided into 9 rectangular areas in the shape of a chessboard, and the 5 points at the intersection of the inside and the center were the sampling points). At each point, 4 rows (row spacing 0.25 m) were taken in width and 2 m in length, and the surveyed area was 0.25×4×2×5 = 10 m 2 .

[0121] The survey data were: point 1: 1158 ears, point 2: 1020 ears, point 3: 1181 ears, point 4: 1203 ears, point 5: 1216 ears. The total number of ears at the 5 points was 5778 ears.

[0122] The number of ears per mu was determined to be 5778 ears / 10m 2 ×667m 2 =385393 spikes

[0123] 2. Determination of grain number per ear

[0124] While investigating the number of ears per mu, 2 whole wheat plants were taken at the starting position of each row (i.e. 5 points × 4 rows per point × 2 plants per row, a total of 40 plants), and the total number of grains was measured to be 3368 and the total number of ears was 92.

[0125] The number of grains per ear was 3368 / 92 ears = 36.61 grains

[0126] 3. Thousand-grain weight / grain weight query

[0127] After checking the "Approval Announcement" of this variety, we learned that its thousand-grain weight is 47.8-49. grams, with an average value of 48.45 grams, and the grain weight is 0.04845 grams.

[0128] 4. Investigate and determine seedling conditions, pests and diseases, weather and mechanical harvest loss coefficient

[0129] (1) Seedling Condition Coefficient: Wheat in the field is growing normally, with a plant height of 79.5 cm, dark green leaves, and a compact plant shape, but the stem hardness and elasticity are low, and the root system is slightly weak. Compared with the "Approval Announcement" of this variety, the plant height and leaf color are consistent, but the stem hardness deviates slightly, and the seedling condition coefficient is determined to be 0.97;

[0130] (2) Pest and disease damage coefficient: Slight infection of stem base rot was occasionally seen at the base of the stem in the field, with an incidence of about 8%, and no white ears were observed due to this; there was slight infection of fusarium head blight at the upper part of the ear head, with an incidence of about 5%; no other pests and diseases were observed; considering that the infection period of these two diseases had passed when the yield was predicted, the pest and disease coefficient was determined to be 0.95;

[0131] (3) Meteorological coefficient: After consulting the 15-day weather forecast and the 7-day rainfall forecast, combined with local historical meteorological records, it is known that the average daily temperature difference in Shenqiu County in early and mid-May (wheat milk maturity period) is about 12 degrees Celsius, with the highest temperature being 29 degrees Celsius. No rainfall has been reported, but the plot has irrigation conditions (if rainfall is insufficient, it can be supplemented by irrigation), which is more suitable for grouting. The meteorological coefficient is determined to be 1;

[0132] (4) Machine harvesting loss coefficient: After communicating with farmers, we learned that the local machine harvesting loss rate is about 5%, and the machine harvesting loss coefficient is determined to be 0.95.

[0133] 5. Yield forecast results

[0134] Yield per mu: 385393 ears × 36.61 grains × 0.04845 grams × 0.97 × 0.95 × 1 × 0.95 / 500 grams = 1196.87 kilograms

[0135] Total output: 1196.87 jin x 14 mu = 16756 jin

[0136] 6. Comparison with actual production

[0137] The farmer is required to record the weight of wheat when it is sold so as to compare it with the predicted yield results. On June 12, 2023, the farmer reported that his wheat dry grain yield was 16,896 catties (equivalent to 1,206.86 catties per mu), with an error of only 9.99 catties and an error rate of less than 3%, which fully demonstrates the advantages of the present invention.

[0138] Comparative Example 1

[0139] This comparative ratio is a method commonly used by agricultural technical departments. During the wheat maturity period (1-3 days before harvest), samples are taken at three points in the plot. Wheat ears within 1 square meter are cut at each point, and weighed after threshing. The reduction coefficient is 85% to convert the per-acre yield. The specific method is as follows:

[0140] Farmer Zhang in Yanzhou District, Jining City, Shandong Province planted 78 acres of wheat in 2023. In order to determine the average yield per acre, the county's agricultural technicians predicted the yield on May 29, 2023 when the wheat was fully mature. The plot was divided into 3 blocks in three equal parts. A point was randomly selected in each block. A yield measurement frame with an area of ​​1 square meter was used to frame the wheat at this point. Then all the wheat in the frame was harvested and bagged. In this way, all the wheat within 3 square meters in the 3 areas was harvested. After the wheat was fully dried and threshed, it was weighed to obtain the overall yield of 3 square meters of wheat, which was converted into the average yield per acre of this plot. The results are as follows:

[0141] Average yield per mu: (1.94 catties + 1.73 catties + 1.88 catties) / 3×667×0.85=1048.9 catties / mu

[0142] Farmer Zhang actually harvested the wheat three days later, then dried all the harvested wheat and put it into storage and weighed it. The average yield per mu was 928.7 catties / mu. It can be seen that there is a large difference between this method and the actual yield per mu, with a difference of 120.2 catties and a difference rate of 12.94%.

[0143] In summary, the method for predicting wheat yield proposed by the present invention has at least the following advantages compared with conventional yield measurement methods:

[0144] (1) Conventional prediction methods measure wheat yield 1-3 days before harvest. Due to time and weather constraints, farmers often find it difficult to cooperate, so this type of yield measurement method is difficult to implement in practice.

[0145] (2) Conventional forecasting methods are not very effective because the time from actual harvest to production is too short and farmers can confirm crop yields through actual harvest.

[0146] (3) Conventional prediction methods have too few sampling points and are difficult to represent the overall wheat growth and yield of a large piece of land. Therefore, the yield measurement results are quite different from the actual harvest yield.

[0147] (4) Conventional prediction methods only measure the wheat yield within the sampling frame, but do not investigate the seedling condition coefficient and disease damage coefficient in the wheat field. Therefore, they cannot provide farmers with constructive suggestions on wheat variety selection, field management, plant protection measures, etc.

[0148] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any simple modification, equivalent change and modification made to the above embodiment according to the technical essence of the present invention still falls within the scope of the technical solution of the present invention.

Claims

1. A method for predicting wheat yield, characterized in that: The wheat yield is predicted during the period from the flowering stage to the milky stage of wheat; wherein the method for predicting the wheat yield comprises the following steps: Block division step: divide the total wheat area to be predicted into one or more blocks; Steps for investigating the number of ears and grains per ear: sample each block, investigate the number of ears and grains per ear at the sampling point, and calculate the number of ears per unit and the number of effective grains per ear; Steps for determining the seedling condition coefficient: observe the growth appearance of the wheat plants in each block to determine the seedling condition coefficient; Steps to determine the pest damage coefficient: Investigate and classify the pest damage situation in each block to determine the pest damage coefficient; Steps for determining the meteorological coefficients in the late growth period: predict the meteorological conditions of wheat in each block from the milky stage to the mature stage, and determine the meteorological coefficients in the late growth period; Steps for predicting yield: Calculate the unit yield of wheat in each block based on the number of ears per unit, the number of effective grains per ear, the seedling condition coefficient, the pest and disease damage coefficient, the meteorological coefficient in the late growth period, and the historical thousand-grain weight and the machine harvest loss reduction coefficient.

2. The method for predicting wheat yield according to claim 1, characterized in that: In the block division step: The total wheat area to be predicted is divided into blocks based on three dimensions: cultivar, soil conditions, and management measures; Preferably, the management measures include one or more of sowing time, seed amount, planting density, fertilizer application, pesticide application, irrigation method and irrigation amount; preferably, the fertilizer application includes fertilizer nutrient content and fertilizer application amount; preferably, the pesticide application includes pesticide composition, content and application amount; Preferably, the soil conditions include sandy soil, clay soil, and loam; If at least one of the dimensions of cultivar, soil condition, or management measure is different, the plots need to be divided into different blocks.

3. The method for predicting wheat yield according to claim 1 or 2, characterized in that: In the step of investigating the number of ears and the number of grains per ear: When performing fixed-point sampling on each block, any one of the sampling methods of diagonal 3-point sampling, device-shaped 5-point sampling, and chessboard-shaped 9-point sampling is adopted; and / or The growth of wheat at the sampling point is consistent with the overall growth of wheat in the block; and / or 1-10 rows of wheat beads were selected at each sampling site, and the length of each row was 1-10m.

4. The method for predicting wheat yield according to any one of claims 1 to 3, characterized in that: In the step of investigating the number of ears and the number of grains per ear: Sampling was carried out at fixed points in each block, the number of ears in multiple sampling points was investigated, and the number of unit ears in each block was calculated according to the following formula: Number of ears per unit = total number of ears surveyed / surveyed area × unit area; Wherein, the survey area = the area of ​​each sampling point × the number of sampling points; The total number of ears surveyed refers to the total number of ears surveyed in multiple sampling sites in each block.

5. The method for predicting wheat yield according to any one of claims 1 to 4, characterized in that: In the step of investigating the number of ears and the number of grains per ear: Fixed-point sampling was performed in each block, and multiple wheat plants were randomly selected at each sampling point to measure the effective number of grains per ear. The calculation formula for the effective number of grains per ear in each block is as follows: Number of effective grains per ear = total number of grains surveyed / total number of ears surveyed; Preferably, at least 10 wheat plants are randomly selected at each sampling point.

6. The method for predicting wheat yield according to any one of claims 1 to 5, characterized in that: In the step of determining the seedling condition coefficient: If the wheat in a certain block is of Class I seedlings, the seedling condition coefficient in that block is determined to be 1.06-1.15; If the wheat in a certain block is of Class II seedlings, the seedling condition coefficient in that block is determined to be 0.95-1.05; If the wheat in a certain block is of Class III seedlings, the seedling condition coefficient in that block is determined to be 0.85-0.94; Preferably, if the plant height, stem hardness, and leaf color of wheat in a certain block are all consistent with the benchmark values ​​by more than or equal to 80%, the wheat is considered to be Class I seedlings; If the degree of fit between the plant height, stem hardness, and leaf color of wheat in a certain block and the benchmark value is less than 80%, the wheat in the block is considered to be Class II seedlings; If the degree of agreement between plant height, stem hardness, and leaf color of two or three items in a certain block and the benchmark value is less than 80%, the wheat in the block is considered to be Class III seedlings; Among them, the plant height, stem hardness and leaf color in the approval announcement of the same variety are used as benchmark values.

7. The method for predicting wheat yield according to any one of claims 1 to 6, characterized in that: In the step of determining the pest damage coefficient: If the wheat in a certain block has no diseases, no insect bites, and no insect eggs, then the disease and insect damage coefficient of the wheat in this block is 1; If the disease incidence of wheat in a certain block is ≤10% and / or the incidence of insect pest infection symptoms is ≤15%, it has been effectively controlled and no further damage is observed, then the disease and insect damage coefficient of wheat in this block is 0.85-0.99; If the disease incidence of wheat in a certain block is greater than 10% and / or the incidence of insect pest infection symptoms is greater than 15%, and white ears and dead ears are found, the disease and insect damage coefficient of wheat in this block is 0.7-0.84; Preferably, the diseases include physiological diseases and infectious diseases; Preferably, the diseases include one or more of sheath blight, stem base rot, head blight, rust, and powdery mildew; Preferably, the insect pests include one or more of wheat spiders, wheat aphids, and midges.

8. The method for predicting wheat yield according to any one of claims 1 to 7, characterized in that: In the step of determining the meteorological coefficient of the late growth period: During the milky to mature stage of wheat, the meteorological conditions of a certain block are predicted as follows: if the temperature difference between day and night is greater than 15°C, the cumulative rainfall and / or irrigation is 50-80mm, and the maximum temperature is less than 30°C, the meteorological coefficient of the block is considered to be 1.01-1.2; During the milky to mature stage of wheat, the meteorological conditions of a certain block are predicted as follows: if the temperature difference between day and night is greater than 10°C, the cumulative rainfall and / or irrigation is 50-80mm, and the number of days with the maximum temperature greater than 30°C is ≤5 days, then the meteorological coefficient of the block is considered to be 1; During the milky to mature stage of wheat, the meteorological conditions of a certain block are predicted as follows: if the temperature difference between day and night is greater than 10°C, the cumulative rainfall and / or irrigation volume is greater than 80mm or less than 50mm, and the number of days with the maximum temperature greater than 30°C is greater than 5 days, then the meteorological coefficient of the block is considered to be 0.8-0.99; Preferably, the meteorological conditions from the milky stage to the mature stage of wheat are predicted based on historical weather records and / or future long-term weather forecasts.

9. The method for predicting wheat yield according to any one of claims 1 to 8, characterized in that: In the yield prediction step, the calculation formula for the unit yield of wheat in each block is as follows: Unit yield = number of ears per unit × number of effective grains per ear × grain weight × seedling condition coefficient × disease and insect damage reduction coefficient × meteorological coefficient in the late growth period × machine harvest loss reduction coefficient; Wherein, the grain weight is obtained based on the historical thousand-grain weight.

10. The method for predicting wheat yield according to claim 9, characterized in that: The mechanical harvesting loss reduction coefficient is 0.95-0.97.

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