JP2022064329A - Wheat yield prediction method - Google Patents

Abstract

To provide a method for predicting a wheat yield at an early stage with high accuracy.SOLUTION: This wheat yield prediction method involves: acquiring analysis data regarding one or more components from a leaf sample of maize; and predicting a wheat yield by using a correlation between the data and the wheat yield.SELECTED DRAWING: None

Description

The present invention relates to a method for predicting wheat yield at an early stage.

Wheat is an important grain called the three major grains along with corn and rice, and is widely eaten in Japan and around the world. It is widely cultivated as one of the important grains, and techniques for increasing the yield are being developed.
The growing period of wheat varies slightly depending on the variety and cultivation conditions, but it usually takes a long period of 3 to 6 months from sowing to harvesting. Therefore, in the development of a technique for increasing the yield of wheat, it takes a lot of time for cultivation to evaluate the yield, and therefore, a method for predicting the yield at an early stage has been required. In addition, if the yield can be predicted at an early stage in the actual production situation, the producer can easily determine whether or not to introduce a costly additional technique in order to secure a stable yield.

Various methods for early evaluation of yield using meteorological data, field images and growth information have been studied so far. For example, in Non-Patent Document 1, a model for predicting production at the national level is constructed using meteorological data such as cumulative precipitation and cumulative average temperature before sowing. Non-Patent Documents 2 and 3 disclose an attempt to evaluate growth and yield from meteorological conditions. Non-Patent Document 4 discloses a technique of estimating NDVI (normalized difference vegetation index) from an image of a field and simply predicting the yield in each field. Further, in Patent Document 1, an attempt is made to measure the amount of nitrogen in the leaf blade, the color of the leaf, or the amount of chlorophyll, evaluate the growth and yield, and determine the amount of fertilizer applied.

However, the model of Non-Patent Document 1 is a model for predicting the yield of wheat using meteorological data predicted before sowing, but the predictable unit is the national unit, and the predictors and yields for each individual are used. It is not suitable for evaluation when you want to correspond. The methods of Non-Patent Documents 2, 3 and 4 and Patent Document 1 can be said to be non-destructive and simple measurement, but the prediction time is after the panicle formation stage, that is, after half of the growth period has passed. Become. Furthermore, since the prediction is made on a field-by-field basis, it is not a technique for predicting the yield at the individual level.

In addition, it has been reported that in rice, metabolites extracted from the above-ground part about 15 days after sowing were comprehensively measured by GC-MS, and a hybrid rice yield prediction model was created using these data. However (Non-Patent Document 5), in this report, the predictability evaluation of the model called cross-validation, which is performed when constructing a normal predictive model, is not performed, and it cannot be said that the verification is sufficient. In addition, it is invasive and is not suitable for evaluation when it is desired to correspond the predictor of each individual with the yield.

Japanese Unexamined Patent Publication No. 2000-300077

Iizumi, T. et al., Climate Services, 2018, vol. 11, p.13-23 Toyohisa Minoda, Journal of the Crop Science Society of Japan, 2010, Vol. 79, No. 1, p. 62-68, "Effects of meteorological conditions on the yield of wheat" Norin 61 "in upland field test fields in Saitama Prefecture" Marletto, V. et al., Agricultural and Forest Meteorology, 2007, vol. 147, p. 71-79 Junichi Ikeda et al., Journal of Japanese Society of Soil Science and Technology, 2001, Vol. 72, No. 6, p. 786-789, "Prediction of wheat yield by image data of high resolution satellite IKONOS" Dan, Z. et al., Scientific Reports, 2016, 6, 21732

The present invention relates to providing a method for predicting wheat yield early and accurately.

As a result of various studies on the evaluation of the yield of wheat, the present inventors have found that the metabolites contained in the leaves have a component whose abundance correlates with the yield, and as early as about one month after sowing. It was found that the final yield can be evaluated at the individual level by collecting a part from the expanded leaves of wheat and analyzing the components contained in the leaves.

That is, the present invention is a method for predicting the yield of wheat by acquiring analytical data of one or more components from a leaf sample collected from wheat and using the correlation between the data and the yield of wheat to predict the yield of wheat. ,I will provide a.

According to the method of the present invention, the yield of wheat can be predicted at an early stage. As a result, for example, it becomes easy to determine the introduction of additional techniques for ensuring the yield, and it is possible to greatly improve the efficiency of the development of the techniques for increasing the yield.

The figure which shows the relationship between the predicted value and the measured value of the yield by the OPLS model constructed using all 23 data. Analytical data of all components from the 11th place to the 500th place, analysis data of all the components from the 21st place to the 500th place, and analysis data of all the components from the 31st place to the 500th place in the model of FIG. ^R2 (indicated as R2Y in the figure) and ^Q2 (indicated as Q2 in the figure) value of each model constructed by the OPLS method using the analysis data of all the components from the 61st place to the 500th place. The figure which shows. Of the analysis data of the components from the top 1 to 10 VIP values in the model of FIG. 1, R ² (R2Y in the figure) of each model constructed by the OPLS method for any 8 combinations (45 combinations). The figure which shows the value (display) value and ^Q2 (displayed as Q2 in the figure) value. Of the analysis data of the components from the top 1 to 10 VIP values in the model of FIG. 1, R ² (R2Y in the figure) of each model constructed by the OPLS method for any 9 combinations (10 combinations). The figure which shows the value (display) value and ^Q2 (displayed as Q2 in the figure) value.

In the present invention, wheat is an annual plant of the Gramineae family and means all plants belonging to the genus Wheat (scientific name: Triticum). There are a total of 18 species belonging to the genus Wheat. thaudar, T.I. monococcum, T.I. dicoccoides, T.I. dicoccum, T.I. pyromidale, T.I. orientale, T.I. durum, T.I. turgidium, T.I. polynicum, T.I. persicum, T.I. aestivum, T.I. spelled, T.I. compactum, T.I. sphereococcum, T.I. maha, T.I. vavilovii, T.I. Timopheevi can be mentioned. One of the representative species is T.I. Aestivum is mentioned, and its varieties include Kitahonami, Yumechi, Kitanokaori, Tsurukichi, Kitasachiho, Haruyo Koi, Harukirari, Haruyutaka, Norin 61 and so on. In addition, other representative species include T.I. Dicoccum is mentioned, and its varieties include Abruzzo, Molise, Umbria and the like. Furthermore, T. Examples of durum varieties include setodur and the like. As described above, the varieties included in the genus Wheat are diverse, but the present invention is not limited thereto.

The growth stages from the germination to the flowering stage of wheat are the germination stage (around 7 days after sowing), the 5 leaf age stage (around 15 days after sowing), the stalk standing stage (around 30 days after sowing), and the panicle formation stage (around 30 days after sowing). It is divided into a flagging stage (around 50 days after sowing), a heading stage (around 60 days after sowing), and a flowering stage (around 65 days after sowing). In the present invention, the leaf sample of wheat may be collected from the sowing stage to the heading stage when the leaves can be collected, and the collection time is preferably from the 5th leaf age to the heading stage, more preferably. Examples thereof include 5 leaf age to stop leaf stage after sowing, preferably 20 days to stop leaf stage after sowing, and more preferably 20 days to young ear formation stage after sowing. It is preferable that the number of days before and after each of the above growth stages is within 5 days.
Alternatively, the time for collecting wheat leaves is 7 days or more after sowing, preferably 15 days or more, more preferably 20 days or more, and preferably 60 days or more, more preferably 50 days or more after sowing. More preferably, it may be before the 40th day. Further, it may be 7 to 60 days after sowing, preferably 15 to 50 days, and more preferably 20 to 40 days. For example, it is preferable to collect leaves from wheat 27 days ± 3 to 5 days after sowing.
Here, the number of days after sowing represents the number of days when spring wheat is cultivated. In autumn wheat, the number of days after sowing of each growth stage is different from the above number of days, but those skilled in the art can understand the number of days after sowing of each growth stage in consideration of the cultivation conditions of autumn wheat. Leaf samples may be taken at the time corresponding to.

In the present invention, the yield means the parenchyma of wheat per individual or the number of grains per individual. The mass is not particularly limited, but a dry mass is preferable. Of these, the actual amount of dried wheat per individual is particularly preferable. The actual amount of dried child in the present invention means the mass measured in a state where the water content contained in the grain after drying at 90 ° C. for 72 hours is reduced to 7% or less. It is preferable that the actual amount of the desiccant is measured only by a balance with a calibration function such as an electronic balance.

The site for collecting the leaf sample is not particularly limited, and for example, about 4 to 7 leaves may be cut from the root of the strain and collected.

In the present invention, the analysis data of the components to be obtained include high performance liquid chromatography (HPLC), gas chromatography (GC), ion chromatography, mass spectrometry (MS), near infrared spectrometric analysis (NIR), and Fourier conversion. Infrared spectroscopic analysis (FT-IR), nuclear magnetic resonance analysis (NMR), Fourier transform nuclear magnetic resonance analysis (FT-NMR), inductively coupled plasma mass spectrometer (ICP-MS), liquid chromatograph and mass spectrometry. Examples thereof include data analyzed / measured using a combined LC / MS or other instrument analysis means, preferably mass spectrometric data, and more preferably mass spectrometric data by LC / MS.
Examples of the mass spectrometric data include precision mass (“m / z value”), ionic strength, holding time, and the like, but precision mass information is preferable.

In order to apply the leaf sample to the above-mentioned instrumental analysis means, it is appropriately pretreated according to the analysis means, but usually, the collected leaves are wrapped in aluminum foil and immediately frozen in liquid nitrogen to stop the metabolic reaction. After being freeze-dried and dried, it is subjected to an extraction operation.
Extraction is performed by pulverizing the freeze-dried leaf sample using a bead crusher or the like, adding an extraction solvent, and stirring the mixture. Examples of the extraction solvent used here include methanol, ethanol, butanol, acetonitrile, chloroform, ethyl acetate, hexane, acetone, isopropanol, water and the like, and a mixture thereof. When LC / MS is used as the analytical means, an 80v / v% aqueous methanol solution to which an internal standard substance is added is preferably used.

In the present invention, examples of the components in the leaves analyzed include wheat metabolites separated and detected by LC / MS. Preferably, the components provided by mass spectrometry have a precise mass (m / z) of 104 to 1178. More preferably, 1,450 components listed in Tables 1a to 1j below, which are defined by the precise mass (m / z value) provided by mass spectrometry, can be mentioned. In the process of separation detection by LC / MS, if different molecular ion peaks of partial decomposition products and adducts (M + H, M + Na, etc.) are generated from the metabolites, the detected partial decomposition products are different from the original metabolites. It was used as a component of.

The 1,450 components are selectively extracted from wheat metabolites, and the selection method is as shown in Examples in detail. In general, 1) wheat (spring-sown wheat) by changing fertilization conditions. ) Is cultivated, 2) 4 to 7 leaves are collected about 1 month after sowing to obtain leaf samples, 3) components are extracted using 80v / v% wheat, and then 4) LC / MS. Analysis is performed to obtain molecular ion information (precision mass, m / z) and structural information derived from fragments, 5) peaks derived from components are extracted, and then each peak is aligned between each sample. Analysis data of 1,450 components is acquired by removing body peaks, correcting peak intensity between samples, and removing noise. The method of correcting the peak intensity between the samples is not particularly limited, and examples thereof include correction using the popled QC method. In the pooled QC method, a sample called pooled QC is prepared by mixing a certain amount from all the samples in the same batch, and the polled QC is analyzed at a constant frequency (about once every 5 to 9 times) between each sample. By implementing, the estimated value "what will happen to each peak intensity if it is assumed that the QC sample was analyzed when each sample was analyzed" is calculated, and the process of correcting with that value is performed. It corrects the sensitivity between each sample. The data correction method does not significantly affect the correlation with yield and the performance of the prediction model.

In addition, the result of correlation analysis between the acquired analysis data of 1,450 components and the corresponding yield data (substantial amount of dried material) (single correlation coefficient r between the peak area and yield of the analysis data of each component and none). The p-value was calculated by the correlation test), and it was shown that certain components significantly correlated with the yield (see Tables 4a-4s below).

Therefore, among the 1,450 components, the component to be analyzed in the present invention is a component having a significant correlation with the yield (p <0.05) and an absolute value of the correlation coefficient | r |> 0.51, that is, Ingredient No. 1, 12, 17, 20, 34, 48, 52, 58, 60, 61, 68, 71, 72, 74, 83, 84, 85, 90, 92, 101, 107, 109, 110, 115, 117, 118, 129, 132, 133, 136, 143, 160, 164, 170, 171, 172, 178, 179, 181, 184, 189, 191, 193, 196, 198, 203, 206, 210, 218, 235, 237, 249, 255, 257, 282, 283, 291 294, 297, 298, 310, 312, 315, 318, 330, 331, 345, 346, 348, 355, 360, 361, 362, 373, 384, 386, 388, 392, 395, 406, 408, 411, 421, 440, 444, 449, 452, 454, 455, 457, 461, 470, 482, 501, 505, 513, 521, 530, 535, 541, 542, 546, 548, 565, 571, 574, 579, 589, 607, 610, 612, 616, 634, 646, 648, 656, 660, 662, 701, 708, 710, 716, 731, 732, 740, 743, 749, 750, 753, 757, 760, 761, 767, 768, 774, 777, 783, 785, 787, 788, 792, 794, 795, 803, 808, 809, 815, 823, 824, 825, 835, 845, 846, 853, 854, 856, 862, 863, 864, 865, 869, 874, 875, 876, 877, 879, 886, 888, 893, 895, 904, 905, 909, 916, 921, 922, 929, 933, 934, 938, 939, 942, 945, 947, 951, 966, 967, 968, 970, 971, 983, 987, 988, 994, 995, 998, 1003, 1005, 1008, 1009, 1010, 1013, 1017, 1018, 1021, 1027, 1031, 1035, 1038, 1039, 1040, 1047, 1052, 1061, 1067, 1068, 1069, 1072, 1077, 1085, 1094, 1096, 1106, 1118, 1121, 1126, 1127, 1132, 1134, 1143, 1153, 1159, 1181, 1183, 1186, 1194, 1198, 1199, 1209, 1213, 1217, 1218, 1232, 1 237, 1238, 1239, 1242, 1244, 1254, 1264, 1275, 1278, 1279, 1284, 1292, 1295, 1308, 1312, 1314, 1315, 1318, 1320, 1330, 1331, 1334, 1337, 1338, 1339, It preferably contains one or more selected from 1345, 1346, 1350, 1355, 1362, 1408, 1412, 1415, 1418, 1419, 1442, 1444 and 1446. In the above component, if the VIP value described later is approximately 0.69 or more and 1.20 or more, the absolute value of the correlation coefficient | r |> 0.51.

Further, among the 1,450 components, the components to be analyzed in the present invention are components having a significant correlation with yield (p <0.05) and an absolute value of the correlation coefficient | r |> 0.66, that is, components. No. 12, 20, 34, 48, 58, 61, 85, 101, 109, 115, 132, 133, 172, 184, 193, 198, 206, 255, 282, 283, 312, 330, 331, 346, 348, 355, 361, 386, 449, 461, 482, 505, 542, 589, 662, 731, 760, 761, 783, 785, 787, 815, 824, 825, 874, 875, 886, 929, 934, 994, It is preferable to include one or more selected from 1005, 1009, 1039, 1040, 1067, 1068, 1085, 1143, 1181, 1183, 1186, 1209, 1232, 1244, 1279, 1284, 1337, 1346, 1350 and 1355. ..

In Tables 1a to 1j, the components of 1,450 are defined by the precise mass obtained by mass spectrometry, and the composition formula of the compound can be estimated from these precise mass data. Further, the partial structure information of the compound can be obtained from the MS / MS data acquired at the same time as the analysis. Therefore, the target component can be estimated from the composition formula and the partial structure information, and the one that can be compared with the reagent can be identified.

For example, as a result of analysis, No. 12 is pyroglutamic acid of composition formula C ₅ H ₇ NO ₃ , No. 20 is L-asparagine of composition formula C ₄ H ₈ N ₂ O ₃ , and No. 48 is composition formula C ₁₂ H. ₁₆ , No. 132 is cloxatin G of composition formula C ₁₂ H ₂₀ O ₂ , No. 184 is kukurublutamic acid of composition formula C ₁₂ H ₂₀ O ₃ , No. 210 is composition formula C ₁₃ H ₁₈ O ₃ , No. 346. Is a composition formula C ₁₆ H ₁₂ O ₄ , No. 355 is the composition formula C ₁₂ H ₁₄ O ₇ , and No. 386 is the composition formula C ₁₄ H ₂₀ N ₄ O ₂ , No. 386. 461 is 5-aminoimidazole ribonucleotide of composition formula C ₈ H ₁₄ N ₃ O ₇ P, and No. 731 is composition formula C ₂₂ H ₂₄ O ₅ , No. It was presumed that 886 was composition formula C ₁₈ H ₂₅ N ₅ O ₆ , No. 929 was composition formula C ₁₇ H ₂₂ O ₁₂ , and No. 1183 was composition formula C ₃₁ H ₃₃ NO ₇ .

Of these, preferred examples of the components to be analyzed in the present invention include pyroglutamic acid, L-asparagine, cloxatin G, coucurbic acid, 5-aminoimidazole ribonucleotide and the like.

As a means for predicting the yield of wheat, the above 1,450 components, preferably components having a significant correlation with the yield (p <0.05) and an absolute value of the correlation coefficient | r |> 0.51. The abundance of components preferably significant (p <0.05) and having an absolute value of the correlation coefficient | r |> 0.66 (eg, precision mass m / z 277.1657 with a correlation coefficient of −0.801). (Peak area) can also be measured for the wheat leaf sample to be predicted, and the yield can be predicted from the correlation between the known yield and the measured peak area.

In addition, the yield can be predicted by using a plurality of the analysis data of the above 1,450 components and collating with the yield prediction model constructed by using the multivariate analysis method.
That is, a wheat leaf sample after a lapse of a predetermined period from sowing is collected, an analysis sample is obtained, the analysis sample is subjected to instrumental analysis to obtain instrumental analysis data, and the instrumental analysis data is collated with a yield prediction model. Therefore, the yield of the wheat can be predicted.

The yield prediction model can be constructed by performing regression analysis using the peak area value of the corrected component analysis data with each precise mass as the explanatory variable and the yield value as the objective variable. Regression analysis methods include, for example, principal component regression analysis, PLS (Partial least squares projection to latent structures) regression analysis, OPLS (Orthogonal projections to latent structures) regression analysis, generalized linear regression analysis, bagging, and support vector machines. Examples include multivariate regression analysis methods such as machine learning / regression analysis methods such as random forest and neural network regression analysis. Of these, it is preferable to use the PLS method, the OPLS method which is an improved version of the PLS method, or the machine learning / regression analysis method. The OPLS method has the same predictability as the PLS method, but is superior in that it is easier to visualize for interpretation for the purpose of this time. Both the PLS method and the OPLS method are methods in which information is aggregated from high-dimensional data, replaced with a small number of latent variables, and the objective variable is expressed using the latent variables. It is important to properly select the number of latent variables, and cross-validation is often used to determine the number of latent variables. That is, the data for model construction is divided into several groups, one group is used for model verification, and the other group is used for model construction to estimate the prediction error, and this work is repeated while exchanging the groups to obtain the prediction error. The number of latent variables with the smallest total is chosen.

The evaluation of the prediction model is mainly judged by two indicators. One is R ² which represents prediction accuracy, and the other is Q ² which represents predictability. R ² is the square of the correlation coefficient between the measured value of the data used for constructing the prediction model and the predicted value calculated by the model, and the closer it is to 1, the higher the prediction accuracy. On the other hand, ^Q2 is the result of the above cross validation, and represents the square of the correlation coefficient between the actually measured value and the predicted value which is the result of repeated model validation. In the wheat yield prediction model of the present invention, it is preferable to use ^Q2 > 0.50 as the standard for model evaluation. Since R ² > Q ² is always satisfied, Q ² > 0.50 satisfies R ² > 0.50 at the same time.
The following shows the results of creating various wheat yield prediction models using the peak area values of the analysis data of the above 1,450 components and the grain yield and verifying their accuracy.

(1) Construction of a yield prediction model using all component information An OPLS model was constructed from a total of 23 data matrices having peak area values and yield values of analysis data of 1,450 components per data. At the time of construction, the peak area value and the yield data of the analysis data of each component were converted into an average of 0 and a variance of 1 by autoscaling.
In the above model, the contribution to the model performance given to each component called the VIP (Variable Impact in the Projection) value is calculated.
The VIP value is obtained by the following formula 1.

The larger the VIP value, the greater the contribution to the model, and it also correlates with the absolute value of the correlation coefficient. A list of the top 500 VIP values is shown in Tables 5a to 5g below.

(2) Model construction using VIP value as an index (2-1) Model using analysis data of components up to the top 500 VIP values Select all components up to the top 500 VIP values and select the 500 per data. An OPLS model was constructed from a total of 23 data matrices with peak area values and yield values of the analysis data of the individual components. At the time of construction, the peak area value and the yield data of the analysis data of each component were converted into an average of 0 and a variance of 1 by autoscaling. R ² = 0.59 and Q ² = 0.503, which means that the model has high predictability.

(2-2) Model using analysis data of components with lower VIP values among the components up to the top 500 VIP values Analysis data of all components up to the 11th and lower 500th VIP values, up to the 500th place below the 21st A model (Fig. 2) was created by the OPLS method using the analysis data of all the components of the above, the analysis data of all the components from the 31st position to the 500th position, and the analysis data of all the components from the 61st position to the 500th position. It was constructed.
^Q2 > 0.5 is satisfied by the model using the analysis data of all the components from the 11th position to the 500th position to the analysis data of all the components from the 31st position to the 500th position. Even if the analysis data of all the components from the VIP value 41st to the 500th are used, ^Q2 > 0.50 does not hold.

(2-3) Model using 8 analysis data of the top 10 VIP values About any combination of 8 components (45 ways) from the analysis data of the top 10 VIP values A model (Fig. 3) was constructed by the OPLS method.
Both models satisfy ^Q2 > 0.50.

(2-4) Model using 9 analysis data of the components from the top 10 VIP values Arbitrary 9 combinations (10 ways) of the analysis data of the components from the top 1 to 10 VIP values A model (Fig. 4) was constructed by the OPLS method.
Both models satisfy ^Q2 > 0.50.

The number of components used for prediction is preferably as small as possible, for example, 10 or less, preferably 5 or less, more preferably 3 or less, and most preferably 3 or less, in the case of simple prediction. It is one. Further, when it is desired to improve the accuracy, it is preferable that the number of components is large, for example, 11 or more, preferably 20 or more, more preferably 50 or more, still more preferably 90 or more, and most preferably 150. More than one. When predicting with a small number of components, it is preferable to use a component having a higher VIP value or a component having a higher correlation coefficient for prediction.

The component having a higher VIP value is, for example, at least one component selected from the top 500 VIP values, preferably at least five components selected from the top 500 VIP values, and more preferably a VIP value. At least 10 components selected from the top 500, more preferably at least 1 component selected from the top 10 VIP values, and even more preferably at least 5 selected from the top 10 VIP values. It is a component of, more preferably at least 8 components selected from the top 10 VIP values, still more preferably at least 9 components selected from the top 10 VIP values, and even more preferably a VIP value. The top 500 components.

The embodiments and preferred embodiments of the present invention are shown below.
<1> A wheat yield prediction method in which analysis data of one or more components is acquired from a leaf sample collected from wheat, and the yield of wheat is predicted by using the correlation between the data and the yield of wheat.
<2> The method according to <1>, wherein the analysis data of the one or more components is corrected by the popled QC method.
<3> The method according to <1> or <2>, wherein the component is at least one selected from the components having a precise mass (m / z) of 104 to 1178 provided by mass spectrometry.
<4> The component is at least one selected from the components shown in Tables 1a to 1i defined by the precision mass (m / z) provided by mass spectrometry, <1> or <2>. The method described in.
<5> The components are the component Nos. No. 1 shown in Tables 1a to 1j. 1, 5, 7, 12, 14, 17, 20, 24, 27, 29, 34, 41, 46, 47, 48, 52, 58, 60, 61, 64, 68, 70, 71, 72, 74, 83, 84, 85, 90, 92, 98, 101, 106, 107, 109, 110, 111, 115, 117, 118, 125, 129, 131, 132, 133, 136, 143, 144, 145, 155, 160, 162, 163, 164, 169, 170, 171, 172, 178, 179, 180, 181, 183, 184, 186, 189, 191, 192, 193, 196, 198, 199, 203, 204, 206, 210, 214, 216, 218, 225, 227, 231, 233, 235, 237, 244, 250, 255, 256, 264, 266, 271, 282, 283, 286, 291, 292, 294, 297, 298, 308, 310, 312, 315, 318, 323, 329, 330, 331, 334, 338, 345, 346, 348, 349, 355, 357, 358, 360, 361, 373, 374, 381, 384, 385, 386, 388, 392, 393, 395, 399, 406, 408, 411, 421, 422, 428, 249, 431, 436, 437, 439, 440, 444, 449, 454, 455, 457, 461, 468, 470, 482, 497, 501, 502, 505, 507, 509, 513, 517, 521, 526, 528, 530, 351, 535, 541, 542, 543, 545, 546, 547, 548, 554, 562, 565, 568, 571, 574, 584, 586, 588, 589, 591, 592, 596, 599, 601, 607, 609, 610, 612, 616, 619, 623, 625, 627, 631, 634, 637, 638, 640, 642, 643, 644, 646, 648, 649, 651, 653, 654, 655, 656, 660, 662, 665, 671, 672, 674, 675, 680, 683, 684, 686, 690, 698, 701, 708, 710, 711, 713, 716, 717, 718, 720, 722, 723, 726, 731, 732, 736, 740, 742, 743, 745, 479, 750, 753, 757, 760, 761, 763, 764, 765, 768, 774, 777, 778, 78 1,783,784,785,786,787,788,792,793,795,796,801,803,805,808,809,815,817,819,823,824,825,826,834,835, 845,846,853,854,856,857,862,864,865,869,874,875,876,877,879,886,888,893,898,899,901,902,904,905,909, 916, 922, 924, 925, 929, 923, 933, 934, 935, 937, 938, 939, 941, 942, 945, 947, 950, 951, 957, 958, 961, 963, 967, 968, 970, 971, 974, 978, 979, 983, 987, 988, 989, 991, 994, 995, 998, 1005, 1007, 1008, 1009, 1010, 1013, 1017, 1018, 1021, 1026, 1027, 1029, 1031, 1035, 1038, 1039, 1040, 1041, 1043, 1046, 1047, 1052, 1060, 1061, 1064, 1067, 1068, 1069, 1072, 1074, 1076, 1077, 1085, 1087, 1094, 1096, 1103, 1106, 1110, 1114, 1116, 1121, 1123, 1126, 1127, 1130, 1132, 1134, 1142, 1143, 1146, 1147, 1148, 1149, 1153, 1157, 1159, 1177, 1179, 1181, 1182, 1183, 1184, 1185, 1186, 1188, 1193, 1194, 1195, 1198, 1199, 1200, 1206, 1209, 1210, 1213, 1215, 1217, 1218, 1219, 1226, 1227, 1232, 1237, 1238, 1239, 1240, 1242, 1244, 1246, 1249, 1250, 1251, 1254, 1257, 1262, 1264, 1274, 1275, 1278, 1279, 1281, 1284, 1287, 1292, 1295, 1296, 1308, 1312, 1314, 1315, 1318, 1320, 1328, 1330, 1331, 1334, 1335, 1336, 1337, 1338, 1339, 1342, 1344, 1345, 1346, 1349, 1350, 1351, 1354, 1355 , 1362, 1370, 1381, 1396, 1400, 1403, 1406, 1408, 1409, 1410, 1411, 1413, 1414, 1415, 1417, 1418, 1419, 1423, 1441, 1442, 1444, 1445, 1446 and 1450. The method according to <4>, which is one or more of the above.
<6> The components are the component Nos. No. 1 shown in Tables 1a to 1j. 1, 12, 17, 20, 34, 48, 52, 58, 60, 61, 68, 71, 72, 74, 83, 84, 85, 90, 92, 101, 107, 109, 110, 115, 117, 118, 129, 132, 133, 136, 143, 160, 164, 170, 171, 172, 178, 179, 181, 184, 189, 191, 193, 196, 198, 203, 206, 210, 218, 235, 237, 249, 255, 257, 282, 283, 291 294, 297, 298, 310, 312, 315, 318, 330, 331, 345, 346, 348, 355, 360, 361, 362, 373, 384, 386, 388, 392, 395, 406, 408, 411, 421, 440, 444, 449, 452, 454, 455, 457, 461, 470, 482, 501, 505, 513, 521, 530, 535, 541, 542, 546, 548, 565, 571, 574, 579, 589, 607, 610, 612, 616, 634, 646, 648, 656, 660, 662, 701, 708, 710, 716, 731, 732, 740, 743, 749, 750, 753, 757, 760, 761, 767, 768, 774, 777, 783, 785, 787, 788, 792, 794, 795, 803, 808, 809, 815, 823, 824, 825, 835, 845, 846, 853, 854, 856, 862, 863, 864, 865, 869, 874, 875, 876, 877, 879, 886, 888, 893, 895, 904, 905, 909, 916, 921, 922, 929, 933, 934, 938, 939, 942, 945, 947, 951, 966, 967, 968, 970, 971, 983, 987, 988, 994, 995, 998, 1003, 1005, 1008, 1009, 1010, 1013, 1017, 1018, 1021, 1027, 1031, 1035, 1038, 1039, 1040, 1047, 1052, 1061, 1067, 1068, 1069, 1072, 1077, 1085, 1094, 1096, 1106, 1118, 1121, 1126, 1127, 1132, 1134, 1143, 1153, 1159, 1181, 1183, 1186, 1194, 1198, 1199, 1209, 1213, 1217, 1218, 1232, 1 237, 1238, 1239, 1242, 1244, 1254, 1264, 1275, 1278, 1279, 1284, 1292, 1295, 1308, 1312, 1314, 1315, 1318, 1320, 1330, 1331, 1334, 1337, 1338, 1339, The method according to <4>, which is one or more selected from 1345, 1346, 1350, 1355, 1362, 1408, 1412, 1415, 1418, 1419, 1442, 1444 and 1446.
<7> The component is the component No. 1 shown in Tables 1a to 1j. 12, 20, 34, 48, 58, 61, 85, 101, 109, 115, 132, 133, 172, 184, 193, 198, 206, 255, 282, 283, 312, 330, 331, 346, 348, 355, 361, 386, 449, 461, 482, 505, 542, 589, 662, 731, 760, 761, 783, 785, 787, 815, 824, 825, 874, 875, 886, 929, 934, 994, One or more selected from 1005, 1009, 1039, 1040, 1067, 1068, 1085, 1143, 1181, 1183, 1186, 1209, 1232, 1244, 1279, 1284, 1337, 1346, 1350 and 1355 <4> The method described in.
<8> The method according to <4>, wherein the component comprises one or more selected from pyroglutamic acid, L-asparagine, cloxatin G, cucurbic acid and 5-aminoimidazole ribonucleotide.
<9> The method according to any one of <1> to <8>, wherein the leaf sample is collected from wheat in the budding stage to the heading stage.
<10> The method according to any one of <1> to <8>, wherein the leaf sample is collected from wheat in the 5th leaf instar to the panicle formation stage.
<11> The method according to any one of <1> to <10>, wherein the analysis data is mass spectrometry data.
<12> At least one of the top 500 VIP values calculated from the yield prediction model constructed using the component information shown in Tables 1a to 1j of the component analysis data obtained from the leaf sample. The method according to any one of <4> to <11>, which comprises a step of collating with a yield prediction model constructed using the analysis data of the components of.
<13> In <12>, the yield prediction model uses at least 5 of the top 500 VIP values calculated from the yield prediction model constructed using the component information shown in Tables 1a to 1i. The method described.
<14> In <12>, the yield prediction model uses at least 10 out of the top 500 VIP values calculated from the yield prediction model constructed using the component information shown in Tables 1a to 1i. The method described.
<15> In <12>, the yield prediction model uses at least one of the top 10 VIP values calculated from the yield prediction model constructed using the component information shown in Tables 1a to 1i. The method described.
<16> In <12>, the yield prediction model uses at least 5 of the top 10 VIP values calculated from the yield prediction model constructed using the component information shown in Tables 1a to 1i. The method described.
<17> In <12>, the yield prediction model uses at least 8 out of the top 10 VIP values calculated from the yield prediction model constructed using the component information shown in Tables 1a to 1j. The method described.
<18> In <12>, the yield prediction model uses at least 9 out of the top 10 VIP values calculated from the yield prediction model constructed using the component information shown in Tables 1a to 1j. The method described.
<19> The method according to <12>, wherein the yield prediction model uses the top 500 VIP values calculated from the yield prediction model constructed by using the component information shown in Tables 1a to 1j.
<20> The method according to any one of <12> to <19>, wherein the yield prediction model is a model constructed by using the OPLS method.
<21> The method according to any one of <1> to <20>, wherein the precision mass is measured with an accuracy of four or more digits after the decimal point.

1. 1. Cultivation test
The data of the indoor pot cultivation test conducted in 2019 will be described in detail.
Medium-term fertilizer-type hilling (Takii water-containing cell hilling, Takii Seedling Co., Ltd.) and vermiculite (Akagi Horticulture Co., Ltd.) are mixed at a volume ratio of 1: 1 and the soil is filled in a polypot (diameter 10.5 cm, height 9 cm). bottom. The cultivation was carried out indoors, and the cultivation conditions were 12 hours in the light period, 25 ° C., an LED light source, a light intensity of 400 to 440 μmol / m2 / s, and the cultivation was carried out under the conditions corresponding to the general cultivation of spring wheat. Fourteen days after sowing, thinning was performed so that there would be one plant per pot. Watering was performed by adding a new amount of water to the bat so that the lower part of the pot could be immersed about 5 cm after the bat placed under the pot had run out of water.
The basic fertilizer amount was set to 1 g per pot, and a chemical fertilizer containing nitrogen, phosphorus and potassium as fertilizer components (trade name "Chemical Fertilizer No. 14" Sun and Hope Co., Ltd.) was mixed with the soil. Cultivation was carried out under a total of four types of fertilization conditions by setting the conditions of the above basic fertilization amount of 1/2 times, 2 times and 4 times. A total of 24 pots were prepared, with 6 pots for each fertilization condition. On October 30, 2019, 3 seeds were sown at 2 places in each pot (6 seeds were used per pot). As the variety, "Norin 61" was used. Fourteen days after sowing, the seeds were thinned out to one per place to make two stocks in each pot. Sampling was done on November 27th. Harvesting was carried out on January 8, 2020 (70 days after sowing). Since one individual was absent, a total of 23 individuals were used for yield prediction.

2. 2. Leaf sampling
Leaf sampling was performed during the daytime, 27 days after sowing (approximately 13:00 to 15:00). The growth stage of wheat at this time was slightly different depending on the individual, but the number of leaves per individual was about 12 to 15, which corresponded to the stalk standing stage at the growth stage. Leaf sampling was taken by cutting 4-7 leaves from the root of the plant. At the time of collection, the whole strain was collected without bias. The collected leaves were wrapped in aluminum foil and immediately frozen in liquid nitrogen to stop the metabolic reaction. The frozen sample was taken back to the laboratory while maintaining the frozen state, and dried by freeze-drying. This dried sample was subjected to the extraction operation described later.

3. 3. Final grain yield measurement
Harvesting was carried out on January 8, 2020, 70 days after sowing. All grains were collected from each individual after the cultivation test and dried at 90 ° C. for 3 days. As the yield data, the actual amount of dried child (mgDW / individual) was measured. The dry cell parenchyma (gDW / individual) was used for the simple correlation analysis between the analysis data of each component and the yield described later and the construction of the prediction model. As shown in Table 2, the data on the actual amount of dried child was 0.4 gDW / individual at the minimum and 4.9 gDW / individual at the maximum.

4. Extraction of collected leaf components
Freeze-dried leaf samples were manually ground as much as possible using a spatula. After pulverization, 10 mg was weighed in a 2 mL tube (Safelock tube, Eppendorf), one zirconia ball having a diameter of 5 mm was added to the tube, and the mixture was pulverized at 25 Hz for 1 minute with a bead crusher (MM400, Resch). As the extraction solvent, an 80v / v% methanol aqueous solution containing lidocaine (Wako Pure Chemical Industries, Ltd., # 120-02671) at a concentration of 500 ng / mL was used as an internal standard. 1 mL of the prepared extraction solvent was added to the crushed tube, and homogenized extraction was performed at 20 Hz for 5 minutes using the same bead crusher. After the extraction is completed, the mixture is centrifuged in a desktop centrifuge (Chibitan) of about 2,000 × g for about 30 seconds, and filtered with a 0.45 μm hydrophilic PTFE filter (DISMIC-13HP 0.45 μm syringe filter, ADVANTEC). An analysis sample was obtained.

5. Analysis of leaf samples by LC / MS
The leaf extraction sample was analyzed by LC / MS analysis using an HPLC system (Infinity1260 series) manufactured by Agilent, and a Q-TOFMS apparatus (TripleTOF4600) manufactured by AB SCIEX as a detector. Separation columns in HPLC include core shell column Capcell core C18 (2.1 mm ID × 100 mm, particle total 2.7 μm) manufactured by Shiseido Co., Ltd. and guard column (2.1 mm ID × 5 mm, A particle meter (2.7 μm) was used, and the column temperature was set to 40 ° C. The autosampler was kept at 5 ° C during the analysis. The analytical sample was injected with 5 μL. A: 0.1 v / v% formic acid aqueous solution and B: 0.1 v / v% formic acid acetonitrile solution were used as eluents. The gradient elution condition was maintained at 1v / v% B (99v / v% A) for 0 to 0.1 minutes, and 1v / v% B to 99.5v / v for 0.1 to 13 minutes. The ratio of eluent B was increased to% B and maintained at 99.5 v / v% B from 13.01 minutes to 16 minutes. The flow velocity was 0.5 mL / min.

As the mass spectrometer conditions, the ionization mode was set to the positive mode, and ESI was used as the ionization method. In this analysis system, the elution ions are scanned by TOFMS for 0.1 seconds, 10 high-intensity ions are selected, and each of them is subjected to MS / MS for 0.05 seconds while repeating the cycle. Molecular ion information (precision mass, m / z) by TOFMS scan and structural information derived from fragments generated by MS / MS scan were acquired. The mass measurement range was set to m / z 100-1,250 for TOFMS and m / z 50-1,250 for MS / MS. The parameters of each scan are set to GS1 = 50, GS2 = 50, CUR = 25, TEM = 450, ISVF = 5500, DP = 80 and CE = 10 for TOFMS scans, and GS1 = for MS / MS scans. It was set to 50, GS2 = 50, CUR = 25, TEM = 450, ISVF = 5500, DP = 80, CE = 30, CES = 15, IRD = 30 and IRW = 15.

6. Creating a data matrix
The data processing was performed as follows. First, peaks were extracted using MarkerView ^TM Software (AB SCIEX). The peak extraction condition (“peak finding option”) is a peak corresponding to a retention time of 0.5 to 16 minutes, and the Subtraction offset in the item of “Enhance Peak Finding” is 20 scans, and the Minimum spectral peak.
The width is 5 ppm, Subtraction multi. Factor was set to 1.2, Minimum RT peak width was set to 10 scans, Noise threshold was set to 5, and the Assign charge state in the "More" item was checked. As a result, peak information of 31,649 was obtained.

Next, an alignment process was performed to align the detected peaks between the analyzed samples. As for the processing conditions of the alignment (“Alightmment & Filtering”), the Retention time tolerance in the item of “Alignment” was set to 0.20 minutes and the Mass tolerance was set to 10.0 ppm. In addition, the Integrity threshold in the item of "Filtering" was set to 10, the Retention time filtering was checked, the Move peaks in <3 samples were set, and the Maximum number of peaks was set to 50,000. The retention time was corrected using the lidocaine peak in the item "Internal standard".

Next, the isotope peak was removed. Since the isotope peak is automatically recognized by the software at the time of peak extraction and labeled as "isotopic" on the peak list, the corresponding peak was deleted by sorting by "isotopic". As a result, the peak decreased to 25,895 peaks.

Next, the peak intensity was corrected between the samples. In this analysis, in addition to the samples, a sample called poored QC was prepared by mixing a certain amount from all the samples, and the polled QC was analyzed once every 6 times. From all these QC analysis results, an estimated value "what will happen to each peak intensity if it is assumed that the QC sample was analyzed when each sample was analyzed" is calculated, and the process of correcting with that value is performed. This was done and the sensitivity between each sample in the same batch was corrected. For this treatment, free software (LOWESS-Normalization-Tool) provided by RIKEN was used. Finally, the relative standard deviation (RSD) of the 10,895 peaks was calculated using the eight measured QC analysis data to remove the highly variable peaks with RSD> 30%, and finally 1,450. Peak data, that is, analytical data of 1,450 components was obtained. The obtained analytical data are shown in Tables 3a to 3s. Subsequent analysis was performed using these data.

7. Correlation analysis
Correlation analysis was performed using the obtained analysis data of 1,450 components in the leaves of 23 individuals and the corresponding yield data (real amount of dried matter), that is, the matrix data of 23 × 1,450. The p-value was calculated by the simple correlation coefficient r and the uncorrelated test between the analysis data of each component and the yield data. The results are shown in Tables 4a-4s. In addition, "component No." in the table is for convenience, in which 1,450 components are arranged in order of mass and numbered from the one with the smallest mass number. Further, the analysis result includes information on the holding time as well as the mass information. It has been shown that mass spectrometric data can be compared and analyzed between mass spectrometric samples. Therefore, the information on the holding time was removed, and only the precise mass information was described.

The results obtained by the correlation analysis showed that the components with a certain correlation coefficient significantly correlated with the yield. It was found that the number of components having an absolute value of the correlation coefficient | r |> 0.51 was 281 and the number of components having | r |> 0.66 was 70.

8. Model construction / evaluation
A multivariate analysis method was used to construct a yield prediction model using analysis data of two or more components, and SIMCA ver. 14 (Umetrics) was used. For the prediction model, regression analysis was performed using the peak area value of the corrected component analysis data with each precise mass as the explanatory variable and the yield value as the objective variable. Regression analysis was performed by the OPLS method, which is an improved version of the PLS method.

The evaluation method of the prediction model is mainly judged by two indexes. One is R ² which represents prediction accuracy, and the other is Q ² which represents predictability. R ² is the square of the correlation coefficient between the measured value of the data used for constructing the prediction model and the predicted value calculated by the model, and the closer it is to 1, the higher the prediction accuracy. On the other hand, Q ² is the result of the cross validation, and represents the square of the correlation coefficient between the actually measured value and the predicted value which is the result of repeated model validation. From a prediction point of view, the model is considered to have good predictability if at least Q ² > 0.50 (Triba, MN et al., Mol. BioSyst. 2015, 11, 13-19. .), ^Q2 > 0.50 was used as the standard for model evaluation. Since R ² > Q ² is always satisfied, Q ² > 0.50 satisfies R ² > 0.50 at the same time.

8-1. Construction and evaluation of a model using all data
An OPLS model that predicts the yield was constructed from a total of 23 data matrices, having peak area values and yield values of analysis data of 1,450 components per data. At the time of construction, the peak area value and the yield data of the analysis data of each component were converted into an average of 0 and a variance of 1 by autoscaling. As a result of model construction, R ² = 0.931 indicating prediction accuracy and Q ² = 0.344 indicating predictability did not meet the criteria of Q ² > 0.50.

8-2. Calculation of VIP value
In the model constructed in 8-1, VIP (Variable Impact in)
The degree of contribution to the model performance given to each component called the projection (variable importance in projection) value is calculated. The larger the VIP value, the greater the contribution to the model, and it also correlates with the absolute value of the correlation coefficient. A list of the top 500 VIP values is shown in Tables 5a-5g.

8-3. Model construction using VIP value as an index
A model was constructed with a plurality of components based on the ranking of VIP values (Tables 5a to 5 g), which is the contribution of each component to the model constructed in 8-1. Although not particularly limited, the model performance standard is set to ^Q2 > 0.50 for convenience.

8-3-1. Model using analysis data of components with VIP values up to the top 500 Select all components up to the top 500 VIP values and have the peak area value and yield value of the analysis data of the 500 components per data. , An OPLS model for predicting yield was constructed from a total of 23 data matrices. At the time of construction, the peak area value and the yield data of the analysis data of each component were converted into an average of 0 and a variance of 1 by autoscaling. As a result of model construction, R ² = 0.59 indicating the prediction accuracy and Q ² = 0.50 indicating the predictability. The results are shown in FIG. From this prediction model, it was shown that a model with high predictability can be constructed and early yield prediction is possible by using the component composition contained in the leaves for about one month of cultivation.

8-3-2. A model using the analysis data of the components with the lower VIP value among the components up to the top 500 of the VIP value. An OPLS model was constructed using the analysis data, the analysis data of all the components from the 31st place to the 500th place, and the analysis data of all the components from the 61st place to the 500th place. As a result, ^Q2 > 0.5 is satisfied by the model using the analysis data of all the components from the 11th place to the 500th place to the analysis data of all the components from the 31st place to the 500th place. As a result of examination, it was found that ^Q2 > 0.50 does not hold even when the analysis data of all the components from the VIP value of 41st place to the 500th place are used (Fig. 2).

8-3-3. A model using 8 analysis data of the components of the top 10 VIP values Build an OPLS model for any 8 combinations (45 ways) of the analysis data of the components of the top 10 VIP values. I did it. As a result, it was found that ^Q2 > 0.50 was satisfied in all the models. From this, it was shown that a model with a certain predictability can be constructed if eight metabolites up to the top 10 VIP values are included (Fig. 3).

8-3-4. A model using 9 analysis data of the components of the top 10 VIP values Build an OPLS model for any 9 combinations (10 ways) of the analysis data of the components of the top 10 VIP values. I did it. As a result, it was found that ^Q2 > 0.50 was satisfied in all the models. From this, it was shown that a model with a certain predictability can be constructed if it contains 9 metabolites up to the top 10 VIP values (Fig. 4).

Claims (20)

A wheat yield prediction method in which analytical data of one or more components is acquired from a leaf sample collected from wheat, and the yield of wheat is predicted by using the correlation between the data and the yield of wheat. The method according to claim 1, wherein the analysis data of the one or more components is corrected by the pooled QC method. The method according to claim 1 or 2, wherein the component is at least one selected from the components having a precise mass (m / z) of 104 to 1178 provided by mass spectrometry. The method according to claim 1 or 2, wherein the component is at least one selected from the components shown in Tables 1a to 1j below, which is defined by the precision mass (m / z) provided by mass spectrometry.

The components are the component Nos. No. 1 shown in Tables 1a to 1j. 1, 5, 7, 12, 14, 17, 20, 24, 27, 29, 34, 41, 46, 47, 48, 52, 58, 60, 61, 64, 68, 70, 71, 72, 74, 83, 84, 85, 90, 92, 98, 101, 106, 107, 109, 110, 111, 115, 117, 118, 125, 129, 131, 132, 133, 136, 143, 144, 145, 155, 160, 162, 163, 164, 169, 170, 171, 172, 178, 179, 180, 181, 183, 184, 186, 189, 191, 192, 193, 196, 198, 199, 203, 204, 206, 210, 214, 216, 218, 225, 227, 231, 233, 235, 237, 244, 250, 255, 256, 264, 266, 271, 282, 283, 286, 291, 292, 294, 297, 298, 308, 310, 312, 315, 318, 323, 329, 330, 331, 334, 338, 345, 346, 348, 349, 355, 357, 358, 360, 361, 373, 374, 381, 384, 385, 386, 388, 392, 393, 395, 399, 406, 408, 411, 421, 422, 428, 249, 431, 436, 437, 439, 440, 444, 449, 454, 455, 457, 461, 468, 470, 482, 497, 501, 502, 505, 507, 509, 513, 517, 521, 526, 528, 530, 351, 535, 541, 542, 543, 545, 546, 547, 548, 554, 562, 565, 568, 571, 574, 584, 586, 588, 589, 591, 592, 596, 599, 601, 607, 609, 610, 612, 616, 619, 623, 625, 627, 631, 634, 637, 638, 640, 642, 643, 644, 646, 648, 649, 651, 653, 654, 655, 656, 660, 662, 665, 671, 672, 674, 675, 680, 683, 684, 686, 690, 698, 701, 708, 710, 711, 713, 716, 717, 718, 720, 722, 723, 726, 731, 732, 736, 740, 742, 743, 745, 479, 750, 753, 757, 760, 761, 763, 764, 765, 768, 774, 777, 778, 78 1,783,784,785,786,787,788,792,793,795,796,801,803,805,808,809,815,817,819,823,824,825,826,834,835, 845,846,853,854,856,857,862,864,865,869,874,875,876,877,879,886,888,893,898,899,901,902,904,905,909, 916, 922, 924, 925, 929, 923, 933, 934, 935, 937, 938, 939, 941, 942, 945, 947, 950, 951, 957, 958, 961, 963, 967, 968, 970, 971, 974, 978, 979, 983, 987, 988, 989, 991, 994, 995, 998, 1005, 1007, 1008, 1009, 1010, 1013, 1017, 1018, 1021, 1026, 1027, 1029, 1031, 1035, 1038, 1039, 1040, 1041, 1043, 1046, 1047, 1052, 1060, 1061, 1064, 1067, 1068, 1069, 1072, 1074, 1076, 1077, 1085, 1087, 1094, 1096, 1103, 1106, 1110, 1114, 1116, 1121, 1123, 1126, 1127, 1130, 1132, 1134, 1142, 1143, 1146, 1147, 1148, 1149, 1153, 1157, 1159, 1177, 1179, 1181, 1182, 1183, 1184, 1185, 1186, 1188, 1193, 1194, 1195, 1198, 1199, 1200, 1206, 1209, 1210, 1213, 1215, 1217, 1218, 1219, 1226, 1227, 1232, 1237, 1238, 1239, 1240, 1242, 1244, 1246, 1249, 1250, 1251, 1254, 1257, 1262, 1264, 1274, 1275, 1278, 1279, 1281, 1284, 1287, 1292, 1295, 1296, 1308, 1312, 1314, 1315, 1318, 1320, 1328, 1330, 1331, 1334, 1335, 1336, 1337, 1338, 1339, 1342, 1344, 1345, 1346, 1349, 1350, 1351, 1354, 1355 , 1362, 1370, 1381, 1396, 1400, 1403, 1406, 1408, 1409, 1410, 1411, 1413, 1414, 1415, 1417, 1418, 1419, 1423, 1441, 1442, 1444, 1445, 1446 and 1450. The method according to claim 4, wherein the method is one or more. The components are the component Nos. No. 1 shown in Tables 1a to 1j. 1, 12, 17, 20, 34, 48, 52, 58, 60, 61, 68, 71, 72, 74, 83, 84, 85, 90, 92, 101, 107, 109, 110, 115, 117, 118, 129, 132, 133, 136, 143, 160, 164, 170, 171, 172, 178, 179, 181, 184, 189, 191, 193, 196, 198, 203, 206, 210, 218, 235, 237, 249, 255, 257, 282, 283, 291 294, 297, 298, 310, 312, 315, 318, 330, 331, 345, 346, 348, 355, 360, 361, 362, 373, 384, 386, 388, 392, 395, 406, 408, 411, 421, 440, 444, 449, 452, 454, 455, 457, 461, 470, 482, 501, 505, 513, 521, 530, 535, 541, 542, 546, 548, 565, 571, 574, 579, 589, 607, 610, 612, 616, 634, 646, 648, 656, 660, 662, 701, 708, 710, 716, 731, 732, 740, 743, 749, 750, 753, 757, 760, 761, 767, 768, 774, 777, 783, 785, 787, 788, 792, 794, 795, 803, 808, 809, 815, 823, 824, 825, 835, 845, 846, 853, 854, 856, 862, 863, 864, 865, 869, 874, 875, 876, 877, 879, 886, 888, 893, 895, 904, 905, 909, 916, 921, 922, 929, 933, 934, 938, 939, 942, 945, 947, 951, 966, 967, 968, 970, 971, 983, 987, 988, 994, 995, 998, 1003, 1005, 1008, 1009, 1010, 1013, 1017, 1018, 1021, 1027, 1031, 1035, 1038, 1039, 1040, 1047, 1052, 1061, 1067, 1068, 1069, 1072, 1077, 1085, 1094, 1096, 1106, 1118, 1121, 1126, 1127, 1132, 1134, 1143, 1153, 1159, 1181, 1183, 1186, 1194, 1198, 1199, 1209, 1213, 1217, 1218, 1232, 1 237, 1238, 1239, 1242, 1244, 1254, 1264, 1275, 1278, 1279, 1284, 1292, 1295, 1308, 1312, 1314, 1315, 1318, 1320, 1330, 1331, 1334, 1337, 1338, 1339, The method according to claim 4, wherein the method is one or more selected from 1345, 1346, 1350, 1355, 1362, 1408, 1412, 1415, 1418, 1419, 1442, 1444 and 1446. The components are the component Nos. No. 1 shown in Tables 1a to 1j. 12, 20, 34, 48, 58, 61, 85, 101, 109, 115, 132, 133, 172, 184, 193, 198, 206, 255, 282, 283, 312, 330, 331, 346, 348, 355, 361, 386, 449, 461, 482, 505, 542, 589, 662, 731, 760, 761, 783, 785, 787, 815, 824, 825, 874, 875, 886, 929, 934, 994, Claim 4 which is one or more selected from 1005, 1009, 1039, 1040, 1067, 1068, 1085, 1143, 1181, 1183, 1186, 1209, 1232, 1244, 1279, 1284, 1337, 1346, 1350 and 1355. The method described in. The method according to claim 4, wherein the component comprises one or more selected from pyroglutamic acid, L-asparagine, cloxatin G, cucurbic acid and 5-aminoimidazole ribonucleotide. The method according to any one of claims 1 to 8, wherein the leaf sample is collected from wheat from the budding stage to the heading stage. The method according to any one of claims 1 to 8, wherein the leaf sample is collected from wheat in the 5th leaf instar to the panicle formation stage. The method according to any one of claims 1 to 10, wherein the analysis data is mass spectrometry data. The analysis data of the components obtained from the leaf sample is used for at least one component from the top 500 VIP values calculated from the yield prediction model constructed by using the component information shown in Tables 1a to 1j. The method according to any one of claims 4 to 11, comprising a step of collating with a yield prediction model constructed using analytical data. The method according to claim 12, wherein the yield prediction model uses at least 5 out of the top 500 VIP values calculated from the yield prediction model constructed by using the component information shown in Tables 1a to 1i. .. The method according to claim 12, wherein the yield prediction model uses at least 10 out of the top 500 VIP values calculated from the yield prediction model constructed by using the component information shown in Tables 1a to 1i. .. The method according to claim 12, wherein the yield prediction model uses at least one of the top 10 VIP values calculated from the yield prediction model constructed by using the component information shown in Tables 1a to 1i. .. The method according to claim 12, wherein the yield prediction model uses at least 5 out of the top 10 VIP values calculated from the yield prediction model constructed by using the component information shown in Tables 1a to 1i. .. The method according to claim 12, wherein the yield prediction model uses at least 8 out of the top 10 VIP values calculated from the yield prediction model constructed by using the component information shown in Tables 1a to 1j. .. The method according to claim 12, wherein the yield prediction model uses at least 9 out of the top 10 VIP values calculated from the yield prediction model constructed by using the component information shown in Tables 1a to 1j. .. The method according to claim 12, wherein the yield prediction model uses the top 500 VIP values calculated from the yield prediction model constructed by using the component information shown in Tables 1a to 1j. The method according to any one of claims 12 to 19, wherein the yield prediction model is a model constructed by using the OPLS method.

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