# The Practical Application of Agricultural Genetic Breeding Technology in Elm Cultivation Based on Big Data Analysis

Authors: Haiguang Huang; Rong Yang; Lei Hao; Guosheng Zhang
Journal: Mobile Information Systems
 (2022)

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## Abstract

In order to explore the role of agricultural genetic breeding technology in elm cultivation, this paper improves the big data technology. According to the test data verification requirements, based on the Monte Carlo idea, a TDSTI statistical test method is proposed, and a set of judgment criteria for the significance of spatio-temporal autocorrelation is given. Moreover, in order to make the observed variables of the time lag series have a clearer geographical significance, the obtained spatio-temporal weight matrix is subjected to row standardization. In addition, this paper combines big data technology and agricultural genetic breeding technology to study the cultivation of elm trees. Finally, this paper conducts a correlation analysis between the distance matrix generated by cluster analysis of leaf shape traits and the distance matrix generated by cluster analysis of SSR molecular markers. The research results show that agricultural genetic breeding technology can play an important role in the cultivation of elm.


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## Body

## 1. Introduction

The elm belongs to the genus Ulmus in the Ulmaceae family. It is widely distributed in China and has 24 species including cultivated species. The elm tree grows fast, likes light, has strong cold and drought resistance, strong salt and alkali resistance, strong anti-pollution ability, and straight wood texture, which can be used for construction, furniture, vehicles, sleepers, and farm tools. Bark can be used as raw material for artificial cotton and paper making [1]. The samara can be squeezed into oil for food, medicine, and the light chemical industry. In addition, it is an important tree species for the construction of timber forests, shelter forests, saline-alkali land afforestation, and “four sides” greening in the vast plains and desert areas of Northeast China, North China, and Huaibei. With the development of science and technology, the use of molecular marker technology to study the genetic diversity of elm germplasm resources has become more and more popular [2].The most widely distributed and important elm species in my country isUlmus pumila. Its material is excellent, and it has good drought resistance, cold resistance, salt and alkali resistance, and wind resistance. Moreover, it is an important tree species for cultivating timber forests, shelter forests, and “four sides” greening on the vast plains, saline-alkali land, and sandy wasteland in the northern country. It grows fast, has a long lifespan, and can grow on dry and barren fixed dunes and chestnut soil. In addition, it has the characteristics of year-by-year fruiting, large fruit size, wide spread of samara, strong germination, rapid rooting, and strong seedlings. Therefore, it has become the most important tree species for soil conservation, sand fixation, and grazing protection in areas with a harsh ecological environment [3].The elm tree has become a good material for making bonsai because of its “root dew, dry thin and dense leaves.” Moreover, it has excellent characteristics such as fast growth, strong germination, pruning resistance, and very good plasticity, and is very popular among bonsai lovers. Because it likes sunlight, elm trees are suitable for growing in a warm and humid environment. Although there are no strict requirements on the soil substrate, the soil must be loose and breathable because it is afraid of water accumulation. As we all know, the material of bonsai comes from trees, and as the trees grow from seedlings, the degree of difficulty in modeling also increases [4].There are usually two ways to obtain the material of pile blanks, one is to dig in the mountains and the other is to reproduce asexually. In the past, many flower farmers usually went to the mountains to dig wild elm trees to cultivate and process them as materials for bonsai. The old elm stumps in the mountains have been eroded by natural conditions and bitten by animals over the years, so their shapes are often unique and old and simple. This method of digging old elm stumps from the mountains can greatly shorten the cultivation time of bonsai, but the continuous excavation over the years has made wild old elm stumps very few now, and farmers continue to cut down elm trees for firewood. As time passed, there were almost no wild elms on the mountain. Therefore, in order to obtain a large number of elm tree stumps, while avoiding damage to the ecological environment, current flower farmers can only cultivate elm tree seedlings by breeding, so that a large amount of elm tree bonsai materials can be quickly obtained. The basic methods of elm reproduction are divided into two categories: sexual reproduction and asexual reproduction. Sexual reproduction means sowing and breeding. It takes a long time to grow old stump bonsai after seedlings are formed by sowing and breeding. Therefore, elm trees generally do not use sowing and reproduction; and the method of asexual reproduction can not only maintain the characteristics of the female parent but also can A large amount of stump material is obtained, and the time for bonsai to be formed is also relatively short. The vegetative propagation of elm trees is often carried out by the cutting or ramification method.This paper combines big data technology and agricultural genetic breeding technology to study the cultivation of elm trees and conducts experimental research in combination with actual conditions.


## 2. Related Work

Ulmus tree species are relatively drought-tolerant, suitable for growing in limestone mountains, and are mostly excellent timber species, and the bark is rich in fiber [5]. Although there are many Ulmaceae species in my country, they generally have huge crowns, slow growth, unsuitable for afforestation in mountainous areas, poor dry shape, serious damage by dry-boring pests, and low utilization value of wood. At present, it is mainly used for village greening, street trees in some northern urban areas, tree planting in the surrounding areas, etc.The literature [6] used AFLP molecular technology to analyze the genetic diversity of Ulmus pumila clones. The literature [7] used RAPD molecular technology to study the genetic diversity of Ulmus pumila in hunshandake. The literature [8] used RAPD molecular technology to study the genetic diversity of natural Ulmus pumila. In the past piece of research on the genetic diversity of elm resources, molecular markers are used individually. At present, there are reports on the combination of phenotypic traits and SSR molecular markers to study plant genetic diversity, and there are few studies on the correlation analysis between phenotypic traits of elm and SSR. Therefore, this experiment uses elm leaf shape traits and SSR molecular marker technology to study the genetic diversity of elm trees.The current research on Ulmus tree species mainly focuses on geographical distribution, population dynamics, biological characteristics, growth and development patterns, community characteristics, etc. There are few reports on special studies on their drought tolerance mechanism and drought tolerance source selection. Moreover, researches mostly focus on a few elm species, such asUpumila, U1.amellosa, Uelongata, etc. On seed germination, growth patterns, quantitative classification, drought tolerance, tissue culture, and other research on a few aspects. Literature [9] studied the seedlings of Ulmus longiflorum. The elm has a short seedling period, the seed germinates out of the soil faster, the seedling time is relatively concentrated, and the initial growth lasts longer. However, its growth is slow and its growth only accounts for 10.18% of the total annual growth. The peak growth period is one-third of the total annual growth period, and its growth accounts for 34.19% of the total annual growth. The results of multiple regression analysis show that temperature is the main meteorological factor affecting the growth of its height. The literature [10] studied the growth of the seedlings of Ulmus chenmoui Cheng and Ulmus gaussenii Cheng. The seedling rate of Ulmus gaussenii Cheng was 20.30% on average, but the preservation rate was low. The causal wing of Ulmus gaussenii Cheng is thicker, and the core is located in the middle of the samara. The emergence rate is only limited to 10%, and the preservation rate is lower. The literature [11] studied the application of auxin in elm seedlings. When the seedling height is about 10 cm, it uses GA3, vitamin B2, choline chloride, high-efficiency auxin, Yemianbao, Aiduoshou, Fengchansu, etc. To spray and observe the 1a elm seedlings. It was found that, except for individual strains that were comparable to the control, the others all increased by 5%–15%, the ground diameter was all better than the control and the increased range was 10%–70%. The literature [12] studied the determination of the wilting coefficient of the too common broad-leaved tree species at the seedling stage and found that the white elm has the same drought resistance as the Chinese locust, black locust, and Ailanthus altissima but is better than walnut, mountain apricot, toon, loblolly pine, and dry willow. The literature [13] adopted P–V technology to measure the moisture parameters of elm and korshinskii, indicating that the elm can tolerate a gradual increase in moisture.The literature [14] used seeds to propagate small seeds like elm, which should be picked and sown at the same time, and the effect is better. There are only sporadic studies on the salt tolerance of Ulmus species. The literature [15] studied the critical value of salt tolerance of Ulmus pumila seeds under compound salt stress of 139/kg, and the critical value of salt tolerance of leaching salt stress of 179/kg. There are some research reports on the salt tolerance of other Ulmus species. There are many research reports on Ulmus sylvestris in foreign countries, which are mainly embodied in the biological characteristics of Ulmus sylvestris and Ulmus crassifolia, cultivation and management, seedling cultivation, and prevention and control techniques of diseases and insect pests, as well as the wood properties of Ulmus sylvestris. However, there has been no report on the salt and drought tolerance characteristics and photosynthesis of Ulmus pumila series.


## 3. TDSTI Statistical Test Method

The null hypothesis H0 of spatial Moran’s I statistical test: the spatial variablesZ are independent of each other. Therefore, when performing the spatial Moran’s I test, it is assumed that the probability distribution of the spatial variable Z is a normal distribution or a random distribution. Under these two assumptions, the expected value and variance of the spatial autocorrelation coefficient are calculated. When the actual expected value is greater than the hypothetical expected value, H0 is rejected and the statistical test is completed [16].The salient feature of spatial Moran’s I test: assumes that the spatial variableZ is independent. If the result of Moran’s I test rejects this null hypothesis, it is considered that the spatial autocorrelation is credible under a certain degree of confidence. Similarly, in the TDSTI statistical test, can this idea be used? That is, it is assumed that the spatio-temporal variable X is also independent.TDSTI is suitable for analyzing the spatio-temporal autocorrelation of data sequences with long-range correlation characteristics. This characteristic is related to two mechanisms: one is the long-range correlation shown by the inherent correlation among data, and the other is the inter-data wide probability density distribution (non-Gaussian normal distribution). The biggest difference between these two mechanisms is that when the attribute values in a time series are randomly sorted to form a new time series, the intrinsic correlation among the new series data will disappear at this time, but the probability among the data remains. The density distribution will not be changed. That is, the new time series is still affected by the wide probability density distribution and still has a certain correlation [17].Obviously, after implementing statistical tests, even if the null hypothesis is rejected, no reliable results can be obtained. This is because the long-range correlation of spatio-temporal series is related to the probability density distribution of the data series and the correlation between the data. When assuming that the space-time variablesX are independent of each other, this assumption only refers to the probability density distribution of the data sequence, that is, assuming that the probability density distribution is independent and identically distributed (i.i.d.), and does not assume the correlation between the data. Therefore, even if the statistical test results reject the null hypothesis, the correlation between the data cannot be determined.Based on the Monte Carlo idea, a TDSTI statistical test method is proposed, and a set of criteria for judging the significance of spatio-temporal autocorrelation is given. Figure1 shows the application process of the TDSTI statistical test method [18].Figure 1
Statistical inspection process.The application of the TDSTI statistical test method mainly includes the following steps:(1)
The algorithm determines the study area, obtains time series data of sufficient length of space objects in the study area, and analyzes and organizes the original data.(2)
The algorithm determines the time series of N spatial objects. It calculates the Hurst index of these N time series and determines the long-range correlation characteristics of the time series.(3)
The algorithm uses the time autocorrelation analysis method to estimate the time lag parameterh of the time series, uses the semivariogram in geostatistics to estimate the spatial range, selects the appropriate time and space weight function, and determines the time and space weight matrix.(4)
The algorithm uses the ARFIMA linear model to construct N artificial simulation time series whose Hurst index is consistent with the real series and connects them end to end according to the specified order to obtain an original spatio-temporal series.(5)
According to the determined time-space weight matrix and the original time-space sequence, the algorithm constructs the time-lag sequence and applies the TDSTI model to calculate the TDSTI value under a certain time scale, TDSTI(s).(6)
The algorithm repeatsn times steps 4 and step 5. Kolmogorov-Smirnov (K–S) test is performed on the calculated n TDSTI(s) values to verify whether the probability density distribution of TDSTI(s) is a normal distribution.(7)
The algorithm gives the result of the probability density distribution of TDSTI(s). According to the set significance levelα and the null hypothesis Io, the expected value and variance of TDSTI are obtained, and the critical value of significant spatio-temporal autocorrelation is obtained.(8)
The algorithm gives a specific TDSTI statistical test criterion.Many real-world geoscience data have long-range correlation characteristics. Granger and Joyeux proposed the Autoregressive Fractional Integrated Moving Average (ARFIMA) model, which can generate long-range correlation time series.We use ARFIMA (0,d, 0) linear model to generate artificial (Artificial) simulation data, which is similar to actual data and also has long-range correlation characteristics. The ARFIMA(0, d, O) model is as follows [19]:(1)yi=∑j=1xajdyi−j+ηi.Among them,ηi in formula (1) is independent of each other and ηi∼N01ajd is the statistical weight, and there is a relationship ajd=−Γj−d/Γ−dΓ1+j, Γ represents the gamma function, d is a free parameter, and the value range is [−0.5, 0.5]. The relationship with the Hurst index is: H=0.5+d. According to the Fractional Brownian Motion Model (FBM), the Iurst index (H) is an important parameter describing the fractal characteristics of time series, and ARFIMA can be used to generate time series with different Hurst indices.WhenH = 0.5, the time series is an independent and identically distributed random series, belonging to Brownian motion, the variables are independent of each other, and the corresponding correlation coefficient is zero [20].When 0 <H < 0.5, the time series is an antipersistent series, and there is a negative correlation between variables. The antipersistence sequence fluctuates more intensely than the random sequence, and the variation span it produces in a certain period of time is smaller than that of the random sequence, and the intensity of antipersistence is related to the distance between H and О. When H is closer to 0, the mutation or variability of the time series is stronger.When 0.5 <H ≤ 1, the time series has state persistence and has the characteristics of long-term memory (long-range correlation), which is expressed as a fractal time series. In theory, current changes will have a lasting impact on subsequent changes. Moreover, the long-range correlation of time series does not change with the time scale, and the time series of different time increments (day, week, month, year, etc.) have the same statistical law, which has the key characteristics of fractal time series. The closer the H is to 1, the stronger the degree of continuity of the sequence [21].Figure2shows the time series with different Hurst exponents generated by the ARFIMA linear model.Figure 2
Relationship diagram betweenajd and j.In Figure3(a)–3(c), the Hurst index is 0.1, 0.5, and 0.9, respectively. The results in Figure 4 show that different Husrt indices have different artificial simulation time series. When H = 0.1, the fluctuation of the time series is the most obvious. When H = 0.5, the time series appears as a random sequence. When H = 0.9, the trend of the time series is the most obvious. This phenomenon is related to the long-range correlation of the fractional Brownian motion.Figure 3
The relationship between artificial simulation time series and Hurst index (a)H = 0.1 (b) H = 0.5 (c) H = 0.9.
(a)(b)(c)Figure 4
Image of elm leaves.Figure3 shows the relationship between the ARFIMA coefficient ajd and j when d = 0.1, 0.2, 0.3, 0.4 (i.e., H = 0.6, 0.7, 0.8, 0.9).Figure4 shows that when j gradually increases, ajd decreases rapidly and quickly approaches zero. This shows that the contribution of the sequence data generated by the ARFIMA linear model mainly comes from the first few items, and the contribution of d to the observed value varies from time to time. As d increases, the contribution increases. Therefore, a significant feature of formula (1) is that y(i) is related to all previous observation points, and this result is consistent with the long-range correlation interpretation of the time series. Moreover, y(i) has a different degree of correlation with all previous observation points.The choice of the space-time weight matrix Wst is very important, where the element Wuvst (the space-time weight function) represents the spatio-temporal geographic process between x(p, i) and y(q, j) u=PT+i,v=qT+j,1≤u≤NT,1≤V≤NT. Generally speaking, we believe that the process of spatio-temporal geography is the result of the joint effect of spatial and temporal interrelationships. Therefore, the space-time weight matrix will be constructed from two aspects: the space weight matrix and the time weight matrix.The spatial weight function indicates the degree of interdependence and correlation among spatial objects, which is the prerequisite and basis for spatial metrological analysis. A reasonable and correct selection of the spatial weight function is essential for spatial model testing and spatial metrological analysis. The spatial correlation WSpq among spatial objects in different locations is more complicated. Generally, the spatial weight function is divided into three types: inverse function, negative exponential function, and step function. In order to take into account the spatial geographic process effect, an exponential decay matrix is constructed for the attenuation as the distance increases, the purpose of that is to describe this spatial effect more accurately. In this study, the negative exponential function exp−dist/λ is used as the spatial weight function. Because this method is derived from the direct-maximization model proposed by Wilson. The spatial weight function can be expressed as:(2)WSpq=exp−dist/λ,dist≠0,0,dist=0.Among them, dist represents the distance between pointp and point q in space, and λ is a constant.The time weight function can be represented by an indicator function, a negative power exponential function, and so on. In order to simplify, this paper chooses the indicator function as the time weight function, which is expressed as:(3)WTij=1,i−j≤h,0,others.Among them,h represents the time lag in the time weight function. Formula (3) indicates that when i−j>h, there is no time correlation between time i and time j, and vice versa.In order to simplify, the space-time weight function can be considered as the product of the space weight function and the time weight function, namelyWUVst=WSpq×WTij, so the space-time weight function can be expressed as:(4)WUVst=WTijdist=0,i−j≤h,i≠j,WSpq×WTij,dist≠0,i−j≤h,0,i−j>h,or(5)WUVst=0,dist=0,i−j≤h,WSpq×WTij,dist≠0,i−j≤h,0,i−j>h.For all spatio-temporal objects,W usually has two situations. Formula (4) shows that two kinds of autocorrelation have an impact on TDGSTI: one autocorrelation originates from the combined effect of space autocorrelation and time autocorrelation, and the other autocorrelation originates from the effect of time autocorrelation. Therefore, in this case, the spatio-temporal weight matrix type is a hybrid spatio-temporal weight matrix.Formula (5) shows that spatio-temporal autocorrelation is only derived from the combined action of spatial autocorrelation and temporal autocorrelation. In this case, the spatio-temporal weight matrix type is a complete spatio-temporal weight matrix.In order to make the observed variables of the time-lag series have a clearer geographical significance, we will perform row-standardization processing on the obtained space-time weight matrix. This is because if we want to analyze how the temperature of a space-time location is affected by the temperature of an adjacent spatio-temporal location, we think that the temperature of each adjacent space-time location will have an impact on the temperature of the spatio-temporal location under study. Therefore, the sum of the weights of the temperature effects on all neighboring space-time locations is 1.


## 4. Application of Agricultural Genetic Breeding Technology Based on Big Data Analysis in Elm Cultivation

The collected natural wild elm species are grafted on a 1-year-old white elm rootstock, and each clone has 10 plants and normal water and fertilizer management.After cultivation, we collect 3–5 young leaves from each plant, put them in a ziplock bag, and store them in a refrigerator at -70 °C for SSR marker analysis. The modified CTAB method (Han Qian et al., 2009) is used to extract cloned genomic DNA. After the DNA is purified and dissolved, it is tested with a Nanodrop-2000 nucleic acid analyzer, and the tested DNA is diluted to 30 ng/μL and used for the PCR reaction. A total of 17 pairs of SSR primers are selected, and gel electrophoresis is performed. Finally, 13 pairs of SSR primers with clear bands and high polymorphism are selected and tested. Figure 4 shows an image of elm leaves.We perform cluster analysis and analysis of variance on the leaf shape traits obtained by using big data technology analysis and calculate the average value, standard deviation, coefficient of variation, and repeatability of each leaf shape trait. In order to eliminate the influence of the dimensions of different phenotypic data, all phenotypic data are standardized, the Euclidean distance between different samples is calculated, and the class average method is used to cluster the phenotypic data. In this paper, the band statistics of the electrophoretic patterns are carried out, and the patterns with bands are recorded as 1, and the patterns without bands are recorded as 0. DPS software is used to calculate the distance coefficient, the class average method (MPGMA) is used for cluster analysis to form a clustering dendrogram, and the PIC software is used to calculate the amount of polymorphic information of each primer. We perform cluster analysis on the phenotypic traits and SSR molecular markers of all varieties and perform correlation analysis based on the genetic distance matrix of the leaf shape trait cluster analysis and the genetic distance matrix of the SSR molecular marker cluster analysis. At the same time, we count the role of agricultural genetic breeding technology in the cultivation of elm trees. The statistical results are shown in Table1 and Figure 5.Table 1
The role of agricultural genetic breeding technology in elm cultivation.
NumberEffect evaluation197.6295.4394.9496.6593.7690.3790.7893.6992.41094.21192.41292.81393.91494.31597.41691.51794.51897.21990.42090.22195.62295.22394.12491.42597.52692.32796.02895.22990.83097.13193.03293.63390.23494.83596.53691.53793.73893.63994.74096.44197.84296.04390.94490.14593.74691.14792.24890.44997.65094.25194.05292.55395.35492.35595.65696.15792.85892.65996.26091.26195.36293.16390.76496.16597.86693.96797.46892.16996.47092.07195.07293.67393.87493.57595.47697.37794.17893.07993.98091.68191.98295.18395.18490.08596.28695.38795.38897.18994.09094.3Figure 5
Line chart of data statistics.In this study, the distance matrix generated by cluster analysis of leaf shape traits and the distance matrix generated by cluster analysis of SSR molecular markers is used for correlation analysis. The experiment showed that the comprehensive leaf shape traits are extremely significantly correlated with SSR molecular markers, and the correlation coefficient reaches 0.48. In order to further understand the relationship between leaf shape traits and SSR molecular markers, each trait of leaf shape and SSR molecular markers are clustered and analyzed. Moreover, the correlation analysis between the distance matrix produced by the cluster analysis of leaf shape traits and the distance matrix produced by the cluster analysis of SSR molecular markers is carried out. The test results showed that in the pairwise analysis of leaf shape traits and SSR molecular markers, the number of primers for leaf shape traits and SSR primers to reach a level of extremely significant correlation (p < 0.001) is between 3 and 11 pairs. There are more than 10 pairs of primers that are extremely significantly related to leaf length traits, the correlation coefficient is up to 0.543, and the repetitive power of leaf length traits is all above 0.9. There are about 10 pairs of primers that are extremely significantly related to leaf width traits, with the highest correlation coefficient being 0.46, and the repetitive power of leaf width traits are all above 0.9. It shows that the variation of leaf shape traits is affected by genetic factors. The results of the study showed that the leaf shape traits of Ulmus pumila clones are extremely significantly correlated with SSR molecular markers, and the correlation level between leaf shape traits and SSR loci is relatively high. This study lays the foundation for future research on other phenotypic traits and SSR molecular markers of elm clones and will play a positive role in promoting the collection and genetic improvement of elm germplasm resources.


## 5. Conclusion

In this paper, the leaf shape traits and SSR molecular markers are performed cluster analysis, and the cluster analysis produced a genetic distance matrix to perform correlation analysis to find the sites closely related to leaf shape traits, to provide a basis for the research on the expression and location of elm-related genes, and to provide technical support for the protection and utilization of elm germplasm resources and molecular marker-assisted breeding.This paper combines big data technology and agricultural genetic breeding technology to study the cultivation of elm trees and conducts experimental research in combination with actual conditions. Moreover, this paper conducts correlation analysis between the distance matrix generated by cluster analysis of leaf shape traits and the distance matrix generated by cluster analysis of SSR molecular markers and combines big data technology to perform data processing and to count the test results. The research results show that agricultural genetic breeding technology can play an important role in elm cultivation.



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