1. ULUSAL TARIM KONGRESİ, EKİM 2013, ANTALYA APPLICATION OF CLASSIFICATION AND REGRESSION TREE METHODS IN AGRICULTURE Ecevit EYDURAN1 Adile TATLIYER2 Mohammad Masood TARIQ3 Abdul WAHEED4 1Iğdir University, Animal Science Department, Biometry And Genetics Unit, Iğdir 2Süleyman Demirel University, Animal Science Department, Biometry And Genetics Unit, Isparta. 3Center for Advanced Studies in Vaccinology and Biotechnology (CASVAB), University of Balochistan, Quetta, Balochistan, Pakistan.  4Faculty of Veterinary Sciences, Bahauddin Zakariya University, Multan, Pakistan  *Corresponding author:
Abstract
The aims of this investigation were to apply classification regression tree methods for different agricultural data sets and to illustrate how to interpret the obtained results, statistically-agriculturally. These analysis methods in visual form could be used instead of General Linear Model (GLM) in the presence of multicollinearity, outliers, and missing data.
As a result, it was emphasized in the investigation that applying classification regression tree methods in place of revealing homogenous sub-groups could yield more detailed information on the data sets examined.
Key Words: Agricultural Sciences, Classification Tree, Regression Tree.
Introduction
In agricultural sciences, describing the sophisticated relationships between significant measurable characteristics and yield characteristics is a considerable matter for attaining desirable genetic progress in yield characteristics overemphasized in animal and plant breeding. In general, the relationships have been probed by the well-recognized statistical techniques such as Pearson correlation, simple linear regression, multiple linear regression, ridge regression, and path analysis on the basis of some assumptions. Simply, investigating a bivariate relationship between the inspected yield characteristics and others by using the first two techniques is not an influent approach, and causes information loss, agriculturally. For instance, a multiple linear regression analysis technique may not submit a good interpretation in the presence of multicollinearity and outliers (Jahan et al., 2013; Eyduran et al., 2013). Due to these reasons, the well-chosen flexible statistical techniques such as Classification and Regression Tree methods should be applied in the sense of properly assessing and commenting the complex relationships, methodologically.
Implementing Classification and Regression Tree methods gain more advantageous results in the occurrence of multicollinearity, outliers, and non-linear problems, and most especially in statistically evaluating high number independent variables (Mendes and Akkartal, 2009; Karabag et al., 2010). Applicability of such methods in agricultural sciences is few, but may bring into prominence.
The current investigation was to apply classification and Regression Tree methods, which exhibit statistical results in visual form, to agricultural data sets.
Material and Method
In the current investigation, data of Mengali lambs in Pakistan were provided for applying classification and regression tree methods. Variable structures can be written as follows:
BWT: Birth weight =continuous variable
TOB: Type of Birth =discrete variable (single and twin)
YEAR: Year of Birth =discrete variable (2006, 2007, 2008 and 2009)
SEX: Discrete variable (male and female)
DAM AGE: continuous variable
DAM WEIGHT: Dam weight at birth (continuous variable)
Motivating examples
Classification tree method
The first example is on applying classification tree method for the data regarding animal science which is one arm of agricultural sciences. The obtained classification tree diagram is illustrated in Fig.1. In the classification tree diagram, sex was considered as a dependent variable and the data of 138 Mengali lambs were evaluated. Of these lambs, 55.1% was female and 44.9% was male at Node 0, a root node, at the top of the classification tree diagram. Node 0 was divided into child Nodes 1 and 2. For example, Node 2, which was the group of lambs with BWT > 3.75 kg, had 63.3% male and 36.7% female. This means that male lambs were biologically heavier than female ones. Node 1, which was observed as the group of lambs with BWT < 3.75 kg, was branched into two nodes (Nodes 3 and 4) on the basis of birth type (TOB), respectively. For instance, male and female proportions were 28.6 % and 71.4% for single lambs with BWT < 3.75 kg.
Regression Tree Method
The second example is on applying regression tree method for the data about animal science which is a significant arm of agricultural sciences. The regression tree diagram is depicted in Fig.2. As the most effectual variable, TOB significantly affected BWT (P<0.01). Node 0 was divided into Node 1 (single lambs) and Node 2 (twin lambs) with respect to TOB, respectively. SEX factor had a statistically significant effect on BWT of only single lambs (P<0.01). Node 2 is called a terminal node due to stopping re-division. Node 1 was branched into Nodes 3 (single-female) and 4 (single-male) according to sex factor. BWT of single-female lambs in Nodes 3 was statistically influenced by year of birth, but dam age factor effected BWT of single-male lambs (P<0.01). Average BWT (4 kg) of male Mengali lambs born from dams with age > 59 months (Node 10) was heavier than Nodes 8 (the male lambs born from dams at age < 36 months) and 9 (the male lambs born from dams at 36 < age < 59 months). Average BWT from the years 2006 to 2009 for single- female lambs increased with a range of to kg.
In conclusion, the heaviest average BWT (4 kg) was taken from Mengali lambs born from dams with age > 59 months (Node 10).
Fig 1. The diagram of Classification Tree Method.
Conclusion
Use of Classification and Regression Tree methods is recommended in the existence of multicollinearity, outliers, and non-linear problems, and especially in assessing complex data sets with high number independent variables. In conclusion, application of such methods in agricultural sciences is scanty, but must be taken into consideration.
References
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E. Eyduran, I. Yilmaz, M. M. Tariq and A. Kaygisiz (2013). Estimation of 305-d Milk Yield using Regression Tree Method in Brown Swiss Cattle. J. Anim. Plant Sci. 23(3).
Mendes, M. and E. Akkartal (2009). Regression tree analysis for predicting slaughter weight in broilers. Italian J. Anim. Sci. 8:
Karabağ, K., Alkan ,S., Mendeş, M (2010). Classification Tree Method for Determining Factors that Affecting Hatchability in Chukar Partridge (Alectoris chukar) Eggs. Kafkas Univ Vet Fak Derg, 16 (5):
Fig 2. The diagram of Regression Tree Method