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ISSN : 1598-5504(Print)
ISSN : 2383-8272(Online)
Journal of Agriculture & Life Science Vol.53 No.5 pp.37-49

Application of Decision Trees for Prediction of Sugar Content and Productivity using Soil Properties for Actinidia arguta ‘Autumn Sense’

Si-Young Ha1, Ji-Young Jung1, Young-Ki Park2, Gi-Young Kweon3, Sang-Yoon Lee3, Jae-Hyeon Park4, Jae-Kyung Yang1*
1Department of Environmental Forest Science, Institute of Agriculture & Life Science, Gyeongsang National University, Jinju, 52828, Republic of Korea
2Forest Medicinal Resources Research Center, National Institute of Forest Science, Yeongju 36040, Republic of Korea
3Department of Bio-Industrial Machinery Engineering, Gyeongsang National University (Institute of Agriculture and Life Science), Jinju, 52828, Republic of Korea
4Department of Forest Resources, College of Life Science & Natural Resources, Gyeongnam National University of Science and Technology, Jinju, 52725, Republic of Korea
Corresponding author: Jae-Kyung Yang Tel: +82-55-772-1862 Fax: +82-55-772-1869 E-mail:
March 6, 2019 July 16, 2019 August 1, 2019


Environmental conditions are important in increasing the fruit sugar content and productivity of the new cultivar Autumn Sense of Actinidia arguta. We analyzed various soil properties at experimental sites in South Korea. A Pearson’s correlation analysis was performed between the soil properties and sugar content or productivity of Autumn Sense. Further, a decision tree was used to determine the optimal soil conditions. The difference in the fruit size, sugar content, and productivity of Autumn Sense across sites was significant, confirming the effects of soil properties. The decision tree analysis showed that a soil C/N ratio of over 11.49 predicted a sugar content of more than 7°Bx at harvest time, and soil electrical capacity below 131.83 μS/cm predicted productivity more than 50 kg/vine at harvest time. Our results present the soil conditions required to increase the sugar content or productivity of Autumn Sense, a new A. arguta cultivar in South Korea.


    Korea Forest Service
    FTIS 2017091C101919AB01


    Kiwifruit is the main representative of the Actinidia genus. It is known worldwide and is highly appreciated for its delicious taste and health-promoting properties (Ferguson & Ferguson, 2003). The other edible and very promising Actinidia species is Actinidia arguta (Siebold & Zucc.) Planch. ex Miq., also known as kiwiberry, hardy kiwi, baby kiwi, or mini kiwi. This exotic species is very interesting and promising given the horticultural advantages it has over kiwifruit, especially its high frost hardiness (down to -30℃ in midwinter) and relatively short vegetation period (Debersaques et al. 2015). A. arguta is indigenous to Siberia, Japan, North East China, and South Korea. It is commercially produced in New Zealand, British Columbia in Canada, and in Oregon, Washington, Pennsylvania, and New York in the USA.

    Stefaniak et al. (2017) reported that the average number of shoots was significantly influenced by both soil nitrogen and the cultivar. Costa et al. (1997) also reported the increase in shoot number of kiwifruit when additional nitrogen was applied. In related studies, Worthington (2001) reported a significantly higher content of vitamin C, iron, magnesium, and phosphorus and significantly less nitrates in kiwifruit grown in soil with high organic matter concentration. Montanaro et al. (2009) reported that the organic carbon content of the soil increased productivity of kiwifruit. Studies on soil properties and productivity of fruit were mostly performed on A. arguta ‘Ananasnaya’ and ‘Hayward’ of Oregon, USA, or A. arguta ‘Weiki’ and ‘Geneva’ of New Zealand.

    In South Korea, A. arguta is commercially produced primarily in Suwon city, Gyeonggi Province, South Korea (37°15.50′N, 127°01.43′E), Yeongwol-gun, Gangwon Province, South Korea (37°11.4′N, 128°28.6′E), and Gwangyang city, Jeollanam-do, South Korea (34°57.21′N, 127°29.25′E). Research on A. arguta began in the 1980s in South Korea, and the new cultivar Autumn Sense was grown in 2013. The average weight of the fruit of this cultivar is 19.9 g, the average fruit size is 36.3 mm in average length and 32.1 mm across the average diagonal, and average sugar content is 19.5°Bx. However, there are few studies on the effects of soil properties on the production of ‘Autumn Sense’. In addition, to the best of our knowledge, there have been few experiments studying the effect of irrigation on mini kiwifruit productivity as well as the sugar content of A. arguta fruit. The variety of cultivar and expansion in arable farming has emphasized a need for more specific information related to factors that affect A. arguta productivity and sugar content of the fruit in the region. It is important to evaluate the soil properties for high productivity and sugar content of the new cultivar because differences in properties depending on the cultivar of A. arguta have been observed.

    Decision tree models are well suited to represent the complexity of interactions between forest communities and the diverse topographic and edaphic variables that determine their environment. Use of decision trees in agriculture has triggered an increasing interest in the management of within-field soil variability, and involves the collection of a lot of information. Decision trees offer many benefits to data mining, such as easy comprehension by the end user, handles a variety of input data, ability to process erroneous datasets or missing values, production of high performance with a small number of data, and can be implemented using data mining packages over a variety of platforms (Bhargava et al. 2013). Earlier research has indicated that it is possible to determine water stress (Yang et al. 2002;Karimi et al. 2005), nitrogen status of various crops (Yang et al. 2002;Goel et al. 2003), and weed stress and distinction between weed species (Goel et al. 2003;Karimi et al. 2005) on the basis of certain spectral wavebands in the visible, nearinfrared, and mid-infrared regions of the spectrum. This study evaluates the use of decision trees to determine the soil conditions that promote the production of high quality fruit.

    Therefore, our objectives were evaluation of soil properties in A. arguta ‘Autumn Sense’ production areas and analysis of the correlation between productivity or sugar content and soil properties. And then, selection of soil conditions leading to high sugar content or productivity in the new A. arguta cultivar Autumn Sense using a decision tree.

    It is hoped that the major limitations and benefits identified in A. arguta ‘Autumn Sense’ cropping and arable land use management will contribute to further research.

    Materials and methods

    1 Experiment site

    The experiment sites located in Yeongwol-gun, Gangwon Province, South Korea (37°12.220′-37°12.248′N and 128°36.622′-128°36.641′E) (Fig. 1, Table 1), had an average annual rainfall of over 1600 mm and an average annual temperature of 21.5°C. The experiment sites were on slopes that were terraced. Most of the plants were 10 to 15-year-old Autumn Sense vines. Above-ground drip irrigation was used for supply of moisture and A. arguta vines were arranged at 1 m spacing. All sites received similar horticultural, disease, and insect control practices.

    2 Soil analysis

    Soil samples (one composite sample per orchard) were collected in September 2017. Each composite soil sample (500-1000 g per sample) consisted of three sub-samples per experiment site taken from 0 to 30 cm depth near the root of the vine. The soil was sampled at three spots per vine and the physical and chemical properties were analyzed using three replicates per spot. The soil temperature (MOISTURE METER, MD-4G, China) and moisture (MIC99300, MIC, China) were measured immediately on-site using portable measuring instruments. After 24 hours of air drying, the samples were gently ground, sieved (2 mm) and properly stored for analysis (Peng et al., 2011).

    The pH and electrical conductivity (EC) were analyzed in a 1:5 (v/v) water extract and measured using a pH meter (HI 8418, HANNA, USA) and EC meter (LQ2- LE, Vernier, China), respectively (European Standard 13037, 1999). Then, all media were analyzed for total organic matter by the dry combustion method at 540℃ (Nelson & Sommers, 1982). Available P was extracted with water or CaCl2 and measured using a UV-spectrophotometer (U-3010, Hitachi, Japan) at 720 nm. Available Si was extracted with monocalcium phosphate and acetic acid and determined using the simple turbidimetric method (Institute of Soil Science, Chinese Academy of Science, 1978) based on the formation of BaSO4 precipitate in colloid form and measured using a UV-spectrophotometer (U-3010, Hitachi, Japan) at 700 nm. The C and N concentrations were analyzed by Kjeldahl digestion (Bremner & Mulvaney, 1982) using a macro elemental analyzer (vario MACRO cube, USA). Samples were digested with hydrofluoric acid mixtures and analyzed for trace elements (K, Ca, and Mg) by inductively coupled plasma-mass spectrometry (ICP-MS) (McBrid and Spiers 2001).

    3 Fruit quality measurements

    3.1 Fruit size

    Fruit size was determined at harvest (September 2017). Fruit were collected and analyzed from nine A. arguta vines per experiment site with a total of 20 fruit per vine. The length, width, and thickness of fruit were determined using digital calipers and immediately assayed after being brought to the laboratory. The fruit length, width, and thickness measurements are shown in Fig. 2. As shown in Fig. 2, it is desirable to measure both the width and the length of the thickness, since the A. arguta fruit is not completely elliptical.

    3.2 Productivity

    Productivity was measured immediately on harvest (September 2017) and weighing scales (GL 6000S, G TECH international, South Korea) were used. The productivity was measured in a total of nine vines per experiment site. Data was expressed as kg/vine in fresh weight.

    3.3 Fruit sugar content of fruit

    Sugar content was determined at harvest by squeezing a drop of juice from the fruit equator onto a digital refractometer (model PR-100, Atago, Japan). Sugar content was measured in a total of 20 fruit on nine vines per experiment site.

    4 Statistical analysis

    Mean and standard deviation were calculated using Microsoft Excel (Microsoft, WA, USA) spreadsheets. We performed Pearson’s correlation analysis to identify two relationships; 1) relationship between the various soil properties and 2) relationship between the soil properties and fruit properties (sugar content and productivity) of the Autumn Sense fruit. Pearson’s correlation coefficient matrix was calculated using R program 3.4.3 (Systat Software Inc., CA, USA).

    5 Prediction method

    We performed a decision tree to examine the conditions of soil properties that promoted an increase in sugar content or productivity of Autumn Sense. Decision tree was presented using Weka 3.8 (Systat Software Inc., CA, USA). The classifier selected was the J48 algorithm and decisions were based on the main soil properties affecting high sugar content or productivity of fruit on A. arguta. The data used for the decision tree were 729 soil property values, 1620 sugar content readings, and 81 productivity data. Prediction of sugar content and productivity were derived only as a result of the agreement of all 729 data on soil properties.

    Results and Discussion

    1 Properties of soil

    Table 2 shows the soil temperature and soil moisture of the nine experiment sites. The results showed that the soil temperature ranged from 19.9 to 22.7℃, which was probably affected by the weather in September when the soil sampling was performed (air temperature of the sampling date was 28.1℃). A. arguta is known to be able to grow at low temperatures and all experiment sites were found to be in the appropriate soil temperature range of 18-23°C. for growth of A. arguta. Soil moisture is an important factor for moisture supply to plants and was found to be in the range of 20.0-43.0 % in the nine experiment sites. Soil moisture is affected by the weather and there was no rain on the date of soil sampling. The main physical properties of the different soil are related to water content (Ozenc & Ozenc, 2007). Also, the sensitivity of fruit growth to soil water availability has been well documented for kiwifruit (Actinidia deliciosa) (Smith & Buwalda, 1994). It is known that in some kiwifruit growing regions of the world a significant reduction in harvest weight will occur if the supply of irrigation water is limited over the summer (Judd et al., 1989).

    The optimum soil pH for hardy kiwi Hayward is between 5.5 and 6.0. Vines show poor growth at a soil pH above 7.2 (Strik & Cahn, 1996). It is not known if other species differ in soil pH requirements, especially the optimal soil pH for Autumn Sense has not been reported. The soil pH of the nine experiment sites varied from 5.54 to 7.20 (Table 3). Electrical conductivity (EC) indicates the amount of soluble salts (cations or anions) in the soil (Friedman, 2005). The EC differed significantly across the experimental sites. The greatest percentage of soil organic matter was found in S1 among the experiment sites. A high soil organic matter content can also indicate an increase in microbial biomass, with a positive relationship seen between organic matter content and bacteria and fungi (Frey et al., 1999). We can infer that the S1 is closest to the original natural state of the land and is of better soil quality than the other sites. The soil available phosphorus was the highest in S1. The highest content in available P in soil indicates that P fertilizer application significantly increases soil P concentration (Wang et al., 2012). Whereas, low content of soil available P in S9 may be attributed to more soil P uptake by the plant, so less P is left for raising the concentration in soil. The available Si in soil was the highest in S2 and S4, on the other hand, S6 had the lowest available Si. In general, fruit trees do not like strong acidic soils, because in soils of pH 5.0 or less, serious problems may arise, such as excessive solubility of Al and Mn, or low availability of P, Ca, Mg, and Mo (Chapman, 1968). None of the nine experiment sites showed a soil pH of 5.0 or less. Although the soil of all experiment sites in this study was found to be in the correct pH range for kiwi growth, soils acidify very slowly under natural conditions over hundreds to millions of years (Guo et al., 2010). Therefore, in the future, proper pH adjustment will be required. The site S1 had a higher soil EC than all other sites. A high soil EC shows an increase in the concentration of dissolved inorganic solutes that are available to plants in this form (Rahman et al., 2012). Therefore, the high soil EC values at S1, suggests that the largest amount of inorganic substances is available to the kiwifruit vines at this site. Our results showed that S1 has the highest soil organic matter content and soil C/N ratio. These findings are corroborated with the previous results in literature (Agbede et al., 2008;Mahmood et al., 2017). Shahzad et al. (2015) reported that high organic content increases nitrogen loss in soil. Therefore, the relatively high soil C/N ratio in S1 is likely due to the increase in the ratio of nitrogen loss due to high soil organic matter content.

    The average content of soil exchangeable Mg was 94.1-134.4 cmol/kg, soil Ca was 63.4-477.4 cmol/kg, and soil Mg was 94.1-134.4 cmol/kg (Table 4). The K, Ca, and Mg are plant nutrients, and Mg is particularly helpful for plant root growth (Bulluck et al., 2002). The content of each cation was found to be significantly different depending on the site of experiment and the highest amount of soil Mg was found in S9.

    2 Fruit quality properties

    The length, width, thickness, productivity, and sugar content range of Autumn Sense fruit were 26.99-34.03 mm, 20.38-27.49 mm, 19.27-24.81 mm, 8.00-53.00 kg/vine, and 5.20-7.90°Bx, respectively (Table 5). We found that the same cultivar (Autumn Sense) differs significantly in fruit size, productivity, and sugar content of fruit across the experiment sites. In particular, the difference in productivity of fruit per vine was very significant. White et al. (2005) reported that A. arguta ‘Hortgem Tahi’ had approximately 7.1°Bx sugar content, ‘Hortgem Wha’ had approximately 10.2°Bx, and ‘Hortgem Toru’ had approximately 7.8°Bx. These three A. arguta cultivars were harvested when mature from vines growing in New Zealand. Latocha et al. (2011) reported that A. arguta ‘Ananasnaya’ and ‘Weiki’ in Germany and A. arguta ‘Jumbo’, ‘Geneva’, and ‘74-49’ in Switzerland had sugar content varying from 14.2 to 20.3°Bx. However, our study showed significant differences in fruit size and productivity of A. arguta varieties despite all fruit being from the same cultivar (Autumn Sense). It is considered that environmental factors such as soil properties or weather affected these properties.

    3 Pearson correlation coefficient matrix between soil properties

    The entire cross-correlation matrix composed of 11 soil properties is shown in Table 6. There was a negative correlation between soil temperature and soil moisture (r=-0.68). Davidson et al. (1998) reported a similar negative correlation between soil temperature and soil moisture. Furthermore, soil moisture-temperature correlations are consistently more negative than correlations between precipitation and temperature (Huang et al., 1996). Our results also show low soil moisture at high soil temperatures. Soil EC, soil organic matter, and soil available P were highly positively correlated to each other. Especially, the correlation between soil organic matter and soil EC was the highest (r=0.92). Soil EC is influenced by a combination of physicochemical properties including organic matter (Corwin & Lesch, 2005). Soil K had the highest correlation with soil Mg (r=0.93) and this is consistent with the results reported by Vågen et al. (2006). The high positive relationship between soil K and soil Mg is due to the use of fertilizers containing K and Mg in A. arguta experiment sites. These results indicate that soil properties have a considerably high effect on each other.

    4 Effects of soil properties on A.arguta quality properties

    Previous studies have shown that the physical and chemical properties of soil played a role in fruit such as orchard efficiency (Srivastava & Singh, 2007). Table 7 shows the results of the correlation analysis between soil properties and quality (fruit size, productivity, and sugar content) of Autumn Sense. Productivity of Autumn Sense fruit was negatively related with soil EC (r=-0.62) and soil C/N ratio (r=-0.61). Our finding that productivity was negatively related with soil EC, agrees with the previous results obtained on kiwifruit (Khan et al., 2018). In addition, soil EC content differed significantly between experiment sites (Table 3). Costa et al. (1997) reported the increase in shoot number of kiwifruit when more nitrogen was applied. This is consistent with our study that soil C/N ratio and productivity are negatively correlated. Mesfine et al. (2005) reported that increased grain production in corn and soybean was mainly due to increased soil moisture content, and our results for Autumn Sense were found to be similar, although the correlation was a comparatively low positive correlation. Our results showed that the sugar content of Autumn Sense is strongly related with soil EC (r=0.61) and soil C/N ratio (r=0.64). We observed a positive relationship between soil C/N ratio and sugar content of A. arguta fruit, which agrees with the reports that soil C/N ratio increased as the fruit granulation developed in two sweet orange cultivars (Munshi et al., 1978). Interestingly, the sugar content and productivity of fruit were found to be negatively correlated. This agrees with previous reports of this correlation between sugar content and productivity of fruit (Kultur et al., 2001).

    5 Decision tree

    The regression tree for the sugar content of fruit of A. arguta is shown in Fig. 3. The first split divides soils by the C/N ratio in their properties. The soil C/N ratio was the most important splitting variable in soils; finer soils are further split into groups with fine soil Ca. Partitions by soil C/N ratio result in forming groups with larger average soil Ca for high sugar content. As a result, sugar content more than 7°Bx in Autumn Sense was recorded when soil C/N ratio was higher than 11.49 and soil Ca was the same or lower than 96.24 cmol/kg. Regression tree for productivity at the experiment sites is shown in Fig. 4. The predictors here were EC and pH. The decision tree shows that soil pH plays a substantial role in defining productivity in soils with soil EC equal to or less than 131.83 μS/cm. As a result, when EC was the same or lower than 131.83 μS/cm, and pH was higher than 6.76, productivity was recorded as more than 50 kg/vine. Using the range of all soil properties as a predictor improves predictions as compared with using only soil properties for sugar content or productivity potential of A. arguta fruit. However, using C/N ratio, Ca, EC, and pH with soil properties makes predictions substantially better and a further addition of the order does not bring much improvement. The C/N ratio makes for a better predictor than the other properties at this matrix potential for sugar content of fruit. The EC and pH make for better predictors than the other properties at this matrix potential for productivity. For the matrix potential sugar content or productivity, using C/N ratio, Ca, EC, and pH order along with the properties range result in a small improvement in predictions.

    Conclusionally, we analyzed the properties of the soil and the quality (productivity and sugar content) of A. arguta ‘Autumn Sense’ fruit at the experiment sites. The aim of the study was to identify the soil properties that affected the productivity and sugar content of the new cultivar Autumn Sense in South Korea, by using Pearson’s correlation. The results showed the effect of changes in sugar content and productivity of fruit in the experiment sites for A. arguta depends on the soil C/N ratio, EC, and pH. In addition, we identified the main soil property conditions required for high sugar content and productivity of Autumn Sense by using a decision tree. We hope that this result will provide useful information for the soil conditions required for a high sugar content and productivity of Autumn Sense.


    This study was carried out with the support of ‘R&D Program for Forest Science Technology (Project No. "FTIS 2017091C101919AB01)’ provided by Korea Forest Service (Korea Forestry Promotion Institute).



    Experiment site in geography of South Korea (Wikipedia).


    Distinction of length, width and thickness for fruit size measurement.


    Decision tree for the prediction of sugar content on A.arguta. The predicted values are presented in the rectangles and ellipses. Numbers under the ellipses and rectangles are variances.


    Decision tree for the prediction of productivity on A.arguta. The predicted values are presented in the rectangles and ellipses. Numbers under the ellipses and rectangles are variances.


    Comparison of irrigation and location in nine experiment site

    Comparison of soil temperature and moisture in the same nine experiment site

    Comparison of soil chemical properties in the same nine experiment site

    Comparison of soil cation content (K, Ca and Mg) in the same nine experiment site

    Comparison of fruit size, productivity and sugar content in the same nine experiment site

    Pearson correlation coefficient matrix between soil properties

    Pearson correlation coefficient matrix for soil properties and A.arguta fruit properties


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