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Revista Ceres

Print version ISSN 0034-737XOn-line version ISSN 2177-3491

Rev. Ceres vol.67 no.5 Viçosa Set./Oct. 2020  Epub Oct 02, 2020 

Soil and Plant Nutrition

Soybean varieties suitability in agroforestry system with kayu putih under influence of soil quality parameters1

Taufan Alam2  *

Sri Muhartini2 

Priyono Suryanto3 

Erlina Ambarwati3 

Dody Kastono2 

Aprilia Ike Nurmalasari5 

Budiastuti Kurniasih2 

2Universitas Gadjah Mada, Department of Agronomy, Yogyakarta, Indonesia.;;

3Universitas Gadjah Mada, Department of Silviculture, Yogyakarta, Indonesia.;

4Universitas Gadjah Mada, Department of Soil, Yogyakarta, Indonesia.

5Universitas Sebelas Maret, Department of Agrotechnology, Surakarta, Indonesia.


The existence of soybean varieties and soil type interaction causes differences in productivity of soybean varieties in agroforestry systems with kayu putih. Soil quality parameters (physical, chemical and biological characteristics) will affect the productivity of soybean varieties. The objective of this study was to reveal the relationship between soil quality parameters with soybean varieties suitability in agroforestry system with kayu putih over three locations in which their soil type were different, i.e. Lithic Haplusterts, Ustic Epiaquerts and Vertic Haplustalfs. The study was conducted from May to August, 2018 in Menggoran Forest Resort, Playen District, Gunungkidul Regency, Special Province of Yogyakarta, Indonesia. The highest yield of soybean per hectare on Dering I grown in Lithic Haplusterts and Ustic Epiaquerts was 1.38 and 1.27 tons.ha-1, respectively, while Grobogan in Ustic Epiaquerts 1.24 tons.ha-1. Dering I showed the mean of the highest yield and most suitable in all soil types, while Gema showed the mean of the lowest yield and not suitable in all soil types. Soil quality parameters that had a significant influenced on the production of soybean varieties in agroforestry systems with kayu putih were chemical characteristic consisting of availability of P, Mg, NH4 +, Mn and Ca.

Keywords: soil type; soil characteristic; yield of soybean


Soybean is the leading commodity for food security in Indonesia. Consumption of soybean per year was projected to continuously rise from 812 thousand tons in 2005 to 946 thousand tons in 2020, indicating an average increase of 1.02% per year. Besides, the average population growth within the same period was also projected as 1.40% per year. Thus, the total soybean production was projected to increase from 1.84 million tons in 2005 to 2.64 million tons in 2020, or an average rise of 2.44% per year (Sudaryanto and Swastika, 2016).

The space between kayu putih stands can be used as an alternative to soybean cultivation. Soybean could be intercropped with kayu putih (Suryanto et al., 2017b). This is possible because kayu putih trees were pruned routinely to harvest the leaves, thus the shade factor did not affect annual crops. Agroforestry with kayu putih could be done continuously for 30 years (Suwignyo et al., 2015).

One of the easiest and cheapest technologies to increase soybean productivity is the introduction of new varieties that have high yield potential (Indonesian Agency for Agricultural Research and Development, 2007). However, varieties that have high yields in one soil type are not necessarily suitable and stable for all soil types. That is causes there is G x E interaction (Gauch, 2006; Piepho et al., 2016). Krisnawati & Adie (2018) reported that G2 and G6 genotype were considered as high yielding and stable promising lines of soybean across environments in which their soil type, seasonal rainfall, and altitude.

The sustainability of soybean varieties productivity can be done by selecting suitable and stable soybean varieties in various soil types. It can maximize the potential yield of these varieties and reduced fertilizer input (European Union, 2012). Alam et al., (2019) reported that Dering I and Devon I varieties had higher stability as well as higher mean of yield and Burangrang, Grobogan and Gema gave medium-high yield and poor yield with poor adaptation. However, the unknown soil quality parameters are a limiting factor for soybean production.

Soil quality is one of the three components of environmental quality in addition to air and water quality. Soil quality is an assessment of how the soil functions and prepared for the future (Andrews et al., 2002; Makalew, 2011). Soil quality depends on a specific soil type and the maintenance of plant and animal sustainability (Andrews et al., 2004). Integration of soil chemical, physical and biological attributes is the concept of soil quality that is dynamic and sensitive to soil management practices (Bilgili et al., 2017). Qi et al. (2009) stated that soil quality evaluation using soil quality measurement could reduce data and saved time and money. Soil quality affected the successful sustainable production of rice in agroforestry system with kayu putih (Suryanto et al., 2017a).

Several studies related to soil quality assessment showed different land evaluation parameters. Soil quality measurement was very dependent on the diversity of location, scale, land management and research objective (Rousseau et al., 2012). Studies related to soil quality assessment had been carried out in the same location for rice. Suryanto et al. (2017a) showed that soil quality parameters that play a role in rice productivity in agroforestry with kayu putih were amount of soil microorganisms, availability of phosphorus and exchange of potassium.

Soil quality parameters consisting of physical, chemical and biological characters in each soil types in the study location will affect the production and suitability of soybean varieties. The prediction of soil quality parameters for each soybean varieties suitability is expected for monitoring and evaluating the production of soybean varieties in different soil types. The objective of this study was to reveal the relationship between soil quality parameters with soybean varieties suitability in agroforestry system with kayu putih. This study would help researchers uncover critical areas of soybean cultivation on agroforestry system with kayu putih that many researchers could not be able to explore. Thus, a new theory on limiting factors in each soybean varieties might arise.


The experiment was conducted in Menggoran Forest Resort, Playen District, Gunungkidul Regency, Special Province of Yogyakarta, Indonesia from May to August 2018. This area was located ±43 km to the south-east of Yogyakarta City (Figure 1).

Figure 1: Geographical locations of the study area (latitude 7º 52` 59.5992`` S to 7º 59` 41.1288`` S and longitude 110º 26` 21.462``E to 110º 35` 7.4868`` E) 

The soybean varieties were from Indonesian Legumes and Tuber Crops Research Institute in Malang Regency, East Java Province, Indonesia. The experimental plots cover an area of 24 m2 (6 x 4 m) in the area between kayu putih stands and the harvest area of 20 m2, excluding the border rows. The plant spacing was 40 x 20 cm. No fertilization and pesticide were carried out in this study. Irrigation did not performed because the field used in this study was rainfed area.

The experiment used a Randomized Complete Block (RCB) design with five blocks as replication. The first factor was soil type in Menggoran Forest Resort consisting of Lithic Haplusterts, Ustic Epiaquerts and Vertic Haplustalfs. The second factor was soybean varieties consisting of Anjasmoro, Argomulyo, Burangrang, Demas I, Dering I, Devon I, Gema and Grobogan.

The soil quality parameters observed was soil properties (physical, chemical and biologycal characteristic) (Table 1). Soil quality parameters observation was carried out at the research site, at the General Soil and General Microbiology Laboratory, Faculty of Agriculture, Universitas Gadjah Mada, the Special Province of Yogyakarta, Indonesia. The observation of soybean yield was seed dry weight per hectare. Soybean seed was dried under sunlight to 11 % of moisture level (Suryanto et al., 2017a).

Table 1: Procedure methods for measurements of each indicator 

Variable Symbol Procedures methods Reference
Soil Texture Silt, Sand, Clay Robinson Pipette Method Aubert et al. (1954)
Bulk Density BD Ring Sample Blake & Hartge (1986)
Available Soil Moisture ASM Gravimetric Alam (2014); Dwidjopuspito (1986)
Permeability Perm Permeameter Blake & Hartge (1986)
pH H2O pH Ratio Soil : Aquadest = 1 : 2,5; pH Meter Van Reeuwijk (1993)
Soil Organic Matter SOM Walkey and Black Black (1965)
Cation Exchange Capacity CEC Ammonium Acetate Extraction Hajek et al. (1972); Van Reeuwijk (1993)
Electrical Conductivity EC Ratio Soil : Aquadest = 1 : 2,5; EC Meter Richards (1954)
Available of Nitrate and Ammonium NO3 - and NH4 + Devarda's Alloy Method Stenhom et al. (2009)
Available of Phosphorus P Olsen Extract (Spectrophotometry) Olsen et al. (1954)
Available of Potassium and Sodium K and Na Ammonium Acetate Extraction; Flame Photometer Jones Jr (2001)
Available of Calcium, Magnesium, Iron, Manganese, Copper and Zinc Ca, Mg, Fe, Mn, Cu and Zn Ammonium Acetate Extraction; Atomic Absorption Spectrophotometry (AAS) Jones Jr (2001); Van Reeuwijk (1993)
Amounts of Bacterium AB Dilution-Plate David & Davidson (2014)
Amounts of Fungi AF Dilution-Plate David & Davidson (2014)

The models must be evaluated so that assumptions can be fulfilled. The normality test was carried out using the Kolmogorov test and Q-Q plot (Moncada et al., 2014). MANOVA was used to determine the significant effects on assessed physical, chemical and biological variables. F-statistics and Wiks’ Lambada tests obtained from MANOVA are used to test the null hypothesis regarding overall treatment (Hatcher & Stepanski, 1994). Two-way ANOVA was used to test the yield of soybean varieties in different soil type, and the separation of means was subject to Tukey’s HSD (α = 5%) (Hinkelman & Kempthorne, 2008).

Soybean varieties suitability were graphically analyzed for interpreting GE interaction using the PCA-Biplot. PCA-Biplot analysis, which consisted of two concepts, the biplot concept (Gabriel, 1971) was employed to visually analyze the soybean varieties in each soil type trial. The PCA-Biplot graphic was made in RStudio (RStudio Team, 2015) using pca3d package (Weiner et al., 2012).

One-way ANOVA, factor Analysis and stepwise regression were used to relationship between the soil quality parameters with soybean varieties suitability (Andrews et al., 2002; Govaerts et al., 2006; Smith et al., 1993). MANOVA, ANOVA, Factor Analysis and Stepwise regression were carried out using SAS software version 9.4 for Windows. Statistical analysis was carried out by PROC GLM, MIXED, PRINCOMP, FACTOR and REG (SAS Institute Inc, 2013).


The study site had an ustic moisture regime. Ustic moisture is a soil regime containing limited moisture but is suitable for plant growth when the environmental conditions favour (Boettinger et al., 2015). The altitude of the study site varied from 100 to 200 meters above sea level. The air temperature ranged between 24.80 to 26.40 °C. The relative humidity ranged between 81.90 % and 86.50 %. The total rainfall in the study area was 2,005 mm year-1. The macro and micro climates in the study site were highly suitable for soybean cultivation (Djaenudin et al., 2011).

Lithic Haplusterts was included into the Vertisol soil type that had a shallow solum and rock contact of 50 cm from the surface. Vertic Haplustalfs was Alfisol soil type with vertic characteristic. Ustic Epiaquerts was Vertisol soil type that had fracture of >5 mm and thickness of >25 cm for 90 days each year in a reasonable condition when it was not irrigated (Soil Survey Staff, 2014). In general soybean was suitable to be planted in Lithic Haplusterts and Vertic Haplusterts. However, it was marginally suitable to be planted in Ustic Epiaquerts because the land was flooded during the wet season (Djaenudin et al., 2011).

The result of two-way ANOVA (p < 0.05) showed that there was an interaction between soil type and soybean varieties concerning the yield of soybean per hectare (Figure 2). The highest yields were produced by Dering I varieties cultivated at Lithic Haplusterts and Ustic Epiaquerts, namely 1.38 and 1.27 tons.ha-1 respectively and in Grobogan at Ustic Epiaquerts of 1.24 tons.ha-1. Soybean varieties showed difference in yield per hectare when grown in Lithic Haplusterts, Ustic Epiaquerts and Vertic Haplustalfs. This was due to the G × E interaction so that each soybean varieties response differently to each soil types (Alam et al., 2019). The relationship between productivity and soil was very complex and highly dependent on the physical, chemical and biological nature of the soil and external factors (Adams, 2016; Sys et al., 1991).

Figure 2: Yield of soybean varieties per hectare on the various of soil types. 

Principal component analysis (PCA) Biplots of the total data set of soil quality parameters were performed with varimax rotation (orthogonal) (Ayoubi et al., 2009; Cox et al., 2003; Shukla et al., 2004b). The results of PCA Biplot showed that Dering I had the mean of highest yield and was suitable for all soil types. Different things were showed by Gema, which had the mean of lowest yield and was not suitable in all soil types. The position of Anjasmoro varieties in the center point. This can be interpreted that Anjasmoro had the mean of moderate yield and suitable for all soil types(Figure 3).

Figure 3: Principal component analysis (PCA) Biplot for soybean varieties and soil types. 

Xu et al. (2014) reported that the presence of G x E interactions gave different responses to rice yields to variations in soil types in various locations. Jandoung et al. (2011) reported that soybean cultivars ‘Kyado’ and ‘Sebore’ have a good performance in soil with pH ranged from 5.5 to 6.5 and have a relative tolerance to moderate acidic soil. It depended on the genetics of each plant (Klee & Tieman, 2013). Giller et al. (2011) suggested that each plant had a different response in absorbing nutrients, fertilizers and lime applications on a site. This showed that the soil had high heterogeneity that affected plant growth.

The result of MANOVA with physical and chemical properties showed a very significant difference and significant difference of (< 0.000**) and (0.019*) respectively but the biological property showed no significant difference (0.508ns). These indices represent the cumulative effects of different soil properties (physical, chemical and biology) as an index from the role of each indicator in soil quality (Drury et al., 2003).

One-way ANOVA was applied on the twenty-four parameters used at different land effecting soil quality parameters (Table 2). The result of ANOVA on the soil quality parameters showed a significant difference (p < 0.05) and had a coefficient of variance of <40% consisting of % clay, % silt, % sand, ASM, CEC, pH H2O, EC, available NH4 +, P, Ca, Mg, Fe, Mn and Cu (Table 2). Those parameters were maintained for continued factor analysis. Factor analysis is the commonly used because of its ability to group related soil properties into a small set of independent factors and to reduce the original data set (Andrews et al., 2002).

Tabel 2: One way analysis of variance (ANOVA) of soil quality parameters 

No. Soil Quality Parameters Unit Soil Type CV(%)
Lithic Haplusterts Ustic Epiaquerts Vertic Haplustalf
1. Clay % 60.34 ± 2.86b 53.59 ± 1.63b 78.50 ± 2.01a 8.78
2. Silt % 32.58 ± 2.37a 35.71 ± 1.18a 16.17 ± 1.49b 14.32
3. Sand % 7.08 ± 0.55ab 10.70 ± 1.65a 5.33 ± 0.70b 36.37
4. Bulk Density g cm-3 1.15 ± 0.03a 1.08 ± 0.04a 1.14 ± 0.04a 6.32
5. Available Soil Moisture mm cm-1 3165.00 ± 364.92a 2002.20 ± 138.44b 3094.10 ± 275.42a 12.33
6. Permeability cm h-1 0.00 ± 0.00a 0.00 ± 0.00a 0.00 ± 0.00a 0.00
7. pH H2O - 8.18 ± 0.04a 7.80 ± 0.06b 7.69 ± 0.05b 1.63
8. Cation Exchange Capacity cmol(+1).kg-1 58.83 ± 0.16b 65.30 ± 0.90a 32.65 ± 0.20c 2.19
9. Electrical Conductivity µS cm-1 1689.90 ± 63.32b 1870.50 ± 27.92a 1154.80 ± 61.07c 6.13
10. Soil Organic Matters % 2.62 ± 0.03a 2.75 ± 0.09a 2.73 ± 0.07a 5.23
11. Ammonium ppm 39.39 ± 3.91b 56.76 ± 7.21a 51.00 ± 1.54ab 18.87
12. Nitrate ppm 86.18 ± 14.42a 143.21 ± 33.65a 82.73 ± 10.24a 39.70
13. Phosphorus ppm 6.87 ± 0.57b 18.76 ± 2.58a 2.46 ± 0.36b 33.76
14. Potassium me % 0.78 ± 0.01a 0.94 ± 0.04a 0.93 ± 0.11a 15.52
15. Sodium me % 0.75 ± 0.04a 0.79 ± 0.12a 0.72 ± 0.07a 23.68
16. Calcium me % 5.85 ± 0.00a 5.83 ± 0.01a 5.70 ± 0.01b 0.34
17. Magnesium me % 0.28 ± 0.00a 0.28 ± 0.00a 0.27 ± 0.00b 1.13
18. Iron ppm 12.22 ± 0.11a 12.58 ± 0.21a 9.21 ± 0.33b 5.49
19. Manganese ppm 32.52 ± 0.27b 32.90 ± 0.59b 35.17 ± 0.18a 2.80
20. Copper ppm 3.20 ± 0.07b 3.44 ± 0.05a 1.72 ± 0.08c 4.78
21. Zinc ppm 1.43 ± 0.04b 1.51 ± 0.80b 4.16 ± 0.08a 46.24
22. Amounts of Bacterium colony 3.44x106 ± 2.45x104a 3.52x106 ± 6.63x104a 3.48x106 ± 3.74x104a 2.13
23. Amounts of Fungi colony 2.54x105 ± 2.45x103a 2.62x105 ± 6.63x103a 2.60x105 ± 3.74x103a 2.34

Number followed by the same letter in the same column were not significantly different by Tukey’s HSD test (p<0.05). The bars was indicated Standard Error of Mean (SEM).

Factor analysis was provided to classify the soil quality parameters into a small set of independent factors and reduced the original data set. The result of the factor analysis showed three sets of soil quality factors formed (Table 3). Factor 1 consisted of % clay, % silt, % sand, CEC, EC, P, Ca, Fe, Mn and Cu. Factor 2 consisted of P and Mg. Factor 3 consisted of % sand and NH4 + (Table 3). The final result of the factor analysis showed that % clay, % silt, % sand, CEC, EC, P, Ca, Fe, Mn, Cu, Mg and NH4 + were suitable to proceed to the stepwise regression analysis since it had a communality value of higher than 0.5.

Table 3: Factor analysis with varimax rotation of physical, chemical and biological properties of soil 

Parameter Factor 1 Factor 2 Factor 3 Communality
% Clay -0.908 * -0.214 -0.137 0.999
% Silt 0.923 * 0.240 -0.065 0.999
% Sand 0.586 * 0.071 0.696 * 0.999
Available Soil Moisture -0.262 -0.059 -0.187 0.974
pH H2O 0.357 -0.030 -0.199 0.979
CEC 0.851 * 0.371 0.095 0.999
Electrical Conductivity 0.936 * 0.176 0.158 0.996
Ammonium -0.051 -0.024 0.976 * 0.995
Phosphorus 0.661 * 0.520 * 0.154 0.991
Calcium 0.873 * 0.194 -0.003 0.998
Magnesium 0.436 0.889 * -0.040 0.988
Iron 0.761 * 0.380 0.119 0.994
Manganese -0.663 * -0.228 -0.240 0.980
Copper 0.880 * 0.299 -0.004 0.993
Eigen-Value 9.120 2.095 1.153

* Significant soil's parameters in each soil set factor.

Factor analysis is widely considered as a suitable method for highly correlated environmental data (Shukla et al., 2004a; Govaerts et al., 2006; Yao et al., 2013). Varimax rotation enhances the interpretability of the uncorrelate components. The derived factors are designated as soil quality indices or complex indicators.

Stepwise regression analysis was used for screening soil quality parameters that affect the production of soybean varieties suitability (Andrews et al., 2002; Govaerts et al., 2006; Makalew, 2011; Smith et al., 1993; Suryanto et al., 2017a).

The result of stepwise regression showed that each soybean varieties had a different soil quality parameters limiting factors (Table 4). Each soybean varieties showed a different response to the availability of nutrients in the soil. The soil quality parameters affecting the yield of Argomulyo also showed P (1.094**) and Mg (-0.692*), Burangrang NH4 + (0.569*), Dering I Mn (-0.684**), Devon I Mg (-1.001**) and Ca (0.648*), Gema Mg (-0.902**), while Grobogan NH4 + (0.5328), and P (0.471*). Specifically for Anjasmoro and Demas I, there was no soil quality parameters limiting factors for these varieties.

Table 4: Stepwise regression analysis of relationship between soil quality parameters with yield of soybean varieties 

Varieties Regression Equation R2
Anjasmoro - -
Argomulyo Y = 6.199* + 1.094 P** - 0.692 Mg* 0.852**
Burangrang Y = 0.854** + 0.569 NH4 +* 0.824*
Demas I - -
Dering I Y = 5.802** - 0.684 Mn** 0.868**
Devon I Y = 3.982ns - 1.001 Mg** + 0.648 Ca* 0.828*
Gema Y = 25.884** - 0.902 Mg** 0.814**
Grobogan Y = 0.496** + 0.532 NH4 +* + 0.471 P* 0.706**

*Significant at α 5%. **Significant at α 1%.

Positive response was shown by Argomulyo and Grobogan for the availability of P, Burangrang and Grobogan for NH4 + and Devon I for Ca. The increased availability of P in the soil was very significant and significantly increased the yield of Argomulyo and Grobogan. The deficiency of P caused a significant decrease in net photosynthetic rate in rice (Xu et al., 2007).

The increased availability of NH4 + in soil was very significant to the increased yield of Grobogan and Burangrang. Nitrogen could be a limiting factor for plant growth after fixed carbon (Marschner, 2011). In a physiological process, urea was an essential internal and external source of N converted to ammonia for N assimilation (Marschner, 2011). Faustino et al. (2015) informed that NH4 + fertilization could increase the root growth of Pinus taeda in drought stress.

Higher increased availability of Ca in soil significantly increased the yield of Devon I varieties. The result of the research conducted by Domingues et al. (2016) described that common bean plants with higher Ca concentrations had a high dry weight of shoots and roots, grain yields and Ca concentrations in leaves and seeds.

Negative response was shown by Argomulyo, Devon I and Grobogan against the availability of Mg nutrients; Mn was available in Dering I. The increased availability of Mg was negatively correlated with K availability and the high concentration of Mg caused a decrease in soil calcium content (Venkatesan & Jayaganesh, 2010). The high concentration of Mg2+ in cytoplasm could block K+ channel in the inner envelope membrane of chloroplasts and thus inhibited the removal of H+ ions from the chloroplast stroma (Wu et al., 1991).

The yield of Dering I decreased in line with the increased availability of Mn in the soil. Silva et al. (2017) reported that Mn poisoning in corn might reduce chlorophyll content, plant biomass and plant antioxidant. The translocation of Mn from the root to leaf triggers decreased in chlorophyll content. High Mn concentration caused the increase in ROS accompanied by a higher level of antioxidant enzyme activity and lipid peroxidation (Rao et al., 2016).

Based on the result of the study it is recommended for soybean varieties that had high yield in each soil type and the fertilizer used, depending on the limiting factors in each soybean varieties. The suggestion for future study is that an optimum dose for soil quality parameters affecting soybean varieties is determined.


The highest yield of soybean per hectare on Dering I grown in Lithic Haplusterts and Ustic Epiaquerts was 1.38 and 1.27 tons.ha-1 respectively while Grobogan in Ustic Epiaquerts 1.24 tons.ha-1.

Dering I showed the mean of the highest yield and most suitable in all soil types, while Gema showed the mean of the lowest yield and not suitable in all soil types.

Soil quality parameters that had a significant influenced on the production of soybean varieties in agroforestry systems with kayu putih were chemical characteristic consisting of availability of P, Mg, NH4 +, Mn and Ca.


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Received: April 09, 2019; Accepted: February 07, 2020

*Corresponding author:

This work is part of the first authors research project.

CONFLICT OF INTERESTS There is no conflict of interests in carrying the research and publishing the manuscript.

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