Agricultural Science Digest

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Agricultural Science Digest, volume 43 issue 1 (february 2023) : 40-45

Soil Function Analysis in Determining the Soil Quality Index of Paddy Fields in Salassae Village, Bulukumba Regency, South Sulawesi Province, Indonesia

Hadija1,2, T. Kuswinanti3, M. Jayadi4,*, S.T. Larekeng5
1Doctoral Program of Agricultural Science, Hasanuddin University Graduate School, Makassar, Indonesia.
2Department of Forestry, Faculty of Agriculture, Moslem University, Maros 90512, South Sulawesi, Indonesia.
3Department of Plant Pests and Diseases, Faculty of Agriculture Hasanuddin University, Makassar 90245, South Sulawesi, Indonesia.
4Department of Soil Science, Faculty of Agriculture, Hasanuddin University, Makassar 90245, South Sulawesi, Indonesia.
5Department of Forestry, Faculty of Forestry, Hasanuddin University, Makassar, 90245, South Sulawesi, Indonesia.
Cite article:- Hadija, Kuswinanti T., Jayadi M., Larekeng S.T. (2023). Soil Function Analysis in Determining the Soil Quality Index of Paddy Fields in Salassae Village, Bulukumba Regency, South Sulawesi Province, Indonesia . Agricultural Science Digest. 43(1): 40-45. doi: 10.18805/ag.DF-476.
Background:  The quality of the function of paddy fields in Indonesia is strongly influenced by the high intensity of the level of management. Degradation of soil quality with improper management practice result in the loss of land resources and functions in the long term.

Methods:  This study uses survey methods, and the determination of soil quality index is calculated based on the criteria of Mausbach and Seybold (1998) modified.

Result: This research shows that the overall soil quality at the research location is still quite good. Locations 1,2,3 and 4 indicated a soil quality index with reasonable criteria, while location five indicated a decrease in soil quality index by 0.1% with moderate criteria. The decline in soil quality shown at location five was due to the level of management behavior in giving compost using only local knowledge. The results of this study indicate the main limiting level of soil quality at the research site is the availability of nitrogen and C-organic in the soil.
In Indonesia, paddy fields are land with intenseve management system. The intensity of intense soil management will significantly affect the function of the soil from a physical, chemical, or biological perspective. The carrying capacity of the soil has many functions, whereas a provider of ecological services includes maintaining the availability of food production (Li et al., 2020). The function of the soil is very varied, soil quality is the foundation (Laishram et al., 2012). Degradation of soil quality can result in loss of land resources and functions in the long term (Doran and Zeiss, 2000). Therefore, it is necessary to calculate the impact of agricultural management practices (Akinbile and Sangodoyin, 2011).

Soil quality is highly dependent on the interaction of physical, chemical and biological characteristics and proper soil quality assessment requires the measurement of many parameters (Martunis et al., 2016). Challenges remain in assessing soil quality because the standards set in assessing soil quality vary widely, both spatially and temporally (Liu et al., 2014). The productivity of paddy fields can decrease due to excess and lack of nutrients because the harvest carried more than the nutrients provided through fertilization or the addition of nutrients from irrigation water (Burauel and Baßmann, 2005). The excess of certain fertilizers and the lack of other fertilizers due to imbalanced fertilization and a decrease in soil organic matter content. This degradation threatens rice yields’ quantity (productivity) and quality (Seaton et al., 2020).

Determining soil quality is very difficult because soil quality and production are very complex and depend on complex relationships between soil attributes and other external factors (Yimer, 2022). Soil quality cannot be measured directly, so it is necessary to determine physical, chemical, and biological indicators (Hadija et al., 2018), which will directly provide a comprehensive measurement of soil quality, known as minimum data set (MDS). An MDS with the right indicators can reduce the need to define many indicators (de Lima et al., 2008) and should adequately represent the complete data set (Sarbu and Pop, 2005). Evaluating the status of paddy soil quality at different management levels will be important to develop sustainable paddy land use management to increase yield production (Sewnet et al., 2020; Turamyenyirijuru et al., 2019).

Evaluation of soil quality needs to be explicitly developed on productive lands such as agricultural land. Anthropogenic lands such as paddy fields that were constantly inundated or this condition is suscept ible to changes in physical, chemical and biological properties (Choudhury and Kennedy, 2005). Rice fields are very vulnerable to soil quality degradation due to management practices (Haefele et al., 2014). The limited evaluation of soil quality functions, which has been limited to the scientific stage, currently requires an approach method that must be easily understood at the farmer level. Farmers have practical knowledge from daily experience in the field and farmers can adjust or adapt their agricultural land management (Prayitno et al., 2019). Specific local information about land conditions and land management systems is needed to conduct a comprehensive evaluation of soil quality functions. Knowing the quality of paddy fields will help the application and adaptation of technology in restoring degraded paddy fields. The application and adaptation of technology by utilizing agricultural resources need to be carried out to maintain the continuity of high soil productivity.
Study area

The location of this research was carried out in the village of Sallasae, Bulukumba Regency with 184 km from the provincial capital of South Sulawesi, which is geographically located at the position of 120011'39.252"E-5022'24,108" S. This research was conducted in April-August 2021. Analysis of soil properties (physical, chemical and biological properties of soil) was carried out in the soil fertility laboratory, majoring in soil science, Faculty of Agriculture, Hasanuddin University. The study used a field observation survey approach to collect primary data (regional biophysical data and data on soil properties and characteristics). The soil quality indicators were approached through laboratory analysis.

Soil sampling and laboratory analysis

The sampling point of soil sampling using the purposive sampling method is the point that has been determined in the paddy field area at the level of application of organic fertilizer in different years (0 years-8 years). Soil sampling was repeated three times at each point with a 0-30 cm depth. Analysis of the soil description in the field uses two methods, namely the method of soil boring and soil profiling. Sampling points of soil sampling using purposive sampling method, namely the point has been determined in the paddy field area at the level of organic fertilizer application in different years (0 years - 8 years) with three repetitions at each point. Analysis of the description of the soil in the field uses two methods, namely the boring soil method, which is intended to determine the thickness of the soil solum in the paddy fields. While the soil morphology data through the description of the soil profile. Soil sampling was devoted only to the topsoil and subsoil layers with a thickness of 0-30 cm and analysis to determine soil quality. Soil quality analysis was based on physical, chemical and biological soil parameters. The selected chemical, physical and biological attributes of the soils were measured using the following standard methods: texture by hydrometer method, pH H2O with soil: water suspension (1:2.5), soil organic carbon by Walkley and black method, total N by Kjeldahl method, available P by the Bray method (acidic soil) and Olsen method (alkaline soil), and Ca, Mg, Na, K and cation exchangeable capacity with 1 N ammonium acetate and 10% sodium chloride extraction. The number of soils microbs ware calculated by (Skinner, F.A., Jones, 1952) the total plate count (TPC) method.

Data analysis of paddy soil quality index
The soil quality index was calculated based on the criteria of (Seybold et al., 1998) modified. The steps for calculating the index were carried out as follows: The weighted index was calculated by multiplying the weight of the soil function (weight 1) by rooting medium weight (weight 2) and root depth weight (weight 3). For example, the weight index for porosity is obtained by multiplying 0.40 (weight 1) by 0.33 (weight 2) by 0.60 (weight 3) and the result is 0.080. The score is calculated by comparing the observed data from the soil indicator and the assessment function (Sarbu and Pop, 2005). Scores ranged from 0 for poor condition and 1 for good condition. Scoring can be done through interpolation or linear equations by the set range based on values or the data obtained. According to Masto 2007, the linear scoring function (FSL) is:
Y = The linear score.
x = The value of soil properties.
x2 = The upper limit value.
x1 = The lower limit value.

Soil quality index is calculated by multiplying the weight index and the score of the indicators. Soil quality assessment uses the soil quality index equation (Vestergaard et al., 2017).

SQI = Soil quality index.
S = Scores on selected indicators.
Wi = Weight index.
n = Number of soil quality indicators.

Furthermore, the soil quality index values are categorized into five criteria classes, as shown in Table 1.

Table 1: Paddy soil quality criteria based on its function.

Spatial analysis analysis of rice soil quality distribution
Mapping of paddy soil quality index was made using spatial interpolation technique. The interpolation method chosen is Co-Kriging using the ARGIS 10.3 program.
Preserving biological activities

Weight The highest preservation of biological activity was obtained at the third location, namely 0.24 and the lowest at the first location, 0.20 (Fig 1). Large amounts of biomass each harvest season, ss explained (Williams and Joseph 1976). The main problems in farming in the wet tropics are low nutrient content of the soil, the availability of soil organic matter and the ability of the soil water content.

Fig 1: Preserving biological activities in paddy soil.

Regulating and distributing water

The soil functions as regulation and distribution of water using the percentage of silt and clay (Fig 2). The highest water regulation and distribution function was achieved at location fifth (0.234) and the lowest at  location sixth (0.153). This function was greatly influenced by the condition of the texture of the soil. The finer or higher the clay content of a soil, the higher the ability to store water. Soil water content ware primarily determined by the soil’s capillarity and pore size distribution. At higher potentials, soil water content was determined by soil texture.

Fig 2: Regulating and distributing water in paddy soil.

Filtering and buffering

The function of the soil as a filter and buffer, in this case, takes into account the function of texture (loam+clay) and microbiological processes, including total microbial, total N, and total soil organic carbon (Fig 3). This study indicates that the highest filter and buffer are locations three and one, with a weighted index value of 0.20. While the lowest weight index value ware found at location fifth (0.13) the low index weight at location fifth was due to the low total Nitrogen value and soil organic carbon content. Las and Setiorini (2010) reported that ±73% of agricultural land in Indonesia has low soil C-organic content (less than 2%). The low content of soil organic carbon will affect soil microbial activity because the microbial population in the soil will increase if a carbon source is available for energy. Microbes need a regular supply of active organic matter to survive in the soil.

Fig 3: Filter and buffer functions in paddy soil.

Paddy soil quality index

The results of the soil quality index analysis using the soil function approach showed the distribution of soil quality at the study site (Table 2; Fig 4). The results of the soil function analysis show that the soil quality index values with moderate criteria are at locations 1, 2, 3 and 4. The soil quality index values   with moderate criteria are at location five. In general, the soil quality is still quite good at the research site. However, the thing that must be observed is the decrease in the soil quality index at location five where this location is a location where the application of compost (organic) fertilizer is quite long from all locations. The decrease in soil quality by 0.1% was thought to be due to the high intensity of management, farmer behavior, management, quality/quantity and variations in the amount of compost and the amount/diversity of microbes. Meanwhile, Sanchez (1976) stated that the potential for soil quality degradation in the tropics is quite vulnerable because climatic factors cause high and fast rates of decomposition and leaching of nutrients. (Subowo and Purwani, 2013), Uncontrolled farming system management  triggering the degradarion  of soil quality.

Table 2: Paddy soil quality based on its function.

Fig 4: Distribution of the soil quality index in Salassa Village, Bulukumba Regency.

The results of the soil function analysis show that the soil quality index value with good criteria is at locations 1,2,3 and 4 the soil quality index value with moderate criteria wares found at location five. In general, the soil quality is still quite good at the research site. However, the thing that must be observed is the decrease in the soil quality index by 0.1% at location five where this location is a location where the application of compost (organic) fertilizer is quite long from all locations. Furthermore, we suggest conducting further studies on farmers’ behavior and their role in influencing the quality of paddy fields at the research site.
The author expresses the most profound gratitude to the Directorate General of Resources for Science, Technology, and Higher Education (752/UN4.22/PT.01.03/2021 and 761/UN4.22/PT.01.0/2021), Ministry of Education and Culture, Indonesia, for funding this research through the Doctoral Dissertation Research scholarship program. Thanks to research team and laboratory colleagues.

  1. Akinbile, C.O. and Sangodoyin, A.Y. (2011). Response of upland rice agronomic parameters to variable water supply. International Journal of Agricultural and Biological Engineering. 4(2): 73-80.

  2. Burauel, P. and Baßmann, F. (2005). Soils as filter and buffer for pesticides-experimental concepts to understand soil functions. Environmental Pollution. Elsevier. 133(1): 11-16.

  3. Choudhury, A.T.M.A. and Kennedy, I.R. (2005). Nitrogen fertilizer losses from rice soils and control of environmental  pollution problems. Communications in Soil Science and Plant Analysis. available at: 200059104.

  4. Doran, J.W. and Zeiss, M.R. (2000). Soil health and sustainability: Managing the biotic component of soil quality, Applied Soil Ecology. available at: 1393(00)00067-6.

  5. Hadija, Dalya, N., Jayadi, M. and Nadir, M. (2018). The abundance of soil microorganisms after planting rice with different fertilization systems. Biology Agricuture and Heathcare. 8(22): 61-65.

  6. Haefele, S.M., Nelson, A. and Hijmans, R.J. (2014). Soil quality and constraints in global rice production. Geoderma, Elsevier B.V. 235-236. 

  7. Laishram, J., Saxena, K.G., Maikhuri, R.K. and Rao, K.S. (2012). Soil quality and soil health: A review. International Journal of Ecology and Environmental Sciences. pp. 250-259.

  8. Las, I. dan D. Setyorini. (2010). Lahan, K. Teknologi, Arah, dan Pengembangan Pupuk Majemuk NPK dan Pupuk Organik. Hlm 47. Dalam Prosiding Semnas Peranan Pupuk NPK dan Organik dalam Meningkatkan Produksi dan Swasembada Beras Berkelanjutan. Balai Besar Litbang Sumberdaya Lahan Pertanian, Bogor 24 Februari 2010. 

  9. Li, P., Wu, M., Kang, G., Zhu, B., Li, H., Hu, F. and Jiao, J. (2020). Soil quality response to organic amendments on dryland red soil in subtropical China. Geoderma, Elsevier. 373(1): 114-416.

  10. Lima, A.C.R.D., Hoogmoed, W. and Brussaard, L. (2008). Soil quality qssessment in rice production systems: Establishing a minimum data set. Journal of Environmental Quality. 37(2): 623-630.

  11. Liu, Z., Zhou, W., Shen, J., Li, S. and Ai, C. (2014). Soil quality assessment of yellow clayey paddy soils with different productivity. Biology and Fertility of Soils. 50(3): 537-548.

  12. Martunis, L., Sufardi, S. and Muyassir, M. (2016). Analisis indeks kualitas tanah di lahan kering kabupaten aceh besar provinsi aceh. Jurnal Budidaya Pertanian. 12(1): 34-40.

  13. Prayitno, A., Sartohadi, J. and Nurudin, M. (2019). Utilization of soil function information for assessing soil quality of rice field in the quaternary-tertiary volcanic transitional zones in central Java. Sains tanah. Journal of Soil Science and Agroclimatology. 16(2): 169.

  14. Sarbu, C. and Pop, H. (2005). Principal component analysis versus fuzzy principal component analysis. Talanta-Oxford Then Amsterdam. 65(5): 1215-1220.

  15. Sanchez, P.A. (1976). Sifat Dan Pengelolaan Tanah Tropika. Terjemahan J.T. Jayadinata. 1992. ITB. Bandung.

  16. Seaton, F.M., Barrett, G., Burden, A., Creer, S., Fitos, E., Garbutt, A., Griffiths, R.I., et al. (2020). Soil health cluster analysis based on national monitoring of soil indicators, available at:

  17. Sewnet, Y., Melesse, B., Dagnaw, A. and Tadesse, D. (2020). Determinants of crop land management practices: The case of North Gondar Zone, north west ethiopia.  Agricultural Science Digest. 40(1): 39-43.

  18. Seybold, C.A., Mausbach, M.J., Karlen, D.L. and Rogers, H.H. (1998). Quantification of Soil Quality, United States Department of Agriculture. No. January 1997, pp. 387-404.

  19. Skinner, F.A., Jones, P.C.T. and M.J.E. (1952). A Comparison of a direct- and a plate-counting technique for the quantitative estimation of soil micro-organisms. American Journal of Nursing. 21(3): 182-183.

  20. Subowo, G. and Purwani, J. (2013). Pemberdayaan sumber daya hayati tanah mendukung pengembangan pertanian ramah lingkungan empowerment of soil biology resource to support ecofriendly agricultural development. J. Litbang Pert. 32(4): 173-179.

  21. Turamyenyirijuru, A., Nyagatare, G., Gesimba, R.M. and Birech, R.J. (2019). Assessment of soil fertility in smallholder potato farms in rwanda. Agricultural Science Digest-A Research Journal. 39(4): 280-285.

  22. Vestergaard, G., Schulz, S., Schöler, A. and Schloter, M. (2017). Making big data smart-how to use metagenomics to understand soil quality. Biology and Fertility of Soils. 53(5): 479-484.

  23. Williams, C.N. and Joseph (1976). Climate, Soil and Crop Production. In: The Humid Tropes. Oxford University Press. Kuala Lumpur. 177 hal.

  24. Yimer, A.H. (2022). Influence of organic fertilizers on productivity of barley: A review. Agricultural Science Digest. 42(2): 121-127.

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