Spatial Variability of Soil Organic Matter Content in Paddy Loamy and Sandy Soils in Amphoe Phra Yuen, Khon Kaen Province Thailand Using Geostatistics
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Abstract
Information on the spatial variability of soil organic matter is very useful for precision agriculture. The objective of this study was to investigate the status of spatial variability and spatial distribution patterns of soil organic matter (SOM) in paddy soils with loamy and sandy textures found in northeast Thailand using geostatistics. The researchers selected 5 study sites in Phra Yuen District, Khon Kaen Province. These were the areas of Dong Yang En (loamy soil) and Ban Phai (sandy soil) soil series.The soil was collected at a depth of 0-15 cm at each site using a non-aligned stratified sampling method in a 5x5 m grid in an area of 50x50 m2. A total of 500 soil samples were analyzed for organic matter content in the laboratory. Basic statistics and geostatistics were employed to study the variability, spatial variability, and spatial distribution patterns of soil organic matter. The results showed differences in spatial variation and spatial distribution patterns of SOM among the study sites, even at sites of the same soil series. The variations in SOM based on CV% were moderate (CV >18.95-33.42%) and high (CV >35.72%) for the Dong Yang En and Ban Phai soil series, respectively. When geostatistics were applied, the best-fitting semivariogram models for all study sites were the Spherical and/or Gaussian models, and judging by R2 values (> 0.600), the models were good enough for the next step - spatial interpolation. The spatial distribution pattern calculated from the Kriging interpolation was reliable (RMSPE% < 40) as the patch of soil organic matter is upon on the effective range between a sample point. Thus, long continuous areas of cultivation had a higher variation. The result also indicated that differences in the spatial variability and spatial distribution patterns of soil organic matter varied according to the landscape, length of time farmed, and agricultural activities at each site.
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King Mongkut's Agricultural Journal
References
กองสำรวจและจำแนกดิน. 2543. วิธีคาดคะเนระดับความอุมสมบูรณ์ของดินโดยการประเมินผลวิเคราะห์ดิน. กรุงเทพฯ : กรมพัฒนาที่ดิน.
สำนักงานเศรษฐกิจการเกษตร. 2564. สถิติการเกษตรของประเทศไทย ปี 2564. กรุงเทพฯ : สำนักงานเศรษฐกิจการเกษตร.
Behera, S. K., Mathur, R. K., Shukla, A. K., Suresh, K., and Prakash, C. 2018. Spatial variability of soil properties and delineation of soil management zones of oil palm plantations grown in a hot and humid tropical region of southern India. Catena 165: 251-259.
Bogunovic, I., Pereira, P., and Brevik, E. 2017. Spatial distribution of soil chemical properties in an organic farm in Croatia. Science of the Total Environment 584-585: 535-545.
Bot, A., and Benites, J. 2005. The importance of soil organic matter – Key to drought-resistant food and production. Rome: Food and Agriculture Organization (FAO) of the United Nations.
Campbell, J.B. 1996. Introduction to Remote Sensing. New York: The Guilford Press.
Gajda, M. A., Czyž, A. E., and Dexter, R. A. 2016. Effects of longterm use of different farming systems on some physical, chemical and microbiological parameters of soil quality. International Agrophysics 30: 165-172.
Gao, X., Xiao, Y., Deng, L., Qi-quan, L, Chang-quan, W., Bing, L., Ou-ping, D., and Min, Z. 2019. Spatial variability of soil total nitrogen, phosphorus and potassium in Renshou County of Sichuan Basin, China. Journal of Integrative Agriculture 18: 279-289.
Gorai T., Ahmed, N., Patra, A., Sahoo, R., Sarangi, A., Meena, M., and Sharma, R. 2015. Site Specific Nutrient Management of an Intensively Cultivated Farm Using Geostatistical Approach. Proceedings of the National Academy of Sciences, India Section B: Biological Sciences 87(2): 477-488.
IT Department. 2001. ILWIS 3.0 Academic User’s Guide. Enschede: International Institute for Aerospace Survey and Earth Sciences.
Khaledian, Y., Pereira, P., Brevik, E., Pundyte, N., and Paliulis, D. 2016. The influence of organic carbon and pH on heavy metals, potassium and magnesium in Lithuanian podzols. Land Degradation Development 28(1): 345-354.
Oliver, M., and Webster, R. 2014. A tutorial guide to geostatistics: Computing and modelling variograms and kriging. Catena 113: 56-69.
Phontusang, P. 2016. Improving Salt-affected Soils Mapping in Northeast Thailand. Doctoral Dissertations. Khon Kaen: Khon Kaen University.
Santra, P., Chopra, U. K., and Chakraborty, D. 2008. Spatial variability of soil properties and its application in predicting surface map of hydraulic parameters in an agricultural farm. Current Science 95(7): 937-945.
Sumner, M. E. 2000. Handbook of Soil Science. Washington D.C.: CRC Press.
Tripler, C., Kaushal, S., Likens, G., and Walter, M. 2006. Patterns in potassium dynamics in forest ecosystems. Ecology Letters 9: 451-466.
Walkley, A., and Black, I. A. 1934. Estimation of soil organic carbon by the chromic acid titration method. Soil Science 37: 29-37.
Whitmore, A. P., Kirk, G. J. D., and Rawlins, B. G. 2015. Technologies for increasing carbon storage in soil to mitigate climate change. Soil Use Management 31: 62-71.
Xue-song, G., Yi, X., Liang-ji, D., Qi-quan, L., Chang-quan, W. Bing, L., Ou-ping, D., and Min, Z. 2019. Spatial variability of soil total nitrogen, phosphorus and potassium in Renshou County of Sichuan Basin, China. Journal of Integrative Agriculture 18(2): 279-289.
Yao, R. J., Yang, J. S., Gao, P., Shao, H. B., Liu, G. M., and Yu, S. P. 2014. Comparison of statistical prediction methods for characterizing the spatial variability of apparent electrical conductivity in coastal salt-affected farmland. Environmental earth sciences 71(1): 233-243.