Zoning of Nutrient’s Elements In Soil and Leaves of Orange Trees Using Gaussian Model (Dezful city as Case Study)
Subject Areas : Optimal management of water and soil resourcesEbtesam Neissian 1 , Kamran Mohsenifar 2 , Ebrahim Panahpour 3 , Teimor Babainejad 4
1 - Ph. D, student of Department of Soil Science, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran.
2 - Department of Soil Science, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran.
3 - Department of Soil Science, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran.
4 - Department of Soil Science, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran.
Keywords: nutrients, Geo-statistical, Semi-variogram, spatial variation,
Abstract :
Background and Aim: Creating land fertility maps are especially important in terms of determining the areas that need particular nutrients, optimizing the use of agricultural fertilizers, and facilitating the optimal management of soil and plant nutrition. Spatial changes in soil and plant nutrients are common, but knowing these changes is essential for accurate planning and management, particularly regarding agricultural lands. This research aims at zoning the spatial distribution pattern of nutrients, aka nitrogen, phosphorus, potassium, sulfur, calcium and magnesium, in the soil and leaves of Dezful orange orchards trees using the Gaussian model and geographic information system (GIS).Method: A total number of 130 sampling points are set on the map in the vicinity of orange orchards of Dezful City with an area of 3200 hectares. Factors such as soil, cultivation and irrigation system, slope, elevation, and the manner of orange trees growth are considered to determine sampling locations. Following sampling the soil (0-60 cm depth) and plants, the samples are transferred to the laboratory and the concentration of the most consumed nutrients is measured. After preliminary statistical analyzes on the data, the correlation level of the variables that are measured in the soil and leaves of orange trees, are calculated with the Pearson correlation test. The location of sampling points is simulated via Gaussian model by using the R software. The interpolation is computed using simple kriging and kernel methods. The model sensitivity analysis for the changes applied in the base values for implementing the algorithm, is done based on the replacement of the desired values from the posterior functions as well.Results: Analysis of dispersion indices show that the highest coefficient of variation is related to phosphorus element in soil and nitrogen element in leaf samples. The results illustrate that the mean square error values for elements of nitrogen, phosphorus, potassium and sulfur are calculated respectively as 0.171, 0.152, 0.132 and 0.153 in simple kriging in soil, and as 0.212, 0.152, 0.229, and 0.166 in kernel method in soil; and respectively as 0.121, 0.188, 0.116 and 0.131 in simple kriging in samples of orange tree leaves, and as 0.184, 0.206, 0.172 and 0.229 in kernel method in the leaves samples as well. The results of the spatial distribution pattern of each of the measured elements in the soil and leaves of orange trees demonstrate that the lowest amount of nitrogen is in the south of the region (0.42 to 1.33 mg/kg) and its distribution pattern is similar to the distribution in the leaves of orange trees (0.9 1 to 1.29 mg/kg). Magnesium has the lowest in the north and part of the south (3.11 to 4.57 mg/kg), and sulfur in most soil of the region is between 21.31 and 26.25 mg/kg.Conclusion: In examining the effectiveness of the Gaussian statistical model in the distribution of nutrients in the soil and leaves of orange trees in the gardens of Dezful city, the results display that the calculated Pearson linear correlation coefficient has the highest correlation between calcium and potassium, as well as magnesium and calcium in the soil, but there is no linear correlation between any of the nutrients in the leaves of orange trees. In estimating the best interpolation method, calcium element in soil has the least error in both kernel and simple kriging methods, whereas in plant leaves, magnesium in kernel method and potassium in simple kriging method have less error. The highest error for soil and plant is related to potassium and calcium respectively, in the Cornell method.
Aiobi, S., Mohamad Zamani, S., and Khormali, F. 2007. Estimation of total soil nitrogen using the amount of organic matter by kriging, cokriging and kriging methods - regression in a part of reddish arable lands of Golestan province. Journal of Agricultural Sciences and Natural Resources, 14. (4). (in Persian with English abstract)
Asadi Kangarshahi, A., Basirat, M., Akhlaghi, N., Haghighatnia, H., Sheikh Ashouri, A., and Sabbah, A. 2016. Instructions for optimal use of fertilizer in fruit trees (Vol. 1). Ministry of Jihad Agriculture. (in Persian)
Bai ,M., Haghizade, A., and Tahmasebipour, N. 2018. Spatial Variations of quality Groundwater use Geostatistical Method. Geographical Space, 18(63), 147-164. http://geographical-space.iau-ahar.ac.ir/article-1-2308-en.html
Baz, I., Geymen, A., and Er, S. N. 2009. Development and application of GIS-based analysis/synthesis modeling techniques for urban planning of Istanbul Metropolitan Area. Advances in Engineering Software, 40(2), 128-140. https://doi.org/https://doi.org/10.1016/j.advengsoft.2008.03.016
Calabi-Floody, M., Medina, J., Rumpel, C., Condron, L. M., Hernandez, M., Dumont, M., and Mora, M. d. l. L. 2018. Chapter Three - Smart Fertilizers as a Strategy for Sustainable Agriculture. In D. L. Sparks (Ed.), Advances in Agronomy (Vol. 147, pp. 119-157) .Academic Press. https://doi.org/https://doi.org/10.1016/bs.agron.2017.10.003
Fabijańczyk, P., and Zawadzki, J. 2019. Using Geostatistical Gaussian Simulation for Designing and Interpreting Soil Surface Magnetic Susceptibility Measurements. International Journal of Environmental Research and Public Health, 16(18), 3497. https://www.mdpi.com/1660-4601/16/18/3497
Farajnia, A. 2015. Investigation of spatial distribution of soil fertility elements in the Miyaneh wheat farms. Agroecology Journal, 11(1), 3.5-45. https://doi.org/10.22034/aej.2015.514346
Goovaerts, P. 1997. Geostatistics for Natural Resource Evaluation. In (Vol. 42) .
Ismail, M. H., and Junusi, R. 2009. Determining and Mapping Soil Nutrient Content Using Geostatistical Technique in a Durian Orchard in Malaysia. Journal of Agricultural Science, 1. https://doi.org/10.5539/jas.v1n1p86
John, K., Afu, S. M., Isong, I. A., Aki, E. E., Kebonye, N. M., Ayito, E. O., Chapman, P. A., Eyong, M. O., and Penížek, V. 2021. Mapping soil properties with soil-environmental covariates using geostatistics and multivariate statistics. International Journal of Environmental Science and Technology, 18(11), 3327-3342. https://doi.org/10.1007/s13762-020-03089-x
Kazemi Poshtmasari, H., Tahmasebi Sarvestani, Z ,.Kamkar, B., Shataei, S., and Sadeghi, S. 2012. Evaluation of Geostatistical Methods for Estimating and Zoning of Macronutrients in Agricultural Lands of Golestan Province. Water and Soil Science, 22(1), 201. https://www.magiran.com/paper/1034000
Khorassani, R. 2016. Determination of norms and limitation of Nutrients for Orange by the Compositional Nutrient Diagnosis (CND) method. Soil Management and Sustainable Production, 6(3), 161-172. in persian. https://www.magiran.com/paper/1648206
Li, J., Wan, H ,.and Shang, S. 2020. Comparison of interpolation methods for mapping layered soil particle-size fractions and texture in an arid oasis. CATENA, 190, 104514. https://doi.org/https://doi.org/10.1016/j.catena.2020.104514
Lloyd, C. D., and Atkinson, P. M. 2004. Increased accuracy of geostatistical prediction of nitrogen dioxide in the United Kingdom with secondary data. International Journal of Applied Earth Observation and Geoinformation, 5(4), 293-305. https://doi.org/https://doi.org/10.1016/j.jag.2004.07.004
Rezaeifard, M., Shariatmadari, H., and Toomanian, N. 2019. Investigation of Interpolation Methods for Determination of Organic Carbon and Nitrogen Spatial Distribution in Lanjanat Region in Isfahan Province. Iranian Journal of Soil Research, 33(3), 349 - .163 https://doi.org/10.22092/ijsr.2019.125196.431
Sadeghikhoo, S. R., and Aliabbaspour, R. 2018. Evaluation of Interpolation Models in Zoning of Heavy Metals in Soil (Case Study: Herris Area). Journal of Environmental Studies, 44(1), 17-32. in persian. https://doi.org/10.22059/jes.2018.242785.1007513
Salma, D., Munaswamy, V., Giridhara Krishna, T., Sumathi, V., and Reddy, B. 2019. GPS and GIS based Soil Fertility Maps and Identification of Soil Related Constraints for Chickpea Growing Soils of Owk Mandal ,Kurnool District (A.P), India. International Journal of Current Microbiology and Applied Sciences, 8, 1241-1247. https://doi.org/10.20546/ijcmas.2019.806.151
Shahinzadeh, N., Babaeinejad, T., Mohsenifar, K., and Ghanavati, N. 2022. Spatial variability of soil properties determined by the interpolation methods in the agricultural lands. Modeling Earth Systems and Environment. https://doi.org/10.1007/s40808-022-01402-w
Tadayon, V. 2017. Bayesian Analysis of Censored Spatial Data Based on a Non-Gaussian Model. Journal of Statistical Research of Iran, 13, 155-180. https://doi.org/10.18869/acadpub.jsri.13.2.155
Tadayon, V., and Khaledi, M. J. 2015. Bayesian Analysis of Skew Gaussian Spatial Models Based on Censored Data. Communications in Statistics - Simulation and Computation, 44(9), 2431-2441. https://doi.org/10.1080/03610918.2013.839036
Zawadzki, J., and Fabijanczyk, P. 2018. The geostatistical reassessment of soil contamination with lead in metropolitan Warsaw and its vicinity. International Journal of Environment and Pollution, 6. (1).
Zawadzki, J., and Fabijańczyk, P. 2008. The geostatistical reassessment of soil contamination with lead in metropolitan Warsaw and its vicinity. International Journal of Environment and Pollution - INT J ENVIRON POLLUTION, 35. https://doi.org/10.1504/IJEP.2008.021127
Zhao, Y., Han, H., Cao, L., and Chen, G. 2011. Study on Soil Nutrients Spatial Variability in Yushu City