An approach to predict soil salinity changes in irrigated pistachio orchards (Ardakan, Yazd Province ): A case study

Document Type : Case Study

Authors

1 Grad. Student, College of Agriculture, Vali-e-Asr University of Rafsanjan. Rafsanjan, 771889711, Iran

2 Associate Professor, College of Agriculture, Vali-e-Asr University of Rafsanjan. Rafsanjan, 771889711, Iran

3 Assistant Professor, National Salinity Research Center (NSRC, AREEO), Yazd, 8917357676, Iran

Abstract

The sustainability production of dryland agriculture is threatened by salt accumulation in soil due to irrigation practices by saline waters. However, the dynamic processes of secondary soil salinization depend on some factors varying in time and space. The aim of this research was to introduce an approach for the prediction of soil salinity in some irrigated pistachio (Pistacia vera L.) orchards facing secondary soil salinization. The study area was Ardakan (Yazd Province, Central Iran). In this approach, the Landsat 8 satellite data bands and satellite–based driven data (indices) were used. The Partial Least Square Regression (PLSR) method was used to predict the variability of soil salinity with minimum (zero) ground measurements. The predicted soil salinity (electrical conductivity) of soil saturated paste extract (ECe) were compared by the measured ECe. The existing conventional methods (e.g. WatSuit computation model) using ancillary measured data of irrigation water salinity (ECiw) and corresponding leaching fractions (LF) were also used for evaluation. The Results of the satellite-based PLSR method showed an R2 of about 64% between predicted and measured soil salinity, while this indicator was about 72% for the conventional model of WatSuit. The higher accuracy of the Watsuit model is owing to its dependence on ground measurements, while the introduced satellite-based PLSR approach was able to predict temporal changes of soil salinity in patterns fitted to the irrigation intervals with zero dependence on the ground truth data.

Keywords

References

Alexandre, C., Borralho, T., & Durão, A. (2018). Evaluation of salinization and sodification in irrigated areas with limited soil data, Case study in southern Portugal. Spanish Journal of Soil Science, 8, 102-120.
AL-Khakani, E. T., Al-Janabi, W. F., & Sa'ad, R. Y., (2018). Using Landsat 8 OLI data to predict and mapping soil salinity for part of An-Najaf governorate. Ecological Environmental Conservation, 24, 572-578.
Ayers, R., & Westcot, D. (1985). Water quality for agriculture. FAO Irrigation and drainage paper 1, vol 1. Food and Agricultural Organization. Rome.
Baghzouz, M., Devitt, D., & Morris, R. (2006). Evaluating temporal variability in the spectral reflectance response of annual ryegrass to changes in nitrogen applications and leaching fractions. International Journal of Remote Sensing, 27(19), 4137-4157.
Cheraghi, S., Hasheminejhad, Y., & Heydari, N. (2008). Causes and management of salt-prone land degradation in lower Karkheh River Basin. Improving On-farm Agricultural Water Productivity in the Karkheh River Basin (CPWF Project no. 8). Research Report no. 1: A Compendium of Review Papers, Edited by: T. Oweis, H. Farhani, M. Qadir, J. Anthofer, H. Siadat, F. Abbasi and A. Bruggeman. ICARDA-AREO), 81-91.
Cheraghi, S., Hasheminejhad, Y., & Rahimian, M. (2007). An overview of the salinity problem in Iran: assessment and monitoring technology. Paper presented at the Advances in the assessment and monitoring of salinization and status of biosaline agriculture. Reports of expert consultation, Dubai, UAE.
Corwin, D. L., & Grattan, S. R. (2018). Are existing irrigation salinity leaching requirement guidelines overly conservative or obsolete?. Journal of Irrigation and Drainage Engineering, 144(8), 02518001.
Corwin, D. L., Rhoades, J. D., & Šimůnek, J. (2007). Leaching requirement for soil salinity control: Steady-state versus transient models. Agricultural Water Management, 90(3), 165-180.
de Soto, I. S., Virto, I., Barré, P., Fernández-Ugalde, O., Antón, R., Martínez, I., Chaduteau, C., Enrique, A., & Bescansa, P. (2017). A model for field-based evidences of the impact of irrigation on carbonates in the tilled layer of semi-arid Mediterranean soils. Geoderma, 297, 48-60.
Ghassemi, F., Jakeman, A. J., & Nix, H. A. (1995). Salinisation of land and water resources: human causes, extent, management and case studies. CAB international.
Gorji, T., Tanik, A., & Sertel, E., (2015). Soil salinity prediction, monitoring and mapping using modern technologies. Procedia Earth and Planetary Science, 15, 507-512.
Graham, M. H. (2003). Confronting multicollinearity in ecological multiple regression. Ecology, 84(11), 2809-2815.
Guo, L., Zhao, C., Zhang, H., Chen, Y., Linderman, M., Zhang, Q., & Liu, Y. (2017). Comparisons of spatial and non-spatial models for predicting soil carbon content based on visible and near-infrared spectral technology. Geoderma, 285, 280-292.
Hasheminejhad, Y. (2011). Irrigation management under saline conditions using wetting front detector (WFD). Iranian Journal of Soil Research (Formerly Soil and Water Sciences), 24(3), 265-272.
Hasheminejhad, Y., Cheraghi, A.M., Abbasi, A., Gholami, H., & Golshan, M. (2008). Investigation of the fate of salt below the root zone of irrigated lands in the north of Yazd-Ardakan plain. https://civilica.com/doc/1096162.
Hasheminejhad, Y., Gholami, M., & Soltani, V. (2012). Valuation of WatSuit model for prediction of alfalfa's root zone salt distribution pattern. Iranian Journal of Water Research in Agriculture, 26, 225-234.
Hassani, A., Azapagic, A., & Shokri, N., (2020). Predicting long-term dynamics of soil salinity and sodicity on a global scale. Proceedings of the National Academy of Sciences, 117(52), 33017-33027.
Ivushkin, K., Bartholomeus, H., Bregt, A. K., Pulatov, A., Kempen, B., & De Sousa, L. (2019). Global mapping of soil salinity change. Remote Sensing of Environment, 231, 111260.
Jin, Y., He, R., Marino, G., Whiting, M., & Kent, E., (2018). Spatially variable evapotranspiration over salt affected pistachio orchards analyzed with satellite remote sensing estimates. Agricultural and Forest Meteorology, 262, 178-191.
Mandal, A. (2019). Modern technologies for diagnosis and prognosis of salt-affected soils and poor-quality waters. In J. C., Dagar, R. K., Yadav, & P. C., Sharma (Eds.), Research Developments in Saline Agriculture (pp. 95-152). Springer Nature, Singapore.
Manzoor, M.Z., Sarwar, G., & Aftab, M. (2019). Role of leaching fraction to mitigate adverse effects of saline water on soil properties. Journal of Agricultural Research, 57(4), 275-280.
Metternicht, G., & Zinck, J. (2003). Remote sensing of soil salinity: potentials and constraints. Remote Sensing of Environment, 85(1), 1-20.
Minasny, B., & McBratney, A. B. (2006). A conditioned Latin hypercube method for sampling in the presence of ancillary information. Computers and Geosciences, 32(9), 1378-1388.
Minhas, P. S., Ramos, T. B., Gal, A. B., & Pereira, L. S. (2020). Coping with salinity in irrigated agriculture: Crop evapotranspiration and water management issues, Agricultural Water Management, 227, 105832.
Moharana, P., Singh, R., Singh, S., Tailor, B., Jena, R., & Meena, M. (2019). Development of secondary salinity and salt migration in the irrigated landscape of hot arid India. Environmental Earth Sciences, 78 (15), 454.
Mokhtari, M. H., Adnan, R., & Busu, I. (2013). A new approach for developing comprehensive agricultural drought index using satellite-derived biophysical parameters and factor analysis method. Natural Hazards, 65(3), 1249-1274.
Noroozi, A. A., Homaee, M., & Abbasi, F. (2012). Integrated application of remote sensing and spatial statistical models to the identification of soil salinity: a case study from Garmsar plain, iran. Environmental Sciences, 9, 59-74.
Oster, J. D., & Rhoades, J. D. (1990). Steady State Root Zone Salt Balance. In K. K. Tanji, (ed.) Agricultural Salinity Assessment and Management Manual. ASCE Manuals and Reports on Engineering Practice No. 71 (pp. 469-481). American Society of Civil Engineers.
Qadir, M., Quillérou, E., Nangia, V., Murtaza, G., Singh, M., Thomas, R.J., Drechsel, P., & Noble, A. D.
(2014). Economics of salt‐induced land degradation and restoration. Natural Resources Forum (4), 282-295.
Qadir, M., Qureshi, A.S., & Cheraghi, S. (2008). Extent and characterization of salt‐affected soils in Iran and strategies for their amelioration and management. Land Degradation and Development, 19(2), 214-227.
Rahimian, M.H., Shayannejad, M., Eslamian, S., & Gheysari, M. (2019). Daily and seasonal pistachio evapotranspiration in saline condition: comprison of sattelite-based and ground-based results. Journal of the Indian Society of Remote Sensing, 47(5), 777-787.
Rhoades, J., Kandiah, A., & Mashali, A. (1992). The use of saline waters for crop production. FAO Irrigation and Drainage Paper, 133(48).
Sahbeni, G., (2021). Soil salinity mapping using Landsat 8 OLI data and regression modeling in the Great Hungarian Plain. SN Applied Sciences, 3(5), 1-13.
Stirzaker, R. (2005). Managing irrigation with a wetting front detector. UK IRRIGATION, 33:22.
Stirzaker, R. J. (2003). When to turn the water off: Scheduling micro-irrigation with a wetting front
detector. Irrigation Science, 22, 177-185.
Vermote, E., Justic, C., Claverie, M., & Belen, F. (2016). Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment, 185, 46-56.
Visconti, F., Paz, J. M. D., Rubio, J., & Sánchez, J. (2012). Comparison of four steady-state models of increasing complexity for assessing the leaching requirement in agricultural salt-threatened soils. Spanish Journal of Agricultural Research, 10(1), 222-237.
Welle, P. D., & Mauter, M. S. (2017). High-resolution model for estimating the economic and policy implications of agricultural soil salinization in California. Environmental Research Letters, 12(9), 094010.
Yang, T., Šimůnek, J., Mo, M., Mccullough-Sanden, B., Shahrokhnia, H., Cherchian, S., & Wu, L. (2019). Assessing salinity leaching efficiency in three soils by the HYDRUS-1D and -2D simulations. Soil and Tillage Research, 194, 104342.
Dry Land Soil Research (DLSR)
Volume 1, Issue 1
December 2022
Pages 1-10

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