Prediction of soil organic matter using an inexpensive colour sensor in arid and semiarid areas of Iran
Maryam Raeesi A , Ali Asghar Zolfaghari
A C , Mohammad Reza Yazdani A , Manouchehr Gorji B and Marmar Sabetizade B
+ Author Affiliations
- Author Affiliations
A Faculty of Desert science, Semnan University, Semnan, Iran.
B Soil Science Department, University of Tehran, Iran.
C Corresponding author email address: azolfaghari@semnan.ac.ir
Soil Research 57(3) 276-286 https://doi.org/10.1071/SR18323
Submitted: 25 October 2018 Accepted: 8 February 2019 Published: 29 March 2019
Abstract
Soil organic matter (SOM) plays a major role in agricultural and ecological processes. For this reason, accurate quantification of SOM is important for precision agriculture and environmental management. Inexpensive sensor technology could be a potential approach to achieving the accurate prediction of SOM. The objective of this study was to evaluate inexpensive colour sensor (Nix™ Pro) data for prediction of SOM in arid and semiarid areas of Iran. A total number of 85 and 152 soil samples from the soil surface (0–20 cm) were collected from the Semnan (arid area) and Qazvin (semiarid area) regions respectively. The nonlinear random forest (RF) method and linear regression were conducted to predict SOM using NixTM pro colour sensor data. The partial least-squares approach was also utilised to reduce the dimensions of the dataset, decrease the number of input variables and avoid multi-collinearity. Soil colour was measured in moist and dry conditions. Root mean square error (RMSE), correlation coefficient (r), r-square (R2), mean square prediction error (MSPE) and ratio of performance to interquartile distance (RPIQ) were used to assess the RF and the linear regression models for prediction of SOM. Moist sample data was used for estimation of the SOM because of the larger correlation between SOM and colour sensor data in moist than dry samples. In estimation of SOM, the RF model represented lower dispersion between the estimated and the actual values of SOM (RMSE = 0.42, 0.43, RPIQ = 2.2, 2.06 and MSPE = 0.19, 0.19 in semiarid and arid regions respectively). In contrast, more dispersion was obtained by applying the linear regression model (RMSE = 0.61 and 0.51, RPIQ = 1.47 and 1.76, and MSPE = 0.39 and 0.26 in semiarid and arid regions respectively). The RPIQ values for linear regression in arid and semiarid areas were 1.76 and 1.47 respectively. Hence, the use of a linear regression model for prediction of SOM in arid areas would result in acceptable reliability; however, its utilisation should be avoided in semiarid areas due to less reliable results.
Additional keywords: Inexpensive colour sensor, nonlinear models, soil organic matter.
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