Gorgan University Of Agricultural Sciences Journal of Water and Soil Conservation 2322-2069 23 3 2016 10 17 Modeling of soil organic carbon content using topographic indices and soil characteristics in rainfed wheat lands Modeling of soil organic carbon content using topographic indices and soil characteristics in rainfed wheat lands 111 129 3189 10.22069/jwfst.2016.3189 FA Ali Reza Vaezi Associate professor/ University of Zanjan Mehdi Taheri Journal Article 2015 07 12 Background and objectives: Soil organic carbon is one of the crucial factors which affects soil quality, water and atmosphere. Soil organic carbon content is a function of climate, vegetation cover, drainage, crop management, land use and soil properties such as texture, mineralogy and structure (7). Slope steepness and different topographic positions are influencing on soil moisture retention and a suitable condition creation for plant establishment and eventually soil organic carbon accumulation and decomposition (2). Among soil properties affecting on soil organic carbon, the most researches have been done on soil texture constitutes and the different results has been reported (16-18). The aim of this study is developing a regression model to estimate soil organic carbon using topographic indices and soil characteristics.<br />Material and methods: In the present study, 110 soil samples were randomly collected from topsoil (0-30 cm) in three replicates from rainfed wheat farms of Khodabandeh County located in South Zanjan, in 2013. Topographic indices including slope steepness, topographic wetness index, multi-resolution valley bottom flatness and profile, plane, general, maximal and minimal curvatures were obtained using digital elevation model in 90m×90m resolution. Soil properties including sand, silt and clay, saturated paste pH and organic carbon content were measured in laboratory. If the data did not follow normal distribution, Johnson Transformation was performed. Applying partial least square regression, a model was developed for describing soil organic carbon variations (n=80). The cross validation by leave one out method was executed to select the optimal model based on principal components number. Then, the selected model were tested using new set of data (n=30). All the modeling, it validation and test processes were performed in three replicates by grouping the data in modeling-validation and test data sets. <br />Results: Among topographic indices, soil organic carbon demonstrated highest Pearson correlation coefficients with normalized topographic wetness index (r=0.59, P Background and objectives: Soil organic carbon is one of the crucial factors which affects soil quality, water and atmosphere. Soil organic carbon content is a function of climate, vegetation cover, drainage, crop management, land use and soil properties such as texture, mineralogy and structure (7). Slope steepness and different topographic positions are influencing on soil moisture retention and a suitable condition creation for plant establishment and eventually soil organic carbon accumulation and decomposition (2). Among soil properties affecting on soil organic carbon, the most researches have been done on soil texture constitutes and the different results has been reported (16-18). The aim of this study is developing a regression model to estimate soil organic carbon using topographic indices and soil characteristics.<br />Material and methods: In the present study, 110 soil samples were randomly collected from topsoil (0-30 cm) in three replicates from rainfed wheat farms of Khodabandeh County located in South Zanjan, in 2013. Topographic indices including slope steepness, topographic wetness index, multi-resolution valley bottom flatness and profile, plane, general, maximal and minimal curvatures were obtained using digital elevation model in 90m×90m resolution. Soil properties including sand, silt and clay, saturated paste pH and organic carbon content were measured in laboratory. If the data did not follow normal distribution, Johnson Transformation was performed. Applying partial least square regression, a model was developed for describing soil organic carbon variations (n=80). The cross validation by leave one out method was executed to select the optimal model based on principal components number. Then, the selected model were tested using new set of data (n=30). All the modeling, it validation and test processes were performed in three replicates by grouping the data in modeling-validation and test data sets. <br />Results: Among topographic indices, soil organic carbon demonstrated highest Pearson correlation coefficients with normalized topographic wetness index (r=0.59, P https://jwsc.gau.ac.ir/article_3189_6db96972d4a162dedc2cd35b4f608906.pdf