SOC stocks prediction on the basis of spatial and temporal variation in soil properties by using partial least square regression

Abstract

Global warming is a wide-scale problem and soil carbon sequestration is its local scale, natural solution. Role of soil as carbon sink has been researched extensively but the knowledge regarding the role of soil variables in predicting soil carbon uptake and its retention is scarce. The current study predicts SOC stocks in the topsoil of Islamabad-Rawalpindi region keeping the soil properties as explanatory variables and applying the partial least square regression model on two different seasons’ datasets. Samples collected from the twin cities of Islamabad and Rawalpindi were tested for soil color, texture, moisture-content, SOM, bulk density, soil pH, EC, SOC, sulphates, nitrates, phosphates, fluorides, calcium, magnesium, sodium, potassium, and heavy metals (nickel, chromium, cadmium, copper and manganese) by applying standard protocols. Afterwards, PLSR was applied to predict the SOC-stocks. Although, current SOC stocks, ranged from 2.4 to 42.5 Mg/hectare, but the outcomes of PLSR projected that if soil variables remain unaltered, the SOC stocks would be likely to get concentrated around 10 Mg/hectare in the region. The study also identified variable importance for both seasons’ datasets so that noisy variables in the datasets could be ruled out in future researches and precise and accurate estimations could be made.