Development of a soil fertility index using on-line Vis-NIR spectroscopy

Soil fertility index (SFI) is commonly used for soil fertility assessment, which is critical for managing in-field variabilities and maximizing crop production with minimum environmental impacts. However, the majority of earlier SFIs were laboratory-based soil analyses. This study developed a novel SFI using on-line collected visible and near-infrared (vis-NIR) spectra. Six agricultural fields were scanned using an on-line vis-NIR sensor (CompactSpec, Tec5 Technology, Germany), when 139 soil samples were collected and analyzed for soil pH, organic carbon, available- phosphorous (P), potassium, magnesium (Mg), calcium, sodium, moisture content (MC) and cation exchange capacity. A minimum dataset was developed comprising the fertility attributes that showed pairwise correlation (r) smaller than 0.75. This was followed by a principal component analysis to calculate the weight factor of each parameter to be used in the SFI formulation using a double-weighted additive function. The data matrix consisting of the SFI and soil spectra was divided into calibration (70 %) and prediction (30 datasets. The former set was subjected to a partial least squares regression to calibrate SFI model, whose accuracy was validated using the prediction set. Results showed that the derived SFI was moderately to highly correlated with P (r = 0.57), pH (r = 0.75), and Mg (r = 0.74) and weakly correlated with MC (r = 0.26). The online vis-NIR sensor predicted SFI with very good accuracy [coefficient of determination (R-2) = 0.75 and ratio of prediction to deviation (RPD) = 2.01]. Therefore, it is concluded that the vis-NIR can accurately predict SFI directly from on-line scanned soil spectra, which can effectively assess soil fertility and manage in-field variability.

Automated summary

A novel Soil Fertility Index (SFI) was developed using online collected visible and near-infrared (vis-NIR) spectra, which accurately predicted soil fertility attributes and helped manage in-field variabilities.