Multivariate modeling of some metals concentrations in agrarian soils: distribution and soil fertility implications in the tropics

Predicting metals concentration in agricultural soils is a sine qua non in establishing environmental policies and evaluating the soils’ agricultural potentials in an area. The relevance of metals to ecological health, agriculture and pollution has sprung a lot of related studies. This study was setup to determine the concentration and profile distribution of aqua regia (AR) extractable Fe, Al, Mn, Mg and K in agricultural soils, and to predict AR extracted elements via Al2O3 (Alx), K2O (Kx), physical and chemical properties for soil fertility interpretations. One soil pit was randomly sited in each slope transition obtained via digital elevation models (DEM), resulting in 27 composite soil samples. Soil samples meant for AR and X-ray florescence were analyzed in triplicate. The soils were dominated by AR extractable Fe with mean concentrations showing the trend; Fea > Ala > Mga > Mna ≈ Ka and ranges of 639.09–125,719.46, 1252.63–14,895.13, 67.61–2408.36, 4.51–2162.91 and 161.84–1356.23 mg/kg, respectively. The distribution of AR metals in the entire soils was quite similar, however, higher values of soluble Fe occurred in the 0–37 cm depth of IH1P1. Multiple linear regression functions were within acceptable and best prediction criteria (R2 = 0.55–0.77). The best performing models were Ka and Mna, with lower errors. The models selected Kx, Mg and CEC which contributed 89.9, 79.9 and 73.4%, respectively to the 44.2% contribution of PC1 to data variation. The dominance of Kx and Alx with ranges of 2381.0–50,401.0 and 57,766.67–119,433.35 mg/kg, respectively, over Ka and Ala is due to limitations associated with AR extraction of elements in silicate minerals, hence the necessity for extracting soil mineral elements by more than one method

Estimating soil organic matter: a case study of soil physical properties for environment-related issues in southeast Nigeria

The different deposition periods in sedimentary geological environment have made the build-up and estimation of soil organic matter ambiguous to study. Soil organic matter has received global attention in the ambience of international policy regarding environmental health and safety. This research was to understand the inter-relationship between soil organic matter and bulk density, saturated hydraulic conductivity (Ksat), total, air-filled and capillary porosities for organic matter estimation, via different multiple linear regression functions (i.e., leapbackward, leap forward, leapseq and lmStepAIC), in soils developed over the sedimentary geological environment. Eight mapping units were obtained in Ishibori, Agoi Ibami and Mfamosing via digital elevation model. Two pits were sited within each mapping unit, and 53 soil samples were used for the study. In soils over shale–limestone–sandstone, two pits were sited, six in alluvium, four in sandstone–limestone and four in limestone. Overall correlation between SOM with Ksat (r = 0.626) and BD (r = − 0.588) was significant (p < 0.001). The strongest correlation was obtained for SOM with BD (r = − 0.783) and Ksat (r = 0.790) in soils over limestone. In contrast, soils over shale–limestone and sandstone geological environment gave the weakest relationship (r < 0.6). Linear regression gave a similar prediction output. The best performing was leapbackward (RMSE = 11.50%, R2 = 0.58, MAE = 8.48%), which produced a smaller error when compared with leap forward, leapseq and lmStepAIC functions in organic matter estimation. Therefore, we recommend applying leapback linear regression when estimating soil organic variation with physical soil properties for solving soil–environmental issues towards sustainable crop production in southeast Nigeria