Soil Use and Management · 2017
Quantifying the interactions of land management practices and agricultural productivity using a soil quality index
de Paul Obade, V.
Export
Quantifying the interactions of land management practices an…
Abstract
It is desirable to develop an objective Soil Quality Index ( SQI ) to guide sustainable agronomic intensification, thereby promoting socio‐economic well‐being. This study pioneers the use of Ward's cluster and principal component regression methods to evaluate soil homogeneity and construct a SQI (expressed as %). Field data were acquired from five different sites within Ohio, USA , that were under no‐till ( NT ), conventional till ( CT ) management and natural vegetation ( NV ) land use. Soil pH , carbon/nitrogen (C/N) ratio, nitrate and soil organic carbon ( SOC ) concentrations were identified as primary drivers of soil quality. Based on Ward's cluster method, the soil properties of croplands were not significantly different from those under NV land use. However, SQI ranked surface soils under CT management as higher in quality than NV and NT managed soils, respectively. The coefficient of determination ( R 2 ) between SQI and corn ( Zea mays L .) and soya bean [ Glycine max (L.) Merr .] yields was 0.7 and 0.9, respectively, implying this SQI effectively relates soil properties, a function of anthropogenic land management practices, with crop yields. In future, time series analyses will be used to assess SQI versus crop yield dynamics, with key socio‐economic and climate variables.
Show all
It is desirable to develop an objective Soil Quality Index ( SQI ) to guide sustainable agronomic intensification, thereby promoting socio‐economic well‐being. This study pioneers the use of Ward's cluster and principal component regression methods to evaluate soil homogeneity and construct a SQI (expressed as %). Field data were acquired from five different sites within Ohio, USA , that were under no‐till ( NT ), conventional till ( CT ) management and natural vegetation ( NV ) land use. Soil pH , carbon/nitrogen (C/N) ratio, nitrate and soil organic carbon ( SOC ) concentrations were identified as primary drivers of soil quality. Based on Ward's cluster method, the soil properties of croplands were not significantly different from those under NV land use. However, SQI ranked surface soils under CT management as higher in quality than NV and NT managed soils, respectively. The coefficient of determination ( R 2 ) between SQI and corn ( Zea mays L .) and soya bean [ Glycine max (L.) Merr .] yields was 0.7 and 0.9, respectively, implying this SQI effectively relates soil properties, a function of anthropogenic land management practices, with crop yields. In future, time series analyses will be used to assess SQI versus crop yield dynamics, with key socio‐economic and climate variables.
It is desirable to develop an objective Soil Quality Index ( SQI ) to guide sustainable agronomic intensification, thereby promoting socio‐economic well‐being. This study pioneers the use of Ward's cluster and principal component regression methods to evaluate soil homogeneity and construct a SQI (expressed as %). Field data were acquired from five different sites within Ohio, USA , that were under no‐till ( NT ), conventional till ( CT ) management and natural vegetation ( NV ) land use. Soil pH , carbon/nitrogen (C/N) ratio, nitrate and soil organic carbon ( SOC ) concentrations were identified as primary drivers of soil quality. Based on Ward's cluster method, the soil properties of croplands were not significantly different from those under NV land use. However, SQI ranked surface soils under CT management as higher in quality than NV and NT managed soils, respectively. The coefficient of determination ( R 2 ) between SQI and corn ( Zea mays L .) and soya bean [ Glycine max (L.) Merr .] yields was 0.7 and 0.9, respectively, implying this SQI effectively relates soil properties, a function of anthropogenic land management practices, with crop yields. In future, time series analyses will be used to assess SQI versus crop yield dynamics, with key socio‐economic and climate variables.