Integrating management information with soil quality dynamics to monitor agricultural productivity

Sustainably utilizing global resources is critical for ensuring soil security which is pertinent for biomass production, climate change mitigation, environmental quality, biodiversity conservation and thus human wellbeing. A plethora of soil quality assessment metrics encapsulated in different concepts exist, with each typically biased towards identifying the interrelationship between agricultural production and specific physical, chemical or biological soil attributes. Because of diversity in soil classifications and crop requirements, considerable variation exist between these metrics making it difficult for end-users to select a suitable method. Here, Partial Least Squares Regression (PLSR) method is used to integrate the physical and chemical soil properties into a Soil Quality Index (SQI) which is then used to evaluate soil quality dynamics vis-à-vis crop yields over two growing seasons. Field data was acquired from 5 sites under No-Till (NT), Conventional Till (CT) management and Natural Vegetation (NV) land use. This SQI was computed under the hypothesis that site specific soil physico-chemical attributes depended on soil type, management, and depth. Under CT management P_w (Pewamo silty clay loam) had the highest soil quality; KbA (Kibbie fine sandy loam) soils had higher quality under NT management; whereas CtA (Crosby Celina silt loams) had relatively higher quality under NV land use. Soil bulk density (ρ_b), Soil Organic Carbon (SOC), Available Water Content (AWC) and Electrical Conductivity (EC) were the significant soil parameters influencing soil quality. The correlation between SQI and corn (Zea mays) yields was 0.6, whereas SQI and Soybean (Glycine max (L.) Merr.) yield was 0.9. Future research will evaluate SQI dynamics vis-à-vis socio-economic indicators and key climate variables.