Integrating management information with soil quality dynamics to monitor agricultural productivity.

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