Start Date: Aug 15, 2012
End Date: Aug 15, 2016
1. Data mining and analysis. Both manual and computerized data mining of the existing literature will be performed to identify appropriate minimum data set components and trends for agricultural systems, by region and management. Where data is limited or variance unobtainable, un-weighted meta-analysis will be performed and the sensitivity of the analysis will be assessed with Monte Carlo simulations. Finally, meta-analysis will provide overall trends in DSPs, by soil, climate, management system, or other factors, as deemed appropriate. 2. Development of PTFs or model subroutines. Using statistical models and in collaboration with modelers, new PTFs and/or model subroutines will be developed to estimate DSPs based on other soil properties in combination with management and climate (or other unforeseen factors.) 3. Recommendation of sampling intensity. Using information on variance obtained from both data mining and model sensitivity analysis, recommendations for sampling design and intensity will be made. The anticipated method will be landscape transects with varying sampling frequency, depending on property variability. Analyzed samples will be used to populate the DSP database, validate models, and provide references for agroecological sites.