Optimizing farming systems will probably require some planned experimentation. It is unlikely that simply observing function systems will provide data for crop and livestock responses over all the relevant inputs and range of rates. This webinar will focus on research by the ISPA On-farm Experimentation Community to make on-farm testing more relevant to farmers and agribusiness.

 

Join us on October 15th, 2020 at 13:30 Central Daylight Time (UTC -5).

 

Register now!

• Welcome and Housekeeping - Nicolas Tremblay
• Learnings from Conducting Co-Ordinated Farm-Centric Field Scale Experiments - Daniel Kindred
  • This presentation will cover our experience of running on-farm experiments with farmers on a commercial basis over the past 5 years, utilising yield mapping and geo-statistics in an approach we call 'Agronomics'.
• On-Farm Testing of Sprinkler Innovations for Pivot - Matt Yost
  • Precision irrigation focuses heavily on varying irrigation rates and to a lesser extent timing. What might be missing? This presentation will cover challenges and lessons learned from on-farm irrigation application method testing.
• High Resolution As-Applied Mapping for Subsurface VRT Fertilizer Application - John Fulton
  • As-applied data archived by variable-rate technology should reflect the applied amount of product and application quality across production fields.  However, many times these as-applied maps are represent the prescription map or just an average of applied product across an applicator.  This presentation overviews a sensing technology developed through our research that measures the mass flow of fertilizer on pneumatic application equipment allowing for collecting detailed application data and associated high-resolution as-applied fertilizer map.
• Optimization of Geospatial Data Modeling for Crop Production by Integrating Proximal Soil Sensing and Remote Sensing Data - Md Saifuzzaman
  • Remote sensing (RS) and proximal soil sensing (PSS) technologies, widely used in quantifying surface and subsurface soil parameters, can be combined to infer spatial patterns of soil heterogeneity and to develop thematic maps for site-specific management. However, the use of these soil sensors must be reviewed constantly to maintain their efficiency and precision in delineating the soil-crop relationship and to inform precision agriculture (PA) approaches. Data mining and model optimization are key to evaluating high-density geospatial data in a dynamic production system. In this study, high-density PSS and RS-based soil characterization was explored and optimization techniques for digital soil mapping in PA were evaluated.
• A Comparison of Geographically Weighted Regression and Bayesian Latent Gaussian Modeling for Analysis of On-Farm Experiments - Fiona Evans
• On-Farm Experimentation Community Business Meeting
  • Agenda
  • Leader Report
  • Election of the Vice-Leader
  • Adjourn