Abstract Detail

Recent Topics Posters

Hu, Zhiqiu [1], Yang, Rong-Cai [1].

A farmer-oriented spatial statistical tool for analysis of precision farming data.

With the recent advent of geomatics technologies such as yield monitors equipped with GPS on a combine harvester and onboard sensors, massive georeferenced crop or soil data across an entire field have become increasingly available from operation of farm machinery. These data are often mapped using freely or commercially available software to detect in-field spatial variability for precision farming activities such as variable rate applications of crop inputs. However, recommended inputs or farm-level decisions may not be reliable when such recommendations or decisions are based just on ‘eyeballing’ yield/soil maps from raw data at one farm in one year. Instead, these recommendations or decisions should be based on the maps or information derived from predicted data at multiple farms/locations over multiple years under tested, statistically sound spatial models. In addition, it is not feasible for most farmers to effectively use the existing software packages/tools that are generally attuned to professional users with research/academic backgrounds. Thus, a user-friendly, farmer-oriented software package would be desired to enable farmers to explore the geospatial data from their own farm fields with minimal computer and statistical knowledge. In this presentation, we will introduce such a package called Precision Farming Rake (PFR) that will be developed in the Microsoft^® Excel environment. Because farmers, like most other users, are very familiar with Excel, the use of PFR/Excel App will enable them to focus on the analysis of data-driven scenarios and concepts for farm-level decisions. The full version of PFR/Excel App is currently still under development, but its existing modules on descriptive spatial statistics have been beta-tested and well received by some farmers and consulting agronomists. Our subsequent development of PFR/Excel App will focus on incorporating advanced spatial statistics required for analysis of large, multi-layer crop and soil data sources.  The PFR/Excel App will have a potential to contribute significantly to the data-driven precision farming activities for more precise and profitable management of farm fields.

1 - University of Alberta, Department of Agricultural, Food and Nutritional Science, 410 Agriculture/Forestry Centre, Edmonton, AB, T6G 2P5, Canada

Keywords:
Precision Agriculture
Autocorrelation
Analysis
yield map
Excel Application
spatial statistics.

Presentation Type: Recent Topics Poster
Session: P
Location: Hall D/The Shaw Conference Centre
Date: Monday, July 27th, 2015
Time: 5:30 PM
Number: PRT014
Abstract ID:1797
Candidate for Awards:None