Agriculture and GIS

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GIS technology has become a vital tool for crop management.  Geographic data about soil condition helps farmers to be more efficient in segmenting arable land in order to apply differential rates of fertilizer, and forecasting to determine when, where, and what to plant in what is known as precision agriculture.  Satellite and aerial imagery is used to analyze existing conditions of the land, soil samples taken from the fields are used to create a more precise understanding of the condition of a farm.  By understanding the condition of the land on a micro scale, farmers and those in the agriculture field can better manage fertilizer and water application, resulting in reduced costs and better crop yields.

More detail about the use of geospatial technologies in precision farming can be found in the article by Peter Rodericks Oisebe entitled “Geospatial Technologies in Precision Agriculture.”

Remote Sensing Organic Crops

The variations in crops grown organically versus conventionally are significant enough to be detected by analysis on satellite imagery.  The European Space Agency  (ESA) has been working with Ecocert, an organic certification organization, as well as consulting firms Keyobs and VISTA, and Belgium’s University of Liège, to develop a methodology to analyze satellite imagery to differentiate crop fields based on whether the crops were grown via organic or conventional methods.

ESA analyzed multi- and hyperspectral imagery from five different satellites, SPOT-4, Kompsat-2, Landsat-5, Proba, and WorldView-2 as part of the study.  Using indicators that included crop spectral reflectance, yield forecasts and spatial heterogeneity, ESA was able to predict with an average accuracy of 90% which crops were grown organically versus conventionally.  Dr Pierre Ott from Ecocert, concluded,  “Accuracy rates of 80% to 100% in discriminating organic from conventional fields are a performance in itself. It seems very promising as far as the potential of future developments is concerned.

Efforts are ongoing to further refine this methodology so that it can be commercially utilized.

Cornfield classification determinations using a WorldView-2 satellite image acquired on August 10, 2010. The fields in light green are classified as organic (KMO) and the ones shaded dark green are classified as conventional (KM). An accuracy of +90% was obtained on the classification between organic and conventional.   Credits: VISTA

Cornfield classification determinations using a WorldView-2 satellite image acquired on August 10, 2010. The fields in light green are classified as organic (KMO) and the ones shaded dark green are classified as conventional (KM). An accuracy of +90% was obtained on the classification between organic and conventional. Credits: VISTA

 

Resources on the use of GIS in Agriculture.

Access to articles, case studies and user groups in agricultural GIS.

Plant Hardiness Zone Map
Showing zones of plant hardiness by the U.S. Department of Agriculture (USDA).

GIS and Agribusiness
ESRI’s web page on agriculture. Articles and support for utilizing GIS in this field.

GIS Agriculture Models
Research article on “Potential for Integrated GIS-Agriculture Models for Precision Farming Systems”.

MapShots
Web site for the EASI suite of software with a focus on data management for agriculture. Find information about this software including conferences and trainings.

Books

GIS Applications in Agriculture – by Francis J. Pierce (Editor), David Clay (Editor)

GIS in Site-Specific Agriculture – by James D. Westervelt (Author), Harold F. Reetz (Author)




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