Extended Abstract (616K)P1.7
Displaying an aridity index as a tool for mid-season evaluation of Midwest corn yield
Darren Miller, Iowa State University, Ames, IA; and S. E. Taylor, R. W. Arritt, D. P. Todey, and P. J. Sherman
Most crop yield models utilize the relationship of yield (percentage of potential) to evapotranspiration (percentage of potential). Sub-monthly indexes involving fairly direct evaluations of evapotranspiration for crop assessment have been developed in the past with satisfactory results. However, the evaluation of actual and potential evapotranspiration for these index computations is not easy to assess in near real-time because some data for input are not readily available (eg., pan evaporation, net radiation, soil water). A different index, which accounts for evapotranspiration indirectly with readily available daily maximum temperature and daily precipitation data, is presented as a near real-time alternative.
Though the term "aridity" becomes a misnomer, the Aridity Index (AI) is defined as the subtraction of weekly maximum temperature's departure from average from weekly precipitation's departure from average. This definition gives a negative (positive) value when the weather is warm and dry (cool and wet). Because warmer and drier than average weather tends to have a negative effect on the corn yield, the slope that occurs when corn yield deviation from trend is plotted against AI generally appears positive and allows users to associate AI less than zero with a better chance for poor yield.
For a growing season, two main items should allow for meaningful dissemination of AI information via a World Wide Web site (http://www.mesonet.agron.iastate.edu/~windmill/AIpage.html). Maps of the current week's accumulation of AI will display the spatial extent of weather warmer and drier or cooler and wetter than average. The other important item is a display of the sequential sample for each district. Such a display would allow users to follow what the AI has been doing in their district and to anticipate what level it might end up at. The display of the spatial distribution of AI is a good application for Geographic Information Systems (GIS). Using GIS allows for fast updates of the AI map for the current growing season. As for the sequential sample displays, there were 85 Midwest districts studied and it would be awkward to produce 85 time series charts each week. An alternative would be to set up the map so that each district has a link to an automatically generated chart. Automatic chart generation has been accomplished with JpGraph 1.6.3 (http://www.aditus.nu/jpgraph/). Methodology, results, and an operational 2002 product are provided on the site.
Supplementary URL: http://www.mesonet.agron.iastate.edu/~windmill/AIpage.html
Poster Session 1, 19th IIPS Poster Session
Monday, 10 February 2003, 2:30 PM-4:00 PM
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