Download Reverse N lookup, sensor based N rates using Weather improved

Rationale
Reverse N lookup, sensor based N rates using Weather improved INSEY
Nicole Remondet
Weather is an aspect of agricultural sciences that cannot be controlled and it has a huge impact on the growth of plants. In the past the reliability of weather
predictions have been relatively questionable. “The Oklahoma Mesonet is a world-class network of environmental monitoring stations. The Oklahoma Mesonet
consists of 120 automated stations covering Oklahoma. At each site, the environment is measured by a set of instruments located on or near a 10-meter-tall tower”
(Mesonet) with the vast amounts of data it is important for us as agriculturalists to use this information to our advantage.“The current model utilized for predicting
grain yield potential was that described by Raun et al. (2005). The INSEY was calculated by dividing the NDVI by the cumulative number of GDD with a growing
threshold value of 4.4ºC. A non-linear relationship was established between INSEY and final grain yield, and the equation from this relationship is thus used to
predict yield” (Jacob T. Bushong. Et al.) “NITROGEN is well documented as a limiting nutrient in crop production and is considered one of the best producer inputs
to increase profitability under an appropriate management system” (R.K. Teal et al.). So because of this it is useful to continue to evaluate the INSEY to make it
more accurate so that fertilizer resources are not wasted.
Objectives
One objective of this study is to develop a model that incorporates climatic parameters with NDVI measurements to increase the
reliability of predicting wheat grain yield in-season. Using current and long term data, the current INSEY will be evaluated from five
locations: Lake Carl Blackwell, Lahoma, Hennessey, Perkins, and Efaw. Another objective of this study is to compare the weather
improved model with the current model.
Methods
NDVI Data and yield data will be collected from current trials then
documented with previous data. First a yield prediction model will be
created through the creation of various graphs, and then using a statistical
program such as SAS to evaluate the significance of the relationships
between different aspects of data. This could be done by comparing “The
maximized adjusted R2 values, to determine the appropriate regression
equation parameters that will best estimate final grain yield” (Jacob Bushog
et al). After this has been accomplished the new predicted yields will be
compared to the predicted yield of the previous yield calculator as well as
including the actual yield that was detected at harvest.
From Jake Bushog’s previous paper where he incorporated soil moisture into his yield calculator.
Linear regression of measured grain yield of plots with no mid-season N fertilizer with estimates of
Conclusion
yield potential
While the current yield predictor is efficient there is always a way to improve it by using accurate weather information in
without added N derived from the Current N Fertilizer Optimization Algorithm (Left) and the
Proposed N Fertilizer Optimization Algorithm (Right).
association with the yield estimation. The aspects of weather that one could include could be: cumulative Growing Degree Days,
Works Cited:
changing the maximum/minimum temperature utilized, or by altering the methods of calculating GDD. Using resources such as
Development of an In-season Estimate of Yield Potential Utilizing Optical Crop Sensors the Mesonet the weather information one can utilize will be accurate. Using less resources to grow our crops will be increasingly
and Soil Moisture Data for Winter Wheat ,Jacob T. Bushong*1, Jeremiah L. Mullock1, Eric
important in the coming years as the human population grows and the resources available begin to dwindle. Since weather has
C. Miller1, William R. Raun1, Arthur R. Klatt1, and D. Brian Arnall1
such a huge impact on the growth of crops it will be beneficial to incorporate such data into the current INSEY so that less
In-Season Prediction of Corn Grain Yield Potential Using Normalized Difference
resources could be used.
Vegetation Index R. K. Teal, B. Tubana, K. Girma, K. W. Freeman, D. B. Arnall, O.
Walsh, and W. R. Raun*
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