Development of an in-season estimate of yield potential utilizing optical crop sensors and soil moisture data for winter wheat
When utilizing optical sensors to make in-season agronomic recommendations in winter wheat, one parameter often required is the in-season grain yield potential at the time of sensing. Current estimates use an estimate of biomass, such as normalized difference vegetation index (NDVI), and growing degree days (GDDs) from planting to NDVI data collection. The objective of this study was to incorporate soil moisture data to improve the ability to predict final grain yield in-season. Crop NDVI, GDDs that were adjusted based upon if there was adequate water for crop growth, and the amount of soil profile (0–0.80 m) water were incorporated into a multiple linear regression model to predict final grain yield. Twenty-two site-years of N fertility trials with in-season grain yield predictions for growth stages ranging from Feekes 3 to 10 were utilized to calibrate the model. Three models were developed: one for all soil types, one for loamy soil textured sites, and one for coarse soil textured sites. The models were validated with 11 independent site-years of NDVI and weather data. The results indicated there was no added benefit to having separate models based upon soil types. Typically, the models that included soil moisture, more accurately predicted final grain yield. Across all site years and growth stages, yield prediction estimates that included soil moisture had an R2 = 0.49, while the current model without a soil moisture adjustment had an R2 = 0.40.
KeywordsOptical sensors Soil moisture Winter wheat Yield potential
Days of potential growth
Fractional water index
Growing degree day
In-season estimate of yield
Normalized difference vegetation index
Plant available water
Root mean square error
The authors would like to thank the Oklahoma Soil Fertility Research and Education Advisory Board for their funding of this research project and their continued financial support of soil fertility research at Oklahoma State University. The authors would also like to express their sincere gratitude to all the current and former soil fertility graduate students who aided in the data collection and maintenance of trials.
Compliance with ethical standards
Conflict of Interest
The mention of any trademarked products or equipment utilized in this experiment was for research purposes only and does not act as an endorsement by Oklahoma State University. The authors and Oklahoma State University have no direct financial relation with any of the named manufacturers, thus the authors declare there is no conflict of interest regarding the publication of this manuscript.
- Black, A.L., & Bauer, A. (1988). Setting winter wheat yield goals. In J.L. Havlin (Ed.), Proceeedings of the workshop central great plains profitable wheat management, wichita, KS. 17–20 1988 (pp. 24–34). Atlanta, GA: Potash and Phosphate Institute.Google Scholar
- Dahnke, W.C., Swenson, L.J., Goos, R.J., & Laholm, A.G. (1988). Choosing a crop yield goal. SF-822. Fargo: North Dakota State Extension Service.Google Scholar
- Fanning, B. (2012). Setting yield goals. South Dakota State University Extension. http://igrow.org/agronomy/wheat/settings-yield-goals/. Accessed 1 Oct. 2015.
- Geisseler, D, & Horwath, W.R. (2013). Determining yield goals. California Department of Food and Agriculture. http://apps.cdfa.ca.gov/frep/docs/Yield_Goals.pdf. Accessed 1 Oct. 2015.
- Oklahoma Mesonet. (2015). Daily data retrieval. University of Oklahoma. http://www.mesonet.org/index.php/weather/category/past_data_files. Accessed 1 Oct. 2015.
- Porter, J.R., & Moot, D.J. (1998). Research beyond the means: Climatic variability and plant growth. In N.R. Dalezios (Ed.), International symposium on applied agrometeorology and agroclimatalology (pp 13–25). Office for Official Publication of the European Commission, LuxembourgGoogle Scholar
- Rehm, G., & Schmitt, M. (1989). Setting realistic crop yield goals. Minnesota Ext. Serv. AG-FS-3873. University of Minnesota.Google Scholar
- SAS Institute Inc. (2011). SAS/STAT® 9.3 User’s Guide. Cary, NC: SAS Institute Inc.Google Scholar
- Soil Survey Staff. 2012a. Official soil series description. USDA-NRCS. http://soils.usda.gov/technical classification/osd/index.html. Accessed 1 April 2012.
- Soil Survey Staff. 2012b. Web soil survey: Soil data mart. USDA-NRCS. http://websoilsurvey.nrcs.usda.gov. Accessed 1 April 2012.
- Stephens, D. J., Lyons, T. J., & Lamond, M. H. (1989). A simple model to forecast wheat yield in Western Australia. Journal of the Royal Society of Western Australia, 71(2–3), 77–81.Google Scholar