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In‐Season Prediction of Potential Grain Yield in Winter Wheat Using Canopy Reflectance
Author(s) -
Raun William R.,
Solie John B.,
Johnson Gordon V.,
Stone Marvin L.,
Lukina Erna V.,
Thomason Wade E.,
Schepers James S.
Publication year - 2001
Publication title -
agronomy journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.752
H-Index - 131
eISSN - 1435-0645
pISSN - 0002-1962
DOI - 10.2134/agronj2001.931131x
Subject(s) - normalized difference vegetation index , yield (engineering) , growing season , agronomy , canopy , fertilizer , growing degree day , grain yield , environmental science , phenology , leaf area index , biology , ecology , materials science , metallurgy
Nitrogen fertilization rates in cereal production systems are generally determined by subtracting soil test N from a specified N requirement based on the grain yield goal, which represents the best achievable grain yield in the last 4 to 5 yr. If grain yield could be predicted in season, topdress N rates could be adjusted based on projected N removal. Our study was conducted to determine if the potential grain yield of winter wheat ( Triticum aestivum L.) could be predicted using in‐season spectral measurements collected between January and March. The normalized difference vegetation index (NDVI) was determined from reflectance measurements under daytime lighting in the red and near‐infrared (NIR) regions of the spectra. In‐season estimated yield (EY) was computed using the sum of two postdormancy NDVI measurements (Jan. and Mar.) divided by the cumulative growing degree days (GDD) from the first to second reading. A significant relationship between grain yield and EY was observed( R 2 = 0.50, P > 0.0001 )when combining all nine locations across a 2‐yr period. Our estimates of potential grain yield (made in early Mar.) differed from measured grain yield (mid‐July) at three sites where yield‐altering factors (e.g., late summer rains delayed harvest and increased grain yield loss due to lodging and shattering) were encountered after the final sensing. Evaluating data from six of the nine locations across a 2‐yr period, EY values explained 83% of the variability in measured grain yield. Use of EY may assist in refining in‐season application of fertilizer N based on predicted potential grain yield.