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Estimation of Corn and Soybean Yields Utilizing Multiple Curvilinear Regression Methods
Author(s) -
Gross E. R.,
Rust R. H.
Publication year - 1972
Publication title -
soil science society of america journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.836
H-Index - 168
eISSN - 1435-0661
pISSN - 0361-5995
DOI - 10.2136/sssaj1972.03615995003600020032x
Subject(s) - sowing , yield (engineering) , agronomy , environmental science , water content , population , precipitation , moisture , crop , regression analysis , mathematics , geography , biology , statistics , demography , materials science , geotechnical engineering , sociology , meteorology , metallurgy , engineering
Analysis of crop yield and soil management records were made to determine relationships between yield, management, and climate. Detailed investigations were made of fertilization, plant population, planting date, soil moisture, and temperature with respect to crop yield and selected soil family groupings in Minnesota. The independent variables used were N, P, K, plant population, maturity rating, planting date, average monthly temperatures for May, June, July and August, and the average available soil moisture for June 1, July 1, August 1, and September 1. The available soil moisture was computed using estimated moisture holding capacity of the soil, the precipitation and the temperature. For corn records three soil groups based on family criteria were used. Variables most highly correlated with yield and retained with greater than 60% frequency were applied N, P, and K, plant population, planting date, May, June, and July temperature, and soil moisture June 1, August 1, and September 1. Substitution of independent management and climatic variables into the prediction equation produces reasonable increases in corn ( Zea mays L.) and soybean ( Glycine max L.) yields and could be used as a method of predicting crop yield.