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Polynomial Functional Models to Simulate Crop Growth in Maize (Zea mays L.) Cultivars
Author(s) -
Prasad T. V. Ramachandra,
Krishnamurthy K.,
Devendra R.,
Kailasam C.
Publication year - 1993
Publication title -
journal of agronomy and crop science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.095
H-Index - 74
eISSN - 1439-037X
pISSN - 0931-2250
DOI - 10.1111/j.1439-037x.1993.tb00436.x
Subject(s) - relative growth rate , mathematics , cultivar , sowing , agronomy , polynomial , dry matter , zea mays , crop , polynomial regression , heteroscedasticity , horticulture , growth rate , regression analysis , biology , statistics , mathematical analysis , geometry
Polynomial and polynomial exponential models of order one to four were tried to describe crop growth in two maize cultivars viz., Deccan hybrid and Deccan 101 of an agronomic trial conducted at the University of Agricultural Sciences, Bangalore, on alfisols. Relative growth rate (RGR) of classical and functional methods was compared. Models performance was assessed based on r 2 , χ 2 residuals sum of squares (RSS), root mean standard deviation (RMSD) and presence or absence of heteroscedasticity in models' predictions. In the cultivars, polynomials of order three and four in Deccan hybrid and cubic polynomial in Deccan 101 simulated dry matter production (DMP) by 99.5 % and showed good distribution of error variance over the observed DMP. The RGR from the third and fourth degree polynomials in both the cultivars was comparable to the classical method and explained better during post‐silking period. Polynomial exponentials predicted very low RGR. Between cultivars, Deccan 101 showed higher RGR during 45–80 days after sowing and contributed significantly more to final DMP.