Journal
COMMUNICATIONS IN SOIL SCIENCE AND PLANT ANALYSIS
Volume 40, Issue 17-18, Pages 2688-2711Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/00103620903173756
Keywords
Artificial neural networks; C; N ratio; discriminant analysis; predictive models; variance components
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Multivariate relationships in and statistical moments of eight biochemical constituents and their impact on estimating carbon/nitrogen (C/N) ratio in alfalfa, corn, soybean, cuphea, and switchgrass residues indicate that (1) equal portions of variation in C/N were explained by differences among crops and among organs; however, the largest variations in N and C were explained by differences among crops and among organs within crops, respectively; (2) variation in N, but not in C or N + C, content explained the greatest variance in C/N ratios; (3) biochemically, stems were closer to roots than to leaves; hence the large portion of variation in C/N ratio in roots explained by variation in biochemical constituents in stems and leaves (R2 = 61.0%) and in stems only (R2 = 58.0%); and (4) statistical moments, other than mean values of biochemical constituents, significantly impacted C/N ratio estimates and the reliability of these estimates, both of which were positively correlated (r = 0.64, p 0.001).
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