Individual Growth Model for Eucalyptus Stands in Brazil Using Artificial Neural Network

Table 5

Statistics of height (Ht) and diameter (d.b.h.), as estimated and observed per age (months) and productive capacity class.

Productive capacity class	Age (months)	Variable
		Ht (m)						d.b.h. (cm)
		𝑟𝑦𝑦	CV % (±)	RMSE	Bias	Bias %	AMD	𝑟𝑦𝑦	CV % (±)	RMSE	Bias	Bias %	AMD
High (𝑆=32)	36	0.979	3.5	0.707	−0.080	−0.489	0.456	0.976	5.5	0.767	−0.081	−0.856	0.516
	48	0.965	4.6	1.040	0.027	0.003	0.741	0.961	7.1	1.065	−0.016	−0.443	0.752
	60	0.953	5.1	1.272	−0.223	−1.067	0.930	0.945	8.5	1.379	−0.184	−1.642	0.961
	72	0.934	5.9	1.540	−0.153	−0.784	1.159	0.925	10.3	1.748	−0.211	−1.890	1.248
Medium (𝑆=26)	36	0.981	3.4	0.621	0.090	0.494	0.453	0.981	4.8	0.624	0.147	1.246	0.461
	48	0.957	5.1	1.091	0.219	1.017	0.848	0.962	7.0	0.971	0.135	1.174	0.754
	60	0.938	6.3	1.437	0.170	0.635	1.154	0.940	9.0	1.345	0.137	0.964	1.067
	72	0.921	7.1	1.747	0.149	0.369	1.425	0.922	10.7	1.685	0.162	0.965	1.351
Low (𝑆=20)	36	0.966	4.1	0.635	0.025	0.101	0.486	0.975	5.4	0.565	0.061	0.559	0.416
	48	0.947	5.7	1.031	0.216	1.015	0.811	0.953	7.7	0.924	0.265	2.163	0.735
	60	0.921	7.0	1.440	0.442	2.017	1.138	0.929	9.6	1.266	0.414	3.138	1.012
	72	0.903	7.8	1.684	0.420	1.642	1.327	0.909	11.0	1.507	0.427	2.806	1.201