A Prediction Model of Forest Preliminary Precision Fertilization Based on Improved GRA-PSO-BP Neural Network

Table 2

Nutrient content of dominant Chinese fir of different forest ages under different site conditions, g/kg.

Organ	Stand age (t/a)	Nitrogen				Phosphorus				Potassium
		Site index S				Site index S				Site index S
		5	18	21	23	5	18	21	23	5	18	21	23
Leaf	5	6.8	10.7	12.6	13.8	0.75	1.02	1.09	1.12	3.2	4.7	4.9	5.0
	19	8.6	12.1	12.4	12.7	0.84	1.16	1.28	1.06	3.3	4.3	4.7	4.9
	22	9.4	13.2	13.5	13.7	0.91	1.12	1.25	0.93	3.4	4.2	4.6	4.8
	25	10.1	13.9	14.1	14.2	0.92	1.04	1.07	0.97	3.2	4.1	4.5	4.7
Branch	5	3.9	6.7	7.1	7.3	0.59	0.71	0.74	0.77	3.1	4.0	4.1	4.2
	19	2.8	4.5	5.7	5.9	0.49	0.55	0.57	0.59	2.7	3.6	3.7	3.9
	22	2.7	4.2	5.5	5.7	0.47	0.52	0.55	0.58	2.6	3.8	3.9	3.6
	25	2.5	4.1	5.3	5.6	0.45	0.54	0.56	0.54	2.5	3.7	4.0	3.5
Stem	5	1.1	1.9	2.1	2.3	0.11	0.20	0.23	0.24	0.8	1.7	1.5	1.4
	19	1.2	1.4	1.9	2.0	0.08	0.09	0.12	0.14	0.4	0.9	1.1	1.3
	22	1.0	1.3	1.6	1.8	0.07	0.08	0.10	0.09	0.5	0.8	1.0	1.1
	25	0.9	1.2	1.4	1.3	0.04	0.06	0.08	0.07	0.4	0.7	0.9	0.8
Bark	5	2.8	4.6	4.5	4.7	0.21	0.32	0.34	0.35	4.7	3.6	3.3	3.1
	19	2.4	1.9	3.5	3.3	0.10	0.15	0.16	0.18	4.5	1.9	1.8	1.7
	22	2.3	3.6	3.4	3.1	0.09	0.11	0.13	0.15	4.1	1.7	1.6	1.5
	25	2.2	3.4	3.2	2.9	0.07	0.09	0.11	0.14	3.8	1.5	1.3	1.4
Root	5	1.7	3.5	3.8	3.9	0.10	0.27	0.29	0.31	3.4	2.1	2.0	1.8
	19	1.4	1.9	2.2	2.5	0.06	0.11	0.14	0.16	2.4	1.6	1.5	1.4
	22	1.6	1.7	2.1	2.4	0.05	0.12	0.12	0.17	2.1	1.7	1.6	1.5
	25	1.2	1.5	2.0	2.2	0.08	0.10	0.14	0.15	2.0	1.4	1.3	1.2