Exploring laboratory-based spectroscopy for estimating NPK content in the hutton soils of Syferkuil Farmlands, South Africa

Figures & data

Figure 1. A map showing the location of the study area (Syferkuil Farm) in Limpopo Province, Mankweng Township.

Figure 2. (A) Raw soil reflectance spectra, (B) raw soil spectra with noisy ends removed, (C) first derivative (FD) and (D) second derivative (SD).

Table 1. A table showing correlation coefficient interpretation.

Correlation coefficient (r)	Interpretation
0.90 to 1.00 or −0.90 to −1.00	very strong correlation
0.70 to 0.90 or −0.70 to −0.90	strong correlation
0.50 to 0.70 or −0.50 to −0.70	moderate correlation
0.30 to 0.50 or −0.30 to −0.50	low correlation
0.00 to 0.30 or −0.00 to −0.30	negligible correlation

Table 2. A table of descriptive statistics of NPK nutrient content of the soil in sykerfuil farm.

Descriptive Statistics
							Skewness		Kurtosis
	N	Minimum	Maximum	Sum	Mean	SD		Std. Error		Std. Error
Nitrogen %	74	0.00	0.25	7.50	0.10	0.06	0.14	0.28	−0.18	0.55
P mg/kg	74	0.64	29.10	453.03	6.12	6.17	1.66	0.28	3.31	0.55
K mg/kg	74	67.23	867.33	15317.28	206,99	125.19	2.65	0.28	10.32	0.55

* N denotes the number of samples, SD denotes standard deviation.

Figure 3. Pearson correlation between (a) N, (b) K, (c) P and spectral data.

Table 3. Table showing SMLR model performance for the prediction of NPK soil content based on raw spectral data (RD), first derivative (FD) and second derivative spectra (SD).

SMLR models
Soil nutrients	Pre-processing	R^2	RMSE	RPD	Important Wavelengths
N	RD	0.06	8.30	0.01	729
	FD	0.91	0.12	2.40	404, 406, 531, 550, 589, 591, 609, 949, 963, 990, 1375, 1390, 1861, 2420, 2488
	SD	0.16	0.12	0.50	453,1003
P	RD	0.85	0.12	2.00	404, 408, 415 426, 430, 431, 433, 444, 445, 461, 974, 997, 1011
	FD	0.99	8.67	2.61	400, 422, 490, 540, 541, 551, 568, 617, 805, 941, 979, 984, 993, 1377, 1448, 1912, 1927, 2138, 2234, 2275, 2305, 2462
	SD	0.17	8.67	0.71	454, 999
K	RD	–	–	–	–
	FD	0.60	241.46	1.84	400, 1405, 1927, 2459, 2368, 2011
	SD	0.19	241.46	0.52	400, 422, 490,

R² is the coefficient of determination, RMSE is the root mean square error and RPD is the ratio of performance to deviation. RD denotes raw spectral data, FD is the first derivative spectra, and SD denotes the second derivative spectra.

Table 4. PLSR model performance for the prediction of NPK soil content based on raw spectral data (RD), first derivative (FD) and second derivative spectra (SD).

PLSR models
Soil nutrients	Pre-processing	R^2	RMSE	RPD	AIC
N	RD	0.72	0.008	1.11	−242.97
	FD	0.67	0.005	1.59	−230.50
	SD	0.98	0.001	7.63	−348.76
P	RD	0.82	0.004	2.03	−253.25
	FD	0.73	0.004	1.62	−246.55
	SD	0.98	0.001	6.66	−348.42
K	RD	0.92	0.004	4.65	−378.45
	FD	0.88	0.003	5.88	−374.17
	SD	0.95	0.004	3.52	−318.64

R² is the coefficient of determination, RMSE is the root mean square error and RPD is the ratio of performance to deviation. RD denotes raw spectral data, FD is the first derivative spectra, SD denotes the second derivative spectra, and AIC denotes Akaike information criterion.

Figure 4. Important wavelength plots for predicting soil N (a- c), P (d-f) and K (g – i) based on various spectral transformations, PLSR model, Variable Importance projection (VIP) and the b-coefficients.

Figure 5. Biplot of the principal components (PC1 -PC3) (red) and scores (black) for the relationship between soil NPK and soil reflectance spectra.

Table 5. Explained and the accumulated explained variances of the first three principal components of soil reflectance spectra.

Principal components	PC1	PC2	PC3
Proportion Explained	34	34	31
Cumulative Proportion	34	69	100

Data availability statement

The data that support the findings of this study are available from the corresponding author, [FM], upon reasonable request.