Spatial analysis and characteristics of pig farming in Thailand

BMC Veterinary Research

Table 4 Important variables modeled by the quantitative Random forests and evaluation of predicted maps modeled by combined models. The variable importance (%) used to predict pig types and pig farm scales and the evaluation of the combined models. Predictor variables include, travel time to the capital city (Bangkok), travel time to the provincial capitals (Meung districts), rainfed croplands irrigated croplands, elevation, and human density)

Categories	Response variables^a	The variable importance^b						Evaluation
		TCapCity	TProCap	RaCrop	IrCrop	Elev	HuDen	RMSE^c	Correlation	RMSE	Correlation
		TCapCity	TProCap	RaCrop	IrCrop	Elev	HuDen	(sub-district)	(sub-district)	(pixel)	(pixel)
Pig types (heads/km2)	Native pigs	41.97	27.54	29.44	23.86	34.64	66.89	0.12	0.94	1.19	0.78
	Breeding pigs	58.74	38.84	36.76	30.46	44.82	75.38	0.23	0.91	1.32	0.79
	Fattening pigs	61.16	37.57	47.71	33.07	63.81	63.55	0.31	0.87	1.28	0.83
Pig farm scales (farms/10 km2)	SM	100.27	46.86	62.10	50.90	77.83	148.82	0.14	0.95	1.43	0.80
Pig farm scales (farms/10 km2)	LF	21.85	21.68	25.33	15.39	36.33	62.09	0.07	0.92	0.74	0.74

^aResponse variables include: number of native pigs, number of breeding pigs, number of fattening pig, number of smallholders (SM), and number of large-scale farming systems (LF)
^bPredictor variables include: travel time to the capital city (TCapCity), travel time to the provincial capitals (TProCap), rainfed croplands (RaCrop), irrigated croplands (IrCrop), elevation (Elev), and human density (HuDen)
^cRMSE stands for root mean square error

ISSN: 1746-6148