USE OF USLE/ GIS TECHNOLOGY FOR IDENTIFYING CRITERIA FOR MONITORING SOIL EROSION LOSSES IN AGRICULTURAL AREAS

Botelho, Thiago H. A.; Jácomo, Simone de A.; Almeida, Rherison T. S.; Griebeler, Nori P.

doi:10.1590/1809-4430-Eng.Agric.v38n1p13-21/2018

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Sumário

Scientific Paper, Soil and Water Engineering • Eng. agríc. (Online) 38 (1) • Jan-Feb 2018 • https://doi.org/10.1590/1809-4430-Eng.Agric.v38n1p13-21/2018 copy

USE OF USLE/ GIS TECHNOLOGY FOR IDENTIFYING CRITERIA FOR MONITORING SOIL EROSION LOSSES IN AGRICULTURAL AREAS¹ 1 Part of the Master's Dissertation of the first author

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

The assessment of land use and occupation is essential for predicting soil loss due to water erosion. The objective of this study was to determine criteria for monitoring soil loss in agricultural areas using the Universal Soil Loss Equation (USLE). Soil loss data were intersected with USLE factors and slope using geoprocessing techniques for the Samambaia River watershed, located in Cristalina, Goiás state, Brazil. A slope of 3% to 8% was found in approximately 50% of the study area. However, mean soil losses <10 t ha^-1 year^-1 were observed in slopes ≤ 3%, where the mean soil length (L) and slope steepness (S) were ≤2 and 0.2, respectively. The intersection of soil loss data with USLE factors was useful for identifying criteria for monitoring soil loss. The mean topographic factor (LS) in non-irrigated crops, pasture, and silviculture was 0.4 ± 0.4, 0.4 ± 0.7, and 2.2 ± 2.6, respectively. Therefore, LS values adequately indicated the type of land use and occupation considering that only silviculture could maintain soil losses below the tolerable limit of 10 t ha^-1 year^-1 for an LS mean of 2.2.

KEYWORDS
soil cover; water erosion; topographic factor; conservation management

Soil mapping units (Symbols)	Erodibility of the soil combination (th MJ^-1 mm^-1)	Mean erodibility (th MJ^-1 mm^-1)	Area (ha)	Reference
RYL and DRL¹ 1 Combination of Red-Yellow Latosols (RYL) and Dark Red Latosols (DRL)	0.020 and 0.014	0.017	33.560	Calixto (2013)Calixto BB (2013) Estimativa indireta da erodibilidade (K) dos solos da Bacia do Ribeirão Pipiripau – DF usando dados pedológicos locais. Monografia, Brasília, Universidade de Brasília, Faculdade de Tecnologia.; Bloise et al. (2001)Bloise GLF, Carvalho Júnior OA, Reatto A, Guimarães RF, Martins ES, Carvalho APF (2001) Avaliação da suscetibilidade natural à erosão dos solos da bacia do Olaria-DF. In: Boletim de Pesquisa de Desenvolvimento 14. Embrapa Cerrados. Available: http://www.cpac.embrapa.br/baixar/28/t/. Accessed: Jun 10, 2013. http://www.cpac.embrapa.br/baixar/28/t/... ; and Silva (2004)Silva VC (2004) Estimativa da erosão atual da bacia do Rio Paracatu (MG/GO/DF). Pesquisa Agropecuária Tropical 34(3):147-159.
C and DRL² 2 Combination of Cambisols (C) and Dark Red Latosols (DRL)	0.024 and 0.014	0.019	20,030
FF and C³ 3 Combination of Petric Plinthosols (FF) and Cambisols (C)	0.018 and 0.024	0.021	32,111	Calixto (2013)Calixto BB (2013) Estimativa indireta da erodibilidade (K) dos solos da Bacia do Ribeirão Pipiripau – DF usando dados pedológicos locais. Monografia, Brasília, Universidade de Brasília, Faculdade de Tecnologia.
C e F⁴ 4 Combination of Cambisols (C) and Plinthosols (F)	0.024 and 0.000^* * Not found in the researched literature	0.024	1.710
L and RYL⁵ 5 Combination of Petroplinthic Latosols (L) and Red-Yellow Latosols (RYL)	0.000^* * Not found in the researched literature and 0.020	0.020	70
C and RL⁶ 6 Combination of Cambisols (C) and Litholic Neosols (RL).	0.048 and 0.040	0.044	10	Silva (2004)Silva VC (2004) Estimativa da erosão atual da bacia do Rio Paracatu (MG/GO/DF). Pesquisa Agropecuária Tropical 34(3):147-159.

Variable	Mean	Median	Standard deviation	CV (%)	AS	K	KS	R_s
¹ 1 (MJ mm ha-1 h-1 years-1); R	7928.94	7901.44	82.55	1.04	1.38	1.75	0.19	0.26^* * The correlation was significant at a level of significance of 0.01 (two-tailed analysis).
² 2 (th MJ-1 mm-1); K	0.019	0.019	1.83 x 10^–3	9.58	0.16	–1.09	0.24	0.42^* * The correlation was significant at a level of significance of 0.01 (two-tailed analysis).
L	2.95	2.16	2.92	98.90	4.58	30.01	0.25	0.86^* * The correlation was significant at a level of significance of 0.01 (two-tailed analysis).
S	0.58	0.45	0.51	86.49	3.28	20.59	0.15	0.86^* * The correlation was significant at a level of significance of 0.01 (two-tailed analysis).
LS	2.04	1.05	3.02	147.92	3.76	18.54	0.26	–
C	0.27	0.18	0.33	123.18	1.68	1.05	0.44	0.26^* * The correlation was significant at a level of significance of 0.01 (two-tailed analysis).
P	0.69	0.5	0.26	37.79	0.11	–1.15	0.37	0.34^* * The correlation was significant at a level of significance of 0.01 (two-tailed analysis).
CP	0.15	0.09	0.16	107.56	1.66	1.05	0.45	0.37
D	5.12	4.25	3.91	76.36	1.93	7.10	0.11	0.86^* * The correlation was significant at a level of significance of 0.01 (two-tailed analysis).
³ 3 (t ha-1 year-1). N	321.90	161.49	485.78	150.91	3.74	18.10	0.26	0.99^* * The correlation was significant at a level of significance of 0.01 (two-tailed analysis).
³ 3 (t ha-1 year-1). A	42.16	14.64	98.88	234.53	8.62	115.65	0.34	0.71^* * The correlation was significant at a level of significance of 0.01 (two-tailed analysis).

Sub-watershed	Slope (%)^* * According to Santos et al. (2013). Smean, mean slope steepness (%); CV, coefficient of variation (%); L, slope length (dimensionless); S, slope steepness (dimensionless); N, natural erosion potential; A, soil losses.	Area (Km²)	Area	S_mean	CV	L	S	N	A
Sub-watershed		Area (Km²)	(%)			(dimensionless)		(t ha^-1 year^-1)
Samambaia River	0-3	217.14	37.23	1.65	50.30	1.86	0.20	90.66	9.83
	3-8	299.05	51.27	5.03	26.24	3.58	0.54	315.00	43.94
	8-20	65.94	11.30	10.72	23.23	4.19	1.25	850.54	103.85
Arrasta-Burro tributary	0-3	51.78	37.66	1.79	42.46	1.93	0.21	61.93	8.77
	3-8	64.11	46.63	4.96	27.62	3.49	0.53	285.29	39.42
	8-20	21.36	15.54	11.04	22.55	4.08	1.30	811.32	96.78
Moreira tributary	0-3	29.64	19.23	1.75	46.86	2.00	0.21	69.84	8.55
	3-8	67.74	43.95	5.35	26.17	3.94	0.58	372.66	48.04
	8-20	53.39	34.64	11.90	24.96	4.39	1.43	992.40	124.62
	20-45	3.38	2.19	23.63	15.79	3.91	3.44	2,089.99	229.28

Associação Brasileira de Engenharia Agrícola SBEA - Associação Brasileira de Engenharia Agrícola, Departamento de Engenharia e Ciências Exatas FCAV/UNESP, Prof. Paulo Donato Castellane, km 5, 14884.900 | Jaboticabal - SP, Tel./Fax: +55 16 3209 7619 - Jaboticabal - SP - Brazil
E-mail: revistasbea@sbea.org.br

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[1] *Corresponding author. Universidade Federal de Goiás/ Goiânia - GO, Brazil. E-mail: thiago2012arbo@hotmail.com

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USE OF USLE/ GIS TECHNOLOGY FOR IDENTIFYING CRITERIA FOR MONITORING SOIL EROSION LOSSES IN AGRICULTURAL AREAS1 1 Part of the Master's Dissertation of the first author

ABSTRACT

USE OF USLE/ GIS TECHNOLOGY FOR IDENTIFYING CRITERIA FOR MONITORING SOIL EROSION LOSSES IN AGRICULTURAL AREAS¹ 1 Part of the Master's Dissertation of the first author