New methods for estimating lime requirement to attain desirable pH values in Brazilian soils

Teixeira, Welldy Gonçalves; Víctor Hugo Alvarez, V.; Neves, Júlio César Lima

doi:10.36783/18069657rbcs20200008

Acessibilidade / Reportar erro

Brasil

Revista Brasileira de Ciência do Solo

Español English

Brasil

Español English

sumário « anterior atual seguinte »

Sumário

Division – Soil Use and Management, Commission – Soil Fertility and Plant Nutrition • Rev. Bras. Ciênc. Solo 44 • 2020 • https://doi.org/10.36783/18069657rbcs20200008 copy

New methods for estimating lime requirement to attain desirable pH values in Brazilian soils

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

In Brazil, empirical models are traditionally used to determine lime requirement (LR), but their reliability is doubtful in most cases, since they can lead to under- or overestimation of LR for different soil types. In this study, the most critical characteristics influencing LR were selected to develop reliable models for predicting LR that raise soil pH to optimum values for crop production in Brazil. Soil samples (n = 22) with varying proportions of clay (5-88 %) and organic matter (OM) levels (3.78-79.35 g kg^-1) were used to develop the models. Organic matter and potential acidity (HAl) combined with ΔpH [target pH(H₂O) - initial pH(H₂O)] were the best predictor variables for estimating LR. Four models were developed (OMpH_5.8, OMpH_6.0, HAlpH_5.8, and HAlpH_6.0) for estimating LR to attain target pH values of 5.8 or 6.0 with reasonably high prediction performance (0.758≤ R² ≤0.886). An algorithm was further developed for selecting the LR to be recommended among those estimated by the models. The proposed algorithm enables to select the minimum LR that ensure the adequate supply of Ca and Mg to plants and does not exceed the levels of soil HAl, thus preventing excessive pH increase. The new predictive models were less sensitive to predict LR high enough to meet Ca²⁺ and Mg²⁺ requirements of plants in soils containing levels of HAl lower than 5 cmol_c dm^-3 and OM lower than 40 g kg^-1. However, they ensured an adequate supply of Ca²⁺ and Mg²⁺ to plants and avoided the overestimation of LR for most soils used in this research. Validation via an independent dataset (n = 100 samples) confirmed the good predictive performance of the models across a wide range of soil types. In summary, the proposed models can be used as good alternatives to traditional methods for predicting LR for a great variety of Brazilian soils. Further, they are versatile and may be easily deployed in soil testing laboratories, since soil pH, OM, and HAl are characteristics determined in routine analysis.

lime requirement prediction; organic matter; potential acidity; algorithm

Method	LR⁽¹⁾ equations	Reference
M^x+(2)	LR = 1.5 M^x+	Cate and Nelson (1965)Cate RB, Nelson LA. A rapid method for correlation of soil test analyses with plant response data. Raleigh: NC State University Agricultural Experiment Station; 1965.
BSAT⁽³⁾	LR = (V₂ - V₁) T/100	van Raij et al. (1996)van Raij B, Cantarella H, Quaggio JA, Furlani AMC. Recomendações de adubação e calagem para o Estado de São Paulo. 2. ed. Campinas: IAC; 1996.
MG₁⁽⁴⁾	LR = Y M^x+ + [X - (Ca + Mg)]	Lopes and Guimarães (1989)Lopes AS, Guimarães PTG. Recomendações para o uso de corretivos e fertilizantes em Minas Gerais - 4ª aproximação. Lavras: Comissão de Fertilidade do Solo do Estado de Minas Gerais; 1989.
MG₂⁽⁵⁾	LR = Y [M^x+ - (m_t t/100)] + [X - (Ca + Mg)]	Alvarez V and Ribeiro (1999)
SMP⁽⁶⁾	LR to raise the soil pH(H₂O) to 6.0 using the SMP buffer	van Raij et al. (1979)van Raij B, Cantarella H, Zullo MAT. O método tampão SMP para determinação da necessidade de calagem de solos do estado de São Paulo. Bragantia. 1979;38:57-69. https://doi.org/10.1590/S0006-87051979000100007 https://doi.org/10.1590/S0006-8705197900...
HAl⁽⁷⁾	LR = -0.086 + 0.7557 HAl	Teixeira et al. (2014)Teixeira WG, Reis JV, Freitas JAD, Alvarez V VH. Determinação da necessidade de calagem para o cafeeiro considerando a acidez potencial. In: Proceedings of the 20th Congreso Latinoamericano y 16th Congreso Peruano de la Ciencia del Suelo; 2014 Nov 9-15; Cusco, PE. Cusco: Sociedad Peruana de la Ciencia del Suelo; 2014.
pHOM⁽⁸⁾	LR = 0.16 (6 - pH) OM	Defelipo et al. (1972)Defelipo BV, Braga JM, Spies C. Comparação entre métodos de determinação da necessidade de calcário de solos de Minas Gerais. Experientiae. 1972;13:111-36.

Soil characteristic⁽¹⁾	Calibration dataset⁽²⁾			Validation dataset⁽³⁾

	Mean	Lowest	Highest	Mean	Lowest	Highest
pH(H₂O)	4.63	4.12	5.26	4.95	4.10	5.87
pH(SMP)	5.72	4.82	6.78	5.90	5.17	7.59
Ca²⁺ (cmol_c dm^-3)	0.45	0.02	1.66	0.61	0.00	3.15
Mg²⁺ (cmol_c dm^-3)	0.20	0.00	0.87	0.23	0.00	1.87
K⁺ (cmol_c dm^-3)	0.10	0.02	0.24	0.12	0.00	0.49
M^x+ (cmol_c dm^-3)	0.87	0.21	1.98	0.85	0.00	3.00
HAl (cmol_c dm^-3)	6.63	1.68	13.06	5.75	0.70	11.30
SB (cmol_c dm^-3)	0.77	0.04	2.04	1.09	0.07	6.55
T (cmol_c dm^-3)	7.40	1.72	14.06	6.84	1.68	11.90
t (cmol_c dm^-3)	1.64	0.50	3.02	1.93	0.07	6.55
V (%)	10.64	0.77	34.53	17.45	1.10	90.30
m (%)	57.41	9.60	95.77	51.39	0.00	94.90
P-rem (mg L^-1)	18.51	5.44	60.00	26.63	0.70	57.20
OM (g kg^-1)	41.87	3.78	79.35	30.17	1.30	89.00
Sand (g kg^-1)	331	49	926	401	37	910
Silt (g kg^-1)	74	6	230	170	10	590
Clay (g kg^-1)	594	54	884	430	60	810

Soil	Lime requirement⁽¹⁾

	Incubation		OMpH		HAlpH		X⁽²⁾	HAl⁽³⁾

	pH 5.8	pH 6.0	pH 5.8	pH 6.0	pH 5.8	pH 6.0
	--------------------------------------t ha^-1--------------------------------------
	Calibration dataset⁽⁴⁾
LVAd₁	4.03	5.28	3.06	4.46	2.55	3.72
LVd₁	3.97	4.70	1.24	2.12	0.83	1.35
Law	3.08	3.95	2.95	4.14	2.74	3.82
LVAd₂	4.32	6.15	2.00	2.88	2.57	3.60
LVAa	6.62	8.72	6.38	8.36	6.28	8.55
LVAd₃	0.84	1.61	0.71	1.13	1.11	1.63	2
LVd₂	4.52	5.94	2.27	3.47	2.01	3.02
LVAd₄	4.54	6.99	4.53	6.07	5.24	7.16
LVAd₅	3.11	4.21	4.11	5.64	3.71	5.17
LVd₃	5.89	7.31	5.48	7.33	4.35	6.00
LVd₄	2.54	4.50	3.47	4.93	3.15	4.49
PVAd₁	0.75	1.31	1.61	2.50	1.41	2.10
CXbd	3.18	4.14	2.24	3.11	2.83	3.84
LVd₅	2.45	3.68	4.84	6.48	3.52	4.81
LVw	2.91	3.65	2.92	4.04	2.75	3.78
PVAd₂	2.40	3.42	1.39	2.36	1.18	1.92
LVAd₆	3.38	4.14	0.89	1.51	0.76	1.21	2
LAd₁	1.87	2.54	2.19	3.07	2.36	3.24
LVd₆	1.08	1.64	1.16	1.73	1.59	2.24
LAd₂	1.59	2.62	2.37	3.27	3.35	4.55
RQo₁	1.60	2.39	1.21	1.85	1.24	1.78	2
RQo₂	0.57	0.77	0.19	0.33	0.48	0.72		1.68

Lowest	0.57	0.77	0.19	0.33	0.48	0.72
Highest	6.62	8.72	6.38	8.36	6.28	8.55
Mean	2.97	4.07	2.60	3.67	2.55	3.58
CV (%)	54.81	50.54	62.89	56.96	58.45	55.35
	Validation dataset⁽⁵⁾
Lowest			0.00	0.04	0.00	0.07
Highest			5.20	7.15	4.57	6.25
Mean			1.46	2.20	1.70	2.48
CV (%)			76.06	67.69	66.45	61.79

Method		target pH	β₀	β₁	R²	F(H₀)	tē	r_Y1Yj ≥ (1 - \|ē\|)	Conclusion⁽²⁾

Standard	Alternative⁽¹⁾
Incubation	OMpH	5.8	0.24^ns	0.8988^ns	0.865	0.64^ns	0.24^ns	No	Y_j ≠ Y₁
Incubation	OMpH	6.0	0.26^ns	0.9202^ns	0.887	0.47^ns	0.03^ns	No	Y_j ≠ Y₁
Incubation	HAlpH	5.8	0.60^ns	0.7738^ns	0.759	2.02^ns	1.02^ns	No	Y_j ≠ Y₁
Incubation	HAlpH	6.0	0.55^ns	0.8514^ns	0.836	1.17^ns	0.73^ns	No	Y_j ≠ Y₁

Soil	LR_R	LR_H	LR_H /LR_R	pH	M^x+	HAl	Ca²⁺	Mg²⁺
	-------t ha^-1-------		%		---------------cmol_c dm^-3---------------
	Calibration dataset⁽¹⁾
LVAd₁	2.55	4.46	175	5.52	0.09	5.59	1.62	0.68
LVd₁	2.12	2.12	100	5.46	0.04	4.33	1.24	0.43
Law	2.74	4.14	151	5.72	0.03	4.40	1.39	0.43
LVAd₂	2.00	3.60	180	5.47	0.19	5.98	1.33	0.40
LVAa	6.28	8.55	136	5.77	0.06	7.35	3.56	1.17
LVAd₃	2.00	2.00	100	6.12	0.02	1.94	1.33	0.34
LVd₂	2.01	3.47	172	5.59	0.04	4.96	2.73	1.08
LVAd₄	4.53	7.16	158	5.80	0.18	7.73	2.72	0.79
LVAd₅	3.71	5.17	139	5.91	0.02	5.60	2.07	0.75
LVd₃	4.35	7.33	168	5.56	0.22	8.03	2.40	1.01
LVd₄	3.15	4.93	157	5.86	0.09	6.11	2.62	0.58
PVAd₁	2.10	2.50	119	6.27	0.01	2.46	1.39	0.73
CXbd	2.24	3.84	172	5.58	0.08	3.09	1.32	0.39
LVd₅	3.52	6.48	184	5.97	0.09	7.04	1.77	0.66
LVw	2.75	4.04	147	5.75	0.03	3.52	1.44	0.43
PVAd₂	2.36	2.36	100	5.79	0.00	3.64	2.45	1.60
LVAd₆	2.00	2.00	100	5.43	0.09	3.50	1.53	0.57
LAd₁	2.19	3.24	148	5.90	0.06	2.65	1.25	0.41
LVd₆	2.24	2.24	100	6.21	0.01	1.78	0.95	0.39
LAd₂	2.37	4.55	192	5.95	0.03	3.87	1.20	0.42
RQo₁	2.00	2.00	100	5.90	0.08	2.76	1.37	0.98
RQo₂	1.68	1.68	100	6.63	0.00	0.55	0.73	0.14

Lowest	1.68	1.68	100	5.43	0.00	0.55	0.73	0.14
Highest	6.28	8.55	192	6.63	0.22	8.03	3.56	1.60
Mean	2.77	3.99	141	5.83	0.07	4.40	1.75	0.65
CV (%)	40.06	49.09	23.40	5.10	92.30	47.01	40.60	52.32

Criteria⁽¹⁾	OMpH		HAlpH

	pH 5.8	pH 6.0	pH 5.8	pH 6.0
	-------------------------------------------------------------------------%-------------------------------------------------------------------------
	Calibration dataset⁽²⁾
LR = 0 (non-recommended)	0.00	0.00	0.00	0.00
0 < LR < X	37.50	25.00	41.67	29.17
X ≤ LR ≤ HAl	62.50	75.00	58.33	70.83
LR > HAl	0.00	0.00	0.00	0.00
LR_R⁽³⁾	26.32	10.53	47.37	15.79
	Validation dataset⁽⁴⁾
LR = 0	3.00	0.00	3.00	0.00
0 < LR < X	74.00	47.00	67.00	39.00
X ≤ LR ≤ HAl	23.00	51.00	30.00	61.00
LR > HAl	0.00	2.00	0.00	0.00
LR_R	29.23	29.23	10.77	30.77

Sociedade Brasileira de Ciência do Solo Sociedade Brasileira de Ciência do Solo, Departamento de Solos - Edifício Silvio Brandão, s/n, Caixa Postal 231 - Campus da UFV, CEP 36570-900 - Viçosa-MG, Tel.: (31) 3612-4542 - Viçosa - MG - Brazil
E-mail: sbcs@sbcs.org.br

Acompanhe os números deste periódico no seu leitor de RSS

[1] * Corresponding author: E-mail: welldygteixeira@gmail.com