Developing a Soil Physical Quality Index (SPQi) for lowlands under different deployment times of no-tillage

Diony Alves Reis Claudia Liane Rodrigues de Lima Adilson Luís Bamberg About the authors

ABSTRACT:

Soil physical quality in lowlands from the Pampa biome under no-tillage (NT) plays an important role; therefore, this study aimed to establish a soil physical quality index (SPQi) from a minimum data set to detect the effects of different deployment times of NT in an Albaqualf. The comparison of areas with one (NT1), three (NT3), five (NT5) and seven (NT7) years of notillage was established using as reference a non-cultivated field plot (NC) for at least thirty years, nearby the sites under NT. Soil samples with undisturbed and disturbed structure were collected to determine the physical quality indicators and soil organic matter (SOM) fractions. The factor analysis (FA) was used to identify and select a minimum data set. The SPQi was elaborated by using the deviations of the measured indicators at different deployment times of NT in relation to NC. The SPQi showed sensibility to identify and explain soil physical quality changes with different deployment times of NT. In well-drained lands, higher deployment times of no-tillage promote the physical quality of lowlands.

Keywords:
factor analysis; minimum data set; management systems; lowland soils

Introduction

In Brazil, the Pampa biome occurs in Rio Grande do Sul State (RS), where 5.4 million hectares are lowlands, and Albaqualf is the predominant class (Parfitt et al., 2014Parfitt, J.M.B.; Timm, L.C.; Reichardt, K.; Pauletto, E.A. 2014. Impacts of land leveling on lowland soil physical properties. Revista Brasileira de Ciência do Solo 38: 315-326.). Albaqualf has high agricultural and economic importance due to its physical characteristics. The presence of a subsurface layer almost impermeable, with expansive clays and low macro/microporosity ratio, favor irrigated rice and livestock. Thus, the agricultural growth of the region is strongly dependent on understanding soil physical properties in this environment.

Rainfed crops have been recently introduced into these soils, mostly cultivated under conventional tillage (CT) (Reis et al., 2016Reis, D.A.; Lima, C.L.R.; Bamberg, A.L. 2016. Physical quality and organic matter fractions of an Alfisol under no-tillage. Pesquisa Agropecuária Brasileira 51: 1623-1632 (in Portuguese, with abstract in English).). The soybean has been an alternative for the traditional flooded rice-livestock sequence, but there is a concern with the sustainability of this production model, as no-tillage (NT) has proven more profitable and environmentally favorable in rainfed agriculture (Crittenden et al., 2015Crittenden, S.J.; Poot, N.; Heinen, M.; van Balen, D.J.M.; Pulleman, M.M. 2015. Soil physical quality in contrasting tillage systems in organic and conventional farming. Soil and Tillage Research 154: 136-144.; Fernández-Romero et al., 2016Fernández-Romero, M.L.; Clark, J.M.; Collins, C.D.; Parras-Alcántara, L.; Lozano-García, B. 2016. Evaluation of optical techniques for characterizing soil organic matter quality in agricultural soils. Soil and Tillage Research 155: 450-460.; Raiesi and Kabiri, 2016Raiesi, F.; Kabiri, V. 2016. Identification of soil quality indicators for assessing the effect of different tillage practices through a soil quality index in a semi-arid environment. Ecological Indicators 71: 198-207.) than other management systems.

Impacts of management systems on soil physical quality (SPQ) have been studied by the S index (Dexter, 2004Dexter, A.R. 2004. Soil physical quality. I. Theory, effects of soil texture, density, and organic matter, and effects on root growth. Geoderma 120: 201-214.), but its inconsistency has also been highlighted (van Lier, 2014Van Lier, Q. de J. 2014. Revisiting the 5-index for soil physical quality and its use in Brazil. Revista Brasileira de Ciência do Solo 38: 1-10.; Moncada et al., 2015Moncada, M.P.; Ball, B.C.; Gabriels, D.; Lobo, D. 2015. Evaluation of soil physical quality index S for some tropical and temperate medium-textured soils. Soil Science Society of America Journal 79: 9-19.). In this sense, soil quality indices (SQI) were developed based on the appropriate selection of soil quality indicators to compose a minimum data set (MDS) (Karlen and Stott, 1994Karlen, D.L.; Stott, D.E. 1994. A framework for evaluating physical and chemical indicators of soil quality. p. 53-72. In: Doran, J.W.; Coleman, D.C.; Bezdicek, D.F.; Stewart, B.A., eds. Defining soil quality for a sustainable environment. Soil Science Society of America, Madison, WI, USA. (SSSA Special Publication, 35).; Karlen et al., 2001Karlen, D.L.; Andrews, S.S.; Doran, J.W. 2001. Soil quality: current concepts and applications. Advances in Agronomy 74: 1-40.; Lima et al., 2008Lima, A.C.R.; Hoogmoed, W.B.; Brussaard, L. 2008. Soil quality assessment in rice production systems: establishing a minimum data set. Journal of Environmental Quality 37: 623-630.; Maia, 2013Maia, C.E. 2013. Environmental quality in soil with different growing season cultivated with muskmelon irrigated. Ciência Rural 43: 603-609 (in Portuguese, with abstract in English).; Chen et al., 2013Chen, Y.; Wang, H.; Zhou, J.; Xing, L.; Zhu, B.; Zhao, Y.; Chen, X. 2013. Minimum data set for assessing soil quality in farmland of northeast China. Pedosphere 23: 564-576.; Yao et al., 2013Yao, R.; Yang, J.; Gao, P; Zhang, J.; Jin, W. 2013. Determining minimum data set for soil quality assessment of typical salt-affected farmland in the coastal reclamation area. Soil and Tillage Research 128: 137-148.; Zornoza et al., 2015Zornoza, R.; Acosta, J.A.; Bastida, F.; Domínguez, S.G.; Toledo, D.M.; Faz, A. 2015. Identification of sensitive indicators to assess the interrelationship between soil quality, management practices and human health. Soil 1: 173-185.; Zhang et al., 2016Zhang, G.; Bai, J.; Xi, M.; Zhao, Q.; Lu, Q.; Jia, J. 2016. Soil quality assessment of coastal wetlands in the Yellow River Delta of China based on the minimum data set. Ecological Indicators 66: 458-466.; Rojas et al., 2016Rojas, J.M.; Prause, J.; Sanzano, G.A.; Arce, O.E.A.; Sánchez, M.C. 2016. Soil quality indicators selection by mixed models and multivariate techniques in deforested areas for agricultural use in NW of Chaco, Argentina. Soil and Tillage Research 155: 250-262.).

In MDS, attributes can be chosen by statistical methods (Paz-Kagan et al., 2014Paz-Kagan, T.; Shachak, M.; Zaady, E.; Karnieli, A. 2014. A spectral soil quality index (SSQI) for characterizing soil function in areas of changed land use. Geoderma 171-184.; Raiesi and Kabiri, 2016Raiesi, F.; Kabiri, V. 2016. Identification of soil quality indicators for assessing the effect of different tillage practices through a soil quality index in a semi-arid environment. Ecological Indicators 71: 198-207.; Obade and Lal, 2016Obade, V.P.; Lal, R. 2016. A standardized soil quality index for diverse field conditions. Science of the Total Environment 541: 424-434.), such as the factor analysis (FA), which reduces redundant information from the original data set and groups soil attributes highly interrelated in a smaller group of representative and independent attributes (Zhang et al., 2016Zhang, G.; Bai, J.; Xi, M.; Zhao, Q.; Lu, Q.; Jia, J. 2016. Soil quality assessment of coastal wetlands in the Yellow River Delta of China based on the minimum data set. Ecological Indicators 66: 458-466.; Raiesi and Kabiri, 2016Raiesi, F.; Kabiri, V. 2016. Identification of soil quality indicators for assessing the effect of different tillage practices through a soil quality index in a semi-arid environment. Ecological Indicators 71: 198-207.), helping to understand the effects of changing from CT to the NT system on soil physical quality.

Thus, given the agricultural importance of lowlands from the Pampa biome, this study aimed to establish an MDS to develop a soil physical quality index (SPQi) and evaluate its sensitivity to different deployment times of NT in Albaqualf from southern Brazil.

Materials and Methods

The study was carried out at the Lowland Experimental Station - Embrapa Temperate Agriculture, located in Capão do Leão, RS, Brazil (31°49′04.13” S, 52°27′53.77” W, 14 m above sea level). The climate is Cfa, according to Köppen classification (Alvares et al., 2013Alvares, C.A.; Stape, J.L.; Sentelhas, P.C.; Gonçalves, J.L.M.; Sparovek, G. 2013. Köppen's climate classification map for Brazil. Meteorologische Zeitschrift 22: 711-728.), a hot mesothermal climate, with average temperature of coldest month between 3 and 18 °C. Average monthly rainfall is not below 60 mm, always humid, and average temperature of the hottest month higher than 22 °C. The soil was classified as Albaqualf (NRCS, 2010Natural Resources Conservation Service [NRCS]. 2010. Keys to Soil Taxonomy. 11ed. United States Department of Agriculture, Washington, DC, USA.) with 460 g kg−1 of sand, 370 g kg−1 of silt and 170 g kg−1 of clay within 0.0 to 0.2 m top layer.

The surface soil layer of the experimental area was historically managed under conventional tillage (CT). For this study, four experimental plots were selected and homogenized before NT deployment through chisel plowing and soil acidity correction by superficial incorporation of dolomitic limestone using disc harrows. Then, different cover crops (Table 1) were established posteriorly, using 2 kg N ha−1, 26.2 kg P ha−1 and 49.8 kg ha−1 of mineral fertilizer to summer crops and 15 kg N ha−1, 26.2 kg P ha−1, 49.8 kg ha−1 (base fertilization) and 100 kg N ha−1 (cover fertilization) to summer and winter grasses. Furthermore, spontaneous plants were not fertilized.

Table 1
Crop sequence cultivated in an Albaqualf under different deployment times of no-tillage (NT).

The study consists of five treatments, four NT [one (NT1), three (NT3), five (NT5) and seven (NT7) years under no-till] and a control treatment consisting of a 30-yr non-cultivated (NC) field located near the no-till treatments.

Soil samples with disturbed and undisturbed structure were collected from the 0.00 to 0.03; 0.03 to 0.06; 0.06 to 0.10 and 0.10 to 0.20 m soil layers. The sampled layers were defined in terms of their susceptibility to physical and hydric changes that originated from tillage and root systems activity of cultivated crops over the time.

Soil samples with undisturbed structure were collected with steel cylinders of 0.05 m diameter and 0.03 m height, totaling 240 samples (three cylinders for each layer × four soil layers × four replicates × five treatments). The soil samples were used to determine total porosity (TP), macroporosity (Ma), microporosity (Mi) (0.006 MPa to distinguish Ma and Mi by the tension table method), soil penetration resistance (PR) (Rousseau et al., 2013Rousseau, L.; Fonte, S.J.; Téllez, O.; Van der Hoek, R.; Lavelle, P. 2013. Soil macrofauna as indicators of soil quality and land use impacts in smallholder agroecosystems of western Nicaragua. Ecological Indicators 27: 71-82.; D'Hose et al., 2014D'Hose, T.; Cougnon, M.; De Vliegher, A.; Vandecasteele, B.; Viaene, N.; Cornelis, W.; Van Bockstaele, E.; Reheul, D. 2014. The positive relationship between soil quality and crop production: a case study on the effect of farm compost application. Applied Soil Ecology 75: 189-198.), bulk density (Bd) (Merrill et al., 2013Merrill, S.D.; Liebig, M.A.; Tanaka, D.L.; Krupinsky, J.M.; Hanson, J.D. 2013. Comparison of soil quality and productivity at two sites differing in profile structure and topsoil properties. Agriculture, Ecosystems and Environment 179: 53-61.) and soil compressive parameters, preconsolidation pressure (σp), bulk density at preconsolidation pressure (Bdσp), compression index (CI) (Krümmelbein et al., 2010Krümmelbein, J.; Horn, R.; Raab, T.; Bens, O.; Hüttl, R.F. 2010. Soil physical parameters of a recently established agricultural recultivation site after brown coal mining in eastern Germany. Soil and Tillage Research 111: 19-25.), degree of compactness (Kondo and Dias Junior, 1999Kondo, M.K.; Dias Junior, M.S. 1999. Management and moisture effects on the compressive behavior of three latosols (oxisols). Brazilian Journal of Soil Science 23: 497-506 (in Portuguese, with abstract in English).), at σp (DCσp, %) and at 1.600 kPa (DC1.600) (Reichert et al., 2016Reichert, J.M.; Rosa, V.T.; Vogelmann, E.S.; Rosa, D.P.; Hornd, R.; Reinert, D.J.; Sattlere, A.; Denardin, J.E. 2016. Conceptual framework for capacity and intensity physical soil properties affected by short and long-term (14 years) continuous no-tillage and controlled traffic. Soil and Tillage Research 158: 123-136.).

Soil samples with disturbed structure were collected, totaling 80 samples (one soil sample × four soil layers × four replicates × five treatments), to determine size classes of water-stable aggregates Ci, where i represents 1, 2, 3, 4, 5 classes (C1 = 9.51 to 4.76 mm; C2 = 4.75 a 2.00 mm; C3 = 1.99 a 1.00 mm; C4 = 0.99 a 0.50 mm; C5 = 0.49 a 0.25 mm and C6 < 0.25 mm), Macroaggregates (Macro), Microaggregates (Micro), mean weight diameter of aggregates (MWD) (Kemper and Rosenau, 1986Kemper, W.D.; Rosenau, R.C. 1986. Aggregate stability and size distribution. p. 425-441. In: Klute, A., ed. Methods of soil analysis. 2ed. Soil Science Society of America, Madison, WI, USA.; Palmeira et al., 1999Palmeira, P.R.T.; Pauletto, E.A.; Teixeira, C.F.A.; Gomes, A.S.; Silva, J.B. 1999. Soil aggregation of an Albaqualf submitted to different soil tillage systems. Revista Brasileira de Ciência do Solo 23: 189-195 (in Portuguese, with abstract in English).; Yoder, 1936Yoder, R.E. 1936. A direct method of aggregate analysis of soil and a study of the physical nature of erosion losses. Journal of American Society of Agronomy 28: 337-351.), the free light fraction (FLF), the occluded light fraction (OLF) and the heavy fraction (HF) of organic matter contained in soil aggregates by performing the densimetric fractionation of soil organic matter (SOM) (Imaz et al., 2010Imaz, M.J.; Virto, I.; Bescansa, P.; Enrique, A.; Fernandez-Ugalde, O.; Karlen, D.L. 2010. Soil quality indicator response to tillage and residue management on semi-arid Mediterranean cropland. Soil and Tillage Research 107: 17-25.).

The total organic carbon content (TOC) was determined (dry combustion - Perkin Elmer elemental analyzer) in the densimetric fractions, and the carbon pool index (CPI), the carbon lability index (CLI) and the carbon management index (CMI) were quantified.

The dataset included 24 indicators: TP, Ma, Mi, PR, Bd, σp, CI, Bdσp, DCσp, DC1600, C2, C3, C4, C5, Macroaggregates, Microaggregates, MWD, FLF, OLF, HF, TOC, CPI, CLI and CMI. The soil attributes were subjected to the factor analysis (FA) to identify highly correlated indicators for subsequent establishment of a minimum data set by eliminating attributes considered as redundant.

The FA was carried out using covariance (raw data) and correlation (standardized data) matrix. Variables with sampling adequacy (Kaiser Criterion) < 0.5 were eliminated from the FA. Using the correlation matrix, factors with eigenvalues > 1 were retained and subjected to varimax rotation to maximize correlation between factors and measured attributes and to constitute the minimum data set (Yao et al., 2013Yao, R.; Yang, J.; Gao, P; Zhang, J.; Jin, W. 2013. Determining minimum data set for soil quality assessment of typical salt-affected farmland in the coastal reclamation area. Soil and Tillage Research 128: 137-148.; Mueller et al., 2013Mueller, L.; Shepherd, G.; Schindler, U.; Ball, B.C.; Munkholm L.J.; Hennings, V.; Smolentseva, E.; Rukhovic, O.; Lukin, S.; Hui, C. 2013. Evaluation of soil structure in the framework of an overall soil quality rating. Soil and Tillage Research 127: 74-84.; Mota et al., 2014Mota, J.C.A.; Alves, C.V.O.; Freire, A.G.; Assis Júnior, R.N. 2014. Uni and multivariate analyses of soil physical quality indicators of a Cambisol from Apodi Plateau - CE, Brazil. Soil and Tillage Research 140: 66-73.). The FA, the Communality and the SPQi were performed by PROC FACTOR and PROC ANOVA in SAS (Statistical Analyses System Institute, version 9.2).

The Measure of Sampling Adequacy (MSA) indicates the proportion of variance in the variables caused by underlying factors. Values close to 1.0 (the measures can range from 0 to 1) generally indicate that the FA may be useful with the data while values lower than 0.5 indicate that the FA is probably not be suitable (Beavers et al., 2013Beavers, A.S.; Lounsbury, J.W.; Richards, J.K.; Huck, S.W.; Skolits, G.J.; Esquivel, S.L. 2013. Practical considerations for using exploratory factor analysis in educational research. Practical Assessment, Research and Evaluation 18: 1-13.).

Equation 1 was used to calculate the MSA value:

1 M S A i j r i j 2 i j r i j 2 + i j a i j 2

MSA represents the ratio of the squared correlation between variables to the squared partial correlation between variables (Kaiser, 1974Kaiser, H.F. 1974. An index of factorial simplicity. Psychometrika 39: 32-35.), where: rij is the correlation coefficient observed between variables i and j; aij is the partial correlation coefficient between the same variables that is, simultaneously, an estimate of the correlation between the factors. The αij is probably close to zero because factors are orthogonal to each other.

The deviations of the measured attribute values in the areas under different deployment times of NT in relation to the reference values measured in the non-cultivated field (NC) were calculated according to equation 2:

2 z i = x x ¯ s

where: zi is the standardized value selected by the FA with mean (μ) and standard deviation (σ) equals to zero and one, respectively; x is the value of the soil attribute evaluated in the sites with different deployment times of NT; x¯ and s is the mean and the standard deviation, respectively, of the soil attribute evaluated in NC.

To estimate the values of quality index (QIi) of the each evaluated soil attribute, we used equations 3, 4 and 5 for the conditions “more is better”, “less is better” and “midpoint optimum”, respectively, with β = exp(−1,7145zi) (Maia, 2013Maia, C.E. 2013. Environmental quality in soil with different growing season cultivated with muskmelon irrigated. Ciência Rural 43: 603-609 (in Portuguese, with abstract in English).).

The curve for the condition “more is better” has positive derivative and is used in indicators that improve soil quality, for example, total porosity, total organic carbon, etc.; “midpoint optimum” has positive derivative until a maximum value and is used in indicators that positively affect soil quality until certain values that, if passed, cause negative influence such as bulk density, penetration resistance, etc. The curve for the condition “less is better” has negative derivative and is used in indicators that negatively affect the soil quality index, such as compactness degree (Chen et al., 2013Chen, Y.; Wang, H.; Zhou, J.; Xing, L.; Zhu, B.; Zhao, Y.; Chen, X. 2013. Minimum data set for assessing soil quality in farmland of northeast China. Pedosphere 23: 564-576.; Nakajima et al., 2015Nakajima, T.; Lal, R.; Jiang, S. 2015. Soil quality index of a Crosby silt loam in central Ohio. Soil and Tillage Research 146: 323-328.; Zhang et al., 2016Zhang, G.; Bai, J.; Xi, M.; Zhao, Q.; Lu, Q.; Jia, J. 2016. Soil quality assessment of coastal wetlands in the Yellow River Delta of China based on the minimum data set. Ecological Indicators 66: 458-466.).

3 Q I = 1 1 + β
4 Q I = β 1 + β
5 Q I = ( 1 + β ) 2

The soil physical quality index (SPQi) in each evaluated site was calculated by equation 6:

6 S P Q i = i = n n O I i n

where: QIi is the quality index of the evaluated characteristic and n is the number of evaluated characteristics. Soil quality evaluated with QIi′ or the conditions “more is better”, “less is better” and “midpoint optimum” and without changes compared to the reference site has QIi equal to one. Thus, values farther from one mean higher changes in relation to NC and reflecting these changes in SPQi (Maia, 2013Maia, C.E. 2013. Environmental quality in soil with different growing season cultivated with muskmelon irrigated. Ciência Rural 43: 603-609 (in Portuguese, with abstract in English).; Chen et al., 2013Chen, Y.; Wang, H.; Zhou, J.; Xing, L.; Zhu, B.; Zhao, Y.; Chen, X. 2013. Minimum data set for assessing soil quality in farmland of northeast China. Pedosphere 23: 564-576.; Nakajima et al., 2015Nakajima, T.; Lal, R.; Jiang, S. 2015. Soil quality index of a Crosby silt loam in central Ohio. Soil and Tillage Research 146: 323-328.; Zhang et al., 2016Zhang, G.; Bai, J.; Xi, M.; Zhao, Q.; Lu, Q.; Jia, J. 2016. Soil quality assessment of coastal wetlands in the Yellow River Delta of China based on the minimum data set. Ecological Indicators 66: 458-466.).

Results and Discussion

The analysis of 24 soil quality indicators of Albaqualf resulted in significant correlation (p < 0.05) in 172 of 276 soil attribute pairs (Table 2). Highest positive correlation coefficients (r ≥ 0.80) were obtained for TP × Mi, FLF × TOC, HF × TOC, HF × CPI, C3 × C4, CLI × CMI, while the highest negative correlation was observed between Macro and Micro (r = 0.97). The carbon content in OLF showed negative correlation with PR (r > 0.60), suggesting that compaction reduces the carbon content between and within aggregates and favors the soil degradation process. In contrast, the TOC presented positive correlation with Ma (r = 0.77).

Table 2
Correlation between attributes of an Albaqualf under different deployment times of no-tillage (NT) in 0.00 to 0.20 m soil layer (n = 192).

Askari and Holden (2015)Askari, M.S.; Holden, N.M. 2015. Quantitative soil quality indexing of temperate arable management systems. Soil and Tillage Research 150: 57-67. evaluated 22 indicators of soil physical quality for assessing the effects of management practices on SQ in temperate maritime soils, while Yao et al. (2013)Yao, R.; Yang, J.; Gao, P; Zhang, J.; Jin, W. 2013. Determining minimum data set for soil quality assessment of typical salt-affected farmland in the coastal reclamation area. Soil and Tillage Research 128: 137-148. used the Factor analysis (FA) to group 22 variables. According to Armenise et al. (2013)Armenise, E.; Redmile-Gordon, M.A.; Stellacci, A.M.; Ciccarese, A.; Rubino, A.P. 2013. Developing a soil quality index to compare soil fitness for agricultural use under different managements in the Mediterranean environment. Soil and Tillage Research 130: 91-98., the FA general rules is to receive high eigenvalues (> 1.00) and to select variables with high factor loadings. These components allow to obtain the best parameter representative and retain it for screening of MDS (Chen et al., 2013Chen, Y.; Wang, H.; Zhou, J.; Xing, L.; Zhu, B.; Zhao, Y.; Chen, X. 2013. Minimum data set for assessing soil quality in farmland of northeast China. Pedosphere 23: 564-576.; Yao et al., 2013Yao, R.; Yang, J.; Gao, P; Zhang, J.; Jin, W. 2013. Determining minimum data set for soil quality assessment of typical salt-affected farmland in the coastal reclamation area. Soil and Tillage Research 128: 137-148.; Zornoza et al., 2015Zornoza, R.; Acosta, J.A.; Bastida, F.; Domínguez, S.G.; Toledo, D.M.; Faz, A. 2015. Identification of sensitive indicators to assess the interrelationship between soil quality, management practices and human health. Soil 1: 173-185.; Zhang et al., 2016Zhang, G.; Bai, J.; Xi, M.; Zhao, Q.; Lu, Q.; Jia, J. 2016. Soil quality assessment of coastal wetlands in the Yellow River Delta of China based on the minimum data set. Ecological Indicators 66: 458-466.; Rojas et al., 2016Rojas, J.M.; Prause, J.; Sanzano, G.A.; Arce, O.E.A.; Sánchez, M.C. 2016. Soil quality indicators selection by mixed models and multivariate techniques in deforested areas for agricultural use in NW of Chaco, Argentina. Soil and Tillage Research 155: 250-262.).

The MSA values are used to keep or exclude attributes from the FA and are given in Table 3. Only 15 from the 24 soil attributes used initially were kept after the Kaiser criterion (MSA > 0.5). Despite the MSA values below 0.5, the following variables DCσp, DC1.600, CPI and CLI were kept because the mean MSA value obtained for the set of variables was higher than 0.5. The FA was performed using a group of attributes with mean MSA higher than 0.5 thus using the parameters: TP, Ma, Bd, PR, FLF, OLF, TOC, σp, CI, Bdσp, DCσp, DC1600, CPI, CLI and CMI similarly to Yao et al. (2013)Yao, R.; Yang, J.; Gao, P; Zhang, J.; Jin, W. 2013. Determining minimum data set for soil quality assessment of typical salt-affected farmland in the coastal reclamation area. Soil and Tillage Research 128: 137-148. and Beniston et al. (2016)Beniston, J.W.; Lal, R.; Mercer, K.L. 2016. Assessing and managing soil quality for urban agriculture in a degraded vacant lot soil. Land Degradation & Development 27: 996-1006..

Table 3
Kaiser-Meyer-Olkin Measure of Sampling Adequacy (MSA) of attributes of an Albaqualf under different deployment times of no-tillage (NT) in 0.00 to 0.20 m soil layer.

Eigenvalues from the correlation matrix indicate that the first four factors explained > 98 % of total data variation (Table 4) (Yao et al., 2013Yao, R.; Yang, J.; Gao, P; Zhang, J.; Jin, W. 2013. Determining minimum data set for soil quality assessment of typical salt-affected farmland in the coastal reclamation area. Soil and Tillage Research 128: 137-148.; Paz-Kagan et al., 2014Paz-Kagan, T.; Shachak, M.; Zaady, E.; Karnieli, A. 2014. A spectral soil quality index (SSQI) for characterizing soil function in areas of changed land use. Geoderma 171-184.; Beniston et al., 2016Beniston, J.W.; Lal, R.; Mercer, K.L. 2016. Assessing and managing soil quality for urban agriculture in a degraded vacant lot soil. Land Degradation & Development 27: 996-1006.; Basak et al., 2016Basak, N.; Datta, A.; Mitran, T.; Roy, S.S.; Saha, B.; Biswas, S.; Mandal, B. 2016. Assessing soil-quality indices for subtropical rice-based cropping systems in India. Soil Research 54: 20-29.).

Table 4
Eigenvalue, difference, proportion and cumulative variance explained by factor analysis using correlation matrix (standardized data) for 0.00 to 0.20 m soil layer of an Albaqualf under different deployment times of no-tillage (NT).

Factors with eigenvalue below 1 explain less variance than an isolated soil attribute and therefore were refused according to the Kaiser Criterion (Armenise et al., 2013Armenise, E.; Redmile-Gordon, M.A.; Stellacci, A.M.; Ciccarese, A.; Rubino, A.P. 2013. Developing a soil quality index to compare soil fitness for agricultural use under different managements in the Mediterranean environment. Soil and Tillage Research 130: 91-98.; Thomazini et al., 2015Thomazini, A.; Mendonça, E.S.; Cardoso, I.M.; Garbin, M.L. 2015. SOC dynamics and soil quality index of agroforestry systems in the Atlantic rainforest of Brazil. Geoderma Regional 5: 15-24.). The first factor, with eigenvalue > 5, explained 53 % of total data variance (Table 4) with FLF and TOC, evidencing the higher positive loadings (0.93 and 0.92, respectively). Nevertheless, contrasted with Bd and PR, that showed greater negative loadings (−0.63 and −0.61, respectively) (Table 5). Similarly, the second factor with an eigenvalue higher than one represents 17 % of total variability, where CLI and CMI evidenced higher positive loadings (0.80 and 0.76), contrasting with TP, OLF and CI, which showed greater negative loadings (−0.24, −0.24 and −0.21, respectively) (Table 5).

Table 5
Proportion of variance using varimax rotation and communality estimates for soil attributes in the 0.00 to 0.20 m soil layer of an Albaqualf under different deployment times of notillage.

Considering the magnitude of the factorial loadings of soil attributes presented in each factor, authors have named factors according to the relationship between soil attributes and factors (Karlen et al., 2013Karlen, D.L.; Kovar, J.L.; Cambardella, C.A.; Colvin, T.S. 2013. Thirty-year tillage effects on crop yield and soil fertility indicators. Soil and Tillage Research 130: 24-41.; Gong et al., 2015Gong, L.; Ran, Q.; He, G.; Tiyip, T. 2015. A soil quality assessment under different land use types in Keriya river basin, southern Xinjiang, China. Soil and Tillage Research 146: 223-229.). For example, factor 1 could be named as “Organic factor” because it presents positive factor loadings > 0.9, and the highest factor loadings were observed with attributes FLF and TOC. However, the factors were not named in this study.

Greater communality estimates were observed in CLI and CMI (0.98) (Table 5), evidencing that these attributes share variability (Yao et al., 2013Yao, R.; Yang, J.; Gao, P; Zhang, J.; Jin, W. 2013. Determining minimum data set for soil quality assessment of typical salt-affected farmland in the coastal reclamation area. Soil and Tillage Research 128: 137-148.; Mueller et al., 2013Mueller, L.; Shepherd, G.; Schindler, U.; Ball, B.C.; Munkholm L.J.; Hennings, V.; Smolentseva, E.; Rukhovic, O.; Lukin, S.; Hui, C. 2013. Evaluation of soil structure in the framework of an overall soil quality rating. Soil and Tillage Research 127: 74-84.), as well as TOC (0.97) and FLF (0.89). Furthermore, high values of communality estimate suggest that a high part of variance was explained by the factor. Lower values of communality estimate means no correlation or low correlation, as observed in CI. Thus, CI was the least important attribute due to the lowest communality estimate.

The rotation of factors was applied to minimize the number of attributes with high factorial loadings within the same factor. Rotation also shows the relation of dependence between each other, negative or positive. High factorial loadings were observed in OLF (0.81), FLF (0.68), TP (0.64), Bd (-0.51) and PR (-0.71) in Factor 1, despite the positive and negative loadings. Therefore, the dependence between them was evident, as seen in their correlation coefficients (Table 2), suggesting that choosing one is enough to represent Factor 1 and the variables that compose it.

CLI and CMI presented positive loadings > 0.9 in Factor 2 while CPI, TOC and Ma showed positive loadings > 0.5. In Factor 4, DC1.600 and DCσp presented positive loadings > 0.8 (Table 6).

Table 6
Varimax orthogonal rotation of the factors for soil attributes in 0.00 to 0.20 m soil layer of an Albaqualf under different deployment times of no-tillage.

According to the three standardization models (“less is better”, “more is better” and “midpoint optimum”), one parameter was selected per factor to compose the quality index of Albaqualf under different NT deployment times: Factor 1 (PR) “less is better”, Factor 3 (Ma) “midpoint optimum” and Factor 4 (DCσp) “less is better”. Attributes for Factor 2 were not chosen, because the higher factor loading (> 0.9) were observed in CLI and CMI, which are already quality indexes of the Albaqualf compared to NC.

In general, a tendency of quality improvement of Albaqualf was seen with higher deployment times in all evaluated soil layers. This can be observed by greater Ma and lower PR and DC p values, linked to the higher deployment time of NT (Table 7). Soil quality, evaluated through several characteristics that influence plant growth and development and considering the three selected parameters, was promoted by the long term of NT. This is evident because of the high correlation coefficient (0.86, p < 0.0001) between SPQi and higher deployment times of NT (Figure 1).

Table 7
Mean values, standard deviation (SD) and variation coefficient (VC, %) Macroporosity (Ma), Penetration resistance (PR) and compactness degree at preconsolidation pressure (DCσp) of an Albaqualf at a non-cultivated field stie and under different deployment times of no-tillage (NT).
Figure 1
Soil physical quality index (SPQi) for different layers of an Albaqualf under different deployment times of no-tillage (NT). Vertical bars represent mean standard deviation; ns = nonsignificant difference; *,** significantly different at 5 % and at 1 %, respectively.*NT1: one; NT3: three; NT5: five and NT7: seven years of no-tillage (NT) deployment, respectively. Points followed by the same letter are not significantly different according to Duncan test at 5 %, considering each evaluated soil layer.

Considering that a better SPQi is equal to 1, NT7 showed the highest SPQi value (0.61) in 0.06 to 0.10 soil layer. In adjacent layers, the SPQi also increased with higher deployment times of NT; however, it tended to decrease in at 0.10 to 0.20 m depth. In this study, deviations of attributes were evaluated in relation to NC, which does not necessarily mean that NC has optimal conditions. Although NC was even not grazed, and remained unmanaged during the last 30 years, NC is representative to a naturally restored area, not a native field.

The SPQi has shown sensitivity and ability to detect changes resulting from soil tillage practices (Figure 1) (Mukherjee and Lal, 2014Mukherjee, A.; Lal, R. 2014. Comparison of soil quality index using three methods. PloS One 9: e105981.; Mota et al., 2014Mota, J.C.A.; Alves, C.V.O.; Freire, A.G.; Assis Júnior, R.N. 2014. Uni and multivariate analyses of soil physical quality indicators of a Cambisol from Apodi Plateau - CE, Brazil. Soil and Tillage Research 140: 66-73.; Askari and Holden, 2015Askari, M.S.; Holden, N.M. 2015. Quantitative soil quality indexing of temperate arable management systems. Soil and Tillage Research 150: 57-67.; Crittenden et al., 2015Crittenden, S.J.; Poot, N.; Heinen, M.; van Balen, D.J.M.; Pulleman, M.M. 2015. Soil physical quality in contrasting tillage systems in organic and conventional farming. Soil and Tillage Research 154: 136-144.; Duval et al., 2016Duval, M.E.; Galantini, J.A.; Martínez, J.M.; López, F.M.; Wall, L.G. 2016. Sensitivity of different soil quality indicators to assess sustainable land management: influence of site features and seasonality. Soil and Tillage Research 159: 9-22.). The evaluated tool has shown that higher deployment time of NT promoted the physical quality of the Albaqualf. Furthermore, the SPQi has shown efficiency to evaluate soil quality. Moreover, it can be used to compare areas subjected to different practices and cultivation conditions.

The physical improvement of lowlands evaluated in this study was demonstrated through several indicators, in particular through that SPQi, which compiled the information of many indicators. The SPQi has shown the ability of NT to ameliorate lowlands for a better adaptation of highland crops in the Pampa biome, as well as to promote soil ecological and conservation functions (i.e. carbon fixation, water infiltration and aeration, drainage regulation and erosion prevention). These benefits are similar to those observed in Brazilian well-drained lands cultivated under NT.

Conclusion

The present study has demonstrated the efficiency of the factorial analysis in selecting the parameters to constitute a minimum data set to evaluate soil quality under different deployment times of no-till. The soil physical quality index (SPQi), constructed from macroporosity, soil resistance to penetration and the compaction degree in the preconsolidation pressure were sensitive to reflect soil physical quality improvements of Albaqualf. This study has also showed that the improvement of physical quality from a cropped Albaqualf is highly dependent of organic matter accumulation in soil surface layers. No-tillage also generated and preserved roots derived and interaggregate macropores, which are essential for gas diffusion and rapid flow of internal water drainage in these soils. Regardless of inherent differences between soil types, the benefits of no tillage for physical status of the studied Albaqualf were comparable to those in Brazilian Oxisols.

Acknowledgments

To Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) for a scholarship granted to the first author; to the Federal University of Pelotas and Embrapa Temperate Agriculture for the opportunity, financial and laboratory support and infrastructure.

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Publication Dates

  • Publication in this collection
    Mar-Apr 2019

History

  • Received
    30 May 2017
  • Accepted
    05 Oct 2017
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