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

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.

Effects of conservation and standard tillage on soil physico-chemical properties and overall quality in a semi-arid agrosystem

“…That is, on our short-term time scale, NT could not be preferred to CT as a sustainable management system. This finding agrees with numerous previous studies, which show that NT needs time to have significant impacts on soil quality (Sağlam et al 2015;Hammac et al 2016;Reis et al 2019;Li et al 2019;Li et al 2020).…”

“…Regression methods such as reduced and partial least squares have been used to synthesize soil physical and chemical properties into a SQI (Masto et al 2008;de Obade and Lal 2016a). Factorial analysis (Raiesi and Kabiri 2016;Reis et al 2019), discriminant analysis (Nehrani et al 2020), and principal component analysis (PCA) (Ghaemi et al 2014;Duval et al 2016) have been employed for the selection of a minimum data set (MDS). The PCA has been shown to be highly effective for this purpose (Bhaduri and Purakayastha 2014;Martínez et al 2020).…”

Indicadores edáficos em sistemas de manejo no cerrado maranhense

“…Outro fator apresentado que pode estar contribuindo para os menores valores de Ds na área de mata, pode estar associado a quantidade de bioporos que permanece no solo provenientes das raízes após a senescência das árvores (Rocha et al, 2015). Na mata, também foi quantificado, em menor magnitude, aumento na Ds de 19,7% na profundidade de 0,10-0,20 m e 16,2% na 0,20-0,40 m, em relação à camada superficial de 0,0-0,10 m. Esse ligeiro aumento mesmo em áreas sem tráfego de máquinas é comumente relatado nos solos de cerrado, principalmente em Latossolos, indicando a presença de adensamento pedogenético em condições originais (Reis et al, 2019).…”

Large-Scale Assessment of Soil Physical Quality of Rural Settlements in the Southeast of Goiás, Brazil

“…For the S index, soil samples should be taken to obtain the SWRC in the laboratory. Other attributes such as penetration resistance, bulk density, hydraulic conductivity, porosity, and aggregation can be used [19,20], but with the same practical restrictions. In an innovative work, Souza et al [7] presented a remote sensing approach to map soil load-bearing capacity using satellite data combined with pedotransfer functions, thus unraveling zones of risk of compaction.…”