Routine particle size analysis of soils using sodium hypochlorite and ultrasonic dispersion

Mikhail, EH; Briner, G.P.

doi:10.1071/sr9780241

Cited by 21 publications

(12 citation statements)

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“…The effective cation exchange capacity (ECEC) was determined from the sum of CEC, H ex and Al ex . Soil mechanical analysis was measured according to Mikhail & Briner (1978) (Table 1).…”

Section: Methodsmentioning

confidence: 99%

Assessing alternative approaches to predicting soil phosphorus sorption

Burkitt

Gourley

Hannah

et al. 2006

Soil Use and Management

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Twenty‐five pasture soils were sampled from high‐rainfall zones of southeastern Australia to examine relationships between soil properties, and between soil properties and P buffering capacity (PBC) measures. Correlations between PBC values and soil properties were generally poor, with the exception of oxalate‐extractable Al (Alox) (r ≥ 0.97). Predictions of PBC were further improved when clay, as well as Alox, was included in a linear regression model (r2 ≥ 0.98). When Alox and oxalate‐extractable Fe were excluded from the modelling exercise, a more complex three‐term linear regression model, including pHH2O, exchangeable H and cation exchange capacity, adequately fitted both PBC values of the 25 soils examined in this study (r2 ≥ 0.76). However, the Alox, Alox plus clay and the three‐term models gave poor predictions of the PBC values when the models were validated using 28 independent soils. These results emphasize the importance of model validation, because predictive models based on soil properties were not robust when tested across a broader range of soil types. In comparison, direct measures of PBC, such as single‐point P sorption measures, are more practical and robust methods of estimating PBC for Australian soils.

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“…The effective cation exchange capacity (ECEC) was determined from the sum of CEC, H ex and Al ex . Soil mechanical analysis was measured according to Mikhail & Briner (1978) (Table 1).…”

Section: Methodsmentioning

confidence: 99%

Assessing alternative approaches to predicting soil phosphorus sorption

Burkitt

Gourley

Hannah

et al. 2006

Soil Use and Management

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“…horizon; Layer 4 = B22, B23 and B3 horizon, and Layer 5 = C and D horizon. Particle size was determined either using the Piper (1944) pipette or the Mikhail and Briner (1978) plummet balance method. The relative composition of sand, silt and clay of the mineral soil which are less than 2mm in diameter is represented in the ternary plot by control section (Fig.…”

Section: Explanatory Variablesmentioning

confidence: 99%

Development of pedotransfer functions by machine learning for prediction of soil electrical conductivity and organic carbon content

et al. 2020

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The pedotransfer function is a mathematical model used to convert direct soil measurements into known and unknown soil properties. It provides information for modelling and simulation in soil research, hydrology, environmental science and climate change impacts, including investigating the carbon cycle and the exchange of carbon between soils and the atmosphere to support carbon farming. In particular, the pedotransfer function can provide input parameters for landscape design, soil quality assessment and economic optimisation. The objective of the study was to investigate the feasibility of using a generalised pedotransfer function derived with a machine learning method to predict soil electrical conductivity (EC) and soil organic carbon content (OC) for different regional locations in the state of Victoria, Australia. This strategy supports a unified approach to the interpolation and population of a single regional soils database, in contrast to a range of pedotransfer functions derived from local databases

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“…Predictions of the contents of clay, silt, fine sand, coarse sand and total sand were made from calibration models based on 7381 samples which had particle size analysis after destruction or removal of aggregating components, e.g. Mikhail and Briner (1978), using either a pipette, hydrometer or plummet balance. The calibration models are based on partial least-squares regression analysis of spectra and laboratory data.…”

Section: Soil Fertilitymentioning

confidence: 99%

Recent trends in soil fertility across the farms of East Gippsland

2020

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Current data are needed to manage our soil resources but there are few recent purpose-built surveys of soil fertility, a critical asset for natural resources management. Instead, data collected for other purposes are re-used as has been the case in our area of interest here: managing the threat of soil erosion on farm land in East Gippsland. The dynamic nature of soil fertility challenges this approach, so we conducted a new survey using 234 paddocks selected at random. These new data were compared to re-purposed legacy data. Plant tissue samples from a selected subset were used to assess micronutrients. Separately, 27 paddocks that had been sampled during 1975–89 were re-tested to examine temporal changes. We concluded that many paddocks are now strongly or very strongly acidic and deficient in molybdenum (Mo) and boron (B). More specifically, those under dairying, cropping and horticulture are likely to have adequate phosphorus (P) while those grazed for meat and wool are likely to be deficient in P. We concluded that grazing enterprises need to either re-adopt soil testing and fertiliser treatment or adopt management practices suitable for acid soils deficient in P, Mo and B, if their paddocks are to support sustainable productive agriculture. Data from the re-tested paddocks suggest that, in general, many were once less acidic, highlighting the difficulties in using legacy data. Also, contrasts in soil pH and available P between paddocks under different management regimes, highlighted the difficulties in using re-purposed data. We concluded that a statistically designed survey is a preferable basis for natural resource management.

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Routine particle size analysis of soils using sodium hypochlorite and ultrasonic dispersion

Cited by 21 publications

References 0 publications

Assessing alternative approaches to predicting soil phosphorus sorption

Assessing alternative approaches to predicting soil phosphorus sorption

Development of pedotransfer functions by machine learning for prediction of soil electrical conductivity and organic carbon content

Recent trends in soil fertility across the farms of East Gippsland

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