Digital soil monitoring of top- and sub-soil pH with bivariate linear mixed models

Filippi, Patrick; Cattle, Stephen R.; Bishop, Thomas; Odeh, Inakwu O. A.; Pringle, M. J.

doi:10.1016/j.geoderma.2018.02.033

Cited by 31 publications

(19 citation statements)

References 23 publications

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“…Soil pH was generally alkaline and increased with depth. This is typical of Vertosols in the cotton-growing valleys, and other studies in similar areas concur [18,40]. The depth in which a soil pH greater than nine was reached was shown to be highly spatially variable across the study area.…”

Section: Soil Alkalinity and Depth-to-soil Ph Constraintsupporting

confidence: 76%

“…Some studies solely produce pH maps for the topsoil (e.g., Kirk et al [17]), which lacks important information about subsoil pH. Other studies create maps of several depth increments (e.g., Filippi et al [18]), but this is often too much information from which to easily make management decisions. The typical broadacre farmer in Australia now has access to a plethora of data and information that relate to their farms [19], which is a great opportunity, but also a challenge to gain useful insights.…”

Section: Introductionmentioning

confidence: 99%

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Mapping the Depth-to-Soil pH Constraint, and the Relationship with Cotton and Grain Yield at the Within-Field Scale

et al. 2019

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Subsoil alkalinity is a common issue in the alluvial cotton-growing valleys of northern New South Wales (NSW), Australia. Soil alkalinity can cause nutrient deficiencies and toxic effects, and inhibit rooting depth, which can have a detrimental impact on crop production. The depth at which a soil constraint is reached is important information for land managers, but it is difficult to measure or predict spatially. This study predicted the depth in which a pH (H2O) constraint (>9) was reached to a 1-cm vertical resolution to a 100-cm depth, on a 1070-hectare dryland cropping farm. Equal-area quadratic smoothing splines were used to resample vertical soil profile data, and a random forest (RF) model was used to produce the depth-to-soil pH constraint map. The RF model was accurate, with a Lin’s Concordance Correlation Coefficient (LCCC) of 0.63–0.66, and a Root Mean Square Error (RMSE) of 0.47–0.51 when testing with leave-one-site-out cross-validation. Approximately 77% of the farm was found to be constrained by a strongly alkaline pH greater than 9 (H2O) somewhere within the top 100 cm of the soil profile. The relationship between the predicted depth-to-soil pH constraint map and cotton and grain (wheat, canola, and chickpea) yield monitor data was analyzed for individual fields. Results showed that yield increased when a soil pH constraint was deeper in the profile, with a good relationship for wheat, canola, and chickpea, and a weaker relationship for cotton. The overall results from this study suggest that the modelling approach is valuable in identifying the depth-to-soil pH constraint, and could be adopted for other important subsoil constraints, such as sodicity. The outputs are also a promising opportunity to understand crop yield variability, which could lead to improvements in management practices.

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Section: Soil Alkalinity and Depth-to-soil Ph Constraintsupporting

confidence: 76%

Section: Introductionmentioning

confidence: 99%

Mapping the Depth-to-Soil pH Constraint, and the Relationship with Cotton and Grain Yield at the Within-Field Scale

et al. 2019

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“…The samples were collected in a survey conducted in 2002, consisting of 384 samples from 87 different sites. The soils in this area are mainly Vertisols, with some soils of sandier texture derived from Aeolian parent material ( Filippi et al, 2018b ). The soil samples were air-dried, ground and passed through a 2-mm sieve.…”

Section: Methodsmentioning

confidence: 99%

In search of an optimum sampling algorithm for prediction of soil properties from infrared spectra

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Malone

et al. 2018

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BackgroundThe use of visible-near infrared (vis-NIR) spectroscopy for rapid soil characterisation has gained a lot of interest in recent times. Soil spectra absorbance from the visible-infrared range can be calibrated using regression models to predict a set of soil properties. The accuracy of these regression models relies heavily on the calibration set. The optimum sample size and the overall sample representativeness of the dataset could further improve the model performance. However, there is no guideline on which sampling method should be used under different size of datasets.MethodsHere, we show different sampling algorithms performed differently under different data size and different regression models (Cubist regression tree and Partial Least Square Regression (PLSR)). We analysed the effect of three sampling algorithms: Kennard-Stone (KS), conditioned Latin Hypercube Sampling (cLHS) and k-means clustering (KM) against random sampling on the prediction of up to five different soil properties (sand, clay, carbon content, cation exchange capacity and pH) on three datasets. These datasets have different coverages: a European continental dataset (LUCAS, n = 5,639), a regional dataset from Australia (Geeves, n = 379), and a local dataset from New South Wales, Australia (Hillston, n = 384). Calibration sample sizes ranging from 50 to 3,000 were derived and tested for the continental dataset; and from 50 to 200 samples for the regional and local datasets.ResultsOverall, the PLSR gives a better prediction in comparison to the Cubist model for the prediction of various soil properties. It is also less prone to the choice of sampling algorithm. The KM algorithm is more representative in the larger dataset up to a certain calibration sample size. The KS algorithm appears to be more efficient (as compared to random sampling) in small datasets; however, the prediction performance varied a lot between soil properties. The cLHS sampling algorithm is the most robust sampling method for multiple soil properties regardless of the sample size.DiscussionOur results suggested that the optimum calibration sample size relied on how much generalization the model had to create. The use of the sampling algorithm is beneficial for larger datasets than smaller datasets where only small improvements can be made. KM is suitable for large datasets, KS is efficient in small datasets but results can be variable, while cLHS is less affected by sample size.

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“…Soil samples from 80 locations from a soil survey conducted in 2002 are used, as well as 140 soil cores from a soil survey from 2015 [ 5 ]. Samples were extracted from soils under a variety of land uses, including irrigated and dryland cropping, irrigated perennial horticulture, and rangeland grazing.…”

Section: Methodsmentioning

confidence: 99%

Combining ancillary soil data with VisNIR spectra to improve predictions of organic and inorganic carbon content of soils

et al. 2018

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Digital soil monitoring of top- and sub-soil pH with bivariate linear mixed models

Cited by 31 publications

References 23 publications

Mapping the Depth-to-Soil pH Constraint, and the Relationship with Cotton and Grain Yield at the Within-Field Scale

Mapping the Depth-to-Soil pH Constraint, and the Relationship with Cotton and Grain Yield at the Within-Field Scale

In search of an optimum sampling algorithm for prediction of soil properties from infrared spectra

Combining ancillary soil data with VisNIR spectra to improve predictions of organic and inorganic carbon content of soils

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