Using soil knowledge for the evaluation of mid‐infrared diffuse reflectance spectroscopy for predicting soil physical and mechanical properties

Mid‐infrared diffuse reflectance spectroscopy can provide rapid, cheap and relatively accurate predictions for a number of soil properties. Most studies have found that it is possible to estimate chemical properties that are related to surface and solid material composition. This paper focuses on prediction of physical and mechanical properties, with emphasis on the elucidation of possible mechanisms of prediction. Soil physical properties that are based on pore‐space relationships such as bulk density, water retention and hydraulic conductivity cannot be predicted well using MIR spectroscopy. Hydraulic conductivity was measured using a tension‐disc permeameter, excluding the macropore effect, but MIR spectroscopy did not give a good prediction. Properties based on the soil solid composition and surfaces such as clay content and shrink‐swell potential can be predicted reasonably well. Macro‐aggregate stability in water can be predicted reasonably as it has a strong correlation with carbon content in the soil. We found that most of the physical and mechanical properties can be related back to the fundamental soil properties such as clay content, carbon content, cation exchange capacity and bulk density. These connections have been explored previously in pedotransfer functions studies. The concept of a spectral soil inference system is reiterated: linking the spectra to basic soil properties and connecting basic soil properties to other functional soil properties via pedotransfer functions.     

Mapping and identifying basal stem rot disease in oil palms in North Sumatra with QuickBird imagery

The application of remote sensing technology and precision agriculture in the oil palm industry is in development. This study investigated the potential of high resolution QuickBird satellite imagery, which has a synoptic overview, for detecting oil palms infected by basal stem rot disease and for mapping the disease. Basal stem rot disease poses a major threat to the oil palm industry, especially in Indonesia. It is caused by Ganoderma boninense and the symptoms can be seen on the leaf and basal stem. At present there is no effective control for this disease and early detection of the infection is essential. A detailed, accurate and rapid method of monitoring the disease is needed urgently. This study used QuickBird imagery to detect the disease and its spatial pattern. Initially, oil palm and non oil palm object segmentation based on the red band was used to map the spatial pattern of the disease. Secondly, six vegetation indices derived from visible and near infrared bands (NIR) were used for to identify palms infected by the disease. Finally, ground truth from field sampling in four fields with different ages of plant and degrees of infection was used to assess the accuracy of the remote sensing approach. The results show that image segmentation effectively delineated areas infected by the disease with a mapping accuracy of 84%. The resulting maps showed two patterns of the disease; a sporadic pattern in fields with older palms and a dendritic pattern in younger palms with medium to low infection. Ground truth data showed that oil palms infected by basal stem rot had a higher reflectance in the visible bands and a lower reflectance in the near infrared band. Different vegetation indices performed differently in each field. The atmospheric resistant vegetation index and green blue normalized difference vegetation index identified the disease with an accuracy of 67% in a field with 21 year old palms and high infection rates. In the field of 10 year old palms with medium rates of infection, the simple ratio (NIR/red) was effective with an accuracy of 62% for identifying the disease. The green blue normalized difference vegetation index was effective in the field of 10 years old palms with low infection rates with an accuracy of 59%. In the field of 15 and 18 years old palms with low infection rates, all the indices showed low levels of accuracy for identifying the disease. This study suggests that high resolution QuickBird imagery offers a quick, detailed and accurate way of estimating the location and extent of basal stem rot disease infections in oil palm plantations.     

Evaluating a low‐cost portable NIR spectrometer for the prediction of soil organic and total carbon using different calibration models

This study aims to assess the performance of a low‐cost, micro‐electromechanical system‐based, near infrared spectrometer for soil organic carbon (OC) and total carbon (TC) estimation. TC was measured on 151 soil profiles up to the depth of 1 m in NSW, Australia, and from which a subset of 24 soil profiles were measured for OC. Two commercial spectrometers including the AgriSpec^TM (ASD) and NeoSpectra^TM (Neospectra) with spectral wavelength ranges of 350–2,500 and 1,300–2,500 nm, respectively, were used to scan the soil samples, according to the standard contact probe protocol. Savitzky–Golay smoothing filter and standard normal variate (SNV) transformation were performed on the spectral data for noise reduction and baseline correction. Three calibration models, including Cubist tree model, partial least squares regression (PLSR) and support vector machine (SVM), were assessed for the prediction of soil OC and TC using spectral data. A 10‐fold cross‐validation analysis was performed for evaluation of the models and devices accuracies. Results showed that Cubist model predicts OC and TC more accurately than PLSR and SVM. For OC prediction, Cubist showed R² = 0.89 (RMSE = 0.12%) and R² = 0.78 (RMSE = 0.16%) using ASD and NeoSpectra, respectively. For TC prediction, Cubist produced R² = 0.75 (RMSE = 0.45%) and R² = 0.70 (RMSE = 0.50%) using ASD and NeoSpectra, respectively. ASD performed better than NeoSpectra. However, the low‐cost NeoSpectra predictions were comparable to the ASD. These finding can be helpful for more efficient future spectroscopic prediction of soil OC and TC with less costly devices.     

Quantifying processes of pedogenesis using optically stimulated luminescence

New analytical techniques have opened up the possibility of addressing rates of soil processes quantitatively. Here, we present the results of an investigation into the use of single‐grain optically stimulated luminescence (OSL) dating to derive rates of soil mixing in the top 50 cm of soil profiles from two toposequences situated in the Werrikimbe National Park in Australia. Of 500 single grains analysed from each sampled depth increment, less than 25% provided a finite age, with the rest of the grains either non‐responsive or dose‐saturated. This proportion of finite‐age grains tended to decrease with soil depth. Median ages of quartz grains increased down the soil profile, with topsoil ages of up to 500 years and subsoil ages of up to 5000 years. Few ‘younger’ grains were found deeper in the profile and few ‘older’ grains near the soil surface. These trends suggest that pedoturbation is resulting in vertical transport of grains through the profile, but that there is a distribution of transport distances, with a poor probability of large transport distances from surface to subsoil or vice versa compared with a more frequent movement of grains to and from the surface in the uppermost 10–35 cm. The calculation of a single age for each soil horizon was unachievable as each horizon contained a heterogeneous mixture of grains with varying histories of transport to and from the soil surface. Soil mixing was confirmed along both toposequences studied. However, the occurrence of minor mixing rates did not lead to a homogenization of the topsoil and adjacent horizons. We postulated that mixing velocities were mostly related to flora at our study site. Vertical soil mixing rates of 0.5 and 0.2 mm year^?1 were calculated from the distribution of finite single‐grain ages.     

Soil–climate contribution to DNDC model uncertainty in simulating biomass accumulation under urban vegetable production on a Petroplinthic Cambisol in Tamale,Ghana

Crop yield simulation using the Denitrification–Decomposition (DNDC) model can help to understand key bottlenecks for improved nitrogen (N) use efficiency and estimate greenhouse gas (GHG) emissions in West African urban vegetable production. The DNDC model was successfully calibrated using high‐resolution weather records, information on management practices and soils, and measured biomass accumulation and N uptake by amaranth (Amaranthus L.), jute mallow (Corchorus olitorius L.), lettuce (Lactuca sativa L.), and roselle (Hibiscus sabdariffa L.) for different input intensities (May 2014–November 2015) in urban vegetable production of Tamale (N‐Ghana, West Africa). The root mean square error (RMSE) and relative error (E) values fell within the confidence interval (α 5%) of the measurements, and there was a high correlation (0.91 to 0.98) between measurements and predictions. However, the analysis of uncertainty and factor importance indicated that soil properties (pH, SOC, and clay content) and weather (precipitation) variability contributed highly to yield uncertainty of vegetable biomass.     

Effect of species grouping and site variables on aboveground biomass models for lowland tropical forests of the Indo-Malay region

Key message

This study assessed the effect of ecological variables on tree allometry and provides more accurate aboveground biomass (AGB) models through the involvement of large samples representing major islands, biogeographical zones and various succession and degradation levels of natural lowland forests in the Indo-Malay region. The only additional variable that significantly and largely contributed to explaining AGB variation is grouping based on wood-density classes.

Context

There is a need for an AGB equation at tree level for the lowland tropical forests of the Indo-Malay region. In this respect, the influence of geographical, climatic and ecological gradients needs to be assessed.

Aims

The overall aim of this research is to provide a regional-scale analysis of allometric models for tree AGB of lowland tropical forests in the Indo-Malay region.

Methods

A dataset of 1300 harvested trees (5 cm ≤ trunk diameter ≤ 172 cm) was collected from a wide range of succession and degradation levels of natural lowland forests through direct measurement and an intensive literature search of principally grey publications. We performed ANCOVA to assess possible irregular datasets from the 43 study sites. After ANCOVA, a 1201-tree dataset was selected for the development of allometric equations. We tested whether the variables related to climate, geographical region and species grouping affected tree allometry in the lowland forest of the Indo-Malay region.

Results

Climatic and major taxon-based variables were not significant in explaining AGB variations. Biogeographical zone was a significant variable explaining AGB variation, but it made only a minor contribution on the accuracy of AGB models. The biogeographical effect on AGB variation is more indirect than its effect on species and stand characteristics. In contrast, the integration of wood-density classes improved the models significantly.

Conclusion

Our AGB models outperformed existing local models and will be useful for improving the accuracy on the estimation of greenhouse gas emissions from deforestation and forest degradation in tropical forests. However, more samples of large trees are required to improve our understanding of biomass distribution across various forest types and along geographical and elevation gradients.

     

Hand-feel soil texture observations to evaluate the accuracy of digital soil maps for local prediction of soil particle size distribution: A case study in Central France

Digital maps of soil properties are now widely available. End-users now can access several digital soil mapping (DSM) products of soil properties, produced using different models, calibration/training data, and covariates at various spatial scales from global to local. Therefore, there is an urgent need to provide easy-to-understand tools to communicate map uncertainty and help end-users assess the reliability of DSM products for use at local scales. In this study, we used a large amount of hand-feel soil texture (HFST) data to assess the performance of various published DSM products on the prediction of soil particle size distribution in Central France. We tested four DSM products for soil texture prediction developed at various scales (global, continental, national, and regional) by comparing their predictions with approximately 3 200 HFST observations realized on a 1:50 000 soil survey conducted after release of these DSM products. We used both visual comparisons and quantitative indicators to match the DSM predictions and HFST observations. The comparison between the low-cost HFST observations and DSM predictions clearly showed the applicability of various DSM products, with the prediction accuracy increasing from global to regional predictions. This simple evaluation can determine which products can be used at the local scale and if more accurate DSM products are required.