Browsing by Author "Prout, Jonah M."
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Item Open Access Changes in organic carbon to clay ratios in different soils and land uses in England and Wales over time(Springer Nature, 2022-03-25) Prout, Jonah M.; Shepherd, Keith D.; McGrath, Steve P.; Kirk, Guy J. D.; Hassall, Kirsty L.; Haefele, Stephan M.Realistic targets for soil organic carbon (SOC) concentrations are needed, accounting for differences between soils and land uses. We assess the use of SOC/clay ratio for this purpose by comparing changes over time in (a) the National Soil Inventory of England and Wales, first sampled in 1978–1983 and resampled in 1994–2003, and (b) two long-term experiments under ley-arable rotations on contrasting soils in the East of England. The results showed that normalising for clay concentration provides a more meaningful separation between land uses than changes in SOC alone. Almost half of arable soils in the NSI had degraded SOC/clay ratios (< 1/13), compared with just 5% of permanent grass and woodland soils. Soils with initially large SOC/clay ratios (≥ 1/8) were prone to greater losses of SOC between the two NSI samplings than those with smaller ratios. The results suggest realistic long-term targets for SOC/clay in arable, ley grass, permanent grass and woodland soils are 1/13, 1/10, and > 1/8, respectively. Given the wide range of soils and land uses across England and Wales in the datasets used to test these targets, they should apply across similar temperate regions globally, and at national to sub-regional scales.Item Open Access Comparing the effect of different sample conditions and spectral libraries on the prediction accuracy of soil properties from near- and mid-infrared spectra at the field-scale(Elsevier, 2021-10-07) Breure, Timo Samuel; Prout, Jonah M.; Haefele, Stephan M.; Milne, Alice E.; Hannam, Jacqueline A.; Moreno-Rojas, S.; Corstanje, RonaldThe prediction accuracy of soil properties by proximal soil sensing has made their application more practical. However, in order to gain sufficient accuracy, samples are typically air-dried and milled before spectral measurements are made. Calibration of the spectra is usually achieved by making wet chemistry measurements on a subset of the field samples and local regression models fitted to aid subsequent prediction. Both sample handling and wet chemistry can be labour and resource intensive. This study aims to quantify the uncertainty associated with soil property estimates from different methods to reduce effort of field-scale calibrations of soil spectra. We consider two approaches to reduce these expenses for predictions made from visible-near-infrared ((V)NIR), mid-infrared (MIR) spectra and their combination. First, we considered reducing the level of processing of the samples by comparing the effect of different sample conditions (in-situ, unprocessed, air-dried and milled). Second, we explored the use of existing spectral libraries to inform calibrations (based on milled samples from the UK National Soil Inventory) with and without ‘spiking’ the spectral libraries with a small subset of samples from the study fields. Prediction accuracy of soil organic carbon, pH, clay, available P and K for each of these approaches was evaluated on samples from agricultural fields in the UK. Available P and K could only be moderately predicted with the field-scale dataset where samples were milled. Therefore this study found no evidence to suggest that there is scope to reduce costs associated with sample processing or field-scale calibration for available P and K. However, the results showed that there is potential to reduce time and cost implications of using (V)NIR and MIR spectra to predict soil organic carbon, clay and pH. Compared to field-scale calibrations from milled samples, we found that reduced sample processing lowered the ratio of performance to inter-quartile range (RPIQ) between 0% and 76%. The use of spectral libraries reduced the RPIQ of predictions relative to field-scale calibrations from milled samples between 54% and 82% and the RPIQ was reduced between 29% and 70% for predictions when spectral libraries were spiked. The increase in uncertainty was specific to the combination of soil property and sensor analysed. We conclude that there is always a trade-off between prediction accuracy and the costs associated with soil sampling, sample processing and wet chemical analysis. Therefore the relative merits of each approach will depend on the specific case in question.Item Open Access What is a good level of soil organic matter? An index based on organic carbon to clay ratio(Wiley, 2020-06-12) Prout, Jonah M.; Shepherd, Keith D.; McGrath, Steve P.; Kirk, Guy J. D.; Haefele, Stephan M.Simple measures of appropriate levels of soil organic matter are needed for soil evaluation, management and monitoring, based on readily‐measurable soil properties. We test an index of soil organic matter based on the soil organic carbon (SOC) to clay ratio, defined by thresholds of SOC/clay ratio for specified levels of soil structural quality. The thresholds were originally delineated for a small number of Swiss soils. We assess the index using data from the initial sampling (1978–83) of the National Soil Inventory of England and Wales, covering 3809 sites under arable land, grassland and woodland. Land use, soil type, annual precipitation and soil pH together explained 21% of the variance in SOC/clay ratio in the dataset, with land use the most important variable. Thresholds of SOC/clay ratio of 1/8, 1/10 and 1/13 indicated the boundaries between ‘very good’, ‘good’, ‘moderate’ and ‘degraded’ levels of structural condition. On this scale, 38.2, 6.6, and 5.6% of arable, grassland and woodland sites, respectively, were degraded. The index gives a method to assess and monitor soil organic matter at national, regional or sub‐regional scales based on two routinely measured soil properties. Given the wide range of soils and land uses across England and Wales in the dataset used to test the index, we suggest it should apply to other European soils in similar climate zones.