Prediction of soil organic matter using different soil classification hierarchical level stratification strategies and spectral characteristic parameters

Hardsetting soils have been defined as soils that set to a hard, structureless mass during drying and are thereafter difficult or impossible to cultivate until the profile is rewetted. Soil strength increases rapidly as the soil dries, and so seedlings must grow quickly before soil strength becomes too high for root growth or shoot emergence. Recent work on the mechanisms of hardsetting confirms that aggregate disruption through slaking and dispersion on wetting leads to coalescence. Bridging by dispersed particles under matric potential makes a soil hardset. Failure to recover from a coalesced state as the soil dries leaves it with a massive structure. This paper reviews the worldwide occurrence of hardsetting soils, the evolution of definitions of hardsetting, and the use of those definitions in soil classification with particular emphasis on Australia. Measurement of hardsetting includes methods such as visual score of slaking and dispersion, penetration resistance, fall-cone penetration, dispersion, fractions of soil organic matter, friability index, modulus of rupture, and a particular use of the soil water retention curve. Overcoming problems associated with hardsetting soils and their ongoing management is difficult. Further work is needed on the reasons for variable responses to tillage, no tillage, and pasture. Modifying soil texture has limited application, and increasing soil organic matter under cropping is difficult in low-rainfall areas. Polymers have been shown to be beneficial. Mulching maintains higher soil moisture and therefore a softer surface, while biochar shows inconsistent effects. Controlled traffic is a key to reducing recompaction. Management of a hardsetting soil must include the whole rotation, including when to till, when to crop, and when to graze or not.

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Soil organic matter as a criteria in soil classification systems

Cereal Research Communications ◽

10.1556/crc.33.2005.1.90 ◽

2005 ◽

Vol 33 (1) ◽

pp. 365-368 ◽

Cited By ~ 2

Author(s):

Márta Fuchs ◽

Barbara Simon ◽

Erika Micheli

Keyword(s):

Organic Matter ◽

Soil Organic Matter ◽

Soil Classification ◽

Classification Systems

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Joint Symposium on “Soil Organic Matter and Soil Classification”

Agronomy Journal ◽

10.2134/agronj1931.00021962002300120013x ◽

1931 ◽

Vol 23 (12) ◽

pp. 1056-1060

Author(s):

Selman A. Waksman

Keyword(s):

Organic Matter ◽

Soil Organic Matter ◽

Soil Classification ◽

Joint Symposium

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Identification and predictability of soil quality factors and indicators from conventional soil and vegetation classifications

10.1101/2021.03.04.433857 ◽

2021 ◽

Author(s):

Paul Simfukwe ◽

Paul W Hill ◽

Davey L Jones ◽

Bridget Emmett ◽

Keyword(s):

Organic Matter ◽

Soil Organic Matter ◽

Soil Quality ◽

Soil Type ◽

Soil Classification ◽

Biological Indicators ◽

Soil Types ◽

Quality Factors ◽

Soil Attributes ◽

Wide Range

Generally, the physical, chemical and biological attributes of a soil combined with abiotic factors (e.g. climate and topography) drive pedogenesis. However, biological indicators of soil quality play no direct role in traditional soil classification and surveys. To support their inclusion in classification schemes, previous studies have shown that soil type is a key factor determining microbial community composition in arable soils. This suggests that soil type could be used as proxy for soil biological function and vice versa. In this study we assessed the relationship between soil biological indicators with either vegetation cover or soil type. A wide range of soil attributes were measured on soils from across the UK to investigate whether; (1) appropriate soil quality factors (SQFs) and indicators (SQIs) can be identified, (2) soil classification can predict SQIs; (3) which soil quality indicators were more effectively predicted by soil types, and (4) to what extent do soil types and/ or aggregate vegetation classes (AVCs) act as major regulators of SQIs. Factor analysis was used to group 20 soil attributes into six SQFs namely; Soil organic matter , Organic matter humification , Soluble nitrogen , Microbial biomass , Reduced nitrogen and Soil humification index . Of these, Soil organic matter was identified as the most important SQF in the discrimination of both soil types and AVCs. Among the measured soil attributes constituting the Soil organic matter factor were, microbial quotient and bulk density were the most important attributes for the discrimination of both individual soil types and AVCs. The Soil organic matter factor discriminated three soil type groupings and four aggregate vegetation class groupings. Only the Peat soil and Heath and bog AVC were distinctly discriminated from other groups. All other groups overlapped with one another, making it practically impossible to define reference values for each soil type or AVC. We conclude that conventionally classified soil types cannot predict the SQIs (or SQFs), but can be used in conjunction with the conventional soil classifications to characterise the soil types. The two-way ANOVA showed that the AVCs were a better regulator of the SQIs than the soil types and that they (AVCs) presented a significant effect on the soil type differences in the measured soil attributes.

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Soil organic matter stocks and spatial distribution in the Rio Grande de Arecibo watershed.

The Journal of Agriculture of the University of Puerto Rico ◽

10.46429/jaupr.v94i1-2.2582 ◽

1969 ◽

Vol 94 (1-2) ◽

pp. 1-23

Author(s):

David Sotomayor-Ramírez ◽

Lionel Cruz ◽

Luis R. Pérez-Alegría

Keyword(s):

Land Use ◽

Organic Matter ◽

Spatial Distribution ◽

Soil Organic Matter ◽

Agricultural Land ◽

Secondary Forest ◽

Rio Grande ◽

Soil Classification ◽

C Sequestration ◽

Mapping Unit

This research evaluated the influence of land use and soil classification, as stratified by taxonomic soil order, on the spatial distribution of soil organic carbon (SOC) and soil organic nitrogen (SON) of the Rio Grande de Arecibo (RGA) watershed, Puerto Rico. The objectives were to quantify the present state of SOC and of SON stocks and potential C sequestration capability of the watershed to 1 -m depth. Samples were taken from representative soils of the watershed occupying 39,361 ha (or 87.3% of the total watershed area) under secondary forest, pasture, or agricultural land use. Soils of the watershed store 5.02 x 106 Mg of SOC and 0.48 x 106 Mg of SON at a depth of 100 cm. The weighted mean SOC and SON contents of the 0- to 15-cm layer of the watershed were 4.33 kg C/m2 and 0.390 kg N/m2, respectively, whereas at 0 to 100 cm it was 11.13 kg C/m2 and 1.08 kg N/m2, respectively. The soil mapping unit x land use interaction represented the best area-wide estimates of soil organic matter because there was improved resolution on a spatial scale. Forest and pasture soils contained higher amounts of SOC (12.8 and 9.79 kg C/m2, respectively) (P < 0.05) than soils under cropland (7.90 kg C/m2) for the 0- to 100-cm depth. The 0- to 15-cm SOC was ranked as Oxisols = Ultisols > Inceptisols, with values of 5.85, 4.77, and 3.18 kg C/m2, respectively (P < 0.05); and for the 0 to 100 cm, were ranked as Oxisols > Ultisols > Inceptisols, with values of 18.3,13.3, and 6.71 kg C/m2, respectively. We estimate that an additional amount of 46,627 Mg C could be sequestered within the watershed if 50% of the agricultural or pasture land were reverted to forest. This estimate represents a modest 1.0% increase above the current watershed C level.

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