Journal Paper Digests 2021 #31
- A clean energy future isn’t set in stone
- Numerical soil horizon classification from South Africa’s legacy database
- Spatial variability-based sample size allocation for stratified sampling
- Soil nutrients increase long-term soil carbon gains threefold on retired farmland
- Linking decomposition rates of soil organic amendments to their chemical composition
- Papatuanuku, Earth Mother: indigenous knowledge in 21st century soil management
A clean energy future isn’t set in stone
Social scientists and geoscientists must work together to critically evaluate and develop feasible visions for a sustainable future. Is a clean-energy economy more viable than a degrowth future?
Numerical soil horizon classification from South Africa’s legacy database
Allocation of soil profiles to one or another soil class depends on the surveyor’s experience and knowledge. Inconsistent class designations can affect the downstream uses of soil information such as hydrological and ecological applications. Therefore, numerical classifications can be useful in creating property-based soil clusters. Quantitative classification of soil horizons can also be beneficial for guiding physicochemical thresholds used in taxonomic criteria. This study aimed at taking a step in applying numerical classification techniques to the South African soil legacy database to quantitatively cluster horizons. For each master horizon, the database was clustered into new “diagnostic” horizons through Partitioning Around Medoids (PAM) implemented in the Clustering Large Applications algorithm (CLARA). This algorithm has the benefit of using multiple subsets of the data making a more reliable initiation of medoids and it is a non-parametric approach making it more robust to outliers. To determine the optimal number of clusters, the silhouette width for 1 through 11 clusters was calculated. The number of clusters with the largest silhouette width was taken as the optimal number of clusters. The results of this study show that for O, E, and G (gleyed) horizons, the algorithm performs relatively well with the first two principle components capturing almost half of the variation in the data. However, for the A, B, and C horizons, the algorithm struggled to separate clusters sufficiently. The A horizon clusters only broadly corresponded to environmental factors (geography, climate, elevation, and geology) and the large clusters straddled numerous taxonomic thresholds. Moreover, the B horizon clusters correspond relatively well with environmental factors. Limitations identified, include bias in geographic location of modal soil profiles of the database, bias in the South African classification system, how to handle non-globular clusters, and outliers which add leverage to the clusters.
Spatial variability-based sample size allocation for stratified sampling
Stratified sampling is one of the most commonly used sampling strategies for soil survey and mapping. An important issue in stratified sampling is the sample size allocation for each stratum. Neyman allocation is a typical sample size allocation approach in which the main idea is to assign more samples to a stratum with a larger internal standard deviation. By applying the spatial variability for sample size allocation, we proposed a new allocation approach (SVNA) for stratified spatial sampling. In this new approach, more samples were allocated to strata with both a bigger statistical standard deviation and larger spatial variability of the target variable. SVNA was evaluated using a simulated experiment based on real-world soil data of Jiangsu province, China. The target variable was soil organic matter (SOM), and land-use type was utilized for stratification. Simple random sampling (SRS) and spatial simulated annealing algorithm (SSA) were applied to determine sample locations. Results showed that the performance of SVNA generally was superior to Neyman allocation in terms of SOM content prediction at all sampling densities (4.28-0.19 points per km(2)) regardless of the application of SRS or SSA for sample location determination. The maximum reduction of mean squared error (MSE) for the SOM content prediction using SVNA was 11% using SRS and was 10% using SSA. Compared with Neyman allocation, SVNA obtained greater prediction accuracies in strata with large spatial variability and similar or slightly smaller prediction accuracies for strata with small spatial variability. Furthermore, the prediction accuracy of the SVNA approach tended to decrease when the sampling density was 0.37 points per km(2), and this sampling density threshold was not the same for each stratum. We concluded that SVNA is an efficient sample size allocation approach for stratified sampling in soil survey and mapping.
Soil nutrients increase long-term soil carbon gains threefold on retired farmland
Abandoned agricultural lands often accumulate soil carbon (C) following depletion of soil C by cultivation. The potential for this recovery to provide significant C storage benefits depends on the rate of soil C accumulation, which, in turn, may depend on nutrient supply rates. We tracked soil C for almost four decades following intensive agricultural soil disturbance along an experimentally imposed gradient in nitrogen (N) added annually in combination with other macro- and micro-nutrients. Soil %C accumulated over the course of the study in unfertilized control plots leading to a gain of 6.1 Mg C ha(-1) in the top 20 cm of soil. Nutrient addition increased soil %C accumulation leading to a gain of 17.8 Mg C ha(-1) in fertilized plots, nearly a threefold increase over the control plots. These results demonstrate that substantial increases in soil C in successional grasslands following agricultural abandonment occur over decadal timescales, and that C gain is increased by high supply rates of soil nutrients. In addition, soil %C continued to increase for decades under elevated nutrient supply, suggesting that short-term nutrient addition experiments underestimate the effects of soil nutrients on soil C accumulation.
Linking decomposition rates of soil organic amendments to their chemical composition
The stock of organic carbon contained within a soil represents the balance between inputs and losses. Inputs are defined by the ability of vegetation to capture and retain carbon dioxide, effects that management practices have on the proportion of captured carbon that is added to soil and the application organic amendments. The proportion of organic amendment carbon retained is defined by its rate of mineralisation. In this study, the rate of carbon mineralisation from 85 different potential soil organic amendments (composts, manures, plant residues and biosolids) was quantified under controlled environmental conditions over a 547 day incubation period. The composition of each organic amendment was quantified using nuclear magnetic resonance and mid- and near-infrared spectroscopies. Cumulative mineralisation of organic carbon from the amendments was fitted to a two-pool exponential model. Multivariate chemometric algorithms were derived to allow the size of the fast and slow cycling pools of carbon to be predicted from the acquired spectroscopic data. However, the fast and slow decomposition rate constants could not be predicted suggesting that prediction of the residence time of organic amendment carbon in soil would likely require additional information related to soil type, environmental conditions, and management practices in use at the site of application.
Papatuanuku, Earth Mother: indigenous knowledge in 21st century soil management
On 20 March 2017 the New Zealand parliament passed the Te Awa Tupua (Whanganui River Claims Settlement) Bill which established the Whanganui River as a legal ‘person’ with all of the rights, powers, duties, and liabilities of the same. The Act endorses and illustrates how Maori perceive their relationship to the natural world. The passing of the Act challenged the river people to restore their ancestral river to good health. Changes in land use beginning in the later part of the 19th century had seen soil fertility decline, water quality deteriorate and the soils that sustained life in its catchment increasingly washed out to sea. These impacts profoundly changed the lifestyles of the people that belonged to it. Describing the issues facing the river iwi (tribes) and their response to them will help illustrate traditional understandings relating to the river, the whenua (the land) and the life sustaining capacity of the soil. It also serves to demonstrate the relevance of traditional knowledge to addressing the current ecological crisis. This viewpoint focuses on key concepts from Maori understandings of the natural world that relate to the primary themes of this conference and suggest how they can contribute towards deepening and broadening our knowledge of soils and what needs to be done to sustain them. In particular the concept of ‘mauri’ will be explored and how that relates to the capacity of soils to support the life that belongs there. Maori, and many traditional peoples, regard the whole landscape as essentially interdependent and consider that the wellness of any part of it, be it soils, vegetation, water quality, etc., can only be understood within the context of the whole network of connections that sustain life. The challenge for researchers, from an indigenous perspective, is to be mindful of the ‘whole’ while focusing on the areas of their particular expertise.