Monitoring and modelling spatio-temporal soil change in a semi-arid irrigated cotton-growing region of south-west NSW, Australia – The impacts of land use and climatic fluctuations

Soil is an invaluable finite resource, and it is essential that any changes in soil condition are adequately monitored. In the semi-arid regions of eastern Australia, there has been an expansion of intensive irrigated cotton production, and these regions have also experienced highly variable rainfall patterns in the last decade or so. This combination of climatic and land use changes has the potential to significantly alter soil attributes. This thesis focuses on monitoring the change in several important soil properties – pH, salinity, sodicity, and organic and inorganic carbon – in the semi-arid irrigated cotton-growing district of Hillston, NSW, between 2002 and 2015. Rather than using traditional digital soil mapping techniques, this study focuses on using bi- and multi-variate linear mixed models, and two-step mixture models to model and map soil properties in space and time. The linear mixed models were particularly advantageous for monitoring changes in soil properties as they can account for correlation in space and time, and improve the sensitivity of detecting statistically significant changes. As traditional laboratory methods of measuring certain soil properties can be expensive and laborious, this study also focused on using visible near infrared (VisNIR) spectroscopic techniques to rapidly predict exchangeable sodium percentage (ESP), organic carbon (SOC) content, and inorganic carbon (SIC) content. Various degrees and extents of soil change were observed during the study period in both the top and sub soil. This included an acidification trend in some areas, a contrasting shift in electrical conductivity (EC) under differing land uses, an increase in soil ESP under irrigated land uses, an increase in SOC content at some locations, and no detectable change in SIC content. Overall, it was clear that fluctuating rainfall patterns and agricultural management practices had a notable impact on the degree and direction of changes in soil properties

Comparison of Artificial Intelligence and Geostatistical Methods in Soil Surface Salinity Prediction in Ghorghori, Hirmand

In this study, geostatistical methods and artificial intelligence models (artificial neural network, decision tree, and support vector machine) were used to simulate the soil salinity of Ghorghori lands in Hirmand city. A total of 130 soil samples were collected from 0-30 cm layers of the soil. The electrical conductivity of each sample was measured using an electrical conductivity device. Soil salinity values were estimated using Geostatistical methods and artificial intelligence methods. Geostatistical and artificial intelligence models were applied and the best model was selected; the accuracy of the methods was compared using independent validation. The results showed that the artificial intelligence methods outperformed the geostatistical method in estimating the soil salinity of the artificial intelligence methods, the decision tree model was the superior model due to its coefficient of determination of 0.99 and RMSE and MAE statistics of 0.26 and 0.18 respectively. The salinity trend showed that the salinity of the soil of the region decreases from west to east first and then increases and decreases from north to south. In order to preserve the environment of the region, the field of planting plant species compatible with the region should be provided in accordance with soil salinity

Assessing the relationship between the soil dielectric constant and electrical conductivity using 5TE sensors in the field conditions

Postprint (published version

Review of soil salinity assessment for agriculture across multiple scales using proximal and/or remote sensors

Mapping and monitoring soil spatial variability is particularly problematic for temporally and spatially dynamic properties such as soil salinity. The tools necessary to address this classic problem only reached maturity within the past 2 decades to enable field- to regional-scale salinity assessment of the root zone, including GPS, GIS, geophysical techniques involving proximal and remote sensors, and a greater understanding of apparent soil electrical conductivity (ECa) and multi- and hyperspectral imagery. The concurrent development and application of these tools have made it possible to map soil salinity across multiple scales, which back in the 1980s was prohibitively expensive and impractical even at field scale. The combination of ECa-directed soil sampling and remote imagery has played a key role in mapping and monitoring soil salinity at large spatial extents with accuracy sufficient for applications ranging from field-scale site-specific management to statewide water allocation management to control salinity within irrigation districts. The objective of this paper is: (i) to present a review of the geophysical and remote imagery techniques used to assess soil salinity variability within the root zone from field to regional scales; (ii) to elucidate gaps in our knowledge and understanding of mapping soil salinity; and (iii) to synthesize existing knowledge to give new insight into the direction soil salinity mapping is heading to benefit policy makers, land resource managers, producers, agriculture consultants, extension specialists, and resource conservation field staff. The review covers the need and justification for mapping and monitoring salinity, basic concepts of soil salinity and its measurement, past geophysical and remote imagery research critical to salinity assessment, current approaches for mapping salinity at different scales, milestones in multi-scale salinity assessment, and future direction of field- to regional-scale salinity assessment

Soil Geography and Geostatistics - Concepts and Applications

Geostatistics are a useful tool for understanding and mapping the variation of soil properties across the landscapes. They can be applied at different scales regarding the initial punctual datasets the soil scientist has been provided, and regarding the target resolution of the study. This report is a collection of various studies, all dealing with geostatistical methods, which have been done in Hungary, Russia and Mexico, with the financial support of various research grants. It provides also a chapter about the general concepts of geostatistics and a discussion about limitations of geostatistics with an opening discussion on the usage of pedodiversity index. This report is then particularly recommended to soil scientists who are not so familiar with geostatistics and who need support for applying geostatistics in specific conditions.JRC.H.7-Land management and natural hazard

Digital Soil Mapping Approaches for Assisting Site-Specific Soil Management in Sugarcane Growing Areas

The Australian sugarcane industry has developed the “Six Easy Steps” nutrient and ameliorant management guidelines with the aim of optimising productivity and profitability, without adversely influencing the soil condition and causing off-farm effects. This involves knowing the spatial variation of soil properties, such as; cation exchange capacity (CEC), exchangeable calcium (Exch. Ca) and magnesium (Mg) and exchangeable sodium percentage (ESP). One way to generate soil information is to use a digital soil mapping (DSM) approach. Specifically, combine limited soil data with easier to collect ancillary data via mathematical models. This thesis focusses on developing digital soil maps (DSM) in different Australia sugarcane growing districts. Chapter 1 describes the need for DSM while Chapter 2 describes the basic components of DSM, including proximal sources of ancillary data and mathematical models. Moreover, the literature is reviewed to provide demonstrated case studies of DSM of various soil properties (e.g. CEC, Exch. Ca, Exch. Mg and ESP), with gaps identified and research chapters presented to bridge these. In Chapter 3, the application of DSM to predict CEC is explored to assist with the quantification of uncertainty due to ancillary data. In Chapter 4, the aim was to determine optimal components for DSM of topsoil Exch. Ca and Mg. In Chapter 5, the potential of wavelet analysis was explored where there was complex variation in ancillary data relative to topsoil ESP. In Chapter 6, a comparison was made of DSM to account for topsoil (0 – 0.3 m) ESP using mathematical or numerical clustering (FKM) models to create soil classes with a conventional Soil Order map (e.g. soils and land suitability of Burdekin River Irrigation Area). The results showed DSM can be applied to a wide range of soil properties and classes, especially when all the available ancillary data was used in combination. Useful guidelines on operational aspects including transect spacing (7.5 – 30 m) and soil samples for calibration (1 per hectare) were described. Future research should explore other ancillary data sources (e.g. crop yield), mathematical models (e.g. machine learning) and follow up improvement in soil condition as a function of the application of nutrient and ameliorants in accordance with the “Six Easy Steps” guidelines in the various study areas

The european framework for soil sustainability: mapping soil quality in model areas in Catalonia

Soil degradation is defined as a decrease in soil quality, which is caused by non sustainable soil management. When the soil is losing its functionality is directly related with decreasing soil quality. This PhD proposes a scheme of intrinsic soil indicators for determining soil quality. This scheme includes three different sets of soil quality indicators derived from basic soil use criteria. Such criteria is based upon indicator availability, suitability and usefulness. These indicators are grouped according to three different soil threats; declining organic matter, desertification and soil salinity. These indicators were chosen under a European framework (COM (2002) 179 final) and by natural processes. With respect to this framework, these indicators should be interpretable in the context of soil quality, whilst also providing an auditable pathway through which soil management decisions can be made. The methods selected showed helpful results determining soil quality being well selected for the use of soil indicators. EM measurements provide relevant information on within-field variability of soil salinity. SOC distribution is important to be calculated in space and in depth. The MEDALUS and RUSLE models can assess the extent, intensity and severity of desertification processes in the target area.La degradació dels sòls es defineix com la disminució de la seva qualitat causada per un mal ús per part de l’espècie humana, o bé per causes generals. Així doncs, la pèrdua de funcionalitat del sòl està lligada a la disminució de la seva qualitat. En aquesta tesi s’ha estudiat el comportament d’indicadors de qualitat del sòl escollits sota un marc polític de la Unió Europea (COM(2002)). En concret, s’han estudiat indicadors relacionats amb tres amenaces/qualitat del sòl, contingut de matèria orgànica, grau de desertificació de les terres i estat de salinitat dels sòls, amb l’objectiu de validar la seva funcionalitat per qualificar el sòl. Els estudis s’han portat a terme en dues àrees ben diferenciades de Catalunya, al marge esquerra del Delta de l’Ebre i a una zona concreta del municipi de Canalda-Odén (Solsona) a la Catalunya central. Els mètodes seleccionats han mostrat donar bons resultats en la determinació de la qualitat del sòl, sent ben seleccionats com indicadors de qualitat del sòl. L’ús de l’electromagnètic sensor proporciona bona informació per a l’estudi de la variabilitat de la salinitat del sòl. La distribució carboni orgànic del sòl és important calcular-lo per veure com canvia tan en l'espai com en profunditat. Els models MEDALUS i RUSLE han mostrat avalar l'abast, la intensitat i la gravetat dels processos de desertificació a la zona d’estudi.La degradación de los suelos se define como la disminución de su calidad del suelo causada por un mal uso por parte de la especie humana, o bien por causas generales. Así pues, la pérdida de la funcionalidad del suelo está ligada a la disminución de calidad de éste. En la presente tesis se ha estudiado el comportamiento de indicadores de calidad del suelo escogidos bajo un marco político de la Unión Europea (COM(2002)). En concreto, se han estudiado indicadores relacionados con tres amenazas del suelo, contenido de materia orgánica, grado de desertificación de las tierras y estado de salinidad de los suelos, con el objetivo de validar su funcionalidad para cualificar el suelo. Los estudios se han realizado en 2 áreas bien diferenciadas de Catalunya, en el margen izquierdo del Delta del Ebro y una zona concreta del municipio de Canalda-Odén (Solsona) en la Catalunya central. Los métodos seleccionados han mostrado dar buenos resultados en la determinación de la calidad del suelo, siendo bien seleccionados como indicadores de calidad del suelo. El uso del sensor electromagnético proporciona buena información para el estudio de la variabilidad de la salinidad del suelo. La distribución carbono orgánico del suelo es importante calcularlo para ver cómo cambia tanto en el espacio como en profundidad. Los modelos MEDALUS y RUSLE han mostrado avalar el alcance, la intensidad y la gravedad de los procesos de desertificación en la zona de estudio

Threats to Soil Quality in Europe

During the recent years, there has been a surge of concern and attention in Europe to soil degradation processes. One of the most innovative aspects of the newly proposed Soil Thematic Strategy for the EU is the recognition of the multifunctionality of soils. This report is summarizing the reserch results on the fields of soil degradation and soil quality reserach. Chapters of the report include: Preface Characterisation of soil degradation risk: an overview Soil quality in the European Union Main threats to soil quality in Europe The Natural Susceptibility on European Soils to Compaction Soil Erosion: a main threats to the soils in Europe Soil Erosion risk assessment in the alpine area according to the IPCC scenarios An example of the threat of wind erosion using DSM techniques Updated map of salt affected soils in the European Union A framework to estimate the distribution of heavy metals in European Soils Application of Soil Organic Carbon Status Indicators for policy-decision making in the EU Main threats on soil biodiversity: The case of agricultural activities impacts on soil microarthropods Implications of soil threats on agricultural areas in Europe MEUSIS, a Multi-Scale European Soil Information System (MEUSIS): novel ways to derive soil indicators through UpscalingJRC.H.7-Land management and natural hazard

Soil threats in Europe

Although there is a large body of knowledge available on soil threats in Europe, this knowledge is fragmented and incomplete, in particular regarding the complexity and functioning of soil systems and their interaction with human activities. The main aim of RECARE is to develop effective prevention, remediation and restoration measures using an innovative trans-disciplinary approach, actively integrating and advancing knowledge of stakeholders and scientists in 17 Case Studies, covering a range of soil threats in different bio-physical and socio-economic environments across Europe. Existing national and EU policies will be reviewed and compared to identify potential incoherence, contradictions and synergies. Policy messages will be formulated based on the Case Study results and their integration at European level. A comprehensive dissemination and communication strategy, including the development of a web-based Dissemination and Communication Hub, will accompany the other activities to ensure that project results are disseminated to a variety of stakeholders at the right time and in the appropriate formats to stimulate renewed care for European soils.JRC.H.5-Land Resources Managemen

Scope to predict soil properties at within-field scale from small samples using proximally sensed gamma-ray spectrometer and EM induction data

Spatial prediction of soil properties are needed for various purposes, including site-specific management, soil quality assessment, soil mapping, and solute transport modelling, to mention a few. However, the costs associated with soil sampling and laboratory analysis are substantial. One way to improve efficiencies is to combine measurement of soil properties with collection of cheaper-to-measure ancillary data. There are two possible approaches. The first is the formation of classes from ancillary data. A second is the use of a simple predictive linear model of the target soil property on the ancillary variables. Here, results are presented and compared where proximally sensed gamma-ray (gamma-ray) spectrometry and electromagnetic induction (EMI) data are used to predict the variation in topsoil properties (e.g. clay content and pH). In the first instance, the proximal data is numerically clustered using a fuzzy k-means (FKM) clustering algorithm, to identify contiguous classes. The resultant digital soil maps (i.e. k = 2 - 10 classes) are consistent with a soil series map generated using traditional soil profile description, classification and mapping methods at a highly variable site near the township of Shelford, Nottinghamshire UK. In terms of prediction, the calculated expected value of mean squared prediction error (i.e. sigma2p,C) indicated that values of k = 7 and 8 were ideal for predicting clay and pH. Secondly, a linear mixed model (LMM) is fitted in which the proximal data are fixed effects but the residuals are treated as a combination of a spatially correlated random effect and an independent and identically distributed error. In terms of prediction, the expected value of the mean squared prediction error from a regression (sigma2p,R) suggested that the regression models were able to predict clay content, better than FKM clustering. The reverse was true with respect to pH, however. It is concluded that both methods have merit. In the case of the clustering, the approach is able to account for soil properties which have non-linearity with the ancillary data (i.e. pH), whereas the LMM approach is best when there is a strong linear relationship (i.e. clay)