Development of a Proximal Soil Sensing System for the Continuous Management of Acid Soil

The notion that agriculturally productive land may be treated as a relatively homogeneous resource at thewithin-field scale is not sound. This assumption and the subsequent uniform application of planting material,chemicals and/or tillage effort may result in zones within a field being under- or over-treated. Arising fromthese are problems associated with the inefficient use of input resources, economically significant yield losses,excessive energy costs, gaseous or percolatory release of chemicals into the environment, unacceptable long-term retention of chemicals and a less-than-optimal growing environment. The environmental impact of cropproduction systems is substantial. In this millennium, three important issues for scientists and agrariancommunities to address are the need to efficiently manage agricultural land for sustainable production, the maintenance of soil and water resources and the environmental quality of agricultural land.Precision agriculture (PA) aims to identify soil and crop attribute variability, and manage it in an accurate and timely manner for near-optimal crop production. Unlike conventional agricultural management where an averaged whole-field analytical result is employed for decision-making, management in PA is based on site-specific soil and crop information. That is, resource application and agronomic practices are matched with variation in soil attributes and crop requirements across a field or management unit. Conceptually PA makes economic and environmental sense, optimising gross margins and minimising the environmental impact of crop production systems. Although the economic justification for PA can be readily calculated, concepts such as environmental containment and the safety of agrochemicals in soil are more difficult to estimate. However,it may be argued that if PA lessens the overall agrochemical load in agricultural and non-agricultural environments, then its value as a management system for agriculture increases substantially.Management using PA requires detailed information of the spatial and temporal variation in crop yield components, weeds, soil-borne pests and attributes of physical, chemical and biological soil fertility. However,detailed descriptions of fine scale variation in soil properties have always been difficult and costly to perform.Sensing and scanning technologies need to be developed to more efficiently and economically obtain accurate information on the extent and variability of soil attributes that affect crop growth and yield. The primary aim of this work is to conduct research towards the development of an 'on-the-go' proximal soil pH and lime requirement sensing system for real-time continuous management of acid soil. It is divided into four sections.Section one consists of two chapters; the first describes global and historical events that converged into the development of precision agriculture, while chapter two provides reviews of statistical and geostatistical techniques that are used for the quantification of soil spatial variability and of topics that are integral to the concept of precision agriculture. The review then focuses on technologies that are used for the complete enumeration of soil, namely remote and proximal sensing.Section two comprises three chapters that deal with sampling and mapping methods. Chapter three provides a general description of the environment in the experimental field. It provides descriptions of the field site,topography, soil condition at the time of sampling, and the spatial variability of surface soil chemical properties. It also described the methods of sampling and laboratory analyses. Chapter four discusses some of the implications of soil sampling on analytical results and presents a review that quantifies the accuracy,precision and cost of current laboratory techniques. The chapter also presents analytical results that show theloss of information in kriged maps of lime requirement resulting from decreases in sample size. The messageof chapter four is that the evolution of precision agriculture calls for the development of 'on-the-go' proximal soil sensing systems to characterise soil spatial variability rapidly, economically, accurately and in a timely manner. Chapter five suggests that for sparsely sampled data the choice of spatial modelling and mapping techniques is important for reliable results and accurate representations of field soil variability. It assesses a number of geostatistical methodologies that may be used to model and map non-stationary soil data, in this instance soil pH and organic carbon. Intrinsic random functions of order k produced the most accurate and parsimonious predictions of all of the methods tested.Section three consists of two chapters whose theme pertains to sustainable and efficient management of acid agricultural soil. Chapter six discusses soil acidity, its causes, consequences and current management practices.It also reports the global extent of soil acidity and that which occurs in Australia. The chapter closes by proposing a real-time continuous management system for the management of acid soil. Chapter seven reports results from experiments conducted towards the development of an 'on-the-go' proximal soil pH and lime requirement sensing system that may be used for the real-time continuous management of acid soil. Assessment of four potentiometric sensors showed that the pH Ion Sensitive Field Effect Transistor (ISFET)was most suitable for inclusion in the proposed sensing system. It is accurate and precise, drift and hysteresis are low, and most importantly it's response time is small. A design for the analytical system was presented based on flow injection analysis (FIA) and sequential injection analysis (SIA) concepts. Two different modes of operation were described. Kinetic experiments were conducted to characterise soil:0.01M CaCl2 pH(pHCaCl2) and soil:lime requirement buffer (pH buffer) reactions. Modelling of the pH buffer reactions described their sequential, biphasic nature. A statistical methodology was devised to predict pH buffer measurements using only initial reaction measurements at 0.5s, 1s, 2s and 3s measurements. The accuracy of the technique was 0.1pH buffer units and the bias was low. Finally, the chapter describes a framework for the development of a prototype soil pH and lime requirement sensing system and the creative design of the system.The final section relates to the management of acid soil by liming. Chapter eight describes the development of empirical deterministic models for rapid predictions of lime requirement. The response surface models are based on soil:lime incubations, pH buffer measurements and the selection of target pH values. These models are more accurate and more practical than more conventional techniques, and may be more suitably incorporated into the spatial decision-support system of the proposed real-time continuous system for the management of acid soil. Chapter nine presents a glasshouse liming experiment that was used to authenticate the lime requirement model derived in the previous chapter. It also presents soil property interactions and soil-plant relationships in acid and ameliorated soil, to compare the effects of no lime applications, single-rate and variable-rate liming. Chapter X presents a methodology for modelling crop yields in the presence of uncertainty. The local uncertainty about soil properties and the uncertainty about model parameters were accounted for by using indicator kriging and Latin Hypercube Sampling for the propagation of uncertainties through two regression functions; a yield response function and one that equates resultant pH after the application of lime. Under the assumptions and constraints of the analysis, single-rate liming was found to be the best management option

Visible and near infrared spectroscopy in soil science

This chapter provides a review on the state of soil visible–near infrared (vis–NIR) spectroscopy. Our intention is for the review to serve as a source of up-to date information on the past and current role of vis–NIR spectroscopy in soil science. It should also provide critical discussion on issues surrounding the use of vis–NIR for soil analysis and on future directions. To this end, we describe the fundamentals of visible and infrared diffuse reflectance spectroscopy and spectroscopic multivariate calibrations. A review of the past and current role of vis–NIR spectroscopy in soil analysis is provided, focusing on important soil attributes such as soil organic matter (SOM), minerals, texture, nutrients, water, pH, and heavy metals. We then discuss the performance and generalization capacity of vis–NIR calibrations, with particular attention on sample pre-tratments, co-variations in data sets, and mathematical data preprocessing. Field analyses and strategies for the practical use of vis–NIR are considered. We conclude that the technique is useful to measure soil water and mineral composition and to derive robust calibrations for SOM and clay content. Many studies show that we also can predict properties such as pH and nutrients, although their robustness may be questioned. For future work we recommend that research should focus on: (i) moving forward with more theoretical calibrations, (ii) better understanding of the complexity of soil and the physical basis for soil reflection, and (iii) applications and the use of spectra for soil mapping and monitoring, and for making inferences about soils quality, fertility and function. To do this, research in soil spectroscopy needs to be more collaborative and strategic. The development of the Global Soil Spectral Library might be a step in the right direction

Magnetic Domain State Diagnosis in Soils, Loess, and Marine Sediments From Multiple First-Order Reversal Curve-Type Diagrams

First-order reversal curve (FORC) diagrams provide information about domain states and magnetostatic interactions that underpin paleomagnetic interpretations. FORC diagrams are a complex representation of remanent, induced, and transient magnetizations that can be assessed individually using additional FORC-type measurements along with conventional measurements. We provide the first extensive assessment of the information provided by remanent, transient, and induced FORC diagrams for a diverse range of soil, loess/paleosol, and marine sediment samples. These new diagrams provide substantial information in addition to that provided by conventional FORC diagrams that aids comprehensive domain state diagnosis for mixed magnetic particle assemblages. In particular, we demonstrate from transient FORC diagrams that particles occur routinely in the magnetic vortex state. Likewise, remanent FORC diagrams provide information about the remanence-bearing magnetic particles that are of greatest interest in paleomagnetic studies

Continental-Scale Soil Organic Carbon Composition and Vulnerability Regulated by Regional Soil and Environmental Controls

Processes that control soil organic carbon (C) composition and dynamics over large scales are not well understood. Thus, our understanding of C cycling is incomplete, making it difficult to predict C gains and losses due to changes in climate, land use and management. In this paper, we show that controls on the composition of organic C, the particulate, humus (or mineral associated) and resistant fractions, and the potential vulnerability of C to decomposition across Australia are distinct, scale-dependent and variable

Continental-scale magnetic properties of surficial Australian soils

Soil magnetism reflects the physical properties of mainly iron oxide and oxyhydroxide minerals, which provides important information for deciphering soil environments. Establishing national scale soil magnetic databases can provide important reference information that can assist mineral surveying and agricultural planning. Our aims are to provide visualizations and to describe multiple magnetic properties across Australia, to evaluate the relationship between soil magnetism and soil forming factors, and to interpret the mechanisms responsible for surface soil magnetism in Australia. We present the first surficial Australian soil magnetic database, which contains 471 topsoil samples of natural and unpolluted materials. The samples were characterized with detailed magnetic measurements, which show that the magnetic properties of Australian soils vary considerably, but most surficial soils have small concentrations of coarse-grained magnetic minerals. The vast central Australian interior is characterized by weak magnetism, with more hematite and goethite contribution. Strong magnetic hotspots occur in the northwestern plateau, Nullarbor Plain, and eastern highlands. Parent material acts as the dominant control on soil magnetic properties, influencing magnetic mineral concentration and grain size, and controlling the contribution and relative importance of hematite to goethite. Temperature and rainfall both have a weak negative influence on superfine ferrimagnetic particles, due to progressive transformation to hematite and particle migration driven by intensive rainfall in sandy soils. Biota and land use changes tend to have a more complex and integrated local influence on hematite and goethite formation and preservationThis work was supported by the Australian Research Council through grants DP160100805 and DP19010087

Diffuse reflectance spectroscopy for estimating soil properties: A technology for the 21st century

Spectroscopic measurements of soil samples are reliable because they are highly repeatable and reproducible. They characterise the samples' mineral-organic composition. Estimates of concentrations of soil constituents are inevitably less precise than estimates obtained conventionally by chemical analysis. But the cost of each spectroscopic estimate is at most one-tenth of the cost of a chemical determination. Spectroscopy is cost-effective when we need many data, despite the costs and errors of calibration. Soil spectroscopists understand the risks of over-fitting models to highly dimensional multivariate spectra and have command of the mathematical and statistical methods to avoid them. Machine learning has fast become an algorithmic alternative to statistical analysis for estimating concentrations of soil constituents from reflectance spectra. As with any modelling, we need judicious implementation of machine learning as it also carries the risk of over-fitting predictions to irrelevant elements of the spectra. To use the methods confidently, we need to validate the outcomes with appropriately sampled, independent data sets. Not all machine learning should be considered 'black boxes'. Their interpretability depends on the algorithm, and some are highly interpretable and explainable. Some are difficult to interpret because of complex transformations or their huge and complicated network of parameters. But there is rapidly advancing research on explainable machine learning, and these methods are finding applications in soil science and spectroscopy. In many parts of the world, soil and environmental scientists recognise the merits of soil spectroscopy. They are building spectral libraries on which they can draw to localise the modelling and derive soil information for new projects within their domains. We hope our article gives readers a more balanced and optimistic perspective of soil spectroscopy and its future. Highlights Spectroscopy is reliable because it is a highly repeatable and reproducible analytical technique. Spectra are calibrated to estimate concentrations of soil properties with known error. Spectroscopy is cost-effective for estimating soil properties. Machine learning is becoming ever more powerful for extracting accurate information from spectra, and methods for interpreting the models exist. Large libraries of soil spectra provide information that can be used locally to aid estimates from new samples

Multi-scale digital soil mapping with deep learning

We compared different methods of multi-scale terrain feature construction and their relative effectiveness for digital soil mapping with a Deep Learning algorithm. The most common approach for multi-scale feature construction in DSM is to filter terrain attributes based on different neighborhood sizes, however results can be difficult to interpret because the approach is affected by outliers. Alternatively, one can derive the terrain attributes on decomposed elevation data, but the resulting maps can have artefacts rendering the approach undesirable. Here, we introduce ‘mixed scaling’ a new method that overcomes these issues and preserves the landscape features that are identifiable at different scales. The new method also extends the Gaussian pyramid by introducing additional intermediate scales. This minimizes the risk that the scales that are important for soil formation are not available in the model. In our extended implementation of the Gaussian pyramid, we tested four intermediate scales between any two consecutive octaves of the Gaussian pyramid and modelled the data with Deep Learning and Random Forests. We performed the experiments using three different datasets and show that mixed scaling with the extended Gaussian pyramid produced the best performing set of covariates and that modelling with Deep Learning produced the most accurate predictions, which on average were 4–7% more accurate compared to modelling with Random Forests

The importance of sampling support for explaining change in soil organic carbon

Soil monitoring networks are used to detect change and ascribe cause to better understand the soil organic carbon (SOC) cycle in terrestrial ecosystems. In particular for SOC, its typically large spatial, and small temporal variability relative to its content may prevent the detection of change because of the complex interactions between the major factors driving change in SOC that cause variation within and between fields. The reduction of estimated SOC spatial variance is readily achieved within e.g., a given land use, by visiting as many fields as resources allow to reduce the estimate of between-field variance. Within-field variance is poorly estimated by sampling within a support (here called a plot), which is a small proportion of the field. We briefly summarise dispersion variances and describe an approximation which makes their calculation readily accessible. Using an example we demonstrate the proportion of total SOC dispersion variance that is omitted and which will ultimately increase noise in the explanation of SOC change. We conclude that when the purpose of monitoring is to explain SOC change we suggest that bulking soil samples from the within-field soil type unit is at least as important as revisiting the same location