Thomas Francis Bishop

• BScAgr PhD GradCert Educational Studies
• Professor (Associate) at The University of Sydney

The normalised difference vegetation index (NDVI) is widely used for crop yield prediction. Several studies have shown that there is a positive correlation between NDVI and crop yield, with higher NDVI values indicating healthier and more productive crops. However, various factors can influence the accuracy of the NDVI-crop yield relationship. A sy...

Soil moisture (SM) estimation from active microwave data remains challenging due to the complex interactions between radar backscatter and surface characteristics. While the water cloud model (WCM) provides a semi-physical approach for understanding these interactions, its empirical component often limits performance across diverse agricultural lan...

Vegetation indices have long been used to monitor vegetation using spectral information. The red-edge (RE) bands have gained attention for improved yield prediction capabilities over traditional red/near-infrared-based indices. This study introduces the triple red-edge index (TREI), a novel vegetation index that leverages the three RE bands provide...

Understanding the causes of spatiotemporal variation in crop yields across large areas is important in closing yield gaps and producing more food for the growing global population. While there has been much focus on using data-driven models to predict crop yield, there is also an opportunity to use these empirical models to understand which factors...

There has been a recent surge in the number of studies that aim to model crop yield using data-driven approaches. This has largely come about due to the increasing amounts of remote sensing (e.g. satellite imagery) and precision agriculture data available (e.g. high-resolution crop yield monitor data), as well as the abundance of machine learning m...

Wheat grain protein content (GPC) is a key determinant of the prices that grain growers receive, yet there is often signification variation within-and between-fields. There is an opportunity to make use of the plethora of publicly-available information and data being collected on-farm to capture, describe and quantitatively assess variability in gr...

Introduction Context Data-driven models (DDMs) are increasingly used for crop yield prediction due to their ability to capture complex patterns and relationships. DDMs rely heavily on data inputs to provide predictions. Despite their effectiveness, DDMs can be complemented by inputs derived from mechanistic models (MMs). Methods This study investi...

Site-specific crop management (SSCM), a key component of precision agriculture, optimises resource management by adjusting practices based on within-field yield variability. However, quantifying the within-field variability and the opportunity for managing this variability can be challenging. In the Australian context, growers grow pulses more than...

Purpose A generalised approach to downscale areal observations of crop production data is illustrated using cotton yield and fibre quality (length and micronaire) data which is measured as a module (areal/block) average. Methods Two features of the downscaling algorithm are; (i) to estimate spatial trends in yield and quality using regression with...

Grain protein content (GPC) is a key determinant of the prices that grain growers receive, and the rising cost of production is shifting management focus towards optimizing this to maximise return on investment. Harvester-mounted grain protein sensors have been used to map grain protein on-the-go for more than 20 years (e.g. CropScan, John Deere)....

Context Digital soil maps (DSM) across large areas have an inability to capture soil variation at within-fields despite being at fine spatial resolutions. In addition, creating field-extent soil maps is relatively rare, largely due to cost. Aims To overcome these limitations by creating soil maps across multiple fields/farms and assessing the valu...

Take home message • Maps of grain protein content are useful for understanding how and why grain protein varies and for informing management decisions • While maps of grain protein content are not available for every field, farm, and season, a combination of on-farm and/or publicly available data can be used to build a model to predict and map grai...

The Murray-Darling Basin is a highly significant agricultural region in Australia, contributing AUD $24 billion to the Australian economy each year through food and fibre production. While the region provides favourable conditions for agriculture, it faces a multitude of challenges, including climate change, drought, and soil degradation. The occur...

The depth-to a constraint determines how much of the soil profile, and the water it contains, can be accessed by plant roots. Spatial information describing the impact of constraints on available water capacity (AWC) and yield is important for farm management, but rarely considered. The interactions between the depth-to three yield-limiting constra...

Hyperspectral imaging spectrometers mounted on unmanned aerial vehicle (UAV) can capture high spatial and spectral resolution to provide cotton crop nitrogen status for precision agriculture. The aim of this research was to explore machine learning use with hyperspectral datacubes over agricultural fields. Hyperspectral imagery was collected over a...

Soil salinization resulting from shallow saline groundwater is a major global environmental issue causing land degradation, especially in semi-arid regions such as Australia. The adverse impact of shallow saline groundwater on soil salinization varies in space and time due to the variation in groundwater levels and salt concentration. Understanding...

Monitoring and mapping organic carbon throughout the soil profile is an important task, as land management and fluctuations in rainfall patterns have the potential to substantially alter the levels of soil organic carbon (SOC). This study aims to monitor the change in SOC content between 2002 and 2015 in a semi-arid, irrigated cotton-growing region...

There is currently limited understanding surrounding the spatial accuracy of soil amelioration advice as a function of sampling density at the sub-field scale. Consequently, soil-based decisions are often made using a data limiting approach, as the value proposition of soil data collection has not been well described. The work presented here invest...

A phenology-based crop type mapping approach was carried out to map cotton fields throughout the cotton-growing areas of eastern Australia. The workflow was implemented in the Google Earth Engine (GEE) platform, as it is time efficient and does not require processing in multiple platforms to complete the classification steps. A time series of Norma...

Forecasts of crop yield are an important tool for a variety of stakeholders, but most studies produce large-scale, late-season yield forecasts that are not appropriate for farmers. Farmers require forecasts of crop yield mid-season and at fine spatial resolutions to guide site-specific adaptive crop management practices. This study created empirica...

Prediction and mapping of soil properties for different soil depths can provide important information for effective land management. Such predictions and maps are often built based on soil data from multiple soil surveys, and the sampled depths will rarely align with depths for which the predictions and maps are required. A recently proposed approa...

Vegetation activity in many parts of Africa is constrained by dynamics in the hydrologic cycle. Using satellite products, the relative importance of soil moisture, rainfall, and terrestrial water storage (TWS) on vegetation greenness seasonality and anomaly over Africa were assessed for the period between 2003 and 2015. The possible delayed respons...

Heavy metals accumulate in soil over time and, with changing land use, humans may be exposed to elevated contaminant concentrations. It is therefore important to delineate contaminated sites in the most efficient and accurate manner. Sensors, such as portable X-ray fluorescence (pXRF) and visible near-infrared (vis-NIR) spectroscopy predict metal c...

Many broadacre farmers have a time series of crop yield monitor data for their fields which are often augmented with additional data, such as soil apparent electrical conductivity surveys and soil test results. In addition there are now readily available national and global datasets, such as rainfall and MODIS, which can be used to represent the cr...

A phenology-based crop type classification was carried out to map cotton in New South Wales and Queensland, Australia. The workflow was implemented in Google Earth Engine (GEE) platform as it is time-efficient and does not require processing in multiple platforms to complete the classification steps. A time-series of images were generated from Land...

We modelled and mapped the distribution of three principal soil organic carbon (SOC) fractions across New South Wales and gained insights into the factors controlling their distribution. Carbon fractions are important for modelling soil carbon dynamics for carbon accounting, and as a potential indicator of soil quality. We considered particulate or...

Climate change will lead to altered soil conditions that will impact on plant growth in both agricultural and native ecosystems. Additionally, changes in soil carbon storage will influence carbon accounting schemes that may play a role in climate change mitigation programs. We applied a digital soil mapping approach to examine and map (at 100-m res...

To improve accuracy and efficiency of monitoring remediated sites, the current study proposed the use of bivariate linear mixed modelling and subsequent hypothesis testing to determine significant change in contaminant concentrations over time. The modelling method integrated soil heavy metal (arsenic–As, lead–Pb and zinc–Zn) concentrations obtaine...

Subsoil alkalinity is a common issue in the alluvial cotton-growing valleys of northern New South Wales (NSW), Australia. Soil alkalinity can cause nutrient deficiencies and toxic effects, and inhibit rooting depth, which can have a detrimental impact on crop production. The depth at which a soil constraint is reached is important information for l...

Soil contamination poses substantial risks to human and ecosystem health, justifying the need for accurate delineation and remediation of contaminated sites. The number of soil samples collected at a site during assessment is limited by cost and time available for assessment, increasing the potential for misclassification due to insufficient sample...

Wildfire can have significant impacts on hydrological processes in forested catchments, and a key area of concern is the impact upon water quality, particularly in catchments that supply drinking water. Wildfire effects runoff, erosion, and increases the influx of other pollutants into catchment waterways. Research suggests that suspended sediment...

Crop production in West Africa is largely under rainfed conditions, making the system vulnerable to the impacts of climate change. However, the impact of future climate on the geographic range of many crops in West Africa is still uncertain. This is exacerbated by considerable uncertainty in projecting future West African climate by global circulat...

Short rainfall events contribute to large portions of annual sediment and nutrient exports. Most water quality sampling schemes rely on regularly spaced temporal sampling and increasingly monitoring schemes are including a form of event-based sampling. A typical approach is to sample each event using equal intervals in time using an automatic sampl...

Climate change will lead to altered soil conditions that will impact on plant growth in both agricultural and native ecosystems. Additionally, changes in soil carbon storage will influence carbon accounting schemes that may play a role in climate change mitigation programs. We applied a digital soil mapping approach to examine and map (at 100-m res...

Soil salinity and sodicity are two of the most limiting constraints for agriculture in arid and semiarid landscapes, but long-term studies are scarce, and most solely focus on the topsoil. This study monitors the change in soil electrical conductivity (EC) and exchangeable sodium percentage (ESP) to 1.2 m depth with bivariate linear mixed models be...

Intensive agricultural management practices and fluctuating rainfall patterns have the potential to significantly impact the status and change of important soil properties. This study looks at the change in soil pH during a 13-year period in a semi-arid, irrigated cotton-growing region in the lower Lachlan River valley in southern NSW, Australia un...

While traditional laboratory methods of determining soil organic carbon (SOC) content are generally simple, this becomes more challenging when carbonates are present in the soil; such is commonly found in semi-arid areas. Additionally, soil inorganic carbon (SIC) content itself is difficult to determine. This study uses visible near infrared (VisNI...

A basic idea concerning collections of soil observations is to obtain statistical parameters from the data distribution. In soil, we recognise two kinds of statistical distributions relating to discrete or continuous random variables.

A soil scientist in most instances can only measure and describe soil at a few points in a landscape; at each location, he has ways to describe and measure soil features. These may be based on field observations, e.g. presence of mottling in the subsoil, or a sample may be collected for subsequent laboratory analysis, e.g. clay content. Many differ...

When we wish to characterize soil, it soon becomes very clear that one or two properties of soil materials, horizons, profiles or pedons will not suffice to give an adequate description. Soil classification, land capability, soil quality, condition and health assessments often involve the observation of tens or scores of soil properties on a single...

There is a need for the up-to-date assessment of desertification/land degradation maps which are dynamic in nature at different scales for comprehensive planning and preparation of action plan. The present paper aims to develop the desertification vulnerability index (DVI) and predict the different desertification processes operating in Anantapur D...

The human population is increasing globally and land use is changing to accommodate for this growth. Soils within urban areas require closer attention as the higher population density increases the chance of human exposure to urban contaminants. One such example of an urban area undergoing an increase in population density is Sydney, Australia. The...

Effective management of soil requires the spatial distribution of its various physical, chemical and hydrological properties. This is because properties, for example clay content, determine the ability of soil to hold cations and retain water. However, data acquisition is labour intensive and time-consuming. To add value to the limited soil data, r...

The cation exchange capacity (CEC) of soil is widely used for agricultural assessment as a measure of fertility and an indicator of structural stability; however, its measurement is time-consuming. Although geostatistical methods have been used, a large number of samples must be collected. Using pedometric methods and incorporating easy-to-measure...

Soil salinisation is a major threat for both crop productivity and environment. Global climate change could affect the salt build-up in agricultural lands due to increase in evapotranspiration and temperature. Simulations of the effects of climate change on soil salinisation are vital to identify the vulnerable areas and to suggest proper reclamati...

Quantitative attribution at the individual pixel level of the relative contributions of climate variability and human activities to vegetation productivity dynamics across Africa is generally lacking. This is because of the difficulty in establishing a baseline or potential vegetation against which the relative impacts of these factors can be asses...

Relationships between parent material and soil are not well understood and generally only reported in qualitative form. We present a classification of parent material for pedologic purposes, which includes twelve lithology classes based on mineralogical and chemical composition. The relationships of these lithology classes with six key soil propert...

Monitoring landscape-scale vegetation responses of resprouter species to wildfire is helpful in explaining post-wildfire recovery. Several previous Australian studies have investigated the temporal recovery of eucalypt obligate- seeder communities (which have a significantly delayed revegetation response), but little research has been conducted for...

Soil bulk density (BD) and effective cation exchange capacity (ECEC) are among the most important soil properties required for crop growth and environmental management. This study aimed to explore the combination of soil and environmental data in developing pedotransfer functions (PTFs) for BD and ECEC. Multiple linear regression (MLR) and random f...

Core Ideas Potential changes in soil organic C to 2070 mapped (100‐m grid) and examined. The direction and magnitude of change varied between the 12 climate projections. Differing changes revealed for 36 current climate–parent material–land use regimes. Digital soil mapping–space‐for‐time substitution is useful for climate change study Digital soi...

Soils naturally change through time, but anthropogenic activity has significantly altered the rate and direction of soil change. As well as further impacts of human activity on soil into the future, it is also expected that recent climatic shifts will have an important effect. There are a variety of methods of monitoring changes in soil, but a shif...

Research in the past 20 years has demonstrated that digital terrain models are a useful secondary information source for the prediction of soil properties and classes. This chapter begins with a brief introduction to digital terrain modeling; in particular, the types of terrain attributes that can be calculated from a digital elevation model (DEM)...

Datasets for modelling and mapping soil properties often consist of samples from many spatial locations, collected from several different soil depth intervals. However, interest may lie in the spatial distribution of the property for a particular target depth interval, which may or may not correspond to the sampled intervals. It is the task of the...

Digital soil models and maps have been developed for pre-European (pre-clearing) levels of soil organic carbon (SOC) over New South Wales, Australia. These provide a useful first estimate of natural, unaltered soil conditions before agricultural development, which are potentially important for many carbon-accounting schemes such as those prescribed...

The impact of grazing on soil carbon (C) and nitrogen (N) cycles is complex, and across a large area it can be difficult to uncover the magnitude of the effects. Here, we have linked two common approaches to statistical modelling-regression trees and linear mixed models-in a novel way to explore various aspects of soil C and N dynamics for a large,...

Soil and land-management interactions in Australian native-forest regrowth remain a major source of uncertainty in the context of the global carbon economy. We sampled soil total organic C (TOC) and soil total N (TN) stocks at 45 sites within the Brigalow ecological community of the Brigalow Belt bioregion, Queensland, Australia. The sites were mat...

Understanding the potential of soil to store organic carbon (SOC) is important for potential climate change mitigation strategies and assessing soil health issues. We examined the factors controlling SOC storage in eastern Australian soils and how these vary with depth. Models were developed using a set of readily interpreted covariates to represen...

In this paper, we present a framework for a space–time observation system for soil organic carbon (STOS-SOC). We propose that the RothC model be embedded within the STOS-SOC, which is driven by satellite-derived inputs and readily available geospatial inputs, such as digital soil maps. In particular, advances in remote sensing have enabled the deve...

In this paper, we present a framework for a space–time observation system for soil organic carbon (STOS-SOC). We propose that the RothC model be embedded within the STOS-SOC, which is driven by satellite-derived inputs and readily available geospatial inputs, such as digital soil maps. In particular, advances in remote sensing have enabled the deve...

Geostatistical methods can be used to calculate predictions of soil variables at unsampled locations, but the methodology is typically based on samples collected on identical sample supports. In this paper, we provide and test theory that allows the inclusion of data from mixed sample supports in a single analysis. In particular, we consider compos...

Study region: South eastern Australia. Study focus: This region is characterised with rainfall events that are associated with large exports of nutrients and sediments. Many water quality monitoring schemes use a form of event-based sampling to quantify these exports. Previous water quality studies that have evaluated different sampling schemes oft...

Predictions of the variation of soil moisture in space and time would improve our management and modelling of the environment in a range of areas from farming to weather forecasting to assessing bushfire risk. Until recently predictions have not been possible at anything but the coarsest scales. However a confluence of newer remote sensing platform...

The Gondwana Rainforests of Australia World Heritage Area (GRWHA) covers approximately 370,000 ha, extending along Australia’s east coast from southeast Queensland to central eastern New South Wales (NSW). The rainforests include cool temperate and subtropical ecosystems supporting a high biodiversity of plant and animal species. More than 200 plan...

There are tens of millions of contaminated soil sites in the world, and with an increasing population and associated risk there is a growing pressure to remediate them. A barrier to remediation is the lack of cost-effective approaches to assessment. Soil contaminants include a wide range of natural and synthetic metallic and organic compounds and m...

In this study we validated digital soil maps of clay content at different spatial supports; point, 48 m blocks and soil–land use complexes (SLU). The aim being to examine the change in prediction quality with different prediction supports. Digital soil maps of clay content at depths of 0–10 cm, 10–30 cm and 30–50 cm were created using linear mixed...

Soil properties can be considerably modified as a result of wildfire. This study examined the impact of wildfire on total carbon and water repellency at two study sites, namely Cranebrook and Wentworth Falls, located 45 and 75 km west of Sydney, Australia, respectively. Within each study site, we measured soil properties at two depth intervals from...

To help meet the increasing need for knowledge and data on the spatial distribution of soils, readily applied multiple linear regression models were developed for key soil properties over eastern Australia. Selected covariates were used to represent the key soil-forming factors of climate (annual precipitation and maximum temperature), parent mater...

Spatial prediction of soil organic carbon (SOC) stock from concentration and bulk density measurements typically employs a two-step procedure. First, SOC concentration and bulk density data are used to calculate the SOC stock at each data location. Second, the calculated SOC stock at each data location is interpolated to give predictions at unsampl...

There is a growing need for spatially continuous and quantitative soil information for environmental modeling and management, especially at the national scale. This study was aimed at predicting soil particle-size fractions (PSF) for Nigeria using random forest model (RFM). Equal-area quadratic splines were fitted to Nigerian legacy soil profile da...

This study aims to map the measurable fractions of soil organic carbon related to the RothC carbon model at the catchment scale and to assess the model and prediction quality. It also discusses how the outputs can be used to provide initial pool estimates for process modelling of soil carbon in a spatial context. The study was carried out in Cox's...

The importance of soil organic carbon (SOC) in maintaining soil health is well understood. However, there is growing interest in studying SOC with an emphasis on quantifying its changes in space and time. This is because of the potential for soil to be used to sequester atmospheric C. There are many issues which make this difficult, for example sho...

The effect of the sample size on prediction quality is well understood. Recently studies have assessed this relationship using near continuous water quality samples. However, this is rarely possible due to financial constraints and therefore many studies have relied on simulation based methods utilising more affordable surrogates. A limitation of s...

Lithology can provide a powerful and easily used covariate to complement other parent material related covariates and improve the statistical performance of digital soil models and maps (DSMM). However, it appears that the use of lithology is not fully utilised as a covariate in digital soil mapping projects around the globe. It appears only 25% or...

Deep drainage can contribute to groundwater table rises and salinity, and is a complex function of rainfall, land management and soil hydraulic properties. Each of these components is uncertain and variable in space and time. This study quantifies the associated uncertainty using a Monte Carlo simulation to calculate deep drainage and estimate deep...

Most water quality monitoring schemes rely on estimation methods as it is often far too expensive to monitor water quality properties continuously. Estimations are used to evaluate management strategies and long term trends. It is critical that the estimation methods provide accurate estimations and an accurate estimate of the associated uncertaint...

The aim of this work is to map the presence the soil-borne fungal pathogen, Phytophthora cinnamomi, in the Gondwana Rainforests of eastern Australia. Logistic regression was used for modeling and the final model included mean and minimum temperature, distance to nearest drainage line, distance to nearest road/trail/lookout/visitor area, region, ins...