Silicon improves root system and canopy physiology in wheat under drought stress

Aims: Root system is an important regulator for unevenly distributed below-ground resource acquisition. In a rainfed cropping environment, drought stress (DS) significantly restricts root growth and moisture uptake capacity. The fact that silicon (Si) alleviates DS in wheat is widely reported, but its effects on the wheat root system remain unclear. Methods: The present study investigated the effect of pre-sowing Si treatment on two contrasting wheat cultivars (RAC875, drought-tolerant; Kukri, drought-susceptible) at early growth stages. The cultivars were grown in a glasshouse in a complete randomized design with four replications and two watering treatments. Various root traits and physiological data, including non-destructive infrared thermal imaging for water stress indices, were recorded. Results: Under DS and Si (DSSi), Kukri had a significant increase in primary root length (PRL,44%) and lateral root length (LRL,28.1%) compared with RAC875 having a substantial increase in PRL (35.2%), but non-significant in LRL. The Si-induced improvement in the root system positively impacted canopy physiology and significantly enhanced photosynthesis, stomatal conductance and transpiration in Kukri and RAC875 under DSSi. Canopy temperature was reduced significantly in Kukri (4.24%) and RAC875 (6.15%) under DSSi, while canopy temperature depression was enhanced significantly in both the cultivars (Kukri,78.6%; RAC875, 58.6%) under DSSi. Conclusion: These results showed that Si has the potential to influence below-ground traits, which regulate the moisture uptake ability of roots for cooler canopy and improved photosynthesis under DS. It also suggests a future direction to investigate the underlying mechanisms involved in wheat’s Si-induced root growth and moisture uptake ability

Water use in a heavily urbanized delta : scenarios and adaptation options for sectorial water use in the Pearl River Basin, China

Water use is increasing globally to meet the growing demand for food and industrial products, and the rising living standard. Water scarcity has been reported in many regions, questioning the long-term sustainability of water use. The objective of this thesis is to better understand sectorial water use development in an urbanizing river delta, and to explore the potential of water use management as an adaptation option to reduce water shortage. The Pearl River Basin in Southern China is taken as study area. The upstream part of the basin is one of the poorer regions of China, whereas the Pearl River Delta (PRD) is the world’s largest urban region in both population and area. This study presents the first consistent analysis of sectorial water use in the PRD. Results show that during the period of 2000-2010, the PRD managed to stabilize its annual total water use. Nevertheless, severe salt intrusion induced water shortages occur. Assessment of water use at a monthly resolution shows that water use contributes to salt intrusion by further reducing the already low dry season river discharge. To investigate the possible future development of water use, this study proposed a method to derive region specific water use scenarios from a global assessment of water use. Scenarios based on regionalised assumptions project substantially lower water use than those based on national assumptions. Nevertheless, hydrological challenges remain for the PRD. The total water use of the PRD may still increase by up to 54% in 2030 in the regionalized scenarios. Also, water use in the upstream regions increases with socio-economic development. To address water shortage, four extreme water allocation strategies were analysed against water use and water availability scenarios under climate change. None of these strategies proved to be sufficient to fully avoid water scarcity in the Pearl River Basin. This study obtains a better understanding of the sectorial water use development and its impact on salt intrusion induced water shortage in a heavily urbanized river delta. The water use framework and methods used to derive regional water use scenarios are transferable to other regions, provided that data is available. Water use scenarios are crucial to sustainably manage water resources in a changing world.</p

Water allocation under future climate change and socio-economic development : the case of Pearl River Basin

Water shortage has become a major challenge in many parts of the world due to climate change and socio-economic development. Allocating water is critical to meet human and ecosystem needs in these regions now and in the future. However, water allocation is being challenged by uncertainties associated with climate change and socio-economic development. This thesis aims to assess the combined effects of climate change and socio-economic development on water supply and demand in the Pearl River Basin (PRB) in China, and identify water allocation plans, which are robust to future climate change and socio-economic development. To do so, the impact of climate change on future water availability is first assessed. Next, different model frameworks are developed to identify robust water allocation plans for improving reservoir management, ensuring sufficient flow into the delta to reduce salt intrusion, and providing sufficient freshwater for human and industrial consumption under future climate change and socio-economic development. Results show that water availability is becoming more variable throughout the basin due to climate change. River discharge in the dry season is projected to decrease throughout the basin. For a moderate climate change scenario (RCP4.5), low flows reduce between 6 and 48 % depending on locations. For a high climate change scenario (RCP8.5), the decreases of low flows can reach up to 72%. In the wet season, river discharge tends to increase in the middle and lower reaches and decreases in the upper reach of the Pearl River Basin. The variation of river discharge is likely to aggravate water stress. Especially the reduction of low flow is problematic as already the basin experiences water shortages during the dry season in the delta. The model frameworks developed in this study not only evaluate the performance of existing water allocation plans in the past, but also the impact of future climate change on robustness of previous and newly generated water allocation plans. The performance of the four existing water allocation plans reduces under climate change. New water allocation plans generated by the two model frameworks perform much better than the existing plans. Optimising water allocation using carefully selected state-of-the-art multi-objective evolutionary algorithms in the Pearl River Basin can help limit water shortage and salt intrusion in the delta region. However, the current water allocation system with six key reservoirs is insufficient in maintaining the required minimum discharge at two selected gauge stations under future climate change. More reservoirs, especially in the middle and lower reaches of the Pearl River, could potentially improve the future low flow into the delta. This study also explored future water shortage in the Pearl River Basin under different water availability and water use scenarios. Four different strategies to allocate water were defined. These water allocation strategies prioritize upstream water use, Pearl River Delta water use, irrigation water use, and manufacturing water use, respectively. Results show that almost all the regions in the Pearl River Basin are likely to face temporary water shortage under the four strategies. The increasing water demand contributes twice as much as the decreasing water availability to water shortage. All four water allocation strategies are insufficient to solve the water scarcity in the Pearl River Basin. The economic losses differ greatly under the four water allocation strategies. Prioritizing the delta region or manufacturing production would result in lower economic losses than the other two strategies. However, all of them are rather extreme strategies. Development of water resources management strategies requires a compromise between different water users. Optimization algorithms prove to be flexible and useful tool in adaptive water resources allocation for providing multiple approximate Pareto solutions. In addition, new technologies and increasing water use efficiency will be important to deal with future water shortage in the Pearl River Basin.</p

A data driven approach for diagnosis and management of yield variability attributed to soil constraints

Australian agriculture does not value data to the level required for true precision management. Consequently, agronomic recommendations are frequently based on limited soil information and do not adequately address the spatial variance of the constraints presented. This leads to lost productivity. Due to the costs of soil analysis, land owners and practitioners are often reluctant to invest in soil sampling exercises as the likely economic gain from this investment has not been adequately investigated. A value proposition is therefore required to realise the agronomic and economic benefits of increased site-specific data collection with the aim of ameliorating soil constraints. This study is principally concerned with identifying this value proposition by investigating the spatially variable nature of soil constraints and their interactions with crop yield at the sub-field scale. Agronomic and economic benefits are quantified against simulated ameliorant recommendations made on the basis of varied sampling approaches. In order to assess the effects of sampling density on agronomic recommendations, a 108 ha site was investigated, where 1200 direct soil measurements were obtained (300 sample locations at 4 depth increments) to form a benchmark dataset for analysis used in this study. Random transect sampling (for field average estimates), zone management, regression kriging (SSPFe) and ordinary kriging approaches were first investigated at various sampling densities (N=10, 20, 50, 100, 150, 200, 250 and 300) to observe the effects of lime and gypsum ameliorant recommendation advice. It was identified that the ordinary kriging method provided the most accurate spatial recommendation advice for gypsum and lime at all depth increments investigated (i.e. 0–10 cm, 10–20 cm, 20–40 cm and 40–60 cm), with the majority of improved accuracy being achieved up to 50 samples (≈0.5 samples/ha). The lack of correlation between the environmental covariates and target soil variables inhibited the ability for regression kriging to outperform ordinary kriging. To extend these findings in an attempt to identify the economically optimal sampling density for the investigation site, a yield prediction model was required to estimate the spatial yield response due to amelioration. Given the complex nonlinear relationships between soil properties and yield, this was achieved by applying four machine learning models (both linear and nonlinear) consisting of a mixed-linear regression, a regression tree (Cubist), an artificial neural network and a support vector machine. These were trained using the 1200 directly measured soil samples, each with 9 soil measurements describing structural features (i.e. soil pH, exchangeable sodium percentage, electrical conductivity, clay, silt, sand, bulk density, potassium, cation exchange capacity) to predict the spatial yield variability at the investigation site with four years of yield data. It was concluded that the Cubist regression tree model produced superior results in terms of improved generalization, whilst achieving an acceptable R2 for training and validation (up to R2 =0.80 for training and R2 =0.78 for validation). The lack of temporal yield information constrained the ability to develop a temporally stable yield prediction model to account for the uncertainties of climate interactions associated with the spatial variability of yield. Accurate predictive performance was achieved for single-season models. Of the spatial prediction methods investigated, random transect sampling and ordinary kriging approaches were adopted to simulate ‘blanket-rate’ (BR) and ‘variable-rate’ (VR) gypsum applications, respectively, for the amelioration of sodicity at the investigated site. For each sampling density, the spatial yield response as a result of a BR and VR application of gypsum was estimated by application of the developed Cubist yield prediction model, calibrated for the investigation site. Accounting for the cost of sampling and financial gains, due to a yield response, the most economically optimum sampling density for the investigation site was 0.2 cores/ha for 0–20 cm treatment and 0.5 cores/ha for 0–60 cm treatment taking a VR approach. Whilst this resulted in an increased soil data investment of $26.4/ha and$136/ha for 0–20 cm and 0–60 cm treatment respectively in comparison to a BR approach, the yield gains due to an improved spatial gypsum application were in excess of 6 t and 26 t per annum. Consequently, the net benefit of increased data investment was estimated to be up to $104,000 after 20 years for 0–60 cm profile treatment. Identifying the influence on qualitative data and management information on soil-yield interaction, a probabilistic approach was investigated to offer an alternative approach where empirical models fail. Using soil compaction as an example, a Bayesian Belief Network was developed to explore the interactions of machine loading, soil wetness and site characteristics with the potential yield declines due to compaction induced by agricultural traffic. The developed tool was subsequently able to broadly describe the agronomic impacts of decisions made in data limiting environments. This body of work presents a combined approach to improving both the diagnosis and management of soil constraints using a data driven approach. Subsequently, a detailed discussion is provided to further this work, and improve upon the results obtained. By continuing this work it is possible to change the industry attitude to data collection and significantly improve the productivity, profitability and soil husbandry of agricultural systems

Next Generation Optical Analysis for Agrochemical Research & Development

The world’s population is increasing rapidly and higher calorific diets are becoming more common; as a consequence the demand for grain is predicted to increase by more than 50% by 2050 without a significant increase in the available agricultural land. Maximising the productivity of the existing agricultural land is key to maintaining food security and agrochemicals continue to be a key enabler for the efficiency gains required. However, agrochemicals can be susceptible to significant losses and thus often require further chemical to be applied to compensate. Sources of such losses include spray drift, poor spray retention/capture by the target and poor penetration through the plant cuticle. The effectiveness of a crop protection agent depends not only on the intrinsic activity of the active ingredient (AI) but also on the physicochemical properties of the formulation. These properties can be modified by using formulation components, known as adjuvants, which can be used to help mitigate such losses. Adjuvants exert their effects by, for example, controlling droplet size and distribution through their effect on surface tension which can also improve penetration into leaves through the cuticle wax which coats the epidermis of leaves and acts as a protective barrier. However, characterising how they alter the movement of the AIs can be challenging. Optical techniques have shown promise in a multitude of scientifically related areas, such as in vivo tissue imaging, but none have yet been applied to aiding the agrochemical industry. By probing the interactions between leaf surface and agrochemical agent, with light, one is able to obtain a large amount of diagnostic information, non-invasively. Whereas techniques like Raman 3 spectroscopy are limited by long acquisition times, coherent Raman techniques such as coherent anti-Stokes Raman scattering (CARS) and stimulated Raman scattering (SRS) are coherently driven and provide an enhanced signal, and also allow for video-rate imaging. In this thesis, I have applied this cutting-edge laser imaging technique as a novel analytical technique that allows the in situ analysis of agrochemicals in living plant tissues at a cellular level. In Chapters 4 through 7, multiple factors essential for a functional and efficient agrochemical were considered and experimented. In Chapter 4, a typical industry study highlights the need for innovative and rapid technologies in the agrochemical industry. The resulting chapters (5, 6, and 7) outline several ways in which Coherent Raman Scattering (CRS) techniques can improve the current capabilities of agrochemical testing. By utilising a model system, paraffin wax, a cheap and rapid protocol can provide accurate diffusion information and repeatable results. Chapters 6 and 7 use both this protocol to gain comparative data on several adjuvants and active ingredients in paraffin wax and in vivo, in a variety of plants. The ability to visualise agrochemical products on a leaf surface to reveal interactions between the materials of the product and with the leaf surface will enable a step change in the agrochemical design process, through determination of the spatial distribution of the materials and their roles within the applied products. It is hoped that the technology developed in this thesis could play a big role in the development of future agrochemical products that are tailored to maximise efficacy and minimise environmental impact

Streamflow and soil moisture forecasting with hybrid data intelligent machine learning approaches: case studies in the Australian Murray-Darling basin

For a drought-prone agricultural nation such as Australia, hydro-meteorological imbalances and increasing demand for water resources are immensely constraining terrestrial water reservoirs and regional-scale agricultural productivity. Two important components of the terrestrial water reservoir i.e., streamflow water level (SWL) and soil moisture (SM), are imperative both for agricultural and hydrological applications. Forecasted SWL and SM can enable prudent and sustainable decisionmaking for agriculture and water resources management. To feasibly emulate SWL and SM, machine learning data-intelligent models are a promising tool in today’s rapidly advancing data science era. Yet, the naturally chaotic characteristics of hydro-meteorological variables that can exhibit non-linearity and non-stationarity behaviors within the model dataset, is a key challenge for non-tuned machine learning models. Another important issue that could confound model accuracy or applicability is the selection of relevant features to emulate SWL and SM since the use of too fewer inputs can lead to insufficient information to construct an accurate model while the use of an excessive number and redundant model inputs could obscure the performance of the simulation algorithm. This research thesis focusses on the development of hybridized dataintelligent models in forecasting SWL and SM in the upper layer (surface to 0.2 m) and the lower layer (0.2–1.5 m depth) within the agricultural region of the Murray-Darling Basin, Australia. The SWL quantifies the availability of surface water resources, while, the upper layer SM (or the surface SM) is important for surface runoff, evaporation, and energy exchange at the Earth-Atmospheric interface. The lower layer (or the root zone) SM is essential for groundwater recharge purposes, plant uptake and transpiration. This research study is constructed upon four primary objectives designed for the forecasting of SWL and SM with subsequent robust evaluations by means of statistical metrics, in tandem with the diagnostic plots of observed and modeled datasets. The first objective establishes the importance of feature selection (or optimization) in the forecasting of monthly SWL at three study sites within the Murray-Darling Basin. Artificial neural network (ANN) model optimized with iterative input selection (IIS) algorithm named IIS-ANN is developed whereby the IIS algorithm achieves feature optimization. The IIS-ANN model outperforms the standalone models and a further hybridization is performed by integrating a nondecimated and advanced maximum overlap discrete wavelet transformation (MODWT) technique. The IIS selected inputs are transformed into wavelet subseries via MODWT to unveil the embedded features leading to IIS-W-ANN model. The IIS-W-ANN outperforms the comparative IIS-W-M5 Model Tree, IIS-based and standalone models. In the second objective, improved self-adaptive multi-resolution analysis (MRA) techniques, ensemble empirical mode decomposition (EEMD) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) are utilized to address the non-stationarity issues in forecasting monthly upper and lower layer soil moisture at seven sites. The SM time-series are decomposed using EEMD/CEEMDAN into respective intrinsic mode functions (IMFs) and residual components. Then the partial-auto correlation function based significant lags are utilized as inputs to the extreme learning machine (ELM) and random forest (RF) models. The hybrid EEMD-ELM yielded better results in comparison to the CEEMDAN-ELM, EEMD-RF, CEEMDAN-RF and the classical ELM and RF models. Since SM is contingent upon many influential meteorological, hydrological and atmospheric parameters, for the third objective sixty predictor inputs are collated in forecasting upper and lower layer soil moisture at four sites. An ANN-based ensemble committee of models (ANN-CoM) is developed integrating a two-phase feature optimization via Neighborhood Component Analysis based feature selection algorithm for regression (fsrnca) and a basic ELM. The ANN-CoM shows better predictive performance in comparison to the standalone second order Volterra, M5 Model Tree, RF, and ELM models. In the fourth objective, a new multivariate sequential EEMD based modelling is developed. The establishment of multivariate sequential EEMD is an advancement of the classical single input EEMD approach, achieving a further methodological improvement. This multivariate approach is developed to allow for the utilization of multiple inputs in forecasting SM. The multivariate sequential EEMD optimized with cross-correlation function and Boruta feature selection algorithm is integrated with the ELM model in emulating weekly SM at four sites. The resulting hybrid multivariate sequential EEMD-Boruta-ELM attained a better performance in comparison with the multivariate adaptive regression splines (MARS) counterpart (EEMD-Boruta-MARS) and standalone ELM and MARS models. The research study ascertains the applicability of feature selection algorithms integrated with appropriate MRA for improved hydrological forecasting. Forecasting at shorter and near-real-time horizons (i.e., weekly) would help reinforce scientific tenets in designing knowledge-based systems for precision agriculture and climate change adaptation policy formulations

Development of a handheld breath analyser for the monitoring of energy expenditure

Metabolic rate is not routinely assessed in healthcare for the general population, nor is it a measure commonly recorded for in-patients (incorrect feeding can slow post-operation recovery rate). For the general community, this lack of knowledge prevents the accurate determination of calorific need and is a factor contributing towards the onset of an overweight and an increasingly obese population. In the UK alone, obesity costs the National Health Service a staggering £5 billion annually. In this thesis a novel low-cost hand-held breath analyser is presented in order to measure human energy expenditure (EE). A unique optical CO2 sensor was developed, capable of sampling exhaled breath with a fast response time ~1 s and resilience to a humidity range of ~30 % to near saturated. The device was tested in a laboratory gas testing rig and a detection limit of ~25 ppm CO2 was measured. A low power metal oxide sensor (~100 mW) was developed to detect volatile organic compounds (VOCs) in the breath, for disease detection and investigation of the variation of inter-individual metabolism processes. The device was sensitive to acetone (100 to 300 ppm, which is a biomarker for type-I diabetes). Other VOCs, such as NO2 were tested (10 to 250 ppb). Further work includes investigating the inter-individual variance of metabolism processes, for which the metal oxide sensor would be well-suited. Software was developed to operate the gas testing rig and acquire sensor output data in real-time. An application was written for smartphones to enable EE measurements with the breath analyser, outside of a laboratory environment. Three hand-held analysers were constructed and tested with a trial of 10 subjects. A counterpart (benchmark) unit with medical grade commercial sensors (cost of ~£2500) and hospital respiratory rooms (reference) were included in the trial. The newly developed analysers improved upon the performance of the benchmark system (average EE measurement error +2.4 % compared to +7.9 %). The affordable device offered far greater accuracy than the traditional method often used by practitioners (predictive equations, error +41.4%). It is proposed a set of periodic (hourly) breath measurements could be used to determine daily EE. The EE analyser and associated low-cost sensors developed in this work offer a potential solution to halt the growing cost of an obese population and provide point-of-care health management