Physiological Basis for Genotypic Variation in Tolerance to and Recovery from Pre-flowering Drought in Peanut.

Drought during the pre-flowering stage can increase yield of peanut. There is limited information on genotypic variation for tolerance to and recovery from pre-flowering drought (PFD) and more importantly the physiological traits underlying genotypic variation. The objectives of this study were to determine the effects of moisture stress during the pre-flowering phase on pod yield and to understand some of the physiological responses underlying genotypic variation in response to and recovery from PFD. A glasshouse and field experiments were conducted at Khon Kaen University, Thailand. The glasshouse experiment was a randomized complete block design consisting of two watering regimes, i.e. fully-irrigated control and 1/3 available soil water from emergence to 40 days after emergence followed by adequate water supply, and 12 peanut genotypes. The field experiment was a split-plot design with two watering regimes as main-plots, and 12 peanut genotypes as sub-plots. Measurements of N-2 fixation, leaf area (LA) were made in both experiments. In addition, root growth was measured in the glasshouse experiment. Imposition of PFD followed by recovery resulted in an average increase in yield of 24 % (range from 10 % to 57 %) and 12 % (range from 2 % to 51 %) in the field and glasshouse experiments, respectively. Significant genotypic variation for N-2 fixation, LA and root growth was also observed after recovery. The study revealed that recovery growth following release of PFD had a stronger influence on final yield than tolerance to water deficits during the PFD. A combination of N-2 fixation, LA and root growth accounted for a major portion of the genotypic variation in yield (r = 0.68-0.93) suggesting that these traits could be used as selection criteria for identifying genotypes with rapid recovery from PFD. A combined analysis of glasshouse and field experiments showed that LA and N-2 fixation during the recovery had low genotype x environment interaction indicating potential for using these traits for selecting genotypes in peanut improvement programs.

Equilibrium studies of ammonium exchange with Australian natural zeolites

This paper is concerned with the study of the equilibrium exchange of ammonium ions with two natural zeolite samples sourced in Australia from Castle Mountain Zeolites and Zeolite Australia. A range of sorption models including Langmuir Vageler, Competitive Langmuir, Freundlich, Temkin, Dubinin Astakhov and Brouers–Sotolongo were applied in order to gain an insight as to the exchange process. In contrast to most previous studies, non-linear regression was used in all instances to determine the best fit of the experimental data. Castle Mountain natural zeolite was found to exhibit higher ammonium capacity than Zeolite Australia material when in the freshly received state, and this behavior was related to the greater amount of sodium ions present relative to calcium ions on the zeolite exchange sites. The zeolite capacity for ammonium ions was also found to be dependent on the solution normality, with 35–60% increase inuptake noted when increasing the ammonium concentration from 250 to 1000 mg/L. The optimal fit ofthe equilibrium data was achieved by the Freundlich expression as confirmed by use of Akaikes Information Criteria. It was emphasized that the bottle-point method chosen influenced the isotherm profile in several ways, and could lead to misleading interpretation of experiments, especially if the constant zeolite mass approach was followed. Pre-treatment of natural zeolite with acid and subsequently sodium hydroxide promoted the uptake of ammonium species by at least 90%. This paper highlighted the factors which should be taken into account when investigating ammonium ion exchange with natural zeolites.

Characterization of north-eastern Australian environments using APSIM for increasing rainfed maize production

Recurring water stresses are a major risk factor for rainfed maize cropping across the highly diverse agro-ecological environments of Queensland (Qld) and northern New South Wales (NNSW). Enhanced understanding of such agro-ecological diversity is necessary to more consistently sample target production environments for testing and targeting release of improved germplasm, and to improve the efficiency of the maize pre-breeding and breeding programs of Qld and New South Wales. Here, we used the Agricultural Production Systems Simulator (APSIM) – a well validated maize crop model to characterize the key distinctive water stress patterns and risk to production across the main maize growing regions of Qld and NNSW located between 15.8° and 31.5°S, and 144.5° and 151.8°E. APSIM was configured to simulate daily water supply demand ratios (SDRs) around anthesis as an indicator of the degree of water stress, and the final grain yield. Simulations were performed using daily climatic records during the period between 1890 and 2010 for 32 sites-soils in the target production regions. The runs were made assuming adequate nitrogen supply for mid-season maize hybrid Pioneer 3153. Hierarchical complete linkage analyses of the simulated yield resulted in five major clusters showing distinct probability distribution of the expected yields and geographic patterns. The drought stress patterns and their frequencies using SDRs were quantified using multivariate statistical methods. The identified stress patterns included no stress, mid-season (flowering) stress, and three terminal stresses differing in terms of severity. The combined frequency of flowering and terminal stresses was highest (82.9%), mainly in sites-soils combinations in the west of Qld and NNSW. Yield variability across the different sites-soils was significantly related to the variability in frequencies of water stresses. Frequencies of water stresses within each yield cluster tended to be similar, but different across clusters. Sites-soils falling within each yield cluster therefore could be treated as distinct maize production environments for testing and targeting newly developed maize cultivars and hybrids for adaptation to water stress patterns most common to those environments.

The critical role of extreme heat for maize production in the United States

Statistical studies of rainfed maize yields in the United States(1) and elsewhere(2) have indicated two clear features: a strong negative yield response to accumulation of temperatures above 30 degrees C (or extreme degree days (EDD)), and a relatively weak response to seasonal rainfall. Here we show that the process-based Agricultural Production Systems Simulator (APSIM) is able to reproduce both of these relationships in the Midwestern United States and provide insight into underlying mechanisms. The predominant effects of EDD in APSIM are associated with increased vapour pressure deficit, which contributes to water stress in two ways: by increasing demand for soil water to sustain a given rate of carbon assimilation, and by reducing future supply of soil water by raising transpiration rates. APSIM computes daily water stress as the ratio of water supply to demand, and during the critical month of July this ratio is three times more responsive to 2 degrees C warming than to a 20% precipitation reduction. The results suggest a relatively minor role for direct heat stress on reproductive organs at present temperatures in this region. Effects of elevated CO2 on transpiration efficiency should reduce yield sensitivity to EDD in the coming decades, but at most by 25%.

Stochastic model for simulating maize yield

Maize is one of the most important crops in the world. The products generated from this crop are largely used in the starch industry, the animal and human nutrition sector, and biomass energy production and refineries. For these reasons, there is much interest in figuring the potential grain yield of maize genotypes in relation to the environment in which they will be grown, as the productivity directly affects agribusiness or farm profitability. Questions like these can be investigated with ecophysiological crop models, which can be organized according to different philosophies and structures. The main objective of this work is to conceptualize a stochastic model for predicting maize grain yield and productivity under different conditions of water supply while considering the uncertainties of daily climate data. Therefore, one focus is to explain the model construction in detail, and the other is to present some results in light of the philosophy adopted. A deterministic model was built as the basis for the stochastic model. The former performed well in terms of the curve shape of the above-ground dry matter over time as well as the grain yield under full and moderate water deficit conditions. Through the use of a triangular distribution for the harvest index and a bivariate normal distribution of the averaged daily solar radiation and air temperature, the stochastic model satisfactorily simulated grain productivity, i.e., it was found that 10,604 kg ha(-1) is the most likely grain productivity, very similar to the productivity simulated by the deterministic model and for the real conditions based on a field experiment. © 2012 American Society of Agricultural and Biological Engineers.

Responses of soil microbial communities to clonal variation of Norway spruce

In boreal forests, microorganisms have a pivotal role in nutrient and water supply of trees as well as in litter decomposition and nutrient cycling. This reinforces the link between above-ground and below-ground communities in the context of sustainable productivity of forest ecosystems. In northern boreal forests, the diversity of microbes associated with the trees is high compared to the number of distinct tree species. In this thesis, the aim was to study whether conspecific tree individuals harbour different soil microbes and whether the growth of the trees and the community structure of the associated microbes are connected. The study was performed in a clonal field trial of Norway spruce, which was established in a randomized block design in a clear-cut area. Since out-planting in 1994, the spruce clones showed two-fold growth differences. The fast-growing spruce clones were associated with a more diverse community of ectomycorrhizal fungi than the slow-growing spruce clones. These growth performance groups also differed with respect to other aspects of the associated soil microorganisms: the species composition of ectomycorrhizal fungi, in the amount of extraradical fungal mycelium, in the structure of bacterial community associated with the mycelium, and in the structure of microbial community in the organic layer. The communities of fungi colonizing needle litter of the spruce clones in the field did not differ and the loss of litter mass after two-years decomposition was equal. In vitro, needles of the slow-growing spruce clones were colonized by a more diverse community of endophytic fungi that were shown to be significant needle decomposers. This study showed a relationship between the growth of Norway spruce clones and the community structure of the associated soil microbes. Spatial heterogeneity in soil microbial community was connected with intraspecific variation of trees. The latter may therefore influence soil biodiversity in monospecific forests.

An improved simulation model to predict pre-harvest aflatoxin risk in maize

Aflatoxin is a potent carcinogen produced by Aspergillus flavus, which frequently contaminates maize (Zea mays L.) in the field between 40° north and 40° south latitudes. A mechanistic model to predict risk of pre-harvest contamination could assist in management of this very harmful mycotoxin. In this study we describe an aflatoxin risk prediction model which is integrated with the Agricultural Production Systems Simulator (APSIM) modelling framework. The model computes a temperature function for A. flavus growth and aflatoxin production using a set of three cardinal temperatures determined in the laboratory using culture medium and intact grains. These cardinal temperatures were 11.5 °C as base, 32.5 °C as optimum and 42.5 °C as maximum. The model used a low (≤0.2) crop water supply to demand ratio—an index of drought during the grain filling stage to simulate maize crop's susceptibility to A. flavus growth and aflatoxin production. When this low threshold of the index was reached the model converted the temperature function into an aflatoxin risk index (ARI) to represent the risk of aflatoxin contamination. The model was applied to simulate ARI for two commercial maize hybrids, H513 and H614D, grown in five multi-location field trials in Kenya using site specific agronomy, weather and soil parameters. The observed mean aflatoxin contamination in these trials varied from <1 to 7143 ppb. ARI simulated by the model explained 99% of the variation (p ≤ 0.001) in a linear relationship with the mean observed aflatoxin contamination. The strong relationship between ARI and aflatoxin contamination suggests that the model could be applied to map risk prone areas and to monitor in-season risk for genotypes and soils parameterized for APSIM.

An improved simulation model to predict pre-harvest aflatoxin risk in maize

Aflatoxin is a potent carcinogen produced by Aspergillus flavus, which frequently contaminates maize (Zea mays L.) in the field between 40° north and 40° south latitudes. A mechanistic model to predict risk of pre-harvest contamination could assist in management of this very harmful mycotoxin. In this study we describe an aflatoxin risk prediction model which is integrated with the Agricultural Production Systems Simulator (APSIM) modelling framework. The model computes a temperature function for A. flavus growth and aflatoxin production using a set of three cardinal temperatures determined in the laboratory using culture medium and intact grains. These cardinal temperatures were 11.5 °C as base, 32.5 °C as optimum and 42.5 °C as maximum. The model used a low (≤0.2) crop water supply to demand ratio—an index of drought during the grain filling stage to simulate maize crop's susceptibility to A. flavus growth and aflatoxin production. When this low threshold of the index was reached the model converted the temperature function into an aflatoxin risk index (ARI) to represent the risk of aflatoxin contamination. The model was applied to simulate ARI for two commercial maize hybrids, H513 and H614D, grown in five multi-location field trials in Kenya using site specific agronomy, weather and soil parameters. The observed mean aflatoxin contamination in these trials varied from <1 to 7143 ppb. ARI simulated by the model explained 99% of the variation (p ≤ 0.001) in a linear relationship with the mean observed aflatoxin contamination. The strong relationship between ARI and aflatoxin contamination suggests that the model could be applied to map risk prone areas and to monitor in-season risk for genotypes and soils parameterized for APSIM.

Applicability of pebble matrix filtration for the pre-treatment of surface waters containing high turbidity and NOM

Purification of drinking water is routinely achieved by use of conventional coagulants and disinfection procedures. However, there are instances such as flood events when the level of turbidity reaches extreme levels while NOM may be an issue throughout the year. Consequently, there is a need to develop technologies which can effectively treat water of high turbidity during flood events and natural organic matter (NOM) content year round. It was our hypothesis that pebble matrix filtration potentially offered a relatively cheap, simple and reliable means to clarify such challenging water samples. Therefore, a laboratory scale pebble matrix filter (PMF) column was used to evaluate the turbidity and natural organic matter (NOM) pre-treatment performance in relation to 2013 Brisbane River flood water. Since the high turbidity was only a seasonal and short term problem, the general applicability of pebble matrix filters for NOM removal was also investigated. A 1.0 m deep bed of pebbles (the matrix) partly in-filled with either sand or crushed glass was tested, upon which was situated a layer of granular activated carbon (GAC). Turbidity was measured as a surrogate for suspended solids (SS), whereas, total organic carbon (TOC) and UV Absorbance at 254 nm were measured as surrogate parameters for NOM. Experiments using natural flood water showed that without the addition of any chemical coagulants, PMF columns achieved at least 50% turbidity reduction when the source water contained moderate hardness levels. For harder water samples, above 85% turbidity reduction was obtained. The ability to remove 50% turbidity without chemical coagulants may represent significant cost savings to water treatment plants and added environmental benefits accrue due to less sludge formation. A TOC reduction of 35-47% and UV-254 nm reduction of 24-38% was also observed. In addition to turbidity removal during flood periods, the ability to remove NOM using the pebble matrix filter throughout the year may have the benefit of reducing disinfection by-products (DBP) formation potential and coagulant demand at water treatment plants. Final head losses were remarkably low, reaching only 11 cm at a filtration velocity of 0.70 m/h.

Taxonomy of factors which influence heavy metal build-up on urban road surfaces

Heavy metals build-up on urban road surfaces is a complex process and influenced by a diverse range of factors. Although numerous research studies have been conducted in the area of heavy metals build-up, limited research has been undertaken to rank these factors in terms of their influence on the build-up process. This results in limitations in the identification of the most critical factor/s for accurately estimating heavy metal loads and for designing effective stormwater treatment measures. The research study undertook an in-depth analysis of the factors which influence heavy metals build-up based on data generated from a number of different geographical locations around the world. Traffic volume was found to be the highest ranked factor in terms of influencing heavy metals build-up while land use was ranked the second. Proximity to arterial roads, antecedent dry days and road surface roughness has a relatively lower ranking. Furthermore, the study outcomes advances the conceptual understanding of heavy metals build-up based on the finding that with increasing traffic volume, total heavy metal build-up load increases while the variability decreases. The outcomes from this research study are expected to contribute to more accurate estimation of heavy metals build-up loads leading to more effective stormwater treatment design.

Build-up of toxic metals on the impervious surfaces of a commercial seaport

In the context of increasing threats to the sensitive marine ecosystem by toxic metals, this study investigated the metal build-up on impervious surfaces specific to commercial seaports. The knowledge generated in this study will contribute to managing toxic metal pollution of the marine ecosystem. The study found that inter-modal operations and main access roadway had the highest loads followed by container storage and vehicle marshalling sites, while the quay line and short term storage areas had the lowest. Additionally, it was found that Cr, Al, Pb, Cu and Zn were predominantly attached to solids, while significant amount of Cu, Pb and Zn were found as nutrient complexes. As such, treatment options based on solids retention can be effective for some metal species, while ineffective for other species. Furthermore, Cu and Zn are more likely to become bioavailable in seawater due to their strong association with nutrients. Mathematical models to replicate the metal build-up process were also developed using experimental design approach and partial least square regression. The models for Cr and Pb were found to be reliable, while those for Al, Zn and Cu were relatively less reliable, but could be employed for preliminary investigations.

Assessing uncertainty in pollutant build-up and wash-off processes

Assessing build-up and wash-off process uncertainty is important for accurate interpretation of model outcomes to facilitate informed decision making for developing effective stormwater pollution mitigation strategies. Uncertainty inherent to pollutant build-up and wash-off processes influences the variations in pollutant loads entrained in stormwater runoff from urban catchments. However, build-up and wash-off predictions from stormwater quality models do not adequately represent such variations due to poor characterisation of the variability of these processes in mathematical models. The changes to the mathematical form of current models with the incorporation of process variability, facilitates accounting for process uncertainty without significantly affecting the model prediction performance. Moreover, the investigation of uncertainty propagation from build-up to wash-off confirmed that uncertainty in build-up process significantly influences wash-off process uncertainty. Specifically, the behaviour of particles <150µm during build-up primarily influences uncertainty propagation, resulting in appreciable variations in the pollutant load and composition during a wash-off event.

Influence of uncertainty inherent to heavy metal build-up and wash-off on stormwater quality

Uncertainty inherent to heavy metal build-up and wash-off stems from process variability. This results in inaccurate interpretation of stormwater quality model predictions. The research study has characterised the variability in heavy metal build-up and wash-off processes based on the temporal variations in particle-bound heavy metals commonly found on urban roads. The study outcomes found that the distribution of Al, Cr, Mn, Fe, Ni, Cu, Zn, Cd and Pb were consistent over particle size fractions <150µm and >150µm, with most metals concentrated in the particle size fraction <150µm. When build-up and wash-off are considered as independent processes, the temporal variations in these processes in relation to the heavy metals load are consistent with variations in the particulate load. However, the temporal variations in the load in build-up and wash-off of heavy metals and particulates are not consistent for consecutive build-up and wash-off events that occur on a continuous timeline. These inconsistencies are attributed to interactions between heavy metals and particulates <150µm and >150µm, which are influenced by particle characteristics such as organic matter content. The behavioural variability of particles determines the variations in the heavy metals load entrained in stormwater runoff. Accordingly, the variability in build-up and wash-off of particle-bound pollutants needs to be characterised in the description of pollutant attachment to particulates in stormwater quality modelling. This will ensure the accounting of process uncertainty, and thereby enhancing the interpretation of the outcomes derived from modelling studies.

Optimisation of in-cell accelerated solvent extraction technique for the determination of organochlorine pesticides in river sediments

Organochlorine pesticides (OCPs) are ubiquitous environmental contaminants with adverse impacts on aquatic biota, wildlife and human health even at low concentrations. However, conventional methods for their determination in river sediments are resource intensive. This paper presents an approach that is rapid and also reliable for the detection of OCPs. Accelerated Solvent Extraction (ASE) with in-cell silica gel clean-up followed by Triple Quadrupole Gas Chromatograph Mass Spectrometry (GCMS/MS) was used to recover OCPs from sediment samples. Variables such as temperature, solvent ratio, adsorbent mass and extraction cycle were evaluated and optimised for the extraction. With the exception of Aldrin, which was unaffected by any of the variables evaluated, the recovery of OCPs from sediment samples was largely influenced by solvent ratio and adsorbent mass and, to some extent, the number of cycles and temperature. The optimised conditions for OCPs extraction in sediment with good recoveries were determined to be 4 cycles, 4.5 g of silica gel, 105 ᴼC, and 4:3 v/v DCM: hexane mixture. With the exception of two compounds (α-BHC and Aldrin) whose recoveries were low (59.73 and 47.66 % respectively), the recovery of the other pesticides were in the range 85.35 – 117.97% with precision < 10 % RSD. The method developed significantly reduces sample preparation time, the amount of solvent used, matrix interference, and is highly sensitive and selective.

Briefs for GSDR - Urban stormwater reuse: An agenda for sustainable development

We treat urban stormwater as a problem as it causes flooding, transports pollutants and degrades the ecosystem health of waterways (Goonetilleke et al., 2014). Municipal authorities devote a significant component of their budget to capture and remove stormwater from urban areas as rapidly as possible. Unfortunately, it is a largely unappreciated fact that urban stormwater is the last available uncommitted water resource for our cities as the demand for potable water escalates due to growing urbanisation, industrialisation and higher living standards.