The limit to wheat water-use efficiency in eastern Australia. II. Influence of rainfall patterns

We investigated the influence of rainfall patterns on the water-use efficiency of wheat in a transect between Horsham (36°S) and Emerald (23°S) in eastern Australia. Water-use efficiency was defined in terms of biomass and transpiration, WUEB/T, and grain yield and evapotranspiration, WUEY/ET. Our working hypothesis is that latitudinal trends in WUEY/ET of water-limited crops are the complex result of southward increasing WUEB/T and soil evaporation, and season-dependent trends in harvest index. Our approach included: (a) analysis of long-term records to establish latitudinal gradients of amount, seasonality, and size-structure of rainfall; and (b) modelling wheat development, growth, yield, water budget components, and derived variables including WUEB/T and WUEY/ET. Annual median rainfall declined from around 600 mm in northern locations to 380 mm in the south. Median seasonal rain (from sowing to harvest) doubled between Emerald and Horsham, whereas median off-season rainfall (harvest to sowing) ranged from 460 mm at Emerald to 156 mm at Horsham. The contribution of small events (≤ 5 mm) to seasonal rainfall was negligible at Emerald (median 15 mm) and substantial at Horsham (105 mm). Power law coefficients (τ), i.e. the slopes of the regression between size and number of events in a log-log scale, captured the latitudinal gradient characterised by an increasing dominance of small events from north to south during the growing season. Median modelled WUEB/T increased from 46 kg/ha.mm at Emerald to 73 kg/ha.mm at Horsham, in response to decreasing atmospheric demand. Median modelled soil evaporation during the growing season increased from 70 mm at Emerald to 172 mm at Horsham. This was explained by the size-structure of rainfall characterised with parameter τ, rather than by the total amount of rainfall. Median modelled harvest index ranged from 0.25 to 0.34 across locations, and had a season-dependent latitudinal pattern, i.e. it was greater in northern locations in dry seasons in association with wetter soil profiles at sowing. There was a season-dependent latitudinal pattern in modelled WUEY/ET. In drier seasons, high soil evaporation driven by a very strong dominance of small events, and lower harvest index override the putative advantage of low atmospheric demand and associated higher WUEB/T in southern locations, hence the significant southwards decrease in WUEY/ET. In wetter seasons, when large events contribute a significant proportion of seasonal rain, higher WUEB/T in southern locations may translate into high WUEY/ET. Linear boundary functions (French-Schultz type models) accounting for latitudinal gradients in its parameters, slope, and x-intercept, were fitted to scatter-plots of modelled yield v. evapotranspiration. The x-intercept of the model is re-interpreted in terms of rainfall size structure, and the slope or efficiency multiplier is described in terms of the radiation, temperature, and air humidity properties of the environment. Implications for crop management and breeding are discussed.

Reducing the risk of herbicide runoff in sugarcane farming through controlled traffic and early-banded application

The off-site transport of agricultural chemicals, such as herbicides, into freshwater and marine ecosystems is a world-wide concern. The adoption of farm management practices that minimise herbicide transport in rainfall-runoff is a priority for the Australian sugarcane industry, particularly in the coastal catchments draining into the World Heritage listed Great Barrier Reef (GBR) lagoon. In this study, residual herbicide runoff and infiltration were measured using a rainfall simulator in a replicated trial on a brown Chromosol with 90–100% cane trash blanket cover in the Mackay Whitsunday region, Queensland. Management treatments included conventional 1.5 m spaced sugarcane beds with a single row of sugarcane (CONV) and 2 m spaced, controlled traffic sugarcane beds with dual sugarcane rows (0.8 m apart) (2mCT). The aim was to simulate the first rainfall event after the application of the photosynthesis inhibiting (PSII) herbicides ametryn, atrazine, diuron and hexazinone, by broadcast (100% coverage, on bed and furrow) and banding (50–60% coverage, on bed only) methods. These events included heavy rainfall 1 day after herbicide application, considered a worst case scenario, or rainfall 21 days after application. The 2mCT rows had significantly (P < 0.05) less runoff (38%) and lower peak runoff rates (43%) than CONV rows for a rainfall average of 93 mm at 100 mm h−1 (1:20 yr Average Return Interval). Additionally, final infiltration rates were higher in 2mCT rows than CONV rows, with 72 and 52 mm h−1 respectively. This resulted in load reductions of 60, 55, 47, and 48% for ametryn, atrazine, diuron and hexazinone from 2mCT rows, respectively. Herbicide losses in runoff were also reduced by 32–42% when applications were banded rather than broadcast. When rainfall was experienced 1 day after application, a large percentage of herbicides were washed off the cane trash. However, by day 21, concentrations of herbicide residues on cane trash were lower and more resistant to washoff, resulting in lower losses in runoff. Consequently, ametryn and atrazine event mean concentrations in runoff were approximately 8 fold lower at day 21 compared with day 1, whilst diuron and hexazinone were only 1.6–1.9 fold lower, suggesting longer persistence of these chemicals. Runoff collected at the end of the paddock in natural rainfall events indicated consistent though smaller treatment differences to the rainfall simulation study. Overall, it was the combination of early application, banding and controlled traffic that was most effective in reducing herbicide losses in runoff. Crown copyright © 2012

Parameter estimation and sensitivity analysis of fat deposition models in beef steers using acslXtreme.

The Davis Growth Model (a dynamic steer growth model encompassing 4 fat deposition models) is currently being used by the phenotypic prediction program of the Cooperative Research Centre (CRC) for Beef Genetic Technologies to predict P8 fat (mm) in beef cattle to assist beef producers meet market specifications. The concepts of cellular hyperplasia and hypertrophy are integral components of the Davis Growth Model. The net synthesis of total body fat (kg) is calculated from the net energy available after accounting tor energy needs for maintenance and protein synthesis. Total body fat (kg) is then partitioned into 4 fat depots (intermuscular, intramuscular, subcutaneous, and visceral). This paper reports on the parameter estimation and sensitivity analysis of the DNA (deoxyribonucleic acid) logistic growth equations and the fat deposition first-order differential equations in the Davis Growth Model using acslXtreme (Hunstville, AL, USA, Xcellon). The DNA and fat deposition parameter coefficients were found to be important determinants of model function; the DNA parameter coefficients with days on feed >100 days and the fat deposition parameter coefficients for all days on feed. The generalized NL2SOL optimization algorithm had the fastest processing time and the minimum number of objective function evaluations when estimating the 4 fat deposition parameter coefficients with 2 observed values (initial and final fat). The subcutaneous fat parameter coefficient did indicate a metabolic difference for frame sizes. The results look promising and the prototype Davis Growth Model has the potential to assist the beef industry meet market specifications.

Classifying genotype by environment interactions for targeted germplasm deployment with a focus on Eucalyptus

A novel methodology for describing genotype by environment interactions estimated from multi-environment field trials is described and an empirical example using an extensive trial network of eucalypts is presented. The network of experiments containing 65 eucalypts was established in 38 replicated field trials across the tropics and subtropics of eastern Australia, with a selection of well-tested species used to provide a more detailed examination of productivity differentials across environmental gradients. By focusing on changes in species’ productivity across environmental gradients, the results are applicable for all species established across the range of environments evaluated in the trial network and simultaneously classify species and environments so that results may be applied across the landscape. The methodology developed was able to explain most (93 %) of the variation in the selected species relative changes in productivity across the various environmental variables examined. Responses were primarily regulated by changes in variables related to water availability and secondarily by temperature related variables. Clustering and ordination can identify groups of species with similar physiological responses to environment and may also guide the parameterisation and calibration of process based models of plant growth. Ordination was particularly useful in the identification of species with distinct environmental response patterns that would be useful as probes for extracting more information from future trials.

Losses of nitrogen in surface runoff from a plantation horticulture farm in coastal Queensland, Australia.

Surface losses of nitrogen from horticulture farms in coastal Queensland, Australia, may have the potential to eutrophy sensitive coastal marine habitats nearby. A case-study of the potential extent of such losses was investigated in a coastal macadamia plantation. Nitrogen losses were quantified in 5 consecutive runoff events during the 13-month study. Irrigation did not contribute to surface flows. Runoff was generated by storms at combined intensities and durations that were 20–40 mm/h for >9 min. These intensities and durations were within expected short-term (1 year) and long-term (up to 20 years) frequencies of rainfall in the study area. Surface flow volumes were 5.3 ± 1.1% of the episodic rainfall generated by such storms. Therefore, the largest part of each rainfall event was attributed to infiltration and drainage in this farm soil (Kandosol). The estimated annual loss of total nitrogen in runoff was 0.26 kg N/ha.year, representing a minimal loading of nitrogen in surface runoff when compared to other studies. The weighted average concentrations of total sediment nitrogen (TSN) and total dissolved nitrogen (TDN) generated in the farm runoff were 2.81 ± 0.77% N and 1.11 ± 0.27 mg N/L, respectively. These concentrations were considerably greater than ambient levels in an adjoining catchment waterway. Concentrations of TSN and TDN in the waterway were 0.11 ± 0.02% N and 0.50 ± 0.09 mg N/L, respectively. The steep concentration gradient of TSN and TDN between the farm runoff and the waterway demonstrated the occurrence of nutrient loading from the farming landscapes to the waterway. The TDN levels in the stream exceeded the current specified threshold of 0.2–0.3 mg N/L for eutrophication of such a waterway. Therefore, while the estimate of annual loading of N from runoff losses was comparatively low, it was evident that the stream catchment and associated agricultural land uses were already characterised by significant nitrogen loadings that pose eutrophication risks. The reported levels of nitrogen and the proximity of such waterways (8 km) to the coastline may have also have implications for the nearshore (oligotrophic) marine environment during periods of turbulent flow.

Runoff, soil loss, and nutrient transport from cropping systems on Red Ferrosols in tropical northern Australia.

Runoff, soil loss, and nutrient loss were assessed on a Red Ferrosol in tropical Australia over 3 years. The experiment was conducted using bounded, 100-m(2) field plots cropped to peanuts, maize, or grass. A bare plot, without cover or crop, was also instigated as an extreme treatment. Results showed the importance of cover in reducing runoff, soil loss, and nutrient loss from these soils. Runoff ranged from 13% of incident rainfall for the conventional cultivation to 29% under bare conditions during the highest rainfall year, and was well correlated with event rainfall and rainfall energy. Soil loss ranged from 30 t/ha. year under bare conditions to <6 t/ha. year under cropping. Nutrient losses of 35 kg N and 35 kg P/ha. year under bare conditions and 17 kg N and 11 kg P/ha. year under cropping were measured. Soil carbon analyses showed a relationship with treatment runoff, suggesting that soil properties influenced the rainfall runoff response. The cropping systems model PERFECT was calibrated using runoff, soil loss, and soil water data. Runoff and soil loss showed good agreement with observed data in the calibration, and soil water and yield had reasonable agreement. Longterm runs using historical weather data showed the episodic nature of runoff and soil loss events in this region and emphasise the need to manage land using protective measures such as conservation cropping practices. Farmers involved in related, action-learning activities wished to incorporate conservation cropping findings into their systems but also needed clear production benefits to hasten practice change.

Spatial impact of projected changes in rainfall and temperature on wheat yields in Australia

Climate projections over the next two to four decades indicate that most of Australia’s wheat-belt is likely to become warmer and drier. Here we used a shire scale, dynamic stress-index model that accounts for the impacts of rainfall and temperature on wheat yield, and a range of climate change projections from global circulation models to spatially estimate yield changes assuming no adaptation and no CO2 fertilisation effects. We modelled five scenarios, a baseline climate (climatology, 1901–2007), and two emission scenarios (“low” and “high” CO2) for two time horizons, namely 2020 and 2050. The potential benefits from CO2 fertilisation were analysed separately using a point level functional simulation model. Irrespective of the emissions scenario, the 2020 projection showed negligible changes in the modelled yield relative to baseline climate, both using the shire or functional point scale models. For the 2050-high emissions scenario, changes in modelled yield relative to the baseline ranged from −5 % to +6 % across most of Western Australia, parts of Victoria and southern New South Wales, and from −5 to −30 % in northern NSW, Queensland and the drier environments of Victoria, South Australia and in-land Western Australia. Taking into account CO2 fertilisation effects across a North–south transect through eastern Australia cancelled most of the yield reductions associated with increased temperatures and reduced rainfall by 2020, and attenuated the expected yield reductions by 2050.

Assessment of internal quality attributes of mandarin fruit. 1. NIR calibration model development

The utility of near infrared spectroscopy as a non-invasive technique for the assessment of internal eating quality parameters of mandarin fruit (Citrus reticulata cv. Imperial) was assessed. The calibration procedure for the attributes of TSS (total soluble solids) and DM (dry matter) was optimised with respect to a reference sampling technique, scan averaging, spectral window, data pre-treatment (in terms of derivative treatment and scatter correction routine) and regression procedure. The recommended procedure involved sampling of an equatorial position on the fruit with 1 scan per spectrum, and modified partial least squares model development on a 720–950-nm window, pre-treated as first derivative absorbance data (gap size of 4 data points) with standard normal variance and detrend scatter correction. Calibration model performance for the attributes of TSS and DM content was encouraging (typical Rc2 of >0.75 and 0.90, respectively; typical root mean squared standard error of calibration of <0.4 and 0.6%, respectively), whereas that for juiciness and total acidity was unacceptable. The robustness of the TSS and DM calibrations across new populations of fruit is documented in a companion study.

Annual forecasting of the Australian macadamia crop - integrating tree census data with statistical climate-adjustment models

To facilitate marketing and export, the Australian macadamia industry requires accurate crop forecasts. Each year, two levels of crop predictions are produced for this industry. The first is an overall longer-term forecast based on tree census data of growers in the Australian Macadamia Society (AMS). This data set currently accounts for around 70% of total production, and is supplemented by our best estimates of non-AMS orchards. Given these total tree numbers, average yields per tree are needed to complete the long-term forecasts. Yields from regional variety trials were initially used, but were found to be consistently higher than the average yields that growers were obtaining. Hence, a statistical model was developed using growers' historical yields, also taken from the AMS database. This model accounted for the effects of tree age, variety, year, region and tree spacing, and explained 65% of the total variation in the yield per tree data. The second level of crop prediction is an annual climate adjustment of these overall long-term estimates, taking into account the expected effects on production of the previous year's climate. This adjustment is based on relative historical yields, measured as the percentage deviance between expected and actual production. The dominant climatic variables are observed temperature, evaporation, solar radiation and modelled water stress. Initially, a number of alternate statistical models showed good agreement within the historical data, with jack-knife cross-validation R2 values of 96% or better. However, forecasts varied quite widely between these alternate models. Exploratory multivariate analyses and nearest-neighbour methods were used to investigate these differences. For 2001-2003, the overall forecasts were in the right direction (when compared with the long-term expected values), but were over-estimates. In 2004 the forecast was well under the observed production, and in 2005 the revised models produced a forecast within 5.1% of the actual production. Over the first five years of forecasting, the absolute deviance for the climate-adjustment models averaged 10.1%, just outside the targeted objective of 10%.

Reassessing spatial and temporal dynamics of kangaroo populations

Long-running datasets from aerial surveys of kangaroos (Macropus giganteus, Macropus [uliginosus, Macropus robustus and Macropus rufus) across Queensland, New South Wales and South Australia have been analysed, seeking better predictors of rates of increase which would allow aerial surveys to be undertaken less frequently than annually. Early models of changes in kangaroo numbers in response to rainfall had shown great promise, but much variability. We used normalised difference vegetation index (NDVI) instead, reasoning that changes in pasture condition would provide a better predictor than rainfall. However, except at a fine scale, NDVI proved no better; although two linked periods of rainfall proved useful predictors of rates of increase, this was only in some areas for some species. The good correlations reported in earlier studies were a consequence of data dominated by large droughtinduced adult mortality, whereas over a longer time frame and where changes between years are less dramatic, juvenile survival has the strongest influence on dynamics. Further, harvesting, density dependence and competition with domestic stock are additional and important influences and it is now clear that kangaroo movement has a greater influence on population dynamics than had been assumed. Accordingly, previous conclusions about kangaroo populations as simple systems driven by rainfall need to be reassessed. Examination of this large dataset has permitted descriptions of shifts in distribution of three species across eastern Australia, changes in dispersion in response to rainfall, and an evaluation of using harvest statistics as an index of density and harvest rate. These results have been combined into a risk assessment and decision theory framework to identify optimal monitoring strategies.

Net radiation dynamics: performance of 20 daily net radiation models as related to model structure and intricacy in two climates.

We compared daily net radiation (Rn) estimates from 19 methods with the ASCE-EWRI Rn estimates in two climates: Clay Center, Nebraska (sub-humid) and Davis, California (semi-arid) for the calendar year. The performances of all 20 methods, including the ASCE-EWRI Rn method, were then evaluated against Rn data measured over a non-stressed maize canopy during two growing seasons in 2005 and 2006 at Clay Center. Methods differ in terms of inputs, structure, and equation intricacy. Most methods differ in estimating the cloudiness factor, emissivity (e), and calculating net longwave radiation (Rnl). All methods use albedo (a) of 0.23 for a reference grass/alfalfa surface. When comparing the performance of all 20 Rn methods with measured Rn, we hypothesized that the a values for grass/alfalfa and non-stressed maize canopy were similar enough to only cause minor differences in Rn and grass- and alfalfa-reference evapotranspiration (ETo and ETr) estimates. The measured seasonal average a for the maize canopy was 0.19 in both years. Using a = 0.19 instead of a = 0.23 resulted in 6% overestimation of Rn. Using a = 0.19 instead of a = 0.23 for ETo and ETr estimations, the 6% difference in Rn translated to only 4% and 3% differences in ETo and ETr, respectively, supporting the validity of our hypothesis. Most methods had good correlations with the ASCE-EWRI Rn (r2 > 0.95). The root mean square difference (RMSD) was less than 2 MJ m-2 d-1 between 12 methods and the ASCE-EWRI Rn at Clay Center and between 14 methods and the ASCE-EWRI Rn at Davis. The performance of some methods showed variations between the two climates. In general, r2 values were higher for the semi-arid climate than for the sub-humid climate. Methods that use dynamic e as a function of mean air temperature performed better in both climates than those that calculate e using actual vapor pressure. The ASCE-EWRI-estimated Rn values had one of the best agreements with the measured Rn (r2 = 0.93, RMSD = 1.44 MJ m-2 d-1), and estimates were within 7% of the measured Rn. The Rn estimates from six methods, including the ASCE-EWRI, were not significantly different from measured Rn. Most methods underestimated measured Rn by 6% to 23%. Some of the differences between measured and estimated Rn were attributed to the poor estimation of Rnl. We conducted sensitivity analyses to evaluate the effect of Rnl on Rn, ETo, and ETr. The Rnl effect on Rn was linear and strong, but its effect on ETo and ETr was subsidiary. Results suggest that the Rn data measured over green vegetation (e.g., irrigated maize canopy) can be an alternative Rn data source for ET estimations when measured Rn data over the reference surface are not available. In the absence of measured Rn, another alternative would be using one of the Rn models that we analyzed when all the input variables are not available to solve the ASCE-EWRI Rn equation. Our results can be used to provide practical information on which method to select based on data availability for reliable estimates of daily Rn in climates similar to Clay Center and Davis.

High-throughput prediction of eucalypt lignin syringyl/guaiacyl content using multivariate analysis: a comparison between mid-infrared, near-infrared, and Raman spectroscopies for model development

BACKGROUND: In order to rapidly and efficiently screen potential biofuel feedstock candidates for quintessential traits, robust high-throughput analytical techniques must be developed and honed. The traditional methods of measuring lignin syringyl/guaiacyl (S/G) ratio can be laborious, involve hazardous reagents, and/or be destructive. Vibrational spectroscopy can furnish high-throughput instrumentation without the limitations of the traditional techniques. Spectral data from mid-infrared, near-infrared, and Raman spectroscopies was combined with S/G ratios, obtained using pyrolysis molecular beam mass spectrometry, from 245 different eucalypt and Acacia trees across 17 species. Iterations of spectral processing allowed the assembly of robust predictive models using partial least squares (PLS). RESULTS: The PLS models were rigorously evaluated using three different randomly generated calibration and validation sets for each spectral processing approach. Root mean standard errors of prediction for validation sets were lowest for models comprised of Raman (0.13 to 0.16) and mid-infrared (0.13 to 0.15) spectral data, while near-infrared spectroscopy led to more erroneous predictions (0.18 to 0.21). Correlation coefficients (r) for the validation sets followed a similar pattern: Raman (0.89 to 0.91), mid-infrared (0.87 to 0.91), and near-infrared (0.79 to 0.82). These statistics signify that Raman and mid-infrared spectroscopy led to the most accurate predictions of S/G ratio in a diverse consortium of feedstocks. CONCLUSION: Eucalypts present an attractive option for biofuel and biochemical production. Given the assortment of over 900 different species of Eucalyptus and Corymbia, in addition to various species of Acacia, it is necessary to isolate those possessing ideal biofuel traits. This research has demonstrated the validity of vibrational spectroscopy to efficiently partition different potential biofuel feedstocks according to lignin S/G ratio, significantly reducing experiment and analysis time and expense while providing non-destructive, accurate, global, predictive models encompassing a diverse array of feedstocks.

Assessment of functional forms of crop yield loss models of invasive plant species applied in decision support tools and bioeconomic modelling

AbstractObjectives Decision support tools (DSTs) for invasive species management have had limited success in producing convincing results and meeting users' expectations. The problems could be linked to the functional form of model which represents the dynamic relationship between the invasive species and crop yield loss in the DSTs. The objectives of this study were: a) to compile and review the models tested on field experiments and applied to DSTs; and b) to do an empirical evaluation of some popular models and alternatives. Design and methods This study surveyed the literature and documented strengths and weaknesses of the functional forms of yield loss models. Some widely used models (linear, relative yield and hyperbolic models) and two potentially useful models (the double-scaled and density-scaled models) were evaluated for a wide range of weed densities, maximum potential yield loss and maximum yield loss per weed. Results Popular functional forms include hyperbolic, sigmoid, linear, quadratic and inverse models. Many basic models were modified to account for the effect of important factors (weather, tillage and growth stage of crop at weed emergence) influencing weed–crop interaction and to improve prediction accuracy. This limited their applicability for use in DSTs as they became less generalized in nature and often were applicable to a much narrower range of conditions than would be encountered in the use of DSTs. These factors' effects could be better accounted by using other techniques. Among the model empirically assessed, the linear model is a very simple model which appears to work well at sparse weed densities, but it produces unrealistic behaviour at high densities. The relative-yield model exhibits expected behaviour at high densities and high levels of maximum yield loss per weed but probably underestimates yield loss at low to intermediate densities. The hyperbolic model demonstrated reasonable behaviour at lower weed densities, but produced biologically unreasonable behaviour at low rates of loss per weed and high yield loss at the maximum weed density. The density-scaled model is not sensitive to the yield loss at maximum weed density in terms of the number of weeds that will produce a certain proportion of that maximum yield loss. The double-scaled model appeared to produce more robust estimates of the impact of weeds under a wide range of conditions. Conclusions Previously tested functional forms exhibit problems for use in DSTs for crop yield loss modelling. Of the models evaluated, the double-scaled model exhibits desirable qualitative behaviour under most circumstances.