Statistical methods for analysis of multi-harvest data from perennial pasture variety selection trials

Variety selection in perennial pasture crops involves identifying best varieties from data collected from multiple harvest times in field trials. For accurate selection, the statistical methods for analysing such data need to account for the spatial and temporal correlation typically present. This paper provides an approach for analysing multi-harvest data from variety selection trials in which there may be a large number of harvest times. Methods are presented for modelling the variety by harvest effects while accounting for the spatial and temporal correlation between observations. These methods provide an improvement in model fit compared to separate analyses for each harvest, and provide insight into variety by harvest interactions. The approach is illustrated using two traits from a lucerne variety selection trial. The proposed method provides variety predictions allowing for the natural sources of variation and correlation in multi-harvest data.

Impacts of feeding system and season on milk composition and Cheddar cheese yield in a subtropical environment

Milk obtained from cows on 2 subtropical dairy feeding systems were compared for their suitability for Cheddar cheese manufacture. Cheeses were made in a small-scale cheesemaking plant capable of making 2 blocks ( about 2 kg each) of Cheddar cheese concurrently. Its repeatability was tested over 10 separate cheesemaking days with no significant differences being found between the 2 vats in cheesemaking parameters or cheese characteristics. In the feeding trial, 16 pairs of Holstein - Friesian cows were used in 2 feeding systems (M1, rain-grown tropical grass pastures and oats; and M5, a feedlot, based on maize/barley silage and lucerne hay) over 2 seasons ( spring and autumn corresponding to early and late lactation, respectively). Total dry matter, crude protein (kg/cow. day) and metabolisable energy (MJ/cow.day) intakes were 17, 2.7, and 187 for M1 and 24, 4, 260 for M5, respectively. M5 cows produced higher milk yields and milk with higher protein and casein levels than the M1 cows, but the total solids and fat levels were similar (P > 0.05) for both M1 and M5 cows. The yield and yield efficiency of cheese produced from the 2 feeding systems were also not significantly different. The results suggest that intensive tropical pasture systems can produce milk suitable for Cheddar cheese manufacture when cows are supplemented with a high energy concentrate. Season and stage of lactation had a much greater effect than feeding system on milk and cheesemaking characteristics with autumn ( late lactation) milk having higher protein and fat contents and producing higher cheese yields.

An analysis of assessment outcomes from eight years' operation of the Australian border weed risk assessment system

The majority of Australian weeds are exotic plant species that were intentionally introduced for a variety of horticultural and agricultural purposes. A border weed risk assessment system (WRA) was implemented in 1997 in order to reduce the high economic costs and massive environmental damage associated with introducing serious weeds. We review the behaviour of this system with regard to eight years of data collected from the assessment of species proposed for importation or held within genetic resource centres in Australia. From a taxonomic perspective, species from the Chenopodiaceae and Poaceae were most likely to be rejected and those from the Arecaceae and Flacourtiaceae were most likely to be accepted. Dendrogram analysis and classification and regression tree (TREE) models were also used to analyse the data. The latter revealed that a small subset of the 35 variables assessed was highly associated with the outcome of the original assessment. The TREE model examining all of the data contained just five variables: unintentional human dispersal, congeneric weed, weed elsewhere, tolerates or benefits from mutilation, cultivation or fire, and reproduction by vegetative propagation. It gave the same outcome as the full WRA model for 71% of species. Weed elsewhere was not the first splitting variable in this model, indicating that the WRA has a capacity for capturing species that have no history of weediness. A reduced TREE model (in which human-mediated variables had been removed) contained four variables: broad climate suitability, reproduction in less or than equal to 1 year, self-fertilisation, and tolerates and benefits from mutilation, cultivation or fire. It yielded the same outcome as the full WRA model for 65% of species. Data inconsistencies and the relative importance of questions are discussed, with some recommendations made for improving the use of the system.

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%.

Scenario analysis of alternative vegetation management options on the greenhouse gas budget of two grazing businesses in north-eastern Australia

The emerging carbon economy will have a major impact on grazing businesses because of significant livestock methane and land-use change emissions. Livestock methane emissions alone account for similar to 11% of Australia's reported greenhouse gas emissions. Grazing businesses need to develop an understanding of their greenhouse gas impact and be able to assess the impact of alternative management options. This paper attempts to generate a greenhouse gas budget for two scenarios using a spread sheet model. The first scenario was based on one land-type '20-year-old brigalow regrowth' in the brigalow bioregion of southern-central Queensland. The 50 year analysis demonstrated the substantially different greenhouse gas outcomes and livestock carrying capacity for three alternative regrowth management options: retain regrowth (sequester 71.5 t carbon dioxide equivalents per hectare, CO2-e/ha), clear all regrowth (emit 42.8 t CO2-e/ha) and clear regrowth strips (emit 5.8 t CO2-e/ha). The second scenario was based on a 'remnant eucalypt savanna-woodland' land type in the Einasleigh Uplands bioregion of north Queensland. The four alternative vegetation management options were: retain current woodland structure (emit 7.4 t CO2-e/ha), allow woodland to thicken increasing tree basal area (sequester 20.7 t CO2-e/ha), thin trees less than 10 cm diameter (emit 8.9 t CO2-e/ha), and thin trees <20 cm diameter (emit 12.4 t CO2-e/ha). Significant assumptions were required to complete the budgets due to gaps in current knowledge on the response of woody vegetation, soil carbon and non-CO2 soil emissions to management options and land-type at the property scale. The analyses indicate that there is scope for grazing businesses to choose alternative management options to influence their greenhouse gas budget. However, a key assumption is that accumulation of carbon or avoidance of emissions somewhere on a grazing business (e.g. in woody vegetation or soil) will be recognised as an offset for emissions elsewhere in the business (e.g. livestock methane). This issue will be a challenge for livestock industries and policy makers to work through in the coming years.

Analysis and Modelling of the Effects of Water Stress on Maize Growth and Yield in Dryland Conditions.

It is essential to provide experimental evidence and reliable predictions of the effects of water stress on crop production in the drier, less predictable environments. A field experiment undertaken in southeast Queensland, Australia with three water regimes (fully irrigated, rainfed and irrigated until late canopy expansion followed by rainfed) was used to compare effects of water stress on crop production in two maize (Zea mays L.) cultivars (Pioneer 34N43 and Pioneer 31H50). Water stress affected growth and yield more in Pioneer 34N43 than in Pioneer 31H50. A crop model APSIM-Maize, after having been calibrated for the two cultivars, was used to simulate maize growth and development under water stress. The predictions on leaf area index (LAI) dynamics, biomass growth and grain yield under rain fed and irrigated followed by rain fed treatments was reasonable, indicating that stress indices used by APSIM-Maize produced appropriate adjustments to crop growth and development in response to water stress. This study shows that Pioneer 31H50 is less sensitive to water stress and thus a preferred cultivar in dryland conditions, and that it is feasible to provide sound predictions and risk assessment for crop production in drier, more variable conditions using the APSIM-Maize model.

Statistical forecasting of the Australian macadamia crop

Three types of forecasts of the total Australian production of macadamia nuts (t nut-in-shell) have been produced early each year since 2001. The first is a long-term forecast, based on the expected production from the tree census data held by the Australian Macadamia Society, suitably scaled up for missing data and assumed new plantings each year. These long-term forecasts range out to 10 years in the future, and form a basis for industry and market planning. Secondly, a statistical adjustment (termed the climate-adjusted forecast) is made annually for the coming crop. As the name suggests, climatic influences are the dominant factors in this adjustment process, however, other terms such as bienniality of bearing, prices and orchard aging are also incorporated. Thirdly, industry personnel are surveyed early each year, with their estimates integrated into a growers and pest-scouts forecast. Initially conducted on a 'whole-country' basis, these models are now constructed separately for the six main production regions of Australia, with these being combined for national totals. Ensembles or suites of step-forward regression models using biologically-relevant variables have been the major statistical method adopted, however, developing methodologies such as nearest-neighbour techniques, general additive models and random forests are continually being evaluated in parallel. The overall error rates average 14% for the climate forecasts, and 12% for the growers' forecasts. These compare with 7.8% for USDA almond forecasts (based on extensive early-crop sampling) and 6.8% for coconut forecasts in Sri Lanka. However, our somewhatdisappointing results were mainly due to a series of poor crops attributed to human reasons, which have now been factored into the models. Notably, the 2012 and 2013 forecasts averaged 7.8 and 4.9% errors, respectively. Future models should also show continuing improvement, as more data-years become available.

Pesticide residues in food - acute dietary exposure

Consumer risk assessment is a crucial step in the regulatory approval of pesticide use on food crops. Recently, an additional hurdle has been added to the formal consumer risk assessment process with the introduction of short-term intake or exposure assessment and a comparable short-term toxicity reference, the acute reference dose. Exposure to residues during one meal or over one day is important for short-term or acute intake. Exposure in the short term can be substantially higher than average because the consumption of a food on a single occasion can be very large compared with typical long-term or mean consumption and the food may have a much larger residue than average. Furthermore, the residue level in a single unit of a fruit or vegetable may be higher by a factor (defined as the variability factor, which we have shown to be typically ×3 for the 97.5th percentile unit) than the average residue in the lot. Available marketplace data and supervised residue trial data are examined in an investigation of the variability of residues in units of fruit and vegetables. A method is described for estimating the 97.5th percentile value from sets of unit residue data. Variability appears to be generally independent of the pesticide, the crop, crop unit size and the residue level. The deposition of pesticide on the individual unit during application is probably the most significant factor. The diets used in the calculations ideally come from individual and household surveys with enough consumers of each specific food to determine large portion sizes. The diets should distinguish the different forms of a food consumed, eg canned, frozen or fresh, because the residue levels associated with the different forms may be quite different. Dietary intakes may be calculated by a deterministic method or a probabilistic method. In the deterministic method the intake is estimated with the assumptions of large portion consumption of a ‘high residue’ food (high residue in the sense that the pesticide was used at the highest recommended label rate, the crop was harvested at the smallest interval after treatment and the residue in the edible portion was the highest found in any of the supervised trials in line with these use conditions). The deterministic calculation also includes a variability factor for those foods consumed as units (eg apples, carrots) to allow for the elevated residue in some single units which may not be seen in composited samples. In the probabilistic method the distribution of dietary consumption and the distribution of possible residues are combined in repeated probabilistic calculations to yield a distribution of possible residue intakes. Additional information such as percentage commodity treated and combination of residues from multiple commodities may be incorporated into probabilistic calculations. The IUPAC Advisory Committee on Crop Protection Chemistry has made 11 recommendations relating to acute dietary exposure.

Inferential, non-parametric statistics to assess quality of probabilistic forecast systems

Many statistical forecast systems are available to interested users. In order to be useful for decision-making, these systems must be based on evidence of underlying mechanisms. Once causal connections between the mechanism and their statistical manifestation have been firmly established, the forecasts must also provide some quantitative evidence of `quality’. However, the quality of statistical climate forecast systems (forecast quality) is an ill-defined and frequently misunderstood property. Often, providers and users of such forecast systems are unclear about what ‘quality’ entails and how to measure it, leading to confusion and misinformation. Here we present a generic framework to quantify aspects of forecast quality using an inferential approach to calculate nominal significance levels (p-values) that can be obtained either by directly applying non-parametric statistical tests such as Kruskal-Wallis (KW) or Kolmogorov-Smirnov (KS) or by using Monte-Carlo methods (in the case of forecast skill scores). Once converted to p-values, these forecast quality measures provide a means to objectively evaluate and compare temporal and spatial patterns of forecast quality across datasets and forecast systems. Our analysis demonstrates the importance of providing p-values rather than adopting some arbitrarily chosen significance levels such as p < 0.05 or p < 0.01, which is still common practice. This is illustrated by applying non-parametric tests (such as KW and KS) and skill scoring methods (LEPS and RPSS) to the 5-phase Southern Oscillation Index classification system using historical rainfall data from Australia, The Republic of South Africa and India. The selection of quality measures is solely based on their common use and does not constitute endorsement. We found that non-parametric statistical tests can be adequate proxies for skill measures such as LEPS or RPSS. The framework can be implemented anywhere, regardless of dataset, forecast system or quality measure. Eventually such inferential evidence should be complimented by descriptive statistical methods in order to fully assist in operational risk management.

The efficacy of a vitamin D3 metabolite for improving the myofibrillar tenderness of meat from Bos indicus cattle

The influence of a once only administration of a metabolite of vitamin D3 (HY [middle dot] D(R)-25-hydroxy vitamin D3) on myofibrillar meat tenderness in Australian Brahman cattle was studied. Ninety-six Brahman steers of three phenotypes (Indo-Brazil, US and US/European) and with two previous hormonal growth promotant (HGP) histories (implanted or not implanted with Compudose(R)) were fed a standard feedlot ration for 70 d. Treatment groups of 24 steers were offered daily 10 g/head HY [middle dot] D(R) (125 mg 25-hydroxyvitamin D3) for 6, 4, or 2 d before slaughter. One other group of 24 steers was given the basal diet without HY [middle dot] D(R). Feed lot performance, blood and muscle samples and carcass quality data were collected at slaughter. Calcium, magnesium, potassium, sodium, iron and Vitamin D3 metabolites were measured in plasma and longissimus dorsi muscle. Warner-Bratzler (WB) shear force (peak force, initial yield) and other objective meat quality measurements were made on the longissimus dorsi muscle of each steer after ageing for 1, 7 and 14 d post-mortem at 0-2 [deg]C.There were no significant effects of HY [middle dot] D(R) supplements on average daily gain (ADG, 1.28-1.45 kg/d) over the experimental period. HY [middle dot] D(R) supplements given 6 d prior to slaughter resulted in significantly higher (P (R)) by phenotype/HGP interaction for peak force (P = 0.028), in which Indo-Brazil steers without previous HGP treatment responded positively (increased tenderness) to HY [middle dot] D(R) supplements at 2 d when compared with Indo-Brazil steers previously given HGP. There were no significant effects of treatment on other phenotypes. HY [middle dot] D(R) supplements did not affect muscle or plasma concentrations of calcium, potassium or sodium, but did significantly decrease plasma magnesium and iron concentrations when given 2 d before slaughter. There were no detectable amounts of 25-hydroxyvitamin D3 in the blood or muscle of any cattle at slaughter.

Henipavirus in Pteropus vampyrus Bats, Indonesia

The emergence of Nipah virus (NiV) in Malaysia in 1999 resulted in 265 known human infections (105 fatal), widespread infection in pigs (with >1 million culled to control the outbreak), and the collapse of the Malaysian pig export market. As with the closely related Hendra virus (HeV) that emerged in Australia in 1994 and caused fatal disease in horses and humans, bats of the genus Pteropus (commonly known as flying foxes) were identified as the major reservoir of Nipah virus in Malaysia. This report describes a serologic survey of Pteropus vampyrus in neighboring Indonesia.

Near infrared spectroscopy as a rapid tool to measure volatile aroma compounds in Riesling wine: Possibilities and limits

Volatile chemical compounds responsible for the aroma of wine are derived from a number of different biochemical and chemical pathways. These chemical compounds are formed during grape berry metabolism, crushing of the berries, fermentation processes (i.e. yeast and malolactic bacteria) and also from the ageing and storage of wine. Not surprisingly, there are a large number of chemical classes of compounds found in wine which are present at varying concentrations (ng L-1 to mg L-1), exhibit differing potencies, and have a broad range of volatilities and boiling points. The aim of this work was to investigate the potential use of near infrared (NIR) spectroscopy combined with chemometrics as a rapid and low-cost technique to measure volatile compounds in Riesling wines. Samples of commercial Riesling wine were analyzed using an NIR instrument and volatile compounds by gas chromatography (GC) coupled with selected ion monitoring mass spectrometry. Correlation between the NIR and GC data were developed using partial least-squares (PLS) regression with full cross validation (leave one out). Coefficients of determination in cross validation (R 2) and the standard error in cross validation (SECV) were 0.74 (SECV: 313.6 μg L−1) for esters, 0.90 (SECV: 20.9 μg L−1) for monoterpenes and 0.80 (SECV: 1658 ?g L-1) for short-chain fatty acids. This study has shown that volatile chemical compounds present in wine can be measured by NIR spectroscopy. Further development with larger data sets will be required to test the predictive ability of the NIR calibration models developed.

Epithiospecifier protein activity in broccoli: The link between terminal alkenyl glucosinolates and sulphoraphane nitrile

The chemical nature of the hydrolysis products from the glucosinolate-myrosinase system depends on the presence or absence of supplementary proteins, such as epithiospecifier proteins (ESPs). ESPs (non-catalytic cofactors of myrosinase) promote the formation of epithionitriles from terminal alkenyl glucosinolates and as recent evidence suggests, simple nitriles at the expense of isothiocyanates. The ratio of ESP activity to myrosinase activity is crucial in determining the proportion of these nitriles produced on hydrolysis. Sulphoraphane, a major isothiocyanate produced in broccoli seedlings, has been found to be a potent inducer of phase 2 detoxification enzymes. However, ESP may also support the formation of the non-inductive sulphoraphane nitrile. Our objective was to monitor changes in ESP activity during the development of broccoli seedlings and link these activity changes with myrosinase activity, the level of terminal alkenyl glucosinolates and sulphoraphane nitrile formed. Here, for the first time, we show ESP activity increases up to day 2 after germination before decreasing again to seed activity levels at day 5. These activity changes paralleled changes in myrosinase activity and terminal alkenyl glucosinolate content. There is a significant relationship between ESP activity and the formation of sulforaphane nitrile in broccoli seedlings. The significance of these findings for the health benefits conferred by eating broccoli seedlings is briefly discussed.

Detecting quantitative trait loci affecting beef tenderness on bovine chromosome 7 near calpastatin and lysyl oxidase

From a study of 3 large half-sib families of cattle, we describe linkage between DNA polymorphisms on bovine chromosome 7 and meat tenderness. Quantitative trait loci (QTL) for Longissimus lumborum peak force (LLPF) and Semitendonosis adhesion (STADH) were located to this map of DNA markers, which includes the calpastatin ( CAST) and lysyl oxidase (LOX) genes. The LLPF QTL has a maximum lodscore of 4.9 and allele substitution of approximately 0.80 of a phenotypic standard deviation, and the peak is located over the CAST gene. The STADH QTL has a maximum lodscore of 3.5 and an allele substitution of approximately 0.37 of a phenotypic standard deviation, and the peak is located over the LOX gene. This suggests 2 separate likelihood peaks on the chromosome. Further analyses of meat tenderness measures in the Longissimus lumborum, LLPF and LL compression (LLC), in which outlier individuals or kill groups are removed, demonstrate large shifts in the location of LLPF QTL, as well as confirming that there are indeed 2 QTL on bovine chromosome 7. We found that both QTL are reflected in both LLPF and LLC measurements, suggesting that both these components of tenderness, myofibrillar and connective tissue, are detected by both measurements in this muscle.

NIR model development and robustness in prediction of melon fruit total soluble solids

Near infrared spectroscopy (NIRS) can be used for the on-line, non-invasive assessment of fruit for eating quality attributes such as total soluble solids (TSS). The robustness of multivariate calibration models, based on NIRS in a partial transmittance optical geometry, for the assessment of TSS of intact rockmelons (Cucumis melo) was assessed. The mesocarp TSS was highest around the fruit equator and increased towards the seed cavity. Inner mesocarp TSS levels decreased towards both the proximal and distal ends of the fruit, but more so towards the proximal end. The equatorial region of the fruit was chosen as representative of the fruit for near infrared assessment of TSS. The spectral window for model development was optimised at 695-1045 nm, and the data pre-treatment procedure was optimised to second-derivative absorbance without scatter correction. The 'global' modified partial least squares (MPLS) regression modelling procedure of WINISI (ver. 1.04) was found to be superior with respect to root mean squared error of prediction (RMSEP) and bias for model predictions of TSS across seasons, compared with the 'local' MPLS regression procedure. Updating of the model with samples selected randomly from the independent validation population demonstrated improvement in both RMSEP and bias with addition of approximately 15 samples.