Predicting Additive and Non-additive Genetic Effects from Trials Where Traits Are Affected by Interplot Competition

There are two key types of selection in a plant breeding program, namely selection of hybrids for potential commercial use and the selection of parents for use in future breeding. Oakey et al. (in Theoretical and Applied Genetics 113, 809-819, 2006) showed how both of these aims could be achieved using pedigree information in a mixed model analysis in order to partition genetic effects into additive and non-additive effects. Their approach was developed for field trial data subject to spatial variation. In this paper we extend the approach for data from trials subject to interplot competition. We show how the approach may be used to obtain predictions of pure stand additive and non-additive effects. We develop the methodology in the context of a single field trial using an example from an Australian sorghum breeding program.

Estimation in a multiplicative mixed model involving a genetic relationship matrix.

Genetic models partitioning additive and non-additive genetic effects for populations tested in replicated multi-environment trials (METs) in a plant breeding program have recently been presented in the literature. For these data, the variance model involves the direct product of a large numerator relationship matrix A, and a complex structure for the genotype by environment interaction effects, generally of a factor analytic (FA) form. With MET data, we expect a high correlation in genotype rankings between environments, leading to non-positive definite covariance matrices. Estimation methods for reduced rank models have been derived for the FA formulation with independent genotypes, and we employ these estimation methods for the more complex case involving the numerator relationship matrix. We examine the performance of differing genetic models for MET data with an embedded pedigree structure, and consider the magnitude of the non-additive variance. The capacity of existing software packages to fit these complex models is largely due to the use of the sparse matrix methodology and the average information algorithm. Here, we present an extension to the standard formulation necessary for estimation with a factor analytic structure across multiple environments.

Non-invasive assessment of pineapple and mango fruit quality using near infra-red spectroscopy

The potential of near infra-red (NIR) spectroscopy for non-invasive measurement of fruit quality of pineapple (Ananas comosus var. Smooth Cayenne) and mango (Magnifera indica var. Kensington) fruit was assessed. A remote reflectance fibre optic probe, placed in contact with the fruit skin surface in a light-proof box, was used to deliver monochromatic light to the fruit, and to collect NIR reflectance spectra (760–2500 nm). The probe illuminated and collected reflected radiation from an area of about 16 cm2. The NIR spectral attributes were correlated with pineapple juice Brix and with mango flesh dry matter (DM) measured from fruit flesh directly underlying the scanned area. The highest correlations for both fruit were found using the second derivative of the spectra (d2 log 1/R) and an additive calibration equation. Multiple linear regression (MLR) on pineapple fruit spectra (n = 85) gave a calibration equation using d2 log 1/R at wavelengths of 866, 760, 1232 and 832 nm with a multiple coefficient of determination (R2) of 0.75, and a standard error of calibration (SEC) of 1.21 °Brix. Modified partial least squares (MPLS) regression analysis yielded a calibration equation with R2 = 0.91, SEC = 0.69, and a standard error of cross validation (SECV) of 1.09 oBrix. For mango, MLR gave a calibration equation using d2 log 1/R at 904, 872, 1660 and 1516 nm with R2 = 0.90, and SEC = 0.85% DM and a bias of 0.39. Using MPLS analysis, a calibration equation with R2 = 0.98, SEC = 0.54 and SECV = 1.19 was obtained. We conclude that NIR technology offers the potential to assess fruit sweetness in intact whole pineapple and DM in mango fruit, respectively, to within 1° Brix and 1% DM, and could be used for the grading of fruit in fruit packing sheds.

Row spacing and planting density effects on the growth and yield of sugarcane. 1. Responses in fumigated and non-fumigated soil.

It has been reported that high-density planting of sugarcane can improve cane and sugar yield through promoting rapid canopy closure and increasing radiation interception earlier in crop growth. It is widely known that the control of adverse soil biota through fumigation (removes soil biological constraints and improves soil health) can improve cane and sugar yield. Whether the responses to high-density planting and improved soil health are additive or interactive has important implications for the sugarcane production system. Field experiments established at Bundaberg and Mackay, Queensland, Australia, involved all combinations of 2-row spacings (0.5 and 1.5 m), two planting densities (27 000 and 81 000 two-eyed setts/ha), and two soil fumigation treatments (fumigated and non-fumigated). The Bundaberg experiment had two cultivars (Q124, Q155), was fully irrigated, and harvested 15 months after planting. The Mackay experiment had one cultivar (Q117), was grown under rainfed conditions, and harvested 10 months after planting. High-density planting (81 000 setts/ha in 0.5-m rows) did not produce any more cane or sugar yield at harvest than low-density planting (27 000 setts/ha in 1.5-m rows) regardless of location, crop duration (15 v. 10 months), water supply (irrigated v. rainfed), or soil health (fumigated v. non-fumigated). Conversely, soil fumigation generally increased cane and sugar yields regardless of site, row spacing, and planting density. In the Bundaberg experiment there was a large fumigation x cultivar x density interaction (P<0.01). Cultivar Q155 responded positively to higher planting density in non-fumigated soil but not in fumigated soil, while Q124 showed a negative response to higher planting density in non-fumigated soil but no response in fumigated soil. In the Mackay experiment, Q117 showed a non-significant trend of increasing yield in response to increasing planting density in non-fumigated soil, similar to the Q155 response in non-fumigated soil at Bundaberg. The similarity in yield across the range of row spacings and planting densities within experiments was largely due to compensation between stalk number and stalk weight, particularly when fumigation was used to address soil health. Further, the different cultivars (Q124 and Q155 at Bundaberg and Q117 at Mackay) exhibited differing physiological responses to the fumigation, row spacing, and planting density treatments. These included the rate of tiller initiation and subsequent loss, changes in stalk weight, and propensity to lodging. These responses suggest that there may be potential for selecting cultivars suited to different planting configurations.

Influence of environmental and instrumental variables on the non-invasive prediction of Brix in pineapple using near infrared spectroscopy

The Brix content of pineapple fruit can be non-invasively predicted from the second derivative of near infrared reflectance spectra. Correlations obtained using a NIRSystems 6500 spectrophotometer through multiple linear regression and modified partial least squares analyses using a post-dispersive configuration were comparable with that from a pre-dispersive configuration in terms of accuracy (e.g. coefficient of determination, R2, 0.73; standard error of cross validation, SECV, 1.01°Brix). The effective depth of sample assessed was slightly greater using the post-dispersive technique (about 20 mm for pineapple fruit), as expected in relation to the higher incident light intensity, relative to the pre-dispersive configuration. The effect of such environmental variables as temperature, humidity and external light, and instrumental variables such as the number of scans averaged to form a spectrum, were considered with respect to the accuracy and precision of the measurement of absorbance at 876 nm, as a key term in the calibration for Brix, and predicted Brix. The application of post-dispersive near infrared technology to in-line assessment of intact fruit in a packing shed environment is discussed.

Effect of the Queensland Shark Control Program on non target species : whale, dugong, turtle and dolphin : a review

The Queensland Shark Control Program (QSCP) aims to protect swimmers at ten beach areas on the east coast of Queensland between Cairns (17°S) and the Gold coast (28°S). Since its inception in 1962 it has deployed shark nets and baited drumlines in a `mixed gear strategy' that adapts the type of gear to the characteristics of a site (e .g . extreme tidal range, high energy wave action, or proximity of turtle breeding areas) . The policy has provided swimmer protection, and the incidental capture of non-target species has been lower than that resulting from deployment of nets alone (Dudley 1997; Gribble et al. 1998b). The QSCP is the only major public-safety shark-control program to routinely use mixed gear. Both the New South Wales (Holt 1998) and KwaZulu-Natal (Dudley 1998) programs use nets exclusively, although the KwaZulu-Natal program has recently tested drumlines on an experimental basis (Dudley 1998; Dudley, personal communication).

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.

Identification and characterization of non-pathogenic Fusarium oxysporum capable of increasing and decreasing Fusarium wilt severity

Fusarium wilt of banana is a potentially devastating disease throughout the world. Options for control of the causal organism, Fusarium oxysporum f.sp. cubense (Foc) are limited. Suppressive soil sites have previously been identified where, despite the presence of Foc, Fusarium wilt does not develop. In order to understand some aspects of this disease suppression, endophytic Fusarium oxysporum isolates were obtained from banana roots. These isolates were genetically characterized and compared with an isolate of Fusarium oxysporum previously identified as being capable of suppressing Fusarium wilt of banana in glasshouse trials. Three additional isolates were selected for glasshouse trials to assess suppression of Fusarium wilt in two different cultivars of banana, Cavendish and Lady Finger. One isolate (BRIP 29089) was identified as a potential biocontrol organism, reducing the disease severity of Fusarium wilt in Lady Finger and Cavendish cultivars. Interestingly, one isolate (BRIP 45952) increased Fusarium wilt disease severity on Cavendish. The implications of an isolate of Fusarium oxysporum, non-pathogenic on banana, increasing disease severity and the potential role of non-pathogenic isolates of Fusarium oxysporum in disease complexes are discussed.

Identification of viral and non-viral reverse transcribing elements in pineapple ( Ananas comosus ), including members of two new badnavirus species

A previously published partial sequence of pineapple bacilliform virus was shown to be from a retrotransposon (family Metaviridae) and not from a badnavirus as previously thought. Two newly discovered sequence groups isolated from pineapple were associated with bacilliform virions and were transmitted by mealybugs. Phylogenetic analyses indicated that they were members of new badnavirus species. A third caulimovirid sequence was also amplified from pineapple, but available evidence suggests that this DNA is not encapsidated, but more likely derived from an endogenous virus.

Non-target impacts of poison baiting for predator control in Australia

1. Mammalian predators are controlled by poison baiting in many parts of the world, often to alleviate their impacts on agriculture or the environment. Although predator control can have substantial benefits, the poisons used may also be potentially harmful to other wildlife. 2. Impacts on non-target species must be minimized, but can be difficult to predict or quantify. Species and individuals vary in their sensitivity to toxins and their propensity to consume poison baits, while populations vary in their resilience. Wildlife populations can accrue benefits from predator control, which outweigh the occasional deaths of non-target animals. We review recent advances in Australia, providing a framework for assessing non-target effects of poisoning operations and for developing techniques to minimize such effects. We also emphasize that weak or circumstantial evidence of non-target effects can be misleading. 3. Weak evidence that poison baiting presents a potential risk to non-target species comes from measuring the sensitivity of species to the toxin in the laboratory. More convincing evidence may be obtained by quantifying susceptibility in the field. This requires detailed information on the propensity of animals to locate and consume poison baits, as well as the likelihood of mortality if baits are consumed. Still stronger evidence may be obtained if predator baiting causes non-target mortality in the field (with toxin detected by post-mortem examination). Conclusive proof of a negative impact on populations of non-target species can be obtained only if any observed non-target mortality is followed by sustained reductions in population density. 4. Such proof is difficult to obtain and the possibility of a population-level impact cannot be reliably confirmed or dismissed without rigorous trials. In the absence of conclusive evidence, wildlife managers should adopt a precautionary approach which seeks to minimize potential risk to non-target individuals, while clarifying population-level effects through continued research.

Non-destructive assessment of Arundo donax (Poaceae) leaf quality

Leaf carbon (C) content, leaf nitrogen (N) content, and C:N ratio are especially useful for understanding plant-herbivore interactions and may be important in developing control methods for the invasive riparian plant Arundo donax L. We measured C content, N content, C:N ratio, and chlorophyll index (SPAD 502 reading) for 768 leaves from A. donax collected over a five year period at several locations in California, Nevada, and Texas. Leaf N was more variable than leaf C, and thus we developed a linear regression equation for estimating A. donax leaf N from the leaf chlorophyll index (SPAD reading). When applied to two independent data sets, the equation (leaf N content % = -0.63 + 0.08 x SPAD) produced realistic estimates that matched seasonal and spatial trends reported from a natural A. donax population. Used in conjunction with the handheld SPAD 502 meter, the equation provides a rapid, non-destructive method for estimating A. donax leaf quality.

The essential and non-essential genes of Bovine herpesvirus 1

Bovine herpesvirus 1 (BoHV-1) is an economically important pathogen of cattle associated with respiratory and reproductive disease. To further develop BoHV-1 as a vaccine vector, a study was conducted to identify the essential and non-essential genes required for in vitro viability. Randominsertion mutagenesis utilizing a Tn5 transposition system and targeted gene deletion were employed to construct gene disruption and gene deletion libraries, respectively, of an infectious clone of BoHV-1. Transposon insertion position and confirmation of gene deletion were determined by direct sequencing. The essential or non-essential requirement of either transposed or deleted open reading frames (ORFs) was assessed by transfection of respective BoHV-1 DNA into host cells. Of the 73 recognized ORFs encoded by the BoHV-1 genome, 33 were determined to be essential and 36 to be non-essential for virus viability in cell culture; determining the requirement of the two dual copy ORFs was inconclusive. The majority of ORFs were shown to conform to the in vitro requirements of BoHV-1 homologues encoded by human herpesvirus 1 (HHV-1). However, ORFs encoding glycoprotein K (UL53), regulatory, membrane, tegument and capsid proteins (UL54, UL49.5, UL49, UL35, UL20, UL16 and UL7) were shown to differ in requirement when compared to HHV-1-encoded homologues.

The suitability of non-target native mangroves for the survival and development of the lantana bug Aconophora compressa, an introduced weed biological control agent

Aconophora compressa (Hemiptera: Membracidae), a biological control agent introduced against the weed Lantana camara (Verbenaceae) in Australia, has since been observed on several non-target plant species, including native mangrove Avicennia marina (Acanthaceae). In this study we evaluated the suitability of two native mangroves, A. marina and Aegiceras corniculatum (Myrsinaceae), for the survival and development of A. compressa through no-choice field cage studies. The longevity of females was significantly higher on L. camara (57.7 ± 3.8 days) than on A. marina (43.3 ± 3.3 days) and A. corniculatum (45.7 ± 3.8 days). The proportion of females laying eggs was highest on L. camara (72%) followed by A. marina (36%) and A. corniculatum (17%). More egg batches per female were laid on L. camara than on A. marina and A. corniculatum. Though more nymphs per shoot emerged on L. camara (29.9 ± 2.8) than on A. marina (13 ± 4.8) and A. corniculatum (10 ± 5.3), the number of nymphs that developed through to adults was not significantly different. The duration of nymphal development was longer on A. marina (67 ± 5.8 days) than on L. camara (48 ± 4 days) and A. corniculatum (43 ± 4.6 days). The results, which are in contrast to those from previous glasshouse and quarantine trials, provide evidence that A. compressa adults can survive, lay eggs and complete nymphal development on the two non-target native mangroves in the field under no-choice condition.

Identifying major contributing sources to odour annoyance using a non-specific gas sensor array

Identification of major contributors to odour annoyance in areas with multiple emission sources is necessary to address and resolve odour disputes. In an effort to develop an appropriate tool for this task, odour samples were collected on-site at a piggery and an abattoir (the major odour sources in the area) and at surrounding off-site areas, then analysed using a commercial non-specific chemical sensor array to develop an odour fingerprint database. The developed odour fingerprint database was analysed using two pattern recognition algorithms including a partial least squares-discriminant analysis (PLS-DA) and a Kohonen self-organising map (KSOM). The KSOM model could identify odour samples sourced from the piggery shed 15, piggery pond 8, piggery pond 9, abattoir, motel and others with mean percentage values of 77.5, 65.0, 90.2, 75.7, 44.8 and 64.6%, respectively.

Non-specific conducting polymer-based array capable of monitoring odour emissions from a biofiltration system in a piggery building

A commercial non-specific gas sensor array system was evaluated in terms of its capability to monitor the odour abatement performance of a biofiltration system developed for treating emissions from a commercial piggery building. The biofiltration system was a modular system comprising an inlet ducting system, humidifier and closed-bed biofilter. It also included a gravimetric moisture monitoring and water application system for precise control of moisture content of an organic woodchip medium. Principal component analysis (PCA) of the sensor array measurements indicated that the biofilter outlet air was significantly different to both inlet air of the system and post-humidifier air. Data pre-processing techniques including normalising and outlier handling were applied to improve the odour discrimination performance of the non-specific gas sensor array. To develop an odour quantification model using the sensor array responses of the non-specific sensor array, PCA regression, artificial neural network (ANN) and partial least squares (PLS) modelling techniques were applied. The correlation coefficient (r(2)) values of the PCA, ANN, and PLS models were 0.44, 0.62 and 0.79, respectively.