Perennial grassland dynamics on fertile plains: Is coexistence mediated by disturbance?

The response of grasslands to disturbance varies with the nature of the disturbance and the productivity of the landscape. In highly productive grasslands, competitive exclusion often results in decreased species richness and grazing may allow more species to coexist. Once widespread, grasslands dominated by Dichanthium sericeum (Queensland bluegrass) and Astrebla spp. (Mitchell grass) occur on fertile plains but have been reduced in extent by cultivation. We tested the effects of exclusion of livestock grazing on these grasslands by comparing the floristic composition of sites in a nature reserve with an adjacent stock reserve. In addition, sites that had been cultivated within the nature reserve were compared with those where grazing but no cultivation had occurred. To partition the effects of temporal variation from spatial variation we sampled sites in three different years (1998, 2002 and 2004). Some 194 taxa were recorded at the nature reserve and surrounding stock routes. Sampling time, the occurrence of past cultivation and livestock grazing all influenced species composition. Species richness varied greatly between sampling periods relating to highly variable rainfall and water availability on heavy clay soils. Native species richness was significantly lower at previously cultivated sites (13-22 years after cultivation), but was not significantly influenced by grazing exclusion. After 8 years it appears that reintroducing disturbance in the form of livestock grazing is not necessary to maintain plant species richness in the reserve. The highly variable climate (e.g. droughts) probably plays an important role in the coexistence of species by negating competitive exclusion and allowing interstitial species to persist.

Atrazine degradation and transport in runoff on a Black Vertosol

In Australia communities are concerned about atrazine being detected in drinking water supplies. It is important to understand mechanisms by which atrazine is transported from paddocks to waterways if we are to reduce movement of agricultural chemicals from the site of application. Two paddocks cropped with grain sorghum on a Black Vertosol were monitored for atrazine, potassium chloride (KCl) extractable atrazine, desethylatrazine (DEA), and desisopropylatrazine (DIA) at 4 soil depths (0-0.05, 0.05-0.10, 0.10-0.20, and 0.20-0.30 m) and in runoff water and runoff sediment. Atrazine + DEA + DIA (total atrazine) had a half-life in soil of 16-20 days, more rapid dissipation than in many earlier reports. Atrazine extracted in dilute potassium chloride, considered available for weed control, was initially 34% of the total and had a half-life of 15-20 days until day 30, after which it dissipated rapidly with a half life of 6 days. We conclude that, in this region, atrazine may not pose a risk for groundwater contamination, as only 0.5% of applied atrazine moved deeper than 0.20 m into the soil, where it dissipated rapidly. In runoff (including suspended sediment) atrazine concentrations were greatest during the first runoff event (57 days after application) (85 μg/L) and declined with time. After 160 days, the total atrazine lost in runoff was 0.4% of the initial application. The total atrazine concentration in runoff was strongly related to the total concentration in soil, as expected. Even after 98% of the KCl-extractable atrazine had dissipated (and no longer provided weed control), runoff concentrations still exceeded the human health guideline value of 40 μg/L. For total atrazine in soil (0-0.05 m), the range for coefficient of soil sorption (Kd) was 1.9-28.4 mL/g and for soil organic carbon sorption (KOC) was 100-2184 mL/g, increasing with time of contact with the soil and rapid dissipation of the more soluble, available phase. Partition coefficients in runoff for total atrazine were initially 3, increasing to 32 and 51 with time, values for DEA being half these. To minimise atrazine losses, cultural practices that maximise rain infiltration, and thereby minimise runoff, and minimise concentrations in the soil surface should be adopted.

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.

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.

Probiotic Bacillus amyloliquefaciens Strain H57 Improves the Performance of Pregnant and Lactating Ewes Fed a Diet Based on Palm Kernel Meal

Probiotic supplements are single or mixed strain cultures of live microorganisms that benefit the host by improving the properties of the indigenous microflora (Seo et al 2010). In a pilot study at the University of Queensland, Norton et al (2008) found that Bacillus amyloliquefaciens Strain H57 (H57), primarily investigated as an inoculum to make high-quality hay, improved feed intake and nitrogen utilisation over several weeks in pregnant ewes. The purpose of the following study was to further challenge the potential of H57 -to show it survives the steam-pelleting process, and that it improves the performance of ewes fed pellets based on an agro-industrial by-product with a reputation for poor palatability, palm kernel meal (PKM), (McNeill 2013). Thirty-two first-parity White Dorper ewes (day 37 of pregnancy, mean liveweight = 47.3 kg, mean age = 15 months) were inducted into individual pens in the animal house at the University of Queensland, Gatton. They were adjusted onto PKM-based pellets (g/kg drymatter (DM): PKM, 408; sorghum, 430; chick pea hulls, 103; minerals and vitamins; Crude protein, 128; ME: 11.1MJ/kg DM) until day 89 of pregnancy and thereafter fed a predominately pelleted diet incorporating with or without H57 spores (10 9 colony forming units (cfu)/kg pellet, as fed), plus 100g/ewe/day oaten chaff, until day 7 of lactation. From day 7 to 20 of lactation the pelleted component of the diet was steadily reduced to be replaced by a 50:50 mix of lucerne: oaten chaff, fed ad libitum, plus 100g/ewe/day of ground sorghum grain with or without H57 (10 9 cfu/ewe/day). The period of adjustment in pregnancy (day 37-89) extended beyond expectations due to some evidence of mild ruminal acidosis after some initially high intakes that were followed by low intakes. During that time the diet was modified, in an attempt to improve palatability, by the addition of oaten chaff and the removal of an acidifying agent (NH4Cl) that was added initially to reduce the risk of urinary calculi. Eight ewes were removed due to inappetence, leaving 24 ewes to start the trial at day 90 of pregnancy. From day 90 of pregnancy until day 63 of lactation, liveweights of the ewes and their lambs were determined weekly and at parturition. Feed intakes of the ewes were determined weekly. Once lambing began, 1 ewe was removed as it gave birth to twin lambs (whereas the rest gave birth to a single lamb), 4 due to the loss of their lambs (2 to dystocia), and 1 due to copper toxicity. The PKM pellets were suspected to be the cause of the copper toxicity and so were removed in early lactation. Hence, the final statistical analysis using STATISTICA 8 (Repeated measures ANOVA for feed intake, One-way ANOVA for liveweight change and birth weight) was completed on 23 ewes for the pregnancy period (n = 11 fed H57; n = 12 control), and 18 ewes or lambs for the lactation period (n = 8 fed H57; n = 10 control). From day 90 of pregnancy until parturition the H57 supplemented ewes ate 17 more DM (g/day: 1041 vs 889, sed = 42.4, P = 0.04) and gained more liveweight (g/day: 193 vs 24.0, sed = 25.4, P = 0.0002), but produced lambs with a similar birthweight (kg: 4.18 vs 3.99, sed = 0.19, P = 0.54). Over the 63 days of lactation the H57 ewes ate similar amounts of DM but grew slower than the control ewes (g/day: 1.5 vs 97.0, sed = 21.7, P = 0.012). The lambs of the H57 ewes grew faster than those of the control ewes for the first 21 days of lactation (g/day: 356 vs 265, sed = 16.5, P = 0.006). These data support the findings of Norton et al (2008) and Kritas et al (2006) that certain Bacillus spp. supplements can improve the performance of pregnant and lactating ewes. In the current study we particularly highlighted the capacity of H57 to stimulate immature ewes to continue to grow maternal tissue through pregnancy, possibly through an enhanced appetite, which appeared then to stimulate a greater capacity to partition nutrients to their lambs through milk, at least for the first few weeks of lactation, a critical time for optimising lamb survival. To conclude, H57 can survive the steam pelleting process to improve feed intake and maternal liveweight gain in late pregnancy, and performance in early lactation, of first-parity ewes fed a diet based on PKM.

Automating Crop Damage Assessments with Unmanned Aerial Systems and Machine Learning

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising methodology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of this approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labelling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means clustering. The results show the algorithm delivers consistent decision boundaries that classify the field into three clusters, one for each crop health level as shown in Figure 1. The methodology presented in this paper represents a venue for further esearch towards automated crop damage assessments and biosecurity surveillance.