
Syeda Hussain
University of Sao Paulo, Brazil
Title: How Signalgrass productive attributes are effected by the environmental factors and fertilizer application
Biography
Syeda Maryam completed his PhD in Animal production and Quality in 2016 at age of 31 years from University of Sao Paulo, Brazil. She has published three articles and 6 resumes.
Abstract
The availability of green herbage (GH) and dead herbage (DH) was evaluated in signalgrass (Brachiaria decumbens) during all the seasons and the highest GH was noted in summer (179.97 g) and three times lower (35.3 g) was reported in winter. The highest GH occurred at dose of 150 and 300 kg at 10 cm height (37.36 g and 25.94 g) in autumn and summer, respectively. While, the lowest GH was reported at 0 kg (2.53 g and 0.70 g) at sward heights of 20 and 10 cm in winter, respectively. The highest DH (14.28 g) was produced at 150 kg N at sward height of 20 cm while lowest at 0 kg N in autumn at 10 cm height (0.14 g). For GH, lower production was at 0 kg N (27.85) and highest in autumn 96.58. While amongst sward heights; in winter at 10 cm (24.4) gives lower values as to 20 cm (33.3). In summer the leaf to stem (L:S) proportions decreases significantly (1.45+0.0447) while it’s been highest in winter (11.41+1.7256). In autumn, a significant difference was observed in L:S by the heights of 10 and 20 (3.1 vs. 1.7), respectively. In green to dead (G:D) proportion, the ratio lessened (13.63) significantly by 2.5 times, producing more green parts at 300 kg N as compare to the highest (32.34) at 0 kg N. For air dry matter (ADM) and G:D, no N*H interactions were found, while for GM and L:S; significant interactions were found in winter and autumn. All the variable behaves quadratically with increase in N dose presenting an increase in ADM and GH, while L:S and G:D showed a decline. The average availability of ADM was 28.60%, green herbage 62.38 g, dead herbage 24.11 %, and L:S (4.7%) of the Signalgrass and ADM and GM increases from April till Jun and the lowest ADM production was reported in winter (23.66) and the highest in autumn (38.56). While the 300 kg N, enhance plants ADM production at its best.

Amalia Berna
Commonwealth Scientific and Industrial Research Organisation, Australia
Title: Indirect detection of ratoon stunting disease in sugar cane
Biography
Amalia Z. Berna is a Senior Research Scientist at CSIRO. She received her PhD degree in Applied Biological Science from Catholic University of Leuven, Belgium. Amalia leads the volatile biomarker discovery component of the Innovative Bioproducts group, her research focuses on the detection of low abundance volatiles release above food, plants and in human breath, with the aim of providing faster tools for predicting quality and health. Amalia is author of over 26 refereed international journal papers with >500 life citations and is inventor on two patent families.
Abstract
Ratoon Stunting Disease (RSD), caused by Leifsonia xyli subsp. xyli, is one of the most significant diseases to affect sugarcane. Incidence of the disease depends on how strictly growers follow control measures aimed at excluding infected cane from the propagation cycle. In the past sixty years farm, hygiene, hot water treatment and use of approved seed plots have remained unchanged at the core of RSD management. Some species of pathogenic bacteria can be characterised by the volatile chemicals they produce. In this work, we harvested more than 10 varieties of sugar-cane from different locations in the Queensland region of Australia over two harvest years. The headspace of sugar sap from infected and uninfected plants was analysed using solid phase micro extraction and gas chromatography-mass spectrometry. We used maximum mutual information (MI) to select the compounds that best differentiate between the infected and uninfected samples. We validated the selected compounds using two simple classifiers – Support Vector Machine and k Nearest Neighbours– through one-against-all cross-validation. In Year 1 (n=146 samples), we were able to predict the infection status of plants with better than 98% accuracy using VOCs signatures. In Year 2 (n=140 samples), larger numbers of cultivars from more diverse sites were analysed with the results showing correct prediction 95% of the time. We also found that there was no correlation between the amount of bacteria and the levels of the diagnostic volatiles indicating that the changes observed are potentially due to a specific systemic response of the plant to this pathogen.