Life cycle and host range of Phycitasp. rejected for biological control of prickly acacia in Australia

Prickly acacia (Vachellia nilotica subsp. indica), a native of the Indian subcontinent, is a serious weed of the grazing areas of northern Australia and is a target for classical biological control. Native range surveys in India identified a leaf webber, Phycita sp. (Lepidoptera: Pyralidae) as a prospective biological control agent for prickly acacia. In this study, we report the life cycle and host-specificity test results Phycita sp. and highlight the contradictory results between the no-choice tests in India and Australia and the field host range in India. In no-choice tests in India and Australia, Phycita sp. completed development on two of 11 and 16 of 27 non-target test plant species, respectively. Although Phycita sp. fed and completed development on two non-target test plant species (Vachellia planifrons and V. leucophloea) in no-choice tests in India, there was no evidence of the insect on the two non-target test plant species in the field. Our contention is that oviposition behaviour could be the key mechanism in host selection of Phycita sp., resulting in its incidence only on prickly acacia in India. This is supported by paired oviposition choice tests involving three test plant species (Acacia baileyana, A. mearnsii and A. deanei) in quarantine in Australia, where eggs were laid only on prickly acacia. However, in paired oviposition choice trials, only few eggs were laid, making the results unreliable. Although oviposition choice tests suggest that prickly acacia is the most preferred and natural host, difficulties in conducting choice oviposition tests with fully grown trees under quarantine conditions in Australia and the logistic difficulties of conducting open-field tests with fully grown native Australian plants in India have led to rejection of Phycita sp. as a potential biological control agent for prickly acacia in Australia.

A universal equation to predict methane production of forage-fed cattle in Australia

The methods for estimating methane emissions from cattle as used in the Australian national inventory are based on older data that have now been superseded by a large amount of more recent data. Recent data suggested that the current inventory emissions estimates can be improved. To address this issue, a total of 1034 individual animal records of daily methane production (MP) was used to reassess the relationship between MP and each of dry matter intake (DMI) and gross energy intake (GEI). Data were restricted to trials conducted in the past 10 years using open-circuit respiration chambers, with cattle fed forage-based diets (forage >70%). Results from diets considered to inhibit methanogenesis were omitted from the dataset. Records were obtained from dairy cattle fed temperate forages (220 records), beef cattle fed temperate forages (680 records) and beef cattle fed tropical forages (133 records). Relationships were very similar for all three production categories and single relationships for MP on a DMI or GEI basis were proposed for national inventory purposes. These relationships were MP (g/day) = 20.7 (±0.28) × DMI (kg/day) (R2 = 0.92, P < 0.001) and MP (MJ/day) = 0.063 (±0.008) × GEI (MJ/day) (R2 = 0.93, P < 0.001). If the revised MP (g/day) approach is used to calculate Australia’s national inventory, it will reduce estimates of emissions of forage-fed cattle by 24%. Assuming a global warming potential of 25 for methane, this represents a 12.6 Mt CO2-e reduction in calculated annual emissions from Australian cattle.

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.