Response to deep placed P, K and S in central Queensland

Two field experiments were established in central Queensland at Capella and Gindie to investigate the immediate and then residual benefit of deep placed (20 cm) nutrients in this opportunity cropping system. The field sites had factorial combinations of P (40 kg P/ha), K (200 kg K/ha) and S (40 kg S/ha) and all plots received 100 kg N/ha. No further K or S fertilizers were added during the experiment but some crops had starter P. The Capella site was sown to chickpea in 2012, wheat in 2013 and then chickpea in 2014. The Gindie site was sown to sorghum in 2011/12, chickpea in 2013 and sorghum in early 2015. There were responses to P alone in the first two crops at each site and there were K responses in half the six site years. In year 1 (a good year) both sites showed a 20% grain yield response to only to deep P. In year 2 (much drier) the effects of deep P were still evident at both sites and the effects of K were clearly evident at Gindie. There was a suggestion of an additive P+K effect at Capella and a 50% increase for P+K at Gindie. Year 3 was dry and chickpeas at Capella showed a larger response to P+K but the sorghum at Gindie only responded to deep K. These results indicate that responses to deep placed P and K are durable over an opportunity cropping system, and meeting both requirements is important to achieve yield responses.

Fertilising for yield and quality in sown grass pastures and forage crops

Sown pasture rundown and declining soil fertility for forage crops are too serious to ignore with losses in beef production of up to 50% across Queensland. The feasibility of using strategic applications of nitrogen (N) fertiliser to address these losses was assessed by analysing a series of scenarios using data drawn from published studies, local fertiliser trials and expert opinion. While N fertilser can dramatically increase productivity (growth, feed quality and beef production gains of over 200% in some scenarios), the estimated economic benefits, derived from paddock level enterprise budgets for a fattening operation, were much more modest. In the best-performing sown grass scenarios, average gross margins were doubled or tripled at the assumed fertiliser response rates, and internal rates of return of up to 11% were achieved. Using fertiliser on forage sorghum or oats was a much less attractive option and, under the paddock level analysis and assumptions used, forages struggled to be profitable even on fertile sites with no fertiliser input. The economics of nitrogen fertilising on grass pasture were sensitive to the assumed response rates in both pasture growth and liveweight gain. Consequently, targeted research is proposed to re-assess the responses used in this analysis, which are largely based on research 25-40 years ago when soils were generally more fertile and pastures less rundown.

An evaluation of the components of a proposed perimeter baiting system for cucumber fly, Bactrocera cucumis

Management of cucumber fly (Bactrocera cucumis) has relied heavily on cover sprays of broad spectrum insecticides such as dimethoate and fenthion. Long term access to these insecticides is uncertain, and their use can disrupt integrated pest management programs for other pests such as whitefly, aphids and mites. Application of a protein bait spray for fruit fly control is common practice in tree crops. However, vegetable crops present different challenges as fruit flies are thought to enter these crops only to oviposit, spending the majority of their time in roosting sites outside of the cropping area. Perimeter baiting of non-crop vegetation was developed overseas as a technique for control of melon fly (B. cucurbitae) in cucurbits in Hawaii. More recent work has refined the technique further, with certain types of perimeter vegetation proving more attractive to melon fly than the sorghum or corn crops which are commonly utilised. Trials were performed to investigate the potential of developing a similar system for cucumber fly. Commercially available fruit fly baits were compared for attractiveness to cucumber fly. Eight plant species were evaluated for their relative attractiveness to cucumber flies as roosting sites. Differences were observed in the number of flies feeding at protein bait applied to each of the plants. Results are discussed in the context of the development of a perimeter baiting system for cucumber fly in cucurbit crops.

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.

Does Fumigation of Durum Wheat and Semolina with Sulfuryl Fluoride affect Quality of the Grain, Semolina and derived Spaghetti and Bread?

There is no information on the effect of sulfuryl fluoride (SF) on durum wheat technological properties and products made from fumigated durum wheat. Durum wheat and semolina were exposed to a range of SF applications under conditions that might be typically encountered in bulk storage facilities used in many countries. SF greatly reduced the germination percentage of fumigated durum wheat with increasing impact under higher SF concentration, grain moisture content, and fumigation temperature. SF greatly reduced seed germination percentage impacting more the higher the SF concentration. SF had little to no effects on grain test weight, 1000 grain weight, hardness, protein content, semolina ash content and mixograph properties. At the highest SF concentration (31.25 mg/L for 48 h) there was a tendency for pasta cooking loss to be increased but still acceptable while other pasta properties were largely unaffected. Fumigation with SF did not have any impact on the baking properties of a wholemeal durum flour-commercial flour mix. Therefore, SF is not recommended if the grains are to be used as seeds for agricultural production but for the production of semolina, pasta and bread, SF used under typical fumigation conditions has little to no impact on technological properties of durum wheat.