Strategies to limit the impact of nematode pressure on sugarcane productivity in the Isis

Two trials were done in this project. One was a continuation of work started under a previous GRDC/SRDC-funded activity, 'Strategies to improve the integration of legumes into cane based farming systems'. This trial aimed to assess the impact of trash and tillage management options and nematicide application on nematodes and crop performance. Methods and results are contained in the following publication: Halpin NV, Stirling GR, Rehbein WE, Quinn B, Jakins A, Ginns SP. The impact of trash and tillage management options and nematicide application on crop performance and plant-parasitic nematode populations in a sugarcane/peanut farming system. Proc. Aust. Soc. Sugar Cane Technol. 37, 192-203. Nematicide application in the plant crop significantly reduced total numbers of plant parasitic nematodes (PPN) but there was no impact on yield. Application of nematicide to the ratoon crop significantly reduced sugar yield. The study confirmed other work demonstrating that implementation of strategies like reduced tillage reduced populations of total PPN, suggesting that the soil was more suppressive to PPN in those treatments. The second trial, a variety trial, demonstrated the limited value of nematicide application in sugarcane farming systems. This study has highlighted that growers shouldn’t view nematicides as a ‘cure all’ for paddocks that have historically had high PPN numbers. Nematicides have high mammalian toxicity, have the potential to contaminate ground water (Kookana et al. 1995) and are costly. The cost of nematicide used in R1 was approx. $320 - $350/ha, adding $3.50/t of cane in a 100 t/ha crop. Also, our study demonstrated that a single nematicide treatment at the application rate registered for sugarcane is not very effective in reducing populations of nematode pests. There appears to be some levels of resistance to nematodes within the current suite of varieties available to the southern canelands. For example the soil in plots that were growing Q183 had 560% more root knot nematodes / 200mL soil compared to plots that grew Q245. The authors see great value in investment into a nematode screening program that could rate varieties into groups of susceptibility to both major sugarcane nematode pests. Such a rating could then be built into a decision support ‘tree’ or tool to better enable producers to select varieties on a paddock by paddock basis.

A crop productivity research protocol for long wall mining

Springsure Creek Coal (SCC) intends to develop a coal mine using the long wall mining process under grain farming land near Emerald in Central Queensland (CQ). While this technology will result in some subsidence of the land surface, SCC wishes to maintain productivity of the grain cropping land in the precinct after coal mining. However, the impact of the surface subsidence resulting from that mining process on productivity of cropping land in any Australian landscape is currently unclear. A research protocol to investigate the impacts of subsidence on grain productivity for when the SCC project becomes operational is proposed. The protocol has wider application for other similar mining projects throughout the country. A copy of the full report is accessible on www.aginstitute.com.au.

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.

Evaluation of chlorothalonil and paraffinic oil alone and in combinations with registered fungicides for the control of yellow Sigatoka (Mycosphaerella musicola) of bananas in Northern Queensland, Australia

The efficacy of chlorothalonil and paraffinic oil alone and in combinations with the registered fungicides propiconazole, tebuconazole, difenoconazole, epoxiconazole and pyrimethanil was evaluated in a field experiment over two cropping cycles in 2013 and 2014 in Northern Queensland, Australia, for control of yellow Sigatoka (caused by Mycosphaerella musicola) of banana. The predominantly applied by the banana industry treatment mancozeb with paraffinic oil was included for comparison. The results from the two cropping cycles suggested that all chemicals used with paraffinic oil were as effective or more effective than when applied with chlorothalonil, and chlorothalonil alone. Difenoconazole and epoxiconazole with paraffinic oil followed by propiconazole with paraffinic oil were the most effective treatments. Pyrimethanil and tebuconazole plus chlorothalonil were the least effective treatments. None of the chemical treatments was phytotoxic or reduced yield.

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.

Non-linear response of soil N2O emissions to nitrogen fertiliser in a cotton-fallow rotation in sub-tropical Australia

Nitrogen fertiliser is a major source of atmospheric N2O and over recent years there is growing evidence for a non-linear, exponential relationship between N fertiliser application rate and N2O emissions. However, there is still high uncertainty around the relationship of N fertiliser rate and N2O emissions for many cropping systems. We conducted year-round measurements of N2O emission and lint yield in four N rate treatments (0, 90, 180 and 270 kg N ha-1) in a cotton-fallow rotation on a black vertosol in Australia. We observed a nonlinear exponential response of N2O emissions to increasing N fertiliser rates with cumulative annual N2O emissions of 0.55 kg N ha-1, 0.67kg N ha-1, 1.07 kg N ha-1 and 1.89 kg N ha-1 for the four respective N fertiliser rates while no N response to yield occurred above 180N. The N fertiliser induced annual N2O EF factors increased from 0.13% to 0.29% and 0.50% for the 90N, 180N and 270N treatments respectively, significantly lower than the IPCC Tier 1 default value (1.0 %). This non-linear response suggests that an exponential N2O emissions model may be more appropriate for use in estimating emission of N2O from soils cultivated to cotton in Australia. It also demonstrates that improved agricultural N management practices can be adopted in cotton to substantially reduce N2O emissions without affecting yield potential.

Legume pastures can reduce N2O emissions intensity in subtropical cereal cropping systems

Alternative sources of N are required to bolster subtropical cereal production without increasing N2O emissions from these agro-ecosystems. The reintroduction of legumes in cereal cropping systems is a possible strategy to reduce synthetic N inputs but elevated N2O losses have sometimes been observed after the incorporation of legume residues. However, the magnitude of these losses is highly dependent on local conditions and very little data are available for subtropical regions. The aim of this study was to assess whether, under subtropical conditions, the N mineralised from legume residues can substantially decrease the synthetic N input required by the subsequent cereal crop and reduce overall N2O emissions during the cereal cropping phase. Using a fully automated measuring system, N2O emissions were monitored in a cereal crop (sorghum) following a legume pasture and compared to the same crop in rotation with a grass pasture. Each crop rotation included a nil and a fertilised treatment to assess the N availability of the residues. The incorporation of legumes provided enough readily available N to effectively support crop development but the low labile C left by these residues is likely to have limited denitrification and therefore N2O emissions. As a result, N2O emissions intensities (kg N2O-N yield−1 ha−1) were considerably lower in the legume histories than in the grass. Overall, these findings indicate that the C supplied by the crop residue can be more important than the soil NO3− content in stimulating denitrification and that introducing a legume pasture in a subtropical cereal cropping system is a sustainable practice from both environmental and agronomic perspectives.

Nitrification (DMPP) and urease (NBPT) inhibitors had no effect on pasture yield, nitrous oxide emissions nor nitrate leaching under irrigation in a hot-dry climate

Modern dairy farming in Australia relies on substantial inputs of fertiliser nitrogen (N) to underpin economic production. However, N lost from dairy systems represents an opportunity cost and can pose a number of environmental risks. Nitrogen cycle inhibitors can be co-applied with N fertilisers to slow the conversion of urea to NH4+ to reduce losses via volatilisation, and slow the conversion of NH4+ to NO3- to minimize leaching of NO3- and gaseous losses via nitrification and denitrification. In a field campaign in a high input ryegrass-kikuyu pasture system we compared the soil N pools, losses and pasture production between a) urea coated with the nitrification inhibitor (3,4-dimethyl pyrazole phosphate - DMPP) b) urea coated with the urease inhibitor (N-(n-butyl) thiophosphoric triamide - NBPT) and c) standard urea. There was no treatment effect (P>0.05) on soil mineral N, pasture yield, N2O flux nor leaching of NO3- cf. standard urea. We hypothesise that at our site, because gaseous losses were highly episodic (rainfall was erratic and displayed no seasonal rainfall nor soil wetting pattern) that there was a lack of coincidence of N application and conditions conducive to gaseous losses, thus the effectiveness of the inhibitor products was minimal and did not result in an increase in pasture yield. There remains a paucity of knowledge on N cycle inhibitors in relation to their effective use in field system to increase N use efficiency. Further research is required to define under what field conditions inhibitor products are effective in order to be able to provide accurate advice to managers of nitrogen in production systems.

Estimating specific yield and transmissivity with magnetic resonance sounding in an unconfined sandstone aquifer (Niger)

The unconfined aquifer of the Continental Terminal in Niger was investigated by magnetic resonance sounding (MRS) and by 14 pumping tests in order to improve calibration of MRS outputs at field scale. The reliability of the standard relationship used for estimating aquifer transmissivity by MRS was checked; it was found that the parametric factor can be estimated with an uncertainty a parts per thousand currency sign150% by a single point of calibration. The MRS water content (theta (MRS)) was shown to be positively correlated with the specific yield (Sy), and theta (MRS) always displayed higher values than Sy. A conceptual model was subsequently developed, based on estimated changes of the total porosity, Sy, and the specific retention Sr as a function of the median grain size. The resulting relationship between theta (MRS) and Sy showed a reasonably good fit with the experimental dataset, considering the inherent heterogeneity of the aquifer matrix (residual error is similar to 60%). Interpreted in terms of aquifer parameters, MRS data suggest a log-normal distribution of the permeability and a one-sided Gaussian distribution of Sy. These results demonstrate the efficiency of the MRS method for fast and low-cost prospection of hydraulic parameters for large unconfined aquifers.

Production of F4 Fimbrial Adhesin in Plants: a Model for Oral Porcine Vaccine against Enterotoxigenic Escherichia coli

F4 fimbriae of enterotoxigenic Escherichia coli (ETEC) are highly stable multimeric structures with a capacity to evoke mucosal immune responses. With these characters F4 offer a unique model system to study oral vaccination against ETEC-induced porcine postweaning diarrhea. Postweaning diarrhea is a major problem in piggeries worldwide and results in significant economic losses. No vaccine is currently available to protect weaned piglets against ETEC infections. Transgenic plants provide an economically feasible platform for large-scale production of vaccine antigens for animal health. In this study, the capacity of transgenic plants to produce FaeG protein, the major structural subunit and adhesin of F4 fimbria, was evaluated. Using the model plant tobacco, the optimal subcellular location for FaeG accumulation was examined. Targeting of FaeG into chloroplasts offered a superior accumulation level of 1% of total soluble proteins (TSP) over the other investigated subcellular locations, namely, the endoplasmic reticulum and the apoplast. Moreover, we determined whether the FaeG protein, when isolated from its fimbrial background and produced in a plant cell, would retain the key properties of an oral vaccine, i.e. stability in gastrointestinal conditions, binding to porcine intestinal F4 receptors (F4R), and inhibition of the F4-possessing (F4+) ETEC attachment to F4R. The chloroplast-derived FaeG protein did show resistance against low pH and proteolysis in the simulated gastrointestinal conditions and was able to bind to the F4R, subsequently inhibiting the F4+ ETEC binding in a dose-dependent manner. To investigate the oral immunogenicity of FaeG protein, the edible crop plant alfalfa was transformed with the chloroplast-targeting construct and equally to tobacco plants, a high-yield FaeG accumulation of 1% of TSP was obtained. A similar yield was also obtained in the seeds of barley, a valuable crop plant, when the FaeG-encoding gene was expressed under an endosperm-specific promoter and subcellularly targeted into the endoplasmic reticulum. Furthermore, desiccated alfalfa plants and barley grains were shown to have a capacity to store FaeG protein in a stable form for years. When the transgenic alfalfa plants were administred orally to weaned piglets, slight F4-specific systemic and mucosal immune responses were induced. Co-administration of the transgenic alfalfa and the mucosal adjuvant cholera toxin enhanced the F4-specific immune response; the duration and number of F4+ E. coli excretion following F4+ ETEC challenge were significantly reduced as compared with pigs that had received nontransgenic plant material. In conclusion, the results suggest that transgenic plants producing the FaeG subunit protein could be used for production and delivery of oral vaccines against porcine F4+ ETEC infections. The findings here thus present new approaches to develop the vaccination strategy against porcine postweaning diarrhea.

Climate-change and agriculture - an indian perspective

In this paper, we examine the major predictions made so far regarding the nature of climate change and its impacts on our region in the light of the known errors of the set of models and the observations over this century. The major predictions of the climate models about the impact of increased concentration of greenhouse gases ave at variance with the observations over the Indian region during the last century characterized by such increases and global warming. It is important to note that as far as the Indian region is concerned, the impact of year-to-year variation of the monsoon will continue to be dominant over longer period changes even in the presence of global warming. Recent studies have also brought out the uncertainties in the yields simulated by crop models. It is suggested that a deeper understanding of the links between climate and agricultural productivity is essential for generating reliable predictions of impact of climate change. Such an insight is also required for identifying cropping patterns and management practices which are tailored for sustained maximum yield in the face of the vagaries of the monsoon.

Visual detection of occluded crop: For automated harvesting

This paper presents a novel crop detection system applied to the challenging task of field sweet pepper (capsicum) detection. The field-grown sweet pepper crop presents several challenges for robotic systems such as the high degree of occlusion and the fact that the crop can have a similar colour to the background (green on green). To overcome these issues, we propose a two-stage system that performs per-pixel segmentation followed by region detection. The output of the segmentation is used to search for highly probable regions and declares these to be sweet pepper. We propose the novel use of the local binary pattern (LBP) to perform crop segmentation. This feature improves the accuracy of crop segmentation from an AUC of 0.10, for previously proposed features, to 0.56. Using the LBP feature as the basis for our two-stage algorithm, we are able to detect 69.2% of field grown sweet peppers in three sites. This is an impressive result given that the average detection accuracy of people viewing the same colour imagery is 66.8%.

Assessment of crop insect damage using unmanned aerial systems: A machine learning approach

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 technology 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 the approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labeling 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. The outcome of this approach is a soft K-means algorithm similar to the EM algorithm for Gaussian mixture models. The results show the algorithm delivers decision boundaries that consistently classify the field into three clusters, one for each crop health level. The methodology presented in this paper represents a venue for further research towards automated crop damage assessments and biosecurity surveillance.