Regularized numerical integration of multibody dynamics with the generalized alpha method*

This paper discusses the consistent regularization property of the generalized α method when applied as an integrator to an initial value high index and singular differential-algebraic equation model of a multibody system. The regularization comes from within the discretization itself and the discretization remains consistent over the range of values the regularization parameter may take. The regularization involves increase of the smallest singular values of the ill-conditioned Jacobian of the discretization and is different from Baumgarte and similar techniques which tend to be inconsistent for poor choice of regularization parameter. This regularization also helps where pre-conditioning the Jacobian by scaling is of limited effect, for example, when the scleronomic constraints contain multiple closed loops or singular configuration or when high index path constraints are present. The feed-forward control in Kane's equation models is additionally considered in the numerical examples to illustrate the effect of regularization. The discretization presented in this work is adopted to the first order DAE system (unlike the original method which is intended for second order systems) for its A-stability and same order of accuracy for positions and velocities.

Phage therapy in livestock methane amelioration

Viruses of prokaryotes (phages) are obligate microbial pathogens that can, in the lytic phase of development, infect and lyse their respective bacterial or archaeal hosts. As such, these viruses can reduce the population density of their hosts rapidly, and have been viewed as possible agents of biological control (phage therapy). Phage therapy is becoming increasingly important as a means of eradicating or controlling microbial populations as the use of antibiotics and chemical treatments becomes both less effective and less publicly acceptable. Phage therapy has therefore been raised as a potential strategy to reduce methane (CH 4) emissions from ruminants, providing an innovative biological approach, harnessing the potent, yet targeted, biocidal attributes of these naturally occurring microbial predators.

Integrating pest population models with biophysical crop models to better represent the farming system

Farming systems frameworks such as the Agricultural Production Systems simulator (APSIM) represent fluxes through the soil, plant and atmosphere of the system well, but do not generally consider the biotic constraints that function within the system. We designed a method that allowed population models built in DYMEX to interact with APSIM. The simulator engine component of the DYMEX population-modelling platform was wrapped within an APSIM module allowing it to get and set variable values in other APSIM models running in the simulation. A rust model developed in DYMEX is used to demonstrate how the developing rust population reduces the crop's green leaf area. The success of the linking process is seen in the interaction of the two models and how changes in rust population on the crop's leaves feedback to the APSIM crop modifying the growth and development of the crop's leaf area. This linking of population models to simulate pest populations and biophysical models to simulate crop growth and development increases the complexity of the simulation, but provides a tool to investigate biotic constraints within farming systems and further moves APSIM towards being an agro-ecological framework.

Phage therapy in livestock methane amelioration.

Viruses of prokaryotes (phages) are obligate microbial pathogens that can, in the lytic phase of development, infect and lyse their respective bacterial or archaeal hosts. As such, these viruses can reduce the population density of their hosts rapidly, and have been viewed as possible agents of biological control (phage therapy). Phage therapy is becoming increasingly important as a means of eradicating or controlling microbial populations as the use of antibiotics and chemical treatments becomes both less effective and less publicly acceptable. Phage therapy has therefore been raised as a potential strategy to reduce methane (CH4) emissions from ruminants, providing an innovative biological approach, harnessing the potent, yet targeted, biocidal attributes of these naturally occurring microbial predators.

Assessment of functional forms of crop yield loss models of invasive plant species applied in decision support tools and bioeconomic modelling

AbstractObjectives Decision support tools (DSTs) for invasive species management have had limited success in producing convincing results and meeting users' expectations. The problems could be linked to the functional form of model which represents the dynamic relationship between the invasive species and crop yield loss in the DSTs. The objectives of this study were: a) to compile and review the models tested on field experiments and applied to DSTs; and b) to do an empirical evaluation of some popular models and alternatives. Design and methods This study surveyed the literature and documented strengths and weaknesses of the functional forms of yield loss models. Some widely used models (linear, relative yield and hyperbolic models) and two potentially useful models (the double-scaled and density-scaled models) were evaluated for a wide range of weed densities, maximum potential yield loss and maximum yield loss per weed. Results Popular functional forms include hyperbolic, sigmoid, linear, quadratic and inverse models. Many basic models were modified to account for the effect of important factors (weather, tillage and growth stage of crop at weed emergence) influencing weed–crop interaction and to improve prediction accuracy. This limited their applicability for use in DSTs as they became less generalized in nature and often were applicable to a much narrower range of conditions than would be encountered in the use of DSTs. These factors' effects could be better accounted by using other techniques. Among the model empirically assessed, the linear model is a very simple model which appears to work well at sparse weed densities, but it produces unrealistic behaviour at high densities. The relative-yield model exhibits expected behaviour at high densities and high levels of maximum yield loss per weed but probably underestimates yield loss at low to intermediate densities. The hyperbolic model demonstrated reasonable behaviour at lower weed densities, but produced biologically unreasonable behaviour at low rates of loss per weed and high yield loss at the maximum weed density. The density-scaled model is not sensitive to the yield loss at maximum weed density in terms of the number of weeds that will produce a certain proportion of that maximum yield loss. The double-scaled model appeared to produce more robust estimates of the impact of weeds under a wide range of conditions. Conclusions Previously tested functional forms exhibit problems for use in DSTs for crop yield loss modelling. Of the models evaluated, the double-scaled model exhibits desirable qualitative behaviour under most circumstances.

Does manure management affect the latent greenhouse gas emitting potential of livestock manures?

With livestock manures being increasingly sought as alternatives to costly synthetic fertilisers, it is imperative that we understand and manage their associated greenhouse gas (GHG) emissions. Here we provide the first dedicated assessment into how the GHG emitting potential of various manures responds to the different stages of the manure management continuum (e.g., from feed pen surface vs stockpiled). The research is important from the perspective of manure application to agricultural soils. Manures studied included: manure from beef feedpen surfaces and stockpiles; poultry broiler litter (8-week batch); fresh and composted egg layer litter; and fresh and composted piggery litter. Gases assessed were methane (CH4) and nitrous oxide (N2O), the two principal agricultural GHGs. We employed proven protocols to determine the manures’ ultimate CH4 producing potential. We also devised a novel incubation experiment to elucidate their N2O emitting potential; a measure for which no established methods exist. We found lower CH4 potentials in manures from later stages in their management sequence compared with earlier stages, but only by a factor of 0.65×. Moreover, for the beef manures this decrease was not significant (P < 0.05). Nitrous oxide emission potential was significantly positively (P < 0.05) correlated with C/N ratios yet showed no obvious relationship with manure management stage. Indeed, N2O emissions from the composted egg manure were considerably (13×) and significantly (P < 0.05) higher than that of the fresh egg manure. Our study demonstrates that manures from all stages of the manure management continuum potentially entail significant GHG risk when applied to arable landscapes. Efforts to harness manure resources need to account for this.

Soybean rotation and crop residue management to reduce nitrous oxide emissions from sugarcane soils

NITROUS OXIDE (N2O) IS a potent greenhouse gas and the predominant ozone-depleting substance in the atmosphere. Agricultural nitrogenous fertiliser use is the major source of human-induced N2O emissions. A field experiment was conducted at Bundaberg from October 2012 to September 2014 to examine the impacts of legume crop (soybean) rotation as an alternative nitrogen (N) source on N2O emissions during the fallow period and to investigate low-emission soybean residue management practices. An automatic monitoring system and manual gas sampling chambers were used to measure greenhouse gas emissions from soil. Soybean cropping during the fallow period reduced N2O emissions compared to the bare fallow. Based on the N content in the soybean crop residues, the fertiliser N application rate was reduced by about 120 kg N/ha for the subsequent sugarcane crop. Consequently, emissions of N2O during the sugarcane cropping season were significantly lower from the soybean cropped soil than those from the conventionally fertilised (145 kg N/ha) soil following bare fallow. However, tillage that incorporated the soybean crop residues into soil promoted N2O emissions in the first two months. Spraying a nitrification inhibitor (DMPP) onto the soybean crop residues before tillage effectively prevented the N2O emission spikes. Compared to conventional tillage, practising no-till with or without growing a nitrogen catch crop during the time after soybean harvest and before cane planting also reduced N2O emissions substantially. These results demonstrated that soybean rotation during the fallow period followed with N conservation management practices could offer a promising N2O mitigation strategy in sugarcane farming. Further investigation is required to provide guidance on N and water management following soybean fallow to maintain sugar productivity.

Crop nutrient application rates

This guide provides information on how to match nutrient rate to crop needs by varying application rates and timing between blocks, guided by soil tests, crop class, cane variety, soil type, block history, soil conditioners and yield expectations.

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.

Description of vectors of arboviruses of livestock in Northern Australia

The project aimed to detect exotic C"11coides species recently established in northern Australia and to map the distribution of Cullcoid"' bi'e\, nth'sis and C. 1.1-, oddiill Western Australia and NT. Between February 1990 and June 1992, collections were Inade throughout Cape York Peninsula, Nortlierii Territory and northern and central Western Australia. Six previously unreported species were collected. These species an'e considered unlikely to be recent jininigrants and seein to pose little threat as potential arboviiT. Is vectors. C. woddi was restricted to coastal 1101tlierii Qld, the northernmost areas of NT and the northern Kiinberley region in WA. 111 NT C. bi'evitai'sis was collected as far soutli as Katlierine. In WA it was collected throughout the Kiinberley and in the Pilbara region ill all area bounded by Nullagine, KanTatha and 300km nortli of Carnalvon. C. bi'evilcii'sis reinains tlie only Guncoide. s species of known 11npoitance as a vector of livestock an'boviruses to extend into Inajor sheep-grazing areas. Generally, CUIicoides distributions in northern Australia between 1990 and 1992 were coinparable but not identical to those defined ill surveys conducted ill tlie 1970's and 1980's. Species distributions were not static and will continue to fluctuate witli variation ill rainfall. . .

Molecular Breeding for Complex Adaptive Traits: How Integrating Crop Ecophysiology and Modelling Can Enhance Efficiency

Progress in crop improvement is limited by the ability to identify favourable combinations of genotypes (G) and management practices (M) in relevant target environments (E) given the resources available to search among the myriad of possible combinations. To underpin yield advance we require prediction of phenotype based on genotype. In plant breeding, traditional phenotypic selection methods have involved measuring phenotypic performance of large segregating populations in multi-environment trials and applying rigorous statistical procedures based on quantitative genetic theory to identify superior individuals. Recent developments in the ability to inexpensively and densely map/sequence genomes have facilitated a shift from the level of the individual (genotype) to the level of the genomic region. Molecular breeding strategies using genome wide prediction and genomic selection approaches have developed rapidly. However, their applicability to complex traits remains constrained by gene-gene and gene-environment interactions, which restrict the predictive power of associations of genomic regions with phenotypic responses. Here it is argued that crop ecophysiology and functional whole plant modelling can provide an effective link between molecular and organism scales and enhance molecular breeding by adding value to genetic prediction approaches. A physiological framework that facilitates dissection and modelling of complex traits can inform phenotyping methods for marker/gene detection and underpin prediction of likely phenotypic consequences of trait and genetic variation in target environments. This approach holds considerable promise for more effectively linking genotype to phenotype for complex adaptive traits. Specific examples focused on drought adaptation are presented to highlight the concepts.

Clays Can Decrease Gaseous Nutrient Losses from Soil-Applied Livestock Manures

Clays could underpin a viable agricultural greenhouse gas (GHG) abatement technology given their affinity for nitrogen and carbon compounds. We provide the first investigation into the efficacy of clays to decrease agricultural nitrogen GHG emissions (i.e., N2O and NH3). Via laboratory experiments using an automated closed-vessel analysis system, we tested the capacity of two clays (vermiculite and bentonite) to decrease N2O and NH3 emissions and organic carbon losses from livestock manures (beef, pig, poultry, and egg layer) incorporated into an agricultural soil. Clay addition levels varied, with a maximum of 1:1 to manure (dry weight). Cumulative gas emissions were modeled using the biological logistic function, with 15 of 16 treatments successfully fitted (P < 0.05) by this model. When assessing all of the manures together, NH3 emissions were lower (×2) at the highest clay addition level compared with no clay addition, but this difference was not significant (P = 0.17). Nitrous oxide emissions were significantly lower (×3; P < 0.05) at the highest clay addition level compared with no clay addition. When assessing manures individually, we observed generally decreasing trends in NH3 and N2O emissions with increasing clay addition, albeit with widely varying statistical significance between manure types. Most of the treatments also showed strong evidence of increased C retention with increasing clay additions, with up to 10 times more carbon retained in treatments containing clay compared with treatments containing no clay. This preliminary assessment of the efficacy of clays to mitigate agricultural GHG emissions indicates strong promise.

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.