Simulation of phosphine flow in vertical grain storage : a preliminary numerical study

To fumigate grain stored in a silo, phosphine gas is distributed by a combination of diffusion and fan-forced advection. This initial study of the problem mainly focuses on the advection, numerically modelled as fluid flow in a porous medium. We find satisfactory agreement between the flow predictions of two Computational Fluid Dynamics packages, Comsol and Fluent. The flow predictions demonstrate that the highest velocity (>0.1 m/s) occurs less than 0.2m from the inlet and reduces drastically over one metre of silo height, with the flow elsewhere less than 0.002 m/s or 1% of the velocity injection. The flow predictions are examined to identify silo regions where phosphine dosage levels are likely to be too low for effective grain fumigation.

Response of Tribolium castaneum and Rhyzopertha dominica to various resources, near and far from grain storage

Tribolium castaneum (Herbst) and Rhyzopertha dominica (F.) are common cosmopolitan pests of stored grain and grain products. We evaluated the relative attraction of T.castaneum and R.dominica to wheat, sorghum and cotton seeds in the field, near grain storage facilities and well away from storages in southern and central Queensland using multiple trapping techniques. The results show that T.castaneum is more strongly attracted to linted cotton seed relative to wheat, whereas R.dominica did not respond to cotton seed at all and was attracted only to wheat. Significantly more adults of T.castaneum (10-15 times) were attracted to traps placed on the ground, near grain storage, than to equivalent traps that were suspended (1.5m above the ground) nearby. These results suggest that Tribolium beetles detect and respond to resources towards the end of their dispersal flight, after which they localize resources while walking. By contrast R.dominica was captured only in suspended traps, which suggests they fly directly onto resources as they localize them. The ability of both species to colonize and reproduce in isolated resource patches within the relatively short time of 1month is illustrated by the returns from the traps deployed in the field (at least 1km from the nearest stored grain) even though they caught only a few beetles. The results presented here provide novel insights about the resource location behaviours of both T.castaneum and R.dominica. In particular, the relationship of T.castaneum with non-cereal resources that are not conventionally associated with this species suggests an emphasis on these other resources in investigating the resource location behaviour of these beetles. This new perspective on the ecology of T. castaneum highlights the potential role of non-cereal resources (such as the lint on cotton seed) in the spread of grain pest infestations.

RP92-217 No. 19 Cost of Grain Storage

Grain marketing decisions are among the toughest the farm owner/operator or manager must make. Grain producers store grain to speculate on receiving higher prices, earn a return above storage costs, or to take advantage of government programs. Prices must increase enough to cover the additional costs, or forward contract prices must exceed current prices by more than the cost of storage in order to justify forward pricing. The gain in prices received can come from both changes in price level and changes in basis. This research publication discusses all the areas of the cost of grain storage.

Grain Storage Costs and Rental Rates

Trends in Grain Storage - Commercial grain storage eliminates the need to monitor grain conditions and, hence, offers the peace of mind that unsold grain will remain in condition. There may be a cost trade-off between this reduced storage risk and the cost of on-farm storage.

Programming and training for small farm grain storage /

September 1978.

FOA grain storage elevators in Pakistan : hearings ... Eighty-fourth Congress, first session.

Pt.1. March 31, April 6, 1955. 120 p.--pt.2. April 14, 15, May 3, 1955. pp. 121-282.

Trafficking of storage proteins in developing grain of wheat

The processing properties of the wheat flour are largely determined by the structures and interactions of the grain storage proteins (also called gluten proteins) which form a continuous visco-elastic network in dough. Wheat gluten proteins are classically divided into two groups, the monomeric gliadins and the polymeric glutenins, with the latter being further classified into low molecular weight (LMW) and high molecular weight (HMW) subunits. The synthesis, folding and deposition of the gluten proteins take place within the endomembrane system of the plant cell. However, determination of the precise routes of trafficking and deposition of individual gluten proteins in developing wheat grain has been limited in the past by the difficulty of developing monospecific antibodies. To overcome this limitation, a single gluten protein (a LMW subunit) was expressed in transgenic wheat with a C-terminal epitope tag, allowing the protein to be located in the cells of the developing grain using highly specific antibodies. This approach was also combined with the use of wider specificity antibodies to compare the trafficking and deposition of different gluten protein groups within the same endosperm cells. These studies are in agreement with previous suggestions that two trafficking pathways occur in wheat, with the proteins either being transported via the Golgi apparatus into the vacuole or accumulating directly within the lumen of the ER. They also suggest that the same individual protein could be trafficked by either pathway, possibly depending on the stage of development, and that segregation of gluten proteins both between and within protein bodies may occur.

Mathematical modelling of fumigant transport in stored grain

Computational fluid dynamics, analytical solutions, and mathematical modelling approaches are used to gain insights into the distribution of fumigant gas within farm-scale, grain storage silos. Both fan-forced and tablet fumigation are considered in this work, which develops new models for use by researchers, primary producers and silo manufacturers to assist in the eradication grain storage pests.

Networks of stored grain diseases and pests: Strategies for sampling and mitigation

Pathogens and pests of stored grains move through complex dynamic networks linking fields, farms, and bulk storage facilities. Human transport and other forms of dispersal link the components of this network. A network model for pathogen and pest movement through stored grain systems is a first step toward new sampling and mitigation strategies that utilize information about the network structure. An understanding of network structure can be applied to identifying the key network components for pathogen or pest movement through the system. For example, it may be useful to identify a network node, such as a local grain storage facility, through which grain from a large number of fields will be accumulated and move through the network. This node may be particularly important for sampling and mitigation. In some cases more detailed information about network structure can identify key nodes that link two large sections of the network, such that management at the key nodes will greatly reduce the risk of spread between the two sections. In addition to the spread of particular species of pathogens and pests, we also evaluate the spread of problematic subpopulations, such as subpopulations with pesticide resistance. We present an analysis of stored grain pathogen and pest networks for Australia and the United States.

Flow field and traverse times for fan forced injection of fumigant via circular or annular inlet into stored grain

Fan forced injection of phosphine gas fumigant into stored grain is a common method to treat infestation by insects. For low injection velocities the transport of fumigant can be modelled as Darcy flow in a porous medium where the gas pressure satisfies Laplace's equation. Using this approach, a closed form series solution is derived for the pressure, velocity and streamlines in a cylindrically stored grain bed with either a circular or annular inlet, from which traverse times are numerically computed. A leading order closed form expression for the traverse time is also obtained and found to be reasonable for inlet configurations close to the central axis of the grain storage. Results are interpreted for the case of a representative 6m high farm wheat store, where the time to advect the phosphine to almost the entire grain bed is found to be approximately one hour.

Mathematical modelling and numerical simulation of phosphine flow during grain fumigation in leaky cylindrical silos

The phosphine distribution in a cylindrical silo containing grain is predicted. A three-dimensional mathematical model, which accounts for multicomponent gas phase transport and the sorption of phosphine into the grain kernel is developed. In addition, a simple model is presented to describe the death of insects within the grain as a function of their exposure to phosphine gas. The proposed model is solved using the commercially available computational fluid dynamics (CFD) software, FLUENT, together with our own C code to customize the solver in order to incorporate the models for sorption and insect extinction. Two types of fumigation delivery are studied, namely, fan- forced from the base of the silo and tablet from the top of the silo. An analysis of the predicted phosphine distribution shows that during fan forced fumigation, the position of the leaky area is very important to the development of the gas flow field and the phosphine distribution in the silo. If the leak is in the lower section of the silo, insects that exist near the top of the silo may not be eradicated. However, the position of a leak does not affect phosphine distribution during tablet fumigation. For such fumigation in a typical silo configuration, phosphine concentrations remain low near the base of the silo. Furthermore, we find that half-life pressure test readings are not an indicator of phosphine distribution during tablet fumigation.