XY-4巖心鉆機升降機的設計

XY-4巖心鉆機升降機的設計,xy,巖心,鉆機,升降機,設計 Journal of Stored Products Resear a,1 ion State September larvae of this species frequently feed on the germ of whole kernels and on fine material in the grain (Rilett, 1949). ARTICLE IN PRESS $ This paper reports the results of research only. Mention of a proprietary product or trade name does not constitute a recommendation or endorsement by the US Department of Agriculture. They leave the germ before becoming adults and do not cause IDK. Nevertheless, grain infested with this species is likely to receive a lower price than uninfested grain. 0022-474X/$-see front matter Published by Elsevier Ltd. doi:10.1016/j.jspr.2006.09.004 C3 Corresponding author. Tel.: +17857762707; fax: +17855375584. E-mail address: paul.flinn@gmprc.ksu.edu (P.W. Flinn). 1 Retired. Please cite this Journal of Stored 1. Introduction Most cereal grain produced in the USA is stored in commercial facilities known locally as grain elevators. Major insect pests of stored wheat in the USA include Rhyzopertha dominica (F.), Sitophilus oryzae (L.), Crypto- lestes ferrugineus (Stephens), Tribolium castaneum (Herbst), and Oryzaephilus surinamensis (L.). The first two species cause the most grain damage because the immature stages develop inside the grain kernels. These internal feeding insects are a major cause of insect contamination in wheat flour because the immature stages and pre-emergent adults cannot be completely removed from the wheat before it is milled. Grain managers and regulators use the number of insect-damaged kernels (IDK) in wheat as an indirect measure of the density of internally-infested kernels. If more than 32 IDK are found per 100g of wheat, the grain is classified as ‘‘sample grade’’ and unfit for human consumption (Hagstrum and Subramanyam, 2006). At most domestic flour mills, the wheat purchasing specifica- tions include a maximum IDK count of either 3 or 5/100g. Cryptolestes ferrugineus is a very common insect pest that often reaches high densities near the grain surface. Young Abstract A decision support system, Stored Grain Advisor Pro (SGA Pro) was developed to provide insect pest management information for wheat stored at commercial elevators. The program uses a model to predict future risk based on current insect density, grain temperature and moisture. A rule-based system was used to provide advice and recommendations to grain managers. The software was tested in a research program conducted at commercial grain elevators in Kansas and Oklahoma, USA. A vacuum-probe sampler was used to take ten 3-kg grain samples in the top 12m of each bin that contained wheat. After the insect species and numbers were determined for each sample, the data were entered into SGA Pro. A risk analysis and treatment recommendation report for all bins was presented to the grain managers every 6 weeks. SGA Pro correctly predicted for 71–80% of bins whether the grain was safe or at high risk of dense infestation and grain damage. SGA Pro failed to predict ‘‘unsafe’’ insect densities in only 2 out of 399 Kansas bins (0.5%) and in none of 114 bins in Oklahoma. Grain managers who followed SGA Pro’s recommendations tended to fumigate only the bins with high insect densities instead of fumigating all bins at their facility. This resulted in more efficient insect pest management because fumigating bins only when insect densities exceeded economic thresholds and treating only the bins that required fumigation minimized the risk of economic losses from insects, reduced the cost of pest management, and reduced the use of grain fumigant. Published by Elsevier Ltd. Keywords: Rhyzopertha dominica; Cryptolestes ferrugineus; Decision support system; Model; Integrated pest management; Stored grain; Area-wide Stored Grain Advisor Pro: Decision management in commercial P.W. Flinn a,C3 , D.W. Hagstrum a USDA-ARS Grain Marketing and Product b Department of Grain Science and Industry, c Department of Entomology, Oklahoma Accepted 20 article as: Flinn, P., et al., Stored Grain Advisor Pro: Decision Products Research (2007), doi:10.1016/j.jspr.2006.09.004 ch ] (]]]]) ]]]–]]] support system for insect grain elevators $ , C.R. Reed b , T.W. Phillips c Research Center, Manhattan, KS, USA Kansas State University, KS, USA University, Stillwater, OK, USA 2006 support system for insect management in commercial grain elevators. ARTICLE IN PRESS Products Typically, control of stored-grain insects in grain elevators in the USA includes monitoring of grain temperature and calendar-based fumigations using phos- phine fumigant (Hagstrum et al., 1999). This approach often fails to distinguish between bins with high and low insect densities and does not optimize the timing of the fumigation treatment. Therefore, grain may be unnecessa- rily fumigated, or the fumigation may not be timed to prevent high insect populations and grain damage from occurring. Although careful monitoring of grain tempera- ture often alerts the manager to potential mold and insect problems (Reed, 2006), large populations of insects or severe mold problems can develop before a temperature increase is noted. In contrast to traditional insect control practices currently used for most stored grain, the integrated pest management (IPM) approach uses sampling to determine if insects have exceeded an economic threshold (Hagstrum and Flinn, 1992). Adapting IPM principles to insect control in a grain elevator is complicated by the structure and operation of the facility. A large elevator may have over 100 bins, and the bins may contain different types of grain, stored for different durations. The grain temperature and moisture often vary greatly between bins, which affects the rate at which insects and molds grow and damage the grain. To facilitate the development and implementation of IPM practices in stored grain in the USA, the USDA’s Agricultural Research Service recently funded a 5-year demonstration project for area-wide IPM for stored wheat in Kansas and Oklahoma (Flinn et al., 2003). This project was undertaken by a collaboration of researchers at the Agricultural Research Service (Manhattan, Kansas), Kan- sas State University (Manhattan, Kansas), and Oklahoma State University (Stillwater, Oklahoma). We used two elevator networks, one in each state, for a total of 28 grain elevators. One of the project goals was the development of a decision support system for insect pest management for grain stored in commercial elevators. A validated insect population growth model was previously developed for R. dominica in concrete elevator storage (Flinn et al., 2004). This model was used in a decision support system to make management recommen- dations based on current insect density, grain temperature and grain moisture. A decision support system (Stored Grain Advisor) was developed previously for farm-stored grain in the USA (Flinn and Hagstrum, 1990b). However, that software was not suitable for large commercial elevators because the grain sampling methods and recom- mendations were specific for farm-stored grain. Decision support systems for stored grain have been developed in several countries. In Canada, CanStore, was developed to assist farmers in stored grain management (www.res2.agr.ca/winnipeg/storage/pages/cnstr_e.htm). In Australia, Pestman ranks insect pest management recom- P.W. Flinn et al. / Journal of Stored2 mendations by their cost and provides a graphical site plan that allow a manager to quickly find information about Please cite this article as: Flinn, P., et al., Stored Grain Advisor Pro: Decision Journal of Stored Products Research (2007), doi:10.1016/j.jspr.2006.09.004 any bin (Longstaff, 1997). In the UK, Integrated Grain Storage Manager (Knight et al., 1999) is a new version of Grain Pest Advisor (Wilkin and Mumford, 1994) that was developed with input from farmers and storekeepers to better suit their needs. Grain Management Expert System (Zonglin et al., 1999), was developed from Pestman for use in China. QualiGrain is an expert system for maintaining the quality of stored malting barley (Ndiaye et al., 2003; Knight and Wilkin, 2004). None of the previously mentioned systems fit the requirements of the USA commercial grain storage system. We needed a management program that was based on intensive grain sampling for insect pests in each elevator bin (at least ten 3-kg grain samples per bin to a depth of 12m). In addition, the system needed to be able to predict insect population growth for up to 3 months, based on current insect density in the bin, grain temperature, and grain moisture. In this paper, we describe the validation of a decision support system that was developed as part of an area-wide IPM demonstration project. The decision sup- port system uses current and predicted insect density estimates to provide grain managers with an overall risk analysis for the grain at their facility and recommended treatment options. 2. Materials and methods 2.1. Grain sampling An area-wide IPM program for grain elevators was started in 1998. Investigators collected data from two elevator networks in south-central Kansas and central Oklahoma. Each network consisted of at least 10 rural elevators and at least one terminal elevator. The rural elevators typically receive grain from farmers and store it for a shorter period of time compared to the terminal elevators, where most grain is received from rural elevators. Storage bins at these elevators were either upright concrete bins, typically 6–9m in diameter and 30–35m tall, or metal bins that are shorter and wider. Maize and other grains were stored in the project elevators, but only the wheat was sampled during this project. Various sampling methods to estimate insect density in upright concrete grain bins were tested: probe traps placed at the grain surface, samples taken as the grain was moved on transport belts, and samples taken from grain dis- charged from the bins onto a stationary transport belt. Samples taken with a vacuum probe as the grain was at rest in the storage bins provided the best estimate of insect density. Data collected with the vacuum probe were highly correlated with grain samples taken as the bin was unloaded (r 2 ? 0.79, N ? 16, P ? 0.001). In addition, unlike the other sampling methods, the power probe allowed the grain to be sampled at any time, and it provided a vertical profile of the insect distribution for each Research ] (]]]]) ]]]–]]] bin. We used a Port-A-Probe (Grain Value Systems, Shawnee Mission, Kansas), which consists of a vacuum support system for insect management in commercial grain elevators. pump powered by a 5.3KW gasoline engine connected by flexible plastic tubing to sections of rigid aluminum tubes 1.2m long by 3.5cm wide. The probe was inserted vertically into the grain and a 3.9l (about 3kg) sample of wheat was taken during every 1.2m transect of grain to a depth of 12m. In the concrete upright bins, the grain was sampled through the entry port. In metal bins, the probe was inserted at 3–5 locations across the surface. Grain samples were extracted from the grain mass by suction and collected in a cyclone funnel. Samples were processed twice over an inclined sieve (89C243cm, 1.6mm aperture) (Hagstrum, 1989) to sepa- rate insects from the wheat. Material that passed through the screen was collected on a pan below the screen, which then slid into a funnel at the bottom of the pan. A re- sealable plastic bag was attached to the funnel to collect the material that was separated from the grain sample. A hopper above the screen held the grain sample and a funnel at the base of the screen directed material passing over the screen into a plastic bucket. The sieve was inclined 241 from horizontal and the opening of the hopper was adjusted such that the sample passed over the screen in about 25s. Each sample was passed over the sieve two times. Validation data for SGA Pro were selected from bins 2.2. Decision support software The Stored Grain Advisor Pro (SGA Pro) software was initially developed using Microsoft Access. The program was then modified and re-written using Visual Basic 6.0. We designed a graphical user interface that provides a bin diagram for each elevator location (Fig. 1). Data were entered using three data-entry forms: insects, grain quality, and grain temperature. Data entered in the insect form were: sample type (bottom, moving sample, probe trap, or vacuum sample) and the number of insects found in each sample for five primary stored-grain insects (Cryptolestes spp., R. domin- ica, O. surinamensis, Sitophilus spp., and Tribolium spp.) (Fig. 2). Data entered for grain quality were: grade, % dockage, test weight, moisture, foreign material, % shrunken or broken kernels, insect damaged kernels, % protein (Fig. 3). Grain temperature data were also entered into the database for each bin (data entry is similar to the grain quality form and is not shown here). Most elevator bins were equipped with one or more cables containing up to 20 thermocouple-type sensors per cable. In bins not equipped with temperature sensors, investiga- tors inserted temporary probes to collect information on ARTICLE IN PRESS P.W. Flinn et al. / Journal of Stored Products Research ] (]]]]) ]]]–]]] 3 that were sampled at least twice, starting in autumn, in which the wheat was not moved or fumigated. In a typical bin (6–9m wide and 30–35m tall), the sampling rate for vacuum probe samples was 0.07–0.13kg/t of grain sampled. In most cases, only the grain in the top 12m of the bin was sampled. Fig. 1. Elevator bin diagram from Stored Grain Advisor Pro; on the computer shown in red, blue and green, respectively. In this figure, bin numbers that are light are at moderate risk. Bin 620 is currently selected (using the mouse), and the Please cite this article as: Flinn, P., et al., Stored Grain Advisor Pro: Decision Journal of Stored Products Research (2007), doi:10.1016/j.jspr.2006.09.004 grain temperature. The SGA Pro system will recommend either fumigation, aeration, or waiting until the next sampling period based on current insect density in the bin, grain tempera- ture, aeration capability, time of year, and predicted insect density in 1, 2, and 3 months. For example, screen, bins of grain at high, moderate, and low risk for insect problems are grey are at low risk, bins 615, 620 and 621 are at high risk, and the rest information for this bin is shown in the bottom half of the screen. support system for insect management in commercial grain elevators. ARTICLE IN PRESS ProductsP.W. Flinn et al. / Journal of Stored4 for bin 620 (Fig. 4), the program indicated that the current insect density was 2.5kg -1 and predicted a density of 14.6insects/kg in 1 month. Twenty-eight percent of the Fig. 2. Insect data entry form for Stored Grain Advisor Pro. The number of common names are used for the insect species (flat ? Cryptolestes spp., lesser ? beetle ? Tribolium spp.). Fig. 3. Grain quality data entry form for Stored Grain Advisor Pro (grade ? grain moisture, DK(%) ? % damaged kernels, FM(%) ? % foreign material, damaged kernels per 100g, protein(%) ? % protein). Please cite this article as: Flinn, P., et al., Stored Grain Advisor Pro: Decision Journal of Stored Products Research (2007), doi:10.1016/j.jspr.2006.09.004 Research ] (]]]]) ]]]–]]] insects from the samples were species that caused IDK, so SGA Pro recommended fumigation followed by aeration to cool the grain. insects for each 3-kg sample were entered into the form. For simplicity, R. dominica, sawtooth ? O. surinamensis, weevil ? Sitophilus spp., flour grain grade, DKG(%) ? % dockage, TW ? test weight, moist(%) ? % SHBK(%) ? % shrunken or broken kernels, IDK ? number of insect support system for insect management in commercial grain elevators. ARTICLE IN PRESS alerts and Products An equation was developed to predict insect population growth based on current insect density, grain temperature and moisture by running simulations for a model of R. dominica (Flinn and Hagstrum, 1990a), over temperatures from 21.5 to 33.51C and moistures from 9.5 to 13.5%. Tablecurve 3D version 3 (SPSS, 1997) was used to fit an equation to the model-generated data, where Z is the rate of increase over 30 days, X is temperature, and Y is moisture: Z ?e9:2004C01:6898X t0:0787 2 C00:0011X 2 Fig. 4. Stored Grain Advisor recommendation report. The report shows Mon ? predicted insect density in 1 month, 2 Mon ? predicted insect density Max SS internal ? highest number of internal insects in any single sample, P.W. Flinn et al. / Journal of Stored t0:1841YT=e1C00:0197X C00:0161YT. e1:0T This equation fitted the data well (R 2 ? 0.98, N ? 25). Because we needed to predict only 1–3 months ahead, this equation was adequate for quickly estimating future populations for many bins present in the database (often more than 100). Although C. ferrugineus was often the most numerous species during the first month of storage, we based Eq. (1.0) on R. dominica because it is the more damaging species, it was more common than C. ferrugineus later in the season, and the predicted rates of increase for both species were fairly similar (Hagstrum and Flinn, 1990). We did not use a model for S. oryzae because this species was found in about 1% of the wheat samples, whereas, R. dominica was found in approximately 60% of the samples. SGA Pro used a rule-based algorithm to determine whether bins were safe, moderate, or at high risk of having insect densities that exceed certain thresholds, based on the current and predicted insect density, grain temperature, and grain moisture. Insect economic thresholds can be adjusted by the user (Fig. 5). In addition, alerts can also be set for: high grain moisture, high thermocouple readings, Please cite this article as: Flinn, P., et al., Stored Grain Advisor Pro: Decision Journal of Stored Products Research (2007), doi:10.1016/j.jspr.2006.09.004 and high numbers of internally feeding insects in an individual sample. SGA Pro was tested during the final 2 years of the area- wide IPM study. Bins at each elevator were sampled at approximately 6-week intervals, data were entered into SGA Pro, and the report recommendations were shown to the elevator managers. SGA Pro was validated by comparing predicted insect densities and control recom- mendations with actual insect densities in the same bins 6 weeks later. Validation data came from bins in which the grain had not been turned or fumigated for at least two for five elevator bins (Current ? average insects per kg grain, 1 in 2 months, IDK insect ? % of the insects in the sample that cause IDK, management option ? recommended actions for the elevator manager). Research ] (]]]]) ]]]–]]] 5 sampling periods. 3. Results In the Kansas data set from 2002, SGA Pro correctly predicted that bins were ‘‘safe’’ or at ‘‘high risk’’ in 285 out of 399 cases (Table 1). Forty-seven of the 399 bins required fumigation. SGA Pro failed to predict ‘‘unsafe’’ insect densities in only two bins (0.5%), and the insects in these isolated instances were mostly near the surface, suggesting recent immigration. The simple growth model used by SGA Pro tended to overestimate insect densities in bins that were at medium risk (112 out of 399 bins). All of the bins that the software predicted to be at high risk contained insect densities greater than the threshold at the next sampling period. In Oklahoma, SGA Pro correctly predicted bins that were ‘‘safe’’ or at ‘‘high risk’’ in 107 out of 133 total bins. Forty-five of the 133 bins needed to be fumigated. All of the bins that the program determined as being ‘‘safe’’ turned out to have insect densities below the threshold of 2insects/kg 6 weeks later. As in Kansas, SGA Pro tended to overestimate insect densities in bins that were at medium risk (26 out of 131 bins). support system for insect management in commercial grain elevators. ARTICLE IN PRESS ProductsP.W. Flinn et al. / Journal of Stored6 4. Discussion Compared to oth