Security breaches and incident handling in organization Research Proposal

Security breaches and incident handling in organization - Research Proposal Example nctions Interest of management to gain control for the security of business functions along with cost These three factors need to be handled to provide better security. In order to tackle all three factors, Christian Fruhwirth, recommended an event based intrusion detection system in 2008. The system will support these three factors by (, SWBC - Thesis: Improving security incident management in multination IT service providers - Software Business Community): Advanced tools incorporated with IDS to detect intrusions and eliminate attacks Standardized frameworks to handle legal compliance Efficient security management application tools to handle the management. Moreover, an article was published related to compromise recovery and incident handling. The article highlighted mishaps from concerned security administrators for installing default programs from a compact disc. These stored programs on a compact disc facilitates hackers to breach security by storing porn contents, configuring an illegal server, initiating attacks on other information assets and breaching server on the network. In order to eliminate all these threats and vulnerabilities, reviewing and learning the functionality of threats is essential. This will certainly reduce the probability of security incident in organizations (Compromise Recovery and Incident Handling. 2003). One more research was conducted related to a Proposed Integrated Framework for Coordinating Computer Security Incident Response Team. Conventionally, computer security incident response teams (CSIRT) are responsive for viruses, hacking and unauthorized access of employees. The CSIRT is defined as â€Å"Computer security incident response team (CSIRT) is a term used by the CERT Coordination Center (CERT/ CC) to describe a service... This will certainly reduce the probability of security incident in organizations (Compromise Recovery and Incident Handling. 2003). One more research was conducted related to a Proposed Integrated Framework for Coordinating Computer Security Incident Response Team. Conventionally, computer security incident response teams (CSIRT) are responsive for viruses, hacking and unauthorized access of employees. The CSIRT is defined as â€Å"Computer security incident response team (CSIRT) is a term used by the CERT Coordination Center (CERT/ CC) to describe a service organization that responds to computer security incidents† (Computer Security Incident Response Team. 2007). The research transformed these teams in to efficient tools that will maintain efficiency of business operations, compliance along with new regulations and homeland security. Those organization possessing incident response teams follows a systematic approach and steps to recover the system efficiently from any securi ty breach or incident. Moreover, the existence of teams, eliminates loss or information theft and service disruption. Furthermore, the information gained by detecting and resolving an incident, facilitates support teams to be more efficient for handling future incidents (, Central Washington University - Networks: Incident Handling).Likewise, these teams are called security incident response teams (SIRT). They are triggered when a security breach shows its existence within the network of an organization. However, these teams conduct investigation of suspect workstations and servers.

Preservation of Biodiversity Essay Example | Topics and Well Written Essays - 250 words

Preservation of Biodiversity - Essay Example In pre-Colombian times, this area supported an estimated 700,000 persons (a multiple of todays population) in a sustainable form† (Lutz, n.d.). Interest of human beings needs to be served first. All countries should not be held to the same standards in the preservation of endangered habitats and species because different countries have different levels of economic strength, and socioeconomic and political issues. It is not practicable to establish same standards of preservation in all countries because different countries give different priority to the conservation of biodiversity, and the stance of individual nations is governed by their cultural and religious beliefs, that often vary across nations. Traditional practices such as whaling and killing wildlife for ivory, tiger bones, and rhinoceros horns, etc. are not justifiable because there are better alternatives to these which are not only environment friendlier but also good for the well-being of animals. Conventionally, b ones retrieved from animals have been of little to no use for humans. Mostly, these bones have been used for ornamental purposes which can be lived

The GM club Essay Example for Free

The GM club Essay The 21st century has become a world of wonders, a world of scientific and technological miracles. Moreover, a world where human kind strives to solve all of its ills without knowing enough about repercussions. If this dream is to be realized, we as an inter-dependant society, have a moral and ethical duty to make fundamental decisions as to the limits of science and technology in our every day life. Technology is emerging as the ruling power in western societies in the 21st century, and therefore, human kind is finding it more and more difficult to survive without constant aid from new hi-tech advances. Computers and the internet has become mens best friend. Children are growing up with Nintendo and Xbox, and consequently without the wondrous knowledge of playing tag, climbing a tree, playing in the dirt or with little insects. They have no familiarity with a world without television and videogames, a natural world where everything is organic and healthy. As the futurist Alvin Toffler points out in an article in the New Scientist, welcome to the latest installment of that (future) shock: the GM revolution. Gene therapy. Spare-part tissues grown from engineered fetal cells. Organ-donor pigs and their viruses. All these are part of it, but they are the remote part that exists only in the labs and the imaginations of scientists. GM food is different, its already left the labs. 1 In this paper, we will examine and try to clarify different philosophies that are competing to control world food production. In particular, we will mainly focus on the use of Genetically Modified Organisms (GM or GMOs) and Organic Farming. Each philosophy has its adherents and its detractors are locked in a boisterous and intransigent battle. This has led to a clouding of issues, making it very difficult for people to develop an informed decision. We believe that the issue at stake is crucial to humanitys existence, since it transcends national and political boundaries. All humans share this planet and ingest its harvest of food, thus, an error in policy can lead to universal catastrophe. As Toffler further points out, suddenly, plant science is no longer a quiet backwater for genial professors and their cuttings. It is the stuff of big business, patent rivalries and closely guarded technical tricks. If you believe biotechs gainsayers, this brave new plant science is also ushering in a dark age in which all genes will bear a no trespassing sign, and the companies that own them will move them from species to species like Lego bricks, to the detriment of whats left of the natural world and our respect for it. Many organisms researchers are manipulating are more complex than bacteria and have greater emotional resonance for humans, either because they are mammals or part of our food supply. 2 On the other hand, as Nathan Batalion points out a farmer may use toxic chemicals for many decades, and then let the land lie fallow for a year or two to convert back to organic farming. The chemicals tend to break down into natural substances within months or years. A few may persist for decades. But genetic pollution (from GMOs) can alter the life in the soil for ever! 3 Background General Background For the past 12,000 years, human kind has interfered with nature in different degrees to guarantee a steady stream of food. From the cultivation of wheat to the domestication of wild animals, humans have manipulated nature for their advantage and survival. Consequently, this has led to a more continuous and reliable source of food that allowed humanity to establish civilizations, pursue knowledge and create the world we know today. Unfortunately, for all the advancements we have accomplished a large portion of the worlds population lives in hunger. As a civilized society, it is our duty and responsibility to try to eradicate hunger and try to raise the standards of less fortunate nations. Farmers, eager to increase their crop yields and number of livestock, have adopted different new methods and technologies with hopes of success. Overall, their efforts have been outstripped by the increase in the worlds population and the failure of some of the technologies to live up to their promises. Over the years, the use of chemicals, pesticides and herbicides has grown to an unprecedented degree. This has led to problems not envisioned by society, such as, soil and water pollution due to overuse, resistance to herbicides and pesticides by organisms, mutagenicity and even resistant forms of bacteria. Today, there have emerged two competing solutions for the farmers woes, Genetically Modified Organisms and Organic farming. Both solutions have their supporters and their detractors. Simply put, Organic Farming is farming without the use of chemicals and farming with GMOs is using genetically modified crops to increase yields and lower the use of chemicals, herbicides and pesticides. Genetically Modified Organism Background The promise of Genetically Modified Organisms (GMOs) or Genetically Modified Foods (GMFs) is increased yields from agriculture, more powerful control of pests and weeds, reduced use of agrochemicals and enhanced nutritional value. The agro-biotechnology industry has announced a revolution: it promises to increase world food production and reduce the requirements for water and other natural resources. Reduction of atmospheric emissions and chemical contamination of soils may be achieved. Another accomplishment this revolution promises is an abundant nutritionally improved diet for malnourished populations. Central to this revolution is genetically modified food (GMF)4. That is the promise of GMOs. Essentially, the process of genetically modifying a plant starts with a piece of DNA that has been isolated from an animal, another plant or a bacterium. This isolated piece of DNA can code for a protein, which has a specific function and could impart the ability of a plant to resist insects, grow at an accelerated rate, require less water and resist disease and chemicals. The isolated DNA, or gene, is then placed into a plant cell. As a result, the plant growing from this modified cell, carries the inserted gene and is enhanced to express new traits; however, the plants exhibit traits that are not possible under natural conditions. In the U. S, GMOs have found their way into a large portion of processed foods. As of January 2002, 5. 5 million farmers worldwide mainly in the U. S, Argentina, Canada and China now grow GM crops covering more than 50 million hectares. And with the vast countries like Indonesia about to join the GM club, next years leap could be bigger still. 5 Meaning, two thirds of all U. S processed foods have GM ingredients and 70-80 million acres of land is growing GM crops. This represents approximately 25% of agricultural lands in the U. S. Furthermore, products such as soybeans, corn, tomatoes and rapeseed (canola), have been genetically modified and are currently in the processed food chain. The problem is not that these products are on the market but, there are no labeling requirements, and today Genetically Modified Foods fill our supermarket shelves, our kitchens and restaurants. Sadly, few consumers are aware this has been going on. 6 The use of GMOs and GMFs is by no means universally accepted. While the U. S has adopted a very aggressive GMO and GMF program, with voluntary labeling, other countries have adopted a more conservative approach. Virtually all of the European nations, many Latin American countries as well as countries in the Near East and Asia have partially banned, restricted or imposed a moratorium on the use of GMOs or GMFs. Many countries require labels indicating that the food has been Genetically Engineered and impose severe legal penalties for non-compliance. Organic Farming Background.

Study on the Variable Star XX Andromeda

Study on the Variable Star XX Andromeda Abstract We present the results of a month long V-Band study on the RRab type variable star XX Andromeda. 4526 data points are used to plot a light curve, with 3 maxima observed and added to data from the GEOS database to create an O-C diagram. Three methods of estimating the pulsation period are used, including two Phase Dispersion Minimisation methods and an O-C method, resulting in a best estimate of the period of days. This value is in excellent agreement with the literature values for the period of XX And, from both the Hipparcos catalogue and the GCVS. The distance to XX And is estimated to be pc using a main sequence fitting method to estimate the absolute magnitude, and the mean radius is estimated to be . A flatfielding improvement to the â€Å"photom.py† pipeline is suggested to combat dust artefacts on the CCD. Physical reasons are discussed for the distinctive features present in the light curve, namely the â€Å"Hump† and the â€Å"Bump†. I. Introduction In 1893 Solon I Bailey started a program of globular cluster study[i]. He noticed that some clusters (e.g ω Centuri) were extremely rich in variable stars with similar properties they had periods of less than a day, and light curve amplitudes of around 1 mag. The mean value of apparent magnitude of these stars in a particular cluster was also approximately the same across the sky. Bailey named these â€Å"Cluster Type Variables†. However an increasing number of stars with these properties were being found outside of clusters indeed the brightest star of this type ever found was a field variable, RR Lyrae (after which the class is now named). Discoveries then began to come thick and fast, and it is currently estimated that over 85000 exist in the Milky Way alonei. RR Lyrae variables have also been observed in the Andromeda Galaxy, the Large Magellanic Cloud and other Local Group dwarf galaxies[ii]. Measuring the properties of these variables has become increasingly important to astronomers, as it was realised that they could be used to gauge astronomical distances through a period-luminosity (P-L) relation, in a similar way to Cepheids. Various catalogues have studied their properties, for example the General Catalogue of Variable Stars[iii] or the more recent Hipparchos Catalogue[iv]. Until recently however, no distinct P-L relation had been found, and instead astronomers had to use a relation between metallicity and visual magnitude or the Baade-Wesselink method, the drawbacks of which are discussed later. Currently there is still no P-L relation for V-band observations, although there are now relations for most of the infrared spectral bands[v]. RR Lyrae variables are also of importance for the study of the population of both the Galactic Bulge (via Baades Window for example) and the Galactic Halo. Their advanced age and low metallicity combined with distinctive pulsation properties provides an excellent â€Å"tracer† for the development of the Milky Way in its early stages, as well as current kinematic analysis[vi]. They have also been used as a means of quantifying the interstellar reddening caused by dust in the galactic plane, thanks to the fact that the colour excess is a function of minimum (V-I) colour only[vii]. Using this reddening data with other distance indicators (for example red clump stars in the bulge[viii]), a meaningful approximation of the distance to the centre of the bulge can be obtained. Clearly then the study of RR Lyrae variables is useful for the understanding of the evolution of both the Milky Way and the rest of the Local Group. The star to be observed in this study is XX Andromeda (abbr. XX And), an F2 spectral class RRab type variable, located in the constellation of Andromeda at RA: 1h 17m 27.4145s, Dec: +38 °57 02.026† (see 1). Its moderately high position in the sky at Durham means that it is circumpolar, whilst not exceeding the +65 ° limit for the telescope fork mount, resulting in minimal atmospheric interference and the maximum possible observing time. The GCVS lists a period of. It is also known to exhibit the Blazhko effect, a long-period modulation of the amplitude of an RR Lyrae star (the cause of which is currently under investigation), with a period ofiii, and has an [Fe/H] value of -1.94. II. Theory Observational Theory CCD Theory Perhaps the most important advance in astronomy in the last 20 years has been the widespread use of Charge-Coupled Devices (CCDs) to replace photographic plates. Invented in 1969 at Bell Labs by Boyle and Smith, CCDs are a thin piece of semiconductor material (e.g. silicon) upon which lies an grid-like array of metal-oxide semiconductor (MOS) capacitors[x]. During an exposure, if a photon impacts on the silicon an electron/hole pair can be produced, as an electron is pushed up into a higher energy state. The MOS capacitors act as deep potential wells (pixels), which hold the electrons until the exposure is finished. The charge is then read-out to an amplifier at one edge, in a specific order so that that the position of the original pixel can be identified, and related to the magnitude of the detected charge. The charge is converted from a raw number of electrons into ADUs (analogue to digital units), the conversion factor of which is the gain of the CCD[xi]. They are preferred to photographic plates in modern astronomical photometry for several reasons: * High quantum efficiency (QE) for each incident photon there is upwards of 90% certainty[1] that an pair will be produced. On the other hand, with photographic plates one can achieve (at best) an efficiency of 3%[xii], so using CCDs will increase the likelihood of detection of distant objects. * Large dynamic range, allowing them to detect objects with a range of magnitudes in the sky in the same exposure. * Strong linearity up to the saturation point, so that for longer exposure times the number of electrons produced is proportional to the integration time, whereas photographic plates will experience a drop in their efficiency. Their linearity will also mean that the magnitude of charge in each pixel is linearly proportional to the luminosity of the object. CCDs have also brought some inherent problems however, for example the noise associated with each image. Because photons obey Gaussian statistics for large counts, there will be a shot noise (uncertainty in the count rate) for each pixel of whereis the number of photons detected. Error in an image also stems from both the bias of the CCD, and the â€Å"dark current† present. The bias of a CCD is a systematic voltage offset across the whole CCD to prevent digital underflow during analogue to digital (A-D) conversion. It includes the read-out noise, a result of the manipulation of the pixel charge values during the A-D process and any charge-loss which occurs during the transfer[xiii]. A CCDs dark current is an unwanted flow of electrons which have been released from the surface of the semiconductor by thermal excitation, and is purely dependant on the surface temperature, rather than being a function of illumination. For this reason the CCD was cooled by both the Peltier method (electrically) and with an active assisting fan[xiv], to around 35 °C below ambient temperature, as the thermal current is approximately halved for each 7 °C reduction in CCD temperaturexii. To remove noise from an image, a set of calibration images may be taken alongside each raw exposure. These are called bias and dark frames. The bias frame is a zero-time exposure which will include both bias and read-out noise. A dark frame can be found by leaving the shutter on the camera closed and taking an exposure seconds long. It can be expressed as , [xv](2) whereis the dark current, andis the thermal noises statistical variation. Ideally one would be taken before each exposure, as temperature routinely varies slightly with time. A â€Å"master dark† frame can be found by taking the average of a large number of dark frames, and will include the equivalent of a master bias. This master dark can then be subtracted from each image to leave a final, processed image with as low a random error as possible. The Automated Photometry Process Since the experiment involved a large number of images, the photometry processes were automated using several Python scripts and FORTRAN routines. The script â€Å"all.py† was used to iterate the â€Å"photom.py† script over a range of images within a directory and print a string detailing which file was currently being processed. â€Å"photom.py† was the main script run, and was used to call several other processes which ran the photometry calculations, among other things. Firstly, it read in the file specified, and split the filename into the file and the extension, by using the find function to search for the full stop as the delimiter. i=file_name.find(.) Using the extension to determine the file type, the script then either subtracted the master_dark.sdf frame (if it was a FITS file, and hence a DRACO output file) or converted it to a FITS file (if it was an ST9 file, and hence 14-inch, which had already had the dark frame removed). The conversion is achieved using two separate routines: sbig2ndf, a routine from the SBIG python module which converts compressed output ST9 files created in CCDOPS into NDF files, and ndf2fits, which is a routine from the convert set of variables that converts the NDF files to FITS images. The subtraction of the dark frame is made using the kappa package from Starlink. â€Å"photom.py† then reads the variable star position from a user-created ‘var_sky_position† file. Using this, the script runs â€Å"find_astrom.py†. This attempts to match the stars in the image to the USNOA2 catalogue, and produce a new FITS file with the derived header solution. Firstly it takes the given star position as the centre of the image, and runs sextractor to find all the objects in the image. Next, it runs the WCS Tools routine scat at that RA and Dec to attempt to find any known objects in the region from the catalogue and prints it to a new file, usnoa_ref.cat: commands.getoutput(scat+ -d -c ua2 -n 200 -m 17 r 600 +ra+ +dec+ j2000 > usnoa_ref.cat) The pixel scale is taken from the directorys automag_driver file, and used by Andrew Pickles starfit script to match each object found by sextractor to the catalogues objects. This is achieved by the matching of triangles created between sets of objects in the sky to similar triangles created from the catalogues objects. Starlinks astrom routine is then used to correct the solution: out=commands.getoutput(/star/bin/astrom fits=asc) print astrom returns:, out Finally, â€Å"find_astrom.py† edits the header keys using pyfits to reflect the newly derived solution, and creates a new FITS image with the file ending â€Å"_ast.fits†. â€Å"photom.py† then runs sextractor again, to product a new catalogue of the objects from the image, complete with their RA and Dec. The script then performs the aperture photometry using â€Å"automag.py†. This measures the relative aperture magnitudes for the objects defined in the new object catalogue, by taking the number of counts within the specified aperture radius from the driver, and applying the formula: (3) Here is a constant offset defined in the driver, is the number of counts within the aperture (which is pixels in size) minus the background, and is the integration time. Background errors are calculated by measuring the counts within the two â€Å"sky† aperture radii to find the mean and rms sky-counts over pixels,and, and firstly deriving the signal to noise ratio for the star, by applying Equation (4) below[xvi]. (4) In the above equation, is the gain of the CCD. By using the flux based definition of the magnitude difference and manipulating the logarithm equation, the signal to noise value can be used to find the error on a measured magnitude, as shown in Equation (5). (5) These instrumental magnitudes are appended to the catalogue file, next to each object. â€Å"auto_mag2list.py† is subsequently run to pull the calibration stars from the catalogue, by matching the RA and Dec to those in the â€Å"cal_sky_positions†. The variable stars data, as well as the calibration stars data and the observation time in Modified Julian Days (MJD) are then appended to a file called â€Å"summary.obs†. Once â€Å"photom.py† completes, the raw2dif routine can then be run to perform the differential magnitude calculation. This routine takes each line from the â€Å"summary.obs† file and subtracts the average of the two comparison stars instrumental magnitudes from the variable stars instrumental magnitude, . A zero-point constant is then added to put the differential magnitude on the standard scale. This can be measured by taking images of photometric standard stars (from the Tycho catalogue for example), and comparing their instrumental magnitudes to their known apparent magnitudes, as described in the next section. (6) raw2dif outputs simply the observation time, variable stars standardised magnitude, and the error on the magnitude to a user-defined file. Magnitude Zero-point Measurement The zero-point is found by measuring the magnitudes of photometric stars with the telescopes, and comparing these to the values found for the stars in the Tycho catalogue. This catalogue uses a separate magnitude system, which can be converted into standard V-band magnitudes using the following formula: (7) The difference in these catalogue magnitudes and the observed values can then be used to show the difference that the specific equipment has made. This is the zero-point magnitude. Period Determination Two programs are used for the period determination, the routine bforce and PDM win 3.0[xvii]. bforce uses a brute force method to find the period of the variability. It attempts to fit the data onto a user generated model of the light curve (with a phase resolution of 0.1), and wrap (or â€Å"fold†) it around a suggested period. The routine then splits the data into a series of bins and estimates the variance in each, as follows; , (8) for observations in each bin. If the trial period is incorrect, there will be a large scatter of magnitudes in each bin, i.e. a large variance. This is compared to the variance of the data set as a whole using an F-test, which is achieved by finding the ratioof bin variance (the explained variance) to total variance (the unexplained variance). For an incorrect estimate of the period ≈ 1, whereas for the correct period The PDM program works in a similar, if more refined way, implementing some of the recent changes in the accepted way of calculating a phase dispersion minimisation period. While still using a variation-based method, it finds the period using a beta-distribution method (designated PDM*) rather than an F-test, as this has been shown to be the correct probability distribution to use[xviii]. It also utilises a GUI with a series of user-set options, for example variable phase resolution. RR Lyrae Theory Subclasses of RR Lyrae Variables From his observations, Bailey noticed three separate subclasses of variable, which have subsequently been compacted into two subclasses (as subclasses a b were very similar). The following is paraphrased from Baileys original description[xix]: * Subclass â€Å"ab†: Very rapid increase of magnitude, with a moderately rapid decrease in mag. Nearly constant mag for approx one half of the full period. Amplitude of roughly one mag and a period of between 12 and 20 hours. * Subclass â€Å"c†: Magnitude always changing, with moderate rapidity. Range generally half a magnitude, with a period of 8 to 10 hours. As our study concerns an RRab type variable, this class shall be primarily discussed. Typical characteristics of RRab stars RR Lyrae stars are large red stars with a low mass, occupying the area of the instability strip on the H-R Diagram (see Fig. 1) between ÃŽ ´-Scuti and Cepheid variables, where it intersects the horizontal branch. They are in the core helium burning stage of their evolution, having exhausted their core hydrogen fuel. Mean physical properties of these variables are under some contention, but a summary of current approximations is provided in Table 1. Period 0.2 1.1 days Mv 0.78  ± 0.02 Te 6404  ± 12 K [Fe/H] -1.56  ± 0.25 Mass 0.55  ± 0.01 Mà ¢Ã‚ ¨Ã¢â€š ¬ Radius 5  ± 1 Rà ¢Ã‚ ¨Ã¢â€š ¬ Table 1. Typical properties of RRab variables. All values are mean values of 335 variable stars[xxi], except period which is a typical rangei. Evolutionary theory It is thought that the progenitor of an RR Lyrae star was a typical low-mass main sequence star, with M* ≈0.8Mà ¢Ã‚ ¨Ã¢â€š ¬. For the first 15 Gyr of its life, the star burns core hydrogen, fusing it into helium. Once the hydrogen supply in the core is exhausted, the star expands to become a red giant, moving off the main sequence and up the giant branch of the Hertzprung-Russell diagram (see Fig. 1), and shell-burning of hydrogen now occurs around an inert helium core. The helium core eventually collapses, becoming electron degenerate, and increases in temperature until the helium in the core ignites using the triple -ÃŽ ± process, causing the â€Å"helium flash†. The cores degeneracy is lost and the star moves off the giant branch asymptotically, down towards the instability strip. At this point it can develop the pulsational properties of an RR Lyrae star, although this will be dependent on its mass, its chemical composition, and its temperaturei. Once the helium core is also used up after around 0.1 Gyr, the star begins to expand and cool again, fuelled only by shell burning of hydrogen and helium. The core never becomes hot enough for the fusion of heavier elements. Eventually all the usable fuel is expended and the star will jettison off its outer layers of material to leave a white dwarf star, shining only through the radiation of internal thermal energy. Pulsation theory The study of pulsation theory owes much to Arthur Eddington, who wrote a series of papers detailing a mathematical description of the properties of stars. Having realised that a radial pulsation in a static star would have a decay time of around 8000 years (much shorter than the length of time stars spend in the instability strip), he proposed that stars behaved as thermodynamic heat engines, using some â€Å"valve mechanism† to regulate energy flow[xxii]. In order to fulfil pulsation, this valve would need to make the star more thermally opaque as the star was compressed, and less opaque as it expanded. Effectively this would cause energy to build up when the star was compressed, forcing the star to swell in size until some turning point was reached and the opacity was small enough that energy could escape, leading to the star contracting again. The Rosseland mean opacity shows the overall opacity of a stellar region, and is defined as follows, (9) where is a constant, is the density of the region, and is temperature. Eddington was unable to come up with a particular material that would possess these properties in a star, particularly as during his time it was not believed that hydrogen or helium made up significant proportions of the inside of stars. It is also the case that neutral hydrogen or helium regions cannot be the â€Å"valve† region, as for these regions and i.e. as increases will decrease. This would lead to the pulsation dying out extremely quickly as all the radiative pressure was lost during contraction. However in 1953 Sergei Zhevakin found that regions of doubly ionised helium would provide an area wherebecomes small or negative, resulting in the desired properties for the gas. It was later shown by R. F. Christy[xxiii] that hydrogen ionisation can play a smaller, but still important, role in the mechanism. Ionisation zones can make another possible contribution to the â€Å"valve† in a star. If the energy from fusion processes cause ionisation in gas regions instead of raising their temperature, then the gas will absorb heat during compression stages, causing a pressure maximum near the minimum volume and thus aiding pulsation. This is known as the mechanism. Different classes of RR Lyrae variable pulsate with different modes. For instance RRab stars all vary in the fundamental mode, whilst RRc stars are pulsating in the first overtone. This is one of the reasons that types â€Å"a† and â€Å"b† can be separated from type â€Å"c† as a separate class. A third class of variables has also been observed, termed RRd type stars, which have a double-mode pulsation, pulsating in the fundamental and first overtone modes simultaneously. However, some RRab stars show a long-timescale second periodicity, known as the Blazhko effect. This is an overarching period that can be anywhere between 30 days and several years. The cause of this effect is unclear, but is believed to come from either a nonlinear resonance effect between the radial fundamental mode and some non-radial mode, or a cyclical rotating magnetic field that deforms the main radial mode of pulsation[xxiv]. Estimation of Absolute Magnitude and Distance RR Lyrae stars are useful for the determination of astronomical distances, especially to regions such as clusters in the Halo, and the Bulge. However, unlike for Cepheids, accurate parallax measurements of distance do not exist for RR Lyrae variables (with the exception of a very few the star RR Lyrae itself for example[xxv]), as the majority of stars are simply too far away for resolution currently[2]. Instead, astronomers look to alternative measurement tools, for example main sequence fitting or the Baade-Wesselink method. Main sequence fitting is the process of determining the distance to a cluster by fitting its colour-magnitude diagram to that of nearby main sequence stars which have a parallax-determined distance. This has produced a wide variety of relations over the last twenty years, but a general relation (that is within error of the majority of current estimates) is given by H. Smithi: (10) The currently favoured method of finding the metallicity is to use the relation, described by Jurcsik Kovà ¡cs in their seminal paper â€Å"Determination of [Fe/H] from the light curves of RR Lyrae stars†[xxvi]. This used a sixth order Fourier decomposition of the light curve to find multiple properties of an RR Lyrae star. When they plotted the data they found the following linear relation: (11) This allows the metallicity to be determined accurately, and then used in the main sequence fitting method to find an accurate absolute magnitude for a star. Finding the absolute magnitudeis important, because it allows for the use of the magnitude equation to determine distance to an object, taking into account the galactic extinction in the direction of the object due to dust and gas in the galactic plane, : (12) The Baade-Wesselink method, originally applied to Cepheid variables, was based on the assumption that a star will have the same surface temperature and brightness at all points of equal colour on the ascending and descending sides of the light curve. This implies that any luminosity variation between two half-phases can be said to be the result of radial differences in the star. Thus a fractional radius change can be measured as. If a radial velocity curve is also plotted for the star, the radius change over the period can be directly measured, and through the combination of these two results a value for the luminosity of the star can be found. This can be used to show the distance to an RR Lyrae star through the relation (13) whereis Stefans Constant, andis the stars effective temperature. However RR Lyrae variables do not behave exactly like Cepheids; for example during stellar expansion the surface gravity is much greater than when the star is contracting, leading to flux redistribution across the surface. This, combined with shock waves permeating through the stellar atmosphere causing distorted radial velocity curves, means that V band photometry is unfortunately useless for applying the Baade-Wesselink method to RR Lyrae stars. The procedure must instead be carried out in (V-H) or (V-K) colours for example, as infra-red wavelengths are less sensitive to the expansion phase distortions[xxvii]. Estimation of Radius Marconi et alxxv have published an equation relating the period of a fundamental mode RR Lyrae star to its average radius; (14) whereis the mean radius (in units of solar radii), is the period (in days), and is the heavier-than-iron metallicity of the star, defined as; xxi, (15) whereis the alpha-enhancement with respect to iron, and is taken to be equal to 1. This is derived from their theoretical predictions of the radial oscillations of a metal poor RR Lyrae, and applies to stars with helium abundances of between (0.24 and 0.28). III. Experimental Methods Preparing the experiment Inital sessions were spent becoming aquainted with the computers Linux-based operating systems, understanding the basics of photometry and exploring the provided software. Several rooftop sessions were attended to gain knowledge of the telescopes provided, and to learn safety procedures associated with the use of the equipment. Due to initial poor weather, previous years data was analysed in order to improve understanding of the provided scripts. A list of RRab targets from the NSVS catalogue[xxviii] was examined to find a suitable object, with a magnitude range visible on the telsecopes available, a period of less than a day, and a high position in the sky. Table 2. Properties of the Telescope and CCD combinations for each dome. Both telescopes were fitted with the same model of V-band filter. Background information on the chosen star (XX And) was found using the SIMBAD database[xxix], and examined to find previous studies, including estimates of period, metallicity, and star type, as well as dates of previously observed maxima. A plot of the field around the star was taken, and used to identify two calibration stars for the photometry ( 3): The calibration stars used were USNOA2.0 numbers 1275-00765817 (cal-star 1) and 1275-00761527 (cal-star 2). They were searched for in various catalogues to verify that they were not known to be variable. The best exposure time for our field was estimated to be 30 seconds with the 14-inch telescope, and 60 seconds with DRACO, so as not to saturate the image. By taking some sample images and viewing them in GAIA, suitable sizes for the apertures were chosen for each telescope. The sizes of the apertures were chosen to enclose the whole star, whilst giving the minimum error. These were then converted from scaled values to numbers of pixels, and entered into seperate â€Å"automag_driver† files for each telescope, along with the specific pixel scale, gain and read-out noise. Telescope Star Sky Inner Sky Outer 14-inch 4.7 14.9 21.4 DRACO 7 25.7 35.1 Table 3. The aperture radii (in pixels) used for each telescope. Firstly, the â€Å"convert† variables were set up. XX Ands RA and Dec in decimal degrees were inserted into a file called â€Å"var_sky_position†, and â€Å"photom.py† was run on the first frame (called for example â€Å"filename.fits†). This produced an output file called â€Å"amag.out† which contained the positions of all the recognised stars in the image, as well as a calibrated image â€Å"dfilename_ast.fits†. By comparing the (x,y) pixel locations in GAIA for the two calibration stars with the data in â€Å"amag.out† the RA and Dec of the calibration stars were noted, and inserted into a text file named â€Å"cal_sky_positions†. Observation of the Variable Observations of the field containing XX And were then taken over a period of 1 month, using both the 14-inch â€Å"Far East† and the 10-inch DRACO telescopes. For the 14-inch, the observing process was as follows: The object was located using the Earth Centre Universe program, the telescope synched and set to track, and the CCD programmed to take around 30 images per sequence at 30 seconds each, with an 8 second dark frame before each new image. For DRACO, the object was found using the provided G.U.I., with care taken to place the variable star and both comparison stars away from dust grains on the CCD. The telescope was set to track, and programmed to take a large number of images with a 60 second exposure. For each new observing session a seperate file was created, containing all the images and the scripts required for automated photometry. For DRACO processing, a master dark file was also copied from the archive. The file â€Å"all.py† was then amended to iterate ov er all the images in the directory, and set running. Once the photometry had completed, the raw2dif routine was run, and the results viewed by running qplot. The data were adjusted to Heliocentric Julian Days by running the cor2hjd routine, and the final tables were copied across to a main results directory to be added to the full table of data. bfplot was run on the full dataset using an estimate for the period, and the phase values from the output file â€Å"fort.30† were killed out and yanked into the dataset file using EMACS. This table was viewed in TOPCAT, and a light curve created. Any clear and accountable anomalies were removed in TOPCAT. To gain a value for the absolute magnitude of XX And, rather than simply an instrumental magnitude, a series of observations were made of photometric stars which had known magnitudes. These are shown in table 4 below: Photometric Star RA Dec Apparent V-band Magnitude 1 1h 18m 20.581s 38 ° 55 38.23 9.847 2 1h 14m 50.729s 38 ° 29 55.80 9.961 3 1h 15m 12.229s 38 ° 49 10.95 9.048 4 1h 16m 39.436s 39 ° 09 38.64 9.735 Table 4: Properties of photometric stars used in the magnitude calibration of XX And. This gave a value for the correction which had to be made to all the observed values for each telescope. The corrections were then applied to the full dataset. An O-C diagram was constructed using the data from the Hipparcos mission, the GEOS RR Lyrae Survey, and also archive data from the GEOS database[xxxi]. The period used was the Hipparcos estimate. Since the newly observed data used HJD, and the archive data was in â€Å"modified HJD†, an addition of 0.5 HJD has to be made to the new data in order to be comparable. The newly observed data was then added to the diagram, and the input period was altered to give the flattest line possible, thus providing a new estimate of the period. The error on the period is given by the slope of the line[xxxii]. Any historical period changes were searched for in the line of the O-C plot. The fast_solve routine was run on all of the summary.obs files, and the comparison stars were checked to see whether or not they were varying. The output model file from fast_solve was edited to include estimates of bin values where there was no actual observational data, and then used in the routine bforce. This was run using the period quoted in the Hipparcos catalogue as the initial period to give an estimate of the new period and its error. The period was also estimated using PDMwin, using an output table from TOPCAT. Errors in the period-finding were estimated using the Jackknife method on both the PDM and bforce programs. This was achieved by recomputing the period, but leaving out one observa

Genetic Engineering and Cryonic Freezing: A Modern Frankenstein? Essay

Genetic Engineering and Cryonic Freezing: A Modern Frankenstein?      Ã‚   In Mary Shelley's Frankenstein, a new being was artificially created using the parts of others. That topic thus examines the ethics of "playing God" and, though written in 1818, it is still a relevant issue today. Genetic engineering and cryogenic freezing are two current technologies related to the theme in the novel of science transcending the limits of what humans can and should do.    Genetic engineering is widely used today. Genetically altered bacteria are used to make human insulin, human growth hormone, and a vaccine for hepatitis B. Two vaccines against AIDS created with genetic engineering have begun clinical trials here in the United States ("The Genetic Revolution" 10), and genetic engineering is used to detect genetic defects in human fetuses ("The Controversy over Genetic Engineering" 18).    Many are now considering using this technology to change humans, such as developing methods that could be used to regenerate or repair faulty organs. It could be also used to find a cure for diseases such as cancer, eventually (Fitzgerald), or to repair genetic defects. Parents could choose the sex and height of their offspring and be able to have more intelligent, more athletic, and better looking children. Also, genetic engineering could also be used to clone humans (Kevles 354), a topic of much discussion of late.    Kevin T. Fitzgerald divided potential scenarios for using cloning technology into three categories: "Producing a clone in order to save the life of an individual who requires a transplant; making available another reproductive option for people who wish to have genetically related children, but face physical or chr... ...Victor may have succeeded in his goal of creating a new being and breaking death's hold over humankind, it appears that it will be us that puts forth the final and most acceptable solution. WORKS CITED   Begley, Sharon. "Designer Babies." Newsweek November 9, 1998: 61,2. "The Controversy over Genetic Engineering." Awake December 8, 1978: 18-20. Fitzgerald, Kevin T. "Little Lamb, Who Made Thee?" America March 29, 1998. . "The Genetic Revolution." Awake July 22, 1989: 10. Kevles, Daniel J. and Leroy Hood. "Will the Human Genome Project Lead to Abuses In Genetic Engineering?" Taking Sides. Ed. Thomas A. Easton. Guilford, Connecticut: Dushkin Publishing Group Inc., 1995. 342-357. Shelley, Mary. "Frankenstein." Puffin Books, Penguin Group. London, England, 1994. Pages 64-65. http://alcor.org. "Alcor Life Extension Foundation." 1998.

The War of the Sexes in The Taming Of The Shrew :: The Taming Of The Shrew William Shakespeare

The Taming Of The Shrew: The Battle Continues in the War of the Sexes  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚        The plot of William Shakespeare’s The Taming Of The Shrew is derived from the popular 'war of the sexes' theme in which males and females are pitted against one another for dominance. Although the play has been condemned for the blatant sexist attitude it has toward women, a close examination of the play reveals that it is not a story of how men should 'put women in their place'. The play is, in fact, a comedy about an assertive woman coping with how she is expected to act in society and of how one must obey the unwritten rules of a society to be accepted by it. Although the play ends with her outwardly conforming to the norms of society, this is in action only, not in mind. Although she assumes the role of the obedient wife, inwardly she still retains her assertiveness. The play begins with an induction in which a drunkard, Christopher Sly, is fooled into believing he is a king and has a play performed for him. The play he watches is what constitutes the main body of The Taming Of The Shrew. In it, a wealthy landowner, Baptista Minola, attempts to have his two daughters married. One is very shrewish, Katherine, while the other is the beautiful and gentle Bianca. In order to ensure Katherine is married, Baptista disallows Bianca to be espoused until Katherine is wed, forcing the many suitors to Bianca to find a mate for Katherine in order for them to vie for Bianca's love. Most of the play's humor comes from the way in which characters create false realities by disguising themselves as other people, a device first introduced by having Christopher Sly believe he is someone he is not and then by having the main play performed for him. By putting The Taming Of The Shrew in a 'play within a play' structure, Shakespeare immediately lets the audience know that the play is not real thus making all events in the play false realities. Almost all characters in the play take on identities other than their own at some point of time during the play. Sly as a king, Tranio as Lucentio, Lucentio as Cambio, Hortensio as Litio and the pedant as Vicentio are all examples of this. Another example of this is Katherine as an obedient wife.

An Analysis of Poems 585 and 754 Essay -- 585 754

An Analysis of Poems 585 and 754 Emily Dickinson’s use of poetic diction in poems 585 and 754 brings to life two inanimate objects, a train and a gun, both of which perform actions that are useful to man. Though these items cannot act on their own, Dickinson’s diction provides them with their own movements, characteristics, and feelings. In poem 585, a train’s daily journey is given a meaning beyond that of a cold, iron machine when Dickinson describes its animal qualities to show its strength, stubbornness, and perseverance. In poem 754, a gun is portrayed as a protective, devoted servant. In both of these poems, Emily Dickinson uses diction to give a train and a gun characteristics of animals to explain their behavior and feelings and to show how man uses them to his advantage and to meet his goals. In poem 585, Dickinson’s diction reveals traits of hunger and determination. In the first stanza, "I like to see it lap the Miles--/And lick the Valleys up--/And stop to feed itself at tanks" (ll. 1-3) describes the train as an animal that runs hungrily over great distances, devouring the land as it goes along, stopping occasionally to eat more substantial food to survive and to continue. Though it is able to perform powerful feats of transportation, the train needs nourishment, just like humans and animals do. With the following lines, Dickinson shows the determination of the train to meet his goal: "And, supercilious, peer/In Shanties—by the sides of Roads—And then a quarry pare/To fit its ribs" (ll. 6-9). These lines also suggest a stubborn determination. Even if the train has to crawl and cut through hundreds of yards of solid rock, nothing will stop this metal animal, not even a huge mountain. The train can drive... ...Why would the master need protection? In both poems, Emily Dickinson uses diction to provide the reader the opportunity to see inanimate objects with some human qualities, first in a determined, powerful train and then in a devoted, non-feeling gun. Though these are inanimate objects, the reader can get a sense of the influences and contributions they give to man. The train made a great impact on travel by allowing him to cover great distances in shorter times. It appears that this iron horse could take man anywhere. In Dickinson’s time the power of trains was an amazement in itself. With the rifle, man has control of something quite powerful, something that can kill but cannot be killed. With her skillful and interesting word choice, Dickinson brings to light the amazing strength of one object, the train, and the fearful power of another, the gun. An Analysis of Poems 585 and 754 Essay -- 585 754 An Analysis of Poems 585 and 754 Emily Dickinson’s use of poetic diction in poems 585 and 754 brings to life two inanimate objects, a train and a gun, both of which perform actions that are useful to man. Though these items cannot act on their own, Dickinson’s diction provides them with their own movements, characteristics, and feelings. In poem 585, a train’s daily journey is given a meaning beyond that of a cold, iron machine when Dickinson describes its animal qualities to show its strength, stubbornness, and perseverance. In poem 754, a gun is portrayed as a protective, devoted servant. In both of these poems, Emily Dickinson uses diction to give a train and a gun characteristics of animals to explain their behavior and feelings and to show how man uses them to his advantage and to meet his goals. In poem 585, Dickinson’s diction reveals traits of hunger and determination. In the first stanza, "I like to see it lap the Miles--/And lick the Valleys up--/And stop to feed itself at tanks" (ll. 1-3) describes the train as an animal that runs hungrily over great distances, devouring the land as it goes along, stopping occasionally to eat more substantial food to survive and to continue. Though it is able to perform powerful feats of transportation, the train needs nourishment, just like humans and animals do. With the following lines, Dickinson shows the determination of the train to meet his goal: "And, supercilious, peer/In Shanties—by the sides of Roads—And then a quarry pare/To fit its ribs" (ll. 6-9). These lines also suggest a stubborn determination. Even if the train has to crawl and cut through hundreds of yards of solid rock, nothing will stop this metal animal, not even a huge mountain. The train can drive... ...Why would the master need protection? In both poems, Emily Dickinson uses diction to provide the reader the opportunity to see inanimate objects with some human qualities, first in a determined, powerful train and then in a devoted, non-feeling gun. Though these are inanimate objects, the reader can get a sense of the influences and contributions they give to man. The train made a great impact on travel by allowing him to cover great distances in shorter times. It appears that this iron horse could take man anywhere. In Dickinson’s time the power of trains was an amazement in itself. With the rifle, man has control of something quite powerful, something that can kill but cannot be killed. With her skillful and interesting word choice, Dickinson brings to light the amazing strength of one object, the train, and the fearful power of another, the gun.

Indias Space Programme Essay

The country is now capable of launching its own spacecraft. In fact, it offers this service to many other countries. Now India has made landmark progress with the launch of Chandrayan for its moon mission. India started its space programme with the launch of first space satellite ‘Aryabhatta’ on April 19, 1975. This space satellite was named after the great Indian astronomer and mathematician of the 5th century, Aryabhatta. It was launched from a soviet cosmodrome with the help of a Soviet rocket. It marked India’s giant leap and made her the eleventh country to join the space club. The second satellite ‘Bhaskara’ was launched on June 7, 1979. It was also launched from a Soviet cosmodrome. It was named after two eminent personalities—Bhaskara I and Bhaskara II. It was followed by ‘Rohini’. It was the first Indian satellite put into the space by SLV-III, an Indian rocket. It was launched from Sriharikota in Andhra Pradesh on July 9, 1980. It was developed by the scientists of ISRO. It was the success of the mission of SLV-III which brought recognition to the space programme of India. India’s fourth satellite Rohini II was launched by the launch vehicle SLV-III from Sriharikota on May 31, 1981. It was designed to provide useful data for 300 days. It was weighted 38 kg. It was known as India’s first development rocket flight. Unfortunately, it burnt in space on June 8, 1981, without completing its mission. Bhaskara II, India’s fifth satellite in space, was launched on November 20, 1981 from Soviet cosmodrome Volgograd. It was the earth observation satellite. It was a milestone in the space journey of India as it brought to India the honour of being a space nation. Apple, an experimental geostationary communication satellite, was launched on June 19, 1981. It was launched with French coordination. With this, India entered the domestic satellite communication era. India launched INSAT-1A on April 10, 1982. India joined the select group of techn ically advanced countries. But this mission failed on September 6, 1982. In April 1983, India successfully launched Rohini satellite (RS-D-2). It marked the opening of new horizons for India. India’s ninth satellite INSAT-1B became fully operational in October 1983. It was the world’s first geo-stationary satellite combining services like telecommunication, mass communication and meteorological. It was launched in August 1983 from US Space Shuttle Challenger. India’s space programme is primarily driven by the vision of great scientist Dr. Vikram Sarabhai. He is considered as the Father of  Indian Space Programme. The main objective of India’s space programme has been to promote the development of application of Space Science and technology for socio-economic benefits of the country. The launching of Chandrayan I in 2008 marked a milestone in the history of space technology of India. Chandrayan will orbit around the earth for two years. During the period, it will send data to scientists. The scientists with the help of the data will study various aspects of moon, and will prepare a map of the moon. The map will further help in the study of moon. Then onward India made successive progress in the field of space research. It launched INSAT series satellite which made India’s position stronger in the comity of nation. India has now become self-reliant in terms of launching vehicles and telecommunications. Now India offers telecommunication services to other countries. The launching of satellites like IRS’s, ASLV’s, PSLV’s have placed India in the exclusive club of four nations—USA, Russia, France and Israel. Captain Rakesh Sharma was the first astronaut of India. Now the country enjoys a respectful position in the countries of the world.

Communication Barrier

Communication Barrier between Local and International Student in Malaysia. The increasing number of international student in Malaysia brings many benefits to the country as well to the local students. However, there are many problems that faced by these international students in this country. One of them is communication barrier between the local students. Communication barrier always bring difficulty to them when interacting with the local students in the campus. This includes both verbal and non-verbal communication. These are the barriers:- * Limited interactionThere are limited interactions between the local and international students. The international students or the local students only communicate with each other when there are group discussions in the class or a meeting on a group assignment. They like to be with their own group of friends that are similar nationality, race and culture with them. These bring them to have less communication with the other group of students. If this thing keeps continuing then the international students cannot learn the host country culture, tradition and beliefs. They also will have less knowledge about the host country.Later on, this will bring difficulty to the international students when they enter in the work field at the host country. * Poor language The local and the international students also have problems with the language. In Malaysia, there are only two languages that are used widely not only in the university but in the whole country, which is Bahasa Malaysia and English Language. The international students or the local students only use English Language when communicating with each other because the international students don’t know the local language which is Bahasa Malaysia.The international students that not from English spoken country like students from China, Turkey, Arab and Japan, always have problem to speak in English with the local students. Some of the local students or the Malaysian studen ts also have the same problems when speaking English with the international students. These problems occur due to poor language skills. This make them to communicate non-verbally more than verbal. * Few close friends The international students in Malaysia have few close friends. They like to be friend with people that make them comfortable.They also make less new friends. This make them always think in the box and not out of the box. Most of the international students don’t like to be friend with the local students. Same goes with the local students. These things occur due to some thought of similarity between them. They make these things more important than the other rest like to make friends to learn new thing and to gain knowledge. * Slang Slang is colloquial language, where words mean something other than their formal meanings, or where words are used that is not actual English words.Sometimes slang or vernacular words will become dictionary words through use and custom. Slang also one of the biggest communication barriers between the local and international students. The international student’s slang or accent also brings difficulty to the local students when talking with them. They hard to understand what the international students try to tell them and sometimes, these will also bring misunderstanding between them. Due to these problems, they communicate less with each other. Reference 1.Hasri Hassan, Zulaikha Nurain Mudzar, Lucien Low, (2013), Corporate Communication, Pearson, Kuala Lumpur. 2. http://blogs. nottingham. ac. uk/chinapolicyinstitute/2013/01/28/integration-of-chinese-internationals-students-with-the-local-community-issues-arising-from-the-sccs-community-building-forum/ 3. http://english. peopledaily. com. cn/90001/90782/90872/7233749. html 4. http://www. awej. org/? article=20 5. http://www. purdueexponent. org/campus/article_f0ea98bd-10f3-5507-b699-b85e32fb1955. html 6. http://www. ijbssnet. com/journals/Vol. _2_No. _7;_Speci al_Issue_April_2011/5. pdf

Sample Costs to Produce Processing Tomatoes

TM-SV-08-1 UNIVERSITY OF CALIFORNIA – COOPERATIVE EXTENSION 2008 SAMPLE COSTS TO PRODUCE PROCESSING TOMATOES TRANSPLANTED IN THE SACRAMENTO VALLEY Prepared by: Gene Miyao Karen M. Klonsky Pete Livingston UC Cooperative Extension Farm Advisor, Yolo, Solano, & Sacramento Counties UC Cooperative Extension Specialist, Department of Agricultural and Resource Economics, UC Davis UC Cooperative Extension Staff Research Associate, Department of Agricultural and Resource Economics, UC DavisUC COOPERATIVE EXTENSION SAMPLE COSTS TO PRODUCE PROCESSING TOMATOES TRANSPLANTED In the Sacramento Valley – 2008 CONTENTS INTRODUCTION †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ 2 ASSUMPTIONS †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ CULTURAL PRACTICES AND MATERIAL INPUTS †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. 3 CASH OVERHEAD †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. 5 NON-CASH OVERHEAD †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ REFERENCES †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢ € ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ 8 TABLE 1. COSTS PER ACRE TO PRODUCE PROCESSING TOMATOES †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. 10 TABLE 2. COSTS AND RETURNS PER ACRE TO PRODUCE PROCESSING TOMATOES †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. 12 TABLE 3.MONTHLY CASH COSTS PER ACRE TO PRODUCE PROCESSING TOMATOES †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. 14 TABLE 4. WHOLE FARM ANNUAL EQUIPMENT, INVESTMENT, AND BUSINESS OVERHEAD COSTS †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ 15 TABLE 5. HOURLY EQUIPMENT COSTS †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. 17 TABLE 6. RANGING ANALYSIS †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. 8 TABLE 7. COSTS AND RETURNS/ BREAKEVEN ANALYSIS †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã ¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. 19 TABLE 8. DETAILS OF O PERATIONS †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ 20 INTRODUCTION The sample costs to produce transplanted processing tomatoes in the Sacramento Valley is based on the 2007 cost and returns study practices using 2008 prices and are presented in this study.The price adjustments are for fuel, fertilizers, pesticides, water, labor rates, interest rates, and some cash overhead costs. This study is intended as a guide only, and can be used to make production decisions, determine potential returns, prepare budgets and evaluate production loans. Pr actices described are based on production practices considered typical for the crop and area, but may not apply to every situation. Sample costs for labor, materials, equipment, and custom services are based on current figures.Blank columns, â€Å"Your Costs†, in Tables 1 and 2 are provided to enter actual costs of an individual farm operation. The hypothetical farm operations, production practices, overhead, and calculations are described under the assumptions. For additional information or an explanation of the calculations used in the study, call the Department of Agricultural and Resource Economics, University of California, Davis, (530) 752-2414 or the local UC Cooperative Extension office.Two additional cost of production study for processing tomatoes grown in this region are also available: â€Å"Sample Costs To Produce Processing Tomatoes, Direct Seeded, In the Sacramento Valley – 2007†, and â€Å"Sample Costs To Produce Processing Tomatoes, Transplante d, In the Sacramento Valley – 2007†. Sample Cost of Production Studies for many commodities are available and can be requested through the Department of Agricultural Economics, UC Davis, (530) 752-2414. Current studies can be downloaded from the department website http://coststudies. ucdavis. edu/ or obtained from selected county UC Cooperative Extension offices.The University of California prohibits discrimination or harassment of any person on the basis of race, color, national origin, religion, sex, gender identity , pregnancy (including childbirth, and medical conditions related to pregnancy or childbirth), physical or mental disability , medical condition (cancer-related or genetic characteristics), ancestry, marital status, age, sexual orientation, citizenship, or service in the uniformed services (as defined by the Uniformed Services Employment and Reemployment Rights Act of 1994: service in the uniformed services includes membership, application for membership, performance of service, application for service, or obligation for service in the uniformed services) in any of its programs or activities. University policy also prohibits reprisal or retaliation against any person in any of its programs or activities for making a complaint of discrimination or sexual harassment or for using or participating in the investigation or resolution process of any such complaint. University policy is intended to be consistent with the provisions of applicable State and Federal laws.Inquiries regarding the University’s nondiscrimination policies may be directed to the Affirmative Action/Equal Opportunity Director, University of California, Agriculture and Natural Resources, 1111 Franklin Street, 6th Floor, Oakland, CA 94607, (510) 987-0096. 2008 Transplanted Processing Tomato Cost and Returns Study Sacramento Valley UC Cooperative Extension 2 ASSUMPTIONS The following assumptions refer to tables 1 to 8 and pertain to sample costs and returns to prod uce transplanted processing tomatoes in the Sacramento Valley. Input prices and interest rates are based on 2008 values. However, production practices were not updated from the 2007 study. Practices described are not recommendations by the University of California, but represent production practices considered typical of a well-managed farm for this crop and area.Some of the costs and practices listed may not be applicable to all situations nor used during every production year and/or additional ones not indicated may be needed. Processing tomato cultural practices and material input costs will vary by grower and region, and can be significant. The practices and inputs used in the cost study serve as a guide only. The costs are shown on an annual, per acre basis. The use of trade names in this report does not constitute an endorsement or recommendation by the University of California nor is any criticism implied by omission of other similar products. Farm. The hypothetical field and row-crop farm consists of 2,900 non-contiguous acres of rented land.Tomatoes are transplanted on 630 acres (70% of the tomato acreage) and direct seeded on 270 acres (30% of the tomato acreage) for a total of 900 acres. Two thousand acres are planted to other rotational crops including alfalfa hay, field corn, safflower, sunflower, dry beans and/or wheat. For direct seeded tomato operations, please refer to the study titled, â€Å"Sample Costs to Produce Processing Tomatoes, Directed Seeded, in the Sacramento Valley – 2007†. The grower also owns various investments such as a shop and an equipment yard. In this report, practices completed on less than 100% of the acres are denoted as a percentage of the total tomato crop acreage.CULTURAL PRACTICES AND MATERIAL INPUTS Land Preparation. Primary tillage which includes laser leveling, discing, rolling, subsoiling, land planing, and listing beds is done from August through early November in the year preceding transplanting. To maintain surface grade, 4% of the acres are laser leveled each year. Fields are stubbledisced and rolled (using a rice roller). Fields are subsoiled in two passes to a 30-inch depth and rolled. A medium-duty disk with a flat roller following is used. Ground is smoothed in two passes with a triplane. Beds on five-foot centers are made with a six-bed lister, and then shaped with a bed-shaper cultivator.Transplanting. Planting is spread over a three-month period (late March through early June) to meet contracted weekly delivery schedules at harvest. The transplants are planted in a single line per bed. Direct seed is for the early season and precedes transplanting. All of the 630 acres are custom planted with greenhouse-grown transplants. Costs for extra seed (15%) purchased to allow for less than 100% germination and for non-plantable transplants are included in the respective categories in Table 2. Fertilization. In the fall, ahead of listing beds, a soil amendment, gypsum at 3. 0 tons per acre is custom broadcast spread on 20% of the acres.After listing, as part of the bed shaping operation, 11-52-0 is shanked into the beds at 100 pounds per acre. Prior to planting, liquid starter fertilizer, 8-24-6 plus zinc, is banded below the seed line at 15 gallons of material per acre. Nitrogen fertilizer, UN-32 at 150 pounds of N per acre is sidedress-banded at layby. Additional N is applied under special needs on 20% of acres as CAN 17 at 100 pounds of product per acre as a sidedress. Irrigation. In this study, water is calculated to cost $31. 92 per acre-foot or $2. 66 per acre-inch and is a combination of 1/2 well water ($47. 67 per acre-foot) and 1/2 canal delivered surface water ($16. 17 per acre-foot).The irrigation costs shown in Tables 1 and 3 include water, pumping, and labor charges. The transplants receive a single sprinkler irrigation after planting. Prior to initial furrow irrigation, fields are all chiseled to 12 inches deep in the furrow. Eight furrow irrigations are applied during the season. In 2008 Transplanted Processing Tomato Cost and Returns Study Sacramento Valley UC Cooperative Extension 3 this study 3. 5 acre-feet (42 acre-inches) is applied to the crop – 2. 0 acre-inches by sprinkler and 40 acreinches by furrow. Although sub-surface drip irrigation is gaining in popularity, it is not used in this study. Pest Management. The pesticides and rates mentioned in this cost study are listed n Integrated Pest Management for Tomatoes and UC Pest Management Guidelines, Tomato. For more information on other pesticides available, pest identification, monitoring, and management visit the UC IPM website at www. ipm. ucdavis. edu. Written recommendations are required for many pesticides and are made by licensed pest control advisors. For information and pesticide use permits, contact the local county agricultural commissioner's office. Weeds. Beginning in January, Roundup plus Goal is sprayed on the fallow beds to control eme rged weeds and repeated later with Roundup only. Before planting, the beds are cultivated twice to control weeds and to prepare the seedbed.Wilcox Performer conditions bed and applies starter fertilizer. Trifluralin is broadcast sprayed at 1. 0 pint per acre and incorporated with a power mulcher. To control nutsedge, Dual Magnum at 1. 5 pints of product per acre is added to trifluralin as a tank-mix and applied to 30% or 189 acres. Matrix is applied to 80% or 504 acres in an 18-inch band at a rate of 2. 0 ounces of material per acre to control a range of weeds. A combination of hand weeding and mechanical cultivation is also used for weed control. The crop is mechanically cultivated with sled-mounted cultivators three times during the season. A contract labor crew hand removes weeds.Insects and Diseases. The primary insect pests of seedlings included in this study are flea beetle, darkling ground beetle, and cutworm. Foliage and fruit feeders included are tomato fruitworm, various a rmyworm species, russet mite, stinkbug, and potato aphid. Diseases are primarily bacterial speck, late blight, and blackmold fruit rot. A Kocide and Dithane tank mix for bacterial speck is applied to 30% of the acres. All of the above applications are made by ground. The following applications are made by aircraft. Sulfur dust for russet mite control is applied to 70% of the acres. Asana for general insect control is applied to 40% of the acres.Confirm for worm control is applied to 100% of the acres. Bravo is applied in June to 5% of the acres for late blight control and again in September as a fruit protectant fungicide on 15% of the acres. Fruit Ripener. Ethrel, a fruit ripening agent, is applied by ground before harvest to 5% of the acres at 4. 0 pints per acre. Harvest. The fruit is mechanically harvested using one primary harvester for 90% of the acres and one older harvester for special harvest situations and as a backup to the primary harvester. Typically growers with this a creage of processing tomatoes own tractors, trailer dollies, generator-light machines, and harvest support equipment.Four manual sorters, a harvester driver, and two bulk-trailer tractor operators are used per harvester. A seasonal average of 1. 5 loads per hour at 25 tons per load are harvested with two (one day and one night) shifts of 10 hours each. Harvest efficiency includes down time, scheduled daily breaks, and transportation between fields. The processor pays the transportation cost of the tomatoes from the field to the processing plant. Costs for harvest operations are shown in Tables 1, 3 and 7; the equipment used is listed in Tables 4 and 5. If tomatoes are custom harvested, harvest expenses are subtracted from harvest costs in Tables 1 and 3, and the custom harvest charges added.The equipment for harvest operations is then subtracted from investment costs in Table 4. Growers may choose to own harvesting equipment, purchased either new or 2008 Transplanted Processing Toma to Cost and Returns Study Sacramento Valley UC Cooperative Extension 4 used, or hire a custom harvester. Many factors are important in deciding which harvesting option a grower uses. The options are discussed in â€Å"Acquiring Alfalfa Hay Harvest Equipment: A Financial Analysis of Alternatives†. Yields. County average annual tomato crop yields in the Sacramento Valley over the past ten years ranged from 26. 34 to 43. 00 tons per acre. The reporting counties are Colusa, Sacramento, Solano, Sutter, Yolo, and sometimes Glenn counties.Butte and Tehama are the only two Sacramento Valley counties that do not report processing tomatoes. The weighted average yields for the Sacramento Valley from 1997 to 2006 are shown in Table A. In this study, a yield of 35 tons per acre is used. Table A. Sacramento Valley Yield and Price †  Tons $ Year per acre per ton 2006 35. 44 59. 28 2005 34. 30 49. 81 2004 40. 51 48. 06 2003 33. 74 48. 82 2002 37. 64 48. 37 2001 35. 23 48. 49 2000 34. 44 49. 54 1999 34. 58 58. 68 1998 29. 90 53. 68 1997 33. 24 50. 85 Average 34. 90 51. 56 Returns. Customarily, growers produce tomatoes under contract with various food processing companies. County †  Source: California Agricultural Commissioner Crop Reports. verage prices in the Sacramento Valley ranged from $45. 66 to $62. 00 per ton over the last 10 years and the Valley-wide weighted averages are shown in Table A. A price of $70. 00 per ton is used in this study to reflect the return price growers are currently receiving. Assessments. Under a state marketing order a mandatory assessment fee is collected and administered by the Processing Tomato Advisory Board (PTAB). The assessment pays for inspecting and grading fruit, and varies between inspection stations. In Yolo County, inspection fees range from $6. 36 to $8. 90 per load with an average of $6. 75. Growers and processors share equally in the fee; growers pay $3. 38 per load in this study.A truckload is assumed to be 25 to ns. Tomato growers are also assessed a fee for the Curly Top Virus Control Program (CTVCP) administered by the California Department of Food and Agriculture (CDFA). Growers in Yolo County (District 111) are charged $0. 019 per ton. Additionally, several voluntary organizations assess member growers. California Tomato Growers Association (CTGA) represents growers’ interest in negotiating contract prices with processors. CTGA membership charges are $0. 17 per ton. The California Tomato Research Institute funds projects for crop improvement. CTRI membership charges are $0. 07 per ton. Labor. Basic hourly wages for workers are $11. 56 and $8. 0 per hour for machine operators and nonmachine (irrigators and manual laborers) workers, respectively. Adding 36% for the employer’s share of federal and state payroll taxes, insurance and other benefits raises the total labor costs to $15. 72 per hour for machine operators and $10. 88 per hour for non-machine labor. The labor for op erations involving machinery is 20% higher than the field operation time, to account for equipment set up, moving, maintenance, and repair. The current minimum wage is $8. 00 per hour. CASH OVERHEAD Cash overhead consists of various cash expenses paid out during the year that are assigned to the whole farm and not to a particular operation.These costs include property taxes, interest on operating capital, office expense, liability and property insurance, share rent, supervisors’ salaries, field sanitation, crop insurance, and investment repairs. Employee benefits, insurance, and payroll taxes are included in labor costs and not in overhead. Cash overhead costs are shown in Tables 1, 2, 3, and 4. Property Taxes. Counties charge a base property tax rate of 1% on the assessed value of the property. In some counties special assessment districts exist and charge additional taxes on property including equipment, buildings, and improvements. For this study, county taxes are calculat ed as 1% of the average value of the property. Average value equals new cost plus salvage value divided by 2 on a per acre basis. 008 Transplanted Processing Tomato Cost and Returns Study Sacramento Valley UC Cooperative Extension 5 Interest o n Operating Capital. Interest on operating capital is based on cash operating costs and is calculated monthly until harvest at a nominal rate of 6. 75% per year. A nominal interest rate is the typical market cost of borrowed funds. Insurance. Insurance for farm investments varies depending on the assets included and the amount of coverage. Property insurance provides coverage for property loss and is charged at 0. 740% of the average value of the assets over their useful life. Liability insurance covers accidents on the farm and costs $1,438 for the entire farm or $0. 50 per acre. Office Expense.Office and business expenses are estimated to be $50,489 for the entire farm or $17. 41 per acre. These expenses include office supplies, telephones, bookkeeping, accounting, legal fees, road maintenance, office and shop utilities, and miscellaneous administrative expenses. Share Rent. Rent arrangements will vary. The tomato land in this study is leased on a share-rent basis with the landowner receiving 12% of the gross returns. The land rented includes developed wells and irrigation system. Field Supervisors’ Salary. Supervisor salaries for tomatoes, including insurance, payroll taxes, and benefits, and are $94,500 per year for two supervisors.Two thirds of the supervisors’ time is allocated to tomatoes. The costs are $70. 00 per acre. Any returns above total costs are considered returns on risk and investment to management (or owners). Field Sanitation. Sanitation services provide portable toilet and washing facilities for the ranch during the crop season. The cost includes delivery and weekly service. Costs will vary depending upon the crops and number of portable units required. Crop Insurance. The insurance pro tects the grower from crop losses due to adverse weather conditions, fire, unusual diseases and/or insects, wildlife, earthquake, volcanic eruption, and failure of the irrigation system.The grower can choose the protection level at 50% to 75% of production history or county yields. In this study, no level is chosen. The cost shown in the study is the average of the costs paid by the growers who reviewed this study. NON-CASH OVERHEAD Non-cash overhead is calculated as the capital recovery cost for equipment and other farm investments. Although farm equipment used for processing tomatoes may be purchased new or used, this study shows the current purchase price for new equipment. The new purchase price is adjusted to 60% to reflect a mix of new and used equipment. Annual ownership costs (equipment and investments) are shown in Tables 1, 2, and 5.They represent the capital recovery cost for investments on an annual per acre basis. Capital Recovery Costs. Capital recovery cost is the ann ual depreciation and interest costs for a capital investment. It is the amount of money required each year to recover the difference between the purchase price and salvage value (unrecovered capital). It is equivalent to the annual payment on a loan for the investment with the down payment equal to the discounted salvage value. This is a more complex method of calculating ownership costs than straight-line depreciation and opportunity costs, but more accurately represents the annual costs of ownership because it takes the time value of money into account (Boehlje and Eidman).The formula for the calculation of the annual capital recovery costs is; Capital *# && # * ,% Purchase † Salvage( ) %Recovery(/ + ,Salvage ) Interest/ % ( Pr ice Value Value Rate + . ‘ $ , / ‘. Factor +$ 2008 Transplanted Processing Tomato Cost and Returns Study ! Sacramento Valley UC Cooperative Extension 6 Salvage Value. Salvage value is an estimate of the remaining value of an investment at the end of its useful life. For farm machinery the remaining value is a percentage of the new cost of the investment (Boehlje and Eidman). The percent remaining value is calculated from equations developed by the American Society of Agricultural Engineers (ASAE) based on equipment type and years of life. The life in years is estimated by dividing the wear out life, as given by ASAE by the annual hours of use in this operation.For other investments including irrigation systems, buildings, and miscellaneous equipment, the value at the end of its useful life is zero. The salvage value for land is equal to the purchase price because land does not depreciate. The purchase price and salvage value for certain equipment and investments are shown in Table 5. Capital Recovery Factor. Capital recovery factor is the amortization factor or annual payment whose present value at compound interest is 1. The amortization factor is a table value that corresponds to the interest rate and the life of t he equipment. Interest Rate. The interest rate of 4. 25% used to calculate capital recovery cost is the effective long-term interest rate in January 2008.The interest rate is used to reflect the long-term realized rate of return to these specialized resources that can only be used effectively in the agricultural sector. Equipment Costs. Equipment costs are composed of three parts: non-cash overhead, cash overhead, and operating costs. Some of the cost factors have been discussed in previous sections. The operating costs consist of repairs, fuel, and lubrication. The fuel, lube, and repair cost per acre for each operation in Table 1 is determined by multiplying the total hourly operating cost in Table 5 for each piece of equipment used for the selected operation by the hours per acre. Tractor time is 10% higher than implement time for a given operation to account for setup, travel and down time. Repairs, Fuel and Lube.Repair costs are based on purchase price, annual hours of use, tot al hours of life, and repair coefficients formulated by the ASAE. Fuel and lubrication costs are also determined by ASAE equations based on maximum Power-Take-Off horsepower, and fuel type. Prices for on-farm delivery of diesel and unleaded gasoline are $3. 54 and $3. 57 per gallon, respectively. Irrigation System. Irrigation equipment owned by the grower consists of main lines, hand moved sprinklers, portable pumps, V-ditchers, and siphon tubes. Risk. Risks associated with processing tomato production are not assigned a production cost. All acres are contracted prior to harvest and all tonnage-time delivery contracts are assumed to have been met. No excess acres are grown to fulfill contracts.While this study makes an effort to model a production system based on typical, real world practices, it cannot fully represent financial, agronomic and market risks which affect the profitability and economic viability of processing tomato production. Table Values. Due to rounding the totals may be slightly different from the sum of the components. 2008 Transplanted Processing Tomato Cost and Returns Study Sacramento Valley UC Cooperative Extension 7 REFERENCES American Society of Agricultural Engineers. 2003. American Society of Agricultural Engineers Standards Yearbook. Russell H. Hahn and Evelyn E. Rosentreter (ed. ) St. Joseph, Missouri. 41st edition. Barker, Doug.California Workers’ Compensation Rating Data for Selected Agricultural Classifications as of January 2008. California Department of Insurance, Rate Regulation Branch. Boehlje, Michael D. , and Vernon R. Eidman. 1984. Farm Management. John Wiley and Sons. New York, NY. Blank, Steve, Karen Klonsky, Kim Norris, and Steve Orloff. 1992. Acquiring Alfalfa Hay Harvest Equipment: A Financial Analysis of Alternatives. University of California. Oakland, CA. Giannini Information Series No. 92-1. http://giannini. ucop. edu/InfoSeries/921-HayEquip. pdf. Internet accessed May, 2008. California State Automobile As sociation. 2008. Gas Price Averages 2007 – 2008.AAA Press Room, San Francisco, CA. http://www. csaa. com/portal/site/CSAA/menuitem. 5313747aa611bd4e320cfad592278a0c/? vgnextoid= 8d642ce6cda97010VgnVCM1000002872a8c0RCRD. Internet accessed April, 2008. California State Board of equalization. Fuel Tax Division Tax Rates. http://www. boe. ca. gov/sptaxprog/spftdrates. htm. Internet accessed April, 2008. CDFA-California County Agricultural Commissioners, California Annual Agricultural Crop Reports. 1998 – 2007. California Department of Food and Agricultural, Sacramento, CA. http://www. nass. usda. gov/ca/bul/agcom/indexcac. htm. Internet accessed May, 2008. Energy Information Administration. 2008.Weekly Retail on Highway http://tonto. eia. doe. gov/oog/info/gdu/gasdiesel. asp. Internet accessed April, 2008. Diesel Prices. Integrated Pest Management Education and Publications. 2008. â€Å"UC Pest Management Guidelines, Tomatoes. † In M. L. Flint (ed. ) UC IPM Pest Man agement Guidelines. University of California. Division of Agriculture and Natural Resources. Oakland, CA. Publication 3339. http://www. ipm. ucdavis. edu/PMG/selectnewpest. tomatoes. html. Internet accessed May, 2008. Miyao, Gene, Karen M. Klonsky, and Pete Livingston. 2007. â€Å"Sample Costs To Produce Processing Tomatoes, Transplanted, In the Sacramento Valley – 2007†. University of California, Cooperative Extension.Department of Agricultural and Resource Economics. Davis, CA. http://coststudies. ucdavis. edu/. Internet accessed April, 2008. Miyao, Gene, Karen M. Klonsky, and Pete Livingston. 2007. Sample Costs to Produce Processing Tomatoes, Direct Seeded, in the Sacramento Valley – 2007. University of California, Cooperative Extension. Department of Agricultural and Resource Economics. Davis, CA. http://coststudies. ucdavis. edu/. Internet accessed, April, 2008. 2008 Transplanted Processing Tomato Cost and Returns Study Sacramento Valley UC Cooperative Exte nsion 8 Statewide Integrated Pest Management Project. 1998. Integrated Pest Management for Tomatoes. Fourth Edition. University of California.Division of Agriculture and Natural Resources. Oakland, CA. Publication 3274. http://www. ipm. ucdavis. edu/PMG/selectnewpest. tomatoes. html. Internet accessed April, 2008. USDA-ERS. 2008. Farm Sector: Farm Financial Ratios. Agriculture and Rural Economics Division, ERS. USDA. Washington, DC. http://usda. mannlib. cornell. edu/reports/nassr/price/zapbb/agpran04. txt; Internet accessed January, 2008. ________________________ For information concerning the above or other University of California publications, contact UC DANR Communications Services at 800994-8849, online at http://anrcatalog. ucdavis. edu/InOrder/Shop/Shop. asp, or your local county UC Cooperative Extension office. 008 Transplanted Processing Tomato Cost and Returns Study Sacramento Valley UC Cooperative Extension 9 Table 1. UC COOPERATIVE EXTENSION COSTS PER ACRE TO PRODUCE TO MATOES SACRAMENTO VALLEY – 2008 TRANSPLANTED Labor Rate: $15. 72/hr. machine labor $10. 88/hr. non-machine labor Interest Rate: 6. 75% Yield per Acre: 35. 0 Ton Operation —————— Cash and Labor Costs per Acre —————–Time Labor Fuel, Lube Material Custom/ Total (Hrs/A) Cost & Repairs Cost Rent Cost 0. 00 0. 14 0. 42 0. 15 0. 36 0. 00 0. 10 0. 25 0. 08 0. 08 0. 26 1. 83 0. 17 0. 33 0. 00 0. 16 3. 00 0. 61 0. 33 0. 25 0. 25 0. 03 0. 04 10. 00 0. 00 0. 04 0. 00 0. 07 0. 00 0. 50 0. 00 0. 00 0. 0 0. 00 0. 32 0. 32 16. 42 0. 10 0. 93 0. 46 1. 49 0. 00 0. 00 0 3 8 3 7 0 2 5 1 1 10 39 3 6 0 3 33 12 6 5 5 1 1 109 0 1 0 1 0 9 0 0 0 0 12 6 212 2 58 32 92 0 0 344 0 18 53 10 22 0 6 12 3 3 19 145 7 13 0 6 0 21 13 15 12 1 2 0 0 2 0 3 0 17 0 0 0 0 8 0 122 4 177 34 215 0 0 482 0 0 0 0 0 79 0 42 12 13 0 146 36 13 354 9 18 0 112 0 0 5 0 107 1 0 15 20 0 0 5 4 27 2 0 0 727 0 0 0 0 14 14 887 7 0 0 0 0 1 0 0 0 0 0 8 0 0 165 0 0 0 0 0 0 0 0 0 0 0 6 0 50 0 3 1 6 0 0 0 231 0 0 0 0 0 0 239 7 20 61 13 29 81 8 59 16 17 28 338 46 33 519 19 51 32 131 20 17 6 3 216 1 3 21 24 50 27 7 4 33 2 20 6 1,292 6 235 66 308 14 14 66 2,017 1 17 0 25 70 294 6 4 6 423 2,440Operation Preplant: Land Preparation – Laser Level – 4% of Acreage Land Preparation – Stubble Disc & Roll Land Preparation – Subsoil & Roll 2X Land Preparation – Disc & Roll Land Preparation – Triplane 2X Land Preparation – Apply Gypsum on 20% of Acreage Land Preparation – List Beds Land Preparation – Shape & Fertilize (11-52-0) Weed Control – Roundup & Goal Weed Control – Roundup Weed Control – Cultivate 2X TOTAL PREPLANT COSTS Cultural: Condition Bed & Starter Fertilizer Mulch Beds & Apply Treflan (& Dual on 30% of Acreage) Transplant Tomatoes Weed Control – Apply Matrix on 80% of Acreage Irrigate – Sprinklers 1X Weed Control – Cultivate 3X Fer tilize – 150 Lbs N Sidedress Chisel Furrows Mulch Beds Disease Control – Bacterial Speck on 30% of Acreage Open Ditches Irrigate – Furrow 8X Disease Control – Late Blight on 5% of Acreage Close Ditches Mite Control – Sulfur on 70% of Acreage Fertilize – 20 Lbs N on 20% of Acreage Weed Control – Hand Hoe – Contract Train Vines Insect Control – Aphid on 40% of Acreage Disease Control – Fruit Rot on 15% of Acreage Insect Control – Worms Fruit Ripener – Ethrel on 5% of Acreage Pickup Truck Use (2 pickups) ATV Use TOTAL CULTURAL COSTS Harvest: Open Harvest Lane on 8% of Acreage Harvest In Field Hauling TOTAL HARVEST COSTS Assessment: Assessments/Fees TOTAL ASSESSMENT COSTS Interest on Operating Capital @ 6. 75% TOTAL OPERATING COSTS/ACRE CASH OVERHEAD: Liability Insurance Office Expense Field Sanitation Crop Insurance Field Supervisors' Salary (2) Land Rent @ 12% of Gross Returns Property Taxes Property I nsurance Investment Repairs TOTAL CASH OVERHEAD COSTS TOTAL CASH COSTS/ACRE Your Cost 008 Transplanted Processing Tomato Cost and Returns Study Sacramento Valley UC Cooperative Extension 10 UC COOPERATIVE EXTENSION Table 1 continued NON-CASH OVERHEAD: Investment Shop Building Storage Building Fuel Tanks & Pumps Shop Tools Booster Pumps Sprinkler Pipe Main Line Pipe – 10†³ Semi Truck & Lowbed Trailer Pipe Trailers Truck-Service – 2 Ton Generators & Light Fuel Wagons Closed Mix System Siphon Tubes Implement Carrier Equipment TOTAL NON-CASH OVERHEAD COSTS TOTAL COSTS/ACRE Per producing Acre 25 10 8 5 21 52 28 12 12 13 3 1 2 4 3 755 953 — Annual Cost -Capital Recovery 2 1 1 0 2 6 3 1 1 3 1 0 0 0 0 94 116 2 1 1 0 2 6 3 1 1 3 1 0 0 0 0 94 116 2,555 008 Transplanted Processing Tomato Cost and Returns Study Sacramento Valley UC Cooperative Extension 11 Table 2. UC COOPERATIVE EXTENSION COSTS and RETURNS PER ACRE to PRODUCE TOMATOES SACRAMENTO VALLEY – 2008 T RANSPLANTED Labor Rate: $15. 72/hr. machine labor $10. 88/hr. non-machine labor Interest Rate: 6. 75% Yield per Acre: 35. 0 Ton Price or Value or Cost/Unit Cost/Acre 70. 00 2,450 2,450 Your Cost Quantity/Acre Unit GROSS RETURNS Processing Tomatoes 35. 00 TOTAL GROSS RETURNS FOR PROCESSING TOMATOES OPERATING COSTS Custom: Laser Level 0. 04 Gypsum Application 0. 20 Transplanting 8. 70 Air Application – Spray 10 Gal/Acre 1. 60 Air Application – Dust 28. 0 Fertilizer: Gypsum 0. 60 11-52-0 100. 00 8-24-6 15. 00 Zinc Chelate 6% 2. 00 UN-32 150. 00 CAN 17 118. 00 Herbicide: Roundup Ultra 2. 50 Goal 2XL 3. 00 Dual Magnum 0. 45 Treflan HFP 1. 00 Matrix DF 0. 48 Seed: Tomato Seed 10. 01 Transplant: Transplants – Growing 8. 70 Irrigation: Water 42. 00 Pump – Fuel, Lube, & Repairs 1. 00 Fungicide: Kocide 101 0. 60 Dithane DF 0. 60 Sulfur, Dust 98% 28. 00 Insecticide: Bravo Weatherstik 0. 60 Warrior T 1. 54 Confirm 12. 00 Contract: Contract Labor 5. 00 Growth Regulato r: Ethrel 0. 03 Assessment: CDFA-CTVP 35. 00 CTGA 35. 00 CTRI 35. 00 PTAB 35. 00 Labor (machine) 9. 34 Labor (non-machine) 18. 08 Fuel – Gas 1. 5 Fuel – Diesel 77. 61 Lube Machinery repair Interest on Operating Capital @ 6. 75% TOTAL OPERATING COSTS/ACRE NET RETURNS ABOVE OPERATING COSTS/ACRE Ton Acre Ton Thou Acre Lb Ton Lb Lb Pint Lb N Lb Pint FlOz Pint Pint Oz Thou Thou AcIn Acre Lb Lb Lb Pint FlOz FlOz Hour Gal Ton Ton Ton Ton Hrs Hrs Gal Gal 165. 00 7. 00 19. 00 6. 25 0. 20 132. 00 0. 419 2. 28 0. 913 0. 745 0. 171 8. 59 1. 03 18. 63 4. 84 19. 25 11. 00 28. 00 2. 67 13. 00 3. 62 3. 89 0. 55 7. 85 3. 05 2. 23 9. 99 63. 00 0. 019 0. 17 0. 07 0. 135 15. 72 10. 88 3. 57 3. 54 7 1 165 10 6 79 42 34 2 112 20 21 3 8 5 9 110 244 112 13 2 2 15 5 5 27 50 2 1 6 2 5 147 197 7 275 42 159 66 2,017 406 008 Transplanted Processing Tomato Cost and Returns Study Sacramento Valley UC Cooperative Extension 12 UC COOPERATIVE EXTENSION Table 2 continued CASH OVERHEAD COSTS: Liability I nsurance Office Expense Field Sanitation Crop Insurance Field Supervisors' Salary (2) Land Rent @ 12% of Gross Returns Property Taxes Property Insurance Investment Repairs TOTAL CASH OVERHEAD COSTS/ACRE TOTAL CASH COSTS/ACRE NON-CASH OVERHEAD COSTS (CAPITAL RECOVERY): Shop Building Storage Building Fuel Tanks & Pumps Shop Tools Booster Pumps Sprinkler Pipe Main Line Pipe – 10†³ Semi Truck & Lowbed Trailer Pipe Trailers Truck-Service – 2 Ton Generators & Light Fuel Wagons Closed Mix SystemSiphon Tubes Implement Carrier Equipment TOTAL NON-CASH OVERHEAD COSTS/ACRE TOTAL COSTS/ACRE NET RETURNS ABOVE TOTAL COSTS/ACRE 1 17 0 25 70 294 6 4 6 423 2,440 2 1 1 0 2 6 3 1 1 3 1 0 0 0 0 94 116 2,555 -105 2008 Transplanted Processing Tomato Cost and Returns Study Sacramento Valley UC Cooperative Extension 13 Table 3. UC COOPERATIVE EXTENSION MONTHLY CASH COST PER ACRE TO PRODUCE TOMATOES SACRAMENTO VALLEY – 2008 TRANSPLANTED SEP 07 7 20 61 13 29 81 8 59 16 17 28 62 46 3 3 519 19 51 14 20 17 6 2 54 2 54 3 21 24 50 27 7 4 33 2 2 0 42 2 12 6 21 14 14 11 87 OCT 07 NOV 07 DEC 07 JAN 08 FEB MAR 08 08 APR MAY 08 08 JUN 08 JUL AUG 08 08 SEP 08 TOTALBeginning SEP 07 Ending SEP 08 Preplant: Laser Level – 4% of Acreage Land Prep – Stubble Disc & Roll Land Prep – Subsoil & Roll 2X Land Prep – Disc & Roll Land Prep – Triplane 2X Land Prep – Apply Gypsum on 20% of Acreage Land Prep – List Beds Land Prep – Shape Beds & Fertilize Weed Control – Roundup & Goal Weed Control – Roundup Weed Control – Cultivate 2X TOTAL PREPLANT COSTS Cultural: Condition Bed & Starter Fertilizer Mulch Beds & Apply Herbicide Transplant Tomatoes Weed Control – Apply Matrix on 80% of Acreage Irrigate – Sprinklers 1X Weed Control – Cultivate 2X Fertilize – 150 Lbs N – Sidedress Chisel Furrows Mulch Beds Disease Control – Bacterial Speck – 30% of Acreage Open Ditches Irrigate – Furrow 8X Disease Control – Late Blight 5% of Acreage Close Ditches Mite Control – Sulfur 70% of Acreage Fertilize – 20 Lb N 20% of Acreage Weed Control – Hand Hoe Train Vines Insect Control – Aphids 40% of Acreage Disease Control – Fruit Rot 15% of Acreage Insect Control – Worms – Confirm Fruit Ripener – Ethrel 5% of Acreage Pickup Truck Use (2 pickups) ATV Use TOTAL CULTURAL COSTS Harvest: Open Harvest Lane 8% of Acreage Harvest In Field Hauling TOTAL HARVEST COSTS Assessment: Assessments/Fees TOTAL ASSESSMENT COSTS Interest on Operating Capital @ 6. 5% TOTAL OPERATING COSTS/ACRE OVERHEAD: Liability Insurance Office Expense Field Sanitation Crop Insurance Field Supervisors' Salary (2) Land Rent @ 12% of Gross Returns Property Taxes Property Insurance Investment Repairs TOTAL CASH OVERHEAD COSTS TOTAL CASH COSTS/ACRE 210 67 7 20 61 13 29 81 8 59 16 17 28 338 46 33 519 19 51 32 131 20 17 6 3 216 1 3 21 24 50 27 7 4 33 2 20 6 1,292 6 235 66 308 14 14 66 2,017 1 17 0 25 70 294 6 4 6 423 2,440 7 131 10 54 54 1 2 0 2 2 0 2 2 0 2 2 0 2 2 0 48 2 0 2 2 0 35 2 0 686 2 0 211 2 0 57 2 0 200 2 111 31 144 2 0 2 2 111 29 143 1 213 2 70 2 4 2 4 2 112 1 1 0 25 5 2 4 2 37 6 693 7 219 8 65 10 354 11 155 1 0 5 1 0 5 1 0 5 1 0 5 1 0 5 3 2 0 12 16 1 0 5 1 0 5 1 0 5 1 0 5 1 0 5 3 2 0 12 367 1 0 5 1 0 5 294 0 7 220 0 7 78 0 7 11 0 7 11 0 33 145 0 7 44 0 7 700 0 7 226 0 7 72 0 7 162 301 388 2008 Transplanted Processing Tomato Cost and Returns Study Sacramento ValleyUC Cooperative Extension 14 Table 4. UC COOPERATIVE EXTENSION WHOLE FARM ANNUAL EQUIPMENT, INVESTMENT, AND BUSINESS OVERHEAD COSTS SACRAMENTO VALLEY – 2008 TRANSPLANTED ANNUAL EQUIPMENT COSTS – Cash Overhead Insurance Taxes 318 430 331 448 477 645 828 1,118 1,060 1,433 211 285 17 24 58 78 45 60 22 30 132 178 58 79 22 29 245 330 195 263 36 49 209 283 1,265 1,710 99 134 91 123 72 97 72 97 9 12 62 83 62 83 35 47 10 14 10 14 10 14 10 14 9 12 175 236 6 8 6 8 6 8 6 8 97 131 70 94 20 26 6,465 8,737 3,879 5,242 Description 110 HP 2WD Tractor 130 HP 2WD Tractor 155 HP 2WD Tractor 200 HP Crawler 425 HP Crawler 92 HP 2WD Tractor ATV Bed Shaper – 3 Row Cultivator –Alloway 3 Row Cultivator – Perfecta 3 Row Cultivator – Performer 3 Row Cultivator – 3 Row Cultivator – Sled 3 Row Disc – Stubble 18†² Disc – Finish 25†² Ditcher – V Harvester Tomato – Used Harvester -Tomato Lister – 3 Row Mulcher – 15†² Pickup Truck – 1/2 Ton Pickup Truck – 3/4 Ton Rear Blade – 8†² Rice Roller – 18†² Flat Roller – 18†² Ringroller – 30†² Saddle Tank – 300 Gallon Saddle Tank – 300 Gallon Saddle Tank – 300 Gallon Saddle Tank – 300 Gallon Spray Boom – 25†² Subsoiler – 16†² – 9 Shank Trailer Dolly Trailer Dolly Trailer Dolly Trai ler Dolly Triplane – 16†² Vine Diverter Vine Trainer TOTAL 60% of New Cost * * Used to reflect a mix of new and used equipment. Yr 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 Price 66,445 69,163 99,594 172,650 221,197 44,015 4,017 13,292 10,236 5,100 30,281 11,868 4,980 49,847 44,743 8,631 46,108 331,980 20,176 20,507 17,655 17,655 2,269 14,139 14,139 7,952 2,374 2,374 2,374 2,374 1,781 35,605 1,451 1,451 1,451 1,451 22,253 16,046 4,800 1,444,424 866,654 Yrs Life 10 10 10 10 10 10 10 10 10 10 10 5 10 5 10 12 8 8 5 9 7 7 15 10 10 10 10 10 10 10 5 5 15 15 15 15 10 10 10Salvage Value 19,627 20,430 29,418 50,998 65,338 13,001 710 2,351 1,810 902 5,355 3,866 881 16,237 7,912 1,195 10,411 10,000 6,572 4,098 1,766 1,766 218 2,500 2,500 1,406 420 420 420 420 580 11,598 139 139 139 139 3,935 2,838 480 302,935 181,761 Capital Recovery 6,678 6,952 10,010 17,353 22,233 4,424 443 1,466 1,129 562 3,339 1,974 549 8,293 4,934 855 5,799 48,743 3,357 2,406 2,747 2,747 197 1,559 1,559 877 262 262 262 262 296 5,923 126 126 126 126 2,454 1,769 560 173,739 104,243 Total 7,427 7,731 11,133 19,299 24,726 4,920 484 1,602 1,234 615 3,649 2,111 600 8,868 5,392 940 6,291 51,718 3,589 2,620 2,916 2,916 219 1,704 1,704 958 286 286 286 286 317 6,334 140 140 140 140 2,682 1,934 606 188,941 113,364 2008 Transplanted Processing Tomato Cost and Returns Study Sacramento Valley UC Cooperative Extension 15UC COOPERATIVE EXTENSION Table 4 continued ANNUAL INVESTMENT COSTS —— Cash Overhead —–Insurance Taxes Repairs 243 18 89 9 31 40 328 132 147 294 59 45 614 118 157 2,325 329 24 121 12 42 54 444 178 199 397 80 61 830 160 212 3,142 1,643 221 439 44 210 487 2,219 700 531 722 145 313 4,152 586 3,860 16,272 Description INVESTMENT Booster Pumps Closed Mix System Fuel Tanks & Pumps Fuel Wagons Generators & Light Implement Carrier Main Line Pipe – 10†³ Pipe Trailers Semi Truck & Lowb ed Trailer Shop Building Shop Tools Siphon Tubes Sprinkler Pipe Storage Building Truck-Service – 2 Ton TOTAL INVESTMENT Price 59,757 4,412 21,949 2,186 7,620 9,742 80,676 35,000 36,170 72,168 14,465 11,066 150,980 29,112 38,600 573,903Yrs Life 10 10 20 10 5 15 10 10 15 25 20 15 10 20 5 Salvage Value 5,976 441 2,195 219 762 974 8,068 700 3,617 7,217 1,447 1,107 15,098 2,911 3,860 54,592 Capital Recovery 6,967 514 1,579 255 1,584 844 9,407 4,311 3,133 4,575 1,041 958 17,604 2,095 8,022 62,889 Total 9,182 778 2,228 320 1,867 1,424 12,398 5,322 4,010 5,988 1,324 1,377 23,201 2,959 12,252 84,629 ANNUAL BUSINESS OVERHEAD Units/ Farm 900 2,900 900 900 2,900 2,900 Price/ Unit 25. 00 0. 48 70. 00 294. 00 0. 50 17. 41 Total Cost 22,500 1,392 63,000 264,600 1,450 50,489 Description Crop Insurance Field Sanitation Field Supervisors' Salary (2) Land Rent @ 12% of Gross Returns Liability Insurance Office ExpenseUnit Acre Acre Acre Acre Acre Acre 2008 Transplanted Processing Tomato Cost and Returns Study Sacramento Valley UC Cooperative Extension 16 Table 5. UC COOPERATIVE EXTENSION HOURLY EQUIPMENT COSTS SACRAMENTO VALLEY – 2008 TRANSPLANTED ——————- COSTS PER HOUR —————————- Cash Overhead ——– Operating ——-InsurFuel & Total Total ance Taxes Repairs Lube Oper. Costs/Hr. 0. 13 0. 18 3. 12 25. 99 29. 11 32. 20 0. 17 0. 22 3. 25 30. 71 33. 96 37. 82 0. 24 0. 32 4. 67 36. 62 41. 29 46. 86 0. 31 0. 42 4. 63 47. 25 51. 88 59. 12 0. 40 0. 54 5. 93 100. 40 106. 33 115. 61 0. 11 0. 14 2. 06 30. 71 32. 77 35. 24 0. 05 0. 07 1. 09 0. 0 1. 09 2. 54 0. 17 0. 24 2. 87 0. 00 2. 87 7. 69 0. 13 0. 18 2. 21 0. 00 2. 21 5. 92 0. 07 0. 09 1. 05 0. 00 1. 05 2. 90 0. 35 0. 47 6. 25 0. 00 6. 25 15. 98 0. 07 0. 09 2. 68 0. 00 2. 68 5. 05 0. 03 0. 05 1. 08 0. 00 1. 08 2. 03 0. 37 0. 50 8. 52 0. 00 8. 52 21. 85 0. 59 0. 79 7. 43 0. 00 7. 43 23. 64 0. 13 0. 18 2. 42 0. 00 2. 42 5. 84 0. 63 0. 85 2. 08 61. 07 63. 15 82. 07 1. 09 1. 47 124. 44 61. 07 185. 51 229. 90 0. 15 0. 21 4. 24 0. 00 4. 24 9. 76 0. 15 0. 20 2. 36 0. 00 2. 36 6. 67 0. 16 0. 22 1. 27 11. 97 13. 24 19. 81 0. 16 0. 22 1. 27 11. 97 13. 24 19. 81 0. 04 0. 06 0. 31 0. 00 0. 31 1. 30 0. 19 0. 25 1. 63 0. 00 1. 63 6. 76 0. 14 0. 9 1. 63 0. 00 1. 63 5. 52 0. 10 0. 14 0. 91 0. 00 0. 91 3. 79 0. 03 0. 04 0. 64 0. 00 0. 64 1. 47 0. 13 0. 17 0. 64 0. 00 0. 64 4. 14 0. 05 0. 07 0. 64 0. 00 0. 64 2. 00 0. 02 0. 02 0. 64 0. 00 0. 64 1. 07 0. 02 0. 02 0. 49 0. 00 0. 49 1. 12 0. 26 0. 35 8. 32 0. 00 8. 32 17. 83 0. 01 0. 01 0. 11 0. 00 0. 11 0. 28 0. 01 0. 01 0. 11 0. 00 0. 11 0. 28 0. 01 0. 01 0. 11 0. 00 0. 11 0. 28 0. 01 0. 01 0. 11 0. 00 0. 11 0. 28 0. 16 0. 21 3. 43 0. 00 3. 43 7. 74 0. 17 0. 23 2. 78 0. 00 2. 78 7. 57 0. 04 0. 05 2. 88 0. 00 2. 88 4. 03 Yr 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 Description 110 HP 2WD Tractor 130 HP 2WD Tractor 155 HP 2WD Tractor 200 HP Crawler 425 HP Crawler 92 HP 2WD Tractor ATV Bed Shaper – 3 Row Cultivator – Alloway 3 Row Cultivator – Perfecta 3 Row Cultivator – Performer 3 Row Cultivator – 3 Row Cultivator – Sled 3 Row Disc – Stubble 18†² Disc – Finish 25†² Ditcher – V Harvester Tomato – Used Harvester -Tomato Lister – 9 Row Mulcher – 15†² Pickup Truck – 1/2 Ton Pickup Truck – 3/4 Ton Rear Blade – 8†² Rice Roller – 18†² Flat Roller – 18†² Ringroller – 30†² Saddle Tank – 300 Gallon Saddle Tank – 300 Gallon Saddle Tank – 300 Gallon Saddle Tank – 300 Gallon Spray Boom – 25†² Subsoiler – 16†² – 9 Shank Trailer Dolly Trailer Dolly Trailer Dolly Trailer Dolly Triplane – 16†² Vine Diverter Vine Trainer Actual Hours Capital Used Recovery 1,443. 2 2. 78 1,200. 0 3. 48 1,199. 3 5. 01 1,599. 4 6. 51 1,599. 8 8. 34 1,199. 2 2. 21 199. 5 1. 33 199. 5 4. 41 199. 8 3. 39 199. 8 1. 69 225. 1 8. 90 533. 0 2. 22 380. 0. 87 399. 2 12. 46 199. 5 14. 84 165. 2 3. 10 199. 4 17. 45 699. 0 41. 84 390. 0 5. 16 365. 4 3. 95 266. 5 6. 18 266. 5 6. 18 132. 2 0. 89 199. 2 4. 70 262. 5 3. 56 199. 5 2. 64 206. 6 0. 76 49. 1 3. 20 126. 0 1. 25 401. 9 0. 39 299. 4 0. 59 399. 5 8. 90 499. 6 0. 15 499. 7 0. 15 499. 3 0. 15 499. 7 0. 15 373. 8 3. 94 241. 9 4. 39 315. 0 1. 07 2008 Transplanted Processing Tomato Cost and Returns Study Sacramento Valley UC Cooperative Extension 17 Table 6. UC COOPERATIVE EXTENSION RANGING ANALYSIS SACRAMENTO VALLEY – 2008 TRANSPLANTED COSTS PER ACRE AT VARYING YIELDS FOR PROCESSING TOMATOES YIELD (TONS/ACRE) 26. 0 29. 0 32. 0 35. 0 38. 0 41. OPERATING COSTS/ACRE: Preplant Cost 338 338 338 338 338 338 Cultural Cost 1292 1,292 1,292 1,292 1,292 1,292 Harvest Cost 228 255 281 308 334 36 0 Assessment Cost 14 14 14 14 14 14 Interest on Operating Capital TOTAL OPERATING COSTS/ACRE TOTAL OPERATING COSTS/TON CASH OVERHEAD COSTS/ACRE TOTAL CASH COSTS/ACRE TOTAL CASH COSTS/TON NON-CASH OVERHEAD COSTS/ACRE TOTAL COSTS/ACRE TOTAL COSTS/TON 65 1937 74 422 2359 91 113 2472 95 65 1,964 68 422 2,386 82 114 2,500 86 65 1,990 62 423 2,413 75 115 2,528 79 66 2,017 58 423 2,440 70 116 2,555 73 66 2,044 54 423 2,466 65 117 2,583 68 66 2,071 51 423 2,493 61 117 2,611 64 44. 0 338 1,292 387 14 67 2,097 48 423 2,520 57 118 2,638 60NET RETURNS PER ACRE ABOVE OPERATING COSTS FOR PROCESSING TOMATOES PRICE YIELD (DOLLARS/TON) (TONS/ACRE) Processing Tomatoes 26. 0 29. 0 32. 0 35. 0 38. 0 41. 0 44. 0 55. 00 -507 -369 -230 -92 46 184 323 60. 00 -377 -224 -70 83 236 389 543 65. 00 -247 -79 90 258 426 594 763 70. 00 -117 66 250 433 616 799 983 75. 00 13 211 410 608 806 1,004 1,203 80. 00 143 356 570 783 996 1,209 1,423 85. 00 273 501 730 958 1,186 1,414 1,643 NET RETURNS PER ACRE ABOVE CASH COS TS FOR PROCESSING TOMATOES PRICE YIELD (DOLLARS/TON) (TONS/ACRE) Processing Tomatoes 26. 0 29. 0 32. 0 35. 0 38. 0 41. 0 44. 0 55. 00 -929 -791 -653 -515 -376 -238 -100 60. 00 -799 -646 -493 -340 -186 -33 120 65. 0 -669 -501 -333 -165 4 172 340 70. 00 -539 -356 -173 10 194 377 560 75. 00 -409 -211 -13 185 384 582 780 80. 00 -279 -66 147 360 574 787 1,000 85. 00 -149 79 307 535 764 992 1,220 NET RETURNS PER ACRE ABOVE TOTAL COSTS FOR PROCESSING TOMATOES PRICE YIELD (DOLLARS/TON) (TONS/ACRE) Processing Tomatoes 26. 0 29. 0 32. 0 35. 0 38. 0 41. 0 44. 0 55. 00 -1,042 -905 -768 -630 -493 -356 -218 60. 00 -912 -760 -608 -455 -303 -151 2 65. 00 -782 -615 -448 -280 -113 54 222 70. 00 -652 -470 -288 -105 77 259 442 75. 00 -522 -325 -128 70 267 464 662 80. 00 -392 -180 32 245 457 669 882 85. 00 -262 -35 192 420 647 874 1,102 2008 Transplanted Processing Tomato Cost and Returns StudySacramento Valley UC Cooperative Extension 18 Table 7. UC COOPERATIVE EXTENSION COSTS AND RETURNS/ BREAKEVEN AN ALYSIS SACRAMENTO VALLEY – 2008 TRANSPLANTED COSTS AND RETURNS – PER ACRE BASIS 1. Gross Returns Crop Processing Tomatoes 2,450 2,017 2. Operating Costs 3. Net Returns Above Oper. Costs (1-2) 433 4. Cash Costs 2,440 5. Net Returns Above Cash Costs (1-4) 10 6. Total Costs 2,555 7. Net Returns Above Total Costs (1-6) -105 COSTS AND RETURNS – TOTAL ACREAGE 1. Gross Returns Crop Processing Tomatoes 1,543,500 2. Operating Costs 1,270,748 3. Net Returns Above Oper. Costs (1-2) 272,752 4. Cash Costs 1,536,994 5. Net Returns Above Cash Costs (1-4) 6,506 6.Total Costs 1,609,965 7. Net Returns Above Total Costs (1-6) -66,465 BREAKEVEN PRICES PER YIELD UNIT Base Yield (Units/Acre) 35. 0 Yield Units Ton ——– Breakeven Price To Cover ——-Operating Cash Total Costs Costs Costs ———— $ per Yield Unit ———–57. 63 69. 70 73. 01 CROP Processing Tomatoes BREAKEVEN YIELDS PER ACRE Yield Units Ton Base Price ($/Unit) 70. 00 ——– Breakeven Yield To Cover ——-Operating Cash Total Costs Costs Costs ———– Yield Units / Acre ———-28. 8 34. 9 36. 5 CROP Processing Tomatoes 2008 Transplanted Processing Tomato Cost and Returns Study Sacramento Valley UC Cooperative Extension 19 Table 8.UC COOPERATIVE EXTENSION DETAILS OF OPERATIONS SACRAMENTO VALLEY – 2008 TRANSPLANTED Operation Laser Level – 4% Of Acreage Land Prep – Stubble Disc & Roll Land Prep – Subsoil & Roll 2X Land Prep – Disc & Roll Land Prep – Triplane 2X Land Prep – Apply Gypsum on 20% of Acreage Land Prep – List Beds Land Prep – Shape Beds & Fertilize Weed Control – Roundup & Goal Weed Control – Roundup Weed Control – Cultivate 2X Condition Beds & Apply Starter Fertilizer Power Mulch & Apply Herbicides – Treflan (& Dual on 30% of Acreage) Transplant Toma toes Operation Month September September Tractor/ Power Unit Custom 425 HP Crawler Implement Laser Level Disc – Stubble 18†² Rice Roller – 18†² Subsoiler – 16†² – 9 Shank Disc – Finish 25†² Ringroller – 30†² Triplane – 16†² Broadcast Material Material Rate/Acre Unit 0. 04 Acre September 425 HP Crawler 200 HP Crawler September 200 HP Crawler September Gypsum Application October October January January January 200 HP Crawler 155 HP 2WD Tractor 130 HP 2WD Tractor 130 HP 2WD Tractor 110 HP 2WD Tractor 92 HP 2WD Tractor 110 HP 2WD Tractor 130 HP 2WD Tractor CustomGypsum Lister – 9 Row Bed Shaper – 3 Row Saddle Tank – 300 Gallon Saddle Tank – 300 Gallon Spray Boom – 25†² Saddle Tank – 300 Gallon Spray Boom – 25†² Cultivator – Alloway 3 Row Cultivator – Perfecta 3 Row Cultivator – Performer 3 Row Mulcher – 15†² Saddle Tan k – 300 Gallon 0. 20 Ton 11-52-0 Zinc Chelate Roundup Ultra Goal 2 XL Roundup Ultra 100. 00 2. 00 1. 00 3. 00 1. 50 Lb Pint Pint FlOz Pint January March April Weed Control – Apply Matrix on 80% of Acreage Irrigate – Sprinklers 1X Weed Control – Cultivate 3X April April April April May May April May April April July April May June July June 130 HP 2WD Tractor Fertilize – 150 Lbs N Sidedress Chisel Furrows Mulch Beds Disease Control – Bacterial Speck – on 30% of Acreage Open Ditches Irrigate – Furrow 8X 10 HP 2WD Tractor 110 HP 2WD Tractor 110 HP 2WD Tractor 130 HP 2WD Tractor 200 HP Crawler 155 HP 2WD Tractor 130 HP 2WD Tractor 200 HP Crawler 200 HP Crawler Saddle Tank – 300 Gallon Cultivator – Sled 3 Row Labor Cultivator – Sled 3 Row Cultivator – Sled 3 Row Cultivator – 3 Row Cultivator – Sled 3 Row Saddle Tank – 300 Gallon Cultivator – 3 Row Cultivator – Sled 3 Row Saddle Tank – 300 Gallon Ditcher – V Ditcher – V Labor Labor Labor Labor 8-24-6 Treflan HFP Dual Magnum Tomato Seed Transplants – Growing Transplanting Matrix DF Water 15. 00 1. 00 0. 45 10. 44 8. 70 8. 70 0. 48 2. 00 Lb Pint Pint Thou Thou Thou Oz AcIn UN-32 150. 00 Lbs N Kocide 101 Dithane DF 0. 60 0. 60 Lb Lb Disease Control – Late Blight on 5% of Acreage Close DitchesAir Application Spray 200 HP Crawler 200 HP Crawler Air Application Dust 130 HP 2WD Tractor Contract Labor 110 HP 2WD Tractor Air Application Spray Rear Blade – 8†² Rear Blade – 8†² Cultivator – Sled 3 Row Saddle Tank – 300 Gallon Vine Trainer Water Water Water Water Bravo Weatherstik 10. 00 10. 00 10. 00 10. 00 0. 15 AcIn AcIn AcIn AcIn Pint July July Mite Control – Sulfur on 70% of Acreage July Fertilize – 20 Lbs N on 20% of Acreage July Weed Control – Hand Hoe Train Vines Insect Control – Aphids on 40% of Acreage Disease Control – Fruit Rot on 15% of Acreage Insect Control – Worms Fruit Ripener – Ethrel on 5% of Acreage Open Harvest Lane on 8% of Acreage July July July Sulfur, Dust 98% CAN 17 Labor Warrior T Bravo Weatherstik Confirm 28. 00 118. 00 5. 00 1. 54 0. 45 12. 00 0. 03