The experiment 1 consisted to evaluate seven rotational crops plus control (fallow) grown before sugarcane was planted.

The soil is as a Typic Paleudult and was chemically characterized at different depths with samples taken after the green manures were cut but before sugarcane was planted (Table 1).

The experimental design was a randomized block with eight treatments and five replications.

The rotational crops were peanut (Arachis hypogaea L.) cv. IAC-Tatu, peanut cv. IAC-Caiapo, sunn hemp cv. IAC-1 (Crotalaria juncea L.), velvet bean (Mucuna aterrima Piper and Tracy), soybean (Glycine max L. Merrill) cv. IAC-17, sunflower (Helianthus annuus L.) cv. IAC — Uruguai, and mung bean (Vigna radiata L. Wilczek).

The green manures were sowed always in December on 7 m * 10 m size plots, with rows 0.50 m apart. The experimental area was weeded 30 d after sowing, and the weed residues were left on the soil surface.

During seed filling, the plants used as green manure were manually cut and spread on the soil covering the entire plot surface in pieces less than 0.25 m and left there for six months. Peanut, soybean, sunflower and mung bean were harvested after physiological maturation for the grain yield, and the remaining plant parts were cut and spread on the soil. Biomass production of the rotational crops was evaluated in 1 m2 of the plot area.

At the harvest stage, the roots of each rotational crop were sampled in order to evaluate the natural colonization level of arbuscular mycorrhizal fungi (AMF). The colonization percentage was estimated using the root coloration technique according to [21]. The percentage of colonization by AMF was estimated by counting the roots’ stained portions using a reticular plate under a microscope following the procedures described by [22].

To evaluate sugarcane stalk yield 2-m sections of each of the three central rows were cut and weighed.

Ten successive stems were separated from each plot for the technological evaluation of the Brix, pol, and total recovered sugar [23]. Sugar yield, expressed in terms of tons of pol per hectare (TPH), was estimated with the stem yield and technological analysis data.

The economic balance considered the costs of production and revenues of the rotational crops as well as three harvests of sugarcane. The basic costs of production of sugarcane (including land preparation, seed stalk, fertilizer, herbicides feedstock and application, and harvesting) were the average of the 2004, 2005, and 2006 prices, based on an average stalk yield of 70 t ha-1. For the control treatment, which did not include the crop rotations, the cost of production of sugarcane was estimated as U$ 3,111 ha-1. The costs of production of the green manures crotalaria and velvet beans, U$ 100 ha-1, include seeds, planting, and cutting. For the grain crops, the costs of grain harvesting and of chemicals needed for phytosanitary control were added: sunflower (U$ 422 ha-1), peanut cv. IAC-Tatu (U$ 1,289 ha-1), Peanut cv. IAC-Caiapo (U$ 1,480 ha-1), mung bean (U$ 2,007 ha-1) and soybean (U$ 513 ha-1). The sales prices of grain and cane stalks for the period between 2004 and 2006 (according to a database of the Institute of Agricultural Economics of the Sao Paulo State Secretary of Agriculture) were: sugarcane stalks, U$ 17.56 t-1; sunflower, U$ 178 t-1; peanut cv. IAC-Tatu, U$ 260 t-1; peanut cv. IAC-Caiapo, U$ 260 t-1; soybean, U$ 197 t-1; and mung bean, U$ 2,222 t-1. Mung bean is not sold as a commodity but as a specialty crop; its prices are highly variable, and the market for it is relatively small; therefore, the data on the economical return for mung bean must be taken with care.

From a long-term sustainability perspective, the reliance on a single species of perennial crops (monoculture) for biomass production may be risky because of less diversity and more chance to prone to certain pests and diseases. Mixture of multiple species may overcome some problems encountered in monoculture crops. In terms of dedicated biofuel crops such as switchgrass and miscanthus, most previous and current studies are focused on monoculture. Little information is known about the mixture of multiple species and their productivity as compared to monoculture.

In ecology studies, the benefits of mixtures of species over monocultures in terms of sustainability and biodiversity have been recognized in both annual and perennial species [6, 40, 41]. For biofuel purpose, specifically, Tilman et al. (2006) argued that the mixtures of different perennial grasses are more stable, more reliable and more productive than monoculture. Also, the mixtures are more environmentally friendly in terms of energy inputs and greenhouse gas emission. From agronomic standpoint, growing mixtures of multiple species in a large farm scale will face challenges such as selecting species, seeding methods, seeds costs, harvesting and so on. In addition, biomass feedstock quality will be an important factor when considering harvesting mixtures.

Brownell and Liu [66] developed a computer model to select the best option, or the best combination of options, for supply of a given bioenergy plant. The options include a direct chop and delivery option, two bale options (round and large rectangular), and a variation of the large rectangular bale option where these bales are compressed at a stationary location before shipment. Specifically, the options are: (1) loose biomass harvest using a self-loading forage wagon; (2) round bale harvest; (3) large rectangular bale harvest; and (4) large rectangular bale with compression before hauling.

The results of the model provide users a decision matrix which shows the optimized handling scenario for all four handling options analyzed by this program. Cost calculation was based on three plant demands (2,000, 5,000, and 10,000 Mg DM/d) to compare costs as the required production area expanded. Satellite storage locations were established based on the distribution of production fields and the existing road network.

The key constraints used for the simulation are:

1. Only loose biomass can be directly hauled from the field to the biorefinery.

2. Biomass cannot be stored at the plant, 20,000 tons (18,144 Mg) will "stand in field" in surrounding area of the plant and collected on demand as loose material.

3. Baled biomass will be transported to and stored at satellite storage locations.

4. Large rectangular bales may be compressed before transportation.

5. Biomass will be grown on one third of the available acreage, with an average yield of 3 Mg/ha (15% average moisture content).

The land close to the plant is more valuable, from the biorefinery viewpoint, for the production of feedstock. The study found that a plant can afford to pay more for feedstock (landowner gets a higher price) if a farm happens to be closer to the chosen plant location.

The results showed that the size of SSLs is sensitive to the amount of material demanded by the plant. The program tests various sizes, distances between SSLs, and possible overlaps of production fields "served" by each SSL. The programming optimizes the simulation by slowly changing the size and location of SSLs. The program solves for the maximum amount of acreage available in the simulation model. The output is a distance material in each SSL can be efficiently hauled based on if it is harvested and hauled directly from the field or if it is baled and stored before hauling. The program also solves for the lowest cost for a set acreage when the closest fields are field chopped and remaining fields are baled.

The growth of microorganism in BAC bed can be divided into three phases: logistic phase, stable phase and decayed phase (see Fig. 16). In the first period, microbe multiplies. In the initial period of biofilm formation, microbe quantity is rather low, and the highest biomass only reaches 70nmolP/gC. When the water temperature is proper and the inflow contains a lot organics, biological multiples rapidly. After one and half a month of the reduction period, it reaches the stable phase. Generally, in this phase, the concentration of the substrate declines, which means the degradation rate is rather high; DO consumption is also in large amount. In the second phase, microbe grows stably. After a period of cultivating when the water temperature is proper, the microbe basically reaches a state of stability, in which the microbe quantity maintains in the range of 450-520nmolP/gC and the microbe grows normally. Meanwhile, the removal effect on pollutants in water is also steady, and at this point, the eco-system of BAC bed is relatively steady. This phase is characterized by equilibrium between biological membranes of new cells and the loss of membranes causing by physical force. In the decay phase, when water temperature as well as other conditions varies fiercely, the microbe quantity decreased rapidly and entering the final phase, in which degradable rate decreased, quality of outflow will be worsen.

As the substrate is oxidized and degraded by the microbe membrane from top to bottom, the concentration decreased gradually, lead to the decline of available organic concentration and the poor nutrition condition of the microbe. Besides, it’s also related to DO concentration. DO distribution in BAC column is gradually decreased from top to bottom, the lower it goes on the carbon bed, the fewer the available DO for microbe to reproduce, so does the biomass. Thus, the biomass distribution in BAC bed shows a feature of gradually decreasing from top to bottom (see Fig. 17).

Composting is defined as a bio-oxidative process involving the mineralization and partial humification of the organic matter, leading to a stabilised final product, free of phytotoxicity and pathogens and with certain humic properties, which can be used to improve and maintain soil quality and fertility [25]. Composting of animal manures has been traditionally carried out by the farmers after manure collection for better handling, transport and management [6]. Frequently, the wastes were heaped up and very little attention was paid to the process conditions (aeration, temperature, ammonia loss, etc.) and using rudimentary methodology.

From a microbial viewpoint continuous composting processes may be described as a sequence of continuous cultures, each of them with their own physical (temperature), chemical (the available substrate), and biological (i. e., the microbial community composition) properties and feedback effects. These changes make it difficult to study the process, which is virtually impossible to simulate in the laboratory since temperature, moisture, aeration, etc., are directly related to the surface/volume ratio. However, in general, composting may be described as a four-phase process in which the energy-rich, abundant and easily degradable compounds like sugars and proteins are degraded by fungi and bacteria (referred to as primary decomposers) during an initial phase called the mesophilic phase (25-40 °C). Although there exists a competition between both microbial groups regarding the easily available substrates, fungi are very soon outcompeted because the maximum of specific growth rates of bacteria exceed those of fungi by one order of magnitude [26]. The importance of bacteria (with the exception of Actinobacteria) during the composting process has long been neglected, probably because of the better visibility of mycelial organisms. A review on the microbial groups involved in the first mesophilic phase is given by [27]. Provided that mechanical influences (like turning) are small, compost fauna including earthworms, mites and millipedes may also act as catalysts, thereby contributing to the mechanical breakdown and offering an intestinal habitat for specialized microorganisms. The contribution of these animals may be negligible or, as in the special case of vermicomposting, considerable (see section 2.2). The number of mesophilic organisms in the original substrate is three orders of magnitude higher than the number of thermophilic organisms; however, the activity of primary decomposers induces a temperature rise and in turn, mesophilic microbiota is, along with the remaining easily degradable compounds, degraded by the succeeding thermophiles. The temperature rise continues to be fast and accelerates up to a temperature of about 62 °C during this second phase of composting, known as the thermophilic phase.

When a temperature exceeding 55 °C is reached in a compost pile, fungal growth is usually inhibited and the thermophilic bacteria and Actinobacteria are the main degraders during this peak-heating phase. Moreover, oxygen supply affects fungi to a greater extent than bacteria, and even in force-aerated systems, temporary anoxic conditions may occur. Hence, fungi play a negligible role during this phase, except for the composting of lignocellulosic residues. Bacteria of the genus Bacillus are often dominant when the temperature ranges from 50 to 65 °C. Moreover, members of the Thermus/Deinococcus group have been found in biowaste composts [28] with an optimum growth between 65 and 75 °C. A number of autotrophic bacteria that obtain their energy by the oxidation of sulfur or hydrogen have been isolated from composts [28]. Their temperature optimum is at 70-75 °C and they closely resemble Hydrogenobacter strains, which were previously found in geothermal sites. Furthermore, obligate anaerobic bacteria are also common in composts, but up to now, there is still a gap of knowledge concerning this microbial group. It is believed that the longer generation times of archaea, in comparison with bacteria, made the archaea unsuitable for the rapidly changing conditions in the composting process. Nevertheless, in recent works, and using the right tools, a considerable number of cultivable (Methanosarcina termophila, Methanothermobacter sp., Methanobacterium formicicum, among others) and yet uncultivated archaea have been detected in composting processes [29-30].

The final temperature increase may exceed 80 °C and it is mainly due to the effect of abiotic exothermic reactions in which temperature-stable enzymes of Actinobacteria might be involved. Such high temperatures are crucial for compost hygienisation in order to destroy human and plant pathogens, and kill weed seeds and insect larvae [31]. The disadvantage of temperatures exceeding 70 °C is that most mesophiles are killed, and therefore the recovery of the decomposer community is retarded after the temperature peak. The inoculation with matter from the first mesophilic stage might, however, solve this problem.

When the activity of thermophilic organisms ceases due to the exhaustion of substrates, the temperature starts to decrease. This constitutes the beginning of the third stage of composting, called the cooling phase or second mesophilic phase. It is characterised by the recolonisation of the substrate with mesophilic organisms, either originating from surviving spores, through the spread from protected microniches, or from external inoculation. During this phase there is an increased number of organisms with the ability to degrade cellulose or starch, such as the bacteria Cellulomonas, Clostridium and Nocardia, and fungi of the genera Aspergillus, Fusarium and Paecilomyces [27]. Finally, during the maturation phase, the ratio of fungi to bacteria increases due to the competitive advantage of fungi under conditions of decreasing water potential and poorer substrate availability. Compounds that are not further degradable, such as lignin-humus complexes, are formed and become predominant. Some authors have proposed a fifth composting phase, known as the curing phase (or storage phase), during which the physico-chemical parameters do not change, but changes in microbial communities still occur [32]. Therefore, the chemical and microbial changes that the substrate undergoes during the different phases of the composting process will largely determine the stability and degree of maturity of the end product and in turn, its safe use as an organic amendment. There exists a wide range of parameters that have been proposed to evaluate compost stability/maturity, as shown in the next section.

Water samples put into hydrobios plankton counting chambers depending on phytoplankton density, after standing overnight by dropping lugol’s solution, counting phytoplankton were made by using inverted microscope [30-31].

Following formula was used to calculate the number of phytoplankton [31]:

C x TA

Number of phytoplankton (piece/ml) =———-

F x A x V

Here;

C= The number of organisms found by counting (number),

TA= Bottom area of the cell count (mm2),

F= Counted field number (number),

A= Field of view of the microscope (mm2),

V= Volume of precipitated sample (ml).

Martin Rulik, Adam Bednarik, Vaclav Mach, Lenka Brablcova, Iva Buriankova, Pavlina Badurova and Kristyna Gratzova

Additional information is available at the end of the chapter http://dx. doi. org/10.5772/52718

1. Introduction

Methane (CH4) is an atmospheric trace gas present at concentration of about 1.8 ppmv, that represents about 15% of the anthropogenic greenhouse effect (Forster et al. 2007). The atmospheric CH4 concentration has increased steadily since the beginning of the industrial revolution (~ 0.7 ppmv) and is stabilized at ~1.8 ppmv from 1999 to 2005 (Forster et al. 2007). An unexpected increase in the atmospheric growth of CH4 during the year 2007 has been recently reported (Rigby et al. 2008), indicating that the sources and sinks of atmospheric CH4 are dynamics, evolving, and not well understood. Freshwater sediments, including wetlands, rice paddies and lakes, are thought to contribute 40 to 50 % of the annual atmospheric methane flux (Cicerone & Oremland 1988; Conrad 2009).

The river hyporheic zone, volume of saturated sediment beneath and beside streams containing some proportion of water from surface channel, plays a very important role in the processes of self-purification because the river bed sediments are metabolically active and are responsible for retention, storage and mineralization of organic matter transported by the surface water (Hendricks 1993; Jones & Holmes 1996, Baker et al. 1999, Storey et al.

1999, Fischer et al. 2005). The seemingly well-oxygenated hyporheic zone contains anoxic and hypoxic pockets („anaerobic microzones") associated with irregularities in sediment surfaces, small pore spaces or local deposits of organic matter, creating a ‘mosaic’ structure of various environments, where different microbial populations can live and different microbially mediated processes can occur simultaneously (Baker et al. 1999, Morrice et al.

2000, Fischer et al. 2005). Moreover, hyporheic-surface exchange and subsurface hydrologic flow patterns result in solute gradients that are important in microbial metabolism. Oxidation processes may occur more readily where oxygen is replenished by surface water infiltration, while reduction processes may prevail where surface-water exchange of oxygen

© 2013 Rulik et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

is less, and the reducing potential of the environment is greater (Hendricks 1993). As water moves through the hyporheic zone, decomposition of the organic matter consumes oxygen, creating oxygen gradients along the flow path. Thus, compared to marine or lake surface sediments, where numerous studies on O2 profiles have showed that O2 concentrations become zero within less than 3 mm from the surface, the hyporheic sediment might be well — oxygenated habitats even up to the depth of 80 cm (e. g. Bretschko 1981, Holmes et al. 1994) . The extent of the oxygen gradient is determined by the interplay between flow path lengh, water velocity, the ratio of surface to ground water, and the amount and quality of organic matter. Organic matter decomposition in sediments is an important process in global and local carbon budgets as it ultimately recycles complex organic compounds from terrestrial and aquatic environments to carbon dioxide and methane. Methane is a major component in the carbon cycle of anaerobic aquatic systems, particularly those with low sulphate concentrations. Since a relatively high production of methane has been measured in river sediments (e. g. Schindler & Krabbenhoft 1998, Hlavacova et al. 2005, Sanders et al. 2007, Wilcock & Sorrell 2008, Sanz et al. 2011), we proposed that river sediments may act as a considerable source of this greenhouse gas which is important in global warming (Hlavacova et al. 2006).

Breakdown of organic matter and gas production are both results of well functioned river self-purification. This degrading capacity, however, requires intensive contact of the water with biologically active surfaces. Flow over various morphological features ranging in size from ripples and dunes to meanders and pool-riffle sequences controls such surface — subsurface fluxes. Highly permeable streambeds create opportunities for subsurface retention and long-term storage, and exchange with the surface water is frequent. Thus, study of the methane production within hyporheic zone and its subsequent emission to the atmosphere can be considered as a measure of mineralization of organic matter in the freshwater ecosystem and might be used in evaluation of both the health and environmental quality of the rivers studied.

Methane (CH4) is mostly produced by methanogenic archaea (Garcia et al. 2000, Chaban et al. 2006) as a final product of anaerobic respiration and fermentation, but there is also aerobic methane formation (e. g. Karl et al. 2008). Methanogenic archaea are ubiquitous in anoxic environments and require an extremely low redox potential to grow. They can be found both in moderate habitats such as rice paddies (Grosskopf et al. 1998a, b), lakes (Jurgens et al. 2000, Keough et al. 2003) and lake sediments (Chan et al. 2005), as well as in the gastrointestinal tract of animals (Lin et al. 1997) and in extreme habitats such as hydrothermal vents (Jeanthon et al. 1999), hypersaline habitats (Mathrani & Boone 1995) and permafrost soils (Kobabe et al. 2004, Ganzert et al. 2006). Rates of methane production and consumption in sediments are controlled by the relative availability of substrates for methanogenesis (especially acetate or hydrogen and carbon dioxide). The most important immediate precursors of methanogenesis are acetate and H2/CO2. The acetotrophic methanogens convert acetic acid to CH4 and CO2 while the hydrogenotrophic methanogens convert CO2 with H2 to CH4 (Conrad 2007).

Methane oxidation can occur in both aerobic and anaerobic environments; however, these are completely different processes involving different groups of prokaryotes. Aerobic methane oxidation is carried out by aerobic methane oxidizing bacteria (methanotrophs, MOB), while anaerobic methane oxidizers, discovered recently, thrive under anaerobic conditions and use sulphate or nitrate as electron donors for methane oxidation (e. g. Strous & Jetten 2004). MOB are a physiologically specialized group of methylotrophic bacteria capable of utilizing methane as a sole source of carbon and energy, and they have been recognized as major players in local and global elemental cycling in aerobic environments (Hanson & Hanson 1996, Murrell et al. 1998, Costelo & Lidstrom 1999, Costelo et al. 2002, McDonald et al. 2008). Aerobic MOB have been detected in a variety of environments, and in some they represent significant fractions of total microbial communities (e. g. Henckel et al. 1999; Carini et al, 2005, Trotsenko & Khmelenina 2005, Kalyuzhnaya et al. 2006). However, the data on the diversity and activity of methanotrophic communities from the river ecosystems are yet fragmentary. Methanotrophs play an important role in the oxidation of methane in the natural environment, oxidizing methane biologically produced in anaerobic environments by the methanogenic archaea and thereby reducing the amount of methane released into the atmosphere.

The present investigation is a part of a long-term study focused on organic carbon and methane dynamics and microbial communities in hyporheic zone of a Sitka, small lowland stream in Czech Republic. The overall purpose of this research was to characterize spatial distribution of both methanogens and methanotrophs within hyporheic sediments and elucidate the differences in methane pathways and methane production/consumption as well as methane fluxes and atmospheric emissions at different sites along a longitudinal profile of the stream.

An acid hydrolysates solution containing 38 g/L of soluble solids was added into a glucose medium to give a predetermined percentage of residual biomass to glucose at 0, 10, 20 and 25% of sugar, respectively. The initial glucose concentration was controlled at a constant level of 9.6 g/L. The flask cultures of no biomass hydrolysates were run in parallel as controls. As shown in Figure 7, the acid biomass hydrolysates were beneficial to both cell growth and PHA formation. Because the residual biomass might also contain some insoluble solids and PHB granules lost in PHB recovery, both cell density and PHB concentration were compared at 24 hours and 48 hours to show the net gains. The benefits of biomass hydrolysates were statistically significant based on the deviations of duplicates.

The acid hydrolysates might have two positive effects on microbial PHA formation. First, the hydrolysates promoted cell activity on glucose utilization, giving higher cell densities than the controls in the first 24 hours. This nutritional effect was similar to those of organic nutrients such as yeast extract and peptone, which are widely used in microbial cultures to provide nutrients and growth factors to the cells. A fast cell growth can reduce the cultivation time, resulting in a high PHB productivity. Second, the biomass hydrolysates might also be used as an extra carbon source to generate more cell mass than the controls in 48 hours. This carbon source effect, however, might play a minor role because the cell density did not increase with cell debris load. In fact, too much acid hydrolysates deteriorated the gains as shown in Figure 7. The reason is not clear yet. A load of acid biomass hydrolysates to glucose from 10 to 20 wt% seems appropriate for both cell growth and PHB formation.

Proposition 1. The dynamics (3) leaves invariant the 3D-space D

(m — ka)

П = (s, x,xd) s + x + ———————— xA

( m — a)

Proof. The invariance of R+ is guaranteed by the following properties:

s = 0 ^ s = kax > 0,

x = 0 =>- x = 0,

xd = (m — a) x > 0.

Consider the quantity M = s + x + ^. One can easily check from equations (3) that one has M = 0, leading to the invariance of the set П. |

Let s be the number s = ц 1 (m) or + to .

Proposition 2. The trajectories of dynamics (3) converge asymptotically toward an equilibrium point

s*, 0, ——p — (s0 + — t0 — s*) m — ka

with s* < min(so + xo, s).

Proof. The invariance of the set О given in Proposition 1 shows that all the state variables remain bounded. From equation xd = (m — a)x with m > a, and the fact that xd is bounded, one deduces that x(-) has to converge toward 0, and xd(•) is non increasing and converges toward x* such that x* Є [0, (so + xo)(m — a) / (m — ka)]. Then, from the invariant defined by the set О, s(-) has also to converges to some s* < so + xo. If s* is such that s* > s, then from equation x = (y(s) — m)x, one immediately see that x{-) cannot converge toward 0. |

A forklift (10-ton or 9.07-Mg capacity) will operate continuously at the receiving facility. This forklift will lift two full racks from the trailer and place them onto a conveyor into the plant for direct processing, or stack these racks in at-plant storage. Then the forklift will lift two empty racks onto the trailer and then the truck returns to the SSL. Empty racks will be stacked in the storage yard until they are lifted onto trailers.

The operational plan calls for two forklifts at the receiving facility, identified as a "work horse" and a "backup." The workhorse will operate continuously and the backup will operate during the day when trucks are waiting in the queue. Key point — the system must have a backup forklift because, if a forklift is not available to lift racks off of and onto trailers, all operations cease.

The handling of the racks emulates the handling of bins at a sugar mill in South Florida. In the bin system, a truck has three bins, two on the first trailer and one on a "pup" trailer. The bins are side-dumped if material is processed directly (Figure 11), or the bins are off-loaded and
stacked two-high in the storage yard for nighttime operation (Figure 12). When the bins are dumped directly, it takes 3 min to dump the bins. For normal operation, one truck hauls 10 loads (30 bins) a day. At 37 tons/load (33.6 Mg/load), each truck hauls 370 ton/d (336 Mg/d). Sugar cane is 80% moisture content, so 370 ton = 74 dry ton/d/truck (67.2 Mg DM/d/truck).

The conveyor for moving the racks into the plant is an adaptation of a piece of commercial technology. Conveyor design and function is similar to the conveyor used to move cotton modules into a cotton gin. Cotton modules and the 16-bale racks have approximately the same dimensions. The conveyor cost used in the analysis was obtained from a cotton equipment manufacturer. The receiving facility operates 6 d/wk, thus, on average, the daily delivery will be:

630 racks/wk = 105 racks/d = 53 trucks/d 6 d/wk

For this example, plant size (23 dry ton/h or 20.9 Mg DM/h) was chosen based on the expected capacity of the two forklifts at the plant. One forklift is expected to lift two full racks and lift and load two empty racks on a trailer at the rate of one truck every 27 minutes averaged over the 24-h a day. The design of the receiving facility and at-plant storage area has to facilitate this operation. A larger at-plant storage area will lower the forklift productivity (ton/h) because the average cycle time to move an individual rack is greater.