5.2: Building Design for Energy Efficient Livestock Housing
Andrea Costantino
TEBE Research Group
Department of Energy
Politecnico di Torino
Torino, Italy
and
Institute of Animal Science and Technology
Universitat Politècnica de València
València, Spain
Enrico Fabrizio
TEBE Research Group
Department of Energy
Politecnico di Torino
Torino, Italy
| Key Terms |
| Energy balance | Supplemental heating | Climate control |
| Mass balance | Heat recovery | Ventilation |
| Energy management | Cooling systems |
Variables
Introduction
Energy usage on farms is considered direct when used to operate machinery and climate control systems or indirect when is used to manufacture feed and agro-chemicals. Direct on-farm energy consumption was estimated to be 6 EJ yr −1 , representing about 1.2% of total world energy consumption (OECD, 2008). If indirect energy is included, total farm energy consumption could be as much as 15 EJ yr −1 , representing about 3.1% of global energy consumption. Housed livestock require adequate indoor climate conditions to maximize both production and welfare, particularly avoiding thermal stress. The task of the engineer is to improve the energy use efficiency of livestock housing and to minimize energy consumption. This can be achieved by improving the energy performance of the equipment used for climate control and the design of the building.
The focus of this chapter is on building design for efficient energy management in livestock housing. Improving building design requires understanding the mass and energy balance of the system to specify materials, dimensions, and equipment needed to maintain safe operating conditions. The importance of understanding the energy needs of buildings is illustrated by the report of St-Pierre et al. (2003), who estimated the economic losses by the dairy industry in the U.S. at $1.69 to $2.36 billion annually due to heat stress. Understanding and being able to use fundamental concepts for animal housing design provides the foundation for desirable welfare and more efficient production-centric animal housing.
Outcomes
After reading this chapter, you should be able to:
- • Describe the energy needs for livestock housing
- • Explain the energy management requirements of a livestock house
- • Describe the main climate control systems used for livestock housing and the features that affect the energy management
- • Calculate energy balances for livestock houses
Concepts
Energy and Mass Balance of a Livestock House
Thermodynamically, a livestock house is an open system that exchanges energy and mass (such as air, moisture, and contaminants) between the indoor and outdoor environments and the animals that occupy the internal volume (the enclosure). The law of conservation of energy and mass is the basic principle for the mass balance. The building walls, floor, and roof represent the control surfaces and enclose the control volume of the thermodynamic system represented by the livestock house and its internal surfaces, such as animals, interior walls, and equipment. Energy and mass balance equations allow the analysis of the thermal behavior of a livestock house, but calculating these balances is challenging because many factors affect the thermal behavior of these buildings. It is essential to understand which terms to consider, and what to assume as negligible.
Energy Balance
Sensible heat is the amount of heat exchanged by a body and the surrounding thermodynamic system that involves a temperature change. Latent heat is the heat absorbed or released by a substance during a phase change without a change in temperature. These two forms of heat can be illustrated using an example of heating a pot of water on a stove. Initially, the water is at room temperature (say, 25°C), and as the water is heated, its temperature increases. The heat causing the temperature increase is sensible heat and for water is equal to 4,186 kJ kg −1 K −1 . When the water temperature reaches 100°C (boiling point of water at atmospheric pressure), the water changes phase from liquid to gas (steam). The heat provided during the phase change breaks the molecular bonds of the liquid water to transition to the gas phase, but the temperature does not change. The heat supplied to effect phase change is latent heat. The latent heat of vaporization for a unit of mass of water is 2,272 kJ kg −1 at 100°C and atmospheric pressure.
The energy balance of a livestock house, considering only the sensible heat, can be written as follows (Panagakis and Axaopoulos, 2008):
\[ \phi_{a}+\phi_{tr}+\phi_{sol}+\phi_{f}+\phi_{v}+\phi_{m}+(\gamma_{fog}\cdot \phi_{H})=\sum^{n}_{k=1}(M_{el,k}\cdot C_{el,k})\cdot \frac{dT_{air,i}}{dt} \]
where \(\phi_{a}\) = sensible heat flow from the animals inside the enclosure (W)
\(\phi_{tr}\) = sensible heat flow due to transmission through the control surfaces but excluding the floor (W)
\(\phi_{sol}\) = sensible heat flow due to solar radiation through both opaque and glazed building elements (W)
\(\phi_{f}\) = sensible heat flow due to transmission through the floor (W)
\(\phi_{v}\) = sensible heat flow due to ventilation (W)
\(\phi_{m}\)= sensible heat flow from internal sources, such as motors and lights (W)
\(\gamma_{fog}\)= Boolean variable for the presence (\(\gamma_{fog}\)=1) or not (\(\gamma_{fog}\)=0) of a fogging system inside the livestock house
\(\phi_{fog}\)= sensible heat flow due to fogging system (W)
\(\gamma_{H}\)= Boolean variable for the presence (\(\gamma_{H}\)=1) or not (\(\gamma_{H}\)=0) of a supplemental heating system inside the livestock house
\(\phi_{H}\)= sensible heat flow due to supplemental heating system (W)
\(M_{el,k}\) = mass of the k th building element (kg)
\(C_{el,k}\) = total heat capacity of k th building element (kJ kg −1 K −1 )
\(\frac{dT_{air,i}}{dt}\) = variation of the indoor air temperature \(T_{air,i}\) with time t
When using Equation 5.2.1 for calculations and sizing, pay attention to the heat flows because each term could be positive or negative depending on the physical context. Usually, heat flows coming into a control volume (the animal house) are positive, and the ones flowing out are negative. For example, in Equation (5.2.1), the terms \(\phi_{a}\) and \(\phi_{sol}\) are always positive or zero, since they represent incoming heat flow from animals and solar radiation, respectively, while the values of \(\phi_{tr}\) and \(\phi_{v}\) could be positive or negative, depending on the difference in temperature inside and outside the animal house. The term \(\phi_{f}\) depends on the floor construction. Although \(\phi_{tr}\) and \(\phi_{f}\) are both transmission heat flows through the control surface, they are always separated. Estimating the heat transfer through the ground is very challenging (Albright, 1990; Panagakis and Axaopoulos, 2008; Costantino et al., 2017), for example, in pig houses with ventilated pits for manure storage. To simplify the energy balance, the term \(\phi_{tr}\) is often considered as the sum of \(\phi_{tr}\) and \(\phi_{f}\) and a corrective coefficient is used when \(\phi_{f}\) is calculated.
The term \(\phi_{fog}\) is always negative because it represents the sensible heat removed by water droplets of a fogging system. A fogging system provides cooling inside the animal house by putting a haze of tiny water droplets in the air to provide evaporative cooling for the animals. The term \(\phi_{H}\) is always positive. The parameters \(\gamma_{fog}\) and \(\gamma_{H}\) should not have a value of 1 at the same time but can both be 0.
Sensible heat from the animals, \(\phi_{a}\), depends on species and body mass and ambient temperature. Sensible and latent heat values can be found in the literature, for example from ASABE (2012), Hellickson & Walker (1983), or Lindley & Whitaker (1996), and more detailed data are available in Pedersen and Sällvik (2002), who express sensible and latent heat from animals as a function of animal weight, indoor air temperature, and animal activity. In complex animal houses the sensible heat flow from internal sources, such as motors (fans and automatic feeding systems) and lights term (\(\phi_{m}\)) can be included (Albright, 1990), but in many calculations it is excluded because is very small compared with \(\phi_{a}\) (Midwest Plan Service, 1987). That exclusion is further justified when energy-efficient technologies such as LED/gas-discharge lamps and brushless motors are used.
The product \(M_{el}\cdot C_{el}\) is the lumped effective heat capacity of a building element expressed in kJ K −1 . For each building element (walls and roof) the amount or mass of material must be known, and the amount of heat energy needed to raise the temperature of a unit mass of the material by one degree Celsius. The fraction \(\frac{dT_{air,i}}{dt}\) represents the variation of the indoor air temperature through time. This side of the equation represents the change in temperature of the building itself.
It is possible to include additional terms to Equation 5.2.1 (Albright, 1990; Esmay and Dixon, 1986) such as the sensible heat flow to evaporate the water inside the control volume from structures such as water troughs and a slurry store (\(\phi_{e}\)). Some authors consider it important (Hamilton et al., 2016), while others do not (Midwest Plan Service, 1987). Liberati and Zappavigna (2005) consider sensible heat exchange between manure (especially when collected in pits) and the air inside the enclosure (\(\phi_{man}\)) to be important in large-scale houses equipped with storage pits and manure when it is not removed frequently. A Boolean variable \(\gamma_{man}\) may generalize Equation 5.2.1 further.
Equation 5.2.1 is a dynamic energy balance. If a large time step (perhaps a week or more) is assumed it can be written for steady-state conditions, meaning that the state variables that describe the system can be considered constant with time, and the terms of the balance represent the average values for the system. For large time steps or in steady-state conditions with constant indoor and outdoor air temperature, heat accumulation by the building itself can be considered to be zero, so Equation 5.2.1 becomes:
\[ \phi_{a}+\phi_{tr}+\phi_{sol}+\phi_{v}+\gamma_{fog}\cdot \phi_{fog}=0 \]
To obtain the energy balance of a livestock house in cold condition requiring supplemental heating, the energy balance becomes:
\[ \phi_{a}+\phi_{tr}+\phi_{H}+\phi_{sol}+\phi_{v}=0 \]
Figure 5.2.1 presents an illustration of the sensible heat balance of Equation 5.2.3 for simple dairy cow housing. Equation 5.2.3 can be used to design a basic livestock house. Undoubtedly, the presented formulation is a simplification, and in literature, other terms are introduced in the energy balance. The calculation of each term of the energy balance of Equation 5.2.3 is provided in greater detail later in this chapter.
Mass Balance
Mass balances are necessary to plan the management of contaminants, such as carbon dioxide (CO 2 ), hydrogen sulfide (H 2 S), and ammonia (NH 3 ), produced by the animals (Esmay and Dixon, 1986) and to regulate the indoor environment temperature, moisture content, and relative humidity. Along with temperature and relative humidity, the indoor air quality (IAQ) must be controlled by ventilation to avoid animal health problems. Calculating ventilation requirements for contaminant control is a mass balance problem. With low indoor air temperatures, a minimum ventilation flow rate (base ventilation) is used to dilute contaminants such as H 2 S and NH 3 . The minimum ventilation flow rate can be increased to reduce the moisture content. When the indoor air temperature is higher than the cooling setpoint temperature used to maintain animal comfort, the ventilation flow rate must be increased to cool the animals (Esmay and Dixon, 1986). The maximum ventilation flow rate must avoid high airspeeds that hurt animal welfare. If cooling cannot be achieved using mass flow, a fogging system can be used. The ventilation airflow can be expressed in m 3 s −1 , m 3 h −1 or as ach (air changes per hour), which indicates how many times the volume of air inside the house is changed in one hour.
To estimate the ventilation flow rate for moisture control in a simple livestock house, Equation 5.2.4 (Panagakis and Axaopoulos, 2008) can be used:
\[ \dot{V}_{air}\cdot \rho_{air}\cdot(x_{air,i}-x_{air,o})+\dot{m}_{a}+(\gamma_{fog} \cdot \dot{m}_{fog})=\rho_{air,i} \cdot V_{air,i} \cdot \frac{dx_{air,i}}{dt} \]
where \(\dot{V}_{air}\) = ventilation air flow rate (m 3 s −1 )
\(\rho_{air}\) = volumetric mass density (kg m 3 )
\(x_{air,o}\) = specific humidity of the outdoor air (kg vapor kg air −1 )
\(x_{air,i}\) = specific humidity of the indoor air (kg vapor kg air −1 )
\(\dot{m}_{a}\)= animal water vapor production (kg vapor s −1 )
\(\gamma_{fog}\) = Boolean variable that indicates the presence of fogging system
\(\dot{m}_{fog}\) = water added through fogging (kg vapor s −1 )
\(\rho_{air,i}\) = volumetric mass density of inside air (kg m −3 )
\(V_{air,i}\) = volume of the inside air (m 3 )
\(\frac{dx_{air,i}}{dt}\) = variation of \(x_{air,i}\) in time t
In steady-state conditions and not considering the presence of fogging systems, the mass balance (Figure 5.2.2) can be simplified as:
\[ \dot{m}_{air} \cdot x_{air,i} - \dot{m}_{air} \cdot x_{air,i} + \dot{m}_{a} = 0 \]
Equation 5.2.5 is the basic formulation of the moisture mass balance in steady-state conditions for livestock houses.
Energy Management Calculations
The basis for sensible energy and mass balance calculations for livestock housing are Equations 5.2.3 and 5.2.5, respectively. In the following sections, the determination of each term of the energy balance (Equation 5.2.3) is presented. A similar approach could be used for Equation 5.2.5.
Heat Flow from the Reared Animals (\(\phi_{a}\))
The animals produce and contribute considerably to heat flow in their housing. In cool conditions, this heat flow can help warm the building and decrease the need for supplemental heat. In warm conditions, this heat flow should be removed to avoid overheating and causing animal heat stress. Animals need to emit heat (both sensible and latent heat) for regulating their body temperature and maintaining their body functions. As an animal grows (usually the desired outcome of a meat production system, but not for dairy and laying hens), the animal produces more heat. The amount of heat also depends on indoor air temperature, production targets (such as the mass of eggs, milk, or meat), and the energy concentration of the feedstuff. Estimating heat production is also essential to calculate ventilation requirements.
Standard values for heat production are available (CIGR, 1999; ASABE, 2012), but a specific calculation is possible (Pedersen and Sällvik, 2002). First the total heat produced, \(\phi_{a,tot}\) (sum of the sensible and latent heat), for the animal house is calculated for an indoor air temperature of 20°C. The formulation of the equation depends on animal species and production:
Broilers: \[ \phi_{a,tot} =10.62\cdot w_{a}^{0.75} \cdot n_{a} \]
Laying hens: \[\phi_{a,tot} =(6.28\cdot w_{a}^{0.75} + 25\cdot Y_{eggs}) \cdot n_{a} \]
Fattening pigs: \[ \phi_{a,tot} =(5.09 \cdot +[1-(0.47+0.003 \cdot w_{a})] \cdot [5.09 \cdot w_{a}^{0.75} \cdot (Y_{feed}-1)]) \cdot n_{a} \]
Dairy cows: \[ \phi_{a,tot} =(5.6\cdot w_{a}^{0.75} + 22\cdot Y_{milk} +1.6\cdot 10^{-5}\cdot Y_{pregnancy}^{3} )\cdot n_{a} \]
where \(w_{a}\) = average animal live weight (kg)
\(n_{a}\) = number of animals inside the livestock house (animals)
\(Y_{eggs}\) = egg production (kg day −1 ), usually between 0.04 (brooding production) and 0.05 kg day −1 (consumer eggs)
\(Y_{feed}\) = dimensionless coefficient related to the daily feed energy intake by the pigs (values of \(Y_{feed}\) are presented in Table 5.2.1)
\(Y_{milk}\) = milk production (kg day −1 )
\(Y_{pregnancy}\) = number of days of pregnancy (days)
Next, the sensible heat produced (\(\phi_{a}\)) at a given indoor air temperature is calculated. If the indoor air temperature is in the thermoneutral zone, that is, a temperature range where the animal heat dissipation is constant (Pedersen and Sällvik, 2002) and the energy fraction used by animals for maintaining their homeothermy is at a minimum, at the house level \(\phi_{a}\) can be calculated as:
|
Pig Body Mass
(kg) |
Y feed | ||
|---|---|---|---|
| Rate of Gain: 700 g day −1 | Rate of Gain: 800 g day −1 | Rate of Gain: 900 g day −1 | |
|
20 |
3.03 |
3.39 |
3.39 |
|
30 |
2.79 |
3.25 |
3.25 |
|
40 |
2.60 |
3.22 |
3.43 |
|
50 |
2.73 |
3.16 |
3.41 |
|
60 |
2.78 |
3.16 |
3.40 |
|
70 |
2.84 |
3.12 |
3.40 |
|
80 |
2.83 |
3.04 |
3.38 |
|
90 |
2.74 |
2.79 |
3.18 |
|
100 |
2.64 |
2.57 |
2.98 |
|
110 |
2.52 |
2.40 |
2.78 |
|
120 |
2.36 |
2.25 |
2.60 |
Broiler house: \[ \phi_{a}=\{0.61 \cdot[1000+20 \cdot (20-T_{air,i})]-0.228 \cdot T_{air,i}^{2} \} \cdot n_{hpu} \]
Laying hen house: \[ \phi_{a}=\{0.67 \cdot[1000+20 \cdot (20-T_{air,i})]-9.8\cdot 10^{-8} \cdot T_{air,i}^{6} \} \cdot n_{hpu} \]
Fattening pig house: \[ \phi_{a}=\{0.62 \cdot[1000+12 \cdot (20-T_{air,i})]-1.15\cdot 10^{-7} \cdot T_{air,i}^{6} \} \cdot n_{hpu} \]
Dairy cow house: \[ \phi_{a}=\{0.71 \cdot[1000+4 \cdot (20-T_{air,i})]-0.408 \cdot T_{air,i}^{2} \} \cdot n_{hpu} \]
where \(T_{air,i}\) = indoor air temperature (°C)
\(n_{hpu}\) = the number of heat-producing units (hpu) that are present inside the livestock house
One hpu is defined as the number of animals that produces 1000 W of total heat (sum of sensible and latent heat) at an indoor air temperature of 20°C and can be calculated as:
\[ n_{hpu} = \frac{\phi_{a,tot}}{1000} \]
where \(\phi_{a,tot}\) is calculated using Equations 5.2.6, 5.2.7, 5.2.8, or 5.2.9 depending on species and production system. Out of the thermoneutral zone, no clear relationship can be found between indoor air temperature and total heat production, but values can be calculated using the formulations present in Pedersen and Sällvik (2002).
Transmission Heat Flow through the Building Envelope (\(\phi_{tr}\))
The term \(\phi_{t}\) is being taken to represent the heat flow through the walls, roof, windows, doors and floor. It is calculated as (European Committee for Standardization, 2007):
\[ \phi_{tr}=[\sum^{n}_{j=1}(b_{tr,j} \cdot U_{j} \cdot A_{j})] \cdot (T_{air,o}-T_{air,i}) \]
where b tr = dimensionless correction factor between 0 and 1
\(U_{j}\) = thermal transmittance of the j building element (W m −2 K −1 )
\(A_{j}\) = total area of the j building element (m 2 )
\(T_{air,o}\) = outdoor air temperature (°C)
The factor \(b_{tr}\) is used to correct the heat flow when the forcing temperature difference is not the difference between the indoor and outdoor air, for example when the heat flow occurs toward unconditioned spaces (e.g. material storages and climate control rooms) or through the ground. In these cases, the air temperature difference between inside and outside can be used but the heat flow is decreased using \(b_{tr}\). This coefficient can be computed in two cases: (1) if the adjacent space temperature is fixed and known, or (2) if all the heat transfer coefficients between the considered spaces can be numerically estimated. In most situations, \(b_{tr}\) (unitless) is obtained from standards, (e.g., Table 5.2.2).
| Type of Unconditioned Space | b tr |
|---|---|
|
Space with 1 wall facing on the outdoor environment |
0.40 |
|
Space with 2 walls facing on the outdoor environment (no doors) |
0.50 |
|
Space with 2 walls facing on the outdoor environment (with doors) |
0.60 |
|
Space with 3 walls facing on the outdoor environment (with doors) |
0.80 |
|
Floor in direct contact with the ground |
0.45 |
|
Ventilated floor (e.g. pits and under-floor cavity) |
0.80 |
Heat Flow Due to a Supplemental Heating System (\(\phi_{H}\))
In most of the cases, \(\phi_{H}\) is the unknown of the problem and the energy balance is solved with the aim of finding its value. A typical example is to solve the energy balance of Equation 5.2.3 for finding \(\phi_{H}\) and sizing the heating capacity of the supplemental heating system. In other cases, \(\phi_{H}\) could be equal to zero and the unknown of the problem could be with the aim of finding the needed ventilation flow rate to maintain a certain indoor air temperature and to cool the reared animals. Rarely, \(\phi_{H}\) has to be estimated. For example, \(\phi_{H}\) has to be estimated when the energy balance is solved with the aim of evaluating the indoor air temperature in given specific boundary conditions. An easy way to estimate \(\phi_{H}\) is to consider the heating capacity reported in the technical datasheet of the equipment for supplemental heating.
More details about the supplemental heating systems are described below, in the Application section.
Heat Flow from Solar Radiation (\(\phi_{H}\))
The heat flow due to solar radiation is dependent on the season, the farm location, and features of the building. In general terms, the solar heat flow can be split into two terms as follows (International Organization for Standardization, 2017):
\[ \phi_{sol} = \sum^{q}_{n=1}\phi_{sol,op,q} + \sum^{k}_{n=1} \phi_{sol,gl,k} \]
where \(\phi_{sol,op,q}\) = heat flows on the q opaque (e.g. walls and roof) surfaces (W)
\(\phi_{sol,gl,k}\)= heat flows on the k glazed (windows) surfaces (W)
For a generic opaque surface \(\phi_{sol,op,q}\) is calculated as:
\[ \phi_{sol, op,q} = A_{q} \cdot U_{q} \cdot \alpha_{q} \cdot R_{ex} \cdot I_{sol,q} \cdot F_{sh,q} \]
where \(\alpha_{q}\) = solar absorption coefficient of the considered surface depending on the surface color (0.3 for light colors, 0.9 for dark colors)
\(R_{ex}\) = external surface heat resistance (m 2 K −1 W −1 ), generally assumed equal to 0.04 m 2 K −1 W −1
\(I_{sol,q}\) = solar irradiance incident on the considered surface (W m −2 )
F sh,q = shading correction factor
For a generic glazed surface k , \(\phi_{s,gl,k}\) is calculated as:
\[ \phi_{s,gl,k} = A_{k} \cdot g_{gl} \cdot I_{sol,k} \cdot (1-F_{fr}) \cdot F_{sh,k} \cdot F_{sh,gl,k} \]
where \(g_{gl}\) = total solar energy transmittance of the transparent surface
\(F_{fr}\) = frame area fraction
\(F_{sh,gl,k}\) = shading reduction factor for movable shading provisions
The shading factors for both opaque and glazed components can be excluded for most livestock housing because they increase the complexity of the calculation, but they do not greatly affect the results.
Heat Flow Due to the Ventilation System (\(\phi_{v}\))
The heat load due to the ventilation system can be expressed as
\[ \phi_{v} = \rho_{air} \cdot c_{air} \cdot \dot{V} \cdot (T_{air,sup}-T_{air,i}) \]
where \(\rho_{air}\) = air volumetric mass density (kg m −3 )
\(c_{air}\) = air specific heat capacity (W h kg −1 K −1 )
\(\dot{V}\) = ventilation flow rate (m 3 h −1 )
\(T_{air,sup}\) = supply air temperature (°C)
In the cool season, \(T_{air,sup}\) usually has the same value of \(T_{air,o}\), since the ventilation uses outdoor air. In the warm season, \(T_{air,sup}\) could have values lower than\(T_{air,o}\), since outdoor air is cooled before entering inside the building. The value of \(T_{air,sup}\) can be estimated using the direct saturation effectiveness \(\varepsilon\) (%) of an evaporative pad system, calculated as (ASHRAE, 2012):
\[ \varepsilon = 100 \cdot \frac{T_{air,o,db}-T_{air,sup,bd}}{T_{air,o,db}-T_{air,o,wb}} \]
where \(T_{air,o,db}\) = dry-bulb outdoor air temperature (°C)
\(T_{air,sup,db}\) = dry-bulb temperature of the supply air leaving the cooling pad (°C)
\(T_{air,o,wb}\) = wet-bulb temperature of the outdoor air entering in the pad (°C)
Equation 5.2.20 can be rearranged to estimate the air supply temperature (\(T_{air,sup,db}\)) in presence of evaporative pads for use in Equation 5.2.19.
Applications
The concepts describe the basis for calculating the energy balance of a simple animal house. These are usually quite straightforward structures built to standard designs, which differ around the world but serve a similar function of making animal production more efficient for the farmer. The calculation for the design of the animal house (the control structure) necessarily assumes a typical or average environment. In reality, weather and production management mean that there have to be components of the system that are dynamic and respond to external conditions. In this section, some of the technology required to help maintain a safe and efficient living environment for the animals are discussed.
Heating Animal Houses
Supplemental Heating Systems
In cold weather, a supplemental heat source may be needed to reach the air setpoint temperature for guaranteeing adequate living conditions for the livestock. This is common at the beginning of the production cycle when animal heat production is small and in cold seasons of the year. This energy consumption represents a major fraction of the total direct energy consumption of the farm (Table 5.2.3) and can be calculated using Equation 5.2.3.
Supplemental heating systems can be classified into localized heating and space heating systems. Localized heating systems create temperature variations in the zones where animals are reared. This allows young animals to move to a zone for optimum thermal comfort. To design a localized heating system, the term \(\phi_{m}\) (as used in Equation 5.2.1) would have to be factored into the calculation to account for heat flow between the internal zones. Localized heating usually uses radiant heat, such as infrared lamps (for piglets) or infrared gas catalytic radiant heaters (for broilers). These systems emit 70% of their heat by radiation and the remaining 30% by convection; the radiation component directly heats the animals and floor while the convection component heats the air.
Space heating systems create a more uniform thermal environment. They are easier to design, manage, and control than localized heating systems, but they tend to have higher energy consumption. Space heating is usually based on a convection system using warm air. Heat is produced in boilers or furnaces and then is transferred into the building when needed.
An alternative is to use direct air heating in the house. Direct heating can be cheaper to install, but requires more maintenance to deal with contaminants, dust, and moisture (Lindley and Whitaker, 1996). Also, there is a need to vent exhaust fumes and CO 2 so ventilation flow rates have to be increased, requiring more energy consumption (Costantino et al., 2020). In other agricultural buildings, such as greenhouses, the warm air is recirculated to decrease energy consumption. In livestock houses this practice is strongly not recommended since the concentration of contaminants that are produced in the enclosure make the IAQ even worse.
| Livestock House | Operation |
Percentage of Electrical Energy
(of the total) |
Percentage of Thermal Energy
(of the total) |
|---|---|---|---|
|
Broiler Houses |
ventilation |
39% |
- |
|
supplemental heating |
27% |
96% |
|
|
lighting |
9% |
- |
|
|
feeding distribution |
20% |
- |
|
|
litter distribution and manure removal |
- |
3% |
|
|
manure transportation and disposal |
- |
1% |
|
|
product collecting and package |
5% |
- |
|
|
Laying Hen Houses |
ventilation |
44% |
- |
|
supplemental heating |
- |
- |
|
|
lighting |
15% |
- |
|
|
feeding distribution |
5% |
- |
|
|
litter distribution and manure removal |
2% |
33% |
|
|
manure treatment |
27% |
- |
|
|
manure transportation and disposal |
- |
67% |
|
|
product collecting and package |
7% |
- |
|
|
Pig Houses |
ventilation and supplemental heating |
48% |
69% |
|
lighting |
2% |
- |
|
|
feeding preparation |
11% |
- |
|
|
feeding distribution |
19% |
- |
|
|
litter care and manure removal |
4% |
1% |
|
|
manure treatment |
4% |
- |
|
|
manure transportation and disposal |
12% |
30% |
|
|
Dairy Cow Houses |
ventilation |
20% |
- |
|
lighting |
8% |
- |
|
|
feeding |
17% |
52% |
|
|
milking |
16% |
6% |
|
|
milk cooling |
12% |
- |
|
|
litter care |
- |
7% |
|
|
manure removal |
8% |
5% |
|
|
manure treatment |
18% |
4% |
|
|
manure transportation and disposal |
1% |
26% |
Localized and space heating systems can be used together or coupled with floor heaters to improve the control of the indoor climate conditions. Floor heating is usually through hot water pipes or electric resistance cables buried directly in the floor, but this can cause greater evaporation and a rise in the air moisture content.
The most common energy sources for heating are electricity, natural gas, propane, and biomass. Solar energy represents an interesting solution for providing supplemental heating, but peak availability is during warm seasons and the daytime when heat demand is lowest.
Heat Recovery Systems
To maintain IAQ, indoor air is replaced by fresh outdoor air to dilute contaminants and decrease moisture content. During heating periods, every cubic meter of fresh air that is introduced inside the livestock house is heated to reach the indoor air set point temperature. The heat of the exhausted air is lost. When the outdoor air is cold, heating the fresh air requires considerable energy; ventilation accounts for 70% to 90% of the heat losses in typical livestock houses during the winter season (ASHRAE, 2011).
To improve energy performance especially in cold climates, heat recovery can be used. In livestock houses, air-to-air heat recovery systems are used to transfer sensible heat from an airstream at a high temperature (exhaust air) to an airstream at a low temperature (fresh supply air) (ASHRAE, 2012). The heat transfer happens through a heat exchange surface (a series of plates or tubes) that separates the two airstreams, avoiding the cross-contamination of fresh supply air with the contaminants in the exhaust air. The most common type of heat exchanger used in livestock houses is cross-flow (Figure 5.2.3). The recovered heat directly increases the temperature of the fresh supply air, decreasing the supplemental heat that is needed to reach the indoor air set point temperature. Heat recovery systems mainly transfer sensible heat but, under certain psychrometric conditions, even part of the latent heat of the exhaust air can be recovered. For example, when the outdoor air is very cool, the water vapor contained in the exhaust air condenses and releases the latent heat of condensation increasing the temperature of the fresh air.
In practice, heat exchanger effectiveness is the ratio between the actual transfer of energy and the maximum possible transfer between the airstreams (ASHRAE, 1991). In livestock houses this is usually between 60% and 80%, because of freezing and dust accumulation on the heat-exchanging surfaces (ASHRAE, 2011). A buildup of dust reduces the heat transfer between the airstreams and reduces the flow rate. In addition, gases and moisture in exhaust air can damage the heat-exchanging surface. Filtration, automatic washing, insulation, and defrost controls can be used to avoid problems with heat exchange.
Cooling Animal Houses
Cooling Systems
In warmer conditions, cooling is required to reduce the indoor air temperature and to alleviate animal heat stress. Air flow driven by fans is used to remove the heat generated by animals and from solar radiation. With high indoor air temperature and in heat stress situations, greater air velocities around the animals are preferred because the skin temperature of the animals is reduced through the increasing convective heat exchange.
When the difference between outdoor air and indoor air temperatures is small, cooling ventilation is less effective because the needed air flow rates require air velocities too great for animal comfort. To overcome this problem, water cooling and evaporative cooling can be used (Lindley and Whitaker, 1996). Water cooling consists of sprinkling or dripping water directly on the animals to remove heat from their bodies through evaporation. Evaporative cooling uses heat from the indoor air to vaporize water and thus decrease indoor air temperature with either a fogging system or evaporative pads. Foggers release a mist of tiny water droplets directly inside the enclosure. Evaporative pads are used in livestock houses with exhaust ventilation systems (Figure 5.2.4). In these systems, exhaust fans force out the indoor air creating a negative pressure difference between inside and outside the house. This pressure difference pulls the fresh outdoor air inside the house through the evaporative pads, decreasing its temperature by some degrees as a function of the direct saturation effectiveness, \(\varepsilon\) (ASHRAE, 2012) (Equation 5.2.20). From a technical point of view, \(\varepsilon\) is the most exciting feature of an evaporative pad, and it ranges between 70% and 95% for commercially available evaporative pads. This value is directly proportional to the pad thickness (from 0.1 to 0.3 m) (ASHRAE, 2012) and inversely proportional to the air velocity through the pad. The highest efficiencies are with air velocity between 1.0 and 1.4 m s −1 (ASHRAE, 2011). The value of \(\varepsilon\) is also influenced by the age and the maintenance of the pad; \(\varepsilon\) can decrease to 30% in old and poorly maintained pads (Costantino et al., 2018).
Evaporative pads affect energy consumption in two ways. On the one hand, they decrease the temperature of the air that is used to ventilate the house, which means a reduction in the ventilation flow rate needed to maintain the indoor air setpoint temperature. On the other hand, they increase the pressure difference between the inside and outside the house, so for the same air flow rate, the fans in a livestock house equipped with evaporative pads require higher electricity consumption. Finally, the use of evaporative pads requires extra electrical energy due to the circulation pumps used to move the water from storage for wetting the top of the pads.
Ventilation Systems
The effectiveness of ventilation inside a livestock house depends on the selection, installation, and operation of the ventilation equipment, such as air inlets, outlets, control systems, and fans.
Fans are classified as centrifugal or axial, according to the direction of the airflow through the impeller (ASHRAE, 2012). Axial fans draw air parallel to the shaft axis (around which the blades rotate) and exhaust it in the same direction. Centrifugal fans exhaust air by deflection and centrifugal force. In centrifugal fans air enters next to the shaft due to the rotation of the impeller and then moves perpendicularly from the shaft to the opening where it is exhausted. Axial fans are usually used in livestock housing because the primary goal is to provide a high airflow rate and not to create a high-pressure difference across the fan. Fans cause considerable energy consumption in livestock houses (Costantino et al., 2016), as shown in Table 5.2.3, but are typically bought based on purchase cost, not operating costs. When fans are installed in the livestock houses, a reduction in efficiency has to be expected due to the wear of the mechanical connections (ASHRAE, 2012).
Examples
Example \(\PageIndex{1}\)
Example 1: Heat flow through a building envelope
Problem:
Determine the total steady-state transmission heat flow through the building envelope of the gable roof broiler house presented in Figure 5.2.5. The thermophysical properties of the envelope elements are shown in Table 5.2.4. For the calculation, assume the indoor air temperature is 23°C and the outdoor air temperature is 20°C.
Solution
The total transmission heat flow through the envelope should be calculated through Equation 5.2.15. In the summation, all the envelope elements of the broiler house must be considered. In this broiler house, the various products \((b_{tr,j} \cdot U_{j} \cdot A_{j})\) of the summation of Equation 5.2.15 are:
|
Building
Element |
Area
(m 2 ) |
U
(W m −2 k −1 ) |
b tr (-) |
|---|---|---|---|
|
North wall |
195 |
0.81 |
1 |
|
South wall |
195 |
0.81 |
1 |
|
East wall |
18 |
0.81 |
1 |
|
West wall |
33 |
0.81 |
1 |
|
Roof |
1320 |
1.17 |
1 |
|
Floor |
1200 |
0.94 |
0.45 |
|
Door (east) |
15 |
1.51 |
1 |
|
North windows |
57 |
3.60 |
1 |
|
South windows |
57 |
3.60 |
1 |
\( \phi_{tr} = [\sum^{n}_{j=1}(b_{tr,j} \cdot U_{j} \cdot A_{j})] \) (Equation \(\PageIndex{15}\))
\( b_{tr,walls} \cdot U_{walls} \cdot A_{walls} = 1 \cdot 0.81 \frac{W}{m^{2} \cdot K} \cdot 441 m^{2} = 357.2 \frac{W}{K} \)
\( b_{tr,roof} \cdot U_{roof} \cdot A_{roof} = 1 \cdot 1.17 \frac{W}{m^{2} \cdot K} \cdot 1320 m^{2} = 1544.4 \frac{W}{K} \)
\( b_{tr, doors} \cdot U_{doors} \cdot A_{doors} = 1 \cdot 1.51 \frac{W}{m^{2} \cdot K} \cdot 15 m^{2} = 22.7 \frac{W}{K} \)
\( b_{tr, windows} \cdot U_{windows} \cdot A_{windows} = 1 \cdot 3.60 \frac{W}{m^{2} \cdot K} \cdot 114 m^{2} = 410.4 \frac{W}{K} \)
The U -value of the floor of the broiler house is 0.94 W m −2 K −1 . This value was calculated considering that the floor was made by a reinforced concrete screed and a waterproofing sheet directly in contact with the ground. In the transmission heat flow via ground, the \(b_{tr}\) coefficient has to be considered. Considering that the floor of the broiler house is in direct contact with the ground, \(b_{tr,floor}\) can be assumed equal to 0.45 (value from Table 5.2.2). The calculation is:
\( b_{tr, floor} \cdot U_{floor} \cdot A_{floor} = 0.45 \cdot 0.94 \frac{W}{m^{2} \cdot K} \cdot 1200 m^{2} = 507.6 \frac{W}{K} \)
Considering the previously calculated values, the sum is:
\( \sum^{n}_{j=1} (b_{tr,j} \cdot U_{j} \cdot A_{j}) = 2842.3 \frac{W}{K} \)
Finally, the heat flow can be calculated considering the temperature difference between inside and outside as:
Example \(\PageIndex{2}\)
Example 2: Sensible heat flow in a broiler house
Problem:
Determine the sensible heat flow produced at the house level by a flock of 14,000 broilers at an indoor air temperature of 23°C. The average weight of the broilers is 1.3 kg.
Solution
The total heat production \(\phi_{a,tot}\) from a broiler flock at an indoor air temperature of 20°C is defined by Equation 5.2.6 that reads
\( \phi_{a,tot}=10.62 \cdot w_{a}^{0.75} \cdot n_{a} \) (Equation \(\PageIndex{6}\))
Considering the given boundary conditions, Equation 5.2.6 becomes:
\( \phi_{a,tot}=10.62 \cdot 1.3^{0.75} \cdot 14,000 = 181,013.1\ W \)
Before calculating \(\phi_{a}\), n hpu has to be calculated according to Equation 5.2.14:
\( n_{hpu} = \frac{\phi_{a,tot}}{1000} \) (Equation \(\PageIndex{14}\))
\( n_{hpu} = \frac{181,031.1\ W}{1000 \frac{W}{hpu}} = 181.01\ hpu \)
Finally, \(\phi_{a}\) calculated at 23°C of \(T_{air,i}\) is (from Equation 5.2.10):
\( \phi_{a} = \{0.61 \cdot [1000+20 \cdot(20-T_{air,i})] -0.228 \cdot T_{air,i}^{2} \} \cdot n_{npu} \) (Equation \(\PageIndex{10}\))
\( \phi_{a} = \{0.61 \cdot [1000+20 \cdot(20-23^\circ C)] -0.228 \cdot (23^\circ C)^{2} \} \cdot 181.01 = 81,959.2\ W \)
The broiler flock in this example produces around 82 kW of sensible heat.
Example \(\PageIndex{3}\)
Example 3: Solar based heat flow
Problem:
Determine the value of \(\phi_{sol}\) considering the boundary conditions shown in Table 5.2.5 and using the same broiler house of Examples 5.2.1 and 5.2.2.
Solution
The first step for determining \(\phi_{sol}\) is to calculate \(\phi_{sol,op}\) for each opaque building element according to Equation 5.2.17, as:
|
Building
Element |
Area
(m 2 ) |
U
(W m −2 k −1 ) |
α (-) | g gl (-) |
I
sol
(W m −2 ) |
|---|---|---|---|---|---|
|
North wall |
195 |
0.81 |
0.3 |
- |
142 |
|
South wall |
195 |
0.81 |
0.3 |
- |
559 |
|
East wall |
18 |
0.81 |
0.3 |
- |
277 |
|
West wall |
33 |
0.81 |
0.3 |
- |
142 |
|
Roof |
1320 |
1.17 |
0.9 |
- |
721 |
|
Floor |
1200 |
0.94 |
- |
- |
- |
|
Door (east) |
15 |
1.51 |
0.9 |
- |
277 |
|
North windows |
57 |
3.60 |
- |
0.6 |
142 |
|
South windows |
57 |
3.60 |
- |
0.6 |
559 |
\( \phi_{sol,op,q} = A_{q} \cdot U_{q} \cdot \alpha_{q} \cdot R_{ex} \cdot I_{sol,q} \cdot F_{sh,q} \) (Equation \(\PageIndex{17}\))
\( \phi_{sol,op,wall, N} = 195 m^{2} \cdot 0.81 \frac{W}{m^{2}\cdot K} \cdot 0.3 \cdot 0.04 \frac{m^{2} \cdot K}{W} \cdot 142 \frac{W}{m^{2}} = 269.1\ W \)
\( \phi_{sol,op,wall, S} = 195 m^{2} \cdot 0.81 \frac{W}{m^{2}\cdot K} \cdot 0.3 \cdot 0.04 \frac{m^{2} \cdot K}{W} \cdot 559 \frac{W}{m^{2}} = 1059.5\ W \)
\( \phi_{sol,op,wall, E} = 18 m^{2} \cdot 0.81 \frac{W}{m^{2}\cdot K} \cdot 0.3 \cdot 0.04 \frac{m^{2} \cdot K}{W} \cdot 277 \frac{W}{m^{2}} = 48.5\ W \)
\( \phi_{sol,op,wall, E} = 33 m^{2} \cdot 0.81 \frac{W}{m^{2}\cdot K} \cdot 0.3 \cdot 0.04 \frac{m^{2} \cdot K}{W} \cdot 142 \frac{W}{m^{2}} = 45.5\ W \)
\( \phi_{sol,op,wall, Roof} = 1320 m^{2} \cdot 1.17 \frac{W}{m^{2}\cdot K} \cdot 0.9 \cdot 0.04 \frac{m^{2} \cdot K}{W} \cdot 721 \frac{W}{m^{2}} = 40,086.4\ W \)
\( \phi_{sol,op,wall, Roof} = 15 m^{2} \cdot 1.51 \frac{W}{m^{2}\cdot K} \cdot 0.9 \cdot 0.04 \frac{m^{2} \cdot K}{W} \cdot 277 \frac{W}{m^{2}} = 225.9\ W \)
The sum of the calculated \(\phi_{sol,op,q}\) values is:
\( \sum^{q}_{n=1}\phi_{sol,op,q} = 41,734.9\ W \)
The solar heat loads on glazed components can be estimated using Equation 5.2.18:
\( \phi_{s,gl,k}=A_{k} \cdot g_{gl} \cdot I_{sol,k} \cdot (1-F_{fr}) \cdot F_{sh,k} \cdot F_{sh,gl,k} \) (Equation \(\PageIndex{18}\))
Considering the given boundary conditions, \(\phi_{sol,gl}\) for the glazed elements can be computed as:
\( \phi_{sol,gl,win,N} = 57\ m^{2} \cdot 0.6 \cdot 142 \frac{W}{m^{2}} \cdot (1-0.2) = 3885.1\ W \)
\( \phi_{sol,gl,win,S} = 57\ m^{2} \cdot 0.6 \cdot 559 \frac{W}{m^{2}} \cdot (1-0.2) = 15,294.2\ W \)
The sum of the calculated \(\phi_{sol,gl,k}\) values is:
\( \sum^{k}_{n=1} \phi_{sol,gl,k} = 19,179.3\ W \)
Finally, the total solar heat load is:
\( \phi_{sol} = 41,734.9\ W + 19,179.3\ W = 60,914.2\ W \)
Example \(\PageIndex{4}\)
Example 4: Ventilation flow rate for temperature control
Problem:
Determine the volumetric ventilation flow rate (m 3 h −1 ) that has to be provided by the exhaust fans of the broiler house to maintain the indoor air temperature at 23°C. For the calculation, consider the absence of supplemental heating flow (\(\phi_{H}=0\ W\)) the heat flows calculated in Example 5.2.1 (\(\phi_{tr}\)), Example 5.2.2 (\(\phi_{a}\)) and Example 5.2.4 (\(\phi_{sol}\)). The supply air temperature is the same of the outdoor air (20°C, as in Example 5.2.1).
Solution
In the previous examples the following heat flows were calculated:
\( \phi_{tr} = -8,526.9\ W = - 8.5\ kW \)
\( \phi_{a} = 81,959.2\ W = 82.0\ kW \)
\( \phi_{sol} = 60,914.2\ W = 60.9\ kW \)
The text of the problem states that no supplemental heating flow is present, therefore:
\( \phi_{H} = 0\ kW \)
Considering the given boundary conditions, the energy balance of Equation 5.2.3 can be written as:
\( 82.0\ kW - 8.5\ kW + 0\ kW + 60.9\ kW + \phi_{v} = 0 \)
That becomes:
\( \phi_{v} = -134.4\ kW \)
Equation 5.2.19 can be expressed in \(\dot{V}\) (the unknown of the problem, in kW) as:
\( \dot{V} = \frac{\phi_{v}}{\rho_{air} \cdot c_{air} \cdot (T_{air,sup} - T_{air, i})} \)
The value of \(\rho_{air}\) is assumed equal to 1.2 kg m −3 and \(c_{air}\) equal to 2.8 × 10 −4 kWh kg −1 K −1 (1010 J kg −1 K −1 ), even though for more detailed calculation \(\rho_{air}\) should be evaluated at the given indoor air temperature and atmospheric pressure. The ventilation air flow is provided with outdoor air, therefore, \(T_{air,sup}\) is equal to \(T_{air,o}\). Inputting the previously calculated value of \(\phi_{v}\), the previous equation reads:
\( \dot{V} = \frac{-134.4\ kW}{1.2 \frac{kg}{m^{3}} \cdot 2.8 \cdot 10^{-4} \frac{kWh}{kg \cdot K} \cdot (20 ^\circ C - 23 ^\circ C)} = 133,333\frac{m^{3}}{h} \)
To maintain the required indoor air temperature inside the livestock house, around 133,000 m 3 h −1 of fresh outdoor air should be provided by the ventilation system.
Image Credits
Figure 1. Fabrizio, E. (CC By 4.0). (2020). The sensible heat balance of equation 3 applied to a generic livestock house.
Figure 2. Fabrizio, E. (CC By 4.0). (2020). The vapor mass balance of equation 5 applied to a generic livestock house.
Figure 3. Costantino, A. (CC By 4.0). (2020). Diagram of the heat exchange surface in an air-to-air heat recovery system.
Figure 4. Costantino, A. (CC By 4.0). (2020). Diagram of a broiler house equipped with evaporative pads.
Figure 5. Costantino, A. (CC By 4.0). (2020). Diagram of the example broiler house with the main geometrical dimensions.
References
Albright, L. (1990). Environmental control for animals and plants . St. Joseph, MI: ASAE.
ASABE Standards. (2012). ASAE EP270.5 DEC1986: Design of ventilation systems for poultry and livestock shelters. St. Joseph, MI: ASABE.
ASHRAE. (2012). 2012 ASHRAE handbook: HVAC systems and equipment . Atlanta, GA: ASHRAE.
ASHRAE. (2011). 2011 ASHRAE handbook: HVAC applications . Atlanta, GA: ASHRAE.
ASHRAE. (1991). ANSI/ ASHRAE Standard 84-1991: Method of testing air-to-air heat exchangers. Atlanta, GA: ASHRAE.
CIGR. (1999). CIGR handbook of agricultural engineering (Vol. II). St. Joseph, MI: ASAE.
Costantino, A., Ballarini, I., & Fabrizio, E. (2017). Comparison between simplified and detailed methods for the calculation of heating and cooling energy needs of livestock housing: A case study. In Building Simulation Applications (pp. 193-200). Bolzano, Italy: Free University of Bozen-Bolzano.
Costantino, A., Fabrizio, E., Biglia, A., Cornale, P., & Battaglini, L. (2016). Energy use for climate control of animal houses: The state of the art in Europe. Energy Proc. 101 , 184-191. https://doi.org/10.1016/j.egypro.2016.11.024
Costantino, A., Fabrizio, E., Ghiggini, A., & Bariani, M. (2018). Climate control in broiler houses: A thermal model for the calculation of the energy use and indoor environmental conditions. Energy Build. 169 , 110-126. https://doi.org/10.1016/j.enbuild.2018.03.056
Costantino, A., Fabrizio, E., Villagrá, A., Estellés, F., & Calvet, S. (2020). The reduction of gas concentrations in broiler houses through ventilation: Assessment of the thermal and electrical energy consumption. Biosyst. Eng . https://doi.org/10.1016/j.biosystemseng.2020.01.002 .
Esmay, M. E., & Dixon, J. E. (1986). Environmental control for agricultural buildings . Westport, CT: AVI.
European Committee for Standardisation. (2009). EN 12831: Heating systems in buildings—Method for calculation of the design heat load. Brussels, Belgium: CEN.
European Committee for Standardization. (2007). EN 13789: Thermal performance of buildings—Transmission and ventilation heat transfer coefficients—Calculation method. Brussels, Belgium: CEN.
Hamilton, J., Negnevitsky, M., & Wang, X. (2016). Thermal analysis of a single-storey livestock barn. Adv. Mech. Eng. 8 (4). https://doi.org/10.1177/1687814016643456 .
Hellickson, M. A., & Walker, J. N. (1983). Ventilation of agricultural structures . St. Joseph, MI: ASAE.
ISO. (2017). ISO 52016-1:2017: Energy performance of buildings—Energy needs for heating and cooling, internal temperatures and sensible and latent heat loads—Part 1: Calculation procedures. International Organization for Standardization.
Liberati, P., & Zappavigna, P. (2005). A computer model for optimisation of the internal climate in animal housing design. In Livestock Environment VII, Proc. Int. Symp . St. Joseph, MI: ASABE.
Lindley, J. A., & Whitaker, J. H. (1996). Agricultural buildings and structures . St. Joseph, MI: ASAE.
Midwest Plan Service. (1987). Structures and environment handbook (11th ed.). Ames, IA: Midwest Plan Service.
OECD. (2008). Environmental performance of agriculture in OECD countries since 1990 . Paris, France: OECD.
Panagakis, P., & Axaopoulos, P. (2008). Comparing fogging strategies for pig rearing using simulations to determine apparent heat-stress indices. Biosyst. Eng. 99 (1), 112-118. https://doi.org/10.1016/j.biosystemseng.2007.10.007 .
Pedersen, S., & Sällvik, K. (2002). 4th report of working group on climatization of animal houses—Heat and moisture production at animal and house levels. Horsens, Denmark: Danish Institute of Agricultural Sciences.
Rossi, P., Gastaldo, A., Riva, G., & de Carolis, C. (2013). Progetto re sole—Linee guida per il risparmio energetico e per la produzione di energia da fonte solare negli allevamenti zootecnici (in Italian). Reggio Emilia, Italy: CRPA.
St-Pierre, N. R., Cobanov, B., & Schnitkey, G. (2003). Economic losses from heat stress by US livestock industries. J. Dairy Sci. 86 , E52-E77. https://doi.org/10.3168/jds.s0022-0302(03)74040-5 .


16.4C: Syntrophy and Methanogenesis
- Assess syntrophy methanogenesis
Syntrophy or cross feeding is when one species lives off the products of another species. A frequently cited example of syntrophy are methanogenic archaea bacteria and their partner bacteria that perform anaerobic fermentation.
Methanogenesis in microbes is a form of anaerobic respiration, performed by bacteria in the domain Archaea. Unlike other microorganisms, methanogens do not use oxygen to respire; but rather oxygen inhibits the growth of methanogens. In methanogenesis, carbon is used as the terminal electron receptor instead of oxygen. Although there are a variety of potential carbon based compounds that are used as electron receptors, the two best described pathways involve the use of carbon dioxide and acetic acid as terminal electron acceptors.
Acetic Acid: CO2+4H2→CH4+2H2OCO2+4H2→CH4+2H2O
Carbon Dioxide: CH3COOH→CH4+CO2CH3COOH→CH4+CO2
Many methanogenic bacteria that live in close association with bacteria produce fermentation products such as fatty acids longer than two carbon atoms, alcohols longer than one carbon atom, and branched chain and aromatic fatty acids. These products cannot be used in methanogenesis. Partner bacteria of the methanogenic archea therefore process these products. By oxydizing them to acetate, they allow them to be used in methanogenesis.
Methanogenic bacteria are important in the decomposition of biomass in most ecosystems. Only methanogenesis and fermentation can occur in the absence of electron acceptors other than carbon. Fermentation only allows the breakdown of larger organic compounds, and produces small organic compounds that can be used in methanogenesis. The semi-final products of decay (hydrogen, small organics, and carbon dioxide) are then removed by methanogenesis. Without methanogenesis, a great deal of carbon (in the form of fermentation products) would accumulate in anaerobic environments.
Methanogenic archea bacteria can also form associations with other organisms. For example, they may also associate with protozoans living in the guts of termites. The protozoans break down the cellulose consumed by termites, and release hydrogen, which is then used in methanogenesis.
Key Points
- Methanogenic bacteria are only found in the domain Archea, which are bacteria with no nucleus or other organelles.
- Methanogenesis is a form of respiration in which carbon rather than oxygen is used as an electron acceptor.
- Bacteria that perform anaerobic fermentation often partner with methanogenic bacteria. During anaerobic fermentation, large organic molecules are broken down into hydrogen and acetic acid, which can be used in methanogenic respiration.
- There are other examples of syntrophic relationships between methanogenic bacteria and mircoorganisms: protozoans in the guts of termites break down cellulose and produce hydrogen which can be used in methanogenesis.
Key Terms
- Archea : A domain of single-celled microorganisms. These microbes have no cell nucleus or any other membrane-bound organelles within their cells.
- syntrophy : A phenomenon where one species lives off the products of another species.
- methanogenesis : The generation of methane by anaerobic bacteria.


5.10: Sentence and Paragraph Construction
You should construct your paragraphs and sentences with intention because these elements are the building blocks of scholarly writing. Plan your writing by briefly outlining the main idea of each paragraph. Each paragraph should convey one main point, usually identified in the first sentence, that links to the overarching purpose of your paper – this is often referred to as your topic sentence. Each paragraph should include several sentences supporting this main point. The end of each paragraph should link to the next paragraph to enhance flow of the overall paper. Watch Film Clip 5.1 about the building blocks of paragraphs.
Film Clip 5.1 : Building blocks of a paragraph [1:31]
Paragraph and sentence length will vary depending on the length of your paper and the complexity of the ideas. For example, you wouldn’t use a one-page paragraph in a three-page paper. Overly long sentences and paragraphs can be unclear and confusing to your reader. The most important thing to keep in mind is that the amount of space needed to develop one idea will likely be different than the amount of space needed to develop another. When is a paragraph complete? When it’s fully developed. Check out Table 5.2 for some pointers on when to end a paragraph and when to combine paragraphs.
Table 5.2: Paragraph pointers
|
Signals that it’s time to end
|
You’re ready to begin developing a new idea. You want to emphasize a new point by setting it apart. You’re getting ready to continue discussing the same idea but in a different way (e.g., shifting from comparison to contrast). You notice that your current paragraph is getting too long (more than three-fourths of a page or so), and you think your writers will need a visual break. |
|
Signals that you may want to combine paragraphs |
You notice that some of your paragraphs appear to be short and choppy. You have multiple paragraphs on the same topic. You have undeveloped material that needs to be united under a clear topic. |
Student Tip
Paragraph and Sentence Length
Although there are exceptions, here are some guiding principles for paragraph and sentence length:
- Sentences should be no longer than three lines. Longer sentences can be complex and confusing: it’s best to write more concisely and/or divide the sentence into two sentences.
- Paragraphs should be at least 3-8 sentences.
- In a double-spaced paper, paragraphs should be about one-half to three-quarters of a page. Longer paragraphs tend to be confusing and usually include more than one idea or repetitive information. Try to make your writing more concise.
The number of paragraphs shares similar qualities to paragraph length. You may have been asked in the past to write a five-paragraph essay – there’s nothing inherently wrong with a five-paragraph essay, but just like sentence length and paragraph length, the number of paragraphs in an essay depends on what’s needed to get the job done. There’s really no way to know that until you start writing. Try not to worry too much about the exact length and number of paragraphs. Start writing and see where the essay and the paragraphs take you. You’ll have plenty of time to sort out the organization during the revision process. You’re letting your ideas unfold: give yourself – and your ideas – the space to let that happen.
Attribution statement
Content from this page was remixed with our own original content, and with editorial and formatting changes, adapted from:
The Word on College Reading and Writing by Carol Burnell, Jaime Wood, Monique Babin, Susan Pesznecker, and Nicole Rosevear, licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.Download for free at: https://openoregon.pressbooks.pub/wrd/


8.12: The Crusades
In 1071, the Great Seljuq Empire, under the leadership of Alp Arslan, defeated the Byzantines at the Battle of Manzikert , near Lake Van, taking the Eastern Roman Emperor, Romanus I, prisoner in the process. This defeat was crushing for the Byzantines, allowing waves of Turkmen ghazis , or raiders, to press deep into the heartland of Anatolia, eventually establishing the Sultanate of Rum, with its capital at Nicaea. A series of weak emperors succeeded Romanus I with Alexios Komnenos (1081 – 1108) eventually ascending to the throne ten years later. As the new emperor, he made peace with the Seljuqs of Rum, and the two states eventually adopted cordial relations. They began to trade with each other and even lent one another military support when needed. Alexius needed this military support in order to secure his borders from groups of Turkic marauders. To that end, he appealed to Pope Urban II (1088 – 1099) for help recruiting mercenary soldiers, namely Frankish knights. An effective cavalry, the Frankish knights had earned an impressive reputation for how they acquitted themselves on the battlefield.
Meanwhile, European leaders had been searching for creative ways of expelling society’s troublemakers and were not averse to sending their soldiers abroad, for the region was suffering from overpopulation and endemic violence. They believed that it was better for the martial groups in their society to fight against the Muslims than amongst themselves. In this way, the Crusades externalized continental violence and promoted European peace.
In 1095, Urban II launched the first crusade from Clermont, a city in southern France. He had benefited from recent church reforms, renewed religious fervor, and a concomitant increase in papal power. While traveling through France, he made an argument for the recovery of the Holy Land: because it belonged to Jesus, it should be controlled by his followers. He also appealed to the greatness of the Franks, promising potential pilgrims a land flowing with milk, honey, and riches. And he offered them well-designed spiritual rewards. For example, salvation applied to those who died on campaign, and anyone who invested in a crusade secured themselves a place in heaven.
The Crusades started in 1096 and were part of a larger process whereby Muslims ceded territory to non-Muslims, sometimes permanently. Provoked by al-Hakim’s treatment of Christians in the Holy Land, as well as the Turkic invasion of Anatolia, Europeans commenced several centuries’ worth of armed crusades against the Muslim states of the eastern Mediterranean and North Africa. Save for the first crusade, in which the Christians established the Crusader states of Edessa, Antioch, Tripoli, and the Latin Kingdom of Jerusalem, all of their campaigns ended in disaster. In fact, they were either looting expeditions or responses to the loss of Crusader states to Muslims. The success that the Latin knights did enjoy related to not only the political fragmentation of the Seljuqs in the eastern Mediterranean, but also the general disinterest of the Fatimid Caliphate in Egypt, which had been dealing with both the repercussions of a religious schism and the consequences of famine and plague. Slow to respond to the challenge posed by the Christians, the Fatimids watched the Crusaders from afar with indifference.
The Muslim counterattack eventually came under the direction of Salah al-Din (Saladin) (d. 1193), a unifier of various Muslim factions in the eastern Mediterranean. An ethnic Kurd, he hailed from a family of soldiers of fortune in the employ of the Zengid Dynasty’s Nur al-Din, a vassal of the Seljuq Turks. Salah al-Din set off in his twenties to fight battles for his uncle, Shirkuh, a Zengid general. A dynamic leader and tactician, he helped his uncle dispatch with the Fatimid opposition in Egypt and solidified Nur al-Din’s rule there. His uncle dying soon thereafter, Salah al-Din eventually became the vizier, or senior minister, to Nur al-Din in 1169. For five years, Salah al-Din ruled Egypt on behalf of Nur al-Din. Then Nur al-Din died in Damascus in 1174, leaving no clear successor.
The Ayyubid Sultanate
In the absence of a formal heir to Nur al-Din, Salah al-Din established the Ayyubid Dynasty (1171 – 1260), named after his father, Ayyub, a provincial governor for the Zengid Dynasty, a family of Oghuz Turks who served as vassals of the Seljuq Empire. Once in power, Salah al-Din established a Sunni government and insisted that the mosque of al-Azhar preach his brand of Islam. He used the concept of jihad to unify the Middle East under the banner of Islam in order to defeat the Christians, but he did not principally direct jihad towards them. A champion of Sunni Islam, he believed that his religion was being threatened mainly from within by the Shi‘a. Like most of their predecessors, the Ayyubids also benefited from tribal ‘asabiyah, or dynastic consensus. Ayyubid ‘asabiyah included a Kurdish heritage, as well as a strong desire to return to Sunni orthodoxy. It was as champions of Sunni Islam that they purposely recruited leading Muslim scholars from abroad, ultimately culminating in Egypt becoming the preeminent state in the Islamic world.
Initially, Salah al-Din displayed no particular interest in the Crusader states. He had clashed with the Crusaders, and King Baldwin IV of Jerusalem; also, Raynald de Chântillon even had handed him a rare defeat at the Battle of Montgisard in 1177. But the Crusaders ultimately brokered an armistice with Salah al-Din. Eventually, Raynald broke their truce when he started attacking Muslim pilgrims and trade caravans in the 1180s. Ensuing skirmishes between the forces of Salah al-Din and Guy de Lusignan, the new King of Jerusalem, presaged a forthcoming battle. In 1187, the two sides met near Tiberias, in modern day Israel. Salah al-Din intentionally attacked the fortress of Tiberius in order to lure the Crusaders away from their well-watered stronghold. His plan worked, and the Christians quickly ran out of water. On the night before the battle, Salah al-Din set brush fires to exacerbate their thirst. He coerced the parched Latin Knights down through the Horns of Hattin towards the cool waters of Lake Tiberius. Salah al-Din bottlenecked the Crusader forces, with the double hill of Hattin acting as a choke point.
The Battle of Hattin represented a smashing victory for Salah al-Din and a major loss for the Crusaders. Tradition dictated that Salah al-Din hold most of the leaders for ransom. Unlike the Crusaders, he treated the defenders of cities with understanding. He showed tolerance of minorities, and even established a committee to partition Jerusalem amongst all the interested religious groups. In this way, he proved his moral superiority to the Crusaders.
With most of their important leaders either killed in battle or captured, no unified Christian leadership remained to fight against Salah al-Din. Deprived of the backbone of their organization, the Crusaders were left with only a few defenseless fortresses along the coast. Salah al-Din pressed his advantage. Increasingly isolated and relying on ever dwindling numbers of Latin Christians willing to remain permanently in the Holy Land, the Latin Crusaders were eventually expelled from the region in 1291.
Although Salah al-Din had maintained direct control over Egypt, he intentionally distributed control over wide swaths of the empire to loyal vassals and family members, whose governance became increasingly autonomous from Cairo. Salah al-Din’s sons and grandsons, who did not have the same ability as their forefather, had trouble managing an increasingly decentralized empire. Widespread mamluk factionalism and family disputes over the control of territory contributed to the weakening of the sultanate. In this vacuum of power, the mamluks came to the fore.


4.6.E: Problems on Compact Sets
Complete the missing details in the proof of Theorem 5.
Verify that any infinite set in a discrete space is closed and bounded but not compact.
[Hint: In such a space no sequence of distinct terms clusters.]
Show that \(E^{n}\) is not compact, in three ways:
\(\left.\text { (i) from definitions (as in Example }\left(\mathrm{a}^{\prime}\right)\right)\) ;
(ii) from Theorem \(4 ;\) and
(iii) from Theorem 5, by finding in \(E^{n}\) a contracting sequence of infinite
closed sets with a void intersection. For example, in \(E^{1}\) take the
closed sets \(F_{m}=[m,+\infty), m=1,2, \ldots(\text { Are they closed?) }\)
Show that \(E^{*}\) is compact under the metric \(\rho^{\prime}\) defined in Problems 5 and 6 in Chapter 3, §11. Is \(E^{1}\) a compact set under that metric?
[Hint: For the first part, use Theorem 2 of Chapter 2, §13, noting that \(G_{q}\) is also a \(\left.\text { globe under } \rho^{\prime} . \text { For the second, consider the sequence } x_{n}=n .\right]\)
Show that a set \(A \subseteq(S, \rho)\) is compact iff every infinite subset \(B \subseteq A\) has a cluster point \(p \in A .\)
[Hint: Select from \(B\) a sequence \(\left\{x_{m}\right\}\) of distinct terms. Then the cluster points of \(\left\{x_{m}\right\}\) are also those of \(B .\) (Why?)]
Prove the following.
(i) If \(A\) and \(B\) are compact, so is \(A \cup B,\) and similarly for unions of \(n\) sets.
(ii) If the sets \(A_{i}(i \in I)\) are compact, so is \(\bigcap_{i \in I} A_{i},\) even if \(I\) is infinite.
Disprove (i) for unions of infinitely many sets by a counterexample.
\( \text { [ Hint: For (ii), verify first that } \bigcap_{i \in I} A_{i} \text { is sequentially closed. Then use Theorem } 1 . ]\)
Prove that if \(x_{m} \rightarrow p\) in \((S, \rho),\) then the set
\[
B=\left\{p, x_{1}, x_{2}, \ldots, x_{m}, \ldots\right\}
\]
is compact.
[Hint: If \(B\) is finite, see Example (b). If not, use Problem \(5,\) noting that any infinite \(\left.\text { subset of } B \text { defines a subsequence } x_{m_{k}} \rightarrow p, \text { so it clusters at } p .\right]\)
Prove, independently, the principle of nested intervals in \(E^{n},\) i.e., Theorem 5 with
\[
F_{m}=\left[\overline{a}_{m}, \overline{b}_{m}\right] \subseteq E^{n} ,
\]
where
\[
\overline{a}_{m}=\left(a_{m 1}, \ldots, a_{m n}\right) \text { and } \overline{b}_{m}=\left(b_{m 1}, \ldots, b_{m n}\right) .
\]
Fixing \(k,\) let \(A_{k}\) be the set of all \(a_{m k}, m=1,2, \ldots .\) Show that \(A_{k}\) is bounded above by each \(b_{m k},\) so let \(p_{k}=\sup A_{k}\) in \(E^{1} .\) Then
\[
(\forall m) \quad a_{m k} \leq p_{k} \leq b_{m k} . \text { (Why?) }
\]
Unfixing \(k,\) obtain such inequalities for \(k=1,2, \ldots, n .\) Let \(\overline{p}=\left(p_{1}, \ldots, p_{k}\right) .\) Then
\[
(\forall m) \quad \overline{p} \in\left[\overline{a}_{m}, \overline{b}_{m}\right], \text { i.e., } \overline{p} \in \bigcap F_{m}, \text { as required. }
\]
Note that the theorem fails for nonclosed intervals, even in \(E^{1} ;\) e.g., take \(F_{m}=\) \((0,1 / m]\) and show that \(\bigcap_{m} F_{m}=\emptyset .\)]
From Problem \(8,\) obtain a new proof of the Bolzano-Weierstrass theorem.
\(\left[\text { Hint: Let }\left\{\overline{x}_{m}\right\} \in[\overline{a}, \overline{b}] \subseteq E^{n} ; \text { put } F_{0}=[\overline{a}, \overline{b}] \text { and set }\right.\)
\[
d F_{0}=\rho(\overline{a}, \overline{b})=d \quad\left(\text { diagonal of } F_{0}\right) .
\]
Bisecting the edges of \(F_{0},\) subdivide \(F_{0}\) into \(2^{n}\) intervals of diagonal \(d / 2 ;\) one of them must contain infinitely many \(x_{m} .\) (Why?) Let \(F_{1}\) be one such inter val; make it closed and subdivide it into \(2^{n}\) subintervals of diagonal \(d / 2^{2} .\) One of them, \(F_{2},\) contains infinitely many \(x_{m} ;\) make it closed, etc.
Thus obtain a contracting sequence of closed intervals \(F_{m}\) with
\[
d F_{m}=\frac{d}{2^{m}}, \quad m=1,2, \ldots
\]
From Problem \(8,\) obtain
\[
\overline{p} \in \bigcap_{m=1}^{\infty} F_{m} .
\]
\(\left.\text { Show that }\left\{\overline{x}_{m}\right\} \text { clusters at } \overline{p} .\right]\)
\(\Rightarrow 10 .\) Prove the Heine-Borel theorem: If a closed interval \(F_{0} \subset E^{n}\) is covered by a family of open sets \(G_{i}(i \in I),\) i.e.,
\[
F_{0} \subseteq \bigcup_{i \in I} G_{i} ,
\]
then it can always be covered by a finite number of these \(G_{i}\).
[Outline of proof: Let \(d F_{0}=d .\) Seeking a contradiction, suppose \(F_{0}\) cannot be covered by any finite number of the \(G_{i}\).
As in Problem \(9,\) subdivide \(F_{0}\) into \(2^{n}\) intervals of diagonal \(d / 2 . A t\) least one of them cannot be covered by finitely many \(G_{i} .\) (Why?) Choose one such interval, make it closed, call it \(F_{1},\) and subdivide it into \(2^{n}\) subintervals of diagonal \(d / 2^{2}\). One of these, \(F_{2},\) cannot be covered by finitely many \(G_{i} ;\) make it closed and repeat the process indefinitely.
Thus obtain a contracting sequence of closed intervals \(F_{m}\) with
\[
d F_{m}=\frac{d}{2^{m}}, \quad m=1,2, \ldots
\]
\(\text { From Problem } 8 \text { (or Theorem } 5),\) get \(\overline{p} \in \bigcap F_{m}\).
As \(\overline{p} \in F_{0}, \overline{p}\) is in one of the \(G_{i} ;\) call it \(G .\) As \(G\) is open, \(\overline{p}\) is its interior point, so let \(G \supseteq G_{\overline{p}}(\varepsilon) .\) Now take \(m\) so large that \(d / 2^{m}=d F_{m}<\varepsilon\). Show that then
\[
F_{m} \subseteq G_{\overline{p}}(\varepsilon) \subseteq G .
\]
\(\left.\text { Thus (contrary to our choice of the } F_{m}\right) F_{m}\) is covered by a single set \(G_{i} .\) This contradiction completes the proof.]
Prove that if \(\left\{x_{m}\right\} \subseteq A \subseteq(S, \rho)\) and \(A\) is compact, then \(\left\{x_{m}\right\}\) converges iff it has a single cluster point.
[Hint: Proceed as in Problem 12 of Chapter 3, §16.]
Prove that if \(\emptyset \neq A \subseteq(S, \rho)\) and \(A\) is compact, there are two points \(p, q \in A\) such that \(d A=\rho(p, q) .\)
\(\text { [Hint: As } A \text { is bounded (Theorem } 3), d A<+\infty .\) By the properties of suprema,
\[
(\forall n)\left(\exists x_{n}, y_{n} \in A\right) \quad d A-\frac{1}{n}<\rho\left(x_{n}, y_{n}\right) \leq d A . \quad \text { (Explain!) }
\]
By compactness, \(\left\{x_{n}\right\}\) has a subsequence \(x_{n_{k}} \rightarrow p \in A .\) For brevity, put \(x_{k}^{\prime}=x_{n_{k}}\), \(y_{k}^{\prime}=y_{n_{k}} .\) Again, \(\left\{y_{k}^{\prime}\right\}\) has a subsequence \(y_{k_{m}}^{\prime} \rightarrow q \in A .\) Also,
\[
d A-\frac{1}{n_{k_{m}}}<\rho\left(x_{k_{m}}^{\prime}, y_{k_{m}}^{\prime}\right) \leq d A .
\]
Passing to the limit \((\text { as } m \rightarrow+\infty),\) obtain
\[
d A \leq \rho(p, q) \leq d A
\]
by Theorem 4 in Chapter 3, §15.]
Given nonvoid sets \(A, B \subseteq(S, \rho),\) define
\[
\rho(A, B)=\inf \{\rho(x, y) | x \in A, y \in B\} .
\]
Prove that if \(A\) and \(B\) are compact and nonempty, there are \(p \in A\) and \(q \in B\) such that \(\rho(p, q)=\rho(A, B) .\) Give an example to show that this \(\left.\text { may fail if } A \text { and } B \text { are not compact (even if they are closed in } E^{1}\right)\).
[Hint: For the first part, proceed as in Problem 12 .]
Prove that every compact set is complete. Disprove the converse by examples.


1.7: Chapter Review - Review Questions
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Review Questions
1 .
What nineteenth-century invention laid the foundation for future developments in computing?
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Jacquard’s loom
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Apple’s Macintosh computer
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Programma 101
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TRS-80
2 .
What is the main operating system for iPhone mobile devices?
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Android
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Google
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iOS
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SMS
3 .
What are direct deposit, high-speed printers/copiers, and automated inventory systems all examples of?
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outdated technology
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future developments in technology
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early-stage developments in technology
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advances in technology
4 .
What is VR?
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a mostly live, real environment with some digital elements embedded
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a computerized game for trying out a new product
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a robot that can perform mundane tasks
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a mostly simulated environment that allows the user to interact in the space
5 .
The autopilot features on an airplane are an example of ________.
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VR
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AI
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AR
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machine learning
6 .
________ is referred to as "the brains of the computer system."
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Printer
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Mainframe
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Microprocessor
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Router
7 .
________ allows the computer to operate and process information quickly.
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ROM
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RAM
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HTTP
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WAN
8 .
What is a network adapter card that organizes information into small packets for movement in the network or over the internet?
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TCP (transmission control protocol)
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HTTP (hypertext transfer protocol)
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UDP (user datagram protocol)
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STP (standard transfer protocol)
9 .
Microsoft 365 is an example of ________.
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HTTP
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LAN
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a Web application
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an e-commerce site
10 .
________ is the language used behind the scenes for website formatting.
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HTTP
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HTML
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URL
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IP address
11 .
What is Wikipedia?
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an internet service provider
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a web browser on mobile phones
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a domain name
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a free source of information
12 .
________ is the protocol used to connect computers together in a LAN.
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HTML
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Ethernet
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IP address
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Web application
13 .
Which operator is used to search partial words on the internet?
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*
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|
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+
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–
14 .
________ refers to a private network meant for internal company use.
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Cloud
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Cloud computing
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Internet
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Intranet
15 .
Cloud computing was first introduced in what year?
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1982
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2002
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1996
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2016
16 .
________ is a set of principles and best practices designed to prevent unauthorized access to a system.
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Two-factor authentication
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Biometrics
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Data masking
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Password management
17 .
Corporate espionage refers to ________.
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trespassing on a competitor’s property
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wiretapping a competitor’s office
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domain hacking
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all of the above
18 .
Cookies are defined as ________.
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the practice of protecting digital information from unauthorized access, corruption, or theft
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small data files from websites that are deposited on a user’s hard disk
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a list of web pages a user visits, stored on a computer’s browser
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ads that entice users to click on them
19 .
________ encrypts information on your computer, making it inaccessible until you give the hacker something valuable.
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Phishing
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Data masking
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Spoofing
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Ransomware


8.3: Psycholinguistics- Traces in the Mind
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-
Last updated
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Save as PDF
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We’ve claimed that when the operation MOVE moves a head or a phrase into a new Surface Structure position, it leaves a silent copy of itself (a trace) behind in the Deep Structure position. This unit presents psycholinguistic evidence that these traces do form part of our mental representation of sentences.
A YouTube element has been excluded from this version of the text. You can view it online here: ecampusontario.pressbooks.pub/essentialsoflinguistics/?p=188
Check Yourself
1. Which of the following illustrates the position of the trace in the wh-question
What did Christina order at Chipotle?
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What did Christina order at Chipotle
what
.
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What did Christina order
what
at Chipotle.
2. Which of the following ungrammatical sentences gives evidence that unpronounced traces exist in our mental representations of sentences?
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*Did you ate what for lunch?
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*What did you eat sandwiches for lunch?
3. Predict which sentence would lead to more eye movements to a picture of a rabbit after the verb
chase
:
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What did the fox chase ^ into the hedge?
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Did the fox chase ^ the rabbit into the hedge?
Answers


5: Special Distributions
In this chapter, we study several general families of probability distributions and a number of special parametric families of distributions. Unlike the other expository chapters in this text, the sections are not linearly ordered and so this chapter serves primarily as a reference. You may want to study these topics as the need arises.
First, we need to discuss what makes a probability distribution special in the first place. In some cases, a distribution may be important because it is connected with other special distributions in interesting ways (via transformations, limits, conditioning, etc.). In some cases, a parametric family may be important because it can be used to model a wide variety of random phenomena. This may be the case because of fundamental underlying principles, or simply because the family has a rich collection of probability density functions with a small number of parameters (usually 3 or less). As a general philosophical principle, we try to model a random process with as few parameters as possible; this is sometimes referred to as the principle of parsimony of parameters . In turn, this is a special case of Ockham's razor , named in honor of William of Ockham, the principle that states that one should use the simplest model that adequately describes a given phenomenon. Parsimony is important because often the parameters are not known and must be estimated.
In many cases, a special parametric family of distributions will have one or more distinguished standard members , corresponding to specified values of some of the parameters. Usually the standard distributions will be mathematically simplest, and often other members of the family can be constructed from the standard distributions by simple transformations on the underlying standard random variable.
An incredible variety of special distributions have been studied over the years, and new ones are constantly being added to the literature. To truly deserve the adjective special , a distribution should have a certain level of mathematical elegance and economy, and should arise in interesting and diverse applications.
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- 5.1: Location-Scale Families
- As usual, our starting point is a random experiment modeled by a probability space (Ω,F,P), so that Ω is the set of outcomes, F the collection of events, and P the probability measure on the sample space (Ω,F). In this section, we assume that we fixed random variable Z defined on the probability space, taking values in R.
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- 5.2: General Exponential Families
- Many of the special distributions studied in this chapter are general exponential families, at least with respect to some of their parameters. On the other hand, most commonly, a parametric family fails to be a general exponential family because the support set depends on the parameter. The following theorems give a number of examples. Proofs will be provided in the individual sections.
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- 5.3: Stable Distributions
- Stable distributions are an important general class of probability distributions on R that are defined in terms of location-scale transformations. Stable distributions occur as limits (in distribution) of scaled and centered sums of independent, identically distributed variables. Such limits generalize the central limit theorem, and so stable distributions generalize the normal distribution in a sense. The pioneering work on stable distributions was done by Paul Lévy.
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- 5.6: The Normal Distribution
- The normal distribution holds an honored role in probability and statistics, mostly because of the central limit theorem, one of the fundamental theorems that forms a bridge between the two subjects. In addition, as we will see, the normal distribution has many nice mathematical properties. The normal distribution is also called the Gaussian distribution, in honor of Carl Friedrich Gauss, who was among the first to use the distribution.
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- 5.7: The Multivariate Normal Distribution
- The multivariate normal distribution is among the most important of multivariate distributions, particularly in statistical inference and the study of Gaussian processes such as Brownian motion. The distribution arises naturally from linear transformations of independent normal variables. In this section, we consider the bivariate normal distribution first, because explicit results can be given and because graphical interpretations are possible.
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- 5.8: The Gamma Distribution
- In this section we will study a family of distributions that has special importance in probability and statistics. In particular, the arrival times in the Poisson process have gamma distributions, and the chi-square distribution in statistics is a special case of the gamma distribution. Also, the gamma distribution is widely used to model physical quantities that take positive values.
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- 5.9: Chi-Square and Related Distribution
- In this section we will study a distribution, and some relatives, that have special importance in statistics. In particular, the chi-square distribution will arise in the study of the sample variance when the underlying distribution is normal and in goodness of fit tests.
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- 5.11: The F Distribution
- In this section we will study a distribution that has special importance in statistics. In particular, this distribution arises from ratios of sums of squares when sampling from a normal distribution, and so is important in estimation and in the two-sample normal model and in hypothesis testing in the two-sample normal model.
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- 5.13: The Folded Normal Distribution
- The folded normal distribution is the distribution of the absolute value of a random variable with a normal distribution. As has been emphasized before, the normal distribution is perhaps the most important in probability and is used to model an incredible variety of random phenomena.
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- 5.14: The Rayleigh Distribution
- The Rayleigh distribution, named for William Strutt, Lord Rayleigh, is the distribution of the magnitude of a two-dimensional random vector whose coordinates are independent, identically distributed, mean 0 normal variables. The distribution has a number of applications in settings where magnitudes of normal variables are important.
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- 5.15: The Maxwell Distribution
- The Maxwell distribution, named for James Clerk Maxwell, is the distribution of the magnitude of a three-dimensional random vector whose coordinates are independent, identically distributed, mean 0 normal variables. The distribution has a number of applications in settings where magnitudes of normal variables are important, particularly in physics. The Maxwell distribution is closely related to the Rayleigh distribution.
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- 5.18: The Beta Prime Distribution
- The beta prime distribution is the distribution of the odds ratio associated with a random variable with the beta distribution. Since variables with beta distributions are often used to model random probabilities and proportions, the corresponding odds ratios occur naturally as well.
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- 5.20: General Uniform Distributions
- This section explores uniform distributions in an abstract setting. If you are a new student of probability, or are not familiar with measure theory, you may want to skip this section and read the sections on the uniform distribution on an interval and the discrete uniform distributions.
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- 5.21: The Uniform Distribution on an Interval
- The continuous uniform distribution on an interval of R is one of the simplest of all probability distributions, but nonetheless very important. In particular, continuous uniform distributions are the basic tools for simulating other probability distributions. The uniform distribution corresponds to picking a point at random from the interval.
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- 5.22: Discrete Uniform Distributions
- The discrete uniform distribution is a special case of the general uniform distribution with respect to a measure, in this case counting measure. The distribution corresponds to picking an element of S at random. Most classical, combinatorial probability models are based on underlying discrete uniform distributions. The chapter on Finite Sampling Models explores a number of such models.
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- 5.25: The Irwin-Hall Distribution
- The Irwin-Hall distribution, named for Joseph Irwin and Phillip Hall, is the distribution that governs the sum of independent random variables, each with the standard uniform distribution. It is also known as the uniform sum distribution. Since the standard uniform is one of the simplest and most basic distributions (and corresponds in computer science to a random number), the Irwin-Hall is a natural family of distributions. It also serves as a nice example of the central limit theorem.
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- 5.30: The Extreme Value Distribution
- Extreme value distributions arise as limiting distributions for maximums or minimums (extreme values) of a sample of independent, identically distributed random variables, as the sample size increases. Thus, these distributions are important in probability and mathematical statistics.
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- 5.31: The Hyperbolic Secant Distribution
- The hyperbolic secant distribution is a location-scale familty with a number of interesting parallels to the normal distribution. As the name suggests, the hyperbolic secant function plays an important role in the distribution, so we should first review some definitions.
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- 5.32: The Cauchy Distribution
- The Cauchy distribution, named of course for the ubiquitous Augustin Cauchy, is interesting for a couple of reasons. First, it is a simple family of distributions for which the expected value (and other moments) do not exist. Second, the family is closed under the formation of sums of independent variables, and hence is an infinitely divisible family of distributions.
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- 5.33: The Exponential-Logarithmic Distribution
- The exponential-logarithmic distribution arises when the rate parameter of the exponential distribution is randomized by the logarithmic distribution. The exponential-logarithmic distribution has applications in reliability theory in the context of devices or organisms that improve with age, due to hardening or immunity.
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- 5.34: The Gompertz Distribution
- The Gompertz distributon, named for Benjamin Gompertz, is a continuous probability distribution on [0,∞) that has exponentially increasing failure rate. Unfortunately, the death rate of adult humans increases exponentially, so the Gompertz distribution is widely used in actuarial science.
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- 5.37: The Wald Distribution
- The Wald distribution, named for Abraham Wald, is important in the study of Brownian motion. Specifically, the distribution governs the first time that a Brownian motion with positive drift hits a fixed, positive value. In Brownian motion, the distribution of the random position at a fixed time has a normal (Gaussian) distribution, and thus the Wald distribution, which governs the random time at a fixed position, is sometimes called the inverse Gaussian distribution.
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- 5.38: The Weibull Distribution
- The Weibull distribution is named for Waloddi Weibull. Weibull was not the first person to use the distribution, but was the first to study it extensively and recognize its wide use in applications. The standard Weibull distribution is the same as the standard exponential distribution. But as we will see, every Weibull random variable can be obtained from a standard Weibull variable by a simple deterministic transformation, so the terminology is justified.
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- 5.39: Benford's Law
- Benford's law refers to probability distributions that seem to govern the significant digits in real data sets. The law is named for the American physicist and engineer Frank Benford, although the law was actually discovered earlier by the astronomer and mathematician Simon Newcomb.
-
- 5.40: The Zeta Distribution
- The zeta distribution is used to model the size or ranks of certain types of objects randomly chosen from certain types of populations. Typical examples include the frequency of occurrence of a word randomly chosen from a text, or the population rank of a city randomly chosen from a country. The zeta distribution is also known as the Zipf distribution, in honor of the American linguist George Zipf.


5.13: For Further Exploration
Online activity/game
https://pronouns.minus18.org.au/ Practicing with different pronouns + explanations.
https://www.itspronouncedmetrosexual.com/2016/05/sexualitree-model-of-comprehensive-sexuality/ Interactive activity, explores how we experience sexuality in different ways
YouTube
https://www.youtube.com/watch?v=Ayi3OJbvePw “ MAN or WOMAN!? | GENDER AND SEX”
https://www.youtube.com/watch?v=N_yBGQqg7kM “Why Pronouns Matter For Trans People”
https://www.youtube.com/watch?v=yu-2i8CJXY4 If Trans People Said The Stuff Cisgender People Say
https://youtu.be/q8bSIuyJHD8 Sam Killermann: Gender from boxes, to -ness.
Other
https://everydayfeminism.com/2016/09/intersex%E2%80%8A-%E2%80%8Amore-than-a-diagnosis/ intersex pov.
http://www.intersexequality.com/wp-content/uploads/2012/10/Brief-Guidelines-for-Intersex-Allies.pdf “brief guidelines for intersex allies”
Vocab
https://www.plannedparenthood.org/learn/gender-identity/transgender/transgender-identity-terms-and-labels glossary terms.
https://pflag.org/glossary glossary terms.
https://www.mypronouns.org/how how to use pronouns
https://rossieronline.usc.edu/blog/gender-identity-glossary/ glossary terms
Spahr Center https://thespahrcenter.org/
Gender Spectrum Institute https://genderspectrum.org/
Positive Images https://www.posimages.org/


7.3: Public Health Initiatives, Delivery Mechanisms, and Implications
By the end of this section, you should be able to:
- Describe Healthy People 2030, a public health initiative that seeks to improve health outcomes.
- Categorize public health policies by mechanisms of delivery (fiscal policy, regulation, education, preventative treatment, and screening).
- Examine the implications of the Institute for Healthcare Improvement (IHI) Triple Aim to improve population health, reduce costs, and improve client satisfaction.
This chapter has discussed health policy and the balance between the right to human health and individual rights. This section will look at one U.S. public health initiative, the mechanisms of public health delivery, and a framework that has been developed to improve population health, reduce costs, and improve client satisfaction.
Healthy People 2030
The Health of the Population described Healthy People , a public health initiative that seeks to improve health outcomes in the United States. Put forth by the HHS, the 10-year plan establishes public health priorities and data-driven objectives to improve public health and well-being over a decade. The most recent iteration of the plan, Healthy People 2030, includes 23 high-priority leading health indicators (LHIs) to improve health and well-being over the lifespan. Most LHIs address factors that affect major causes of death and disease, such as the proportion of people with access to health insurance and the number of people exposed to unhealthy air (ODPHP, n.d.-c).
Public health policies and programs are what drive and establish the mechanisms by which such health outcomes are achieved. Table 7.5 provides examples of LHI objectives as established by Healthy People 2030 and examples of policies and programs necessary to achieve them.
| Leading Health Indicator Objective | Public Health Policy or Program to Achieve the Objective |
|---|---|
| Increase the proportion of people with health insurance | The passage of the ACA improved access to health insurance through the expansion of Medicaid and the health care marketplace. The HHS (2022) reports that the total enrollment for Medicaid expansion , Marketplace coverage, and the Basic Health Program reached a high of over 35 million people as of early 2022. |
| Increase the proportion of persons who know their HIV status | According to the CDC, increasing the number of people who are aware of their HIV status is critical to preventing HIV infections. To increase Americans’ awareness of their HIV status, the CDC has established the Expanded Testing Initiative , through which three programs have been launched (see Expanded Testing Initiative) (CDC, 2020b). |
| Reduce household food insecurity and, in doing so, reduce hunger |
The
Special Supplemental Nutrition Program for Women, Infants, and Children
(WIC) provides federal grants to states for “supplemental foods, health care referrals, and nutrition education for low-income pregnant, breastfeeding, and non-breastfeeding postpartum women, and to infants and children up to age 5 who are found to be at nutritional risk” (USDA Food and Nutrition Service, n.d.)
In a 2015 policy statement, the American Academy of Pediatrics (AAP) recommended that “pediatricians engage in efforts to mitigate food insecurity at the practice level and beyond,” including connecting families to and advocating for federal nutrition programs (AAP, 2015). |
| Increase the proportion of persons who are vaccinated annually against seasonal influenza | State laws establish requirements regarding the vaccination of schoolchildren (CDC, 2016). In addition, the CDC works with public health agencies and private organizations to “improve and sustain immunization coverage and to monitor the safety of vaccines” (CDC, 2022c). |
| Reduce drug overdose deaths |
The
Overdose Response Strategy
(ORS) is a
collaboration
between the CDC and the High Intensity Drug Trafficking Areas (HIDTA) program designed to help communities reduce drug overdoses through evidence-based intervention strategies at the local, regional, and state levels (CDC, 2023b).
The Bureau of Justice Assistance’s Comprehensive Opioid, Stimulant, and Substance Abuse Program (BJA COSSAP) supports state, local, and tribal responses to substance use to decrease overdose deaths, improve public safety, and improve access to treatment services in the criminal justice system (CDC, 2023b). |
| Reduce the number of days people are exposed to unhealthy air | The U.S. Environmental Protection Agency (EPA) Clean Air Act programs have lowered the levels of six common pollutants by 78 percent and have led to an improvement in air quality in the United States (EPA, 2023). |
Public Health Policy Delivery Mechanisms
Health policy interventions are how health policies are carried out (Hadler et al., 2018). Public health officials responsible for making decisions must consider certain factors, including selecting the appropriate intervention, facilitating the implementation of the intervention, and assessing the effectiveness of the intervention. Mechanisms for delivering public health policy interventions include fiscal policy, regulation, education, preventative treatment, and screening. Table 7.6 defines public health policy delivery mechanisms and provides examples of each.
| Category | Explanation | Example(s) |
|---|---|---|
| Fiscal Policy | The U.S. government can use fiscal policy, including subsidies, free services, and taxes, to incentivize healthy behaviors and discourage unhealthy ones (Nugent & Knaul, 2006). |
|
| Regulation | Any law, ordinance, rule, and so on issued by a government agency or organization. | The FDA is the U.S. agency responsible for regulating food- and health-related products. For example, under the Federal Food, Drug, and Cosmetic Act (FFDCA), the FDA regulates the safety and effectiveness of medical devices. |
| Education | The information, tools, and resources needed to obtain and sustain positive health outcomes (also referred to as health literacy ). | The CDC’s Diabetes Self-Management Education and Support (DSMES) Toolkit program provides individuals living with diabetes the necessary education and support to make the best health care decisions, leading to more positive health outcomes (CDC, 2022b). |
| Preventative Treatment | Routine care that people receive to maintain their health or prevent disease. | Well-child visits , vaccinations, and blood tests as required by the Children’s Health Insurance Program (CHIP) are examples of preventative treatment . |
| Screening | Tests that screen people for diseases or other issues, often catching them before symptoms are noticed. | CDC guidelines on disease-specific screening s, such as mammograms, colonoscopies, and blood pressure screenings, are examples of screenings to improve public health by preventing disease progression. |
Education is a crucial social determinant of health. Access to education increases the probability that individuals will have the necessary skills to make the best decisions related to their health. Education also assists in increasing one’s health literacy, which is the degree to which individuals have the capacity to obtain, process, and understand basic health information and services needed to make appropriate health decisions.
Institute for Healthcare Improvement Triple Aim
Despite its high costs, the U.S. health care system lags in factors relating to access, affordability, and quality. As discussed in What Is Population Health? the Triple Aim is a framework developed by the Institute for Healthcare Improvement (IHI) that aims to improve health care system performance on three levels (IHI, 2023):
- Improving the client experience of care (including quality and satisfaction)
- Improving the health of populations
- Reducing the per capita cost of health care
The IHI Triple Aim recommends a change process that includes identifying target population s; defining systems, aims, and measures to target the population; developing a portfolio of project work that is strong enough to drive system changes; and performing rapid testing that is specific to the needs of the local communities and the issues the community faces (IHI, 2023). The key to the success of the IHI Triple Aim is the utilization of and adherence to five components, shown in Table 7.7.
| Component | Explanation/Examples |
|---|---|
| Focus on Individuals and Families |
|
| Redesign of Primary Care Services and Structures |
|
| Population Health Management (Prevention and Health Promotion) |
|
| Cost Control Platform |
|
| System Integration and Execution |
|
The results and benefits of the IHI Triple Aim are as follows (IHI, 2023):
- Populations are healthier.
- Clients receive more coordinated care, which lessens the burden of disease and illness.
- The per capita cost of care for populations is stabilized.
- The burden on publicly funded health care budgets (such as Medicaid and Medicare) is lessened.
- The economic well-being of populations is improved.
Organizations and programs such as primary care coalitions, regional health care systems, and others have experienced success implementing the IHI Triple Aim (IHI, 2023).
Since the inception of the Triple Aim in 2008, two more aims have been proposed, including a Quintuple Aim (Mate, 2022). Those two aims are improving workforce well-being and safety and advancing health equity.
This video describes a population health initiative in Minnesota that focuses on improving the health, well-being, and quality of life of the residents. Note this video is over 22 minutes.
Watch the video, and then respond to the following questions.
- How does the Heart of New Ulm study use electronic health records and data warehousing to meet clients’ needs?
- What are examples of health interventions at a population level?
- How does the Triple Aim relate to the Heart of New Ulm?
- Do you agree that improving health outcomes requires making it easy for clients to do the right thing? Why or why not?