Robustness in farm animals was defined by Knap as ‘the ability to combine a high production potential with resilience to stressors, allowing for unproblematic expression of a high production potential in a wide variety of environmental conditions’. The importance of robustness-related traits in breeding objectives is progressively increasing towards the production of animals with a high production level in a wide range of climatic conditions and production systems, together with a high level of animal welfare. Current strategies to increase robustness include selection for ‘functional traits’, such as skeletal and cardiovascular integrity, disease resistance and mortality in various stages. It is also possible to use global evaluation of sensitivity to the environment (e.g. reaction norm analysis or canalization), but these techniques are difficult to implement in practice. The hypothalamic–pituitary–adrenocortical (HPA) axis is the most important stress-responsive neuroendocrine system. Cortisol (or corticosterone) released by the adrenal cortices exerts a large range of effects on metabolism, the immune system, inflammatory processes and brain function, for example. Large individual variations have been described in the HPA axis activity with important physiopathological consequences. In terms of animal production, higher cortisol levels have negative effects on growth rate and feed efficiency and increase the fat/lean ratio of carcasses. On the contrary, cortisol has positive effects on traits related to robustness and adaptation. For instance, newborn survival was shown to be directly related to plasma cortisol levels at birth, resistance to bacteria and parasites are increased in animals selected for a higher HPA axis response to stress, and tolerance to heat stress is better in those animals that are able to mount a strong stress response. Intense selection for lean tissue growth during the last decades has concomitantly reduced cortisol production, which may be responsible for the negative effects of selection on piglet survival. One strategy to improve robustness is to select animals with higher HPA axis activity. Several sources of genetic polymorphism have been described in the HPA axis. Hormone production by the adrenal cortices under stimulation by adrenocorticotropin hormone is a major source of individual differences. Several candidate genes have been identified by genomic studies and are currently under investigation. Bioavailability of hormones as well as receptor and post-receptor mechanisms are also subject to individual variation. Integration of these different sources of genetic variability will allow the development of a model for marker-assisted selection to improve animal robustness without negative side effects on production traits.

In the UK, mean stillbirth rate is reported to be 6.7% (BPEX, 2008), which indicates a considerable loss for pig producers. Lately much research has been carried out to improve piglet survival by genetic improvement, but correlations between survival and reproduction traits showed contradictory results and seldom distinguished between maternal and direct piglet genetic effects. Some studies considered birth weight as a trait that is likely to be closely related to survival, but few looked at individual piglet birth weight. The aim of this study was to estimate genetic correlations between birth weight or its variation within litter and survival to examine if selection on individual birth weight is a viable way to improve survival.

A Bayesian procedure was used to estimate linear reaction norms (i.e. individual G × E plots) on 297 518 litter size records of 121 104 sows, daughters of 2040 sires, recorded on 144 farms in North and Latin America, Europe, Asia and Australia. The method allowed for simultaneous estimation of all parameters involved. The analysis was carried out on three subsets, comprising (i) parity 1 records of 33 641 sows of line B, (ii) all parity records of 52 120 sows of line B and (iii) all parity records of 121 104 sows of lines A, B and A × B. Estimated heritabilities ranged from 0.09 to 0.10 (smallest to largest subset) for the intercept of the reaction norms, and were 0.15, 0.08 and 0.02 (ditto) for the slope. Estimated genetic correlations between intercept and slope were −0.09, +0.26 and +0.69 (ditto). The three subsets therefore showed a progressively lower genetic component to environmental sensitivity, and progressively less re-ranking of genotypes across the environmental (herd–year–season) range. In a genetic evaluation that does not include reaction norms in the statistical model, part of the G × E effect remains confounded with the additive genetic effect, which may lead to errors in the estimates of the additive genetic effect; the reaction norms model removes this confounding. The intercept estimates from the largest data subset show correlations with litter size estimated breeding values (EBV) from routine genetic evaluation (without reaction norms included) of 0.78 to 0.85 for sows with one to seven litter records, and 0.75 for sires. Hence, including reaction norms in genetic evaluation would increase the reliability of the EBV of young selection candidates without own performance or progeny data by considerably more than 100 × (1/0.75−1) = 33%. Reaction norm slope estimates turn out to be very demanding statistics; environmental sensitivity must therefore be classified as a ‘hard-to-measure’ trait.

Piglet survival is based on a complex interaction between the piglets own genetic component (direct genetic effects), the dams genetic contribution (maternal genetic effects) and environmental effects (systematic environmental such as year-season, common litter and individual environmental effects). Disentanglement of direct and maternal genetic effects needs a powerful design of genetic relationships. In order to accomplish this, a two generation selection experiment was designed with different selection groups for direct and maternal effects and cross-classification of these selection groups. Survival at birth and survival during the nursing period may have genetically independent components and would then be treated as different traits. In addition, piglet survival traits are reported to have low direct and maternal heritabilities and traits genetically associated with survival, such as birth weight, may result in a more efficient change in survival than using survival per se. Therefore, the objective of the research was to estimate the genetic parameters of direct and maternal genetic effects of survival and birth weight in order to enhance the selection strategies for piglet survival.

Mechanistic animal growth models can incorporate a description of the genotype as represented by underlying biological traits that aim to specify the animal's genetic potential for performance, independent from the environmental factors captured by the models. It can be argued that these traits may therefore be more closely associated to genetic potential, or components of genetic merit that are more robust across environments, than the environmentally dependent phenotypic traits currently used for genetic evaluation. The prediction of merit for underlying biological traits can be valuable for breeding and development of selection strategies across environments.

Model inversion has been identified as a valid method for obtaining estimates of phenotypic and genetic components of the biological traits representing the genotype in the mechanistic model. The present study shows how these estimates were obtained for two existing pig breeds based on genetic and phenotypic components of existing performance trait records. Some of the resulting parameter estimates associated with each breed differ substantially, implying that the genetic differences between the breeds are represented in the underlying biological traits. The estimated heritabilities for the genetic potentials for growth, carcass composition and feed efficiency as represented by biological traits exceed the heritability estimates of related phenotypic traits that are currently used in evaluation processes for both breeds. The estimated heritabilities for maintenance energy requirements are however relatively small, suggesting that traits associated with basic survival processes have low heritability, provided that maintenance processes are appropriately represented by the model.

The results of this study suggest that mechanistic animal growth models can be useful to animal breeding through the introduction of new biological traits that are less influenced by environmental factors than phenotypic traits currently used. Potential value comes from the estimation of underlying biological trait components and the explicit description of their expression across a range of environments as predicted by the model equations.

Peri- and postnatal mortality of piglets is reported to be around 20% and genetic improvement in piglet survival has great potential benefits in terms of animal welfare, economics and the environment. The indication of an unfavourable genetic correlation between litter size and survival in particular points to the importance of including piglet survival in those pig breeding programmes that currently only aim to increase litter size. Phenotypically, individual birth weight is closely associated with piglet survival (Roehe and Kalm, 2000). Genetic parameters for piglet survival traits and individual birth weight therefore need to be estimated in order to genetically improve piglet survival efficiency

Mixing of commercial pigs frequently leads to intense aggression. Considerable phenotypic variation exists between individuals and selection against aggressiveness may offer a long-term reduction in aggression without incurring additional costs to the primary producer. The genetic contribution to aggressiveness was quantified in this study using the number of skin lesions as an indicator of involvement in aggression. A sample of 1132 pigs were mixed at an average weight of 27·9 (s.d. 4·6) kg into 96 pens on a commercial sire line nucleus unit. Post-mixing aggressiveness of pigs was assessed using the lesion score (LS) approach. Growth rate, between 27·9 and 91·9 kg, and backfat depth at 91·9 kg were recorded for a subsample of 658 pigs. With a pedigree file of 1947 animals, a heritability of 0·22 was estimated for the LS trait. No significant genetic or phenotypic correlations were found between LS and growth rate or backfat depth, but standard errors of estimates were large. The response to selection, when all selection pressure was placed on the LS trait, was a 25% reduction in LS per generation. It is therefore technically possible to select for a reduced LS without substantially inhibiting genetic progress in growth rate or backfat depth through antagonistic genetic relationships.

The objective of this study was to develop accurate mathematical-statistical functions to estimate body composition of live pigs between 20 and 140 kg weight from total body water (TBWA) determined by the deuterium dilution technique. Chemical body compositions during the growth period are essential input parameters for biological pig growth models, which are used to estimated the nutrient requirements, improve the entire production system, determine optimal slaughter weight, optimize selection for food intake, etc. In the present study, 48 pigs (17 female and 31 castrated males) were used in an experimental station to obtain protein, lipid, ash and water content at 20, 30, 60, 90, 120 and 140 kg live weight. At each target weight, body water of the animals was determined by the deuterium dilution technique. Eight pigs of each live-weight group were slaughtered and chemically analysed. Water content of the empty body decreased from 74 to 53%, whereas lipid content rose from 7 to 30%. Between 20 and 30 kg body weight, protein content increased from 16 to 17% and thereafter decreased to 16%. Ash content was constant at 3%. To estimate body composition of the remaining animals from TBWA (%) determined by deuterium dilution technique, two sets of exponential prediction functions were used to describe the relationship between chemically analysed body components and TBWA (%). The first set of prediction functions fitted one intercept for the entire growth period and the second set of prediction functions fitted a different intercept for each weight class. Correlation coefficients between estimated and chemically determined empty body water, lipid, protein and ash for the first set of functions were 0·93, 0·86, 0·83 and 0·65, respectively. The second set of prediction functions showed higher accuracy (2 to 10%), but had the disadvantage of non-continuous estimates over the entire growth period. In contrast, by using the first set of prediction functions, a continuous accurate estimation of body composition of live pigs was obtained over a large range of growth (20 to 140 kg) based on deuterium dilution space.

Random regression models were used to analyse the daily growth data for a total of 25 pigs of two commercial crossbred types between 75 and 140 days of age. A visual imaging system placed above a feeding station provided daily the plan area and length measurements of different body parts. Daily live-weight measurements of the pigs were obtained from a platform balance integrated into an electronic feeding station. Growth curves associated with different measures, pigs and types were compared. Significant differences in the age growth curves between the pig types could only be found in the ham width measurements (P < 0.05). The linear measure of ham width showed the greatest difference between the two types, and the lowest coefficient of variation among individual animals. Size measures were shown to be a more consistent indicator of pig performance during growth than live weight: pigs with a relatively large surface area or ham width at the early growth stage also have relatively large surface area or ham width at later stages and the between-animal variation in these measurements remains constant with age. Gain in live weight relative to increase in size differed significantly between the two pig types (P < 0.05). Pigs of the two types had significantly different shapes, but the change of shape during growth did not differ significantly between them. The allometric relationships between surface area and ham width1.85 and between body length and ham width0.85 indicate that the ham widths of pigs increase faster in proportion to full body measures. Variations between individual animals in size increase and shape change are significant (P < 0.05). The analysis suggests that VIA size and shape measurements provide valid descriptors of pig growth.

The conformation, tissue composition, and chemical composition of three types of pigs, given food ad libitum and slaughtered over a nominal live weight range of 35 to 115 kg, was assessed in relation to data provided on the live animals by a visual image analysis (VIA) system. The pig types were named as ‘3⁄4 Landrace’, ‘1⁄2 Pietrain’, and ‘1⁄4 Meishan’ types, representing ‘attenuated’, ‘blocky’, and ‘flabby’ types. Three analyses of the shape, conformation and composition data were performed. First, the relationship between conformation and age/size was assessed using linear regression of logarithmically transformed VIA and carcass data. In relation to age, ‘1⁄2 Pietrain’ pigs were found by both VIA and carcass measurements to have the widest shoulders. Both analyses also found this type to have the widest ham, trunk, and shoulders in relation to body length across most of the body length range studied, although the greatest rate of increase in ham width in relation to body length was found in the ‘1/4 Meishan’ type pigs. Second, the relationship between composition and VIA shape was examined using linear regression of transformed and standardized data. Significant relationships were found between fat, lipid, muscle, and protein weight and VIA shape, although relationships were weaker for protein and muscle weight. For fat and lipid, the VIA shape measures from the trunk region proved the most informative, whereas the VIA ham measures proved the most informative for muscle and protein. Third, detrended measures of composition/conformation and shape were used to remove the effect of animal size from the data. Removal of the variation due to growth generally led to substantial decreases in the adjusted R2 statistics and in the R2-like statistics for prediction. Although in the models without detrending, relative fat and lipid weight had been found most strongly correlated with VIA shape, relative muscle was found most strongly correlated with shape in the detrended data. This was considered to result from the low between-animal variation in the data set combined with greater across-weight variation in fat and lipid weights than muscle and protein weights in the data without trend removal. Future trials with greater between-animal variation imposed would allow more precise determination of the relationship between conformation and shape.

A review of work reported in the literature was used to present quantitative descriptions of energy dispositioning in the growing pig. These are detailed in the text, which points to preferred values, as well as to anomalies and lacunae. The review was prepared with the objective of allowing from its content the inclusive and quantitative modelling of energy requirement. Requirement is approached as the sum of the component factors; maintenance, protein retention and lipid retention. Conventional expressions of maintenance requirement, as some function of pig mass, were found unconvincing in their variety of expression of coefficients and exponents. The review concluded that maintenance is properly related to protein turn-over, and thereby requires at least to include elements of concomitant protein metabolic activity. It was also judged that maintenance costs might be farm-specific. The energy requirements for activity, gaseous losses and disease were identified as important, but unsatisfactory in their quantification. Exploration of the energy costs of uncomfortable ambient temperatures suggested that whilst the responses of the pig are open to sophisticated and relatively exact calculation, the description of comfort remained inexact. The efficiency of retention of lipid by direct incorporation was high and may comprise a substantial proportion of the dietary lipid supply. There was little evidence of variation in the efficiency of utilization of metabolizable energy from carbohydrate for lipid retention. The linear-plateau paradigm for protein retention was adopted. The efficiency of utilization of energy for protein retention measured by a variety of approaches was found to be highly variable, prone to error and the literature confused. It was concluded that the efficiency of use of metabolizable energy for protein retention would be a function of at least: (a) the absorbed substrate being metabolized for the synthesis of body protein, (b) the rate of total protein tissue turn-over associated with the retention of newly accreted protein and not already accounted in the estimate of maintenance, (c) the mass of protein tissue involved in turn-over, and (d) the degree of maturity attained, and any influence maturity may have upon the rate of turn-over of total body protein. Algorithms for energy requirement are presented based upon protein turn-over and these appear to have some consistency with empirical findings.

Food intake in pigs is highly variable across different production circumstances. This report concludes from a critical review of published observations that it was unrealistic to expect from the scientific literature purporting to express nutrient requirement any reasonable prediction of the particular food intake of groups of pigs. None the less, such knowledge is essential for the practical purposes of their day-to-day nutrition. The literature does however yield general principles from which may be derived: (a) the likely forms (but not the parameter values) of intake functions relating food intake to pig live weight; and (b) the likely factors involved in the modulation of food intake at any given live weight. Using these principles two methods for determining on-farm food intake from the use of simple and available records were proposed. The first requires knowledge only of start and final weight, the time elapsed, and total food intake: it involves two steps, the determination of a suitable growth curve followed by the fitting of a suitable food intake curve. The second method is appropriate in the absence of information on total food intake, and requires a minimum number of spot measurements through the growth period. Different functions were tested for the curve of best fit. As a further benefit it appeared that models could be constructed from the information presented that would speculate for diagnostic purposes upon the likely modulators of food intake. Such models could explore the constraints of gut capacity, the energetic requirements of maintenance and potential growth, the influence of excessive or inadequate environmental temperature, the quality of housing and stocking density.

A dynamic model for simulation of growth in pigs, extended to describe thermoregulatory processes, was made stochastic to simulate groups of pigs with between-animal variation in mature body protein (Pα) and lipid mass (Lα), in the potential rate at which mature mass is attained (B⋆), and in the distribution of body protein and lipid over pools and depots. The resulting variation in body composition leads to variation in energy requirements for protein turn-over and thermoregulation, causing between-animal variation in maintenance requirements (MEmaint).

Simulated population means for Pα, Lα /Pα and B⋆ were varied in three steps each. Excluding unrealistic parameter combinations this led to 33 – 6 = 21 simulated genotypes. Simulated within-population coefficients of variation (CV) were 7, 15 and 3%. Random replicates of each genotype were simulated five times, in climatic conditions that were in turn severely cold, mildly cold (about 5 and 1ºC below lower critical temperature), thermoneutral, mildly hot and severely hot (about 1 and 5ºC above upper critical temperature), during the entire growth period of 23 to 100 kg live weight. Simulated food intake was ad libitum.

Simulated thermoneutral within-population standard deviations of body protein and lipid content were 0·21 to 0·46 kg and 0·78 to 2·14 kg at 100 kg body weight. On average, the corresponding values in cold and hot conditions were slightly higher.

MEmaint showed a protein-turn-over-related within-population CV of 1·5% at thermoneutrality. Thermoregulatory action contributed about 4% extra variance in cold and hot conditions but CV values were not affected. A genetic increase in the maximum protein deposition rate from 100 to 250 g/day would increase MEmaint as related to protein turn-over and thermoregulation by 11% at thermoneutrality, and by 6 to 11% in cold or hot conditions. Two relevant groups of genotypes could be distinguished based on the within-population regression coefficients of MEmaint on daily or cumulative protein deposition (bdailyPdep, bcumPdep). These ranged from 0·250 to 0·428 kJ/kg0·75 per day per g/day and from 2·77 to 5·45 kJ/kg0·75 per day per kg, respectively, in 12 ‘conventional’ genotypes at thermoneutrality. On average, bdailyPdep was increased by 48%, 20%, –11% and –36% in the other climatic conditions mentioned above, respectively. The corresponding increase of bcumPdep was 32%, 14%, 8% and 48%. Three fast-growing lean genotypes showed similar bdailyPdep and bcumPdep at thermoneutrality, but much more pronounced increases in cold and hot conditions: 137%, 49%, –12% and + 88% for bdailyPdep and 248%, 108%, 17% and 196% for bcumPdep.

It is concluded that differences in body composition traits between pig genotypes do not cause important between-genotype differences in thermoregulatory MEmaint, and that thermoregulatory processes contribute little body-composition-related variation to hot or cold MEmaint within most genotypes.

The inferences to be made from this with regard to experimental design are discussed. The verification of the above predictions will require a very elaborate and large-scale experiment.

Differences in the metabolic resource situation between non-reproductive male and female mice of a line selected for high litter size at birth (average of 22 born per litter) and a non-selected control line (average of 10 born per litter) were investigated in two replicates. Brody curves were fitted to individual data on body weight against age and linear regression lines were fitted to individual data on cumulative food intake against age. Mature body weight and mature daily food intake were higher in selected mice than in control mice and higher in males than in females. Selected males matured faster than selected females and control mice. In general, differences in growth and food intake curves between species or lines can mostly be explained by differences in mature size. Therefore, parameters were subsequently scaled by individual estimates of mature body weight. Differences that remain after scaling are a consequence of what have been called specific genetic factors. Scaled mature food intake was higher in selected mice than in control mice and higher in females than in males. Scaled maturation rate was higher in selected mice than in control mice and higher in selected males than in selected females. This shows that in the present study, specific genetic factors have been detected for both body weight and food intake, which suggests that selection for increased litter size has disproportionally changed the resource allocation pattern.

In the accompanying paper, specific genetic factors for body weight and food intake were identified in non-reproductive male and female mice of a line selected for high litter size at birth (average of 22 born per litter) and a non-selected control line (average of 10 born per litter). The existence of these factors are indicated by variation in efficiency parameters such as growth efficiency and maintenance requirements. Residual food intake (RFI) and Parks’ estimates of growth efficiency (AB) and maintenance requirements (MEm) were used to quantify these factors. In the growing period, females had a higher RFI (are less efficient) than males. At maturity, selected mice had higher RFI than control mice and selected females had higher RFI than selected males. AB was higher in selected-line mice than in control-line mice, and higher in males than in females. MEm was higher in selected-line mice than in control-line mice, and higher in females than in males. The results indicate the existence of specific genetic factors for both growth efficiency and maintenance requirements. Selected females may increase RFI in the adult state to anticipate the metabolically stressful periods of pregnancy and lactation, to support a genetically highly increased litter size.

Previously published data from serial slaughter trials on growing pigs of five genotypes were reanalysed. Gompertz curves were fitted to body protein and lipid mass in order to estimate mature protein and lipid mass (P∞, L∞) and the rate parameter (BGomp J that was presumed to be equal for the protein and lipid curves. L∞ was expressed as its ratio to P∞, R L∞P∞. The maximum rate of protein deposition was derived as Pdep,max = P∞ X BGomp/e. The analysed data encompass body weights of 10 to 133 kg, 13 to 217 kg, 18 to 106 kg, 20 to 110 kg and 11 to 145 kg. The Gompertz function fitted all data sets well, as judged by the standard deviations and distribution patterns of the residual terms. Autocorrelations among the residuals were non-significant.

Averaged over sexes (females and entire and castrated males), the P estimates were all close to 31 kg; the RL∞/P∞ estmates ranged from 1·4 to 4·7 kg/kg; the BGomp estimates ranged from 0·009 to 0·017 kg/day per kg. The resulting Pdep,max estimates ranged from 110 to 193 g/day. The genotypes were placed in 1969, 1976, 1984, 1990 and 1993. Plotting the estimates against time (year) showed distinct time trends for all parameters except P∞. RL∞/P∞ seems to gradually reach a plateau around unity, whereas BGomp and Pdep/max increase linearly. These trends were confirmed by an analysis of body weight based on the same data plus data on three other genotypes that spanned the same time period. Analyses of the same protein and lipid data to fit a sigmoid growth function with a flexible point of inflexion did not change the apparently absent time trend of F. The estimates of the inflexion points of the fitted protein accretion curves, expressed as proportions ofF, were indistinguishable from the fixed 0·368 value of the Gompertz function for the earliest three genotypes and then showed a tendency to increase, up to 0·46 for the 1993 population. These time trends must be the consequence of a combination of changes in nutritional and other environmental factors and genetic changes. They cannot be the sole result of within-line selection for growth and body composition traits, since this should increase P∞. It seems as if pig breeders have repeatedly initiated their sire lines from genetic resources with small mature size, to subsequently increase this trait as an indirect result of within-line selection.

Body composition in the pig, and its variation, is mostly referred to in terms of body protein and lipid content of the whole body. This study was made to check for animal-intrinsic variation in the partitioning of body protein into protein pools and of body lipid into lipid depots. Results from serial slaughter trials on 316 Danish Landrace and 76 Danish Yorkshire pigs were used to estimate additive genetic and litter-associated variance components for several traits. These traits were total body protein and lipid mass (TOTPROT and TOTLIPD), the proportions of total body protein that are present in the muscles (PROTMUS) or in the (sub-)cutaneous tissue plus bones (connective tissue protein, PROTCON), and the proportions of total body lipid that are present in the (sub-)cutaneous tissue (LIPDSUB), in the muscles (inter- and intramuscular fat, LIPDMUS), or in the bones (LIPDBON). TOTPROT and TOTLIPD were adjusted by regression for body weight; PROTMUS and PROTCON were adjusted for PROTCON, and LIPDSUB, LIPDMUS and LIPDBON were adjusted for TOTLIPD. The pooled estimates (± s.e.) of the degree of genetic determination (the sum of the additive genetic and litter-associated variance components, which approximates the repeatability) of these traits were 0·48 ± 0·19 for TOTPROT, 0·56 ± 0·20 for TOTLIPD, 0·56 ± 0·12 for PROTMUS, 0·57 ± 0·15 for PROTCON, 0·32 ± 0·10 for LIPDMUS, 0·33 ± 0·12 for LIPDSUB, and 0·22 ± 0·10 for LIPDBON. It is concluded that there is animal-intrinsic variation in partitioning of body protein and lipid.

We present the results of the high magnetic field studies of properties of two-dimensional electron gas (2DEG) in AlGaN/GaN heterostructures grown over high-pressure bulk GaN, sapphire, and insulating SiC substrates. The experimental results include the low field Hall measurements, cyclotron resonance measurements, and cryogenic temperature Quantum Hall Effect studies as well as room-temperature characteristics of High Electron Mobility Transistors fabricated on all these substrates. The room temperature high field measurements allow us to clearly separate the contributions of a parasitic parallel conduction from 2DEG conduction in all investigated heterostructures.

The magnetotransport measurements are performed in the magnetic fields up to 30 Tesla for temperatures between 50mK-300K. This high magnetic field in combination with very high mobilities (over 60.000 cm2/Vs) in the sample on the bulk GaN substrates allow us to observe features related both to cyclotron resonance and spin splitting. The temperature dependence of this splitting determines the spin and cyclotron resonance energy gaps and, in combination with cyclotron resonance and tilted field experiments, allows us to determine the complete energy structure of 2DEG conduction band. We also present the first experimental results showing so called “the exchange enhancement” of the energy gaps between spin Landau levels.

We present the results of the experimental and theoretical studies of the low field mobility of two-dimensional electrons in the homoepitaxial AlGaN/GaN heterostructures and in the AlGaN/GaN heterostructures grown on SiC. We show that, at cryogenic temperatures, the temperature dependence of the mobility is primarily determined by the deformation potential scattering and that most of other important scattering mechanisms are temperature independent. We show also that two-dimensional (2D) and three-dimensional (3D) mobility models yield very close results. The analysis of the mobility dependence on the electron sheet density ns shows two possible explanations of the non monotonic mobility versus carrier density dependence: i) the alloy/interface scattering and ii) transfer of the 2D electrons into 3D states in GaN. We present experimental data suggesting that for high 2D gas densities in the investigated structure grown on SiC the 2D-3D transition should take place and might be responsible for the mobility decrease at high electron sheet densities.

Low frequency noise has been investigated in hexagonal n-type GaN with equilibrium electron concentration n ∼ 1017 cm−3 at T=300 K. The frequency and temperature dependencies of the spectral density of the current noise, SI, have been studied in the frequency range f from 20 Hz to 20 kHz. Over the whole temperature range from T=80K to 400K the SI(f) dependence is very close to 1/f. The value of the Hooge constant, α, is very large: α ∼ 5 – 7 and is found to be temperature independent. The effects of illumination on the low frequency noise in GaN are studied for the first time. The noise is unaffected by illumination with photon energy Eph < Eg, while band-to band illumination (Eph∼Eg ) influences the low frequency noise, increasing the noise at higher temperatures, and decreasing it at lower temperatures.