Development and application of an integrated sustainability index for small-holder dairy farms in Rajasthan,India

In agrarian economy of developing nations like India, smallholder dairy production is an important enterprise and its sustainability is vital for ensuring livelihood and nutritional security to the masses. Studies on methodological aspects of farm sustainability at micro-level are limited, either confined to a particular dimension of sustainability or based on complex data requirement which is not feasible to obtain in the context of smallholder dairy farms. This study has developed a multi-attribute farm level sustainability assessment method encompassing economic, social and ecological dimensions of sustainability and has applied it to assess the sustainability of dairy farming in north-western part of India. Based on data from 120 dairy farms located in rural area of Jaipur district from the state of Rajasthan, the study computed the composite Sustainable Dairy Farming Index (SDFI). The overall substantiality status of the smallholder dairy farms in the study area was not encouraging, implying that it may not be viable for future generation to take up the enterprise. Among the three dimensions of sustainability, the average scores of ecological dimension were highest followed by the economic and social sustainability scores. Some of the core attributes like feed productivity, management of animal genetic potential and gender equality are particularly weak aspects of the dairy production system in India. The direct relationship of economic sustainability with herd size suggests for farmers with very small herds (one to two) animals, increasing the number of dairy animals to about five to six, would be a good strategy to economize on input costs and generate more marketed surplus of milk.     

Evaluation of nitrogen excretion equations for ryegrass pasture-fed dairy cows

Accurate and precise estimates of nitrogen (N) excretion in faeces and urine of dairy cattle may provide direct tools to improve N management and thus, to mitigate environmental pollution from dairy production. Empirical equations of N excretion have been evaluated for indoor dairy cattle but there is no evaluation for cows fed high proportions of fresh forage. Therefore, the objective of the current study was to evaluate N excretion equations with a unique data set of zero-grazing experiments. Through literature searches, 89 predictive equations were identified from 13 studies. An independent data set was developed from seven zero-grazing experiments with, in total, 55 dairy Holstein-Friesian cows. Models’ performance was evaluated with statistics derived from a mixed-effect model and a simple regression analysis model. Squared sample correlation coefficients were used as indicators of precision and based on either the best linear unbiased predictions (R²_BLUP) or model-predicted estimates (R²_MDP) derived from the mixed model and simple regression analysis, respectively. The slope (β₀), the intercept (β₁) and the root mean square prediction error (RMSPE_m%) were calculated with the mixed-effect model and used to assess accuracy. The root mean square prediction error (RMSPE_sr%) and the decomposition of the mean square prediction error were calculated with the simple regression analysis and were used to estimate the error due to central tendency (mean bias), regression (systematic bias), and random variation. Concordance correlation coefficient (CCC) were also calculated with the simple regression analysis model and were used to simultaneously assess accuracy and precision. Considering both analysis models, results suggested that urinary N excretion (UN; R²_MDP = 0.76, R²_BLUP = 0.89, RMSPE_m% = 17.2, CCC = 0.82), total manure N excretion (ManN; R²_MDP = 0.83, R²_BLUP = 0.90, RMSPE_m% = 11.0, CCC = 0.84) and N apparently digested (NAD; R²_MDP = 0.97, R²_BLUP = 0.97, RMSPE_m% = 5.3, CCC = 0.95) were closely related to N intake. Milk N secretion was better predicted using milk yield as a single independent variable (MilkN; R²_MDP = 0.77, R²_BLUP = 0.97, RMSPE_m% = 6.0, CCC = 0.74). Additionally, DM intake was a good predictor of UN and ManN and dietary CP concentration of UN and ManN. Consequently, results suggest that several evaluated empirical equations can be used to make accurate and precise predictions concerning N excretion from dairy cows being fed on fresh forage.     

Application of computed tomography for the evaluation of obstetrically relevant pelvic parameters in German Holstein-Friesian cows

The aim of this study was to use computed tomography for the evaluation of the suitability of external pelvimetry to determine obstetrically relevant parameters. External pelvimetric measurements obtained in vivo using calipers and internal pelvimetric measurements obtained in vitro using computed tomography were taken in 30 German Holstein-Friesian cows (Bos taurus). All measurements were highly reproducible with intraclass correlation coefficients ≥98%. Hip width was the external variable with the highest correlation to internal variables, particularly pelvic inlet area and circumference, pelvic volume, medial horizontal diameter of the pelvic inlet, and the narrowest horizontal diameter of the midpelvis (r² > 0.60, P < 0.0001). The pelvic inlet area and circumference, the pelvic volume, and the diagonal diameter of the pelvis were sufficiently predicted with the aid of external pelvic measurements and age (r²≥ 0.80, P < 0.0001). The results of this study show that external pelvimetry yields reliable information about the size of the pelvis when the age of the cow is considered.     

A note on overdispersion as an index of behavioural synchrony: a pilot study in dairy cows

We developed a method for studying the synchrony of behaviour based on calculations of overdispersion of a binomial process. The lying behaviour of cows was investigated under two different housing units inside the same barn. The first unit housed 30 cows undergoing conventional milking and the second unit housed 27 cows undergoing automatic milking. The lying behaviour of the cows was observed over 3 weeks in 12 periods of 6 h each. Every 5 min, we counted the number of cows lying down in the cubicles. As external cues, like feeding and conventional milking, can promote synchrony in dairy cows, we expected that cows conventionally milked would show more behavioural synchrony than automatically milked cows. Cows lied down synchronously in both units (overdispersion 1.67, P < 0.01). Lying synchrony tended to be slightly bigger in automatically than in conventionally milked cows (overdispersion 1.76 v. 1.58, P = 0.09), although the proportion of cows lying down was on average greater in conventionally than in automatically milked cows (60.7% v. 45.6%). This suggests that synchronized lying behaviour in cows is a constant phenomenon that depends on social facilitation rather than on external cues. The overdispersion index appears to be a useful tool for studying the synchrony of animal behaviour when observations are made at the group level.     

Spatial variability and exploratory inference of abiotic factors in barn compost confinement for cattle in the semiarid

The assessment of environmental conditions in the creation of dairy cattle in feedlots must be carried out to identify possible causes of stress and assist producers in the decision-making process. The objective was to characterize the abiotic factors and bed temperature, in the comfort conditions of dairy cows, through geostatistics and exploratory data inference, in a Compost Barn System in the Brazilian semiarid region. The data were obtained in a milk production unit, located in the municipality of Lajedo, Pernambuco, Brazil. The variables air temperature (Tair), relative humidity (RH), wind speed (WS), illuminance (Lux), skin temperature (Ts), bed temperature (Tb) were recorded and the temperature index was determined and humidity (THI). Data were recorded at 9:00 a.m., 12:00 p.m., and 03:00 p.m., over 5 days in the summer season. For geostatistical analysis, the classic semivariances were determined. The principal component analysis was performed to establish an index that characterized the condition of animal comfort. The variables Tair, RH, Tb, and THI showed a low coefficient of variation for all times. The best fit to the models of the semivariograms was the Gaussian at 9:00 a.m. and 03:00 p.m., and the spherical at 12:00 p.m. The Tb spatial variability was low for all studied hours. Tair showed a strong correlation with Tb, due to the process of heat transfer by convection from the floor to the environment. Geostatistics and exploratory data analysis allowed the establishment of a comfort index for Compost Barn production systems in the Brazilian semiarid region (R² = 0.996; p < 0.0001).     

Associations between genetic merit for milk production and animal parameters and the fertility performance of dairy cows

Relationships between genetic merit for milk production and animal parameters and various parameters of reproductive performance were examined using multilevel binary response analysis in a study of 19 dairy herds for three successive years, representing approximately 2500 cows per year. The proportion of cows intended for rebreeding that were back in-calf again within 100 days of calving (ICR-100) and the proportion of cows that reappeared again with 365 (RR-365) and 400 days (RR-400) of a previous calving were considered in addition to the traditional measures of reproductive performance. Each 100-kg increase in genetic merit for milk yield was associated with an increased interval to first service (IFS) and calving index (CI) of 1.4 ( P < 0.001) and 1.8 days ( P < 0.001), respectively, a 0.5% increase ( P < 0.05) in calving rate to first insemination (CR-1) and 0.8% increase in RR-400. Each £10 increase in £PIN (the economically weighted yield selection index used in the UK that takes account of butterfat and protein yields) was associated with an increased IFS and CI of 1.5 ( P < 0.001) and 3.0 days ( P < 0.001), respectively. Cows with increased genetic merit for milk yield and £PIN were more likely to re-calve (RR-overall; P < 0.001). Each 1000-kg increase in 305-day milk yield was associated with an increased IFS and CI of 3.2 ( P < 0.001) and 7.8 days ( P < 0.001), respectively, and a 13.6 ( P < 0.001), 22.4 ( P < 0.001), 19.9 ( P < 0.001) and 19.0% ( P < 0.001) decrease in CR-1, ICR-100, RR-365 and RR-400, respectively. A 10-kg increase in maximum yield was associated with a 6.6-day increase in CI ( P < 0.001) and a 14.9 ( P < 0.001), 18.3 ( P < 0.001), 9.6 ( P < 0.05) and 14.2% ( P < 0.001) decrease in CR-1, ICR-100, RR-365 and RR-400, respectively. Fertility performance was also associated with season of calving, lactation number and dystocia score. Level of production had a larger effect on fertility performance than genetic merit for milk production suggesting that infertility at an individual cow level is more likely to be associated with increased production and an inability to meet the nutritional requirements of the cow.     

Development of an expert system for the integration of biomarker responses in mussels into an animal health index

Biomarkers on sentinel organisms are utilised worldwide in biomonitoring programs. However, the lack of effective interpretational capacity has hampered their uptake for use for assessment of risk in environmental management. The aim of the present study was to develop and test an objective decision-support or expert system capable of integrating biomarker results into a five-level health-status index. The expert system is based on a set of rules derived from available data on responses to natural and contaminant-induced stress of marine mussels. Integration of parameters includes: level of biological organization; biological significance; mutual inter-relationship; and qualitative trends in a stress gradient. The system was tested on a set of biomarker data obtained from the field and subsequently validated with data from previous studies. The results demonstrate that the expert system can effectively quantify the biological effects of different levels of pollution. The system represents a simple tool for risk assessment of the harmful impact of contaminants by providing a clear indication of the degree of stress syndrome induced by pollutants in mussels.     

Development of an expert system for the integration of biomarker responses in mussels into an animal health index

Biomarkers on sentinel organisms are utilised worldwide in biomonitoring programs. However, the lack of effective interpretational capacity has hampered their uptake for use for assessment of risk in environmental management. The aim of the present study was to develop and test an objective decision-support or expert system capable of integrating biomarker results into a five-level health-status index. The expert system is based on a set of rules derived from available data on responses to natural and contaminant-induced stress of marine mussels. Integration of parameters includes: level of biological organization; biological significance; mutual inter-relationship; and qualitative trends in a stress gradient. The system was tested on a set of biomarker data obtained from the field and subsequently validated with data from previous studies. The results demonstrate that the expert system can effectively quantify the biological effects of different levels of pollution. The system represents a simple tool for risk assessment of the harmful impact of contaminants by providing a clear indication of the degree of stress syndrome induced by pollutants in mussels.     

Benchmarking the environmental performance of specialized milk production systems: selection of a set of indicators

Dairy production across the world contributes to environmental impacts such as eutrophication, acidification, loss of biodiversity, and use of resources, such as land, fossil energy and water. Benchmarking the environmental performance of farms can help to reduce these environmental impacts and improve resource use efficiency. Indicators to quantify and benchmark environmental performances are generally derived from a nutrient balance (NB) or a life cycle assessment (LCA). An NB is relatively easy to quantify, whereas an LCA provides more detailed insight into the type of losses and associated environmental impacts. In this study, we explored correlations between NB and LCA indicators, in order to identify an effective set of indicators that can be used as a proxy for benchmarking the environmental performance of dairy farms. We selected 55 specialised dairy farms from western European countries and determined their environmental performance based on eight commonly used NB and LCA indicators from cradle-to-farm gate. Indicators included N surplus, P surplus, land use, fossil energy use, global warming potential (GWP), acidification potential (AP), freshwater eutrophication potential (FEP) and marine eutrophication potential (MEP) for 2010. All indicators are expressed per kg of fat-and-protein-corrected milk. Pearson and Spearman Rho’s correlation analyses were performed to determine the correlations between the indicators. Subsequently, multiple regression and canonical correlation analyses were performed to select the set of indicators to be used as a proxy. Results show that the set of selected indicator, including N surplus, P surplus, energy use and land use, is strongly correlated with the eliminated set of indicators, including FEP (r = 0.95), MEP (r = 0.91), GWP (r = 0. 83) and AP (r = 0.79). The canonical correlation between the two sets is high as well (r = 0.97). Therefore, N surplus, P surplus, energy use and land use can be used as a proxy to benchmark the environmental performance of dairy farms, also representing GWP, AP, FEP and MEP. The set of selected indicators can be monitored and collected in a time and cost-effective way, and can be interpreted easily by decision makers. Other important environmental impacts, such as biodiversity and water use, however, should not be overlooked.     

A copula-based approach for creating an index of micronutrient intakes at household level in Pakistan

Deficiency of micronutrients is considered as the basic cause of health issues. There are a large number of micronutrients to be considered for good health, which are analyzed separately. However, such analyses involve practical as well as methodological complications and it requires construction of an index representing malnutrition of micronutrients. This study proposes copula methodology to categorize malnutrition of micronutrients at household level by combining the dependence structure of various correlated variables. Data of eleven micronutrients are extracted from HIICS- 2015–16 published by Pakistan -Bureau of Statistics. Seven out of the eleven variables are highly correlated, which are considered to construct the index. These include calcium, iron, iodine, zinc, riboflavin, thiamine and phosphorus intakes per capita at household level. Normal probability distribution is found as the best fit to the sample data of all variables. Gaussian copula function is used to derive multivariate probability distribution by combining univariate marginal probability distribution of each micronutrient. The Multivariate distribution of Gaussian copula model is used to calculate cumulative probabilities, which provide a base to categorize households’ malnutrition w.r.t. micronutrients. The results show that 60% households lie in very low or low category of micronutrient intakes, 20% of households fall into medium category while 20% fall into high or very high category of micronutrient consumption. The proposed methodology might be helpful to combine other micronutrients as well as a variety of correlated variables in many other fields having a survey data     

Toxoplasma gondii: Infection natural congenital in cattle and an experimental inoculation of gestating cows with oocysts

Two studies, of a natural infection and an experimental infection, were performed in order to study congenital transmission of Toxoplasma gondii in cattle. In the first study, 50 fetuses were harvested from gestating cows that were eutanasied at a municipal slaughterhouse in Jaboticabal, São Paulo state, Brazil. In the second study, 11 gestating cows were divided into four groups for inoculation with T. gondii: GI consisted of three cows inoculated with 1.0 × 10⁵ oocysts during their first trimester of gestation; GII consisted of three cows inoculated with 1.0 × 10⁵ oocysts during their second trimester of gestation; GIII consisted of three cows inoculated with 1.0 × 10⁵ oocysts during their last trimester of gestation; and GIV consisted of two control cows, one during its first and the other during its second trimester of gestation. In both studies, the presence of T. gondii was confirmed both indirectly by immunofluorescence assay (IFAT). In the natural infection experiment, 18% (9/50) of the gestating cows were confirmed to have specific antibodies (IFAT – 1:64) against T. gondii. The bioassay was able to diagnose the presence of T. gondii in the tissue samples from three calves. In the second experiment, the nine cows from groups I, II and III presented with specific antibodies (IFAT) against T. gondii. In contrast, T. gondii could not be detected by IFAT, histopathological examination or the bioassay in any of the nine calves born to cows experimentally infected with T. gondii oocysts. Based on the results from both studies, we conclude that congenital infection of T. gondii in cattle, while infrequent, does occur naturally. The pathogenicity of the strain of T. gondii may influence the likelihood of this route of transmission.     

Cornell Net Carbohydrate and Protein System: A model for precision feeding of dairy cattle

The Cornell Net Carbohydrate and Protein System (CNCPS) predicts cattle requirements and nutrient supply for site-specific situations. This paper describes the CNCPS version 6 (CNCPSv6), which represents a re-engineering and updating of CNCPS version 5 with the following objectives: (1) improve the organization of the model and user interface to improve speed and accuracy in formulating diets for a herd of dairy cattle, (2) expand the carbohydrate pools to include sugars, soluble fibers, and organic and volatile fatty acids, (3) integrate a fat model to account for ruminal lypolization and biohydrogenation, and absorption of fatty acids in the small intestine, and (4) update the computational sub-models with new information. The CNCPSv6 model was re-designed using object-oriented programming in which physiological functions (e.g. growth, lactation, pregnancy) and anatomical compartments (e.g. rumen, intestines) were programmed as objects. The interface uses farm, location, and group flow, which decreases the number of inputs required per cattle group and allows for more rapid evaluation of diets, feed requirements, and nutrient excretion by location, group, and herd. The revised implementation of the body reserves sub-model allows accounting for fluxes in energy reserves when formulating diets. Updated equations and coefficients include the prediction of rumen ammonia balance and feed passage rates, indigestible DM, MP lactation efficiency, and DMI. The CNCPSv6 was evaluated with data from individually fed lactating dairy cows from three independent studies. As implemented, CNCPSv6 accounted for a similar proportion of the variation (86%) in first limiting (ME or MP) milk production as CNCPSv5 but with a lower bias (1% versus 4%, respectively). We concluded the re-designing and updating of the CNCPS improved its ability to formulate and evaluate a feeding program for a herd of dairy cattle with greater accuracy and efficiency.     

Evaluation and application potential of an accelerometer-based collar device for measuring grazing behavior of dairy cows

The commercially available collar device MooMonitor+ was evaluated with regards to accuracy and application potential for measuring grazing behavior. These automated measurements are crucial as cows feed intake behavior at pasture is an important parameter of animal performance, health and welfare as well as being an indicator of feed availability. Compared to laborious and time-consuming visual observation, the continuous and automated measurement of grazing behavior may support and improve the grazing management of dairy cows on pasture. Therefore, there were two experiments as well as a literature analysis conducted to evaluate the MooMonitor+ under grazing conditions. The first experiment compared the automated measurement of the sensor against visual observation. In a second experiment, the MooMonitor+ was compared to a noseband sensor (RumiWatch), which also allows continuous measurement of grazing behavior. The first experiment on n = 12 cows revealed that the automated sensor MooMonitor+ and visual observation were highly correlated as indicated by the Spearman’s rank correlation coefficient (r_s) = 0.94 and concordance correlation coefficient (CCC) = 0.97 for grazing time. An r_s-value of 0.97 and CCC = 0.98 was observed for rumination time. In a second experiment with n = 12 cows over 24-h periods, a high correlation between the MooMonitor+ and the RumiWatch was observed for grazing time as indicated by an r_s-value of 0.91 and a CCC-value of 0.97. Similarly, a high correlation was observed for rumination time with an r_s-value of 0.96 and a CCC-value of 0.99. While a higher level of agreement between the MooMonitor+ and both visual observation and RumiWatch was observed for rumination time compared to grazing time, the overall results showed a high level of accuracy of the collar device in measuring grazing and rumination times. Therefore, the collar device can be applied to monitor cow behavior at pasture on farms. With regards to the application potential of the collar device, it may not only be used on commercial farms but can also be applied to research questions when a data resolution of 15 min is sufficient. Thus, at farm level, the farmer can get an accurate and continuous measurement of grazing behavior of each individual cow and may then use those data for decision-making to optimize the animal management.     

Evaluation of the factors affecting silage intake of dairy cows: a revision of the relative silage dry-matter intake index

An evaluation of the factors affecting silage dry-matter intake (SDMI) of dairy cows was conducted based on dietary treatment means. The data were divided into six subsets based on the silage treatments used in the experiments: concentration of digestible organic matter in dry matter (D-value) influenced by the maturity of grass ensiled (n = 81), fermentation quality influenced by silage additives (n = 240), dry matter (DM) concentration influenced by wilting of grass prior to ensiling (W; n = 85), comparison of silages made from primary growth or regrowth of grass (n = 46), and replacement of grass silage with legume (L; n = 53) or fermented whole-crop cereal (WC; n = 37) silages. The data were subjected to the mixed model regression analysis. Both silage D-value and fermentation quality significantly affected SDMI. The average effects of D-value and total acid (TA) concentration were 17.0 g and − 12.8 per 1 g/kg DM, respectively. At a given D-value, silage neutral-detergent fibre (NDF) concentration tended to decrease SDMI. Silage TA concentration was the best fermentation parameter predicting SDMI. Adding other parameters into the multivariate models did not improve the fit and the slopes of the other parameters remained insignificant. Total NDF intake was curvilinearly related to silage D-value the maximum intake being reached at a D-value of 640 g/kg DM. Results imply that physical fill is not limiting SDMI of highly digestible grass silages and that both physical and metabolic factors constrain total DM intake in an interactive manner. Silage DM concentration had an independent curvilinear effect on SDMI. Replacing primary growth silage with regrowth, L or WC silages affected SDMI significantly, the response to regrowth silage being linearly decreasing and to L and WC quadratically increasing. The outcome of factors affecting SDMI was used to update the relative SDMI index as follows: SDMI index = 100+10 × [(D-value − 680) × 0.0170 − (TA − 80) × 0.0128+(0.0198 ×  (DM − 250) − 0.00002364 × (DM² − 250 ²)) − 0.44 × a+4.13 × b − 2.58 × b²+5.90 × c − 6.14 × c² − 0.0023 × (550 − NDF)], where a, b and c represent the proportions (0–1) of regrowth, L or WC silages from total silage DM. For the whole data set, one index unit corresponded to the default value of 0.10 kg in SDMI. The SDMI index explained proportionally 0.852 of the variation in SDMI with 0.34 kg DM per day residual. The updated SDMI index provides improved basis for the practical dairy cow ration formulation and economic evaluation.     

Evaluation of concentrate factors affecting silage intake of dairy cows: a development of the relative total diet intake index

The aim of this work was to develop an index describing the relative intake of the total diet by dairy cows, and hence the ability to predict intake responses to changes in both forage and concentrate variables. An evaluation of concentrate factors affecting silage dry matter (DM) intake of dairy cows was conducted based on dietary treatment means from milk production experiments. The data were divided into four subsets according to concentrate treatments used within the experiments: the amount of concentrate supplementation (n = 217), protein supplementation (n = 336), carbohydrate composition (n = 114) and fat concentration of the concentrate (n = 29). The data were subjected to mixed-model regression analysis. Increased concentrate DM intake (CDMI) decreased silage DM intake (SDMI) quadratically. The substitution rate (substitution of silage DM for concentrate DM) increased with improved silage intake potential. SDMI increased quadratically with concentrate protein intake, the response being negatively related to the effective protein degradability (EPD) of concentrates. Replacement of starchy concentrate ingredients with fibrous supplements had a small positive effect on silage intake, whereas increased concentrate fat concentration slightly decreased SDMI. The outcome of concentrate factors influencing total DM intake (TDMI) was used to create a relative CDMI index as follows: CDMI index = 100 + 10 × [(CDMI - 0.1629 × CDMI - 0.01882 × CDMI2 - 5.49) + ((0.9474 × CCPI - 0.4965 × CCPI2) - 2.017 × (CEPD - 0.74)) + 0.00225 × (CNDF - 250) - 0.0103 × (40 - Cfat) - 0.00058 × (CDMI - 8.0) × (SDMI index - 100)], where CDMI = concentrate DM intake (kg/day), CCPI = supplementary concentrate CP intake (kg/day; CP>170 g/kg DM), CEPD = concentrate EPD (g/g), CNDF = concentrate NDF concentration (g/kg DM), Cfat = concentrate fat concentration (g/kg DM) and SDMI index is the relative intake potential of silage (Huhtanen, Rinne and Nousiainen 2007. Animal 1, 758-770). TDMI index was calculated as SDMI index + CDMI index - 100 to describe the relative intake potential of the total diet. For the whole data set (n = 943), one TDMI index unit was equivalent to 0.095 kg/day DM intake, i.e. close to the default value of 0.100 kg. The CDMI index explained proportionally 0.88 of the variation in TDMI within a study with a 0.27 kg/day residual mean-square error (n = 616). The corresponding values for the TDMI index were 0.81 and 0.37 kg/day (n = 943), respectively. The residual mean-square errors in cross-validation were marginally higher. The developed TDMI index can be used to estimate the intake responses to diet changes. It provides an improved basis for practical dairy cow ration formulation and economic evaluation.     

The impact of rabbits and cattle on an arid calcareous shrubby grassland in central Australia

Multivariate methods of classification and ordination were used to examine changes in plant composition over a three year period on a calcareous shrubby grassland grazed by rabbits and cattle in central Australia. In a site-time ordination of the data for all years, the season and amount of rainfall was the most important influence, followed by several soil factors and rabbit density. In a site ordination of each year, rabbit density was a minor influence for the pretreatment data but increased in importance over the three years until it was highly correlated with vector one in the final year. This was a result of the exclosure treatments diverging from the controls with extremely high rabbit populations at two of the eighteen plots. When site-time trajectories were plotted through species space, most sites tended to move with season over the three year period towards a common domain. The two heavily grazed sites exhibited a different trajectory; this was due to the behaviour of seven plant species under grazing. In multiple regression analysis, rabbit density, cattle presence and yearly rainfall explained 66% of the variation in total standing biomass. In general, total cover was not affected by grazing treatment, although short term utilisation effects were usually visible in the late dry season. Given its present degraded state, this arid vegetation type is seen as reasonably resilient, requiring extremely heavy rabbit grazing to prevent the plant community responding to the dominating influence of season.Nomenclature follows J. Jessop (ed.), 1981. Flora of Central Australia. The Australian Systematic Botany Society. Reed Press, Sydney.I thank G. Pearce and K. Jones for their diligent data collection, preparation and analysis.     

Development of an in vitro incubation technique for the estimation of the utilizable crude protein (uCP) in feeds for cattle

An in vitro incubation technique based on the first stage of the in vitro digestion technique published by Tilley and Terry (1963) was developed to estimate the utilizable crude protein (uCP) of single feeds and feed mixtures as non ammonia‐N after 24 h of incubation. The results of 25 feed samples showed that there was a significant relationship between the uCP values calculated by regression based on in vivo data sets (Y, CP [g‐kg^‐1 DM]) and those measured by the in vitro incubation technique (X, CP [g‐kg^‐1 DM], 24 h incubation):

y=0.85x+18.0, r²=0.84, P≤0.001.

It was concluded that it can be possible to determine the uCP value of single feeds or feed mixtures by this in vitro incubation technique and to estimate the uCP value of feeds by this regression equation.     

Duration tenacity: A method for assessing acclimatory capacity of the Antarctic limpet, Nacella concinna

The limpet, Nacella concinna, collected from the Antarctic Peninsula (67°S), was incubated at − 0.3 °C and 2.9 °C for 9 months to test if the previously reported absence of acclimation capacity in Antarctic marine ectotherms could be due to the extended time it takes for them to adjust their physiology to a new stable state. Acclimation was tested through acute measurements of upper lethal limit and a modified measure of tenacity, that tested muscle capacity by measuring the length of time that N. concinna were able to remain attached to the substratum at different temperatures. Both measures acclimated in response to incubation to the higher temperature. Lethal limits were elevated in N. concinna incubated at 2.9 °C (8.1 ± 0.3 °C) compared to those incubated at − 0.3 °C (6.9 ± 0.4 °C). 2.9 °C incubated N. concinna also had a maximum tenacity at 2.1 °C, a higher temperature than the maximum tenacity of those incubated at − 0.3 °C, which occurred at − 1.0 °C. This study is the first to show that the Antarctic limpet can acclimate its physiology, but that it requires a greater period of time for acclimation to occur than previous studies have allowed for.     

e-Cow: an animal model that predicts herbage intake, milk yield and live weight change in dairy cows grazing temperate pastures, with and without supplementary feeding

This animal simulation model, named e-Cow, represents a single dairy cow at grazing. The model integrates algorithms from three previously published models: a model that predicts herbage dry matter (DM) intake by grazing dairy cows, a mammary gland model that predicts potential milk yield and a body lipid model that predicts genetically driven live weight (LW) and body condition score (BCS). Both nutritional and genetic drives are accounted for in the prediction of energy intake and its partitioning. The main inputs are herbage allowance (HA; kg DM offered/cow per day), metabolisable energy and NDF concentrations in herbage and supplements, supplements offered (kg DM/cow per day), type of pasture (ryegrass or lucerne), days in milk, days pregnant, lactation number, BCS and LW at calving, breed or strain of cow and genetic merit, that is, potential yields of milk, fat and protein. Separate equations are used to predict herbage intake, depending on the cutting heights at which HA is expressed. The e-Cow model is written in Visual Basic programming language within Microsoft Excel^R. The model predicts whole-lactation performance of dairy cows on a daily basis, and the main outputs are the daily and annual DM intake, milk yield and changes in BCS and LW. In the e-Cow model, neither herbage DM intake nor milk yield or LW change are needed as inputs; instead, they are predicted by the e-Cow model. The e-Cow model was validated against experimental data for Holstein–Friesian cows with both North American (NA) and New Zealand (NZ) genetics grazing ryegrass-based pastures, with or without supplementary feeding and for three complete lactations, divided into weekly periods. The model was able to predict animal performance with satisfactory accuracy, with concordance correlation coefficients of 0.81, 0.76 and 0.62 for herbage DM intake, milk yield and LW change, respectively. Simulations performed with the model showed that it is sensitive to genotype by feeding environment interactions. The e-Cow model tended to overestimate the milk yield of NA genotype cows at low milk yields, while it underestimated the milk yield of NZ genotype cows at high milk yields. The approach used to define the potential milk yield of the cow and equations used to predict herbage DM intake make the model applicable for predictions in countries with temperate pastures.