This study investigated the potential for accurate detection of clinical mastitis (CM) in an automatic milking system (AMS) using electronic data from the support software. Data from cows were used to develop the model, which was then tested on 2 independent data sets, 1 with 311 cows (same farm but from a different year) and 1 with 568 cows (from a different farm). In addition, the model was used to test how well it could predict CM 1 to 3 d before actual clinical diagnosis. Logistic mixed models were used for the analysis. Twelve measurements were included in the initial model before a backward elimination, which resulted in the following 6 measurements being included in the final model: quarter-level milk yield (MY; kg), electrical conductivity (EC; mS/cm), average milk flow rate (MF; kg/min), occurrence of incompletely milked quarters in each milking session (IM; yes or no), MY per hour (MYH; kg/h), and EC per hour (ECH; mS/cm/h) between successive milking sessions. The other 6 measurements tested but not included in the final model were peak milk flow rate (kg/min), kick-offs (yes or no) in each milking session, lactation number, days in milk (d), blood in milk (yes or no), and a calculated mastitis detection index used by DeLaval (DelPro software; DeLaval International AB, Tumba, Sweden). All measurements were assessed to determine their ability to detect CM as both individual variables and combinations of the 12 above-mentioned variables. These were assessed by producing a receiver operating characteristic curve and calculating the area under the curve (AUC) for each model. Overall, 9 measurements (i.e., EC, ECH, MY, MYH, MF, IM, peak flow rate, lactation number, and mastitis detection index) had significant mastitis detection ability as separate predictors. The best mastitis prediction was possible by incorporating 6 measurements (i.e., EC, ECH, MY, MYH, MF, and IM) as well as the random cow and quarter effects in the model, resulting in 90% sensitivity and 91% specificity with excellent AUC (0.96). Assessment of the model was found to produce robust results (AUC >0.9) in different data sets and could detect CM with reductions in sensitivity and specificity with increasing days before actual diagnosis. This study demonstrated that improved mastitis status prediction can be achieved by using multiple measurements, and new indexes based on that are expected to result in improved accuracy of mastitis alerts, thereby improving the detection ability and utility on farm.

The aim of the present study was to evaluate the accuracy of a newer version of an activity- and rumination-monitoring system by comparison against direct visual observations, for the following three different types of behaviour: grazing, resting (described as lying or standing idle) and ruminating for cows grazing either annual ryegrass or chicory-based swards. Eight non-lactating Holstein–Friesian cows were fitted with the sensor tags, and grazed on annual ryegrass pasture for a target consumption of 10 kg DM ryegrass/cow.day for 7 days. The experiment was then repeated with cattle offered a similar allowance of chicory. Observations were conducted by two trained observers in two observation periods each day, to capture the above described behaviours. In each period, electronic behavioural measurements were recorded continuously by the sensors, while visual observations were also continuous (during observation periods), and the two datasets were matched. On average, each cow was visually observed for 87.2 min/day. For each behavioural state (at 1-min intervals, n = 6963), probability of agreement, sensitivity, specificity and positive predicted value were determined for grazing as 98%, 98.3%, 97.3% and 98.9% respectively, for resting as 80%, 77.5%, 99.1% and 92.9% and for ruminating as 87%, 86.9%, 98.4% and 90.68%. Concordance correlation coefficient (CCC) and Pearson correlations (r) were used to investigate the relationships between visual observations and data generated from the tags. Different behaviours were analysed separately. Significant correlations were found for the three behaviours (grazing: CCC = 0.99, r = 0.99; resting: CCC = 0.95, r = 0.97; ruminating: CCC = 0.80, r = 0.80), with no differences detected between the two forages. We conclude that, under the conditions of the present study, the activity- and rumination-monitoring system tag measured grazing, resting and ruminating behaviours with high accuracy on the basis of comparison to visual observations.

A methodology is presented that explores soil survey information at the national level (1:1 M), generating sustainability indicators for wheat cultivation in Uruguay. Potential yields were calculated for simplified crop production situations under several constraints, such as limitation of water availability calculated from soil physical properties and climatic conditions, and limitation of nutrient availability calculated from soil fertility and climatic conditions. Land quality sufficiency was examined by comparing these yields with the constraint-free yield conditioned only by solar radiation, temperature and the crop's photosynthetic properties. Crop growth was simulated only for areas suitable for the defined agricultural use. Model runs were repeated with inclusion of a topsoil loss scenario over 20 years as defined from an erosion risk analysis. Comparison between crop growth simulations for the two situations, gives an indication of the changes in land quality status, which supplies an indicator for agroecological sustainability.On the basis of crop growth simulation it is concluded that wheat production constraints in Uruguay appear to be mainly related to water availability limitations, while nutrient availability is near optimal for the suitable soils. The simulated loss of topsoil impacts most on soil physical properties, expressed in reduced water-limited yields. Soil fertility status, evaluated by change in nutrient-limited yields, was little affected by the scenario.

The process of agricultural intensifi cation in Uruguay brought up questions about the impacts on the soil. In order to analyze the soil quality status in the departamentos of Soriano and Río Negro, we sampled 108 fi elds to determine soil organic carbon (SOC) and potentially mineralizable nitrogen (PMN). Average losses were 20% in SOC and 42% in PMN in the 0-15cm sampling depth. About one third of the sampled fi elds had losses between 30 and 60% in SOC and between 35 and 80% in PMN. Losses in SOC and PMN varied among edaphic environments. Results about the effects of soil use and management on SOC and PMN are not conclusive. This paper concludes with reference values for SOC and PMN for the different edaphic environments studied.