Measuring dairy cow welfare with real-time sensor-based data and farm records: a concept study

Welfare assessment of dairy cows by in-person farm visits provides only a snapshot of welfare and is time-consuming and costly. Possible solutions to reduce the need for in-person assessments would be to exploit sensor data and other routinely collected on-farm records. The aim of this study was to develop an algorithm to classify dairy cow welfare based on sensors (accelerometer and/or milk meter) and farm records (e.g. days in milk, lactation number). In total, 318 cows from six commercial farms located in Finland, Italy and Spain (two farms each) were enrolled for a pilot study lasting 135 days. During this time, cows were routinely scored using 14 animal-based measures of good feeding, health and housing based on the Welfare Quality® (WQ®) protocol. WQ® measures were evaluated daily or approximately every 45 days, using disease treatments from farm records and on-farm visits, respectively. WQ® measures were supplemented with daily temperature-humidity index to account for heat stress. The severity and duration of each welfare measure were evaluated, and the final welfare index was obtained by summing up the values for each cow on each pilot study day, and stratifying the result into three classes: good, moderate and poor welfare. For model building, a machine-learning (ML) algorithm based on gradient-boosted trees (XGBoost) was applied. Two model versions were tested: (1) a global model tested on unseen herd, and (2) a herd-specific model tested on unseen part of the data from the same herd. The version (1) served as an example on the model performance on a herd not previsited by the evaluator, while version (2) resembled a custom-made solution requiring in-person welfare evaluation for model training. Our results indicated that the global model had a low performance with average sensitivity and specificity of 0.44 and 0.68, respectively. For the herd-specific version, the model performance was higher reaching an average of 0.64 sensitivity and 0.80 specificity. The highest classification performance was obtained for cows in poor welfare, followed by cows in good and moderate welfare (balanced accuracy of 0.77, 0.71 and 0.68, respectively). Since the global model had low classification accuracy, the use of the developed model as a stand-alone system based solely on sensor data is infeasible, and a combination of in-person and sensor-based welfare evaluation would be preferable for a reliable welfare assessment. ML-based solutions, even with fair discriminative abilities, have the potential to enhance dairy welfare monitoring.

The economic and environmental value of genetic improvements in fattening pigs: An integrated dynamic model approach

The selection of animals for improved performance affects the profitability of pig fattening and has environmental consequences. The goal of this paper was to examine how changes in genetic and market parameters impact the biophysical (feeding patterns, timing of slaughter, nitrogen excretion) and economic (return per pig space unit) results describing pig fattening in a Finnish farm. The analysis can be viewed as focusing on terminal line breeding goals. An integrated model using recursive stochastic dynamic programming and a biological pig growth model was used to estimate biophysical results and economic values. Combining these models allowed us to provide more accurate estimates for the value of genetic improvement and, thus, provide better feedback to animal breeding programs than the traditional approach, which is based on fixed management patterns. Besides the benchmark scenario, the results were simulated for 5 other scenarios. In each scenario, genotype was improved regarding daily growth potential, carcass lean meat content, or the parameters of the Gompertz growth curve (maturing rate [], adult weight of protein [α], and adult weight of lipid mass []). The change in each parameter was equal to approximately 1 SD genetic improvement (ceteris paribus). Increasing , , daily growth potential, or carcass lean meat content increased the return on pig space unit by €12.60, €7.60, €4.10, or €2.90 per year, respectively, whereas an increase in decreased the return by €3.10. The genetic improvement in and resulted in the highest decrease in nitrogen excretion calculated in total or per kilogram of carcass gain but only under the optimal feeding pattern. Simulated changes in the Gompertz growth function parameters imply greater changes in ADG and lean meat content than changes in scenarios focusing on improving ADG and lean meat content directly. The economic value of genetic improvements as well as the quantity of nitrogen excreted during the fattening period largely depends on feeding. Improved genotypes can require changes in pig management pattern. Estimating the influence of the genotype on the nitrogen excretion without considering changes in the management pattern can result in flawed conclusions. To improve overall economic performance and to decrease the environmental footprint of fattening pig production, the pig producer can adjust the herd management pattern according to the pigs' genetics.

Monitoring growth in finishers by weighing selected groups of pigs - A dynamic approach

Application of BW monitoring methods for the whole batch of pigs is not common in commercial herds. Instead, farm managers may regularly weigh a chosen subset of pigs (observed group) and use the obtained information for monitoring, forecasting, and decision support. The objective of this study was to construct a model for growth monitoring and forecasting in pig fattening herds and use the developed model framework to quantify the value of information on BW. The dynamic process of pig growing was described by means of a dynamic linear model (DLM) with Kalman filtering. For this study, data from 9 fattening cycles with the total registration for 9,800 pigs were used. The variance components were estimated by fitting a mixed-effects linear model on selected BW measurements. The obtained model was evaluated on its performance in forecasting the number of pigs ready to deliver from the whole batch and from a particular pen given the level of information on a reference data set consisting of 2 batches (Batch 3 [B1] and Batch 4 [B2]). Scenarios with a different frequency of observations (only 1 selected week, every second week, or weekly) on individual and aggregated levels for an observed group comprising 1 pen (36 pigs, which constitute 7.5% of pigs in a batch) or 2 pens (15.5% of pigs) were analyzed. Moreover, results with only initial herd information and insertion BW at the batch, pen, and pig level were presented. The model can be used for growth monitoring of the batch and for prediction of the number of pigs ready for slaughter in a given week (i.e., with a BW exceeding a threshold, which, in this study, is set to 105 kg). With an increased level of information, both accuracy (measured by the mean absolute deviation [MAD] of actual number of pigs above 105 kg from predicted number) and precision (measured by CV) of the model continue to improve. When monitoring all pigs at insertion and the observed groups every week (15.5% of pigs) compared with predictions based on only initial herd information, the MAD between the observed and predicted number of pigs above 105 kg in a single pen decreased by 1.4 and 2 pigs whereas CV was reduced by 147 and 78% for B1 and B2, respectively. The DLM was able to detect variation between pens already at insertion; therefore, data on initial BW had high value for the prediction procedure. Moreover, the aggregation had a marginal effect on model performance.

Optimal management of replacement heifers in a beef herd: a model for simultaneous optimization of rearing and breeding decisions

The aim of this study was to provide farmers an efficient tool for supporting optimal decisions in the beef heifer rearing process. The complexity of beef heifer management prompted the development of a model including decisions on the feeding level during prepuberty (age <10 mo), the time of weaning (age, BW, calendar month), the feeding level during the reproductive period (age ≥10 mo), and time of breeding (age, BW, and calendar month). The model was formulated as 3-level hierarchic Markov process. A founder level of the model has 12 states resembling all possible birth months of a heifer. Based on the birth month information from the founder level, for the indoor season (November to April) and outdoor season (May to October), feeding and breeding costs (natural service cost in the outdoor and AI cost in the indoor season) were applied. The optimal rearing strategy was found by maximizing the total discounted net revenues from the predicted future productivity of the Polish Limousine heifers defined as the cumulative BW of calves born from a cow calved until the age of 5 yr, standardized on the 210th day of age. According to the modeled optimal policy, heifers fed during the whole rearing period at the ADG of 810 g/d and generally weaned after the maximum suckling period of 9 mo should already be bred at the age of 13.2 mo and BW constituting 55.6% of the average mature BW. Based on the optimal strategy, 52% of all heifers conceived from May to July and calved from February to April. This optimal rearing pattern resulted in an average net return of EUR 311.6 per pregnant heifer. It was found that the economic efficiency of beef operations can be improved by applying different herd management practices to those currently used in Poland. Breeding at 55.6% of the average mature BW, after a shorter and less expensive rearing period, resulted in an increase in the average net return per heifer by almost 18% compared to the conventional system, in which heifers were bred after attaining 65% of the mature BW. Extension of the rearing period by 2.5 mo (breeding at the age 15.7 mo), due to a prepubertal growth rate lowered by 200 g, reduced the average net return per heifer by 6.2% compared to the results obtained under the basic model assumptions. In the future, the model may also be extended to investigate additional aspects of the beef heifer development, such as the environmental impacts of various heifer management decisions.