The DairyPrint Model: A Decision-Support Model to Help Dairy Farmers and Other Stakeholders Towards Improved Sustainability.

Dairy farmers face increasing pressure to reduce greenhouse gas (GHG) emissions [i.e., carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O)], but measuring on-farm GHG emissions directly is costly or impractical. Therefore, the dairy industry has relied upon mathematical models to estimate those emissions. However, current models tend to be not user-friendly, difficult to access or sometimes very research-focused, limiting their practical use. To address this, we introduce the DairyPrint model, a user-friendly tool designed to estimate GHG emissions from dairy farming. The model integrates herd dynamics, manure management, crop, and feed costs considerations, simplifying the estimation process while providing comprehensive insights. The herd module simulates monthly herd dynamics based on inputs as total cows, calving interval, and culling rate, outputting average annual demographics and estimating various animal related variables (i.e., dry matter intake, milk yield, manure excretion, and enteric CH4 emissions). These outputs feed into other modules, such as the manure module, which calculates emissions based on manure, weather data, and facility type. The manure module processes manure according to farm practices, and the crop module accounts for GHG emissions from manure, fertilizers, and limestone application, also estimating nutrient balances. The DairyPrint model was developed using the Shiny framework and the Golem package for robust production-grade shiny applications in the R programming language. We evaluated the model across 32 simulation scenarios by combining various factors and considering a standard free-stall system with 1000 dairy cows averaging 40 kg/day of milk production. These factors included 2 levels of NDF-ADF in the diet (28-22.8% and 24-19.5%), the presence or absence of 3-NOP dietary addition (yes or no) at an average dose of 70 mg/kg DM per cow daily, the type of bedding used (sawdust or sand), the frequency of manure pond emptying [once (only Fall) or twice a year (Fall and Spring)], and the utilization or non-utilization of a biodigester plus solid-liquid separator (Biod + SL). In our results across the 32 scenarios simulated, the average GHG emission was 0.811 kgCO2eq/kg of milk corrected for fat and protein contents (4% and 3.3%, respectively), ranging from 0.644 to 1.082. Notably, the scenario yielding the lowest GHG emission (i.e., 0.644 kgCO2eq/kg) involved a combination of factors, including a lower level of NDF-ADF in the diet in addition to incorporation of 3-NOP, utilization of sand as bedding, application of Biod + SL, and strategic manure pond emptying in both Fall and Spring. Conversely, the scenario that resulted in the highest GHG emission (i.e., 1.082 kgCO2eq/kg) involved a combination of higher level of NDF-ADF in the diet and excluded the incorporation of 3-NOP, utilization of sawdust as bedding, no application of Biod + SL, and manure pond emptying only in Fall. All these scenarios can be easily simulated in the DairyPrint model and results obtained immediately for user evaluation. Therefore, the DairyPrint model can help farmers move toward improved sustainability, providing a user-friendly and intuitive graphical user interface allowing the user to ask what-if questions.

Comparison of behavior, thermoregulation, and growth of pair-housed versus individually housed calves in outdoor hutches during continental wintertime.

Cold stress negatively affects the welfare of calves in outdoor hutches. No studies have examined the potential benefits of pair housing calves to buffer against cold stress. Our study evaluated the effects of pair versus individual housing on thermoregulatory, behavioral, and growth performance responses of calves in outdoor hutches during a Wisconsin continental winter. Forty-eight Holstein-Friesian heifer calves were enrolled into 1 of 2 housing treatments: individually (n = 16 calves) or pair housed (n = 16 pairs; 32 calves). Calves were fed milk twice daily, with ad libitum access to starter and water. Step-down weaning began on d 42 of life, and all milk was removed on d 54. Data collection continued through d 59. Calves were restricted inside a hutch (pair-housed calves in the same hutch) for 1 h during wk 4, 6, and 9 of life; internal hutch air temperature (T) was recorded with data loggers, and rectal temperature (RT) was recorded outside the hutch before and after restriction. On the subsequent 3 d in those weeks, calves' locations (outside or inside a hutch) were recorded at 15-min intervals using time-lapse cameras. Linear mixed models (change in T and RT after 1 h) and generalized linear mixed models with a ß distribution (proportion of time spent inside hutches) were used to evaluate the fixed effects of housing treatment, week of life, and their interaction. For pair-housed calves, preference to be together was evaluated using one-sample t-tests comparing the proportion of time they were observed in the same location against 50% (chance, no preference), separately for each week of life. Predicted dry matter intake (DMI) of starter and body weight (BW) were standardized by day of life using regression models and used to calculate average daily gain (ADG) and feed conversion ratio (FCR; DMI of starter/ADG). Linear mixed models were constructed for each measure, separately for the preweaning, weaning, and postweaning periods, with a fixed effect of housing treatment; the models for BW included birth weight as a covariate. All mixed models included a random term for housing unit (individual or pair of calves) nested within treatment. Hutch T increased more after 1 h with pair-housed calves inside than with those housed individually (+2.3 vs. 1.4°C, respectively; standard error of the mean = 0.26°C). However, no treatment differences were detected in RT. Individually housed calves spent more time inside the hutches than pair-housed ones (93.9 vs. 90.7% of total time, respectively; standard error of the mean = 0.8%), and the latter chose to be together most of the time, regardless of location (90.0 ± 1.3%, 88.6 ± 1.2%, and 79.4 ± 4.2% in wk 4, 6, and 9 of life, respectively). After weaning, there was some evidence suggesting that pair-housed calves had greater starter DMI than those housed individually. No effects of housing type were found on FCR, BW, or ADG. Our study is the first to explicitly examine the potential benefits of pair housing for alleviating cold stress in outdoor-housed dairy calves, and we found limited evidence in support of our hypotheses.

Effect of different air speeds at cow resting height in freestalls on heat stress responses and resting behavior in lactating cows in Wisconsin.

Heat abatement (e.g., soakers, fans) effectively reduces the negative physiological and production effects of heat stress, but no previous studies have documented effective interventions for the reduced lying times observed in response to hot weather. Although likely adaptive for heat dissipation, the reduction in motivated lying behavior may be an animal welfare concern. We evaluated the effect of air speed from fans with variable frequency drives on the heat stress responses of cows in a naturally ventilated freestall barn. Eight groups of lactating Holsteins (16 cows/group) were exposed to 3 treatments in a replicated crossover design: control (fans off, 0.4 ± 0.2 m/s, measured 0.5 m above the stall surface to represent cow resting height) versus 60% (1.7 ± 0.5 m/s; ≥1 m/s in all stalls) and 100% (2.4 ± 0.8 m/s) fan power. Each treatment was applied for 3 d of acclimation and 4 d of data collection. The effects of treatment on daily maximum vaginal temperature (VT) and lying time (LT; both measured with data loggers), respiration rate (RR; recorded from video), unshaved scapular skin temperature (ST), milk yield (MY), and dry matter intake (DMI) were analyzed using linear mixed models. All models included the fixed effect of treatment and a repeated term for treatment day within group of cows, with group as the subject. The models for LT, VT, and RR also included a fixed effect for same-day temperature-humidity index (THI; recorded in the pens with data loggers) and the THI × treatment interaction. The models for DMI and MY, using data from the latter 3 d of each treatment period, also included a fixed effect for the previous day's THI and the -1 d THI × treatment interaction. Lying time differed among treatments (100% vs. 60% fan power vs. control: 14.2 vs. 13.9 vs. 13.2 h/d, respectively, SEM = 0.15 h/d), but both fan treatments prevented the reduction in LT observed in the control treatment as THI increased. Relative to the control, both fan treatments effectively reduced ST, RR, and VT and increased DMI and MY. In the control, average values were elevated for both RR (68.7 ± 1.5 breaths/min, mean ± SEM, greater than a common benchmark of 60 breaths/min) and VT (39.3 ± 0.05°C) but remained in the normal range in both fan treatments (54.2 vs. 50.7 breaths/min in the 60% vs. 100% fan power treatments; 39.0°C in both fan treatments). Both fan treatments resulted in greater overall MY (42.6 vs. 43.0 ± 0.4 kg/d in the 60% vs. 100% fan power treatments) relative to the control (41.0 kg/d) and similarly avoided the reduction in MY when -1 d THI increased. Compared with natural ventilation alone, fans delivering air speeds of at least 1 m/s at cow resting height were effective not only for reducing thermoregulatory responses, but also for maintaining lying time, DMI, and MY in heat stress conditions. This is the first study to demonstrate an intervention to improve animal welfare by maintaining lying times during periods of heat stress.

Integrated Decision Support Systems (IDSS) for Dairy Farming: A Discussion on How to Improve Their Sustained Adoption.

Dairy farm decision support systems (DSS) are tools which help dairy farmers to solve complex problems by improving the decision-making processes. In this paper, we are interested in newer generation, integrated DSS (IDSS), which additionally and concurrently: (1) receive continuous data feed from on-farm and off-farm data collection systems and (2) integrate more than one data stream to produce insightful outcomes. The scientific community and the allied dairy community have not been successful in developing, disseminating, and promoting a sustained adoption of IDSS. Thus, this paper identifies barriers to adoption as well as factors that would promote the sustained adoption of IDSS. The main barriers to adoption discussed include perceived lack of a good value proposition, complexities of practical application, and ease of use; and IDSS challenges related to data collection, data standards, data integration, and data shareability. Success in the sustainable adoption of IDSS depends on solving these problems and also addressing intrinsic issues related to the development, maintenance, and functioning of IDSS. There is a need for coordinated action by all the main stakeholders in the dairy sector to realize the potential benefits of IDSS, including all important players in the dairy industry production and distribution chain.