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Artificial intelligence (AI) refers to the ability of a digital computer or computer-controlled robot to perform reasoning tasks commonly associated with intelligent beings. Powered by novel and affordable software and hardware capabilities, AI is finding its way into dairy farms. The purpose of this review is to give examples of research on AI for dairy farms and highlight some emerging opportunities. Many AI applications are based on machine learning, including the technique of deep learning.

The objective of this study was to analyze the associations of body condition score (BCS) and BCS change (∆BCS) during the dry period and the first 100 d of lactation with daily milk yield. Examining the involvement of health status in the associations between BCS and milk yield was a secondary objective of this research. Data included 12,042 lactations in 7,626 Holstein cows calving between April 2019 and January 2022 in a commercial dairy operation located in Colorado, USA. BCSs were generated daily by an automated BCS camera system located at the exit of the milking parlor. The assessment points selected for this study were dry-off (BCSdry), calving (BCS1), 7 DIM (BCS7), 14 DIM (BCS14), 21 DIM (BCS21), and nadir (nBCS; defined as the lowest daily BCS from calving to 100 DIM). Subsequently, these BCS were categorized considering quartiles (Q1 = 25% lowest BCS; Q4 = 25% greatest BCS), separately for primiparous and multiparous cows. Changes in BCS were calculated from dry-off to calving (multiparous); and from calving to 7 DIM, 14 DIM, 21 DIM, and nadir and assigned into quartile categories considering Q1 as the 25% of cows with the greatest decrease of BCS. Lactations were classified based on the number of health events before nadir as healthy, affected by one event, or having multiple events. Data were examined in primiparous and multiparous cows separately using ANOVA. The least square means for daily milk at 60 DIM and 305 DIM were calculated by category of BCS and ∆BCS at multiple time points and time periods. Subsequently, lactation curves were created by BCS and ∆BCS categories and by health status. Multivariable models included calving season and BCS1 as covariables. The largest differences in milk yield among categories of BCS and ∆BCS were identified for BCS originated at nadir and for the ∆BCS between calving and nadir. The differences in average daily milk yield between cows in the lowest and the greatest nBCS category (Q1 vs. Q4) were 3.3 kg/d (60 DIM) and 3.4 kg/d (305 DIM) for primiparous cows and 2.4 kg/d (60 DIM) and 2.1 kg/d (305 DIM) for multiparous cows. During the period from calving to nadir, primiparous cows in Q1 (greatest decrease of BCS) produced 4.3 kg/d (60 DIM) and 3.8 kg/d (305 DIM) more than cows in Q4. For multiparous cows, the differences were 3.0 kg/d (60 DIM) and 1.9 kg/d (305 DIM) in favor of Q1 cows. Overall, the associations between BCS and ∆BCS categories and milk yield were not consistent across time and they depended on the parity category. Nonetheless, as the assessment of BCS and ∆BCS approached the nadir, the association between greater milk yield and lower BCS or greater reduction in BCS became more evident.•The association between greater milk yield and use of energy reserves represented by loss in body condition score (BCS) became more evident at the time of the nadir BCS.•The magnitude of the nadir BCS reflects the interrelations among energy balance, use of energy reserves, and potential for milk production.The objective of this study was to analyze the associations of body condition score (BCS) and BCS change (∆BCS) during the dry period and the first 100 d of lactation with daily milk yield. The involvement of health status in these associations was also evaluated. The relationship between BCS and ∆BCS with milk yield was more evident for nadir BCS (nBCS; defined as the lowest daily BCS from calving to 100 d in milk [DIM]) and for the ∆BCS occurring between calving and nBCS. Cows categorized in the lowest nBCS category and cows with greater loss in BCS from calving to nadir had the greatest average milk yields at 60 DIM and 305 DIM. In agreement, clear patterns for lactation curves were identified when the study cows were categorized by nBCS and ∆BCS for the calving to nBCS period. There was no evident association of health status with the reported relationships between BCS and milk yield.

The objective of this study was to identify variables associated with the time and magnitude of nadir BCS (nBCS) during early lactation and to characterize the effects of these two measurements on the subsequent pregnancy at first artificial insemination (P/AI1) and pregnancy loss of Holstein cows. A retrospective observational study was completed using data collected from 12,042 lactations in 7,626 Holstein cows calving between April 2019 and January 2022 in a commercial dairy operation located in Colorado, USA. Scores generated by an automated BCS camera system at dry-off (BCSdry), calving (BCS1), nadir (nBCS; defined as the lowest daily BCS from calving to 100 DIM), and 21 d post nadir were selected for the analyses and subsequently categorized into quartiles (Q1= lower nBCS). Changes in BCS were calculated from dry-off to calving (multiparous cows); from calving to nadir; and from nadir to 21 d post nadir and assigned into quartile categories considering Q1 as the 25% of cows with greater loss (pre nadir) or smaller gain (post nadir) of BCS. Data were examined using logistic regression, ANOVA, and time to event analyses. Initial univariable models were followed by multivariable models that considered parity category, calving season, BCS1, and average milk yield up to 30 DIM (M30) as covariables. Sick cows and high producing cows had increased DIM at nadir, while lower BCSdry, greater M30, and occurrence of disease were associated with lower nBCS. Cows that conceived at AI1 had their nBCS earlier (45.1 ± 0.32 d) than cows that remained open after first AI (46.4 ± 0.35 d). On the contrary, DIM at nBCS did not differ by pregnancy loss status. The logistic regression analyses reported smaller odds of P/AI1 in cows in the lower nBCS categories. Likewise, cows with larger loss in BCS between calving and nadir had smaller odds of P/AI1. Although cows that lost their pregnancy had lower nBCS, this association was not consistent across the analyses. The time to and magnitude of nBCS, together with the ∆BCS from calving to nadir could be useful measures to evaluate the transition and early lactation status of dairy cows.Lower BCS at dry-off, greater milk yields up to 30 DIM, and occurrence of disease were associated with lower nadir BCS, while sick and high producing cows had increased DIM at nadir. Cows in the low nadir BCS category and cows with large loss in BCS between calving and nadir had smaller odds of P/AI1.The transition from gestation to lactation requires adjustments in nutrient consumption that are challenging for the postpartum dairy cow. As milk energy output increases more rapidly than energy intake, most cows mobilize fat and labile protein from body energy reserves, which results in reductions in body condition score during early lactation. The objective of this study was to identify variables associated with the extent of the lossin body condition score (BCS) during early lactation and to characterize the effects of the nadir BCS on the subsequent pregnancy at first artificial insemination and pregnancy loss in Holstein cows. Parity, disease presentation, and milk yield were associated with the time and magnitude of nadir BCS. Sick cows and high producing cows had increased DIM at nadir, while lower BCS at dry-off, greater milk yield, and occurrence of disease were associated with lower nadir BCS. Overall, cows with later and lower nadir BCS and greater loss of BCS between calving and nadir had poorer fertility. Monitoring nadir BCS could be a useful tool for evaluating the transition and early lactation status of dairy cows.

The objective of this study was to characterize the associations between body condition score (BCS) and BCS change (∆BCS), determined by an automated camera system during early lactation and close to artificial insemination (AI), and the subsequent pregnancy loss (PL) in Holstein cows. A secondary objective was to determine the impact of disease on PL, considering multiple time periods relative to AI. Data from 9,430 lactations in 6,884 Holstein cows in a commercial dairy operation located in Colorado, USA were included in this retrospective observational study. Cows were subject to first AI at about 80 DIM (primiparous) and 60 DIM (multiparous), following a double OvSynch protocol. Pregnancy diagnosis was performed via transrectal ultrasonography on day 32 ± 3 after AI and reconfirmed on day 80 ± 3 after AI. Cameras mounted on the sorting gate at each exit (n= 2) of the milking parlor generated BCS on a five-point scale with 0.1 increments. The BCS at calving (BCS1), 21 DIM (BCS21), 56 DIM (BCS56), AI resulting in pregnancy (BCSAI), and 90 d post AI (BCSAI90) were selected for the analyses and subsequently categorized as low (≤ lower quartile), moderate (interquartile range), and high (≥ upper quartile). Changes in BCS were calculated by periods of interest as change from calving to 21 DIM; change from calving to 56 DIM; change from 56 DIM to AI; and change from AI to 90 d post AI and assigned into categories to facilitate the analysis. Data were examined using logistic regression, considering parity category, season at calving and AI, DIM at AI, milk yield up to 60 DIM, and occurrence of disease as covariables. The logistic regression analyses indicated that the odds of PL were greater in cows in the low BCS category relative to cows in the high BCS category at 56 DIM (OR 95% CI = 1.41 [1.12–1.79]), AI (1.31 [1.05–1.65]), and 90 d post AI (1.38 [1.10–1.74]). Likewise, cows with large loss in BCS between calving and 21 DIM (1.46 [1.10–1.94]) and loss in BCS between AI and 90 d post AI (1.44 [1.15–1.81]) had greater odds of PL compared with cows with no loss of BCS within the same period. Occurrence of disease at all the time periods considered in the analysis had a consistent detrimental impact on maintenance of the pregnancy, supporting the concept that pre and postconceptional disease affects embryonic survival. Overall, low BCS, more pronounced reductions in BCS occurring closer to AI, and occurrence of disease resulted in greater PL in this Holstein population.The effects of low body condition scores (BCS) and loss in BCS on early embryonic mortality were evident when occurring closer to the time of insemination. Occurrence of disease in all the time periods in the analysis had a consistent detrimental impact on maintenance of pregnancy, supporting the concept that pre and postconceptional disease affects embryonic survival.Maintenance of the pregnancy until term is a key factor for adequate fertility in dairy operations, as early embryonic mortality in lactating dairy cows is significant. The objective of this study was to investigate the association between the changes in body condition scores (BCS) during early lactation and in the proximity of artificial insemination (AI) and embryo mortality in Holstein cows. A secondary objective was to determine the impact of disease on pregnancy loss (PL), considering multiple time periods relative to AI. Our study population included 9,430 lactations in 6,884 Holstein cows that had their daily BCS determined by an automated camera system during early lactation and close to AI. Cows were diagnosed pregnant via transrectal ultrasonography on day 32 after AI and reconfirmed on day 80 after AI. Overall, the dynamics of BCS differed between cows that lost or did not lose their pregnancy. Low BCS and more pronounced reductions in BCS occurring closer to the AI, as well as concurrent disease, resulted in greater levels of PL.