A time series analysis of milk productivity in US dairy states

As US dairy cow production evolves, it is important to characterize trends and seasonal patterns to project amounts and fluctuations in milk and milk components by states or regions. Hence, this study aimed to (1) quantify historical trends and seasonal patterns of milk and milk components production associated with calving date by parities and states; (2) classify parities and states with similar trends and seasonal patterns into clusters; and (3) summarize the general pattern for each cluster for further application in simulation models. Our data set contained 9.18 million lactation records from 5.61 million Holstein cows distributed in 17 states during the period January 2006 to December 2016. Each record included a cow's total milk, fat, and protein yield during a lactation. We used time series decomposition to obtain each state's annual trend and seasonal pattern in milk productivity for each parity. Then, we classified states and parities with agglomerative hierarchical clustering into groups according to 2 methods: (1) dynamic time warping on the original time series and (2) Euclidean distance on extracted features of trend and seasonality from the decomposition. Results showed distinguishable trends and seasonality for all states and lactation numbers for all response variables. The clusters and cluster centroid pattern showed a general upward trend for all yields [energy-corrected milk (ECM), milk, fat, and protein] and a steady trend for fat and protein percent for all states except Texas. We also found a larger seasonality amplitude for all yields (ECM, milk, fat, and protein) from higher lactation numbers and a similar amplitude for fat and protein percent across lactation numbers. The results could be used for advising management decisions according to farm productivity goals. Furthermore, the trend and seasonality patterns could be used to adjust the production level in a specific state, year, and season for farm simulations to accurately project milk and milk components production.

A stochastic animal life cycle simulation model for a whole dairy farm system model: Assessing the value of combined heifer and lactating dairy cow reproductive management programs

This analysis introduces a stochastic herd simulation model and evaluates the estimated reproductive and economic performance of combinations of reproductive management programs for both heifers and lactating cows. The model simulates the growth, reproductive performance, production, and culling for individual animals and integrates individual animal outcomes to represent herd dynamics daily. The model has an extensible structure, allowing for future modification and expansion, and has been integrated into the Ruminant Farm Systems model, a holistic dairy farm simulation model. The herd simulation model was used to compare outcomes of 10 reproductive management scenarios based on common practices on US farms with combinations of estrous detection (ED) and artificial insemination (AI), synchronized estrous detection (synch-ED) and AI, timed AI (TAI, 5-d CIDR-Synch) programs for heifers; and ED, a combination of ED and TAI (ED-TAI, Presynch-Ovsynch), and TAI (Double-Ovsynch) with or without ED during the reinsemination period for lactating cows. The simulation was run for a 1,000-cow (milking and dry) herd for 7 yr, and we used the outcomes from the final year to evaluate results. The model accounted for incomes from milk, sold calves, and culled heifers and cows, as well as costs from breeding, AI, semen, pregnancy diagnosis, and calf, heifer, and cow feed. We found that the interaction between heifer and lactating dairy cow reproductive management programs influences herd economic performance primarily due to heifer rearing costs and replacement heifer supply. The greatest net return (NR) was achieved when combining heifer TAI and cow TAI without ED during the reinsemination period, whereas the lowest NR was obtained when combining heifer synch-ED with cow ED.

Investigating the effect of temporal, geographic, and management factors on US Holstein lactation curve parameters

We fit the Wood's lactation model to an extensive database of test-day milk production records of US Holstein cows to obtain lactation-specific parameter estimates and investigated the effects of temporal, spatial, and management factors on lactation curve parameters and 305-d milk yield. Our approach included 2 steps as follows: (1) individual animal-parity parameter estimation with nonlinear least-squares optimization of the Wood's lactation curve parameters, and (2) mixed-effects model analysis of 8,595,413 sets of parameter estimates from individual lactation curves. Further, we conducted an analysis that included all parities and a separate analysis for first lactation heifers. Results showed that parity had the most significant effect on the scale (parameter a), the rate of decay (parameter c), and the 305-d milk yield. The month of calving had the largest effect on the rate of increase (parameter b) for models fit with data from all lactations. The calving month had the most significant effect on all lactation curve parameters for first lactation models. However, age at first calving, year, and milking frequency accounted for a higher proportion of the variance than month for first lactation 305-d milk yield. All parameter estimates and 305-d milk yield increased as parity increased; parameter a and 305-d milk yield rose, and parameters b and c decreased as year and milking frequency increased. Calving month estimates parameters a, b, c, and 305-d milk yield were the lowest values for September, May, June, and July, respectively. The results also indicated the random effects of herd and cow improved model fit. Lactation curve parameter estimates from the mixed-model analysis of individual lactation curve fits describe well US Holstein lactation curves according to temporal, spatial, and management factors.

Comparison of reproductive management programs for submission of Holstein heifers for first insemination with conventional or sexed semen based on expression of estrus, pregnancy outcomes, and cost per pregnancy

Our objective was to evaluate reproductive management programs for submission of Holstein heifers for first insemination with conventional or sexed semen. In experiment 1, nulliparous Holstein heifers (n = 462) were submitted to a 5-d progesterone-releasing intravaginal device (PRID)-Synch protocol [d 0, GnRH + PRID; d 5, PGF_2α - PRID; d 6, PGF_2α; d 8, GnRH + TAI] and were randomly assigned for PRID removal on d 5 or 6 of the protocol followed by timed artificial insemination (TAI) with conventional semen. Delaying PRID removal decreased early expression of estrus before scheduled TAI (0.9 vs. 12.2%), and pregnancies per AI (P/AI) did not differ between treatments. In experiment 2, nulliparous Holstein heifers (n = 736) from 3 commercial farms were randomized within farm to 1 of 3 treatments for first AI with sexed semen: (1) CIDR5 [d -6, GnRH + controlled internal drug release (CIDR); d -1, PGF_2α - CIDR; d 0, PGF_2α; d 2, GnRH + TAI]; (2) CIDR6 (d -6, GnRH + CIDR; d -1, PGF_2α; d 0, PGF_2α - CIDR; d 2, GnRH + TAI); and (3) EDAI (PGF_2α on d 0 followed by once-daily estrous detection and AI). Delaying CIDR removal decreased early expression of estrus before scheduled TAI (0.004 vs. 27.8%); however, CIDR5 heifers tended to have more P/AI at 35 (53 vs. 45 vs. 46%) and 64 (52 vs. 45 vs. 45%) days after AI than CIDR6 and EDAI heifers, respectively. Overall, CIDR5 and CIDR6 heifers had fewer days to first AI and pregnancy than EDAI heifers which resulted in less feed costs than EDAI heifers due to fewer days on feed until pregnancy. Despite greater hormonal treatment costs for CIDR5 heifers, costs per pregnancy were $16.66 less for CIDR5 than for EDAI heifers. In conclusion, delaying PRID removal by 24 h within a 5-d PRID-Synch protocol in experiment 1 suppressed early expression of estrus before TAI, and P/AI for heifers inseminated with conventional semen did not differ between treatments. By contrast, although delaying CIDR removal by 24 h within a 5-CIDR-Synch protocol in experiment 2 suppressed early expression of estrus before TAI, delaying CIDR removal by 24 h tended to decrease P/AI for heifers inseminated with sexed semen. Further, submission of heifers to a 5-d CIDR-Synch protocol for first AI tended to increase P/AI and decrease the cost per pregnancy compared with EDAI heifers.

Measuring farmers' dynamic technical and udder health management inefficiencies: The case of Wisconsin dairy farms

This study measures the dynamic technical and udder health management inefficiencies of a sample of Wisconsin dairy farms. Udder health management inefficiency is defined as a farmer's failure to achieve lower levels of milk somatic cell counts compared with those of the best-practice farmers within the sample. The study proposes the treatment of somatic cell count as an undesirable output. We measured inefficiency using a dynamic directional distance function that accounts simultaneously for the expansion of desirable outputs and investments in capital assets, and contraction of undesirable output and variable inputs. In a second step, a bootstrap truncated regression was used to analyze factors that cause differences in dynamic technical and udder health management inefficiencies. Results showed that the sample farmers had considerably higher udder health management inefficiency scores than technical inefficiency scores. The results of the second-stage analysis indicated that technical inefficiency was influenced by summer precipitation and farmers' financial characteristics, and was regionally heterogeneous. Udder health management inefficiency was affected by summer temperature and nonfarm income. By ranking farms in this study in terms of technical and udder health management inefficiency, we allowed inefficient farms to compare their performance with that of their efficient peers, and thus identify targets for production and udder health management improvement efforts. Finally, although our study focused on farmers' performances with respect to udder health management, the proposed modeling framework can be applied to the management of other animal diseases and welfare conditions.

Development of a linear programming model for the optimal allocation of nutritional resources in a dairy herd

A linear programming model that selects the optimal cropping plan and feeds allocation for diets to minimize the whole dairy farm feed costs was developed. The model was virtually applied on 29 high-yielding Holstein-Friesian herds, confined, total mixed ration dairy farms. The average herd size was 313.2 ± 144.1 lactating cows and the average land size was 152.2 ± 92.5 ha. Farm characteristics such as herd structure, nutritional grouping strategies, feed consumption, cropping plan, intrinsic farm limitations (e.g., silage and hay storage availability, water for irrigation, manure storage) and on farm produced forage costs of production were collected from each farm for the year 2017. Actual feeding strategies, land availability, herd structure, crop production costs and yields, and milk and feed market prices for the year 2017 were used as model inputs. Through optimization, the feeding system was kept equal to the actual farm practice. The linear program formulated diets for each animal group to respect actual herd dry matter intake and fulfill actual consumption of crude protein, rumen-degradable and rumen-undegradable fractions of crude protein, net energy for lactation, neutral detergent fiber, acid detergent fiber, forage neutral detergent fiber, and nonfiber carbohydrate. Production levels and herd composition were considered to remain constant as the nutritional requirement would remain unchanged. The objective function was set to minimize the whole-farm feed costs including cash crop sales as income, and crop production costs and purchased feed costs as expenses. Optimization improved income over feed costs by reducing herd feed costs by 7.8 ± 6.4%, from baseline to optimized scenario, the improved was explained by lower feed costs per kilogram of milk produced due to a higher feed self-sufficiency and higher income from cash crop. In particular, the model suggested to maximize, starting from baseline to optimized scenario, the net energy for lactation (+8.5 ± 6.3%) and crude protein (+3.6 ± 3.1%) produced on farm, whereas total feed cost (€/100 kg of milk) was greater in the baseline (20.4 ± 2.3) than the optimized scenario (19.0 ± 1.9), resulting in a 6.7% feed cost reduction with a range between 0.49% and 21.6%. This meant €109 ± 96.9 greater net return per cow per year. The implementation of the proposed linear programming model for the optimal allocation of the nutritional resources and crops in a dairy herd has the potential to reduce feed cost of diets and improve the farm feed self-sufficiency.

Short communication: Economic impact among 7 reproductive programs for lactating dairy cows, including a sensitivity analysis of the cost of hormonal treatments

Although hormonal synchronization programs can improve reproductive efficiency of dairy herds, some farmers question the economics of these programs based on the upfront cost of hormonal treatments as opposed to the economic value of the resulting reproductive performance. Our aim was to compare the economic impact of reproductive management programs that incorporate varying degrees of detection of estrus and timed artificial insemination (AI) in dairy herds with year-round calving in confinement total mixed ration systems. A reproductive economic analysis simulation model was used to compare the economic impact of pairs of reproductive management programs. We simulated sets of scenarios for 2 analyses. In the first analysis, we calculated the economic impact of switching from a Presynch-Ovsynch program to a Double-Ovsynch program that included a second PGF_2α treatment during the Breeding-Ovsynch portion of the program (Double-Ovsynch+PGF). In the second analysis, we conducted a break-even analysis in which the cost of hormonal treatments was incrementally increased within various reproductive management programs. Our analyses revealed that a Double-Ovsynch+PGF program, the most intensive program evaluated, was more profitable than other programs compared, including a Presynch-Ovsynch program with 100% timed AI or a Presynch-Ovsynch program that incorporated detection of estrus, despite the higher upfront cost incurred by using more hormonal treatments. This advantage remained until the cost of hormones was increased 5 to 14 times current US market prices and 2 to 6 times current European market prices. The cost of GnRH had a greater impact on net profit gain than the cost of PGF_2α. In conclusion, more intensive reproductive programs that use more hormonal treatments but result in substantially increased reproductive performance are more profitable than less intensive programs and remain so even if hormone prices are unusually high.

The relationship between the number of consecutive days with heat stress and milk production of Holstein dairy cows raised in a humid continental climate

Heat stress is known to affect performance of dairy cows experiencing prolonged periods of high temperature and relative humidity. Less is known about its effects in cooler climates. The goals of the present study were to determine the prevalence of days susceptible to cause mild heat stress in dairy cows living in a humid continental climate and to investigate the relationship between the number of consecutive days of mild heat stress and milk, fat, protein, and lactose production. A 6-yr data set (2010-2015) containing 606,031 milk analysis records for 34,360 Holstein dairy cows at different parities was matched with the corresponding daily maximum temperature-humidity index. Exposure to heat stress conditions was divided into 5 categories corresponding to 0, 1 to 2, 3 to 4, 5 to 6, and 7 to 8 consecutive days before milk test date. On average, cows were exposed to heat stress conditions for 135.8 ± 5.9 d/yr in Southwest Quebec and 95.3 ± 10.2 d/yr in Eastern Quebec. Cows experiencing heat stress conditions produced on average less fat, protein, and energy-corrected milk and lower fat and protein concentrations. The decrease in milk fat reached 6% for category 7 to 8 exposure of cows in parity 3 or more. The association between exposure category and milk yield and lactose yield and concentration was weak. Heat stress lowered milk fat and protein production but had little effect on milk volume output. Further research is necessary to better understand the mechanism underlying the effects of sporadic low- to medium-intensity heat stress on dairy productivity.

An economic evaluation of management strategies to mitigate the negative effect of twinning in dairy herds

Our objectives were to develop an economic model to estimate the economic impact of twinning in dairy cows and to evaluate management strategies to mitigate the negative economic impact of twinning in dairy herds. A probabilistic tree considering spontaneous embryo reduction, early pregnancy loss, abortion, metritis, retained placenta, and culling rate at 120 d of the second, at the end of the second, and at the end of the third lactation was developed for a single pregnancy; we also developed 3 management options upon diagnosis of a twin pregnancy: (1) do nothing, (2) induce abortion using PGF_2α, or (3) attempt manual embryo reduction. A value was given to each branch of the tree by simulating cow states on a farm for 1,400 d to encompass 4 consecutive lactations. The incomes considered in the simulations included milk income over feed cost, income from calves, and slaughter value upon culling. The expenses taken into account depending on each branch included additional inseminations and synchronization protocols, embryo reduction, induction of abortion, replacement heifers, and costs due to metritis and retained placenta. The gross value for a singleton pregnancy and the 3 management options upon diagnosis of a twin pregnancy were calculated by adding the value of all braches multiplied by their probability. The costs for the 3 management options were calculated by subtracting its gross value minus the gross value of a singleton pregnancy. The negative economic impact of a twin pregnancy ranged from $97 to $225 depending on the type of twin pregnancy (unilateral vs. bilateral), parity, and DIM when the twin pregnancy occurred. The overall negative economic impact of twinning on dairy farm profitability in the United States was estimated to be $96 million per year. Attempting manual embryo reduction early during gestation upon diagnosis of a twin pregnancy was the optimal management strategy for mitigating the negative economic impact of twinning under a wide variety of scenarios.

From cold to hot: Climatic effects and productivity in Wisconsin dairy farms

This study examined the effects of climatic conditions on dairy farm productivity using panel data for the state of Wisconsin along with alternative stochastic frontier models. A noteworthy feature of this analysis is that Wisconsin is a major dairy-producing area where winters are typically very cold and snowy and summers are hot and humid. Thus, it is an ideal geographical region for examining the effects of a range of climatic factors on dairy production. We identified the effects of temperature and precipitation, both jointly and separately, on milk output. The analysis showed that increasing temperature in summer or in autumn is harmful for dairy production, whereas warmer winters and warmer springs are beneficial. In contrast, more precipitation had a consistent adverse effect on dairy productivity. Overall, the analysis showed that over the past 17 yr, changes in climatic conditions have had a negative effect on Wisconsin dairy farms. Alternative scenarios predict that climate change would lead to a 5 to 11% reduction in dairy production per year between 2020 and 2039 after controlling for other factors.

Feeding strategies and manure management for cost-effective mitigation of greenhouse gas emissions from dairy farms in Wisconsin

Greenhouse gas (GHG) emissions from dairy farms are a major concern. Our objectives were to assess the effect of mitigation strategies on GHG emissions and net return to management on 3 distinct farm production systems of Wisconsin. A survey was conducted on 27 conventional farms, 30 grazing farms, and 69 organic farms. The data collected were used to characterize 3 feeding systems scaled to the average farm (85 cows and 127ha). The Integrated Farm System Model was used to simulate the economic and environmental impacts of altering feeding and manure management in those 3 farms. Results showed that incorporation of grazing practices for lactating cows in the conventional farm led to a 27.6% decrease in total GHG emissions [-0.16kg of CO2 equivalents (CO2eq)/kg of energy corrected milk (ECM)] and a 29.3% increase in net return to management (+$7,005/yr) when milk production was assumed constant. For the grazing and organic farms, decreasing the forage-to-concentrate ratio in the diet decreased GHG emissions when milk production was increased by 5 or 10%. The 5% increase in milk production was not sufficient to maintain the net return; however, the 10% increase in milk production increased net return in the organic farm but not on the grazing farm. A 13.7% decrease in GHG emissions (-0.08kg of CO2eq/kg of ECM) was observed on the conventional farm when incorporating manure the day of application and adding a 12-mo covered storage unit. However, those same changes led to a 6.1% (+0.04kg of CO2eq/kg of ECM) and a 6.9% (+0.06kg of CO2eq/kg of ECM) increase in GHG emissions in the grazing and the organic farms, respectively. For the 3 farms, manure management changes led to a decrease in net return to management. Simulation results suggested that the same feeding and manure management mitigation strategies led to different outcomes depending on the farm system, and furthermore, effective mitigation strategies were used to reduce GHG emissions while maintaining profitability within each farm.

A simple formulation and solution to the replacement problem: a practical tool to assess the economic cow value, the value of a new pregnancy, and the cost of a pregnancy loss

This study contributes to the research literature by providing a new formulation for the cow replacement problem, and it also contributes to the Extension deliverables by providing a user-friendly decision support system tool that would more likely be adopted and applied for practical decision making. The cow value, its related values of a new pregnancy and a pregnancy loss, and their associated replacement policies determine profitability in dairy farming. One objective of this study was to present a simple, interactive, dynamic, and robust formulation of the cow value and the replacement problem, including expectancy of the future production of the cow and the genetic gain of the replacement. The proven hypothesis of this study was that all the above requirements could be achieved by using a Markov chain algorithm. The Markov chain model allowed (1) calculation of a forward expected value of a studied cow and its replacement; (2) use of a single model (the Markov chain) to calculate both the replacement policies and the herd statistics; (3) use of a predefined, preestablished farm reproductive replacement policy; (4) inclusion of a farmer's assessment of the expected future performance of a cow; (5) inclusion of a farmer's assessment of genetic gain with a replacement; and (6) use of a simple spreadsheet or an online system to implement the decision support system. Results clearly demonstrated that the decision policies found with the Markov chain model were consistent with more complex dynamic programming models. The final user-friendly decision support tool is available at http://dairymgt.info/ → Tools → The Economic Value of a Dairy Cow. This tool calculates the cow value instantaneously and is highly interactive, dynamic, and robust. When a Wisconsin dairy farm was studied using the model, the solution policy called for replacing nonpregnant cows 11 mo after calving or months in milk (MIM) if in the first lactation and 9 MIM if in later lactations. The cow value for an average second-lactation cow was as follows: (1) when nonpregnant, (a) $897 in MIM = 1 and (b) $68 in MIM = 8; (2) when the cow just became pregnant,(a) $889 for a pregnancy in MIM = 3 and (b) $298 for a pregnancy in MIM = 8; and (3) the value of a pregnancy loss when a cow became pregnant in MIM = 5 was (a) $221 when the loss was in the first month of pregnancy and (b) $897 when the loss was in the ninth month of pregnancy. The cow value indicated pregnant cows should be kept. The expected future production of a cow with respect to a similar average cow was an important determinant in the cow replacement decision. The expected production in the rest of the lactation was more important for nonpregnant cows, and the expected production in successive lactations was more important for pregnant cows. A 120% expected milk production for a cow with MIM = 16 and 6 mo pregnant in the present lactation or in successive lactations determined between 1.52 and 6.48 times the cow value, respectively, of an average production cow. The cow value decreased by $211 for every 1 percentage point of expected genetic gain of the replacement. A break-even analysis of the cow value with respect to expected milk production of an average second-parity cow indicated that (1) nonpregnant cows in MIM = 1 and 8 could still remain in the herd if they produced at least 84 and 98% in the present lactation or if they produced at least 78 and 97% in future lactations, respectively; and (2) cows becoming pregnant in MIM = 5 would require at least 64% of milk production in the rest of the lactation or 93% in successive lactations to remain in the herd.

Prediction of nitrogen excretion in dairy farms located in north Florida: a comparison of three models

The increasing N concentrations in surface and groundwater in north Florida emphasize the need to identify sources of N loss and ways to reduce them. The amount of N excretion produced by dairy farms and deposited into the Suwannee River agro-ecosystem is being heavily scrutinized by regulatory agencies because it is believed to contribute significantly to the high N concentrations in water. Models developed by Van Horn and the USDA-Natural Resource and Conservation Service are used to estimate N balances on dairy farms. This study explores ways to improve these estimates by using dynamic simulation of N excretion over time. The Livestock Dynamic North Florida Dairy Farm model (LiDyNoFlo), which was created for this purpose, is described. The amount of N excretion on a dairy farm depends on crude protein in the diet, milk production, the presence of mature bulls and heifers, and seasonality of production. The LiDyNoFlo considered more variables than earlier models, and estimates of N excretion differed from those of other models. Comparisons consistently showed the LiDyNoFlo predictions of N excretion were between those predicted by the Van Horn model (upper end) and the Natural Resource and Conservation Service model (lower end). The LiDyNoFlo predicted that a 1,000-cow operation produced 324 kg of N excretion/d in February and 307 kg of N excretion/d in August because of seasonal milk production and herd dynamics. Seasonal differences in N excretion are important because they determine the opportunity for N recycling in the crop fields such that total N losses into the Suwannee River agro-ecosystem may be minimized.