A system dynamics approach to model heat stress accumulation in dairy cows during a heatwave event.

Climate change is expected to increase the number of heat wave events, leading to prolonged exposures to severe heat stress (HS) and the corresponding adverse effects on dairy cattle productivity. Modelling dairy cattle productivity under HS conditions is complicated because it requires comprehending the complexity, non-linearity, dynamicity, and delays in animal response. In this paper, we applied the System Dynamics methodology to understand the dynamics of animal response and system delays of observed milk yield (MY) in dairy cows under HS. Data on MY and temperature-humidity index were collected from a dairy cattle farm. Model development involved: (i) articulation of the problem, identification of the feedback mechanisms, and development of the dynamic hypothesis through a causal loop diagram; (ii) formulation of the quantitative model through a stock-and-flow structure; (iii) calibration of the model parameters; and (iv) analysis of results for individual cows. The model was successively evaluated with 20 cows in the case study farm, and the relevant parameters of their HS response were quantified with calibration. According to the evaluation of the results, the proposed model structure was able to capture the effect of HS for 11 cows with high accuracy with mean absolute percent error <5%, concordance correlation coefficient >0.6, and R2 > 0.6, except for two cows (ID #13 and #20) with R2 less than 0.6, implying that the rest of the nine animals do not exhibit heat-sensitive behaviour for the defined parameter space. The presented HS model considered non-linear feedback mechanisms as an attempt to help farmers and decision makers quantify the animal response to HS, predict MY under HS conditions, and distinguish the heat-sensitive cows from heat-tolerant cows at the farm level.

Feedback thinking in dairy farm management: system dynamics modelling for herd dynamics.

Systems perspectives and system dynamics have been widely used in decision-making for agricultural problems. However, their use in dairy farm management remains limited. This work demonstrates the use of systems approaches and feedback thinking in modelling for dairy farm management. The application of feedback thinking was illustrated with causal loop and stock-and-flow diagrams to disentangle the complexity of the relationship among farm elements. The study aimed to identify the dynamic processes of an intensive dairy farm by mapping the animal stocks (e.g., heifers, lactating cows, dry cows) with the final objective of anticipating the expected milk deliveries over a long time period. The project was conducted for a reference dairy farm that was intensively managed with a herd size of >2 500 cattle heads, which provided monthly farm records from Jan 2016 to Dec 2019. Model development steps included: (i) problem articulation with farm interviews and data analysis; (ii) the development of a dynamic hypothesis and a causal loop diagram; (iii) the development of a stock-and-flow cattle model describing ageing chains of heifers and cows and subsequent calibration of the model parameters; (iv) the evaluation of the model based on lactating cows and milk deliveries against farm historical records; and (v) the analysis of the model results. The model characterized the farm dynamics using three main feedback loops: one balancing loop of culling and two reinforcing loops of heifers' replacement and cows' pregnancy, pushing milk delivery. The model reproduced the historical oscillation patterns of lactating cows and milk deliveries with high accuracy (root mean square percentage error of 2.8 and 5.2% for the number of lactating cows and milk deliveries, respectively). The model was shown to be valid for its purpose, and applications of this model in dairy farm management can support decision-making practices for herd composition and milk delivery targets.

Review: The challenge to integrate animal welfare indicators into the Life Cycle Assessment.

The transition to a more sustainable livestock sector represents one of the major challenges of our time. Life Cycle Assessment (LCA) is recognised as the gold standard methodology for assessing the environmental impact of farming systems. Simultaneously, animal welfare is a key component of livestock production and is intrinsically related to human and environmental well-being. To perform an overall on-farm sustainability assessment, it would be desirable to consider both the environmental impact and the welfare of the animals. The present work aimed to summarise and describe the methodologies adopted in peer-reviewed papers published to date, that combine animal welfare evaluation with LCA. Citations, retrieved from four bibliographical databases, were systematically evaluated in a multi-stage approach following the JBI and PRISMA scoping review guidelines. The searches identified 1 460 studies, of which only 24 were compliant with the inclusion criteria. The results highlighted how the environmental LCA was undertaken with a much more homogenous and standardised method than animal welfare assessment. When studies were grouped based on the type of animal welfare assessment performed: 16.7% used single welfare indicators, 45.8% multiple indicators, 8.3% applied existing validated protocols (i.e., TGI-200 and TGI-35L), 16.7% used non-validated protocols and 12.5% employed other methods. The papers were further classified with respect to the "5 Animal Welfare Domains Model": the most assessed domain was "environment" (90.5% of the papers%), followed by "health" (52.4%), "nutrition" (33.3%), "behavioural interactions" (28.6%) and "mental state" (9.5%). None of the studies assessed all the domains simultaneously. In addition, 66.7% of papers (n = 16) aggregated the animal welfare indicators into a final score. Within these, only four papers proposed to associate the animal welfare scores with the LCA functional unit. An overall sustainability score, calculated with several different approaches to summarise the information, was provided by 46% of the papers. In summary, despite the topic's relevance, to date, there is neither a consensus on the animal welfare assessment approach to be carried out (indicators selection and their aggregation) nor on the standardisation of an integrated animal welfare-LCA evaluation. The present review provides a basis for the development of common future guidelines to carry out a comprehensive, true-to-life and robust farm sustainability assessment.

Milk metabolome reveals pyrimidine and its degradation products as the discriminant markers of different corn silage-based nutritional strategies.

The purpose of this study was to evaluate the effect of 6 different feeding systems (based on corn silage as the main ingredient) on the chemical composition of milk and to highlight the potential of untargeted metabolomics to find discriminant marker compounds of different nutritional strategies. Interestingly, the multivariate statistical analysis discriminated milk samples mainly according to the high-moisture ear corn (HMC) included in the diet formulation. Overall, the most discriminant compounds, identified as a function of the HMC, belonged to AA (10 compounds), peptides (71 compounds), pyrimidines (38 compounds), purines (15 compounds), and pyridines (14 compounds). The discriminant milk metabolites were found to significantly explain the metabolic pathways of pyrimidines and vitamin B6. Interestingly, pathway analyses revealed that the inclusion of HMC in the diet formulation strongly affected the pyrimidine metabolism in milk, determining a significant up-accumulation of pyrimidine degradation products, such as 3-ureidopropionic acid, 3-ureidoisobutyric acid, and 3-aminoisobutyric acid. Also, some pyrimidine intermediates (such as l-aspartic acid, N-carbamoyl-l-aspartic acid, and orotic acid) were found to possess a high discrimination degree. Additionally, our findings suggested that the inclusion of alfalfa silage in the diet formulation was potentially correlated with the vitamin B6 metabolism in milk, being 4-pyridoxic acid (a pyridoxal phosphate degradation product) the most significant and up-accumulated compound. Taken together, the accumulation trends of different marker compounds revealed that both pyrimidine intermediates and degradation products are potential marker compounds of HMC-based diets, likely involving a complex metabolism of microbial nitrogen based on total splanchnic fluxes from the rumen to mammary gland in dairy cows. Also, our findings highlight the potential of untargeted metabolomics in both foodomics and foodomics-based studies involving dairy products.

Assessment of feed and economic efficiency of dairy farms based on multivariate aggregation of partial indicators measured on field.

Many of the metrics used to evaluate farm performance are only partial indicators of farm operations, which are assumed to be best predictors of the whole farm efficiency. The main objective of this work was to identify aggregated multiple indexes of profitability using common partial indicators that are routinely available from individual farms to better support the short-term decision-making processes of the cattle-feeding process. Data were collected from face-to-face interviews with farmers from 90 dairy farms in Italy and used to calculate 16 partial indicators that covered almost all indicators currently used to target feeding and economic efficiency in dairy farms. These partial indicators described feed efficiency, energy utilization, feed costs, milk-to-feed price ratio, income over feed costs, income equal feed cost, money-corrected milk, and bargaining power for feed costs. Calculations of feeding costs were based on lactating cows or the whole herd, and income from milk deliveries was determined with or without considering the milk quality payment. Multivariate factor analysis was then applied to the 16 partial indicators to determine simplified and latent structures. The results indicated that 5 factors explained 70% of the variability. Each of the original partial indicator was associated with all factors in different proportions, as indicated by loading scores from the multivariate factor analysis. Based on the loading scores, we labeled these 5 factors as "economic efficiency," "energy utilization," "break-even point," "milk-to-feed price," and "bargaining power of the farm," in decreasing order of explained communality. The first 3 factors shared 83% of the total communality. Feed efficiency was similarly associated with factor 1 (53% loading) and factor 2 (66% loading). Only factor 4 was significantly affected by farm location. Milk production and herd size had significant effects on factor 1 and factor 2. Our multivariate approach eliminated the problem of multicollinearity of partial indicators, providing simple and effective descriptions of farm feeding economics. The proposed method allowed the evaluation, benchmarking, and ranking of dairy herd performance at the level of single farms and at territorial level with high opportunity to be used or replicated in other areas.

Animal board invited review - Beef for future: technologies for a sustainable and profitable beef industry.

The global consumption, notably in developing countries, and production of beef are increasing continuously, and this requires the industry to improve performance and to reduce the environmental impact of the production chain. Since the improvement in efficiency and the highest impacts occur at farm level, it is appropriate to focus on the profitability and environmental sustainability of these enterprises. In many areas of the world, beef production is economically and socially relevant because it accounts for a significant portion of the agricultural production and represents a vital economic activity in mountain and hill districts of many regions, where few alternatives for other agricultural production exist. Due to the important role in the agricultural and food economy worldwide, the future of the beef industry is linked to the reduction of ecological impacts, mainly adopting the agroecological mitigation practices, and the simultaneous improvement of production performances and of product quality. This review analyses the technical and managerial solutions currently available to increase the efficiency of the beef industry and, at the same time, to reduce its environmental impacts in response to the growing concerns and awareness of citizens and consumers.

Metabolic and hormonal control of energy utilization and partitioning from early to mid lactation in Sarda ewes and Saanen goats.

In a recent study, we observed that starch-rich diets used in mid lactation induced lower milk production persistency and higher body fat accumulation in dairy ewes compared with dairy goats. Because these species differences could be linked to hormonal mechanisms that drive energy partitioning, in the same experiment, we explored the evolution of metabolic and hormonal status during lactation to test this hypothesis. Twenty mature Sarda dairy ewes and 20 mature Saanen goats [15-134 ± 11 d in milk (DIM), mean ± SD] were compared simultaneously. In early lactation, each species was allocated to one dietary treatment: high-starch diet [HS: 20.4% starch, on dry matter (DM) basis], whereas from 92 ± 11 DIM, each species was allocated to 1 of 2 dietary treatments: HS (20.0% starch, on DM basis) and low-starch (LS: 7.8% starch, on DM basis) diets. Blood samples were collected in the morning to analyze glucose, nonesterified fatty acids (NEFA), growth hormone (GH), insulin, and insulin-like growth factor I (IGF-I). Data were analyzed using the PROC MIXED procedure of SAS with repeated measurements (SAS Version 9.0). The HS and LS diets applied in mid lactation did not affect metabolic status of the animal within species; thus, only a comparison between species was carried out. From early to mid lactation, plasma glucose concentration was higher in ewes than in goats (54.57 vs. 48.35 ± 1.18 mg/dL), whereas plasma NEFA concentration was greater in goats than in ewes (0.31 vs. 0.25 ± 0.03 mmol/L). Goats had higher plasma GH concentration and lower plasma insulin content than ewes (4.78 vs. 1.31 ng/mL ± 0.47; 0.11 vs. 0.26 µg/L ± 0.02). Plasma IGF-I concentration did not vary between species. The comparison of metabolic and hormonal status of lactating Sarda dairy ewes and Saanen goats, carried out by studying simultaneously the 2 species in the same stage of lactation and experimental conditions, suggests that the higher insulin and glucose concentration observed in Sarda ewes explains why they partitioned more energy toward body reserves than to the mammary gland, especially in mid lactation. This can justify the negative effect of high-starch diets in mid-lactating Sarda ewes. Conversely, the highest GH and NEFA concentration observed in Saanen goats explain why they partitioned more energy of starch diets toward the mammary gland than to body reserves and justify the positive effect of high-starch diet in mid lactation. Together, these different responses contribute to explain why specialized dairy goats, such as the Saanen breed, have a higher milk production persistency than specialized dairy sheep breeds, such as the Sarda.

Exposure of dairy cows to high environmental temperatures and their lactation status impairs establishment of the ovarian reserve in their offspring.

The objectives of this study were to establish if exposure of pregnant dairy cows to high environmental temperatures and humidity during the first trimester of pregnancy impairs the establishment of the ovarian reserve (total number of healthy follicles and oocytes in ovaries) and fertility in their offspring. Serum anti-Müllerian hormone (AMH) concentrations and number of follicles ≥3 mm (antral follicle count; AFC) were assessed on a random day of the estrous cycle in 310 sixteen-month-old dairy heifers. Based on season of their conception and early fetal life, heifers were separated into 2 groups: summer (mean monthly temperature-humidity index = 69.33 ± 2.6) and winter (temperature-humidity index = 54.91 ± 1.08). The AMH and AFC were lower in summer (419.27 ± 22.81 pg/mL and 9.32 ± 0.42 follicles, respectively) compared with winter heifers (634.91 ± 47.60 pg/mL and 11.84 ± 0.46 follicles, respectively) and were not influenced by farm and age at sampling. Heifers born to dams that were not being milked during gestation had lower AMH and AFC compared with offspring of cows on their first lactation, whereas no difference was detected between offspring of cows on their first and subsequent lactations. Summer and winter heifers had similar age at first service and at first calving, and similar number of services per conception. Regardless of season in early fetal life, heifers were classified into 3 groups based on AMH and AFC (low = 20%, intermediate = 60%, high = 20%). Heifers with the lowest AMH were older at first service compared with herd mates with intermediate AMH, but age at first calving and number of services per conception were similar among AMH categories. No difference was detected in any of the fertility measures among AFC categories. Heifers born to mothers exposed to high environmental temperatures in early gestation had smaller ovarian reserves compared with herd mates conceived in winter, but no association between season of early fetal life and fertility at first conception was established. Season of conception and maternal lactation status affect the size of the ovarian reserve, but not fertility, at first conception in the progeny.

Prenatal exposure to different diets influences programming of glucose and insulin metabolism in dairy ewes.

Nutrition in fetal and postnatal life can influence the development of several biological systems, with permanent effects in adult life. The aim of this work was to investigate in dairy sheep whether diets rich in starch or fiber during intrauterine life (75 d before lambing) and postnatal life (from weaning to first pregnancy; growth phase) program glucose and insulin metabolism in the female offspring during their first pregnancy. Starting from intrauterine life, 20 nulliparous Sarda ewes were exposed to 4 dietary regimens (n = 5 per group) based on different dietary carbohydrates during their intrauterine life and their subsequent growth phase: (1) the fiber (FI) diet during both intrauterine and growth life, (2) the starch (ST) diet during both intrauterine and growth life, (3) the FI diet in intrauterine life followed by the ST diet in the growth phase, and (4) the ST diet in intrauterine life followed by the FI diet in the growth phase. After the end of the growth phase, all growing ewes were fed the same diet and naturally mated. When ewes were pregnant, on average at 124 ± 2 d of gestation they were challenged with an intravenous glucose tolerance test, and peripheral concentrations of glucose and insulin were determined. Basal insulin concentrations were higher in ewes exposed to the ST diet (0.97 µg/L) than in ewes exposed to the FI diet (0.52 µg/L) in intrauterine life. After glucose infusion, glucose and insulin concentrations were not affected by intrauterine diet. Insulin resistance, determined by the homeostasis model assessment, was affected by the intrauterine × growth phases interaction. Insulin sensitivity, assessed by the quantitative insulin check index, was lower in ewes exposed to the ST diet than in those exposed to the FI diet in intrauterine life (ST = 0.28; FI = 0.30). Diet in growth life had no effect on glucose and insulin metabolism. In conclusion, starchy diets offered during intrauterine life but not during postnatal life increased basal insulin level and lowered insulin sensitivity during the first pregnancy. Nutritional strategies of metabolic programming should consider that exposure to starchy diets in late fetal life might favor the programming of dietary nutrient partitioning toward organs with high requirements, such as the gravid uterus or the mammary gland.

Short communication: Cocoa husks can effectively replace soybean hulls in dairy sheep diets-Effects on milk production traits and hematological parameters.

The aim of this study was to test the effect of replacing soybean hulls with different doses of cocoa husk (CH) on milk production traits and the hematological profile of dairy ewes. Twenty-four mid-lactating Sarda dairy ewes were allotted to 3 homogeneous experimental groups (8 animals per group divided into 4 pens). Each group received a total mixed ration as a basal diet and a supplement that differed among groups. The first group was supplemented with 100 g of soybean hulls/d per head (SBH group). In the second group, soybean hulls were replaced with 50 g of CH/d (CH50 group). In the third group, soybean hulls were replaced with 100 g of CH/d per head (CH100 group). The study lasted 8 wk, with 3 wk of adaptation and 5 wk for the experimental period. The replacement of soybean hulls with 50 and 100 g of CH/d did not affect dry matter intake, milk production, and milk coagulation properties. Milk fat, protein, casein, and somatic cell count concentration and curd-firming time showed a significant interaction between treatment and sampling date. During the experiment, the somatic cell counts were lower in both the CH50 and CH100 groups than in the SBH group. Most of the hematological parameters were not affected by treatments except for basophiles, which were significantly higher in the SBH group than in the CH50 and CH100 groups. In conclusion, CH can be substituted for soybean hulls in the diet of dairy sheep without adverse effects on milk production or apparent negative effects on animal health conditions.

Meta-analysis of spineless cactus feeding to meat lambs: performance and development of mathematical models to predict dry matter intake and average daily gain.

Spineless cactus is a useful feed for various animal species in arid and semiarid regions due to its adaptability to dry and harsh soil, high efficiency of water use and carbohydrates storage. This meta-analysis was carried out to assess the effect of spineless cactus on animal performance, and develop and evaluate equations to predict dry matter intake (DMI) and average daily gain (ADG) in meat lambs. Equations for predicting DMI and ADG as a function of animal and diet characteristics were developed using data from eight experiments. The dataset was comprised of 40 treatment means from 289 meat lambs, in which cactus was included from 0 to 75% of the diet dry matter (DM). Accuracy and precision were evaluated by cross-validation using the mean square error of prediction (MSEP), which was decomposed into mean bias, systematic bias and random error; concordance correlation coefficient, which was decomposed into accuracy (Cb) and precision (ρ); and coefficient of determination (R2). In addition, the data set was used to evaluate the predicting accuracy and precision of the main lamb feeding systems (Agricultural and Food Research Council, Small Ruminant Nutritional System, National Research Council and Institut National de la Recherche Agronomique) and also two Brazilian studies. The DMI, CP intake (CPI), metabolizable energy (ME) intake and ADG increased when cactus was included up to 499 g/kg DM (P<0.001). In contrast, animals fed high levels of cactus (>500 g/kg DM) had a decreased DMI, CPI and NDF intake, but increased feed efficiency (P<0.001) and similar ADG compared with those without cactus addition. The DMI was positively correlated with initial BW, final BW, concentrate and ADG, while it was negatively correlated with cactus inclusion and ME of the diet. On other hand, ADG was positively correlated with DMI, initial and mean BW and concentrate, and it was negatively correlated with cactus inclusion. The two developed equations had high accuracy (Cb of 0.95 for DMI and 0.94 for ADG) and the random error of MSEP was 99% for both equations. The precision of both equations was moderate, with R2 values of 0.53 and 0.50 and ρ values of 0.73 and 0.71 for DMI and ADG, respectively. In conclusion, the developed equation to predict DMI had moderate precision and high accuracy, nonetheless, it was more efficient than those reported in the literature. The proposed equations can be a useful alternative to estimate intake and performance of lambs fed cactus.

Short communication: In vitro rumen gas production and starch degradation of starch-based feeds depend on mean particle size.

Our objective was to model the effect of mean particle size (mPS) on in vitro rumen starch degradation (IVSD) and the kinetics of gas production for different starch-based feeds. For each feed, 2 batches of the same grains were separately processed through 2 different mills (cutter or rotor speed mills), with or without different screens to achieve a wide range of mPS (0.32 to 3.31 mm for corn meals; 0.19 to 2.81 mm for barley meals; 0.16 to 2.13 mm for wheat meals; 0.28 to 2.32 mm for oat meals; 0.21 to 2.36 mm for rye meals; 0.40 to 1.79 for sorghum meals; 0.26 to 4.71 mm for pea meals; and 0.25 to 4.53 mm for faba meals). The IVSD data and gas production kinetics, obtained by fitting to a single-pool exponential model, were analyzed using a completely randomized design, in which the main tested effect was mPS (n = 6 for all tested meals, except n = 7 for corn meals and n = 5 for sorghum meals). Rumen inocula were collected from 2 fistulated Holstein dairy cows that were fed a total mixed ration consisting of 16.2% crude protein, 28.5% starch, and 35.0% neutral detergent fiber on a dry matter basis. The IVSD, evaluated after 7 h of rumen incubation, decreased linearly with increasing mPS for corn, barley, wheat, rye, pea, and faba meals, and decreased quadratically with increasing mPS for the other meals. The y-axis intercept for 7-h IVSD was below 90% starch for corn, barley, and rye feeds and greater than 90% for the other tested feeds. The mPS adjustment factors for the rate of rumen starch degradation varied widely among the different tested feeds. We found a linear decrease in starch degradation with increasing mPS for barley, wheat, rye, and pea meals, whereas we noted a quadratic decrease in starch degradation for the other tested meals. Further, we observed a linear decrease in the rate of gas production with increasing mPS in each tested feed, except for pea meal, which had a quadratic relationship. For each 1 mm increase in mPS, the gas production was adjusted by -0.009 h-1 for corn, -0.011 h-1 for barley, -0.008 h-1 for wheat, and -0.006 h-1 for faba, whereas numerically greater adjustments were needed for oat (-0.022 h-1), rye (-0.017 h-1), and sorghum (-0.014 h-1). These mPS adjustment factors could be used to modify the starch-based feed energy values as a function of mean particle size, although in vivo validation is required.

The comparison of the lactation and milk yield and composition of selected breeds of sheep and goats.

The objective of this study was to characterize the milk yield (MY) and milk composition of relevant sheep and goat breeds raised around the world to be used with nutrition models for diet formulation and nutrient balancing. A 2-step approach was used. First, a database developed by the Food and Agriculture Organization was used to identify relevant breeds (i.e., frequently raised) by comparing the occurrence of transboundary breed names across countries. We selected transboundary breeds that occurred more than 3 times and other relevant breeds obtained from the specialized literature that had milk production information (e.g., MY, days in milk, and milk fat, protein, and lactose). The majority of sheep breeds were classified as nondairy (76%) because they lacked milk production information. Karakul and Merino accounted for up to 2.4% of sheep breeds raised around the world, whereas the other individual breeds accounted for less than 1%. In contrast, nondairy breeds of goats accounted for 46.3% and of the remaining 53.7%, Saanen, Boer, Anglo-Nubian, Toggenburg, and Alpine accounted for 6.5, 5, 4.4, 4, and 3%, respectively, of the transboundary breeds. Second, a database compiled from published studies for the selected sheep (n = 65) and goats (n = 78) breeds were analyzed using a random coefficients model (studies and treatments within studies as random effects). For sheep breeds, the average and SD were 1.1 ± 0.3 kg/d for MY, 6.9 ± 1% for milk fat, 5.4 ± 0.4% for milk protein, 5 ± 0.3% for milk lactose, 17.7 ± 1.4% for milk total solids, and 1,073 ± 91 kcal/kg of milk energy. Lacaune had the greatest MY compared to Comisana and Tsigai (1.65 versus 0.83 and 0.62 kg/d; respectively, P < 0.05), but milk components were not different among breeds. For goats breeds, the average and SD across breeds were 1.7 ± 0.6 kg/d for MY, 4.2 ± 0.9% for milk fat, 3.3 ± 0.4% for milk protein, 4.4 ± 0.4% for milk lactose, 12.7 ± 1.1% for milk total solids, and 750 ± 75 kcal/kg of milk energy. Alpine had similar MY to Saanen (2.66 versus 2.55 kg/d, respectively; P > 0.05), but greater (P < 0.05) than other breeds. The Boer breed had the greatest milk fat, protein, lactose, and total solids than several other breeds, leading to the greatest milk energy content (907 kcal/kg). Because there are many factors that can alter MY and milk composition, averages provided in this study serve as guidelines, and nutritionists must obtain observed values when using nutrition models.

Effect of extruded linseed supplementation on blood metabolic profile and milk performance of Saanen goats.

This study assessed the effects of dietary supplementation with extruded linseed on milk yield and composition, milk fatty acid (FA) profile and renal and hepatic metabolism of grazing goats in mid-lactation. Forty Saanen goats were divided into two isoproductive groups: one group was fed the control diet (CON) composed of hay and pelleted concentrate and the other group was supplemented with additional 180 g/day of extruded linseed (LIN; dry matter basis), which supplied 70 g/day of fat per head for 9 weeks. Animals grazed on pasture for ∼3 h/day after the first of the 2 daily milkings. Milk samples were collected weekly and analyzed for fat, protein, lactose, milk urea nitrogen (MUN) and somatic cell count. Blood samples were collected every 2 weeks and analyzed for total bilirubin, creatinine, aspartate transaminase (AST), alanine transaminase (ALT), gamma glutamyl transpeptidase, alkaline phosphatase, total protein and urea nitrogen. Milk yield was higher in the LIN than in the CON group (2369 v. 2052 g/day). LIN group had higher milk fat (37.7 v. 33.4 g/kg) and protein (30.7 v. 29.1 g/kg) concentration and lower MUN (35.0 v. 43.3 mg/dl) than CON group. Goats fed LIN had greater proportions of 18:1 trans11, 18:2 cis9trans11 and total polyunsatured fatty acids n-3 in milk fat, because of higher 18:3n-3 and 20:5n-3 FA, and lower proportions of short- and medium-chain FAs than goats fed CON. All kidney and liver function biomarkers in serum did not differ between dietary groups, except for AST and ALT, which tended to differ. Extruded linseed supplementation to grazing mid-lactating goats for 2 months can enhance the milk performance and nutritional profile of milk lipids, without altering the general hepatic and renal metabolism.

A multivariate and stochastic approach to identify key variables to rank dairy farms on profitability.

The economic efficiency of dairy farms is the main goal of farmers. The objective of this work was to use routinely available information at the dairy farm level to develop an index of profitability to rank dairy farms and to assist the decision-making process of farmers to increase the economic efficiency of the entire system. A stochastic modeling approach was used to study the relationships between inputs and profitability (i.e., income over feed cost; IOFC) of dairy cattle farms. The IOFC was calculated as: milk revenue + value of male calves + culling revenue - herd feed costs. Two databases were created. The first one was a development database, which was created from technical and economic variables collected in 135 dairy farms. The second one was a synthetic database (sDB) created from 5,000 synthetic dairy farms using the Monte Carlo technique and based on the characteristics of the development database data. The sDB was used to develop a ranking index as follows: (1) principal component analysis (PCA), excluding IOFC, was used to identify principal components (sPC); and (2) coefficient estimates of a multiple regression of the IOFC on the sPC were obtained. Then, the eigenvectors of the sPC were used to compute the principal component values for the original 135 dairy farms that were used with the multiple regression coefficient estimates to predict IOFC (dRI; ranking index from development database). The dRI was used to rank the original 135 dairy farms. The PCA explained 77.6% of the sDB variability and 4 sPC were selected. The sPC were associated with herd profile, milk quality and payment, poor management, and reproduction based on the significant variables of the sPC. The mean IOFC in the sDB was 0.1377 ± 0.0162 euros per liter of milk (€/L). The dRI explained 81% of the variability of the IOFC calculated for the 135 original farms. When the number of farms below and above 1 standard deviation (SD) of the dRI were calculated, we found that 21 farms had dRI<-1 SD, 32 farms were between -1 SD and 0, 67 farms were between 0 and +1 SD, and 15 farms had dRI>+1 SD. The top 10% of the farms had a dRI greater than 0.170 €/L, whereas the bottom 10% farms had a dRI lower than 0.116 €/L. This stochastic approach allowed us to understand the relationships among the inputs of the studied dairy farms and to develop a ranking index for comparison purposes. The developed methodology may be improved by using more inputs at the dairy farm level and considering the actual cost to measure profitability.