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Solving complex livestock production problems is a pressing issue for achieving long-term sustainability and profitability and often requires modeling techniques. The use of mathematical models is undoubtedly a daily practice in the livestock industry, especially nutrition, and has improved animal performance, productivity, and environmental sustainability while maintaining or reducing costs. Although many students and professionals use spreadsheets and existing empirical models for nutrition and management [NASEM (2016)], there is still a need to understand the complexity of livestock systems and the utility of flight simulator models. At the same time, more complex models (although robust) may fail to provide new insights for experienced nutritionists due to poor userfriendliness. A systems understanding goes beyond simply obtaining a desired output, such as optimizing a total mixed ration, but instead leads to identifying high-leverage solutions and gaining insight. Further, parameterizing and calibrating variables and equations and testing management scenarios is straightforward. However, developing causal feedback linkages (A to B and B to A) and identifying time delays is less intuitive and more challenging for novices. Model flight simulators grounded in fully documented, calibrated models provide a means to introduce practitioners to a methodology of insight generation because the user designs and runs the model scenarios for themselves, challenging their mental models. Such approaches are generally more impactful (compared with someone telling them) because they have gained insight into the system themselves, and, in the case of open source (white box models), they can explore equations and parameters. Therefore, understanding how to utilize dynamic models in scenario-based simulations is critical in training current and future modelers in animal science. This hands-on model training will cover the basics of System Dynamics modeling and allow participants to run real-time animal production simulations. Finally, participants will be “debriefed” to unpack “ah ha” moments that were unexpected. The debrief will include using models to develop accurate guidelines and recommendations for those who cannot use computer models and developing experimental designs to test hypotheses and “validate” model recommendations (proof in the pudding). Participants will gain knowledge of System Dynamics applications for animal production systems, experience using flight simulators, and their utility in teaching, informing, and guiding their livestock production or that of a client. The “flight simulator” will focus on production and nutrition with species-agnostic principles. Participants will also have a new tool to identify areas for improvement in model development (i.e., what is missing?), research questions, or industry needs. The need for modelers who can turn big data into insight, knowledge, and wisdom using a systems approach is becoming even more critical due to the increasing use of precision livestock farming. Thus, providing the livestock industry with trained systems modelers will help achieve current and future sustainability challenges. [ABSTRACT FROM AUTHOR]

The use of partial indicators helps to improve technical and economic efficiency of dairy farms. Nevertheless, complete monitoring of farm cash flow and economic balances is needed to better support the decision making. The aim of this work was to propose a practical approach to monitor farm costs and incomes and related indicators of profitability. A methodological framework to calculate farm gross margin was developed and applied to a dairy cattle farm with 227 milking cows from Arborea (Or, Italy). Farm data were gathered at daily scale and elaborated twice per month. Replacement and dry cattle, other costs were monitored separately. Considered variables included: diet formulas, dry matter (DM) and costs of the produced and purchased feeds, milk delivered and price, meat and cattle sold (culled cows, live animals). Through the management software Ecostallawere collected the other costs concerning health and reproduction cost, energy and other costs that included: fuel, electricity and other materials. The farmer was asked daily to input in an excel spreadsheet for lactation groups feed and milk delivered, milking cows, feed orts and for all the groups: consistency, feed delivered and weekly the unifeed DM. Partial indicators were calculated for feed efficiency as milk/feed (FE), cost in €/kg of DM, IOFC, gross margin (revenues -considered costs). All the calculations were referred to the year 2022 the average milk price and cost per kg of DM was 0.465 €/l 0.39 €/kg respectively. The average milk production and DMI per cow was 33 l and 25.5 kg respectively, with a FE of 1.29. Pooling daily data over the whole year a variation of ± 0.1 in FE corresponded to ±1.01 €/head daily change in IOFC. At the end of the year the feeding costs were equal to 817,798 € for lactating cows, 229,354 € for heifers, dry cows and calves, health and reproduction cost were 60,852 € and energy and other cost were 212,132 €. The final gross margin was 18,461 €, ranging from a max of 14,282 € in March and a min of - 15,501 € in September. Improving FE from 1.29 to 1.43 with 26.5 kg/DM and 38 l/d with other costs that remain equal except improving cost per kg of DM of. 0.02 € it will be a gross margin about 128,707 € more than 110,246 € respect the actual situation. This approach can be useful to stimulate farmer nutirionist and vet to improve a discussion to research goal to economic sustainability of farm. [ABSTRACT FROM AUTHOR]

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 (C b ) and precision (ρ); and coefficient of determination (R 2). 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 (C b 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 R 2 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. [ABSTRACT FROM AUTHOR]

Sardinia (Italy),one of the main European regions for sheep milk production and where a broad variety of dairy sheep farming systems coexist, can represent a special context for testing strategies of climate change mitigation for the small ruminant sector. The Sheep ToShip LIFE - Looking for an eco-sustainable sheep supply chain is a EU project launched in 2016 to develop and implement an intervention model for the sheep-dairy supply chain of Sardinia, able to reduce GHG emissions by 20% over the next 10 years through eco-innovative actions. The core of Sheep ToShip LIFE strategy is the evaluation, with a Life Cycle Assessment (LCA) approach, of the environmental impacts of the main Sardinian dairy sheep farming and manufacturing systems by using a case study methodology. The project's goal seems technically feasible by increasing farm efficiency at both flock and field levels. However, the greening of the dairy sheep sector strongly depends on attractive agro-environmental measures (based on effective eco-innovation criteria) within the next Rural Development Plan. [ABSTRACT FROM AUTHOR]

Modeling the individual animal response to heat stress (HS) conditions is challenging because of the complex interactions that characterize the system behavior. In explaining the resulting animal behavior, the dynamicity, nonlinearity, and delays in the HS response are often unaccounted for or misinterpreted. The system dynamics (SD) methodology, a mathematical modeling approach based on feedback loop structures, allows for modeling and understanding the behavior of complex systems over time. By applying SD methodology, this study developed a preliminary conceptual model to capture the cow response and observed milk yield (MY) under HS. The data on the temperature-humidity index (THI) and MY used for model development were collected from a dairy cattle farm in August 2021. The parameters related to the HS response of 20 selected cows were used for calibration and parameterization of the model. To minimize the effect of the lactation stage on milk production and model results, 20 cows were selected for days in milk (DIM) to be between 70 and 220 d. After the parameter calibration using MY data, it was found that the historical data pattern of 13 out of 20 cows followed the expected behavioral pattern generated by the model. In contrast, the behavior of the remaining seven cows did not align with that generated by the model. Therefore, based on their patterns, the cows were identified as fitting or non-fitting the model's structure. The structure of the model captured the effect of HS on fitting cows with high accuracy (mean absolute percentage error, MAPE < 5%; R2 > 0.6; concordance correlation coefficient, CCC > 0.6). At the same time, the behavior of the non-fitting cows could not be explained by the defined parameter space. We believe they either had heat-resistant behavior or experienced different biological delays than average. Based on the obtained results, the evaluation of parameter values should be done only for the fitting cows, as the work aimed to develop a model to understand the HS response. The behaviors generated by the model can help farmers and decision-makers distinguish heat-sensitive from heat-tolerant cows and quantify the animal response in terms of MY so that mitigation strategies can be implemented. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

Simple Summary: In the Mediterranean region, artichoke and broccoli are major crops with a high number of by-products that can be used as alternative feedstuffs for ruminants, lowering feed costs and enhancing milk sustainability while reducing the environmental impact of dairy production. However, the nutritional quality of milk needs to be assured under these production conditions. A 40% inclusion of these silage by-products has proven to be a good short-term option (1 month). Therefore, it is interesting to study the effect of feeding animals with these by-products for a longer time, such as full lactation (6 months). With this type of feeding, the performance of the animals fed with the artichoke plant silage was similar to that of those fed with a conventional diet (alfalfa and cereals), even improving the milk quality from the point of view of bioactive compounds. However, the performance of goats fed with broccoli by-product silage in high amounts worsened due to a low intake of the diet containing that by-product. Therefore, it is desirable to reduce its level of inclusion. The aim of this experiment was to study the effects of a 40% inclusion of broccoli by-product (BB) and artichoke plant (AP) silages in dairy goat diets on the milk yield, composition and animal health status during a full lactation. Feed consumption was lower in AP and BB animals due to their composition and higher moisture content, and BB animals showed a significant reduction in body weight. Milk from the BB treatment had the highest fat content, total solids and useful dry matter content (5.02, 13.9 and 8.39%, respectively). The Se level was slightly lower in AP and BB animals; however, the milk of these treatments was the lowest in Na and, in the case of BB animals, the richest in Ca (1267 mg/kg). Control and AP milk showed a similar fatty acid profile, although AP had a more beneficial aptitude for human health (lower ratio of n6/n3, 12.5). Plasma components, as metabolic parameters, were adequate for goats. It was concluded that a 40% inclusion of AP is an adequate solution to reduce the cost of feeding without harming the animals' health or performance and to improve the nutritional milk quality. It is necessary to lower the BB level of inclusion to increase feed consumption. [ABSTRACT FROM AUTHOR]

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., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

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 R 2  > 0.6, except for two cows (ID #13 and #20) with R 2 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., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

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., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

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 B 6 . 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 B 6 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., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

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., (Copyright © 2021 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

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., (Copyright © 2021 The Author(s). Published by Elsevier B.V. All rights reserved.)