A dairy herd model for use in whole farm simulations

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.

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.

The application of nonlinear programming on ration formulation for dairy cattle

The objective of this study was to compare the application of iterative linear programming (iteLP), sequential quadratic programming (SQP), and mixed-integer nonlinear programming-based deterministic global optimization (MINLP_DGO) on ration formulation for dairy cattle based on Nutrient Requirements of Dairy Cattle (NRC, 2001). Least-cost diets were formulated for lactating cows, dry cows, and heifers. Nutrient requirements including energy, protein, and minerals, along with other limitations on dry matter intake, neutral detergent fiber, and fat were considered as constraints. Five hundred simulations were conducted, with each simulation randomly selecting 3 roughages and 5 concentrates from the feed table in NRC (2001) as the feed resource for each of 3 animal groups. Among the 500 simulations for lactating cows, 57, 45, and 21 simulations did not yield a feasible solution when using iteLP, SQP, and MINLP_DGO, respectively. All the simulations for dry cows and heifers were feasible when using SQP and MINLP_DGO, but 49 and 11 infeasible simulations occurred when using iteLP for dry cows and heifers, respectively. The average ration costs per animal per day of the feasible solutions obtained by iteLP, SQP, and MINLP_DGO were $4.78 (±0.71), $4.45 (±0.65), and $4.44 (±0.65) for lactating cows; $2.39 (±0.52), $1.48 (±0.26), and $1.48 (±0.26) for dry cows; and $0.98 (±0.72), $0.97 (±0.15), and $0.91 (±0.14) for heifers, respectively. The average computation time of iteLP, SQP, and MINLP_DGO were 0.59 (±1.87) s, 1.15 (±0.62) s, and 58.69 (±68.45) s for lactating cows; 0.041 (±0.070) s, 0.76 (±0.37) s, and 14.84 (±39.09) s for dry cows; and 1.60 (±2.90) s, 0.51 (±0.19) s, and 16.45 (±45.56) s for heifers, respectively. In conclusion, iteLP had limited capability of formulating least-cost diets when nonlinearity existed in the constraints. Both SQP and MINLP_DGO handled the nonlinear constraints well, with SQP being faster, whereas MINLP_DGO was able to return a feasible solution under some situations where SQP could not.

ASAS-NANP SYMPOSIUM: prospects for interactive and dynamic graphics in the era of data-rich animal science1

Statistical graphics, and data visualization, play an essential but under-utilized, role for data analysis in animal science, and also to visually illustrate the concepts, ideas, or outputs of research and in curricula. The recent rise in web technologies and ubiquitous availability of web browsers enables easier sharing of interactive and dynamic graphics. Interactivity and dynamic feedback enhance human-computer interaction and data exploration. Web applications such as decision support systems coupled with multimedia tools synergize with interactive and dynamic graphics. However, the importance of graphics for effectively communicating data, understanding data uncertainty, and the state of the field of interactive and dynamic graphics is underappreciated in animal science. To address this gap, we describe the current state of graphical methodology and technology that might be more broadly adopted. This includes an explanation of a conceptual framework for effective graphics construction. The ideas and technology are illustrated using publicly available animal datasets. We foresee that many new types of big and complex data being generated in precision livestock farming create exciting opportunities for applying interactive and dynamic graphics to improve data analysis and make data-supported decisions.

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.

Improve Pasture or Feed Grain? Greenhouse Gas Emissions, Profitability, and Resource Use for Nelore Beef Cattle in Brazil's Cerrado and Amazon Biomes

Simple Summary

Deforested areas in Brazil’s Amazon and Cerrado savannah have historically transitioned to pasture for grazing tropical beef cattle. Brazil’s projected growth in beef exports emphasizes the importance of sustainably intensifying Brazil’s cattle industry on existing agricultural land without increasing deforestation nor accelerating land conversion. We adapted a widely used simulation model for cattle, pasture, and crops to closely match two cooperating beef farms, one in the Cerrado and one in the Amazon. We then simulated the adoption of pasture fertilization, pasture re-seeding, and pasture-based grain supplementation of cattle by a model beef farm. These three sustainable agricultural intensification strategies were compared to extensive cattle grazing, the status quo in Brazil. Beef productivity and economic returns were greater for grain supplementation, followed by pasture fertilization and pasture re-seeding. Grain supplementation had the lowest greenhouse gas emissions, with less energy and nitrogen use compared to extensive grazing, as measured as a “footprint” for every unit of beef body weight produced. Pasture re-seeding and fertilization had lower greenhouse gas footprints compared to extensive; however, water and energy use and nitrogen losses were greater. Grain supplementation used more human edible livestock feed than other strategies, so pasture intensification could increase future human food availability.

Abstract

Economic development, international food and feed demand, and government policies have converted Brazil’s natural ecosystems into agricultural land. The Integrated Farm System Model (IFSM) was evaluated using production, economic, and weather data collected on two cooperating farms in the Legal Amazon and Cerrado biomes in the Midwest state of Mato Grosso, Brazil. Three sustainable agricultural intensification strategies, namely grain supplementation, pasture re-seeding, and pasture fertilization were simulated in IFSM with double the beef cattle stocking density compared to extensive grazing. Livestock dry matter consumption simulated in IFSM was similar for pasture grazing estimates and actual feed consumed by beef cattle on the two collaborating farms. Grain supplementation best balanced beef production and profitability with lower carbon footprint compared to extensive grazing, followed by pasture fertilization and pasture re-seeding. However, pasture re-seeding and fertilization had greater use of water and energy and more nitrogen losses. Human edible livestock feed use was greatest for grain supplementation compared to other modeled systems. While grain supplementation appears more favorable economically and environmentally, greater use of human edible livestock feed may compete with future human food needs. Pasture intensification had greater human edible feed conversion efficiency, but its greater natural resource use may be challenging.

ASN-ASAS SYMPOSIUM: FUTURE OF DATA ANALYTICS IN NUTRITION: Mathematical modeling in ruminant nutrition: approaches and paradigms, extant models, and thoughts for upcoming predictive analytics1,2

This paper outlines typical terminology for modeling and highlights key historical and forthcoming aspects of mathematical modeling. Mathematical models (MM) are mental conceptualizations, enclosed in a virtual domain, whose purpose is to translate real-life situations into mathematical formulations to describe existing patterns or forecast future behaviors in real-life situations. The appropriateness of the virtual representation of real-life situations through MM depends on the modeler's ability to synthesize essential concepts and associate their interrelationships with measured data. The development of MM paralleled the evolution of digital computing. The scientific community has only slightly accepted and used MM, in part because scientists are trained in experimental research and not systems thinking. The scientific advancements in ruminant production have been tangible but incipient because we are still learning how to connect experimental research data and concepts through MM, a process that is still obscure to many scientists. Our inability to ask the right questions and to define the boundaries of our problem when developing models might have limited the breadth and depth of MM in agriculture. Artificial intelligence (AI) has been developed in tandem with the need to analyze big data using high-performance computing. However, the emergence of AI, a computational technology that is data-intensive and requires less systems thinking of how things are interrelated, may further reduce the interest in mechanistic, conceptual MM. Artificial intelligence might provide, however, a paradigm shift in MM, including nutrition modeling, by creating novel opportunities to understand the underlying mechanisms when integrating large amounts of quantifiable data. Associating AI with mechanistic models may eventually lead to the development of hybrid mechanistic machine-learning modeling. Modelers must learn how to integrate powerful data-driven tools and knowledge-driven approaches into functional models that are sustainable and resilient. The successful future of MM might rely on the development of redesigned models that can integrate existing technological advancements in data analytics to take advantage of accumulated scientific knowledge. However, the next evolution may require the creation of novel technologies for data gathering and analyses and the rethinking of innovative MM concepts rather than spending resources in collecting futile data or amending old technologies.

Linkage between predictive transmitting ability of a genetic index, potential milk production, and a dynamic model

With the increased use of information and communication technology-based tools and devices across traditional desktop computers and smartphones, models and decision-support systems are becoming more accessible for farmers to improve the decision-making process at the farm level. However, despite the focus of research and industry providers to develop tools that are easy to adopt by the end user, milk-production prediction models require substantial parameterization information for accurate milk production simulations. For these models to be useful at an individual animal level, they require the potential milk yield of the individual animals (and possibly potential fat and protein yields) to be captured and parameterized within the model to allow accurate simulations of the interaction of the animal with the system. The focus of this study was to link 3 predicted transmitting ability (PTA) traits from the Economic Breeding Index (PTA for milk yield, fat, and protein) with potential index parameters for milk, fat, and protein required as inputs to a herd-based dynamic milk model. We compiled a data set of 1,904 lactations that included different experiments conducted at 2 closed sites during a 14-yr period (2003-2016). The treatments implied different stocking rates, concentrate supplementation levels, calving dates, and genetic potential. The first step, using 75% of the data randomly selected, was to link the milk, fat, and protein yields achieved within each lactation to their respective PTA value, stocking rate, parity, and concentrate supplementation level. The equations generated were transformed to correspond to inputs to the pasture-based herd dynamic milk model. The equations created were used in conjunction with the model to predict milk, fat, and protein production. Then, using the remaining 25% data of the data set, the simulations were compared against the actual milk produced during the experiments. When the model was tested, it was capable of predicting the lactation milk, fat, and protein yield with a relative prediction error of <10% at the herd level and <13% at the individual animal level.

Measures of nitrogen use efficiency and nitrogen loss from dairy production systems

In dairy production systems, tradeoffs can occur between fertilizer N applications and crop N use, feed N consumption and manure N excretion, and environmental impacts. This paper examines (i) how stocking rates affect N imports and management on dairy farms, N use efficiency (NUE; i.e., the amount of applied N incorporated into product N), and N loss; (ii) how reductions in fertilizer N and feed N may affect crop and milk production, NUE, and N loss; and (iii) why tradeoffs in N use outcomes should be considered when attempting to enhance overall NUE and reduce N loss. The Integrated Farm Simulation Model simulations of two representative dairy farm types and analyses of regional studies, long-term field experiments, and cow nutrition trials were used to demonstrate that (i) stocking rate affects cropping patterns, fertilizer and feed imports, and N loss; (ii) although fertilizer N reductions of 20 kg N ha may reduce slightly the crude protein (CP) content of corn silage (which would require purchase of additional CP supplements), this practice should not affect long-term corn yield but would reduce nitrate (NO) and nitrous oxide (NO) losses by 13 to 38%; (iii) dietary CP could be reduced on many dairy farms, which would not affect milk production but would reduce ammonia (NH) and NO emissions by 15 to 43%; and (iv) greater recognition of the tradeoffs in N use and N loss are needed to provide a better understanding of the potentials to enhance overall NUE and reduce environmental N loss from dairy production systems.

Ammonia emission model for whole farm evaluation of dairy production systems

Ammonia (NH) emissions vary considerably among farms as influenced by climate and management. Because emission measurement is difficult and expensive, process-based models provide an alternative for estimating whole farm emissions. A model that simulates the processes of NH formation, speciation, aqueous-gas partitioning, and mass transfer was developed and incorporated in a whole farm simulation model (the Integrated Farm System Model). Farm sources included manure on the floor of the housing facility, manure in storage (if used), field-applied manure, and deposits on pasture (if grazing is used). In a comprehensive evaluation of the model, simulated daily, seasonal, and annual emissions compared well with data measured over 2 yr for five free stall barns and two manure storages on dairy farms in the eastern United States. In a further comparison with published data, simulated and measured barn emissions were similar over differing barn designs, protein feeding levels, and seasons of the year. Simulated emissions from manure storage were also highly correlated with published emission data across locations, seasons, and different storage covers. For field applied manure, the range in simulated annual emissions normally bounded reported mean values for different manure dry matter contents and application methods. Emissions from pastures measured in northern Europe across seasons and fertilization levels were also represented well by the model. After this evaluation, simulations of a representative dairy farm in Pennsylvania illustrated the effects of animal housing and manure management on whole farm emissions and their interactions with greenhouse gas emissions, nitrate leaching, production costs, and farm profitability.

A teleonomic model describing performance (body, milk and intake) during growth and over repeated reproductive cycles throughout the lifespan of dairy cattle. 1. Trajectories of life function priorities and genetic scaling

The prediction of the control of nutrient partitioning, particularly energy, is a major issue in modelling dairy cattle performance. The proportions of energy channelled to physiological functions (growth, maintenance, gestation and lactation) change as the animal ages and reproduces, and according to its genotype and nutritional environment. This is the first of two papers describing a teleonomic model of individual performance during growth and over repeated reproductive cycles throughout the lifespan of dairy cattle. The conceptual framework is based on the coupling of a regulating sub-model providing teleonomic drives to govern the work of an operating sub-model scaled with genetic parameters. The regulating sub-model describes the dynamic partitioning of a mammal female's priority between life functions targeted to growth (G), ageing (A), balance of body reserves (R) and nutrient supply of the unborn (U), newborn (N) and suckling (S) calf. The so-called GARUNS dynamic pattern defines a trajectory of relative priorities, goal directed towards the survival of the individual for the continuation of the specie. The operating sub-model describes changes in body weight (BW) and composition, foetal growth, milk yield and composition and food intake in dairy cows throughout their lifespan, that is, during growth, over successive reproductive cycles and through ageing. This dynamic pattern of performance defines a reference trajectory of a cow under normal husbandry conditions and feed regimen. Genetic parameters are incorporated in the model to scale individual performance and simulate differences within and between breeds. The model was calibrated for dairy cows with literature data. The model was evaluated by comparison with simulations of previously published empirical equations of BW, body condition score, milk yield and composition and feed intake. This evaluation showed that the model adequately simulates these production variables throughout the lifespan, and across a range of dairy cattle genotypes.

e-Cow: an animal model that predicts herbage intake, milk yield and live weight change in dairy cows grazing temperate pastures, with and without supplementary feeding

This animal simulation model, named e-Cow, represents a single dairy cow at grazing. The model integrates algorithms from three previously published models: a model that predicts herbage dry matter (DM) intake by grazing dairy cows, a mammary gland model that predicts potential milk yield and a body lipid model that predicts genetically driven live weight (LW) and body condition score (BCS). Both nutritional and genetic drives are accounted for in the prediction of energy intake and its partitioning. The main inputs are herbage allowance (HA; kg DM offered/cow per day), metabolisable energy and NDF concentrations in herbage and supplements, supplements offered (kg DM/cow per day), type of pasture (ryegrass or lucerne), days in milk, days pregnant, lactation number, BCS and LW at calving, breed or strain of cow and genetic merit, that is, potential yields of milk, fat and protein. Separate equations are used to predict herbage intake, depending on the cutting heights at which HA is expressed. The e-Cow model is written in Visual Basic programming language within Microsoft Excel®. The model predicts whole-lactation performance of dairy cows on a daily basis, and the main outputs are the daily and annual DM intake, milk yield and changes in BCS and LW. In the e-Cow model, neither herbage DM intake nor milk yield or LW change are needed as inputs; instead, they are predicted by the e-Cow model. The e-Cow model was validated against experimental data for Holstein-Friesian cows with both North American (NA) and New Zealand (NZ) genetics grazing ryegrass-based pastures, with or without supplementary feeding and for three complete lactations, divided into weekly periods. The model was able to predict animal performance with satisfactory accuracy, with concordance correlation coefficients of 0.81, 0.76 and 0.62 for herbage DM intake, milk yield and LW change, respectively. Simulations performed with the model showed that it is sensitive to genotype by feeding environment interactions. The e-Cow model tended to overestimate the milk yield of NA genotype cows at low milk yields, while it underestimated the milk yield of NZ genotype cows at high milk yields. The approach used to define the potential milk yield of the cow and equations used to predict herbage DM intake make the model applicable for predictions in countries with temperate pastures.

NitroScape: a model to integrate nitrogen transfers and transformations in rural landscapes

Modelling nitrogen transfer and transformation at the landscape scale is relevant to estimate the mobility of the reactive forms of nitrogen (N(r)) and the associated threats to the environment. Here we describe the development of a spatially and temporally explicit model to integrate N(r) transfer and transformation at the landscape scale. The model couples four existing models, to simulate atmospheric, farm, agro-ecosystem and hydrological N(r) fluxes and transformations within a landscape. Simulations were carried out on a theoretical landscape consisting of pig-crop farms interspersed with unmanaged ecosystems. Simulation results illustrated the effect of spatial interactions between landscape elements on N(r) fluxes and losses to the environment. More than 10% of the total N(2)O emissions were due to indirect emissions. The nitrogen budgets and transformations of the unmanaged ecosystems varied considerably, depending on their location within the landscape. The model represents a new tool for assessing the effect of changes in landscape structure on N(r) fluxes.

SIMS(DAIRY): a modelling framework to identify sustainable dairy farms in the UK. Framework description and test for organic systems and N fertiliser optimisation

Multiple demands are placed on farming systems today. Society, national legislation and market forces seek what could be seen as conflicting outcomes from our agricultural systems, e.g. food quality, affordable prices, a healthy environmental, consideration of animal welfare, biodiversity etc., Many of these demands, or desirable outcomes, are interrelated, so reaching one goal may often compromise another and, importantly, pose a risk to the economic viability of the farm. SIMS(DAIRY), a farm-scale model, was used to explore this complexity for dairy farm systems. SIMS(DAIRY) integrates existing approaches to simulate the effect of interactions between farm management, climate and soil characteristics on losses of nitrogen, phosphorus and carbon. The effects on farm profitability and attributes of biodiversity, milk quality, soil quality and animal welfare are also included. SIMS(DAIRY) can also be used to optimise fertiliser N. In this paper we discuss some limitations and strengths of using SIMS(DAIRY) compared to other modelling approaches and propose some potential improvements. Using the model we evaluated the sustainability of organic dairy systems compared with conventional dairy farms under non-optimised and optimised fertiliser N use. Model outputs showed for example, that organic dairy systems based on grass-clover swards and maize silage resulted in much smaller total GHG emissions per l of milk and slightly smaller losses of NO(3) leaching and NO(x) emissions per l of milk compared with the grassland/maize-based conventional systems. These differences were essentially because the conventional systems rely on indirect energy use for 'fixing' N compared with biological N fixation for the organic systems. SIMS(DAIRY) runs also showed some other potential benefits from the organic systems compared with conventional systems in terms of financial performance and soil quality and biodiversity scores. Optimisation of fertiliser N timings and rates showed a considerable scope to reduce the (GHG emissions per l milk too).

The carbon footprint of dairy production systems through partial life cycle assessment

Greenhouse gas (GHG) emissions and their potential effect on the environment has become an important national and international issue. Dairy production, along with all other types of animal agriculture, is a recognized source of GHG emissions, but little information exists on the net emissions from dairy farms. Component models for predicting all important sources and sinks of CH(4), N(2)O, and CO(2) from primary and secondary sources in dairy production were integrated in a software tool called the Dairy Greenhouse Gas model, or DairyGHG. This tool calculates the carbon footprint of a dairy production system as the net exchange of all GHG in CO(2) equivalent units per unit of energy-corrected milk produced. Primary emission sources include enteric fermentation, manure, cropland used in feed production, and the combustion of fuel in machinery used to produce feed and handle manure. Secondary emissions are those occurring during the production of resources used on the farm, which can include fuel, electricity, machinery, fertilizer, pesticides, plastic, and purchased replacement animals. A long-term C balance is assumed for the production system, which does not account for potential depletion or sequestration of soil carbon. An evaluation of dairy farms of various sizes and production strategies gave carbon footprints of 0.37 to 0.69kg of CO(2) equivalent units/kg of energy-corrected milk, depending upon milk production level and the feeding and manure handling strategies used. In a comparison with previous studies, DairyGHG predicted C footprints similar to those reported when similar assumptions were made for feeding strategy, milk production, allocation method between milk and animal coproducts, and sources of CO(2) and secondary emissions. DairyGHG provides a relatively simple tool for evaluating management effects on net GHG emissions and the overall carbon footprint of dairy production systems.

Effect of nutrient composition of feeds on digestibility of organic matter by cattle: a review

Estimates of nutrient availability, calculated as TDN for 106 different feedstuffs generated from various published equations, were compared with TDN for similar feeds listed in the 1961 text by F. B. Morrison titled Feeds and Feeding. Incomplete analysis of carbohydrate fractions limited accuracy of evaluations. Although published equations may satisfactorily rank feeds in energy value, the absolute values, correlations, and SE of the estimates revealed that most equations were inaccurate. Across all feeds and forages, TDN was related most closely to crude fiber (R(2) = 0.68) within data sets from Morrison's text and from the NRC publications concerning Nutrient Requirements for Dairy and for Beef Cattle in 1989 and 2000, respectively. Within the latter data set, of the total variation, ADF accounted for 65% of the variation in TDN across all feeds, 62% of the variation in TDN for concentrate feeds, but only 41% of the variation in TDN of forages. Within the 2001 publication for dairy cattle from the NRC, ADF content was related most closely to TDN for all feeds, but nonfiber carbohydrate was most closely related to TDN of forages (R(2) = 0.81 and 0.69, respectively). To separate true from apparent digestibility of nutrients, fecal excretions of components (i.e., CP, fat, crude fiber, nitrogen free extract) were regressed against concentrations of these nutrients in feedstuffs and summed to estimate fecal loss. Metabolic fecal loss of OM (MFOM), the difference between true and apparent OM digestibility, was correlated closely with crude fiber content of feedstuffs (R(2) = 0.86) and increased from 7 to 25 g/100 g of diet as dietary crude fiber concentration increased. This may explain why most TDN equations are based on crude fiber or ADF. Whether dietary NDF similarly increases metabolic OM excretion is not certain, but when humans were fed NDF-enriched diets, fecal excretion of nonfiber carbohydrate increased markedly. The impact of crude fiber on nutrient availability of feeds appears to be related more closely to its adverse effect on apparent digestibility of other nutrients than to the amount of energy that fiber itself contributes. Refinements to laboratory methods for measuring fiber digestibility that match apparent in vivo digestibility coefficients for fiber by ruminants is needed, and the origin, composition, and cost of replacing MFOM need additional research.

Organic dairy production systems in Pennsylvania: a case study evaluation

The current market demand and price for organic milk is encouraging dairy producers, particularly those on smaller farms, to consider organic production as a means for improving the economic viability of their operations. Organic production systems vary widely in scale, in practices, and across agroclimatic settings. Within this context, case studies of 4 actual organic dairy farms were used to characterize existing systems in Pennsylvania. Based on data from these farms, a whole-farm simulation model (Integrated Farm System Model) was used to compare 4 production systems representing organic grass, organic crop, conventional crop with grazing, and conventional confinement production. The performance of each of these systems was simulated over each year of 25 yr of central Pennsylvania weather data. Simulation results indicated that farm level accumulation of soil P and K may be a concern on organic farms that use poultry manure as a primary crop nutrient source, and that erosion and runoff loss of P may be of concern on organic farms producing annual crops because more tillage is required for weed control. Whole-farm budgets with prices that reflect recent conditions showed an economic advantage for organic over conventional production. A sensitivity analysis showed that this economic advantage depended on a higher milk price for producers of organic milk and was influenced by the difference in milk production maintained by herds using organic and conventional systems. Factors found to have little effect on the relative profitability of organic over conventional production included the differences between organic and conventional prices for seed, chemicals, forage, and animals and the overall costs or prices assumed for organic certification, machinery, pasture fencing, fuel, and labor. Thus, at the current organic milk price, relative to other prices, the case study organic production systems seem to provide an option for improving the economic viability of dairy operations of the scale considered in Pennsylvania. To motivate transition to organic systems, the economic advantage found requires the persistence of a substantial difference between conventional and organic raw milk prices.

Economic and Phosphorus-Related Effects of Precision Feeding and Forage Management at a Farm Scale

Structural best management practices were implemented throughout the Cannonsville Reservoir Watershed (CRW) in an effort to reduce P losses to the reservoir. Yet long-term water quality control efforts within CRW are hindered by continuous P build-up in the soils resulting from dairy farm P imports exceeding exports. Addressing the P imbalance problems and maintaining economic viability of the farms requires a system-level redesign of farm management. One possible innovative strategy, precision feed management (PFM), reduces soil-P build-up by limiting feed and fertilizer purchases, and increasing high-quality homegrown forage production. This study applied the integrated farm system model (IFSM) to 2 CRW dairy farms to quantify the benefits of a PFM farm planning strategy in controlling P imbalance problems, and maintaining farm profit-ability and reducing off-farm P losses. The IFSM accurately simulated the 2 farms based on farm data supplied by farm planners; these scenarios were used as the baseline conditions. The IFSM simulations of more accurate feeding of P (based on P required in animal diets) integrated with increased productivity of grass-forage and increased proportion of forage in the diet reduced the P imbalance of 1 farm from 5.3 to 0.5 kg/ ha and from 9.6 to 0.0 kg/ha for the second farm. For both farms, soluble P lost to the environment was reduced by 18%. Feed supplement purchases declined by 7.5 kg/cow per year for dietary mineral P, and by 1.04 and 1.29 t/cow per year for protein concentrates through adoption of the PFM system. Moreover, when a land management practice of converting corn to grass was coupled with the precision feeding of P and improved forage management, IFSM predicted reductions of 5.8 and 9.3 kg/ha of converted land sediment-bound P in erosion loss each year. The model predicted slight purchase increases in corn grain to offset reductions in corn silage production and feeding rates, but no appreciable change in the farm P balance due to land conversion. The model-based studies conducted on a farm-by-farm basis complement farm planning efforts in exploring innovative farming systems. Moreover, the results set a benchmark for potential benefits of PFM strategies, economically and environmentally.

Economic and Environmental Feasibility of a Perennial Cow Dairy Farm

More efficient and economical production systems are needed to improve the sustainability of dairy farms. One concept to consider is using perennial cows. Perennial cows are those that maintain a relatively high milk production for >or=2 yr without going through the typical dry period followed by calving. Farm records show that some cows have produced over 20 kg/d after 4 yr of continuous lactation. A farm simulation model was used to evaluate the long-term performance, environmental impact, and economics of a conceptual perennial cow production system on a typical dairy farm in Pennsylvania. Compared with a traditional 100-cow farm with replacement heifers produced on the farm, a perennial herd of 100 cows and purchased replacements provided environmental benefit but sustained a substantial economic loss. However, increasing the perennial herd to 128 cows better utilized the feed produced on the farm. Compared with the traditional 100-cow farm, use of the perennial 128-cow herd reduced supplemental protein and mineral feed purchases by 38%, increased annual milk sales by 21%, reduced nitrogen losses by 17%, maintained a phosphorus balance, and increased annual net return to farm management by 3200 dollars. A traditional 120-cow dairy farm with purchased replacements also used a similar amount of farm-produced feed. Compared with this option, the farm with 128 perennial cows reduced protein and mineral feed purchases by 36%, maintained similar annual milk sales, increased manure production by 7%, reduced N losses by 10%, and increased annual net return by 12,700 dollars. The economic feasibility of the perennial-cow dairy farm was very sensitive to the milk production maintained by the perennial herd and market prices for milk and perennial replacement animals. The analysis was relatively insensitive to the assumed useful life of perennial cows as long as they could be maintained in the herd for at least 3 yr. Thus, a perennial cow production system can improve the economic and environmental sustainability of a traditional dairy farm if a similar level in annual milk production per cow can be maintained.

Automatic milking systems, farm size, and milk production

Automatic milking systems (AMS) offer relief from the demanding routine of milking. Although many AMS are in use in Europe and a few are used in the United States, the potential benefit for American farms is uncertain. A farm-simulation model was used to determine the long-term, whole-farm effect of implementing AMS on farm sizes of 30 to 270 cows. Highest farm net return to management and unpaid factors was when AMS were used at maximal milking capacity. Adding stalls to increase milking frequency and possibly increase production generally did not improve net return. Compared with new traditional milking systems, the greatest potential economic benefit was a single-stall AMS on a farm size of 60 cows at a moderate milk production level (8600 kg/cow). On other farm sizes using single-stall type robotic units, losses in annual net return of 0 dollars to 300 dollars/cow were projected, with the greatest losses on larger farms and at high milk production (10,900 kg/cow). Systems with one robot serving multiple stalls provided a greater net return than single-stall systems, and this net return was competitive with traditional parlors for 50- to 130-cow farm sizes. The potential benefit of AMS was improved by 100 dollars/cow per year if the AMS increased production an additional 5%. A 20% reduction in initial equipment cost or doubling milking labor cost also improved annual net return of an AMS by up to 100 dollars/cow. Annual net return was reduced by 110 dollars/cow, though, if the economic life of the AMS was reduced by 3 yr for a more rapid depreciation than that normally used with traditional milking systems. Thus, under current assumptions, the economic return for an AMS was similar to that of new parlor systems on smaller farms when the milking capacity of the AMS was well matched to herd size and milk production level.

Production and feeding strategies for phosphorus management on dairy farms

Long-term accumulation of soil phosphorus (P) is becoming a concern on some watersheds heavily populated with animal feeding facilities, including dairy farms. Management changes in crop production and feeding may help reduce the accumulation of excess P, but farm profitability must be maintained or improved to assure adoption of such changes. Whole-farm simulation was used to evaluate the long-term effects of changes in feeding, cropping, and other production strategies on P loading and the economics of 100-cow and 800-cow dairy farms in southeastern New York. Simulated farms maintained a long-term P balance if the following occurred: 1) animals were fed to meet recommended minimum amounts of dietary P, 2) the cropping strategy and land base supplied all of the forage needed, 3) all animals were fed a high forage diet, and 4) replacement heifers were produced on the farm to utilize more forage. The most easily implemented change was to reduce the supplemental mineral P fed to that required to meet current NRC recommended amounts, and this provided an annual increase in farm profit of about $22/cow. Intensifying the use of grassland and improving grazing practices increased profit along with a small reduction in excess P. Conversion from dairy production to heifer raising or expansion from 100 cows to a 250-cow "state-of-the-art" confinement facility (with a 70% increase in land area) were also profitable options. These options provided a long-term P balance for the farm as long as the production and use of forage was maximized and minimum dietary P amounts were those recommended by the NRC. Thus, management changes can be made to prevent the long-term accumulation of soil P on dairy farms while improving farm profitability.

Ryegrass or alfalfa silage as the dietary forage for lactating dairy cows

Renewed interest exists in using grass forages to dilute the higher crude protein (CP) and lower digestible fiber present in legumes fed to lactating dairy cows. A 3 x 3 Latin square feeding study with 4-wk periods was conducted with 24 Holstein cows to compare ryegrass silage, either untreated control or macerated (intensively conditioned) before ensiling, with alfalfa silage as the sole dietary forage. Ryegrass silages averaged [dry matter (DM) basis] 18.4% CP, 50% neutral detergent fiber (NDF), and 10% indigestible acid detergent fiber (ADF) (control) and 16.6% CP, 51% NDF, and 12% indigestible ADF (macerated). Alfalfa silage was higher in CP (21.6%) and lower in NDF (44%) but higher in indigestible ADF (26%). A lower proportion of the total N in macerated ryegrass silage was present as nonprotein N than in control ryegrass and alfalfa silages. Diets were formulated to contain 41% DM from either rye-grass silage, or 51% DM from alfalfa silage, plus high moisture corn, and protein concentrates. Diets averaged 17.5% CP and 28 to 29% NDF. The shortfall in CP on ryegrass was made up by feeding 7.6% more soybean meal. Intake and milk yields were similar on control and macerated ryegrass; however, DM intake was 8.3 kg/d greater on the alfalfa diet. Moreover, feeding the alfalfa diet increased BW gain (0.48 kg/d) and yield of milk (6.1 kg/d), FCM (6.8 kg/d), fat (0.26 kg/d), protein (0.25 kg/d), lactose (0.35 kg/d), and SNF (0.65 kg/d) versus the mean of the two ryegrass diets. Both DM efficiency (milk/DM intake) and N efficiency (milk-N/N-intake) were 27% greater, and apparent digestibility was 16% greater for DM and 53% greater for NDF and ADF, on the ryegrass diets. However, apparent digestibility of digestible ADF was greater on alfalfa (96%) than on ryegrass (average = 91%). Also, dietary energy content (estimated as net energy of lactation required for maintenance, milk yield, and weight gain) per unit of digested DM was similar for all three diets. Results of this trial indicated that, relative to ryegrass silage, feeding alfalfa silage stimulated much greater feed intake, which supported greater milk production.

Economic and environmental impact of four levels of concentrate supplementation in grazing dairy herds

Low-cost, pasture-based forage systems are a viable management alternative for small to moderately sized dairy farms in the Northeast United States. A whole farm analysis was conducted to evaluate the potential long-term environmental impact and economic benefit of varying the level of concentrate supplementation on seasonal grazing dairies. A representative dairy farm was simulated with various production strategies over 25 yr of historical Pennsylvania weather using the Dairy Forage System Model. A representative grazing farm (81 ha) was simulated with four levels of daily concentrate supplementation: 1) no supplement, 2) 3 kg of DM/cow in early lactation, 3) 6 kg of DM/cow in early lactation, and 4) 9 kg of DM/cow in early lactation fed daily to the lactating cows to meet annual milk production levels of 5000, 6068, 6968, and 7700 kg/cow, respectively. These farm systems were then compared to an alfalfa- and corn-based confinement system on the same land base where total mixed rations were fed to maintain an annual milk production level of 9000 kg/cow. The five systems were simulated for three scenarios. In the first, total milk sold per farm (625,000 kg) was similar across all systems. In the second, cow numbers were held constant across all systems (100 mature cows), and total milk sold per farm varied. In the third, stocking rate was set so that forage consumed equaled forage production on the farm. Profitability increased as supplementation level increased in the grazing systems, but at a decreasing rate with each successive level of supplementation. At higher levels of supplementation, the grazing dairy farms showed greater profitability than the confinement systems. Economic risk or year-to-year variation also decreased as concentrate supplementation level increased. The grazing systems showed an environmental benefit compared with the confinement systems by decreasing nitrogen leaching losses. Concentrate supplementation of grazing lactating dairy cows provided an increase in profitability and a mixed impact on nutrient balance of the farm.

Whole-herd optimization with the Cornell Net Carbohydrate and Protein System. III. Application of an optimization model to evaluate alternatives to reduce nitrogen and phosphorus mass balance

The objectives of this paper were to use a linear programming model previously described to evaluate different alternatives for reducing excess nutrients that may influence water quality on a case study farm (300 lactating cows on 430 ha of cropland growing alfalfa, grass, and corn). Several alternatives perceived to influence farm nutrient balance were evaluated for their potential to reduce N and P mass balance. Dividing lactating cow diets into three groups according to their level of milk production versus a one-group total mixed ration decreased mass balance (tonne/yr) from 51.7 to 44.7 for N, from 6.7 to 6.1 for P and from 16.2 to 14.8 for K with little influence on return over feed costs. Increasing forage quality (lower neutral detergent fiber and higher crude protein) did not improve N balance because of the increased N fixation from the air to the soil, but it increased returns over feed costs by $31,385. Improving yields to the maximum potential for the farm reduced mass balance by 29, 51, and 100% for N, P, and K, respectively, and increased returns over feed costs by $70,579. Changing the crop hectare proportions to more corn and less alfalfa reduced N and K balances by 19 and 29%, respectively, and increased returns over feed costs $39,383. Increasing annual milk production 10% by increasing milk production per head 10% compared with increasing animal numbers at the current average milk production per cow until total milk increased 10% gave $34,132 more return over feed costs with less N, P, and K retained on the farm.

Performance of lactating dairy cows fed alfalfa or red clover silage as the sole forage

Three Latin square trials, with 20 (two trials) or 24 (one trial) multiparous lactating Holstein cows (four in each trial with ruminal cannulae), compared the feeding value of red clover and alfalfa silages harvested over 3 yr. Overall, the forages contained similar amounts of neutral detergent fiber and acid detergent fiber; however, red clover silage contained more hemicellulose, less ash and crude protein (CP), and only 67% as much nonprotein N, as a proportion of total N, as did alfalfa silage. Diets were formulated with equal dry matter (DM) from alfalfa or red clover silage and contained on average 65% forage, 33 or 30% ground high moisture ear corn, and 0 or 3% low soluble fishmeal (DM basis). Diets fed in the Latin squares contained (mean dietary CP): 1) alfalfa (17.8% CP); 2) red clover (15.1% CP); 3) alfalfa plus fishmeal (19.6% CP); and 4) red clover plus fishmeal (16.9% CP). Although performance varied somewhat among trials, overall statistical analysis showed that replacing alfalfa with red clover reduced yields of milk, fat-corrected milk, fat, protein, lactose, and SNF; these effects were related to the 1.2 kg/d lower DM intake for cows fed red clover. Replacing alfalfa with red clover improved body weight gain and reduced concentrations of milk and blood urea and ruminal NH3. Apparent digestibility of DM, organic matter, neutral detergent fiber, acid detergent fiber, and hemicellulose all were greater when red clover was fed. There were no significant forage x fishmeal interactions for DM intake and yield of milk and milk components, indicating that supplementation with rumen undegradable protein gave similar increases in production on both forages. Net energy of lactation (NE(L)), estimated from maintenance, mean milk yield, and body weight change, in alfalfa and red clover silage were, respectively, 1.25 and 1.38 Mcal NE(L)/kg of DM, indicating 10% greater NE(L) in red clover.

Feeding strategy, nitrogen cycling, and profitability of dairy farms

On a typical dairy farm today, large amounts of N are imported as feed supplements and fertilizer. If this N is not recycled through crop growth, it can lead to large losses to the atmosphere and ground water. More efficient use of protein feed supplements can potentially reduce the import of N in feeds, excretion of N in manure, and losses to the environment. A simulation study with a dairy farm model (DAFOSYM) illustrated that more efficient feeding and use of protein supplements increased farm profit and reduced N loss from the farm. Compared to soybean meal as the sole protein supplement, use of soybean meal along with a less rumen degradable protein feed reduced volatile N loss by 13 to 34 kg/ha of cropland with a small reduction in N leaching loss (about 1 kg/ha). Using the more expensive but less degradable protein supplement along with soybean meal improved net return by $46 to $69/cow per year, dependent on other management strategies of the farm. Environmental and economic benefits from more efficient supplementation of protein were generally greater with more animals per unit of land, higher milk production, more sandy soils, or a daily manure hauling strategy. Relatively less benefit was obtained when either alfalfa or corn silage was the sole forage on the farm or when relatively high amounts of forage were used in animal rations.