Molly reborn in C++ and R.

The dairy cow model 'Molly' is a mixed discrete event-continuous system model that simulates feeding, metabolism and lactation of dairy cows. Decades of model development have resulted in a valuable tool in dairy science. Due to the deprecation of the ACSL (Advanced Continuous Simulation Language) programming language, Molly has been translated into C++. This paper describes the translation process and discusses the advantages of the new implementation, one of which is the ability to run Molly within RStudio, a popular integrated development environment (IDE) for data science.

A redefinition of the modeled responses of mammary glands to once-daily milking.

Milking cows once daily is a management tool that has been implemented to improve physical and financial results of seasonal pasture-based dairy farms. The Molly cow model integrates physiology and metabolism of dairy cattle; however, milk production during short-term changes in milking frequency (e.g., 1× milking) is not well represented. The model includes a representation of variable rates of cell quiescence and death. However, the rate constants governing cell death and the return of quiescent to active cells are not affected by milking frequency. An empirical assessment of the problem was conducted, and it was hypothesized that changing the current representation of the rate of cell death in response to short-term 1× milking would more accurately represent active and quiescent cells and improve predictions of milk production. An extra senescent cell flux was added to account for cell loss during periods of 1× milking. Additional changes included a gradual decline in the rate of 1× stimulated senescence during 1× milking, and a structural change in cell cycling between active and quiescent cells during and after short-term 1× milking. Data used for parameter estimation were obtained from 5 studies where 1× milking or different feeding strategies were tested. Parameter estimates of cell loss indicated that 1× milking would affect a small proportion of quiescent cells to cause extra cell death. This added cell senescence was influenced by the length of 1× milking such that cell senescence peaked on d 1 of 1× milking and decayed from that point. The new structure in the model includes a variable rate of cell death in response to 1× milking and a gradual rate of return of quiescent cells back to the active pool in response to switching to 2× milking after short-term 1× milking. Root mean square errors, mean bias, and slope bias declined by at least 50% for predictions of energy-corrected milk yield and fat percent. The model showed quantitative agreement with production data from short-term 1× milking. The accuracy of predictions was improved and the error was reduced by implementing modifications in the model in response to changes in milking frequency.

The performance of an efficient dairy system using a combination of nitrogen leaching mitigation strategies in a variable climate.

An efficient dairy system, that implemented a combination of nitrogen (N) leaching mitigation strategies including lower N fertilizer input, standing cows off pasture for part of the day in autumn and winter (stand-off), and importing limited amounts of low protein supplements was evaluated over four consecutive years of a farmlet study. This efficient system consistently demonstrated a lower measured annual N leaching of 40 to 50% compared with a baseline system representing current practice with no mitigations. To maximize return from this system fewer cows but of higher genetic merit were used resulting in an average decrease in milk production of 2% and operating profit by 5% compared with the baseline system. The magnitude of the N leaching reduction from mitigation strategies was predicted in pre-trial modelling. Using similar mechanistic models in a post-trial study, we were able to satisfactorily predict the trends in the observed N leaching data over the four years. This enabled us to use the calibrated models to explore the contributions of the different mitigation strategies to the overall leaching reduction in the efficient system. In one of the years half of the leaching reduction was achieved by the 'input' component of the strategy (less feed N flowing through the herd from lower fertilizer use, less grass grown, and low-protein supplement use), while the other half was achieved by the stand-off strategy. However, these contributions are determined by the weather of a particular year. We estimate that on average stand-off would contribute 60% and 'input' 40% to the reduction. The implication is that farmers facing nutrient loss limitations have some current and some future technologies available to them for meeting these limitations. A shift towards the mitigations described here can result in a downward trend in their own N-loss metrics. The challenge will be to negate any reductions in production and profit, and remain competitive.

Incorporating a prediction of postgrazing herbage mass into a whole-farm model for pasture-based dairy systems.

The DairyNZ whole-farm model (WFM; DairyNZ, Hamilton, New Zealand) consists of a framework that links component models for animal, pastures, crops, and soils. The model was developed to assist with analysis and design of pasture-based farm systems. New (this work) and revised (e.g., cow, pasture, crops) component models can be added to the WFM, keeping the model flexible and up to date. Nevertheless, the WFM does not account for plant-animal relationships determining herbage-depletion dynamics. The user has to preset the maximum allowable level of herbage depletion [i.e., postgrazing herbage mass (residuals)] throughout the year. Because residuals have a direct effect on herbage regrowth, the WFM in its current form does not dynamically simulate the effect of grazing pressure on herbage depletion and consequent effect on herbage regrowth. The management of grazing pressure is a key component of pasture-based dairy systems. Thus, the main objective of the present work was to develop a new version of the WFM able to predict residuals, and thereby simulate related effects of grazing pressure dynamically at the farm scale. This objective was accomplished by incorporating a new component model into the WFM. This model represents plant-animal relationships, for example sward structure and herbage intake rate, and resulting level of herbage depletion. The sensitivity of the new version of the WFM was evaluated and then the new WFM was tested against an experimental data set previously used to evaluate the WFM and to illustrate the adequacy and improvement of the model development. Key outputs variables of the new version pertinent to this work (milk production, herbage dry matter intake, intake rate, harvesting efficiency, and residuals) responded acceptably to a range of input variables. The relative prediction errors for monthly and mean annual residual predictions were 20 and 5%, respectively. Monthly predictions of residuals had a line bias (1.5%), with a proportion of square root of mean square prediction error (RMSPE) due to random error of 97.5%. Predicted monthly herbage growth rates had a line bias of 2%, a proportion of RMSPE due to random error of 96%, and a concordance correlation coefficient of 0.87. Annual herbage production was predicted with an RMSPE of 531 (kg of herbage dry matter/ha per year), a line bias of 11%, a proportion of RMSPE due to random error of 80%, and relative prediction errors of 2%. Annual herbage dry matter intake per cow and hectare, both per year, were predicted with RMSPE, relative prediction error, and concordance correlation coefficient of 169 and 692kg of dry matter, 3 and 4%, and 0.91 and 0.87, respectively. These results indicate that predictions of the new WFM are relatively accurate and precise, with a conclusion that incorporating a plant-animal relationship model into the WFM allows for dynamic predictions of residuals and more realistic simulations of the effect of grazing pressure on herbage production and intake at the farm level without the intervention from the user.

Comparison of updates to the Molly cow model to predict methane production from dairy cows fed pasture.

Molly is a deterministic, mechanistic, dynamic model representing the digestion, metabolism, and production of a dairy cow. This study compared the predictions of enteric methane production from the original version of Molly (MollyOrigin) and 2 new versions of Molly. Updated versions included new ruminal fiber digestive parameters and animal hormonal parameters (Molly84) and a revised version of digestive and ruminal parameters (Molly85), using 3 different ruminal volatile fatty acid (VFA) stoichiometry constructs to describe the VFA pattern and methane (CH4) production (g of CH4/d). The VFA stoichiometry constructs were the original forage and mixed-diet VFA constructs and a new VFA stoichiometry based on a more recent and larger set of data that includes lactate and valerate production, amylolytic and cellulolytic bacteria, as well as protozoal pools. The models' outputs were challenged using data from 16 dairy cattle 26 mo old [standard error of the mean (SEM)=1.7], 82 (SEM=8.7) d in milk, producing 17 (SEM=0.2) kg of milk/d, and fed fresh-cut ryegrass [dry matter intake=12.3 (SEM=0.3) kg of DM/d] in respiration chambers. Mean observed CH4 production was 266±5.6 SEM (g/d). Mean predicted values for CH4 production were 287 and 258 g/d for MollyOrigin without and with the new VFA construct. Model Molly84 predicted 295 and 288 g of CH4/d with and without the new VFA settings. Model Molly85 predicted the same CH4 production (276 g/d) with or without the new VFA construct. The incorporation of the new VFA construct did not consistently reduce the low prediction error across the versions of Molly evaluated in the present study. The improvements in the Molly versions from MollyOrigin to Molly84 to Molly85 resulted in a decrease in mean square prediction error from 8.6 to 8.3 to 4.3% using the forage diet setting. The majority of the mean square prediction error was apportioned to random bias (e.g., 43, 65, and 70% in MollyOrigin, Molly84, and Molly85, respectively, on the forage setting, showing that with the updated versions a greater proportion of error was random). The slope bias was less than 2% in all cases. We concluded that, of the versions of Molly used for pastoral systems, Molly85 has the capability to predict CH4 production from grass-fed dairy cows with the highest accuracy.

The relationship between milk production and farm-gate nitrogen surplus for the Waikato region, New Zealand.

As the scope and scale of New Zealand (NZ) dairy farming increases, farmers and the industry are being challenged by Government and the New Zealand public to address growing environmental concerns. Dairying has come under increasing scrutiny from local authorities tasked with sustainable resource management. Despite recent efforts of farmers and industry to improve resource use efficiency, there is increasing likelihood of further regulatory constraints on water use and nutrient management. This study uses available data on farm-gate nitrogen (N) surpluses and milk production from the Waikato, New Zealand's largest dairying region, together with a farm scale modeling exercise, to provide a perspective on the current situation compared to dairy farms in Europe. It also aims to provide relevant guidelines for N surpluses and efficiencies under NZ conditions. Waikato dairy farms compare favorably with farms in Europe in terms of N use efficiency expressed as L milk/kg farm-gate N surplus. Achievable and realistic good practice objectives for Waikato dairy farmers could be 15,000 L milk/ha (1200 kg milk fat plus protein/ha) with a farm-gate N surplus of 100 kg/ha giving an eco-efficiency (L milk/kg N surplus) of 150, and long-term average nitrate leaching losses of approximately 25-30 kg/ha/yr. This can be achieved by increasing the N conversion efficiency through lower replacement rates (16 versus 22%), lower stocked (< 3 cows/ha) high genetic merit cows (30 L milk/day at peak) milked for longer (277 versus 240 days), feeding effluent-irrigated, home-grown, low-protein supplements to cows on high-protein, grass-clover pastures to dilute N concentration in the diet, removing some of the urinary N from the paddocks during critical times by standing cows on a loafing pad for part of the day, and through lower N fertilizer rates (50-70 kg/ha/yr compared to the norm of 170-200 kg/ha/yr) and using a nitrification inhibitor and gibberellins to boost pasture growth and the former to reduce N leaching.

Technical note: a simple model to estimate changes in dietary composition of strip-grazed cattle during progressive pasture defoliations.

Methodological problems occur in measuring herbage intake and diet quality during short-term (4-24h) progressive defoliations by grazing. Several models were developed to describe pasture component selection by grazing ruminants, particularly sheep. These models contain empirical coefficients to determine preferences that require laborious and data-demanding calibration. The objective was to develop a simple and practical model of changes in diet composition (green:dead) of pastures strip-grazed by dairy cows. The model was based on 3 premises when cows are strip-grazed in relatively homogeneous swards: 1) cows eat dead material only when green leaf and uncontaminated material have been removed; 2) dead material increases toward the bottom of the sward canopy; and 3) cows progressively defoliate pasture in layers. The main simplification in this model was assuming a linear decrease of green mass from the top to the bottom of the sward canopy. Thus, the proportion of green mass in the stratum eaten depended on the proportion of green in the entire sward canopy and its vertical profile. The model offers a simple solution to estimate changes in dietary compositions in pastures strip-grazed by dairy cattle during progressive pasture defoliations. It uses 2 inputs, the green mass proportion of the total herbage mass and the proportion of total herbage mass eaten during grazing. This can be optionally complemented with inputs of herbage chemical composition. The main outputs of the model are the proportions of green and dead herbage mass in the diet. For example, if the green proportion in the sward was 0.5 and the proportion of herbage mass eaten was 0.5, then the diet would be 0.75 green:0.25 dead; assuming 0.8 and 0.4 digestibility for green and dead material, respectively, the diet digestibility would be 0.7.

Using a whole farm model to determine the impacts of mating management on the profitability of pasture-based dairy farms.

An approach to assessing likely impacts of altering reproductive performance on productivity and profitability in pasture-based dairy farms is described. The basis is the development of a whole farm model (WFM) that simulates the entire farm system and holistically links multiple physical performance factors to profitability. The WFM consists of a framework that links a mechanistic cow model, a pasture model, a crop model, management policies and climate. It simulates individual cows and paddocks, and runs on a day time-step. The WFM was upgraded to include reproductive modeling capability using reference tables and empirical equations describing published relationships between cow factors, physiology and mating management. It predicts reproductive status at any time point for individual cows within a modeled herd. The performance of six commercial pasture-based dairy farms was simulated for the period of 12 months beginning 1 June 2005 (05/06 year) to evaluate the accuracy of the model by comparison with actual outcomes. The model predicted most key performance indicators within an acceptable range of error (residual<10% of observed). The evaluated WFM was then used for the six farms to estimate the profitability of changes in farm "set-up" (farm conditions at the start of the farming year on 1 June) and mating management from 05/06 to 06/07 year. Among the six farms simulated, the 4-week calving rate emerged as an important set-up factor influencing profitability, while reproductive performance during natural bull mating was identified as an area with the greatest opportunity for improvement. The WFM presents utility to explore alternative management strategies to predict likely outcomes to proposed changes to a pasture-based farm system.

Evaluation of a whole-farm model for pasture-based dairy systems.

In the temperate climate of New Zealand, animals can be grazed outdoors all year round. The pasture is supplemented with conserved feed, with the amount being determined by seasonal pasture growth, genetics of the herd, and stocking rate. The large number of factors that affect production makes it impractical and expensive to use field trials to explore all the farm system options. A model of an in situ-grazed pasture system has been developed to provide a tool for developing and testing novel farm systems; for example, different levels of bought-in supplements and different levels of nitrogen fertilizer application, to maintain sustainability or environmental integrity and profitability. It consists of a software framework that links climate information, on a daily basis, with dynamic, mechanistic component-models for pasture growth and animal metabolism, as well as management policies. A unique feature is that the component models were developed and published by other groups, and are retained in their original software language. The aim of this study was to compare the model, called the whole-farm model (WFM) with a farm trial that was conducted over 3 yr and in which data were collected specifically for evaluating the WFM. Data were used from the first year to develop the WFM and data from the second and third year to evaluate the model. The model predicted annual pasture production, end-of-season cow liveweight, cow body condition score, and pasture cover across season with relative prediction error <20%. Milk yield and milksolids (fat + protein) were overpredicted by approximately 30% even though both annual and monthly pasture and supplement intake were predicted with acceptable accuracy, suggesting that the metabolic conversion of feed to fat, protein, and lactose in the mammary gland needs to be refined. Because feed growth and intake predictions were acceptable, economic predictions can be made using the WFM, with an adjustment for milk yield, to test different management policies, alterations in climate, or the use of genetically improved animals, pastures, or crops.