Modeling the economic impacts of mobility scores in dairy cows under Irish spring pasture-based management.

Moderate to severe forms of suboptimal mobility on dairy cows are associated with yield losses, whereas mild forms of suboptimal mobility are associated with elevated somatic cell count and an increased risk to be culled. Although the economic consequences of severe forms of suboptimal mobility (also referred as clinical lameness) have been studied extensively, the mild forms are generally ignored. Therefore, the aim of the current study was to determine the economic consequences associated with varying prevalence and forms of suboptimal mobility within spring calving, pasture-based dairy herds. A new submodel predicting mobility scores was developed and integrated within an existing pastured-based herd dynamic model. Using a daily timestep, this model simulates claw disorders, and the consequent mobility score of individual cows. The impact of a cow having varying forms of suboptimal mobility on production and reproduction was simulated. The economic impact was simulated including treatment costs, as well as the production and reproductive impacts of varying levels of suboptimal mobility. Furthermore, different genetic predispositions for mobility issues and their interaction with herd-level management associated with each level of suboptimal mobility were simulated. Overall, 13 scenarios were simulated, representing a typical spring calving, pasture-based dairy herd with 100 cows. The first scenario represents a perfect herd wherein 100% of the cows had mobility score 0 (optimal mobility) throughout the lactation. The remaining 12 scenarios represent a combination of (1) 3 different herd-management levels, and (2) 4 different levels of a genetic predisposition for suboptimal mobility. The analysis showed that a 17% decrease in farm net profit was achieved in the worst outcome (wherein just 5% of the herd had optimal mobility) compared with the perfect herd. This was due to reduced milk yield, increased culling, and increased treatment costs for mobility issues compared the ideal scenario.

Associating mobility scores with production and reproductive performance in pasture-based dairy cows.

Lameness in dairy cows can have significant effects on cow welfare, farm profitability, and the environment. To determine the economic and environmental consequences of lameness, we first need to quantify its effect on performance. The objective of this study, therefore, was to determine the associations of various production and reproductive performance measurements (including milk, fat, and protein yield, somatic cell count, calving interval, cow death, or cow slaughter), and mobility scores in spring-calving, pasture-based dairy cows. We collected mobility scores (0 = good, 1 = imperfect, 2 = impaired, and 3 = severely impaired mobility), body condition scores, and production data for 11,116 cows from 68 pasture-based dairy herds. Linear mixed modeling was used to determine the associations between specific mobility scores and milk, fat and protein yield, and somatic cell count and calving interval. Binomial logistic regression was used to determine the association between mobility score and cow death, or slaughter. Significant yield losses of up to 1.4% of the average yield were associated with mobility score 2 and yield losses of up to 4.7% were associated with mobility score 3 during the early scoring period. Elevated somatic cell count was associated with all levels of suboptimal mobility during the late scoring period. Cows with a mobility score of 2 during the early scoring period were associated with longer calving interval length, whereas only cows with a mobility score of 3 during the late scoring period were associated with longer calving interval length. Cows with a mobility score ≥1 were more likely to be culled during both scoring periods. Our study, therefore, shows an association between specific mobility scores and production and reproductive performance in spring-calving, pasture-based dairy cows scored during the summer grazing period.

LiGAPS-Beef, a mechanistic model to explore potential and feed-limited beef production 2: sensitivity analysis and evaluation of sub-models.

The model LiGAPS-Beef (Livestock simulator for Generic analysis of Animal Production Systems - Beef cattle) has been developed to assess potential and feed-limited growth and production of beef cattle in different areas of the world and to identify the processes responsible for the yield gap. Sensitivity analysis and evaluation of model results with experimental data are important steps after model development. The first aim of this paper, therefore, is to identify which parameters affect the output of LiGAPS-Beef most by conducting sensitivity analyses. The second aim is to evaluate the accuracy of the thermoregulation sub-model and the feed intake and digestion sub-model with experimental data. Sensitivity analysis was conducted using a one-at-a-time approach. The upper critical temperature (UCT) simulated with the thermoregulation sub-model was most affected by the body core temperature and parameters affecting latent heat release from the skin. The lower critical temperature (LCT) and UCT were considerably affected by weather variables, especially ambient temperature and wind speed. Sensitivity analysis for the feed intake and digestion sub-model showed that the digested protein per kg feed intake was affected to a larger extent than the metabolisable energy (ME) content. Sensitivity analysis for LiGAPS-Beef was conducted for ¾ Brahman×» Shorthorn cattle in Australia and Hereford cattle in Uruguay. Body core temperature, conversion of digestible energy to ME, net energy requirements for maintenance, and several parameters associated with heat release affected feed efficiency at the herd level most. Sensitivity analyses have contributed, therefore, to insight which parameters are to be investigated in more detail when applying LiGAPS-Beef. Model evaluation was conducted by comparing model simulations with independent data from experiments. Measured heat production in experiments corresponded fairly well to the heat production simulated with the thermoregulation sub-model. Measured ME contents from two data sets corresponded well to the ME contents simulated with the feed intake and digestion sub-model. The relative mean absolute errors were 9.3% and 6.4% of the measured ME contents for the two data sets. In conclusion, model evaluation indicates the thermoregulation sub-model can deal with a wide range of weather conditions, and the feed intake and digestion sub-model with a variety of feeds, which corresponds to the aim of LiGAPS-Beef to simulate cattle in different beef production systems across the world.

LiGAPS-Beef, a mechanistic model to explore potential and feed-limited beef production 1: model description and illustration.

The expected increase in the global demand for livestock products calls for insight in the scope to increase actual production levels across the world. This insight can be obtained by using theoretical concepts of production ecology. These concepts distinguish three production levels for livestock: potential (i.e. theoretical maximum) production, which is defined by genotype and climate only; feed-limited production, which is limited by feed quantity and quality; and actual production. The difference between the potential or limited production and the actual production is the yield gap. The objective of this paper, the first in a series of three, is to present a mechanistic, dynamic model simulating potential and feed-limited production for beef cattle, which can be used to assess yield gaps. A novelty of this model, named LiGAPS-Beef (Livestock simulator for Generic analysis of Animal Production Systems - Beef cattle), is the identification of the defining factors (genotype and climate) and limiting factors (feed quality and available feed quantity) for cattle growth by integrating sub-models on thermoregulation, feed intake and digestion, and energy and protein utilisation. Growth of beef cattle is simulated at the animal and herd level. The model is designed to be applicable to different beef production systems across the world. Main model inputs are breed-specific parameters, daily weather data, information about housing, and data on feed quality and quantity. Main model outputs are live weight gain, feed intake and feed efficiency (FE) at the animal and herd level. Here, the model is presented, and its use is illustrated for Charolais and Brahman × Shorthorn cattle in France and Australia. Potential and feed-limited production were assessed successfully, and we show that FE of herds is highest for breeds most adapted to the local climate conditions. LiGAPS-Beef also identified the factors that define and limit growth and production of cattle. Hence, we argue the model has scope to be used as a tool for the assessment and analysis of yield gaps in beef production systems.

LiGAPS-Beef, a mechanistic model to explore potential and feed-limited beef production 3: model evaluation.

LiGAPS-Beef (Livestock simulator for Generic analysis of Animal Production Systems - Beef cattle) is a generic, mechanistic model designed to quantify potential and feed-limited growth, which provides insight in the biophysical scope to increase beef production (i.e. yield gap). Furthermore, it enables identification of the bio-physical factors that define and limit growth, which provides insight in management strategies to mitigate yield gaps. The aim of this paper, third in a series of three, is to evaluate the performance of LiGAPS-Beef with independent experimental data. After model calibration, independent data were used from six experiments in Australia, one in Uruguay and one in the Netherlands. Experiments represented three cattle breeds, and a wide range of climates, feeding strategies and cattle growth rates. The mean difference between simulated and measured average daily gains (ADGs) was 137 g/day across all experiments, which equals 20.1% of the measured ADGs. The root mean square error was 170 g/day, which equals 25.0% of the measured ADGs. LiGAPS-Beef successfully simulated the factors that defined and limited growth during the experiments on a daily basis (genotype, heat stress, digestion capacity, energy deficiency and protein deficiency). The simulated factors complied well to the reported occurrence of heat stress, energy deficiency and protein deficiency at specific periods during the experiments. We conclude that the level of accuracy of LiGAPS-Beef is acceptable, and provides a good basis for acquiring insight in the potential and feed-limited production of cattle in different beef production systems across the world. Furthermore, its capacity to identify factors that define or limit growth and production provides scope to use the model for yield gap analysis.

Production, partial cash flows and greenhouse gas emissions of simulated dairy herds with extended lactations.

The transition period is the most critical period in the lactation cycle of dairy cows. Extended lactations reduce the frequency of transition periods, the number of calves and the related labour for farmers. This study aimed to assess the impact of 2 and 4 months extended lactations on milk yield and net partial cash flow (NPCF) at herd level, and on greenhouse gas (GHG) emissions per unit of fat- and protein-corrected milk (FPCM), using a stochastic simulation model. The model simulated individual lactations for 100 herds of 100 cows with a baseline lactation length (BL), and for 100 herds with lactations extended by 2 or 4 months for all cows (All+2 and All+4), or for heifers only (H+2 and H+4). Baseline lactation length herds produced 887 t (SD: 13) milk/year. The NPCF, based on revenues for milk, surplus calves and culled cows, and costs for feed, artificial insemination, calving management and rearing of youngstock, was k€174 (SD: 4)/BL herd per year. Extended lactations reduced milk yield of the herd by 4.1% for All+2, 6.9% for All+4, 1.1% for H+2 and 2.2% for H+4, and reduced the NPCF per herd per year by k€7 for All+2, k€12 for All+4, k€2 for H+2 and k€4 for H+4 compared with BL herds. Extended lactations increased GHG emissions in CO2-equivalents per t FPCM by 1.0% for All+2, by 1.7% for All+4, by 0.2% for H+2 and by 0.4% for H+4, but this could be compensated by an increase in lifespan of dairy cows. Subsequently, production level and lactation persistency were increased to assess the importance of these aspects for the impact of extended lactations. The increase in production level and lactation persistency increased milk production of BL herds by 30%. Moreover, reductions in milk yield for All+2 and All+4 compared with BL herds were only 0.7% and 1.1% per year, and milk yield in H+2 and H+4 herds was similar to BL herds. The resulting NPCF was equal to BL for All+2 and All+4 and increased by k€1 for H+2 and H+4 due to lower costs for insemination and calving management. Moreover, GHG emissions per t FPCM were equal to BL herds or reduced (0% to -0.3%) when lactations were extended. We concluded that, depending on lactation persistency, extending lactations of dairy cows can have a positive or negative impact on the NPCF and GHG emissions of milk production.

Influence of distance to urban markets on smallholder dairy farming systems in Kenya.

We studied influence of distance to urban markets on smallholder dairy farming system development. Farms were chosen from three locations that varied in distance to the urban market of Nakuru Town in the Kenyan highlands: urban location (UL, n = 10) at less than 15 km distance, mid-rural location (MRL, n = 11) in between 20 and 50 km west of Nakuru and extreme rural location (ERL, n = 9) beyond 50 km west and south-west of Nakuru. In-depth interviews with farmers and focus group discussions with eight groups of stakeholders were held to collect narratives and data about market quality, production factors, farm performance and functions of dairy cattle. We applied thematic content analysis to qualitative information by clustering narratives according to predefined themes and used ANOVA to analyse farm data. In UL, markets were functional, with predominantly informal market chains, with a high milk price (US $ 45.1/100 kg). Inputs were available in UL markets, but prices were high for inputs such as concentrates, fodder, replacement stock and hired labour. Moreover, availability of grazing land and the high opportunity costs for family labour were limiting dairy activities. In UL, milk production per cow (6.9 kg/cow/day) and per farm (20.1 kg/farm/day) were relatively low, and we concluded that farm development was constrained by scarcity of inputs and production factors. In rural locations (MRL and ERL), markets were functional with relatively low prices (average US $ 32.8/100 kg) for milk in both formal and informal market chains. Here, concentrates were relatively cheap but also of low quality. Fodder, replacement stock and labour were more available in rural locations than in UL. In rural locations, milk production per cow (average 7.2 kg/cow/day) and per farm (average 18.5 kg/farm/day) were low, and we concluded that farm development was constrained by low quality of concentrates and low price of milk. In all locations, production for subsistence was valued since income generated was used for non-dairy expenses. A tailor-made package of interventions that targets the above constraints is recommended for farm development.

Estimating the economic impact of subclinical ketosis in dairy cattle using a dynamic stochastic simulation model.

The objective of this study was to estimate the economic impact of subclinical ketosis (SCK) in dairy cows. This metabolic disorder occurs in the period around calving and is associated with an increased risk of other diseases. Therefore, SCK affects farm productivity and profitability. Estimating the economic impact of SCK may make farmers more aware of this problem, and can improve their decision-making regarding interventions to reduce SCK. We developed a dynamic stochastic simulation model that enables estimating the economic impact of SCK and related diseases (i.e. mastitis, metritis, displaced abomasum, lameness and clinical ketosis) occurring during the first 30 days after calving. This model, which was applied to a typical Dutch dairy herd, groups cows according to their parity (1 to 5+), and simulates the dynamics of SCK and related diseases, and milk production per cow during one lactation. The economic impact of SCK and related diseases resulted from a reduced milk production, discarded milk, treatment costs, costs from a prolonged calving interval and removal (culling or dying) of cows. The total costs of SCK were €130 per case per year, with a range between €39 and €348 (5 to 95 percentiles). The total costs of SCK per case per year, moreover, increased from €83 per year in parity 1 to €175 in parity 3. Most cows with SCK, however, had SCK only (61%), and costs were €58 per case per year. Total costs of SCK per case per year resulted for 36% from a prolonged calving interval, 24% from reduced milk production, 19% from treatment, 14% from discarded milk and 6% from removal. Results of the sensitivity analysis showed that the disease incidence, removal risk, relations of SCK with other diseases and prices of milk resulted in a high variation of costs of SCK. The costs of SCK, therefore, might differ per farm because of farm-specific circumstances. Improving data collection on the incidence of SCK and related diseases, and on consequences of diseases can further improve economic estimations.

Effect of production quotas on economic and environmental values of growth rate and feed efficiency in sea cage fish farming.

In sea cage fish farming, production quotas aim to constrain the impact of fish farming on the surrounding ecosystem. It is unknown how these quotas affect economic profitability and environmental impact of genetic improvement. We combined bioeconomic modelling with life cycle assessment (LCA) to calculate the economic (EV) and environmental (ENV) values of thermal growth coefficient (TGC) and feed conversion ratio (FCR) of sea bass reared in sea cages, given four types of quota commonly used in Europe: annual production (Qprod), annual feed distributed (Qannual_feed), standing stock (Qstock), and daily feed distributed (Qdaily_feed). ENV were calculated for LCA impact categories climate change, eutrophication and acidification. ENV were expressed per ton of fish produced per year (ENV(fish)) and per farm per year (ENV(farm)). Results show that irrespective of quota used, EV of FCR as well as ENV(fish) and ENV(farm) were always positive, meaning that improving FCR increased profit and decreased environmental impacts. However, the EV and the ENV(fish) of TGC were positive only when quota was Qstock or Qdaily_feed. Moreover, the ENV(farm) of TGC was negative in Qstock and Qdaily_feed quotas, meaning that improving TGC increased the environmental impact of the farm. We conclude that Qstock quota and Qdaily_feed quota are economically favorable to a genetic improvement of TGC, a major trait for farmers. However, improving TGC increases the environmental impact of the farm. Improving FCR represents a good opportunity to balance out this increase but more information on its genetic background is needed to develop breeding programs improving FCR.

Dutch dairy farms after milk quota abolition: Economic and environmental consequences of a new manure policy.

The abolition of the Dutch milk quota system has been accompanied by the introduction of a new manure policy to limit phosphate production (i.e., excretion via manure) on expanding dairy farms. The objective of this study was to evaluate the effect of these recent policy changes on the farm structure, management, labor income, nitrogen and phosphate surpluses, and greenhouse gas emissions of an average Dutch dairy farm. The new manure policy requires that any increase in phosphate production be partly processed and partly applied to additional farmland. In addition, phosphate quotas have been introduced. Herein, we used a whole-farm optimization model to simulate an average farm before and after quota abolition and introduction of the new manure policy. The objective function of the model maximized labor income. We combined the model with a farm nutrient balance and life-cycle assessment to determine environmental impact. Based on current prices, increasing the number of cows after quota abolition was profitable until manure processing or additional land was required to comply with the new manure policy. Manure processing involved treatment so that phosphate was removed from the national manure market. Farm intensity in terms of milk per hectare increased by about 4%, from 13,578kg before quota abolition to 14,130kg after quota abolition. Labor income increased by €505/yr. When costs of manure processing decreased from €13 to €8/t of manure or land costs decreased from €1,187 to €573/ha, farm intensity could increase up to 20% until the phosphate quota became limiting. Farms that had already increased their barn capacity to prepare for expansion after milk quota abolition could benefit from purchasing extra phosphate quota to use their full barn capacity. If milk prices increased from €355 to €420/t, farms could grow unlimited, provided that the availability of external inputs such as labor, land, barn capacity, feed, and phosphate quota at current prices were also unlimited. The milk quota abolition, accompanied by a new manure policy, will slightly increase nutrient losses per hectare, due to an increase in farm intensity. Greenhouse gas emissions per unit of milk will hardly change, so at a given milk production per cow, total greenhouse gas emissions will increase linearly with an increase in the number of cows.

Economic and environmental effects of providing increased amounts of solid feed to veal calves.

Traditionally, veal calves receive most of their nutrients from milk replacer (MR). Nowadays, however, solid feed (SF; i.e., concentrates and roughages) increasingly substitutes for MR. Studies have shown that providing SF reduces different types of nonnutritive oral behaviors. The objective of this study was to assess the economic and environmental effects of substituting SF for MR in veal calf diets. With respect to environmental effects, we considered the emission of greenhouse gases and land occupation. Substitution rates were based on an experiment in which 160 calves were provided 2 mixtures of SF at 4 levels of dry matter (DM) intake. Mixtures of SF contained either 80% concentrates, 10% corn silage, and 10% straw on DM basis (C80) or 50% concentrates, 25% corn silage, and 25% straw (C50). The 4 levels of SF during the last 17 wk of the fattening period were 20, 100, 180, and 260 kg of DM SF. Additionally, provision of MR was adjusted to achieve equal rates of carcass gain. Substitution rates, representing the SF equivalent needed to substitute for 1 kg of DM MR, were 1.43 kg of DM for C80 and 1.61 kg of DM for C50. Economic effects were assessed based on prices and substitution rates of SF for MR and the possible penalty for carcass color. Environmental effects were assessed based on effects related to the production of feed ingredients, substitution rates, and changes in enteric methane emission and energy use for feed preparation. Costs of feeding SF needed to substitute for 1 kg of DM MR were €0.68 lower for C80 and €0.71 lower for C50, compared with the costs of feeding 1 kg of DM MR. When carcass color scores became too high, however, lower feeding costs were offset by lower revenues from meat. Emissions of greenhouse gases were hardly affected when SF intake was increased. In general, increased enteric methane emission were offset by lower emissions from feed production and energy use. Land occupation increased when intake of SF was increased, mostly because of the high land occupation associated with some concentrate ingredients. In conclusion, this study only showed a negative effect on land occupation when substituting SF for part of the MR in diets of veal calves. Effects on costs and greenhouse gas emissions were neutral or positive.

Investment appraisal of technology innovations on dairy farm electricity consumption.

The aim of this study was to conduct an investment appraisal for milk-cooling, water-heating, and milk-harvesting technologies on a range of farm sizes in 2 different electricity-pricing environments. This was achieved by using a model for electricity consumption on dairy farms. The model simulated the effect of 6 technology investment scenarios on the electricity consumption and electricity costs of the 3 largest electricity-consuming systems within the dairy farm (i.e., milk-cooling, water-heating, and milking machine systems). The technology investment scenarios were direct expansion milk-cooling, ice bank milk-cooling, milk precooling, solar water-heating, and variable speed drive vacuum pump-milking systems. A dairy farm profitability calculator was combined with the electricity consumption model to assess the effect of each investment scenario on the total discounted net income over a 10-yr period subsequent to the investment taking place. Included in the calculation were the initial investments, which were depreciated to zero over the 10-yr period. The return on additional investment for 5 investment scenarios compared with a base scenario was computed as the investment appraisal metric. The results of this study showed that the highest return on investment figures were realized by using a direct expansion milk-cooling system with precooling of milk to 15°C with water before milk entry to the storage tank, heating water with an electrical water-heating system, and using standard vacuum pump control on the milking system. Return on investment figures did not exceed the suggested hurdle rate of 10% for any of the ice bank scenarios, making the ice bank system reliant on a grant aid framework to reduce the initial capital investment and improve the return on investment. The solar water-heating and variable speed drive vacuum pump scenarios failed to produce positive return on investment figures on any of the 3 farm sizes considered on either the day and night tariff or the flat tariff, even when the technology costs were reduced by 40% in a sensitivity analysis of technology costs.

Economic values of growth and feed efficiency for fish farming in recirculating aquaculture system with density and nitrogen output limitations: a case study with African catfish (Clarias gariepinus).

In fish farming, economic values (EV) of breeding goal traits are lacking, even though they are key parameters when defining selection objectives. The aim of this study was to develop a bioeconomic model to estimate EV of 2 traits representing production performances in fish farming: the thermal growth coefficient (TGC) and the feed conversion ratio (FCR). This approach was applied to a farm producing African catfish (Clarias gariepinus) in a recirculating aquaculture system (RAS). In the RAS, 2 factors could limit production level: the nitrogen treatment capacity of the biofilter or the fish density in rearing tanks at harvest. Profit calculation includes revenue from fish sales, cost of juveniles, cost of feed, cost of waste water treatment, and fixed costs. In the reference scenario, profit was modeled to zero. EV were calculated as the difference in profit per kilogram of fish between the current population mean for both traits (µt) and the next generation of selective breeding (µt+Δt) for either TGC or FCR. EV of TGC and FCR were calculated for three generations of hypothetical selection on either TGC or FCR (respectively 6.8% and 7.6% improvement per generation). The results show that changes in TGC and FCR can affect both the number of fish that can be stocked (number of batches per year and number of fish per batch) and the factor limiting production. The EV of TGC and FCR vary and depend on the limiting factors. When dissolved NH3-N is the limiting factor for both µt and µt+Δt, increasing TGC decreases the number of fish that can be stocked but increases the number of batches that can be grown. As a result, profit remains constant and EVTGC is zero. Increasing FCR, however, increases the number of fish stocked and the ratio of fish produced per kilogram of feed consumed ("economic efficiency"). The EVFCR is 0.14 €/kg of fish, and profit per kilogram of fish increases by about 10%. When density is the limiting factor for both µt and µt+Δt, the number of fish stocked per batch is fixed; therefore, extra profit is obtained by increasing either TGC, which increases the annual number of batches, or by decreasing FCR, which decreases annual feed consumption. EVTGC is 0.03 €/kg of fish and EVFCR is 0.05-0.06 €/kg of fish. These results emphasize the importance of calculating economic values in the right context to develop efficient future breeding programs in aquaculture.

A mechanistic model for electricity consumption on dairy farms: definition, validation, and demonstration.

Our objective was to define and demonstrate a mechanistic model that enables dairy farmers to explore the impact of a technical or managerial innovation on electricity consumption, associated CO2 emissions, and electricity costs. We, therefore, (1) defined a model for electricity consumption on dairy farms (MECD) capable of simulating total electricity consumption along with related CO2 emissions and electricity costs on dairy farms on a monthly basis; (2) validated the MECD using empirical data of 1yr on commercial spring calving, grass-based dairy farms with 45, 88, and 195 milking cows; and (3) demonstrated the functionality of the model by applying 2 electricity tariffs to the electricity consumption data and examining the effect on total dairy farm electricity costs. The MECD was developed using a mechanistic modeling approach and required the key inputs of milk production, cow number, and details relating to the milk-cooling system, milking machine system, water-heating system, lighting systems, water pump systems, and the winter housing facilities as well as details relating to the management of the farm (e.g., season of calving). Model validation showed an overall relative prediction error (RPE) of less than 10% for total electricity consumption. More than 87% of the mean square prediction error of total electricity consumption was accounted for by random variation. The RPE values of the milk-cooling systems, water-heating systems, and milking machine systems were less than 20%. The RPE values for automatic scraper systems, lighting systems, and water pump systems varied from 18 to 113%, indicating a poor prediction for these metrics. However, automatic scrapers, lighting, and water pumps made up only 14% of total electricity consumption across all farms, reducing the overall impact of these poor predictions. Demonstration of the model showed that total farm electricity costs increased by between 29 and 38% by moving from a day and night tariff to a flat tariff.

Cost-effectiveness of feeding strategies to reduce greenhouse gas emissions from dairy farming.

The objective of this paper was to evaluate the cost-effectiveness of 3 feeding strategies to reduce enteric CH4 production in dairy cows by calculating the effect on labor income at the farm level and on greenhouse gas (GHG) emissions at the chain level (i.e., from production of farm inputs to the farm gate). Strategies included were (1) dietary supplementation of an extruded linseed product (56% linseed; 1kg/cow per day in summer and 2kg/cow per day in winter), (2) dietary supplementation of a nitrate source (75% nitrate; 1% of dry matter intake), and (3) reducing the maturity stage of grass and grass silage (grazing at 1,400 instead of 1,700kg of dry matter/ha and harvesting at 3,000 instead of 3,500kg of dry matter/ha). A dairy farm linear programing model was used to define an average Dutch dairy farm on sandy soil without a predefined feeding strategy (reference situation). Subsequently, 1 of the 3 feeding strategies was implemented and the model was optimized again to determine the new economically optimal farm situation. Enteric CH4 production in the reference situation and after implementing the strategies was calculated based on a mechanistic model for enteric CH4 and empirical formulas explaining the effect of fat and nitrate supplementation on enteric CH4 production. Other GHG emissions along the chain were calculated using life cycle assessment. Total GHG emissions in the reference situation added up to 840kg of CO2 equivalents (CO2e) per t of fat- and protein-corrected milk (FPCM) and yearly labor income of €42,605. Supplementation of the extruded linseed product reduced emissions by 9kg of CO2e/t of FPCM and labor income by €16,041; supplementation of the dietary nitrate source reduced emissions by 32kg of CO2e/t of FPCM and labor income by €5,463; reducing the maturity stage of grass and grass silage reduced emissions by 11kg of CO2e/t of FPCM and labor income by €463. Of the 3 strategies, reducing grass maturity was the most cost-effective (€57/t of CO2e compared with €241/t of CO2e for nitrate supplementation and €2,594/t of CO2e for linseed supplementation) and had the greatest potential to be used in practice because the additional costs were low.

Energy demand on dairy farms in Ireland.

Reducing electricity consumption in Irish milk production is a topical issue for 2 reasons. First, the introduction of a dynamic electricity pricing system, with peak and off-peak prices, will be a reality for 80% of electricity consumers by 2020. The proposed pricing schedule intends to discourage energy consumption during peak periods (i.e., when electricity demand on the national grid is high) and to incentivize energy consumption during off-peak periods. If farmers, for example, carry out their evening milking during the peak period, energy costs may increase, which would affect farm profitability. Second, electricity consumption is identified in contributing to about 25% of energy use along the life cycle of pasture-based milk. The objectives of this study, therefore, were to document electricity use per kilogram of milk sold and to identify strategies that reduce its overall use while maximizing its use in off-peak periods (currently from 0000 to 0900 h). We assessed, therefore, average daily and seasonal trends in electricity consumption on 22 Irish dairy farms, through detailed auditing of electricity-consuming processes. To determine the potential of identified strategies to save energy, we also assessed total energy use of Irish milk, which is the sum of the direct (i.e., energy use on farm) and indirect energy use (i.e., energy needed to produce farm inputs). On average, a total of 31.73 MJ was required to produce 1 kg of milk solids, of which 20% was direct and 80% was indirect energy use. Electricity accounted for 60% of the direct energy use, and mainly resulted from milk cooling (31%), water heating (23%), and milking (20%). Analysis of trends in electricity consumption revealed that 62% of daily electricity was used at peak periods. Electricity use on Irish dairy farms, therefore, is substantial and centered around milk harvesting. To improve the competitiveness of milk production in a dynamic electricity pricing environment, therefore, management changes and technologies are required that decouple energy use during milking processes from peak periods.

Economic, ecological, and social performance of conventional and organic broiler production in the Netherlands.

1. In this study, we compared a conventional broiler production system keeping fast growing broilers with an organic broiler production system keeping slow growing broilers in the Netherlands, both managed by one person working a full time year (Full Time Equivalent, FTE). This comparison was based on a quantification of economic, ecological and social indicators. Indicators were quantified using scientific literature and national data sets. 2. The organic system performed better for the economic indicator net farm income per FTE than the conventional system. 3. Regarding ecological indicators, calculations showed a higher on-farm emission of ammonia per kg live weight for the organic system. Moreover, an organic system includes a higher risk for eutrophication per ha due to outdoor access. Emission of green house gasses, use of fossil fuels and use of land required for the production of one kg of live weight is higher for an organic than for a conventional system. This is mainly due to a lower feed conversion in organic production and use of organic feed. 4. The organic system performed better than the conventional system for the social indicators related to animal welfare time spent on walking, footpad lesions, mortality, and sound legs. Regarding the social indicator food safety was found that meat from an organic system contained less antibiotic residues and Salmonella contaminations but more Campylobacter contaminations than meat from a conventional system. 5. Changing from a conventional to an organic broiler production system, therefore, not only affects animal welfare, but also affects economic, ecological and other social issues. In this study, we ran into the situation that some information needed was lacking in literature and quantifications had to be based upon several sources. Therefore, an integrated on-farm assessment is needed, which can be used to develop a broiler production system that is economically profitable, ecologically sound, and acceptable for society.

On-farm quantification of sustainability indicators: an application to egg production systems.

1. On-farm quantification of sustainability indicators (SI) is an effective way to make sustainable development measurable. The egg production sector was used as a case study to illustrate this approach. 2. The objective was to select SI for economic, ecological and societal issues, and to analyse the performance on selected SI of different production systems. 3. For the case study, we compared 4 egg production systems, characterised by differences in the housing systems which are most common in the Netherlands: the battery-cage system, the deep-litter system with and without outdoor run, and the aviary system with outdoor run. 4. Based on a clear set of criteria, we selected SI for animal welfare, economics, environmental impact, ergonomics and product quality. 5. We showed that on-farm quantification of SI was an appropriate method to identify the strengths and weaknesses of different systems. 6. From this analysis it appears that the aviary system with outdoor run is a good alternative for the battery-cage system, with better scores for the aviary system on animal welfare and economics, but with worse scores on environmental impact.

A method using sustainability indicators to compare conventional and animal-friendly egg production systems.

The objective of this paper was to describe a method that enables a comparison of egg production systems for their contribution to sustainable development of egg production, using data from the Netherlands for demonstration purposes. One conventional system, the battery cage system, and two animal-friendly systems, the deep-litter and the aviary systems, illustrate this method. The method is based on a three-phase framework that identifies relevant issues regarding sustainable development (Phase 1), translates issues into sustainability indicators (Phase 2), and assesses the contribution of sustainability indicators to sustainable development (Phase 3). Phases 1 and 2 are based on analysis of literature and consultation with experts. Phase 3 is based on graphical and numerical comparisons. A graphical comparison presents the relative deviation between actual and target values for each sustainability indicator. A numerical comparison combines the relative deviation for each sustainability indicator into an overall contribution of an egg production system to sustainable development. Sustainability indicators selected include economic performance, ammonia emission, energy use, hen welfare, farmer welfare, and egg quality. Based on equal importance of these indicators, the battery cage system shows the least negative contribution to sustainable development. The aviary system is considered a better animal-friendly alternative for a battery cage system when compared with a deep-litter system. The aviary system especially needs to improve economic performance and farmer welfare. The conclusion can only be tentative, however, because methodological aspects such as selection and weighting of sustainability indicators will need careful attention and further research.