P.B.M. Berentsen

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.

Effect of yield and price risk on conversion from conventional to organic farming

Although the benefits of organic farming are already well known, the conversion to organic farming does not proceed as the Dutch government expected. In order to investigate the conversion decisions of Dutch arable farms, a discrete stochastic dynamic utility-efficient programming (DUEP) model is developed with special attention for yield and price risk of conventional, conversion and organic crops. The model maximizes the expected utility of the farmer depending on the farmer’s risk attitude. The DUEP model is an extension of a dynamic linear programming model that maximized the labour income of conversion from conventional to organic farming over a 10 year planning horizon. The DUEP model was used to model a typical farm for the central clay region in the Netherlands. The results show that for a risk-neutral farmer it is optimal to convert to organic farming. However, for a more risk-averse farmer it is only optimal to fully convert if policy incentives are applied such as taxes on pesticides or subsidies on conversion, or if the market for the organic products becomes more stable.

Increasing the revenues from automatic milking by using individual variation in milking characteristics

The objective of this study was to quantify individual variation in daily milk yield and milking duration in response to the length of the milking interval and to assess the economic potential of using this individual variation to optimize the use of an automated milking system. Random coefficient models were used to describe the individual effects of milking interval on daily milk yield and milking duration. The random coefficient models were fitted on a data set consisting of 4,915 records of normal uninterrupted milkings collected from 311 cows kept in 5 separate herds for 1 wk. The estimated random parameters showed considerable variation between individuals within herds in milk yield and milking duration in response to milking interval. In the actual situation, the herd consisted of 60 cows and the automatic milking system operated at an occupation rate (OR) of 64%. When maximizing daily milk revenues per automated milking system by optimizing individual milking intervals, the average milking interval was reduced from 0.421 d to 0.400 d, the daily milk yield at the herd level was increased from 1,883 to 1,909 kg/d, and milk revenues increased from euro498 to euro507/d. If an OR of 85% could be reached with the same herd size, the optimal milking interval would decrease to 0.238 d, milk yield would increase to 1,997 kg/d, and milk revenues would increase to euro529/d. Consequently, more labor would be required for fetching the cows, and milking duration would increase. Alternatively, an OR of 85% could be achieved by increasing the herd size from 60 to 80 cows without decreasing the milking interval. Milk yield would then increase to 2,535 kg/d and milk revenues would increase to euro673/d. For practical implementation on farms, a dynamic approach is recommended, by which the parameter estimates regarding the effect of interval length on milk yield and the effect of milk yield on milking duration are updated regularly and also the milk production response to concentrate intake is taken into account.

Modelling conventional and organic farming : a literature review

Literature shows a significant development of organic farming in Europe but with considerable differences between countries. These depend on general agricultural policy (the set of regulations and laws), specific policy incentives, and also on differences in consumer behaviour. This paper reviews scientific literature on the evaluation of the technical, economic and environmental aspects of conversion from conventional towards organic production. The methods and results of empirical and normative modelling studies at the farm level, with special regard to farm management and policy, are analysed. Empirical modelling studies show the importance of incentives and agricultural policy, and the usefulness of integrated modelling for determining the effects of different policies on farm management. Normative modelling shows the effects of new policy instruments and technology, and allows the high level of detail needed for what-if analysis. Normative models of conversion to organic farming confirm the importance of incentives and the agricultural policy context.Additional keywords: farm modelling, conversion, empirical modelling, normative modelling, policy, context.

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.

Economic potential of individual variation in milk yield response to concentrate intake of dairy cows

The objectives of the current study were to quantify the individual variation in daily milk yield response to concentrate intake during early lactation and to assess the economic prospects of exploiting the individual variation in milk yield response to concentrate intake. In an observational study, data from 299 cows on four farms in the first 3 weeks of the lactation were collected. Individual response in daily milk yield to concentrate intake was analysed by a random coefficient model. Marked variation in individual milk yield response to concentrate intake was found on all four farms. An economic simulation was carried out, based on the estimated parameter values in the observational study. Individual optimization of concentrate supply is compared with conventional strategies for concentrate supply based on averaged population response parameters. Applying individual economic optimal settings for concentrate supply during early lactation, potential economic gain ranges from €0·20 to €2·03/cow/day.

Methods to determine the relative value of genetic traits in dairy cows to reduce greenhouse gas emissions along the chain

Current decisions on breeding in dairy farming are mainly based on economic values of heritable traits, as earning an income is a primary objective of farmers. Recent literature, however, shows that breeding also has potential to reduce greenhouse gas (GHG) emissions. The objective of this paper was to compare 2 methods to determine GHG values of genetic traits. Method 1 calculates GHG values using the current strategy (i.e., maximizing labor income), whereas method 2 is based on minimizing GHG per kilogram of milk and shows what can be achieved if the breeding results are fully directed at minimizing GHG emissions. A whole-farm optimization model was used to determine results before and after 1 genetic standard deviation improvement (i.e., unit change) of milk yield and longevity. The objective function of the model differed between method 1 and 2. Method 1 maximizes labor income; method 2 minimizes GHG emissions per kilogram of milk while maintaining labor income and total milk production at least at the level before the change in trait. Results show that the full potential of the traits to reduce GHG emissions given the boundaries that were set for income and milk production (453 and 441kg of CO2 equivalents/unit change per cow per year for milk yield and longevity, respectively) is about twice as high as the reduction based on maximizing labor income (247 and 210kg of CO2 equivalents/unit change per cow per year for milk yield and longevity, respectively). The GHG value of milk yield is higher than that of longevity, especially when the focus is on maximizing labor income. Based on a sensitivity analysis, it was shown that including emissions from land use change and using different methods for handling the interaction between milk and meat production can change results, generally in favor of milk yield. Results can be used by breeding organizations that want to include GHG values in their breeding goal. To verify GHG values, the effect of prices and emissions factors should be considered, as well as the potential effect of variation between farm types.

Comparing risk in conventional and organic dairy farming in the Netherlands: an empirical analysis.

This study was undertaken to contribute to the understanding of why most dairy farmers do not convert to organic farming. Therefore, the objective of this research was to assess and compare risks for conventional and organic farming in the Netherlands with respect to gross margin and the underlying price and production variables. To investigate the risk factors a farm accountancy database was used containing panel data from both conventional and organic representative Dutch dairy farms (2001-2007). Variables with regard to price and production risk were identified using a gross margin analysis scheme. Price risk variables were milk price and concentrate price. The main production risk variables were milk yield per cow, roughage yield per hectare, and veterinary costs per cow. To assess risk, an error component implicit detrending method was applied and the resulting detrended standard deviations were compared between conventional and organic farms. Results indicate that the risk included in the gross margin per cow is significantly higher in organic farming. This is caused by both higher price and production risks. Price risks are significantly higher in organic farming for both milk price and concentrate price. With regard to production risk, only milk yield per cow poses a significantly higher risk in organic farming.

Adaptive models for online estimation of individual milk yield response to concentrate intake and milking interval length of dairy cows

Automated feeding and milking of dairy cows enables the application of individual cow settings for concentrate supply and milking frequency. Currently, general settings are used, based on knowledge about energy and nutrient requirements in relation to milk production at the group level. Individual settings, based on the actual individual response in milk yield, have the potential for a marked increase in economic profits. In the present study, adaptive dynamic models for online estimation of milk yield response to concentrate intake and length of milking interval are evaluated. The parameters in these models may change over time and are updated through a Bayesian approach for online analysis of time series. The main use of dynamic models lies in their ability to determine economically optimal settings for concentrate intake and milking interval length for individual cows at any day in lactation. Three adaptive dynamic models are evaluated, a model with linear terms for concentrate intake and length of milking interval, a model that also comprises quadratic terms, and an enhanced model (EM) in order to obtain more stable parameter estimates. The linear model is useful only for forecasting milk production and the estimated parameters of the quadratic model were found to be unstable. The parsimony of the EM leads to far more stable parameter estimates. It is shown that the EM is suitable for control and monitoring, and therefore promises to be a valuable tool for application within precision livestock farming

Quantifying the effect of heat stress on daily milk yield and monitoring dynamic changes using an adaptive dynamic model

Automation and use of robots are increasingly being used within dairy farming and result in large amounts of real time data. This information provides a base for the new management concept of precision livestock farming. From 2003 to 2006, time series of herd mean daily milk yield were collected on 6 experimental research farms in the Netherlands. These time series were analyzed with an adaptive dynamic model following a Bayesian method to quantify the effect of heat stress. The effect of heat stress was quantified in terms of critical temperature above which heat stress occurred, duration of heat stress periods, and resulting loss in milk yield. In addition, dynamic changes in level and trend were monitored, including the estimation of a weekly pattern. Monitoring comprised detection of potential outliers and other deteriorations. The adaptive dynamic model fitted the data well; the root mean squared error of the forecasts ranged from 0.55 to 0.99 kg of milk/d. The percentages of potential outliers and signals for deteriorations ranged from 5.5 to 9.7%. The Bayesian procedure for time series analysis and monitoring provided a useful tool for process control. Online estimates (based on past and present only) and retrospective estimates (determined afterward from all data) of level and trend in daily milk yield showed an almost yearly cycle that was in agreement with the calving pattern: most cows calved in winter and early spring versus summer and autumn. Estimated weekly patterns in terms of weekday effects could be related to specific management actions, such as change of pasture during grazing. For the effect of heat stress, the mean estimated critical temperature above which heat stress was expected was 17.8±0.56°C. The estimated duration of the heat stress periods was 5.5±1.03 d, and the estimated loss was 31.4±12.2 kg of milk/cow per year. Farm-specific estimates are helpful to identify management factors like grazing, housing and feeding, that affect the impact of heat stress. The effect of heat stress can be decreased by modifying these factors.