Browsing by Subject "Dairy farming"

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Integration von tiergesundheitsrelevanten Daten in betriebliche Managemententscheidungen
(2010) Fick, Johanna; Doluschitz, Reiner
For many years agriculture in Germany has been subject of changes due to economic, technical and social developments. These changes are structural, market and environmental induced. Livestock owner may react with increasing turnover and/or cost reduction to face the changes and to achieve sustained success in livestock farming. Expenditures for veterinarian and pharmaceuticals in dairy farming have a large share in the expenditures. Furthermore revenue losses due to animal diseases have to be added to the costs. Strategies to reduce costs and revenue losses can only lead to success if farmers and veterinarians act jointly. Basis for this can be an established digital information exchange between the process partners. Existing data of the process participants are used, consolidated and analysed in such a digital information exchange. Previous activities to improve the animal health were mostly partial solutions of individual members of the process chain. These are partial solutions due to the fact that the existing data are not brought together and that the use as an entity is not possible. The available software merges the animal health data and the animal performance data merely partial. The target of this research focuses on the integration of relevant data on animal health in operating management decisions by conception and development of an IT-model. This IT-model links relevant but distributed, incomplete, and redundant animal health data of the process participants (farmer, veterinarians, and professional associations) in livestock farming. So this contribution shall improve the use of animal health and animal performance data. Thereby issues of veterinary medicine and dairy farming will be joined by using information technology (IT). The procedure is structured in drafting the principles, an analysis of the status quo, and a market- and potential analysis consisting the use and application of sector software by farmers and veterinarians. It is followed by the conception and development of the animal health system and the conception of the implementation with a first product test. To answer the research questions of this paper a process model has been developed which was used as methodical frame. Different explanation strategies were necessary and therefore qualitative and quantitative methods of social research were used.
Modelling nitrogen use and excretion in dairy cattle herds grazing temperate, semi-natural grasslands
(2025) Perdana-Decker, Sari; Dickhöfer, Uta
Grazing-based dairy cattle systems exhibit several benefits, such as preserving biodiverse grassland habitats, improving animal welfare, or turning grassland protein into human-edible protein. However, grazing-based diets are prone to greater nitrogen (N) losses via urine than balanced stall-fed diets, leading to a greater risk for N emissions. Strategies for improving the N use in grazing-based systems are predominantly investigated on homogenous clover-ryegrass pastures with high yields and nutritional quality. In contrast, grazing-based systems reliant on less external inputs (e.g., synthetic fertilisers or concentrates) using semi-natural grassland as main feed source received less attention. The present thesis addressed the knowledge gap on the N use of such low-input grazing-based systems by adapting an existing dynamic, process-based herd model (i.e., the LIVestock SIMulator, LIVSIM) for simulating animal performance and N use and excretion of dairy herds. For this, a broad dataset was gathered on nine commercial organic dairy cattle farms in Baden-Württemberg during two grazing periods (2019, 2020). This dataset fulfilled two purposes: firstly, to get a basic understanding on N use and excretion of dairy cows under low-input grazing conditions (study 1); secondly, to serve as reference dataset for adapting and evaluating LIVSIM for such production systems (studies 2 and 3). The reference dataset represented the wide range of grazing and production factors found on commercial farms in South Germany using semi-natural grasslands for grazing. The dataset applied for study 1 covered n = 323 individual animal observations with mean (± one standard deviation) milk production, dry matter intake (DMI), and pasture DMI (PDMI) of 23.9 (± 5.35), 21.0 (± 3.21), and 11.3 (± 4.83) kg/d, respectively. Milk N use efficiency (MNE) averaged 24.7 g/100 g N intake (± 5.91), which is greater than observations in temperate, high-input grazing-based systems but lower than in cows receiving balanced stall-fed diets. Nevertheless, MNE and other indicators of N use and excretion varied greatly among farms and seasons, highlighting the need to identify the drivers for this variation. Supplement feeding had the greatest potential for manipulating the N use and excretion. Increasing shares of fresh forages as well as of hay of total supplement DMI increased N use (e.g., MNE) and decreased urinary N excretion (e.g., urinary N to creatinine ratio), while increasing shares of concentrates of supplement DMI were related to lower N losses via urine. Study 1 highlighted that using semi-natural grasslands for grazing can potentially reduce environmentally harmful N losses compared to high-input grazing systems. For future research endeavours, a modelling approach may simplify the investigation of more feeding scenarios, their interactions, different local conditions, and considering the spatial and temporal variation of pasture herbage quality and yield. Hence, studies 2 and 3 focused on adaptating LIVSIM for low-input grazing-based dairy farms. The DMI and N intake are among the most decisive factors for determining animal performance and N excretion. Therefore, a module for predicting the PDMI of cows grazing semi-natural grassland was identified in study 2, using a subset of the reference dataset (n = 233 individual animal observations). Among the thirteen tested models, behaviour-based and semi-mechanistic models specifically developed for grazing animals had the lowest prediction adequacy. Their underlying empirical equations likely did not fit the grazing and production conditions of farms employing semi-natural grasslands. Modelling performance of a semi-mechanistic model developed for stall-based feeding situations (Mertens II) with slight modifications was best (relative prediction error = 13.4%) when evaluated based on the mean observed PDMI (i.e., averaged across animals per farm and period (n = 28)). Consequently, the modified Mertens II model was integrated in LIVSIM in study 3. Additionally, the modules for energy requirements, lactation, N excretion, and herd management were adopted, and breed-specific model coefficients added to represent Simmental, Brown Swiss, and Holstein-Friesian cattle breeds. Dairy cow characteristics, herd composition, annual milk yield, and DMI were predicted accurately (i.e., with a relative difference ≤ 10 % between observed and predicted outputs for the majority of outputs). The absolute total N excretion (g/d) was underpredicted by 23 % (= relative difference between observed and predicted values) mainly due to the underprediction of urinary N excretion by 43 %. The relative differences in N excretion between farming systems, in contrast, were predicted reliably. The observed faecal, urinary, and total N excretion (in % of N intake) differed by 30, -23, and -7 %, respectively, between the two reference herds, which is similar to the respective relative differences for the predicted faecal, urinary, and total N excretion of 32, -36, and -4 %. Further model improvements should focus on increasing the prediction accuracy of N excretion and its partitioning due to the varying degree of susceptibility of faecal or urinary N to volatilisation and leaching. The scenario and sensitivity analyses further confirmed that the adapted LIVSIM plausibly simulated differences in animal performance and nutrient excretions based on differences in supplement feeds and pasture herbage. Core input and model coefficients are the dietary ME, CP, and rumen-undegradable CP concentrations, as well as the available herbage biomass on pastures, for which precise measurements are thus needed. The findings of studies 2 and 3 demonstrate that existing models can be adopted for low-input grazing-based dairy production systems. There is further potential for adapting LIVSIM for production systems beyond the ones investigated in the present study, and/or for adding more outputs (e.g., enteric methane) and scales (e.g., grassland) to better capture the multifaceted aspects determining farm sustainability.
Technikfolgenabschätzung und Diffusionsforschung in der Landwirtschaft
Beschreibung, Analyse und Weiterentwicklung im Kontext der Einführung Automatisierter Melkverfahren
(2009) Hein, Klaus A.; Grosskopf, Werner
With the fully automatic milking systems (AMS), we have a technique available to perform the milking process largely independent from the dairy farmer for the first time. On German farms we can observe first utilization of this innovation since the mid-nineties. The complexity of technical innovations in farming that is shown in the AMS has led to a higher interest in the subsequent effects of increased mechanization. A concept of a comprehensive analysis of the technological impact for AMS doesn?t exist so far. Further, AMS have not been described in terms of their classification within innovation-theory yet. At the same time, we get the impression that with the adoption process of the AMS technology, limitations of the traditional demand-based theory become obvious in real-ity. Based on this knowledge, we have to confront the agricultural economic research with the task to carefully investigate the explanatory-models for the development, impact and expan-sion of technical advancement as a basis of a technological assessment for AMS. The objec-tive of this thesis is to investigate the diffusion process of innovations in farming following the example of introducing fully automated milking systems on the market. Coming from observations of innovation processes in the context of various models based on innovation and economic theory, certain approaches and methods for the technological assessment in farming will be discussed against the background of theories to analyse technical advancement. The gained insights will be combined in the development of an overall concept for the analysis of the technical impact for fully automated milking systems. Part of the analysis of technical impact for AMS is to capture the motivation of potential users to adopt this new technology in an empirical study. The available results from literature were supplemented by a written survey of 5.210 dairy farms in four German dairy farming areas. The high investment costs of AMS are the main obstacle for this innovation at the moment. Also the associated costs for adaptation of the milking technique as well as the expectation of a high maintenance effort are important arguments from non-adopters against an investment in AMS. Social arguments are on the other hand the main factors for potential adopters in their decision making process. Possible examples are health aspects or the expected flexibility and time saving. Another reason can be found in the pursuit for greater independence from contract workforce. Therefore it can be shown, that even if the relative cost of purchasing AMS is higher than its relative productivity, we still see adoption of the innovative milking technique based on individual benefits. With the introduction of the individual benefit theory in the decision-making process of competitive techniques, it is now possible to explain the diffusion of AMS, even though the adoption of AMS in terms of maximizing profit would have to be rejected in the single case. A further specification of the decision-making factors with regards to personal benefits could be very helpful for ex ante estimations of diffusion processes in agriculture. For the majority of potential users of AMS we can assume that expectations of performance and profit will outweigh the mostly social and economical benefits expected in the long term. This insight is opposed to the expected S-shaped distribution of AMS adopters. Contrary to the present tradition of demand-based diffusion research, for AMS we were able to prove a diffusion process that is determined more by supply-side. Due to the fact of increased com-plexity of innovations in the diffusion processes, where processes are more and more described in terms of industrial coordination, one should give up the onesided emphasis of socio-economical characteristics of potential adopters in favour of a growing supply-oriented diffusion research. Accordingly, we also have to expand future technological assessments specifically on manufacturers of these innovations. All in all, we are not able to explain the diffusion of AMS sufficiently with the concept of factorprice-induced technical change. This is also shown in the fact that in spite of the introduction of AMS, we don?t necessarily use more effective capital in dairy farms. The question, if we can see AMS as a technical advancement from an economical point of view, is therefore dependent on the individual circumstances for their use on dairy farms. In view of the future development we can therefore realize, that we won?t see big changes in the structure of the dairy farms that can be traced back to the introduction of this technology.