Die Landwirtschaftliche Betriebslehre ist ein Kernbereich der Agrarökonomie. Das Verständnis der speziellen betriebswirtschaftlichen Grundlagen ist für Studierende der Agrarwissenschaften und praktische Landwirte gleichermaßen wichtig. Die komplett überarbeitete 4. Auflage stellt zuerst die Prinzipien vor, die Planungen und Entscheidungen zugrunde liegen. Ressourcen, Institutionen und Informationen als Voraussetzungen betriebswirtschaftlicher Entscheidungen sind Gegenstand des Folgekapitels. Methoden, die das klassische Handwerkszeug landwirtschaftlicher Betriebswirte darstellen, werden ausführlich erläutert, ebenso wie Werkzeuge für strategische Entscheidungen. Die Inhalte wurden leicht verständlich aufbereitet: Studium, Prüfungsvorbereitung und Wissenstransfer auf neue Situationen und Probleme werden so erleichtert.

Bio-economic simulation models are widely established in Farming Systems Research; they are used to investigate complex real-world phenomena in agricultural production. Such simulation models are largely designed and created by scientists from different disciplines who are not modeling experts. Thus, IT knowledge is required, but this area of expertise falls outside of most agricultural researchers' background. IT knowledge is essential for the maintenance, development, and applicability of simulation models. Often, bio-economic simulation models require a fair amount of time to ensure basic functionality before specific research questions can be answered. Researchers who contribute to the creation of a bio-economic simulation model often spend the majority of their time ensuring basic model functionality. This integrative literature review provides a few basic rules that are intended to ensure more efficient model development. There is an increased need for support from IT personnel who are not researchers in their own field but who can increase the quality of such models and their reusability in different contexts. (C) 2017 Elsevier B.V. All rights reserved.

In agricultural production, land-use decisions are components of economic planning that result in the strategic allocation of fields. Climate variability represents an uncertainty factor in crop production. Considering yield impact, climatic influence is perceived during and evaluated at the end of crop production cycles. In practice, this information is then incorporated into planning for the upcoming season. This process contributes to attitudes toward climate-induced risk in crop production. In the literature, however, the subjective valuation of risk is modeled as a risk attitude toward variations in (monetary) outcomes. Consequently, climatic influence may be obscured by political and market influences so that risk perceptions during the production process are neglected. We present a utility concept that allows the inclusion of annual risk scores based on mid-season risk perceptions that are incorporated into field-planning decisions. This approach is exemplified and implemented for winter wheat production in the Kraichgau, a region in Southwest Germany, using the integrated bio-economic simulation model FarmActor and empirical data from the region. Survey results indicate that a profitability threshold for this crop, the level of “still-good yield” (sgy), is 69 dt ha-1 (regional mean Kraichgau sample) for a given season. This threshold governs the monitoring process and risk estimators. We tested the modeled estimators against simulation results using ten projected future weather time series for winter wheat production. The mid-season estimators generally proved to be effective. This approach can be used to improve the modeling of planning decisions by providing a more comprehensive evaluation of field-crop response to climatic changes from an economic risk point of view. The methodology further provides economic insight in an agrometeorological context where prices for crops or inputs are lacking, but farmer attitudes toward risk should still be included in the analysis.

The organic market depends on an effective and efficient certification system. Control bodies or authorities are pivotal to this system. From a social point of view, our objective is to theoretically optimize inspection strategies. For this, sanctions and inspection frequencies have to be implemented in a way that the net social damage arising from farmers' noncompliance with an organic standard will be minimized. In scenarios that combine different kinds of social damages, fines and compliance cost distributions for an assumed set of farms we use Monte Carlo simulations to model farmers' non-compliance and resulting social damages. Depending on potential reputation losses and compliance cost variability among farms we identify different adequate control frequencies.

This study investigates the effects of planting rights liberalization on the largest wine-producing region in Germany, Rheinland-Pfalz. Introduced by the reform of the Common Agricultural Policy (CAP) of the European Union (EU) in 2008, the abolishment of restrictions on the planting of new vineyards is still a subject of controversial discussions regarding possible effects on the sector's structure and the quantity of wine production. For a simulation of these effects, this study uses partial equilibrium modeling and Markov chain projection. The results reveal that the effects of abolishment of planting rights depend on the assumed wine must prices. Relatively high market prices for wine must would lead to increases in the production of both standard and basic quality wine must and in the number of more cost-effective wine farms in the region. If low prices for wine must are assumed, the reform might result only in minor impacts.

Natural peatlands are the world's most area-effective carbon sinks. However, over 90% of German, 40% of European and 10-20% of global peatlands have been degraded and converted into carbon sources, primarily because of agricultural drainage. Against this background, rewetting and more sensible uses of peat soils for agriculture are internationally recognized as effective potential options to mitigate greenhouse gas (GHG) emissions. This paper presents estimates of the GHG mitigation potential and abatement costs of different peatland management options by using the example of farm models that represent typical farm types in an intensive grassland-use peatland region in southern Germany. Therefore, an optimization model at the farm level that includes the emissions from all relevant sources in the production process is used.The current net GHG emissions of the farm models range from 10 to 12.9 tCO(2)e ha(-1) a(-1), of which the peat soil borne emissions make up a noticeable share. The rewetting and conversion of medium-drained grassland into wet grassland lead to considerable reductions of GHG emissions. However, in intensively managed dairy farms, the full emission Mitigation potential of these peatland management options is not realized, because necessary adaptations increase emissions from other sources. Because of its low GHG mitigation potential, the conversion of arable land into medium drained intensive grassland leads to high abatement costs of up to 92(sic)/tCO(2)e, while the abatement costs of the rewetting and conversion into wet grassland range from 5 to 57(sic)/tCO(2)e. The results vary by farm type, intensity of agricultural peatland use to date and the share of peatland area on a farm. This stresses the need for individual farm approaches and impact analyses for planned peatland renaturations. The findings also show that the overall costs to compensate farmer income loss from planned peatland renaturations will be as high as they are for more intensively managed dairy farms, which will be located in and affected in the respective area. The modelling approach presented has the potential to be adapted to the needs of peatland farm systems in other countries of the temperate zone. (C) 2016 Elsevier Ltd. All rights reserved.

This study assesses the potential impact of future climate change on agricultural land rents in Germany using a Ricardian approach. In addition to including common explanatory variables, we focus on the effects of different indicators of soil characteristics when explaining land rental prices. The analysis is based on data from the official farm census 1999, weather data from the German National Meteorological Service and different soil data-bases at the county level. Different classifications of soil quality do not influence the results of our Ricardian analysis. The results of spatial error models indicate higher land rental prices for locations with more productive soils and higher mean annual temperatures. Also a lower land slope, a smaller share of rented land and (in some cases) less spring precipitation increase land rental prices. To estimate the effects of changing climatic conditions on future land rents, we draw on data from the regional climate model REMO for 2011-2040. Our models show an average land rent increase of 10-17% resulting from the expected changes in temperature and spring precipitation. According to our results future climate change will have an overall positive but spatially heterogeneous impact on the agricultural income in Germany.

Assuming that agglomeration effects do matter in organic farming we analyse (a) the difficulties due to data aggregation arising when trying to statistically verify neighbourhood effects and (b) whether results can be confirmed at different spatial resolutions. Explaining the spatial distribution of organic farming in southern Germany (2007) we compare results of spatial lag models at two measurement scales. The results suggest that essential factors determining the decision to convert from conventional to organic farming are found at different spatial resolutions. The results at the lower spatial resolution are not artificially generated through the aggregation process in this case, strengthening the relevance of previous studies.

This paper describes the Italian and German organic certification systems, including the institutions involved and the definitions of non-compliance and sanctions. Although they are both implementations of the same EU regulatory framework, these systems differ in many respects. Case study data from control bodies on non-compliance and sanctions are presented and analysed using binary choice models. This analysis shows that the occurrence of slight non-compliance and greater farm acreage are significant risk factors that explain severe non-compliance in both countries. However, to implement an efficient risk-based inspection system in the future, the data collection process must be improved and extended to examine personal attributes of farmers and operators.

Applying the economics of crime theory , we model the decision of an opportunistic and/or careless organic farmer and derive hypotheses to explain noncompliance. Where empirical data are available, hypotheses are tested. We use data for the years 2007 through 2009 of organic farms certified by Bio Suisse. Imposed sanctions are used as a proxy variable for noncompliance and farm characteristics as explanatory variables. Random effects logit models show that processing activities and livestock diversity significantly increase a farm's sanction probability. Past noncompliances also indicate a higher present sanction probability. Finally, we discuss some methodological issues and suggest a way to organize risk-based inspections more effectively.

This work summarizes the main findings of a workshop conducted at the Universitat Hohenheim in February 2014 with the aim to develop and discuss supporting measures for organic farmers who are willing to (continue to) rent agricultural land. Surprisingly, only a few organic-farming specific challenges could be identified in the German land rental market. As supporting measures for organic farmers the participants suggested (i) to adapt (e.g., the time frame of) governmental support such as agri-environmental payments, (ii) to develop a positive image of organic farming at the regional level (e.g., via information at the organic field) and (iii) to systematically improve the relationship between landlord and organic tenant.

Organic food markets substantially rely on a reliable control system. To identify and isolate the effect of different factors potentially influencing the uniform implementation of organic controls, we apply a stepwise estimation of different logit models. Using control data of German farms from five important control bodies we identify, first, risk factors for non-compliance of farms, second, the impact of the control body as well as, third, the potential impact of governmental institutions which are in charge for the implementation of the control system. The results indicate a need for a more harmonized implementation of the German organic control system.

This article introduces a special section in Food Policy that discusses organic certification systems. Among the various issues in this field, the three articles in this special section address (i) the determinants of farmers’ decisions regarding whether to utilise organic production practices and whether to apply for organic certification; (ii) consumer preferences for different organic labels; and (iii) how to organise effective risk-based inspections. Key policy recommendations derived from this special section include continuously striving to reduce the transaction costs of all actors in the organic certification process, implementing a risk-based inspection system based on the collection of adequate farm data by certification bodies and harmonising the supervision of the certification system within the EU.