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Publications (10 of 69) Show all publications
Ritter, M., Djabarian, Y., Grosse, M., Holm-Mueller, K., Leonhardt, H. & Uhlemann, J.-P. (2022). Digitale Lehre – was bleibt? Die Lehren aus den Digitalsemestern für die Hochschullehre in den Wirtschaftsund Sozialwissenschaften des Landbaus [Digital Teaching - what remains? The Lessons from the Digital Semesters for University Teaching in the Economic and Social Sciences of Agriculture]. Berichte über Landwirtschaft, 100(1), 1-22
Open this publication in new window or tab >>Digitale Lehre – was bleibt? Die Lehren aus den Digitalsemestern für die Hochschullehre in den Wirtschaftsund Sozialwissenschaften des Landbaus [Digital Teaching - what remains? The Lessons from the Digital Semesters for University Teaching in the Economic and Social Sciences of Agriculture]
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2022 (German)In: Berichte über Landwirtschaft, ISSN 0005-9080, Vol. 100, no 1, p. 1-22Article in journal (Refereed) Published
Abstract [en]

The spontaneous conversion of university teaching to digital teaching in the summer semester of 2020 due to the Corona pandemic provided a digitalization boost to German universities. After much experience and a gradual return to on-campus teaching, it is time to consider how a meaningful combination of on-campus and digital teaching might look like after the end of the Corona pandemic. This article presents various student and lecturer experiences and lessons learned and their plans for future teaching. It also discusses in more detail the conceptual implementation of blended learning, the benefits and challenges of digital tools, and digital exams. Many opportunities emerge to retain elements of digital teaching in on-campus teaching. This requires support from the universities and an increased exchange among teachers.

Place, publisher, year, edition, pages
Bundesministerium Ernahrung Landwirtschaft, 2022
National Category
Educational Sciences
Identifiers
urn:nbn:se:hj:diva-56113 (URN)10.12767/buel.v100i1.402 (DOI)000761689000001 ()2-s2.0-85132635664 (Scopus ID)POA;intsam;803862 (Local ID)POA;intsam;803862 (Archive number)POA;intsam;803862 (OAI)
Available from: 2022-03-29 Created: 2022-03-29 Last updated: 2023-02-20Bibliographically approved
Schmidt, L., Odening, M., Schlanstein, J. & Ritter, M. (2022). Exploring the weather-yield nexus with artificial neural networks. Agricultural Systems, 196, Article ID 103345.
Open this publication in new window or tab >>Exploring the weather-yield nexus with artificial neural networks
2022 (English)In: Agricultural Systems, ISSN 0308-521X, E-ISSN 1873-2267, Vol. 196, article id 103345Article in journal (Refereed) Published
Abstract [en]

CONTEXT: Weather is a pivotal factor for crop production as it is highly volatile and can hardly be controlled by farm management practices. Since there is a tendency towards increased weather extremes in the future, understanding weather-related yield factors becomes increasingly important not only for yield prediction, but also for the design of insurance products. Although insurance products mitigate financial losses for farmers, they suffer from considerable basis risk, i.e., a discrepancy between losses and the indemnity payment. OBJECTIVE: The objective of this paper was to explore the potential of machine learning for estimating the relationship between crop yield and weather conditions at the farm level and to use it as a tool for reducing basis risk in index insurance applications. METHODS: An artificial neural network was set up and calibrated to a rich set of farm-level yield data in Germany, covering the period from 2003 to 2018. A nonlinear regression model, which uses rainfall, temperature, and soil moisture as explanatory variables for yield deviations, served as a benchmark. RESULTS AND CONCLUSIONS: The empirical application revealed that compared with traditional estimation approaches, the gain in forecasting precision by using machine learning techniques was substantial. Moreover, the use of regionalized models and disaggregated high-resolution weather data improved the performance of artificial neural networks. A considerable part of yield variability at the farm level, however, could not be captured by statistical methods which solely use “big weather data”. SIGNIFICANCE: Our findings have important implications for the design of weather-index based insurance because they document that a rather high level of basis risk remains if insurance products are based on an estimation of the weather-yield relationship.

Place, publisher, year, edition, pages
Elsevier, 2022
Keywords
Basis risk, Index insurance, Machine learning, Weather risk, Yield prediction, artificial diet, artificial nest, artificial neural network, crop production, crop yield, exploration, soil moisture, spatiotemporal analysis, Germany
National Category
Economics
Identifiers
urn:nbn:se:hj:diva-55392 (URN)10.1016/j.agsy.2021.103345 (DOI)000793272400001 ()2-s2.0-85120945024 (Scopus ID)HOA;intsam;785329 (Local ID)HOA;intsam;785329 (Archive number)HOA;intsam;785329 (OAI)
Available from: 2021-12-20 Created: 2021-12-20 Last updated: 2023-02-20Bibliographically approved
Plogmann, J., Mußhoff, O., Odening, M. & Ritter, M. (2022). Farm growth and land concentration. Land use policy, 115, Article ID 106036.
Open this publication in new window or tab >>Farm growth and land concentration
2022 (English)In: Land use policy, ISSN 0264-8377, E-ISSN 1873-5754, Vol. 115, article id 106036Article in journal (Refereed) Published
Abstract [en]

Structural change in agriculture is characterized by the interdependency of farms’ growth decisions due to the scarcity of agricultural land. This paper adds to the sparse empirical literature on the relation between land market concentration and farm size changes, considering different definitions of the relevant market. Using data from the Integrated Administrative Control System (IACS) from 2005 until 2017 for Brandenburg, Germany, we find that about half of the land transactions occur beyond municipality borders. This emphasizes the importance of carefully defining the relevant market. The descriptive analysis shows that although concentration rates, on average, did not increase over time, spatial differences are present. In the econometric analysis, we apply a two-stage model to analyze how competition for agricultural land impacts the probability and relative level of expansion. For farms that remained active between 2005 and 2017, we find a negative relation between farm size and relative growth. Our conjecture that higher inequality of land distribution fosters the expansion of large farms was not confirmed.

Place, publisher, year, edition, pages
Elsevier, 2022
Keywords
Agricultural land markets, Concentration measures, Farm growth, IACS, Structural change
National Category
Economics
Identifiers
urn:nbn:se:hj:diva-55962 (URN)10.1016/j.landusepol.2022.106036 (DOI)000784232200003 ()2-s2.0-85124396323 (Scopus ID)
Funder
German Research Foundation (DFG)
Available from: 2022-03-02 Created: 2022-03-02 Last updated: 2023-02-20Bibliographically approved
Plogmann, J., Mußhoff, O., Odening, M. & Ritter, M. (2022). Farmland sales under returns and price uncertainty. Economic Modelling, 117, Article ID 106044.
Open this publication in new window or tab >>Farmland sales under returns and price uncertainty
2022 (English)In: Economic Modelling, ISSN 0264-9993, E-ISSN 1873-6122, Vol. 117, article id 106044Article in journal (Refereed) Published
Abstract [en]

This paper investigates the observed heterogeneity in the liquidity of agricultural land markets. We adopt a real options model to determine the value of an opportunity to sell farmland and derive the optimal disinvestment triggers. A proportional hazards model is applied to estimate the duration between land sales in Germany and test the implications of the real options model. In contrast to expectations, we find an ambiguous effect of returns and price volatility on the optimal timing of land sales. There is no evidence that non-agricultural investors buy and sell land more frequently than farmers. Our results contribute to the current discussion on land market regulations, one major point of which is capping land prices. According to our results, such policies could increase the rent–price ratio and thus discourage land sales. In turn, the land supply would be reduced, causing further price pressure on farmland markets.

Place, publisher, year, edition, pages
Elsevier, 2022
Keywords
Cox proportional hazards model, Duration model, Farmland supply, Land market liquidity, Land price uncertainty, Real options model
National Category
Economics
Identifiers
urn:nbn:se:hj:diva-58617 (URN)10.1016/j.econmod.2022.106044 (DOI)000868890100001 ()2-s2.0-85138658726 (Scopus ID)
Funder
German Research Foundation (DFG)
Available from: 2022-10-10 Created: 2022-10-10 Last updated: 2023-02-20Bibliographically approved
Kionka, M., Odening, M., Plogmann, J. & Ritter, M. (2022). Measuring liquidity in agricultural land markets. Agricultural Finance Review, 82(4), 690-713
Open this publication in new window or tab >>Measuring liquidity in agricultural land markets
2022 (English)In: Agricultural Finance Review, ISSN 0002-1466, E-ISSN 2041-6326, Vol. 82, no 4, p. 690-713Article in journal (Refereed) Published
Abstract [en]

Purpose: Liquidity is an important aspect of market efficiency. The purpose of this paper is threefold: first, this paper aims to discuss indicators that provide information about liquidity in agricultural land markets. Second, this paper aims to reflect on determinants of market liquidity and analyze the relationship with land prices. Third, this paper aims to conduct an empirical analysis for Germany that illustrates these concepts and allows hypothesis testing.

Design/methodology/approach: This study reviews liquidity dimensions and measurement in financial markets and derives indicators applicable to farmland markets. In an empirical analysis, this study exhibits the spatial and temporal variability of land market liquidity in Lower Saxony, a German federal state with the highest agricultural production value. This study uses a rich dataset that includes 72,547 sale transactions of arable land between 1990 and 2018. The research focuses on volume-based (number of transactions, volume and turnover) and time-based (trading frequency and durations) measures. A panel vector autoregression and Granger causality tests are applied to investigate the relation between land turnover and land prices.

Findings: The paper confirms the thinness of farmland markets but also reveals regional and temporal heterogeneity of land market liquidity. This study finds that the relation between market liquidity and prices is ambiguous. This study concludes that a high demand from expanding farms absorbs supply shocks regardless of the current price level in agricultural land markets.

Originality/value: Even though the relevance of agricultural land markets’ thinness is widely acknowledged in the literature, this paper is one of the first attempts to measure liquidity in agricultural land markets and to explain its relationship with land prices.

Place, publisher, year, edition, pages
Emerald Group Publishing Limited, 2022
Keywords
Agricultural land markets, Granger causality, Liquidity indicators, Panel vector autoregressive model
National Category
Economics
Identifiers
urn:nbn:se:hj:diva-55159 (URN)10.1108/AFR-03-2021-0037 (DOI)000696666700001 ()2-s2.0-85114897508 (Scopus ID)
Available from: 2021-11-26 Created: 2021-11-26 Last updated: 2022-12-11Bibliographically approved
Kionka, M., Musshoff, O., Ritter, M., Uhlemann, J.-P. R. & Odening, M. (2022). Optimal reserve prices for land auctions in Eastern Germany. Applied Economics Letters
Open this publication in new window or tab >>Optimal reserve prices for land auctions in Eastern Germany
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2022 (English)In: Applied Economics Letters, ISSN 1350-4851, E-ISSN 1466-4291Article in journal (Refereed) Epub ahead of print
Abstract [en]

Privatization auctions are seen as culprit for rising prices in eastern Germany. This study aims to analyse whether privatization auctions could have earned even higher revenues by setting optimal reserve prices. For this purpose, a structural estimation approach is applied to determine reserve prices and to compare the resulting expected revenues with actual revenues. The data set includes land auctions in eastern Germany from 2005-2019. The empirical results illustrate that room for increasing revenues exists, but reserve prices would be required to be higher than actual land prices. A potential consequence of high reserve prices is the risk of delays in the privatization process.

Place, publisher, year, edition, pages
Taylor & Francis, 2022
Keywords
Land prices, privatization, first price sealed bid auctions, optimal reserve prices
National Category
Economics
Identifiers
urn:nbn:se:hj:diva-58978 (URN)10.1080/13504851.2022.2140752 (DOI)000878558300001 ()2-s2.0-85141434535 (Scopus ID)HOA;intsam;1713066 (Local ID)HOA;intsam;1713066 (Archive number)HOA;intsam;1713066 (OAI)
Available from: 2022-11-23 Created: 2022-11-23 Last updated: 2024-01-15
Ritter, M., Ott, D. V. M., Paul, F., Haynes, J.-D. & Ritter, K. (2021). COVID-19: a simple statistical model for predicting intensive care unit load in exponential phases of the disease. Scientific Reports, 11(1), Article ID 5018.
Open this publication in new window or tab >>COVID-19: a simple statistical model for predicting intensive care unit load in exponential phases of the disease
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2021 (English)In: Scientific Reports, E-ISSN 2045-2322, Vol. 11, no 1, article id 5018Article in journal (Refereed) Published
Abstract [en]

One major bottleneck in the ongoing COVID-19 pandemic is the limited number of critical care beds. Due to the dynamic development of infections and the time lag between when patients are infected and when a proportion of them enters an intensive care unit (ICU), the need for future intensive care can easily be underestimated. To infer future ICU load from reported infections, we suggest a simple statistical model that (1) accounts for time lags and (2) allows for making predictions depending on different future growth of infections. We have evaluated our model for three heavily affected regions in Europe, namely Berlin (Germany), Lombardy (Italy), and Madrid (Spain). Before extensive containment measures made an impact, we first estimate the region-specific model parameters, namely ICU rate, time lag between infection, and ICU admission as well as length of stay in ICU. Whereas for Berlin, an ICU rate of 6%, a time lag of 6 days, and a stay of 12 days in ICU provide the best fit of the data, for Lombardy and Madrid the ICU rate was higher (18% and 15%) and the time lag (0 and 3 days) and the stay in ICU (3 and 8 days) shorter. The region-specific models are then used to predict future ICU load assuming either a continued exponential phase with varying growth rates (0–15%) or linear growth. By keeping the growth rates flexible, this model allows for taking into account the potential effect of diverse containment measures. Thus, the model can help to predict a potential exceedance of ICU capacity depending on future growth. A sensitivity analysis for an extended time period shows that the proposed model is particularly useful for exponential phases of the disease. 

Place, publisher, year, edition, pages
Springer Nature, 2021
Keywords
epidemiology, Europe, forecasting, Germany, hospitalization, human, intensive care, intensive care unit, Italy, pandemic, pathogenicity, procedures, Spain, statistical model, COVID-19, Critical Care, Humans, Intensive Care Units, Models, Statistical, Pandemics, SARS-CoV-2
National Category
Public Health, Global Health, Social Medicine and Epidemiology Economics
Identifiers
urn:nbn:se:hj:diva-54509 (URN)10.1038/s41598-021-83853-2 (DOI)000626138700013 ()33658593 (PubMedID)2-s2.0-85101989962 (Scopus ID)
Available from: 2021-11-23 Created: 2021-11-23 Last updated: 2022-09-15Bibliographically approved
Schmidt, L., Odening, M., Schlanstein, J. & Ritter, M. (2021). Estimation of the farm-level yield-weather-relation using machine learning. In: : . Paper presented at GEWISOLA2021, Annual Conference of the German Society of Economic and Social Sciences in Agriculture, September 22-24, 2021 online, Humboldt-Universität zu Berlin Faculty of Life Sciences, Department of Agricultural Economics.
Open this publication in new window or tab >>Estimation of the farm-level yield-weather-relation using machine learning
2021 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Weather is a pivotal factor for crop production as it is highly volatile and can hardly be controlled by farm management practices. Since there is a tendency towards increased weather extremes in the future, understanding the weather-related yield factors becomes increasingly important not only for yield prediction, but also for the design of insurance products that mitigate financial losses for farmers. In this study, an artificial neural network is set up and calibrated to a rich set of farm-level wheat yield data in Germany covering the period from 2003 to 2018. A nonlinear regression model, which uses rainfall, temperature, and soil moisture as explanatory variables for yield deviations, serves as a benchmark. The empirical application reveals that the gain in estimation precision by using machine learning techniques compared with traditional estimation approaches is quite substantial and that the use of regionalized models and high-resolution weather data improve the performance of ANN.

Keywords
Yield Prediction, Machine Learning, Weather Risk, Risk Management, Index Insurance
National Category
Economics
Identifiers
urn:nbn:se:hj:diva-55170 (URN)
Conference
GEWISOLA2021, Annual Conference of the German Society of Economic and Social Sciences in Agriculture, September 22-24, 2021 online, Humboldt-Universität zu Berlin Faculty of Life Sciences, Department of Agricultural Economics
Available from: 2021-11-27 Created: 2021-11-27 Last updated: 2022-10-18Bibliographically approved
Wilke, A., Shen, Z. & Ritter, M. (2021). How much can small-scale wind energy production contribute to energy supply in cities?: A case study of Berlin. Energies, 14(17), Article ID 5523.
Open this publication in new window or tab >>How much can small-scale wind energy production contribute to energy supply in cities?: A case study of Berlin
2021 (English)In: Energies, E-ISSN 1996-1073, Vol. 14, no 17, article id 5523Article in journal (Refereed) Published
Abstract [en]

In light of the global effort to limit the temperature rise, many cities have undertaken initiatives to become climate-neutral, making decentralized urban energy production more relevant. This paper addresses the potential of urban wind energy production with small wind turbines, using Berlin as an example. A complete framework from data selection to economic feasibility is constructed to enable the empirical assessment of wind energy for individual buildings and Berlin as a whole. Based on a detailed dataset of all buildings and hourly wind speed on a 1 km² grid, the results show that multiple turbines on suitable buildings can significantly contribute to households’ energy consumption but fall short of covering the full demand. For individual households, our economic evaluation strongly recommends the self-consumption of the produced electricity. The findings suggest that while the use of small wind turbines should be continuously encouraged, exploring other renewable resources or combination of wind and photovoltaic energy in the urban environment remains important.

Place, publisher, year, edition, pages
MDPI, 2021
Keywords
Energy transition, Renewable energy, Urban wind energy, Wind potential assessment, Economic analysis, Energy utilization, Wind, Wind turbines, Economic evaluations, Economic feasibilities, Empirical assessment, Photovoltaic energy, Renewable resource, Small-scale wind energies, Urban environments, Wind energy production, Wind power
National Category
Economics Energy Engineering
Identifiers
urn:nbn:se:hj:diva-55158 (URN)10.3390/en14175523 (DOI)000694223000001 ()2-s2.0-85114483648 (Scopus ID)
Available from: 2021-11-27 Created: 2021-11-27 Last updated: 2023-08-28Bibliographically approved
Balmann, A., Graubner, M., Müller, D., Hüttel, S., Seifert, S., Odening, M., . . . Ritter, M. (2021). Market Power in Agricultural Land Markets: Concepts and Empirical Challenges. German Journal of Agricultural Economics (GJAE), 70(4), 213-235
Open this publication in new window or tab >>Market Power in Agricultural Land Markets: Concepts and Empirical Challenges
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2021 (English)In: German Journal of Agricultural Economics (GJAE), ISSN 0002-1121, E-ISSN 0515-6866, Vol. 70, no 4, p. 213-235Article in journal (Refereed) Published
Abstract [en]

This paper provides review about challenges and opportunities to assess and quantify market power in agricultural land markets. Measuring land market power is challenging because the characteristics of this production factor hinder the direct application of familiar concepts from commodity markets. Immobility, fixed availability, and large heterogeneity of land and potential users contradict assumptions of fictitious point market for homogeneous goods. Moreover, the use of concentration indicators for policy assessments is hampered by two problems. First, defining the relevant regional size of the market is challenging and concentration indicators are not robust with regard to market size and number of actors. Second, high concentration of land ownership or land operation may point at potential market power, but it may also be the result of an efficient allocation of land due to structural change in agriculture. The aforementioned challenges are illustrated with a case study for the Federal State of Brandenburg in Germany. Using available data for land sales, a regression analysis reveals a negative relationship between land use concentration and farmland prices. This result can be interpreted as an indication of market power on the buyer side in agricultural land markets. However, it is hardly possible to translate this finding into recommendations for land market regulations because the evaluation of the potential misuse of dominant positions in land markets requires a case-specific analysis. Providing evidence for the exertion of market power in land markets is extremely complex and deserves further attention from researchers and politicians.

Place, publisher, year, edition, pages
Deutscher Fachverlag GmbH, 2021
National Category
Economics
Identifiers
urn:nbn:se:hj:diva-55168 (URN)10.30430/gjae.2021.0117 (DOI)
Available from: 2021-11-27 Created: 2021-11-27 Last updated: 2021-11-27Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0003-2543-3673

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