Humboldt-Universität zu Berlin - Department of Agricultural Economics

Current Research Projects

more research projects: research data base of Humboldt-Universität zu Berlin

contact: Prof. Dr. Oliver Mußhoff, oliver.musshoff@hu-berlin.de

Climate Change and Agriculture

Weather is both a key production factor and one of the greatest sources of risk in agriculture worldwide. As climate change progresses, fluctuations in temperature and precipitation patterns are expected to intensify, increasing uncertainty and vulnerability across farming systems in both the Global North and the Global South. While farmers in temperate regions face new challenges such as changing growing seasons, extreme rainfall, and heat stress, smallholder farmers in tropical and subtropical regions remain particularly exposed to droughts, floods, and shifting weather patterns that threaten food security and livelihoods. This research project aims to analyze the economic impacts of climate change on agricultural production systems at the farm and sectoral levels. To do so, we combine climate, yield, farm, and sector models to simulate how changing climatic conditions affect productivity, profitability, and risk exposure. By integrating these models, we can capture the complex interactions between biophysical and economic processes and provide insights into how different regions and farming systems may adapt to a changing climate. The findings are intended to inform strategies that enhance the resilience and sustainability of agriculture globally, promoting adaptive measures tailored to local conditions in both developed and developing regions.

Credit Markets

Our research on rural credit markets aims to better understand the functioning and inclusiveness of agricultural finance in countries where smallholder farming is a central economic activity, including Mali, Madagascar, and Tanzania. Access to formal credit is a critical factor for productivity, investment, and resilience, yet rural credit systems in these contexts often remain underdeveloped and may exhibit inequalities. Studying how loans are allocated, who receives them, and under what conditions is therefore essential to identify structural barriers to financial inclusion and agricultural growth. By combining detailed loan-level data with empirical and machine learning methods, our work provides a comprehensive view of lending practices, credit demand, and borrower characteristics across diverse agricultural settings. This research is particularly relevant in environments where climate shocks, liquidity constraints, and gender disparities limit the potential of rural households. Understanding the factors that shape access to finance can inform policies and financial innovations that promote more equitable, resilient, and productive credit systems in both the Global South and comparable contexts worldwide.

Oil Palm in Indonesia

The massive development of oil palm plantation in South East Asia and in Indonesia in particular, as the largest oil palm producer in the world, benefits the socio-economics condition of Indonesian smallholder farmers. At the same time, this eventually also contributes to the environmental damage. The issues of oil palm plantation and the environmental damage have been discussed globally, and the concept of sustainable palm oil was later evolved in the form of sustainable palm oil certification including Indonesian Sustainable Palm Oil (ISPO) and Roundtable Sustainable Palm Oil (RSPO). However, although smallholder farmers contribute to 40% of national production, while most of them are not certified. The objective of our research is to examine whether an individual smallholder farmer reacts on environmental policies including environmental information, price premium, and recognition by others. In order to do so, we employ experimental techniques.

Price Risk Management

In July 2010, the European Parliament identified increasing price volatility resulting from the ongoing liberalization of agricultural markets as a key challenge for individual farms in shaping agricultural policy after the 2013 reform. To address this issue, the need for instruments to mitigate fluctuations was emphasized. Contracts traded on commodity futures exchanges (e.g., futures contracts) were highlighted as potentially effective tools for dealing with volatile market and climate conditions. However, within the EU, farmers have so far made very limited use of futures contracts. The aim of this research project is to examine the use of commodity futures exchanges and farmers’ perceptions and associations related to futures contracts, in order to understand why their use remains relatively low in Europe compared to farmers in the United States.

Risk Mitigation Potential of Index-Based and Satellite-Based Insurance

Our research examines how innovative financial instruments, particularly index-based and satellite-based insurance, can help farmers manage weather-related production risks in both the Global North and the Global South. Weather is a critical determinant of agricultural productivity, and as climate change intensifies the frequency of droughts, floods, and other extreme events, farmers across all regions face increasing income instability. Traditional on-farm risk management strategies, such as irrigation systems or drought-tolerant crop varieties, can mitigate some of these risks but are often insufficient on their own. Complementary instruments like agricultural insurance are therefore essential to enhance resilience and stabilize farm incomes. Since the mid-1990s, index-based weather insurance, also known as weather derivatives, has been discussed as a promising alternative to traditional loss-based insurance. These contracts link pay-outs to objectively measurable weather indices, such as rainfall or temperature, reducing issues of moral hazard and adverse selection. However, despite their theoretical appeal, markets for such instruments remain underdeveloped worldwide due to pricing challenges, limited data availability, and imperfect correlations between weather indices and actual yield outcomes, known as basis risk. To address these limitations, our research explores the use of satellite-based indicators to improve the design and effectiveness of index-based insurance. Remotely sensed measures such as the Normalized Difference Vegetation Index (NDVI) and Brightness Temperature (BT) provide globally available, spatially detailed, and consistent datasets that can reduce geographical basis risk and improve the precision of pay-out mechanisms. These indices, reflecting vegetation health and surface temperature, can be combined into Vegetation Health Indices (VHI) that correlate strongly with crop yields and thus enhance the reliability of insurance products. By applying whole-farm risk modelling and cross-regional comparisons, our research seeks to evaluate the economic and resilience benefits of these innovative tools and to identify the design features and policy frameworks that encourage their adoption. Ultimately, by integrating satellite data and financial innovation, index-based insurance can become a more reliable, scalable, and equitable mechanism for managing agricultural risk—supporting farmers in both developed and developing regions in adapting to the growing volatility of agricultural production under climate change.

Smallholder Farmers in India

Our research in India explores how behavioural, psychological, and environmental factors intersect to shape the daily lives and decision-making of smallholder farmers. By combining field-based measurements with survey and experimental data, we study how aspects such as sleep, cognitive performance, and economic preferences interact in real-world rural settings. Focusing on smallholder farmers near Bengaluru, we aim to provide insights that go beyond laboratory studies or urban samples, highlighting the lived realities of individuals whose livelihoods depend on agriculture and variable environmental conditions. Through this work, we seek to better understand how factors like rest, stress, and socioeconomic conditions influence productivity, well-being, and economic decision-making, thereby contributing to a more inclusive understanding of human behaviour in developing country contexts.

Land Markets

The formation of prices on agricultural and forestry land markets has long been a subject of debate among economists. In particular, further empirical analysis is needed to examine the determinants and statistical dependencies between land rental and purchase prices. In efficient markets, rental and purchase prices should be cointegrated. Empirical studies show that rental and purchase price developments do not follow the relationship expected in asset pricing models. In addition to the question of whether speculative bubbles could explain the discrepancy between theoretical and empirically observed land prices, conditional copula regression models are used to examine the relationship between rental and purchase prices.

Agricultural Structural Change, including Efficiency and Competitiveness of Farms

The description and analysis of agricultural structural change has long been a topic of interest in agricultural economics. On the one hand, business structure is seen as an important determinant of the competitiveness of businesses in the agricultural and food industry. On the other hand, it has considerable socio-political significance due to the income aspects associated with it. In addition, there are significant innovations (e.g., digitalization, drone technology, field robots, and artificial intelligence) that agricultural enterprises (can) use. A fundamental understanding of agricultural structural change is necessary in order to predict structural change and make recommendations for action based on political goals. However, it can be observed, for example, that adaptation processes to changing economic conditions often do not take place at the speed at which they should according to superficial expectations (economic hysteresis). Existing approaches only allow structural change to be captured in its complexity to a limited extent. With the help of numerical option valuation methods and econometric approaches, the explanatory potential and explanatory content of the real option approach for agricultural structural change will be investigated.

Analysis of Entrepreneurial Decision-making Behavior and Ex Ante Policy Impact Assessment

Social and political stakeholders are interested in influencing the behavior of entrepreneurs by changing the framework conditions. In policy impact assessment in particular, predictions must be made about how companies will react to changed conditions (e.g., a changed form of support for measures that increase biodiversity or animal welfare). Since changes in framework conditions are only reflected in decisions to the extent that they are perceived by the actors and incorporated into their planning, such forecasts must take into account the “nature” of real economic entities, including their bounded rationality. Otherwise, there is a risk of designing measures for actors that do not exist in reality. Within the framework of classical business analyses and economic experiments, the aim is to investigate how different political measures actually work and how limited the rationality of agricultural decision-makers is.

 

Price Formation and Agricultural Land Markets

Land is a crucial production factor in agriculture. In developed countries this input factor is usually in short supply and its overall availability shrinks permanently. On the contrary, the increasing demand of growing farms and more recently of non-agricultural investors causes price pressure on the land market. Thus, it is not surprising that land prices increased in recent years. Though the analysis of land markets is a core topic in agricultural economics, many questions are still unanswered. For example, do land prices reflect the price boom for agricultural commodities and bio-energy? Can land prices be fully explained by fundamental factors or are they also driven by speculative bubbles? What is the role of non-agricultural investors? Should land markets be regulated and if so, what are the most efficient instruments?

Against this background the project aims at understanding the recent developments on land markets, particularly in Germany and the EU. The focus will be on quantitative modelling and empirical (econometric) analyses.

Contact: Prof. Dr. Martin Odening, m.odening(at)agrar.hu-berlin.de
Cooperation: Prof. Dr. Silke Hüttel, Universität Göttingen

Liquidity on the agricultural land market

Liquidity constitutes a decisive factor for the efficient functioning of a market. It describes the ability of market participants to realize desired buy or sell transactions without a time delay. Poor liquidity bears the risk of either paying additional premia on top of a “fundamental” value or losing money if an immediate transaction shall be enforced. The determinants and impact of liquidity are well-explored in financial markets and real estate markets, both theoretically and empirically. When analyzing liquidity on agricultural land markets, one has to consider the special characteristics of farmland: It is limited,immobile, extremely heterogeneous, and in short supply. Since it is not traded on exchanges, common liquidity measures as the bid-ask spread cannot be derived for farmland markets. Moreover, there is no counterpart for exchange traded Real Estate Investments Trusts (REITs) on agricultural land markets. Further, there is little knowledge about the relationship between market liquidity and prices on land markets. Itis even unclear if this relationship is positive or negative. Thus, the objectives are to measure market liquidity on land markets with different indicators and methods as well as to explore its relationship with prices.

Contact: Marlene Kionka, marlene.kionka(at)agrar.hu-berlin.de

Machine Learning in Agricultural Risk Management and Agricultural Land Markets - Prediction, Classification and Causal Inference

Machine learning offers a great variability of tools and techniques for various tasks and allows with the flexible structure of most of the machine learning models an adaption to specific problems. Especially when it comes to prediction machine learning techniques in particular supervised machine learning techniques can provide an enormous improvement in comparison to classical statistical approaches. With higher computational power and the higher availability of large data sets, applications of machine learning models are rising. The thematic focus in agriculture is often on plant sciences or animal sciences. In the field of agricultural economics, the application of such methods is not as widespread as it is in other research areas and applications that are going beyond the basic prediction and classification tasks are rare, especially in agricultural risk management and agricultural land markets. Therefore, this research project aims to develop and apply machine learning models to these specific topics to address the existing shortcomings of traditional statistical approaches.

Contact: Lorenz Schmidt, lorenz.schmidt.1(at)hu-berlin.de