Climate and soil conditions shape farmers’ climate change adaptation preferences

Christian Stetter^a, Carla Cronauer^b

^a Agricultural Economics and Policy Group, ETH Zurich
^b Potsdam Institute for Climate Impact Research (PIK)

3 April 2025 | Research Seminar | Land Use, Ecosystem Services and Biodiversity Group |
Centre for Environmental Research (UFZ)

Climate Change Puts Harvests at Risk in Germany

Figure 1: Estimated marginal revenue losses in Euro/ha for winter wheat in Euro/ha of one additional summer drought day.[1]

Climate Change: A Significant Threat to (Arable) Agriculture

Agriculture is one of the most vulnerable sectors to the negative effects of climate change.

Global farming productivity is 21% lower than it could have been without climate change [2]

Arable crop cultivation is particularly susceptible to climate change due to its unprotected weather exposure.

Crop yield losses [1] – Product quality loss [3] – Financial losses [4]

Crop farms need to offset these negative impacts by adapting to the changing climate

Unprecedented rate and intensity of climate change and associated uncertainty will test the adaptability of farmers [5]

What does the literature say?

There are ample adaptation options for farmers:

Irrigation
Use of resistant crop varieties
Insurance
Diversification
…

Many studies have focused on:

Identifying the factors that drive farmers to adopt specific adaptation strategies [6].
The impact of adaptation itself [7]
Or both [8].
More generally measuring the extent and potential of adaptation on the whole [9].
Analyzing the (cost-)effectiveness of specific climate change adaptation strategies in agriculture [10].

… why yet another study on this topic?

Reason 1: The need to understand farmers’ preferences among adaptation measures

Many previous studies have been criticized for not sufficiently considering the needs and preferences of farmers in the context of climate change adaptation [11]

Many previous studies either
- Focused on one specific adaptation measure
- Measured adaptation in an abstract sense

This might not reflect farmers’ actual decision making process

Given the abundance of available adaptation options and given farmers’ limited resources, understanding how they evaluate and prioritize among multiple available adaptation measures is key for successful implementation.

Reason 2: Farms are heterogenous: The need for context-specific adaptation

Farmers operate under diverse production environments (climate, soil).

Environmental conditions significantly influence farmers’ production choices and adaptation needs.

No One-Size-Fits-All Solution: Climate change adaptation isn’t uniform; solutions vary by region and farm.

Understanding this heterogeneity is vital for effective policy and has often been neglected by previous studies.

Research Question and Objectives:
Understanding Farmers’ Adaptation Priorities

Which climate change adaptation activities are considered most relevant by farmers, and how are their preferences linked to local environmental conditions?

Objectives:

Determine the relative preference farmers have for different adaptation strategies.
Explore the role of climatic (temperature, precipitation) and soil conditions in shaping these preferences.
Focus on German arable farming, a sector particularly vulnerable to climate change.

Results outlook:

Farmers tend to prefer low-cost, incremental adaptation measures
Diversifying one’s crop rotation is most preferred
Insurance is consistently the least preferred adaptation measure
There exists substantial preference heterogeneity w.r.t to environmental conditions

Contribution to the Literature: Focusing on Farmers’ Perspectives

Farmer Relevance:
Shifts focus from just effective strategies to what is relevant to farmers’ decision-making.

Comparative Evaluation:
Uses Best-Worst Scaling (BWS) to evaluate adaptation strategies collectively and in mutual comparison, offering a more nuanced understanding than isolated assessments.

Environmental Context:
Examines how farmers’ preferences are linked to local climatic and soil conditions, acknowledging the interconnectedness of adaptation choices and environmental factors.

Research design (i)

Adaptation options inventory

Extensive literature search: >250 measures

Filter and aggregate most relevant options

Based on literature, interviews with farmers and stakeholders

Final list of adaptation measures

Adjustment of management rhythm

Use of irrigation

Insurance

Diversify crop rotation

Business diversification

Use of drought-resistant crops

Use of catch crops

Adjustment of fertilizer use

Adjustment of farm chemicals use

Use of precision farming

Use of mixed crops

Conservation tillage

Use of drought-resistant crop varieties

Research design (ii)

Generate BWS experiment

The respondent was asked to choose the most and least preferred

Brief description of each of the measures as provided to respondents

Each respondent was shown 13 choice sets

Conduct survey & experiment

The survey was conducted online from January to March 2021

German apprenticing farmers were approached via e-mail

Effective sample size of 624

Combining Survey Data with Environmental Information

Environmental data:

Climate: Average temperature and precipitation during the growing season (1991-2020) from the German Meteorological Service (DWD).
Soil Quality: Soil biomass productivity index from the European Soil Data Centre (ESDAC).
Spatial Aggregation: Environmental data linked to farm locations based on postcode areas.

Empirical analysis

A land user \(n\) obtains a certain level of indirect utility \(U_{nit}\) from an adaptation measure \(i\) in a choice situation \(t\): \[ U_{nit} = V_{nit} + \epsilon_{nit} \]

with a systematic component \(V\) and a random component \(\epsilon\) (unobserved decision-relevant elements):

\[ V_{int}= \sum^{12}_{i=1}\beta_{in} X_{int} \]

with \(X=1\) if selected as best, \(X=-1\) if selected as worst, 0 otherwise.

The distribution of the coefficient estimates follow: \[ \beta_{in} = \beta_i + \delta_{i1} Rainfall_n + \delta_{i2} Temperature_n \\ + \delta_{i3} SoilQuality_n + \Omega_{in} v_{in} \]

with:

\(ProdSys=1\) if conventional farm, \(-1\) if organic farm
\(Loc=1\) if farm is located in East Germany, \(-1\) if farm is located in West Germany
\(ManFoc=1\) if farm is non-specialized crop farm, \(-1\) if farm is specialized crop farm
\(\Omega_{in} v_{in}\) captures individual-specific unexplained variation around the mean and allows for correlation across the attribute-related random coefficients

Use of simulated ML to estimate different versions of mixed logit models using 1,000 Halton draws to estimate coefficients (preference ranking).

Descriptive statistics

Key Findings - Overall Preferences

Key findings: Climate and Soil Conditions Shape Farmers’ Climate Change Adaptation Preferences

Drier Conditions: Linked to preferences for crop rotation adjustments and conservation tillage.

Wetter Conditions: Associated with the use of cover crops and mixed cropping.

Hotter Conditions: Conservation tillage, resilient crops and varieties become more interesting.

Cooler Conditions: Linked to diversified rotations.

Poorer Soils: Adaptation preferences more varied.

Better Soils: Strong preference for crop rotation diversification.

Results that did not make it to the paper:
Heterogeneity w.r.t. farm characteristics

Results that did not make it to the paper:
Heterogeneity w.r.t. behavioral traits

Adaptation by others: Do you know any farmers who are implementing measures to adapt to climate change?

Feel climate impact: Has your farm been affected by one or more negative climate impacts in recent years?

Risk seeker: Are you generally a risk-taking person or do you try to avoid risks? (Likert scale)

Perception: Is it important to you how you are perceived by others? (Likert scale)

Discussion - Implications for Policy

Heterogeneity is key: Farmers’ adaptation preferences are diverse and context-specific.

One-size-fits-all policies are likely to be suboptimal.

Need for tailored approaches: Policies should consider local farming conditions.

Incentivizing Effective Measures:
- Tailored Support: Given diverse preferences based on climate/soil, policies need localized information and incentives.
- Financial support can encourage adoption of currently less preferred but impactful strategies (e.g., business diversification, irrigation).
- Invest in research for locally relevant climate-resilient solutions.

Policymakers need to understand farmers’ preferences to design effective and acceptable adaptation policies.

Conclusion - Key Takeaways

Farmers prioritize incremental adaptation: Closing this effectiveness gap will be key for successful adaptation to climate change at the farm level

Farmers have heterogeneous preferences: Climate and soil conditions shape adaptation preferences.

Policy needs to be tailored and consider local context.

THANK YOU

cstetter@ethz.ch | LinkedIn | BlueSky | Google Scholar

References

[1]

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