Agroforestry adoption in the face of regional weather extremes

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# Agroforestry adoption in the face of regional weather extremes
## Christian Stetter & Johannes Sauer
### AES conference 2022
### Production & Resource Economics Group, Technical University Munich <br /> 2022/04/05 <br />
### <br /> Slides available at: rpubs.com/cstetter/aecp

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# Farming and climate change: a two-fold challenge
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**Driver of climate change**
- Agricultural production causes greenhouse gas emissions

Consequence:<br/>
Agriculture, Forestry and Other Land Use activities accounted for around 23% of total net GHG emissions from human activities globally during 2007-2016 .font70[(Shukla et al., 2019)]
]

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**Vulnerable to climate change**

- Agricultural production strongly depends on weather and climate conditions

Consequence:<br/>
Anthropogenic climate change has reduced global agricultural total factor productivity by about 21% since 1961 .font70[(Ortiz-Bobea et al. 2021)]

]

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# The problem of more more extreme weather events

- With climate change extreme weather events occur more often

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- Extreme events become lengthier

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- Prime examples are 2003 heat wave, 2018 drought in Europe or the 2000s Australian drought

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- Climate extremes, such as droughts or heat waves .font70[(Vogel et al. 2019)]:

- can lead to harvest failures
    - may threaten the livelihoods of agricultural producers
    - may threaten food security of communities worldwide

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# Agroforestry: providing mitigation and adaptation synergies

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**Mitigation**

Mitigate climate change through carbon sequestration

Also indirect mitigation by reducing deforestation and help replace fossil fuels
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**Adaptation**

Positive regulation effects on hydrological cycles, soil, and the microclimate 
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Provision of multiple ecosystem services with positive effects
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**Agroforestry** <br/>
Land-use systems where woody perennials are deliberately integrated with agricultural crops and/or livestock on a piece of land, either in some sort of spatial arrangement or temporal sequence
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- **Focus of this study: Short rotation alley cropping (AC)** <br/>
Crops are grown in the alleyways between widely spaced rows of short rotation coppices

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# Research Questions

Despite its great potential for tackling climate change, agroforestry is not widely adopted by farmers.
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There is very little evidence on farmers' intention to adopt agroforestry in the context of climate change and extreme weather.
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We want to answer the following research questions:

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**1. How likely are farmers to adopt agroforestry in response to regional weather extremes?**
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**2. Under what conditions are farmers willing to adopt agroforestry?**
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To answer these questions, we used the combination of a discrete choice experiment, gridded weather data and a recently developed approach to simulate the impact of extreme weather .font70[(Ramsey et al., 2020)].

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# Theoretical Framework - Land Use, Random Utility and Weather Expectations

- When it comes to planning the usage of their land, farmers face a choice among a set of alternative land uses

- Each farmer obtains a certain level of indirect utility from a land-use alternative. 
- The indirect utility of an alternative cannot be directly measured but can be expressed by a .content-box-blue[**systematic component**] and a *random component*

.pull-left[.content-box-blue[**Land use attributes** <br/>
- Returns per hectare and year
- Returns variability
- Minimum useful lifetime
- Payments for ecosystem services
- Greening area
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.pull-right[.content-box-blue[**Expected weather at the time of the planting decision** <br/>
Assumption: Farmers have adaptive weather expectations based on past local weather history, where both short-term ("noise") and long-term trends ("signal") might affect land use choices
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# Empirical Strategy

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# Data

- Online survey with crop-cultivating farmers in Bavaria, Germany, Oct 2020, final N=198

.pull-left[.content-box-blue[**Discrete Choice Experiment** <br/>
- 3 land use types considered:
   - Short rotation coppice
   - Alley cropping 
   - Common crop rotation
   
- Participants were asked to state their preferred choice between the 3 land use options

- 12 choice tasks, each task representing a unique combination of attribute levels

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<br/>
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.pull-right[.content-box-blue[**Weather history** <br/>
- 0.1 degree gridded daily data

- Weather indicators (Mar – Oct)
    - Average temperature
    - Precipitation sum
    - Number of dry days
    - Number of hot days
    - Number of heavy rain days

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<div style="text-indent: 3em"> *Short-term*: average of years t-1 to t-3 <div>
<div style="text-indent: 3em"> *Long-term*: average of years t-4 to t-10 <div>

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- Data fusion via zip codes
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# Predicting land-use probabilities
1. **Estimation** via correlated mixed logit (simulated maximum likelihood, 1000 Halton draws)