Factors other than climate change are currently more important in predicting how well fruit farms are doing financially
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Abstract (arXiv)
Machine learning and statistical modeling methods were used to analyze the impact of climate change on financial wellbeing of fruit farmers in Tunisia and Chile. The analysis was based on face to face interviews with 801 farmers. Three research questions were investigated. First, whether climate change impacts had an effect on how well the farm was doing financially. Second, if climate change was not influential, what factors were important for predicting financial wellbeing of the farm. And third, ascertain whether observed effects on the financial wellbeing of the farm were a result of interactions between predictor variables. This is the first report directly comparing climate change with other factors potentially impacting financial wellbeing of farms. Certain climate change factors, namely increases in temperature and reductions in precipitation, can regionally impact self-perceived financial wellbeing of fruit farmers. Specifically, increases in temperature and reduction in precipitation can have a measurable negative impact on the financial wellbeing of farms in Chile. This effect is less pronounced in Tunisia. Climate impact differences were observed within Chile but not in Tunisia. However, climate change is only of minor importance for predicting farm financial wellbeing, especially for farms already doing financially well. Factors that are more important, mainly in Tunisia, included trust in information sources and prior farm ownership. Other important factors include farm size, water management systems used and diversity of fruit crops grown. Moreover, some of the important factors identified differed between farms doing and not doing well financially. Interactions between factors may improve or worsen farm financial wellbeing.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 4 of its 82 references that are in the map. First posted on arXiv 11 Jan 2023.
Builds on
4 of 82 references.
References · 4
- 2019Tackling Climate Change with Machine Learningcited by 264
- 2018Deep learning in agriculture: A surveycited by 160
- 2020Coupling Machine Learning and Crop Modeling Improves Crop Yield Prediction in the US Corn Beltcited by 17
- 2022Sparse-group boosting -- Unbiased group and variable selectioncited by 3
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Citing papers per year: 2023: 1.
1 paper.