Towards assessing agricultural land suitability with causal machine
learning
- URL: http://arxiv.org/abs/2204.12956v1
- Date: Wed, 27 Apr 2022 14:13:47 GMT
- Title: Towards assessing agricultural land suitability with causal machine
learning
- Authors: Georgios Giannarakis, Vasileios Sitokonstantinou, Roxanne Suzette
Lorilla, Charalampos Kontoes
- Abstract summary: We use causal machine learning to estimate the effect of crop rotation and landscape crop diversity on Net Primary Productivity in the Flanders region of Belgium.
We find that the effect of crop rotation was insignificant, while landscape crop diversity had a small negative effect on NPP.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Understanding the suitability of agricultural land for applying specific
management practices is of great importance for sustainable and resilient
agriculture against climate change. Recent developments in the field of causal
machine learning enable the estimation of intervention impacts on an outcome of
interest, for samples described by a set of observed characteristics. We
introduce an extensible data-driven framework that leverages earth observations
and frames agricultural land suitability as a geospatial impact assessment
problem, where the estimated effects of agricultural practices on
agroecosystems serve as a land suitability score and guide decision making. We
formulate this as a causal machine learning task and discuss how this approach
can be used for agricultural planning in a changing climate. Specifically, we
extract the agricultural management practices of "crop rotation" and "landscape
crop diversity" from crop type maps, account for climate and land use data, and
use double machine learning to estimate their heterogeneous effect on Net
Primary Productivity (NPP), within the Flanders region of Belgium from 2010 to
2020. We find that the effect of crop rotation was insignificant, while
landscape crop diversity had a small negative effect on NPP. Finally, we
observe considerable effect heterogeneity in space for both practices and
analyze it.
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