Abstract
Spatial econometrics in agriculture focuses on modelling spatial dependencies in data, recognizing that agricultural outcomes are often influenced by geographic proximity and spatial interactions. This approach acknowledges that agricultural phenomena, such as crop yields, pest outbreaks, and soil quality, can exhibit spatial patterns that traditional econometric models may overlook. By incorporating spatial elements into econometric analysis, researchers can better understand how neighbouring regions or locations influence each other's agricultural outcomes. This is crucial for policymakers and farmers seeking to optimize resource allocation, manage environmental impacts, and enhance productivity in agriculture. Spatial econometrics provides a robust framework to uncover hidden relationships and spatial interactions within agricultural data, thereby supporting informed decision-making and sustainable agricultural practices in a spatially interconnected world.
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