Spatial Econometrics in Agriculture: Modelling Spatial Dependencies in Data
A. Meena
Department of Statistics and Mathematics, College of Agriculture, Pjtsau, Telengana, India.
B. Manjunatha *
Agricultural Statistics, Indian Agricultural Research Institute-New Delhi, India.
A. Anil Kumar
Agricultural Statistics, Indian Agricultural Research Institute-New Delhi, India.
Jhade Sunil
Department of Farm Engineering, Institute of Agricultural Sciences, Banaras Hindu University, India.
S. Vishnu Shankar
Department of Physical Science and Information Technology, AEC&RI, TNAU, Coimbatore, India.
M. Manjubala
Department of Social Sciences, RVS Agricultural College, Thanjavur, India.
Abinayarajam D
Department of Farm Engineering, Institute of Agricultural Sciences, Banaras Hindu University, India.
Himanshu Sekhar Behera
Department of Soil Science and Agricultural Chemistry, Lovely Professional University, Punjab, 144411, India.
*Author to whom correspondence should be addressed.
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.
Keywords: Spatial, quality, analysis, agricultural, economics