Projection models for agriculture risk analysis

Abstract

Due to increasing food demands, high rates of population growth (2-5% per year), and major changes in political, economic and social fragmentation, the agriculture system will be deprived. In the interest of sustainability, we should develop and revise many existing agricultural policies at the national level. Estimation of agriculture risk is the key role for scientists in the revision of crop patterns in different agro climatic zones. In any science, statistics are the fundamental and formidable tool for decision-making theory (projection of agricultural productivity and economic prosperity of the country). More advanced analytical based research will be necessary to make accurate decisions about policy implementation. As of now, there are fewer analytical-based publications cited across the globe. In this pragmatic research gap, statistical methods will play an important role in the development of decision support algorithms by considering the large number of parameters that are collected from the laboratory to the agriculture field. However, decision-makers at all levels need an increasing amount of information to help them understand the possible outcomes of their decisions. For the abovementioned research gap, we attempt to formulate a user-friendly decision-support GIS-based interactive model with greater accuracy to manage the existing agricultural system. The present model will be very useful for agriculture scientists, meteorologists, and policymakers to make the right decisions at the right time for the projection of rainfall with an ambient large substitution of weather parameters, spatial and agricultural management traits. These newer models have eagerly fulfilled all the necessary analytical research interventions for agricultural scientists and policy planners.