Adoption of Climate-Smart Agriculture in Farm Production: A Machine Learning Approach

Abstract

Productivity gains in agriculture are crucial for economic and employment development, yet understanding how they interact is still evolving. Agricultural productivity, particularly crop production, is expected to increase by more than 60% to prevent a global food crisis by 2050. Increased farm production and productivity require inputs and technical services. The farming sector must meet global food demands amid weather changes and unexpected health crises.

Agricultural production must increase by about 70% to cater to food needs in the coming years. This paper utilizes machine learning applications in crop production as an alternative to the current agricultural systems in the United States. Applying machine learning techniques ensures agricultural productivity and is a fruitful step toward mitigating the possibility of a global food crisis.