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Agriculture, as the primary industry in every country, significantly contributes to GDP and sustains large populations. As an effect of greenhouse gas emissions, global warming have increased and they have become a topic of concern for the whole globe for many years now and continue to become even more serious as their effects are more visible in agriculture factors like extreme weather fluctuations significantly impact crop yields in whole world. We need a comprehensive understanding of the environmental factors affecting agricultural production to address this. One approach is to develop a model that links crop productivity to climate change. Additionally, species distribution modelling (SDM) plays a crucial role in predicting future yield production, considering the impact of greenhouse gases in future. SDM, often powered by machine learning tools, helps in solving ecological problems. Analysing climatic variables can predict the occurrence of species. This chapter explores various machine learning models used in SDM, including support vector machines, random forests, MaxEnt, and relevant terminology.

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Predictive Analytics in Agriculture: Assessing Crop Yield Through Species Distribution Models and Machine Learning in the Context of Greenhouse Gas Dynamics Over Time

  • 章节

  • 发表日期：

    2025年

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  • Neelam Bohra,
  • Anil Gupta

摘要

Agriculture, as the primary industry in every country, significantly contributes to GDP and sustains large populations. As an effect of greenhouse gas emissions, global warming have increased and they have become a topic of concern for the whole globe for many years now and continue to become even more serious as their effects are more visible in agriculture factors like extreme weather fluctuations significantly impact crop yields in whole world. We need a comprehensive understanding of the environmental factors affecting agricultural production to address this. One approach is to develop a model that links crop productivity to climate change. Additionally, species distribution modelling (SDM) plays a crucial role in predicting future yield production, considering the impact of greenhouse gases in future. SDM, often powered by machine learning tools, helps in solving ecological problems. Analysing climatic variables can predict the occurrence of species. This chapter explores various machine learning models used in SDM, including support vector machines, random forests, MaxEnt, and relevant terminology.