Logo Lanfrica
  • Home
  • Atlas
  • Insights
  • Docs
  • Sign in

© 2026 Lanfrica. All rights reserved. All copyrights of the resources shown on the Lanfrica website belong to the original copyright holders, unless explicitly stated otherwise.

A Machine Learning Approach to Predicting and Mitigating Climate-Induced Agricultural Risks in Nigeria

Domain:

agricultureclimate
Creator:
OnyDr.AmaOli
Publisher:
Que
Host:
Agricultural productivity in Nigeria is significantly challenged by climate change given that the nation is heavily reliant on rain-fed farming systems. This study explores the role of machine learning in agroclimatic risk modelling, discussing its capability to predict and mitigate climate-induced risks such as droughts, pest outbreaks and floods. The study further investigates the integration of climatic and non-climatic factors in risk evaluation and the application of machine learning algorithms for predictive purposes. Furthermore, it discusses the practical implications for relevant stakeholders including farmer, extension workers, and policymakers, focusing on strategies to enhance resilience and sustainability. The findings illustrate the transformative potential of machine learning in mitigating agro-climatic risks in a changing climatic condition.

Visit

doi.org

Similar

Predicting Childhood Anaemia in Nigeria: A Machine Learning Approach to Uncover Key Risk FactorsPredicting Potato Diseases in Smallholder Agricultural Areas of Nigeria Using Machine Learning and Remote Sensing-Based Climate DataA Machine Learning Approach to Predicting Wellbore Instability in the Niger DeltaClimate-Informed Machine Learning for Predicting Heat-Related Health Risks: Towards Resilient and Personalized Early Warning SystemsOn predicting school dropouts in Egypt: A machine learning approachA Machine Learning Approach to Defining and Predicting the Scale of Typhoid Fever Outbreaks

Predicting Childhood Anaemia in Nigeria: A Machine Learning Approach to Uncover Key Risk Factors

Background : Childhood anaemia remains a significant public health challenge, par

Predicting Potato Diseases in Smallholder Agricultural Areas of Nigeria Using Machine Learning and Remote Sensing-Based Climate Data

Crop disease management is crucial for sustainable food production. Although farmers in Nigeria cont

A Machine Learning Approach to Predicting Wellbore Instability in the Niger Delta

Abstract Wellbore instability is a major drilling challenge in the Niger Delta whic

Climate-Informed Machine Learning for Predicting Heat-Related Health Risks: Towards Resilient and Personalized Early Warning Systems

Severe heat events cause over 489,000 deaths each year worldwide, with forecasts suggesting a 2.3–4.

On predicting school dropouts in Egypt: A machine learning approach

Abstract Compulsory school-dropout is a serious problem affecting not only the education systems, b

A Machine Learning Approach to Defining and Predicting the Scale of Typhoid Fever Outbreaks

Abstract Despite improvements in access to clean water and sanitation, typhoid fever outbreaks cont