Abstract

Climate change poses significant challenges to agribusiness by increasing uncertainty and disrupting traditional agricultural practices. This chapter explores the development of climate resilient agribusiness models through the integration of advanced agro-meteorological data and comprehensive risk minimization frameworks. Emphasizing the synergy between algorithmic intelligence and social pedagogy, the proposed framework leverages machine learning, predictive analytics, and participatory approaches to enhance adaptive capacity across diverse agricultural contexts. Key technological innovations such as mobile-based advisory services, blockchain-enabled supply chains, digital twins, and crowdsourcing mechanisms are examined for their roles in improving data accessibility, transparency, and real-time decision-making. The chapter also addresses barriers related to data interoperability, digital literacy, and institutional coordination, proposing policy and governance solutions to facilitate large-scale adoption. By bridging technological advancement with community engagement, the chapter contributes to sustainable agribusiness development that can withstand climatic variability and promote food security. The insights presented serve as a foundation for researchers, policymakers, and practitioners aiming to implement resilient agricultural systems in a changing climate.

Introduction

Agricultural systems worldwide are increasingly vulnerable to the adverse impacts of climate change, which manifest through altered rainfall patterns, rising temperatures, and increased frequency of extreme weather events [1]. These environmental shifts disrupt conventional farming practices, reduce crop yields, and intensify economic uncertainty for farmers and agribusiness stakeholders. In this context, building climate resilience has become imperative to safeguard food security [2], sustain rural livelihoods, and ensure the stability of agricultural value chains. Climate resilient agribusiness models focus on enhancing the adaptive capacity of farming systems by incorporating innovative tools and strategies that can anticipate, absorb, and recover from climatic shocks [3]. One promising approach involves the integration of agro-meteorological data—detailed weather and climate information collected through satellites, weather stations, and remote sensing technologies—into decision-making frameworks. Such integration enables stakeholders to access accurate, timely, and location-specific climate intelligence that informs farm management, supply chain logistics, and risk assessment [4]. This chapter investigates how these data-driven approaches, coupled with comprehensive risk minimization frameworks, can foster agribusiness resilience, thereby supporting sustainable agricultural development amid climate variability [5].

Technological advancements in artificial intelligence (AI) and data analytics have revolutionized the ability to process complex agro-meteorological datasets and generate actionable insights [6]. Machine learning models, for instance, analyze historical and real-time climate data to forecast weather patterns, predict pest outbreaks, and estimate crop yields with increasing accuracy. These predictive capabilities enable agribusinesses to anticipate risks and optimize resource allocation [7], reducing losses and enhancing productivity. In addition, decision support systems built on AI algorithms provide farmers and agribusiness managers with customized recommendations that align with site-specific conditions [8]. The utilization of mobile-based advisory services further extends the reach of these innovations, delivering weather forecasts and agronomic advice directly to end-users, including smallholder farmers who often lack access to traditional extension services. Integration of blockchain technology into supply chains enhances traceability and transparency, facilitating climate-resilient procurement and distribution processes [9]. Furthermore, the development of digital twins and simulation models allows stakeholders to explore various climate scenarios and test management strategies virtually before implementation. These technologies collectively contribute to a more responsive, flexible, and sustainable agribusiness environment [10].