Determinants Of Households’ Crop Production Decision And Its Risk In Adea Berga Distinct, West Shoa Zone, Oromiya, Ethiopia

Abstract

This study aims to analyse the determinants that influencing the households’ crop production decision and its risk in Adea Berga Distinct. Agricultural production is subject to various risks that affect farmers' crop choices and productivity. Primary data were collected from 369 randomly selected farming households through surveys and interviews. The data were analysed using descriptive statistics and econometric modelling, specifically the Negative Binomial Regression Model and the Multinomial Logistic Regression Model, with SPSS version 25 and STATA version 14. The findings indicated that key factors such as the age of household heads, total land size, climate change, and conflict significantly increased crop production risks. Conversely, livestock size, access to extension services, and training were found to mitigate risks. Regarding crop production choices, the multinomial logistic regression model revealed that factors such as education level, total land size, availability of agricultural inputs, access to agricultural infrastructure, and agro-ecological conditions significantly influenced farmers’ decisions. Education and input availability positively influenced the selection of cereal crops, while land size, climate change, and conflict had a negative association with this choice. Agricultural inputs, infrastructure, and agro-ecological zone positively impacted the choice of cereal crops and pulses, while land size had a negative association. Based on these findings, the study recommends enhancing support for older farmers, improving access to extension services and training, promoting sustainable land management, ensuring the availability of agricultural inputs, investing in agricultural infrastructure, and supporting crop diversification. Addressing security issues and mitigating climate change impacts are also crucial for improving agricultural resilience and productivity. These insights can guide policymakers and stakeholders in designing targeted interventions to enhance farmers’ adaptive capacity and overall agricultural performance