Assessing the effectiveness of AGRI UDAAN accelerator program on agri-startups in India: an integrated PCA and machine learning approach

Agri-startups are transforming India’s agricultural landscape by providing innovative solutions and shifting youth from job seekers to job creators. Accelerator programs play a crucial role in strengthening these startups, yet systematic evaluations of their effectiveness remain limited. This study assesses the impact of the AGRI UDAAN accelerator program implemented by a-IDEA on selected agri-startups across multiple performance dimensions. The study employed a semi-structured questionnaire and in-depth interviews with founders and core team members of 52 agri-startups supported under the AGRI UDAAN program. An adapted and modified version of Kirkpatrick’s four-level training evaluation model-reaction, learning, behaviour, and results-was applied to assess program effectiveness. Both qualitative and quantitative analyses were conducted using R software, along with machine learning models (Random Forest, SVM, and XGBoost) to evaluate the influence of demographic and educational factors on learning and behavioural outcomes. Results indicate high program effectiveness, with 85% of participants reporting strong satisfaction and 90% demonstrating significant learning and behavioural changes. The startups recorded a 150% increase in average gross revenue and a 550% rise in funds raised after program participation. A composite performance index revealed that Agri Supply Chain, Agri-Biotechnology, and Agritech sectors achieved values exceeding 0.40, indicating superior outcomes. Machine learning results further validated the predictive role of demographic and educational attributes in driving behavioural change. The findings demonstrate the substantial positive impact of the AGRI UDAAN accelerator on the performance and growth of agri-startups in a developing economy. The program effectively enhanced entrepreneurial capabilities, financial outcomes, and sectoral performance during early-stage growth. This study contributes by identifying key strengths, weaknesses, and critical success factors of accelerator-driven agri-startups, offering insights for policymakers and ecosystem stakeholders aiming to scale innovation-led agricultural entrepreneurship.