Modeling the impact of improved seed varieties on common beans productivity in agroecological zones of Tanzania: A stochastic simulation approach

Beans are a critical source of food and income for smallholder farmers in Tanzania; yet, their productivity remains low due to a reliance on traditional varieties and limited adoption of inputs. This study evaluates the impact of improved seed varieties on bean productivity across diverse agroecological zones (AEZs) in Tanzania, employing a stochastic simulation approach. Drawing on nationally representative data from the 2019/20 National Sample Census of Agriculture (NSCA), we compared yield distributions for farms using local versus improved seeds. Monte Carlo simulations assessed the probabilities of achieving productivity thresholds of 0.6 t/ha (low) and 1.5 t/ha (high) during both long and short rainy seasons. Findings reveal that improved seeds significantly increase the likelihood of higher yields, particularly in the short rainy season. In zones such as the Lake and Eastern regions, improved seed users had a 42–49% probability of surpassing the global standard of 1.5 t/ha, compared to only 34% for local seed users. However, this yield gain was accompanied by slightly higher variability and risk of yield failure. The long rainy season presented less favorable conditions, with over 50% of farms, regardless of seed type, yielding less than 0.6 t/ha and minimal chances of exceeding 1.5 t/ha. Spatial variability was evident, with improved seeds showing stronger effects in the Lake, Eastern, and Northern zones. Notably, local seeds continued to demonstrate profitability during the short rainy season. The study recommends improving access to improved seeds, enhancing extension services, and implementing input subsidies for marginalized AEZs. These findings support policy interventions that boost resilience, productivity, and food security, aligning with Sustainable Development Goal 2 (Zero Hunger). The study also highlights the need for spatially differentiated strategies and provides empirical evidence to inform adaptive agricultural policy and investment planning.