Abstract
{ "background": "Agricultural productivity in sub-Saharan Africa is constrained by inefficient resource management and variable climatic conditions. While automated process-control systems (PCS) offer potential for optimisation, rigorous empirical evaluations of their impact on yield in real-world farming contexts are scarce.", "purpose and objectives": "This study aimed to quantify the causal effect of a sensor-based irrigation and nutrient delivery PCS on crop yield, compared to traditional manual farming practices. The primary objective was to estimate the average treatment effect on the treated (ATT) for yield improvement.", "methodology": "A quasi-experimental design was employed, using propensity score matching to create a counterfactual. Data were collected from 120 smallholder farms, half adopting the PCS and half using manual methods. The core impact was estimated using a weighted least squares model: $Yi = \\beta0 + \\beta1 \\text{PCS}i + \\mathbf{X}i\\boldsymbol{\\beta} + \\epsiloni$, where $Yi$ is yield for farm $i$, and $\\mathbf{X}i$ is a vector of pre-treatment covariates. Robust standard errors were calculated.", "findings": "Adoption of the PCS resulted in a statistically significant yield increase of 22.4% (95% CI: 18.1 to 26.7). The system's efficacy was particularly pronounced in managing water stress periods, with sensor-driven interventions preventing significant moisture deficits observed in control plots.", "conclusion": "The implemented process-control system substantively enhanced agricultural yield under the studied conditions. The quasi-experimental design provides strong evidence for a positive causal impact, moving beyond correlational analysis.", "recommendations": "Policy initiatives should consider subsidies or financing mechanisms to lower the capital cost barrier for smallholder adoption. Further research should investigate the long-term economic viability and system robustness across different agro-ecological zones.", "key words": "precision agriculture, causal inference, propensity score matching, irrigation automation, sub-Saharan Africa", "contribution