Effect of Technology Adoption on Livestock Farm Modernization and Projects Performance: A Case Study of the Grow with Agriculture, Livestock and Environment Project (Grow ALE) by Yalla Yalla Group in Rwanda (2021-2024)

The study examines the Effect of Technology Adoption on Livestock farm Modernization and Projects Performance, focusing on the Grow with Agriculture, Livestock, and Environment (GrowALE) Project of Yalla Yalla Group. The research specifically evaluates the extent of technology adoption, its impact on key performance indicators such as productivity, efficiency, sustainability, and cost-effectiveness, and the correlation between technology adoption and project performance. A non-experimental research design integrating descriptive and correlational methodologies was employed. The study targeted 179 stakeholders, including project managers, agricultural engineers, veterinarians, agronomists, and IT officers, with a sample size of 179 respondents selected through stratified random sampling. Data were collected using structured questionnaires and analyzed using IBM SPSS version 23.0. Findings for Objective One, which assessed the level of technology adoption, revealed high adoption rates across key technological interventions, with IoT devices (mean = 4.6936, SD = 0.6447), precision farming technologies (mean = 4.7274, SD = 0.5865), automated feeding systems (mean = 4.6823, SD = 0.6139), remote monitoring solutions (mean = 4.6935, SD = 0.6348), and data analytics platforms (mean = 4.6968, SD = 0.6401). These findings indicate that respondents strongly agree on the benefits of technology adoption, with moderate variability in perceptions. For Objective Two, which analyzed the performance outcomes of livestock farm modernization projects, results showed a significant positive impact on productivity (mean = 4.7145, SD = 0.5987), efficiency improvement (mean = 4.7274, SD = 0.5707), sustainability promotion (mean = 4.7113, SD = 0.6040), cost-effectiveness (mean = 4.7064, SD = 0.5887), and animal welfare enhancement (mean = 4.5984, SD = 0.7307). These findings confirm that modernization initiatives contribute to improved project performance, although slight variations exist in respondents' perceptions. Spearman’s correlation coefficient analysis showed a strong positive relationship between technology adoption and project performance (Spearman’s rho = 0.640, p < 0.01). Multiple linear regression results revealed that implementing IoT devices (B = 0.726, p = 0.003) and utilizing data analytics platforms (B = 0.309, p = 0.003) had the strongest impact on project performance, while precision farming technologies (B = 0.018, p = 0.004) and remote monitoring solutions (B = 0.008, p = 0.002) had marginal but statistically significant contributions.