Temporal similarity of the mechanization level and crop yield in Eritrea

BACKGROUND: The dynamics of agricultural production have gradually moved toward mechanization and maximizing efficiency. Understanding the relationship between mechanization and crop yield is crucial to improve productivity. AIM: This study assesses the temporal pattern similarity between crop yield and the level of mechanization (LOM) and examines how LOM influences crop productivity over time. MATERIALS AND METHODS: The study used ordinary least squares (OLS) regression with interaction terms for trend analysis and dynamic time warping (DTW) for pattern similarity. Descriptive statistics (standard deviation, mean, minimum, and maximum) and error metrics (MAE and RMSE) were used to assess the DTW distance performance between sequences. RESULTS: The OLS analysis showed almost parallel trend lines (slopes 0.038% and 0.053%). The DTW analysis showed significant temporal alignment, with a 44.4% perfect match and a similarity score of 34 (34 optimal paths across 28 dataset pairs). Performance evaluation metrics—standard deviation, mean, minimum, and maximum—were 7.56 × 10−3, 1.08 × 10−2, 1.42 × 10−5, and 3.22 × 10−2, respectively. MAE and RMSE values were 6.33 × 10−3 and 7.56 × 10−3, respectively. Based on these values, average similarity, consistency, alignment quality, and error metrics were used to assess the level of similarity. These values indicate high similarity and consistency (based on the low mean DTW distances, standard deviation, and error metrics), despite occasional poor alignment. CONCLUSION: The temporal similarity between LOM and crop yield showed that variations in LOM significantly impacted cereal crop yields. Agricultural productivity could benefit from mechanization through the use of contemporary technologies, improved supportive policies, and the integration of sustainable practices.