Abstract
<title>Abstract</title> <p>This study investigates the effects of data missingness on five agro-climatic indices: Maximum Consecutive Dry Days (CDD), Maximum Consecutive Wet Days (CWD), Seasonal Totals, Season Onset, and Season Cessation. These impacts were assessed under three patterns of data missingness, Missing at Random (MAR), Missing Completely at Random (MCAR), and Missing Not at Random (MNAR), as well as at three missingness volumes: 5%, 10%, and 20%. To characterise the effects, three error metrics were used to compare indices calculated from a degraded time series against those from a complete one, using a Monte Carlo simulation that cycled through all possible combinations of missingness pattern, volume, and temporal position within the time series. The results showed that the MNAR mechanism caused the most severe bias in the indices, while MAR caused the least severe bias. The results also showed that uncertainty in the indices increased as the missingness volume increased. Additionally, drier areas were less affected by missingness than wetter regions. Among the indices, onset and cessation were the most resilient to data missingness, while CDD was the most sensitive.</p>