Assessment of the spatial distribution of Banana xanthomonas wilt (BXW) Disease incidence in Rwanda

Abstract

Banana is the second most important food crop in Rwanda with 99% of households growing it. Banana Xanthomonas Wilt (BXW) is a major threat to banana production. All cultivators have reported susceptibiliyu to BXW and with up to 100% yield loss. BXW was first reported in 2005 when it was detected in one district but incidences increased over time with now reports from all 30 districts of Rwanda. BXW is caused by Xanthomonas Compstrus Pv. Mosacearum (xcm) and can spread through farms tools, infected planting materials and even pollinating insects. Most BXW management practices as mat removal, tool sterilization etc. have failed to prevent/halt the spread of BXW and studies are being conducted to understand about the current spatial distribution of BXW incidence and the relation between disease incidence and climatic factors. Climatic factors and BXW incidence are not much studied to aid in effective planning and prevention measures in Rwanda. Disease surveys were conducted in Gasabo, Rwamagana, Rulindo, Rubavu and Gisagara districts. The presence of BXW was found in four provinces and Kigali city with 192 banana farms (identified by sector agronomist) in 192 villages between 2018 to 2019. The relationship between BXW and environmental variables was examined using bivariate correlation modeling approaches, MaxEnt (Maximum Entropy). Highest BXW incidence was recorded in Rubavu (42%) and lowest in Gasabo (22%) districts. This research confirmed 30 banana varieties in farms fields. The highest BXW incidence was found in beer banana variety called Intuntu (15%). Inzizi_inziga (83%), Kivuvu (81%), Gisukari (75%), Inyamunyo (78%), Poyo (71%), Ingenge (71%) were found to be more susceptible to BXW disease while no BXW was observed in Isha, Kayinja, Ingumba and Fhia0 1m. The entropy modeling of BXW incidence showed that the disease is positively correlated with rainfall and negatively with high temperature and altitude. Precipitation was the main explanatory factor for BXW (49.9%) while high temperature had the least relevance to the BXW incidence. A detailed study on BXW incidence in all farms (affected and non-affected) including additional factors (soil, wind) in the model for comprehensive information on BXW incidence associating environmental variables is recommended. Finally, regular monitoring using GIS and remote sensing is recommended for proper planning and effective prevention of BXW Disease.