LAUSR

Abstract: Stagnating potato (Solanum tuberosum L.) yields in eastern Canada have resulted in loss of competitive advantage in global potato markets. Therefore, there is a need to investigate the potential to increase yield by adopting precision agriculture technology. This study evaluated the efficiency of an apparent soil electrical conductivity (ECa) sensor to delineate management zones (MZs) in two commercial potato fields in New Brunswick, Canada, using an unsupervised fuzzy k-means clustering algorithm. Georeferenced soil samples from 0 to 15 cm depth were analyzed for physicochemical properties. Tuber yields were recorded using a yield monitor. The two MZs delineated using soil ECa differed significantly in soil physicochemical properties for both fields; however, tuber yield differed significantly between MZs only in Field 1. The yield difference (7.1 Mg ha-1) in Field 1 was attributed to a difference in soil moisture (23.5% vs 28.5%) resulting from a difference in clay content (141 vs 189 g kg-1). The lack of a yield difference between MZs in Field 2 may reflect relatively low within-field spatial variability. The soil ECa sensor showed promise for use in commercial potato production in New Brunswick, especially in fields with high spatial variability.