Emerging technologies for integrated nematode management: remote sensing or proximal sensing as a potential tool to detect and identify nematode infestation.

Remote or proximal sensing defines the use of optical sensors, in combination with a carrier platform, to obtain information from objects in a non-invasive manner. Optical properties of plants provide valuable information on the health status, vitality or developmental stages of plants. The difference among remote-sensing and proximal-sensing technologies is mainly characterized by the distance between the measurement system and the object of interest. This chapter discusses physiological reactions influencing optical characteristics in nematode infested plants, remote sensing with satellites, the use of robots and drones for a more flexible infield assessment, as well as the analysis and interpretation of remote-sensing data. Some case studies with pine wood nematode (Bursaphelenchus xylophilus) and sugarbeet cyst nematode (Heterodera schachtii) are presented. Further use of remote and proximal sensing for the advancement of agriculture is also mentioned.

A model of egg production by Heterodera schachtii (Nematoda: Heteroderidae)

Egg production by the sugarbeet cyst nematode, Heterodera schachtii, infecting sugarbeet, Beta vulgaris, was assessed at temperatures of 13, 18, 24, and 30 °C in constant-temperature tanks. The minimum-threshold temperature for degree-day accumulation relative to egg production was identified as 8 °C. The relationship between cumulative degree-days (DD) (base 8 °C) and egg production was examined. Egg production began between 160 and 270 DD after hatch and reached a maximum between 390 and 480 DD. Observed egg production (Y) across all temperatures was described as a logistic function of degree-days: Y = 202/(1 + 23726 e−0.034 DD) (r2 = 0.84; P < 0.05). The model depicts initiation of egg production at 140 DD, a maximum average egg production of 202 eggs per female reached at approximately 410 DD, and a maximum rate of egg production of 1.7 eggs/DD reached at approximately 290 DD. The accuracy of the model was limited because observed cumulative egg production was confounded by egg hatch. Accordingly, the cumulative egg production data were adjusted for egg hatch. The adjusted cumulative egg production (Y) was described as a logistic function of cumulative degree-days: Y = 420/(1 + 3319 e−0.023 DD) (r2 = 0.98; P < 0.05). The function depicts egg production initiated at approximately 120 DD. Average maximum egg production is 420 eggs per female occurring at approximately 680 DD, with a maximum egg-production rate of 2.4 eggs/DD occurring at approximately 350 DD.