A thermal forecasting model for the overwintering generation of cotton bollworm by remote sensing in the southeast of Caspian Sea

Keywords: Degree-Day (DD) model, Land Surface Temperature (LST), Helicoverpa armigera


Aim of study: Cotton bollworm (Helicoverpa armigera) is a key pest of cotton all around the world. The Degree-Day (DD) model, as a reliable forecasting approach, is based on the cumulatively effective temperature which must be received by the pests to complete their life cycle. The main objective of the current research was the feasibility of using two accessible thermal data to predict the emergence time of the first generation of H. armigera.

Area of study: Golestan province of Iran

Material and methods: The lower temperature threshold (T0) and the thermal constant (k) were calculated by separately incubating batches of 10 pupae (≥24 h) at a wide range of temperatures (20, 25, 30, 35, and 40 ) in laboratory conditions. The thermal requirements of the overwintering generation were estimated via two types of thermal data sources, i.e., Land Surface Temperature (LST) of Terra® satellite and synoptic meteorological stations from January 21st, 2020 to the end of May 2020.

Main results: T0 and k of the pupal stage were found to be 9.75±1.41°C and 250.57±4.66 (DD), respectively, via the linear regression and 10.26±1.09°C and 240.85±6.71 (DD) through Ikemoto & Takai’s model. The time series of satellite thermal data (LST-day and LST-night) modified through laboratory DD parameters was validly identified to determine high-risk areas and predict the emergence times of the first generation of cotton bollworm. This was in agreement with the reports of the governmental Plant Protection Organization.

Research highlights: If there is a lack of meteorological synoptic stations in some agricultural areas, the LST data of Terra® satellite could be replaced by the meteorological data for DD forecasting models.


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How to Cite
JokarM. (2022). A thermal forecasting model for the overwintering generation of cotton bollworm by remote sensing in the southeast of Caspian Sea. Spanish Journal of Agricultural Research, 20(2), e1001. https://doi.org/10.5424/sjar/2022202-18439
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