Field-based infrastructure and cyber-physical system for the study of high night temperature stress in irrigated rice

Authors: QUINONES, CHERRYL - Arkansas State University; Adviento-Borbe, Arlene; LARAZO, WENCESLAO - Arkansas State University; HARRIS, RODNEY - Arkansas State University; MENDEZ, KHARLA - Arkansas State University; CUNNINGHAM, SHANNON - Arkansas State University; CAMPBELL, ZACHARY - Arkansas State University; MEDINA-JIMENEZ, KARINA - Arkansas State University; HEIN, NATHAN - Kansas State University; OTTIS, BRIAN - Ricetec, Inc; WALIA, HARKAMAL - University Of Nebraska; LORENCE, ARGELIA - Arkansas State University

Submitted to: The Plant Phenome Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/12/2023
Publication Date: 11/20/2023
Citation: Quinones, C., Adviento-Borbe, A.A., Larazo, W., Harris, R.S., Mendez, K., Cunningham, S., Campbell, Z., Medina-Jimenez, K., Hein, N., Ottis, B., Walia, H., Lorence, A. 2023. Field-based infrastructure and cyber-physical system for the study of high night temperature stress in irrigated rice. The Plant Phenome Journal. 6(1):1-19. https://doi.org/10.1002/ppj2.20085.
DOI: https://doi.org/10.1002/ppj2.20085

Interpretive Summary: High night temperature (HNT) in paddy rice has been extensively investigated because it significantly reduces rice yield and grain quality which consequently lowers the price of rice in the US and global market. HNT studies are mostly conducted in the greenhouse because of ease in heat implementation at critical rice growth stage. However, greenhouses do not reflect actual growing field conditions, hence results from controlled environment are likely not accurate to apply in commercial rice fields. This study used a computerized greenhouse equipped with smart temperature sensors, module, generator, fan and gas heater to raise automatically specific air temperature at night. The study used 310 rice accessions representing diverse rice parental lines across the globe and 10 US hybrid cultivars. The results showed that the computerized greenhouses accurately increased 4oC night air temperature relative to ambient night air temperature at flowering stage of rice during 2 years of study. The physical structure of the greenhouse was able to withstand strong winds and rains and suitable for moving within paddy fields. Our results demonstrate the validity of the computerized infrastructure to control air temperature inside a covered area in the field at specific temperature and time of the day. This dynamic infrastructure can be utilized by crop breeders, researchers and extension workers for field experimentation such as rain out shelter for water stress study and cyber tunnel to study crop growth under future climate.

Technical Abstract: High night temperature stress (HNT) negatively impacts both rice yield and grain quality and have been extensively investigated because of the significant yield loss observed (10%) for every increase of air temperature (1°C). Most of the rice HNT studies have been conducted under greenhouse conditions, with limited information on field-level responses for the major rice sub-populations. This is due to lack of a field-based phenotyping infrastructure that can accommodate a diverse set of accessions representing the wider rice germplasm and impose growth stage specific HNT stress imposition. In this study, we built six high tunnel greenhouses in the field and screened 310 rice accessions from Rice Diversity Panel 1 (RDP1) and 10 commercial hybrid cultivars in a replicated design. Each high tunnel greenhouse had heating and cyber-physical system that sensed ambient air temperature and automatically increased night air temperature to about 4 °C relative to ambient temperature in the field for two cropping seasons. On average, the system successfully imposed HNT stress of 4.01°C and 3.94°C as recorded by Raspberry Pi sensors for two weeks in 2019 and 2020, respectively. Similarly, HOBO data loggers recorded 2.9°C and 2.07°C temperature differential of ambient air between control and heated greenhouses in 2019 and 2020, respectively. These greenhouses were able to withstand constant flooding, heavy rain, strong winds, and thunderstorms. Several US rice cultivars showed an average of 24% and 15% yield reduction under HNT during 2019 and 2020 cropping seasons, respectively. Our study highlights the potential of a computer-based infrastructure for accurate implementation of HNT or other abiotic stresses, flexible to different crops with specific crop environment under field growing conditions.