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Tropentag 2022, September 14 - 16, Prague, Germany

"Can agroecological farming feed the world? Farmers' and academia's views."

Genotype by environment interactions affecting simulation of rice phenology

Linda Groot Nibbelink1, Folkard Asch1, Kazuki Saito2

1University of Hohenheim, Inst. of Agric. Sci. in the Tropics (Hans-Ruthenberg-Institute), Sweden
2Africa Rice Center (AfricaRice), Côte d'Ivoire


Adapting rice production in Sub-Saharan Africa to future challenges such as climate change and maintaining food security requires functional crop models to evaluate the potential of a production environment in combination with selected rice varieties. The backbone of such models is accurately simulating phenology across a wide spectrum of environments. Rice garden experiments were conducted at five of AfricaRice’s research locations with 25 sowing dates (SD): Cotonou, Benin, 2SD; Mbe, Ivory Coast, 5SD; Ambohibary, Madagascar, 5SD; Fanaye, Senegal, 7SD; Ruvu, Tanzania, 6SD. We simulated days from sowing to flowering (DTF) for 80 varieties across all these environments using cardinal temperatures derived from three existing phenology models developed by Summerfield et al. (1992), Dingkuhn et al. (1995), and Stuerz et al. (2020). The data from this experiment showed that the relationship between development rate (DR) and mean temperature is not linear as assumed in Summerfield’s model, but rather stagnates as temperature increases. We therefore developed a model (Asch-Groot Nibbelink; AGN) where this relationship was captured by fitting a second order regression (DR=a*Tmean2+b*Tmean+c) and taking two tangents: one horizontal at the vertex and one sloped with tangency point where DR is half of DR at the vertex. Tbase is where DR=0 while Topt is where the two tangents intersect. Temperature sum is the inverse of the slope of the sloped tangent. When regressing residuals (simulated DTF – observed DTF) against other climatic factors such as photoperiod, radiation, vapour pressure deficit, and relative humidity (RH), we found that RH explained 38,4% of the residuals. Therefore, AGN was adjusted to include a genotype-specific RH adjustment factor resulting in Topt increasing with increasing RH. With a slope of 0.937, an r2 of 0.938 and RMSE of 12.3 days when regressing simulated DTF on observed DTF, AGN proofed to simulate genotype by environment effects on phenology better than the three existing rice phenology models. We suggest an RH adjustment factor for optimum temperature to be included into existing rice growth models, e.g. RIDEV and ORYZA2000.

Keywords: Oryza sativa, phenology, relative humidity, rice, temperature

Contact Address: Linda Groot Nibbelink, University of Hohenheim, Inst. of Agric. Sci. in the Tropics (Hans-Ruthenberg-Institute), Spolegatan 24, 22219 Lund, Sweden, e-mail: linda.grootnibbelink@gmail.com

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