Climatic determinants of lowland rice development

Abstract

Accurate modelling of plant development is the basis for any assessment of climate change impact on crop yields. Most rice models simulate development (phenology) based on temperature and photoperiod, but often the reliability of these models is reduced beyond the environment they were calibrated for. In our study, we tested the effects of relative air humidity and solar radiation on leaf appearance rate in greenhouse experiments and analysed data sets from field studies conducted in two extremely different rice‐growing environments in Nepal and Senegal. We also analysed environmental effects on duration to flowering of one popular IRRI material (IR64) for eight different sites covering the entire temperature range where rice is widely cultivated. Both low relative air humidity and low solar radiation significantly decreased leaf appearance rate. Mean air temperature explained 81% of the variation in duration to flowering across sites, which was furthermore significantly influenced by relative air humidity. Across all sites, a simple linear regression approach including mean air temperature and mean relative humidity in the calculation of duration to flowering led to a root mean square error (RMSE) of 10 days, which was slightly lower than the RMSE of 11 days achieved with an automated calibration tool for parameter optimization of cardinal temperatures and photoperiod sensitivity. Parameter optimization for individual sites led to a much smaller prediction error, but also to large differences in cardinal temperatures between sites, mainly lower optimum temperatures for the cooler sites. To increase the predictive power of phenological models outside their calibration range and especially in climate change scenarios, a more mechanistic modelling approach is needed. A starting point could be including relative air humidity and radiation in the simulation procedure of crop development, and presumably, a closer link between growth and development procedures could help to increase the robustness of phenological models.

Season‐specific varietal management as an option to increase rainfed lowland rice production in East African high altitude cropping systems

“…For the short‐duration genotypes, the only difference in weather between the two years was the lower radiation in 2017, which was a result of both early sowing and differences between years. Stuerz et al (2020) showed that low radiation can have a prolonging effect on rice development, but they did not find this effect under field conditions. Since early sowing resulted in a longer photoperiod, photoperiodism can also not be excluded in explaining the increased cycle length.…”

“…Consequently, for medium‐ and long‐duration varieties, temperature does not explain the observed differences in duration between years very well. Since low air humidity can extend crop duration (Stuerz et al, 2020), the difference in air humidity between the two years is the most likely weather parameter explaining the shortened duration in 2017. Since air humidity declined slowly after the end of the rainy season, early sowing could enhance rate of development of medium‐ and long‐duration varieties during the vegetative stage.…”

Creating the data basis to adapt agricultural decision support tools to new environments, land management and climate change—A case study of the RiceAdvice App

“…Modelling approaches similar to the engine running RiceAdvice are being followed by established, more complex crop growth models such as APSIM (Agricultural Production Systems sIMulator, a multi‐module model focusing on plant, soil and management aspect, simulating biophysical processes and impacts of management decisions, http://www.apsim.info), DSSAT (Decision Support System for Agrotechnology Transfer, including dynamic crop growth simulation related to soil‐plant‐atmosphere dynamics, http://www.dssat.net) and Oryza2000/V3 (a rice growth and development centred cropping model family focusing on abiotic stresses, Boumann et al, 2001). There are different approaches implemented to model ecophysiological processes, some of which might need to be readjusted based on current findings (Stuerz et al, 2020). One possibility to improve RiceAdvice could be to implement a modelling engine similar to Oryza2000, but adapted for the needs of a modern, mobile, app‐based extension tool.…”

Development of EST-SSR Markers Linked to Flowering Candidate Genes in Elymus sibiricus L. Based on RNA Sequencing

“…Grenier et al [ 53 ] reported flowering time were highly correlated with latitudinal and racial distributions of landraces that can be affected by day-length variation. Stuerz et al [ 54 ] found that the key climatic factors affecting flowering were humidity and temperature. Mean air temperature explained 81% variation in duration to flowering across sites, which was furthermore significantly influenced by relative air humidity in lowland rice.…”