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.

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Accurate measurement of leaf transpiration (E) and related stomatal conductance (g s ) is fundamental to understanding plant energy dynamics and water relations at all scales, from pot to field (Pearcy et al., 2000). It is inextricably linked to photosynthesis as E and carbon assimilation share the same route in their way out of and into the leaf (Stanhill, 1986). It is a major component of the hydrological cycle and is a useful reflection of the adjustments of a plant to the surrounding environment. Available methods to measure E in herbaceous plants include: (1) porometers or infrared gas analyzers (IRGAs) that measure either at the single leaf or whole-plant level; (2) lysimeters that measure intact soil column weight loss (usually combining E and evaporation of individual plants or smaller communities) (Bello & Van Rensburg, 2017); (3) Eddy covariance; and (4) remote sensing methods encompassing landscape and regional scales with satellite image analysis (Talsma, 2018). However, measurements on individual leaves using hand-held IRGAs/porometers are unable to capture whole plant heterogeneity arising from differences in leaf age, leaf position, plant architecture, as well as temperature and boundary layer (Pearcy et al., 2000). Whole-plant chambers linked to an IRGA, on the other hand, are costly and replication is timeconsuming (Ryan et al., 2016). In both cases, it is difficult to relate cuvette to field conditions (Tardieu & Simonneau, 1998). Whereas

Indoor plant production systems with artificial lighting are considered an emerging technology contributing to biomass-based value webs. The viability of this concept greatly relies on the energy requirements (ER, Watt) for lighting. We estimated the ER for plant growth by calculating the conversion efficiency of electricity to light of solid-state light-emitting diodes (LED) and the quantum requirements for plant growth of a fictional plant stand producing 2500 g of dry weight per m2 of ground during 100 days, representing a high productivity benchmark of field crops. The quantum output (µmol s−1 W−1) of eight LEDs of different colours varied between 0.78 for green and 2.54 for deep red. Uncertainty in the H+ demand for ATP synthesis during photosynthesis, the relative portion of photorespiration and the fraction of light intercepted by plant canopies (fabs) were considered in a pessimistic (PA) and optimistic (OA) approach of calculation of ER. Cumulative ER were 606 and 265 kWh m−2 for the PA and OA scenarios. The energy conversion efficiencies in the PA and OA scenarios were 2.07 and 4.72%. Estimates of energy savings by suppressing photorespiration and increasing fabs vary between 24 and 38%. The peak daily ER were 9.44 and 4.14 kWh in the PA and OA scenarios. Results are discussed in the context of the design of lighting in indoor plant production systems and commercial greenhouses where natural fluctuation in solar radiation could be balanced by dimmable LED panels.