No abstract

Rising global mean temperatures open opportunities in high‐altitude production systems for temperature‐sensitive crops such as lowland rice. Currently, the cropping window for rice in higher altitudes is still narrow, and thus, genotypes that tolerate a certain degree of chilling are needed to achieve their potential yield. Final yield depends on the interaction between genotype and environmental conditions. Exposing the genotype to a wide range of environments is a way to evaluate its adaptability into an expanding cropping calendar. Over a 2‐year period, an experiment was conducted in lowland rice systems in Madagascar at two locations differing in altitude. Twenty genotypes with contrasting levels of tolerance to low temperature were sown monthly in a non‐replicated rice garden trial. Plant development was monitored and yield and yield components were determined. Yield stability across the different growing environments was investigated. While crop duration was affected by sowing dates and altitude, yield was mainly determined by sowing date. Panicle number per m2 and number of spikelets per panicle were the most limiting factors for yield potential in mid‐altitude, while in high altitude, yield was mainly limited by spikelet fertility. Resulting cropping calendar and genotype recommendations are discussed.

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.

Increasing demand for land to ensure human food security in the future has already impelled agricultural production into marginal areas. The environmental conditions found there have a more pronounced impact on agricultural productivity than in the systems used so far under favourable conditions. In addition to this challenge, climate change is expected to increase the unreliability of weather conditions (through increased variability and occurrence of extremes) for farmers considerably. This unreliability is even more serious in developing countries’ farming system where food security is vulnerable. Current efforts in digitalization offer great possibilities to improve farmers` decision‐making processes. A wide range of online tools and smartphone applications is available to support both agricultural extension services and smallholder farmers alike. These apps are often parameterized and validated to certain environments and are troubled when applied to new geographical locations and different environmental conditions. We have conducted field trials to demonstrate potential methods to close knowledge gaps in the data background for one of these apps, RiceAdvice, concerning three key aspects: shifting of cropping calendar, adjustment of fertilizer management and genotype selection. Sites in Ethiopia, Madagascar and Rwanda were selected to represent altitudinal gradients, with overlapping elevations reflecting differences in temperature to enable cross‐country comparisons. Planting dates were distributed throughout three calendar years, with continuous iterative planting dates taking place in Madagascar, in‐ and off‐season planting dates in Rwanda with different fertilizer applications, and one planting date during each rainy season in Ethiopia with different management options. With these trials, we have been able to identify key data sets needed for the adaptation of agricultural decision support tools to new environments. These include the assessment of climatic constraints on innovations to cropping calendars (e.g. double cropping), informed selection of alternative varieties able to complete crucial parts of their phenological development to avoid temperature‐related stress inducing, for example spikelet sterility in rice in late development stages and the effectivity of potential innovations in fertilizer management strategies.