Development of methods for the probabilistic assessment of climate change impacts on crop production

Various attempts have been made to determine the relative importance of uncertainties in climate change impact assessments stemming from climate projections and crop models, respectively, and to analyse yield outputs probabilistically. For example, in the ENSEMBLES project, probabilistic climate projections (Harris et al. 2010) have been applied in conjunction with impact response surfaces (IRS), constructed by using impact models, to estimate the future likelihood (risk) of exceeding critical thresholds of crop yield impact (see, Fronzek et al., 2011, for an explanation of the method). In this task, we aimed to further develop and operationalize these methods and testing them in different case study regions in Europe. The method combines results of a sensitivity analysis of (one or more) impact model(s) with probabilistic projections of future temperature and precipitation (Fronzek et al., 2011). Such an overlay is one way of portraying probabilistic estimates of future impacts. By further accounting for the uncertainties in crop and biophysical parameters (using perturbed parameter approaches), the outcome represents an ensemble of impact risk estimates, encapsulating both climate and crop model uncertainties

Effects of climate and historical adaptation measures on barley yield trends in Finland

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Using impact response surfaces to analyse the likelihood of impacts on crop yield under probabilistic climate change

Conventional methods of modelling impacts of future climate change on crop yields often rely on a limited selection of projections for representing uncertainties in future climate. However, large ensembles of climate projections offer an opportunity to estimate yield responses probabilistically. This study demonstrates an approach to probabilistic yield estimation using impact response surfaces (IRSs). These are constructed from a set of sensitivity simulations that explore yield responses to a wide range of changes in temperature and precipitation. Options for adaptation and different levels of future atmospheric carbon dioxide concentration [CO2] defined by representative concentration pathways (RCP4.5 and RCP8.5) were also considered. Model-based IRSs were combined with probabilistic climate projections to estimate impact likelihoods for yields of spring barley (Hordeum vulgare L.) in Finland during the 21st century. Probabilistic projections of climate for the same RCPs were overlaid on IRSs for corresponding [CO2] levels throughout the century and likelihoods of yield shortfall calculated with respect to a threshold mean yield for the baseline (1981–2010). Results suggest that cultivars combining short pre- and long post-anthesis phases together with earlier sowing dates produce the highest yields and smallest likelihoods of yield shortfall under future scenarios. Higher [CO2] levels generally compensate for yield losses due to warming under the RCPs. Yet, this does not happen fully under the more moderate warming of RCP4.5 with a weaker rise in [CO2], where there is a chance of yield shortfall throughout the century. Under the stronger warming but more rapid [CO2] increase of RCP8.5, the likelihood of yield shortfall drops to zero from mid-century onwards. Whilst the incremental IRS-based approach simplifies the temporal and cross-variable complexities of projected climate, it was found to offer a close approximation of evolving future likelihoods of yield impacts in comparison to a more conventional scenario-based approach. The IRS approach is scenario-neutral and existing plots can be used in combination with any new scenario that falls within the sensitivity range without the need to perform new runs with the impact model. A single crop model is used for demonstration, but an ensemble IRS approach could additionally capture impact model uncertainties.peerReviewe

Projected impacts of sowing date and cultivar choice on the timing of heat and drought stress in spring barley grown along a European transect

Publisher Copyright: © 2022Barley is one of the most important cereals for animal and human consumption. Barley heading and grain filling are especially vulnerable to heat and drought stress, which are projected to increase in the future. Therefore, site-specific adaptation options, like cultivar choice or shifting sowing dates, will be necessary. Using a global climate model ensemble and a phenology model we projected spring barley heading and maturity dates for 2031–50 for climatically contrasting sites: Helsinki (Finland), Dundee (Scotland) and Zaragoza (Spain). We compared the projected future heading and maturity dates with the baseline period (1981–2010) and described corresponding heat and drought stress conditions and how they were affected by adaptation options, i.e. shifting the sowing date by + /- 10–20 days, choosing early or late heading cultivars or combining both adaptation options, with agroclimatic indicators. At all sites and sowing dates, heading and maturity in 2031–50 occurred earlier (up to three weeks with earliest sowing) than in the baseline period. Along the European transect, the projected heading and grain filling periods were hotter than under baseline conditions but advancing heading alleviated heat stress notably. Different indicators signaled more severe drought conditions for 2031–50. At Helsinki, delayed heading periods were exposed to less drought stress, likely because the typical early summer droughts were avoided. At Zaragoza, fewer, yet more intense, rainfall events occurred during grain filling of the early cultivars. Only under scenario RCP4.5, heading and grain filling periods at Dundee were slightly wetter for the early cultivars. Our study provides a unique overview of agroclimatic conditions for heading and grain filling periods projected for 2031–50 along a climatic transect and quantifies the effects of different adaptations for spring barley. The approach can be extended by coupling the agroclimatic indicators with crop modelling.Peer reviewe

A crop model ensemble analysis of temperature and precipitation effects on wheat yield across a European transect using impact response surfaces

Impact response surfaces (IRSs) of spring and winter wheat yields were constructed from a 26-member ensemble of process-based crop simulation models for sites in Finland, Germany and Spain across a latitudinal transect in Europe. The sensitivity of modelled yield to systematic increments of changes in temperature (-2 to +9°C) and precipitation (-50 to +50%) was tested by modifying values of 1981–2010 baseline weather.In spite of large differences in simulated yield responses to both baseline and changed climate between models, sites, crops and years, several common messages emerged. Ensemble average yields decline with higher temperatures (3–7% per 1°C) and decreased precipitation  (3–9% per 10% decrease), but benefit from increased precipitation (0-8% per 10% increase). Yields are more sensitive to temperature than precipitation changes at the Finnish site while sensitivities are mixed at the German and Spanish sites. Precipitation effects diminish under higher temperature changes. Inter-model variability is highest for baseline climate at the Spanish site, but relatively insensitive to changed climate. Modelled responses diverge most at the Finnish and German sites for winter wheat under temperature change. The IRS pattern of yield reliability tracks average yield levels. Inter-annual yield variability is more sensitive to precipitation than temperature, except at the Spanish site for spring wheat.Optimal temperatures for present-day cultivars are close to the baseline under Finnish conditions but below the baseline at the German and Spanish sites. This suggests that adoption of later maturing cultivars with higher temperature requirements might already be advantageous, and increasingly so under future warming

Cultivating resilience by empirically revealing response diversity

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Analysis of rainfall variability and trends for better climate risk management in the major agro-ecological zones in Tanzania

Managing climate risk in agriculture requires a proper understanding of climatic conditions, regional and global climatic drivers, as well as major agricultural activities at the particular location of interest. Critical analyses of variability and trends in the historical climatic conditions are crucial in designing and implementing action plans to improve resilience and reduce the risks of exposure to harsh climatic conditions. However, in Tanzania, less is known about the variability and trends in the recent climatological conditions. The current study examined variability and trends in rainfall of major agroecological zones in Tanzania (1o - 12oS, 21o - 41oE) using station data from seven locations i.e. Hombolo, Igeri, Ilonga, Naliendele, Mlingano, Tumbi, and Ukiliguru which had records from 1981 to 2020 and two locations i.e. Dodoma and Tanga having records from 1958 to 2020. The variability in annual rainfall was high in Hombolo and Tanga locations (CV ≥ 28%) and low in Igeri (CV = 16%). The OND season showed the highest variability in rainfall (34% to 61%) as compared to the MAM (26% to 36%) and DJFMA (20% to 31%) seasons. We found increasing and decreasing trends in the number of rainy days in Ukiliguru and Tanga respectively, and a decreasing trend in the MAM rainfall in Mlingano. The trends in other locations were statistically insignificant. We assessed the forecast skills of seasonal rainfall forecasts issued by the Tanzania Meteorological Authority (TMA) and IGAD (Intergovernmental Authority on Development) Climate Prediction and Application Center (ICPAC). We found TMA forecasts had higher skills compared to ICPAC forecasts, however, our assessment was limited to MAM and OND seasons due to the unavailability of seasonal forecasts of the DJFMA season issued by ICPAC. Moreover, we showed that Integration of SCF with SSTa increases the reliability of the SCF to 80% at many locations which present an opportunity for better utilization of the SCF in agricultural decision making and better management of climate risks