Belay T. Kassie

Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations

We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.

Climate variability and change in the Central Rift Valley of Ethiopia: challenges for rainfed crop production

Ethiopia is one of the countries most vulnerable to the impacts of climate variability and change on agriculture. The present study aims to understand and characterize agro-climatic variability and changes and associated risks with respect to implications for rainfed crop production in the Central Rift Valley (CRV). Temporal variability and extreme values of selected rainfall and temperature indices were analysed and trends were evaluated using Sens slope estimator and Mann–Kendall trend test methods. Projected future changes in rainfall and temperature for the 2080s relative to the 1971–90 baseline period were determined based on four General Circulation Models (GCMs) and two emission scenarios (SRES, A2 and B1). The analysis for current climate showed that in the short rainy season (March–May), total mean rainfall varies spatially from 178 to 358 mm with a coefficient of variation (CV) of 32–50%. In the main (long) rainy season (June–September), total mean rainfall ranges between 420 and 680 mm with a CV of 15–40%. During the period 1977–2007, total rainfall decreased but not significantly. Also, there was a decrease in the number of rainy days associated with an increase (statistically not significant) in the intensity per rainfall event for the main rainy season, which can have implications for soil and nutrient losses through erosion and run-off. The reduced number of rainy days increased the length of intermediate dry spells by 0·8 days per decade, leading to crop moisture stress during the growing season. There was also a large inter-annual variability in the length of growing season, ranging from 76 to 239 days. The mean annual temperature exhibited a significant warming trend of 0·12–0·54 °C per decade. Projections from GCMs suggest that future annual rainfall will change by +10 to -40% by 2080. Rainfall will increase during November–December (outside the growing season), but will decline during the growing seasons. Also, the length of the growing season is expected to be reduced by 12–35%. The annual mean temperature is expected to increase in the range of 1·4–4·1 °C by 2080. The past and future climate trends, especially in terms of rainfall and its variability, pose major risks to rainfed agriculture. Specific adaptation strategies are needed for the CRV to cope with the risks, sustain farming and improve food security.

Towards a comprehensive climate impacts assessment of solar geoengineering

Despite a growing literature on the climate response to solar geoengineering – proposals to cool the planet by increasing the planetary albedo – there has been little published on the impacts of solar geoengineering on natural and human systems such as agriculture, health, water resources, and ecosystems. An understanding of the impacts of different scenarios of solar geoengineering deployment will be crucial for informing decisions on whether and how to deploy it. Here we review the current state of knowledge about impacts of a solar geoengineered climate and identify major research gaps. We suggest that a thorough assessment of the climate impacts of a range of scenarios of solar geoengineering deployment is needed and can build upon existing frameworks. However, solar geoengineering poses a novel challenge for climate impacts research as the manner of deployment could be tailored to pursue different objectives making possible a wide range of climate outcomes. We present a number of ideas for approaches to extend the survey of climate impacts beyond standard scenarios of solar geoengineering deployment to address this challenge. Reducing the impacts of climate change is the fundamental motivator for emissions reductions and for considering whether and how to deploy solar geoengineering. This means that the active engagement of the climate impacts research community will be important for improving the overall understanding of the opportunities, challenges and risks presented by solar geoengineering.

Multimodel ensembles improve predictions of crop–environment–management interactions

A recent innovation in assessment of climate change impact on agricultural production has been to use crop multimodel ensembles (MMEs). These studies usually find large variability between individual models but that the ensemble mean (e-mean) and median (e-median) often seem to predict quite well. However, few studies have specifically been concerned with the predictive quality of those ensemble predictors. We ask what is the predictive quality of e-mean and e-median, and how does that depend on the ensemble characteristics. Our empirical results are based on five MME studies applied to wheat, using different data sets but the same 25 crop models. We show that the ensemble predictors have quite high skill and are better than most and sometimes all individual models for most groups of environments and most response variables. Mean squared error of e-mean decreases monotonically with the size of the ensemble if models are added at random, but has a minimum at usually 2-6 models if best-fit models are added first. Our theoretical results describe the ensemble using four parameters: average bias, model effect variance, environment effect variance, and interaction variance. We show analytically that mean squared error of prediction (MSEP) of e-mean will always be smaller than MSEP averaged over models and will be less than MSEP of the best model if squared bias is less than the interaction variance. If models are added to the ensemble at random, MSEP of e-mean will decrease as the inverse of ensemble size, with a minimum equal to squared bias plus interaction variance. This minimum value is not necessarily small, and so it is important to evaluate the predictive quality of e-mean for each target population of environments. These results provide new information on the advantages of ensemble predictors, but also show their limitations.