Frank Ewert

Ecosystem service supply and vulnerability to global change in Europe

Global change will alter the supply of ecosystem services that are vital for human well-being. To investigate ecosystem service supply during the 21st century, we used a range of ecosystem models and scenarios of climate and land-use change to conduct a Europe-wide assessment. Large changes in climate and land use typically resulted in large changes in ecosystem service supply. Some of these trends may be positive (for example, increases in forest area and productivity) or offer opportunities (for example, “surplus land” for agricultural extensification and bioenergy production). However, many changes increase vulnerability as a result of a decreasing supply of ecosystem services (for example, declining soil fertility, declining water availability, increasing risk of forest fires), especially in the Mediterranean and mountain regions.

Crops and climate change: progress, trends, and challenges in simulating impacts and informing adaptation

Assessments of the relationships between crop productivity and climate change rely upon a combination of modelling and measurement. As part of this review, this relationship is discussed in the context of crop and climate simulation. Methods for linking these two types of models are reviewed, with a primary focus on large-area crop modelling techniques. Recent progress in simulating the impacts of climate change on crops is presented, and the application of these methods to the exploration of adaptation options is discussed. Specific advances include ensemble simulations and improved understanding of biophysical processes. Finally, the challenges associated with impacts and adaptation research are discussed. It is argued that the generation of knowledge for policy and adaptation should be based not only on syntheses of published studies, but also on a more synergistic and holistic research framework that includes: (i) reliable quantification of uncertainty; (ii) techniques for combining diverse modelling approaches and observations that focus on fundamental processes; and (iii) judicious choice and calibration of models, including simulation at appropriate levels of complexity that accounts for the principal drivers of crop productivity, which may well include both biophysical and socio-economic factors. It is argued that such a framework will lead to reliable methods for linking simulation to real-world adaptation options, thus making practical use of the huge global effort to understand and predict climate change.

Simulating the effects of elevated CO2 on crops: approaches and applications for climate change

Several crop models may be used to simulate the effects of elevated CO2 on crop productivity. Yet no summary exists in the literature attempting to describe differences among models and how simulations might differ under climate change conditions. We provide an introductory review focusing on simulating the impacts of elevated CO2 on crops. We describe and discuss modeling approaches, component modules, applications to climate change and model validation and inter-comparison studies. By searching the recent peer-reviewed literature from 1995 to present, we found that about 20% of published crop modeling studies have focused on climate change impacts. About half of these studies explicitly analyzed the effects of elevated CO2 on crop growth and yield. Our analysis further suggested that the crop models that have been used the most in climate change assessments are also those that have been evaluated the least using available data from elevated CO2 experiments. Based on our review, we identify a set of recommendations aimed at improving our confidence in predictions of crop production under elevated CO2 and climate change conditions. These include continued model evaluation with existing field experiment data; increased focus on limiting factors such as pest, weeds, and disease; and attention to temporal and spatial scaling issues.

Effects of elevated CO2 and drought on wheat: Testing crop simulation models for different experimental and climatic conditions

Effects of increasing carbon dioxide concentration [CO2] on wheat vary depending on water supply and climatic conditions, which are difficult to estimate. Crop simulation models are often used to predict the impact of global atmospheric changes on food production. However, models have rarely been tested for effects on crops of [CO2] and drought for different climatic conditions due to limited data available from field experiments. Simulations of the effects of elevated [CO2] and drought on spring wheat (Triticum aestivum L.) from three crop simulation models (LINTULCC2, AFRCWHEAT2, Sirius), which differ in structure and mechanistic detail, were compared with observations. These were from 2 years of free-air carbon dioxide enrichment (FACE) experiments in Maricopa, Arizona and 2 years of standardised (in crop management and soil conditions) open-top chamber (OTC) experiments in Braunschweig and Giessen, Germany. In a simulation exercise, models were used to assess the possible impact of increased [CO2] on wheat yields measured between 1987 and 1999 at one farm site in the drought prone region of Andalucia, south Spain. The models simulated well final biomass (BM), grain yield (GY), cumulative evapotranspiration (ET) and water use efficiency (WUE) of wheat grown in the FACE experiments but simulations were unsatisfactory for OTC experiments. Radiation use efficiency (RUE) and yield responses to [CO2] and drought were on average higher in OTC than in FACE experiments. However, there was large variation among OTC experiments. Plant growth in OTCs was probably modified by several factors related to plot size, the use (or not use) of border plants, airflow pattern, modification of radiation balance and/or restriction of rooting volume that were not included in the models. Variation in farm yields in south Spain was partly explained by the models, but sources of unexplained yield variation could not be identified and were most likely related to effects of pests and diseases that were not included in the models. Simulated GY in south Spain increased in the range between 30 and 65% due to doubling [CO2]. The simulated increase was larger when a [CO2]×drought interaction was assumed (LINTULCC2, AFRCWHEAT2) than when it was not (Sirius). It was concluded that crop simulation models are able to reproduce wheat growth and yield for different [CO2] and drought treatments in a field environment. However, there is still uncertainty about the combined effects of [CO2] and drought including the timing of drought stress and about relationships that determine yield variation at farm and larger scales that require further investigation including model testing.

Multimodel ensembles of wheat growth: many models are better than one.

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.

Regional Farm Diversity Can Reduce Vulnerability of Food Production to Climate Change

Food production must adapt in the face of climate change. In Europe, projected vulnerability of food production to climate change is particularly high in Mediterranean regions. Increasing agricultural diversity has been suggested as an adaptation strategy, but empirical evidence is lacking. We analyzed the relationship between regional farm diversity (i.e., diversity among farm types) and the effects of climate variability on regional wheat ( Triticum spp.) productivity. An extensive data set with information from more than 50 000 farms from 1990 to 2003 was analyzed, along with observed weather data. Our results suggest that the diversity in farm size and intensity, particularly high in Mediterranean regions, reduces vulnerability of regional wheat yields to climate variability. Accordingly, increasing regional farm diversity can be a strategy through which regions in Europe can adapt to unfavorable conditions, such as higher temperatures and associated droughts.

Modelling CO2 effects on wheat with varying nitrogen supplies.

Abstract Crop simulation models are an essential tool for testing whether predicted global atmospheric changes are likely to have impact on food production. Any confidence in model predictions must be based on their ability successfully to predict performance in experiments. Accordingly, the predictions of three daily time step wheat simulation models (AFRCWHEAT2, FASSET and Sirius) were tested against data from wheat (Triticum aestivum L.) experiments in AZ in which the amount of applied N and the atmospheric CO2 concentration were both varied. Although there were differences between predicted and observed yields, all the three models predicted yield trends with treatments very similar to those observed. They all predicted, both in absolute terms and in the magnitude of responses, very similar effects of the variations on green area index (GAI), shoot and grain biomass accumulation, and shoot and grain biomass yield to observations and to each other. Comparison of simulated and observed results showed that CO2 effects were expressed through effects on light use efficiency (LUE), whereas N effects were expressed by causing variations in GAI. The exercise showed that the models used have potential for assessing climate change impacts on wheat production.

Changes in time of sowing, flowering and maturity of cereals in Europe under climate change

The phenological development of cereal crops from emergence through flowering to maturity is largely controlled by temperature, but also affected by day length and potential physiological stresses. Responses may vary between species and varieties. Climate change will affect the timing of cereal crop development, but exact changes will also depend on changes in varieties as affected by plant breeding and variety choices. This study aimed to assess changes in timing of major phenological stages of cereal crops in Northern and Central Europe under climate change. Records on dates of sowing, flowering, and maturity of wheat, oats and maize were collected from field experiments conducted during the period 1985–2009. Data for spring wheat and spring oats covered latitudes from 46 to 64°N, winter wheat from 46 to 61°N, and maize from 47 to 58°N. The number of observations (site–year–variety combinations) varied with phenological phase, but exceeded 2190, 227, 2076 and 1506 for winter wheat, spring wheat, spring oats and maize, respectively. The data were used to fit simple crop development models, assuming that the duration of the period until flowering depends on temperature and day length for wheat and oats, and on temperature for maize, and that the duration of the period from flowering to maturity in all species depends on temperature only. Species-specific base temperatures were used. Sowing date of spring cereals was estimated using a threshold temperature for the mean air temperature during 10 days prior to sowing. The mean estimated temperature thresholds for sowing were 6.1, 7.1 and 10.1°C for oats, wheat and maize, respectively. For spring oats and wheat the temperature threshold increased with latitude. The effective temperature sums required for both flowering and maturity increased with increasing mean annual temperature of the location, indicating that varieties are well adapted to given conditions. The responses of wheat and oats were largest for the period from flowering to maturity. Changes in timing of cereal phenology by 2040 were assessed for two climate model projections according to the observed dependencies on temperature and day length. The results showed advancements of sowing date of spring cereals by 1–3 weeks depending on climate model and region within Europe. The changes were largest in Northern Europe. Timing of flowering and maturity were projected to advance by 1–3 weeks. The changes were largest for grain maize and smallest for winter wheat, and they were generally largest in the western and northern part of the domain. There were considerable differences in predicted timing of sowing, flowering and maturity between the two climate model projections applied.

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.

Temperature increase reduces global yields of major crops in four independent estimates

Significance Agricultural production is vulnerable to climate change. Understanding climate change, especially the temperature impacts, is critical if policymakers, agriculturalists, and crop breeders are to ensure global food security. Our study, by compiling extensive published results from four analytical methods, shows that independent methods consistently estimated negative temperature impacts on yields of four major crops at the global scale, generally underpinned by similar impacts at country and site scales. Multimethod analyses improved the confidence in assessments of future climate impacts on global major crops, with important implications for developing crop- and region-specific adaptation strategies to ensure future food supply of an increasing world population. Wheat, rice, maize, and soybean provide two-thirds of human caloric intake. Assessing the impact of global temperature increase on production of these crops is therefore critical to maintaining global food supply, but different studies have yielded different results. Here, we investigated the impacts of temperature on yields of the four crops by compiling extensive published results from four analytical methods: global grid-based and local point-based models, statistical regressions, and field-warming experiments. Results from the different methods consistently showed negative temperature impacts on crop yield at the global scale, generally underpinned by similar impacts at country and site scales. Without CO2 fertilization, effective adaptation, and genetic improvement, each degree-Celsius increase in global mean temperature would, on average, reduce global yields of wheat by 6.0%, rice by 3.2%, maize by 7.4%, and soybean by 3.1%. Results are highly heterogeneous across crops and geographical areas, with some positive impact estimates. Multimethod analyses improved the confidence in assessments of future climate impacts on global major crops and suggest crop- and region-specific adaptation strategies to ensure food security for an increasing world population.