A volumetric integral approach to estimate the rhizosphere extent from measured and simulated concentration profiles - Ahmet Kürşad Sırcan; Andrea Schnepf; Adrian Lattacher; Ellen Kandeler; Guoting Shen; Evgenia Blagodatskaya; Christian Poll; Thilo Streck; Holger Pagel (Soil Biology and Biochemistry, 2026)

Integrating a dynamic pathogen infection risk module into the Noah-MP-Gecros model for assessing the spread of diseases in maize and wheat stands - Shehan Morandage; Joachim Ingwersen; Thilo Streck (Journal of Plant Diseases and Protection, 2026)
Enhancing agricultural productivity requires a thorough assessment of fungal-induced crop diseases under varying climatic conditions. We implemented infection risk response functions into a coupled land surface-crop model (Noah-MP-Gecros) for both winter wheat and maize. For wheat, the infection risk module covers Septoria tritici blotch ( Zymoseptoria tritici ), brown rust ( Puccinia triticina ), yellow rust ( Puccinia striiformis ), and Fusarium head blight ( Fusarium spp. , mainly F. graminearum ). For maize, the focus is on Fusarium ear and stalk rot (primarily Fusarium verticillioides ) and Gibberella ear rot ( Fusarium graminearum) . We evaluated model performance by simulating infection risks for major wheat and maize diseases across three growing between 2020 and 2023, analyzing infection dynamics and the sensitivity of crop-specific parameters. Model outputs were partially evaluated against field observations. Simulated pathogen infection varied significantly from year to year within the same region. Rust diseases in wheat and Fusarium ear rot and stalk rot in maize displayed similar interannual trends, while Fusarium head blight in wheat, and Gibberella ear rot in maize exhibited divergent patterns across growing seasons. The maximum amount of water intercepted by the canopy and the land-surface parameter momentum roughness length were identified as the most sensitive crop parameters for infection risk. The model, which relies exclusively on meteorological variables, has shown potential for real-time disease risk prediction and guiding pesticide application. The model performance could be improved by integrating biological processes such as host-pathogen interactions and spore availability. On the crop side, pathogen impacts could potentially be mitigated by management and cultivar traits influencing canopy water interception and aerodynamic roughness.

Trait-based modeling of microbial interactions and carbon turnover in the rhizosphere - Ahmet Kürşad Sırcan; Thilo Streck; Andrea Schnepf; Mona Giraud; Adrian Lattacher; Ellen Kandeler; Christian Poll; Holger Pagel (Soil Biology and Biochemistry, 2025)

Improving winter wheat yield prediction by accounting for weather and model parameter uncertainty while assimilating LAI and updating weather data within a crop model - Hossein Zare; Michelle Viswanathan; Tobias KD Weber; Joachim Ingwersen; Wolfgang Nowak; Sebastian Gayler; Thilo Streck (European Journal of Agronomy, 2024)

Harvest residues: A relevant term in the carbon balance of croplands? - Joachim Ingwersen; Arne Poyda; Pascal Kremer; Thilo Streck (Agricultural and Forest Meteorology, 2024)

Within-season crop yield prediction by a multi-model ensemble with integrated data assimilation - Hossein Zare; Tobias KD Weber; Joachim Ingwersen; Wolfgang Nowak; Sebastian Gayler; Thilo Streck (Field Crops Research, 2024)

Gibbs energy or enthalpy—What is relevant for microbial C‐turnover in soils? - Matthias Kästner; Thomas Maskow; Anja Miltner; Marcel Lorenz; Sören Thiele‐Bruhn; Tobias Bölscher; Sergey Blagodatsky; Thilo Streck; Holger Pagel; Evgenia Blagodatskaya (Global Change Biology, 2024)

Diagnosing similarities in probabilistic multi-model ensembles: an application to soil–plant-growth-modeling - Aline Schäfer Rodrigues Silva; Tobias K. D. Weber; Sebastian Gayler; Anneli Guthke; Marvin Höge; Wolfgang Nowak; Thilo Streck (Modeling Earth Systems and Environment, 2022)
There has been an increasing interest in using multi-model ensembles over the past decade. While it has been shown that ensembles often outperform individual models, there is still a lack of methods that guide the choice of the ensemble members. Previous studies found that model similarity is crucial for this choice. Therefore, we introduce a method that quantifies similarities between models based on so-called energy statistics. This method can also be used to assess the goodness-of-fit to noisy or deterministic measurements. To guide the interpretation of the results, we combine different visualization techniques, which reveal different insights and thereby support the model development. We demonstrate the proposed workflow on a case study of soil–plant-growth modeling, comparing three models from the Expert-N library. Results show that model similarity and goodness-of-fit vary depending on the quantity of interest. This confirms previous studies that found that “there is no single best model” and hence, combining several models into an ensemble can yield more robust results.

Global wheat production with 1.5 and 2.0°C above pre‐industrial warming - Bing Liu; Pierre Martre; Frank Ewert; John R. Porter; Andy J. Challinor; Christoph Müller; Alex C. Ruane; Katharina Waha; Peter J. Thorburn; Pramod K. Aggarwal; Mukhtar Ahmed; Juraj Balkovič; et al. (Global Change Biology, 2019)
Efforts to limit global warming to below 2°C in relation to the pre‐industrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming >2°C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5 and 2.0°C warming above the pre‐industrial period) on global wheat production and local yield variability. A multi‐crop and multi‐climate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by −2.3% to 7.0% under the 1.5°C scenario and −2.4% to 10.5% under the 2.0°C scenario, compared to a baseline of 1980–2010, when considering changes in local temperature, rainfall, and global atmospheric CO 2 concentration, but no changes in management or wheat cultivars. The projected impact on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields (bottom 5 percentile of baseline distribution) and yield inter‐annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer—India, which supplies more than 14% of global wheat. The projected global impact of warming

Climate change impact and adaptation for wheat protein - Senthold Asseng; Pierre Martre; Andrea Maiorano; Reimund P. Rötter; Garry J. O’Leary; Glenn J. Fitzgerald; Christine Girousse; Rosella Motzo; Francesco Giunta; M. Ali Babar; Matthew P. Reynolds; Ahmed M. S. Kheir; et al. (Global Change Biology, 2019)
Wheat grain protein concentration is an important determinant of wheat quality for human nutrition that is often overlooked in efforts to improve crop production. We tested and applied a 32‐multi‐model ensemble to simulate global wheat yield and quality in a changing climate. Potential benefits of elevated atmospheric CO 2 concentration by 2050 on global wheat grain and protein yield are likely to be negated by impacts from rising temperature and changes in rainfall, but with considerable disparities between regions. Grain and protein yields are expected to be lower and more variable in most low‐rainfall regions, with nitrogen availability limiting growth stimulus from elevated CO 2 . Introducing genotypes adapted to warmer temperatures (and also considering changes in CO 2 and rainfall) could boost global wheat yield by 7% and protein yield by 2%, but grain protein concentration would be reduced by −1.1 percentage points, representing a relative change of −8.6%. Climate change adaptations that benefit grain yield are not always positive for grain quality, putting additional pressure on global wheat production.

Multimodel ensembles improve predictions of crop–environment–management interactions - Daniel Wallach; Pierre Martre; Bing Liu; Senthold Asseng; Frank Ewert; Peter J. Thorburn; Martin van Ittersum; Pramod K. Aggarwal; Mukhtar Ahmed; Bruno Basso; Christian Biernath; Davide Cammarano; et al. (Global Change Biology, 2018)
A recent innovation in assessment of climate change impact on agricultural production has been to use crop multimodel ensembles ( MME s). 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.

Evidence for the importance of litter as a co-substrate for MCPA dissipation in an agricultural soil - Omar Saleh; Holger Pagel; Esther Enowashu; Marion Devers; Fabrice Martin-Laurent; Thilo Streck; Ellen Kandeler; Christian Poll (Environmental Science and Pollution Research, 2016)

Imidacloprid concentrations in paddy rice fields in northern Vietnam: measurement and probabilistic modeling - Nguyen La; Marc Lamers; Matthias Bannwarth; Vien Van Nguyen; Thilo Streck (Paddy and Water Environment, 2015)

Multimodel ensembles of wheat growth: many models are better than one - Pierre Martre; Daniel Wallach; Senthold Asseng; Frank Ewert; James W. Jones; Reimund P. Rötter; Kenneth J. Boote; Alex C. Ruane; Peter J. Thorburn; Davide Cammarano; Jerry L. Hatfield; Cynthia Rosenzweig; et al. (Global Change Biology, 2015)
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.

The influence of the herbicide 2-methyl-4-chlorophenoxyacetic acid (MCPA) on the mineralization of litter-derived alkanes and the abundance of the alkane monooxygenase gene (alkB) in the detritusphere of Pisum sativum (L.) - Stephan Schulz; Tatiana Yuyunkina; Holger Pagel; Lukas Y. Wick; Christian Poll; Thilo Streck; Ellen Kandeler; Michael Schloter (Biology and Fertility of Soils, 2012)

Nanoparticulate Zeolitic Tuff for Immobilizing Heavy Metals in Soil: Preparation and Characterization - Ayoup M. Ghrair; Joachim Ingwersen; Thilo Streck (Water, Air, & Soil Pollution, 2009)