Agroecological transitions reveal trade-offs and synergies among ecosystem services - Mathieu Delandmeter; Bruno Basso; Jérôme Bindelle; Benjamin Dumont (Science of The Total Environment, 2026)

Livestock Integration Into Cropping Systems Enhances Their Climate Change Resistance and Mitigation While Reducing Their Environmental Impacts - Mathieu Delandmeter; Bruno Basso; Xavier Fettweis; Christophe Lacroix; Pierre Aubry; Jérôme Bindelle; Benjamin Dumont (Global Change Biology, 2026)
The sustainability of cropping systems is linked to their circularity, which is their ability to close resource cycles such as carbon and nitrogen through strategies for managing crop residues, byproducts, and other inputs. Here, we investigate three crop rotations—business‐as‐usual (BAU), vegan, and integrated crop‐livestock systems (ICLS)—varying in livestock integration, crop residue fate, and human diet sustained. Under ten climate change scenarios, we compare their impacts on multiple ecosystem services during 24 years over 541,800 ha in Belgium using a validated crop model. All three circularity scenarios are found to be net greenhouse gas (GHG) emitters, with increasing intensity under climate change. The BAU system, favoring cash crops such as sugarbeet or potato, demonstrates the highest productivity, which, however, is highly variable across years and comes with greater environmental impacts such as GHG emissions (+45% and +23% compared to ICLS and Vegan in average—i.e., across all sites and climate scenarios). The Vegan system has lower carbon sequestration than the ICLS due to the lack of pasture and livestock, which, however, is partly offset by the regular incorporation of crop residues into the soil. Finally, ICLS, which include temporary pastures and sheep, demonstrate intermediate productivity levels compared to the other systems. However, they offer the greatest stability and resistance to extreme weather (+43% and +86% for stability compared to BAU and Vegan, in average), with better environmental performance. Therefore, our study reveals the benefits of crop‐livestock systems in terms of climate change adaptation, through stability and resistance to extreme climate events, and mitigation, through soil carbon sequestration and reduced greenhouse gas emissions and nitrate leaching. Moreover, our findings highlight the critical links between farm‐level circularity, soil‐crop feedbacks, human diet, and climate change.

Evaluation of multimodel averaging approaches for ensembling evapotranspiration and yield simulations from maize models - Viveka Nand; Zhiming Qi; Liwang Ma; Matthew J. Helmers; Chandra A. Madramootoo; Ward N. Smith; Tiequan Zhang; Tobias K.D. Weber; Elizabeth Pattey; Ziwei Li; Jiaxin Wang; Virginia L. Jin; et al. (Journal of Hydrology, 2025)

Sequence‐Based Multi Ancestry Association Study Reveals the Polygenic Architecture of Varroa destructor Resistance in the Honeybee Apis mellifera - Sonia E. Eynard; Fanny Mondet; Benjamin Basso; Olivier Bouchez; Yves Le Conte; Benjamin Dainat; Axel Decourtye; Lucie Genestout; Matthieu Guichard; François Guillaume; Emmanuelle Labarthe; Barbara Locke; et al. (Molecular Ecology, 2025)
Honeybees, Apis mellifera , have experienced the full impacts of globalisation, including the recent invasion by the parasitic mite Varroa destructor , now one of the main causes of colony losses worldwide. The strong selection pressure it exerts has led some colonies to develop defence strategies conferring some degree of resistance to the parasite. Assuming these traits are partly heritable, selective breeding of naturally resistant bees could be a sustainable strategy for fighting infestations. To characterise the genetic determinism of varroa resistance, we conducted the largest genome wide association study performed to date on whole genome sequencing of more than 1500 colonies on multiple phenotypes linked to varroa resistance of honeybees. To take into account some genetic diversity of honeybees, colonies belonging to different ancestries representing the main honeybee subspecies in Western Europe were included and analysed both as separate populations and combined in a meta‐analysis. The results show that varroa resistance is substantially heritable and polygenic: while 60 significant associations were identified, none explain a substantial part of the trait genetic variance. Overall our study highlights that genomic selection for varroa resistance is promising but that it will not be based on managing a few strong effect mutations and rather use approaches that leverage the genome wide diversity of honeybee populations. From a broader perspective, these results point the way towards understanding the genetic adaptation of eusocial insects to parasite load.

Simulation of soil temperature under maize: An inter-comparison among 33 maize models - Bruce A. Kimball; Kelly R. Thorp; Kenneth J. Boote; Claudio Stockle; Andrew E. Suyker; Steven R. Evett; David K. Brauer; Gwen G. Coyle; Karen S. Copeland; Gary W. Marek; Paul D. Colaizzi; Marco Acutis; et al. (Agricultural and Forest Meteorology, 2024)

Simulation of evapotranspiration and yield of maize: An inter-comparison among 41 maize models - Bruce A. Kimball; Kelly R. Thorp; Kenneth J. Boote; Claudio Stockle; Andrew E. Suyker; Steven R. Evett; David K. Brauer; Gwen G. Coyle; Karen S. Copeland; Gary W. Marek; Paul D. Colaizzi; Marco Acutis; et al. (Agricultural and Forest Meteorology, 2023)

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.

Systematic analysis of site-specific yield distributions resulting from nitrogen management and climatic variability interactions - Benjamin Dumont; Bruno Basso; Vincent Leemans; Bernard Bodson; Jean-Pierre Destain; Marie-France Destain (Precision Agriculture, 2015)