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Examples of recent publications

• Albert I., Makowski D. 2018. Ranking crop species using mixed treatment comparisons. Research Synthesis Method, in press. https://doi.org/10.1002/jrsm.1328
• Schauberger B., Ben-Ari T., Makowski D., Kato T., Kato H., and Ciais Ph. 2018. Yield trends, variability and stagnation analysis of major crops in France over more than a century. Scientific report 8, 16865. https://www.nature.com/articles/s41598-018-35351-1
• Martin P, Bladier C., Bruyere O., Feinblatt E., Meek B., Touvier M., Watier L. Makowski D. 2018. Weight of Evidence for Hazard Identification: A Critical Review of the Literature. Environmental Health Perspectives, in press
• Chen M., Raynal M., Brun F., Makowski D. 2018. Timing of grape downy mildew onset in Bordeaux vineyards. Phytopathology. doi: 10.1094/PHYTO-12-17-0412-R.
• Li W., Ciais P., Makowski D., Peng S. 2018. A global yield dataset for major lignocellulosic bioenergy crops based on field measurements. Scientific data 5, 1801169.
• El Akkari M., Rechauchere O., Bispo A., Gabrielle B, Makowski D. 2018. A meta-analysis of the greenhouse gas abatement of bioenergy factoring in land use changes. Scientific report 8, 8563. https://www.nature.com/articles/s41598-018-26712-x
• Cernay C., Makowski D., Pelzer E. 2018. Preceding cultivation of grain legumes increases cereal yields under low nitrogen input conditions. Environmental Chemistry Letters 16(2), 631-636.
• Ben-Ari T., Boé J., Ciais Ph., Lecerf R., Van der Velde M., Makowski D. 2018. Causes and implications of the unforeseen 2016 extreme yield loss in France’s breadbasket. Nature communications 9, 1627. doi:10.1038/s41467-018-04087-x
• Ramanantenasoa MMJ, Genermont S, Gilliot J-M., Mignolet C., Bedos C., Mathias E., Eglin T., Makowski D. 2018. A new framework to estimate spatio-temporal ammonia emissions due to nitrogen fertilization in France. Science of the Total Environment, in press.
• Zhu P, Jin Z, Zhuang Q, Ciais P, Bernacchi C, Wang X, Makowski D, Lobell D. 2018. The important but weakening maize yield benefit of grain filling prolongation in the US Midwest. Global Change Biology 24(10):4718-4730. doi: 10.1111/gcb.14356
• Makowski D., Piraux F., Brun F. 2018. De l’analyse des réseaux à la méta-analyse. Quae.
• Rechauchere O., Bispo A. Gabrielle B., Makowski D. 2018. Sustainable Agriculture Reviews 30: Environmental Impact of Land Use Change in Agricultural Systems. Springer International Publishing, 30, 239 p., 2018, Sustainable Agriculture Reviews, 978-3-319-96288-7
• Lechenet M., Dessaint F., Py G., Makowski D., Munier-Jolain N. 2017. Reducing pesticide use while preserving crop productivity and profitability in arable farms. Nature Plants 3, 17008.  https://www.nature.com/articles/nplants20178.

Full list of peer-reviewed publications

1. Makowski, D., D. Wallach, J-M. Meynard. 1999. Models of yield, grain protein, and residual mineral nitrogen responses to applied nitrogen for winter wheat. Agronomy Journal 91:377-385.
2. Makowski, D., E.M.T. Hendrix, M.K. van Ittersum, W.A.H. Rossing. 2000. A framework to study nearly optimal solutions of linear programming models developed for agricultural land use exploration. Ecological Modelling 131:65-77.
3. Makowski, D., E.M.T. Hendrix, M.K. van Ittersum, W.A.H. Rossing. 2001. Generation and presentation of nearly optimal solutions for mixed-integer linear programming, applied to a case in farming system design. European Journal of Operational Research 132:425-438.
4. Makowski, D., D. Wallach, J-M. Meynard. 2001. Statistical methods for predicting responses to applied nitrogen and for calculating optimal nitrogen rates. Agronomy Journal 93:531-539.
5. Makowski, D., D. Wallach. 2001. How to improve model-based decision rules for nitrogen fertilization. European Journal of Agronomy 15:197-208.
6. Makowski, D., D. Wallach. 2002. It pays to base parameter estimation on a realistic description of model errors. Agronomie 22:179-189.
7. Monod, H., D. Makowski, M. Sahmoudi, D. Wallach. 2002. Optimal experimental designs for estimating model parameters, applied to yield response models. Agronomie 22:229-338.
8. Makowski, D., D. Wallach, M. Tremblay. 2002. Using a Bayesian approach to parameter estimation; comparison of the GLUE and MCMC methods. Agronomie 22:191-203.
9. Makowski, D. 2002. Modèle non linéaire mixte pour simuler la réponse du blé à la dose d’engrais azoté. Journal de la Société Française de Statistique 143:219-227.
10. Meynard J-M., M. Cerf, L. Guichard, M-H. Jeuffroy, D. Makowski. 2002. Which decision support tools for the Environmental Management of nitrogen? Agronomie 22:817-829.
11. Le Bail, M., D. Makowski. 2004. A model-based approach for optimizing segregation of soft wheat in country elevators. European Journal of Agronomy 21:171-180.
12. Houlès, V., B. Mary, M. Guérif, D. Makowski, E. Justes. 2004. Evaluation of the crop model STICS to recommend nitrogen fertilization rates according to agro-environmental criteria. Agronomie 24:339-349. 
13. Makowski, D., A. Maltas, M. Morison, R. Reau. 2005. Calculating N fertilizer rates for oil-seed rape using plant and soil data. Agronomy for Sustainable Development 25:159-161. 
14. Makowski, D., M. Taverne, J. Bolomier, M. Ducarne. 2005. Comparison of risk indicators for sclerotinia control in oilseed rape. Crop Protection 24:527-531. 
15. Lacroix, A., N. Beaudoin, D. Makowski. 2005. Agricultural water nonpoint pollution control under uncertainty and climate variability. Ecological Economics 53:115-127.
16. Ennaïfar, S., P. Lucas, J.M. Meynard, D. Makowski. 2005. Effects of Summer-fallow Management on Take-all of Winter Wheat caused by Gaeumannomyces graminis var. tritici. European Journal of Plant Pathology 112:167-181.
17. Hillier, J., D. Makowski, B. Andrieu. 2005. Maximum likelihood inference and bootstrap methods for plant organ growth via multi-phase kinetic models and their application to maize. Annals of Botany 96:137-148.
18. Primot, S., M. Valantin-Morison, D. Makowski. 2006. Predicting the risk of weed infestation in winter oilseed rape crops. Weed Research 46:22-33.
19. Makowski, D., M. Lavielle. 2006. Using SAEM (stochastic approximation of EM) to estimate parameters of models of response to applied fertilizer. Journal of Agricultural, Biological, and Environmental Statistics 11 (1):45-60. 
20. Makowski, D., C. Naud, M-H. Jeuffroy, A. Barbottin. H. Monod. 2006. Global sensitivity analysis for calculating the contribution of genetic parameters to the variance of crop model predictions. Reliability Engineering and System Safety 91:1142-1147.
21. Makowski D., T. Doré, H. Monod. 2007. A new method to analyse relationships between yield components with boundary lines. Agronomy for Sustainable Development 27:119-128.
22. Makowski D, T. Doré, N. Munier-Jolain, J. Gasquez. 2007. Modelling land use strategies to optimize crop production and protection of ecologically important weed species. Weed research 47:202-211.
23. Ennaïfar, S., D. Makowski, J-M. Meynard, Ph. Lucas. 2007. Evaluation of models to predict take-all incidence on winter wheat as a function of cropping practices, soil, and climate. European Journal of Plant Pathology 118:127-143.
24. Naud C, Makowski D, Jeuffroy MH. 2007. An interacting particle filter to improve model-based predictions of nitrogen nutrition index for winter wheat. Ecological modelling 207:251-263.
25. Naud C, Makowski D, Jeuffroy MH. 2008. Is it useful to combine measurements taken during the growing season with a dynamic model to predict the nitrogen status of winter wheat?  European journal of Agronomy 28:291-300.
26. Makowski D., Denis J-B., Ruck L., Penaud A. 2008. A Bayesian approach to assess the accuracy of a diagnostic test based on plant disease measurement. Crop Protection 27:1187-1193.
27. Barbottin A, Makowski D, Le Bail M, Jeuffroy M-H, Bouchard Ch, Barrier C. 2008. Comparison of models and indicators for categorizing soft wheat fields according to their grain protein contents. European Journal of Agronomy 29, 159-183.
28. Prost L, Makowski D, Jeuffroy M-H. 2008. Comparison of stepwise selection and Bayesian model averaging for yield gap analysis. Ecological Modelling 219, 66-76.
29. Pelosi C., Bertrand M., Makowski D., Roger-Estrade J. 2008. WORMDYN: A model of Lumbricus terrestris population dynamics in agricultural fields Ecological Modelling 218, 219-234.
30. Doré T., Clermont-Dauphin C., Crozat Y., David Ch., Jeuffroy M-H., Loyce C., Makowski D., Malézieux E., Meynard J-M., Valantin-Morison M. 2008. Methodological progress in on-farm regional agronomic diagnosis. A review. Agronomy for Sustainable Development 28:151-161.  
31. Bockstaller Ch., Guichard L., Makowski D., Aveline A., Girardin Ph., Plantureux S. 2008. Agri-environmental indicators to assess cropping and farming systems. A review. Agronomy for Sustainable Development 28:139-149.  
32. Malézieux E., Crozat Y., Dupraz C., Laurans M., Makowski D., Ozier-Lafontaine H., Rapidel B., de Tourdonnet S., Valantin-Morison M. 2008. Mixing plant species in cropping systems: concepts, tools and models. A review. Agronomy for Sustainable Development 29, 43-62.
33. Naud C., Makowski D., Jeuffroy M-H. 2009. Transmittance measurements can improve predictions of the nitrogen status for winter wheat crop. Field Crop Research 110, 27-34.
34. Stobbelaar D.J., Groot J.C.J., Makowski D., Tichit M. 2009. Multifunctional agriculture – From farm diagnosis to farm design and institutional innovation. Editorial. Journal of Environmental Management 90, S109-S111.
35. Makowski D., Tichit M., Guichard L., van Keulen H., Beaudoin N. 2009. Measuring the accuracy of agro-environmental indicators. Journal of Environmental Management 90, S139-S146.
36. Lehuger S., Van Oijen M., Makowski D., Gabrielle B. 2009. Bayesian calibration of the nitrous oxide emission module of CERES-EGC agro-ecosystem model. Agriculture, Ecosystem and Environment 133, 208-222.
37. Casagrande M., David Ch., Valantin-Morison M., Makowski D., Jeuffroy M-H. 2009. Factors limiting grain protein content of organic winter wheat in South-eastern France : a model-mixing approach. Agronomy for Sustainable Development 29, 565-574.
38. Lamboni M., Makowski D., Lehuger S. Gabrielle B., Monod H. 2009. Multivariate global sensitivity analysis for dynamic crop models. Field Crop Research 113, 312-320.
39. Barbottin A., Tichit M., Cadet C., Makowski D. 2010. Accuracy and cost of models predicting bird distribution in agricultural grasslands. Agriculture, Ecosystem and Environment 136, 28-34.
40. Casagrande M., Makowski D., Valantin-Morison M., Jeuffroy M-H., David Ch. 2010. The benefits of using quantile regression for analyzing the effect of weeds on organic winter wheat. Weed Research 50, 199-208.
41. Tichit M., Barbottin A., Makowski D. 2010. A cost effectiveness approach to identify cheap and accurate indicators to assess livestock impact on biodiversity. Animal 4:6, 819-826.
42. Makowski D., Mittinty M. 2010. Comparison of scoring systems for invasive pests using ROC analysis and Monte Carlo simulations. Risk Analysis 30, 906-915.
43. Makowski D., Chauvel B., Munier-Jolain N. 2010. Improving weed population model using a sequential Monte Carlo method. Weed Research 50, 373-382
44. Lamboni M., Monod H., Makowski D. 2011. Multivariate sensitivity analysis to measure global contribution of input factors in dynamic models. Reliability Engineering and System Safety 96, 450-459
45. Lamboni M., Makowski D., Monod H. 2011. Indices de sensibilité, sélection de paramètres et erreur quadratique de prediction: des liaisons dangereuses ? Journal de la Société Française de Statistique 152, 26-48.
46. Makowski D. 2011. Uncertainty and sensitivity analysis for models used in pest risk analysis. HPPJ 4, 1-11.
47. Doré T. D. Makowski, E. Malézieux, N. Munier-Jolain, M. Tchamitchian, P. Tittonell. 2011. Facing up to the paradigm of ecological intensification in agronomy : Revisiting methods, concepts and knowledge. European Journal of Agronomy 34, 197-210.  
48. Dupin M., Reynaud P., […], Makowski D. 2011. Effects of training dataset characteristics on the performance of models for predicting the distribution of Diabrotica virgifera virgifera. Plos One 6, 1-11.
49. Philibert A., Desprez-Loustau M-L., […], Makowski D. 2011. Predicting invasion success in forest pathogenic fungi from species traits. Journal of Applied Ecology 48, 1381–1390  
50. Makowski D., Bancal R., Vincent A. 2011. Estimation of wetness duration requirements of foliar fungal pathogens with uncertain data. Application to Mycosphaerella nawae. Phytopathology 101, 1346-1354.
51. Philibert A., Loyce C., Makowski D. 2012. Assessment of the quality of the meta-analysis in agronomy. Agriculture, Ecosystem and Environment 148, 72-82.
52. Pelzer E, Bazot M, Makowski D, Corre-Hellou G, Naudin C, Al Rifaï M, Baranger E, Bedoussac L, Biarnès V, Boucheny P, Carrouée B, Dorvillez D, Foissy D, Gaillard B, Guichard L, Mansard MC, Omon b, Prieur l, Yvergniaux m, Justes E, Jeuffroy MH, 2012. Pea–wheat intercrops in low-input conditions combine high economic performances and low environmental impacts. European Journal of Agronomy 40, 39–53.
53. Philibert A., Loyce C., Makowski D. 2012. Quantifying uncertainties in N2O emission due to N fertilizer application in cultivated areas. Plos One 7(11): e50950. doi:10.1371/journal.pone.0050950.
54. Pelosi C., S. Joimel, D. Makowski. 2013. Searching for a more sensitive earthworm species to be used in pesticide homologation tests – a meta-analysis. Chemosphere 90 (2013) 895–900.
55. J.-E. Bergez, P. Chabrier, C. Gary, M.H. Jeuffroy, D. Makowski, G. Quesnel, E. Ramat, H. Raynal, N. Rousse, D. Wallach, P. Debaeke, P. Durand, M. Duru, J. Dur, P. Faverdin, C. Gascuel-Odoux, F. Garcia. 2013. An open platform to build, evaluate and simulate integrated models of farming and agro-ecosystems. Environmental Modelling and Software 39, 39-49.
56. Gouache D, Bensadoum A, Brun F, Pagé C., Makowski D, Wallach D. 2013. Modelling climate change impact on Septoria tritici blotch in France: accounting for climate model and disease model uncertainty. Agricultural and Forest Meteorology, 170, 242-252.  
57. Philibert A., Loyce C., Makowski D. 2013. Prediction of N2O emission from local information with Random Forest. Environmental Pollution 177, 156-163.
58. Rachel Licker,  Christopher J Kucharik, Thierry Doré, Mark J Lindeman, D. Makowski. 2013. Climatic impacts of winter wheat yields in Picardy, France and Rostov, Russia: 1973-2010, Agricultural and Forest Meteorology 176, 25-37
59. Lesur C., C. Loyce, D. Makowski, A Riche, N Yates, M Fritz, B Formowitz, M Grunert, U Jorgensen, P E Laerke, M-H Jeuffroy. 2013. Modeling long term yield trends of Miscanthus x giganteus using experimental data from across Europe. Field Crop Research 149, 252-260.
60. Makowski D. 2013. Uncertainty and sensitivity analysis in quantitative pest risk assessments; practical rules for risk assessors. Neobiota 18: 157–171.
61. Michel L., Makowski D. 2013. Comparison of statistical models for analyzing wheat yield time series. Plos one 8(10).
62. Pélosi C, Lucile Toutous; François Chiron; Florence Dubs; Mickaël Hedde; Audrey Muratet; Jean-François Ponge; Sandrine Salmon, Makowski D. 2013. Reduction of pesticide use can increase earthworm populations in wheat crops in a European temperate region. Agriculture Ecosystem and Environment 181, 223-230.
63. Makowski D., Nesme T., Papy F., Doré T. 2014. Global agronomy, a new field of research. Agronomy for Sustainable Development.  34:293-307.
64. Bassu S, Brisson N, Durand J-L, Boote K, Lizaso J, Jones JW, Rosenzweig C, Ruane AC, Adam M, Baron C, Basso B, Biernath C, Boogaard H, Conijn S, Corbeels M, Deryng D, De Sanctis G, Gayler S, Grassini P, Hatfield J, Hoek S, Izaurralde C, Jongschaap R, Kemanian A, Kersebaum KC, Kumar N, Makowski D, Müller C, Nendel C, Priesack E, Pravia Maria Virginia, Soo Hyung K, Sau F, Shcherbak I, Tao F, Teixeira E, Timlin D, Waha K., 2014.   How do various maize crop models vary in their responses to climate change factors? Global Change Biology 20:2301–2320. 
65. Wilcox J., Makowski D. 2014. A meta-analysis of the predicted effects of climate change on wheat yields using simulation studies. Field Crop Research 156, 180–190.
66. Makowski D., Vicent A., Pautasso M., Stancanelli G., Rafoss T. 2014. Comparison of statistical models in a meta-analysis of fungicide treatments for the control of citrus black spot caused by Phyllosticta citricarpa. European journal of plant pathology 139, 79–94
67. Hossard L., Philibert A., Bertrand M., Colnenne-David C., Debaeke P., Munier-Jolain N., Jeuffroy M.H., Richard G., Makowski D. 2014. Effects of having pesticide use on wheat production. Scientific Reports 4, doi:10.1038/srep04405. https://www.nature.com/articles/srep04405.
68. Pelzer E., Hombert N., Jeuffroy M-H. Makowski D. 2014. Meta-analysis of the effect of nitrogen fertilization on annual cereal-legume intercrop production. Agronomy Journal 106:1775–1786.
69. Philibert A., Loyce C., Makowski D. 2014. Predicting N2O emission with random effect models. Environmental Modelling & Software 61, 12-18.
70. Ben Ari T., Makowski D. 2014. Decomposing global crop yield variability. Environmental Research Letters 9 114011 doi:10.1088/1748-9326/9/11/114011.
71. Laurent A., Loyce C., Pelzer E., Makowski D. 2015. Ranking yields of energy crops: a meta-analysis using direct and indirect comparisons. Renewable & Sustainable Energy Reviews 46, 41-50.
72. Cernay C., Ben-Ari T., Pelzer E., Meynard J-M., Makowski D. 2015. Estimating variability in grain legume yields across Europe and the Americas. Scientific report 5:11171. https://www.nature.com/articles/srep11171
73. Yu Y., Stomph T-J., Makowski D., van der Werf W. 2015. Temporal niche differentiation increases the land equivalent ratio of annual intercrops: a meta-analysis Field Crop Research 184, 133–144.
74. Laurent A., Loyce C., Makowski D., Pelzer E. 2015. Using site-specific data to estimate energy crop yield. Environmental Modelling & Software 74 104-113.
75. Makowski D. et al. 2015. A statistical analysis of ensembles of crop model responses to climate change factors. Agriculture and Forest Meteorology 214–215, 483–493
76. Vogel L., D. Makowski, P. Garnier, L. Vieublé-Gonod, Y. Coquet, X. Raynaud, N. Nunan, C. Chenu, R. Falconer, V. Pot. 2015. Modeling the effect of soil meso- and macropores topology on the biodegradation of a soluble carbon substrate. Advances in Water Resouces. 2015, 83, 123-136
77. Mouysset L, Miglianico M, Makowski D, Jiguet F, Doyen L. 2016. Dynamic models for bird community in farming landscapes. Environmental Modeling and Assessment 21 (3), 407-418.
78. Bensadoun A., Monod H., Makowski D., Messéan A. 2016. A Bayesian approach to model dispersal for decision support. Environmental Modelling & Software 78, 179-190.
79. Hossard L. Archer, D.W., Bertrand, M., Colnenne-David, C., Debaeke, P., Erfors, M., Jensen, E.S., Jeuffroy, M.H., Munier-Jolain, N., Nilsson, C., Sanford, G.R., Snapp, S.S., Makowski, D. 2016. A Meta-Analysis of Maize and Wheat Yields in Low-Input vs. Conventional and Organic systems. Agronomy Journal, 108:1155-1167.
80. Ben-Ari T., Adrian J., Klein T., Calanca P. Van der Velde M., Makowski D. 2016. Identification of accurate indicators for extreme wheat and maize yield loss. Agriculture and Forest Meteorology 220, 130-140.
81. Michel L., Brun F., Piraux F., Makowski D. 2016. Estimating the incidence of Septoria tritici in wheat crops from in-season measurements. European journal of Plant Pathology. doi:10.1007/s10658-016-0887-9.
82. Gerber J.S., Kimberly M. C., Makowski D, Iñaki Garcia de Cortazar-Atauri, Petr Havlík , Mario Herrero, Marie Launay, Nathaniel D. Mueller,  Christine S. O’Connell, Pete Smith, Paul C. West. 2016. Spatially explicit estimates of N2O emissions from croplands suggest climate mitigation opportunities from improved fertilizer management. Global Change Biology 22 3383–3394.
83. Ben-Ari T., Makowski D. 2016. Analysis of the trade-off between high crop yield and low yield instability at the global scale. Environmental Research Letter 11 104005.
84. Yu Y., Makowski D., Stomph T-J., van der Werf W. 2016. Robust increases of land equivalent ratio with temporal niche differentiation: a meta-quantile regression. Agronomy Journal 108 2269-2279.
85. Yu Y., Stomph T-J., Makowski D., van der Werf W. 2016. A meta-analysis of relative crop yields in cereal/legume mixtures suggests options for management. Field Crop Research 198, 269–279
86. Lechenet M., Makowski D., Py G., Munier-Jolain N. 2016. Profiling farming management strategies with contrasting pesticide use in France. Agricultural Systems149 40–53
87. Cernay C., Pelzer E., Makowski D. 2016. A global experimental dataset for assessing grain legume production. Scientific data 3, 160084. https://www.nature.com/articles/sdata201684.
88. Perronne R., Makowski D., Goffaux R., Montalenta P., Goldringer I. 2017. Temporal evolution of varietal, spatial and genetic diversity of bread wheat between 1980 and 2006 strongly depends upon agricultural regions in France. Agriculture, Ecosystem & Environment 236, 12-20.
89. Hossard L., Guichard L., Pelosi C., Makowski D. 2017. Lack of evidence for a decrease in synthetic pesticide use on the main arable crops in France.  Science of the Total Environment 575, 152-161.
90. Michel L., Brun F., Makowski D. 2017. A framework based on generalised linear mixed models for analysing pest and disease surveys. Crop protection 94, 1-12.
91. Makowski D., Bancal R., Bensadoum A., Monod H., Messéan A. 2017. Sampling strategies to evaluate rate of transgene adventitious presence in maize fields. Risk analysis 37, No. 9, 1693-1705. DOI: 10.1111/risa.12745
92. Lammoglia S-K., Makowski D., Moeys J., Justes E., Barriuso E., Mamy L. 2017. Sensitivity analysis of the STICS-MACRO model to identify cropping practices reducing pesticides losses. Science of the Total Environment 580, 117-129.
93. Sharif B., Makowski D., Plauborg F., Olesen J.O. 2017. Comparison of regression techniques to predict response of oilseed rape yield to variation in climatic conditions in Denmark. European Journal of Agronomy 82A, 11-20. dx.doi.org/10.1016/j.eja.2016.09.015
94. Makowski D. 2017. A simple Bayesian method for adjusting ensemble of crop model outputs to yield observations. European journal of Agronomy 88, 76-83. dx.doi.org/10.1016/j.eja.2015.12.012.
95. Lechenet M., Dessaint F., Py G., Makowski D., Munier-Jolain N. 2017. Reducing pesticide use while preserving crop productivity and profitability in arable farms. Nature Plants 3, 17008.  https://www.nature.com/articles/nplants20178.
96. Verret V., Gardarin A., Pelzer E., Médiène S., Makowski D., Morison M. 2017. Can legume companion plants control weeds without decreasing crop yield? A meta-analysis. Field Crops Research 204, 158-168.
97. Lesur C., Malézieux E., Ben-Ari T., Langlais C., Makowski D. 2017. Lower average yields but similar yield variability in organic versus conventional horticulture. A meta-analysis. Agronomy for sustainable development 37, 45.
98. Verret V., Gardarin A., Makowski D., Lorin M., Cadoux S., Butier A., Valantin-Morison M. 2017. Assessment of the benefits of frost-sensitive companion plants in winter rapeseed. European journal of agronomy 91, 93-103.
99. Cantalapiedra-Hijar G., Dewhurst R.J., Cheng L., Cabrita R., Fonseca A., Nozière P., Makowski D., Fouillet H., Ortigues-Marty I. Nitrogen isotopic fractionation as a biomarker for nitrogen use efficiency in ruminants: A meta-analysis. Animal, 12, 1827-1837. https://doi.org/10.1017/S1751731117003391
100. Cernay C., Makowski D., Pelzer E. 2018. Preceding cultivation of grain legumes increases cereal yields under low nitrogen input conditions. Environmental Chemistry Letters 16(2), 631-636.
101. Ben-Ari T., Boé J., Ciais Ph., Lecerf R., Van der Velde M., Makowski D. 2018. Causes and implications of the unforeseen 2016 extreme yield loss in France’s breadbasket. Nature communications 9, 1627. doi:10.1038/s41467-018-04087-x
102. El Akkari M., Rechauchere O., Bispo A., Gabrielle B, Makowski D. 2018. A meta-analysis of the greenhouse gas abatement of bioenergy factoring in land use changes. Scientific report 8, 8563. www.nature.com/articles/s41598-018-26712-x
103. Vogel L.E., Pot V., Makowski D., Garnier P., Baveye P.C. 2018. To what extent do uncertainty and sensitivity analyses help unravel the influence of microscale physical and biological drivers in soil carbon dynamics models? Ecological Modelling 383, 10-22.
104. Ramanantenasoa MMJ, Genermont S, Gilliot J-M., Mignolet C., Bedos C., Mathias E., Eglin T., Makowski D. 2018. A new framework to estimate spatio-temporal ammonia emissions due to nitrogen fertilization in France. Science of the Total Environment, in press.
105. Martin P, Bladier C., Bruyere O., Feinblatt E., Meek B., Touvier M., Watier L. Makowski D. 2018. Weight of Evidence for Hazard Identification: A Critical Review of the Literature. Environmental Health Perspectives, in press.
106. Zhu P, Jin Z, Zhuang Q, Ciais P, Bernacchi C, Wang X, Makowski D, Lobell D. 2018. The important but weakening maize yield benefit of grain filling prolongation in the US Midwest. Global Change Biology 24(10):4718-4730. doi: 10.1111/gcb.14356
107. Li W., Ciais P., Makowski D., Peng S. 2018. A global yield dataset for major lignocellulosic bioenergy crops based on field measurements. Scientific data 5, 1801169.
108. Chen M., Raynal M., Brun F., Makowski D. 2018. Timing of grape downy mildew onset in Bordeaux vineyards. Phytopathology. doi: 10.1094/PHYTO-12-17-0412-R.
109. Albert I., Makowski D. 2018. Ranking crop species using mixed treatment comparisons. Research Synthesis Method, in press. https://doi.org/10.1002/jrsm.1328
110. Schauberger B., Ben-Ari T., Makowski D., Kato T., Kato H., and Ciais Ph. 2018. Yield trends, variability and stagnation analysis of major crops in France over more than a century. Scientific report 8, 16865.

Books, special issues, and thesis

1. Makowski D. 2001. Models and statistical methods for optimizing nitrogen fertilization. PhD thesis, AgroParisTech, Paris.
2. Wallach D., Makowski D., Jones J. (eds) 2006. Working with dynamic crop models. Elsevier. Book written in collaboration with the University of Florida. 
3. Makowski D. 2007. Assessment of mathematical models in crop science. HDR (accreditation for supervising research), University Paris-Sud, Orsay.
4. Stobbelaar D.J., Groot J.C.J., Makowski D., Tichit M. 2009. Multifunctional agriculture – From farm diagnosis to farm design and institutional innovation. Guest editors of the special issue 90 (supplement 2) of Journal of Environmental Management. Fait marquant 2008 du département SAD INRA.
5. Makowski D, Monod H. 2011. Analyse statistique des risques agro-environnementaux. Springer.
6. Makowski D. (dir.). 2012. Méthodes statistiques bayésiennes. INRA/FormaSciences.
7. Faivre R., Iooss B., Mahévas S., Makowski D. Monod H. (eds). 2013. Analyse de sensibilité et exploration de modèles. Quae éditions.
8. Wallach D, Makowski D, Jones J, Brun F. 2014. Working with dynamic crop models. Methods, Tools and Examples for Agriculture and Environment. Elsevier (third edition published in 2018).
9. Collectif BioBayes. 2015. Initiation à la statistique bayésienne. Bases théoriques et applications en alimentation, environnement, épidémiologie et génétique. Ellipses. 
10. Makowski D., Piraux F., Brun F. 2018. De l’analyse des réseaux à la méta-analyse. Quae.
11. Rechauchere O., Bispo A. Gabrielle B., Makowski D. 2018. Sustainable Agriculture Reviews 30: Environmental Impact of Land Use Change in Agricultural Systems. Springer International Publishing, 30, 239 p., 2018, Sustainable Agriculture Reviews, 978-3-319-96288-7

Book chapters

1. Makowski, D., M-H. Jeuffroy, M. Guérif. 2004. Bayesian methods for updating crop model predictions, applications for predicting biomass and grain protein content. In : « Bayesian Statistics and quality modelling in the agro-food production chain ». Van Boekel et al. (eds). Kluwer Academic Publishers, Dordrecht. p.57-68.
2. Beaudoin, N., V. Parnaudeau, B. Mary, D. Makowski, J.-M. Meynard. 2004. Simulation de l'impact de différents scénarios agronomiques sur les pertes de nitrate à l'échelle d'un bassin hydrologique. In : Organisation spatiale des activités agricoles et processus environnementaux. P. Monestiez, S. Lardon et B. Seguin Eds, Coll. Science Update, INRA Editions, p. 117-141.
3. Makowski, D., J. Hillier, D. Wallach, B. Andrieu, MH. Jeuffroy. 2006. Parameter estimation for crop models. In: Working with dynamic crop models. D. Wallach, D. Makowski, J. Jones Eds, Elsevier. p. 101-150. 
4. Monod, H., C. Naud, D. Makowski. 2006. Uncertainty and sensitivity analysis for crop models. In: Working with dynamic crop models. D. Wallach, D. Makowski, J. Jones Eds, Elsevier. p. 55-100. 
5. Makowski, D., M. Guérif, J. Jones., W. Graham. 2006. Data assimilation with crop models. In: Working with dynamic crop models. D. Wallach, D. Makowski, J. Jones Eds, Elsevier. p. 151-172.
6. Guérif, M., V. Houlès, D. Makowski, C. Lauvernet. 2006. Data assimilation and parameter estimation for precision agriculture using the crop model STICS. In: Working with dynamic crop models. D. Wallach, D. Makowski, J. Jones Eds, Elsevier. p. 391-398.
7. 128.    Houlès V., B. Mary, M. Guérif, D. Makowski, E. Justes, J.-M. Machet. 2007. Critères agro-environnementaux fondés sur le modèle de culture STICS pour la modulation intra-parcellaire de la fertilisation azotée du blé. In: Agriculture de précision, Guérif M. and King D. (eds), QUAE, Paris, France. p. 199-224.
8. Makowski D., M. Le Bail, A. Barbottin, M-H. Jeuffroy, C. Barrier, Ch. Bouchard, C. Pasquier. 2009. Utilisation de modèles pour prédire la qualité du blé. In : Concevoir et construire la décision. E. de Turckheim, B. Hubert, A. Messéan (coord.). Quae. P.57-70.
9. Makowski D. 2013. Incertitude des modèles utilisés pour l’analyse des risques agro-environnementaux. In : « Agir en situation d’incertitude en agriculture. Regards pluridisciplinaires au Nord et au Sud ». Ancey V., Avelange IS., Dedieu B. (eds.). PIE Peter Lang S.A. p.327-339.
10. Wilcox J.C., Barbottin A., Durant D., Tichit M., Makowski D. 2014. Farmland Birds and Arable Farming, a Meta-Analysis. Sustainable Agriculture Reviews 13, 35-63.
11. Makowski et al. 2015. Statistical analysis of large simulated yield datasets for studying climate effects. Handbook of climate change and agroecosystems : the agricultural model intercomparison and improvement project (AgMip). Imperial College Press, 279-295.

Software

1. Brun F., Makowski D., Wallach D., Jones J. 2013. ZeBook : Working with dynamic models for agriculture and environment. R package. http://cran.r-project.org/.
2. Makowski D., Piraux F., Brun F. 2017.  KenSyn : Knowledge synthesis in Agiculture. http://cran.r-project.org/.
3. Base de données : Cernay C, Pelzer E, Makowski D. 2016. A global experimental dataset for assessing grain legume production. DOI: http://dx.doi.org/10.5061/dryad.mf42f

Popularization papers and scientific opinions

1. Beaudoin, N., D. Makowski, V. Parnaudeau, B. Parisseaux, D. Wallach, B. Mary, J-M. Meynard. 1998. Evaluation de l'impact économique et environnemental de la mesure agri-environnementale "réduction d'intrants" au moyen de modèles agronomiques. Rapport Ministère de l'Agriculture, 79 pages + annexes.
2. Makowski, D., M. Tremblay, D. Debroize, F. Laurent. 2000. Apports d'engrais hétérogènes, quel impact économique et environnemental ? Perspectives Agricoles 263:56-61. 
3. Makowski, D. 2001. Evaluation et optimisation des recommandations de doses d’engrais azoté à l’aide de modèles agronomiques simples. Dans « Les nouveaux défis de la fertilisation raisonnée ». GEMAS-COMIFER. G. Thévenet et A. Joubet (eds.), p.269-279.
4. Makowski, D., P. Castillon. 2002. Fertilisation azotée du blé dur. Rémunérer la teneur en protéines du grain. Perspectives Agricoles 277 :56-58.
5. Makowski, D., A. Maltas, M. Morison, R. Reau. 2004. Réglette azote colza : même revenu, moins d’engrais, plus d’huile. Oléoscope 74 :12-16.
6. Bouthier A, Makowski D. 2005. Fertilisation azotée de l’orge de printemps : concilier marge du producteur avec objectifs de qualité. Perspectives Agricoles 318 : 62-63.
7. Scientific Opinion of the Panel on Plant Heath on a request from the Commission on an evaluation of asymptomatic citrus fruit as a pathway for the introduction of citrus canker disease (Xanthomonas axonopodis pv. citri) made by the US Animal and Plant Health Inspection Service, The EFSA Journal (2006) 439, 1-41.
8. Makowski, D., L. Ruck. 2007. Des perspectives prometteuses pour la PCR quantitative. Oléoscope 88, 14-15.
9. Laurent, F., D. Makowski. 2007. Quels sont les effets du prix du blé et de l’engrais azoté? Perspectives Agricoles 339, 46-50.
10. Scientific Opinion of the Panel on Plant Heath on a request from the Commission on the pest risk assessment made by Lithuania on Ambrosia spp. The EFSA Journal (2007) 527, 1-33. Co-rapporteur.
11. Scientific Opinion of the Panel on Plant Heath on a request from the Commission on the pest risk assessment made by Poland on Ambrosia spp. The EFSA Journal (2007) 528, 1-32. Co-rapporteur.
12. Scientific Opinion of the Panel on Plant Heath on a request from the European Commission on Guignardia citricarpa Kiely. The EFSA Journal (2008) 925, 1-108.
13. Scientific Opinion of the Panel on Plant Health on a pest risk assessment made by France on Sphaeropsis tumefaciens Hedges considered by France as harmful in French overseas departments of French Guiana, Guadeloupe and Martinique. The EFSA Journal (2008) 683, 1-16. Rapporteur.
14. Scientific Opinion of the Panel on Plant Health on a request from the European Commission on Pest risk assessment made by France on Fusarium oxysporum. f. sp. cubense considered by France as harmful in French overseas departments of French Guiana, Guadeloupe, Martinique and Réunion. The EFSA Journal (2008) 668, 1-22.
15. Scientific Opinion of the Panel on Plant Health on a request from the European Commission on Pest risk assessment made by France on Trachysphaera fructigena considered by France as harmful in French overseas departments of French Guiana, Guadeloupe, Martinique and Réunion. The EFSA Journal (2008) 664, 1-23.
16. Scientific Opinion of the Panel on Plant Heath on a request from the European Commission on mortality verification of pinewood nematode from high temperature treatment of shavings. The EFSA Journal (2009) 1055, 1-19. Rapporteur.
17. Guidance of the Panel on Plant Health following a request from EFSA on the evaluation of pest risk assessments and risk management options prepared to justify requests for phytosanitary measures under Council Directive 2000/29/EC. The EFSA Journal (2009) 2654, 1-18.
18. Makowski D., Ruck L. 2008. Le taux de fleurs contaminés comme indicateur. Dossier « Sclérotinia du colza ». Perspectives Agricoles 343, 6-8.
19. Makowski D., Guichard L. 2008. Niveaux de précision des indicateurs utilisés pour évaluer les risques de pollution nitrique en parcelles agricoles. Report for the Comifer association.
20. Bazot M., Makowski D., Bouchard Ch. 2008. Techniques d’échantillonnage sur blé tendre d’hiver. Optimiser les procédures d’échantillonnage pour mesurer la biomasse, le nombre de plantes et le poids de mille grains. Cahiers Techniques de l’INRA 64, 5-12.
21. EFSA Panel on Plant Health (PLH); Guidance on a harmonised framework for pest risk assessment and the identification and evaluation of pest risk management options by EFSA. EFSA Journal 2010; 8(2):1495. Co-rapporteur.
22. EFSA Panel on Plant Health; Statement on a study proposal prepared by the US to support a future derogation request from the EU import requirements for wood packaging material originating in the US and used to pack and transport military ammunition. EFSA Journal 2010; 8(1):1497. Rapporteur.
23. Avis de l’Agence française de sécurité sanitaire des aliments relative à son auto-saisine sur l’article publié dans International Journal of Biological Sciences et intitulé « A comparison of the effects of three GM corn varieties on mammalian health ». Saisine n°2009-SA-0322. Rapporteur.
24. Morfin M., Makowski D. 2010. MMIX : un package R pour combiner des modèles en agronomie. Cahiers Techniques INRA 69, 39-47.
25. Scientific opinion on a quantitative pathway analysis of the likelihood of Tilletia indica M. introduction into EU with importation of US wheat. EFSA Journal 2010; 8(6):1621. [88 pp.]. Chairman.
26. Scientific Opinion on a technical file submitted by the Japanese Authorities to support a derogation request from the EU import requirements for bonsai and topiary trees that are host plants of Anoplophora chinensis. EFSA Journal 2010;8(10):1849. [13 pp.]. Chairman.
27. Scientific Opinion on a composting method proposed by Portugal as a heat treatment to eliminate pine wood nematode from the bark of pine trees. EFSA Journal 2010;8(9):1717. [10 pp.]. Chairman.
28. Scientific Opinion on a technical file submitted by the US Authorities to support a request to list a new option among the EU import requirements for wood of Agrilus planipennis host plants. EFSA Journal. 2011; 9(7):2185 [51 pp.]. Rapporteur.
29. Recommandations pour la mise en œuvre de l’analyse statistique des données issues des études de toxicité sub-chronique de 90 jours chez le rat dans le cadre des demandes d’autorisation de mise sur le marché d’OGM. Avis de l’ANSES et rapport d’expertise collective, janvier 2011, 54pp + annexes.
30. Dupin M., S. Brunel, R. Baker, D. Eyre and D. Makowski. 2011. A comparison of methods for combining maps in pest risk assessment: application to Diabrotica virgifera virgifera. Bulletin OEPP/EPPO Bulletin 41, 217–225.
31. Baker RHA, J. Benninga, J. Bremmer, S. Brunel, M. Dupin, D. Eyre, Z. Ilieva, V. Jarosˇ ı´k, H. Kehlenbeck, D. J. Kriticos, D. Makowski, J. Pergl, P. Reynaud, C. Robinet, T. Soliman, W. Van der Werf, and S. Worner. 2012. A decision-support scheme for mapping endangered areas in pest risk analysis. Bulletin OEPP/EPPO Bulletin 42 (1), 65–73.
32. EFSA Panel on Plant Health (PLH); Statement on a heat treatment to control Agrilus planipennis. EFSA Journal 2012;10(4):2646. [14 pp.]. Rapporteur.
33. European Food Safety Authority; Guidance on methodology for evaluation of the effectiveness of options for reducing the risk of introduction and spread of organisms harmful to plant health in the EU territory. EFSA Journal 2012;10(6):2755. [92 pp.]. Chaiman.
34. Avis de l’ANSES relatif à l’analyse de l’étude de Séralini et al. (2012) “Long term toxicity of a roundup herbicide and a roundup-tolerant genetically modified maize”. Saisine n°2012-SA-0227.
35. Jeger M., Schans J., Lövei G.L., van Lenteren J., Navajas M., Makowski D., Stancanelli G., Tramontini SS., Ceglarska E.B. 2012. Risk assessment in support of plant health. EFSA journal 10, 100-107.
36. Makowski D. 2013. Brèves de math: L’agriculture est-elle responsable des gaz à effet de serre ? http://www.breves-de-maths.fr/agriculture-est-elle-responsable-des-gaz-a-effet-de-serre/
37. F. Brun, J. Veslot, L. Michel, B. Cichosz, A. Petit, D. Makowski. 2015. Quelles pistes d'amélioration pour mieux valoriser les données et les simulations dans le Bulletin de Santé du Végétal ?. AFPP CIMA 2015.
38. EFSA Panel on Plant Health (PLH); ‘Scientific Opinion on the risk of Phyllosticta citricarpa (Guignardia citricarpa) for the EU territory with identification and evaluation of risk reduction options’, EFSA Journal 2014; 12:3557. Rapporteur.
39. EFSA Panel on Plant Health (PLH); Risks to plant health posed by EU import of soil or growing media. EFSA Journal 2015;13 (6):4132[133 pp.]. doi:10.2903/j.efsa.2015.4132. Chairman.
40. EFSA Panel on Plant Health, 2016. Evaluation of new scientific information on Phyllosticta citricarpa in relation to the EFSA PLH Panel (2014) Scientific Opinion on the plant health risk to the EU. EFSA Journal 2016;14(6):4513, 53 pp. doi:10.2903/j.efsa.2016.4513. Rapporteur.
41. ANSES. 2016. Evaluation du poids des preuves à l'Anses : revue critique de la littérature et recommandations à l'étape d'identification des dangers. Rapport d’expertise collective (GT MER). Edition scientifique (Makowski D., président). https://www.anses.fr/fr/system/files/AUTRE2015SA0089Ra.pdf
42. ANSES. 2016. Prise en compte de l’incertitude en évaluation des risques : revue de la littérature et recommandations pour l’Anses. Rapport d’expertise collective (GT MER). Édition scientifique (Makowski D., président).

https://www.anses.fr/fr/system/files/AUTRE2015SA0090Ra.pdf

1. Hardy A, Benford D, Halldorsson T, Jeger MJ, Knutsen HK, More S, Naegeli H, Noteborn H, Ockleford C, Ricci A, Rychen G, Schlatter JR, Silano V, Solecki R, Turck D, Benfenati E, Chaudhry QM, Craig P, Frampton G, Greiner M, Hart A, Hogstrand C, Lambre C, Luttik R, Makowski D, Siani A, Wahlstroem H, Aguilera J, Dorne J-L, Fernandez Dumont A, Hempen M, Valtuena Martınez S, Martino L, Smeraldi C, Terron A, Georgiadis N and Younes M, 2017. Scientific Opinion on the guidance on the use of the weight of evidence approach in scientific assessments. EFSA Journal 2017;15(8):4971, 69 pp. https://doi.org/10.2903/j.efsa.2017.4971
2. Bispo. A., Gabrielle B., Makowski D. (coord.). 2017. Effets environnementaux des changements d'affectation des sols liés à des réorientations agricoles, forestières, ou d'échelle territoriales : une revue critique de la littérature scientifiques. Synthèse du rapport d'étude DEPE. http://prodinra.inra.fr/record/398059.
3. Makowski D. 2017. Synthétiser les connaissances en agronomie. Notes académiques de l’académie d’agriculture de France / Academic Notes from the French Academy of Agriculture 3, 1-7.
4. Cernay C, Makowski D, Lescoat P, Pelzer E, 2017. Comparaison des performances de différentes espèces de légumineuses à graines. Innovations Agronomiques 60, 21-41
5. Lechenet M., Py G., Dessaint F., Makowski D., Munier-Jolain N. 2017. Réduire l’usage des pesticides sans dégrader la productivité. Phytoma 705, 43-47.
6. ANSES. 2017. Illustrations et actualisation des recommandations pour l’évaluation du poids des preuves et l’analyse d’incertitude à l’Anses. Rapport d’expertise collective (GT MER). Édition scientifique (Makowski D., président).
7. https://www.anses.fr/fr/system/files/AUTRE2015SA0089Ra-2.pdf
8. Mamy L., Lammoglia S.K., Alletto L., Bedos C., Brun F., Justes E., Makowski D., Marin-Benito J.M., Moeys J., Munier-Jolain N., Nicolardot B., Pot V., Quemar T., Ubertosi M., Barriuso E. 2017. Modélisation des flux de pesticides dans les systèmes de culture : effets de la variabilité du climat, des pratiques agricoles et des propriétés du sols et des pesticides. Synthèse des résultats du projet Perform. Innovations Agronomiques 59, 171-189.
9. Andriamampianina L., Temple L., de Bon H., Malézieux E., Makowski D. 2018. Evaluation pluri-critères de l’agriculture biologique en Afrique subsaharienne par élicitation probabiliste des connaissances d’experts. Cah. Agric. 27, 45002.

Invited communications in conferences and workshops

1. Makowski, D., M-H. Jeuffroy, M. Guérif. 2003. Bayesian methods for updating crop model predictions, applications for predicting biomass and grain protein content. Workshop « Bayesian Statistics and quality modelling in the agro-food production chain », 12-14 May 2003, Wageningen, The Netherlands.
2. Makowski, D. 2005. Parameter estimation for dynamic crop models. Workshop “Crop modelling” INRA – ICTA - ACTA, 28 november – 1st december 2005, La Rochelle, France.
3. Makowski, D. 2006. How to use mathematical models for pest management. Workshop “Models for crop protection”, 20-21 November 2006, Paris, France.  
4. Makowski, D., Baker, R. 2007. Methodologies in pest risk assessment: qualitative vs. quantitative approaches in the assessment of introduction potential. EFSA scientific colloquium 10: Pest Risk Assessment. Science in support of phytosanitary decision-making in the European Community. 6-7 December 2007, Parma, Italy.
5. McRoberts N., Makowski D. 2008. Bayesian methods in model development in plant disease epidemiology. VIIth meeting of the French society of phytopathology/mycology. 20-24 january 2008, Aussois, France.
6. Makowski D., Naud C., Ramat E., Quesnel G. 2008. Implementation of the interacting particle filter with dynamic crop models using the Discrete Event System Specification. Environmetrics workshop, Queensland University of Technology, Brisbane, Australia. 17-18 July 2008.
7. Introduction to dynamic system modeling in biology. 18-20 November 2008, Poznan, Poland. Organization of a three-days course with J-N. Aubertot and D. Wallach for the Institute of Plant Genetics – Polish Academy of Sciences.
8. Makowski D., Guichard L. 2009. Measuring the accuracy of agri-environmental indicators. EUROFERT 2009. February 2-3, Rennes.
9. Makowski D. 2009. Analyse d’incertitude, analyse de sensibilité. Objectifs et principales étapes. Présentation invitée. Ecole chercheur « Analyse de sensibilité et exploration de modèles ». 11-14 mai 2009. Giens, France.
10. Makowski D. 2009. L’analyse ROC en agronomie. 12 novembre 2009, Paris, Séminaire ACTA « Validation de modèles et outils en agronomie et élevage ».
11. Makowski D. 2010. Introduction aux méthodes de filtrage pour corriger les modèles dynamiques. Journée de formation organisée dans le cadre du RMT Modélisation. 9 mars 2010, Paris.
12. International course on « Methods for working with dynamic system models » organised by INRA (D Wallach, D. Makowski, S Roux), the University of Florida (J. Jones) and Montpellier SupAgro (A. Metay). 2-4 september 2010, Montpellier.
13. Makowski D. 2010. Méthodes fréquentistes et bayésiennes pour combiner les modèles. Séminaire statistique CIRAD. 7 avril 2010, Montpellier.
14. Makowski D., Bel L., Parent E. 2010. Simulation-based optimal design for estimating weed density in agricultural fields. 21th Annual Conference of the International Environmetrics Society. Isla Margarita, Venezuela.
15. Makowski D et al. 2010. Guidance document on statistical methods for analyzing 90-day rat feeding studies. WG meeting of the GMO panel of EFSA, Brussels, October 26 2010.
16. Makowski D. (coord.). Ecole chercheur BioBayes 2011: Méthodes bayésiennes pour la biologie, l’alimentation et l’environnement. Coordinateur. La Rochelle, décembre 2011. 
17. Makowski D. La variabilité du rendement augmente-t-elle ? Séminaire “Statistique bayésienne”, Rochebrune, Avril 2012. 
18. Philibert, A., C. Loyce, D. Makowski. 2012. Etude de la sensibilité des émissions de N2O à la méthode statistique utilisée. 44ème journées de la Statistique, Bruxelles, Mai 2012.
19. Makowski D. 2012. Uncertainty and sensitivity analysis for pest risk analysis; practical rules for risk assessors. International Pest Risk Mapping Workgroup Sixth Annual Workshop will be held 23-26 July 2012 in Tromsø, Norway.
20. Makowski D. 2012. Méthodes statistiques pour l’analyse des risques agro-environnementaux. Session invitée des journées de la Société française de Biométrie, CNAM, Paris, novembre 2012.
21. Makowski et al. 2013. Statistical Analysis of Large Simulated Yield Datasets for Studying Climate Effects. American Society of Agronomy annual meeting, Tampa, FL, USA, nov. 2013.
22. Makowski D. 2014. Statistical models for plant health studies. An introduction. Mathematical Modelling for Sustainable Management of Crop Health. Volterra, Italy, January 13-17 2014.
23. Makowski D. 2014. Model evaluation. Eclaire European Winter school on ”Measurement and modelling of biosphere-atmosphere exchanges of trace gases and aerosols”. Feb. 2014, Paris, France.
24. Makowski D. 2014. Uncertainty and sensitivity analysis. Macsur modelling workshop. ZALF, Müncheberg, Germany, May 2014.
25. Makowski D. 2014. Data assimilation with dynamic models. Macsur modelling workshop. ZALF, Müncheberg, Germany, May 2014.
26. Makowski D. 2014. Bayesian parameter estimation. Macsur modelling workshop. ZALF, Müncheberg, Germany, May 2014.
27. Makowski D. 2015. The Price equation; history and application in agriculture. Mini-symposium on "Phenotypic plasticity, diversity and productivity in plant communities". 8 January 2015, Wageningen, The Netherlands.
28. Makowski D. 2015. Intérêts et limites des modèles pour l’analyse des risques liés à la santé des plantes. Symposium “Risques et modèles en épidémiologie”. Université des Antilles, Pointe-à-Pitre, France.
29. Makowski D. 2015. Global agronomy. Workshop “New perspectives on food security”. INRA LEI-Wageningen workshop, Paris, France.
30. Makowski D., Ben-Ari T. et al. 2015. Is it possible to predict extreme yield loss using climate indicators? Farming System Design/ModExtreme conference. September 10 2015. Montpellier, France.
31. Makowski D., Miguez F. 2015. Should we use Bayesian method in meta-analysis? Annual meeting of the American Society of Agronomy, Minneapolis, USA, nov. 2015.
32. Makowski D. 2015. Modèles statistiques pour la protection intégrée. Formation « Introduction à la modélisation pour la protection intégrée des cultures », ACTA, (30 novembre au 4 décembre 2015, Paris).
33. Makowski D. 2015. Principes de l’analyse statistique Bayésienne. Séminaire-Atelier ACTA-Arvalis « Intégration de différentes sources de connaissances pour valoriser nos données : Intérêt des approches bayésiennes », 11 décembre 2015, Paris.
34. Makowski D. 2016. Data synthesis in agricultural and environmental sciences. AnaEE (analysis and experimentation on ecosystem) international conference, Paris, 2-3 Mars, 2016.
35. Makowski D. 2016. Meta-modelling of climate change impact on crop yields. NIAES, Tsukuba, Japan, July 2016. 
36. Makowski D. 2016. Incertitude dans les modèles mathématiques : caractérisation, description et représentation. Séminaire GIP-Ecofor « Quelles nouvelles approches de l’incertitude pour la gestion des forêts et de leur biodiversité ? ». Paris, 16 novembre 2016.
37. Makowski D. 2016. Meta-analysis: a two-day course. The C.T. De Wit Graduate school. Wageningen University.
38. Makowski D. 2016. Synthétiser les connaissances : de l’analyse de réseaux expérimentaux à la méta-analyse. Montpellier, Agropolis, 5, 6, 7 septembre 2016.
39. Makowski D., Sauvant D. 2016. Initiation à la méta-analyse, Clermont-Ferrand, 17-18 novembre 2016.
40. Makowski D. 2016. What role for meta-analysis in plant health risk assessment? Joint EFSA-EPPO Workshop: Modelling in Plant Health – how can models support risk assessment of plant pests and decision-making? Parma, 12-14 December 2016.
41. Makowski D. 2017. Statistical methods for predicting crop yield. Joint Research Center meeting on yield prediction. Ispra, Italy. 23-24 January 2017.
42. Makowski D. 2017. Bayesian statistics; a short introduction. Séminaire « méthodes » ANSES, Maison-Alfort, 24 mars 2017.
43. Makowski D. 2017. Méta-analyse des émissions liées à la production de biomasse à vocation énergétique. Journées scientifiques et techniques, INRA, Ademe, GIS CAS, Paris. 29 mars 2017.
44. Makowski D., Ben-Ari. 2017. Optimization of weather index thresholds for improving crop yield insurances. Workshop “Near-term crop yield forecasts to mitigate production risks-Impacts World 2017”. Postdam, Germany, 11-13 oct. 2017.
45. Makowski D. 2017. Effet du changement climatique sur la production : panorama des études disponibles pour la France. Rencontre régionale céréalière, Mâcon, 30 novembre 2017.
46. Makowski D. 2018. Introduction à la méta-analyse. Séminaire “méthodes” ANSES, Maison-Alfort, 18 janvier, 2018.
47. Organisation du séminaire de recherche « Méthodes probabilistes innovantes appliquées aux enjeux de l’agriculture et de la sécurité alimentaire : élicitation probabiliste et statistique bayésienne ». Université Ouaga 2, Burkina-Faso, 24-26 janvier 2018.
48. Makowski D. 2018. Méta-analyse pour les biosciences. Symposium « Modélisation et statistique en écologie et biosciences ». Université des Antilles, 15-16 mai, 2018.
49. Makowski D. 2018. Formation sur la méta-analyse organisée avec l’Institut de recherche pour le développement (IRD). Dakar, Sénégal, 4-6 juin 2018.
50. Makowski D. 2018. Statistical models for quantitative synthesis of climate change impact studies. NARO-FFTC-MARCO Symposium “Climate smart agriculture for the small-scale farmers in the Asian and Pacific region”. 27-28 september 2018, Tsukuba, Japan.
51. Makowski D. 2018. Impact of pesticide use reduction on crop yields and profitability. Scientific Advisory Board NAP meeting at Bundesministerium für Ernährung und Landwirtschaft, Berlin, Germany, 29 October 2018.