Axe 1 - Plant plasticity and ideotype

Colorado J. D., Calderon F., Mendez D., Petro E., Rojas J. P., Correa E. S., Mondragon I. F., Rebolledo M. C., Jaramillo-Botero A. 2020. A novel NIR-image segmentation method for the precise estimation of above-ground biomass in rice crops. PloS One, 15 (10):e0239591, 20 p. https://doi.org/10.1371/journal.pone.0239591

Dingkuhn M., Luquet D., Fabre D., Muller B., Yin X., Paul M. 2020. The case for improving crop carbon sink strength or plasticity for a CO2-rich future. Curr. Op. Plant Biol., https://doi.org/10.1016/j.pbi.2020.05.012

Dingkuhn M., Luquet D., Fabre D., Muller B., Yin X., Paul M. J. 2020. The case for improving crop carbon sink strength or plasticity for a CO2-rich future. Current Opinion in Plant Biology, 56, n.spéc. Biotic interactions AGRI 2019: 259-272. https://doi.org/10.1016/j.pbi.2020.05.012

Echeverri-Rico J., Petro E., Fory P. A., Mosquera G., Lang J. M., Leach J. E., Lobaton J. D., Garcés G., Perafán R., Amezquita N., Toro S., Mora B., Cuasquer J., Ramirez-Villegas J., Rebolledo M. C., Torres Edgar A. 2021. Understanding the complexity of disease-climate interactions for rice bacterial panicle blight under tropical conditions. PloS One, 16 (5):e0252061, 18 p. https://doi.org/10.1371/journal.pone.0252061

Fabre D., Dingkuhn M. 2022. Why is rice Amax (at saturating CO2) more heritable than Asat (at ambient CO2)? Plant Breeding, 141 (4) : 542-545. https://doi.org/10.1111/pbr.13000

Fabre D., Dingkuhn M., Yin X., Clément-Vidal A., Roques S., Soutiras A., Luquet D. 2020. Genotypic variation in source and sink traits affects the response of photosynthesis and growth to elevated atmospheric CO2. Plant, Cell and Environment, 43 (3) : 579-593. https://doi.org/10.1111/pce.13693

Gano B.; Dembele J.S.B.; Ndour A.; Luquet D.; Beurier G.; Diouf D.; Audebert, A. 2021 Using UAV Borne, Multi-Spectral Imaging for the Field Phenotyping of Shoot Biomass, Leaf Area Index and Height of West African Sorghum Varieties under Two Contrasted Water Conditions. Agronomy, 11, 850.

Shrestha S., Laza M. R., Mendez K.V., Bhosale S., Dingkuhn M. 2020. The blaster: A methodology to induce rice lodging at plot scale to study lodging resistance. Field Crops Research, 245:107663, 12 p. https://doi.org/10.1016/j.fcr.2019.107663

Luquet D., Perrier L., Clément-Vidal A., Jaffuel S., Verdeil J.-L., Roques S., Soutiras A., Baptiste C., Fabre D., Bastianelli D., Bonnal L., Sartre P., Rouan L., Pot D. (2019) Genotypic covariations of traits underlying sorghum stem biomass production and quality and their regulations by water availability: Insight from studies at organ and tissue levels. GCB Bioenergy 11(2): 444-462. DOI: 10.1111/gcbb.12571

Thomas H. L., Pot D., Jaffuel S., Verdeil J.-L., Baptiste C., Bonnal L., Trouche G., Bastianelli D., Latrille E., Berger A., Calatayud C., Chauvergne C., Rossard V., Jeanson P., Alcouffe J., Carrere H. 2021. Mobilizing sorghum genetic diversity: Biochemical and histological-assisted design of a stem ideotype for biomethane production. Global Change Biology. Bioenergy, 13 (12): 1874-1893. https://doi.org/10.1111/gcbb.12886

Yang Y., Wilson L. T., Li T., Paleari ., Confalonieri R., Zhu Y., Tang L., Qiu X., Tao F., Chen Y., Hoogenboom G., Boote K., Gao Y., Onogi A., Nakagawa H., Yoshida H., Yabe S., Dingkuhn M., Lafarge T., Wang J., Hasegawa T. 2021. Integration of genomics with crop modeling for predicting rice days to flowering: A multi-model analysis. Field Crops Research, 276:108394, 15 p. https://doi.org/10.1016/j.fcr.2021.108394

Yin X., Gu J., Dingkuhn M., Struik P. 2022. A model-guided holistic review of exploiting natural variation of photosynthesis traits in crop improvement. Journal of Experimental Botany, 73 (10) : 3173-3188.

Axe 2 - Plant interaction and cropping systems

Adam M., MacCarthy D.S., Traoré P.C.S., Nenkam A., Freduah B.S., Ly M., Adiku S.G.K. 2020. Which is more important to sorghum production systems in the Sudano-Sahelian zone of West Africa: Climate change or improved management practices? Agricultural Systems, 185: 14 p.. DOI: 10.1016/j.agsy.2020.102920

Adam M, Boote K J., Falconnier G N., Porter C, Eyshi R E., Webber H. (2020) Modeling the effects of climate change on agriculture: a focus on cropping systems. In : Climate change and agriculture. Deryng Delphine (ed.). Cambridge : Burleigh Dodds Science Publishing (Burleigh Dodds Series in Agricultural Science). https://doi.org/10.19103/as.2020.0064.07

Assogba, G.G.C., Adam, M., Berre, D., Descheemaeker, K. 2022. Managing biomass in semi-arid Burkina Faso: Strategies and levers for better crop and livestock production in contrasted farm systems. Agricultural Systems, 201. DOI: 10.1016/j.agsy.2022.103458   

Barrios-Perez C., Okada K., Garcés Varón G., Ramirez-Villegas J., Rebolledo M. C., Prager S. D. 2021. How does El Niño Southern Oscillation affect rice-producing environments in central Colombia? Agricultural and Forest Meteorology, 306:108443, 14 p.

Berre D., Adam M., Koffi C.K., Vigne M., Gautier D. 2022. Tailoring management practices to the structure of smallholder households in Sudano-Sahelian Burkina Faso: Evidence from current practices. Agricultural Systems,198. https://doi.org/10.1016/j.agsy.2022.103369

Huet, E.K., Adam, M., Traore, B., Giller, K.E., Descheemaeker, K. 2022. Coping with cereal production risks due to the vagaries of weather, labour shortages and input markets through management in southern Mali.  European Journal of Agronomy, 140. https://doi.org/10.1016/j.eja.2022.126587.

Ganyo K. K., Muller B., Ndiaye M., Gaglo E. K., Guisse A., Adam M. 2019. Defining fertilization strategies for sorghum (Sorghum bicolor (L.) Moench) production under Sudano-Sahelian conditions: Options for late basal fertilizer application. Agronomy (Basel), 9 (11):697, 18 p. https://doi.org/10.3390/agronomy9110697

Gérardeaux E., Falconnier G., Gozé E., Defrance D., Kouakou P.-M., Loison R., Sultan B., Affholder F.,  Muller B. (2021) Adapting rainfed rice to climate change: A case study in Senegal. Agronomy for Sustainable Development, 41:57, 16 p. https://doi.org/10.1007/s13593-021-00710-2

Ndiaye M., Muller B., Ganyo K.K., Guissé A., Cissé N., Adam M. 2021. Phenotypic plasticity of plant traits contributing to grain and biomass yield of dual-purpose sorghum. Planta, 253 : 14 p. DOI: 10.1007/s00425-021-03599-z

Axe 3 - Plant and crop modeling

Blanc E., Barbillon P., Fournier C., Lecarpentier C., Pradal C., Enjalbert J. 2021. Functional–structural plant modeling highlights how diversity in leaf dimensions and tillering capability could promote the efficiency of wheat cultivar mixtures. Frontiers in Plant Science, 12:734056, 15 p. https://doi.org/10.3389/fpls.2021.734056

Boudon F., Persello S., Jestin A., Briand A-S, Grechi I., Fernique P., Guédon Y., Lechaudel M., Lauri P-E., Normand F. 2020. V-Mango: A functional-structural model of mango tree growth, development and fruit production. Annals of Botany, 126 (4), In.spéc. Functional-Structural Plant Growth Modelling : pp. 745-763.https://doi.org/10.1093/aob/mcaa089

Braghiere Renato K., Gérard F., Evers J., Pradal C., Pagès L. 2020.Simulating the effects of water limitation on plant biomass using a 3D functional-structural plant model of shoot and root driven by soil hydraulic. Annals of Botany, 126 (4), n.spec. Functional-Structural Plant Growth Modelling : 713-728. https://doi.org/10.1093/aob/mcaa059

Ehounou Adou E., Cornet D., Desfontaines L., Marie-Magdeleine C., Maledon E., Nudol E., Beurier G., Rouan L., Brat P., Lechaudel M., Nous C., N'Guetta Assanvo S-P., Kouakou Amani M., Arnau G. (2021) Predicting quality, texture and chemical content of yam (Dioscorea alata L.) tubers using near infrared spectroscopy. Journal of Near Infrared Spectroscopy, 29 (3): 128-139.  https://doi.org/10.1177/09670335211007575

Falconnier G.N., Corbeels M., Boote K.J., Affholder F., Adam M., MacCarthy D.S., Ruane A.C., Nendel C., Whitbread A.M., Justes É., Ahuja L.R., Akinseye F.M., Alou I.N., Amouzou K.A., Anapalli S.S., Baron C., Basso B., Baudron F., Bertuzzi P., Challinor A.J., Chen Y., Deryng D., Elsayed M.L., Faye B., Gaiser T., Galdos M., Gayler S., Gerardeaux E., Giner M., Grant B., Hoogenboom G., Ibrahim E.S., Kamali B., Kersebaum K.C., Kim S.-H., van der Laan M., Leroux L., Lizaso J.I., Maestrini B., Meier E.A., Mequanint F., Ndoli A., Porter C.H., Priesack E., Ripoche D., Sida T.S., Singh U., Smith W.N., Srivastava A., Sinha S., Tao F., Thorburn P.J., Timlin D., Traore B., Twine T., Webber H. 2020. Modelling climate change impacts on maize yields under low nitrogen input conditions in sub-Saharan Africa.               Global Change Biology, 26 (10) : p. 5942-5964. DOI: 10.1111/gcb.15261

Gao Y., Wallach D., Liu B., Dingkuhn M., Boote K. J., Singh U., Asseng S., Kahveci T., He J., Zhang R., Confalonieri R., Hoogenboom G. 2020. Comparison of three calibration methods for modeling rice phenology. Agricultural and Forest Meteorology, 280:107785, 12 p. https://doi.org/10.1016/j.agrformet.2019.107785

Gauthier M., Barillot R., Schneider C., Chambon C., Fournier C., Pradal C., Robert C., Andrieu B. 2020. A functional structural model of grass development based on metabolic regulations and coordination rules. Journal of Experimental Botany, 71 (18) : 5454-5468. https://doi.org/10.1093/jxb/eraa276

Heidsieck G., De Oliveira D., Pacitti E., Pradal C., Tardieu F., Valduriez P. 2021.Cache-aware scheduling of scientific workflows in a multisite cloud. Future Generation Computer Systems, 122 : 172-186. https://doi.org/10.1016/j.future.2021.03.012

Hoogenboom G., Justes E., Pradal C., Launay M., Asseng S., Ewert F., Martre P. 2020. iCROPM 2020: Crop modeling for the future. Journal of Agricultural Science, 158 (10): 791-793. https://doi.org/10.1017/S0021859621000538

Mamassi A., Marrou H., Wellens J., El Gharous M., Hammad A., Jabbour F., Tychon B. 2022. Relevance of soil fertility spatial databases for parameterizing APSIM-wheat crop model in Moroccan rainfed areas. Agronomy for Sustainable Development.42: 83.

Midingoyi C. A., Pradal C., Athanasiadis I., Donatelli M., Enders A., Fumagalli D., Garcia F., Holzworth D., Hoogenboom G., Porter C., Raynal H., Thorburn P. J., Martre P. 2020. Reuse of process-based models: Automatic transformation into many programming languages and simulation platforms. In Silico Plants, 3 (1):diaa007 : -20. https://doi.org/10.1093/insilicoplants/diaa007

Midingoyi C. A., Pradal C., Enders A., Fumagalli D., Raynal H., Donatelli M., Athanasiadis I., Porter C., Hoogenboom G., Holzworth D., Garcia F., Thorburn P. J., Martre P. 2021. Crop2ML: An open-source multi-language modeling framework for the exchange and reuse of crop model components. Environmental Modelling and Software, 142:105055, 15 p. https://doi.org/10.1016/j.envsoft.2021.105055

Perez R., Vezy R., Brancheriau L., Boudon F., Grand F., Ramel M., Raharjo D. A., Caliman J.-P., Dauzat J.. 2022. When architectural plasticity fails to counter the light competition imposed by planting design: An in silico approach using a functional–structural model of oil palm. In Silico Plants, 4 (1), n.spéc. : Functional–Structural Plant Models:diac009, 16 p.

Saint Cast C., Lobet G., Cabrera-Bosquet L., Couvreur V., Pradal C., Tardieu F., Draye X. 2022. Connecting plant phenotyping and modelling communities: Lessons from science mapping and operational perspectives. In Silico Plants, 4 (1) : 1-13.https://doi.org/10.1093/insilicoplants/diac005

Takahashi H, Pradal C. 2021. Root phenotyping: Important and minimum information required for root modeling in crop plants. Breeding Science, 71 (1): 109-116. https://doi.org/10.1270/jsbbs.20126

Vaillant J., Grechi I., Normand F., Boudon F. 2021. Towards virtual modelling environments for functional–structural plant models based on Jupyter notebooks: Application to the modelling of mango tree growth and development. In Silico Plants, 4 (1), n.spec. Functional-structural plant models:diab040, 16 p. https://doi.org/10.1093/insilicoplants/diab040

Van Oort P.A.J., Dingkuhn M. 2021. Feet in the water and hands on the keyboard: A critical retrospective of crop modelling at AfricaRice. Field Crops Research, 263:108074, 15 p.

Vasseur F., Cornet D., Beurier G., Messier J., Rouan L., Bresson J., Ecarnot M., Stahl M., Heumos S., Gérard M., Reijnen H., Tillard P., Lacombe B., Emanuel A., Floret J., Estarague A., Przybylska S., Sartori K., Gillespie L.M., Baron E., Kazakou E., Vile D., Violle C. (2022) A Perspective on Plant Phenomics: Coupling Deep Learning and Near-Infrared Spectroscopy. Front. Plant Sci. 13:836488. doi: 10.3389/fpls.2022.836488