Articles | Volume 19, issue 2
https://doi.org/10.5194/gmd-19-627-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-627-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
22 Jan 2026
Model description paper |
| 22 Jan 2026
| 22 Jan 2026
A modelling system for identification of maize ideotypes, optimal sowing dates and nitrogen fertilization under climate change – PREPCLIM-v1
National Meteorological Administration Romania (NAM), Sos. Bucureşti-Ploieşti nr. 97, Sector 1, 013686 Bucureşti, România
Catalin Lazar
National Agricultural Research and Development Institute (NARDI) Fundulea, 915200 Călăraşi, România
Petru Neague
National Meteorological Administration Romania (NAM), Sos. Bucureşti-Ploieşti nr. 97, Sector 1, 013686 Bucureşti, România
Antoanela Dobre
National Meteorological Administration Romania (NAM), Sos. Bucureşti-Ploieşti nr. 97, Sector 1, 013686 Bucureşti, România
Vlad Amihaesei
National Meteorological Administration Romania (NAM), Sos. Bucureşti-Ploieşti nr. 97, Sector 1, 013686 Bucureşti, România
Zenaida Chitu
National Meteorological Administration Romania (NAM), Sos. Bucureşti-Ploieşti nr. 97, Sector 1, 013686 Bucureşti, România
Adrian Irasoc
National Meteorological Administration Romania (NAM), Sos. Bucureşti-Ploieşti nr. 97, Sector 1, 013686 Bucureşti, România
Andreea Popescu
National Meteorological Administration Romania (NAM), Sos. Bucureşti-Ploieşti nr. 97, Sector 1, 013686 Bucureşti, România
George Cizmas
National Agricultural Research and Development Institute (NARDI) Fundulea, 915200 Călăraşi, România
Cited articles
Adams, R. M., Hurd, B. H., Lenhart, S., and Leary, N.: Effects of global climate change on agriculture: an interpretative review, Climate Research, 11, 19–30, https://doi.org/10.3354/cr011019,1998.
Angus, J. F., Mackenzie, D. H., Morton, R., and Schafer, C. A.: Phasic development in field crops II. Thermal and photoperiodic responses of spring wheat, Field Crops Research, 4, 269–283, https://doi.org/10.1016/0378-4290(81)90078-2, 1981.
Arnell, N. W. and Freeman, A.: The effect of climate change on agro-climatic indicators in the UK, Climatic Change, 165, 40, https://doi.org/10.1007/s10584-021-03054-8, 2021.
Asseng, S., Ewert F., Martre, P., Rötter R. P., Lobell, D. B., Cammarano, D., Kimball, B. A., Ottman M. J., Wall G. W., White J. W., Reynolds, M. P., Alderman, P. D., Prasad, P. V. V., Aggarwal, P. K., Anothai, J., Basso, B., Biernath, C., Challinor, A. J., De Sanctis, G., Doltra, J., Fereres, E., Garcia-Vila, M., Gayler, S., Hoogenboom, G., Hunt, L. A., Izaurralde, R. C., Jabloun, M., Jones, C. D., Kersebaum, K. C., Koehler, A. K., Müller, C., Kumar, S. N., Nendel, C., O'Leary, G., Olesen, J. E., Palosuo, T., Priesack, E., Rezaei, E. E., Ruane, A. C., Semenov, M. A., Shcherbak, I., Stockle, C., Stratonovitch, P., Streck, T., Supit, I., Tao, F., Thorburn, P. J., Waha, K., Wang, E., Wallach, D., Wolf, J., Zhao, Z., and Zhu, Y.: Rising temperatures reduce global wheat production, Nat. Clim. Change, 5, 143–147, https://doi.org/10.1038/nclimate2470, 2015.
Baez-Gonzalez, A. D., Kiniry, J. R., Maas, S. J., Tiscareno, M., Macias, J., Mendoza, J. L., Richardson, C. W., Salinas, J., and Manjarrez, J. R.: Large-area maize yield forecasting using leaf area index based yield model, Agron. J., 97, 418–425, https://doi.org/10.2134/agronj2005.0418, 2005.
Bai, Y., Yue, W., and Ding, C.: Optimize the Irrigation and Fertilizer Schedules by Combining DSSAT and genetic algorithm, Environ. Sci. Pollut. Res., 29, 52473–52482, https://doi.org/10.1007/s11356-022-19525-z, 2022.
Banterng, P., Patanothai, A., Pannangpetch, K., Jogloy, S., and Hoogenboom, G.: Determination of genetic coefficients of peanut lines for breeding applications, European Journal of Agronomy, 21, 297–310, https://doi.org/10.1016/j.eja.2003.10.002, 2004.
Basso, B., Shuai, G., Zhang, J., and Robertson, G. P.: Yield stability analysis reveals sources of large-scale nitrogen loss from the US Midwest, Sci. Rep., 9, 5774, https://doi.org/10.1038/s41598-019-42271-1, 2019.
Bell, B., Hersbach, H., Simmons, A., Berrisford, P., Dahlgren, P., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Radu, R., Schepers, D., Soci, C., Villaume, S., Bidlot, J. R., Haimberger, L., Woollen, J., Buontempo, C., and Thepaut, J. N.: The ERA5 flobal reanalysis: Preliminary extenssion to 1950, Quaerterly Journal of the Royal Meteorological Society, 147, 4186–4227, https://doi.org/10.1002/qj.4174, 2021.
Benestad, R., Buonomo, E., Gutiérrez, J. M., Haensler, A., Hennemuth, B., Illy, T., Jacob, D., Keup-Thiel, E., Katragkou, E., Kotlarski, S., Nikulin, G., Otto, J., Rechid, D., Remke, T., Sieck, K., Sobolowski, D., Szabó, P., Szépszó, G., Teichmann, C., Vautard, R., Weber, T., Zsebeháziet, G., and Horányi, A.: Guidance for EURO-CORDEX climate projections data use, Version 1.1,-2021.02, https://www.euro-cordex.net/imperia/md/content/csc/cordex/guidance_for_euro-cordex_climate_projections_data_use__2021-02_1_.pdf (last access: 17 November 2025), 2021.
Bernardo, R.: Reinventing quantitative genetics for plant breeding: something old, something new, something borrowed, something BLUE, Heredity, 125, 375–385, https://doi.org/10.1038/s41437-020-0312-1, 2020.
Berti, A., Maucieri, C., Bonamano, A., and Borin, M.: Short-term climate change effects on maize phenological phases in northeast Italy, Italian Journal of Agronomy, https://doi.org/10.4081/ija.2019.1362, 2019.
Birch, C. J., Vos, J., Kiniry, J., Bos, H. J., and Elings, A.: Phyllochron responds to acclimation to temperature and irradiance in maize, Field Crops Research, 59, 187–200, https://doi.org/10.1016/S0378-4290(98)00120-8, 1998.
Boogaard, H., Wolf, J., Supit, I., Niemeyer, S., and van Ittersum, M.: A regional implementation of WOFOST for calculating harvest gaps of autumn-sown wheat across the European Union, Field Crops Research, 143, 130–142, https://doi.org/10.1016/j.fcr.2012.11.005, 2013.
Caian, M., Lazar, C., and Neague, P.: DSSAT code used in PREPCLIM project, Zenodo [code], https://doi.org/10.5281/zenodo.13145522, 2024a.
Caian, M., Lazar, C., and Neague, P.: PREPCLIM software – additional material for publication in “Geoscientific Model Development”, Zenodo [code], https://doi.org/10.5281/zenodo.13132587, 2024b.
Caian, M., Lazar, C., and Neague, P.: PREPCLIM sample data set for publication in “Geoscientific Model Development”, Zenodo [data set], https://doi.org/10.5281/zenodo.13133107, 2024c.
Ceglar, A., Zampieri, M., Gonzalez-Reviriego, N., Ciais, P., Schauberger, B., and Van der Velde M.: Time-varying impact of climate on maize and wheat harvests in France since 1900, Environmental Research Letters, 15, https://doi.org/10.1088/1748-9326/aba1be, 2020.
Chang, Y., Latham, J., Licht, M., and Wang L.: A data-driven crop model for maize yield prediction, Commun. Biol., 6, 439, https://doi.org/10.1038/s42003-023-04833-y, 2023.
Chapagain, R., Remenyi, T. A., Huth, N., Mohammed, C. L., and Ojeda, J. J.: Investigating the effects of APSIM model configuration on model outputs across different environments, Frontiers in Agronomy, https://doi.org/10.3389/fagro.2023.1213074, 2023.
Chazarreta, Y. D., Amas, J. I., and Otegui, M. E.: Kernel filling and desiccation in temperate maize: Breeding and environmental effects, Field Crops Research, 271, 108243, https://doi.org/10.1016/j.fcr.2021.108243, 2021
Chen, Y. and Tao, F.: Potential of remote sensing data-crop model assimilation and seasonal weather forecasts for early-season crop yield forecasting over a large area, Field Crops Research, 276, 108398, https://doi.org/10.1016/j.fcr.2021.108398, 2022.
Cooper, M. and Messina, C. D.: Breeding crops for drought-affected environments and improved climate resilience, The Plant Cell, 35, 162–186, https://doi.org/10.1093/plcell/koac321, 2023.
Copernicus report: European State of the Climate 2024, https://doi.org/10.24381/14j9-s541, 2024.
Dainelli, R., Calmanti, S., Pasqui, M., Rocchi, L., Di Giuseppe, E., Monotti, C., Quaresima, S., Matese, A., Di Gennaro, S. F., and Toscano, P.: Moving climate seasonal forecasts information from useful to usable for early within-season predictions of durum wheat yield, Climate Services, 28, 100324, https://doi.org/10.1016/j.cliser.2022.100324, 2022.
Djaman, K., Samuel Allen, S., Djaman, D. S., Koudahe, K., Irmak, S., Puppala, N., Darapuneni, M. K., and Angadi, S. V.: Planting date and plant density effects on maize growth, yield and water use efficiency, Environmental Challenges, 6, 100417, https://doi.org/10.1016/j.envc.2021.100417, 2022.
Dos Santos, C. L., Abendroth L. J., Coulter, J. A., Nafziger. E. D., Suyker, A., Yu, J., Schnable. P. S., and Archontoulis, S. V.: Maize Leaf Appearance Rates: A Synthesis From the United States Corn Belt, Frontiers in Plant Science, 13, https://doi.org/10.3389/fpls.2022.872738, 2022.
Espadafor, M., Orgaz Rosua, F., Testi, L., Lorite, I. J., González-Dugo, M. P., and Fereres Castiel, E.: Responses of transpiration and transpiration efficiency of almond trees to moderate water deficits, Scientia Horticulturae, 225, 6–14, https://doi.org/10.1016/j.scienta.2017.06.028, 2017.
Eyring, V., Mishra, V., Griffith, G. P., Chen, L., Keenan, T., Turetsly, M. R., Vrown, S., Jotzo, F., Moore, F. C., and der Linden, S.: Reflections and projections on a decade of climate science, Nature Climate Change, 11, 279–285, doi.org/10.1038/s41558-021-01020-x, 2021.
Fahad, S., Bajwa, A. A., Nazir, U., Anjum, S. A., Farooq, A., Zohaib, A., Sadia, S., Nasim, W., Adkins, S., Saud, S., Ihsan, M. Z., Alharby, H., Wu, C., Wang, D., and Huang, J.: Crop Production under Drought and Heat Stress: Plant Responses and Management Options, Frontiers in Plant Science, 8, https://doi.org/10.3389/fpls.2017.01147, 2017.
Godfray, H. C., Beddington, J. R., Crute, I. R., Haddad, L., Lawrence, D., Muir, J. F., Pretty, J., Robinson, S., Thomas, S. M., and Toulmin, C.: Food Security: The Challenge of Feeding 9 Billion People, Science, 327, 812–818, https://doi.org/10.1126/science.1185383, 2010.
Grewer, U., Kim, D. H., and Waha, K.: Too much, too soon? Early-maturing maize varieties as drought escape strategy in Malawi, Food Policy, 129, 102766, https://doi.org/10.1016/j.foodpol.2024.102766, 2024.
Hatfield, J. L., Antle, J., Garret, K. A., Izaurralde, R., C., Mader, T., Marshall, E., Nearing, M., Robertson, G. P., and Ziska, L.: Indicators of climate change in agricultural systems, Climatic Change, 163, 1719–1732, https://doi.org/10.1007/s10584-018-2222-2, 2020.
Hoogenboom, G., Porter, C. H., Sheila, V., Boote, K. J., Singh, U., White, J. W., Hunt, L. A., Ogoshi, R., Lizaso, J. I., Koo, J., Asseng, S., Singels, A., Moreno, L. P., and Jones, J. W.: The DSSAT crop modeling system, e-chapter Advances in crop modelling for a sustainable agriculture, edited by: Boote, K., https://doi.org/10.19103/AS.2019.0061.10, 2019.
Hu, H., Ming, B., Wang, K., Xue, J., Hou, P., Li, S., and Xie R.: The effects of photoperiod and temperature-related factors on maize leaf number and leaf positional distribution in the field, Frontiers in Plant Science, 14, https://doi.org/10.3389/fpls.2023.1006245, 2023.
Huang, M., Wang, J., Wang, B., Liu, D. L., Yu, Q., He, D., Wang, N., and Pan, X.: Optimizing sowing window and cultivar choice can boost China's maize yield under 1.5 °C and 2 °C global warmin, Environmental Research Letters, 15, 024015, https://doi.org/10.1088/1748-9326/ab66ca, 2020.
Huang, N., Lin, X., Lun, F., Zeng, R., Sassenrath, G. F., and Pan, Z.: Nitrogen fertilizer use and climate interactions: Implications for maize yields in Kansas, AgriculturalSystems, 220, 104079, https://doi.org/10.1016/j.agsy.2024.104079, 2024.
IPCC: Climate Change 2022: Impacts, Adaptation and Vulnerability, IPCC Sixth Assessment Report, https://doi.org/10.1017/9781009325844, 2022.
Jin, X., Jin, Y., Zhai, J., Fu, D., and Mao, X.: Identification and prediction of crop Waterlogging Risk Areas under the impact of climate change, Water, 14, 1–21, https://doi.org/10.3390/w14121956, 2022.
Jones, J., Hoogenboom, G., Porter, C., Porter, C. H., Boote, K. J., Batchelor, W. D., Hunt, L. A., Wilkens, P. W., Singh, U., Gijsman, A. J., and Ritchie, J. T.: DSSAT Cropping System Model. European Journal of Agronomy, 18, 235–265, https://doi.org/10.1016/S1161-0301(02)00107-7, 2003.
Kothari, K., Ale, S., Marek, G. W., Munster, C. L., Singh, V. P., Chen, Y., Marek, T. H., and Xue, Q.: Simulating the climate change impacts and evaluating potential adaptation strategies for irrigated corn production in Northern High Plains of Texas, Climate Risk Management, 37, 100446, https://doi.org/10.1016/j.crm.2022.100446, 2022.
Langworthy, A. D., Rawnsley, R. P., Freeman, M. J., Pembleton, K., G., Corkrev, R., Harrison M. T., and Lane, P. A.: Potential of summer-active temperate (C3) perennial forages to mitigate the detrimental effects of supraoptimal temperatures on summer home-grown feed production in south-eastern Australian dairying regions, Crop Pasture Sci., 69, 808–820, https://doi.org/10.1071/CP17291, 2018.
Li, M., Tang, Y. L., Li, C., Wu, X. L., Tao, X., and Liu, M.: Climate warming causes changes in wheat phenological development that benefit yield in the Sichuan Basin of China, European Journal of Agronomy, 139, 126574, https://doi.org/10.1016/j.eja.2022.126574, 2022.
Liu, K., Harrison, M. T., Hunt, J., and Angessa, T. T.: Identifying optimal sowing and flowering periods for barley in Australia: a modelling approach, Agr. Forest Meteorol., 282–283, 107871, https://doi.org/10.1016/j.agrformet.2019.107871, 2020.
Liu, K., Harrison, M. T., Yan, H., Liu, D. L., Meinke, H., Hoogenboom, G., Wang, B., Peng, B., Guan, K., Jaegermeyr, J., Wang, E., Zhang, F., Yin, X., Archontoulis, S., Nie, L., Badea, A., Man, J., Wallach, D., Zhao, J., Benjumea, A. B., Fahad, S., Tian, X., Wang, W., Tao, F., Zhang, Z., Rötter, R., Yuan, Y., Zhu, M., Dai, P., Nie, J., Yang, Y., Zhang, Y., and Zhou, M.: Silver lining to a climate crisis in multiple prospects for alleviating crop waterlogging under future climates, Nature Communications, 14, 765, https://doi.org/10.1038/s41467-023-36129-4, 2023.
Lu, J., Stomph, T. J., Mi, G., Yuan, L., and Evers, J.: Identifying and quantifying the contribution of maize plant traits to nitrogen uptake and use through plant modelling, in silico Plants, 6, diae018, https://doi.org/10.1093/insilicoplants/diae018, 2024.
Malhi, G. S., Kaur, M., and Kaushik, P: Impact of Climate Change on Agriculture and Its Mitigation Strategies: A Review, Sustainability, 13, 1318, https://doi.org/10.3390/su13031318, 2021.
Mamassi, A., Balaghi, R., Devkota, K. P., Bouras, H., El Gharous, M., and Tychon, B.: Modelling genotype × environment × management interactions for a sustainable intensification under rainfed wheat cropping system in Morocco, Agriculture and Food Security, https://doi.org/10.1186/s40066-023-00428-2, 2023.
Marcinkowski, P. and Piniewski, M.: Effect of climate change on sowing and harvest dates of spring barley and maize in Poland, International Agrophysics, 32, 265–227, https://doi.org/10.1515/intag-2017-0015, 2018.
McKay, J. K., Richards, J. H., and Mitchell-Olds, T.: Genetics of drought adaptation in Arabidopsis thaliana: I. Pleiotropy contributes to genetic correlations among ecological traits, Molecular ecology, 12, https://doi.org/10.1046/j.1365-294X.2003.01833.x, 2003.
McKee, T. B., Doesken, N. J., and Kleist, J.: The Relationship of Drought Frequency and Duration to Time Scales, In Proceedings of the Eighth Conference on Applied Climatology, Anaheim, CA, USA, Boston, MA, American Meteorological Society, 17, 179–183, 1993.
Meehl, G. A., Stocker T. F., Collins, W. D., Friedlingstein, P., Gaye, A. T., Gregory, J. M., Kitoh, A., Knutti, R., Murphy J. M., Noda, A., Raper, S. C. B., Watterson, I. G., Weaver A. J., and Zhao, Z. C.: Global Climate Projection, in: Climate Change 2007, The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B., Tignor, M., and Miller, H. L., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, ISBN 978-052188009-1, 2007.
Meroni, M., Waldner, F., Seguini, L., Kerdiles, H., and Rembold, F.: Yield forecasting with machine learning and small data: What gains for grains?, Agricultural and Forest Meteorology, 308–309, 108555, https://doi.org/10.1016/j.agrformet.2021.108555, 2021.
Mi, N., Cai, F., Zhang, S., Zgang Y., Ji, R., Chen, N., Ji, Y., and Wang, D.: Thermal Time Requirements for Maize Growth in Northeast China and Their Effects on Yield and Water Supply under Climate Change Conditions, Water, 13, 2612, https://doi.org/10.3390/w13192612, 2021.
Mitchell, R. J., Bellamy, P. E., Broome, A., Ellis, C. J., Hewison, R. L., Iason, G. R., Litlewood, N. A., Newey, S., Pozsgai, G., Ray, D., Stockan, J. A., Stokes, V., and Taylor, A. F. S.: Cumulative impact assessments of multiple host species loss from plant diseases show disproportionate reductions in associated biodiversity, Journal of Ecology, https://doi.org/10.1111/1365-2745.13798, 2021.
Morales, A. and Villalobos, F.: Using machine learning for crop harvest prediction in the past or the future, Frontiers in Plant Science, 14, 1128388, https://doi.org/10.3389/fpls.2023.1128388, 2023.
Morell, F. J., Yang, H. S., Cassman, K. G., Van Wart, J., Elmore, R. W., Licht, M., Coulter, J. A., Ciampitti, I. A., Pittelkow, C. M., Brouder, S. M., Thomison, P., Lauer, J., Graham, C., Massey, R., and Grassini, P.: Can crop simulation models be used to predict local to regional maize harvests and total production in the U.S. Corn Belt? Field Crops Research, 192, 1–12, https://doi.org/10.1016/j.fcr.2016.04.004, 2016.
O'Reilly, C. H., MacLeod, D., Befort, D. J., Shepherd, T. G., and Weisheimer, A.: Evaluating seasonal forecast improvements over the past two decades, Quarterly Journal of the Royal Meteorological Society, https://doi.org/10.1002/qj.70036, 2025.
Patra, A., Sharma, V. K., Nath, D. J., Dutta, A., Purakayastha, T. J., Kumar, S., Barman, M., Chobhe, K. A., Nath, C. P., and Kumawat, C.: Long-term impact of integrated nutrient management on sustainable harvest index of rice and soil quality under acidic inceptisol, Archives of Agronomy and Soil Science, https://doi.org/10.1080/03650340.2022.2056597, 2022.
Paudel, D., de Wit, A., Boogaard, H., Marcos, D., Osinga, S., and Athanasiadis, I. N.: Interpretability of deep learning models for crop yield forecasting, Computers and Electronics in Agriculture, 206, 107663, https://doi.org/10.1016/j.compag.2023.107663, 2023.
Peleman, J. D. and Van der Voort, J. R.: Breeding by Design, Trends in Plant Science, 8, 330–334, https://doi.org/10.1016/S1360-1385(03)00134-1, 2003.
Pfeiffer, W. H. and McClafferty, B.: HarvestPlus: Breeding Crops for Better Nutrition, Crop Science, 47, https://doi.org/10.2135/cropsci2007.09.0020IPBS, 2007.
Popescu, A., Dinu, T. A., Stoian, E., and Şerban, V.: The use of chemical fertilizers in Romania's agriculture, Scientific Papers, Series “Management, Economic Engineering in Agriculture and rural development”, 21, 469–476, 2021.
Qiao, L., Zhang, X., Li, X., Yang, Z., Li, R., Jia, J., Yan, L., and Chang, Z.: Genetic incorporation of genes for the optimal plant architecture in common wheat, Molecular Breeding, 42, https://doi.org/10.1007/s11032-022-01336-2, 2022.
Rezaei, E. E., Webber, H., Asseng, S., Boote, K., Durand, J. L., Ewert, F., Martre, P., and MacCarthy, D. S.: Climate change impacts on crop harvests, Nature Reviews Earth and Environment, https://doi.org/10.1038/s43017-023-00491-0, 2023.
Roeber, V., Schmulling T., and Cortleven, A.: The Photoperiod: Handling and Causing Stress in Plants, Frontiers in Plant Science, 12, https://doi.org/10.3389/fpls.2021.781988, 2022.
Rosenzweig, C., Jones, J. W., Hatfield, J. L., Ruane, A., C., Boote, K. J., Thorburn, P., Antle, J. M., Nelson, G. C., Porter, C., Janssen, S., Asseng, S., Basso, B., Ewert, F., Wallach, D., Baigorria, G., and Winter, J. M.: The Agricultural Model Intercomparison and Improvement Project (AgMIP): Protocols and pilot studies, Agricultural and Forest Meteorology, 170, 166–182, https://doi.org/10.1016/j.agrformet.2012.09.011, 2013.
Sardans, J., Grau, O., Chen, H. Y. H., Janssens, I. A., Ciais, P., Piao, S., and Peñuelas, J.: Changes in nutrient concentrations of leaves and roots in response to global change factors, Global Change Biology, https://doi.org/10.1111/gcb.13721, 2017.
Schauberger, B., Jägermeyr, J., and Gornott, C.: A systematic review of local to regional harvest forecasting approaches and frequently used data resources, European Journal of Agronomy, 120, 126153, https://doi.org/10.1016/j.eja.2020.126153, 2020.
Schwalbert, R., Amado, T., Nieto, L., Corassa, G., Rice, C., Peralta, N., Schauberger, B., Gornott, C., and Ciampitti, I.: Mid-season county-level corn yield forecast for US Corn Belt integrating satellite imagery and weather variables, Crop Sci., 69, 739–750, https://doi.org/10.1002/csc2.20053, 2020.
Selvaraju, R., Gommes, R., and Bernardi, M.: Climate science in support of sustainable agriculture and food security, Climatic Research, 47, 95–110, https://doi.org/10.3354/cr00954, 2011.
Semenov, M. and Stratonovitch, P.: Adapting wheat ideotypes for climate change: Accounting for uncertainties in CMIP5 climate projections, Climate Research, 65, 123–139, https://doi.org/10.3354/cr01297, 2015.
Singh, R., Vara Prasad, P. V., and Raja Reddy, K.: Impacts of Changing Climate and Climate Variability on Seed Production and Seed Industry, edited by: Sparks, D. L., Advances in Agronomy, Academic Press, 118, 49–110, ISBN 9780124059429, https://doi.org/10.1016/B978-0-12-405942-9.00002-5, 2013.
Smil, V.: The next 50 years: Unfolding trends, Population and Development Review, 31, 605–643, https://doi.org/10.1111/j.1728-4457.2005.00091.x, 2005.
Stehr, N. and Von Storch, H.: Climate and society: climate as resource, climate as risk, World Scientific, ISBN: 978-981-4280-53-2, https://doi.org/10.1142/7391, 2009.
Su, Z., Liu, Z., Bai, F., Zhang, Z., Sun, S., Huang, Q., Liu, T., Liu, X., and Yang, X.: Cultivar selection can increase yield potential and resource use efficiency of spring maize to adapt to climate change in Northeast China, Journal of Integrative Agriculture, 20, 371–382, https://doi.org/10.1016/S2095-3119(20)63359-7, 2021.
Tao, F., Zhang, Z., Liu, J., and Yokozawa, M.: Modelling the impacts of weather and climate variability on crop productivity over a large area: A new super-ensemble-based probabilistic projection, Agricultural and Forest Meteorology, 149, 1266–1278, https://doi.org/10.1016/j.agrformet.2009.02.015, 2009.
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An Overview of CMIP5 and the experiment design, Bull. Amer. Meteor. Soc., 93, 485–498, https://doi.org/10.1175/BAMS-D-11-00094.1, 2012.
Tsvetsinskaya, E. A., Mearns, L. O., and Easterling, W. E.: Investigating the Effect of Seasonal Plant Growth and Development in Three-Dimensional Atmospheric Simulations. Part I: Simulation of Surface Fluxes over the Growing Season, Journal of Climate, 14, 711–729, https://doi.org/10.1175/1520-0442(2001)014, 2001.
Tsimba, R., Edmeades, G. O., Millner, J. P., and Kemp, P. D.: The effect of planting date on maize: Phenology, thermal time durations and growth rates in a cool temperate climate, Field Crops Research, 150, 145–155, https://doi.org/10.1016/j.fcr.2013.05.021, 2013.
Trnka, M., Rötter, R. P., Ruiz-Ramos, M., Kersebaum, K. C., Olesen., J. E., Žalud, Z., and Semenov, M. A.: Adverse weather conditions for European wheat production will become more frequent with climate change, Nature Climate Change, 4, 637–643, https://doi.org/10.1038/nclimate2242, 2014.
Ursu, A.: Efficiency of chemical fertilizer use in Romanian agriculture. Comparative study based on FAOSAT data, Lucrari Stiintifice Management Agricol, 27, Agroprint Timisoara, Romania, https://www.lsma.ro/index.php (last access: 12 December 2025), 2025.
Van Ittersum, M. K., Cassman, K. G., Grassini, P., Wolf, J., Tittonell, P., and Hochman, Z.: Yield gap analysis with local to global relevance – A review, Field Crops Research, 143, 4–17, https://doi.org/10.1016/j.fcr.2012.09.009, 2013.
Villalobos, F., Pérez-Priego, O., Testi, L., Morales, A., and Orgaz, F.: Effects of water supply on carbon and water exchange of olive trees, European Journal of Agronomy, 40, 1–7, https://doi.org/10.1016/j.eja.2012.02.004, 2012.
Wang, Y., Jiang, K., Shen, H., Wang, N., Liu, R., Wu, J., and Ma, X.: Decision-making method for maize irrigation in supplementary irrigation areas based on the DSSAT model and a genetic algorithm, Agricultural Water Management, 280, 108231, https://doi.org/10.1016/j.agwat.2023.108231, 2023.
Webber, H., Ewert, F., Olesen, J. E., Müller, C., Fronzek, S., Ruane, A. C., Bourgault, M., Martre, P., Ababaei, B., Bindi, M., Ferrise, R., Finger, R., Fodor, N., Gabaldón-Leal, C., Gaiser, T., Jabloun, M., Kersebaum, K. C., Lizaso, J. I., Lorite, I. J., Manceau, L., Moriondo, M., Nendel, C., Rodríguez, A., Ruiz-Ramos, M., Semenov, M., Siebert, S., Stella, T., Stratonovitch, P., Trombi, G., and Wallach, D.: Diverging importance of drought stress for maize and winter wheat in Europe, Nature Communications, 9, 1–11, https://doi.org/10.1038/s41467-018-06525-2, 2018.
Webber, H., Cooke, D. K., Ewert, F., Olesen, J. E., Fronzek, S., Ruane, A. C., Martre, P., Collins, B., Bindi, M., Ferrise, R., Fodor, N., Gabaldon-Leal, C., Gaiser, T., Jabloun, M., Kersebaum, K., C., Lizaso, J. I., Lorite, I. J., Manceau, L., Moriondo, M., Nendel, C., Rodriguez, A., Ruiz-Ramos, M., Semenov, M., Stella, T., Stratonovitch, P., and Trombi, G.: Pan-European multi-crop model ensemble simulations of wheat and grain maize under climate change scenarios, Open Data Journal for Agricultural Research, 6, 21–27, https://doi.org/10.18174/odjar.v6i0.16326, 2020.
Wheeler, T. and von Braun, J.: Climate change impacts on global food security, Science, 341, 508–513, https://doi.org/10.1126/science.1239402, 2013.
World Bank, World Development Report: Agriculture for Development, https://doi.org/10.1596/978-0-8213-6807-7, 2008.
Wu, J. Z., Zhang, J., Ge, Z., Xing, L., Han, S., Shen, C., and Kong, F.: Impact of climate change on maize harvest in China from 1979 to 2016, Journal of Integrative Agriculture, 20, 289–299, https://doi.org/10.1016/S2095-3119(20)63244-0, 2021.
Xie, W., Zhu, A., Ali, T., Zhang, Z., Chen, X., Wu, F., Huang, J., and Davis, K. F.: Crop switching can enhance environmental sustainability and farmer incomes in China, Nature, 616, 300–305, https://doi.org/10.1038/s41586-023-05799-x, 2023.
Xu, H., Ming, B., Wang, K., Xue, J., Hou, P., Li, S., and Xie, R.: Quantitative analysis of maize leaf collar appearance rates, Plant Physiology and Biochemistry, 196, 454–462, https://doi.org/10.1016/j.plaphy.2023.01.016, 2023.
Yang, Y., Yin, J., Kang, S., Slater, L. J., Gu, X., and Volchak, A.: Quantifying the drivers of terrestrial drought and water stress impacts on carbon uptake in China, Agricultural and Forest Meteorology, 344, 109817, https://doi.org/10.1016/j.agrformet.2023.109817, 2024.
Zhuang, H., Zhang, Z., Cheng, F., Han, J., Luo, Y., Zhang, L., Cao, J., Zhang, J., He, B., Xu, J., and Tao, F.: Integrating data assimilation, crop model, and machine learning for winter wheat yield forecasting in the North China Plain, Agricultural and Forest Meteorology, 347, 109909, https://doi.org/10.1016/j.agrformet.2024.109909, 2024.
Short summary
We present the implementation of a new climate-phenology integrated system for adaptation to climate change, using high-resolution scenarios and the Decision Support System for Agrotechnology Transfer crop model, with new modules developed for optimal agromanagement and genotypes identification using a hybrid deterministic/machine learning Genetic-Algorithms method. The system is user-interactive in real time. It has been implemented for South Romania and is applicable and extendable for Europe.
We present the implementation of a new climate-phenology integrated system for adaptation to...
