FAO. Food and Agriculture Organization of the United Nations. FAOSTAT Data; www.faostat.fao.org (last access 15.06.21), (2016).
Gomez, D., Salvador, P., Sanz, J. & Casanova, J. L. Modelling wheat yield with antecedent information, satellite and climate data using machine learning methods in Mexico. Agric. For. Meteorol. 300, 108317. https://doi.org/10.1016/j.agrformet.2020.108317 (2021).
Google Scholar
Wrigley, C. W. Wheat: A unique grain for the world. In Wheat chemistry and technology 4th edn (eds Khan, K. & Shewry, P. R.) 1–17 (AACC Int. Inc, St Paul, 2009).
Awika, J. M. Major cereal grains production and use around the world. In Advances in Cereal Science: Implications to Food Processing and Health Promotion, Vol. 1089 (eds Awika, J. M., Piironen, V. & Bean, S.) 1–13 (American Chemical Society, 2011).
Gupta, R., Meghwal, M. & Prabhakar, P. K. Bioactive compounds of pigmented wheat (Triticum aestivum): Potential benefits in human health. Trends Food Sci. Technol. 110, 240–252. https://doi.org/10.1016/j.tifs.2021.02.003 (2021).
Google Scholar
FAO. Food and Agriculture Organization of the United Nations. FAOSTAT Data; www.faostat.fao.org (last access 15.06.21), (2020).
USDA. Grain and Feed Annual. United States Department of Agriculture (USDA), Foreign Agricultural Service (FAS), MO2020-0007; https://www.fas.usda.gov/data/morocco-grain-and-feed-annual-3 (last access 15.06.21), (2020).
McIntyre, C. L. et al. Molecular detection of genomic regions associated with grain yield and yield-related components in an elite bread wheat cross evaluated under irrigated and rainfed conditions. Theor. Appl. Genet. 120, 527–541. https://doi.org/10.1007/s00122-009-1173-4 (2010).
Google Scholar
UN. World population prospects. United Nations (UN), Department of Economic and Social Affairs (DESA); https://www.un.org/development/desa/en/news/population/world-population-prospects-2017.html (last access 15.06.21), (2017).
Gomez-Macpherson, H. & Richards, R. A. Effect of sowing time on yield and agronomic characteristics of wheat in south-eastern Australia. Aust. J. Agric. Res. 46, 1381–1399. https://doi.org/10.1071/AR9951381 (1995).
Google Scholar
Stone, P. J. & Nicolas, M. E. Effect of timing of heat stress during grain filling on two wheat varieties differing in heat tolerance. I. Grain growth. Aust. J. Plant Physiol. 22, 927–934. https://doi.org/10.1071/PP9950927 (1995).
Google Scholar
Mahdi, L., Bell, C. J. & Ryan, J. Establishment and yield of wheat (Triticum turgidum L.) after early sowing at various depths in a semi-arid Mediterranean environment. Field Crops Res. 58, 187–196. https://doi.org/10.1016/S0378-4290(98)00094-X (1998).
Google Scholar
Radmehr, M., Ayeneh, G. A. & Mamghani, R. Responses of late, medium and early maturity bread wheat genotypes to different sowing date. I. Effect of sowing date on phonological, morphological, and grain yield of four breed wheat genotypes. Iran. J. Seed. Sapling 21, 175–189 (2003).
Turner, N. C. Agronomic options for improving rainfall use efficiency of crops in dryland farming systems. J. Exp. Bot. 55, 2413–2425. https://doi.org/10.1093/jxb/erh154 (2004).
Google Scholar
Pickering, P. A. & Bhave, M. Comprehensive analysis of Australian hard wheat cultivars shows limited puroindoline allele diversity. Plant Sci. 172, 371–379. https://doi.org/10.1016/j.plantsci.2006.09.013 (2007).
Google Scholar
Zheng, B., Chenu, K., Fernanda Dreccer, M. & Chapman, S. C. Breeding for the future: What are the potential impacts of future frost and heat events on sowing and flowering time requirements for Australian bread wheat (Triticum aestivium) varieties?. Glob. Change Biol. 18, 2899–2914. https://doi.org/10.1111/j.1365-2486.2012.02724.x (2012).
Google Scholar
Wu, X. S., Chang, X. P. & Jing, R. L. Genetic insight into yield-associated traits of wheat grown in multiple rain-fed environments. PLoS ONE 7, e31249. https://doi.org/10.1371/journal.pone.0031249 (2012).
Google Scholar
Mueller, B. et al. Lengthening of the growing season in wheat and maize producing regions. Weather Clim. Extrem. 9, 47–56. https://doi.org/10.1016/j.wace.2015.04.001 (2015).
Google Scholar
Hunt, J. R., Hayman, P. T., Richards, R. A. & Passioura, J. B. Opportunities to reduce heat damage in rainfed wheat crops based on plant breeding and agronomic management. Field Crops Res. 224, 126–138. https://doi.org/10.1016/j.fcr.2018.05.012 (2018).
Google Scholar
Ababaei, B. & Chenu, K. Heat shocks increasingly impede grain filling but have little effect on grain setting across the Australian wheatbelt. Agric. For. Meteorol. 284, 107889. https://doi.org/10.1016/j.agrformet.2019.107889 (2020).
Google Scholar
Anderson, W. K. & Smith, W. R. Yield advantage of two semi-dwarf compared with two tall wheats depends on sowing time. Aust. J. Agric. Res. 41, 811–826. https://doi.org/10.1071/AR9900811 (1990).
Google Scholar
Connor, D. J., Theiveyanathan, S. & Rimmington, G. M. Development, growth, water-use and yield of a spring and a winter wheat in response to time of sowing. Aust. J. Agric. Res. 43, 493–516. https://doi.org/10.1071/AR9920493 (1992).
Google Scholar
Owiss, T., Pala, M. & Ryan, J. Management alternatives for improved durum wheat production under supplemental irrigation in Syria. Eur. J. Agron. 11, 255–266. https://doi.org/10.1016/S1161-0301(99)00036-2 (1999).
Google Scholar
Bassu, S., Asseng, A., Motzo, R. & Giunta, F. Optimizing sowing date of durum wheat in a variable Mediterranean environment. Field Crops Res. 111, 109–118. https://doi.org/10.1016/j.fcr.2008.11.002 (2009).
Google Scholar
Bannayan, M., Eyshi Rezaei, E. & Hoogenboom, G. Determining optimum sowing dates for rainfed wheat using the precipitation uncertainty model and adjusted crop evapotranspiration. Agric. Water Manag. 126, 56–63. https://doi.org/10.1016/j.agwat.2013.05.001 (2013).
Google Scholar
Liang, Y. F. et al. Subsoiling and sowing time influence soil water content, nitrogen translocation and yield of dryland winter wheat. Agronomy 9, 37. https://doi.org/10.3390/agronomy9010037 (2019).
Google Scholar
Farooq, M., Basra, S. M. A., Rehman, H. & Saleem, B. A. Seed priming enhances the performance of late sown wheat (Triticum aestivum L.) by improving chilling tolerance. J. Agron. Crop Sci. 194, 55–60. https://doi.org/10.1111/j.1439-037X.2007.00287.x (2008).
Google Scholar
Kudair, I. M. & Adary, A. H. The effects of temperature and planting depth on coleoptile length of some Iraqi local and introduced wheat cultivars. Mesopotamia J. Agric. 17, 49–62 (1982).
Leoncini, E. et al. Phytochemical profile and nutraceutical value of old and modern common wheat cultivars. PLoS ONE 7, e45997. https://doi.org/10.1371/journal.pone.0045997 (2012).
Google Scholar
Busko, M. et al. The effect of Fusarium inoculation and fungicide application on concentrations of flavonoids (apigenin, kaempferol, luteolin, naringenin, quercetin, rutin, vitexin) in winter wheat cultivars. Am. J. Plant Sci. 5, 3727–3736. https://doi.org/10.4236/ajps.2014.525389 (2014).
Google Scholar
Bannayan, M., Kobayashi, K., Marashi, H. & Hoogenboom, G. Gene-based modeling for rice: An opportunity to enhance the simulation of rice growth and development?. J. Theor. Biol. 249, 593–605. https://doi.org/10.1016/j.jtbi.2007.08.022 (2007).
Google Scholar
Soler, C. M. T., Sentelhas, P. C. & Hoogenboom, G. Application of the CSM-CERES-Maize model for sowing date evaluation and yield forecasting for maize grown off-season in a subtropical environment. Eur. J. Agron. 18, 165–177. https://doi.org/10.1016/j.eja.2007.03.002 (2007).
Google Scholar
Andarzian, B. et al. WheatPot: A simple model for spring wheat yield potential using monthly weather data. Biosyst. Eng. 99, 487–495. https://doi.org/10.1016/j.biosystemseng.2007.12.008 (2008).
Google Scholar
Andarzian, B., Hoogenboom, G., Bannayan, M., Shirali, M. & Andarzian, B. Determining optimum sowing date of wheat using CSM-CERES-Wheat model. J. Saudi Soc. Agric. Sci. 14, 189–199. https://doi.org/10.1016/j.jssas.2014.04.004 (2015).
Google Scholar
Palosuo, T. et al. Simulation of winter wheat yield and its variability in different climates of Europe: A comparison of eight crop growth models. Eur. J. Agron. 35, 103–114. https://doi.org/10.1016/j.eja.2011.05.001 (2011).
Google Scholar
Rötter, R. P. et al. Simulation of spring barley yield in different climatic zones of Northern and Central Europe: A comparison of nine crop models. Field Crops Res. 133, 23–36. https://doi.org/10.1016/j.fcr.2012.03.016 (2012).
Google Scholar
Ran, H. et al. Capability of a solar energy-driven crop model for simulating water consumption and yield of maize and its comparison with a water-driven crop model. Agric. For. Meteorol. 287, 107955. https://doi.org/10.1016/j.agrformet.2020.107955 (2020).
Google Scholar
Keating, B. A. et al. An overview of APSIM, a model designed for farming systems simulation. Eur. J. Agron. 18, 267–288. https://doi.org/10.1016/S1161-0301(02)00108-9 (2003).
Google Scholar
Probert, M. E. & Dimes, J. P. Modelling release of nutrients from organic resources using APSIM. In Modelling nutrient management in tropical cropping systems Vol. 114 (eds Delve, R. J. & Probert, M. E.) 25–31 (ACIAR Proceedings, 2004).
Mohanty, M. et al. Simulating soybean–wheat cropping system: APSIM model parameterization and validation. Agric. Ecosyst. Environ. 152, 68–78. https://doi.org/10.1016/j.agee.2012.02.013 (2012).
Google Scholar
George, N., Thompson, S. E., Hollingsworth, J., Orloff, S. & Kaffka, S. Measurement and simulation of water-use by canola and camelina under cool-season conditions in California. Agric. Water Manag. 196, 15–23. https://doi.org/10.1016/j.agwat.2017.09.015 (2018).
Google Scholar
Bahri, H., Annabi, M., M’Hamed, H. C. & Frija, A. Assessing the long-term impact of conservation agriculture on wheat-based systems in Tunisia using APSIM simulations under a climate change context. Sci. Total Environ. 692, 1223–1233. https://doi.org/10.1016/j.scitotenv.2019.07.307 (2019).
Google Scholar
Ahmed, M. et al. Novel multimodel ensemble approach to evaluate the sole effect of elevated CO2 on winter wheat productivity. Sci. Rep. 9, 7813. https://doi.org/10.1038/s41598-019-44251-x (2019).
Google Scholar
Eyni-Nargeseh, H., Deihimfard, R., Rahimi-Moghaddam, R. & Mokhtassi-Bidgoli, A. Analysis of growth functions that can increase irrigated wheat yield under climate change. Meteorol. Appl. 27, 1–10. https://doi.org/10.1002/met.1804 (2020).
Google Scholar
Rahimi-Moghaddam, S., Eyni-Nargeseh, H., Kalantar Ahmadi, S. A. & Azizi, K. Towards withholding irrigation regimes and resistant genotypes as strategies to increase canola production in drought-prone environments: A modeling approach. Agric. Water Manag. 243, 106487. https://doi.org/10.1016/j.agwat.2020.106487 (2021).
Google Scholar
Berghuijs, H. N. C. et al. Calibrating and testing APSIM for wheat-faba bean pure cultures and intercrops across Europe. Field Crops Res. 264, 108088. https://doi.org/10.1016/j.fcr.2021.108088 (2021).
Google Scholar
METLE. National Report. Ministry of Equipment, Transport, Logistics and Water (last access 15.06.21), (2019).
HCP. Voluntary national review of the implementation of the sustainable development goals. High Comm. Plng. p. 188 (2020).
Hammer, G. L. et al. Adapting APSIM to model the physiology and genetics of complex adaptive traits in field crops. J. Exp. Bot. 61, 2185–2202. https://doi.org/10.1093/jxb/erq095 (2010).
Google Scholar
Holzworth, D. P. et al. APSIM—evolution towards a new generation of agricultural systems simulation. Environ. Model. Softw. 62, 327–350. https://doi.org/10.1016/j.envsoft.2014.07.009 (2014).
Google Scholar
Gaydon, D. S. et al. Evaluation of the APSIM model in cropping systems of Asia. Field Crops Res. 204, 52–75. https://doi.org/10.1016/j.fcr.2016.12.015 (2017).
Google Scholar
Climate Kelpie website. http://www.climatekelpie.com.au/manage-climate/decision-support-tools-for-managing-climate (2010).
McCown, R. L., Hammer, G. L., Hargreaves, J. N. G., Holzworth, D. P. & Freebairn, D. M. APSIM: A novel software system for model development, model testing and simulation in agricultural systems research. Agric. Syst. 50, 255–271. https://doi.org/10.1016/0308-521X(94)00055-V (1996).
Google Scholar
Cichota, R., Vogeler, I., Werner, A., Wigley, K. & Paton, B. Performance of a fertiliser management algorithm to balance yield and nitrogen losses in dairy systems. Agric. Syst. 162, 56–65. https://doi.org/10.1016/j.agsy.2018.01.017 (2018).
Google Scholar
Laurenson, S., Cichota, R., Reese, P. & Breneger, S. Irrigation runoff from a rolling landscape with slowly permeable subsoils in New Zealand. Irrig. Sci. 36, 121–131. https://doi.org/10.1007/s00271-018-0570-3 (2018).
Google Scholar
Rodriguez, D. et al. Predicting optimum crop designs using crop models and seasonal climate forecasts. Sci. Rep. 8, 2231. https://doi.org/10.1038/s41598-018-20628-2 (2018).
Google Scholar
Archontoulis, S. V., Miguez, F. E. & Moore, K. J. A methodology and an optimization tool to calibrate phenology of short-day species included in the APSIM PLANT model: Application to soybean. Environ. Model. Softw. 62, 465e477. https://doi.org/10.1016/j.envsoft.2014.04.009 (2014).
Google Scholar
Brown, H., Huth, N. & Holzworth, D. Crop model improvement in APSIM: Using wheat as a case study. Eur. J. Agron. 100, 141–150. https://doi.org/10.1016/j.eja.2018.02.002 (2018).
Google Scholar
Yang, X. et al. Cropping system productivity and evapotranspiration in the semiarid Loess Plateau of China under future temperature and precipitation changes: An APSIM-based analysis of rotational vs. Continuous systems. Agric. Water Manag. 229, 105959. https://doi.org/10.1016/j.agwat.2019.105959 (2020).
Google Scholar
Balboa, G. R. et al. A systems-level yield gap assessment of maize-soybean rotation under highand low-management inputs in the Western US Corn Belt using APSIM. Agric. Syst. 174, 125–154. https://doi.org/10.1016/j.agsy.2019.04.008 (2019).
Google Scholar
Yang, X. et al. Modelling the effects of conservation tillage on crop water productivity, soil water dynamics and evapotranspiration of a maize-winter wheat-soybean rotation system on the Loess plateau of China using APSIM. Agric. Syst. 166, 111–123. https://doi.org/10.1016/j.agsy.2018.08.005 (2018).
Google Scholar
Mohanty, M. et al. Soil carbon sequestration potential in a Vertisol in central India- results from a 43-year long-term experiment and APSIM modeling. Agric. Syst. 184, 102906. https://doi.org/10.1016/j.agsy.2020.102906 (2020).
Google Scholar
Vogeler, I., Thomas, S. & van der Weerden, T. Effect of irrigation management on pasture yield and nitrogen losses. Agric. Water Manag. 216, 60–69. https://doi.org/10.1016/j.agwat.2019.01.022 (2019).
Google Scholar
Bosi, C. et al. APSIM-tropical pasture: A model for simulating perennial tropical grass growth and its parameterisation for palisade grass (Brachiaria brizantha). Agric. Syst. 184, 102917. https://doi.org/10.1016/j.agsy.2020.102917 (2020).
Google Scholar
Smethurst, P. J., Valadares, R. V., Huth, N. I., Almeida, A. C. & Júlio, C. L. N. Generalized model for plantation production of Eucalyptus grandisand hybrids forgenotype-site-management applications. For. Ecol. Manag. 469, 118164. https://doi.org/10.1016/j.foreco.2020.118164 (2020).
Google Scholar
Xiao, D. P., Liu, D. L., Wang, B., Feng, P. Y. & Tang, J. Z. Climate change impact on yields and water use of wheat and maize in the north China plain under future climate change scenarios. Agric. Water Manag. 238, 1–15. https://doi.org/10.1016/j.agwat.2020.106238 (2020).
Google Scholar
Seyoum, S., Rachaputi, R., Chauhan, Y., Prasanna, B. & Fekybelu, S. Application of the APSIM model to exploit G × E × M interactions for maize improvement in Ethiopia. Field Crops Res. 217, 113–124. https://doi.org/10.1016/j.fcr.2017.12.012 (2018).
Google Scholar
Basche, A. D. & DeLonge, M. S. Comparing infiltration rates in soils managed with conventional and alternative farming methods: A meta-analysis. PLoS ONE 14, e0215702. https://doi.org/10.1371/journal.pone.0215702 (2019).
Google Scholar
Holzworth, D. et al. The development of a farming systems model (APSIM): A disciplined approach. In Proceedings of the iEMSs Third Biennial Meeting, Burlington, VT, USA, 9–13 July 2006 (International Environmental Modelling and Software Society, Manno, Switzerland, 2006).
Gaydon, D. S. The APSIM model—an overview. In SAC Monograph: The SAARC-Australia Project Developing Capacity in Cropping Systems Modelling for South Asia (eds Dr. Donald S. Gaydon et al.) 15–31 (2014).
Pinheiro, J. C. & Bates, D. M. Mixed Effects Models in S and S-Plus (Statistics and Computing) (Springer, New York, 2000).
Google Scholar
El Halimi, R. Nonlinear Mixed-effects Models and Bootstrap resampling: Analysis of Non-normal Repeated Measures in Biostatistical Practice. Amazon Books. 320 (2009).
Vock, D. M., Davidian, M., Tsiatis, A. A. & Muir, A. J. Mixed model analysis of censored longitudinal data with flexible random-effects density. Biostat. 13, 61–73. https://doi.org/10.1093/biostatistics/kxr026 (2012).
Google Scholar
Beroho, M. et al. Analysis and prediction of climate forecasts in Northern Morocco: Application of multilevel linear mixed effects models using R Software. Heliyon 6, e05094. https://doi.org/10.1016/j.heliyon.2020.e05094 (2020).
Google Scholar
Laird, N. M. & Ware, J. H. Random-effects models for longitudinal data. Biometrics 38, 963–974. https://doi.org/10.2307/2529876 (1982).
Google Scholar
Littell, R. C., Henry, P. R. & Ammerman, C. B. Statistical analysis of repeated measures data using SAS procedures. J. Anim. Sci. Biotechnol. 76, 1216–1231. https://doi.org/10.2527/1998.7641216x (1998).
Google Scholar
Bouyoucos, G. J. Direction for making mechanical analysis of soils by the hydrometer method. Soil Sci. 42, 225–230. https://doi.org/10.1097/00010694-193609000-00007 (1936).
Google Scholar
Nash, J. E. & Sutcliffe, J. V. River flow forecasting through conceptual models, part I: A discussion of principles. J. Hydrol. 10, 282–290. https://doi.org/10.1016/0022-1694(70)90255-6 (1970).
Google Scholar
Willmott, C. J., Robeson, S. M. & Matsuura, K. A refined index of model performance. Int. J. Climatol. 32, 2088–2094. https://doi.org/10.1002/joc.2419 (2011).
Google Scholar
Loague, K. & Green, R. E. Statistical and graphical methods for evaluating solute transport models; overview and application. J. Contam. Hydrol. 7, 51–73. https://doi.org/10.1016/0169-7722(91)90038-3 (1991).
Google Scholar
Willmott, C. J. et al. Statistic for the evaluation and comparison of models. J. Geophys. Res. 90, 8995–9005. https://doi.org/10.1029/JC090iC05p08995 (1985).
Google Scholar
Jones, C. A., Kiniry, J. R. & Dyke, P. T. CERES-Maize, A simulation model of maize growth and development 1st edn. (Texas University Press, College Station, 1986).
Dardanelli, J. L., Bacheier, O. A., Sereno, R. & Gil, R. Rooting depth and soil water extraction patterns of different crops in a silty loam Haplustoll. Field Crops Res. 54, 29–38. https://doi.org/10.1016/S0378-4290(97)00017-8 (1997).
Google Scholar
Probert, M. E., Dimes, J. P., Keating, B. A., Dalal, R. C. & Strong, W. M. APSIM’s water and nitrogen modules and simulation of the dynamics of water and nitrogen in fallow systems. Agric. Syst. 56, 1–28. https://doi.org/10.1016/S0308-521X(97)00028-0 (1998).
Google Scholar
Littleboy, M., Freebairn, D. M., Silburn, D. M., Woodruff, D. R., Hammer, G. L. PERFECT version 3. A computer simulation model of productivity erosion runoff functions to evaluate conservation techniques. Queensland department of natural resources and department of plant industries. Queensland Dep. Prim. Ind., Queensland, Australia (1999).
Dalgliesh, N. P. & Foale, M. A. Soil matters: Monitoring soil water and nutrients in dryland farming. Agric. Prod. Sys. Res. Unit, Toowoomba, Australia; http://hdl.handle.net/102.100.100/217161?index=1 (1998).
Malone, R. W. et al. Evaluating and predicting agricultural management effects under tile drainage using modified APSIM. Geoderma 140, 310–322. https://doi.org/10.1016/j.geoderma.2007.04.014 (2007).
Google Scholar
Cresswell, H. P. et al. Catchment response to farm scale land use change. CSIRO and NSW Dept. of Ind. & Invest. (2009).
Hammer, G. L. et al. Can changes in canopy and/or root system architecture explain historical maize yield trends in the U.S. Corn Belt?. Crop Sci. 49, 299–312. https://doi.org/10.2135/cropsci2008.03.0152 (2009).
Google Scholar
Archontoulis, S. V., Miguez, F. E. & Moore, K. J. Evaluating APSIM maize, soil water, soil nitrogen, manure, and soil temperature modules in the Midwestern United States. Agron. J. 106, 1025. https://doi.org/10.2134/agronj2013.0421 (2014).
Google Scholar
MacCarthy, D. S., Sommer, R. & Vlek, P. L. G. Modeling the impacts of contrasting nutrient and residue management practices on grain yield of sorghum (Sorghum bicolor (L.) Moench) in a semi-arid region of Ghana using APSIM. Field Crops Res. 113, 105–115. https://doi.org/10.1016/j.fcr.2009.04.006 (2009).
Google Scholar
Yang, Y. et al. Water use efficiency and crop water balance of rainfed wheat in a semi-arid environment: Sensitivity of future changes to projected climate changes and soil type. Theor. Appl. Climatol. 123, 565–579. https://doi.org/10.1007/s00704-015-1376-3 (2016).
Google Scholar
Deihimfard, R., Eyni-Nargeseh, H. & Mokhtassi-Bidgoli, A. Effect of future climate change on wheat yield and water use efficiency under semi-arid conditions as predicted by APSIM-wheat model. Int. J. Plant Prod. 12, 115–125. https://doi.org/10.1007/s42106-018-0012-4 (2018).
Google Scholar
Zhao, P. et al. The adaptability of Apsim-wheat model in the middle and lower reaches of the Vangtze river plain of china: A case study of winter wheat in hubei province. Agronomy 10, 981. https://doi.org/10.3390/agronomy10070981 (2020).
Google Scholar
SHNP, D. S., Takahashi, T., Okada, K. Evaluation of APSIM-wheat to simulate the response of yield and grain protein content to nitrogen application on an Andosol in Japan. Plant Prod. Sci. https://doi.org/10.1080/1343943X.2021.1883989 (2021).
O’Leary, G. J. et al. Response of wheat growth, grain yield and water use to elevated CO2 under afree-air CO2 Enrichment (FACE) experiment and modelling in a semi-arid environment. Glob. Change Biol. 21, 2670–2686. https://doi.org/10.1111/gcb.12830 (2015).
Google Scholar
Lilley, J. M. & Kirkegaard, J. A. Farming system context drives the value of deep wheat roots in semi-arid environments. J. Exp. Bot. 67, 3665–3681. https://doi.org/10.1093/jxb/erw093 (2016).
Google Scholar
Whitbread, A. M., Hoffmann, M. P., Davoren, C. W., Mowat, D. & Baldock, J. A. Measuring and modeling the water balance in low-Rainfall cropping systems. Trans. ASABE 60, 2097–2110. https://doi.org/10.13031/trans.12581 (2017).
Google Scholar
Silungwe, F. R. et al. Crop upgrading strategies and modelling for rainfed cereals in a semi-arid climate—a review. Water 10, 356. https://doi.org/10.3390/w10040356 (2018).
Google Scholar
Hussain, J., Khaliq, T., Ahmad, A. & Akhtar, J. Performance of four crop model for simulations of wheat phenology, leaf growth, biomass and yield across planting dates. PLoS ONE 13, e0197546. https://doi.org/10.1371/journal.pone.0197546 (2018).
Google Scholar
Asseng, S., Turner, N. C. & Keating, B. A. Analysis of water- and nitrogen-use efficiency of wheat in a Mediterranean climate. Plant Soil 233, 127–143. https://doi.org/10.1023/A:1010381602223 (2001).
Google Scholar
Moeller, C., Pala, M., Manschadi, A. M., Meinke, H. & Sauerborn, J. Assessing the sustainability of wheat-based cropping systems using APSIM: Model parameterisation and evaluation. Aust. J. Agric. Res. 58, 75–86. https://doi.org/10.1007/s11625-013-0228-2 (2007).
Google Scholar
Bassu, S., Asseng, S., Giunta, F. & Motzo, R. Optimizing triticale sowing densities across the Mediterranean Basin. Field Crops Res. 144, 167–178. https://doi.org/10.1016/j.fcr.2013.01.014 (2013).
Google Scholar
Bationo, A., Mokwunye, U., Vlek, P. L. G., Koala, S. & Shapiro, B. I. Soil fertility management for sustainable land use in the West African Sudano-Sahelian Zone. In Soil Fertility Management in Africa: A Regional Perspective, African Academy of Sciences Centro Internacional de Agricultura Tropical (CIAT); Tropical Soil Biology and Fertility (TSBF) (eds Gichuri, M. P. et al.) 253–292 (Academic and Scientific Publishing, Nairobi, 2003).
Bernstein, L. et al. IPCC, 2007: Climate Change 2007: Synth. Rep. Geneva: IPCC. ISBN 2-9169-122-4 (2008).
Tramblay, Y. et al. Climate change impacts on extreme precipitation in Morocco. Glob. Planet Change 82, 104–114. https://doi.org/10.1016/j.gloplacha.2011.12.002 (2012).
Google Scholar
Tramblay, Y., Ruelland, D., Somot, S., Bouaicha, R. & Servat, E. High-resolution Med-CORDEX regional climate model simulations for hydrological impact studies: A first evaluation of the ALADIN-Climate model in Morocco. Hydrol. Earth Syst. Sci. 17, 3721–3739. https://doi.org/10.5194/hess-17-3721-2013 (2013).
Google Scholar
Seif-Ennasr, M. et al. Climate change and adaptive water management measures in Chtouka Aït Baha region (Morocco). Sci. Total Environ. 573, 862–875. https://doi.org/10.1016/j.scitotenv.2016.08.170 (2016).
Google Scholar
Hirich, A., Fatnassi, H., Ragab, R. & Choukr-Allah, R. Prediction of climate change impact on corn grown in the South of Morocco using the saltmed model. J. Irrigat. Drain. Eng. 65, 9–18. https://doi.org/10.1002/ird.2002 (2016).
Google Scholar
Ouhamdouch, S. & Bahir, M. Climate change impact on future rainfall and temperature in semi-arid areas (Essaouira basin, Morocco). Environ. Process. 4, 975–990. https://doi.org/10.1007/s40710-017-0265-4 (2017).
Google Scholar
Brouziyne, Y. et al. Modelling sustainable adaptation strategies toward a climate-smart agriculture in a Mediterranean watershed under projected climate change scenarios. Agric. Syst. 162, 154–163. https://doi.org/10.1016/j.agsy.2018.01.024 (2018).
Google Scholar
Dosio, A. & Panitz, H.-J. Climate change projections for CORDEX-Africa with COSMO-CLM regional climate model and differences with the driving global climate models. Clim. Dyn. 46, 1599–1625. https://doi.org/10.1007/s00382-015-2664-4 (2016).
Google Scholar
Zeroual, A., Assani, A. A., Meddi, M. & Alkama, R. Assessment of climate change in Algeria from 1951 to 2098 using the Köppen-Geiger climate classification scheme. Clim. Dyn. 52, 227–243. https://doi.org/10.1007/s00382-018-4128-0 (2018).
Google Scholar
Mami, A. et al. Future climatic and hydrologic changes estimated by bias-adjusted regional climate model outputs of the Cordex-Africa project: Case of the Tafna basin (North-Western Africa). Int. J. Glob. Warm. 23, 58–90. https://doi.org/10.1504/IJGW.2021.112489 (2021).
Google Scholar
Arora, V. K. & Gajri, P. R. Evaluation of a crop growth–water balance model for analyzing wheat responses to climate and water-limited environments. Field Crops Res. 59, 213–224. https://doi.org/10.1016/S0378-4290(98)00124-5 (1998).
Google Scholar
Aggarwal, P. K., Talukdar, K. K., Mall, R. K. Potential yields of rice–wheat system in the Indo-Gangetic plains of India. Rice–Wheat Consortium Paper Series 10. New Delhi, India. RWCIGP, CIMMYT. p. 16 (2000).
Arora, V. K., Singh, H. & Singh, B. Analyzing wheat productivity responses to climatic, irrigation and fertilizer–nitrogen regimes in a semi-arid sub–tropical environment using the CERES-Wheat model. Agric. Water Manag. 94, 22–30. https://doi.org/10.1016/j.agwat.2007.07.002 (2007).
Google Scholar
Timsina, J. et al. Evaluation of options for increasing yield and water productivity of wheat in Punjab, India using the DSSAT–CSM-CERES-wheat model. Agric. Water Manag. 95, 1099–1110. https://doi.org/10.1016/j.agwat.2008.04.009 (2008).
Google Scholar
Balwinder-Singha, Humphreys & E., Gaydon, D. S., Eberbach, P. L.,. Evaluation of the effects of mulch on optimum sowing date and irrigation management of zero till wheat in central Punjab, India using APSIM. Field Crops Res. 197, 83–96. https://doi.org/10.1016/j.fcr.2016.08.016 (2016).
Google Scholar
Choudhury, A. K. et al. Optimum Sowing Window and Yield Forecasting for Maize in Northern and Western Bangladesh Using CERES Maize Model. Agronomy 11, 635. https://doi.org/10.3390/agronomy11040635 (2021).
Google Scholar
Sun, H., Shao, I., Chen, S. & Zhang, X. Effects of sowing time and rate on crop growth and radiation use efficiency of winter wheat in the North China Plain. Int. J. Plant Prod. 7, 117–138 (2013).
Qu, H. J. et al. Effects of plant density and seeding date on accumulation and translocation of dry matter and nitrogen in winter wheat cultivar Lankao Aizao 8. Acta Agron. Sin. 35, 124–131. https://doi.org/10.3724/SP.J.1006.2009.00124 (2009).
Google Scholar
Liu, P. et al. Effect of seeding rate and sowing date on population traits and grain yield of drip irrigated winter wheat. J. Triticeae Crops 33, 1202–1207 (2013).
Google Scholar
Lu, H. D., Xue, J. Q., Hao, Y. C., Zhang, R. H. & Gao, J. Effects of sowing time on spring maize (Zea mays L.) growth and water use efficiency in rainfed dryland. Acta Agron. Sin. 41, 1906–1914 (2015).
Google Scholar
Taylor, S. & Evans, C. Wheat: Susceptibility of varieties to common root rot. CWFS Research Compendium (2005).
Bowden, P. et al. Wheat growth & development. NSW Department of Primary Industries, State of New South Wales, p. 104 (2008).
DEEDI. Wheat varieties. Queensland Department of Employment, Economic Development and Innovation (DEEDI). p. 20 (2010).
Lush, D. et al. Queensland wheat varieties. Grains Research and Development Corporation (GRDC) and the Queensland Department of Agriculture, Fisheries and Forestry (DAFF). p. 20 (2015).
Greenwood, J. R. Wheat inflorescence architecture. Thesis report, Australian National University, p. 218 (2017).
Lush, D., Forknall, C., Neate, S., Sheedy, J. Queensland wheat varieties. Grains Research and Development Corporation (GRDC) and the Queensland Department of Agriculture and Fisheries (DAF). p. 20 (2018).
Hines, S., Andrews, M., Scott, W. R. & Jack, D. Sowing depth and nitrogen effects on emergence of a range of New Zealand wheat cultivars. Proc. Agron. Soc. N. Z. 21, 67–72 (1991).
Zaicou, C. et al. Wheat variety guide 2008 Western Australia. Department of Agriculture and Food, Western Australia, Perth. Bull. 4733 (2008).
Kelbert, A. J., Spaner, D., Briggs, K. G. & King, J. R. The association of culm anatomy with lodging susceptibility in modern spring wheat genotypes. Euphytica 136, 211–221. https://doi.org/10.1023/B:EUPH.0000030670.36730.a4 (2004).
Google Scholar
Mason, H., Navabi, A., Frick, B., O’Donovan, J. & Spaner, D. Cultivar and seeding rate effects on the competitive ability of spring cereals grown under organic production in northern Canada. Agron. J. 99, 1199–1207. https://doi.org/10.2134/agronj2006.0262 (2007).
Google Scholar
Shah, L. et al. Improving lodging resistance: Using wheat and rice as classical examples. Int. J. Mol. Sci. 20, 4211. https://doi.org/10.3390/ijms20174211 (2019).
Google Scholar
Mitter, V. et al. A high-throughput greenhouse bioassay to detect crown rot resistance in wheat germplasm. Plant Pathol. 55, 433–441. https://doi.org/10.1111/j.1365-3059.2006.01384.x (2006).
Google Scholar
Hare, R. Agronomy of the durum wheats Kamilaroi, Yallaroi, Wollaroi and EGA Bellaroi. NSW Department of Primary Industries, State of New South Wales, Primefact 140 (2006).
DPI&F. Wheat varieties for Queensland. Department of Primary Industries and Fisheries (DPI&F), State of Queensland, p. 12 (2007).
Singh, B. et al. Inheritance and chromosome location of leaf rust resistance in durum wheat cultivar Wollaroi. Euphytica 175, 351–355. https://doi.org/10.1007/s10681-010-0179-y (2010).
Google Scholar
Bansal, U. K., Kazi, A. G., Singh, B., Hare, R. A. & Bariana, H. S. Mapping of durable stripe rust resistance in a durum wheat cultivar Wollaroi. Mol Breed 33, 51–59. https://doi.org/10.1007/s11032-013-9933-x (2014).
Google Scholar
Source: Ecology - nature.com