Importância dos modelos de simulação de culturas diante os impactos das alterações climáticas sobre a produção agrícola - Revisão

Revista Brasileira de Geografia Física

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Title Importância dos modelos de simulação de culturas diante os impactos das alterações climáticas sobre a produção agrícola - Revisão
 
Creator Pinheiro, Antonio Gebson
Souza, Luciana Sandra Bastos de
Jardim, Alexandre Maniçoba da Rosa Ferraz
Araújo Júnior, George do Nascimento
Alves, Cleber Pereira
Souza, Carlos André Alves de
Silva, Gabriel Ítalo Novaes da
Silva, Thieres George Freire da
 
Subject Meteorologia; Agrometeorologia


 
Description O efeito climático é o principal responsável pelas oscilações no rendimento produtivo. Logo, é esperado que as mudanças do clima promovam alterações na agricultura, comprometam a sustentabilidade e a segurança alimentar, especialmente, em áreas semiáridas. O entendimento da amplitude desses fatores e suas consequências no rendimento agrícola mediante os diferentes cenários climáticos, regionais e tecnológicos são fundamentais nas tomadas de decisões. Para as análises desses diversos cenários, os modelos de simulação de culturas se caracterizam como ferramentas funcionais e com eficientes performances na estimativa dos níveis de produtividades, desde que devidamente calibrados e validados com dados consistentes e representativos. Dentre os modelos de simulação podemos destacar: AquaCrop - FAO, ZAE - FAO, CROPGRO e Apsim como aqueles de maiores aplicabilidades nas culturas agrícolas, sendo utilizados de maneira recorrente em diversos estudos para fins do conhecimento das lacunas de produtividade agrícola, ou “Yield Gap”. Esta revisão analisou os impactos das alterações climáticas na agricultura e o levantamento de informações dos principais modelos de simulação de culturas. Mediante síntese das informações levantadas, pode-se evidenciar o eminente impacto das alterações climáticas sobre o cenário agrícola futuro, proporcionando maior vulnerabilidade agrícola. Logo, destaca-se a importância do uso de modelos de simulação de culturas para conhecimento das lacunas de produtividade e potencial produtivo. Contudo, é evidente a necessidade de pesquisas mais detalhadas sobre a aplicabilidade dos modelos em cenários agrícolas diversos e situações climáticas amplas.Palavras-chave: modelos de simulação; sazonalidade climática; práticas resilientes; “yield gap”. Importance of crop simulation models in view of the impacts of climate change on agricultural production – Review A B S T R A C TThe climatic effect is the main responsible for the fluctuations in the productive yield. Therefore, it is expected that climate change will promote changes in agriculture, compromise sustainability and food security, especially in semi-arid areas. Understanding the breadth of these factors and their consequences on agricultural income through different climatic, regional and technological scenarios are fundamental in decision-making. For the analysis of these different scenarios, the crop simulation models are characterized as functional tools and with efficient performances in the estimation of the productivity levels, as long as they are properly calibrated and validated with consistent and representative data. Among the simulation models we can highlight: AquaCrop - FAO, ZAE - FAO, CROPGRO and Apsim as those with the greatest applicability in agricultural crops, being used in a recurring manner in several studies for the purpose of understanding agricultural productivity gaps, or “Yield Gap”. This review analyzed the impacts of climate change on agriculture and the gathering of information on the main crop simulation models. By synthesizing the information collected, it is possible to highlight the imminent impact of climate change on the future agricultural scenario, providing greater agricultural vulnerability. Therefore, the importance of using crop simulation models to understand the gaps in productivity and productive potential is highlighted. However, there is a clear need for more detailed research on the applicability of models in diverse agricultural scenarios and broad climatic situations.Keywords: simulation models; climatic seasonality; resilient practices; yield gap.
 
Publisher Universidade Federal de Pernambuco
 
Contributor FACEPE, CAPES e CNPq
 
Date 2021-12-31
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion



 
Format application/pdf
 
Identifier https://periodicos.ufpe.br/revistas/rbgfe/article/view/250281
10.26848/rbgf.v14.6.p3648-3666
 
Source Revista Brasileira de Geografia Física; v. 14, n. 6 (2021): Revista Brasileira de Geografia Física; 3648-3666
Brazilian Journal of Physical Geography; v. 14, n. 6 (2021): Revista Brasileira de Geografia Física; 3648-3666
1984-2295
 
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Relation https://periodicos.ufpe.br/revistas/rbgfe/article/view/250281/40135
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/*ref*/Abd-Elmabod, S.K., Muñoz-Rojas, M., Jordán, A., Anaya-Romero, M., Phillips, J.D., Laurence, J., Zhang, Z., Pereira, P., Fleskens, L., Van der Ploeg, M., Rosa, D., 2020. Climate change impacts on agricultural suitability and yield reduction in a Mediterranean region. Geoderma, 374(4), 114453. https://doi.org/10.1016/j.geoderma.2020.114453 Adeboye, O.B., Schultz, B., Adekalu, K.O., Prasad, K.C., 2019. Performance evaluation of AquaCrop in simulating soil water storage, yield, and water productivity of rainfed soybeans (Glycine max L. merr) in Ile-Ife, Nigeria. Agricultural Water Management, 213, 1130–1146. https://doi.org/10.1016/j.agwat.2018.11.006 Aggarwal, P., Vyas, S., Thornton, P., Campbell, B. M., Kropff, M. 2019. Importance of considering technology growth in impact assessments of climate change on agriculture. Global Food Security, 23(4), 41–48. https://doi.org/10.1016/j.gfs.2019.04.002 Akhavizadegan, F., Ansarifar, J., Wang, L., Huber, I., Archontoulis, S.V., 2021. A time-dependent parameter estimation framework for crop modeling. Scientific Reports, 11(1), 11437. https://doi.org/10.1038/s41598-021-90835-x Akumaga, U., Tarhule, A., Yusuf, A.A., 2017. Validation and testing of the FAO AquaCrop model under different levels of nitrogen fertilizer on rainfed maize in Nigeria, West Africa. Agricultural and Forest Meteorology, 232, 225–234. https://doi.org/10.1016/j.agrformet.2016.08.011 Alderman, P.D., Boote, K.J., Jones, J.W., Bhatia, V.S., 2015. Adapting the CSM-CROPGRO model for pigeonpea using sequential parameter estimation. Field Crops Research, 181, 1–15. https://doi.org/10.1016/j.fcr.2015.05.024 Ali, U., Jing, W., Zhu, J., Omarkhanova, Z., Fahad, S., Nurgazina, Z., Khan, Z.A., 2021. Climate change impacts on agriculture sector: A case study of Pakistan. Ciência Rural, 51(8), 2021. https://doi.org/10.1590/0103-8478cr20200110 Ammar, M.E., Davies, E.G.R., 2019. On the accuracy of crop production and water requirement calculations: Process-based crop modeling at daily, semi-weekly, and weekly time steps for integrated assessments. Journal of Environmental Management, 238, 460–472. https://doi.org/10.1016/j.jenvman.2019.03.030 Araya, A., Kisekka, I., Holman, J., 2016. Evaluating deficit irrigation management strategies for grain sorghum using AquaCrop. Irrigation Science, 34(6), 465–481. https://doi.org/10.1007/s00271-016-0515-7 Asseng, S., Foster, I., Turner, N.C., 2011. The impact of temperature variability on wheat yields. Global Change Biology, 17(2), 997–1012. https://doi.org/10.1111/j.1365-2486.2010.02262.x Asseng, S., Zhu, Y., Wang, E., Zhang, W., 2015. Crop modeling for climate change impact and adaptation. In Crop Physiology (pp. 505–546). Elsevier. https://doi.org/10.1016/B978-0-12-417104-6.00020-0 Balboa, G.R., Archontoulis, S.V, Salvagiotti, F., Garcia, F.O., Stewart, W.M., Francisco, E., Prasad, P.V.V., Ciampitti, I.A., 2019. A systems-level yield gap assessment of maize-soybean rotation under high- and low-management inputs in the Western US Corn Belt using APSIM. Agricultural Systems, 174, 145–154. https://doi.org/10.1016/j.agsy.2019.04.008 Bao, Y., Hoogenboom, G., McClendon, R.W., Paz, J.O., 2015. Potential adaptation strategies for rainfed soybean production in the south-eastern USA under climate change based on the CSM-CROPGRO-Soybean model. The Journal of Agricultural Science, 153(5), 798–824. https://doi.org/10.1017/S0021859614001129 Basso, B., Hyndman, D.W., Kendall, A. D., Grace, P. R., & Robertson, G. P., 2015. Can Impacts of Climate Change and Agricultural Adaptation Strategies Be Accurately Quantified if Crop Models Are Annually Re-Initialized? PLOS ONE, 10(6), e0127333–e0127333. https://doi.org/10.1371/journal.pone.0127333 Battisti, R, Sentelhas, P.C., 2015. Drought tolerance of brazilian soybean cultivars simulated by a simple ag rometeorological yield model. 51(2), 285–298. https://doi.org/10.1017/S0014479714000283 Battisti, R., Parker, P.S., Sentelhas, P.C., Nendel, C., 2017. Gauging the sources of uncertainty in soybean yield simulations using the MONICA model. Agricultural Systems, 155, 9–18. https://doi.org/10.1016/j.agsy.2017.04.004 Battisti, R, Sentelhas, P.C. 2019. Characterizing Brazilian soybean-growing regions by water deficit patterns. Field Crops Research, 240, 95–105. https://doi.org/10.1016/j.fcr.2019.06.007 Battisti, R., Sentelhas, P.C., Boote, K.J., 2017. Inter-comparison of performance of soybean crop simulation models and their ensemble in southern Brazil. Field Crops Research, 200, 28–37. https://doi.org/10.1016/j.fcr.2016.10.004 Battisti, R., Sentelhas, P.C., Boote, K.J., 2018. Sensitivity and requirement of improvements of four soybean crop simulation models for climate change studies in Southern Brazil. International Journal of Biometeorology, 62(5), 823–832. https://doi.org/10.1007/s00484-017-1483-1 Battisti, R., Sentelhas, P.C., Pilau, F.G., Wollmann, C.A., 2013. Eficiência climática para as culturas da soja e do trigo no estado do Rio Grande do Sul em diferentes datas de semeadura. Ciência Rural, 43(3), 390–396. https://doi.org/10.1590/S0103-84782013000300003 Berghuijs, H.N.C., Weih, M., Van der Werf, W., Karley, A.J., Adam, E., Villegas-Fernández, Á.M., Kiær, L.P., Newton, A.C., Scherber, C., Tavoletti, S., Vico, G., 2021. Calibrating and testing APSIM for wheat-faba bean pure cultures and intercrops across Europe. Field Crops Research, 264, 108088. https://doi.org/10.1016/j.fcr.2021.108088 Bhatia, V.S., Singh, P., Wani, S.P., Chauhan, G.S., Rao, A.V.R.K., Mishra, A.K., Srinivas, K., 2008. Analysis of potential yields and yield gaps of rainfed soybean in India using CROPGRO-Soybean model. Agricultural and Forest Meteorology, 148(8–9), 1252–1265. https://doi.org/10.1016/j.agrformet.2008.03.004 Bhattacharya, A. 2019. Global Climate Change and Its Impact on Agriculture. In Changing Climate and Resource Use Efficiency in Plants. https://doi.org/10.1016/b978-0-12-816209-5.00001-5 Boote, K.J., Jones, J.W., Hoogenboom, G., Pickering, N.B., 1998. The CROPGRO model for grain legumes. In Understanding Options for Agricultural Production, 7, 99–128. https://doi.org/10.1007/978-94-017-3624-4_6 Bosi, C., Sentelhas, P.C., Pezzopane, J.R.M., Santos, P.M., 2020. CROPGRO-Perennial Forage model parameterization for simulating Piatã palisade grass growth in monoculture and in a silvopastoral system. Agricultural Systems, 177(5), 102724. https://doi.org/10.1016/j.agsy.2019.102724 Camargo, G.G.T., Kemanian, A.R., 2016. Six crop models differ in their simulation of water uptake. Agricultural and Forest Meteorology, 220, 116–129. https://doi.org/10.1016/j.agrformet.2016.01.013 De Wit, A., Boogaard, H., Fumagalli, D., Janssen, S., Knapen, R., Van Kraalingen, D., Supit, I., Van der Wijngaart, R., Van Diepen, K., 2019. 25 years of the WOFOST cropping systems model. Agricultural Systems, 168, 154–167. https://doi.org/10.1016/j.agsy.2018.06.018 Dias, H.B., Sentelhas, P.C., 2018. Sugarcane yield gap analysis in Brazil – A multi-model approach for determining magnitudes and causes. Science of the Total Environment, 637–638, 1127–1136. https://doi.org/10.1016/j.scitotenv.2018.05.017 Elli, E.F., Sentelhas, P.C., Freitas, C.H., Carneiro, R.L., Alvares, C. A., 2019. Intercomparison of structural features and performance of Eucalyptus simulation models and their ensemble for yield estimations. Forest Ecology and Management, 450, 117493. https://doi.org/10.1016/j.foreco.2019.117493 Foster, T., Brozović, N., Butler, A.P., Neale, C.M. U., Raes, D., Steduto, P., Fereres, E., Hsiao, T.C., 2017. AquaCrop-OS: An open source version of FAO’s crop water productivity model. Agricultural Water Management, 181, 18–22. https://doi.org/10.1016/j.agwat.2016.11.015 Funatsu, B.M., Dubreuil, V., Racapé, A., Debortoli, N.S., Nasuti, S., Le Tourneau, F. M., 2019. Perceptions of climate and climate change by Amazonian communities. Global Environmental Change, 57(May), 101923. https://doi.org/10.1016/j.gloenvcha.2019.05.007 Gaydon, D.S., Balwinder-Singh, Wang, E., Poulton, P.L., Ahmad, B., Ahmed, F., Akhter, S., Ali, I., Amarasingha, R., Chaki, A.K., Chen, C., Choudhury, B.U., Darai, R., Das, A., Hochman, Z., Horan, H., Hosang, E.Y., Kumar, P.V., Khan, A.S.M.M.R., Roth, C.H., 2017. Evaluation of the APSIM model in cropping systems of Asia. Field Crops Research, 204, 52–75. https://doi.org/10.1016/j.fcr.2016.12.015 Andrioli, G.K., Cesar Sentelhas, P., 2009. Brazilian maize genotypes sensitivity to water deficit estimated through a simple crop yield model. In Pesq. agropec. bras (Issue 7). http://www.scielo.br/pdf/pab/v44n7/01.pdf Hadebe, S.T., Mabhaudhi, T., Modi, A.T., 2020. Sorghum best practice management recommendations based on AquaCrop modeling scenario analysis in various agro-ecologies of KwaZulu Natal, South Africa. Physics and Chemistry of the Earth, Parts A/B/C, 102866. https://doi.org/10.1016/j.pce.2020.102866 Hochman, Z., Gobbett, D., Horan, H., Navarro Garcia, J., 2016. Data rich yield gap analysis of wheat in Australia. Field Crops Research, 197, 97–106. https://doi.org/10.1016/j.fcr.2016.08.017 Holzworth, D., Huth, N.I., Fainges, J., Brown, H., Zurcher, E., Cichota, R., Verrall, S., Herrmann, N.I., Zheng, B., Snow, V., 2018. APSIM Next Generation: Overcoming challenges in modernising a farming systems model. Environmental Modelling & Software, 103, 43–51. https://doi.org/10.1016/j.envsoft.2018.02.002 Holzworth, D.P., Huth, N.I., De Voil, P.G., Zurcher, E.J., Herrmann, N.I., McLean, G., Chenu, K., Van Oosterom, E.J., Snow, V., Murphy, C., Moore, A.D., Brown, H., Whish, J.P.M., Verrall, S., Fainges, J., Bell, L.W., Peake, A.S., Poulton, P.L., Hochman, Z., Keating, B.A., 2014. APSIM – Evolution towards a new generation of agricultural systems simulation. Environmental Modelling & Software, 62, 327–350. https://doi.org/10.1016/j.envsoft.2014.07.009 Holzworth, D.P., Snow, V., Janssen, S., Athanasiadis, I.N., Donatelli, M., Hoogenboom, G., White, J. W., Thorburn, P., 2015. Agricultural production systems modelling and software: Current status and future prospects *. Environmental Modelling and Software, 72, 276–286. https://doi.org/10.1016/j.envsoft.2014.12.013 Hsiao, T.C., Heng, L., Steduto, P., Rojas-Lara, B., Raes, D., Fereres, E., 2009. AquaCrop-The FAO Crop Model to Simulate Yield Response to Water: III. Parameterization and Testing for Maize. Agronomy Journal, 101(3), 448–459. https://doi.org/10.2134/agronj2008.0218s Jardim, A.M.R.F., Souza, L.S.B., Alves, C.P., Araújo, J.F.N., Souza, C.A.A., Pinheiro, A.G., Araújo, G.G.L., Campos, F.S., Tabosa, J.N., Silva, T.G.F., 2021. Intercropping forage cactus with sorghum affects the morphophysiology and phenology of forage cactus. African Journal of Range and Forage Science, 38, 1-12. https://doi.org/10.2989/10220119.2021.1949749 Jones, J.W., Hoogenboom, G., Porter, C.H., Boote, K.J., Batchelor, W.D., Hunt, L.A., Wilkens, P.W., Singh, U., Gijsman, A.J., Ritchie, J.T. 2003. The DSSAT cropping system model. European Journal of Agronomy, 18(3–4), 235–265. https://doi.org/10.1016/S1161-0301(02)00107-7 Karimi, V., Karami, E., Keshavarz, M., (2018). Climate change and agriculture: Impacts and adaptive responses in Iran. Journal of Integrative Agriculture, 17(1), 1–15. https://doi.org/10.1016/S2095-3119(17)61794-5 Karuku, G.N., Mbindah, B.A., 2020. Validation of aquacrop model for simulation of rainfed bulb onion (Allium cepa l.) yields in west ugenya sub-county, Kenya. Tropical and Subtropical Agroecosystems, 23(06), 1–11. Keating, B., Carberry, P., Hammer, G., Probert, M., Robertson, M., Holzworth, D., Huth, N., Hargreaves, J. N., Meinke, H., Hochman, Z., McLean, G., Verburg, K., Snow, V., Dimes, J., Silburn, M., Wang, E., Brown, S., Bristow, K., Asseng, S., Smith, C., 2003. An overview of APSIM, a model designed for farming systems simulation. European Journal of Agronomy, 18(3–4), 267–288. https://doi.org/10.1016/S1161-0301(02)00108-9 Khaliq, T., Gaydon, D.S., Ahmad, M.D., Cheema, M.J.M., Gull, U., 2019. Analyzing crop yield gaps and their causes using cropping systems modelling–A case study of the Punjab rice-wheat system, Pakistan. Field Crops Research, 232, 119–130. https://doi.org/10.1016/J.FCR.2018.12.010 Lamichhane, J.R., Constantin, J., Aubertot, J.N., Dürr, C., 2019. Will climate change affect sugar beet establishment of the 21st century? Insights from a simulation study using a crop emergence model. Field Crops Research, 238, 64–73. https://doi.org/10.1016/j.fcr.2019.04.022 Leng, G., Zhang, X., Huang, M., Asrar, G.R., Leung, L.R., 2016. The Role of Climate Covariability on Crop Yields in the Conterminous United States OPEN. Nature Publishing Group. https://doi.org/10.1038/srep33160 Makuvaro, V., Walker, S., Masere, T.P., Dimes, J., 2018. Smallholder farmer perceived effects of climate change on agricultural productivity and adaptation strategies. Journal of Arid Environments, 152, 75–82. https://doi.org/10.1016/j.jaridenv.2018.01.016 Manivasagam, V.S., Rozenstein, O., 2020. Practices for upscaling crop simulation models from field scale to large regions. Computers and Electronics in Agriculture, 175, 105554. https://doi.org/10.1016/j.compag.2020.105554 Martini, L.C.P., 2018. Sensitivity analysis of the AquaCrop parameters for rainfed corn in the South of Brazil. Pesquisa Agropecuária Brasileira, 53(8), 934–942. https://doi.org/10.1590/s0100-204x2018000800008 Martins, M.A., Tomasella, J., Dias, C.G., 2019. Maize yield under a changing climate in the Brazilian Northeast: Impacts and adaptation. Agricultural Water Management, 216(8), 339–350. https://doi.org/10.1016/j.agwat.2019.02.011 Martins, M.A., Tomasella, J., Rodriguez, D.A., Alvalá, R.C.S., Giarolla, A., Garofolo, L.L., Júnior, J.L.S., Paolicchi, L.T.L.C., Pinto, G.L.N., 2018a. Improving drought management in the Brazilian semiarid through crop forecasting. Agricultural Systems, 160(11), 21–30. https://doi.org/10.1016/j.agsy.2017.11.002 Martins, M.A., Tomasella, J., Rodriguez, D.A., Alvalá, R.C.S., Giarolla, A., Garofolo, L.L., Júnior, J.L.S., Paolicchi, L.T.L.C., Pinto, G.L.N., 2018b. Improving drought management in the Brazilian semiarid through crop forecasting. Agricultural Systems, 160, 21–30. https://doi.org/10.1016/j.agsy.2017.11.002 McCown, R.L., Hammer, G.L., Hargreaves, J.N.G., Holzworth, D., Huth, N.I., 1995. APSIM: an agricultural production system simulation model for operational research. Mathematics and Computers in Simulation, 39(3–4), 225–231. https://doi.org/10.1016/0378-4754(95)00063-2 Mccown, R.L., Hammer, G.L., Hargreaves, J.N.G., Holzworth, D.P., Freebairn, D.M., 1996. APSIM: a Novel Software System for Model Development, Model Testing and Simulation in Agricultural Systems Research. Agricultural Systems, 50, 255–271. Minoli, S., Egli, D.B., Rolinski, S., Müller, C., 2019. Modelling cropping periods of grain crops at the global scale. Global and Planetary Change, 174, 35–46. https://doi.org/10.1016/j.gloplacha.2018.12.013 Mohanty, M., Probert, M.E., Reddy, K.S., Dalal, R.C., Mishra, A.K., Rao, A.S., Singh, M., Menzies, N.W., 2012. Simulating soybean-wheat cropping system: APSIM model parameterization and validation. “Agriculture, Ecosystems and Environment,” 152, 68–78. https://doi.org/10.1016/j.agee.2012.02.013 Monteiro, L. A., & Sentelhas, P. C. (2014). <b>Calibration and testing of an agrometeorological model for the estimation of soybean yields in different Brazilian regions. Acta Scientiarum. Agronomy, 36(3), 265. https://doi.org/10.4025/actasciagron.v36i3.17485 Morel, J., Kumar, U., Ahmed, M., Bergkvist, G., Lana, M., Halling, M., Parsons, D., 2021. Quantification of the Impact of Temperature, CO2, and Rainfall Changes on Swedish Annual Crops Production Using the APSIM Model. Frontiers in Sustainable Food Systems, 5(5), 1–11. https://doi.org/10.3389/fsufs.2021.665025 Silva, E.H.F.M., Silva Antolin, L.A., Zanon, A.J., Andrade, A.S, Souza, H.A, Santos Carvalho, K.S., Vieira, N.A., Marin, F.R., 2021. Impact assessment of soybean yield and water productivity in Brazil due to climate change. European Journal of Agronomy, 129, 126329. https://doi.org/10.1016/j.eja.2021.126329 Neset, T.S., Wiréhn, L., Opach, T., Glaas, E., Linnér, B.O., 2019. Evaluation of indicators for agricultural vulnerability to climate change: The case of Swedish agriculture. Ecological Indicators, 105(6), 571–580. https://doi.org/10.1016/j.ecolind.2018.05.042 Nóia Júnior, R. S., Sentelhas, P.C., 2019. Soybean-maize succession in Brazil: Impacts of sowing dates on climate variability, yields and economic profitability. European Journal of Agronomy, 103. https://doi.org/10.1016/j.eja.2018.12.008 Palit, P., Kudapa, H., Zougmore, R., Kholova, J., Whitbread, A., Sharma, M., Varshney, R.K., 2020. An integrated research framework combining genomics, systems biology, physiology, modelling and breeding for legume improvement in response to elevated CO2 under climate change scenario. Current Plant Biology, 22, 100149. https://doi.org/10.1016/j.cpb.2020.100149 Palosuo, T., Hoffmann, M.P., Rötter, R.P., Lehtonen, H.S., 2021. Sustainable intensification of crop production under alternative future changes in climate and technology: The case of the North Savo region. Agricultural Systems, 190, 103135. https://doi.org/10.1016/j.agsy.2021.103135 Pedreira, B.C., Pedreira, C.G.S., Boote, K.J., Lara, M.A.S., Alderman, P.D., 2011. Adapting the CROPGRO perennial forage model to predict growth of Brachiaria brizantha. Field Crops Research, 120(3), 370–379. https://doi.org/10.1016/j.fcr.2010.11.010 Pequeno, D.N.L., Pedreira, C.G.S., Boote, K.J., Alderman, P.D., Faria, A.F.G., 2018. Species-genotypic parameters of the CROPGRO Perennial Forage Model: Implications for comparison of three tropical pasture grasses. Grass and Forage Science, 73(2), 440–455. https://doi.org/10.1111/gfs.12329 Piffer Cardozo, N., Oliveira Bordonal, R., La Scala Jr, N., 2018. Sustainable intensification of sugarcane production under irrigation systems, considering climate interactions and agricultural efficiency. Journal of Cleaner Production, 204, 861–871. https://doi.org/10.1016/j.jclepro.2018.09.004 Pirmoradian, N., Davatgar, N., 2019. Simulating the effects of climatic fluctuations on rice irrigation water requirement using AquaCrop. Agricultural Water Management, 213, 97–106. https://doi.org/10.1016/j.agwat.2018.10.003 Raes, D., Steduto, P., Hsiao, T.C., Fereres, E., 2009. AquaCrop - The FAO Crop Model to Simulate Yield Response to Water: II. Main Algorithms and Software Description. Agronomy Journal, 101(3), 438–447. https://doi.org/10.2134/agronj2008.0140s Sandhu, R., Irmak, S., 2019. Performance of AquaCrop model in simulating maize growth, yield, and evapotranspiration under rainfed, limited and full irrigation. Agricultural Water Management, 223, 105687. https://doi.org/10.1016/j.agwat.2019.105687 Santos, M.G., Faria, R.T., Palaretti, L.F., Dantas, G.D.F., Dalri, A.B., Lopes, A.D.S., 2016. Calibration and testing of CS-CROPGRO Model for Common Beans. Engenharia Agrícola, 36(6), 1239–1249. https://doi.org/10.1590/1809-4430-eng.agric.v36n6p1239-1249/2016 Scheelbeek, P.F.D., Bird, F.A., Tuomisto, H.L., Green, R., Harris, F.B., Joy, E.J.M., Chalabi, Z., Allen, E., Haines, A., & Dangour, A.D., 2018. Effect of environmental changes on vegetable and legume yields and nutritional quality. Proceedings of the National Academy of Sciences, 115(26), 6804–6809. https://doi.org/10.1073/pnas.1800442115 Singh, S., Boote, K.J., Angadi, S.V., Grover, K.K., 2017. Estimating water balance, evapotranspiration and water use efficiency of spring safflower using the CROPGRO model. Agricultural Water Management, 185, 137–144. https://doi.org/10.1016/j.agwat.2017.02.015 Steduto, P., Hsiao, T.C., Fereres, E., 2007. On the conservative behavior of biomass water productivity. Irrigation Science, 25(3), 189–207. https://doi.org/10.1007/s00271-007-0064-1 Steduto, P., Hsiao, T.C., Raes, D., Fereres, E., 2009. AquaCrop-The FAO Crop Model to Simulate Yield Response to Water: I. Concepts and Underlying Principles. Agronomy Journal, 101(3), 426–437. https://doi.org/10.2134/agronj2008.0139s Takatani, N., Ito, T., Kiba, T., Mori, M., Miyamoto, T., Maeda, S.I., Omata, T., 2014. Effects of High CO2 on Growth and Metabolism of Arabidopsis Seedlings During Growth with a Constantly Limited Supply of Nitrogen. Plant and Cell Physiology, 55(2), 281–292. https://doi.org/10.1093/pcp/pct186 Tooley, B.E., Mallory, E.B., Porter, G.A., Hoogenboom, G., 2021. Predicting the response of a potato-grain production system to climate change for a humid continental climate using DSSAT. Agricultural and Forest Meteorology, 307, 108452. https://doi.org/10.1016/j.agrformet.2021.108452 van Bussel, L.G.J., Müller, C., van Keulen, H., Ewert, F., Leffelaar, P.A., 2011. The effect of temporal aggregation of weather input data on crop growth models’ results. Agricultural and Forest Meteorology, 151(5), 607–619. https://doi.org/10.1016/j.agrformet.2011.01.007 Vanuytrecht, E., Raes, D., Steduto, P., Hsiao, T.C., Fereres, E., Heng, L.K., Garcia Vila, M., Mejias Moreno, P., 2014. AquaCrop: FAO’s crop water productivity and yield response model. Environmental Modelling & Software, 62, 351–360. https://doi.org/10.1016/j.envsoft.2014.08.005 Wang, X., Wang, H., Si, Z., Gao, Y., Duan, A., 2020. Modelling responses of cotton growth and yield to pre-planting soil moisture with the CROPGRO-Cotton model for a mulched drip irrigation system in the Tarim Basin. Agricultural Water Management, 241(July), 106378. https://doi.org/10.1016/j.agwat.2020.106378 Xu, J., Bai, W., Li, Y., Wang, H., Yang, S., Wei, Z., 2019. Modeling rice development and field water balance using AquaCrop model under drying-wetting cycle condition in eastern China. Agricultural Water Management, 213, 289–297. https://doi.org/10.1016/j.agwat.2018.10.028 Zhao, C., Liu, B., Xiao, L., Hoogenboom, G., Boote, K.J., Kassie, B.T., Pavan, W., Shelia, V., Kim, K.S., Hernandez-Ochoa, I.M., Wallach, D., Porter, C.H., Stockle, C.O., Zhu, Y., Asseng, S., 2019. A SIMPLE crop model. European Journal of Agronomy, 104(2), 97–106. https://doi.org/10.1016/j.eja.2019.01.009
 
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Rights Direitos autorais 2021 Revista Brasileira de Geografia Física
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