Caracterização da variabilidade da precipitação no MATOPIBA, região produtora de soja

Layara Campelo dos Reis, Cláudio Moisés Santos e Silva, Bergson Guedes Bezerra, Maria Helena Constantino Spyrides

Resumo


A análise sobre a variabilidade dos padrões climatológicos espaciais e temporais das chuvas fornecem informações valiosas para a condução de cultivos agrícolas, principalmente em condições de sequeiro. Assim, o presente estudo objetivou caracterizar a variabilidade da precipitação no MATOPIBA, região produtora de soja, sob influência das fases do ENSO e do gradiente térmico do Atlântico Tropical. Foram utilizados dados diários de precipitação do período de 1980-2013 dispostos em uma grade de espaçamento de 0,25º x 0,25°, abrangendo 963 pontos sobre a região. O acumulado mensal da precipitação foi especializado por meio de sistemas geográficos de informação e da geoestatística. A variabilidade da precipitação foi analisada por meio da aplicação do teste de Mann-Kendall, considerando três cenários de condições meteorológicas (climatologia, favorável-wet e desfavorável-dry) à ocorrência da precipitação. Os volumes de chuvas foram relativamente maiores no cenário da fase fria do ENSO combinado com o gradiente inter-hemisférico apontando para o Sul (favorável-wet), em contrapartida, verificou-se um aumento de condições de risco hídrico nos anos com ocorrência da fase quente do ENSO e o gradiente apontando para o Norte (desfavorável-dry), embora com exceções registradas em algumas áreas no mês de Janeiro e Fevereiro. Tendências positivas e negativas foram identificadas, constatando indícios de alterações nos padrões da variável, previamente para os cenários da climatologia e desfavorável (dry). Os resultados poderão contribuir para o desenvolvimento de soluções e direcionamento na tomada de decisões pelos agentes da cadeia produtiva que visem a mitigação de impactos em decorrência da variabilidade da precipitação na região estudada. 

 

Characterization of rainfall variability in the MATOPIBA, soybean producing region

 

Abstract

The analysis of spatial and temporal rainfall patterns variability provides invaluable information for the development of dryland agriculture systems. Therefore, the aim of the present study was to characterize the variability of rainfall in the MATOPIBA, an important soybean producing region, under the influence of ENSO phases and the tropical Atlantic thermal gradient. We used daily rainfall data for the period from 1980-2013 arranged in a 0.25º x 0.25º spacing grid, comprising 963 points over the study region. Monthly accumulated rainfall has been specialized through geographic information systems and geostatistics. Variability rainfall was analyzed by applying the Mann-Kendall test, considering three scenarios of meteorological conditions (climatology, favorable-wet and unfavorable-dry) to the occurrence of precipitation. Rainfall volumes were relatively higher in the ENSO cold phase scenario combined with the southward-favorable interhemispheric gradient. ENSO and the gradient pointing north (unfavorable-dry), although with exceptions recorded in some areas in January and February. Positive and negative trends were identified, showing evidence of changes in the variable's patterns, previously for the climatology and unfavorable (dry) scenarios. The results may contribute to the development of solutions and decision making direction by the agents of the productive chain aiming at mitigating impacts due to the variability of precipitation in the studied region.

Keywords: Climate variability; ENSO; Agrometeorology

 


Palavras-chave


Mudanças Climáticas

Texto completo:

PDF

Referências


Aceituno, P., 1988. On the functioning of the Southern Oscillation in the South American sector. Surface climate, Monthly Weather Review 116, 505–524.

Alvares, C.A., Stape, J.L., Sentelhas, P.C., de Moraes, G., Leonardo, J., Sparovek, G., 2014. Köppens climate classification map for Brazil. Meteorologische Zeitschrift 22(6), 711–728.

Anderson, M.C., Zolin, C.A., Sentelhas, P.C., Hain, C R., Semmens, K., Yilmaz, M.T., Gao, F., Otkin, J.A., Tetrault, R., 2016. The Evaporative Stress Index as an indicator of agricultural drought in Brazil: An assessment based on crop yield imapcts. Remote Sensing of Environment 174, 82-99. https://doi.org/10.1016/j.rse.2015.11.034.

Araújo, M.L.S., Sano, E.E., Bolfe, É.L., Santos, J.R.N., Santos, J.L., Silva, F.B., 2019. Spatiotemporal dynamics of soybean crop in the Matopiba region, Brazil (1990-2015). Land Use Policy 80, 57-67. https://doi.org/10.1016/j.landusepol.2018.09.040

Assad, E.D., Marin, F.R., Evangelista, S.R., Pilau, F.G., Farias, J.R.B., Pinto, H.S., Zullo Júnior, J. 2007. Sistema de previsão da safra de soja para o Brasil. Pesquisa Agropecuária Brasileira 42(5), 615-625. https://doi.org/10.1590/S0100-204X2007000500002

Balbinot Junior, A.A., Hirakuri, M.H., Franchini, J.C., Debiasi, H., Ribeiro, R.H., 2017. Análise da área, produção e produtividade da soja no Brasil em duas décadas (1997-2016) [recurso eletrônico]. Londrina: Embrapa Soja, 2017. 21 p. https://www.embrapa.br/busca-de-publicacoes/-/publicacao/1065512/analise-da-area-producao-e-produtividade-da-soja-no-brasil-em-duas-decadas-1997-2016. Acesso: 20 jul. 2019.

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., Pascoalino, J.A.L., Sako, H., de Sá Dantas, J.P., Moraes, M.F., 2018. Soybean yield gap in the areas of yield contest in Brazil. International Journal of Plant Production. https://doi.org/10.1007/s42106-018-0016-0.

Berlato, M.A., Fontana D.C., 2003. El Niño e La Niña: impactos no clima, na vegetação e na agricultura do Rio Grande do Sul; aplicações de previsões climáticas na agricultura. UFRGS, Porto Alegre, 110 p.

Bezerra, B.G., Silva, L.L., Santos e Silva, C.M., Carvalho, G.G., 2018. Changes of precipitation extremes indices in São Francisco River Basin, Brazil from 1947 to 2012. Theoretical and Applied Climatology 135, 565–576. https://doi.org/10.1007/s00704-018-2396-6.

Bhatia, V.S., Singh, P., Wani, S.P., Chauhan, G.S., Kesava Rao, A.V.R. 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, 1252–1265.

Blain, G.C., 2010. Séries anuais de temperatura máxima média do ar no estado de São Paulo: Variações e tendências climáticas. Revista Brasileira de Meteorologia 25(1), 114 - 124.

Butt, N., De Oliveira, P.A., Costa, M.H., 2011. Evidence that deforestation affects theonset of the rainy season in Rondonia. Brazil. Journal of Geophysical Research: Atmospheres 116, 2–9, http://dx.doi.org/10.1029/2010JD015174.

Carleton, T.A, Hsiang, S.M., 2016. Social and economic impacts of climate. Science 353, aad9837. https://doi.org/10.1126/science.aad9837.

Castanheira, E.G., Grisoli, R., Coelho, S., Silva, G.A., Freire, F., 2015. Life-cycle assessment of soybean-based biodiesel in Europe: comparing grain, oil and biodiesel import from Brazil. Journal of Cleaner Production 102, 188-201. https://doi.org/10.1016/j.jclepro.2015.04.036

Chaves, R.R., Cavalcanti, I.F.A., 2001. Atmospheriuc Circulation Features Associated with Rainfall Variability over Saouthern Northeast Brazil. Monthly Weather Review 129, 2614 – 2626.

CONAB - Companhia Nacional de Abastecimento, 2019. Acompanhamento da Safra Brasileira de Grãos, Safra 2018/19 - Oitavo Levantamento, p. 1-135, https://www.conab.gov.br/info-agro/safras/graos/boletim-da-safra-de-graos. Acesso: 25 out. 2019.

Costa, M.H., Pires, G.F., 2010). Effects of Amazon and Central Brazil deforestationscenarios on the duration of the dry season in the arc of deforestation. International Journal of Climatology 30, 1970–1979, http://dx.doi.org/10.1002/joc.2048.

da Rocha, R.P., Reboita, M.S., Dutra, L.M.M., Llopart, M.P., Coppola, E., 2014. Variability associated with ENSO: present and future climate projections of RegCM4 for South America-CORDEX domain. Nature Climate Change 125, 95–109. https://doi.org/10.1007/s10584-014-1119-y.

da Silva, R.M., Santos, C.A.G., Moreira, M., Corte-Real, J., Silva, V.C.L., Medeiros, I.C., 2015. Rainfall and river flow trends using Mann –Kendall and Sen’s slope estimator statistical tests in the Cobres River basin. Nat Hazards, 77, 1205–1221. https://doi.org/10.1007/s11069-015-1644-7.

da Silva, L.L., Costa, R.F., Campos, J.H.B., Dantas, R.T., 2009. Influência das precipitações na produtividade agrícola no Estado da Paraíba. Revista Brasileira de Engenharia Agrícola e Ambiental 13(4): 454–461.

da Silva, P.E., Santos e Silva, C.M., Spyrides, M.H.C., Andrade, L.M.B., 2019a. Precipitation and air temperature extremes in the Amazon and northeast Brazil. International Journal of Climatology, 1-17. https://doi.org/10.1002/joc.5829.

da Silva, P.E., Santos e Silva, C.M., Spyrides, M.H.C., Andrade, L.M.B., 2019b. Análise de Índices de Extremos Climáticos no Nordeste e Amazônia Brasileira para o Período entre 1980 a 2013. Anuário do Instituto de Geociências 42(2), 137-148. http://dx.doi.org/10.11137/2019_2_137_148.

de Oliveira, P.T, Santos e Silva, C.M., Lima, K.C. 2014. Linear trend of occurrence and intensity of heavy rainfall events on Northeast Brazil. Atmospheric Science Letters 15, 172-177. https://doi.org/10.1002/asl2.484.

de Oliveira, P.T., Santos e Silva, C. M., Lima, K.C. 2017. Climatology and trend analysis of extreme precipitation in subregions of Northeast Brazil. Theoretical and Applied Climatology 130, 77-90. https://doi.org/10.1007/s00704-016-1865-z.

Del Ponte, E.M., Esker, P.D., 2008. Meteorological factors and Asian Soybean Rust epidemics - a systems approach and implications for risk assessment. Scientia Agricola 65, 88-97. https://doi.org/10.1590/S0103-90162008000700014.

dos Reis, L.C., Silva, C.M.S.e., Bezerra, B.G., Mutti, P.R., Spyrides, M.H.C., da Silva, P.E., 2020. Analysis of Climate Extreme Indices in the MATOPIBA Region, Brazil. Pure and Applied Geophysics. https://doi.org/10.1007/s00024-020-02474-4.

dos Reis, L.C., Santos e Silva, C.M., Spyrides, M.H.C., Bezerra, B.G., 2017. Climate Trends in Bom Jesus, soybean production region in Piauí. Revista Geama, 196-200.

Erasmi, S., Schucknecht, A., Barbosa, M., Matschullat, J., 2014. Vegetation greenness in Northeastern Brazil and its relation to ENSO warm events. Remote Sensing 6, 3041–3058. https://doi.org/10.3390/rs6043041.

FAO - Food and Agriculture Organization of the United Nations, 2016. OECD-FAO Agricultural Outlook 2016-2025, especial focus: Sub-Saharan Africa. [s.l.] OECD-FAO.

Farooq, M., Wahid, A., Kobayashi, N., Fujita, D., Basra, S. M.A., 2009. Plant drought stress: effects, mechanisms and management. Agronomy for Sustainable Development 29, 185–212. https://doi.org/10.1051/agro:2008021.

Ferreira, D.B., Rao, V.B., 2011. Recent climate variability and its impacts on yields in Southern Brazil. Theoretical and Applied Climatology 105, https://doi.org/83-97. 10.1007/s00704-010-0358-8.

Ferreira, N.J., Sanches, M., Silva Dias, M.A.F., 2004. Composição da Zona de Convergência do Atlântico Sul em Períodos de El Niño e La Niña. Revista Brasileira de Meteorologia 19(1), 89–98.

Fu, R., Yin, L., Li, W., Arias, P., Dickinson, R.E., Huang, L., Chakraborty, S., Fernandes, K., Liebmann, B., Fisher, R., Myneni, R.B., 2013. Increased dry-season lengthover southern Amazonia in recent decades and its implication for futureclimate projection. Proceedings of the National Academy of Sciences 110, 18110–18115, http://dx.doi.org/10.1073/pnas.1302584110.

Gelcer, E., Fraisse, C.W., Dzotsi, K., Hu, Z., Mendes, R., Zotarelli, L., 2013. Effects of El Niño Southern Oscillation on the space–time variability of Agricultural Reference Index for Drought in midlatitudes. Agricultural and Forest Meteorology 174(175), 110–128.

Gelcer, E., Fraisse, C.W., Zotarelli, L. Stevensd, F.R., Perondi, D., Barreto, D.D., Malia, H.A., Ecole, C.C., Montone, V., Southworth, J., 2018. Influence of El Niño-Southern oscillation (ENSO) on agroclimatic zoning for tomato in Mozambique. Agricultural and Forest Meteorology 248, 316–328. https://doi.org/10.1016/j.agrformet.2017.10.002.

Goossens, C., Berger, A., 1986. Annual and seasonal climatic variations over the northern hemisphere and Europe during the last century. Annales Geophysicae, Berlin 4(B-4), 385-400.

Grimm, A.M., 2011 Interannual climate variability in South America: impacts on seasonal precipitation, extreme events and possible effects of climate change. Stochastic Environmental Research and Risk Assessment 25(4), 537–554.

Hastenrath, S., 1984. Interannual variability and annual cycle: mechanisms of circulation and climate in the tropical Atlantic. Monthly Weather Review 112, 1097-1107.

Hastenrath, S., 2012. Exploring the climate problems of Brazil’s Nordeste: a review. Climatic Change 112(2), 243-251.

Hu, M., Wiatrak, P. 2012. Effect of planting date on soybean growth, yield, and grain quality: review. Agronomy Journal 104, 785–790.

IBGE – Instituto Brasileiro de Geografia e Estatística, 2018. 3º Levantamento Sistemático da Produção Agrícola (LSPA). https://www.ibge.gov.br/estatisticas/economicas/agricultura-e-pecuaria/9201-levantamento-sistematico-da-producao-agricola.html. Acesso: 7 mai. 2019.

IPCC - Intergovernmental Panel on Climate Change, 2014. Central and South America. In: Barros, V. (ed) Climate change 2014: impacts, adaptation and vulnerability. Part B: Regional aspects, Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, 1499–1566.

Karmeshu, N., 2012. Trend Detectionin Annual Temperature & Precipitation using the Mann Kendall Test - A Case Study to Assess Climate Change on Select States in the Northeastern United States. Master of Environmental Studies Capstone Projects. University of Pennslvania. Department of Earth and Environmental Science.

Kayano, M.T., Andreoli, R.V., 2006. Relationships between rainfall anomalies over northeastern Brazil and the El Nino–Southern Oscillation, Journal of Geophysical Research 111, D13101.

Kendall, M.G., 1975. Rank Correlation Methods. Charles Griffin, London.

Kousky, V.E., 1979. Frontal influences on Northeast Brazil. Monthly Weather Review 107, 1140–1153.

Lacerda, F.F., Nobre, P., Sobral, M.C., Lopes, G.M.B., Chou, S.C., Assad, E.D., Brito, E., 2015. Long-term temperature and rainfall trends over Northeast Brazil and Cape Verde. Journal of Earth Science and Climatic Change 6(8), 1–8. https://doi.org/10.4172/2157-7617.1000296.

Li, D., Li, H., Qiao, Y., Wang, Y., Cai, Z., Dong, B., Shi, C., Liu, Y., Li, X., Liu, M., 2013. Effects of elevated CO2 on the growth, seed yield, and water use efficiency of soybean (Glycine max (L.) Merr.) under drought stress. Agricultural Water Management 129, 105–112

Liang, X.Z., Wu, Y., Chambers, R.G., Schmoldt, D.L., Gao, W., Liu, C., Liu, Y. A., Sun, C., Kennedy, J.A., 2017. Determining climate effects on US total agricultural productivity. Proceedings of the National Academy of Sciences 114, E2285–E2292. https://doi.org/10.1073/pnas.1615922114.

Magalhães, L.A., Miranda, E.E., 2014. MATOPIBA: Quadro Natural. Nota técnica 5. EMBRAPA. Available at: Grupo de Inteligência Territorial Estratégica (GITE). https://www.embrapa.br/gite/publicacoes/NT5_ Matopiba_Quadro_Natural.pdf. Acesso: 12 fev. 2019.

Mann, H.B., 1945. Nonparametric tests against trend. Econometrica 13(3), 245–259.

Marengo, J.A., Alves, L.M., Alvala, R.C.S., Cunha, A.P., Brito, S., Moraes, O.L.L., 2018. Climatic characteristics of the 2010-2016 drought in the semiarid Northeast Brazil region. Anais da Academia Brasileira de Ciências 90, 1973-1985.

Marengo, J.A., Bernasconi, M., 2015. Regional differences in aridity / drought conditions over Northeast Brazil: present state and future projections. Nature Climate Change 129, 103–115. https://doi.org/10.1007/s10584-014-1310-1.

Minuzzi, R.B., Caramori, P.H., 2011. Variabilidade climática sazonal e anual da chuva e veranicos no Estado do Paraná. Revista Ceres 58(5), 593 – 602.

Miranda, E.E., Magalhães, L.A., Carvalho, C.A., 2014. Proposta de delimitação territorial do Matopiba. Nota técnica 1. EMBRAPA. Grupo de Inteligência Territorial Estratégica (GITE) Available at: https://www.embrapa.br/gite/ publicacoes/ NT1_DelimitacaoMatopiba.pdf. Acesso: 12 mar. 2019.

Moura, M.M., dos Santos, A.R., Pezzopane, J.E.M., Alexandre, R.S., da Silva, S.F., Pimentel, S.M., de Andrade, M.S.S., Silva, F.G.R., Branco, E.R.F., Moreira, T.R., da Silva, R.G., de Carvalho, J.R., 2019. Relation of El Niño and La Niña phenomena to precipitation, evapotranspiration and temperature in the Amazon basin. Science of the Total Environment 651, 1639-1651. https://doi.org/10.1016/j.scitotenv.2018.09.242.

Mutti, P.R., Abreu, L.P., Andrade, L.M.B., Spyrides, M.H.C., Lima, C.K., de Oliveira, C.P., Dubreuil, V., Bezerra, B. G., 2019. A detailed framework for the characterization of rainfall climatology in semiarid watersheds. Theoretical and Applied Climatology. https://doi.org/10.1007/s00704-019-02963-0.

Nóia Junior, R.D.S., Sentelhas, P.C., 2019. Soybean-maize off-season double crop system in Brazil as affected by El Niño Southern Oscillation phases. Agricultural Systems 173, 254–267. https://doi.org/10.1016/j.agsy.2019.03.012

Obregón Párraga. G.O., 2003. Dinâmica da variabilidade climática da precipitação sobre a América do Sul. Tese (Doutorado). São José dos Campos, INPE.

Penalba, O.C., Rivera, J.A., 2016. Precipitation response to El Niño/La Niña events in southern South America – emphasis in regional drought occurrences. Advances Geosciences 42, 1-14. https://doi.org/10.5194/adgeo-42-1-2016.

Pierozan Junior, C., Kawakami, J., Schwarz, K., Umburanas, R.C., Del Conte, M.V., Müller, M.M.L., 2017. Sowing dates and soybean cultivars influence seed yield, oil and protein contents in subtropical environment. Journal of Agricultural Science 9, 188. https://doi.org/10.5539/jas.v9n6p188.

Pinheiro, A., Graciano, R.L.G., Severo, D.L., 2013. Tendência das séries temporais de precipitação da região Sul do Brasil. Revista Brasileira de Meteorologia 28(3), 281 - 290.

Pinto, H.S., Assad, E.D., 2008. Aquecimento global e a nova geografia da produção agrícola no Brasil. Embrapa Informática Agropecuária, 84 p. https://www.agritempo.gov.br/climaeagricultura/CLIMA_E_AGRICULTURA_BRASIL_300908_FINAL.pdf.

Pires, G.F., Abrahão, G.M., Brumatti, L.M., Oliveira, L.J.C., Costa, M.H., Liddicoat, S., Kato, E., Ladle, R.J, 2016. Increased climate risk in Brazilian double cropping agriculture systems: implications for land use in Northern Brazil. Agricultural and Forest Meteorology 228 (229), 286–298. https://doi.org/10.1016/j.agrformet.2016.07.005.

Ramirez-Rodrigues, M., Asseng, S., Fraisse, C., Stefanova, L., Eisenkolbi, A., 2014. Tailoring wheat management to ENSO phases for increased wheat production in Paraguay. Climate Risk Management, 1–15.

Rao, V.B., Hada, K., 1990. Characteristics of rainfall over Brazil: Annual and variations and connections with the Southern Oscillation, Theoretical and Applied Climatology 42, 81–91.

Rigo, A.A., Dahmer, A.M., Steffens, C., Steffens, J., Carrão-Panizzi, M.C., 2015. Characterization of soybean cultivars genetically improved for human consumption. International Journal of Food Engineering 1, 1-7. https://doi.org/10.18178/ijfe.1.1.1-7.

Rodrigues R.Á., Pedrini, J.E., Fraisse, C.W., Fernandes, J.M.C., Justino, F.B., Heinemann, A.B., Costa, L.C., Vale, F.X.R., 2012. Utilization of the cropgro-soybean model to estimate yield loss caused by Asian rust in cultivars with different cycle. Bragantia 71(2), 308-317.

Saath, K.C.O., Fachinello, A.L., 2018. Crescimento da demanda mundial de alimentos e restrições do fator terra no Brasil. Revista de Economia e Sociologia Rural. 56: 2. http://dx.doi.org/10.1590/1234-56781806-94790560201.

Salvador, M.A., Brito, J.I.B., 2017. Trend of annual temperature and frequency of extreme events in the MATOPIBA region of Brazil. Theoretical and Applied Climatology. http://dx.doi.org/10.1007/s00704-017-2179-5.

Schoffel, E.R., Saccol, A.V., Manfron, P.A., Medeiros, S.L.P., 2001. Excesso hídrico sobre os componentes do rendimento da cultura da soja. Ciência Rural 31, 7-12.

Sentelhas, P. C., Battisti, R., 2015. Clima e produtividade da soja: efeitos nas produtividades potencial, atingível e real. In: Fundação MT (Org.). Boletim de pesquisa 2015/2016, 17. ed. Rondonópolis: Fundação MT, 18-43.

Sentelhas, P.C., Battisti, R., Câmara, G.M.S., Farias, J.R.B., Hampf, A., Nendel, C., 2015.The soybean yield gap in Brazil - magnitude, causes and possible solutions for a sustainable production. Journal of Agricultural Science 153, 1394–1411. https://doi.org/10.1017/S0021859615000313.

Sentelhas, P.C., Battisti, R., Sako, H., Zeni, R., Rodrigues, L.A., 2018. Clima e produtividade da soja: variabilidade climática como fator controlador da produtividade. Boletim de Pesquisa, 2017/2018. https://edisciplinas.usp.br/mod/resource/view.php?id=2221718. Acesso: 24 nov. 2017

Sneyers, R. 1975. Sur l’ analyse statistique des series d’ observations. Genève: Organisation Météorologique Mondial, 1975. p 192. (OMM Note Technique, 143).

Souza, E.B., Kayano, M.T., Ambrizzi, T., 2005. Intraseasonal and submonthly variability over the Eastern Amazon and Northeast Brazil during the autumn rainy season. Theoretical and Applied Climatology 81, 177–191.

Spera, S.A., Galford, G.L., Coe, M.T., Macedo, M.N., Mustard, J.F., 2016. Land-use change affects water recycling in Brazil’s last agricultural frontier. Global Change Biology 22, 3405–3413. http://dx.doi.org/10.1111/gcb.13298.

St-Marseille, A-F.G., Bourgeois, G., Brodeur, J., Mimee, B., 2019. Simulating the impacts of climate change on soybean cyst nemoted and dristibution of soybean. Agricultural and Forest Meteorology, v. 264, p. 178-187. https://doi.org/10.1016/j.agrformet.2018.10.008.

Taherzadeh, O., Caro, D., 2019. Drivers of water and land use embodied in international soybean trade. Journal of Cleaner Production 223, 83-93. https://doi.org/10.1016/j.jclepro.2019.03.068.

Tedeschi, R.G., Grimm, M., Cavalcanti, I.F.A., 2016. Influence of Central and East ENSO on precipitation and its extreme events in South America during austral autumn and winter. International Journal of Climatology 36, 4797–4814. https://doi.org/10.1002/joc.4670.

Timmermann, A., AN, S.I., Kug, J.S., Jin, F.F., Cai, W., Capotondi, A., Cobb, K.M., Lengaigne, M., Mcphaden, M.J., Stuecker, M.F., Stein, K., Wittenberg, A.T., Yun, K.S., Bayr, T., Chen, H.C., Chikamoto, Y., Dewitte, B., Dommenget, D., Grothe, P., Guilyardi, E., Ham, Y. G., Hayashi, M., Ineson, S., Kang, D., Kim, S., Kim, W.M., Lee, J.Y., Li, T., Luo, J.J., Mcgregor, S., Planton, Y., Power, S., Rashid, H., Ren, H.L., Santoso, A., Takahashi, K., Todd, A., Wang, G., Wang, G., Xie, R., Yang, W.H., Yeh, S.W., Yoon, J., Zeller, E., Zhang, X., 2018. El Niño – Southern Oscillation complexity. Nature Springer 559, 535–545. https://doi.org/10.1038/s41586-018-0252-6.

Woli, P., Ortiz, B.V., Johnson, J., Hoogenboom, G., 2015. El Niño-Southern Oscillation effects on winter wheat in the Southeastern United States. Agronomy Journal 107. https://doi.org/10.2134/agronj14.0651.

Wu, H., Qian, H., 2017. Innovative trend analysis of annual and seasonal rainfall and extreme values in Shaanxi, China, since the 1950s. International Journal of Climatology 37, 2582–2592. https://doi.org/10.1002/joc.4866.

Xavier, A.C., King, C.W., Scanlon, B.R., 2015. Daily gridded meteorological variables in Brazil (1980–2013). International Journal of Climatology 36, 2644–2659. https://doi.org/10.1002/joc.4518.

Yu, P.S., Tao, T.C., Chou, C.C., 2002. Effects of climate change on evapotranspiration from paddy fields in Southern Taiwan. Climatic Change 54, 165‑179. https://doi.org/10.1023/A:1015764831165

Zhu, X., Troy, T.J., 2018. Agriculturally Relevant Climate Extremes and Their Trends in the World’s Major Growing Regions. Earth’s Future 6, 656–672. https://doi.org/10.1002/2017EF000687.




DOI: https://doi.org/10.26848/rbgf.v13.4.p1425-1441

Licença Creative Commons
Esta obra está licenciada sob uma licença Creative Commons Atribuição 4.0 Internacional.

      

Revista Brasileira de Geografia Física - ISSN: 1984-2295

Creative Commons License
Esta obra está licenciada com uma Licença Creative Commons Attribution-NonCommercial 4.0 International License