Assessing cloudiness effect on soybean yield in the Southeast Brazil

Izael Martins Fattori Junior, Evandro Henrique Figueiredo Moura da Silva, Juliano Mantellatto Rosa, Fábio Ricardo Marin

Resumo


The solar radiation is one of the most important weather variables for determining the potential yield of agricultural crops. Soybean is the main Brazilian agricultural commodity, with great social and economical importance for the country. It is well recognized that cloudiness is a limiting factor for crop growth rate. Few studies have been conducted to systematically evaluate how much cloud affect soybean yield and none, to our knowledge, is available for tropical soybean. The objective of this paper was to quantify the implications of cloudiness on soybean growth and development in tropical Brazil. To do so, experimental data associated with the simulations of the DSSAT/CROPGRO (DC) model was used, and two treatments were simulated: a) the first used measured solar radiation and b) the second used estimated solar radiation for non-cloud days. Thus, based on the model output variables (growth rate of aerial dry matter, grain yield, specific leaf weight, and light saturation point) and comparing the data with the literature review. We found that simulations exhibited an increase in the dry matter production rate of up to 23%, during days of clear sky, resulting in a yield increase between 26 and 37%. Also, the DC could simulate some adaptation in the plant when the solar radiation changes, like the specific foliar weight and the light-saturated photosynthesis rate.


Palavras-chave


solar radiation; simulation; DSSAT; crop modeling; Glycine max (L.) Merrill

Texto completo:

PDF (English)

Referências


BATTISTI, R.; SENTELHAS, P. C. Improvement of soybean resilience to drought through deep root system in Brazil. Agronomy Journal, v. 109, n. 4, p. 1612–1622, 2017.

BOOTE, K. J. et al. Evaluation of the CROPGRO-Soybean model over a wide range of experiments. In: [s.l.] Springer, Dordrecht, 1997. p. 113–133.

BOOTE, K.; PICKERING, N. Modeling photosynthesis of row crop canopies. Hortscience, 1994. Disponível em: . Acesso em: 25 abr. 2019.

CONAB. Acompanhamento da Safra Brasileira de Grãos Safra 2018/19. Companhia Nacional de Abastecimento, 2019. .

DOHLEMAN, F. G.; LONG, S. P. More Productive Than Maize in the Midwest: How Does Miscanthus Do It? 2009. Disponível em: . Acesso em: 11 jun. 2019.

ERBS, D. G.; KLEIN, S. A.; DUFFIE, J. A. Estimation of the diffuse radiation fraction for hourly, daily and monthly-average global radiation. Solar Energy, v. 28, n. 4, p. 293–302, 1 jan. 1982. Disponível em: . Acesso em: 25 abr. 2019.

FEHR, W. R.; CAVINESS, C. E. Stages of soybean development. [s.l: s.n.]. Disponível em: . Acesso em: 19 maio. 2019.

GONG, W. et al. Transcriptome Analysis of Shade-Induced Inhibition on Leaf Size in Relay Intercropped Soybean. PLoS ONE, v. 9, n. 6, p. e98465, 2 jun. 2014. Disponível em: . Acesso em: 11 jun. 2019.

GOUDRIAAN, J. Crop micrometeorology : a simulation study, 1977. Disponível em: . Acesso em: 9 set. 2019.

HUSSAIN, S. et al. Shade effect on carbohydrates dynamics and stem strength of soybean genotypes. Environmental and Experimental Botany, v. 162, p. 374–382, 1 jun. 2019. Disponível em: . Acesso em: 10 jun. 2019.

JONES, J. W. et al. Volume 4 Cropping System Model Documentation. Cropping System Model Documentation, 2010.

KIMBALL, B. A.; BELLAMY, L. A. Generation of diurnal solar radiation, temperature, and humidity patterns. Energy in Agriculture, v. 5, n. 3, p. 185–197, 1 nov. 1986. Disponível em: . Acesso em: 25 abr. 2019.

KOKUBUN, M. Physiological Mechanisms Regulating Flower Abortion in Soybean. Soybean - Biochemistry, Chemistry and Physiology, p. 541–554, 2011. Disponível em: . Acesso em: 22 maio. 2019.

KUROSAKI, H.; YUMOTO, S. Effects of Low Temperature and Shading during Flowering on the Yield Components in Soybeans. Plant Production Science, v. 6, n. 1, p. 17–23, 2003. Disponível em: . Acesso em: 20 abr. 2019.

LIU, B. et al. Soybean yield and yield component distribution across the main axis in response to light enrichment and shading under different densities. PLANT SOIL ENVIRON, v. 56, p. 384–392, 2010. Disponível em: . Acesso em: 11 jun. 2019.

MARCHIORI, P. E. R.; MACHADO, E. C.; RIBEIRO, R. V. Photosynthetic limitations imposed by self-shading in field-grown sugarcane varieties. Field Crops Research, v. 155, p. 30–37, 1 jan. 2014. Disponível em: . Acesso em: 7 jun. 2019.

MARIN, F.R. et al. Parametrization and evaluation of predictions of DSSAT/CANEGRO for Brazilian sugarcane. Agronomy Journal, v. 103, n. 2, p. 304–315, 2011.

MATHEW, J. P. et al. Differential Response of Soybean Yield Components to the Timing of Light Enrichment. Agronomy Journal, v. 92, n. 6, p. 1156–1161, 2000. Disponível em: . Acesso em: 9 maio. 2019.

MELGES, E.; LOPES, N.; OLIVA, M. Crescimento e conversão da energia solar em soja cultivada sob quatro níveis de radiação solar. Pesquisa Agropecuária Brasileira, v. 24, n. 9, p. 1065- 1072, 1989. Disponível em: . Acesso em: 27 jan. 2019.

PARTON, W. J.; LOGAN, J. A. A model for diurnal variation in soil and air temperature. Agricultural Meteorology, v. 23, p. 205–216, 1 jan. 1981. Disponível em: . Acesso em: 25 abr. 2019.

PEART, R. M.; BOOTE, K. J. Simulation of Crop Growth: CROPGRO ModelAgricultural Systems modeting and Simulation, 2018. .

POPPER, K. The logic of scientific discovery. [s.l.] Routledge, 2005.

RODRIGUES, R. de Á. et al. Utilization of the cropgro-soybean model to estimate yield loss caused by Asian rust in cultivars with different cycle. Bragantia, v. 71, n. 2, p. 308–317, 2012. Disponível em: . Acesso em: 30 set. 2019.

SALMERÓN, M.; PURCELL, L. C. Simplifying the prediction of phenology with the DSSAT-CROPGRO-soybean model based on relative maturity group and determinacy. Agricultural Systems, v. 148, p. 178–187, 1 out. 2016. Disponível em: . Acesso em: 2 abr. 2019.

SILVA, E. H. F. M. et al. Análise de sensibilidade com base em parâmetros relacionados a temperatura e fotoperíodo no modelo DSSAT/CROPGRO-SOYBEAN. Agrometeoros, v. 25, n. 1, 29 nov. 2018a. Disponível em: . Acesso em: 16 jun. 2019.

SILVA, E. H. F. M. et al. Simulação de produtividade futura de soja em Piracicaba-SP com base em projeções de mudanças climáticas. Agrometeoros, v. 25, n. 1, 29 nov. 2018b. Disponível em: . Acesso em: 8 set. 2019.

SPITTERS, C. J. T. Separating the diffuse and direct component of global radiation and its implications for modeling canopy photosynthesis Part II. Calculation of canopy photosynthesis. Agricultural and Forest Meteorology, v. 38, n. 1–3, p. 231–242, 1 out. 1986. Disponível em: . Acesso em: 19 abr. 2019.

SPITTERS, C. J. T.; TOUSSAINT, H. A. J. M.; GOUDRIAAN, J. Separating the diffuse and direct component of global radiation and its implications for modeling canopy photosynthesis Part I. Components of incoming radiation. Agricultural and Forest Meteorology, v. 38, n. 1–3, p. 217–229, 1 out. 1986. Disponível em: . Acesso em: 19 abr. 2019.

TERASHIMA, I. et al. Leaf functional anatomy in relation to photosynthesis. Plant Physiology, v. 155, n. 1, p. 108–116, 2011.

VAN ITTERSUM, M. K. et al. Yield gap analysis with local to global relevance—A review. Field Crops Research, v. 143, p. 4–17, 1 mar. 2013. Disponível em: . Acesso em: 28 abr. 2019.

WU, Y.; GONG, W.; YANG, W. Shade Inhibits Leaf Size by Controlling Cell Proliferation and Enlargement in Soybean OPEN. Scientific reports, v. 7, p. 9259, 2017. Disponível em: .

WU, Y. shan et al. Shade adaptive response and yield analysis of different soybean genotypes in relay intercropping systems. Journal of Integrative Agriculture, v. 16, n. 6, p. 1331–1340, 1 jun. 2017.

YANG, F. et al. Auxin-to-Gibberellin Ratio as a Signal for Light Intensity and Quality in Regulating Soybean Growth and Matter Partitioning. Frontiers in Plant Science, v. 9, p. 56, 30 jan. 2018. Disponível em: . Acesso em: 11 jun. 2019.

YAO, X. et al. Effect of shade on leaf photosynthetic capacity, light-intercepting, electron transfer and energy distribution of soybeans. Plant Growth Regulation, v. 83, n. 3, p. 409–416, 2 dez. 2017. Disponível em: . Acesso em: 11 jun. 2019.




DOI: http://dx.doi.org/10.31062/agrom.v27i1.26598

Apontamentos

  • Não há apontamentos.


Embrapa Trigo

Rodovia BR-285, km 294, Caixa Postal: 3081

CEP 99050-970 Passo Fundo/RS