Digital agrometeorology: the biophysical basis for the digital revolution in the field

Authors

DOI:

https://doi.org/10.23925/1984-3585.2019i20p59-76

Keywords:

Biophysics, Ecophysiology, Crop Modeling, Information Technology, Operational Agrometeorology

Abstract

An estimated world population of over 10 billion by 2100 and the uncertainties concerning the future climate heat up the debate on world food security, water supply and energy availability. Brazil is a key player in supplying a large part of world food demand. Hence, research directed towards the full understanding of soil-plant-atmosphere relations becomes increasingly important. This paper reviews literature on the fundamentals of biophysical bases for conceptualizing Digital Agrometeorology. Model-based decision support systems might help with the quantification of climate influence and with the management of agricultural production. It may bring a range of agrometeorological services that assist in management decisions, and increase the efficiency of shortand long-term processes of decision-making.

Author Biographies

Felipe Gustavo Pilau, University of São Paulo, São Paulo, São Paulo, Brazil

PhD in Agronomy (Agricultural Environmental Physics) from University of São Paulo (USP).

Fabio Ricardo Marin, University of São Paulo, São Paulo, São Paulo, Brazil

PhD in Agronomy (Agricultural Environmental Physics) from University of São Paulo (USP).

References

ADAMS, Richard M. et al. Global climate change and US agriculture. Nature, v. 345, p. 219–24, 1990. Disponível em: nature.com/nature/journal/v345/n6272/abs/345219a0.html. Acesso em: 02 out. 2019.

ALEXANDRATOS, N.; BRUINSMA, J. World Agriculture Towards 2030/2050: the 2012 revision. Rome: FAO, 2012. (ESA working paper nº 12-03).

ANGELOCCI, L. R. et al. Transpiration, leaf diffusive conductance, and atmospheric water demand relationship in an irrigated acid lime orchard. Brazilian Journal of Plant Physiology, v. 16, p. 53-64, 2004.

ANGELOCCI, L. R. et al. Radiation balance of coffee hedgerows. Revista Brasileira de Engenharia Agrícola e Ambiental, v. 12, n. 3, p. 274-281, 2008.

ASSAD, E. D. et al. Sistema de previsão da safra de soja para o Brasil. Pesquisa Agropecuária Brasileira, v. 42, n. 5, p. 615-625, 2007.

ASSENG, S. et al. Uncertainty in simulating wheat yields under climate change. Nature Climate Change, v. 3, p. 827-832, 2013. Disponível em: . Acesso em: 02 out. 2019.

BAIGORRIA, G. A.; JONES, J. W. GIST: A stochastic model for generating spatially and temporally correlated daily rainfall data. Journal of Climate, v. 23, 5990-6008, 2010. Disponível em: doi.org/10.1175/2010JCLI3537.1. Acesso em: 02 out. 2019.

BOOTE, K. J. et al. Potential uses and limitations of crop models. Agronomy Journal, v. 88, n. 5, p. 704–16, 1996. doi.org/10.2134/agronj1996.00021962008800050005x. Acesso: 02 out. 2019.

BRUINSMA, J. World Agriculture: towards 2015/2030. Rome: FAO; London: Earthscan, 2003. Disponível em: fao.org/3/a-y4252e.pdf. Acesso em: 13 jan. 2016.

CARVALHO, K. S. et al. Assessing sugarcane evapotranspiration based on a biophysical approach. International Journal of Current Research, v. 9, n. 4, p. 48601-48610, 2017.

CARVALHO, K. S. et al. Effect of soil straw cover on evaporation, transpiration, and evapotranspiration in sugarcane cultivation. Australian Journal of Crop Science, v. 13, n. 8, p. 1835–2707, 2019.

DOURADO-NETO, D. et al. Principles of crop modelling and simulation: II. The implications of the objective in model development. Scientia Agricola, v. 55, p. 46-57, 1998.

JONES, J. W. et al. The DSSAT cropping system model. European Journal of Agronomy, v. 18, n. 3-4, p. 235-65, 2003.

KEATING, B. A. et al. An overview of APSIM, a model designed for farming systems simulation. European Journal of Agronomy, v. 18, n. 3-4, p. 267-88, 2003.

LOBELL, D.B. et al. Crop yield gaps: their importance, magnitudes, and causes. Annual Review of Environment and Resources, v. 34, p. 179-204, 2009. Disponível em: doi.org/10.1146/annurev.environ.041008.093740. Acesso em: 02 out. 2019.

MARIN, F. R. Evapotranspiração, transpiração e balanço de energia em pomar de lima ácida ‘Tahiti’. 2000. 74 f. Dissertação (Mestrado em Agrometeorologia) – Escola Superior de Agricultura “Luiz de Queiroz”, Universidade de São Paulo, Piracicaba, 2000.

MARIN, F. R. Evapotranspiração e transpiração máxima em cafezal adensado. 2003. 118 p. Tese (Doutorado em Física do Ambiente Agrícola) – Escola Superior de Agricultura “Luiz de Queiroz”, Universidade de São Paulo, Piracicaba.

MARIN, F. R. Eficiência de produção da cana-de-açúcar brasileira: estado atual e cenários futuros baseados em simulações multimodelos. 2014. 262 p. Tese (Livre Docência) – Escola Superior de Agricultura “Luiz de Queiroz”, Universidade de São Paulo, Piracicaba, 2014.

MARIN, F. R.; ANGELOCCI, L. R. Irrigation requirements and transpiration coupling to the atmosphere of a citrus orchard in southern Brazil. Agricultural Water Management, v. 98, n. 6, p. 1091-1096, 2011.

MARIN, F. R. et al. Construção e avaliação de psicrômetro aspirado de termopar. Scientia Agricola, v. 58, n. 4, p. 839-844, 2001. Disponível em: dx.doi.org/10.1590/S0103-90162001000400028. Acesso em: 02 out. 2019.

MARIN, F. R. et al. Evapotranspiration and irrigation requirements of a coffee plantation in Southern Brazil. Experimental Agriculture, v.41, n. 2, p. 1-11, 2005.

MARIN, F. R. et al. Fluxo de seiva pelo método do balanço de calor: base teórica, qualidade das medidas e aspectos práticos. Bragantia, v. 67, n. 1, p. 1-14, 2008.

MARIN, F. R. et al. Climate change impacts on sugarcane attainable yield in southern Brazil. Climatic Change, v. 117, n. 1-2, p. 227-39, 2013.

MARIN, F. R. et al. How can crop modeling and plant physiology help to understand the plant responses to climate change? A case study with sugarcane. Theoretical and Experimental Plant Physiology, v. 26, n. 1, p. 49-63, 2014. Disponível em: doi.org/10.1007/s40626-014-0006-2. Acesso em: 02 out. 2019.

MARIN, F. R. et al. Crop coefficient changes with reference evapotranspiration for highly canopy-atmosphere coupled crops. Agricultural Water Management, v. 163, p. 139-145, 2016. Disponível em: doi.org/10.1016/j.agwat.2015.09.010. Acesso em: 02 out. 2019.

MARIN, F. R. et al. Revisiting the crop coefficient–reference evapotranspiration procedure for improving irrigation management. Theoretical and Applied Climatology, 2019. Disponível em: doi.org/10.1007/s00704-019-02940-7. Acesso em: 02 out. 2019.

NASSIF, D. S. P. et al. The role of decoupling factor on sugarcane crop water use under tropical conditions. Experimental Agriculture, 2019, p. 1–11. Disponível em: doi.org/10.1017/S0014479718000480. Acesso em: 02 out. 2019.

OLIVEIRA, R. K. Fluxos de CO2, água e energia em área de renovação de canavial com cultivo de soja. 2018. 61 p. Dissertação (Mestrado em Engenharia de Sistemas Agrícolas) – Escola Superior de Agricultura “Luiz de Queiroz”, Universidade de São Paulo, Piracicaba.

OVERMAN, A. R.; SCHOLTZ, R. V. III. Mathematical models of crop growth and yield. Annals of Botany, v. 91, n. 3, p. 403-404, 2003. Disponível em: . Acesso em: 02 out. 2019.

PALOSUO, T. et al. 2013. How to assess climate change impacts on farmers’ crop yields? In: IMPACTTS WORLD 2013 – International Conference on Climate Change Effects, 2013, Postdam. Anais… Postdam: Potsdam Institute for Climate Impact Research, 2013, p. 327-334. Disponível em: gfzpublic.gfz-potsdam.de/pubman/item/escidoc:152514:5/component/escidoc:152588/Impacts_World_2013.pdf. Acesso em: 15 nov. 2019.

PILAU, F. G. Saldo de radiação da copa de laranjeira num pomar e de renques de cafeeiros: medidas e estimativas, 2005. Tese (Doutorado) – Escola Superior de Agricultura “Luiz de Queiroz”, Universidade de São Paulo, Piracicaba, 2005.

PILAU, F. G.; ANGELOCCI, L. R. Radiation balance of an orange tree in orchard and its relation with global solar radiation and grass net radiation. Revista Brasileira de Agrometeorologia, v. 15, p. 257-266, 2007.

PILAU, F. G.; ANGELOCCI, L. R. Balanço de radiação de copas de cafeeiros em renques e suas relações com radiação solar global e saldo de radiação de gramado. Bragantia, Campinas, v. 73, p. 335-342, 2014.

PILAU, F. G.; ANGELOCCI, L. R. Leaf area and solar radiation interception by orange tree top. Bragantia, Campinas, v. 74, n. 4, p. 476-482, 2015.

PILAU, F. G.; ANGELOCCI, L. R. Padrões de interceptação de radiação solar por cafeeiros em função da área foliar. Coffee Science, v. 11, n. 1, p. 127-136, 2016.

RIGHI, E. Z. et al. Energy balance of a young drip-irrigated coffee crop in southeast Brazil: an analysis of errors and reliability of measurements by the Bowen ratio method. Revista Brasileira de Agrometeorologia, v. 15, p. 267-279, 2007.

ROSENZWEIG, C.; PARRY, M. L. Potential impact of climate change on world food supply. Nature 367 (6459): 133–38, 1994. Disponível em: . Acesso em: 02 out. 2019.

ROSENZWEIG, C. et al. Assessing cgricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proceedings of the National Academy of Sciences, January, 201222463, 2013. Disponível em: doi.org/10.1073/pnas.1222463110. Acesso em: 02 out. 2019.

RÖTTER, R. P. et al. Crop-climate models need an overhaul. Nature Climate Change, v. 1, p. 175-77, 2011.

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 Research, v. 133, p. 23-36, 2012. Disponível em: sciencedirect.com/science/article/pii/S0378429012001098. Acesso em: 02 out. 2019

SEMENOV, M.; STRATONOVITCH, P. Use of multi-model ensembles from global climate models for assessment of climate change impacts. Climate Research, v. 41, n. 1, p. 1-14, 2010.

SILVA, Evandro H. F. M. da et al. Soybean irrigation requirements and canopy-atmosphere coupling in southern Brazil. Agricultural Water Management, v. 218, p. 1-7, 2019. Disponível em: doi.org/10.1016/j.agwat.2019.03.003. Acesso em: 02 out. 2019.

SINCLAIR, T. R.; SELIGMAN, N. G. Crop modeling: from infancy to maturity. Agronomy Journal, v. 88, n. 5, p. 698-704, 1996.

SOBENKO, L. R. et al. Irrigation requirements are lower than those usually prescribed for a maize crop in southern Brazil. Experimental Agriculture, v. 55, n. 4, 662-671. Disponível em: doi.org/10.1017/S0014479718000339. Acesso em: 02 out 2019.

THORNLEY J. H. M.; JOHNSON I. R. Plant and crop modeling: a mathematical approach to plant and crop physiology. Caldwell, NJ: Blackburn, 2000.

UNITED NATIONS, Department of Economic and Social Affairs, Population Division. World Population Prospects 2019, Online Edition, 2019. Disponível em: population.un.org/wpp/Download/Standard/Population. Acesso: 01 set. de 2019.

VAN REES, H. et al. Leading farmers in South East Australia have closed the exploitable wheat yield gap: prospects for further improvement. Field Crops Research, v. 164, p. 1-11, 2014.

VIANNA, M. S. et al. Modelo Agrometeorológico Genérico de Produção Vegetal (MAGé). Piracicaba: ESALQ, 2017.