Published

2020-05-01

Influence of the AquaCrop soil module on the estimation of soybean and maize crop yield in the State of Parana, Brazil

Influencia del módulo suelo del AquaCrop en la estimación del rendimiento de los cultivos de soja y maíz en el Estado de Paraná, Brasil

DOI:

https://doi.org/10.15446/agron.colomb.v38n2.78659

Keywords:

simulation, soil attributes, Glycine max, Zea mays (en)
simulación, atributos del suelo, Glycine max, Zea mays (es)

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Authors

  • Jorge Luiz Moretti de Souza Universidade Federal do Paraná - Programa de Pós-Graduação em Ciência do Solo -
  • Stefanie Lais Kreutz Rosa Universidade Federal do Paraná - Programa de Pós-Graduação em Ciência do Solo
  • Karla Regina Piekarski Emater Empresa Paranaense de Assistência Técnica e Extensão Rural
  • Rodrigo Yoiti Tsukahara Fundação ABC Pesquisa e Desenvolvimento Agropecuário

The values of the physical-water attributes of soils for use in agricultural simulation models are usually obtained using difficult and time-consuming methods. The objective of this study was to analyze the performance of the AquaCrop model to estimate soybean and maize crop productivity in the region of Campos Gerais (Brazil), with the option of including soil physical-water attributes in the model. Real crop productivities and input data (soil, climate, crop and soil management) were obtained from experimental stations of the ABC Foundation for the crop years 2006 to 2014. Sixty-four yield simulations were performed for soybean (four municipalities) and 42 for maize (three municipalities), evaluating input soil data scenarios of AquaCrop as follows: i) all soil physical-water attributes were measured (standard) and ii) the attributes were measured only using textural classification of the area (alternative). Real and simulated yields were verified by simple linear regression analyses and statistical indices (r, d, c). The standard scenario yielded performances between very good and excellent (0.75<c≤1.0) for soybean and between bad and excellent (0.40<c≤1.0) for maize. The alternative scenario was more variable, with performances between terrible and excellent (0.0<c≤1.0) for soybean and terrible and medium (0.0<c≤0.65) for maize. Using only the soil texture classification in AquaCrop indicated an easier way to estimate crop yields, but low performances may restrict estimates of soybean and maize yields in Campos Gerais.

Los valores de los atributos físico-hídricos del suelo para uso en modelos de simulación agrícola generalmente se obtienen usando métodos difíciles y demorados. El objetivo del presente trabajo fue analizar el desempeño del modelo AquaCrop para estimar la productividad de los cultivos soja y maíz en la región de los Campos Gerais (Brasil), de acuerdo con la opción de incluir los atributos físico-hídricos del suelo en el modelo. Las productividades reales de los cultivos y datos de entrada (suelo, clima, cultivo y manejo del suelo) se obtuvieron de las estaciones experimentales de la Fundación ABC, para los años cosecha entre 2006 y 2014. Se llevaron a cabo 64 simulaciones de productividad para soya (cuatro municipios) y 42 para maíz (tres municipios), evaluando escenarios de entrada de los datos del suelo en el AquaCrop de la siguiente manera: i) todos los atributos físico-hídricos del suelo medidos (estándar) y ii) sólo la clasificación textural del área (alternativo). Las productividades reales y simuladas se verificaron por análisis de regresión lineal simple e índices estadísticos (r, d, c). El escenario estándar obtuvo desempeños entre muy bueno y excelente
(0.75<c≤1.0) para la soya y entre malo y excelente (0.40<c≤ 1.0) para el maíz. El escenario alternativo fue más variable, con desempeños entre pésimo y excelente (0.0<c≤1.0) para soya y entre pésimo y mediano (0.0<c≤0.65) para maíz. Utilizar solamente la clasificación de textura del suelo en el AquaCrop indicó una forma más fácil de estimar los rendimientos de los cultivos, pero los bajos desempeños pueden restringir estimativas de las productividades de los cultivos soja y maíz en Campos Gerais.

References

Alvares, C.A., J.L. Stape, P.C. Sentelhas, J.L.M. Gonçalves, and G. Sparovek. 2013. Köppens’s climate classification map for Brazil. Meteorol. Z. 22(6), 711-728. Doi: 10.1127/0941-2948/2013/0507

ASCE-EWRI. 2005. The ASCE standardized reference evapotranspiration equation. Report of the technical committee on standardization of reference evapotranspiration. Environmental and Water Resources Institute of the American Society of Civil Engineers, Reston, USA. URL: <https://ascelibrary.org/doi/book/10.1061/9780784408056> (accessed 01 February 2019)

Bünemann, E.K., G. Bongiorno, Z. Bai, R.E. Creamer, G. Deyn, R. Goede, L. Fleskens, V. Geissen, T.W. Kuyper, P. Mäder, M. Pulleman, W. Sukkel, J.W. Van Groenigen, and L. Brussaard. 2018. Soil quality - A critical review. Soil Biol. Biochem. 120, 105-125. Doi: 10.1016/j.soilbio.2018.01.030

Camargo, A.P. and P.C. Sentelhas. 1997. Avaliação do desempenho de diferentes métodos de estimativas da evapotranspiração potencial no Estado de São Paulo, Brasil. Rev. Bras. de Agrometeorol. 5, 89-97.

Erickson, A.E. 1982. Tillage effects on soil aeration. pp. 91-104. In: Unger, P.W., D.M. Van Doren Jr., F.D. Whisler, and E.L. Skidmore (eds.). Predicting tillage effects on soil physical attributes and processes. American Society of Agronomy, Soil Science Society of America, Madison, USA. Doi: 10.2134/asaspecpub44.c6

García, J., G. Fischer, and N. Riaño. 2017. Effect of fertilization level on water use and production of corn (Zea mays L.) in a cereal producing area in Colombia - a modeling exercise using AquaCrop-FAO. Agron. Colomb. 35(1), 68-74. Doi: 10.15446/agron.colomb.v35n1.61428

Issoufou, A.A., I. Soumana, G. Maman, S. Konate, and A. Mahamane. 2020. Dynamic relationship of traditional soil restoration practices and climate change adaptation in semi-arid Niger. Heliyon 6(1), e03265. Doi: 10.1016/j.heliyon.2020.e03265

Jones, J.W., J.M. Antle, B. Basso, K.J. Boote, R.T. Conant, I. Foster, H.C.J. Godfray, M. Herrero, R.E. Howitt, S. Janssen, B.A. Keating, R. Munoz-Carpena, C.H. Porter, C. Rosenzweig, and T.R. Wheeler. 2017. Brief history of agricultural systems modeling. Agr. Syst. 155, 240-254. Doi: 10.1016/j.agsy.2016.05.014

Lin, H.S., K.J. McInnes, L.P. Wilding, and C.T. Hallmark. 1999. Effects of soil morphology on hydraulic attributes II: Hydraulic pedotransfer functions. Soil Sci. Soc. Am. J. 63(4), 955-961. Doi: 10.2136/sssaj1999.634955x

López-Urrea, R., A. Domínguez, J.J. Pardo, F. Montoya, M. García-Vila, and A. Martínez-Romero. 2020. Parameterization and comparison of the AquaCrop and MOPECO models for a highyielding barley cultivar under different irrigation levels. Agr. Water Manag. 230, 105931. Doi: 10.1016/j.agwat.2019.105931

Oliveira, L.B., M.R. Ribeiro, P.K.T. Jacomine, J.J.V. Rodrigues, and F.A. Marques. 2002. Funções de pedotransferência para predição da umidade retida a potenciais específicos em solos do estado de Pernambuco. Rev. Bras. Ciên. Solo, 26(2), 315-323. Doi: 10.1590/S0100-06832002000200004

Pinheiro, E.A.R., Q.J. van Lier, and J. Šimůnek. 2019. The role of soil hydraulic properties in crop water use efficiency: a processbased analysis for some Brazilian scenarios. Agr. Syst. 173, 364-377. Doi: 10.1016/j.agsy.2019.03.019

Prevedello, C.L. 1999. Programa Splintex para estimar a curva de retenção de água a partir da granulometria (composição) do solo. Versão 1.0. UFPR, Curitiba, Brazil.

Raes, D., P. Steduto, T.C. Hsiao, and E. Fereres. 2013. Reference manual: Section 2.21 input files (Chapter 2). Version 4.0. FAO, Rome.

Raes, D., P. Steduto, T.C. Hsiao, and E. Fereres. 2018a. Reference Manual: Chapter 1: FAO crop-water productivity model to simulate yield response to water. Version 6.0 - 6.1. FAO, Rome.

Raes, D., P. Steduto, T.C. Hsiao, and E. Fereres. 2018b. Reference Manual: Chapter 2: Users guide. Version 6.0 - 6.1. FAO, Rome.

Raes, D., P. Steduto, T.C. Hsiao, and E. Fereres. 2018c. Reference Manual: Chapter 3: Calculation procedures. Version 6.0 - 6.1. FAO, Rome.

Reichardt, K. and L.C. Timm. 2004. Solo, planta e atmosfera: conceitos, processos e aplicações. Manole, Barueri, Brazil.

Rosa, S.L.K., J.L.M. Souza, and R.Y. Tsukahara. 2020. Performance of the AquaCrop model for the wheat crop in the subtropical zone in Southern Brazil. Pesq. Agropec. Bras. 55, e01238. Doi: 10.1590/s1678-3921.pab2020.v55.01238

Scheraiber, C.F. 2012. Adaptação metodológica para a estimativa e caracterização das relações hídricas como suporte ao planejamento agrícola. MSc thesis, Universidade Federal do Parana, Curitiba, Brazil.

Siad, S.M., V. Iacobellis, P. Zdruli, A. Gioia, I. Stavi, and G. Hoogenboom. 2019. A review of coupled hydrologic and crop growth models. Agr. Water Manag. 224, 105746. Doi: 10.1016/j.agwat.2019.105746

Souza, J.L.M. 2018. Fundamentos de matemática e estatística para formulação de modelos e análise de dados: aplicado às ciências agrárias. Plataforma Moretti/DSEA/SCA/UFPR (Série Didática), Curitiba, Brazil.

Souza, J.L.M., E. Gerstemberger, and M.A. Araujo. 2013. Calibração de modelos agrometeorológicos para estimar a produtividade da cultura do trigo, considerando sistemas de manejo do solo, em Ponta Grossa-PR. Rev. Bras. Meteorol. 28(4), 409-418. Doi: 10.1590/S0102-77862013000400007

Steduto, P., T.C. Hsiao, E. Fereres, and D. Raes. 2012. Crop yield response to water. FAO Irrigation and Drainage Paper N° 66. FAO, Rome.

Steduto, P., T.C. Hsiao, D. Raes, and E. Fereres. 2009. Aquacrop - The FAO crop model to simulate yield response to water: I. Concepts and underlying principles. Agron. J. 101, 426-437.

Teixeira, P.C., G.K. Donagemma, A. Fontana, and W.G. Teixeira. 2017. Manual de métodos de análise de solo. Embrapa, Brasilia.

Tonitto, C., P.B. Woodbury, and E.L. Mclellan. 2018. Defining a best practice methodology for modeling the environmental performance of agriculture. Environ. Sci. Policy 87, 64-73. Doi: 10.1016/j.envsci.2018.04.009

Willmott, C.J. 1982. Some comments on the evaluation of model performance. Bull. Am. Meteorol. Soc. 63(11), 1309-1313.

Yevtushenko, T.V., O.L. Tonkha, and O.V. Pikovskaa. 2016. Changes in balk density and porosity of chernozem typical under different cultivation systems. Ann. Agrar. Sci. 14(4), 299-302. Doi: 10.1016/j.aasci.2016.09.005

How to Cite

APA

Moretti de Souza, J. L., Kreutz Rosa, S. L., Piekarski, K. R. & Yoiti Tsukahara, R. (2020). Influence of the AquaCrop soil module on the estimation of soybean and maize crop yield in the State of Parana, Brazil. Agronomía Colombiana, 38(2), 234–241. https://doi.org/10.15446/agron.colomb.v38n2.78659

ACM

[1]
Moretti de Souza, J.L., Kreutz Rosa, S.L., Piekarski, K.R. and Yoiti Tsukahara, R. 2020. Influence of the AquaCrop soil module on the estimation of soybean and maize crop yield in the State of Parana, Brazil. Agronomía Colombiana. 38, 2 (May 2020), 234–241. DOI:https://doi.org/10.15446/agron.colomb.v38n2.78659.

ACS

(1)
Moretti de Souza, J. L.; Kreutz Rosa, S. L.; Piekarski, K. R.; Yoiti Tsukahara, R. Influence of the AquaCrop soil module on the estimation of soybean and maize crop yield in the State of Parana, Brazil. Agron. Colomb. 2020, 38, 234-241.

ABNT

MORETTI DE SOUZA, J. L.; KREUTZ ROSA, S. L.; PIEKARSKI, K. R.; YOITI TSUKAHARA, R. Influence of the AquaCrop soil module on the estimation of soybean and maize crop yield in the State of Parana, Brazil. Agronomía Colombiana, [S. l.], v. 38, n. 2, p. 234–241, 2020. DOI: 10.15446/agron.colomb.v38n2.78659. Disponível em: https://revistas.unal.edu.co/index.php/agrocol/article/view/78659. Acesso em: 12 nov. 2025.

Chicago

Moretti de Souza, Jorge Luiz, Stefanie Lais Kreutz Rosa, Karla Regina Piekarski, and Rodrigo Yoiti Tsukahara. 2020. “Influence of the AquaCrop soil module on the estimation of soybean and maize crop yield in the State of Parana, Brazil”. Agronomía Colombiana 38 (2):234-41. https://doi.org/10.15446/agron.colomb.v38n2.78659.

Harvard

Moretti de Souza, J. L., Kreutz Rosa, S. L., Piekarski, K. R. and Yoiti Tsukahara, R. (2020) “Influence of the AquaCrop soil module on the estimation of soybean and maize crop yield in the State of Parana, Brazil”, Agronomía Colombiana, 38(2), pp. 234–241. doi: 10.15446/agron.colomb.v38n2.78659.

IEEE

[1]
J. L. Moretti de Souza, S. L. Kreutz Rosa, K. R. Piekarski, and R. Yoiti Tsukahara, “Influence of the AquaCrop soil module on the estimation of soybean and maize crop yield in the State of Parana, Brazil”, Agron. Colomb., vol. 38, no. 2, pp. 234–241, May 2020.

MLA

Moretti de Souza, J. L., S. L. Kreutz Rosa, K. R. Piekarski, and R. Yoiti Tsukahara. “Influence of the AquaCrop soil module on the estimation of soybean and maize crop yield in the State of Parana, Brazil”. Agronomía Colombiana, vol. 38, no. 2, May 2020, pp. 234-41, doi:10.15446/agron.colomb.v38n2.78659.

Turabian

Moretti de Souza, Jorge Luiz, Stefanie Lais Kreutz Rosa, Karla Regina Piekarski, and Rodrigo Yoiti Tsukahara. “Influence of the AquaCrop soil module on the estimation of soybean and maize crop yield in the State of Parana, Brazil”. Agronomía Colombiana 38, no. 2 (May 1, 2020): 234–241. Accessed November 12, 2025. https://revistas.unal.edu.co/index.php/agrocol/article/view/78659.

Vancouver

1.
Moretti de Souza JL, Kreutz Rosa SL, Piekarski KR, Yoiti Tsukahara R. Influence of the AquaCrop soil module on the estimation of soybean and maize crop yield in the State of Parana, Brazil. Agron. Colomb. [Internet]. 2020 May 1 [cited 2025 Nov. 12];38(2):234-41. Available from: https://revistas.unal.edu.co/index.php/agrocol/article/view/78659

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1. Rongkun Zhao, Yujing Ma, Shangrong Wu. (2024). A Review of the Research Status and Prospects of Regional Crop Yield Simulations. Agronomy, 14(7), p.1397. https://doi.org/10.3390/agronomy14071397.

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