ISSN: 2316-4093 Acta Iguazu, Cascavel, V.9, N.2, P. 45-58, 2020 Si
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ISSN: 2316-4093 Simulation and performance yield of soybean and maize crops by AquaCrop, in Campos Gerais, Paraná State Jorge Luiz Moretti de Souza1*, Karla Regina Piekarski2, Stefanie Lais Kreutz Rosa1, Rodrigo Yoiti Tsukahara3, Mariana Vasconcelos Barroca4 1Programa de Pós-Graduação em Ciência do Solo, Universidade Federal do Paraná, Setor de Ciências Agrárias, Departamento de Solos e Engenharia Agrícola. Rua dos Funcionários, 1540 - Cabral, Curitiba, PR, Brasil. 2Emater Empresa Paranaense de Assistência Técnica Extensão Rural. Contenda, PR, Brasil. 3Fundação ABC Pesquisa e Desenvolvimento Agropecuário, Castro, PR, Brasil. 4Ben-Gurion University of the Negev, Israel. *Autor correspondente: [email protected] Artigo enviado em 04/09/2019, aceito em 01/05/2020 Abstract: The computational models that simulate yield of agricultural crops are important to planning activities. The objective of this study was to verify the performance of AquaCrop model to simulate soybean and maize yield in Campos Gerais region, in different soil types. The AquaCrop was used to estimate yield, requiring climate, soil, crop and soil management input data. In the analysis were used data from 21 and 32 experiments with maize and soybeans, respectively, carried out in the ABC Foundation, from years harvest between 2006 and 2014. For soybean crop, the highest absolute and relative errors of productivity simulations occurred in less productive crops, due to the lack of rain during sowing, water deficit in the harvest or high temperatures in the first weeks after the plants emergence. The highest absolute and relative errors verified in the simulations with maize crop experiments did not allow defined pattern identification. The AquaCrop achieved “very good” and “excellent” performances in the simulations of soybean and maize yield it the analyzed locations. The soil type affected the results from the analyzes of the two crops, and the Latossolos provided better performance and higher correlation compared to other soil types. Keywords: software, mathematical model, estimative, agricultural crops. Simulação e desempenho da produtividade das culturas soja e milho no AquaCrop, nos Campos Gerais, Estado do Paraná Resumo: Modelos computacionais que simulam a produtividade de culturas agrícolas são cada vez mais necessários às atividades de planejamento. Teve-se por objetivo no presente trabalho verificar o desempenho do modelo AquaCrop para simular a produtividade das culturas soja e milho na região dos Campos Gerais, em diferentes tipos de solo. O AquaCrop foi utilizado para estimar as produtividades, sendo necessários dados de clima, solo, cultura e manejo do solo como entrada. Nas análises foram utilizados dados de 21 e 32 experimentos com soja e milho, respectivamente, realizados na Fundação ABC, dos anos safra entre 2006 e 2014. Para cultura da soja, os maiores erros absoluto e relativo das simulações de produtividade ocorreram nas safras menos produtivas, tendo como motivo a falta de chuvas na semeadura, déficit hídrico na safra ou altas temperaturas nas primeiras semanas após a emergência das plantas. Os maiores erros absoluto e relativo verificados nas simulações com os experimentos da cultura do milho não possibilitaram a identificação de um padrão definido. O AquaCrop _____________________________________________________________ Acta Iguazu, Cascavel, v.9, n.2, p. 45-58, 2020 45 Souza et al. obteve desempenhos entre “muito bom” e “ótimo” nas simulações das produtividades das culturas soja e milho, nos locais analisados. O tipo de solo interferiu nos resultados das análises das duas culturas, sendo que os Latossolos proporcionaram melhor desempenho e maiores correlações comparado aos demais solos estudados. Palavras-chave: software, modelo matemático, estimativa, cultivos agrícolas. Introduction Organization (FAO) developed AquaCrop. Computational models that simulate Steduto et al. (2012) and Raes et al. growth, development, and yield of (2018a) consider AquaCrop an agricultural crops are more important evolution, differing from main and are increasingly being used in agricultural yield simulation models for countries that have technified its simplicity. The AquaCrop was agriculture. They are important systems developed from the Doorenbos & in sustainable management Kassam equation in two main aspects: development, contributing for the separation of evapotranspiration (ET) in accomplishment of studies and yield soil evaporation (E) and the crop increase of diverse crops. transpiration (Tr); and yield (Y) Simulation models of agricultural estimation from biomass production (B) production generally use water relations and harvest index (HI). in the estimates, since there is a direct The main advantage of models relationship between crop yield (Y) and computer simulation is related to the the ratio between crop transpiration quickness productivity results obtained, (Trc) and potential evaporation (Eo) when compared to field experiments. (Steduto et al., 2012; Raes et al., 2018a). The use of crop simulation models Equations relating grain yield to results in reductions in the planting meteorological variables are being crops costs, obtaining a greater amount developed and tested, trying to settle of information regarding plants functional relations of yield prediction responses to the environmental for models or simulation systems conditions evaluated and the creation of (Scheraiber, 2012; Souza et al., 2013; unknown scenarios (Corrêa et al., 2011). Islam et al., 2016). In the literature there are several In search for higher precision and examples of crop simulations with robustness, the models became more AquaCrop in the world, where complex, being one of the largest satisfactory results have been obtained: difficulties to the use of agricultural wheat production submitted to water simulation models, due to the deficit in Northern China (Iqbal et al., complexity, knowledge degree and 2014); potato cultivation under different understanding of functionality. There is irrigation conditions in Spain (Montoya also an incompatibility between models, et al., 2016); amaranth, chard, and since they are very heterogeneous and spider-plant production in different elaborated without any interaction. water regimes in Gauteng Province, Another serious problem is to find data South Africa (Nyathi et al., 2018). The that accurately describes the variability applicability of the AquaCrop model in in agricultural crop systems (Janssen et several crops and countries has shown al., 2017). In order to minimize some of its robustness and comprehensiveness, the problems mentioned, researchers but few studies are aimed at its associated to the Food and Agriculture validation and use in Brazil. The Campos _____________________________________________________________ Acta Iguazu, Cascavel, v. 9, n.2, p. 45-58, 2020 46 Souza et al. Gerais region is a reference point in tillage with homogeneous vegetable grain research and production in Paraná mulching. The crop rotation system used State. Data collected at Experimental is alternated between soybean and Stations in the region have already maize in the summer, and wheat and supported numerous studies aiming to black oats in winter. Pest and disease identify the performance of water- control is performed according to usual culture functions or testing models to methods pattern in the region, and estimate crop yields (Araujo et al., 2011, fertilization is performed by supplying Rosa et al., 2018) all the nutrients necessary for the full As a model that simulates crop development. agricultural production, AquaCrop To verify the performance of allows the assumption of ideal scenarios AquaCrop under agricultural production for plant growth and development, conditions, 21 and 32 yield simulations being used in decision support and crop of maize and soybean crops (kg ha−1), management, being able to identify respectively, were carried out for better sowing times, cultivars choices, comparison with real productivities evaluation of risks and investment, observed in the field (kg ha−1), harvests aiming at achieving highest productivity from 2006/07 to 2013/14. To perform and solving problems mainly regarding the analyzes, it was necessary to insert drought periods (Oliveira, 2018). the following input data into the model Considering the AquaCrop (Raes et al., 2018b): contributions to crop planning and a) Climate: The climate data used research, as well as the limited evidence came from the agrometeorological of its use in Brazil, it is believed that the stations in each Experimental Field. The verification of its performance in minimum and maximum air simulation of agricultural yield may be temperature (°C) and rainfall (mm interesting for the national scenario. In day−1) data were obtained from the this sense, the aim of this study was to climatic databases provided by ABC verify the performance of AquaCrop to Foundation Agrometeorology Sector. simulate soybean and maize crops yields The reference evapotranspiration (mm in Campos Gerais region, in different soil day−1) was estimated using the Penman- types. Monteith method (ASCE-EWRI, 2005). The mean yearly atmospheric CO2 Material and methods concentrations (ppm) are provided by AquaCrop program, measured at the The present study was carried out in Mauna Loa observatory in Hawaii (Raes Campos Gerais-PR, a reference region of et al., 2018b); grains production in Brazil, using maize b) Crop: the data required was the and soybean historical crop data planting date, duration of