Sensitivity analysis of the AquaCrop parameters for rainfed corn in the South of Brazil

Martini, Luiz Carlos Pittol

doi:10.1590/s0100-204x2018000800008

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…A.; Silva, G. Í. N.; Silva, T. G. F. Quando comparado a outros modelos de simulação, o AquaCrop necessita de um número reduzido de variáveis e parâmetros de entrada, os quais são facilmente computados. Apesar das características de simplicidade do modelo, quando bem calibrado e validado, o mesmo oferece um grande equilíbrio na precisão da produção (Martini, 2018;Steduto et al, 2007;Vanuytrecht et al, 2014;Xu et al, 2019).…”

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Importância dos modelos de simulação de culturas diante os impactos das alterações climáticas sobre a produção agrícola - Revisão

Pinheiro¹,

Souza²,

Jardim³

et al. 2021

Rev. Bras. Geog. Fis.

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O efeito climático é o principal responsável pelas oscilações no rendimento produtivo. Logo, é esperado que as mudanças do clima promovam alterações na agricultura, comprometam a sustentabilidade e a segurança alimentar, especialmente, em áreas semiáridas. O entendimento da amplitude desses fatores e suas consequências no rendimento agrícola mediante os diferentes cenários climáticos, regionais e tecnológicos são fundamentais nas tomadas de decisões. Para as análises desses diversos cenários, os modelos de simulação de culturas se caracterizam como ferramentas funcionais e com eficientes performances na estimativa dos níveis de produtividades, desde que devidamente calibrados e validados com dados consistentes e representativos. Dentre os modelos de simulação podemos destacar: AquaCrop - FAO, ZAE - FAO, CROPGRO e Apsim como aqueles de maiores aplicabilidades nas culturas agrícolas, sendo utilizados de maneira recorrente em diversos estudos para fins do conhecimento das lacunas de produtividade agrícola, ou “Yield Gap”. Esta revisão analisou os impactos das alterações climáticas na agricultura e o levantamento de informações dos principais modelos de simulação de culturas. Mediante síntese das informações levantadas, pode-se evidenciar o eminente impacto das alterações climáticas sobre o cenário agrícola futuro, proporcionando maior vulnerabilidade agrícola. Logo, destaca-se a importância do uso de modelos de simulação de culturas para conhecimento das lacunas de produtividade e potencial produtivo. Contudo, é evidente a necessidade de pesquisas mais detalhadas sobre a aplicabilidade dos modelos em cenários agrícolas diversos e situações climáticas amplas.Palavras-chave: modelos de simulação; sazonalidade climática; práticas resilientes; “yield gap”. Importance of crop simulation models in view of the impacts of climate change on agricultural production – Review A B S T R A C TThe climatic effect is the main responsible for the fluctuations in the productive yield. Therefore, it is expected that climate change will promote changes in agriculture, compromise sustainability and food security, especially in semi-arid areas. Understanding the breadth of these factors and their consequences on agricultural income through different climatic, regional and technological scenarios are fundamental in decision-making. For the analysis of these different scenarios, the crop simulation models are characterized as functional tools and with efficient performances in the estimation of the productivity levels, as long as they are properly calibrated and validated with consistent and representative data. Among the simulation models we can highlight: AquaCrop - FAO, ZAE - FAO, CROPGRO and Apsim as those with the greatest applicability in agricultural crops, being used in a recurring manner in several studies for the purpose of understanding agricultural productivity gaps, or “Yield Gap”. This review analyzed the impacts of climate change on agriculture and the gathering of information on the main crop simulation models. By synthesizing the information collected, it is possible to highlight the imminent impact of climate change on the future agricultural scenario, providing greater agricultural vulnerability. Therefore, the importance of using crop simulation models to understand the gaps in productivity and productive potential is highlighted. However, there is a clear need for more detailed research on the applicability of models in diverse agricultural scenarios and broad climatic situations.Keywords: simulation models; climatic seasonality; resilient practices; yield gap.

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Importância dos modelos de simulação de culturas diante os impactos das alterações climáticas sobre a produção agrícola - Revisão

Pinheiro¹,

Souza²,

Jardim³

et al. 2021

Rev. Bras. Geog. Fis.

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“…From these results, it can be seen that both measured data and AquaCrop simulation show no significant difference between the two sowing rates. For rainfed corn, Martini [35] showed that AquaCrop is less sensitive to crop management factors such as planting density. Figure 13.…”

Section: Effect Of Sowing Ratementioning

confidence: 99%

AquaCrop Calibration and Validation for Faba Bean (Vicia faba L.) under Different Agronomic Managements

Zeleke

2019

Agronomy

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Faba bean (Vicia faba L.) is an important pulse crop known for its nitrogen-fixing characteristics and as a disease-break crop in crop rotations. Sowing time, scheduling of supplemental irrigation, and sowing rate are some of the agronomic managements which affect faba bean growth and yield. The effect of these on faba bean yield can be evaluated using calibrated models. The Food and Agriculture Organization (FAO) AquaCrop model was calibrated and tested using two-year experimental data of different watering regimes, sowing dates, and sowing rates in a semiarid environment of South-Eastern Australia. AquaCrop adequately simulated the green canopy cover (CC), biomass development, grain yield, and soil water dynamics under different agronomic management conditions. AquaCrop simulated faba bean yield with 3% deviation, root mean square error (RMSE) of 0.49 t ha−1, normalised root mean square error (NRMSE) of 12.4%, index of agreement (d) of 0.95, and R2 of 0.86. The CC was simulated with RMSE of 14.1%, R2 of 0.85, and d of 0.90. The above-ground dry matter was predicted with RMSE of 2.6 t ha−1, R2 of 0.95, and d of 0.93. Except for end-of-season values, the total soil water was also adequately simulated at RMSE of 21 mm, R2 of 0.89, and d of 0.87. The response of faba bean to supplemental irrigation, sowing time, and sowing rate was adequately simulated by the calibrated model. AquaCrop is a valuable decision support tool for predicting faba bean growth, yield, and soil water dynamics under different agronomic managements.

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“…They estimated the yields in these seasons based on the irrigation volume, where they showed that irrigating maize with 72.15 mm of water in the rainy season could increase the yield by 0.24 t/ha and irrigating with 427.03 mm in the dry season could increase the yield by 1.22 t/ha [13]. Martini (2018) simulated rainfed maize yields using climate data from 1987 to 2016 in southern Brazil and analyzed the sensitivity of parameters such as the soil water stress level, maximum effective rooting depth, root zone crop coefficient, groundwater recharge, and planting density, which are required by the AquaCrop model. The results show that the crop cycle duration, planting density, and field practices had minimal effects, whereas the root zone crop coefficient, WUE, soil water storage, and groundwater recharge were the parameters with the greatest effects on rainfed maize yields [14].…”

Section: Introductionmentioning

confidence: 99%

“…Martini (2018) simulated rainfed maize yields using climate data from 1987 to 2016 in southern Brazil and analyzed the sensitivity of parameters such as the soil water stress level, maximum effective rooting depth, root zone crop coefficient, groundwater recharge, and planting density, which are required by the AquaCrop model. The results show that the crop cycle duration, planting density, and field practices had minimal effects, whereas the root zone crop coefficient, WUE, soil water storage, and groundwater recharge were the parameters with the greatest effects on rainfed maize yields [14]. In addition, in order to improve the effectiveness of the model, ACOSP is an open-source Python implementation of AquaCrop developed by Kelly and Foster, which can be implemented for integration with other Python modules [15].…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Maize Irrigation Strategy and Yield Prediction under Future Climate Scenarios in the Yellow River Delta

Shan

Tan

et al. 2023

Agronomy

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The contradiction between water demand and water supply in the Yellow River Delta restricts the corn yield in the region. It is of great significance to formulate reasonable irrigation strategies to alleviate regional water use and improve corn yield. Based on typical hydrological years (wet year, normal year, and dry year), this study used the coupling model of AquaCrop, the multi-objective genetic algorithm (NSGA-III), and TOPSIS-Entropy established using the Python language to solve the problem, with the objectives of achieving the minimum irrigation water (IW), maximum yield (Y), maximum irrigation water production rate (IWP), and maximum water use efficiency (WUE). TOPSIS-Entropy was then used to make decisions on the Pareto fronts, seeking the best irrigation decision under the multiple objectives. The results show the following: (1) The AquaCrop-OSPy model accurately simulated the maize growth process in the experimental area. The R2 values for canopy coverage (CC) in 2019, 2020, and 2021 were 0.87, 0.90, and 0.92, respectively, and the R2 values for the aboveground biomass (BIO) were 0.97, 0.96, and 0.96. (2) Compared with other irrigation treatments, the rainfall in the test area can meet the water demand of the maize growth period in wet years, and net irrigation can significantly reduce IW and increase Y, IWP, and WUE in normal and dry years. (3) Using LARS-WG (a widely employed stochastic weather generator in agricultural climate impact assessment) to generate future climate scenarios externally resulted in a higher CO2 concentration with increased production and slightly reduced IW demand. (4) Optimizing irrigation strategies is important for allowing decision makers to promote the sustainable utilization of water resources in the study region and increase maize crop yields.

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Sensitivity analysis of the AquaCrop parameters for rainfed corn in the South of Brazil

Cited by 7 publications

References 19 publications

Importância dos modelos de simulação de culturas diante os impactos das alterações climáticas sobre a produção agrícola - Revisão

Importância dos modelos de simulação de culturas diante os impactos das alterações climáticas sobre a produção agrícola - Revisão

AquaCrop Calibration and Validation for Faba Bean (Vicia faba L.) under Different Agronomic Managements

Optimizing the Maize Irrigation Strategy and Yield Prediction under Future Climate Scenarios in the Yellow River Delta

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