Abstract:
The objective of this work was to calibrate and validate the AquaCrop model for the wheat (Triticum aestivum) crop in the Campos Gerais region, in Southern Brazil. Five cultivars were evaluated in the harvests from 2007 to 2017. The input data for AquaCrop - related to climate, crop, soil, and soil management -, collected in the field, were obtained from the database of Fundação ABC and from the literature. From 35 to 43% of total harvests were selected for calibration, and the remaining, for validation. Calibration was performed for the parameters most sensitive to crop potential yield penalty. The simulated yields were compared with those observed in the field through simple linear regression analysis, root mean square error (RMSE), Pearson’s correlation coefficient (r), the index of agreement (d), and the performance index (c). Calibration showed good results (RMSE ≤ 609.78 kg ha-1; r ≥ 0.72; d ≥ 0.80) for all assessed cultivars and locations, but validation did not have the same performance (c ≤ 0.46). The attempted adjustment, considering the range of calibrated parameters in the harvests, indicated “very good” and “excellent” performances (Supera and Quartzo, respectively) for the cultivars in Castro and “tolerable” to “excellent” in Ponta Grossa.
Index terms:
Triticum aestivum; crop yield; mathematical modeling
Resumo:
O objetivo deste trabalho foi calibrar e validar o modelo AquaCrop para a cultura de trigo (Triticum aestivum) na região dos Campos Gerais, no Sul do Brasil. Foram avaliadas cinco cultivares nas safras de 2007 a 2017. Os dados de entrada no AquaCrop - referentes a clima, cultura, solo e manejo do solo -, coletados em campo, foram obtidos do banco de dados da Fundação ABC e da literatura. De 35 a 43% do total das safras foram selecionadas para calibração, e as demais, para validação. A calibração foi realizada para os parâmetros mais sensíveis à penalização da produtividade potencial da cultura. As produtividades simuladas foram comparadas às observadas em campo por meio de análises de regressão linear simples, raiz quadrada do erro médio (RMSE), coeficiente de correlação de Pearson (r), índice de concordância (d) e índice de desempenho (c). A calibração apresentou bons resultados (RMSE ≤ 609,78 kg ha-1; r ≥ 0,72; d ≥ 0,80) para todas as cultivares e locais avaliados, mas a validação não teve o mesmo desempenho (c ≤ 0,46). O ajuste por tentativa, tendo-se considerado a faixa de parâmetros calibrados nas colheitas, indicou desempenhos “muito bom” e “excelente” (Supera e Quartzo, respectivamente) para as cultivares em Castro, e “ruim” a “excelente” em Ponta Grossa.
Termos para indexação:
Triticum aestivum; produtividade da cultura; modelagem matemática
Introduction
Wheat (Triticum aestivum L.) is widely cultivated worldwide (FAO, 2018aFAO. Food and Agriculture Organization of the United Nations. Food outlook: biannual report on global food markets. Rome. 2018a. Available at: <Available at: http://www.fao.org/3/CA2320EN/ca2320en.pdf
>. Accessed on: Aug. 5 2018.
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). In Brazil, the use of wheat cultivars with a high potential productivity, combined with the country’s soil and climatic conditions, promotes high yields and crop expansion (Silva et al., 2014SILVA, A.A.V. da; SILVA, I.A.F.; TEIXEIRA FILHO, M.C.M.; BUZETTI, S.; TEIXEIRA, M.C.M. Estimativa da produtividade de trigo em função da adubação nitrogenada utilizando modelagem neuro fuzzy. Revista Brasileira de Engenharia Agrícola e Ambiental, v.18, p.180-187, 2014. DOI: https://doi.org/10.1590/S1415-43662014000200008.
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). In the state of Paraná, the winter crop is the most important, reaching about 1.081 million hectares of planted area in the 2015/2016 harvest, with a production of 3.3 million tons (Oliveira Neto & Santos, 2017OLIVEIRA NETO, A.A. de; SANTOS, C.M.R. A cultura do trigo. Brasília: Conab, 2017. 218p. Available at: <Available at: https://www.conab.gov.br/uploads/arquivos/17_04_25_11_40_00_a_cultura_do_trigo_versao_digital_final.pdf
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). The Campos Gerais region stands out in the state due to its agricultural potential, which is above the national average (Shimandeiro et al., 2008SHIMANDEIRO, A.; KANTELHARDT, J.; WEIRICH NETO, P.H. Characterization of major crop management in the buffer zone of Vila Velha State Park, state of Paraná, Brazil. Acta Scientiarum. Agronomy, v.30, p.225-230, 2008. DOI: https://doi.org/10.4025/actasciagron.v30i2.1732.
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).
Campos Gerais is located in the Southeastern and Southern regions of Brazil. It presents a territory band of 11,761.41 km2 with a northwest convexity (Melo et al., 2014MELO, M.S. de; MORO, R.S.; GUIMARÃES, G.B. (Ed.). Patrimônio natural dos Campos Gerais do Paraná. Ponta Grossa: Ed. da UEPG, 2014. 227p. Available at: <Available at: http://www.uepg.br/editora
>. Accessed on: Aug. 7 2019.
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). The predominant climate is Cfa and Cfb according to the climate map for the country based on Köppen’s classification (Alvares et al., 2013ALVARES, C.A.; STAPE, J.L.; SENTELHAS, P.C.; GONÇALVES, J.L. de M.; SPAROVEK, G. Köppen’s climate classification map for Brazil. Meteorologische Zeitschrift, v.22, p.711-728, 2013. DOI: https://doi.org/10.1127/0941-2948/2013/0507.
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). The region is characterized by agriculture focused mainly on grain production in the no-tillage system (Melo et al., 2014MELO, M.S. de; MORO, R.S.; GUIMARÃES, G.B. (Ed.). Patrimônio natural dos Campos Gerais do Paraná. Ponta Grossa: Ed. da UEPG, 2014. 227p. Available at: <Available at: http://www.uepg.br/editora
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).
Understanding the soil-plant-atmosphere system through modeling has been increasingly important for researchers (Jin et al., 2014JIN, X.-L.; FENG, H.-K.; ZHU, X.-K.; LI, Z.-H.; SONG, S.-N.; SONG, X.-Y.; YANG, G.-J.; XU, X.-G.; GUO, W.-S. Assessment of the AquaCrop model for use in simulation of irrigated winter wheat canopy cover, biomass, and grain yield in the North China Plain. PLoS ONE, v.9, e86938, 2014. DOI: https://doi.org/10.1371/journal.pone.0086938.
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). However, the highest difficulty in carrying out simulations consists in the collection or availability of input data, which are generally difficult and costly to obtain. Searching for solutions to these limitations, the Food and Agriculture Organization (FAO) developed the AquaCrop model (Raes et al., 2018bRAES, D.; STEDUTO, P.; HSIAO, T.C.; FERERES, E. Chapter 1: FAO crop-water productivity model to simulate yield response to water: AquaCrop: version 6.0-6.1: reference manual. Rome: FAO, 2018b. 19p.).
AquaCrop is a simulation model that describes the interaction between soil and plants, presenting good results, with a high accuracy. It requires few input parameters, which are explicit and intuitive (Foster et al., 2017FOSTER, T.; BROZIVIĆ, N.; BUTLER, A.P.; NEALE, C.M.U.; RAES, D.; STEDUTO, P.; FERERES, E.; HSIAO, T.C. AquaCrop-OS: an open source version of FAO’s crop water productivity model. Agricultural Water Management, v.181, p.18-22, 2017. DOI: https://doi.org/10.1016/j.agwat.2016.11.015.
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), obtained easily and at a low cost. In addition, the model is able to simulate accurately biomass production and crop yield under different water content and fertility conditions (Jin et al., 2014JIN, X.-L.; FENG, H.-K.; ZHU, X.-K.; LI, Z.-H.; SONG, S.-N.; SONG, X.-Y.; YANG, G.-J.; XU, X.-G.; GUO, W.-S. Assessment of the AquaCrop model for use in simulation of irrigated winter wheat canopy cover, biomass, and grain yield in the North China Plain. PLoS ONE, v.9, e86938, 2014. DOI: https://doi.org/10.1371/journal.pone.0086938.
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).
To increase the reliability and reduce the uncertainties of a model, the used parameters must be subjected to a calibration process (He et al., 2017HE, D.; WANG, E.; WANG, J.; ROBERTSON, M.J. Data requirement for effective calibration of process-based crop models. Agricultural and Forest Meteorology, v.234-235, p.136-148, 2017. DOI: https://doi.org/10.1016/j.agrformet.2016.12.015.
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), which consists in adjusting the input parameter value so that the simulated results in the software are similar to those observed in the field. The next step is the validation process, considering the quality of the output data (Xiangxiang et al., 2013XIANGXIANG, W.; QUANJIU, W.; JUN, F.; QIUPING, F. Evaluation of the AquaCrop model for simulating the impact of water deficits and different irrigation regimes on the biomass and yield of winter wheat grown on China’s Loess Plateau. Agricultural Water Management, v.129, p.95-104, 2013. DOI: https://doi.org/10.1016/j.agwat.2013.07.010.
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), which indicates if the calibration was adequate under the studied conditions.
The AquaCrop model has been calibrated and validated for several crops and locations, and its accuracy has been widely confirmed (Darko et al., 2016DARKO, R.O.; SHOUQI, Y.; HAOFANG, Y.; JUNPING, L.; ABBEY, A. Calibration and validation of AquaCrop for deficit and full irrigation of tomato. International Journal of Agricultural and Biological Engineering, v.9, p.104-110, 2016. DOI: https://doi.org/10.3965/j.ijabe.20160903.1812.
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; Montoya et al., 2016MONTOYA, F.; CAMARGO, D.; ORTEGA, J.F.; CÓRCOLES, J.I.; DOMÍNGUEZ, A. Evaluation of Aquacrop model for a potato crop under different irrigation conditions. Agricultural Water Management, v.164, p.267-280, 2016. DOI: https://doi.org/10.1016/j.agwat.2015.10.019.
https://doi.org/10.1016/j.agwat.2015.10....
; Oiganji et al., 2016OIGANJI, E.; IGBADUN, H.E.; MUDIARE, O.J.; OYEBODE, M.A. Calibrating and validating AquaCrop model for maize crop in Northern zone of Nigeria. Agricultural Engineering International, v.
18
, p.
1-13, 2016.; Pareek et al., 2017PAREEK, N.; ROY, S.; SAHA, S.; NAIN, A. Calibration & validation of Aquacrop model for wheat crop in Tarai region of Uttarakhand. Journal of Pharmacognosy and Phytochemistry, v.6, p.1442-1445, 2017.; Silva et al., 2018SILVA, V. de P.R. da; SILVA, R.A. e; MACIEL, G.F.; BRAGA, C.C.; SILVA JÚNIOR, J.L.C. da; SOUZA, E.P. de; ALMEIDA, R.S.R.; SILVA, M.T.; HOLANDA, R.M. de. Calibration and validation of the AquaCrop model for the soybean crop grown under different levels of irrigation in the Motopiba region, Brazil. Ciência Rural, v.48, e20161118, 2018. DOI: https://doi.org/10.1590/0103-8478cr20161118.
https://doi.org/10.1590/0103-8478cr20161...
). However, there are no researches that prove its efficiency in simulating wheat yield under Brazilian conditions. Despite this, due to its high precision, simplicity and robustness (Raes et al., 2018aRAES, D.; STEDUTO, P.; HSIAO, T.C.; FERERES, E. AquaCrop: version 6.0-6.1: Reference manual: annexes. Rome: FAO, 2018a. Available at: <Available at: http://www.fao.org/3/a-br244e.pdf
>. Accessed on: Feb. 21 2018.
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), it is believed that AquaCrop can accurately simulate wheat yield in the Campos Gerais region.
The objective of this work was to calibrate and validate the AquaCrop model, developed by FAO, for the wheat crop in the Campos Gerais region, in Southern Brazil.
Materials and Methods
The study was carried out using climate and wheat crop data obtained in the municipalities of Castro and Ponta Grossa, located in the Campos Gerais region, in the state of Paraná, in the subtropical zone of Southern Brazil, from 2007 to 2017. The analyzed cultivars were: Quartzo, Supera, TBIO Sinuelo, TBIO Tibagi, and TBIO Toruk, cultivated under field conditions, with fertilization and phytosanitary control performed by Fundação ABC (Castro, PR, Brazil), as required for wheat crops.
According to Köppen’s climate classification for Brazil (Alvares et al., 2013ALVARES, C.A.; STAPE, J.L.; SENTELHAS, P.C.; GONÇALVES, J.L. de M.; SPAROVEK, G. Köppen’s climate classification map for Brazil. Meteorologische Zeitschrift, v.22, p.711-728, 2013. DOI: https://doi.org/10.1127/0941-2948/2013/0507.
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), Castro is classified as Cfa, a humid subtropical, oceanic climate without dry season and with a hot summer; and Ponta Grossa is classified as Cfb, a humid subtropical, oceanic climate without dry season and with a temperate summer. The pluvial precipitation (mm) and medium daily air temperature (°C) of the analyzed locations are shown in Figure 1.
Pluvial precipitation and medium daily air temperature of the experimental locations (municipalities), in the state of Paraná, Southern Brazil: A, Castro; and B, Ponta Grossa.
The used model was AquaCrop, version 6.0 (FAO, 2018bFAO. Food and Agriculture Organization of the United Nations. Land & Water: AquaCrop. version 6.0. 2018b. Available at: <Available at: http://www.fao.org/land-water/databases-and-software/Aquacrop/en/
>. Accessed on: Mar. 3 2018.
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). The climate, crop, and management data were obtained from the database of Fundação ABC (Castro, PR, Brazil), which follows the recommendations for sensors of American Society of Agricultural and Biological Engineers (St. Joseph, MI, USA).
The climatic data inserted in AquaCrop, obtained from the agrometeorological stations installed in the respective study locations, were: pluvial precipitation (mm per day); maximum, minimum, and medium daily air temperature (°C); incident solar radiation (MJ m-2 per day); relative humidity (%); and wind speed (m s-1).
The soil fertility level was considered as non-limiting to crop development, and canopy cover was made up of 75% organic plant materials. The management adopted in the experimental areas did not affect surface runoff, and there was no presence of weeds.
Wheat phenological stages were considered achieved when the cultivars reached the following Zadoks decimal codes (Zadoks et al., 1974ZADOKS, J.C.; CHANG, T.T.; KONZAK, C.F. A decimal code for the growth stages of cereals. Weed Research, v.14, p.415-421, 1974. DOI: https://doi.org/10.1111/j.1365-3180.1974.tb01084.x.
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): 09, emergence, with leaf just at coleoptile tip; 54, maximum coverage, with half of the inflorescences emerged; 60, flowering, beginning of anthesis; 60 to 68, flowering duration; 71, senescence, with kernel watery ripe; and 91, maturity, with kernel hard.
Soil data from Ponta Grossa were collected in the field and analyzed according to Teixeira et al. (2017)TEIXEIRA, P.C.; DONAGEMMA, G.K.; FONTANA, A.; TEIXEIRA, W.G. (Ed.). Manual de métodos de análise de solo. 3.ed. rev. e ampl. Brasília: Embrapa, 2017. 573p. (Table 1), while data from Castro were obtained from an experiment carried out in the same area by Piekarski et al. (2017)PIEKARSKI, K.R.; SOUZA, J.L.M.; TSUKAHARA, R.Y.; ROSA, S.L.K.; OLIVEIRA, C.T. Estimativa da produtividade da cultura da soja considerando a influência dos atributos físico-hídricos do solo na região dos Campos Gerais. In: CONGRESSO ONLINE DE AGRONOMIA, 5., 2017, Road Town. Anais online. São Paulo: Instituto Pantex de Pesquisa, 2017.. The initial soil water content was considered equal to the total available soil water in the root zone, which consists in the difference between the water content at field capacity and permanent wilting point, selected in AquaCrop.
The calibration of AquaCrop was performed for the most sensitive parameters identified in Castro and Ponta Grossa, which were: maximum canopy cover (CCx, %), dependent on environment and/or management, calibrated in Castro and Ponta Grossa; canopy decline coefficient (CDC, percentage per day), conservative and calibrated only in Castro; crop coefficient when the canopy is complete but prior to senescence (KcTR,x, unitless), also conservative and calibrated both in Castro and Ponta Grossa; normalized water productivity (WP*) for reference evapotranspiration and CO2 (g m-2), conservative and calibrated in Castro and Ponta Grossa; reference harvest index (HIo, %), cultivar specific and also calibrated in Castro and Ponta Grossa; and minimum growing degrees required for full biomass production (°C per day), conservative and calibrated only in Ponta Grossa. Of the total harvests, 35 to 43% were randomly selected for calibration in the evaluated municipalities (Table 2). The parameters that did not receive calibration were defined according to the recommendation of Raes et al. (2018a)RAES, D.; STEDUTO, P.; HSIAO, T.C.; FERERES, E. AquaCrop: version 6.0-6.1: Reference manual: annexes. Rome: FAO, 2018a. Available at: <Available at: http://www.fao.org/3/a-br244e.pdf
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for wheat crops.
Experiments, harvests, early and later planting and harvesting dates, lower and higher grain yields, and finality (calibration or validation) of the wheat (Triticum aestivum) cultivars evaluated with the AquaCrop model, in the municipalities of Castro and Ponta Grossa, in the state of Paraná, Southern Brazil, from 2007 to 2017.
For each simulation, the initial canopy cover at 90% emergence was automatically determined by AquaCrop, based on the number of plants per hectare inserted in the software, which followed the protocols of Fundação ABC. The minimum and maximum effective rooting depths were 0.1 and 0.3 m, respectively. The canopy growth coefficient (percentage per day) was automatically adjusted in AquaCrop, established on the date of maximum canopy expansion, which was indirectly determined based on the phenological cycle. As the cycle considered in the present study was in days after planting, the value of 8.0% per day was adopted for the CDC.
After the calibration of the most sensitive AquaCrop parameters, validation was carried out with harvests that were not used in calibration (Table 2). However, the used soil and soil management data were the same for both processes, and the parameter values obtained in calibration were also used for the validation analyzes for each cultivar and location.
Yields (kg ha-1) were simulated by the calibration and validation processes in AquaCrop and compared with the real yields observed in the field (kg ha-1), using a simple linear regression analysis. The absolute and relative errors, root mean square error (RMSE) (Jacovides & Kontoyiannis, 1995JACOVIDES, C.P.; KONTOYIANNIS, H. Statistical procedures for the evaluation of evapotranspiration computing models. Agricultural Water Management, v.27, p.365-371, 1995. DOI: https://doi.org/10.1016/0378-3774(95)01152-9.
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), Pearson’s correlation coefficient (r), and index of agreement (d) (Willmott, 1982WILLMOTT, C.J. Some comments on the evaluation of model performance. Bulletin American Meteorological Society, v.63, p.1309-1313, 1982. DOI: https://doi.org/10.1175/1520-0477(1982)063<1309:SCOTEO>2.0.CO;2.
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) were also used to compare simulated to real data. For the validation process, performance was calculated with the c index, proposed by Camargo & Sentelhas (1997)CAMARGO, A.P. de; SENTELHAS, P.C. Avaliação do desempenho de diferentes métodos de estimativa da evapotranspiração potencial no Estado de São Paulo, Brasil. Revista Brasileira de Agrometeorologia, v.5, p.89-97, 1997., and was classified as: “excellent”, c > 0.85; “very good”, 0.75 < c ≤ 0.85; “good”, 0.65 < c ≤ 0.75; “medium”, 0.60 < c ≤ 0.65; “tolerable”, 0.50 < c ≤ 0.60; “bad”, 0.40 < c ≤ 0.50; and “terrible”, c ≤ 0.40.
Results and Discussion
The values obtained in the calibration process for the most sensitive AquaCrop parameters in Castro and Ponta Grossa are shown in Table 3. The regression analysis between real and simulated yields presented excellent results in calibration (Figure 2) according to Akoglu (2018)AKOGLU, H. User’s guide to correlation coefficients. Turkish Journal of Emergency Medicine, v.18, p.91-93, 2018. DOI: https://doi.org/10.1016/j.tjem.2018.08.001.
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, with Pearson’s correlation coefficient ranging from 0.72 to 0.92, the d index from 0.80 to 0.94, and RMSE from 150.40 to 609.78 kg ha-1 (Table 4). Kumar et al. (2014)KUMAR, P.; SARANGI, A.; SINGH, D.K.; PARIHAR, S.S. Evaluation of Aquacrop model in predicting wheat yield and water productivity under irrigated saline regimes. Irrigation and Drainage, v.63, p.474-487, 2014. DOI: https://doi.org/10.1002/ird.1841.
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found better adjustments for wheat with AquaCrop at different levels of soil salinity in Delhi (r = 0.99 and d = 0.99). Good adjustments were also reported by Toumi et al. (2016)TOUMI, J.; ER-RAKI, S.; EZZAHAR, J.; KHABBA, S.; JARLAN, L.; CHEHBOUNI, A. Performance assessment of AquaCrop model for estimating evapotranspiration, soil water content and grain yield of winter wheat in Tensift Al Haouz (Morocco): application to irrigation management. Agricultural Water Management, v.163, p.219-235, 2016. DOI: https://doi.org/10.1016/j.agwat.2015.09.007.
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in Morocco (r = 0.99 and RMSE = 30 kg ha-1), considering HIo = 46% and WP* = 16 g m-2.
Linear regression analysis obtained from the association of simulated vs. observed yield in the calibration process by the AquaCrop model for wheat (Triticum aestivum) cultivars evaluated in the municipalities of Castro (A) and Ponta Grossa (B) in the state of Paraná, Southern Brazil.
Absolute error (AE), relative error (RE), root mean square error (RMSE), Pearson’s correlation coefficient (r), and d and c indexes obtained from the association of simulated vs. observed yield in the calibration and validation processes by the AquaCrop model for wheat (Triticum aestivum) cultivars evaluated in the municipalities of Castro and Ponta Grossa, in the state of Paraná, Southern Brazil(1).
Both the absolute and relative errors obtained in the calibration of the wheat cultivars with AquaCrop, in all analyzed locations, were small (Table 4). The largest RMSE was observed in Ponta Grossa for the TBIO Toruk cultivar; however, this error was still considered small compared with the real yield of the assessed variable.
The HIo was 62% for cultivar Supera and 56% for Quartzo in Castro. In Ponta Grossa, the values of this index ranged from 55 to 58% for the different cultivars. According to Raes et al. (2018a)RAES, D.; STEDUTO, P.; HSIAO, T.C.; FERERES, E. AquaCrop: version 6.0-6.1: Reference manual: annexes. Rome: FAO, 2018a. Available at: <Available at: http://www.fao.org/3/a-br244e.pdf
>. Accessed on: Feb. 21 2018.
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, the recommended HIo for the wheat crop should range from 45 to 50%; however, in the present study, the obtained values were higher. It should be noted that HIo is a cultivar-specific parameter (Raes et al., 2018aRAES, D.; STEDUTO, P.; HSIAO, T.C.; FERERES, E. AquaCrop: version 6.0-6.1: Reference manual: annexes. Rome: FAO, 2018a. Available at: <Available at: http://www.fao.org/3/a-br244e.pdf
>. Accessed on: Feb. 21 2018.
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), and its value may vary under different cultivation conditions or water regimes. Bouazzama et al. (2017)BOUAZZAMA, B.; KARROU, M.; BOUTFIRASS, M.; BAHRI, A. Assessment of Aquacrop model in the simulation of durum wheat (Triticum aestivum L.) growth and yield under different water regimes. Revue Marocaine des Sciences Agronomiques et Vétérinaires, v.5, p.222-230, 2017. and Toumi et al. (2016)TOUMI, J.; ER-RAKI, S.; EZZAHAR, J.; KHABBA, S.; JARLAN, L.; CHEHBOUNI, A. Performance assessment of AquaCrop model for estimating evapotranspiration, soil water content and grain yield of winter wheat in Tensift Al Haouz (Morocco): application to irrigation management. Agricultural Water Management, v.163, p.219-235, 2016. DOI: https://doi.org/10.1016/j.agwat.2015.09.007.
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found HIo = 46% in Morocco, Andarzian et al. (2011)ANDARZIAN, B.; BANNAYAN, M.; STEDUTO, P.; MAZRAEH, H.; BARATI, M.E.; BARATI, M.A.; RAHNAMA, A. Validation and testing of the AquaCrop model under full and deficit irrigated wheat production in Iran. Agricultural Water Management, v.100, p.1-8, 2011. DOI: https://doi.org/10.1016/j.agwat.2011.08.023.
https://doi.org/10.1016/j.agwat.2011.08....
HIo = 40% in Iran, and Pareek et al. (2017)PAREEK, N.; ROY, S.; SAHA, S.; NAIN, A. Calibration & validation of Aquacrop model for wheat crop in Tarai region of Uttarakhand. Journal of Pharmacognosy and Phytochemistry, v.6, p.1442-1445, 2017. HIo = 34% in India. Trombetta et al. (2016)TROMBETTA, A.; IACOBELLIS, V.; TARANTINO, E.; GENTILE, F. Calibration of the AquaCrop model for winter wheat using MODIS LAI images. Agricultural Water Management, v.164, p.304-316, 2016. DOI: https://doi.org/10.1016/j.agwat.2015.10.013.
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calibrated HIo = 43% for winter wheat in Rocchetta Sant’Antonio and Sant’Agata di Puglia, both in Italy. Raes et al. (2018b)RAES, D.; STEDUTO, P.; HSIAO, T.C.; FERERES, E. Chapter 1: FAO crop-water productivity model to simulate yield response to water: AquaCrop: version 6.0-6.1: reference manual. Rome: FAO, 2018b. 19p. pointed out that this index increases gradually from flowering onwards until reaching its reference value at physiological maturity, and that a short grain-filling stage due to early canopy senescence may cause an inadequate photosynthesis and a reduction in HIo.
The values obtained for WP* ranged from 17 to 19 g m-2 in all locations, and the lowest value was higher than that observed by Trombetta et al. (2016)TROMBETTA, A.; IACOBELLIS, V.; TARANTINO, E.; GENTILE, F. Calibration of the AquaCrop model for winter wheat using MODIS LAI images. Agricultural Water Management, v.164, p.304-316, 2016. DOI: https://doi.org/10.1016/j.agwat.2015.10.013.
https://doi.org/10.1016/j.agwat.2015.10....
in Italy and by Zhang et al. (2013)ZHANG, W.; LIU, W.; XUE, Q.; CHEN, J.; HAN, X. Evaluation of the AquaCrop model for simulating yield response of winter wheat to water on the southern Loess Plateau of China. Water Science & Technology, v.68, p.821-828, 2013. DOI: https://doi.org/10.2166/wst.2013.305.
https://doi.org/10.2166/wst.2013.305...
in China (WP* = 15 g m-2). Moreover, the WP* values in the present study were in the range of 15 to 20 g m-2 recommended by FAO for C3 cycle crops (Raes et al., 2018bRAES, D.; STEDUTO, P.; HSIAO, T.C.; FERERES, E. Chapter 1: FAO crop-water productivity model to simulate yield response to water: AquaCrop: version 6.0-6.1: reference manual. Rome: FAO, 2018b. 19p.). Toumi et al. (2016)TOUMI, J.; ER-RAKI, S.; EZZAHAR, J.; KHABBA, S.; JARLAN, L.; CHEHBOUNI, A. Performance assessment of AquaCrop model for estimating evapotranspiration, soil water content and grain yield of winter wheat in Tensift Al Haouz (Morocco): application to irrigation management. Agricultural Water Management, v.163, p.219-235, 2016. DOI: https://doi.org/10.1016/j.agwat.2015.09.007.
https://doi.org/10.1016/j.agwat.2015.09....
and Bouazzama et al. (2017)BOUAZZAMA, B.; KARROU, M.; BOUTFIRASS, M.; BAHRI, A. Assessment of Aquacrop model in the simulation of durum wheat (Triticum aestivum L.) growth and yield under different water regimes. Revue Marocaine des Sciences Agronomiques et Vétérinaires, v.5, p.222-230, 2017. found WP* values of 16.0 and 15.3 g m-2, respectively, for winter wheat in Morocco.
The KcTR,x values ranged from 1.25 to 1.37 for all cultivars and locations. Other authors obtained lower KcTR,x values for the wheat crop, as Toumi et al. (2016)TOUMI, J.; ER-RAKI, S.; EZZAHAR, J.; KHABBA, S.; JARLAN, L.; CHEHBOUNI, A. Performance assessment of AquaCrop model for estimating evapotranspiration, soil water content and grain yield of winter wheat in Tensift Al Haouz (Morocco): application to irrigation management. Agricultural Water Management, v.163, p.219-235, 2016. DOI: https://doi.org/10.1016/j.agwat.2015.09.007.
https://doi.org/10.1016/j.agwat.2015.09....
(KcTR,x = 1.07) for winter wheat in the semiarid region of the Tensift basin, in central Morocco. In the literature, there are also reports of KcTR,x = 1.13 (Bouazzama et al., 2017BOUAZZAMA, B.; KARROU, M.; BOUTFIRASS, M.; BAHRI, A. Assessment of Aquacrop model in the simulation of durum wheat (Triticum aestivum L.) growth and yield under different water regimes. Revue Marocaine des Sciences Agronomiques et Vétérinaires, v.5, p.222-230, 2017.) and KcTR,x = 1.10 (Trombetta et al., 2016TROMBETTA, A.; IACOBELLIS, V.; TARANTINO, E.; GENTILE, F. Calibration of the AquaCrop model for winter wheat using MODIS LAI images. Agricultural Water Management, v.164, p.304-316, 2016. DOI: https://doi.org/10.1016/j.agwat.2015.10.013.
https://doi.org/10.1016/j.agwat.2015.10....
; Pareek et al., 2017PAREEK, N.; ROY, S.; SAHA, S.; NAIN, A. Calibration & validation of Aquacrop model for wheat crop in Tarai region of Uttarakhand. Journal of Pharmacognosy and Phytochemistry, v.6, p.1442-1445, 2017.).
The CCx ranged from 89 to 93% for all cultivars and locations, and the highest value was observed for the Supera cultivar in Castro. The obtained values are within the limits of 80 to 99% recommended by Raes et al. (2018a)RAES, D.; STEDUTO, P.; HSIAO, T.C.; FERERES, E. AquaCrop: version 6.0-6.1: Reference manual: annexes. Rome: FAO, 2018a. Available at: <Available at: http://www.fao.org/3/a-br244e.pdf
>. Accessed on: Feb. 21 2018.
http://www.fao.org/3/a-br244e.pdf...
. In the literature, the CCx values due to calibration were also high and variable for winter wheat: 98.7% in different water conditions in Morocco (Bouazzama et al., 2017BOUAZZAMA, B.; KARROU, M.; BOUTFIRASS, M.; BAHRI, A. Assessment of Aquacrop model in the simulation of durum wheat (Triticum aestivum L.) growth and yield under different water regimes. Revue Marocaine des Sciences Agronomiques et Vétérinaires, v.5, p.222-230, 2017.); 98% in China (Xiangxiang et al., 2013XIANGXIANG, W.; QUANJIU, W.; JUN, F.; QIUPING, F. Evaluation of the AquaCrop model for simulating the impact of water deficits and different irrigation regimes on the biomass and yield of winter wheat grown on China’s Loess Plateau. Agricultural Water Management, v.129, p.95-104, 2013. DOI: https://doi.org/10.1016/j.agwat.2013.07.010.
https://doi.org/10.1016/j.agwat.2013.07....
); 95% in Pantnagar, India (Pareek et al., 2017PAREEK, N.; ROY, S.; SAHA, S.; NAIN, A. Calibration & validation of Aquacrop model for wheat crop in Tarai region of Uttarakhand. Journal of Pharmacognosy and Phytochemistry, v.6, p.1442-1445, 2017.); 90 and 79% in Rocchetta Sant’Antonio and Sant’Agata di Puglia, respectively, in Italy (Trombetta et al., 2016TROMBETTA, A.; IACOBELLIS, V.; TARANTINO, E.; GENTILE, F. Calibration of the AquaCrop model for winter wheat using MODIS LAI images. Agricultural Water Management, v.164, p.304-316, 2016. DOI: https://doi.org/10.1016/j.agwat.2015.10.013.
https://doi.org/10.1016/j.agwat.2015.10....
); and 90% in China (Zhang et al., 2013ZHANG, W.; LIU, W.; XUE, Q.; CHEN, J.; HAN, X. Evaluation of the AquaCrop model for simulating yield response of winter wheat to water on the southern Loess Plateau of China. Water Science & Technology, v.68, p.821-828, 2013. DOI: https://doi.org/10.2166/wst.2013.305.
https://doi.org/10.2166/wst.2013.305...
).
The CDC, which was only evaluated in Castro, was 7.6 and 7.7% per day for the Supera and Quartzo cultivars, respectively. Xiangxiang et al. (2013)XIANGXIANG, W.; QUANJIU, W.; JUN, F.; QIUPING, F. Evaluation of the AquaCrop model for simulating the impact of water deficits and different irrigation regimes on the biomass and yield of winter wheat grown on China’s Loess Plateau. Agricultural Water Management, v.129, p.95-104, 2013. DOI: https://doi.org/10.1016/j.agwat.2013.07.010.
https://doi.org/10.1016/j.agwat.2013.07....
found 8.4% per day for this parameter. However, Kumar et al. (2014)KUMAR, P.; SARANGI, A.; SINGH, D.K.; PARIHAR, S.S. Evaluation of Aquacrop model in predicting wheat yield and water productivity under irrigated saline regimes. Irrigation and Drainage, v.63, p.474-487, 2014. DOI: https://doi.org/10.1002/ird.1841.
https://doi.org/10.1002/ird.1841...
observed CDC variations between 11.0 and 12.9% per day when evaluating the response of AquaCrop to parameter adjustments at different soil salinity levels in Delhi, India. Andarzian et al. (2011)ANDARZIAN, B.; BANNAYAN, M.; STEDUTO, P.; MAZRAEH, H.; BARATI, M.E.; BARATI, M.A.; RAHNAMA, A. Validation and testing of the AquaCrop model under full and deficit irrigated wheat production in Iran. Agricultural Water Management, v.100, p.1-8, 2011. DOI: https://doi.org/10.1016/j.agwat.2011.08.023.
https://doi.org/10.1016/j.agwat.2011.08....
obtained good results in the simulation for wheat with CDC adjusted to 6.2% per day in Iran, under conditions of maximum and minimum temperatures higher than those of the Campos Gerais region.
The validation analyzes for each cultivar and location were performed using the same parameter values obtained in calibration (Table 3), as well as protocol data on harvests from Fundação ABC that were not used in the calibration process.
In AquaCrop, the results for calibration were considered good (RMSE ≤ 609.78 kg ha-1; r ≥ 0.72; d ≥ 0.80) for all cultivars and locations; however, the performance of the validation process was “terrible” to “bad” (RMSE ≤ 2,680.50 kg ha-1; r ≤ 0.69; d ≤ 0.67; c ≤ 0.46 (Table 4). Figure 3 shows the best and worst associations between real and estimated yields, obtained in validation when considering the same fixed parameters used in calibration (Table 3).
Linear regression analysis obtained from the association between observed vs. simulated yield in the validation process by the AquaCrop model for wheat (Triticum aestivum) cultivars, considering fixed calibration parameters. A, better analysis (“bad”) for the Supera cultivar (r = 0.69; d = 0.67; c = 0.46); and B, worst analysis (“terrible”) for the TBIO Toruk cultivar (r = 0.06; d = 0.18; c = 0.01), in Ponta Grossa, in the state of Paraná, Southern Brazil.
Due to sensitivity, small changes in the evaluated input parameters (CCx, CDC, KcTR,x, WP*, HIo, and minimum growing degrees required for full biomass production) considerably modified the simulated yield in AquaCrop. Therefore, using only one value for each parameter analyzed in the model to estimate crop yield under different conditions of wheat crop growth and development was not satisfactory and accurate for different conditions, including different times to reach the established phenological stages, different population of plants, different cultivars throughout the years, possible problems with pests and/or diseases, and climatic conditions (drought periods).
Since even small modifications in input parameters caused large changes in the simulated yield in AquaCrop, scenarios were used to test the reliability of a calibrated parameter range for the harvests (Table 3). It should be highlighted that the same harvests evaluated in the validation process were used in the parameter range analysis (Table 2).
The analysis of the calibrated parameter range of the harvests showed “very good” and “excellent” performances (for the Quartzo and Supera cultivars, respectively) in Castro and “tolerable” to “excellent” ones in Ponta Grossa (Table 4 and Figure 4). The results indicated that the functional relationships between the most sensitive parameters (CCx, CDC, KcTR,x, WP*, HIo, and minimum growing degrees required for full biomass production), not considered or specified in the AquaCrop options, can significantly improve the model’s performance (5.76 kg ha-1 ≤ RMSE ≤ 1,219.86 kg ha-1; 0.81 ≤ r ≤ 1.0; 0.69 ≤ d ≤ 1.0; 0.56 ≤ c ≤ 1.0).
Linear regression analysis obtained from the association of simulated vs. observed yield in the validation process by the AquaCrop model, considering calibration ranges for: A, each cultivar evaluated in Castro; B, each cultivar evaluated in Ponta Grossa; and C, all cultivars evaluated in the municipalities of Castro and Ponta Grossa, located in the state of Paraná, Southern Brazil.
Some cultivars presented absolute and relative errors in validation that were larger than those found in calibration (Table 4 and Figure 4). The AquaCrop model indicated temperature stress in two harvests of the Quartzo cultivar in Castro due to 18 and 25 days of temperature below 5°C in each crop before flowering. According to Raes et al. (2018a)RAES, D.; STEDUTO, P.; HSIAO, T.C.; FERERES, E. AquaCrop: version 6.0-6.1: Reference manual: annexes. Rome: FAO, 2018a. Available at: <Available at: http://www.fao.org/3/a-br244e.pdf
>. Accessed on: Feb. 21 2018.
http://www.fao.org/3/a-br244e.pdf...
, 5°C is the minimum air temperature below which pollination starts to fail (cold stress) in the wheat crop. Therefore, the period in which the temperature was below 5°C in the harvests resulted in the observed errors (Table 4).
The Quartzo cultivar in Ponta Grossa presented harvests with high water restriction at the beginning of the cycle. Only 8 mm of rainfall were registered in the first 35 days after planting, also reflecting the obtained statistical errors (Table 4).
The TBIO Sinuelo and TBIO Toruk cultivars also showed water deficit along the growing cycles. This limitation occurred in periods that preceded flowering, more regularly for the TBIO Toruk cultivar, resulting in the largest errors obtained in the software (Figure 4 and Table 4).
In Castro, the observed performances were “excellent” for Supera and “very good” for Quartzo (Figure 4 A), indicating that the range of parameters was suitable for the cultivars in the region. The d index in Castro was similar to that found by Kumar et al. (2014)KUMAR, P.; SARANGI, A.; SINGH, D.K.; PARIHAR, S.S. Evaluation of Aquacrop model in predicting wheat yield and water productivity under irrigated saline regimes. Irrigation and Drainage, v.63, p.474-487, 2014. DOI: https://doi.org/10.1002/ird.1841.
https://doi.org/10.1002/ird.1841...
in India (d = 0.96) for final grain yield. Andarzian et al. (2011)ANDARZIAN, B.; BANNAYAN, M.; STEDUTO, P.; MAZRAEH, H.; BARATI, M.E.; BARATI, M.A.; RAHNAMA, A. Validation and testing of the AquaCrop model under full and deficit irrigated wheat production in Iran. Agricultural Water Management, v.100, p.1-8, 2011. DOI: https://doi.org/10.1016/j.agwat.2011.08.023.
https://doi.org/10.1016/j.agwat.2011.08....
, studying irrigated wheat in Iran, also reported good results in the validation process (d = 0.97).
According to Camargo & Sentelhas (1997)CAMARGO, A.P. de; SENTELHAS, P.C. Avaliação do desempenho de diferentes métodos de estimativa da evapotranspiração potencial no Estado de São Paulo, Brasil. Revista Brasileira de Agrometeorologia, v.5, p.89-97, 1997., the c index resulted in a “tolerable” performance for the TBIO Toruk cultivar in Ponta Grossa, probably due to the small number of experiments used in calibration (Figure 4 B and Table 2). Therefore, it was not possible to adequately adjust the obtained parameters in the sensitivity and linear regression analyzes for this cultivar, impairing data interpretation. For the other cultivars, the performance of the model was satisfactory, varying between “very good” and “excellent”.
Considering the statistical adjustments by the c index, the best results were obtained for the Supera and TBIO Tibagi cultivars in Ponta Grossa (Table 4), with r and the d and c indexes equal to 1.00 (Figure 4 A, B, and C). Similar results were reported by Toumi et al. (2016)TOUMI, J.; ER-RAKI, S.; EZZAHAR, J.; KHABBA, S.; JARLAN, L.; CHEHBOUNI, A. Performance assessment of AquaCrop model for estimating evapotranspiration, soil water content and grain yield of winter wheat in Tensift Al Haouz (Morocco): application to irrigation management. Agricultural Water Management, v.163, p.219-235, 2016. DOI: https://doi.org/10.1016/j.agwat.2015.09.007.
https://doi.org/10.1016/j.agwat.2015.09....
(r = 0.99 and RMSE = 100 kg ha-1).
The values obtained for the TBIO Sinuelo cultivar in Ponta Grossa were the closest to those found by Kale (2016)KALE, S. Assessment of Aquacrop model in the simulation of wheat growth under different water regimes. Scientific Papers. Series A. Agronomy, v.59, p.308-314, 2016. (RMSE = 330 kg ha-1; d index = 0.83), which validated AquaCrop for the wheat crop considering the parameters suggested by Raes et al. (2009)RAES, D.; STEDUTO, P.; HSIAO, T.C.; FERERES, E. Crop water productivity. Calculation procedures and calibration guidance. Aquacrop Version 3.0. Rome: FAO, 2009.. Iqbal et al. (2014)IQBAL, M.A.; SHEN, Y.; STRICEVIC, R.; PEI, H.; SUN, H.; AMIRI, E.; PENAS, A.; DEL RIO, S. Evaluation of the FAO AquaCrop model for winter wheat on the North China Plain under deficit irrigation from field experiment to regional yield simulation. Agricultural Water Management, v.135, p.61-72, 2014. DOI: https://doi.org/10.1016/j.agwat.2013.12.012.
https://doi.org/10.1016/j.agwat.2013.12....
also observed similar values (RSME = 580 kg ha-1; d index = 0.92) in China.
The results of the simulated and real yield analyses used to determine the calibration range of AquaCrop, considering all analyzed cultivars (Table 4 and Figure 4 C), were similar to those of the analysis performed for each cultivar (Figure 4 A and B). In Castro, cultivars achieved an “excellent” performance and, in Ponta Grossa, a “very good” one.
The obtained results, considering the calibrated parameter range, are very promising for future studies in the region involving the planning and simulation of agricultural scenarios. These results are also an indicative that the functional relationships between the most sensitive parameters, not considered or specified in the AquaCrop options, can significantly improve the performance of the model.
Conclusions
-
Calibration by the AquaCrop model shows good results for all wheat (Triticum aestivum) cultivars and locations analyzed in the Campos Gerais region in Southern Brazil.
-
The attempted adjustment indicates different performances for the evaluated cultivars: “very good” for Quartzo and “excellent” for Supera in the municipality of Castro; and “tolerable” for TBIO Toruk, “very good” for TBIO Sinuelo, and “excellent” for Supera, Quartzo and TBIO Tibagi in the municipality of Ponta Grossa, in the Campos Gerais region.
Acknowledgments
To Fundação ABC Pesquisa e Desenvolvimento Agropecuário, for support in conducting field experiments.
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Publication Dates
-
Publication in this collection
20 Dec 2019 -
Date of issue
2020
History
-
Received
21 Jan 2019 -
Accepted
08 Oct 2019