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.
Crop productivity evaluation with models simulations can help in the prediction of harvests and in the understanding of the interactions resulting from the soil-plant-atmosphere continuum. The aim of this study was to calibrate and validate the AquaCrop model for maize crop in the edaphoclimatic conditions of Campos Gerais region, Paraná State, Brazil. The analyses were carried out for maize crop with model input data (climate, crop, soil and soil management) obtained from the ABC Foundation Experimental Station in Castro, Ponta Grossa and Socavão. The climate in the region is humid subtropical, with rainfall evenly distributed. The relief varies from flat to gently undulating. The period analyzed in the calibration and validation process comprised 2011 to 2016 and 2012 to 2016 harvests, respectively. The data used in the calibration of AquaCrop was different from those used in the validation process. Observed and simulated yields were evaluated by simple linear regression analyses, absolute and relative errors, correlation coefficient (r), concordance (d) and performance (c) indexes. The calibration of AquaCrop was satisfactory in the locations studied for maize crop, obtaining absolute errors varying from 6 to 121 kg ha–1. The highest calibration errors occurred in Castro. However, the errors were not enough to reduce the performance in the validation process for this localitie. The model validation resulted in “excellent” performance in all locations evaluated. The AquaCrop can be used to predict the maize yield with acceptable accuracy in the Campos Gerais Region, Paraná State, Brazil.
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.
The objective of this study was to verify the magnitude and trend of hourly reference evapotranspiration (EToh), as well as associate and analyze daily ETo (ETod) series and the sum of hourly ETo (ETo24h) in 24 h, estimated with the Penman-Monteith ASCE model for Paraná State (Cfa and Cfb climate type). Relative humidity (RH), temperature (T), solar radiation (Rs) and wind speed (u2) data were obtained from 25 meteorological stations from the National Meteorological Institute (INMET), between December 1, 2016 to November 8, 2018. The analyzes were performed by linear regression and associations considering the root mean square error, correlation coefficient and index of agreement. The EToh trend has a Gaussian distribution, with the highest values between 12:00 p.m. and 2:00 p.m., with the maximum average being 0.44 mm h−1 (Cfa climate type) and 0.35 mm h−1 (Cfb climate type). The average difference between the ETo24h and ETod values was small (5.1% for Cfa and 7.4% for Cfb), resulting in close linear associations. The results obtained indicate that EToh has good potential to be used in planning and management in the field of soil and water engineering, in Paraná State.
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