TITLE:
Aquacrop Model Calibration in Potato and Its Use to Estimate Yield Variability under Field Conditions
AUTHORS:
Antonio de la Casa, Gustavo Ovando, Luciano Bressanini, Jorge Martínez
KEYWORDS:
Potato; Canopy Cover; AquaCrop Model; Soil Water Content
JOURNAL NAME:
Atmospheric and Climate Sciences,
Vol.3 No.3,
July
8,
2013
ABSTRACT:
AquaCrop model estimates the crop productivity decrease in response to water stress, determining the biomass (B) based on water productivity (WP) and accumulated transpiration (ΣTr); and the yield (Y) is calculated according to B and the harvest index (HI). AquaCrop was evaluated considering different WP values for 2010 late growing season to simulate crop yield of potato (Solanum tuberosum L.) cv. Spunta, in a commercial production field of 9 ha located in the green belt of Cordoba city (31°30'S, 64°08'W, 402 m asl), while monitoring in 2009 was used to verify the model. Canopy cover estimation by AquaCrop was adjusted using observed field data obtained from vertical digital photographs acquired at 2.5 m height. WP values of 15.8 and 31.6 (for C3 and C4 species, respectively) and two intermediate values 21 and 26.3 g·mˉ2 were considered to evaluate the model performance. While linear function between observed tuber yields and estimated by AquaCrop had always a correlation coefficient greater than 0.94 (p 0.001), using WP = 26.3 and WP =31.6 g·mˉ2 presented overestimation, whereas with 15.8 g·mˉ2 had an opposite behavior, while WP = 21 g·mˉ2 was the value that produced the lowest estimation error. In addition, soil moisture from this estimated value of WP was highly correlated with measured water content in different areas of production field. The verification test shows that while the model slightly underestimates canopy cover, biomass was overestimated. After setting the coefficients of canopy cover development, the AquaCrop crop model estimated adequately potato yield for high production values that are less affected by lack of water, but in both years showed a tendency to overestimate the lowest yields, as was observed for other crops. Meanwhile, the dispersion between the observed and estimated yield was higher in the verification test because the sampling this year was more random.