ACS  Vol.3 No.3 , July 2013
Aquacrop Model Calibration in Potato and Its Use to Estimate Yield Variability under Field Conditions

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.

Cite this paper
A. Casa, G. Ovando, L. Bressanini and J. Martínez, "Aquacrop Model Calibration in Potato and Its Use to Estimate Yield Variability under Field Conditions," Atmospheric and Climate Sciences, Vol. 3 No. 3, 2013, pp. 397-407. doi: 10.4236/acs.2013.33041.
[1]   M. Mosciaro, “Caracterización de la Producción y Comercialización de Papa en Argentina,” área de Economía y Sociología Rural, EEA-INTA, Balcarce, 2004.

[2]   D. O. Caldiz and P. C. Struik, “Survey of Potato Production and Possible Yield Constraints in Argentina,” Potato Research, Vol. 42, No. 1, 1999, pp. 51-71. doi:10.1007/BF02358391

[3]   J. Kadaja and H. Tooming, “Potato Production Model Based on Principle of Maximum Plant Productivity,” Agricultural and Forest Meteorology, Vol. 127, No. 1-2, 2004, pp. 17-33. doi:10.1016/j.agrformet.2004.08.003

[4]   D. O. Caldiz, F. J. Gaspari, A. J. Haverkort and P. C. Struik, “Agro-Ecological Zoning and Potential Yield of Single or Double Cropping of Potato in Argentina,” Agricultural and Forest Meteorology, Vol. 109, No. 4, 2001, pp. 311-320. doi:10.1016/S0168-1923(01)00231-3

[5]   F. J. Pierce and P. Novak, “Aspects of Precision Agriculture,” Advances in Agronomy, Vol. 67, 1999, pp. 1-85. doi:10.1016/S0065-2113(08)60513-1

[6]   E. A. Po, S. S. Snapp and A. Kravchenko, “Potato Yield Variability across the Landscape,” Agronomy Journal, Vol. 102, No. 3, 2010, pp. 885-894. doi:10.2134/agronj2009.0424

[7]   Y. M. Oliver, M. J. Robertson and M. T. F. Wongbet, “Integrating Farmer Knowledge, Precision Agriculture Tools, and Crop Simulation Modelling to Evaluate Management Options for Poor-Performing Patches in Cropping Fields,” European Journal of Agronomy, Vol. 32, 2010, pp. 40-50. doi:10.1016/j.eja.2009.05.002

[8]   J. Doorenbos and A. H. Kassam, “Yield Response to Water, FAO Irrigation and Drainage Paper No. 33,” Food and Agriculture Organization of United Nations, Rome, 1979.

[9]   P. Steduto, T. C. Hsiao, D. Raes and E. Fereres, “AquaCrop—The FAO Crop Model for Predicting Yield Response to Water: I. Concepts and Underlying Principles,” Agronomy Journal, Vol. 101, No. 3, 2009, pp. 426-437. doi:10.2134/agronj2008.0139s

[10]   C. B. Tanner and T. R. Sinclair, “Efficient Water Use in Crop Production,” In: H. M. Taylor, et al., Ed., Limitations to Water Use in Crop Production, ASA, CSSA, and SSSA, Madison, 1983.

[11]   P. Steduto, T. C. Hsiao and E. Fereres, “On the Conservative Behavior of Biomass Water Productivity,” Irrigation Science, Vol. 25, No. 3, 2007, pp. 189-207. doi:10.1007/s00271-007-0064-1

[12]   T. C. Hsiao, L. K. Heng, P. Steduto, B. Rojas-Lara, D. Raes and E. Fereres, “AquaCrop—The FAO Crop Model for Predicting Yield Response to Water: III. Model Parameterization and Testing for Maize,” Agronomy Journal, Vol. 101, No. 3, 2009, pp. 448-459. doi:10.2134/agronj2008.0218s

[13]   L. K. Heng, S. R. Evett, T. A. Howell and T. C. Hsiao, “Calibration and Testing of FAO AquaCrop Model for Rainfed and Irrigated Maize,” Agronomy Journal, Vol. 101, No. 3, 2009, pp. 488-498. doi:10.2134/agronj2008.0029xs

[14]   H. J. Farahani, G. Izzi, P. Steduto and T. Y. Oweis, “Parameterization and Evaluation of AquaCrop for Full and Deficit Irrigated Cotton,” Agronomy Journal, Vol. 101, 2009, pp. 469-476. doi:10.2134/agronj2008.0182s

[15]   M. R. Todorovic, R. Albrizio, L. Zivotic, M. T. Abi Saab, C. St?ckle and P. Steduto, “Assessment of AquaCrop, CropSyst, and WOFOST Models in the Simulation of Sunflower Growth under Different Water Regimes,” Agronomy Journal, Vol. 101, No. 3, 2009, pp. 509-521. doi:10.2134/agronj2008.0166s

[16]   S. Geerts, D. Raes, M. Garcia, R. Miranda, J. A. Cusicanqui, C. Taboada, J. Mendoza, R. Huanca, A. Mamani, O. Condori, J. Mamani, B. Morales, V. Osco and P. Steduto, “Simulating Yield Response to Water of Quinoa (Chenopodium quinoa Willd.) with FAO-AquaCrop,” Agronomy Journal, Vol. 101, No. 3, 2009, pp. 499-508. doi:10.2134/agronj2008.0137s

[17]   D. Raes, P. Steduto, T. C. Hsiao and E. Fereres, “AquaCrop—The FAO Crop Model to Predict Yield Response to Water: II Main Algorithms and Software Description,” Agronomy Journal, Vol. 101, No. 3, 2009, pp. 438-447. doi:10.2134/agronj2008.0140s

[18]   P. L. Kooman, M. Fahem, P. Tegera and A. J. Haverkort, “Effects of Climate on Different Potato Genotypes. 1. Radiation Interception, Total and Tuber Dry Matter Production,” European Journal of Agronomy, Vol. 5, 1996, pp. 193-205. doi:10.1016/S1161-0301(96)02031-X

[19]   B. Jarsún, J. Gorgas, E. Zamora, H. Bosnero, E. Lovera, A. Ravelo and J. Tassile, “Los Suelos de Córdoba,” Agencia Córdoba Ambiente e Instituto Nacional de Tecnología Agropecuaria, EEA Manfredi, Córdoba, 2006.

[20]   F. J. Adamsen, P. J. Pinter Jr., E. M. Barnes, R. L. La Morte, G. W. Wall, S. W. Leavitt and B. A. Kimball, “Measuring Wheat Senescence with a Digital Camera,” Crop Science, Vol. 39, No. 3, 1999, pp. 719-724. doi:10.2135/cropsci1999.0011183X003900030019x

[21]   A. A. Gitelson, Y. J. Kaufman, R. Stark and D. Rundquist, “Novel Algorithms for Remote Estimation of Vegetation Fraction,” Remote Sensing of Environment, Vol. 80, No. 1, 2002, pp. 76-87. doi:10.1016/S0034-4257(01)00289-9

[22]   Y. Li, D. Chen, C. N. Walker and J. F. Angus, “Estimating the Nitrogen Status of Crops Using a Digital Camera,” Field Crops Research, Vol. 118, No. 2, 2010, pp. 221-227. doi:10.1016/j.fcr.2010.05.011

[23]   A. de la Casa, G. Ovando, L. Bressanini, á. Rodríguez and J. Martínez, “Determinación de la Fracción de Suelo Cubierta con el Follaje de Papa a Partir del Cociente Entre Bandas de Fotografías Digitales,” Actas de la XIII Reunión Argentina y VI Latinoamericana de Agrometeorología, Bahía Blanca, Buenos Aires, 2010.

[24]   J. D. Dardanelli, O. A. Bachmeier, R. Sereno and R. Gil, “Rooting Depth and Soil Water Extraction Patterns of Different Crops in a Silty Loam Haplustoll,” Field Crop Research, Vol. 54, 1997, pp. 29-38. doi:10.1016/S0378-4290(97)00017-8

[25]   K. E. Saxton and W. J. Rawls, “Soil Water Characteristics by Texture and Organic Matter for Hydrologic Solutions,” Soil Science Society of America Proceedings of Annual Conference, Seattle, 2004.

[26]   D. Raes, “ETo Calculator v3.1,” Land and Water Digital Media Series No. 36, Food and Agriculture Organization of United Nations, Rome, 2009.

[27]   Soil Conservation Service, “Estimation of Direct Runoff from Storm Rainfall,” In: National Engineering Handbook, Soil Conservation Service, USDA, Washington DC, 1964.

[28]   T. R. Sinclair and R. C. Muchow, “Radiation Use Efficiency,” Advances in Agronomy, Vol. 65, 1999, pp. 215-265. doi:10.1016/S0065-2113(08)60914-1

[29]   A. de la Casa, G. Ovando, L. Bressanini, á. Rodríguez and J. Martínez, “Uso del índice de área Foliar y del Porcentaje de Cobertura del Suelo Para Estimar la Radiación Interceptada en Papa,” Agricultura Técnica (Chile), Vol. 67, 2007, pp. 78-85.

[30]   A. de la Casa, G. Ovando, L. Bressanini, J. Martínez and á. Rodríguez, “Eficiencia en el Uso de la Radiación en Papa Estimada a Partir de la Cobertura del Follaje,” Agriscientia, Vol. 26, 2011, pp. 21-30.

[31]   S. R. Evett and J. A. Tolk, “Introduction: Can Water Use Efficiency Be Modeled Well Enough to Impact Crop Management?” Agronomy Journal, Vol. 101, No. 3, 2009, pp. 423-425. doi:10.2134/agronj2009.0038xs

[32]   N. S. Boyd, R. Gordon and R. C. Martin, “Relationship between Leaf Area Index and Ground Cover in Potato under Different Management Conditions,” Potato Research, Vol. 45, No. 2, 2002, pp. 117-129. doi:10.1007/BF02736107

[33]   A. Hornung, R. Khosla, R. Reich, D. Inman and D. G. Westfall, “Comparison of Site-Specific Management Zones: Soil-Color-Based and Yield-Based,” Agronomy Journal, Vol. 98, No. 2, 2006, pp. 407-415. doi:10.2134/agronj2005.0240