Validating the FAO AquaCrop Model for Rainfed Maize in Pennsylvania

Abstract

It is widely known that a close relationship exists between crop production and water stress. In this study, field‐measured data were used to test the performance of AquaCrop and its ability to capture this relationship for rainfed maize (Zea mays L.) in Pennsylvania. The objectives were to evaluate AquaCrop’s ability to simulate the progression of cumulative biomass and grain yield with time, final biomass and harvestable yield, and volumetric water content at six depths. Two years of data from a study conducted in Rock Springs, PA, were used to validate AquaCrop’s ability to accurately simulate the progression of cumulative biomass and grain yield with time, as well as final biomass and harvestable yield. Data collected from January 2004 to March 2006 from a study conducted near Landisville, PA, were used to assess AquaCrop’s ability to effectively simulate soil moisture content at six depths. In addition, the 2004 and 2005 seasonal final biomass measurements obtained from the Landisville location were compared with the model’s simulated values. The results indicated that AquaCrop was able to accurately simulate the progression of cumulative biomass and grain yield with time, with index of agreement values ranging from 0.96 to 0.99. Comparisons between simulated and measured final biomass and final harvestable yield produced biomass deviations ranging from 2.4 to 20.7% and yield deviations of 2.9 and 15.3%. The water balance evaluation indicated that, averaged across all depths, the results were consistent with other validation studies of soil water balance models, with RMSE ranging from 1.5 to 9.8% (v/v).

Modeling Maize Yield and Soil Water Content with AquaCrop Under Full and Deficit Irrigation Managements

“…Measurements showed that SWC was more variable in the upper soil layers implying that roots were mostly active in extracting water from the upper soil layers. This is in agreement with the finding of Cabelguenne and Debaeke (1998), who reported that maize extracts most water from the top 0.5 m; therefore accurate simulation of SWC in this zone would positively influence simulated maize development and yield (Mebane et al 2013;Araya et al 2010b). In top soil layers, maize root density and its spatial variability is very high (Trachsel et al 2013); therefore detailed information of root length density, specific root length, and precisely-determined root distribution in AquaCrop considerably leads to more accurate simulations of SWC as it directly affects the simulation of transpiration.…”

“…It is most likely that the measured SWC in 67 DAP in calibration are not correct since the SWC in the top soil layer is below or equal to PWP. Mebane et al (2013) used AquaCrop to simulate SWC of two rainfed maize experiments in Pennsylvania and reported RMSEs of 0.025 and 0.044 m 3 m −3 , which agree with the results of our study. Other studies have also reported satisfactory performance of AquaCrop in simulating SWC of various crops, for instance quinoa (Geerts et al 2009), barley (Abrha et al 2012), Soil water depletion threshold above which canopy expansion starts declining, P upper 0.2 Soil water depletion threshold above which canopy expansion ceases, P lower 0.5…”

“…Indeed, this is in agreement with Hsiao et al (2009), Farahani et al (2009), and Zeleke et al (2011 that reported AquaCrop tended to overestimate SWC for maize, cotton, and canola, respectively. In contrary, other studies showed that AquaCrop underestimated SWC for maize (Mebane et al 2013;Biazin and Stroosnijder 2012) and wheat (Igbal et al 2014;Mkhabela and Bullock 2012). Such discrepancies might be due to several reasons that would cause overestimation or underestimation of SWC (Abrha et al 2012;Zeleke et al 2011;Hsiao et al 2009;Heng et al 2009;Farahani et al 2009) or accurate simulation of SWC (Andarzian et al 2011;Araya et al 2010b).…”

Water Footprint of main crops in Austria / Wasser-Fußabdruck wichtiger Nutzpflanzen in Österreich

“…The model is developed to simulate the yield response of crops as a function of water consumption . The elements of the AquaCrop soil-plantatmosphere continuum focus on (i) the soil water balance, (ii) the plant's development, growth, and yield processes, and (iii) the atmospheric temperature regime, evaporative demand, rainfall and CO 2 concentration Mebane et al, 2013). Compared to other crop growth models, AquaCrop uses a significantly smaller number of input parameters to predict daily biomass and water requirement ).…”

Quantitative analysis of yield and soil water balance for summer maize on the piedmont of the North China Plain using AquaCrop

“…The global applicability of AquaCrop is dependent on its being tested in a diverse environment under differing soil conditions, crops, agronomic practices, and climatic conditions [32,33] . For example, the calibration and evaluation of the performance of AquaCrop has been carried out for quinoa [34] , wheat [35][36][37][38][39] , sorghum [40] , maize [28,[41][42][43][44][45][46] , potato [32,47] , and cabbage [48,49] . Previous studies have demonstrated that AquaCrop is able to accurately simulate crop canopy cover, biomass yield and grain yield in diverse environments and under a variety of meteorological conditions and management practices.…”