Several maize field experiments, including deficit and full irrigation, were performed in Ribatejo region, Portugal and were used to assess water stress impacts on yields using the AquaCrop model. The model was assessed after its parameterization using field observations relative to leaf area index (LAI), crop evapotranspiration, soil water content, biomass and final yield data and also using default parameters. LAI data were used to calibrate the canopy cover (CC) curve. Results showed that when the CC curve is properly calibrated, with root mean square errors (RMSE) smaller than 7.4%, model simulations, namely relative to crop evapotranspiration and its partition, show an improved accuracy. The model performance relative to soil water balance simulation revealed a bias in estimation but low estimation errors, with RMSE < 13% of the total available soil water. However the model tends to overestimate transpiration and underestimate soil evaporation. A good model performance was obtained relative to biomass and yield predictions, with RMSE lower than 11% and 9% of the average observed biomass and yield, respectively. Overall results show adequacy of AquaCrop for estimating maize biomass and yield under deficit irrigation conditions, mainly when an appropriate parameterization is adopted. The model showed less good performance when using the default parameters but errors are likely acceptable when field data are not available.

Olive production has expanded significantly from the Mediterranean Basin into the New World over the last two decades. In some cases, cultivars of European origin have been introduced at a large commercial scale with little previous evaluation of potential productivity. The objective of this study was to evaluate whether a temperature-driven simulation model developed in the Mediterranean Basin to predict normal flowering occurrence and flowering date using cultivar-specific thermal requirements was suitable for the low latitude areas of Northwest Argentina. The model was validated at eight sites over several years and a wide elevation range (350–1200 m above mean sea level) for three cultivars (‘Arbequina’, ‘Frantoio’, ‘Leccino’) with potentially different chilling requirements. In ‘Arbequina’, normal flowering was observed at almost all sites and in all years, while normal flowering events in ‘Frantoio’ and ‘Leccino’ were uncommon. The model successfully predicted if flowering would be normal in 92% and 83% of the cases in ‘Arbequina’ and ‘Frantoio’, respectively, but was somewhat less successful in ‘Leccino’ (61%). When flowering occurred, the predicted flowering date was within ± 7 days of the observed date in 71% of the cases. Overall, the model results indicate that cultivar-specific simulation models may be used as an approximate tool to predict whether individual cultivars will be successful in new growing areas. In Northwest Argentina, the model could be used to identify cultivars to replace ‘Frantoio’ and ‘Leccino’ and to simulate global warming scenarios.

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Several simulation models of the olive crop have been formulated so far, but none of them is capable of analyzing the impact of environmental conditions and management practices on water relations, growth and productivity under both well-irrigated and water-limiting irrigation strategies. This paper presents and tests OliveCan, a process-oriented model conceived for those purposes. In short, OliveCan is composed of three main model components simulating the principal elements of the water and carbon balances of olive orchards and the impacts of some management operations. To assess its predictive power, OliveCan was tested against independent data collected in two 3-year field experiments conducted in Córdoba, Spain, each of them applying different irrigation treatments. An acceptable level of agreement was found between measured and simulated values of seasonal evapotranspiration (ET, range 393 to 1016 mm year-1; RMSE of 89 mm year-1), daily transpiration (Ep, range 0.14–3.63 mm d-1; RMSE of 0.32 mm d-1) and oil yield (Yoil, range 13–357 g m-2; RMSE of 63 g m-2). Finally, knowledge gaps identified during the formulation of the model and further testing needs are discussed, highlighting that there is additional room for improving its robustness. It is concluded that OliveCan has a strong potential as a simulation platform for a variety of research applications.

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