Regulated deficit irrigation (RDI), which applied 75% of the water received by CON (i.e., rainfall plus irrigation) with a midsummer deficit period (15 July to 15 September) without irrigation. Fernández, J. E., Moreno, F., Cabrera, F., Arrue, J. L., and Martin-Aranda, J. (1991). Drip irrigation, soil characteristics and the root activity of olive trees. Plant Soil 133, 239–251. doi: 10.1007/BF00009196

Vialet-Chabrand, S., Dreyer, E., and Brendel, O. (2013). Performance of a new dynamic model for predicting diurnal time courses of stomatal conductance at the leaf level. Plant Cell Environ. 36, 1529–1546. doi: 10.1111/pce.12086 Bustan, A., Avni, A., Lavee, S., Zipori, I., Yeselson, Y., Schaffer, A., et al. (2011). Role of carbohydrate reserves in yield production of intensively cultivated oil olive ( Olea europaea L.) trees. Tree Physiol. 31, 519–530. doi: 10.1093/treephys/tpr036 Ritchie, J. T. (1972). Model for predicting evaporation from a row crop with incomplete cover. Water Resour. Res. 8, 1204–1213. doi: 10.1029/WR008i005p01204Model tests generally revealed a high level of agreement between simulations and experimental measurements. Given the variety of the simulated treatments and the many assumptions that a model like OliveCan must take, we found the results satisfactory. Notwithstanding that, there were situations in which model estimates departed from observations. For example, some discrepancies were found for some of the simulations of Y oil, ET (Table 1) and E p (Figure 3), but, considering that the general trends and differences between treatments were captured by the model, we believe that the results are highly acceptable. Some of the divergences between measured and simulated Y oil might be attributed to the fact that the approach followed by OliveCan to simulate alternate bearing is limited, as far as the physiological bases of alternate bearing are not completely understood yet ( Connor, 2005; Dag et al., 2010). However, biennial comparisons (Table 2) only improved slightly the results. Apart from that, the remarkable lag between the simulated and measured diurnal courses of E p (Figure 4) was to be expected: measurements were performed in the trunk with sap flow sensors and OliveCan does not simulate the buffering effect of the water stored in aboveground organs ( Cermák et al., 2007). Also, the model assumes that stomatal conductance responds instantaneously to changes in environmental conditions, but the slow dynamics of stomatal opening and closing can cause lags in diurnal transpiration ( Vialet-Chabrand et al., 2013). Obviously, the comprehensive nature and the wide range of simulated processes come at the expense of both model complexity and high input requirements. The latter is likely to be its main limitation, as far as some of the inputs (e.g., soil depth, L v distribution) are not easy to measure in the field. In any case, it is noteworthy to emphasize that OliveCan has not been primarily conceived as a decision support system for farmers, but as a research tool. Further Research Gardner, W. R. (1960). Dynamic aspects of water availability to plants. Soil Sci. 89, 63–73. doi: 10.1097/00010694-196002000-00001 Finally, the soil carbon balance and heterotrophic respiration ( RESP H) are computed with an adaptation of the model proposed by Huang et al. (2009) and modified to take into account the effect of soil moisture on the rate of decomposition according to Verstraeten et al. (2006). Then, by considering the different computed fluxes of assimilation and respiration within the orchard, OliveCan provides estimates of the ecosystem respiration ( RESP eco) and net ecosystem exchange ( NEE). Management Component Simulating the water balance of an irrigated olive orchard is a particularly challenging task as the trees are typically watered by point-source emitters that keep a small fraction of the surface frequently wet while the remaining area remains dry, unless it rains. This fact results in differences between these two soil areas in relation to soil water content, the water fluxes determining the water balance (i.e., runoff, drainage, redistribution along the soil profile, soil evaporation, and root water uptake) and root length density ( Fernández et al., 1991). Therefore, traditional modeling approaches based on the use of the average soil water content can lead to large errors, besides giving a poor insight into the system. One alternative consists of using a two-compartment model that solves the water balance separately for each zone of the soil. In this regard, Testi et al. (2006) proposed a model capable of simulating potential transpiration, separately calculating runoff, drainage and soil evaporation from the wet and dry fractions of the soil surface under localized irrigation. The model was developed to determine the potential irrigation needs of olive orchards, so its use is unfortunately limited to unstressed conditions. Lately, García-Tejera et al. (2017a) have formulated a soil-plant-atmosphere-continuum (SPAC) model capable of calculating root water uptake from soils with spatially heterogeneous distributions of water content and root length densities. Such a model also discretizes the soil into different soil zones and layers and, for the canopy, it considers two leaf classes (i.e., sunlit and shaded). Furthermore, the model by García-Tejera et al. (2017a) provides estimates of gross assimilation ( A), offering an opportunity to link the water and carbon balances of olive trees.

P.S. Another olive favorite are these Olive Puffs (I think they’re delicious year-round, but especially at Halloween). Tuzet, A., Perrier, A., and Leuning, R. (2003). A coupled model of stomatal conductance, photosynthesis and transpiration. Plant Cell Environ. 26, 1097–1116. doi: 10.1046/j.1365-3040.2003.01035.xWhen available, the values of the different parameters were taken from the literature. Supplementary Table S2 provides a complete list with the parameter values used for the simulations and the source from which they were taken. In short, the parameters of the SPAC model were taken from García-Tejera et al. (2017a, b), who, in turn, gathered most of the parameter values from different sources. Parameters related to phenology were obtained from reports by De Melo-Abreu et al. (2004) and López-Bernal et al. (2014, 2017). The studies by Mariscal et al. (2000) and Pérez-Priego et al. (2014) were used for setting the maintenance respiration and PV coefficients, respectively. Parameters related to the calculation of fruit number and yield were taken from several sources, including experimental data (see section “Number of Fruits and Alternate Bearing” in Supplementary Material). The coefficient of oil yield to dry fruit matter was taken from experimental data collected in a hedgerow cv. ‘Arbequina’ orchard ( López-Bernal et al., 2015). Partitioning coefficients were based on findings by Mariscal et al. (2000); Villalobos et al. (2006) and Scariano et al. (2008). Reports from Barranco et al. (2005) and Koubouris et al. (2009) were used to parametrize the routines modeling the impacts of frost damage and heat stress, respectively. Coefficients modulating fine root growth distribution were directly taken from Jones and Kiniry (1986). Finally, parameters implied in the soil carbon balance were taken from Verstraeten et al. (2006); Huang et al. (2009) and, to a lesser extent, from other studies. Model Testing

Transfer blue cheese filling to a piping bag or a sturdy plastic bag with a round piping tip (or cut the end off with kitchen scissors). Continuous deficit irrigation (CDI), which also applied 75% of the water received by CON (i.e., rainfall plus irrigation), but for the whole irrigation season. Olive orchards represent the main component of agricultural systems in many semiarid regions with Mediterranean climate, reaching 10.1 Mha worldwide in 2011 ( FAOSTAT, 2014). In countries where the cultivation of this tree species is done in extensive areas, olive cropping systems have become of high relevance not only from an economic perspective, but also from an ecological one. Olive orchards have been traditionally cultivated at low planting densities under low-input rainfed conditions. However, the increase in the demand for oil of recognized and consistently high quality in recent years has triggered the development and adoption of farming techniques aimed to improve productivity, such as localized irrigation, fertigation and mechanical pruning and harvesting. As a result, traditional rainfed olive orchards (<200 trees ha -1) coexist nowadays with new intensive (250–850 trees ha -1) or super-intensive (1200–3000 trees ha -1) irrigated plantations. The rapid changes in olive farming have raised questions on the economic and environmental sustainability of the different olive cropping systems under present and future climate scenarios. Given that an olive orchard is a complex system, its quantitative study via modeling is a crucial step in understanding its behavior in response to climatic and management factors. Spanish Passion Foods & Wines specialises in sourcing and supplying authentic Spanish food & Spanish wine products to the UK. We work directly with artisan Spanish food producers from the heart of rural Spain, using only the very best and freshest ingredients to create food bursting with traditional flavours and authenticity. If you’re looking for high-quality Spanish food and wine to create Traditional Spanish meals, Mediterranean-style dishes or Tapas at Home, we’re sure you’ll find what you’re looking for. Experimental measurements conducted in two mature olive orchards located in the Alameda del Obispo Research Station, Córdoba, Spain (37.8°N, 4.8°W, 110 m) were used for assessing the reliability of OliveCan. The climate in the area is typically Mediterranean, with around 600 mm of average annual rainfall and 1390 mm and average annual ET 0 of 1390 mm ( Testi et al., 2004), respectively. The soil for both orchards is classified as a Typic Xerofluvent of sandy loam texture and exceeds 2 m in depth, with field capacity (𝜃 UL) and permanent wilting point (𝜃 LL) water contents of 0.23 m 3 m -3 and 0.07 m 3 m -3, respectively ( Testi et al., 2004). Weather data were collected using a station placed 500 m away from the orchards. Within both orchards, irrigation experiments comprising several irrigation treatments were performed. Each irrigation treatment was simulated separately with OliveCan. Experiment IRomero, P., Castro, G., Gómez, J. A., and Fereres, E. (2007). Curve number values for olive orchards under different soil management. Soil Sci. Soc. Am. J. 71, 1758–1769. doi: 10.2136/sssaj2007.0034 Want to change it up? Swap out blue cheese for a creamy goat cheese or opt for a milder blue cheese and go with gorgonzola cheese. Two phenological stages are considered for the vegetative organs: (i) a dormant stage characterized by an absence of growth that is induced by chilling accumulation during autumn and (ii) a phase of active growth that starts in late winter, by the time average temperature is above a threshold. In relation to the reproductive growth, the date of flowering is determined with the two-phase model by De Melo-Abreu et al. (2004). Fruit growth is assumed to start after a given amount of thermal time is accumulated from the date of flowering and ceases when either maturity or the harvest date is reached. Verstraeten, W., Veroustraete, F., and Feyen, J. (2006). On the temperature and water limitation of net ecosystem productivity: implementation in the C-Fix model. Ecol. Modell. 199, 4–22. doi: 10.1016/j.ecolmodel.2006.06.008

Four management operations are considered in OliveCan: tillage, irrigation, harvest and pruning. In the model, tillage operations have an impact on CN whereas irrigation provides an additional water input for the wetted soil zone. Irrigation amounts and dates can either be defined explicitly by the users or implicitly calculated through a dedicated routine that, at customizable intervals, applies a fraction of the maximum ET lost since the last irrigation. Harvesting takes place on a user-defined day of the year and results in the removal of fruits. At harvest, the model provides an estimate of oil yield ( Y oil) by multiplying the dry biomass of fruits and a fixed coefficient representing the ratio of oil content to dry matter. Finally, pruning is simulated by setting a customizable fraction of LAI to be removed ( F prune) and an interval between pruning operations. The model also reduces the biomasses of shoots and branches by the same fraction F prune. The user should indicate whether pruning residues are incorporated into the soil or exported. Initialization Requirements García-Tejera, O., López-Bernal, A., Villalobos, F. J., Orgaz, F., and Testi, L. (2016). Effect of soil temperature on root resistance: implications for different trees under Mediterranean conditions. Tree Physiol. 36, 469–478. doi: 10.1093/treephys/tpv126 The research leading to these results has received funding from Ministerio de Economía y Competitividad (Grant Nos. AGL-2010-20766 and AGL2015-69822), from Junta de Andalucía (Grant No. P08-AGR-04202), from the European Community’s Seven Framework Programme-FP7 (KBBE.2013.1.4-09) under Grant Agreement No. 613817. 2013–2016 “MODelling vegetation response to EXTREMe Events” (MODEXTREME, modextreme.org) and from ERA-NET FACCE SURPLUS (Grant No. 652615, project OLIVE-MIRACLE), the latter co-funded by INIA (PCIN-2015-259). Besides, ÁL-B was funded by a postdoctoral fellowship (‘Juan de la Cierva-Formación 2015’ Programme, FJCI-2015-24109) from Ministerio de Economía y Competitividad. Conflict of Interest StatementThe measurements (only performed for the central trees of the replicates) used for testing the model were oil yield ( Y oil, g m -2), seasonal ET and daily transpiration ( E p, mm d -1). With regard to the former, each tree was manually harvested and the fresh yield weighed in the field. Y oil was subsequently determined from sub-samples of 5 kg of fresh fruits. Cumulative ET was determined by water balance for the whole 2005 and 2006 seasons by measuring soil water content with a neutron probe (model 503, Campbell Pacific Nuclear Corp, Pacheco, CA, United States). Eight access tubes were installed between two trees per replicate, normal to tree rows. Measurements were taken at several depths (from 0.075 to 2.65 m deep). Finally, E p was measured in 2006 with a sap-flow system device developed and assembled in the IAS-CSIC in Córdoba and described by Testi and Villalobos (2009). The system uses the Compensated Heat Pulse (CHP) method in combination with the Calibrated Average Gradient (CAG) procedure. The probes performed readings every 15 min at 4 depths in the xylem, spaced 10 mm. Six RDI, six CDI and four CON trees were instrumented with two probes per tree, at a height of 30 cm. The outputs of each probe were integrated first along the trunk radius and then around the azimuth angle. Average sap flow records for each treatment were calibrated against the estimates of E p deduced from the difference between the measured ET and soil evaporation in a period of several weeks with no rainfall events during the summer. The model of Orgaz et al. (2006) was used to calculate soil evaporation. The calibrated sap flow data have not been published so far. Values of GC, LAD, and R zx required to initialize the model were taken from measurements of tree silhouettes. A record of Y dry of the year preceding simulations was also considered. Initial L v values were taken from records measured by Moriana (2001) for the trees of Experiment II. Experiment II Considering its mechanistic approach, the vast quantity of simulated processes and its potential uses, OliveCan represents a momentous step forward in relation to previous olive growth simulation models. In this regard, OliveCan enables one to assess the combined effects of management operations and weather over crop performance for different olive orchard and soil typologies both under unstressed and water deficit conditions. Thus, the model shows potential for a broad range of research applications. For instance, OliveCan seems particularly suitable for assessing the performance of olive orchards under future climatic scenarios, as the model explicitly accounts for the multiple effects of reduced rainfall and increased environmental CO 2 and temperature for the water and carbon balances of the orchard and the development of trees.