López-Bernal 1 Alejandro Morales 2 Omar García-Tejera 3 Luca Testi 3* Francisco Orgaz 3 J. P. De Melo-Abreu 4 Francisco J. Villalobos 1,3 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

P.S. Another olive favorite are these Olive Puffs (I think they’re delicious year-round, but especially at Halloween). The measurements (only performed for the central trees of the replicates) used for the model were Y oil and seasonal ET. On the one hand, trees were harvested between December 15th and January 15th for the 3 years. Individual fruit weight of each tree was measured and a subsample of 150 fruits from each tree was used for determining oil content. On the other, cumulative ET was determined by water balance for each season 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 in the four irrigation treatments and six tubes were placed in the rainfed treatment. Measurements were taken were performed at several depths (from 0.075 to 2.4 m deep). Values of GC, LAD, and R zx required to initialize the model were taken from dedicated measurements. 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). Statistical Analysis

ORIGINAL RESEARCH article

Variables related to canopy characteristics such as leaf area index ( LAI) or GC are updated from the estimates of biomass of leaves assuming that the crowns present an spheroidal shape with constant leaf area density ( LAD) and ratio of vertical to horizontal canopy radiuses ( R zx). Similarly, the biomass of fine roots in each soil compartment is used to compute root length density ( L v) by adopting a constant specific root length ( SRL). Further research regarding genetic variability in model parameters is also desirable. With the exception of those related to the simulation of flowering date ( De Melo-Abreu et al., 2004) and frost damage ( Barranco et al., 2005), all parameters have been taken from past experiments carried out either with only one cultivar each (‘Arbequina’ being the most frequent) or averaging the results obtained for a few of them. Although the scarce literature does not allow us to disentangle how many of these crop parameters are cultivar-specific, it is clear that exploring their genetic variability might be important for enhancing model reliability. Moreover, the quantification of such cultivar variability may be used for evaluating its impact on tree physiology and productivity under different management, weather or orchard characteristics using OliveCan, which may be useful for breeding purposes. The model presented here targets the simulation of the interactions between olive trees and their environment through a detailed characterization of the water and carbon balances of the orchard as affected by weather variables, soil attributes and management operations. The generally high level of agreement found between measured and simulated data evidence the suitability of OliveCan for estimating olive orchard dynamics. These results encourage the application of the model to simulate the growth, carbon exchange and water relations of olive orchards in a wide range of research contexts, including studies on the performance of olive trees under climate change scenarios. The development of OliveCan has also highlighted significant knowledge gaps in relation to some physiological processes and the cultivar specificity of some of the parameters. Further research on these aspects may contribute to improve the reliability of the model. Author Contributions The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Supplementary Material

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. Want more olive appetizers? Try my Olive Dip and Olive Cheese Ball! What to Serve with Blue Cheese Stuffed Olives These stuffed olives are one of my favorite blue cheese recipes. They’re simple to make, you can prep them in advance of your party and pull them out of the fridge when your guests arrive, and the blue cheese and green olive combination is salty perfection! Stuffed Olives

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. Runoff and infiltration are calculated following a Soil Conservation Service curve number methodology that was specifically calibrated and validated for different typologies of olive orchards ( Romero et al., 2007). The approach requires information on the canopy ground cover ( GC) and the soil hydrological condition ( SHC) -i.e., an indicative of the capacity of infiltration of the soil when it is wet. The water content at field capacity (𝜃 UL), wilting point (𝜃 LL) and saturation (𝜃 sat) are also needed for the computation of infiltration and all the remaining simulated processes. Apart from the weather dataset and some orchard (e.g., planting density, age, and latitude) and soil (e.g., depth, 𝜃 UL, 𝜃 LL) basic traits, the user is required to enter the initial values of GC and L v to deduce the biomasses of the different organs following simple criteria (see Supplementary Material). For the computation of FN in the first season, an estimate of dry yield for the year preceding the start of the simulation is also needed. To initialize the state variables related to phenology, simulations must start at the beginning of a year and the temperature records of the preceding 3 months must be provided. Some simulation settings such as the number of years to simulate and N must also be provided. Finally, the user is to indicate the management operations to be implemented and provide values to their parameters. Model Parameterization Continuous deficit irrigation (CDI), which applied 25% of the irrigation supplied to CON, distributed throughout the irrigation season. During the vegetative rest period and provided that fruits are not present, all the available assimilates after discounting maintenance respiration are allocated to a virtual pool of reserves. Such reserve pool is subsequently used for the growth of vegetative organs and fruits during the growth season. Fruit growth can either be source-limited or sink-limited. In the former case, the associated partitioning coefficient is fixed whereas in the latter, it is calculated as a function of the number of fruits ( FN), which in turn is modeled as a function of the number of fruits and nodes produced in the previous year. In doing so, the model may be prone to errors in the estimates of productivity and vegetative growth for a given year when performing long runs, but such errors are to be compensated if those model outputs are averaged over biennia. With regard to the vegetative organs, fixed partitioning coefficients are adopted. Whenever fruits are present, the model considers that they become the prioritary sink of assimilates, thus the vegetative partitioning coefficients are applied after discounting the fruit demand from the daily pool of assimilates. Therefore, partitioning coefficients to vegetative organs are assumed to be independent of tree size, management factors and environmental conditions, as in the model of Morales et al. (2016). As a final remark, inspired by the CERES-type models ( Jones and Kiniry, 1986), the growth of fine roots is distributed among the different layers in the two soil zones as a function of the size and water content of each soil compartment.