Bernal Pitted Green Manzanilla Olives - Catering Size 4.25kg, Stoneless

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P.S. Another olive favorite are these Olive Puffs (I think they’re delicious year-round, but especially at Halloween). All authors played a significant role in the conception and development of the model. FV led out the coding, with contributions from ÁL-B, AM, OG-T, and LT. ÁL-B, LT, and FV gathered the datasets for testing the model. ÁL-B led out the writing with significant contributions from all co-authors. Funding 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 OliveCan is subdivided into three main components (Supplementary Figure S1) that are devoted to the computation of the water and carbon balances of the olive orchard and to simulate the impacts of some management operations. The water and carbon balance components are interdependent (i.e., each one needs data provided by the other) and both of them require information on soil traits and weather data. Regulated deficit irrigation (RDI), which applied the same seasonal water as CDI, with a midsummer (July 1st to September 10th–15th) deficit period without irrigation.

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). 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 de Ciencias e Engenharia de Biossistemas, Instituto Superior de Agronomia, Universidade de Lisboa, Lisbon, Portugal When 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 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.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. 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 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. Where M i is the ith measured variable, M ¯ is the average value of all measurements, S i is the ith simulated variable and n is the number of measured values. In addition, the slope, intercept and coefficient of determination ( r 2) obtained by regressing the simulated and measured values were also used. Results 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 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

Introduction

Finally, future improvements of OliveCan might include additional sub-models for simulating nutrient uptake and the impact of pests and diseases. Apart from that, the model shows potential for being adapted to other tree species, so its interest may not be only restricted to olive researchers. Conclusion

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 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 I The model by García-Tejera et al. (2017a) is used to compute root water uptake ( RWU) from each layer in the two soil zones, canopy transpiration ( E p) and gross assimilation ( A′). By analogy with the Ohm’s law for electric circuits, the model assumes that water transport through the SPAC is driven by differences in water potential and hydraulic resistances. In this regard, three hydraulic resistances are considered: from the soil to the root-soil-interface ( R s), from the soil-root interface to the root xylem ( R r) and from the root xylem to the canopy ( R x). R s depends on soil texture, root length density ( L v), soil water content (𝜃) ( Gardner, 1960). R r is a function of L v and root permeability, the latter being mediated by 𝜃 ( Bristow et al., 1984) and temperature ( García-Tejera et al., 2016). Finally, R x is calculated from xylem anatomical traits and tree height. In the canopy, two leaf populations are considered (i.e., sunlit and shaded). For each one, gross assimilation ( A′), stomatal conductance ( g s), intercellular CO 2 concentration ( C i) and leaf water potential (Ψ l) are calculated iteratively, considering both the models by Farquhar et al. (1980) and Tuzet et al. (2003). In doing so, the environmental CO 2 concentration ( C a) is explicitly taken into account for calculating both A′ and g s on the one hand. On the other, the model requires information on the intercepted photosynthetically active radiation ( IPAR) as well as the sunlit and shaded fractions of the canopy. These inputs are provided by a simple geometric model of radiation interception which assumes a spheroidal shape for the crown and accounts for the shadowing from neighboring trees. Finally, E p is estimated from the imposed evaporation equation assuming that the canopy is coupled to the atmosphere, whereas RWU is deduced in each layer of each soil zone from the corresponding water potential differences and hydraulic resistances. Carbon Balance Component 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.This section provides an overview of the main features and processes within OliveCan. An in-depth description of the model, along with its equations and scientific rationale is given as Supplementary Material. The code of OliveCan was written in Visual Basic 6.0. Want more olive appetizers? Try my Olive Dip and Olive Cheese Ball! What to Serve with Blue Cheese Stuffed Olives