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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. 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. RESP M is calculated as a function of temperature and biomass, and it is subtracted directly from the pool of assimilates. Whenever maintenance respiration exceeds the pool of assimilates, the deficit is discounted from the reserve pool. The remaining assimilates are distributed among the different organs with partitioning rules being mediated by phenology. The loss of carbon during the synthesis of new biomass was included by calculating a production value ( PV) ( Penning de Vries et al., 1974) for each type of organ according to its biochemical composition.
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. Control irrigation (CON), which applied the required water to match the maximum ET, based on the fully replenishing soil water extraction from April to October. 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 A dry martini! Ha, ok so you’re having a party and including these stuffed olives on the menu? Here are a few other party appetizers that would pair nicely with these blue cheese olives.
Supplementary Material
Want more olive appetizers? Try my Olive Dip and Olive Cheese Ball! What to Serve with Blue Cheese Stuffed Olives 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
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 MaterialOlive 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.
The goal of this study is to present and test a process-oriented model integrating existing knowledge on the growth and development processes of olive orchards and capable to account for the impacts of water stress, management and climate on their productivity, in the absence of nutrient deficiencies, diseases and pests. The model, hereafter named ‘OliveCan’ -which comes from ‘Olive Canopy-,’ was formulated using the models by Testi et al. (2006); Morales et al. (2016) and García-Tejera et al. (2017a) as starting point. Materials and Methods Model Description Model 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). 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.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). 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 During the development of the model, it became apparent that our current understanding of some of the physiological processes to be simulated was limited. For example, timing of vegetative bud break, dynamics of leaf senescence, fruit photosynthesis and the use of reserves are among the phenomena that have received less attention in the literature. Also, OliveCan is missing a sub-model aimed to properly simulate the dynamics of oil accumulation during the fruit growth period. Further research on these and other topics (e.g., alternate bearing) are clearly needed and might result in model improvements through either a more consistent parametrization or the formulation of better equations for simulating such processes. 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. Conclusionde Ciencias e Engenharia de Biossistemas, Instituto Superior de Agronomia, Universidade de Lisboa, Lisbon, Portugal 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 Considering all the simulations together, the maximum simulated oil yield was 358 g m -2 (Table 1), which is comparable to the maximum values estimated by the model of Morales et al. (2016) and to available experimental data ( Villalobos et al., 2006; Pastor et al., 2007). Simulated values of radiation use efficiency for oil production (i.e., the amount of oil produced per unit of intercepted PAR) averaged over biennia ranged between 0.17 and 0.10 g MJ -1. These estimates are within the range of variation found by Villalobos et al. (2006) across a wide range of commercial orchards in Southern Spain. 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 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
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