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A Nature Research Journal. Predicting photosynthetic production in olive trees is a key feature in managing the effect of climate change on arid areas.
Functional-structural plant modelling is a promising tool for achieving this goal. We used a photosynthetic sub-model that accounted for water and temperature stress and implemented it into LIGNUM model. We then conducted an experiment to validate the model at the leaf level using olive trees Olea europaea grown under various climatic condition. Then, we simulated photosynthetic production of three static olive tree models aged 1, 2, and 3 years.
Results revealed a good fit between observed and predicted photosynthesis, with coefficient of determination R 2 values of 0. These results showed that the impact of water stress on photosynthetic production was marginal.
The olive tree Olea europaea L. However, predicting the responses of olive trees to different climate scenarios remains a key challenge for agriculture. The empirical approach cannot predict novel or non-analog responses. Thus, models based on that approach are less effective when it comes to predicting crop responses to climate change 2 , 3 , 4. Furthermore, the application of empirical models in regions characterized by diverse climatic conditions is often criticized.
Hence, an approach is needed that: makes future projections that include novel responses is robust to generalization across regions takes account of horticultural techniques.
The process-based approach can meet the first two conditions but is insufficient to model the effects of cultivation techniques such as pruning 5 , 6 , 7. The functional-structural plant model, which combines process-based models with three-dimensional plant structure, can meet all three requirements 5.
This approach allows us to simulate the canopy architecture in the environment and incorporate the effect of pruning techniques, sunlight, and carbon allocation 8 , 9 , 10 , 11 , Photosynthetic production is a key feature for developing an accurate functional-structural plant model, particularly in environments with an inconsistent climate As a first step, developing a static functional-structural plant model is a conservative method of predicting photosynthesis production, without the effects of other growth-related aspects, such as carbon allocation and dynamic growth patterns.
The model can also explore the performance of the functional-structural tree model in response to temperature-stressed and water-stressed environments. This is the first time this model has been applied to arid land Hence, photosynthesis predictions must be consistent and accurate across a wide range of temperatures, soil-moistures, vapour pressure deficits, and light intensities.
We tested the validity of a parameterized Farquhar photosynthesis sub-model coupled with the LIGNUM sky model on young olive trees grown under controlled climatic conditions. In addition, we simulated the annual photosynthetic production of three different ages of static olive tree models aged 1, 2,and 3-years-old. Fifty-four 1-year-old own-rooted olive tree cultivars Olea europaea L.
Chemlali and cv. In which, trees were placed outside the growth chamber. The experimental site bioclimate is arid with hot and dry summers and mild winters.
The experiments were conducted at a average of temperature varied from 5. Meanwhile, the average of the relative humidity was The Fig. The maps were created using QGIS software version 3. Then the composed image was treated and labelled using GIMP version 2. In addition, measurements were taken under sunny and shaded conditions, and a 1-m 2 wooden board was used to create the shade.
The amount of incoming radiation was divided into direct and diffused radiation, where the diffuse photon flux density originates from the midpoint of each sector, which was estimated by applying standard overcast sky radiation. The total diffused radiation was the sum of the diffused radiation generated from all the sectors. However, the direct radiation was generated from one sector according to the position of the sun. The upper hemisphere was divided into sectors.
The sun position, direct radiation, and diffused radiation were updated every hour. The amount of photon flux received by a leaf depends on intensity of the radiation, the leaf normal direction, and the shading caused by other leaves of the crown. As in earlier applications of LIGNUM to broadleaf trees, the shadow of the woody part was ignored and leaves were fitted into ellipses 6 , However, due to the natural elliptic shape of an olive tree, we assumed that the ellipses created were the actual leaves i.
Therefore, a shading effect occurs when the light beam is intercepted by this ellipse. Therefore, we added the cumulative shading effects as a coefficient for each leaf. The calculations were done separately for direct and diffused radiation. For the direct radiation, for each time step, the light beam was tracked from the centre of the leaf to the centre of the sector where the sun existed and then the cumulative shading effect was calculated. For the diffused radiation, the light beam was tracked from the centre of the leaf to the centre of each sector.
However, since the trees were static, the calculation of this coefficient for each leaf was done only for the first-time step. The radiation intercepted by the leaf from a light beam was the dot product of the leaf normal vector and the light beam vector:.
Photosynthesis was modelled at the leaf level using a parameterized Farquhar model for olive trees. Also, the photosynthesis model was coupled to a model of stomatal conductance to account for the effects of water stress on the photosynthesis productivity. The photosynthetic sub-model takes temperature, total photon flux intercepted by the leaf, water soil content, vapour pressure deficit, and reference stomatal conductance as inputs for each time step.
Then, it calculates the hourly net CO 2 assimilation of a leaf A after subtracting leaf respiration. Weather data were provided by the national institute of meteorology-Tunisia. The vapour pressure deficit was calculated according to following equations:. The HelioClim 5 database was used for solar radiation data Horizontal sensor data were used for the diffused radiation, and normal irradiance data, i. Three static olive tree models aged 1, 2, and 3 years were used in the simulations.
All the models were based on real olive trees from which the following geometric characteristics were taken: length, diameter, segment girth, branching positions, reduction ratio, bending angles, leaf number and position.
The total leaf-areas of the three static models were 0. A systematic simulation using all possible values of input parameters was conducted to detect possible anomalies in the calculation processes or the parameters used in the model e. This fits the description of the temperature dependency function for C3 plants, such as solanaceae, gramineae, and fruit-trees 17 , 18 , Likewise, photosynthesis responses to variation in light intensity were as expected.
In fact, photosynthesis followed a logarithmic curve showing that it was very responsive to the increase in light when the intensity ranged from weak to mild. However, the sensitivity decreased as the light intensity increased, until it reached a plateau.
This can be explained by the saturation of electron excitation VPD-photosynthesis dependency is controlled directly by stomatal closure; when the deficit increases, the plant reacts by closing its stomata to avoid excessive water loss.
This makes CO 2 less available in the intracellular spaces 20 , Therefore, photosynthesis was limited, and inversely proportional to VPD see Fig. Changes in net photosynthesis production according to systematic changes in temperature, vapour pressure deficit, and photosynthetic photon flux. Under uncontrolled climatic conditions, the average of the photosynthetic production for the Chemlali cultivar was 5. Meanwhile, under the same climatic conditions the average of the photosynthetic production for the Zarrazi cultivar was 5.
Light is a sensitive environmental parameter compared to temperature or relative humidity. Light can be highly variable over a short period of time and is affected by weather conditions e.
The trend in the mean values of direct radiation during the experiment is shown in Fig. We observed that the general trend was normal, i. It is worth noting that light intensity showed mild irregularity near its maximum values from to This fluctuation had a small effect on photosynthesis production as shown in the light dependency function. However under natural sunlight these minor fluctuations were normal The relationship between measured and modelled net photosynthetic production.
According to Fig. This indicated that the model was robust across a wide range of temperatures. Similarly, for all SWC the model explained at least This clearly showed that the model predicted photosynthesis for both water-stressed and well-irrigated trees.
These results confirm the findings of previous studies 23 , 24 , The model accurately predicted the rate of photosynthesis under water stress. However, this is potentially misleading as the RMSE shows that photosynthesis tended to be smaller when the trees were under water stress, which can increase the R 2 without a real increase in significance. However, further observations revealed that the model tended to underestimate photosynthesis for the Zarrazi cultivar.
Otherwise, when the photosynthetic production was close to its peak, the Chemlali cultivar recorded higher rate of photosynthesis production than the Zarrazi cultivar. This might have been due to the difference in the genetic characteristics between these two cultivars 26 , Thus, the Zarrazi cv appears to be a distant relative of the Chemlali cv Such relatedness would be observable some phenotypic characteristics, such as tree canopy or growth dynamic The Chemlali cv leaf area appeared to be more closely related to the Manzanilla cv, which was used to calibrate and validate the photosynthetic model.
This could explain the slightly better performance of the model for the Chemlali cv These cultivars adopt different strategies to overcome water stress.
Drought Stress Effects and Olive Tree Acclimation under a Changing Climate
Increasing consciousness regarding the nutritional value of olive oil has enhanced the demand for this product and, consequently, the expansion of olive tree cultivation. Although it is considered a highly resilient and tolerant crop to several abiotic stresses, olive growing areas are usually affected by adverse environmental factors, namely, water scarcity, heat and high irradiance, and are especially vulnerable to climate change. In this context, it is imperative to improve agronomic strategies to offset the loss of productivity and possible changes in fruit and oil quality. To develop more efficient and precise measures, it is important to look for new insights concerning response mechanisms to drought stress.
Photosynthetical activity modelisation of olive trees growing under drought conditions
C Corresponding author. Email: sofo unibas. After 20 d without irrigation, mean predawn leaf water potential fell from —0. In particular, a marked increase in APX activity was found in leaves of plants at severe drought stress. CAT activity increased during severe water deficit conditions in leaves and fine roots. The patterns of POD and IAA oxidase activity ran in parallel and showed increases in relation to the degree of drought. In contrast, PPO activity decreased during the progression of stress in all the tissues studied.
Olive trees Olea europaea L. This species has developed a series of physiological mechanisms, that can be observed in several plants of the Mediterranean macchia, to tolerate drought stress and grow under adverse climatic conditions. These mechanisms have been investigated through an experimental campaign carried out over both irrigated and drought-stressed plants in order to comprehend the plant response under stressed conditions and its ability to recover. Experimental results show that olive plants subjected to water deficit lower the water content and water potentials of their tissues, establishing a particularly high potential gradient between leaves and roots, and stop canopy growth but not photosynthetic activity and transpiration. Active and passive osmotic adjustment due to the accumulation of carbohydrates in particular mannitol and glucose , proline and other osmolytes have key roles in maintaining cell turgor and leaf activities. At severe drought-stress levels, the non-stomatal component of photosynthesis is inhibited and a light-dependent inactivation of the photosystem II occurs.