Changes in Water-Use Efﬁciency of Eucalyptus Plantations and Its Driving Factors in a Small County in South China

: Ecosystem water-use efﬁciency ( WUE ) has been central in revealing the variability in terrestrial carbon and water cycles. Short-rotation plantations such as Eucalyptus plantations can simultaneously impact net primary production ( NPP ) and actual evapotranspiration ( ET a ), components of WUE , resulting in changes in terrestrial carbon and water cycles. However, there are few detailed studies on the changes in the WUE of Eucalyptus plantations at the catchment scale with high spatial remote sensing imagery. Here, we present the changes in the WUE of Eucalyptus plantations and its driving factors (i.e., NPP and ET a ) using satellite-based models combined with 5-m spatial resolution RapidEye imagery in a small county in South China. The increases in ET a of Eucalyptus plantations are primarily the result of climate warming and result in low WUE of Eucalyptus plantations. The management practice used (short rotation in this study) can enhance the effect of climate warming on WUE by varying the NPP of Eucalyptus plantations. A high value of NPP leads to a high WUE of Eucalyptus plantations at the end of a short rotation, while a low value of NPP results in a low WUE at the beginning of another short rotation. Changes in the WUE of Eucalyptus plantations indicated large spatial and temporal variability, associated with climate warming and short-rotation practices.


Introduction
Ecosystem water-use efficiency (WUE) has been central in revealing the variability in terrestrial carbon and water cycles and exploring the relationship between productivity and water use [1][2][3].However, spatially significant differences in environmental factors can lead to different changes in WUE.Any disturbance of one element of WUE (i.e., photosynthesis or ET a ) can cause the other to change [3].Tree planting can constantly disturb the terrestrial carbon and water cycles.Tree planting is also considered as a successful strategy for achieving carbon neutrality targets in China [4] and a mitigated strategy for attaining the targets of the Paris Agreement in fighting climate change [5].Therefore, there will be an increasing trend in establishing tree plantations in the future, which will have a significant impact on carbon and water cycles.Hence, to explore the relationship between tree plantations and WUE would improve our understanding of the coupling between terrestrial carbon and the water cycle under global warming.
Tree plantations usually increase both ecosystem productivity or carbon storage and ET a simultaneously [6][7][8].However, the different magnitudes, representing the sensitivity of different types of plantations to environmental disturbance, have not been fully explored.The presence of plantations is expanding steadily worldwide, especially in China [9], but they disrupt the water cycle as well [10].For example, Eucalyptus is a fast-growing Water 2023, 15, 2754 2 of 13 and preferred genus for plantations and has been planted worldwide due to its great commercial importance, specifically in South China.Eucalyptus plantations can increase carbon storage with increasing stand age [11], but they can consume more water than other forests [12], which may result in low WUE.The WUE of Eucalyptus plantations varies among genotypes [13][14][15].These variations have also been commonly attributed to the environment [15] and management practices [16][17][18].Therefore, the most important issue is how to sustainably manage plantations to attain carbon neutrality targets and reduce water stress under climate warming, as commercial forestry should be repudiated if it cannot benefit the environment [19].
Due to its large Eucalyptus plantation coverage, the Guangxi Zhuang Autonomous Region in South China is ideal for estimating NPP and ET a and then exploring the changes in WUE of Eucalyptus plantations at the catchment scale.Here, we reveal a high spatial variability of WUE in Eucalyptus plantations by exploring the NPP and ET a responses to short rotations and climate warming using RapidEye imagery with a 5-m spatial resolution.First, Eucalyptus plantations were classified using a classification and regression tree (CART) method for 2011-2019 in a small county in South China.Second, the NPP and ET a of Eucalyptus plantations were calculated with modified satellite-based Carnegie-Ames-Stanford Approach (CASA) and Priestly-Taylor Jet Propulsion Laboratory (PT-JPL) models, respectively.In addition, changes in the WUE of Eucalyptus plantations and their driving factors were analyzed.

Study Area
Huangmian, a small county covering approximately 499 km 2 in the Guangxi Zhuang Autonomous Region (Figure 1), South China, was chosen as the experimental site because Eucalyptus (mainly Eucalyptus grandis) has been cultivated in plantations there for at least 20 years [20].Eucalyptus plantations currently occupy more than 30% of the total land area of Huangmian County.and preferred genus for plantations and has been planted worldwide due to its great commercial importance, specifically in South China.Eucalyptus plantations can increase carbon storage with increasing stand age [11], but they can consume more water than other forests [12], which may result in low  .The  of Eucalyptus plantations varies among genotypes [13][14][15].These variations have also been commonly attributed to the environment [15] and management practices [16][17][18].Therefore, the most important issue is how to sustainably manage plantations to attain carbon neutrality targets and reduce water stress under climate warming, as commercial forestry should be repudiated if it cannot benefit the environment [19].Due to its large Eucalyptus plantation coverage, the Guangxi Zhuang Autonomous Region in South China is ideal for estimating  and  and then exploring the changes in  of Eucalyptus plantations at the catchment scale.Here, we reveal a high spatial variability of  in Eucalyptus plantations by exploring the  and  responses to short rotations and climate warming using RapidEye imagery with a 5-m spatial resolution.First, Eucalyptus plantations were classified using a classification and regression tree (CART) method for 2011-2019 in a small county in South China.Second, the  and  of Eucalyptus plantations were calculated with modified satellite-based Carnegie-Ames-Stanford Approach (CASA) and Priestly-Taylor Jet Propulsion Laboratory (PT-JPL) models, respectively.In addition, changes in the  of Eucalyptus plantations and their driving factors were analyzed.

Study Area
Huangmian, a small county covering approximately 499 km 2 in the Guangxi Zhuang Autonomous Region (Figure 1), South China, was chosen as the experimental site because Eucalyptus (mainly Eucalyptus grandis) has been cultivated in plantations there for at least 20 years [20].Eucalyptus plantations currently occupy more than 30% of the total land area of Huangmian County.The RapidEye multispectral satellite image data with a 5-m spatial resolution (https: //earth.esa.int/eogateway/missions/rapideye,accessed on 19 May 2022) was used in this study.We tried to separate Eucalyptus plantations from other forest types and then estimated their ET a and transpiration with the PT-JPL model in late summer and autumn (mainly August to October) once every two years (biennially) from 2011 to 2019.As four RapidEye images were needed to cover the study area at each time slice, it required 20 RapidEye images for the full study period (Table 1).There was less than 0.2% cloud cover in images on 16 October 2011, but all clouds were located outside the Huangmian County territory.Approximately 0.5% of pixels with cloud cover in the northwest part of the study area in images on 24 August 2019 were omitted from image processing.

Meteorological Data
We collected data for eight key variables: daily mean air temperature; maximum and minimum air temperature; precipitation; air pressure; relative humidity; sunshine hours; and wind speed from four meteorological stations (Guilin, Liuzhou, Rong'an, and Mengshan: Figure 1b) from the China Meteorological Data Service Center (http: //data.cma.cn(accessed on 10 July 2022)).Gridded meteorological data for all eight key variables from these four meteorological stations were generated with the inverse distance weighting (IDW) interpolation module of ArcMap (Esri Inc., Redlands, CA, USA, Version 10.3).Finally, the gridded net radiation (R n ) and potential evapotranspiration (PET) were computed using the Food and Agricultural Organization (FAO) Penman-Monteith equation [21] combined with meteorological data.

Classification and Regression Tree (CART)
CART was proposed by Breiman et al. [22].It uses a Gini index for its impurity function [23].First, it builds a large tree and then trims the tree to a smaller size with 10-fold (default) cross-validation to minimize the estimate error rates.This trimming procedure can solve underfitting and overfitting problems [24].In image classification, CART analyzes all explanatory variables, including spectral and ancillary data, and determines the best binary division of a single explanatory variable for reducing the deviance in the response variable, such as the list of land use types [25].
The red edge spectral band can be used as a sensitive discriminator of Eucalyptus plantations [26].We therefore applied the normalized difference red edge index (NDV I Re ) [27] to enhance the differences in spectral information between Eucalyptus plantations and other forest types.
where red edge and red are the red edge and red bands of the multispectral RapidEye imagery, respectively.Principal component analysis (PCA) was applied to transform the spectral information in the RapidEye imagery.Next, the spectral traits (i.e., mean, variance, contrast, dissimilarity, homogeneity, correlation, entropy, and second moment) were extracted to represent the texture information with the Co-occurrence Measures tool of ENVI, version 5.3 (Exelis Visual Information Solutions, Boulder, Colorado).Finally, the CART algorithm combined with vegetation indices and spectral and texture information was used to separate Eucalyptus plantations from all other forest types (Figure 2).CART was proposed by Breiman et al. [22].It uses a Gini index for its impurity function [23].First, it builds a large tree and then trims the tree to a smaller size with 10-fold (default) cross-validation to minimize the estimate error rates.This trimming procedure can solve underfitting and overfitting problems [24].In image classification, CART analyzes all explanatory variables, including spectral and ancillary data, and determines the best binary division of a single explanatory variable for reducing the deviance in the response variable, such as the list of land use types [25].
The red edge spectral band can be used as a sensitive discriminator of Eucalyptus plantations [26].We therefore applied the normalized difference red edge index ( ) [27] to enhance the differences in spectral information between Eucalyptus plantations and other forest types.

𝑁𝐷𝑉𝐼 =
(1) where red edge and red are the red edge and red bands of the multispectral RapidEye imagery, respectively.Principal component analysis (PCA) was applied to transform the spectral information in the RapidEye imagery.Next, the spectral traits (i.e., mean, variance, contrast, dissimilarity, homogeneity, correlation, entropy, and second moment) were extracted to represent the texture information with the Co-occurrence Measures tool of ENVI, version 5.3 (Exelis Visual Information Solutions, Boulder, Colorado).Finally, the CART algorithm combined with vegetation indices and spectral and texture information was used to separate Eucalyptus plantations from all other forest types (Figure 2).

Spectral Vegetation Indices and Fractional Green Vegetation Cover (𝐹 )
As , ration vegetation index (), soil adjusted vegetation index (), leaf area index (), fractional green vegetation cover ( ), and other aforementioned meteorological variables are the main inputs of the CASA and PT-JPL models, we calculated the  ,  , and  with the near infrared (  ) andred bands of multispectral RapidEye imagery as follows: where the values of NDV I max and NDV I min are 0.95 and 0.05, respectively [28].

Carnegie-Ames-Stanford Approach (CASA) Model
The CASA model has been widely applied to address the spatial and temporal variability of NPP at different scales [29][30][31].The NPP can be computed as: where LUE is the light-use efficiency, FPAR is the fraction of absorbed photosynthetically active radiation, and SOL is the solar radiation.FPAR can be calculated as follows: where FPAR max and FPAR min are 0.95 and 0.001, respectively, and have nothing to do with vegetation types.NDV I min and NDV I max are the values of 5% and 95% of the NDV I, respectively.RV I min and RV I max are the values of 5% and 95% of the RV I, respectively.
To overcome the overestimation and underestimation of FPAR, this value was set to the average of the FPAR NDV I and FPAR RV I values [32]: LUE can be calculated as follows: where T ε1 is the temperature stress in production under high-and low-temperature conditions; T ε2 is the temperature stress of changing from optimal temperature condition to highor low-temperature conditions; and W ε is the water stress: Water 2023, 15, 2754 where T opt is the optimum temperature and was set to 25 • C [28]; LUE max is the maximum light-use efficiency and was set to 0.768 g C MJ −1 according to Reference [33] in this study.

Priestly-Taylor Jet Propulsion Laboratory (PT-JPL) Model
The PT-JPL model was developed by Fisher et al. [34] for estimating ET a .It partitions the evapotranspiration rate into canopy transpiration (T r ), soil evaporation (E soil ), and canopy interception evaporation (E i ).These components can be computed as: where λ is the latent heat of vaporization and is set to 2.45 MJ kg −1 , α is a constant and is set to 1.26 (unitless), ∆ is the slope of the saturation-to-vapor pressure curve (kPa • C −1 ), γ is the psychrometric constant (0.665 × 10 −3 × P kPa • C −1 ), G is the ground heat flux (W m −2 ) and can be ignored for daily time steps [21], R ns is the net radiation to the soil (W m −2 ) calculated as R ns = R n × (1 − F c ) [28], and R nc is the net radiation to the canopy (W m −2 ) calculated as R n × F c .f wet is the relative surface wetness, f SM is the soil moisture constraint, f T and f M are constraints for plant temperature and moisture, respectively.f wet , f SM , f T , and f M can be calculated as follows: where RH is the relative humidity (%), VPD is the saturation vapor pressure deficit (kPa), β is the soil moisture constraint to VPD and is set to 1.0 kPa [34], T a is the air temperature ( • C), f APAR is the fraction of photosynthetically active radiation (PAR) (unitless), and f APAR max is the maximum of f APAR .
Here, f APAR is calculated as follows [35]:

Water-Use Efficiency (WUE ) of Eucalyptus Plantations
We defined WUE of Eucalyptus plantations as the ratio of NPP to ET a in this study, and it can be calculated with simulated NPP and ET a values as follows (g C m −2 mm −1 ):

Validation of the Classification of Eucalyptus Plantations
To validate the classification results of Eucalyptus plantations based on CART, we used historical data of cultivated Eucalyptus plantations in Huangmian County from 2009 to 2018.These data were obtained from the Guangxi Huangmian State Owned Forest Farm.
The Kappa coefficient (k) test was used to evaluate the CART classification performance, and is defined as [36]: where P o and P e are the probabilities of the observed agreement and expected agreement by chance, respectively.

Classification of Eucalyptus Plantations
Our evaluation of the classification results indicated a reasonably accurate separation of Eucalyptus plantations, as inferred from relatively high Kappa accuracy coefficients when using historical data of annual Eucalyptus plantation cultivation (Table 2

NPP of Eucalyptus Plantations
The increasing trend of NDV I and F c was similar to the change in the area of Eucalyptus plantations (Table 3), increasing during the study period.In contrast, the mean daily NPP peaked in 2017 and decreased to approximately 3.7 g C m −2 in 2019, indicating that 2017 might be the end of a short rotation and that 2019 should be the beginning of another short rotation of Eucalyptus plantations.Correlation analysis confirmed that for all areas with Eucalyptus plantations, NDV I, and F c did not have a significant impact on NPP.Therefore, information about stand ages and structure should significantly improve the accuracy in estimating productivity and its change trend in future studies.

Actual Evapotranspiration (ET a ) and Transpiration of Eucalyptus Plantations
The daily ET a of Eucalyptus plantations displayed different spatial patterns associated with different periods (Figure 3).The daily ET a and transpiration of Eucalyptus plantations were highest on 24 August 2019, indicating that ET a and transpiration might have been affected by other factors, especially by temperature (Table 4).Temperature had a significant impact on both ET a (p-value = 0.009) and transpiration (p-value = 0.01) in Eucalyptus plantations according to correlation analysis.In contrast, neither the area of Eucalyptus plantations, NDV I, nor F c had a significant impact on ET a and transpiration.The ratio of transpiration to ET a of Eucalyptus plantations was significantly high, higher than 66% for all study time slices, indicating that Eucalyptus plantations lose more water via transpiration to the atmosphere.In particular, we highlight that transpiration accounted for more than 82% of ET a on 16 October 2011.

Water-Use Efficiency (𝑊𝑈𝐸) of Eucalyptus Plantations
The  of Eucalyptus plantations also showed various spatial patterns associated with different periods (Figure 4).However, in contrast to  , the  of Eucalyptus plantations was the lowest on 24 August 2019, indicating that high evapotranspiration or transpiration with low net primary production results in low  of Eucalyptus plantations in the late summer and autumn period (Table 5).Neither forest cover changes (including the area of Eucalyptus plantations, , and  ) nor climate warming had an impact on the  of Eucalyptus plantations.Indirect effects of forest cover changes and

Water-Use Efficiency (WUE) of Eucalyptus Plantations
The WUE of Eucalyptus plantations also showed various spatial patterns associated with different periods (Figure 4).However, in contrast to ET a , the WUE of Eucalyptus plantations was the lowest on 24 August 2019, indicating that high evapotranspiration or transpiration with low net primary production results in low WUE of Eucalyptus plantations in the late summer and autumn period (Table 5).Neither forest cover changes (including the area of Eucalyptus plantations, NDV I, and F c ) nor climate warming had an impact on the WUE of Eucalyptus plantations.Indirect effects of forest cover changes and climate warming on the WUE might occur by altering other vegetation properties (i.e., stand ages and structure) and evapotranspiration, respectively.Climate warming increased ET a (Table 4) and then led to decreases in the WUE of Eucalyptus plantations.

Discussion 219
Eucalyptus is not native to China; however, it has been widely planted and its plantation estate has 220 expanded owing to its high wood productivity and economic benefits [5].By the end of 2015, Eucalyptus 221 plantation covered an area of 4.5 million hectares across China [30]; this is expected to increase by 0.2 222 million hectares annually [31,32].Guangxi Zhuang Autonomous Region, as noted before, is the province 223 in China with the largest extent of Eucalyptus plantation; the area has expanded to more than 1.8 million 224

Discussion
Eucalyptus is not native to China.However, it has been widely planted and its plantation estate has expanded owing to its high wood productivity and economic benefits [6].By the end of 2015, Eucalyptus plantations covered an area of 4.5 million hectares across China [37].This is expected to increase by 0.2 million hectares annually [38,39].The Guangxi Zhuang Autonomous Region, as noted before, is the province in China with the largest extent of Eucalyptus plantations.The area has expanded to more than 1.8 million hectares in just the last decade and is predicted to increase in years to come.Huangmian, a small county in the Guangxi Zhuang Autonomous Region, witnessed an increase in the total area of Eucalyptus plantations from 2011 to 2019, accounting for at least 38% of the county's territory, which has come at the expense of declines in areas of other forest types.Such large-scale Eucalyptus plantations could have serious impacts, such as biodiversity reduction, soil degradation, and regional water security [38,40], consequently threatening the attainment of sustainable plantations.Hence, evaluating the relationship between Eucalyptus plantations and regional water cycles should be a priority for sustainable plantation forestry.
Accurately estimating evapotranspiration helps in achieving sustainable tree plantations and efficient water use management [8,41].We analyzed the ET a and transpiration of Eucalyptus plantations using a modified PT-JPL model combined with RapidEye multispectral images taken at a 5-m spatial resolution.We found that the value of daily ET a , especially the transpiration of Eucalyptus plantations, was consistent with the transpiration of Eucalyptus plantations worldwide, ranging from 0.5 to 6 mm per day [8,42].ET a and transpiration depend not only on vegetation growth dynamics but also on climatic factors and management practices [43].Within a small county under the same site and climate conditions in South China, our study found that both the ET a and transpiration of Eucalyptus plantations were significantly impacted by climate warming.Climate warming can indeed increase evapotranspiration [44,45], including in Eucalyptus plantations [46,47], and result in low WUE in Eucalyptus plantations.There was significant evidence of lower WUE in Eucalyptus plantations on 24 August 2019, with a high daily mean temperature of 30.32 • C, which was predominantly reflected by a higher ET a and a lower NPP in Eucalyptus plantations.WhiteHead and Beadle [48] mentioned that when temperature extremes are high, the productivity (i.e., photosynthesis) of Eucalyptus plantations is much lower than expected, which may result in lower WUE.Hubbard et al. [15] also confirmed that high temperature could result in low growth, high transpiration, and low WUE in Eucalyptus plantations in Brazil.The lower net photosynthesis rate is primarily due to stomatal regulation, which could be a water-saving strategy to address the drier conditions induced by warming [49].The daily mean temperature was higher than 30 • C on 24 August 2019, it was not extremely high but could still induced water stress in South China even with adequate water conditions.Therefore, management practice (i.e., short rotation in this study) enhanced the effect of climate warming on NPP and then decreased the WUE of Eucalyptus plantations.There was a higher WUE at the end of a short rotation (during 2017 in this study) and a lower WUE at the beginning of another short rotation (during 2019 in this study).A previous study confirmed that successive rotations decreases carbon storage in Eucalyptus plantations [50].In addition, other management practices, such as fertilization, mixed-species plantations, and irrigation, could enhance the WUE of Eucalyptus plantations by promoting available resource use [51] and increasing photosynthetic capacity [16] to gain much more biomass and carbon stock per unit water use [18].
The WUE is not a constant for any species but varies according to the combination of stand age, growth dynamics, climate, and site conditions [52,53].Therefore, in addition to management practices, stand ages, canopy structure, and physiological processes should be monitored to better evaluate productivity, ET a and transpiration which will then improve accuracy when estimating the WUE of Eucalyptus plantations under future global warming.Other net productivity datasets such as forest inventory or airborne LiDAR productions, which could capture the aboveground forest biomass, should be considered to improve the accuracy of net primary production capturing and thus, WUE estimation.A previous study pointed out that plantations almost always had a higher water demand than the corresponding reference native forests [54].Therefore, expanding short-rotation Eucalyptus plantations should strive to reach a compromise between production goals and eco-environmental benefits under future global warming.

Conclusions
We estimated the NPP with a modified CASA model, ET a and transpiration of Eucalyptus plantations with a modified PT-JPT model combined with 5-m spatial resolution RapidEye imagery, and then explored the changes in the WUE of Eucalyptus plantations and its driving factors.Our results showed that high daily ET a significantly decreased the WUE of Eucalyptus plantations.Both the daily ET a and transpiration of Eucalyptus plantations were primarily impacted by temperature.Climate warming will increase evapotranspiration, decrease net photosynthesis, and result in low WUE in Eucalyptus plantations.Management practices (i.e., short rotation in this study) can enhance the effect of climate warming on WUE by varying NPP of Eucalyptus plantations.High NPP results in high WUE of Eucalyptus plantations at the end of rotations, while low NPP leads to low WUE at the beginning of successive rotations.However, some uncertainty remains as to whether there is interannual or growing season variation in the daily aboveground primary productivity of Eucalyptus plantations, which will result in different temporal and spatial patterns of WUE.Detection of changes in aboveground primary productivity using forest inventory data, LiDAR, or remote sensing-based primary productivity productions with high spatial resolution and subdividing species-specific stand levels will be helpful in improving the accuracy in exploring the WUE of Eucalyptus plantations.As both the ET a and transpiration were high and WUE was low in Eucalyptus plantations under increased temperature conditions, any practices (i.e., intensive rotation or expanding) in changing the NPP of Eucalyptus plantations should balance production goals and sustainable water resource management under future global warming.This is particularly important in South China, especially in the Guangxi Zhuang Autonomous Region, since this region is the country's leader in terms of Eucalyptus plantation coverage.
The digital elevation model (DEM) of Huangmian County shows elevations ranging from 60 to 845 m.The soil type is mainly Ach Haplic Acrisols according to the Soil and Terrain (SOTER) database obtained from the National Earth System Science Data Center (http://soil.geodata.cn(accessed on 30 June 2022)).Huangmian County has a monsoonal climate with an average annual precipitation ranging from 1750 to 2000 mm and an average annual temperature of approximately 21 • C. Water 2023, 15, x FOR PEER REVIEW 2 of 13 The digital elevation model (DEM) of Huangmian County shows elevations ranging from 60 to 845 m.The soil type is mainly Ach Haplic Acrisols according to the Soil and Terrain (SOTER) database obtained from the National Earth System Science Data Center (http://soil.geodata.cn(accessed on 30 June 2022)).Huangmian County has a monsoonal climate with an average annual precipitation ranging from 1750 to 2000 mm and an average annual temperature of approximately 21 °C.

Figure 1 .
Figure 1.Study site location and soil types.(a) Guangxi Zhuang Autonomous Region within China, and (b) Huangmian County located in the Guangxi Zhuang Autonomous Region.(c) Soil units of Huangmian County.

Figure 2 .
Figure 2. Flow chart illustrating the separation of Eucalyptus plantations from other forest areas.

Figure 2 . 2 )
Figure 2. Flow chart illustrating the separation of Eucalyptus plantations from other forest areas.2.3.2.Spectral Vegetation Indices and Fractional Green Vegetation Cover (F c ) As NDV I, ration vegetation index (RV I), soil adjusted vegetation index (SAV I), leaf area index (LAI), fractional green vegetation cover (F c ), and other aforementioned meteorological variables are the main inputs of the CASA and PT-JPL models, we calculated

Table 1 .
Details of RapidEye multispectral images used in this study.
).The total coverage of Eucalyptus plantations increased from approximately 152 km 2 in 2011 to 191 km 2 in 2019, reaching approximately 38% of Huangmian County territory, indicating that the area occupied by Eucalyptus plantations has increased over time.

Table 2 .
Accuracy of distinguishing Eucalyptus plantations in the study region.

Table 3 .
Change characteristics of NDV I, F c , and NPP of Eucalyptus plantations.

Table 4 .
A high ratio of transpiration to ET a was revealed in Eucalyptus plantations during late summer and autumn.

Table 5 .
Characteristics of the WUE of Eucalyptus plantations during late summer and autumn periods.