Simulation of Photosynthetic Quantum Efficiency and Energy Distribution Analysis Reveals Differential Drought Response Strategies in Two (Drought-Resistant and -Susceptible) Sugarcane Cultivars

Abstract

Selections of drought-tolerant cultivars and drought-stress diagnosis are important for sugarcane production under seasonal drought, which becomes a crucial factor causing sugarcane yield reduction. The main objective of this study was to investigate the differential drought-response strategies of drought-resistant (‘ROC22’) and -susceptible (‘ROC16’) sugarcane cultivars via photosynthetic quantum efficiency (Φ) simulation and analyze photosystem energy distribution. Five experiments were conducted to measure chlorophyll fluorescence parameters under different photothermal and natural drought conditions. The response model of Φ to photosynthetically active radiation (PAR), temperature (T), and the relative water content of the substrate (rSWC) was established for both cultivars. The results showed that the decreasing rate of Φ was higher at lower temperatures than at higher temperatures, with increasing PAR under well-watered conditions. The drought-stress indexes (ε_D) of both cultivars increased after rSWC decreased to the critical values of 40% and 29% for ‘ROC22’ and ‘ROC16’, respectively, indicating that the photosystem of ‘ROC22’ reacted more quickly than that of ‘ROC16’ to water deficit. An earlier response and higher capability of nonphotochemical quenching (NPQ) accompanied the slower and slighter increments of the yield for other energy losses (Φ_NO) for ‘ROC22’ (at day5, with a rSWC of 40%) compared with ‘ROC16’ (at day3, with a rSWC of 56%), indicating that a rapid decrease in water consumption and an increase in energy dissipation involved in delaying the photosystem injury could contribute to drought tolerance for sugarcane. In addition, the rSWC of ‘ROC16’ was lower than that of ‘ROC22’ throughout the drought treatment, suggesting that high water consumption might be adverse to drought tolerance of sugarcane. This model could be applied for drought-tolerance assessment or drought-stress diagnosis for sugarcane cultivars.

1. Introduction

Sugarcane is a major crop for sugar and bio-energy worldwide and essential for agricultural productivity and economic growth [1]. In China, sugar from sugarcane exceeds 90% of the total produced sugar; however, over 80% of the sugarcane planting area spans the slopes of arid lands in south China without irrigation facilities and where seasonal drought has become a major natural disaster. Seasonal drought is reflected in spatial–temporal characteristics based on the drought index of continuous days without precipitation [2]. Yield reduction caused by seasonal drought has gravely restricted the development of the sugarcane industry.

Several pot experiments and field studies have been conducted to understand the biological properties and improve the phenotypic and genotypic characteristics of sugarcane varieties under drought. Morphological parameters, including the leaf area, root and shoot weight, and stalk characteristics (length/height and diameter) of sugarcane, have been analyzed under drought induced via polyethylene glycol (PEG)treatment and withheld irrigation [3,4,5]. Both the drought-resistant and drought-susceptible genotypes showed decreased net photosynthetic rates (P_n), stomatal conductance (g_s), PSII quantum efficiency (Φ_PSII), and leaf relative water content; however, the genotypes exhibited increased nonphotochemical quenching (NPQ), malondialdehyde (MDA), proline, superoxide dismutase (SOD),and ascorbate peroxidase (APX) contents, which were higher in drought-resistant than in drought-susceptible genotypes [6,7,8]. Yield component analysis confirmed that the drought-resistant variety exhibited higher productivity than did the drought-susceptible variety due to its superior stalk characteristics (height, weight, and number) [9]. Moreover, studies on the roles of specific candidate genes (belonging to the abscisic-acid (ABA)-dependent pathway) in the drought-stress responses of resistant and susceptible sugarcane clones laid the foundation and direction for improving drought resistance of sugarcanes through marker-assisted breeding [10].

Process-based models have also been developed to estimate photosynthesis, biomass production, dry matter partitioning, and sugar accumulation for informed decision-making in sugarcane production [11,12]. Combining the irrigation module and the gross margin calculator with the crop growth and water balance model could simulate the yield and survival of newly planted ratoon sugarcane under different water-allocation and climate scenarios, supported with field irrigation management [13]. The accuracy of biomass simulation was substantially increased via further improving the transpiration efficiency and root-length density aspects of the model. These factors are determined throughthe genetic variations of sugarcane to water deficit; hourly transpiration traits, such as midday flattening and the conductance limit; and the hydraulic conductivity in each soil layer [14]. Moreover, the sugarcane model could achieve remote and large-scale monitoring of sugarcane irrigation when improved with innovative techniques and methods. These techniques involve adding a thermal infrared index coupled with a remote sense of the fraction of intercepted photosynthetically active radiation (fiPAR), or satellite thermal infrared [15,16,17]. However, almost all models are inseparable from simulation of photosynthetic performance, which is essential to plant growth as the source of matter and energy.

As a photosynthesis probe, chlorophyll fluorescence (ChlF) parameters have been applied to measure photosystem operation efficiency rapidly and nondestructively under normal and stressful circumstances [18]. The photosystem is directly affected by drought via stomatal closure triggered by ABA signals transferred from the roots [19,20], and thus could be used to evaluate the performances of plants subjected to drought stress. Decreases in the maximum quantum efficiency of PSII photochemistry (F_v/F_m), Φ_PSII, photochemical quenching (q_P), and the electron transport rate (ETR) accompanied increases in nonphotochemical quenching (NPQ), fast-relaxing NPQ (q_E), slowly relaxing NPQ (q_I), and the yield induced via downregulatory processes (Φ_NPQ); Φ_NO had been shown under drought stress in many studies [21,22,23,24].

Among the chlorophyll fluorescence parameters, the reliability of F_v/F_m in evaluating the performance of sugarcane cultivars under drought stress was verified in a previous study [23]. The decreasing rate of F_v/F_m was significantly higher in drought-tolerance cultivars than that in drought-susceptible cultivars under drought stress [25]. Though F_v/F_m could become a stable drought-resistance indicator for sugarcane cultivars, considering its slow and slight decrease during drought stress (mostly 2~15%) [3,23,25], we chose to simulate another important parameter, Φ (namely Φ_PSII), under various water statuses, and evaluated sugarcane drought tolerance through analysis of the fitted parameters for two reasons: (1) Φ provides information on the noncyclic electron transport rate through PSII [18] and photoinhibition based on inactivation of PSII reaction centers [26] and exhibits a large varied amplitude (47.5%~64.3% decrease) with aggravation of drought in C₄ plants, including sugarcane and reed [24,27]; (2) Φ is directly related to the ETR, which provides energy to biochemical reactions of CO₂ assimilation [28], thus having been used to estimate photosynthetic rates via the modified FvCB model, which describes the relationship between the rate of carboxylation for the C₄ cycle (V_p) and the rate of ATP production driven via e^- transport (J_ATP), calculated from ΔF/F_m′ (equal to Φ, where ΔF = F_m′ − F_s,) [29]. Two other parameters, NPQ and Φ_NO, were chosen in this study to analyze energy distribution under drought. The former represented total energy dissipation into heat loss containing energy-dependent, zeaxanthin-dependent, and photoinhibitory quenching [30], and the latter has been used to evaluate the occurrence of physiological damage accumulation during drought [24] and post-drought recovery [31].

Thus, the main objective of this study was to establish the relationship between photosynthetic quantum efficiency and water status and reveal the different drought-response patterns in drought-tolerant and -susceptible cultivars via analyzing the energy distribution associated with drought stress. This study provides a new perspective for sugarcane drought-tolerance assessment and drought-stress diagnosis.

2. Results

2.1. Photothermal Model Parameterization and Its Biological Significance

Φ decreased with rising or falling temperature from T_o under three given PAR levels, and the decreasing rate intensified with increasing PAR. The Φ_bL values were lower than the Φ_bH values under all three given PAR levels, indicating that the sugarcane photosystem is more sensitive to low temperatures. All of the Φ-related parameters (Φ_bL, Φ_bH, and Φ_To) declined with the increase in PAR, and the difference between Φ_bL and Φ_bH gradually increased, but both values occurred at the same PAR level in ‘ROC22’ and ‘ROC16’ (Figure 1). The F_v/F_m value showed no significant difference between ‘ROC22’ (0.7693 ± 0.003) and ‘ROC16’ (0.7685 ± 0.0027). Parameter Ε reflected the amplitude decrease in Φ_To with increasing PAR and had the value of 5.91×10⁻⁴ for both tested cultivars (

Table 1. List of parameters and empirical coefficients with definitions, units, and values (standard error shown in brackets) determined in this study.

Parameters	Definition	Unit	Value
aL, bL	Empirical coefficient describing the change of ΦbL with PAR	—	0.688, 6.77 × 10−4 for both cultivars
aH, bH	Empirical coefficient describing the change of ΦbH with PAR	—	0.676, 5.16 × 10−4 for both cultivars
c, d	Empirical coefficient describing the change of ΕD with rSWC when rSWC was below rSWCc	—	39.63, 10.01 for ROC22 286.78, 21.53 for ROC16
Fv/Fm	Maximum quantum efficiency of PSII photochemistry	—	0.769 for both cultivars without water deficit
NPQ	Nonphotochemical quenching	—	—
PAR	Photosynthetically active radiation	μmol photons m−2 s−1	—
rSWC	Relative substrate water content calculated from VWC/VWCs	%	—
rSWCc	Critical value of rSWC	%	40 for ROC22 29 for ROC16
T	Temperature	°C	—
Tmin	Minimum temperature for sugarcane photosynthesis and growth	°C	15
To	Optimum temperature for sugarcane photosynthesis and growth	°C	30
Tmax	Maximum temperature for sugarcane photosynthesis and growth	°C	40
VWC	Volumetric water content of substrate	%	—
VWCs	Saturated VWC	%	38 (0.66) for Exp1~3 38.3 (1.11) for Exp4~5
Ε	The amplitude decrease in ΦTo with increasing PAR	—	5.91 × 10−4 for both cultivars
ΕD	Ε varied with rSWC to describe the drought-stress degree	—	—
Φ	Photosynthetic quantum efficiency	mol e−1 (mol photon)−1	—
ΦbL	Basic Φ under low temperature, varied with PAR	mol e−1 (mol photon)−1	—
ΦbH	Basic Φ under high temperature, varied with PAR	mol e−1 (mol photon)−1	—
ΦTo	Optimum Φ under To, varied with PAR	mol e−1 (mol photon)−1	—
ΦNO	Yield for other energy losses (indicate non-regulated energy dissipation)	mol e−1 (mol photon)−1	—

). In general, the cultivars exhibited high light efficiency at low Ε.

2.2. Response of the Estimated Parameter to Drought Treatment

The parameter Ε_D increased with the decrease in rSWC for both cultivars, and a higher Ε_D value of denoted severe effects of drought-induced stress on the plant thus could be used for drought-stress assessment. Different downward trends were observed between the two cultivars during drought stress. The Ε_D of ‘ROC22’ increased gradually when the rSWC decreased from 29% to 18%, while that of ‘ROC16’ increased drastically when the rSWC dropped below the 29% level. Based on the corresponding Ε _D for drought-stress assessment, the slight, moderate, and severe drought conditions were described as 7 <Ε _D< 9, 9 <Ε _D< 13, and Ε _D> 13, respectively (Figure 2a).

According to our experiments, the rSWC_c values for ‘ROC22’ and ‘ROC16’ were 40% and 29%, respectively, showing that the photochemical reaction for ‘ROC22’ responded at a higher water status in contrast to that of ‘ROC16’ (Figure 2b). Additionally, rSWC decreased faster for ‘ROC16’ than for ‘ROC22’ during drought stress, indicating that the two cultivars possessed different water consumption characteristics under drought conditions.

2.3. Energy Distributionin PSII in Response to Drought

We used the parameters Φ and NPQ to assess the transfer of excitation light energy to photochemical reaction and its decay via heat loss, respectively. Five days after the drought treatment, the Φ of ‘ROC16’ was slightly higher than that of ‘ROC22’; however, both cultivars exhibited the same NPQ level on day 1, and it differed with the increasing PAR from day 3 to day 5 (Figure 3a–c). ‘ROC22’ maintained high Φ compared to ‘ROC16’ at day 7 after the drought treatment, but the two cultivars had the same NPQ level when the PAR was below 900 μmol photons m⁻² s⁻¹. After 9 days of drought stress, both the Φ and the NPQ of ‘ROC16’ were significantly lower than those of ‘ROC22’ (Figure 3d,e). The results demonstrated that the photoprotective mechanism based on the heat loss of ‘ROC16’ did not involve excessive energy consumptionwhen compared with that of ‘ROC22’. Another piece of evidence that showed less energy consumption was the increased Φ_NO levels observed on day 3 (rSWC 56%) and day 5 (rSWC 40%) for ‘ROC16’ and ‘ROC22’, respectively, after the drought treatment. The Φ_NO value of ‘ROC16’ was significantly higher than that of ‘ROC22’ after day 3, and the difference was magnified with aggravation of drought stress, indicating higher drought-induced damage imposed on ‘ROC16’ than on ‘ROC22’ (Figure 3f).

Furthermore, the Ε_D values of the two cultivars increased on day 5 after the drought treatment, but the rSWC values of ‘ROC22’ and ‘ROC16’ decreased to 40% and 29%, respectively. This differed from the rSWC values (56% for ‘ROC16’ and 40% for ‘ROC22’) that corresponded to the turning point of Φ_NO, implying that the PSII of ‘ROC22’ responded to the water deficit more quickly than did that of ‘ROC16’. Contemporaneously, for ‘ROC22’, the NPQ rose to 1.6 when the PAR approached 1200 μmol photons m⁻² s⁻¹ but remained below 1.3 for ‘ROC16’ throughout the drought treatment. NPQ merely increased when the PAR ranged from 100 to 900 μmol photons m⁻² s⁻¹ on day 7 and day 9 for ‘ROC16’ and on day 5 for ‘ROC22’. This phenomenon demonstrated that rapid and high energy dissipation in drought is essential for drought tolerance in sugarcane.

2.4. Different Drought-Response Patterns for Two Cultivars

We investigated the diverse drought-response patterns of the two sugarcane cultivars. The results showed that ‘ROC22’ exhibited a quick response and a high capability of NPQ (Figure 2b) instead of liner electron transport (exhibited via low Ε _D) at the onset of drought (rSWC of 40%) (Figure 3c). This delayed the conversion from the drought-induced reaction to the physiological damage of the PSII, which was indicated with an increase in Φ_NO (Figure 3f). However, ‘ROC16’ maintained a relatively lower Ε _D value to ensure photochemical efficiency until the rSWC dropped to 29%, after which the NPQ increased at PAR of merely 100~900 μmol photons m⁻² s⁻¹. Moreover, the Φ_NO of ‘ROC16’ was significantly higher than that of ‘ROC22’. The different water consumption characteristics of the two cultivars were revealed in the differentially decreasing rates of rSWC, which showed that ‘ROC16’ had higher rSWC than ‘ROC22’ throughout the drought treatment.

2.5. Model Validation

The coefficient of determination (r²) and relative root mean-squared error (rRMSE) of the predicted values were 0.922 and 0.11 (Figure 4a), while those of the measured values were 0.826 and 0.309 (Figure 4b), respectively, for Φ under different photothermal and drought conditions, indicating that the model could be applied for drought-tolerance assessment or drought-stress diagnosis for sugarcane cultivars.

3. Discussion

The minimum, optimal, and maximum temperatures obtained from the curve fitting of the net photosynthetic rate under saturated light (P_n,max) and air temperature (Ta) were used to describe the fundamental temperature for crop growth [32]. The relationship between Φ and T at limited PAR observed in our study was similar to those reported in previous studies [33]; however, in our study, the fitted parameters varied with different PAR levels (Figure 1). Unlike with P_n,max, the occurrence of Φ_b (Φ_bL or Φ_bH) at low or high temperatures was associated with alternative electron fluxes beyond photochemical reaction processes, such as photorespiration, the Mehler reaction, or cyclic and pseudocyclic electron transport under normal or drought conditions [34,35]. For instance, photorespiration and the Mehler reaction constituted 20% and 30% of the total electron flux, respectively [36,37]. Moreover, the rETR remained at 50% under normal conditions, even when photosynthesis ceased due to stomatal closure under drought stress [38]. This could also explain the existence of Φ_b and the gradual decrease in Φ with increases or decreases in temperature in our study compared with the P_n,max-Ta of the C₃, C_4, and CAM plants [39].

The Φ of sugarcane, a typical C₄ crop, showed higher sensitivity to lower temperatures than to higher temperatures because C₄ pathway enzymes are cold-labile due to the limitations of phosphoenolpyruvate carboxylase (PEPC) and pyruvate phosphate dikinase (PPDK) [40,41]. This explains why the Φ_bL value was lower than that of Φ_bH, and the increasing disparity between Φ_bL and Φ_bH with increasing PAR. The assumption that Φ_bL and Φ_bH exhibited the same downward trend as Φ_To in our study resulted in underestimation of Φ, especially when a value below 0.2 was obtained with a combination of drought, high PAR, and low (or high) temperature (Figure 4b). This phenomenon resulted from the fact that both net photosynthetic rate and Φ_PSII significantly decrease more under drought–cold stress than under drought stress only [42]. Additionally, combined heat and drought stress severely affect crop leaves at the physiological and biochemical levels via detrimental variation of photosynthetic pigments, osmolytes, and enzymatic antioxidant activities more than heat or drought stress alone [43,44].

We supposed that light energy was allocated between liner electron transport (Φ) and heat loss (NPQ) at the onset of drought stress until the occurrence of potential damage in the PSII, indicated with the concurrence of increased Φ_NO and decreased NPQ (or q_N) shown in studies of salt stress [45,46] and disease influence [47]. The increase in NPQ compromised the decrease in Φ based on their new relationship acquired in the dark that followed illumination, which could be more appropriate to describe the photoprotective potential of NPQ [48]. This phenomenon also appeared in sweet sorghum and maize, with the degree of NPQ change differing in different cultivars under drought stress [49,50]. However, a significant increase in Φ_NO was found when sorghum suffered from drought [51]; this might be related to quenching of singlet oxygen via β-carotene [52], which could also cause photoinhibition via degradation of the D1 protein in the PSII center [53]. Similar results appeared in coastal halophytic marsh grasses with increasing salinity, and the high levels of the Mehler reaction and antioxidant enzymes improved adaptation in photoprotection [54]. Another study reported a decrease in Φ_NO with enhanced Φ_NPQ in maize under a long time field condition, which probably represented heat energy loss in protection of the stressed younger leaves [55].

Both the Φ and the water consumption of ‘ROC22’ were lower than those of ‘ROC16’ before the 5-day marker after drought, similarly to a previous study, where the sugarcane response to drought involved a rapid decrease in P_n and g_s for the drought-tolerance cultivar rather than the drought-sensitive one [7]. This might be associated with rapid stomatal closure, to reduce water loss, induced via ABA [56], which is synthesized in arid root systems to induce specific genes and proteins in response to water deficit [10,57]. In conversion of the value range of the Ε_D to water status, the drought degree of sugarcane plantation was divided into three stages: 29% < rSWC < 40% (slight), 18 < rSWC< 29% (moderate), and rSWC < 18% (severe). These ranges were lower than those reported in the previous study (which were 65~70% (slight), 45~50% (moderate), and 25~30% (severe)) [58], since we concentrated on the critical value of irrecoverable physiological injury instead of the yield reduction caused by the stress reaction. The accuracy, persuasiveness, and application scope of the model could be improved after more cultivars (or varieties) participate in parameter fitting and model validation.

4. Materials and Methods

4.1. Plant Materials and Treatment

Two sugarcane cultivars, ‘ROC22’ (drought-resistant) and ‘ROC16’ (drought-sensitive), cultivated by the Taiwan Sugar Research Institute, were used in this study; both were main cultivars introduced and applied in production in south China [59] and used in many drought-response researches [24,60,61]. Five experiments were conducted with the same planting, management, and drought treatment during different seasons in Zhanjiang, China (21° N, 110° E), from 2019 to 2021. Bucket planting was adopted for the experiment, with 30 buckets for each cultivar and three stems (1 bud per segment) per bucket, arranged based on field planting density (90,000 buds·ha⁻¹). The caliber, bottom diameter, and height of each bucket were 40 cm, 30 cm, and 40 cm, respectively. The buckets contained red soil (70%), sand (20%), and organic manure (10%) as a substrate. The buckets were placed on hardened ground in an open environment without shading to implement photothermal experiments at the 8^th/euphylla stage until the drought experiments were initiated. Five and ten buckets with uniform-growth seedlings were selected for the photothermal experiments and the drought experiments, respectively. The environmental conditions and substrate properties for each experiment are displayed in

Table 2. Detailed information on the experimental conditions and physicochemical properties of the substrate.

		Exp1 aM, bV	Exp2 aM, bV	Exp3 aM, bV	Exp4 aV, bV	Exp5 aV, bM
Experimental and Environmental Conditions
Photothermal Experiment	Start date	9 April 2020	11 July 2020	30 November 2020	20 May 2021	23 October 2021
	Finish date	11 April 2020	13 July 2020	2 December 2020	22 May 2021	25 October 2021
	Average daily high temp (°C)	24.3 ± 2	36.2 ± 1.3	21.7 ± 1.8	35.2 ± 1.7	23.3 ± 4
	Average daily low temp (°C)	19.4 ± 1.4	28.6 ± 0.2	11.4 ± 3.3	27.5 ± 0.1	16.7 ± 1.1
	Average daily PARave (μmol photons m−2 s−1)	233 ± 129	575 ± 61	364 ± 27	523 ± 139	314.6 ± 165
	Average daily PARmax (μmol photons m−2 s−1)	1477 ± 623	2119 ± 66	1573 ± 231	2085 ± 253	1474 ± 647
Drought Experiment	Start date	13 April 2020	14 July 2020	3 December 2020	23 May 2021	26 October 2021
	Finish date	22 April 2020	20 July 2020	14 December 2020	2 June 2021	4 November 2021
	Average daily high temp (°C)	26.6 ± 2.5	35.3 ± 1.2	23 ± 1.3	34.8 ± 1.2	28.2 ± 0.8
	Average daily low temp (°C)	20 ± 4.7	27.1 ± 1	15.3 ± 2.8	26.9 ± 1.1	21.4 ± 2.1
	Average daily PARave (μmol photons m−2·s−1)	341 ± 124	535 ± 60	262 ± 93	498 ± 74	321 ± 81
	Average daily PARmax (μmol photons m−2 s−1)	1896 ± 388	2313 ± 138	1441 ± 322	2412 ± 208	2012 ± 311
Physicochemical Properties of the Substrate
	Bulk density (g·cm−3)	1.19	1.19	1.19	1.21	1.21
	Saturated volumetric water content (%)	38	38	38	38.3	38.3
	Organic C (g·kg−1)	17.51	17.51	17.51	19.22	19.22
	Avaliable N (mg·kg−1)	36.34	36.34	36.34	33.88	33.88
	Avaliable P (mg·kg−1)	15.62	15.62	15.62	12.96	12.96
	Avaliable K (mg·kg−1)	44.70	44.70	44.70	46.16	46.16
	pH	4.82	4.82	4.82	5.07	5.07

Note: ^a and ^b denote photothermal and drought experiments, while ^M and ^V denote experimental data used for modeling and validation, respectively. Temperature and PAR data were selected from the daily data set recorded with a farm land environment monitoring system (CR1000).

.

Photothermal experiments were conducted under field conditions. The average F_v/F_m value of 9 leaves, measured at T_o under well-watered conditions and selected from 15 plants (5 buckets) in experiment 5 (Exp5) and with 30 points of Φ obtained under 3 given PAR levels (285, 625, and 1150 μmol photons m⁻² s⁻¹), with T ranging from 11.8 °C to 42.7 °C for each cultivar from 30 plants (10 buckets) in Exp1-3, was used to fit the photothermal response model (evaluation of Φ under different light and temperature backgrounds). Furthermore, 60 sets of the Φ, PAR, and T measured for each cultivar fromExp4 and Exp5were used for model validation. The buckets were kept in a nearby greenhouse for a short time (10 min) for Φ acquisition at a higher T (over 38 °C).

For the subsequent drought experiments, the buckets were placed infield conditions, except for those in Exp5, which were moved into the phytotron from 6:00 to 12:00, with the PAR increasing from 0 to 1200 μmol photons m⁻² s⁻¹ (20% light intensity increased per hour using artificial light) under optimal T (29 ± 1.5 °C) at 1, 3, 5, 7, and 9 d after withholding of irrigation. The measured Φ, VWC, F_v/F_m, PAR, and T were used to estimate Ε_D, and the energy dissipation parameters obtained from Exp5 were used to analyze the different responses of the photoprotective mechanisms between the two cultivars under drought conditions. A total of 125 sets of the Φ, VWC, F_v/F_m, PAR, and T obtainedfor each cultivarfrom 8:00 to 12:00 am in Exp1-4 were used for model validation.

4.2. Chlorophyll Fluorescence and Substrate Water Content Measurement

The chlorophyll fluorescence (ChlF) parameters were measured on the middle part of each +1 leaf (the first fully expanded leaf when counting from the top down) using a MINI-PAM-II analyzer (Walz, Effeltrich, Germany), which measures PAR and temperature via a self-contained sensor. All of the ChlF parameters were obtained under natural light conditions, except for those measured in the phytotron in Exp5, where artificial light had been used for light adaptation. The leaf clip was slightly adjusted to ensure relatively constant PAR (within ±10), and a stable F_s was waited for to initiate the process of measuring F_m′. Then, F_o and F_m were measured after 30 min of dark adaptation using dark clips at the same leaf position. The next round of F_s and F_m′ measurement was conducted after 30 min of natural light adaptation (artificial light in the phytotron in Exp5). Four basic fluorescence parameters, F_s (steady fluorescence from light-adapted leaves), F_m′ (maximal fluorescence from light-adapted leaves), F_o (minimal fluorescence from 30 min dark-adapted leaves), and F_m (maximal fluorescence from 30 min dark-adapted leaves), were measured from morning to midday. The obtained parameters were then used to calculate the minimal fluorescence from light-adapted leaves (F_o′) [62], photosynthetic quantum efficiency (Φ), the maximum quantum efficiency of PSII photochemistry (F_v/F_m), nonphotochemical quenching (NPQ), and the yield for other energy losses (Φ_NO) [63] indicating non-regulated energy dissipation.

F_o′ = F_o/[(F_v/F_m) + (F_o/F_m′)]

Φ = (F_m′ − F_s)/F_m′

F_v/F_m = (F_m − F_o)/F_m

NPQ = F_m/F_m′ − 1

Φ_NO = 1/[NPQ + 1 + q_L(F_m/F_o − 1)], where q_L = (F_o′/F_s)(F_m′ − F_s)/(F_m′ − F_o′)

Parameter Φ was equivalent to and symbolized as ΔF/F_m′, Φ_PSII, Φ_II, or Φ₂ in different studies, with the same calculation result [21,22,23,24,27,29,31,45,46,47,48,49,51,54,55,63].

Volumetric water content (VWC) was measured at a depth of 0~16 cm in each bucket via AZS-100 (Aozuo, China) at 8:00 a.m., with 5 repetitions during the drought treatment. The rSWC was calculated with the formula rSWC = VWC/VWC_S (VWC_S was measured at sufficient irrigation with 3 repetitions; Table 1).

4.3. Model Construction

4.3.1. Estimation of Photosynthetic Quantum Efficiency (Φ) under Different Photothermal Conditions

Three levels of PAR (285, 625, and 1150 μmol photons m⁻² s⁻¹) were adopted to measure Φ under different air temperatures (T) using artificial light in field conditions in different seasons, calculated using the formulae transformed from the calibration model proposed to evaluate the response of Φ to photothermal backgrounds [64] (Figure 1a):

$$\Phi (T)=\{\begin{array}{c}{\Phi }_{\mathrm{b}\mathrm{L}}+({\Phi }_{\mathrm{T}\mathrm{o}}-{\Phi }_{\mathrm{b}\mathrm{L}})\times \sin (\frac{\pi }{2}\times \frac{T-T_{\min }}{T_{\mathrm{o}}-T_{\min }})T_{\min }\le T\le T_{\mathrm{o}}\\ {\Phi }_{\mathrm{b}\mathrm{H}}+({\Phi }_{\mathrm{T}\mathrm{o}}-{\Phi }_{\mathrm{bH}})\times \sin (\frac{\pi }{2}\times \frac{T_{\max }-T}{T_{\max }-T_o})T_{\mathrm{o}}\le T\le T_{\max }\end{array}$$

(1)

where Φ_To, Φ_bL, and Φ_bH represent the optimum Φ under T_o and the basic Φ values under low T and high T at limited PAR. T_min, T_o, and T_max represent the minimal, optimal, and maximal temperatures for sugarcane photosynthesis and vegetative growth, with values of 15 °C, 30 °C, and 40 °C, respectively, according to previous studies [65,66,67]. The relationship between Φ_bL, Φ_bH, Φ_To, and PAR was established as follows (Figure 1b):

$${\Phi }_{\mathrm{bL}}=a_{\mathrm{L}}\times {\mathrm{e}}^{-PAR\times b_{\mathrm{L}}}$$

(2)

$${\Phi }_{\mathrm{bH}}=a_{\mathrm{H}}\times {\mathrm{e}}^{-PAR\times b_{\mathrm{H}}}$$

(3)

$${\Phi }_{\mathrm{T}\mathrm{o}}=(F_{\mathrm{v}}/F_{\mathrm{m}})\times {\mathrm{e}}^{-\epsilon \times {10}^{-4}\times PAR}$$

(4)

The mean value of the F_v/F_m measured from Exp1~3 was used for Equation (4)’s fitting, and a_L, b_L, a_H, and b_H are empirical coefficients fitted from Equations (2) and (3). The parameter Ε, fitted from Equation (4), was merely related to different sugarcane cultivars.

4.3.2. Estimation of the Parameters under Drought Conditions

We inserted Equations (2)–(4) into Equation (1) to determine the Φ, F_v/F_m, PAR, T, and rSWC measured under drought and used the values to describe the relationship between ε and rSWC (Figure 2a):

$${\epsilon }_{\mathrm{D}}=\{\begin{array}{cc}\epsilon \hfill & rSWC>rSW{C}_c\hfill \\ \epsilon +\mathrm{c}\times {\mathrm{e}}^{-\mathrm{d}\times rSWR}& rSWC\le rSW{C}_c\hfill \end{array}$$

(5)

where rSWC_c indicates the critical limit of rSWC for the two sugarcane cultivars, Ε _D is Ε under drought stress, and c and d are the empirical coefficients fitted from Equation (5).

The effect of drought stress on Φ_bL and Φ_bH was assumed to be the same as that of Φ_To, and these values were calculated as:

$${\Phi }_{\mathrm{bL}\mathrm{D}}={\Phi }_{\mathrm{bL}}\times {\mathrm{e}}^{-\epsilon \times {10}^{-4}\times PAR}$$

(6)

$${\Phi }_{\mathrm{bHD}}={\Phi }_{\mathrm{bH}}\times {\mathrm{e}}^{-\epsilon \times {10}^{-4}\times PAR}$$

(7)

where Φ_bLD and Φ_bHD represent Φ_bL and Φ_bH, respectively, under drought stress.

4.3.3. Model Validation

The coefficient of determination (r²) and relative root mean-squared error (rRMSE) were adopted to analyze the conformity and accuracy between the predicted and the measured values and were calculated as follows:

$$r^2=\frac{{\left({\displaystyle \sum \left(x-\overline{x}\right)\left(y-\overline{y}\right)}\right)}^2}{{\displaystyle \sum {\left(x-\overline{x}\right)}^2{\displaystyle \sum {\left(y-\overline{y}\right)}^2}}}$$

(8)

$$rRMSE=\frac{\sqrt{\frac{{\displaystyle \sum _{i=1}^n{\left(OB{S}_{\mathrm{i}}-SI{M}_{\mathrm{i}}\right)}^2}}{n}}}{M}$$

(9)

where x, y, $\overline{x}$, $\overline{y}$, OBS_i, SIM_i, M, and n are the measured value, predicted value, average measured variables, average predicted variables, observed value, simulated value, average observed value, and sample size for Equations (8) and (9), respectively.

The definitions, units, and values (standard error shown in brackets) of the parameters and empirical coefficients are listed in Table 1.

5. Conclusions

The simulation and energy distribution analyses exhibited an opposition between water consumption and drought tolerance. ‘ROC22’ (drought-resistant) decreased its water consumption and activated a photoprotective mechanism to delay the drought-induced physiological injury, while ‘ROC16’ (drought-susceptible) maintained relatively high water consumption to guarantee photochemical reactions but was severely injured with aggravation of drought.