Available nitrogen (AN) was determined by the Kjeldahl method [39]. Available phosphorus (AP) was determined by the diacid extraction spectrophotometric colorimetry method [40], and available potassium (AK) was determined using a flame photometer method by ammonium acetate extraction [41]. Moreover, soil moisture content (SMC) was calculated based on wet and dry weight, whereas soil pH was determined using a potentiometric method (1:2.5 soil:water). Details about the soil laboratory analysis are also available in our published papers [42,43].

#### *2.6. Data Analysis*

Two-way analysis of variance (ANOVA) at a 5% probability level was performed to determine the effects of forest types and different soil depth profiles on the natural abundance of C and N isotopes, soil TC and TN content, and soil nutrient availability. Oneway ANOVA at 5% probability level was performed to determine the differences between foliar δ13C, δ15N and, foliar TC and TN content among forest types. A regression analysis was conducted to analyze the foliar and soil isotopic abundance relationship. The Pearson correlation test was performed to observe the relationship between isotopic dynamics and influential factors. Means that exhibited significant differences were compared using post-hoc Tukey's HSD significance test. All statistical analyses were performed using the SPSS Statistical Package (SPSS 17.0, Chicago, IL, USA).

#### **3. Results**

#### *3.1. Soil and Foliar TC, TN Content, and C:N Ratio*

Two-way ANOVA revealed that soil TC, TN content, and C:N ratio varied significantly among forest types (*p* < 0.05) and soil depths (*p* < 0.001) (Tables 2 and A1). Soil TN content ranged from 0.56 g·kg−1 to 3.33 g·kg−1, while the soil TC content varied from 1.69 g·kg−1 to 46.88 g·kg−1. Moreover, the soil C:N ratio ranged from 3.01 to 14.06 (Table 2). Soil TN, TC, and C:N ratio decreased as the soil depth increased among all forest types. In the topmost soil layer, TC, TN, and soil C:N ratio was higher in the mixed forest stand, while for the remaining two soil layers, it was greater in CPF (Table 2).

**Table 2.** Soil and foliar TC, TN content, and C:N ratio in different forest types at different soil depths.

Note: Values are means ±SE. The different small letter represents the significant differences between forest types and different capital letters between different vertical soil depths at *p* ≤ 0.05. **a** TN, total nitrogen; TC, total carbon.

In terms of foliage values, one-way ANOVA showed that foliar C content varied significantly among forest types (*p* < 0.05), whereas foliar N content and foliar C:N ratio were not significantly different (*p* = 0.242 and *p* = 0.155, respectively) (Table 2).

#### *3.2. C13 and 15N Natural Abundance*

Two-way ANOVA depicted that soil δ15N varied significantly among forest types (*p* > 0.001) and soil depths (*p* = 0.048). However, forest type (*p* = 0.078) and interaction of forest type × soil depth (*p* = 0.329) had no significant effect on soil δ13C, whereas soil δ13C varied significantly with soil depth (*p* = 0.028) (Figure 2 and Table A1). A positive correlation was present between soil δ15N-soil depth (*r* = 0.606) and soil δ13C-soil depth (*r* = 0.470). Overall, soil δ15N values ranged from −1.61‰ to 5.19‰, and soil δ13C values ranged from −28.23% to −25.00% (Figure 2).

One-way ANOVA illustrated that forest type had a considerable effect on foliar δ13C (*p* = 0.03) and foliar δ15N (*p* = 0.047). Foliar δ15N values ranged from 6.39‰ to 8.90‰, and foliar δ13C values ranged from −25.4‰ to −29.7‰. Foliar δ15N was enriched in the NBF stand, and δ13C was less negative in the mixed forest stand (Figure 2).

**Figure 2.** The natural abundance of (**A**) soil 15N (‰) and (**B**) soil 13C (‰) at different soil depths and (**C**) foliar 15N (‰) and 13C (‰) in different forest types. Values are means ±SE. The different small letters represent the significant differences between forest types, and different capital letters represent the significant difference between different soil depths *p* ≤ 0.05.

#### *3.3. Relationship between Soil, Foliar Isotopic Abundance, and C:N Ratio*

A strong positive linear relationship was observed between soil and foliar δ13C (*r* = 0.78, *p* = 0.04) and soil and foliar δ15N (*r* = 0.56, *p*= 0.03) across forest types and soil depths (Figure 3A,B).

**Figure 3.** Linear relationships between (**A**) soil and foliar δ13C and (**B**) soil and foliar δ15N (‰) across three forest types at a 0–40 cm soil depth.

#### *3.4. Correlation between Isotopic Abundance and Potentially Influential Factors*

Soil δ13C was positively correlated to SMC and foliar C:N ratio, while negatively correlated to soil and foliar TN and soil TC whereas soil δ15N was positively related to soil C:N ratio, foliar C:N ratio, and negatively correlated to SMC, foliar TC, and TN content (Table 3).

**Table 3.** Pearson correlation between soil and foliar δ13C and δ15N and potentially influential factors across forest types and soil depths.

Note: Correlation was significant at the 0.05 \* and 0.01 \*\* level (2-tailed). **a** TN, total nitrogen; TC, total carbon; BD, soil bulk density; SMC, soil moisture content.

> Foliar δ13C was positively and negatively correlated to soil BD and foliar C:N ratio, respectively, whereas foliar δ15N was positively related to foliar TC and TN content, and negatively correlated to foliar C:N ratio and SMC (Table 3).

#### *3.5. Soil Nutrient Availability*

Two-way ANOVA illustrated that AN, AP, and AK varied significantly in different forest types and soil depths (*p* < 0.05) (Tables 4 and A1). In the 0–10 cm and 20–40 cm soil layers, AN content (177 mg·kg−1 and 109 mg·kg−1) was observed to be higher in the mixed forest; in 10–20 cm, it was higher in the NBF (155 mg·kg−1). AN content decreased as the soil depth increased in the mixed forest and NBF. AP content ranged from 29 mg·kg−1 to 82 mg·kg−1. In the 0–10 cm soil layer, the highest AP content was observed in CPF (71 mg·kg−1), while in the remaining two layers (10–20 and 20–40 cm), the maximum content was measured in the mixed forest. Among all forest types and soil depths, AK ranged from 12 g·kg−1 to 147 g·kg−1. In the topmost soil layer, it was higher in the mixed forest stand. In both the 10–20 cm and 20–40 cm layers, it was higher in NBF. Generally, soil nutrient content decreased as the soil depth increased across all forest types (Table 4).

**Table 4.** Available soil nutrients in different forest types at different soil depths.

Note: Values are means ±SE. The different small letters represent the significant differences between forest types, and different capital letters represent the significant difference between soil depths at *p* < 0.05.

#### **4. Discussion**

Among all forest types, soil TN content decreased as the soil depth increased. In the topsoil layer, both TC and TN contents were enriched in the mixed forest. It indicates that litter input was higher in the mixed forest due to mixed species composition and more understory vegetation, which increased soil organic matter (SOM) more than both NBF and CPF stands. Since soil organic carbon changes with the vertical soil depth profiles, soil depth is widely used as a SOM decomposition rate indicator [44–46]. It also points toward the better leaf litter quality in mixed forest with higher N release potential during decomposition. This study observed a considerable difference in soil and foliar TC, TN content, and C:N ratio among the NBF, CPF, and mixed forest. Tree species vary in their nutrient transformation potential with changing forest structure and canopy dynamics. These factors influence the various biogeochemical cycles [12,26,42]. Species autecology affects the decomposition and the turnover rate of C and N dynamics [47]. Neirynck et al. [48] mentioned that different tree species such as *Acer psedoplatanus*, *Fraxinus excelsior*, and *Tilia platyphyllos* significantly affect the C:N ratio and it was related mainly to litter quality. Lovett et al. [49] also revealed that forest structure alters the C and N dynamics because species composition affects nutrient cycling under their different canopy structures, which can substantially influence the forest ecosystems' overall nutrient turnover rates [50,51].

In this study, a higher abundance of soil 15N and 13C was observed in CPF. Enriched soil δ15N and δ13C in CFP could be due to more intensive forest management practices than that in the NBF and mixed forest stands because the plantations are artificially managed forests with added activities of humans and livestock. Pardo et al. [13] and Yang et al. [52] also reported that different silvicultural techniques in plantations such as clear-cutting, slash burning, site preparation, thinning, and pruning could enrich the soil δ13C and δ15N in plantation forests because of higher soil C and N loss such as enhanced nitrification followed by nitrate loss, which could have a direct impact on isotopic abundance. Rehman et al. [53] reported that various forest protection activities could influence stable isotopic abundance and C:N ratios. Moreover, it is generally believed that multiple factors such as light availability, litter input, water use efficiency, soil physical, chemical, and biological properties altered by the forest types may affect natural isotopic abundance in forest ecosystems [50,51].

Forest type and soil depth significantly impacted soil δ15N, whereas soil δ13C was only impacted by soil depth; moreover, both the soil δ13C and δ15N were enriched in the topmost soil layer. Higher isotopic values in the upper soil layer can be due to higher SOC. Ellert and Janzen [54] reported higher C content in the uppermost layer, providing ample SOM that strongly influences the δ13C in the topsoil, whereas lesser C content in the deeper layers determines the lesser δ13C values. Ngaba et al. [9] also reported that soil depth increment significantly impacted the C and N isotopic abundance. Forest type had a significant effect on foliar C (highest in CPF) and N (highest in NBF) abundance. Usually, foliar δ15N values increased under N-rich conditions; in our study, higher foliar and soil δ15N were observed in CPF. On average, foliar and soil δ15N patterns in sub-tropical forests are higher than other forest types because of the gaseous N losses related to microbial activities. Our study showed foliar C and N positively correlated with the soil isotopic values; this indicates that foliar isotopic values of different species are consistent concerning soil values.

When we studied the correlation of potentially influential factors concerning δ13C and δ15N in soil and foliage, we observed that SMC, foliar TN, and foliar C:N ratio was usually significantly related to soil foliar 13C and 15N natural abundance, either positively or negatively. These factors can affect biogeochemical processes such as N mineralization, nitrification/denitrification, soil respiration, root dynamics, thus altering the rate of δ15N [55,56]. Moisture content is a vital factor influencing the nutrient content and δ13C of the plant and soil. Philips et al. [57] stated that the δ13C depends on moisture conditions, either caused by low atmospheric humidity, less precipitation, or low soil

moisture. Moreover, drought can also potentially influence the C fluxes and soil δ13C [58]. C isotopic abundance primarily depends on the water use efficiency; therefore, soil water resources can affect the isotopic abundance [59]. Ngaba et al. [9] also stated mean annual precipitation and land-use as the significant factor altering soil δ13C. While analyzing the 15N abundance between the north and south slopes of forests, Chen et al. [5] also reported soil moisture content, leaf N content, and leaf C:N as the significant factors related to isotopic abundance. This inferred that water retention capacity should be the critical factor concerning the isotopic abundance of plant tissues.

In our study, a significant difference was observed in variability in macronutrient availability among all forest types. Generally, AN and AK were higher in mixed forest stand and AP in CPF. Plantation forests mostly have fast-growing species with successive short rotations; hence, the fast growth demands more extensive nutrient availability, which causes an imbalance in soils under plantations compared to mixed-species forest. Kooch et al. [47] reported the decline in soil fertility with successive planting of fast-growing monoculture species at the same site. The decline in soil available nutrients with soil depth increase could be due to a large amount of plant litter deposition in the topsoil layer as its decomposition results in a higher accumulation of soil nutrients in the uppermost soil than the lower soil layers. Farooq et al. [43] cited that species structure influences the soil properties, which is more noticeable at the upper layer of soil. Our study is also in line with Selvaraj et al. [60], who stated that both available and soil total nutrient content decreased with the soil depth, irrespective of stand age; moreover, Groppo et al. [61] and Breulmann et al. [62] also supported this phenomenon regardless of species and land use. However, the relative importance of the various soil-forming factors always remains debated [28–30,60]. Soil conditions solely do not affect directly, but with an interplay of other related factors such as the autecology of tree species, litter quality, parent material, soil community effects, human activities, and local climatic conditions [3,63–65]. These impacts are likely to have significant consequences for belowground communities [1]. Similarly, different land uses cannot directly affect soil fertility, but it can influence through indirect effects such as organic/inorganic amendments [66–68], various stresses for plant and nutrient availability [69,70], different planting materials [71,72], and atmospheric deposition levels and turnover rates [73].

#### **5. Conclusions**

This study explored how δ13C, δ15N, TC, and TN content of soil and foliage and soil nutrient availability is affected by different forest types. Forest type and soil depth significantly affected the soil δ15N, while forest type effect on soil δ13C was not significant. Significant changes in soil δ15N among different forest types indicated that 15N signatures might dominate the processes of N cycling in different forest types. Across forest types and soil depths, the observed δ13C and δ15N variations were attributed to SMC, soil TC, soil TN, and soil C:N ratio. Variations in foliar δ13C were accredited to differences in soil BD and foliar C:N ratio, whereas variation in foliar δ15N was accredited to SMC, foliar TN, and TC. Our study also highlights that, on average, available forms of soil nutrients were less in monoculture Chinese fir stands (CPF) than NBF and mixed forests. This difference was possibly caused by higher litter biomass accumulation from broadleaved and mixed-species litter compared to CPF. Thus, the presence of broadleaved species within a monoculture stand along with other management practices such as the introduction of multi-layering and multi-aged plantations, avoiding clear-cutting practice, and burying the cutting leftover rather than burning could improve soil health, sustainable management, and production, which would enhance both economic and environmental aspects to the forest industry. Overall, our results highlight the importance of forest types and composition when studying biogeochemical cycling and its response in different terrestrial ecosystems. Moreover, this study will also help to further understand the role of C and N cycles concerning soil productivity.

**Author Contributions:** Conceptualization, T.H.F., U.K. and W.Y.; Data curation, A.S.; Formal analysis, T.H.F. and Y.L.; Funding acquisition, W.Y.; Methodology, T.H.F., X.C. and M.H.U.R.; Project administration, J.W. and W.Y.; Resources, W.Y.; Software, T.H.F. and A.S.; Validation, A.S. and W.Y.; Writing—original draft, T.H.F.; Writing—review & editing, X.C. and W.Y. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was financially supported by postdoctoral research funding from the Central South University of Forestry and Technology (70702-45200003), Scientific Research Fund of Hunan Provincial Education Department (18A150) and Key Research and Development Project of Hunan (2020NK2022).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** All the available data have been presented in the manuscript.

**Acknowledgments:** We would like to thank Xiang Zhang and Arfien Sayman for their help in conducting fieldwork.

**Conflicts of Interest:** The authors declare that they have no known competing interests.

#### **Abbreviations**

NBF—natural broadleaved forest; CPF—coniferous plantations forest; SMC—soil moisture content; BD—soil bulk density; SOM—soil organic matter; MAT—mean annual temperature; MAP—mean annual precipitation; AN—available nitrogen; AP—available phosphorus; AK—available potassium.

#### **Appendix A**

**Table A1.** Two-way ANOVA results (*p* values) for the studied soil variables at *p* ≤ 0.05.

#### **References**

## *Article* **Intercropping of Peanut–Tea Enhances Soil Enzymatic Activity and Soil Nutrient Status at Different Soil Profiles in Subtropical Southern China**

**Taimoor Hassan Farooq 1,2,† , Uttam Kumar 3,† , Jing Mo 1,2, Awais Shakoor 4 , Jun Wang 1,2, Muhammad Haroon U. Rashid 5 , Muhammad Aammar Tufail 6 , Xiaoyong Chen 1,7,\* and Wende Yan 1,2,\***

**Abstract:** Intercropping is one of the most widely used agroforestry techniques, reducing the harmful impacts of external inputs such as fertilizers. It also controls soil erosion, increases soil nutrients availability, and reduces weed growth. In this study, the intercropping of peanut (*Arachishypogaea* L.) was done with tea plants (*Camellia oleifera*), and it was compared with the mono-cropping of tea and peanut. Soil health and fertility were examined by analyzing the variability in soil enzymatic activity and soil nutrients availability at different soil depths (0–10 cm, 10–20 cm, 20–30 cm, and 30–40 cm). Results showed that the peanut–tea intercropping considerably impacted the soil organic carbon (SOC), soil nutrient availability, and soil enzymatic responses at different soil depths. The activity of protease, sucrase, and acid phosphatase was higher in intercropping, while the activity of urease and catalase was higher in peanut monoculture. In intercropping, total phosphorus (TP) was 14.2%, 34.2%, 77.7%, 61.9%; total potassium (TK) was 13.4%, 20%, 27.4%, 20%; available phosphorus (AP) was 52.9%, 26.56%, 61.1%; 146.15% and available potassium (AK) was 11.1%, 43.06%, 46.79% higher than the mono-cropping of tea in respective soil layers. Additionally, available nitrogen (AN) was 51.78%, 5.92%, and 15.32% lower in the 10–20 cm, 20–30 cm, and 30–40 cm layers of the intercropping system than in the mono-cropping system of peanut. Moreover, the soil enzymatic activity was significantly correlated with SOC and total nitrogen (TN) content across all soil depths and cropping systems. The depth and path analysis effect revealed that SOC directly affected sucrase, protease, urease, and catalase enzymes in an intercropping system. It was concluded that an increase in the soil enzymatic activity in the intercropping pattern improved the reaction rate at which organic matter decomposed and released nutrients into the soil environment. Enzyme activity in the decomposition process plays a vital role in forest soil morphology and function. For efficient land use in the cropping system, it is necessary to develop coherent agroforestry practices. The results in this study revealed that intercropping certainly enhance soil nutrients status and positively impacts soil conservation.

**Keywords:** *Camellia oleifera*; *Arachis hypogaea*; soil nutritional status; soil quality; cropping pattern; silvicultural methods; sustainable production

**Citation:** Farooq, T.H.; Kumar, U.; Mo, J.; Shakoor, A.; Wang, J.; Rashid, M.H.U.; Tufail, M.A.; Chen, X.; Yan, W. Intercropping of Peanut–Tea Enhances Soil Enzymatic Activity and Soil Nutrient Status at Different Soil Profiles in Subtropical Southern China. *Plants* **2021**, *10*, 881. https://doi.org/10.3390/ plants10050881

Academic Editor: M. Iftikhar Hussain

Received: 2 April 2021 Accepted: 23 April 2021 Published: 27 April 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

#### **1. Introduction**

The enhancement and maintenance of soil productivity and sustainability through the long-term use of different silvicultural practices, such as varying planting density and spacing, introducing native and exotic beneficial species, agroforestry and intercropping, has been widely used worldwide [1–6]. Intercropping can enhance soil quality by incorporating a significant amount of topsoil and subsoil organic matter and releasing and recycling nutrients [7–9]. It is the general opinion that intercropping is more suitable than mono-cropping for the long-term maintenance of soil fertility [10]. Intercropping systems are rarely dependent on external inputs such as fertilizers [11]; this also reduces the adverse environmental effects such as soil erosion [12]. In China, trees/legume intercropping is most commonly used because it reduces crop failure risk and improves land use [13].

Tea (*Camellia oleifera*) is an evergreen shrub of the Theaceae family, and it is widely found in central and south China [14]. Moreover, it can also survive in nutrient-depleted soils. Peanut (*Arachishypogaea* L.) is a leguminous crop that can fix the atmospheric N and increase soil fertility [15]. According to the US Department of Agriculture, it produces a considerable amount of organic N, improves soil organic matter (SOM), helps in nutrient release and recycling, and improves soil structure. When peanut was intercropped with maize, it played a crucial role in changing soil health by influencing soil microbes composition [16]. Additionally, the dominant microbial species altered due to the peanut intercropping, which shows a close and significant relationship between improving available soil nutrients and soil enzyme activities [17].

Soil enzymes are continually playing dynamic roles in the maintenance of soil health. Soil enzymes are the direct mediators for the biological catabolism of soil organic and mineral components. They are often closely associated with SOM, soil physical properties, and microbial activities and biomass. They are the better indicators of soil health as changes in enzymes occurred much earlier than other soil parameters, thus providing early indications of changes in soil health. Their activities can also be used as measures of microbial activity and soil productivity [18,19]. Although they are present in a very nominal quantity, their role in soil quality can never be ignored. Likewise, a soil nutrient's total content and soil nutrient availability directly affect the plant's growth and development, reflecting soil health [20–22]. In the southern Chinese province of Hunan, the adoption of agroforestry (intercropping) in tea interests the growers because intercropping controls weed growth and soil erosion. However, many of the sites where these plantations were established are deficient in available macronutrients, albeit a high level of total phosphorus (P). The available P rapidly forms insoluble complexes with cations, particularly aluminum and iron. Less nutrient availability in forest soils is considered as one of the most important causes of productivity decline [23]. Nutrient total status and availability are essential for plant growth and development, and thus, macronutrient availability is vital for sustainable productivity. Soil enzymes play a significant role in nutrient recycling in soil ecosystems. In addition, soil enzymes play a crucial role in maintaining soil quality. They can provide essential and early detection signals for soil metabolic activity and nutrient status changes. However, few publications have focused on how legume intercropping with tea plantation, specifically related soil enzymatic activities, influences soil fertility.

Therefore, the objectives of our current study were (1) to investigate the soil total nutrient content and soil nutrient availability; (2) and the activities of soil enzymes (sucrase, protease, urease, acid phosphatase, and catalase); and (3) to explore a correlation between soil enzymatic activity and soil nutrients in different cropping systems and soil depths. Three cropping systems, including peanut–tea intercropping, tea mono-cropping, peanut mono-cropping, and four different soil depths (0–10 cm, 10–20 cm, 20–30 cm, and 30–40 cm) were used in this study. Our hypotheses were that: (i) intercropping favors better nutrient availability and total nutrient content than monoculture systems due to better litter quality and decomposition, and nutrient availability will decrease with the increase in soil depth; (ii) soil enzymatic activity will be higher in the peanut–tea intercropping system than in tea monoculture because peanut influences soil microbial composition—thus, it can enhance

soil microbial activity, which has a significant relationship with the improvement of soil enzyme activities; and (iii) there will be a positive and considerable relationship between soil nutrients and soil enzymatic activity across all cropping types and soil depths.

#### **2. Results**

#### *2.1. Soil Organic Carbon*

Among all the cropping systems and soil depth, SOC content ranged from 5.61 g.kg−1 to 19.93 g.kg−1. The topmost layer had the highest SOC content among all the cropping systems. While comparing each cropping system at the same depth, the SOC content was significantly different except at a 10–20 cm depth of CPM and CMM. SOC content decreased as the soil depth increased in the PMM and CMM. However, in CPM, it was observed to be highest at 20–30 cm. Overall, SOC was with the order of PMM = CPM > CMM (Table 1).

**Table 1.** Soil organic carbon (g.kg-1) content at different soil depths in *C. oleifera* mono-cropping model (CMM), peanut mono-cropping model (PMM), and Camellia–peanut inter-cropping model (CPM).

**Note:** Two-way analysis of variance (ANOVA) test was performed and the values are mean ± SD. Different uppercase letters indicate significant differences between different cropping systems at *p* < 0.05, while different lowercase letters indicate significant differences in different depths *p* < 0.05.

#### *2.2. Fluctuations in Soil Total Nutrient Status*

TN ranged from 0.72 g.kg−1 to 1.51 g.kg−1 among all cropping types and soil depths. PMM had the highest TN content among all the cropping systems at the topmost layer. TN content decreased as the soil depth increase, apart from CPM, where it was observed as the highest at 20–30 cm. There was a significant difference present in TN at each soil layer among the three cropping systems, except at a 10–20 cm depth of CPM and CMM. In terms of TP, among all the cropping patterns and soil depths, its content ranged from 0.21 g.kg−1 to 0.57 g.kg−1. CPM had the highest TP observed at 10–20 cm.

The TK content ranged from 0.05 g.kg−1 to 0.07 g.kg−1 among all soil layers and cropping patterns. CPM had the highest TK content, and it was observed to be the highest at 10–20 cm depth. At the same depths, there was a non-significant difference observed between PMM and CMM; however, both were significantly different to PMM. Overall, the TP and TK contents were in the order of CPM > CMM > PMM (Figure 1).

#### *2.3. Variability in Soil Nutrient Availability*

Among all the soil layers and cropping patterns, AN content ranged from 5.04 mg.kg−1 to 9.69 mg.kg−1. There was a non-significant difference observed for AN between different cropping systems at each soil layer, except 10–20 cm depth. The AP content ranged from 7.55 mg.kg−1 to 110.58 mg.kg−1 among all the soil depths and cropping systems, and it was observed to be the highest in the topsoil layer. Overall, the AP content was observed highest in CPM. Similar to AP, AK was also observed to be higher in CPM compared to both mono-cropping systems. On average, the AN content followed the order of PMM > CPM > CMM, whereas AP content followed the order of CPM > CMM > PMM, and the AK content followed the order CPM > PMM > CMM (Figure 2).

**Figure 1.** Variability in (**A**) soil total nitrogen (TN), (**B**) total phosphorus (TP) and (**C**) total potassium (TK) content at different soil depths in *C. oleifera* mono-cropping model (CMM), peanut mono-cropping model (PMM), and Camellia–peanut inter-cropping model (CPM). Two-way analysis of variance (ANOVA) test was conducted and the values in the columns are mean ± SD. Different uppercase letters indicate significant differences between the cropping systems at *p* < 0.05, while the different lowercase letters indicate significant differences among the soil depths *p* < 0.05.

#### *2.4. Soil Enzymatic Responses in Different Cropping Types*

All enzymes had the highest activity in 0–10 cm for each cropping system except CMM. In CMM, soil urease and protease activity were most increased at a 10–20 cm depth. The protease content varied significantly for all the cropping systems in the 0–10 cm and 30–40 cm layer and was observed to be the highest in CPM. The sucrase content followed the order PMM > CPM > CMM in all soil layers. For acid phosphatase, all layers followed the order CPM > CMM > PMM. The urease activity varied significantly among all cropping systems in the 0–10 cm, 10–20 cm, and 30–40 cm layers; only in the 20–30 cm layer was there no significant difference observed. Catalase content was not significantly different in the 0–10 cm, 20–30 cm and 30–40 cm among the cropping systems, and it was the highest in PMM. However, in the 10–20 cm layer, it was observed highest in CPM (Figure 3).

#### *2.5. Correlation between Soil Nutrients and Soil Enzymatic Activity*

(1) In PMM, sucrase, protease and catalase had no significant correlation with any nutrient. Urease was positively correlated to SOC, TN and AK, while negatively correlated to TK. Acid phosphatase was positively correlated with SOC, TN and AK. (2) In CPM, sucrase was positively related to TN; protease to SOC; urease to SOC, AN and AK; acid phosphatase to SOC; and catalase to SOC and AK. (3) In CMM, sucrase was positively

related to AP, while protease was negatively associated with AN. Urease, acid phosphatase and catalase enzymes were positively associated with SOC, TN, TP, AP and AK (Table 2).

**Figure 2.** Variability in (**A**) soil available nitrogen (AN), (**B**) available phosphorus (AP) and (**C**) available potassium (AK) content in different soil depths in *C. oleifera* mono-cropping model (CMM), peanut mono-cropping model (PMM), and Camellia–peanut inter-cropping model (CPM). Two-way analysis of variance (ANOVA) test was conducted and the values in the columns are mean ± SD. Different uppercase letters indicate significant differences between cropping systems at *p* < 0.05, while different lowercase letters indicate significant differences among soil depths *p* < 0.05.

#### *2.6. Path Coefficients between Soil Nutrients and Soil Enzyme Activity*

In PMM, among all the soil nutrients, the direct path coefficients of soil TN (0.821) and SOC (−0.534) on soil sucrase activity were relatively large. The direct path coefficients of SOC (0.689) and AP (0.524) indicate the substantial direct effect of these nutrients on protease activity. SOC also has a robust direct path coefficient with acid phosphatase activity (1.045). The direct path coefficients of SOC (2.203) and TN (−2.069) on catalase activity indicate that both had strong positive and negative effects on catalase activity, respectively (Table A1).

In CPM, soil TN, SOC and AN had a robust positive effect on sucrase enzyme activity. However, the path coefficient was small. SOC (0.469) had a sizeable direct path coefficient with protease indicates the substantial direct effect on protease activity. Moreover, SOC (0.7074) also has a robust positive path coefficient with urease activity. The direct path coefficients of all soil nutrients with catalase activity were also positive. Compared with the other four enzymes, the direct and indirect path coefficients of soil nutrient factors with acid phosphatase were relatively small, indicating that soil nutrient factors had a more negligible effect on acid phosphatase activity (Table A2).

In CMM, soil AP (0.532) had a more significant direct path coefficient on sucrase and TP (0.905), which strongly affected protease activity shows. The effect of soil nutrients on urease activity was positive with a relatively more minor path coefficient. The direct path coefficient of AP (0.885) on acid phosphatase was rather significant. In addition, TN (0.436) had a direct effect on catalase activity (Table A3). Two-way ANOVA results of the testes soil variables are shown in Table 3.

**Figure 3.** The activity of soil enzymes (**A**) protease; (**B**) sucrase; (**C**) acid phosphatase; (**D**) urease; and (**E**) catalase (mg.g−1) at different soil depths in the three cropping models in the study site. A two-way analysis of variance (ANOVA) test was conducted and the values in the columns were the mean ± SD. Different uppercase letters indicated significant differences between the cropping systems at *p* < 0.05, while different lowercase letters indicated significant differences among soil depths *p* < 0.05.

**Table 2.** Correlation analysis of soil nutrients and enzyme activities in *C. oleifera* mono-cropping model (CMM), peanut mono-cropping model (PMM), and the Camellia–peanut inter-cropping model (CPM).

**Note:** Significant values are bold. Values given are the Pearson correlation coefficients. \* Correlation is significant at the 0.05 level (2-tailed). \*\* Correlation is significant at the 0.01 level (2-tailed).

**Table 3.** Two-way ANOVA results for soil variables. \*, \*\* and \*\*\* indicate a significant level at *p* < 0.05, *p* < 0.01 and *p* < 0.001, respectively.

#### **3. Discussion**

In the present study, SOC was unanimously higher in the 0–10 cm soil layer among all the cropping systems because of the higher litterfall on the topmost layer. Compared to the mono-cropping of tea, SOC was higher in peanut–tea intercropping, possibly due to the higher litterfall and tea biomass decomposition inputs. Growing different crops on the same land simultaneously helps maintain the SOC and improve nutrient cycling [24,25]. A similar kind of increase in tea soil SOC content was also observed by [26,27]. N is vital for plant growth and development [22,28]. It is available in many soil forms, such as nitrate and ammonia [29,30]. Peanut being a legume plant could fix atmospheric nitrogen [31,32] and as a result, we found out that PMM had the highest amount of TN for the upper two layers, while in the remaining two layers, it was observed to be highest in CPM. This could be due to the tea plant deep root system, which makes way in the soil deeper layers for the atmospheric N that the peanut plant fixes. Both TP and TK contents were higher in the CPM among all the soil layers than the tea and peanut mono-cropping system. The probable reason was that intercropping can provide a much more comprehensive ground cover along with better water use efficiency [31,32].

Leguminous plants can convert the unavailable N form into a useable form [33]. The comparison of CPM and PMM depicted that AN was 51.78%, 5.92%, and 15.32% lower

in the 10–20 cm, 20–30 cm, and 30–40 cm layers of the intercropping system than in the peanut mono-cropping system because the of higher urease activity in CPM, which led to an increase in the NH3 loss from soil [34,35]. AP in the intercropping of tea and peanut was higher in all the soil layers than the tea and peanut monoculture cropping. This is because leguminous plant led to the acidification of the rhizosphere with the help of roots, and these roots release the organic acids [36–39]. Due to acidification, the enzyme acid phosphatase starts the dissolution of P-based minerals, which increases the P availability. Maurya and Lal [40] obtained similar results, where the amount of P increased due to the release of acid phosphatase by the chickpea, which led to the conversion of organic P into inorganic P. AK content was higher in CPM compared to CMM and PMM in all soil layers. AK was 11.1%, 43.06%, 46.79% higher in 0–10 cm, 10–20 cm, and 20–30 cm layers of the intercropping system than the mono-cropping of tea. Similarly, AK was 13.4%, 9.29%, and 22.06% higher in 0–10 cm, 10–20 cm, and 20–30 cm layers of intercropping system than the mono-cropping system of peanut. The increase in AK in the intercropping system can be attributed to the increase in the soil enzymatic activity.

Soil enzymes play a significant role in the soil ecosystem's overall biochemical functioning [41–43]. A better understanding of the soil enzymatic activity in different cropping systems with a depth effect can provide better knowledge about how intercropping systems can improve soil fertility. It is evident from the previous studies that the mono-cropping system could potentially harm the soil enzyme mechanism, which results in a significant decrease in the soil enzymatic activity [44]. In our current study, a very considerable increase in the enzyme activities was observed in the intercropping system of the tea and peanut than in the mono-cropping systems. Protease is the main enzyme involved in the catalysis of N minerals and N cycling [45]. The protease activity was higher in the layers of 0–10 cm and 30–40 cm for the intercropping system than both mono-cropping systems. This change in protease activity might be attributed to a higher SOC content in the topsoil [46]. However, in the 30–40 cm layer, the increase might be due to a higher N content. Soil sucrase enzymes catalyze sucrase to glucose and fructose with hydrolysis, and it is also connected with the biomass of the soil microbes [47,48]. The mono-cropping of peanut had the highest sucrase enzyme activity, possibly due to a higher SOM. These findings are supported by Li et al. [41], who observed that soil enzyme activity was significantly enhanced due to intercropping. Acid phosphatase is the main enzyme involved in ester hydrolysis and phosphoric acid anhydrides [49,50]. It converts the esters and anhydrides into phosphate, and is a key enzyme in P cycling in soil [51].

In our current study, acid phosphatase activity was higher in the intercropping of peanut–tea than tea mono-cropping because the peanut was identified as a species whose roots release an ample amount of acid phosphatase in the soil [52,53]. The soil urease enzyme catalyzes urea into NH3 and CO2 with hydrolysis [54]. This is a necessary process that regulates N availability to the plants after the application of urea. To some level, urease indicates the availability of N in different cropping systems. The catalase's primary function is to decompose the organic matter into the plant's useable form [55,56]. In our current study, the mono-cropping of peanut had a higher activity of urease and catalase than intercropping because of the SOM content of the mono-cropping of peanut. In this study, the enzyme activity was more or less associated with the content and distribution of SOC, TN and AK among all the cropping systems. Our results are in line with Tian et al. [57] and Udawatta et al. [58]; they also observed a stronger correlation between SOC and enzymes. An increase in the SOM and litter quantity enhances the soil activity [59,60] and this increment has direct involvement in the improvement of nutrient cycling, along with greenhouse emission [61–63], which, in return, has a positive impact on the ecosystem, plant growth and overall SOC.

#### **4. Materials and Methods**

#### *4.1. Study Site*

The study was conducted in Hunan Botanical Garden in Changsha city, Hunan Province, China (113◦02 –113◦03 E, 28◦06 –28◦07 N) (Figure 4). The study area had a typical subtropical humid monsoon climate, with a mean annual precipitation of 1378 mm, mean annual temperature of 17.2 ◦C, and a mean annual relative humidity of 81%. The mean annual average sunshine was 1814.8 h and the frost-free period was 275 days. The site was the hillside, with a slope of about 5–15◦. The soil was classified as typical red earth developed from the quaternary red clay reticulated parent material. The soil texture ranged from clay loam to sandy loam, with a depth of about 1 m. The soil was acid with a pH of 4.5–5.5. Soil bulk density was ranged between 1.16 to 1.22 (g/m3).

**Figure 4.** Location of the study area, Hunan botanical garden, Changsha, Hunan.

#### *4.2. Experimental Design*

In this study, three cropping models were set up which included: (1) a *C. oleifera* mono-cropping model (CMM); (2) a peanut mono-cropping model (PMM), and (3) a Camellia–peanut inter-cropping model (CPM). The *C. oleifera* monoculture stand was established in 2010, and the line-row spacing of the *C. oleifera* was planted in 4 m × 3 m. In CMM, an area with three lines and three rows of *C. oleifera* trees was selected as a plot (about 110 m2). Four plots were set up for CMM in this study.

However, in PMM, the peanuts were planted in the open space beside the *C. oleifera* forests according to the 0.25 m × 0.1 m spacing in April 2018. An area with 40 lines and 100 rows of peanut plants was selected as a plot (about 100 m2). Four plots were set up for PMM in this study. Moreover, in CPM, an area with three lines and three rows of *C. oleifera* trees was selected as a plot (about 110 m2). Peanuts were intercropped between *C. oleifera* trees in April 2018. The spacing between the peanut and tea plant was 1 m. Four plots of CPM were set up in this study. The three cropping models employed similar field management practices during the study period.

#### *4.3. Soil Sampling*

Soil samples were collected in September 2019. Four plots of 20 × 20 m were established in each of CMM, PMM, and CPM plots; four pits were dug diagonally. A soil corer was used to obtain soil samples from 0–10, 10–20, 20–30, and 30–40 cm depths from each pit. Four replicates were taken from each plot for each depth. The rocks and plant residues were removed from the soil samples. Soil samples were placed into self-sealing plastic bags, labelled, and delivered to the laboratory for further analysis. For enzymatic activity analysis, the soil samples were placed in −80 ◦C until further use. For nutrients analysis, after air drying, the soil samples were passed through a 0.15 mm and 0.20 mm sieve for the determination of soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), total potassium (TK), available nitrogen (AN), available phosphorus (AP), and available potassium (AK).

#### *4.4. Soil Chemical Analysis*

Urease activity was measured following the method described in [64]. Five grams of soil was incubated with 10 mL of citrate phosphate buffer (pH 6.7) and 5 mL of 10 % urea solution at 38 ◦C for 3 h. Activity was determined by measuring the released NH4 + with a spectrophotometer at 578 nm. Acid phosphatase activity was analyzed with nitrophenyl phosphate disodium (PhOH mg g1, 37 ◦C, 24 h), and catalase with KMnO4 (0.1 mol L−1 KMnO4 ug g−1, 30 ◦C, 20 h) [65]. Sucrase activity was determined by the method of [66]. For sucrose, the air-dried soil (5 g) was incubated with 15 mL sucrose. Five microliters (5 mL) of phosphate buffer (pH 5.5) and five drops of toluene at 37 ◦C for 24 h, and the reaction solution was filtered through the quantitative filter paper as rapidly as possible after incubation. Filtrate (1 mL) was mixed with 3 mL salicylic acid at 100 ◦C for 5 min in the water bath, and the mixture was adjusted to 50 mL and cooled with deionized water. Sucrase activity was determined spectrophotometrically at 508 nm. The protease activity was determined by ninhydrin colorimetry, expressed in milligrams of amino nitrogen in 1 g of soil cultured for 24 h in a 37 ◦C incubator [67].

SOC was determined by the hydrated potassium thermo-dichromate oxidation method. TN was determined using the CN elemental analyzer, while the colorimetric method of the molybdenum–antimony solution with royal acid was used to determine TP. The flame photometer method was used to determine TK. AN was analyzed by the Kjeldahl method [68]; AP was determined by the diacid extraction spectrophotometric colorimetry method [69], and AK was determined using a flame photometer method by ammonium acetate extraction [70]. Detailed information about the total and available nutrients was also mentioned in our published paper [6].

#### *4.5. Statistical Analysis*

Two-way analysis of variance (ANOVA) was performed to analyze the difference in soil enzymatic activity, soil total nutrient status and soil nutrient availability between different cropping systems at four different soil depths. Correlation analysis was performed using the Pearson statistical method to analyze the association among the studied soil parameters. Moreover, the path analysis using multiple linear analysis was also performed to study the relationships betweesn soil enzyme and soil nutrients. Results were declared statistically significant at *p* < 0.05 and the means that exhibited significant differences were compared using Tukey's significance test. All statistical analyses were performed using the SPSS Statistical Package (SPSS 17.0, Chicago, IL, USA).

The multiple linear regression equations of soil sucrase activity (Y1), protease activity (Y2), urease activity (Y3), acid phosphatase activity (Y4), catalase activity (Y5) and soil nutrient factors are as follows:

The multiple linear regression equations in PMM are:

$$\text{Y1} = -0.534 \text{X1} + 0.821 \text{X2} + 0.173 \text{X3} - 0.258 \text{X4} - 0.012 \text{X5} - 0.065 \text{X6} - 0.257 \text{X7} \tag{1}$$

$$\text{Y5} = 2.023\text{X1} - 2.069\text{X2} - 0.181\text{X3} + 0.065\text{X4} + 0.065\text{X5} + 0.244\text{X6} + 0.169\text{X7} \tag{5}$$
  $\text{The multiple linear regression equations in CPM are:}$

$$\text{Y1} = 0.104 \text{X1} + 0.517 \text{X2} - 0.297 \text{X3} - 0.046 \text{X4} + 0.018 \text{X5} - 0.219 \text{X6} + 0.329 \text{X7} \tag{6}$$

$$\text{Y4} = 0.219\text{X1} + 0.239\text{X2} + 0.053\text{X3} - 0.139\text{X4} + 0.154\text{X5} - 0.131\text{X6} + 0.031\text{X7} \tag{9}$$

$$\text{Y5} = 0.243\text{X1} + 0.143\text{X2} + 0.197\text{X3} - 0.037\text{X4} + 0.015\text{X5} - 0.274\text{X6} + 0.264\text{X7} \tag{10}$$

The multiple linear regression equations in CMM are:

$$\text{Y1} = 0.009\text{X1} + 0.213\text{X2} - 0.368\text{X3} - 0.089\text{X4} + 0.001\text{X5} + 0.532\text{X6} + 0.248\text{X7} \tag{11}$$

$$\text{Y5} = -0.200\text{X1} + 0.436\text{X2} + 0.088\text{X3} - 0.132\text{X4} - 0.199\text{X5} + 0.174\text{X6} + 0.274\text{X7} \tag{15}$$

X1, X2, X3, X4, X5, X6 and X7 represent SOC, TN, TP, TK, AN, AP and AK, respectively. These equations present the direct path coefficients. The indirect path coefficients were obtained using the direct path coefficients multiplied by each soil nutrient's correlation coefficient. The direct path coefficient was the direct effect of soil nutrient factors on enzyme activity.

#### **5. Conclusions**

In our study, the activity of enzymes, SOC, and TN was higher in the topsoil than the subsoil because of the higher accumulation of SOM on the topsoil and the much favorable moisture and temperature conditions of topsoil. SOC and most of the soil total nutrient content and nutrient availability were significantly higher in the intercropping system. Moreover, apart from soil catalase and urease, other enzymes' soil enzymatic activities were higher in the intercropping than in the mono-cropping system. Path analysis provides the weighted effect of soil nutrients on soil enzymes in each cropping type. Intercropping may also lead to an increase in the other soil quality indicators, such as SOM. Intercropping of peanut with tea could significantly increase soil fertility. The long-term sustainability of the soil ecosystem in tea farming can be achieved with the help of peanut–tea intercropping.

**Author Contributions:** Conceptualization, T.H.F., J.M., X.C.; Data curation, A.S. and M.H.U.R.; Formal analysis, T.H.F. and J.M.; Funding acquisition, W.Y.; Investigation, J.W. and W.Y.; Methodology, T.H.F., U.K. and J.M.; Project administration, J.W. and W.Y.; Resources, X.C.; Software, A.S., M.H.U.R. and M.A.T.; Writing—original draft, T.H.F. and U.K.; Writing—review & editing, M.A.T., X.C. and W.Y. All authors have read and agreed to the published version of the manuscript.

**Funding:** The funding sources include the National Science and Technology Support Grant of China (2015BAD07B0503), Forestry Science and Technology Promotion Project of China (No. 12 2017) and Postdoctoral research funding of Central South University of Forestry and Technology (70702-45200003).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** No additional data available.

**Conflicts of Interest:** The authors declare there is no conflict of interest exists.

#### **Appendix A**

**Table A1.** Path coefficients between soil nutrients factors (X) and soil enzyme activity (Y) in PMM.

\* indicate a significant level at *p* < 0.05.

**Table A2.** Path coefficients between soil nutrients factors (X) and soil enzyme activity (Y) in in CPM.

**Table A2.** *Cont.*

\* indicate a significant level at *p* < 0.05.

**Table A3.** Path coefficients between soil nutrients factors (X) and soil enzyme activity (Y) in in CMM.

**Table A3.** *Cont.*

\* indicate a significant level at *p* < 0.05.

#### **References**

### *Article* **Evaluation of Physiological and Morphological Traits for Improving Spring Wheat Adaptation to Terminal Heat Stress**

**Hafeez ur Rehman 1, Absaar Tariq 1, Imran Ashraf 1, Mukhtar Ahmed 2,3,\* , Adele Muscolo 4 , Shahzad M. A. Basra 1 and Matthew Reynolds 5**

**Abstract:** Wheat crop experiences high temperature stress during flowering and grain-filling stages, which is termed as "terminal heat stress". Characterizing genotypes for adaptive traits could increase their selection for better performance under terminal heat stress. The present study evaluated the morpho-physiological traits of two spring wheat cultivars (Millet-11, Punjab-11) and two advanced lines (V-07096, V-10110) exposed to terminal heat stress under late sowing. Early maturing Millet-11 was used as heat-tolerant control. Late sowing reduced spike length (13%), number of grains per spike (10%), 1000-grain weight (13%) and biological yield (15–20%) compared to timely sowing. Nonetheless, higher number of productive tillers per plant (19–20%) and grain yield (9%) were recorded under late sowing. Advanced lines and genotype Punjab-11 had delayed maturity and better agronomic performance than early maturing heat-tolerant Millet-11. Advanced lines expressed reduced canopy temperature during grain filling and high leaf chlorophyll *a* (20%) and *b* (71–125%) contents during anthesis under late sowing. All wheat genotypes expressed improved stem water-soluble carbohydrates under terminal heat stress that were highest for heat-tolerant Millet-11 genotype during anthesis. Improved grain yield was associated with the highest chlorophyll contents showing stay green characteristics with maintenance of high photosynthetic rates and cooler canopies under late sowing. The results revealed that advanced lines and Punjab-11 with heat adaptive traits could be promising source for further use in the selection of heat-tolerant wheat genotypes.

**Keywords:** canopy temperature; water soluble carbohydrates; heat stress; stay green; seed yield

#### **1. Introduction**

Wheat (*Triticum aestivum* L.) is a staple crop and nourishes billions of people daily, while its productivity is significantly decreased under high temperature. Owing to global climate changes, wheat yields are expected to decline by 6% for each 1 ◦C increase in temperature. Therefore, gain yield of wheat crop must be increased by 60% until 2050 to fulfil the food demands of burgeoning global population [1]. According to the International Maize and Wheat Improvement Center (CIMMYT), world wheat producing regions are grouped into eight different mega environments (MEs) [2]. The ME1 and ME5 are highly productive irrigated environments in South Asia; however, wheat crop is exposed to terminal heat stress in these MEs [3]. South Asia includes India, Nepal, Pakistan and

**Citation:** Rehman, H.u.; Tariq, A.; Ashraf, I.; Ahmed, M.; Muscolo, A.; Basra, S.M.A.; Reynolds, M. Evaluation of Physiological and Morphological Traits for Improving Spring Wheat Adaptation to Terminal Heat Stress. *Plants* **2021**, *10*, 455. https://doi.org/10.3390/ plants10030455

Academic Editor: Ornella Calderini

Received: 25 December 2020 Accepted: 24 February 2021 Published: 28 February 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Bangladesh where wheat is sown late due to the delay in the harvesting of previous crops, i.e., rice and cotton in rice-wheat and cotton-wheat zones owing to delayed harvest of long maturing semi-dwarf rice varieties and delayed picking of cotton respectively [4,5]. The delay in wheat cultivation exposes the crop to high temperatures during flowering and grain-filling stages termed as "terminal heat stress" [6]. Nonetheless, wheat grown on ≥13.9 million ha in the rice-wheat cropping system of South Asia [1] including rice-wheat and cotton-wheat zones of Pakistan experience terminal heat stress [4,5].

Spring wheat in South Asia is sown in the months of November and December while harvested during April and May particularly in rice-wheat and cotton-wheat cropping zones. The timely planted wheat crop in these zones has temperature requirement of 12–22 ◦C during vegetative and reproductive growth periods particularly at anthesis and grain filling stages.

Late sown wheat experiences higher canopy temperature (>31 ◦C) during reproductive period adversely affecting many physiological processes resulting in shortened crop growth cycle and reduced yields [6]. For example, high temperature during anthesis reduces pollen viability by restricting embryo development and promotes anther sterility resulting in less grain numbers [7,8]. High temperature exposure after anthesis decreases grain filling rate which is associated with declined grain yield [7–10]. These decreases in grain filling rate and duration accelerate senescence with the loss of chlorophyll and limits assimilate supply towards developing grains [11].

Nonetheless, wheat plants evolve several physiological mechanisms to cope with terminal heat stress which include early maturity [3,6], stay green [11,12], reduced canopy temperature [13], accumulation of high stem water soluble carbohydrates [14] and high biomass accumulation [13] to translate assimilates into yield. For instance, early maturity as an adaptation strategy provides explanation for variation to physiological responses and grain yield under high temperature among wheat genotypes during reproductive period [3,6]. Stay green trait maintains high leaf chlorophyll contents that are associated with grain yield and its components under heat stress [11,12,15]. Likely, the loss in green area affects grain size that can be compensated by improving the remobilization of watersoluble carbohydrates stored in stem and leaf sheaths to developing grains under high temperature and drought stress [14]. However, studies showing the direct relationship of water-soluble carbohydrates with grain yield under heat or drought stress are limited. In addition, wheat plants with cooler canopy during grain filling, have capability to access subsoil moisture which helps to maintain evaporation and photosynthesis under hot irrigated conditions [16]. Wheat plants with cooler canopies also have positive association with grain yield [13,17,18]. Therefore, these physiological traits can be the best combination for genetic improvement of wheat genotypes to mitigate heat and drought stress because of their common genetic basis [16,19,20].

Exploring combination of physiological traits needs identification of wheat genotypes which produce high yield and express adaptation traits under high temperature exposure. Therefore, developing heat tolerant genotypes through breeding is a major objective in wheat improvement programs [3,6]. In view of global climate changes, these efforts should be accelerated to reduce high temperature effects [1,21] as rise in the global average temperature is a serious threat to wheat production in different MEs prone to terminal drought or heat [3,22,23].

To cope with situation, at present, efforts for evaluation of exotic germplasm developed by CIMMYT are in progress through National Agriculture Research Programs (NARPs). For this, elite nurseries of wheat germplasm are tested in MEs of South Asia for their adaptability to heat and associated traits which are later assembled into mega varieties for both improved productivity and heat tolerance [3,6,24]. Similarly, an international wheat phenotyping network has been established in South Asia for application of phenotyping techniques to accelerate selection and support breeding program for their success by incorporating physiological traits into new generation of lines for heat or drought tolerance [24]. Thus, physiological characterization of existing genotypes or plant genetic

resources through phenotyping tools may provide better understanding of heat adaptive traits and their integration into breeding programs may help to translate these genotypes into desirable plant types [25,26]. The present study, therefore, compared the performance of wheat genotypes including one widely grown and two promising advanced lines with early maturing heat tolerant cultivar to identify morpho-physiological traits for adaptation to terminal heat stress under late sowing. Most specifically the association of these traits with grain yield was determined for their further use in cultivar selection.

#### **2. Results**

#### *2.1. Crop Phenological Development*

High temperature significantly reduced crop development period, including booting (7–8 d), heading (2 d), anthesis (3 d), grain filling (10 d) and maturity period (10–12 d) under late sowing. Among the genotypes, earlier heading (3, 3, 4 d), booting (9–11, 10–12, 11–15 d), anthesis (1–2, 2–5, 4 d) and maturity (10–13, 10–12, 10–11 d) were observed for Punjab-11, V-07096 and V-10110 respectively compared to heat tolerant Millet-11 under late sowing condition. However, delayed heading (2 d), booting (3 d) and anthesis (7 d) were observed for heat tolerant Millet-11 while grain filling period was reduced for all genotypes under late sowing condition. Interactions were significant for all phenological traits (Table 1).

#### *2.2. Physiological Traits*

Wheat crop expressed a 20% and 71–125% increase in Chl *a* and *b* contents respectively, under late sowing compared to timely sown crop during anthesis. Heat tolerant check Millet-11 and advanced line V-07096 expressed the highest Chl *a* and *b* contents during the first growing season (2012–2013), while these were highest in Punjab-11 and V-10110 during the second growing season (2013–2014) under late sowing. Reduced canopy temperature was observed in late sown crop during 2012–2013 and vice versa for 2013–2014. Heat tolerant Millet-11 and advanced lines V-07096 and V-10110 expressed reduced canopy temperature during both seasons. Maximum increase (50%) in stem water-soluble carbohydrates was found under late sowing. Nonetheless, all genotypes expressed similar stem water-soluble carbohydrates under late sowing (Table 1).

**Table 1.** Mean comparison for crop phenology and physiological traits in wheat genotypes under late sown condition.

HSD SD = 0.71; G = 1.07, SD × G = 1.84 SD = 0.62; G = 1.09, SD × G = 1.88

Means SD 91.08 A 83.75 B 93.00 A 84.75 B

**Table 1.** *Cont*.

**Table 1.** *Cont*.

Letters among columns denote significant differences in means for sowing dates while letters within columns denote significant differences between cultivars at *P* ≤ 0.05; SD = Sowing dates; G = Genotype; n.s. = non-significant; Fwt = Fresh weight basis.

#### *2.3. Gas Exchange Traits*

No significant difference was observed for photosynthetic (A) and transpiration rates (E) under timely and late sown wheat while intercellular CO2 concentration (Ci) and stomatal conductance (Gs) were reduced under late sowing. However, lowest photosynthetic and transpiration rates were found for advanced line V-07096. Regarding interactions, the highest Gs was found for timely sown advanced line V-07096 and it was drastically reduced under late sowing condition. On the other hand, highest Ci was found in advanced line V-07096 under both sowing conditions while Gs was significantly reduced under late sowing (Figure 1).

**Figure 1.** *Cont*.

**Figure 1.** Gas exchange traits (**a**) photosynthetic rate (**b**) transpiration rate (**c**) sub-stomatal CO2 (**d**) stomatal conductance in wheat genotypes under late sowing. Different lowercase letters indicate significant differences among treatments; Means with same letters show non-significant difference at *p* > 0.05.

#### *2.4. Yield Related Traits*

Terminal heat stress significantly reduced yield related traits in all the wheat genotypes under late sowing. Among these traits, plant height, spike length, grains per spike, thousand grain weight and biological yield were reduced more under late sowing than timely sowing (Table 2). Total number of productive tillers were highest during both years, while grain yield was high during first growing season and was similar under both timely and late sowing in 2nd growing season. Advanced lines V-07096 and V-10110 expressed the highest plant height, spike length, number of grains per spike, thousand grain weight including biological and grain yields. Genotype Punjab-11 also expressed highest biological and grain yields significantly similar to advanced line V-07096. For total productive tillers, the differences among genotypes were non-significant. The interactions were significant for spike length, thousand grain weight and biological yield, while these traits decreased during second growing season, grain yield during both years and plant height during first growing season were significant (Table 2).

**Table 2.** Mean comparison for yield related traits in wheat genotypes under late sowing condition.

#### **Table 2.** *Cont*.

Letters among columns denote significant differences in means for sowing dates while letters within columns denote significant differences between cultivars at *P* ≤ 0.05; SD = Sowing dates; G = Genotype; n.s. = non-significant.

> Positive correlation was recorded for Chl *a* and *b* contents, and canopy temperature with grain yield while no relationship was noted between stem water-soluble carbohydrates and grain yield under both timely and late sowing (Figure 2). A significantly strong, and positive relationship of 1000-grain weight was found with days to heading during both years while with maturity time during the 2nd year only. Nonetheless, the relationship of grain yield with heading and maturity time was non-significant during both years and negative with maturity time during the 1st growing season (Table 3).

> **Table 3.** Correlation co-efficient of heading and maturity time with thousand seed weight and seed yield.

#### **3. Discussion**

Identification of plant traits and developing wheat cultivars with improved adaptation to terminal heat stress is priority for plant breeders around the globe. The present study compared the performance of wheat genotypes to identify the morpho-physiological traits for terminal heat stress tolerance under late sowing. Results showed that late sowing reduced yield traits, including spike length, number of grains per spike, thousand grain weight and biological yield than timely sown crop, albeit response varied between sowing conditions and years. The reduced number of grains per spike in the present study might be due to low grain fertility and floral spikelets associated with increased temperature even by 1 ◦C during booting and anthesis stages [27]. Reduction in number of grains depends on developmental stage, at which high temperature occurs and determined by supply of carbohydrates during floral development, which is a sensitive process to high temperature [28]. Higher number of grains per spike in advanced lines of the present study might be attributed to increased availability of stem water-soluble carbohydrates. Advanced lines including genotype Punjab-11 had higher thousand grain weight and grain yield than heat tolerant early maturing check Millet-11 and maintained it under terminal heat stress. Better agronomic and yield performance of the advanced lines and Punjab-11 seemed to be genotypic specific effects [29], while achieving and maintaining the optimal grain weight is considered an index of heat stress tolerance and adaptation [30]. Another possible reason for the decrease in yield traits of the present study might be attributed to

reduced duration between different crop stages and less translation of biomass to yield (Tables 1 and 2). Reduced biological yield, thousand grain weight and 6–20% decrease in grain yield is reported in wheat crop exposed to terminal heat stress [3,23,31].

**Figure 2.** Regression relationship of seed yield with (**a**) leaf Chl a, (**b**) Chl b, (**c**) canopy temper-ature (**d**) stem water soluble carbohydrates. Filled box with black color indicate the data val-ues for timely planted and white filled for late planted wheat crop.

Nonetheless, a higher number of total productive tillers and grain yield during the first growing season under late sowing might be due to superiority in physiological traits of genotypes expressing high leaf chlorophyll contents and cooler canopies during anthesis or grain filling stages. Both traits help the plants to maintain better photosynthetic performance and remobilization of stem reserves to the developing grains during grain filling under high temperature or drought stress [3]. High temperature increased transpiration and keep crop canopies cool and turgid to maintain the photosynthetic performance [16]. Although relatively low transpiration and photosynthetic rates were found especially for advanced line V-07096 under late sowing, these were significantly similar with timely sowing supporting the hypothesis that genotypes maintained photosynthetic performance under terminal heat stress.

Reduced canopy temperature response of wheat lines V-07096 and V-10110 was also reflected with a decrease in gas exchange traits including A, E and Ci under late sowing in the present study [16]. Higher leaf chlorophyll contents at anthesis in wheat genotypes are an indicative of delayed senescence, high photosynthetic rate and remobilization of assimilates under terminal heat stress. Both traits contributed to higher grain yield in wheat genotypes under late sowing as evident from direct association of leaf chlorophyll contents and canopy temperature with grain yield (Figure 2a,b). Stay green trait is highly dependent on the environment and has a strong positive relationship with grain filling rate, duration and grain yield under heat stress [20]. Grain yield and stay grain are controlled by similar quantitative traits loci (QTLs) which are co-localized for productivity enhancement under heat stressed environments [20]. Heat tolerant wheat lines developed with physiological traits having cooler canopies and stay green showed superior yield and higher thousand grain weight with better adaptability under terminal heat stress [3,24].

The stem water-soluble carbohydrate is a potential adaptive trait for developing heat or drought tolerant wheat and 10–50% variation in stem water-soluble carbohydrates to total stem dry weight has been reported in wheat [32,33]. Higher accumulation of stem water-soluble carbohydrates in advanced lines along with early maturing heat tolerant check Millet-11 of the present study indicated increased buffering capacity of these genotypes to remobilize carbon reserves towards the developing grains accumulated during stem elongation period under terminal heat stress [14].

Nonetheless, no relationship of stem water-soluble carbohydrates with grain yield in the present study (Figure 2d) validates that weak association of water-soluble carbohydrates with yield as reported earlier and response is dependent on environment [16,26]. Higher stem water-soluble carbohydrates expressed under late sowing in the present study validate the potential of trait under stress condition and should be further investigated. In addition to superior physiological traits, wheat genotypes showed delayed maturity, however, response was compensated with higher yields ranging 5.38–23.82% compared to early maturing heat tolerant check Millet-11. Early maturity is as considered a breeding criterion to escape the effects of terminal heat stress, however, short duration may be accompanied with grain yield losses [3]. Interestingly, association of thousand grain weight with days to heading was significant and strong during both years and with days to maturity in the 1st year (Table 3).

Nonetheless, superior performance of advanced lines and Punjab-11 for grain yield and physiological traits demonstrated their potential to be used for the physiological breeding programs. Thus genotypes with heat adaptive traits should be considered in parent selection for a targeted environment or for the identification of one or more adaptive traits [24–46].

#### **4. Materials and Methods**

#### *4.1. Study Site*

This study was conducted during the winter (rabi) growing season of 2013–2014 and 2104–2015 at University of Agriculture Faisalabad (latitude, 31◦ 26' N; longitude, 73◦ 06 E; altitude 184.4 m). The soil had sandy loam texture with Lyallpur series, an Aridisol, a fine

silty, mixed, hyperthermic Ustalfic Haplargid according to USDA classification. Seeds of wheat genotypes were obtained from Wheat Research Institute, Ayub Agriculture Research Institute (AARI), Faisalabad.

#### *4.2. Plant Material*

The four wheat genotypes used in the present study were two widely cultivated (Millet-11, Punjab-11) and two promising lines (V-07096, V-10110).

#### *4.3. Creation of Heat Stress Environment and Experimental Design*

Genotypes were cultivated at two sowing times viz. timely and late sowing. The crop planted on the 10th and 13th of November was considered as normal sowing while on the 10th and 13th of December was recorded as late sowing during 2013 and 2014, respectively. The delayed sowing was done with the objective to create a heat stress environment at anthesis and during reproductive stages. The early maturing genotype Millet-11 was used as heat tolerant check while Punjab-11 was selected for its high yield potential [47]. The experimental treatments were randomized in complete block design (RCBD) with split-plot arrangement with sowing dates into main plots and wheat genotypes into sub-plots and each replicated thrice. Wheat genotypes were planted into net plot size of 5.7 m × 3.5 m at inter-row spacing of 22.5 cm.

#### *4.4. Climate and Weather Conditions*

The Faisalabad features semi-arid sub-tropical climate and is located in Punjab-Pakistan. The rice-wheat rotation is generally practiced as a cropping pattern in this ecological zone. Wheat is grown as a spring crop starting from November and harvested in April. The average maximum and minimum temperatures during winter were 21 ◦C and 6 ◦C, respectively. May, June and July are the hottest months during summer and December, January and February are the coldest ones during winter [48]. Average annual rainfall is about 300 mm and that is highly seasonal and 50% of it is received during monsoon in July and August. However, in the present study, the temperature from sowing to booting ranged between 16 to 28 ◦C during 2012–2013 and 9.6 to 28.1 ◦C during 2013–2014. The maximum temperature of wheat crop sown under normal and late sowing from anthesis to maturity including grain filling period ranged from 16.8 to 31.5 ◦C in 2012–2013 and 17.3 to 33.6 ◦C in 2013–2014 of present study and similar trend was observed for low temperature (Figure 3).

#### *4.5. Crop Husbandry*

The seed rate of 125 kg ha–1 was used for wheat cultivation. Recommended fertilizers doses of nitrogen (N), phosphorus (P) and potassium (K) were applied at rate of 100–85–60 kg ha−1 using urea, single super phosphate and sulfate of potash, respectively. Whole of the P and K were applied during land preparation and while N was applied in three splits with the 1st half during land preparation as basal, 2nd at the 1st irrigation and 3rd at the 2nd irrigation. Crop was irrigated four times including the 1st as pre-saturated, 2nd at crown initiation, 3rd at tillering and 4th during anthesis stages. All plant protection measures were performed as recommended and weeds were controlled manually.

#### *4.6. Observations*

#### 4.6.1. Crop Phenology

Phenological measurements at different crop developmental stages such as days to booting, heading, anthesis and maturity including grain filling period were recorded following Zadoks scale [49].

#### 4.6.2. Physiological Traits

For canopy temperature, measurements were taken during mid grain-filling stage using Infrared Thermometer between 10:00 h to 14:00 h under clear bright sky with no wind. Two readings per plot were taken and averaged. Leaf chlorophyll contents were measured according to Arnon et al. [50]. Five flag leaves were harvested from each plot at 10th days after anthesis and 0.5 g leaf sample was extracted in acetone (80% *v*/*v*) and kept overnight in sealed falcon tubes at 5 ◦C. The absorbance of the supernatant was determined at 645 nm and 663 nm using a spectrophotometer (Hitachi-220 Japan). Water soluble carbohydrates i.e., stem reserves were measured according to Yemm and Willis [51] and stem from main primary tillers was collected at the 7th day after anthesis. Fresh material (0.5 g) was boiled in 5 mL distilled water for 1 h. The extract was filtered and distilled water was added up to 50 mL. The 5 mL anthrone reagent was added into 1 mL of the extract along the sidewall of the test tube and vortexed. The mixture was heated in a water bath at 90–95 ◦C for 20 min, cooled down and absorbance was taken at 620 nm using a spectrophotometer (Hitachi-220 Japan).

**Figure 3.** Maximum and minimum temperatures during crop growing period (**a**) 2013–2014 and (**b**) 2014–2015.

#### 4.6.3. Gas Exchange Traits

Stomatal conductance (gs), sub-stomatal CO2 concentration (Ci), photosynthetic (A), and transpiration rates (E) were recorded at anthesis stage from top the 3rd leaf of every plant using infra-red gas analyzer (IRGA) (model, LCA-4; Analytical Development Company, Hoddesdon, England) [52]. All these measurements were recorded at 13.00–14.00 h. During measurements, leaf chamber molar gas flow rate was 248 μmol s−1, ambient CO2 conc. (Cref) 352 μmol mol−1, ambient pressure (P) of 98.01 k Pa, molar flow of air/leaf area 221.06 mol m−2s−1 and leaf chamber volume with gas flow rate (v) of 380 mL/min.

#### 4.6.4. Yield Related Traits

Average plant height, spike length and number of grains per spike were recorded at maturity by threshing 10 primary tillers manually. Numbers of productive tillers of each genotype from selected plants were counted at maturity and averaged. The grains of threshed plants for each replication were bulked separately for 1000-grains and weighed. For straw yield, an area of 2 m2 was randomly harvested twice per plot, tagged and weighed separately with the help of an electric balance. The harvested samples were threshed manually and grain yield for each genotype was measured. Both straw and grain yields were expressed as g m−2.

#### 4.6.5. Statistical Analysis

Analysis of variance (ANOVA) was performed for data comparison using Statistix 8.1 software. The year was taken as factor to find the homogeneity or heterogeneity and where year was significant, the data of both years were analyzed and presented separately, otherwise pooled. MS-Excel was used to present data graphically. To overcome confounding effects of maturity among cultivars for physiological traits, co-variance analysis was performed. The significant difference among treatment means was compared following Tukey's range test at 5% probability level. Pearson's correlation was also performed for heading and maturity time with thousand grain wight and grain yield to determine relationship between them.

#### **5. Conclusions**

The present study revealed necessary variation among genotypes for various morphophysiological traits associated with adaptation to terminal heat stress. Advanced lines and cv. Punjab-11 had better agronomic performance for most of the traits than heat tolerant check Millet-11 under late sowing. A positive relationship of leaf chlorophyll contents and canopy temperature was found with grain yield. Therefore, advanced lines and Punjab-11 with high biomass and grain yield, reduced canopy temperature and high leaf chlorophyll contents can be promising sources to be utilized in physiological breeding for developing climate resilience in wheat.

**Author Contributions:** Conceptualization, H.u.R. and M.R.; Data curation, A.T.; Formal analysis, H.u.R. and A.T.; Investigation, H.u.R.; Methodology, H.u.R. and S.M.A.B.; Project administration, H.u.R. and S.M.A.B.; Supervision, H.u.R., S.M.A.B. and M.R.; Visualization, M.A.; Writing—original draft, H.u.R., A.T. and M.A.; Writing—review & editing, I.A., M.A. and A.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** Higher Education Commission, Pakistan.

**Data Availability Statement:** Data is contained within the article.

**Acknowledgments:** The authors acknowledge the Higher Education Commission, Pakistan for providing financial support in completion of these studies and Shahid Farooq, Academic Editor, Plos One for critically editing the manuscript.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Abbreviations**

CIMMYT: International Maize and Wheat Improvement Center, MEs: mega environments, NARPs: National Agriculture Research Programs, SD: Sowing dates, G: Genotype, n.s: non-significant, Fwt: Fresh weight, A: Photosynthetic rate, E: Transpiration rate, Ci: Intercellular CO2 concentration, Gs: Stomatal conductance, AARI: Ayub Agriculture Research Institute, RCBD: Randomized complete

block design, N: Nitrogen, P: Phosphorus, K:Potassium, IRGA: Infra-red gas analyzer and ANOVA: Analysis of variance

#### **References**

### *Article* **Elevated Nitrogen Priming Induced Oxinitro-Responses and Water Deficit Tolerance in Rice**

**Kamolchanok Umnajkitikorn 1,\* , Mitsutaka Fukudome 2,3 , Toshiki Uchiumi 2 and Neung Teaumroong 4**

**Abstract:** Under water deficit conditions, the essential macronutrient nitrogen becomes limited as a result of reduced dissolved nitrogen and root nitrogen uptake. An elevated nitrogen level might be able to mitigate these effects, integrated with the idea of using nitric oxide as abiotic stress tolerant inducers. In this study, we evaluated the potential of using elevated nitrogen priming prior to water shortage to mitigate plant stress through nitric oxide accumulation. We grew rice plants in 300 mg L−1 nitrogen for 10 weeks, then we primed plants with four different nitrogen concentrations: 100, 300 (control), 500 and 1000 mg L−1 nitrogen prior to inducing water deficit conditions. Plants primed with 500 mg L−1 nitrogen possessed a higher photosynthetic rate, relative water content, electrolyte leakage and lipid peroxidation under water deficit conditions, compared to control plants. The induction of water deficit tolerance was supported with the activation of antioxidant defense system, induced by the accumulation of nitric oxide in leaves and roots of rice plants. We originally demonstrated the accumulation of nitric oxide in leaves of rice plants. The elevated nitrogen priming can be used to enhance water deficit tolerance in irrigated paddy fields, instead of nitric oxide donors.

**Keywords:** rice; nitrogen; water stress; drought; antioxidant; reactive oxygen species; reactive nitrogen species

#### **1. Introduction**

Water deficit conditions cause widespread crop loss globally due to abiotic stress. By the end of the 21st century, drought and water deficit stress may account for more than 70 percent of crop loss below optimal productivity [1]. There are many methods to cope with stresses from water deficit conditions: breeding strategies, cultural practices, and irrigation approaches [2,3]. Drought stress induces mature and young leaf senescence. The primary site of damage during stress is the chloroplasts, where more than 70% of the leaf nitrogen (N) is sequestrated. In many annual crop species, chloroplast degradation requires a main strategy to cope with drought stress—known as an escape strategy [4]. This strategy aims to utilize the accumulated nutrients, particularly nitrogen and energy for acclimation and survival during stress episodes [5].

Under water deficit conditions, the loss of transpiration and cell turgor pressure lead to a reduction in uptake and transport of nitrogen, silicon, magnesium and calcium and other important nutrients [6]. Water deficit does not only limit nitrogen uptake but also restricts nitrogen assimilation through the inhibition of enzymes involved in nitrogen metabolism, such as nitrate reductase and glutamine synthetase [7]. Leaf yellowing commonly appears as the consequence of water deficit due to low chlorophyll biosynthesis and high chloroplast and chlorophyll degradation [5].

**Citation:** Umnajkitikorn, K.; Fukudome, M.; Uchiumi, T.; Teaumroong, N. Elevated Nitrogen Priming Induced Oxinitro-Responses and Water Deficit Tolerance in Rice. *Plants* **2021**, *10*, 381. https:// doi.org/10.3390/plants10020381

Academic Editor: M. Iftikhar Hussain Received: 27 January 2021 Accepted: 11 February 2021 Published: 17 February 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Nitric oxide (NO) has been proposed as a regulator of the antioxidant defense system during stress conditions [8–11]. Cai et al. 2015 found that the activity of nitric oxide synthase (NOS) and the accumulation of NO in rice plants were activated under salinity and drought stress [9]. Transgenic rice plants with overexpression of rat neuronal NO synthase (nNOS) showed an improvement in drought and salt tolerance [9]. External application of nitric oxide (NO) donor S-nitroso-N-acetylpenicillamine (SNAP) also alleviated the damage from salinity stress on chickpea (Cicer arietinum L.) plants [11]. The application of sodium nitroprussiate (SNP) (NO donor) also improved the chilling tolerance of winter wheat via the accumulation of fructan [12]. Spraying rice seedlings with SNP also enhanced drought tolerance, maintained leaf water potential, enhanced the antioxidant defense system and improved the stability of membranes [13]. However, NO donors or the transgenic approach are costly and inapplicable in many parts of the world. We were eager to search for a simplified method to utilize NO-triggered tolerant mechanisms. NO can be generated as a byproduct from a reaction catalyzed with nitrate reductase (NR) [14,15]. NR-generated NO had a pivotal role in lateral root formation and nitrogen uptake under partial nitrate nutrition in rice [16]. A high level of nitrogen application therefore becomes an interesting option due to the fact that farmers can access and afford nitrogen fertilizer.

Long-term application of high amounts of chemical N fertilizer lessened the water and nutrient use efficiency as well as inducing environmental problems and sensitivity to insects and diseases [17,18]. However, with moderate drought stress an appropriate level of N application can benefit the growth and yield of rice plants. Applying nitrogen can induce leaf production and thus accelerate transpiration and drought stress severity, and elevated nitrogen application levels may further stimulate rice shoot/root growth, promote photosynthesis, transpiration and yield [19,20]. High nitrogen levels also enhanced plasticity of roots, allowing plants to extract more of the water available from the soil [21]. Moreover, high N application induced a reduction in mesophyll conductance (gm), which limits photosynthetic capacity and N-use efficiency [17]. Some recent studies have also shown that a high level of N in growing media enhanced the antioxidant defense system and some nitrogen assimilation enzymes, such as glutamine synthetase (GS) and glutamate synthase (GOGAT) but decreased N-use efficiency [22]. Although the effect of a large amount of N in growing media or in soil has long been seen to promote photosynthesis and N assimilation in various crop plants, regulation of high N levels triggering drought tolerance through NO signaling has never been investigated.

We therefore hypothesized that if we increased the nitrogen concentration in the fertilizer for only a short-period of time (priming), NO accumulation would be activated. Thus, the antioxidant defense system would also be activated. An addition of nitrogen could delay protein and chlorophyll degradation during a short period of water scarcity. In this study we aim to identify the appropriate level of N for priming treatment prior to stress episodes and to assess the mechanisms of nitrogen induced water deficit tolerance.

#### **2. Results**

#### *2.1. Elevated Nitrogen Priming Enhanced Photosynthesis and Leaf Growth under Water Deficit Conditions*

An elevated nitrogen level is well-known for promoting plant growth. However, the effects of an elevated nitrogen level for short term application (priming) on photosynthesis and growth have not been investigated. Rice plants primed with an elevated N level (500 mg L−1 and 1000 mg L−1 N) maintained higher photosynthetic activity (Figure 1a), but only the 500 mg L−1 N promoted rice plant growth as indicated by increasing leaf area under water deficit conditions (Figure 1b,c) when compared to the control plants (at 300 mg L−1 N). Elevated nitrogen priming failed to alter the net photosynthetic rate or leaf area of rice plants under well-watered conditions. Low N level priming had lower photosynthetic activity and growth under well-watered conditions (Figure 1).

**Figure 1.** Growth and photosynthesis responses of rice plants to different nitrogen concentration priming. (**a**) Net photosynthesis of the youngest fully expanded leaves of the greenhouse-grown 11-week old rice plants after maintaining relative soil moisture content at 10–15% for 7 days (WS), compared with those under well-watered conditions (WW). (**b**) Leaf area of plants subjected to different level of nitrogen priming. (**c**) Plant growth under different level of nitrogen priming. The values shown are the Mean ± SE (n = 6 and 10, respectively). The different letters above the bars indicate significant differences by one-way ANOVA and Duncan's test (*p* ≤ 0.05).

*2.2. Elevated Nitrogen Priming Promoted Relatively Higher Leaf Relative Water Content (RWC) under Water Deficit Conditions*

Elevated N-primed rice had higher RWC under water deficit conditions than normal N and lower N-primed rice (Figure 2a). Stomata conductance and transpiration rate from different priming nitrogen concentrations under water deficit conditions showed no significant differences. However, under well-watered conditions N-primed plants showed significantly lower stomata conductance (Figure 2b,c). A dramatically increased instantaneous water use efficiency occurred only in rice plants subjected to 500 mg L−1 N-priming treatment (Figure 2d).

#### *2.3. Elevated Nitrogen Priming Alleviated Chlorophyll and Protein Degradation as Well as Cell Damage under Water Deficit Conditions*

The elevated N-primed leaf maintained significantly higher chlorophyll and protein content under water deficit conditions (Figure 3a,b) but had only significantly higher protein content under well-watered conditions (Figure 3b). Electrolyte leakage (EL) was lower in the 500 mg L−1 N-primed plants and low N-primed plants (Figure 3c). However, excessive N (1000 mg L−1 N) had significantly higher electrolyte leakage (Figure 3c).

#### *2.4. Elevated Nitrogen Priming Enhanced Antioxidant Defense Mechanisms under Water Deficit Conditions*

To elucidate the mechanisms of elevated-N priming in delaying leaf senescence, we investigated the antioxidant defense system. Under well-watered conditions, SOD and CAT activity were the same between different N priming treatments (Figure 4a,b), while APX activity was slightly lower 100 mg L−1 and 500 mg L−1 N-primed plants and significantly lower in 1000 mg L−1 N-primed plants, compared to nonprimed (300 mg L−1 N) plants (Figure 4c). In contrast, under water deficit conditions, SOD and APX activities significantly increased in plants primed with 500 mg L−1 N, compared to control (Figure 4a,b). However, CAT activity in elevated N-primed plants significantly increased under water deficit conditions, compared to control (300 mg L−1 N) under well-watered conditions (Figure 4b).

**Figure 2.** Physiological responses associated with plant water status. (**a**) relative leaf water content (RWC), (**b**) stomatal conductance, (**c**) transpiration rate and (**d**) instantaneous water use efficiency of the youngest fully expanded leaves of the greenhouse-grown 11-week old rice plants after maintaining relative soil moisture content at 10–15% for 7 days, compared with those under well-watered conditions (WW). Values are the Mean ± SE (n = 6). The different letters above the bars indicate significant differences by one-way ANOVA and Duncan's test (*p* ≤ 0.05).

**Figure 3.** Biochemical responses associated with leaf senescence: (**a**) Total chlorophyll content, (**b**) total leaf protein content and (**c**) electrolyte leakage of the youngest fully expanded leaves of the greenhouse-grown 11-week old rice plants after maintaining relative soil moisture content at 10–15% for 7 days (WS), compared with those under well-watered conditions (WW). Values shown are the Mean ± SE (n = 4, 6 and 4, respectively). The different letters above the bars indicate significant differences by one-way ANOVA and Duncan's test (*p* ≤ 0.05).

**Figure 4.** Enzymatic reactive oxygen species (ROS)-scavenging antioxidant responses: (**a**) Superoxide dismutase (SOD), (**b**) catalase (CAT) and (**c**) ascorbate peroxidase (APX) specific activities of the youngest fully expanded leaves of the 5 greenhouse-grown 11-week old rice plants after maintaining relative soil moisture content at 10–15% for 7 days (WS), compared with those under well-watered conditions (WW). Values are the Mean ± SE (n = 4). The different letters above the bars indicate significant differences by one-way ANOVA and Duncan's test (*p* ≤ 0.05).

#### *2.5. Nitric Oxide Accumulation in Elevated Nitrogen Primed-Plants Alleviated Reactive Oxygen Species (ROS) Accumulation under Water Deficit Conditions*

To further investigate the mechanisms underlining the elevated nitrogen priming in alleviation of oxidative damage, we therefore focused on the nitric oxide signaling which has been reported to trigger antioxidant defense mechanisms and set up an in-lab experiment to investigate the process with the application of nitric oxide donor and scavenger. Nitric oxide (NO) accumulation occurred under the PEG-induced water deficit conditions for elevated nitrogen primed plant roots and leaves (500D), and plants without elevated nitrogen priming but supplied with a nitric oxide donor, sodium nitroprusside (SNP) (300D + SNP) (Figure 5). In contrast, the released NO and histological NO accumulation remained low under PEG-induced water deficit in the plant without elevated N priming (300D) and plants-primed with 500 mg L−1 N but supplied with a NO scavenger, 2-(4-carboxyphenyl)-4,4,5,5-tetramethylimidazoline-1-oxyl-3-oxide (cPTIO) (500D + cPTIO) (Figure 5).

Additionally, H2O2 content and staining showed low ROS levels in roots and leaves in plants without PEG treatment (control condition) (Figures 6 and 7a,b). The accumulation of ROS and lipid peroxidation were induced by PEG as shown in 300D. However, ROS and MDA content remained low in 500D plants and 300D +SNP (Figures 6 and 7), while the endogenous NO and released NO were more pronounced in these treatments (Figures 6 and 7). The cPTIO also inverted elevated nitrogen priming effects, leading to ROS accumulation and lipid peroxidation (Figures 6 and 7).

**Figure 5.** Reactive nitrogen species accumulation responses. The epifluorescent images of rice (**a**) leaves and (**b**) roots after 1-h incubation in 20 μM DAF-FM DA. The plants were subjected to the different NO-associated treatments for 3. days: 300 mg L−1 N-primed (300C); 500 mg L−1 N-primed (500C); 300 mg L−1 N-primed with PEG water deficit induction (300D); 500 mg L−1 N-primed with PEG water deficit induction (500D); 300 mg L−1 N-primed with PEG water deficit induction + Nitric oxide donor (300D + sodium nitroprussiate—SNP); 500 mg L−1 N-primed with PEG water deficit induction + Nitric oxide scavenger (500D + cPTIO) (**c**) Relative fluorescent intensity of the histochemical nitric oxide (NO) accumulation of the leaves and roots (**d**) Relative fluorescent units (RFUs) of the released NO after 2-h incubation in 7 μM DAF-FM. Values shown are the Mean ± SE (n ≥ 30 and n = 4, respectively). The different letters above the bars indicate significant differences by one-way ANOVA and Duncan's test (*p* ≤ 0.05), and the small letters represent the statistics of the leaves and the capital letters represent the statistics of the roots.

**Figure 6.** Reactive oxygen species accumulation responses. Hydrogen peroxide accumulation detected by 3,3' diaminobenzidine (DAB) staining of (**a**) leaves and (**c**) roots. Superoxide radical accumulation detected by nitro blue tetrazolium (NBT) staining of (**b**) leaves and (**d**) roots of rice plants after 3-day growth in the different NO-associated treatments.

**Figure 7.** Quantitative ROS accumulation and lipid peroxidation responses: quantitative hydrogen peroxide accumulation in (**a**) leaves and (**b**) roots, and malondialdehyde (MDA) content of (**c**) leaves and (**d**) roots of rice plants after 3-day growth in the different NO-associated treatments. Values shown are the Mean ± SE (n = 4). The different letters above the bars indicate significant differences by one-way ANOVA and Duncan's test (*p* ≤ 0.05).

#### **3. Discussion**

Nitrogen is an essential nutrient that plays the main role in leaf development. In many cases, high nitrogen application induced leaf area increment and total plant transpiration increment while N also increases root growth, leading to the added water and nutrient uptakes to support the increased transpiration and maintain high photosynthetic activity and chloroplast functions during water deficit conditions [23]. However, high nitrogen priming during a short period of nitrogen application effects were first investigated here (prior to drought episodes). We found rice plants primed with 500 mg L−1 N prior to water deficit conditions maintained higher photosynthetic rates (Figure 1a) but did not alter the transpiration rate (Figure 2c), resulting in a dramatic increase in instantaneous water use efficiency (Figure 2d). Elevated nitrogen priming in our studies also induced plant responses similar to the high soil-incorporated nitrogen application [20,21]. Haefele et al. [24] and Zhong et al. [25] both suggested the use of high nitrogen fertilizer increases water-use efficiency, drought tolerance and survival rates for C3 and C4 crops. The improvement of photosynthetic capacity mainly depended on the activity of ribulose-1,5-bisphosphate carboxylase (Rubisco). Around 15 to 30% of leaf nitrogen are invested in Rubisco [26]. The high nitrogen levels induced higher Rubisco content, which was generally degraded during stress episodes [27]. The elevated N-primed rice might delay the degradation of Rubisco and maintain a high photosynthetic rate under water deficit conditions, as well as less sensitive stomatal behavior [24,25]. The maintenance of relative water content in elevated N-primed plants may be explained by the accumulation of NO as shown in Li et al. 2013 where the NO donor could have induced fructans accumulation, which resulted in plant cell water content regulation [12].

On the other hand, Gao et al. 2019 indicated that low nitrogen priming levels reduced the transpiration rate, stomatal conductance and maintained a higher leaf relative water content to mitigate drought-induced damage [28]. We also found slightly lower transpiration rates (Figure 2c) and significantly higher leaf relative water content (Figure 2a) in 100 mg L−1-N primed plants. Low nitrogen priming may also have induced lower chlorophyll and protein content, slowing down the process of stress recovery [29].

Although elevated N (500 mg L−1-N) priming benefits the tolerance of rice plants, excessive N (1000 mg L−1-N) priming leads to stress symptoms, like decreasing photosynthetic activity (Figure 1a), lower RWC (Figure 2a) and higher electrolyte leakage (Figure 3c), compared to plants primed with 500 mg L−1 N under control conditions. Excessive nitrogen application can also generate an imbalance in the carbon/nitrogen ratio which causes mature leaf senescence, leading to yield reduction after drought episodes [30]. Excessive nitrogen has impacts on the antioxidant defense systems in wheat [31], and in our study (Figure 4). Many nonenzymatic antioxidant molecules, such as GABA, 4-hydroxybenzoylcholine and several phenolic compounds were downregulated in the leaves of rice plants grown under excessive N, which led to the overaccumulation of ROS [31].

Redox imbalance under water deficit conditions is accountable for significant plant cell damage. The antioxidant induction system is a viable approach to alleviate cellular damage and a higher tolerability of plants to water deficit conditions [10,32,33]. We observed enhanced antioxidant enzyme activity in 500 mg L−1 N primed rice (Figures 1a and 2a). Perhaps the one-day elevated N priming was sufficient to activate antioxidant systems. Five-hundred mg L−1 N priming reduced membrane damage as demonstrated by the lower electrolyte leakage (Figure 3c) and MDA content (Figure 7d), which were supported by increasing SOD and APX activities (Figure 4a,c). Antioxidant defense systems act as drought tolerance mechanisms in many crop species, including rice, creeping bentgrass (*Agrostis stolonifera* L.), wheat (*Triticum aestivum* L.) seedlings and peanuts (*Arachis hypogaea* L.) [34–38].

The antioxidant defense system that controls ROS status in plant cells has also been reported to be regulated by the level of reactive nitrogen species (RNS) from both endogenous production and exogenous application [8,9,35,36,39]. Cai et al. 2015 [9] found that the overexpression of rat neuronal NO synthase (nNOS) in rice enhanced drought and salt tolerance of rice with a higher NOS activity and the accumulation of NO, together with a reduction in H2O2 accumulation, electrolyte leakage and MDA content. Using NO donors, SNP as a seed priming solution and foliar spray has previously induced rice drought tolerance in rice [13]. Because NO acts as a signaling molecule it can enhance antioxidant capacity thus reducing oxidative damage [13]. The 500 mg L−1 N leaves and roots accumulated higher endogenous NO and released NO, which was inhibited by the NO scavenger, cPTIO (Figure 5b–d). The level of NO accumulation was close to that of the normal N-primed (300 mg L−1 N) plants supplied with 1 mM SNP (Figure 5b,d). In contrast, 500 mg L−1 N primed plants possessed less O2 •− and H2O2 accumulation evidenced by the NBT, DAB staining and H2O2 quantification (Figures 6 and 7a,b). We measured only O2 •− and H2O2 because these two species have been reported to directly interact with NO [40,41]. Adding cPTIO may mitigate such effects (Figures 6 and 7a,b). Control plants (300 mg L−1 N) had less O2 •− and H2O2 further suggesting a relationship between elevated N priming and increased NO (Figures 6 and 7a,b). This phenomenon might be explained by the fact that NO in plants can be generated by NO synthase (NOS), nitrate reductase (NR) and xanthine oxidoreductase [10]. When priming rice with elevated N, it induced NR activity, promoting greater nitrite levels (precursors for both NO production and N assimilation [42]. Higher NO levels in elevated N-primed rice promoted the antioxidant defense system, causing less accumulated ROS and less oxidative damage while maintaining photosynthetic functions. NO acted as a potent inhibitor of lipid peroxidation by scavenging lipid alcoxyl (LO˙) and peroxyl (LOO˙) radicals [43]. NO also directly quenches the ROS, such as superoxide radical (O2 •−) [44], limits oxidative damage and prevents the onset of cell death [45]. Moreover, NO maintained membrane fluidness, cell wall relaxation, cell enlargement and plant growth under stressful conditions [11,42]. Therefore, elevated nitrogen priming demonstrated a water deficit tolerance through NO-mediated antioxidant defense mechanisms.

#### **4. Materials and Methods**

#### *4.1. Plant Material and Greenhouse Growth Conditions*

Seeds of Thai rice (*Oryza sativa* L. subspecies indica cv. Pathumthani 1) were germinated on moist germination paper for 10 d at 28 ◦C in the dark. Seedlings were transplanted into 2-L pots filled with 1:1 vermiculite: perlite. The seedlings were grown in the greenhouse with day lengths of 11–12 h and daily average temperatures of 35 ± 4/24 ± 7 ◦C day/night and 700–1500 μmol m−2 s−1 mid-day photosynthetically active radiation (PAR). Plants were fertilized every other day with (N 300 mg L−1 equal in nitrate and ammonium forms), P 20 mg L−1, K 75 mg L−1, Ca 25 mg L−1, Mg 17 mg L−1, S 55 mg L−1, Fe 3.30 mg L−1, Mn 0.50 mg L−1, Zn 0.05 mg L−1, Mo 0.01 mg L−1, Cu 0.02 mg L−1) for 10 weeks until the plants were one week before panicle initiation. Then, different concentrations of nitrogen (N) fertilizer were applied by using N 100 mg L−1 (equal in nitrate and ammonium forms), P 20 mg L−1, K 75 mg L−1, Ca 25 mg L−1, Mg 17 mg L−1, S 55 mg L−1, Fe 3.30 mg L−1, Mn 0.50 mg L−1, Zn 0.05 mg L−1, Mo 0.01mg L−1, Cu 0.02 mg L−1 as base fertilizer and added ammonium nitrate (NH4NO3) to reach target concentrations of 300, 500 and 1000 mg L−1 N and used double volume of the field capacity of the pots to wash the previous nitrogen fertilizer. Water deficit stress was applied by withholding water for ~7 d when visual stress symptoms (i.e., leaf rolling) appeared (10–15% relative soil water content) [7], when the gas exchange parameters were measured and the leaf samples were taken for biochemical assays. The leaves were collected between 9.00 am and 11.00 am and immediately frozen in liquid nitrogen and kept at −80 ◦C until use.

#### *4.2. Gas-Exchange Measurements*

Net photosynthetic rate and stomatal conductance were measured with the photosynthesis system ADC LCi-SD (BioScientific, UK). The measurements were conducted under 900 ± 50 μmol m−2 s−1 light intensity and 380 ± 10 μmol mol−1 CO2 surrounding the leaf (Ca) at 32 ± 2 ◦C. To calculate instantaneous transpiration, the transpiration rate was divided by the net photosynthetic rate.

#### *4.3. Leaf Area*

After one-day post recovery, all remaining leaves were removed and all surface dust was wiped away prior to measuring the area with an LI-3100C area meter (LI-COR, Lincoln, NE, USA) in square centimeters.

#### *4.4. Relative Water Content (RWC) of Leaves*

RWC was determined according to Gao et al. 2019 [28]. Leaves were weighed immediately to obtain fresh weight (FW), soaked in water overnight in the dark and weighed again to obtain turgid fresh weight (TW), and then dried at 75 ◦C until constant weight (DW). RWC was then calculated as follows:

$$\text{RWC} = (\text{FW} - \text{DW}) / (\text{TW} - \text{DW}) \tag{1}$$

#### *4.5. Electrolyte Leakage (EL)*

The electrolyte leakage measurement was adjusted based on the method described previously by Cai et al. 2015 [9]. After placing six 1 cm2 leaves from each treatment into a 50-mL-tube with 20 mL distilled deionized water, the tubes were shaken and electroconductivity (*ECi*) was immediately measured with a pH/cond meter (WTW, inoLab, Germany). After soaking the leaves for 12 h, the second conductivity was measured (*ECf*). Then, the leaves were boiled for 1 h and the total electro-conductivity was measured (*ECt*). The electrolyte leakage was calculated as follows:

$$\% \text{ EL} = \left(\frac{\text{EC}\_f - \text{EC}\_i}{\text{EC}\_t - \text{EC}\_i}\right) \times 100 \tag{2}$$

#### *4.6. Determining SOD, CAT and APX Activities*

Enzymes were extracted by the modified method of Umnajkitikorn et al. 2013 [46]. The frozen leaves were ground in liquid nitrogen with a mortar and pestle. Two hundred fifty mg of the leaf powder were homogenized in 1 mL of the extraction buffer containing 50 mM potassium phosphate buffer (pH 7.8), 0.1 mM disodium EDTA, 1 mM ascorbic acid and, 2% PVPP (*w*/*v*). The homogenate was centrifuged at 15,000× *g* for 20 min at 4 ◦C. The supernatant was collected for SOD, CAT and APX activity assays.

SOD activity was assayed based on the method described by Beauchamp and Fridovich 1971 [47] and Vaidyanathan et al. 2003 [48]. One point two milliliters of the reaction mixture, containing 50 mM sodium phosphate buffer (pH 7.8), 10 mM EDTA, 1 mM NBT, 5 mM L-methionine, 0.2 mM riboflavin and mixed with 80 μL of extracts. Each reaction was carried out at 25 ± 2 ◦C under a light intensity, which is sufficient to increase absorbance of 0.110/10 min (in the absence of the enzyme) for 30 min. Two hundred microliters of the reactions were taken to Nunc microwell 96-well plate (Thermo ScientificTM, Shanghai, China) for the absorbance at 560 nm (A560), using spectrophotometer (Biotex, Epoch, Winooski, VT, USA). The nonirradiated reaction mixture served as a blank and was deducted from A560. One unit of SOD activity was defined as the amount of enzyme required to inhibit the reduction of NBT by 50%.

CAT activity was assayed based on the method of Sunohara and Matsumoto 2004 [49]. One hundred and ninety microliters of the assay mixture contained 20 mM H2O2 in 50 mM potassium phosphate buffer (pH 7.0), 0.1 mM disodium-EDTA and 10 μL of extracts. The reactions were carried in Nunc 96 well UV transparent plate (Thermo ScientificTM, Vantaa, Finland). The absorbance was measured at 240 nm with a UV/VIS spectrophotometer (Biotex, Epoch, Winooski, VT, USA). The enzyme activity was defined as the amount of H2O2 decomposed min−1 mg protein−1. The molar coefficient of H2O2 (E) is 39.4 M−1 cm−1) at 240 nm.

APX activity was assayed based on the method of Sunohara and Matsumoto 2004 [49]. One hundred and eighty microliters of the assay mixture contained120 μL of 50 mM potassium phosphate buffer (pH 7.0), 20 μL of 1 mM EDTA, 20 μL of 5 mM L-ascorbic acid, 20 μL of 1 mM H2O2 and 20 μL of extracts. The subsequent decrease in ascorbic acid was observed at 290 nm (E = 2.8 mM−1 cm−1) with a UV/VIS spectrophotometer (Biotex, Epoch, Winooski, VT, USA). The enzyme activity was defined as the amount of ascorbic acid (ASA) which decomposed min−1 mg protein−1.

#### *4.7. Protein Quantification*

The Bradford assay [50] was used for protein quantification using bovine serum albumin as the standard.

#### *4.8. The RNS and ROS Experiment*

Rice seeds (*Oryza sativa* subspecies indica cv. Pathumthani 1) were germinated and grown for 5 d at 25 ◦C in darkness. Seedlings were transplanted into 9-cm pots filled with vermiculite 4 plants/pot and grown in the growth room with 12 h/12 h light/dark cycles and 25/20 ◦C day/night cycles for 3 weeks with fertilizer containing 300 mg L−1 N. Then, the plants were randomly divided into 6 groups and transferred to glass tubes with the following treatments: (1) 300 mg L−1 N (300 C); (2) 500 mg L−1 a day and switched back to the 300 mg L−1 (500 C); (3) 300 mg L−1 N + gradually increased polyethylene glycol (PEG) 6000 (300 D); (4) 500 mg L−1 a day and switched back to the 300 mg L−1 + gradually increased PEG (500 D); (5) 300 mg L−1 N + PEG 6000 + nitric oxide donor, 1 mM SNP (300 D + SNP); (6) 500 mg L−1 N + PEG 6000 + 3 mM of Nitric oxide scavenger, 2-(4 carboxyphenyl)-4,4,5,5-tetramethylimidazoline-1-oxyl-3-oxide (cPTIO) (500 D + cPTIO). After 3 days under each treatment, the seedlings were harvested for further ROS and RNS analyses.

#### *4.9. NO Determination*

To histologically detect NO, the leaf and root segments were soaked for 1 h with 20 μM 4-amino-5-methylamino-2 ,7 -difluorescein diacetate (DAF-FM DA; Sekisui Medical, Tokyo, Japan). The epifluorescence images were captured with an Eclipse 90i microscope (Nikon, Tokyo, Japan). The intensity of the fluorescent signal from at least 30 pictures of each treatment was quantified by using Image J software (by Wayne Rasband, NIH, MD, USA).

The concentration of NO released from the roots was assessed in leaf and root segments of the seedlings in each treatment after incubation in 7 μM DAF-FM for 2 h. The fluorescence intensity of the DAF-FM solution was measured as described in Fukudome et al. 2016 [51] but adapted by adding 100 μL of the solution into the 96 well black plate (SPL life sciences, Gyeonggi-do, Korea), then measured with a fluorescence spectrophotometer (Varioskan Lux, Finland) at 495 nm and 519 nm as excitation and emission wavelengths, respectively. The released NO was expressed as relative fluorescence units (RFUs) per fresh weight of leaves or roots.

#### *4.10. Histochemical Detection of H2O2 and O2* •−

H2O2 was detected in situ according to Fukudome et al. 2019 [39] and Signorelli et al. 2013 [52]. Detached leaves and roots were vacuum-infiltrated in the dark with 10 mM potassium phosphate buffer, and 0.1% (*w*/*v*) 3,3'-diaminobenzidine (DAB), at pH 7.8. Samples were incubated overnight in the dark. The leaf segments were boiled in 95% ethanol at 90oC to remove chlorophyll. The clear leaf segments were then photographed. The staining areas were calculated by Image J software (by Wayne Rasband, NIH, USA), in the red channel according to procedure described in [53]. Data were shown in Supplementary Figure S2. The detailed method of staining area identification is given in Supplementary file 2.

Superoxide radical (O2 •−) was detected in situ essentially as described by to Fukudome et al. 2019 [39] and Signorelli et al. 2013 [52]. Detached leaves and roots were vacuum-infiltrated with 10mM potassium phosphate buffer, 0.1% (*w*/*v*) nitro blue tetrazolium (NBT), and 0.05% (*v*/*v*) Tween 20, pH 7.8. Treated samples were incubated overnight in the dark, cleared and photographed as described above. The staining areas were calculated by Image J software (by Wayne Rasband, NIH, MD, USA), in the blue channel according to the procedure described in [53]. Data are shown in Supplementary Figure S2.

#### *4.11. H2O2 Quantification*

The frozen leaves were ground and 5–10 mg of samples were extracted with 1 mL of 20 mM K2HPO4 (pH 6.5), homogenized at 1500 rpm for 5 min at 4 ◦C and then centrifuged for 5 min at 16,200× *g* at 4 ◦C. The H2O2 was colorimetrically quantified by Amplex Red detection assay (Thermo Fisher Scientific). The measurement was performed on the supernatant as described in Brumbarova et al. 2016 [54] with some modification of the incubation time from 30 min to 15 min. The assay solutions were analyzed at 560 nm with a UV/VIS spectrophotometer (Biotex, Epoch, Winooski, VT, USA). The standard curve was also generated with H2O2 concentration from 0–5 μM.

#### *4.12. Malondialdehyde (MDA) Measurements*

The youngest fully expanded leaves were homogenized with 5 mL of 50 mM potassium phosphate buffer pH 7.5 and centrifuged at 20,000× *g* for 25 min. For measurements of MDA concentration, 4 mL of 20% trichloroacetic acid containing 0.5% thiobarbituric acid were added to a 1 mL aliquot of the supernatant. The mixture was heated at 95 ◦C for 30 min, quickly cooled in ice and then centrifuged at 10,000× *g* for 10 min. The absorbance of the supernatant was measured at 532 nm. The results are shown as MDA content mg–1 FW, using malondialdehyde tetrabutylammonium salt as a standard (Sigma-Aldrich, Cat 63287, Singapore).

#### *4.13. Statistical Analysis*

The SPSS 25 statistical package was used for statistical analyses. The experiments were based on a randomized complete block design.

#### **5. Conclusions**

Elevated N priming at approximately 60% more than normal N level enhanced water deficit tolerance with NO-triggered antioxidant defense systems. The elevated N priming also supported the sustainability of photosynthetic activity and relative water contentof the leaves, together with the reduction of membrane damage. This approach has potential for in-situ investigation in aerobic rice fields with fertigation systems as a potential mitigating factor for enhancing drought tolerance in rice.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/2223-774 7/10/2/381/s1, Supplementary file 1: Fertilizer Information and Formulation sheet. Supplementary file 2: Figure S1: Examples of the picture calibrated for staining area evaluation. Figure S2: The ROS staining area of tissue segments of rice plants after 3–day growth in the different NO-associated treatments; Procedure for staining area quantification by image J.

**Author Contributions:** Experiment planning, K.U. and T.U.; Performing experiments K.U. and M.F.; Statistical analyses and data interpretation, K.U.; Manuscript preparation, K.U., M.F., T.U and N.T. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by Suranaree University of Technology (SUT), Thailand.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data available in a publicly accessible repository.

**Acknowledgments:** The authors would like to thank Colin T. Strine, School of Biology, Institute of Science, Suranaree University of Technology, for English corrections in this manuscript.

**Conflicts of Interest:** The authors declare that there is no conflict of interest.

#### **References**

### *Review* **Rising Atmospheric Temperature Impact on Wheat and Thermotolerance Strategies**

**Adeel Khan 1 , Munir Ahmad 1, Mukhtar Ahmed 2,3,\* and M. Iftikhar Hussain 4,5**

	- Rawalpindi 46300, Pakistan<EMAIL_ADDRESS>(A.K<EMAIL_ADDRESS>(M.A.)

**Abstract:** Temperature across the globe is increasing continuously at the rate of 0.15–0.17 ◦C per decade since the industrial revolution. It is influencing agricultural crop productivity. Therefore, thermotolerance strategies are needed to have sustainability in crop yield under higher temperature. However, improving thermotolerance in the crop is a challenging task for crop scientists. Therefore, this review work was conducted with the aim of providing information on the wheat response in three research areas, i.e., physiology, breeding, and advances in genetics, which could assist the researchers in improving thermotolerance. The optimum temperature for wheat growth at the heading, anthesis, and grain filling duration is 16 ± 2.3 ◦C, 23 ± 1.75 ◦C, and 26 ± 1.53 ◦C, respectively. The high temperature adversely influences the crop phenology, growth, and development. The pre-anthesis high temperature retards the pollen viability, seed formation, and embryo development. The postanthesis high temperature declines the starch granules accumulation, stem reserve carbohydrates, and translocation of photosynthates into grains. A high temperature above 40 ◦C inhibits the photosynthesis by damaging the photosystem-II, electron transport chain, and photosystem-I. Our review work highlighted that genotypes which can maintain a higher accumulation of proline, glycine betaine, expression of heat shock proteins, stay green and antioxidant enzymes activity viz., catalase, peroxidase, super oxide dismutase, and glutathione reductase can tolerate high temperature efficiently through sustaining cellular physiology. Similarly, the pre-anthesis acclimation with heat treatment, inorganic fertilizer such as nitrogen, potassium nitrate and potassium chloride, mulches with rice husk, early sowing, presoaking of a 6.6 mM solution of thiourea, foliar application of 50 ppm dithiothreitol, 10 mg per kg of silicon at heading and zinc ameliorate the crop against the high temperature. Finally, it has been suggested that modern genomics and omics techniques should be used to develop thermotolerance in wheat.

**Keywords:** heat stress; photosynthesis; antioxidant enzymes; HSPs; QTLs; omics

#### **1. Introduction**

Climate change is the result of a higher level of greenhouse gases such carbon dioxide (CO2), nitrous oxide, and methane (CH4). These gases can entrap the sun rays leading towards the severity of extreme events for crops development [1,2]. It has been observed that CO2 was increased 0.6 ± 0.1 ppm/year in the early 1960s and 2.3 ± 0.6 ppm/year during the last decade. Meanwhile, the CH4 gas was doubled after the industrial revolution until the 1980s and it is increasing at the rate of 12 parts per billion per year. However, during the last three decades it was increasing 2–5 parts per billion per year. The nitrous

**Citation:** Khan, A.; Ahmad, M.; Ahmed, M.; Iftikhar Hussain, M. Rising Atmospheric Temperature Impact on Wheat and Thermotolerance Strategies. *Plants* **2021**, *10*, 43. https://doi.org/ 10.3390/plants10010043

Received: 21 November 2020 Accepted: 22 December 2020 Published: 27 December 2020

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/).

oxide concentration was enhanced 18% more than the 1970s and increased 0.8 parts per billion per year [3,4].

The escalating global warming evokes an extreme weather pattern, increases disease incidences, insect pest survival, and ultimately influences crop productivity [5,6]. Global warming potential (GWP) is the contribution of one molecule of compound over 100 years to global warming as compared to CO2. It allows the comparison of different gas contributions to global warming and how much energy emissions of 1 ton of gas absorbs more than 1 ton of CO2 over a given time period. The larger global warming potential represents more potential of the given gas to persist and the ability to warm the Earth temperature over a given time period. The GWP of carbon dioxide is 1, CH4 28–36, and nitrous oxide 265–298 over 100 years. However, these gases possess more potential and persistency to entrap the sun rays than CO2 but a major contributor in global warming is CO2 [7].

Agricultural crop productivity depends on biotic (diseases and insect pest) and abiotic (heat, drought, and salinity) factors [8]. Among the abiotic stresses, the higher temperature is a major concern influencing crop growth and development. The global temperature roughly increased by 1.5 ◦C with the same accelerating trend in all regions from the 1970s, as reported by the intergovernmental panel of climatic change (IPCC) and was predicted to increase 2.5–5.8 ◦C until the 2100s [3]. The global average temperature annually increased by 0.04–0.07 ◦C and 0.15–0.17 ◦C per decade since the 1880s and 1970s, respectively according to the National Oceanic and Atmospheric Administration (NOAA, 2018). Therefore, global warming characterized by an extreme temperature possesses the challenge to improve the yield potential of crops.

Terminal and continual heat stresses are two major constraints influencing crops growth and development. The temperature threshold levels were reported at different stages for crops viz., cotton [9–13], rice [14–19], sorghum [20,21], barley [12], maize [9,22,23], and soybean [23]. Wheat is an imperative staple food, the cheapest energy source, provides 8–20% of protein, and 70–75% of calories in our average diet [24], but a high temperature restricts the wheat crop to express its full genetic potential. Therefore, there is a dire need to understand the wheat response against the high temperature and a suitable strategy to improve its productivity.

#### **2. Impact of High Temperature on Wheat**

High temperature influences the wheat productivity in tropical, subtropical, arid and semi-arid regions of the world. The optimum temperature for wheat growth and development are given in Table 1. The high temperature in the tropical region is an inevitable constraint for wheat during germination and early growth stages, whereas in the Mediterranean region, the reproductive stage is highly sensitive [25]. A high temperature of 3–4 ◦C above the optimum temperature at grain filling reduces 10–50% of the wheat yield in Asia with the current production technology and varieties [26]. High temperature declines 0.07% per ◦C grain yield depending on the wheat variety [27]. Each degree increase in the temperature at the grain filling duration reduces 6% of wheat yield globally [28,29] and 3–17% in South Asia including India and Pakistan [30]. It accredited directly or indirectly the disturbance in different cellular, physiological functions and metabolic pathways associated with the grain yield in wheat (Figure 1).

**Table 1.** Optimal temperature requirements of wheat at different growth stages.

[28,30–47].

**Figure 1.** Schematic illustration of the high temperature impact on wheat associated with the grain yield.

#### *2.1. Cellular Metabolism*

The plasma membrane is a highly organized structure composed of lipids and proteins. It regulates the enzymatic activity and transport of ions. High temperature alters the microtubules organization, expansion, elongation, and cell differentiation [48]. It increases the kinetic energy of hydrogen bonds between adjacent fatty acids, weakens the bonds, and leads to membrane fluidity. This fluidity, unsaturation of fatty acids, and disruption of different proteins trigger the electrolyte leakage [49,50]. High temperature causes 25–55% electrolyte leakage at 45 ◦C for 1 h [51], while 21–40% leakage at 40 ◦C for 30 min [52]. Therefore, the cell damages its internal composition and sustainable physiological processes (e.g., photosynthesis, respiration, and transpiration) associated with the synthesis and translocation of carbohydrates into the grains.

#### *2.2. Grain Filling Duration*

High temperature enforces the plant to complete the growing degree days earlier, which results in early maturity and shorter life cycle of plant, lesser biosynthetic products accumulation, and ultimately poor grain development [32,53,54]. Vernalization (*VRN1, VRN2*) and the photoperiodic (*PPD-A1, PPD-D1*) sensitive gene determines the developmental phases at volatile temperature events and triggers earliness in wheat by limiting various growth phases [55,56]. The longer grain filling duration determines the appropriate grains development associated with the grain yield [57]. However, high temperature reduces the duration to uptake the available nutrients and translocation of photosynthates.

#### *2.3. Grain Formation and Development*

Vital events at the reproductive stage such as flowering initiation, pollen germination, pistil receptiveness, and embryo development determine the florets fertility [58,59]. The embryo sac and embryo formation are sensitive to high temperature [60]. Microgametogenesis and microsporogenesis are sensitive to high temperature, which hinder the gametes development and cause spores abortion [61,62].

Wheat grain contains 60–70% starch content and gradually drops under high temperature [63,64]. High temperature inhibits the starch accumulation into grains ascribable to the enzymes inactivity viz., granule bound starch, soluble starch, and sucrose synthase

during the grain filling phase [65,66]. It also declines the starch content synthesis [67,68], stem reserves carbohydrates translocation [69,70], alters the structure of aleurone layer, and endosperm of seed [71,72], which ultimately influences grain development.

#### *2.4. Leaf Senescence*