The interactive responses of fertigation levels under buried straw layer on growth, physiological traits and fruit yield in tomato plant

ABSTRACT

The experiments were conducted on tomato plants to study the interactive responses of water levels (W_70%: 70% of water consumption and W_90%: 90% water consumption) and nitrogen rates (N_100%: 100% of recommended and N_80%:80% of N_1.0) under two straw mulching conditions (NS; no straw introduced and WS: with buried straw layer) on growth and physiological parameters for two fruit growing years to assess the interactive responses of fertigation under buried straw layer on the changes in plant fresh and dry biomass, roots biomass, photosynthesis rate (P_N), stomatal conductance (G_S) and chlorophyll fluorescence of tomato plants. Buried straw layer was proved to improve plant biomass, photosynthesis rate and other physiological traits such as chlorophyll contents (Chl), maximum electron transport rate (ETRmax), maximum quantum yield (F_V/F_M) and G_S under the lower fertilizer (N_80%) and irrigation levels (W_70%). However, increasing fertilizer and irrigation level decreased these parameters significantly (p < .05 to p < .001) under buried straw layer. Conversely, increased fertilizer (N_100%) and irrigation (W_90%) levels increased these parameters significantly (p < .05 to p < .001) under no straw condition. The overall findings revealed that buried straw layer could relieve stress developed by limited irrigation water and fertilizer and benefit plant growth, physiological parameters and fruit yield of tomato plant.

KEYWORDS:

• Buried straw layer
• photosynthesis
• light curve
• electron transport rate and quantum yield

1. Introduction

Tomato is a widely grown plant due to the high nutritional value of its fruit and great economic importance (Savić et al. 2008). China is one of the leading tomato producing countries with a production of 50,552,200.0 metric tons, and it is 6.82% of the world’s grown tomatoes (FAO 2016). Tomato growth parameters are strongly influenced by two significant cultural manageable factors such as nutrients and irrigation water (Ozbahce and Tari 2010; Chen et al. 2013). Inefficient and overuse of nitrogen fertilizer is a severe issue in China with an adverse economic and environmental impacts (Zhang et al. 2015). According to the investigation by the Chinese Ministry of Agriculture, the amount of fertilizer applied is higher and beyond the safe level (Shuqin and Fang 2018).

Conventional methods of high input of nutrient and water management are still being used by most of the farmer’s community to achieve higher yield. However, the extreme usage of nutrients and water have also led to decrease fertilization efficiency by increasing leaching of nutrients and resulted in severe degradation of soil environment especially in the Southern China, which produces most of the food (Delang 2017) as well as water pollution (Zhang et al. 2015). Therefore an appropriate management is a crucial measure to improve crop yield, quality and fertilizer use efficiency (Kennedy et al. 2013; Luo and Li 2018).

Fertigation is a method by which irrigation and fertilization can be applied precisely. However, fertigation requires sufficient skills and knowledge of soil and vegetables that farmers are lacking in many vegetable production areas. Over fertilization and irrigation are still common under fertigation. A higher application of nitrogen fertilizer may cause leaching of NO₃–N correspondingly (Ullah et al. 2017) and ultimately disturb the nitrate contents in fruits of tomato (Wang et al. 2015). At the same time, higher application of water will inevitably promote leaching and waste of fertilizer (Ullah et al. 2017; Li et al. 2018). To fulfill the high demand of food, there is conflict to recommendations to lessen the use of fertilizer in China as it is considered as a risk of production decrease, despite the extensive evidence. Too much fertilization results in low fertilizer use efficiency, increases costs of production, and needlessly larger losses of nutrients triggering severe ecological problems such as groundwater nitrate contamination and greenhouse gas emissions. In the current situation, additional measures are immensely needed to minimize fertilizer wastage and mitigate the soil degradation (Delang 2017).

Photosynthesis is the key physiological process of plants and can provide 90% of plant biomass (Wang et al. 2018). Leaves are the main contributors to the crop productivity (Ashraf and Bashir 2003), and their photosynthetic activities are crucial significantly for harvestable fruit yield. Leaf gas-exchange such as photosynthesis rate and stomatal conductance or chlorophyll fluorescence parameters such as light curve, maximum electron transport rate and maximum quantum yield based on the operating quantum efficiency of electron transport can be used to estimate leaf photosynthetic capacities.

Water deficit negatively affects water potential of plants which results in, dehydration, stomatal conductance reduction and changes in fluorescence (Pessarakli 2005). The decline in photosynthesis can be credited to a decrease in intercellular carbon dioxide concentration partially to metabolic elements (Lawlor and Cornic 2002) and partially because of closing of stomata, which subsequently leads to photo-inhibitory damage of PSII reaction centers (Souza et al. 2004). The deficiency of nutrients can directly disturb photosynthetic actions and restrict partitioning of assimilates from the leaves to the fruits (Kanai et al. 2011). On the other hand, applied nutrients can be used to synthesize the components of the photosynthetic apparatus (Sugiharto et al. 1990), including rubisco, chlorophyll and carotenoid containing membrane proteins (Bungard et al. 1997).

The crop yield of plant is mainly based on its photosynthetic capacity (Pessarakli 2005). Leaf photosynthesis is the main reason for the majority of the variation in biomass and crop production (Takai et al. 2010). However, three differing correlations between leaf photosynthesis and crop yield have been reported by different researchers: no correlation (Chongo and McVetty 2001), positive (Ashraf 2003; Hubbart et al. 2007) and negative (Long et al. 2006). Lawlor (1995) described that these clashing findings could be clarified by the fact that limiting plant and environmental dynamics interact strongly for regulating photosynthesis, which subsequently affects crop yield. Therefore, it is important to improve understanding of the approaches underlying biomass production under different conditions (Jaimez et al. 2008).

The response of either fertilizer or irrigation on photosynthetic effects of tomato plants have been well studied, however, the interactive effects of fertigation under an isolated straw layer on these factors have not been studied. Furthermore, how could leaf photosynthesis affects other physiological traits under these conditions? Therefore, the objectives of this study were to assess the interactive responses of different fertilizer and irrigation levels under a buried straw layer on growth, photosynthesis rate, stomatal conductance and Chlorophyll fluorescence parameters for two growing season.

2. Materials and methods

2.1. Site description and cropping conditions

The experiments were carried out in April–July 2017 and 2018 in a greenhouse without temperature control under nature light environments, situated at State Key Laboratory of Efficient Irrigation and Drainage of Hohai University, Nanjing, China at 31°95′ N, 118°83′ E and 15 m above the sea level. Microclimatic parameters inside the greenhouse are shown in Table 1. The soil physical and chemical properties are presented in Table 2. Six-week-old uniform seedlings of tomato (Solanum lycopersicum L.) were transplanted in beds, and each bed consisted of three tomato plants having a distance of 50 cm between two plants. Plant density was maintained as 3.33 plants.m⁻². A nylon cord was used for plants vertical support. Pruning was also done to uphold the appropriate growth by following the well managed local agronomic practices.

Table 1. Microclimatic parameters inside the greenhouse during growing seasons.

Parameter	2017	2018
Avg. Max T(°C)	44.9	42.50
Avg. min T(°C)	19.6	16.99
Avg. Max RH (%)	86.2	87.69
Avg. Min RH (%)	18.1	11.50
Epan (mmday^−1)	4.1	3.11

T: Temperature, RH: Relative humidity, Epan: Daily evaporation from Pan.

Table 2. Soil physical and chemical properties.

Soil property	Range	Soil property	Range
Soil type	Clay	pH	7.12
Sand	18.8%	Organic matter	8.06 mgkg^−1
Silt	36.67%	Total N	0.1%
Clay	45.53%	Available N	10.12 mgkg^−1
Bulk density	1.35 gcm^−3	Total P	330.9 mgkg^−1
Field capacity	0.392 mm^−1	Available P	64 mgkg^−1

2.2. Experimental design

Three factors randomized complete block design with three replications was established to assess the effects of irrigation and nitrogen regimes under buried straw layer on growth and physiological traits of greenhouse-grown tomato. Two water levels (W_70%: 70% of water consumption; and W_90%: 90% water consumption) and two nitrogen rates (N_100%: 100% of recommended and N_80%:80% of N_100%) under two straw mulching conditions (NS; no straw introduced and WS: with buried straw layer) were applied (Table 3). A 5 cm thick straw layer was buried at a 20 cm depth in the soil. Each block had eight rows and 24 plants. Before transplanting, the top soil was rototilled and the beds were manually raised. For N_100%, 225 kg.ha⁻¹ and for N_80%, 180 kg.ha⁻¹ of pure nitrogen was applied in the form of CO(NH₂)₂, 112.5 kg.ha⁻¹ of P₂O₅ was applied to all the treatments in the form of KH₂PO₄ and 135 kg.ha⁻¹ of K₂O was applied to all the treatments in the form of KH₂PO₄ and K₂SO₄.

Table 3. Treatments information.

Treatments	Treatment Detail	Straw mulching	Nitrogen (%)	Water Applied (%)
T1	NSN80%W90%	No straw	80	90
T2	WSN80%W90%	With buried straw	80	90
T3	NSN80%W70%	No straw	80	70
T4	WSN80%W70%	With buried straw	80	70
T5	NSN100%W90%	No straw	100	90
T6	WSN100%W90%	With buried straw	100	90
T7	NSN100%W70%	No straw	100	70
T8	WSN100%W70%	With buried straw	100	70

Note: T₅ was taken as CK.

2.3. Irrigation requirements

The same level of irrigation water was applied for the first 21 days to ensure the proper growth of the plants and treatments were imposed on day 22. Volumetric soil moisture contents θ_v were monitored by a Time Domain Reflectometer with 4-days interval during both growing seasons. The irrigation frequency was kept at every two days from commencement of irrigation regimes until the harvesting of the crop. Irrigation was done by the gravity drip irrigation bags having a capacity of 1.5 L. The storage bags were hanged at a height of 1.5 m to store irrigation water to irrigate a single plant. Every bag had two drippers, which were buried at a depth of 2 cm on either side of a plant at a distance of 10 cm from the stem of the plant. Fertigation was done in four equal splits along the season.

Soil water deficit (W_SD) was computed by using Equation (1). (1) $W_{\mathrm{S}\mathrm{D}}=\frac{{\theta }_{t1}-{\theta }_{t2}}{100}\ast Z_{rt}\ast 10$(1) where soil water deficit (mm), θ_t1 and θ_t2 are the volumetric water contents (%) in root zone at time t1 and t2, respectively and Z_rt is the depth of root zone (cm)

Amounts of irrigation water for treatments were computed by using Equation (2). (2) $W_I=W_{\mathrm{S}\mathrm{D}}\ast K_n$(2) where W_I is irrigation water (mm), W_SD is water deficit before irrigation (mm) and K_n is the rate of water deficit (0.9 for W_90% and 0.7 for W_70%).

Crop consumptive use (ET) was estimated using the following water balance method: (3) $\mathrm{E}\mathrm{T}=I_W+\mathrm{\Delta }S-R-D$(3) where ET (mm) is the crop consumptive use, I_W (mm) is irrigation water, ΔS (mm) is a change in soil water storage, D and R, are losses due to deep percolation and run-off (mm), respectively. Deep percolation and Run-off can be ignored for drip irrigation system (Yuan et al. 2001; Chen et al. 2015). Thus, Equation (3) can be simplified: (4) $\mathrm{E}\mathrm{T}=I_{\mathrm{W}}+\mathrm{\Delta }S$(4)

2.4. Plant growth parameters

The plant material was collected at the end of the experiments for both seasons and washed by tap water. After recording fresh weight, all the material was dried at 70° until the sample weight was held constant. Then the dry weight of biomass and roots were recorded.

2.5. Photosynthesis rate and stomatal conductance

Photosynthesis rate (P_N) and stomatal conductance (G_S) of two mature leaflets in the upper canopy were measured during both years on 38, 64 and 86 DAT after transplanting for flowering, fruiting and harvesting stages respectively using a portable photosynthesis system (Li-6400) from 9:00 to 11:00 in the morning under natural conditions. The measurements were conducted at temperature natural conditions and CO₂ at 400 µmol. mol⁻¹.

2.6. Chlorophyll fluorescence

Light response curves and F_V/F_M were measured using Mini-PAM II (Walz, Germany) on 36, 62 and 85 days after transplanting during both years. Light curve measurements were done at 12 PAR levels from 0 to 1500 µmol.m⁻².s⁻¹. ETRmax was determined using the following method developed by (Silsbe and Kromkamp 2012). (5) $\mathrm{E}\mathrm{T}\mathrm{R}=\alpha \text{β}\cdot \left(F_V/F_m\right)\cdot \mathrm{P}\mathrm{A}{\mathrm{R}}_{\mathrm{s}\mathrm{a}\mathrm{t}}\cdot [1-\mathrm{e}\mathrm{x}\mathrm{p}(-I/\mathrm{P}\mathrm{A}{\mathrm{R}}_{\mathrm{s}\mathrm{a}\mathrm{t}})]$(5)

Using the above expression, ETRmax can be determined as: (6) $\mathrm{E}\mathrm{T}{\mathrm{R}}_{\mathrm{m}\mathrm{a}\mathrm{x}}=\alpha \text{β}\cdot \left(F_V/F_m\right)\cdot \mathrm{P}\mathrm{A}{\mathrm{R}}_{\mathrm{s}\mathrm{a}\mathrm{t}}\cdot [1-\mathrm{e}\mathrm{x}\mathrm{p}(-1)]$(6)

where ETR; electron transfer rate, F_V/F_M; maximum quantum yield PAR_sat; saturation light intensity (µmol.m⁻².s⁻¹) and α, β and I are leaf absorptance coefficient, distribution of light energy between PSI and PSII and light intensity (µmol.m⁻².s⁻¹) respectively and were derived from the light response curves.

2.7. Chlorophyll content

A non-destructive chlorophyll meter SPAD-502 (Japan) was used to record the relative leaf greenness using the same leaves used for other physiological parameters.

2.8. Statistical analysis

Three-way analysis of variance was achieved using Statistix 8.1 statistical software and LSD was applied for means comparisons at p < .05 significance level. Linear regression was performed on Microsoft excel 2016 to test the correlation of maximum electron transport rate, chlorophyll contents and stomatal conductance with photosynthesis rate for both seasons.

3. Results

3.1. Water consumption

The amount of water consumed under different treatments during different growth stages is shown in Table 4. During the seedling stage, the same quantity of irrigation was applied to all the treatments for plant optimum establishment. The lower fertilizer and irrigation levels under buried straw layer utilized lowest amount of water for both years. On the other hand, higher fertilizer and high irrigation levels with no straw layer used highest amount of water during both years.

Table 4. Irrigation water consumed under different growth stages in 2017 and 2018 growing seasons.

Treatments	Water consumed under different growth stages (mm)
	Seedling	Flowering	Fruiting	Harvesting	Total
2017
T1	40.32	40.58	56.08	83.57	220.56
T2	40.32	29.30	51.93	77.79	199.34
T3	40.32	32.71	46.77	69.27	189.08
T4	40.32	29.85	46.29	69.83	186.30
T5	40.32	40.87	58.82	82.78	222.79
T6	40.32	32.99	52.94	79.28	205.54
T7	40.32	25.66	41.18	61.67	168.82
T8	40.32	32.60	46.56	65.01	184.49
2018
T1	39.90	29.43	42.68	72.77	184.78
T2	39.90	20.92	39.44	66.69	166.94
T3	39.90	22.71	34.80	55.80	153.21
T4	39.90	18.28	31.10	51.48	140.76
T5	39.90	30.02	45.29	75.20	190.41
T6	39.90	27.43	44.81	76.66	188.80
T7	39.90	23.18	35.68	56.85	155.62
T8	39.90	19.65	34.62	56.51	150.68

3.2. Plant and roots fresh and dry biomass

The aboveground plant biomass, root biomass and root length at harvesting time are shown in Table 5. In general, plant fresh weight (PFW) and plant dry weight (PDW) were both significantly increased under buried straw layer in both growing seasons (p < .001). The mean values of FPW increase with increasing fertilizer significantly (p < .05 in 2017 and p < .001 in 2018) and no significant increase was observed in PDW with increasing fertilizer in 2017 but a significant increase was noticed in 2018 (p < .05). Similarly, increasing irrigation water also increased both FPW and DPW in both experimental seasons (p < .001). The interactions of the buried straw layer with fertilizer, fertilizer with irrigation and irrigation with buried straw layer increased both FPW and DPW significantly (p < .001) in both growing seasons. The highest PFW and PDW values were recorded under the T₅ (NSN_100%W_90%) treatment (645.25 and 96.66 g, respectively, in 2017; and 683.39 and 116.12 g, respectively, in 2018) followed by T₄ (WSN_80%W_70%). The smallest FPW and DPW values were observed under T₇ (NSN_100%W_70%) treatment (342.36 and 53.88 g, respectively, in 2017; and 380.47 and 60.94, respectively, in 2018).

Table 5. Tomato plant growth parameters grown under two fertilizer levels and two irrigation regimes with and without buried straw layer during 2017 and 2018 growing seasons.

Interactions	PFW (g plant^−1)		PDW (g plant^−1)		RFW (g plant^−1)		RDW (g plant^−1)		RL (cm plant^−1)
Season	2017	2018	2017	2018	2017	2018	2017	2018	2017	2018
NS	447.68b	469.93b	66.72b	78.42b	22.82b	25.89b	6.07b	6.38b	45.85a	44.20a
WS	495.37a	531.86a	73.92a	83.87a	26.23a	28.26a	6.61a	6.79a	44.15b	42.43a
N100%	478.94a	520.71a	71.28a	82.79a	24.98a	27.77a	6.33a	6.68a	46.71a	46.00a
N80%	464.10b	481.08b	69.36a	79.50b	24.07a	26.38b	6.35a	6.50a	43.29b	40.63b
W90%	514.67a	545.02a	76.09a	87.64a	25.83a	28.48a	6.57a	6.91a	49.72a	48.33a
W70%	428.38b	456.77b	64.54b	74.65b	23.23b	25.67b	6.103b	6.27b	40.29b	38.30b
NS N100%	493.81b	531.93ab	75.27b	88.53a	26.05b	29.50a	6.95a	6.78ab	47.45a	46.78a
WS N100%	464.07c	509.49b	67.29c	77.06b	23.91c	26.04b	6.27bc	6.57b	45.98ab	45.21a
NS N80%	401.54d	407.93c	58.162d	68.31c	19.60d	22.29c	5.75c	5.98c	44.26bc	41.61b
WS N80%	526.66a	554.23a	80.55a	90.69a	28.54a	30.47a	6.95a	7.01a	42.33c	39.64b
N100% W90%	588.53a	638.20a	85.99a	100.35a	29.62a	32.44a	7.1650a	7.53a	53.22a	52.20a
N100% W70%	369.35d	403.22d	56.57d	65.24d	20.34d	23.10d	6.72a	5.83d	40.21c	39.79c
N80% W90%	440.80c	451.84c	66.19c	74.94c	22.03c	24.53c	5.98b	6.29c	46.22b	44.45b
N80% W70%	487.41b	510.32b	72.52b	84.06b	26.11b	28.23b	5.49b	6.71b	40.37c	36.80d
NS W90%	533.68a	549.44a	78.83a	94.21a	27.38a	30.45a	6.59a	7.25a	50.28a	49.78a
WS W90%	495.66b	540.60a	73.35b	81.07c	24.28b	26.51b	6.55a	6.57b	49.15a	46.87b
NS W70%	361.67c	390.42b	54.60c	62.63d	18.27c	21.34c	5.55b	5.52c	41.43b	38.61c
WS W70%	495.08b	523.13a	74.48ab	86.68b	28.18a	30.00a	6.66a	7.01a	39.15b	37.98c
T1:NSN80%W90%	422.11e	415.49de	61.01d	72.29e	21.16c	23.55e	5.01d	6.32c	47.08b	45.62b
T2:WSN80%W90%	459.50d	488.18c	71.38c	77.58d	22.90c	25.51d	6.54b	6.26c	45.35b	43.28bc
T3:NSN80%W70%	380.98f	400.36de	55.32de	64.32f	18.04d	21.03f	6.54b	5.65d	41.43c	37.60de
T4:WSN80%W70%	593.83b	620.28b	89.72b	103.81b	34.18a	35.44b	7.35a	7.76a	39.30c	36.00e
T5:NSN100%W90%	645.25a	683.39a	96.66a	116.12a	33.59a	37.35a	7.76a	8.17a	53.48a	53.95a
T6:WSN100%W90%	531.82c	593.02b	75.33c	84.57c	25.65b	27.52c	6.57b	6.88b	52.95a	50.45a
T7:NSN100%W70%	342.36g	380.47e	53.88e	60.94f	18.50d	21.65f	5.42cd	5.39d	41.42c	39.62cde
T8:WSN100%W70%	396.33ef	425.97d	59.25de	69.54e	22.18c	24.55de	5.97bc	6.26c	39.00c	39.97cd
Straw layer	***	***	***	***	***	***	**	**	*	NS
Fertigation	*	***	NS	*	NS	**	NS	NS	***	***
Irrigation	***	***	***	***	***	***	*	***	***	***
S*N	***	***	***	***	***	***	**	***	NS	NS
N*W	***	***	***	***	***	***	***	***	***	NS
S*W	***	***	***	***	***	***	**	***	NS	*
S*N*W	NS	NS	NS	NS	NS	NS	*	NS	NS	NS

Note: PFW; Plant Fresh Weight, PDW; Plant Dry Weight, RFW; Root Fresh Weight and RDW; Root Dry Weight, RL; root length. Data represented are the means of three replications. Values within the same columns followed with different letters are significantly different at p < .05 according to LSD test. NS; not significant, *; significant at p ≤ .05, **; significant at p ≤ .01 and ***; significant at p ≤ .001 between the treatments.

The buried straw layer significantly increased roots fresh weight (RFW) (p < .001) and root dry weight (RDW) (p < .01) for both experimental season. Increasing fertilizer did not increase RFW significantly for both seasons but RDW was significantly increased in 2017 experimental season (p < .01). Irrigation also had significant effect on both RFW and RDW and increased RFW for both seasons (p < .001) but different significance was observed in RDW (p < .05 in 2017 and p < .001 in 2018). The interactions of the buried straw layer with fertilizer, fertilizer with irrigation and irrigation with buried straw layer significantly increased RFW and RDW (p < .01 to p < .001) in both growing seasons. The T₄ (WSN_80%W_70%) treatment resulted in the highest RFW values (34.18 and 35.44 g) followed by T₅ (NSN_100%W_90%) (33.59 and 37.35 g) in 2017 and 2018, respectively. The lowest values of RFW (18.04 and 21.03 g) were recorded in T₃ (NSN_80%W_70%) in 2017 and 2018 respectively. The highest RDW was achieved in T₅ treatment (7.76 and 8.17 g) followed by T₄ treatment (7.35 and 7.76 g) in consecutive seasons. The lowest RDW values (5.01 and 5.39 g) were recorded in T₁ (NSN_80%W_90%) and T₇ (NSN_100%W_70%) in 2017 and 2018 respectively. Root length (RL) was significantly decreased under buried straw layer in 2017 (p < .05) but no significant effect was observed in 2018. RL was significantly increased by increasing fertilizer and irrigation water (p < .001). The interaction of buried straw layer and fertilizer had no significant effect on RL. A significant increase in RL was noticed when fertilizer interacted with irrigation in 2017 but no significant effect was observed in 2018. Similarly, the interaction of irrigation and buried straw mulching had no significant effect on RL in 2017 but RL increased significantly in 2018. The highest (53.48 and 53.95 cm) and lowest (39.30 and 36.00 cm) RL was observed in T₅ and T₄.

3.3. Photosynthesis and stomatal conductance

The effects of buried straw layer, fertilizer, irrigation and their interactions on P_N and G_S is presented in Table 6. During both years, at the flowering stage no significant change in P_N and G_S was observed among all the treatments. The interaction between fertilizer and irrigation significantly affected the P_N (p < .01 and p < .001 respectively) and G_S (p < .05 and p < .001 respectively) at the flowering stage for both years. At the fruiting stage, all the factors and their interactions affected both P_N and G_S significantly. In 2017 the highest and lowest decrease in P_N was observed in T₇ (NSN_100%W_70%) (48.24%) and T₄ (WSN_80%W_70%) (5.98%) respectively whereas T₈ (WSN_100%W_70%) (44.84%) and T₄ (6.91%) had highest and lowest decrease in 2018 when compared with T₅ (NSN_100%W_90%) at the fruiting stage. While studying the effect of different treatments on G_S at fruiting stage, the lowest and highest decrease was recorded in T₄ (7.58%) and T₇ (46.54%) in 2017 experimental season when compared with T₅ however in 2018, highest and lowest decrease was noted in T₃ (NSN_80%W_70%) (48.21%) and T₄ (8.21%) respectively. The interactive factors significantly affected P_N during the harvesting stage for both years (p < .01 to p < .001). Comparing with T₅, the highest and lowest decrease in P_N was observed in T₇ (42.76 and 30.62%) and T₄ (6.21 and 5.61%) in 2017 and 2018 respectively. During the harvesting stage, all the factors and their interaction affected G_S significantly except their combined interaction (S*N*W) for both years (p < .05 to p < .001). The lowest and highest decrease in G_S was observed in T₄ (7.91 and 7.29%) and T₃ (NSN_80%W_70%) (49.64 and 34.11%) for 2017 and 2018 respectively.

Table 6. Effect of buried straw layer, fertilizer, irrigation and their interactions on P_N and G_S.

Interactions	PN for different stages						GS for different stages
	Flowering		Fruiting		Harvesting		Flowering		Fruiting		Harvesting
	2017	2018	2017	2018	2017	2018	2017	2018	2017	2018	2017	2018
NS	14.52a	15.93a	16.73a	16.72b	10.19a	14.19a	0.275a	0.301a	0.315a	0.319b	0.190b	0.268b
WS	14.84a	15.93a	16.78a	18.04a	10.55a	14.53a	0.280a	0.301a	0.329a	0.362a	0.207a	0.286a
N100%	14.86a	16.17a	15.78b	17.51a	10.43a	14.34a	0.282a	0.308a	0.315a	0.354a	0.209a	0.288a
N80%	14.50a	15.68b	17.73a	17.25a	10.32a	14.38a	0.273a	0.295b	0.330a	0.327b	0.189b	0.266b
W90%	14.82a	16.14a	17.40a	18.49a	10.53a	14.70a	0.278a	0.303a	0.338a	0.367a	0.203a	0.286a
W70%	14.54a	15.71b	16.11b	16.27b	10.22a	14.03b	0.278a	0.299a	0.307b	0.314b	0.194a	0.468b
NS N100%	14.72a	16.18a	18.13b	19.08a	11.40a	15.10a	0.281a	0.309a	0.354b	0.374a	0.222a	0.298a
WS N100%	15.00a	16.15a	13.42d	15.93b	9.45b	13.58b	0.283a	0.307ab	0.275c	0.335b	0.195b	0.279b
NS N80%	14.32a	15.67a	15.33c	14.35c	8.98b	13.28b	0.429a	0.294b	0.276c	0.263c	0.158c	0.239c
WS N80%	14.68a	15.70a	20.13a	20.15a	11.65a	15.48a	0.277a	0.296ab	0.384a	0.390a	0.219a	0.294ab
N100% W90%	15.45a	17.22a	18.37a	21.22a	11.60a	15.92a	0.290a	0.324a	0.360a	0.421a	0.227a	0.312a
N100% W70%	14.27b	15.12c	13.18c	13.80d	9.25b	12.77c	0.275ab	0.291c	0.270c	0.288c	0.191b	0.264b
N80% W90%	14.18b	15.07c	16.43b	15.77c	9.45b	13.48b	0.266b	0.282c	0.316b	0.313c	0.179b	0.260b
N80% W70%	14.82ab	16.30b	19.03a	18.73b	11.18a	15.28a	0.280ab	0.308b	0.344a	0.341b	0.198b	0.272b
NS W90%	14.72a	16.20a	19.48a	19.37a	11.85a	15.33a	0.277a	0.304a	0.376a	0.378a	0.227a	0.297a
WS W90%	14.92a	16.08a	15.32c	17.62b	9.20b	14.07b	0.279a	0.302a	0.299b	0.356a	0.179b	0.275b
NS W70%	14.32a	15.65a	13.98d	14.07c	8.53b	13.05c	0.273a	0.298a	0.254c	0.260b	0.153c	0.240c
WS W70%	14.77a	15.77a	18.23b	18.47ab	11.90a	15.00a	0.282a	0.301	0.360a	0.369a	0.235a	0.297a
T1:NSN80%W90%	14.00c	15.03c	15.07de	14.13d	9.20bcd	12.83ef	0.260c	0.278e	0.291d	0.281de	0.176c	0.251cd
T2:WSN80%W90%	14.37bc	15.10c	17.80c	17.40c	9.70bc	14.13c	0.272abc	0.285e	0.340c	0.345c	0.183c	0.269bc
T3:NSN80%W70%	14.63abc	16.30b	15.60d	14.57d	8.77cd	13.73cde	0.279abc	0.310bc	0.261de	0.246e	0.140d	0.226d
T4:WSN80%W70%	15.00abc	16.30b	22.47b	22.90b	13.60a	16.83b	0.282abc	0.306bcd	0.427b	0.436b	0.256a	0.318a
T5:NSN100%W90%	15.43ab	17.37a	23.90a	24.60a	14.50a	17.83a	0.294a	0.331a	0.462a	0.475a	0.278a	0.343a
T6:WSN100%W90%	15.47a	17.07ab	12.83fg	17.83c	8.70cd	14.00cd	0.285ab	0.318ab	0.258de	0.367c	0.175c	0.281b
T7:NSN100%W70%	14.00c	15.00c	12.37g	14.03d	8.30d	12.37f	0.268bc	0.286de	0.247e	0.273de	0.167cd	0.253cd
T8:WSN100%W70%	14.53abc	15.23c	14.00ef	13.57d	10.20b	13.17def	0.282abc	0.295cde	0.292d	0.302d	0.215b	0.276bc
Straw layer	ns	ns	ns	**	ns	ns	ns	ns	ns	***	*	*
Fertigation	ns	*	***	ns	ns	ns	ns	*	ns	**	**	**
Irrigation	ns	*	***	***	ns	**	ns	ns	***	***	ns	*
S*N	ns	ns	***	***	***	***	ns	ns	***	***	***	***
N*W	**	***	***	***	***	***	*	***	***	***	***	***
S*W	ns	ns	***	***	***	***	ns	ns	***	***	***	***
S*N*W	ns	ns	***	ns	**	**	ns	ns	***	ns	ns	ns

Note: S: Straw, N: nitrogen, W: Irrigation water. Values within the same columns followed with different letters are significantly different at p < .05 according to LSD test. ns; not significant, *; significant at p ≤ .05, **; significant at p ≤ .01 and ***; significant at p ≤ .001 between the treatments.

3.4. Fluorescence parameters

The response of the buried straw layer, fertilizer levels, irrigation regimes and their interactions on chlorophyll contents (Chl) and maximum electron transport rate (ETRmax) are represented in Table 7. A polynomial relationship between light intensity and electron transport rate was fitted (Figure 1) in all three growth stages (p < .001). During the flowering stage, irrigation affected the Chl significantly whereas irrigation and the interactive effect of fertilizer and irrigation had significant effects on ETRmax (p < .05) for both growing seasons. Comparing with T₅ (NSN_100%W_90%), highest decrease in Chl was noticed in T₃ (NSN_80%W_70%) (7.18%) and T₇ (NSN_100%W_70%) (6.24%) during 2017 and 2018 respectively, however, T₈ (WSN_100%W_70%) had highest decrease in ETRmax for both years during flowering stage. Fertilizer, irrigation, interactive effects of buried straw layer and fertilizer, and the interactions of straw layer and irrigation significantly affected Chl but fertilizer, interactive effect of fertilizer and buried straw layer, and the interaction between irrigation and buried straw layer significantly affected ETRmax during both years (p < .05 to p < .001) at the fruiting stage. Comparing with T₅, the highest decrease in Chl and ETRmax was noted in T₆ (WSN_100%W_90%) (8.98%) in 2017 whereas in 2018, highest decrease was observed in T₄ (4.39%) and T₃ (NSN_80%W_70%) (19.65%) respectively during the fruiting stage. However, for both years, T₃ gave highest decrease in ETRmax (17.64 and 16.92%) at fruiting stage while comparing with T₅. During the harvesting stage, Chl was significantly affected by buried straw layer, fertilizer, the interaction between straw layer and fertilizer and the interactive effects of straw layer and irrigation (p < .05 to p < .001) for both years. On the other hand, fertilizer, interaction of fertilizer with buried straw layer, irrigation with buried straw layer and fertilizer with irrigation significantly affected ETRmax (p < .05 to p < .001) during both years Comparing with T₅, highest decrease in Chl was observed in T₃ (21.70%) and T₂ (WSN_80%W_90%) (16.36%) in 2017 and 2018 respectively during the harvesting stage. On the other hand, T₃ had highest decrease in ETRmax (19.60 and 18.91%) during harvesting stage for consecutive years. The Table 8 shows the effects of buried straw layer, fertilizer levels, irrigation regimes and their interactions on the maximum quantum yield (F_V/ F_M) for flowering, fruiting and harvesting stages during 2017 and 2018 growing seasons. At the flowering stage, fertilizer significantly affected F_V/F_M during 2017 (p < .05) and 2018 (p < .01) respectively. Comparing with T₅ (NSN_100%W_90%), T₆ (WSN_100%W_90%) and T₃ (NSN_80%W_70%) had lowest (0.29 and 4.81%) and highest (17.0 and 19.76%) decrease in F_V/F_M during 2017 and 2018 respectively. During the fruiting stage, the interactive effects of buried straw layer and irrigation significantly affected F_V/F_M (p < .05) in both years whereas the interaction of straw with fertilizer and irrigation with fertilizer had a significant effect on F_V/F_M (p < .05) only in 2018. The highest and lowest decrease was observed in T₃ (16.20 and 14.66%) and T₄ (WSN_80%W_70%) (7.44 and 1.01%) respectively in the consecutive years when compared with T5 at the fruiting stage. At the harvesting stage, a significant interactive effect of straw and irrigation was noted during both years (p < .05) whereas straw and fertilizer significantly affected F_V/F_M in 2018 season. Comparing with T5, T₄ and T₃ had lowest (4.79 and 5.24%) and highest (19.66 and 22.80%) decrease in F_V/F_M during consecutive growing years at the harvesting stage.

Figure 1. Light curves as affected by the interaction of fertilizer and irrigation levels with and without buried straw layer at flowering stages (a) in 2017 and (b) in 2018, fruiting stages (c) in 2017 and (d) in 2018, and harvesting stages (e) in 2017 and (f) in 2018. Each curve was constructed using three replicates for all the treatments for both years.

Table 7. Effect of buried straw layer, fertilizer, irrigation and their interactions on Chl and ETRmax.

Parameter	Chl (SDPAD)						ETRmax
Interactions	Flowering		Fruiting		Harvesting		Flowering		Fruiting		Harvesting
Season	2017	2018	2017	2018	2017	2018	2017	2018	2017	2018	2017	2018
NS	56.18b	52.70a	63.33a	59.78a	47.54b	45.93a	103.61a	114.20a	105.52a	115.42a	97.78a	101.79a
WS	57.54a	51.99a	63.47a	61.54a	49.59a	46.59a	101.61a	111.88a	105.70a	118.28a	100.02a	104.28a
N100%	57.68a	52.84a	62.96b	61.17a	48.30a	44.18b	101.12a	113.38a	107.38a	118.69a	101.06a	105.07a
N80%	56.05b	51.85a	63.83a	60.15a	48.83a	48.33a	104.10a	112.69a	103.83b	115.01b	96.74b	100.99b
W90%	57.10a	53.18a	63.67a	62.78a	50.25a	47.51a	105.09a	119.22a	105.33a	117.13a	99.33a	103.39a
W70%	56.63a	51.51b	63.13a	58.53b	46.88b	45.01a	100.13b	106.86b	105.88a	116.57a	98.47a	102.68a
NS N100%	57.85a	53.22a	64.23b	62.12ab	49.70b	45.45ab	105.58a	114.30a	110.75a	121.12a	103.52a	107.53a
WS N100%	57.50a	52.47a	61.68c	60.22ab	46.90c	42.92b	96.65b	112.47a	104.02bc	116.27a	98.60b	102.62a
NS N80%	54.52b	52.18a	62.42c	57.43b	45.38d	46.40ab	101.63ab	114.10a	100.28c	109.73b	92.03c	96.05b
WS N80%	57.58a	51.52a	65.25a	62.87a	52.28a	50.27a	106.57a	111.28a	107.38ab	120.28a	101.45ab	105.93a
N100% W90%	57.25a	53.83a	63.83a	63.33a	50.02a	46.62ab	106.20a	124.02a	107.53a	123.52a	105.62a	109.72a
N100% W70%	58.10a	51.85b	62.08b	62.23a	46.58b	41.75b	96.03b	114.42ab	107.23a	113.87b	96.50bc	100.43bc
N80% W90%	56.95a	52.53ab	63.50a	59.00a	50.48a	48.40a	103.98a	102.75c	103.13a	110.75b	93.05c	97.07c
N80% W70%	55.15b	51.17b	64.17a	58.07a	47.18b	48.27a	104.22a	110.97bc	104.53a	119.27a	100.43b	104.92ab
NS W90%	56.57ab	53.68a	65.23a	63.92a	51.13a	48.42a	109.95a	122.55a	110.53a	120.80a	103.10a	107.12a
WS W90%	57.63a	52.68ab	62.10b	61.65a	49.37b	46.60a	100.23b	115.88ab	100.13b	113.47b	95.57b	99.67b
NS W70%	55.80b	51.72b	61.42b	55.63b	43.95c	43.43a	97.27b	105.85b	100.50b	110.05b	92.45b	96.47b
WS W70%	57.45a	51.30b	64.83a	61.43a	49.82b	46.58a	102.98b	107.87b	111.27a	123.08a	104.48a	108.88a
T1:NSN80%W90%	56.03b	52.43ab	63.63b	61.83ab	49.27b	47.33abc	105.13bc	116.80ab	104.83cd	110.77bc	93.27bcd	97.20bc
T2:WSN80%W90%	57.87ab	52.63ab	63.37b	62.63ab	51.70a	41.40c	102.83bcd	112.03bc	101.43de	110.73bc	92.83cd	96.93bc
T3:NSN80%W70%	53.00c	51.93b	61.20d	53.03c	41.50d	45.47abc	98.13cd	111.40bc	95.73e	108.70c	90.80d	94.90c
T4:WSN80%W70%	57.30ab	50.40b	67.13a	63.10ab	52.87a	51.07a	110.30ab	110.53bc	113.33ab	129.83a	110.07a	114.93a
T5:NSN100%W90%	57.10ab	54.93a	66.83a	66.00a	53.00a	49.50ab	114.77a	128.30a	116.23a	130.83a	112.93a	117.03a
T6:WSN100%W90%	57.40ab	52.73ab	60.83d	60.67ab	47.03c	43.73abc	97.63cd	119.73ab	98.83de	116.20b	98.30bc	102.40b
T7:NSN100%W70%	58.60a	51.50b	61.63cd	58.23bc	46.40c	49.47ab	96.40d	100.30c	105.27cd	111.40bc	94.10bcd	98.03bc
T8:WSN100%W70%	57.60ab	52.20b	62.53bc	59.77abc	46.77c	42.10bc	95.67d	105.20bc	109.20bc	116.33b	98.90b	102.83b
Straw layer	ns	ns	ns	ns	***	*	ns	ns	ns	ns	ns	ns
Fertigation	ns	ns	**	*	*	*	ns	ns	*	*	**	*
Irrigation	*	*	*	*	ns	ns	*	*	ns	ns	ns	ns
S*N	ns	ns	***	*	***	*	**	ns	***	***	***	***
N*W	ns	ns	ns	ns	ns	ns	*	*	ns	***	***	***
S*W	ns	ns	***	*	***	*	***	ns	***	***	***	***
S*N*W	ns	ns	ns	ns	ns	ns	ns	ns	ns	ns	ns	ns

Note: S: Straw, N: nitrogen, W: Irrigation water. Values within the same columns followed with different letters are significantly different at p < .05 according to LSD test. ns; not significant, *; significant at p ≤ .05, **; significant at p ≤ .01 and ***; significant at p ≤ .001 between the treatments.

Table 8. Effect of buried straw layer, fertilizer, irrigation and their interactions on F_V/F_M and Fruit Yield.

	FV/FM
Interactions	Flowering		Fruiting		Harvesting		Fruit Yield (kgm^−2)
Season	2017	2018	2017	2018	2017	2018	2017	2018
NS	0.621a	0.671a	0.668a	0.718a	0.511a	0.553b	6.10a	7.81a
WS	0.640a	0.671a	0.664a	0.742a	0.509a	0.594a	5.69b	7.86a
N100%	0.654a	0.698a	0.675a	0.736a	0.512a	0.594a	6.35a	8.15a
N80%	0.607b	0.644b	0.657a	0.724a	0.509a	0.553a	5.43b	7.51b
W90%	0.653a	0.696a	0.678a	0.743a	0.517a	0.590a	6.00a	8.02a
W70%	0.609a	0.647b	0.654a	0.717a	0.503a	0.558a	5.78a	7.65b
NS N100%	0.652ab	0.707a	0.696a	0.743ab	0.534a	0.591a	6.60a	8.67a
WS N100%	0.657a	0.689ab	0.654a	0.729ab	0.489a	0.598a	4.96c	7.38b
NS N80%	0.592b	0.653bc	0.640a	0.694b	0.488a	0.591a	5.59b	6.95b
WS N80%	0.623ab	0.635c	0.674a	0.754a	0.529a	0.591a	6.41a	8.34a
N100% W90%	0.699a	0.731a	0.705a	0.768a	0.531a	0.631a	6.73a	9.07a
N100% W70%	0.610b	0.666b	0.644b	0.704b	0.492a	0.557b	4.83c	6.98c
N80% W90%	0.606b	0.660b	0.650ab	0.719ab	0.504a	0.548b	5.97b	7.24c
N80% W70%	0.609b	0.628b	0.664ab	0.729ab	0.514a	0.558b	6.03b	8.05b
NS W90%	0.651a	0.709a	0.701a	0.752a	0.546a	0.589a	7.21a	8.78a
WS W90%	0.654a	0.682ab	0.654ab	0.735ab	0.489ab	0.590a	5.49bc	7.52c
NS W70%	0.593a	0.633b	0.635b	0.685b	0.477b	0.516b	4.98c	6.84d
WS W70%	0.626a	0.661ab	0.673ab	0.748a	0.529ab	0.599a	5.88b	8.19b
T1:NSN80%W90%	0.602b	0.669bc	0.649b	0.712bc	0.506ab	0.529c	5.69cd	6.99cd
T2:WSN80%W90%	0.611ab	0.651bc	0.651b	0.725abc	0.501ab	0.567abc	6.25bc	7.49c
T3:NSN80%W70%	0.581b	0.601c	0.631b	0.675c	0.470b	0.501c	5.49cde	6.92cd
T4:WSN80%W70%	0.636ab	0.656bc	0.697ab	0.783ab	0.557ab	0.615a	6.57b	9.19b
T5:NSN100%W90%	0.700a	0.749a	0.753a	0.791a	0.585a	0.649a	8.73a	10.58a
T6:WSN100%W90%	0.698a	0.713b	0.658b	0.744abc	0.477b	0.613ab	4.73ef	7.55c
T7:NSN100%W70%	0.604b	0.665bc	0.638b	0.695c	0.484b	0.532bc	4.47f	6.76d
T8:WSN100%W70%	0.616ab	0.666bc	0.649b	0.713bc	0.501ab	0.583abc	5.19def	7.20cd
Straw layer	ns	ns	ns	ns	ns	*	*	ns
Fertigation	*	**	ns	ns	ns	*	***	***
Irrigation	ns	**	ns	ns	ns	ns	ns	*
S*N	ns	ns	ns	*	*	ns	***	***
N*W	*	ns	ns	*	ns	*	***	***
S*W	ns	ns	*	*	*	*	***	***
S*N*W	ns	ns	ns	ns	ns	ns	***	*

Note: S: Straw, N: nitrogen, W: Irrigation water. Values within the same columns followed with different letters are significantly different at p < .05 according to LSD test. ns; not significant, *; significant at p ≤ .05, **; significant at p ≤ .01 and ***; significant at p ≤ .001 between the treatments.

3.5. Correlation of G_S, ETRmax and Chl with P_N

The linear regression relationships of G_S, ETRmax, Chl and G_S with P_N during 2017 and 2018 is shown in Figure 2 which shows that correlations obtained for ETRmax, Chl and G_S with P_N during both years were highly significant (p < .001). The linear regression coefficients (R²) between P_N and G_S were 0.963 for 2017 and 0.9173 for 2018. Although, the correlation between ETRmax and P_N was significant but its values were 0.3819 and 0.4847 in 2017 and 2018, respectively. The correlation between Chl and P_N was also significant and the R² of these relations were 0.6677 for 2017 and 0.3441 for 2018.

Figure 2. Linear regression relationships of G_S, ETRmax and Chl with P_N in 2017 (a, c, e) and 2018 (b, d, f). Each equation was obtained from 72 data (8 treatments × 3 growth stage × 3 replications), means significance at p < .001.

3.6. Fruit yield

During both years, the highest decrease in yield was observed in T₇ (M_NF_CI₇₀) when compared to T₅ (M_NF_CI₉₀)_. The highest fruit yield (8.73 and 10.58 kgm⁻²) was achieved by T₅ (NSN_100%W_90%) followed by T₄ (WSN_80%W_70%) (6.57 and 9.19 kgm⁻²) whereas lowest fruit yield (4.47 and 6.76 kgm⁻²) were achieved inT₇ (NSN_100%W_70%) in 2017 and 2018 respectively (Table 8). T₅ resulted with the highest yield, but it increased the input cost of irrigation water and fertilizer. On the other hand, T₄ has no significant decrease in the fruit yield but it decreased the input costs of the water and fertilizer respectively.

4. Discussions

Irrigation is a substantial factor of plant growth therefore increased irrigation deficiency can lead to decrease plant vegetative measurements by decreasing soil water constants and consequently reduce water availability to the plant growth. On the other hand, the buried straw layer can hold moisture and decreases the chances of plant stress. In our findings, buried straw mulching significantly affected the soil water contents when compared with the treatments having no straw layer. Similar trends have been reported by (Zhang et al. 2010; Mingze et al. 2016). The treatments T₄ and T₅ (NSN_100%W_90%) recorded lowest and highest water consumption but no significant difference for most vegetative measurements was noticed between these two treatments except the plant root attributes. Similar trends for roots have been reported by Man et al. (2016) and (Rasool et al. 2019) who stated that keeping soil water contents at a moderate level enhances the root distribution in the topsoil layers. Li et al. (2010) also noted that deficit irrigation levels increase root density in the topsoil layers. These results match with our findings which reveal that optimum levels of moisture in the root zone can improve roots density compared to deficit or full irrigation.

It was concluded by Harris (1992) that root length is possibly a better measure of the absorbing capability of roots, which is also proved by our results that under W_90% treatments, roots had vertical whereas roots had a horizontal pattern under W_70% treatments due to the difference in wetting pattern of irrigation under different irrigation levels. There is a close correlation between plant nutrient absorption and water uptake. During the process of water absorption by plant roots, the nutrients move to the roots surface. Limiting the plant water uptake also restricts the delivery of nutrients to the plant roots. The processes of diffusion and mass flow, responsible for bathing the plant roots with nutrients slow down as the soil dries. The tomato plant requires a balanced management of fertilizer for optimum growth because it is a heavy remover of nutrients and responds well to the fertigation (Hebbar et al. 2004). In addition, deficit irrigation and fertilizer levels may lead to adversely affect the plant growth. On the other hand, buried straw layer can decrease the impact of water and fertilizer deficiency by holding water and nutrients in the topsoil layer (Zhao et al. 2014). As in our findings, application of higher water and fertilizer with no straw layer and lower water and fertilizer with buried straw layer had no significant difference in most vegetative growth parameters. Similar trends have also been reported by (Chen et al. 2016; Chen et al. 2019). It is found in our results that plant growth parameters are more sensitive to mulching and irrigation than fertilizer. It is also reported by (Xiukang and Yingying 2016) that irrigation has more effects on plant growth than fertilizer.

The interactive effect of fertilizer and irrigation significantly affected P_N and G_S at the flowering stage during both years (p < .001) but no significant difference was notice because all the plants were treated with same practices for the day 1 to day 21 and treatments were established on 22nd day. At fruiting and harvestings stages, combination of higher levels of fertilizer and irrigation improved P_N and G_S under no buried straw conditions whereas lower fertilizer (N_80%) and irrigation (W_70%) levels had a positive effect on P_N and G_S under buried straw layer. The reason of improved P_N and G_S by the lower irrigation and fertilizer under buried straw layer treatment (T₄) could be the effect of better plant growth under T₄, which was improved due to the horizontal distribution of roots at the topsoil layer, which increased plant nutrients and water uptake. Under increased fertilizer and lower irrigation, both P_N and G_S were decreased at fruiting and harvesting stages. Liu et al. (2012) advised that nitrogen and water are closely associated, and drought tolerance cab be enhanced by applying nitrogen at a lower rate which subsequently can improve water uptake and photosynthetic capacity. Consequently, an optimal low nitrogen supply would be adopted under the deficit conditions. It is also reported by Garcia et al. (2007) that the water-stressed tomato plants showed a decrease in stomatal conductance due to an increase in leaf osmotic potential and decrease in leaf water potential. The optimal addition of nitrogen can contribute to recover the adversative drought effect by a process of elastic and osmotic adjustment. In our findings, a direct relation was observed between P_N and G_S, as P_N was significantly increased with the rise in G_S (Figure 2) _.

Hebbar et al. (2004) stated that chlorophyll contents of tomato were significantly affected by the interaction between fertilization and irrigation. However, no significant effect of the interaction between fertilization and irrigation was noticed during our study but the interactive effects of buried straw layer with fertilizer as well as with irrigation affected Chl significantly. Photosynthesis was significantly influenced by chlorophyll contents (Figure 2) which is also stated by Hebbar et al. (2004) and Xiukang and Yingying (2016), who described that photosynthesis is the main function of leaves however, fertilization and irrigation are the main factors which progress leaves growth and subsequently improves plant growth, crop production and crop quality. Chlorophyll fluorescence analysis allows near-instantaneous, non-invasive measurement of main aspects of electron transport and photosynthetic light capture (Campbell et al. 1998) and responds to quantum yield, state of energy distribution in the thylakoid membrane and the extent of photoinhibition (Wang et al. 2007). ETRmax and P_N were found to have a significant correlation (Figure 2, p < .001). The possible reason due to which T₄ (WSN_80%W_70%) could get higher yield and T₆ (WSN_100%W_90%) decreased the fruit yield is horizontal distribution of plant roots in T₄, which supports higher nutrients availability. While, under T_6, most part of the roots was in the lower side of straw layer, which reduced the nutrients uptake ability of the plants as most of the nutrients, sustained by straw layer. Also, the straw layer could reduce leaching loss of nitrogen and water due to the characteristic of straw-lack of capillary (Zhao et al. 2014). If the soil moisture of the upper-layer is decreased due to evapotranspiration loss then straw use could overcome the adversative effects of water stress by increasing plant water status, improving plant growth and water use efficiencies (Abd El-Mageed et al. 2018). The increment in nitrogen fertilization reduced tomato yield under no straw and W_70% treatment (T₇). This result indicated that increasing nitrogen rate under water deficit will be relatively inefficient, could be because of an adverse effects of excessive nitrogen on yields (El Mokh et al. 2015; Ullah et al. 2017).

5. Conclusions

It was revealed in the present study that growth and physiological parameters were all affected by fertilizer, irrigation and buried straw layer. Buried straw layer improved both growth and physiological parameters under lower fertilizer (N_80%) and irrigation levels (W_70%) but decreased under higher fertilizer (N_100%) and irrigation levels (W_90%) during both years. Plant and roots fresh and dry biomass, photosynthesis, stomatal conductance, ETRmax, F_V/F_M and chlorophyll contents were significantly improved in T₅ (NSN_100%W_90%) followed by T₅ (WSN_80%W_70%). P_N was noticed to be correlated with G_S, ETRmax, and Chl significantly suggesting that osmotic effect was the key factor influencing the photosynthetic process. It was observed that under limited water conditions, buried straw layer could be applied to relieve the adverse effect of deficit irrigation and fertilizer levels for improving plant growth and physiology and fruit yield.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research was supported by the National Key R&D Program of China (grant number 2017YFD0201507) and Jiangsu Water Science and Technology Program [Grant Number 2018046].

Notes on contributors

Ghulam Rasool

Ghulam Rasool is PhD candidate at Hohai University in the College of Agricultural Engineering. His research interests include Agricultural Soil and Water Engineering, Soil and Water Conservation, and Soil, Water and Plant relationship.

Xiangping Guo

Xiangping Guo is Professor at Hohai University in the College of Agricultural Engineering. His research focuses Soil and Water Engineering, and Water Saving irrigation.

Zhenchang Wang

Zhenchang Wang is Associate Professor at Hohai University in the College of Agricultural Engineering. His research interests are; deficit irrigation, Salinity, Soil, Water and Plant relationship.

Sheng Chen

Sheng Chen is Post-doctorate researcher at Hohai University in the College of Water Conservancy and Hydropower Engineering. His research focus is agricultural water engineering.

Ikram Ullah

Ikram Ullah is Post-doctorate researcher at Jiangsu University, Zhenjiang in the School of Agricultural Equipment Engineering. His research directions are Agricultural Soil and Water Engineering, and Soil, Water and Plant relationship.