Agronomic response of subsurface versus surface drip irrigation in Nagpur mandarin (Citrus reticulata Blanco) on Indian Vertisol

Introduction: Citrus is a highly water- and nutrient-demanding crop, with several previous efforts focusing on improving water productivity (WP) and nutrient use efficiency (NUE). However, studies highlighting the performance of subsurface drip irrigation (SDI) versus drip irrigation (DI) in black clay soil (Vertisol) for Nagpur mandarin are limited.

Methods: This study evaluated six treatments: T₁ (SDI with double inline laterals at 100 cm radially from the trunk and 30 cm vertically placed into the soil, drippers spaced 30 cm apart); T₂ (SDI with double inline laterals at 100 cm radially from the trunk and 30 cm vertically placed into the soil, drippers spaced 40 cm apart); T₃ (SDI with double inline laterals at 100 cm radially from the trunk and 30 cm vertically placed into the soil, drippers spaced 50 cm apart); T₄ (SDI with double inline laterals at 100 cm radially from the trunk and 30 cm vertically placed into the soil, drippers spaced 60 cm apart); T₅ (DI with double online laterals at 100 cm from the trunk with 6 drippers spaced 60 cm apart; control as farmers’ practice); and T₆ (DI with ring-type online laterals at 10 cm from the trunk with 12 drippers tree-1 placed 60 cm apart). These treatments were for their effects on growth performance, fruit yield, fruit quality, WP, and leaf nutrient composition.

Results and discussion: Significantly higher WP and lower water requirement were observed with SDI (7.96–10.73 kgm^-3 and 13,260 L plant^-1 year^-1) compared with DI treatments (2.79–3.04 kg m-³ and 32,670 L plant^-1 year^-1). These observations were associated with a 25.29% higher fruit yield in SDI over DI treatments. Similarly, fruit quality parameters, viz., juice content and total soluble solids (TSS), were 6.65% and 1.17°Brix higher, respectively, in SDI than in DI treatments. Consequently, SDI treatments registered higher leaf N, P, K, Ca, Mg, Fe, Mn, and Zn contents by 4.81%, 15.68%, 6.60%, 17.51%, 3.18%, 7.70%, 5.04%, and 15.67%, respectively, over DI treatments. Thus, SDI treatments saved.

Conclusion: The T3 treatment was the most effective SDI treatment, registering 47.32% higher fruit yield and 64.87% higher WP than DI treatments, along with significantly improved leaf and fruit nutrient composition.

1 Introduction

Nagpur mandarin (Citrus reticulata Blanco), a globally recognized commercial citrus cultivar, is highly valued for its unique taste, nutritional benefits, and adaptability to varied climatic conditions. The cultivar is extensively grown on black clay soils (Entisols, Inceptisols, and Vertisols) in the Vidarbha region of Maharashtra and the Jhalawad/Sriganganagar districts of Rajasthan, India, with strong consumer preference due to its excellent taste and high economic returns (Ladaniya et al., 2021).

Despite the cultivar’s prominence, sustaining its high productivity is severely challenged by water scarcity (Panigrahi, 2023a) and by improper irrigation management, particularly in the absence of information on crop phenology-based drip irrigation (DI) scheduling (Puig-Sirera et al., 2021). Previous studies have highlighted the adverse effects of suboptimal irrigation at any critical growth stage on fruit yield and quality (Panigrahi et al., 2014; Panigrahi, 2023a, b). DI scheduling has provided considerable impetus for sustaining optimum productivity worldwide (Shirgure et al., 2004; Panigrahi and Srivastava, 2017; Jamshidi et al., 2020) compared with conventional basin irrigation (Panigrahi et al., 2012a), resulting in water and nutrient savings (Panigrahi et al., 2012b; Martínez-Gimeno et al., 2018) and superior fruit quality attributes (Robles et al., 2016).

In this context, optimizing irrigation water for improved water productivity (WP) by aligning water requirements with critical growth stages, along with enhancing nutrient use efficiency (NUE), is an essential component of profitable citrus cultivation (Shirgure and Srivastava, 2013; Kadam, 2020).

Combining, WP, and NUE has given rise to the concept of fertigation—an irrigation method that has created a strong niche across crops and agroclimates, significantly reducing nutrient leaching losses while enhancing nutrient uptake efficiency to as high as 90% compared with 40%–60% in traditional basin irrigation (Sravani et al., 2020; Pimpale et al., 2022). As in many leading citrus-growing countries, the extensive adoption of DI in citrus orchards in India has transformed water and nutrient management, lowering input costs and improving both yield and quality (Shirgure et al., 2016; Panigrahi and Srivastava, 2016).

More recently, as a further advancement of DI, SDI has emerged as a more advanced micro-irrigation technique, offering the additional advantages of delivering water directly into the root zone, minimizing evaporation losses, and further enhancing and WP (Martinez and Reca, 2014; Robles et al., 2016; Meshram et al., 2019; Franco et al., 2019). SDI has been successfully applied to many woody perennial crops, improving yield, WP, and NUE, while also reducing alternate bearing (Consoli et al., 2014; Ayars et al., 2015; Martínez-Gimeno et al., 2018). Other studies (Robles et al., 2016; Zhang et al., 2017) have demonstrated that SDI increases and WP by reducing water losses compared with surface DI, without compromising fruit yield, and enhances NUE by decreasing water losses by up to 43% (Camp, 1998; Bonachela et al., 2001; Provenzano et al., 2014; Martinez and Reca, 2014).

Despite these clear advantages, effective implementation of SDI is often limited by high initial costs and potential challenges such as emitter clogging and rodent damage (Phene, 1995; Sinobas et al., 2012; Yao et al., 2021; Muhammad et al., 2022; Wang et al., 2024), which may be more prevalent in the black clay soils typical of Vertisols supporting Nagpur mandarin or lemon trees (Robles et al., 2016). Unlike surface DI, where lateral pipes are placed above ground, SDI involves burying these pipes below the soil surface, thereby reducing canopy humidity and weed prevalence (Singh and Rajput, 2007; Camp and Lamm, 2003; Cahn and Hutmacher, 2024).

In India, knowledge about SDI—especially in mandarin cultivation—remains limited. Given its potential to enhance, WP, and NUE, SDI is increasingly being adopted in row-crop agriculture (Gao et al., 2014; Ardenti et al., 2022), offering strong potential for value-added application in citriculture. Previous research has indicated that SDI can save 60%–70% of water and increase fruit yield by 40%–50% compared with surface DI (Meshram et al., 2019). Considering the persistent challenge of water scarcity, coupled with the growing need for efficient irrigation practices in citrus orchards—particularly for Nagpur mandarin—this study was conducted to compare the efficacy of DI and SDI, focusing on yield attributes, water productivity, and nutritional responses of index leaves and fruit. The outcomes of such a study would be useful in providing some useful insights for sustainable citrus farming in central India’s hot, subhumid tropical climate.

2 Materials and methods

2.1 Description of experimental site

The field experiment was carried out during 2021–2023 on 12-year-old bearing Nagpur mandarin (Citrus reticulata Blanco) budded onto Rough lemon (Citrus jambhiri Lush.) rootstock 6.0 × 6.0 m at ICAR–Central Citrus Research Institute (ICAR-CCRI) in Nagpur (21⁰ 08’’ N latitude, 79⁰ 01’’ E longitude at an altitude of 349 m MSL), India. The soil was taxonomically classified as alkaline and calcareous Typic Haplustert (soil pH 7.73, EC 0.23 dS m^-1, sand 22%, silt 25.9%, clay 52.2%, field capacity 35.50%, permanent wilting point 16.34%, available water content 15.66%, organic carbon 0.55%, bulk density 1.35 g cm^-3, hydraulic conductivity 2.03 cm h^-1, alkaline KMnO₄–N 115.63 mg kg^-1, Olsen P 11.25 mg kg^-1, NH₄OAc-K 274.25 mg kg^-1, DTPA Fe 14.17 mg kg^-1, DTPA Mn 10.92 mg kg^-1, DTPA-Zn 0.62 mg kg^-1, and DTPA-Cu 2.58 mg kg^-1) showing the occurrence of multiple nutrient constraints in the form of N, P, Fe, Mn, and Zn.

The experiment was set up through six treatments viz., T₁ (SDI with double inline laterals placed at 100 cm from tree trunk at 30 cm soil depth with 30 cm dripper spacing), T₂ (SDI with double inline laterals placed at 100 cm from tree trunk at 30 cm soil depth with 40 cm dripper spacing and 30 drippers tree^-1 area); T₃ (SDI with double inline laterals placed at 100 cm from tree trunk at 30 cm soil depth with 50 cm dripper spacing and 26 drippers tree^-1 area); T₄ (SDI with double inline laterals placed at 100 cm from tree trunk at 30 cm soil depth with 60 cm dripper spacing and 20 drippers tree^-1 area); T₅ (DI with double online laterals placed 100 cm from tree trunk with 6 drippers tree^-1 area, as control, a common farmers’ practice); and T₆ (DI with ring-type online laterals placed at 100 cm from tree trunk with 12 drippers tree^-1 area). The experiment was carried out in a randomized block design consisting of 6 treatments with 4 trees for each treatment and a total of 24 experimental units replicated four times (location of experimental site and micro-irrigation layout map shown schematically through Figure 1). All the experimental trees were maintained under uniform cultural and management practices. The physical, hydraulic, and chemical characteristics of the soil profile (Supplementary Tables), along with mean monthly meteorological parameters during the study period at the experimental site (ICAR-CCRI, Nagpur, Maharashtra, India) (Supplementary Figure 1), are presented in the Supplementary Files.

Figure 1

Figure 1. Location map and block diagram of SDI and DI irrigation setup at the experimental site.

Growing citrus in central India is characterized by two flowering seasons, January-February bloom with flowering taking place in winter and June-July bloom with flowering taking place with initial showers of monsoon rains, the former is more dependent on water availability than latter (Srivastava et al., 1998; Huchche et al., 1999; Wilkie et al., 2008). In our experiment, we experimented with January-February bloom, induced by withholding irrigation water for 40–50 days during November–December followed by resumption of irrigation at water holding capacity of soil for flowering to take place. The frequency of fertigation was administered on 2^nd and 16^th day of each month starting from February to June and September to October (August–September as peak rainy days). The recommended doses of nutrients (600 N: 400 P₂O₅: 200 K₂O: 50 borax: 100 FeS0₄: 100 MnS0₄: 100 ZnS0₄, g plant^-1 year^-1) (Srivastava, 2013) as recommended doses of fertilizers using conventional fertilizers (N as urea, P as phosphoric acid, and K as muriate of potash). These fertilizers were applied at critical phenological growth stages viz., initiation of leaf flush (250 N:100 P₂0₅:50 K₂0: 25 FeS0₄: 25 MnS0₄: 25 ZnS0₄: 25 Borax, g plant^-1), fruit development period (250 N: 200 P₂0₅: 100 K₂0: 50 FeS0₄: 50 MnS0₄: 50 ZnS0₄: 25 borax, g plant^-1) and 10-days before fruit maturity (100 N: 100 P₂0₅: 50 K₂0: 25 FeS0₄: 25 MnS0₄: 25 ZnS0₄, g plant^-1) through fertigation.

2.2 Irrigation setup

The drip irrigation system and fertigation unit were installed in the experimental block, which accommodated 96 plants, with four plants per replication per treatment. In-line and on-line laterals with 2L and 4 L h^-1 discharge rate were equipped with SDI and DI drip lines, respectively. The sub-surface drip lines were installed at 30 cm depth, as recommended by Leskovar et al. (2001). In-line (2 L h^-1) and online (4 L h^-1) pressure-compensating drippers were positioned 100 cm away from the tree trunk (Panigrahi et al., 2008). The daily irrigation scheduling for Nagpur mandarin plants was based on crop evapotranspiration (ET_NM) of dry days, while fertigation was applied using a liquid fertilizer injector based on venturi principle.

The water quantity for SDI and DI was determined based on reference crop evapotranspiration (ET_r). The crop coefficient (K_c), ranging from 0.30 to 1.10, was estimated at various phenological stages through the shaded area approach at solar noon hour. ET of crop was calculated using the Penman–Monteith method, varying between 2.56 to 10.47 mm day^-1. The daily water requirement (ET_NM) and irrigation time for mandarin plants were calculated (Meshram et al., 2019) using the following formulas.

$${\mathrm{ET}}_{\mathrm{NM}}={\mathrm{ET}}_r\text{x}{K}_c\text{x}\mathrm{WA}\text{x}A/\mathrm{IE}$$

where, ET_NM is water requirement (L day^-1 tree^-1); ET_r is reference crop evapotranspiration (mm); K_c is crop coefficient, fraction; WA is wetted area, fraction; A is area occupied by each tree (m²); and IE is irrigation efficiency of the micro-irrigation system, fraction.

$$\text{IT }=\text{ E}{\text{T}}_{\text{NM}}/\text{DC}$$

where, IT is irrigation time (hr); ET_NM is water requirement (L day^-1 tree^-1); and DC is dripper discharge capacity (L h^-1).

2.3 Estimation of ET_r and K_c

ET_r, as the key factor influencing evapotranspiration, represents the water transpired by vegetation during growth, retain in the plant tissue, and the reduction in water evaporated from the soil and vegetation surface. ET_r was calculated using the Penman–Monteith method with daily, weekly, and monthly datasets. ET_r was calculated based on daily climatic data from January 2021 to December 2023 as per the procedure suggested by Allen et al. (1998). The following equation estimates daily ET_r values:

${\text{ET}}_{\text{r}}=\frac{0.408\Delta ({\text{R}}_{\text{n }}-\text{G})+\text{γ}(\frac{900}{\text{T}+273}){\text{u}}_{2({\text{e}}_{\text{s}}-{\text{e}}_{\text{a}})}}{\Delta +\text{γ}(1+0.34{\text{u}}_2)}$

where, ET_r refers to reference ETc (mm day^-1); G is soil heat flux density (MJ m^-2 day^-1); R_n is net radiation (MJ m^-2 day^-1); T is mean daily air temperature (⁰C); γ is psychometric constant (kPa ⁰C^-1); Δ is slope of saturation vapor pressure function (kPa ⁰C^-1); e_s is saturation vapor pressure at air temperature T (kPa); e_a is actual vapor pressure at dew point température (kPa); and u is average daily wind speed at 2 m height (m sec^-1).

K_c values for various crop phenophases (initiation of new leaf start, development, maturity, and harvesting) were determined using shaded area techniques. The following equation, designed for deciduous fruit crops (Ayars et al., 2003; Gorantiwar et al., 2011), was employed:

${\text{ K}}_{\text{c}}=0.014\text{x}+0.08$

where, K_c represents the crop coefficient and x represents the percentage of shaded area (%). Water demand for Nagpur mandarin was estimated using K_c, wetted area (WA), and 90% irrigation efficiency (IE) (Kisekka et al., 2010; Gorantiwar et al., 2011; Meshram et al., 2019).

2.4 Estimation of wetted area (WA), WP, SWC, and θv

The wetted area factor is the proportion of effective root zone. Canopy area was measured weekly, and wetted area was computed using the following equation: Wetted area (WA)=SA/A, where, SA represents the shaded area (m²) and A as the area occupied by each tree (m²) according to Gorantiwar et al. (2011). The yield and daily water requirement were determined from the observed data; while WP was computed using formula: WP= Yield (kg)/WU (m³) The WP was calculated under different treatments as the ratio of yield in kg ha^-1 to water used in m³ ha^-1, and expressed in kg m^-3. The root zone of Nagpur mandarin trees typically extends up to 120 cm, making this depth of soil profile suitable for studying soil moisture dynamics (Panigrahi and Srivastava, 2016). To assess soil water suction, four tensiometers were installed as per treatment depths viz., 15 cm, 30 cm, 60 cm, and 100 cm, allowing for a detailed evaluation of soil water availability throughout the active root zone. Simultaneously, SWC was monitored daily in the morning at the same depths using a neutron moisture meter (Troxler Model 4300, USA) before initiating irrigation. It was measured directly using the neutron moisture meter, which provided rapid and depth-specific readings by detecting hydrogen atoms in soil water. The volumetric water content (θv) was calculated based on the formula: θv = soil water content × bulk density of soil Monitoring θv enables precise irrigation by identifying moisture depletion zones and aligning water application with crop demand.

2.5 Observations on growth-contributing parameters

Growth-contributing parameters such as stock girth diameter (stem diameter measured at 0.01 m above the bud union), canopy diameter (mean of canopy spread diameter measured in N-S and E-W direction), plant height (distance from the ground surface to the top of the plant crown), and stem height (distance from the ground surface to the base of the first branch) were measured using a metric tape. The canopy volume was calculated using the formula developed by Panigrahi (2023a): Vc = 0.5238HW², where V_c denotes canopy volume (m³); H is the difference between tree height and stem height (m), and W is the mean canopy width (m) measured in the north–south and east–west direction.

2.6 Leaf physiological parameters

The leaf physiological parameter relative leaf water content (RLWC) was calculated according to the procedure suggested by Turner (1988). Leaf water use efficiency (LWUE) was estimated as the ratio of the net photosynthesis rate to transpiration rate of leaves (Garcia Sanchez et al., 2007). The net photosynthesis rate (Pn), transpiration rate (Tr), and stomatal conductance (gs) were measured fortnightly on clear days using a CO₂ (Model 301PS, CID Bio-Science, USA) during the period from 9:00 a.m. to 5:00 p.m. at one-hour intervals.

2.7 Yield and fruits analysis data

Fruit weight (g) and number of fruits per tree were observed in each treatment, and mean yield per tree was calculated. Yield (t ha^-1) was calculated, and the total yield (t ha^-1) based on yield per tree was computed considering 277 trees ha^-1. The weekly fruit growth was recorded using a digital slide caliper. Ten random fruits per plant (4 plants treatment^-1) were tagged immediately after fruit set (mid-April). The fruit quality parameters such as fruit length (mm), fruit diameter (mm), fruit axis diameter (mm), rind thickness (mm), number of segments, juice content (%), TSS (⁰Brix), acidity (%), and TSS: acid ratio were recorded as per standard procedures described by Ranganna (2001). The yield increase percentage was calculated as per the method suggested by Abd El-Naby et al. (2019) using the equation:

$$\text{Yield increase }(\%)=(\text{Yield }(\text{Treatment})–\text{ Yield }(\text{Control}))/\text{Yield }(\text{Control})\times 100$$

The fruit size distribution observations, based on the percentage of graded fruits, were also recorded in all six treatments. Fruit samples from each treatment were used to measure the fruit diameters for this purpose using a Vernier caliper (0.01 mm accuracy). Grading of Nagpur mandarin fruits was done on the basis of fruit size grades (A, B, C), with Grade A for fruits with a diameter greater than 80 mm, Grade B for fruits with a diameter between 70 and 80 mm, and Grade C for fruits with a diameter less than 70 mm.

2.8 Determination of leaf and fruit nutrients composition

Three- to five-month-old Nagpur mandarin leaves (3^rd and 4^th leaf from the tip of non-fruiting branches) at a height of 1.5 m from the ground surface surrounding the plant canopy were collected in paper bags from different treatments at the end of October (Srivastava et al., 1998; Srivastava and Singh, 2009). The collected leaf samples were thoroughly washed (Bhargava, 2002; Huchche et al., 2001), initially dried under shade, followed by drying at 65°C in an oven to constant weight, and finally ground to obtain a homogeneous sample ready for acid digestion. Likewise, ten mature, fresh, and undamaged Nagpur mandarin fruits were randomly selected from each treatment, washed thoroughly with distilled water to remove dirt, air-dried, followed by drying at 65°C in an oven to constant weight, and finally ground to obtain a homogeneous sample ready for acid digestion. Both leaf and fruit samples were digested in a di-acid mixture of 2 parts HNO₃ and 1 part H₂SO₄ until clear digests were obtained. They were then analyzed for macronutrients (N, P, K, Ca, and Mg), expressed as percentages, and micronutrients (Fe, Mn, Cu, and Zn), expressed as ppm, following standard procedures (AOAC, 1995).

2.9 Statistical analysis

The data from the experimentation were analyzed statistically using analysis of variance (ANOVA). The F-test was employed to assess the significance of the treatments at the 5% significance level. Principal component analysis (PCA) and development of the regression equation for prediction of fruit yield were carried out using IBM-SPSS statistical software (version 27). The multivariate analysis, including correlation matrix and PCA, and multiple linear regression analysis correlating fruit yield to other plant-based parameters were also performed using the software. The correlation matrix (using PROC CORR) was prepared to determine the strength of the linear relationship among the variables, whereas regression analysis (using PROC REG) was conducted to establish the mathematical relationship between the dependent variable (yield) and the independent variable having maximum variability in the dataset. PCA was performed using the PROC PRINCOMP procedure to determine the variables with maximum variability. Data for different years were tested by comparing treatment means using the t-test.

3 Results and discussion

3.1 Climatic analogues vis-à-vis crop phenology

The agro-met observatory at ICAR–Central Citrus Research Institute, Nagpur, Maharashtra (India) was used to generate mean monthly meteorological parameters (averaged over three years). Cooler December temperatures with lower humidity (26.04°C max., 11.09°C min., and 61.21% RH) compared to November (27.88°C max., 15.13°C min., and 69.41% RH) aided in developing plant stress under no-irrigation scenarios. Rising temperature, humidity, and long sunshine hours during January–February favored the induction of new flush and, consequently, fruit set.

The fruit development phase experienced high temperatures, elevated evaporation rates, and low humidity, especially during April–May, when temperatures sometimes reached up to 47.8°C. Substantial June rainfall reduced temperatures, aiding in comparatively faster fruit development.

The initial growth phase (January–February) saw a rise in maximum temperatures (30.34°C–33.24°C) and low minimum temperatures (10.11°C–12.33°C), with high relative humidity (68.58%–70.85%) and extended sunshine hours (8.72–10.08 h). These conditions, along with minimal rainfall during January (15.10 mm), favored early vegetative growth, floral induction, and subsequent anthesis.

The fruit development phase coincided with April–May (peak temperatures of 47.8°C in May), a period known for high temperatures, high evaporation, and low humidity, which began to decline from June onwards due to substantial rainfall, aiding faster fruit development. July recorded the highest monthly rainfall (563 mm), followed by September (363 mm) and August (256 mm), collectively supporting fruit growth towards the mid-development stage.

This was followed by comparatively cooler temperatures and reduced evaporation during September–October (2.41–3.25 mm day^-1 evaporation; temperature range 19.12°C–30.78°C). The fruit maturity period featured moderate temperatures, humidity, and sunshine hours, leading into the color-break stage, when both temperature and humidity were lower (temperature range 10.11°C –42.41°C; humidity 40.25%–85.71%), allowing the final color change in the peel (Supplementary Tables).

3.2 ET_NM, K_c, and WA vis-à-vis crop phenology

ET_r is an important parameter in understanding the crop water requirement, computed from weather data (Pereira et al., 2015; Latief et al., 2017). The ET_r data (Figure 2) revealed that the highest ET_NM coincided with the pea-size fruitlet stage, increasing along with rising temperatures and longer daylight hours, which further enhanced evaporation and transpiration rates. The lowest ET_NM occurred towards the completion of the fruit development stage in September. Beyond this stage, the occurrence of cooler temperatures and shorter days aided in reducing water loss. The average ET_r values were 1,812.30 mm, with daily minimum and maximum values ranging from 2.56 to 10.47 mm, respectively. The K_c values and wetted area values for different growth stages of 12-year-old Nagpur mandarin trees varied in the ranges of 0.20–1.10 and 0.30–1.20, respectively. The K_c values showed four distinct phases, initially with values of 0.60 at the flowering–fruit set stage, reaching stable values between 1.10 and 1.20 during the fruit development stage, and then declining towards crop maturity and the color-break stage. These K_c values at various growth stages of Nagpur mandarin are comparable with previous studies (Petillo and Castel, 2007; Bhantana and Lazarovitch, 2010; Gorantiwar et al., 2011; Jamshidi et al., 2020) on other fruit crops such as pomegranate, peach, and Citrus. Phenophase-wise wetted area of Nagpur mandarin showed average values ranging from 0.20 to 0.70, 0.30 to 0.80, and 0.40 to 1.10 during the 12^th and 13th years of orchard age, respectively.

Figure 2

Figure 2. Reference crop evapotranspiration (ET_r) in mm, Crop coefficient (K_c), Wetted area (WA) in fraction, Water applied through SDI and DI in Lday^-1 during study period (Pooled data of two seasons). (*SP, Stress period; NLIP, New leaf initiation period; CDP, Crop development phase; CM, Crop maturation; CH, Crop harvesting).

3.3 Soil moisture variation under SDI versus DI

Daily water applied through SDI and DI treatments (Figure 2) showed an increasing trend from January–February (46 and 115 L day^-1) to May–June (86 and 216 L day^-1), indicating 60.00% less water with the former treatments. The water used by mandarin trees varied considerably compared with Valencia trees grown in Florida, USA, due to differences in age, climate, nature of citrus cultivars, type of irrigation system, and method of scheduling irrigation (Fares and Alva, 1999).

The total water applied through SDI and DI during the crop production cycle of Nagpur mandarin showed a contrasting variation from 13,260 to 32,670 L tree^-1 with the former using 60% less water. These observations indicated that SDI-irrigated trees used a higher quantity of stored soil moisture in the root zone than DI. The mean (θv) observed at different soil depths (15, 30, 60, and 100 cm) in SDI and DI (Supplementary File with Figures 1a–d) further supported these observations. The θv decreased progressively from the onset of stress until the end of the stress period (45 days of stress) in both years of the experiment. The fortnightly estimated soil water content (SWC) was highest r SDI, 1.72% to 14.40%, and accompanied θv values, ranging 2.24% to 16.91%, considering different soil depths (Turner, 1988). The maximum SWC was observed at a 30 cm soil depth, followed by 15 cm, indicating the most active root zone (effective root depth) of mandarin trees between 15 and 30 cm. Earlier findings reported a shallow active root zone of 15 cm for drip-irrigated Nagpur mandarin budded on Rangpur lime grown on Vertisol in central India (Autkar et al., 1988; Panigrahi et al., 2008) and 30 cm for sweet orange budded on Rangpur lime in red sandy clay loam soil in South India (Iyengar and Shivananda, 1990). This difference was due to variation in citrus cultivars and rootstocks used under different pedoclimatic conditions. The SWC value at 30 cm depth under SDI was significantly higher than under DI due to greater root activity up to 60 cm soil depth under water stress conditions with SDI treatments.

3.4 Response on canopy volume and fruit quality

The effects of SDI and DI on canopy volume varied among treatments (Table 1). A 29.19% higher canopy volume was observed with SDI over DI, with treatment T₃ consistently outperforming the others. Earlier studies (Abo-Taleb et al., 1998; Ibrahim and Abd El-Samad, 2009) also reported maximum canopy volume with various SDI treatments.

Table 1

Table 1. Effect of micro-irrigation treatments on growth characteristics and fruit quality parameters (Pooled data of two seasons).

Fruit quality, on the other hand, responded significantly to micro-irrigation treatments. Treatment T₃ exhibited the highest juice content, TSS, acidity, and TSS:acid ratio. These results indicated that T₃ was more effective in enhancing fruit quality compared with other treatments. Greater canopy light interception in T₃ likely improved photosynthetic activity, thereby aiding the development of superior fruit quality parameters. These findings emphasize the importance of selecting optimal micro-irrigation treatments to improve the quality and marketability of Nagpur mandarin fruits.

3.5 Response on fruit yield

Fruit yield was more influenced by SDI than by DI treatments (Table 2). The highest yield and yield increase (%) were obtained with treatment T₃ (56.23%), followed by T₄ (35.56%), and the lowest yield with T₁ (15.86%) and T₂ (29.16%) among the SDI treatments.

Table 2

Table 2. Effect of micro-irrigation treatments on yield attributes and water productivity of Nagpur mandarin (pooled data of two seasons).

Among the DI treatments, and considering T₅ as the control (farmers’ practice), SDI treatments registered 15.86–56.23% higher fruit yield. Compared with T₆ (a DI treatment), SDI treatments recorded 6.95–47.32% higher fruit yield. The significantly superior response of Nagpur mandarin to SDI treatments over DI treatments was attributable to reduced water losses, which translated into higher water- and nutrient-use efficiency.

Similarly, Ayars et al. (2003) reported a 30.0% saving in water over alternate-day irrigation using drip irrigation in a lysimetric study.

An improved yield was directly associated with the number of fruits tree^-1, with treatment T₃ recording the highest number (718.75 fruits tree^-1) among SDI treatments, compared to 600.62 fruits tree^-1 under DI (Table 2), showing the lesser effectiveness of the latter. Fruit size distribution is another indicator of improved fruit yield. A higher percentage of B-grade fruits was recorded with treatment T₃ (64%), statistically on par with treatments T₄ and T₂, compared to the rest of the treatments. A similar pattern was observed with respect to A-grade fruit size, with T3 as the best treatment followed by T4 and T₂. Hence, implementing SDI could be a highly remunerative strategy to increase the proportion of A-grade fruits over B- or C-grade fruits, alongside fruit size, thereby enhancing marketability (Figure 3).

Figure 3

Figure 3. Percentage distribution of fruit size in response to among various treatments (Pooled means of two harvesting seasons).

3.6 Improvements in WP and NUE

Use efficiency of applied nutrients and water provided strong clues towards the magnitude of crop response. WU was observed to be quite consistent in treatments such as T₁ and T₄ (13.26 m³). However, T₅ and T₆ showed significantly higher WU (32.67 m³), indicating a 60% water saving with SDI over DI treatments on account of restricted deep percolation losses below the root zone (Table 2). WP, measuring the efficiency of water use in producing higher fruit yield, was highest with treatment T₃ (73.99%) compared to T₁ (69.94%), T₂ (65.58%), or T₄ (70.06%) among SDI treatments, and to T₆ as a DI treatment (8.22%), considering T₅ as the control treatment. This highlighted the greater effectiveness of SDI over DI. Water stress at any growth stage adversely affects fruit yield; the role of SDI in this regard was not only to ensure uniform delivery of water but also to maintain uniform water distribution within the effective root zone, thereby keeping the plants in a water-friendly mode—an agro-pedological condition necessary for elevated WP. The WP values in our study were lower than some previously reported under more humid tropical environments (Howell, 2001). These differences could be explained by the fact that our study was conducted in a more semi-arid environment.

3.7 Changes in leaf nutrients composition

Treatments expressed a differential response in leaf nutrient composition, except for leaf P content (Figure 4). The highest concentrations of leaf nutrients were recorded with treatment T₃, followed by T_4. However, in all treatments, regardless of SDI or DI, the leaf macro- and micronutrient contents were higher than the suggested optimum concentrations for Nagpur mandarin. Treatment T₃ was the most effective, with the highest leaf nutrient concentrations. In contrast, treatments T₅ and T₆ exhibited the lowest nitrogen and potassium contents, indicating their reduced efficacy compared to other treatments. Phosphorus levels in the leaves remained unaffected by the different treatments. Despite the lack of significant differences, T₃ still demonstrated the highest phosphorus content, implying slightly better phosphorus uptake.

Figure 4

Figure 4. Response of micro-irrigation treatments on leaf macro-nutrients (N, P, K, Ca, and Mg) and micro-nutrients (Fe, Mn, Cu, and Zn).

Micronutrients play a vital role in photosynthesis and nutrient transport, thereby increasing yield and nutrient content. Treatment T₃ had the highest Fe, Mn, Cu, and Zn contents, making it the most effective in enhancing micronutrient accumulation. Treatments T₅ and T₆ exhibited progressively lower concentrations of different micronutrients. These observations underscore the importance of selecting an optimal micro-irrigation system to enhance nutrient accumulation and improve both fruit yield and quality (Shirgure et al., 2001; Srivastava, 2013; Kuchanwar et al., 2017).

The correlation between fruit yield and other plant-based variables (CV, canopy volume; nutrient content in leaves and fruits [N, P, K, Ca, Mg, Fe, Mn, Cu, and Zn]; and physiological parameters [RLWC, Pn, gs, Tr, LWUE]) (Figure 5) showed notable patterns. Fruit yield was positively correlated with leaf nutrients such as N (r = 0.94**), K (r = 0.88**), Ca (r = 0.91**), Fe (r = 0.93**), Mn (r = 0.78**), Cu (r = 0.79**), and Zn (r = 0.91**), indicating that higher leaf nutrient content contributed significantly to yield. Similarly, yield was positively correlated with fruit nutrients such as N (r = 0.89**), P (r = 0.87**), K (r = 0.79**), Ca (r = 0.90**), Mg (r = 0.81**), Fe (r = 0.74**), and Zn (r = 0.79*), highlighting the importance of nutrient-rich fruits for better yield outcomes. The physiological parameters, including CV (r = 0.57), RLWC (r = 0.83*), Pn (r = 0.92**), gs (r = 0.91**), Tr (r = 0.95**), and LWUE (r = 0.93**), also showed positive correlations with yield.

Figure 5

Figure 5. Correlation matrix (Pearson’s) for plant-based observations (leaf nutrients, fruit nutrients, and physiological parameters) and fruit yield using the pooled data of two seasons. Data followed by “*” indicate a significant correlation at p < 5, and data followed by “+” indicate a significant correlation at P<1.

The strong relationship of fruit yield with Tr and gs was earlier reported by Dzikiti et al. (2010) in citrus under micro-irrigation treatments. Principal component analysis (PCA) using 23 variables indicated that the first three PCs explained 87% of the variability under micro-irrigation (Figure 6). Therefore, the variables involved in these three PCs were considered for further analysis. Variables from PC1 (Leaf-N, Leaf-P, Leaf-K, Leaf-Fe, Leaf-Mn, Leaf-Cu, Leaf-Zn, RLWC, LWUE, Pn, gs, and Tr), PC2 (Fruit-N, Fruit-P, Fruit-K, Fruit-Ca, Fruit-Na, Fruit-Fe, Fruit-Mn, Fruit-Cu, Fruit-Zn, RLWC, LWUE, Pn, gs, and Tr), and PC3 (CV, RLWC, LWUE, Pn, gs, and Tr) were retained for interpretation, considering their eigenvalues > 1.

Figure 6

Figure 6. Principal components (PCs) for plant-based variables (leaf nutrients, fruit nutrients, and physiological parameters) with Eigen values in response to different treatments in EFGP.

However, due to the strong correlations among gs, Tr, and Leaf-K; between Pn and CV (r = 0.87–0.89**); and between Leaf-N and SD (Figure 5), CV, RLWC, LWUE, Pn, gs, and Tr were not considered for the PCRAA multiple regression model. A regression model (Figure 7) developed between yield and other selected plant variables (Leaf-N, Leaf-P, Leaf-K, Leaf-Fe, Leaf-Mn, Leaf-Cu, and Leaf-Zn, RLWC, LWUE, Pn, gs, and Tr) is proposed as:

Figure 7

Figure 7. Predicated fruit yield versus observed fruit yield of Nagpur mandarin under various micro-irrigation treatments.

$$\begin{array}{c}\text{Fruit yield}=-49.99+23.46*(\text{Leaf}-\text{N})-28.35*(\text{Leaf}-\text{K})\\ +0.14*(\text{Leaf}-\text{F}e)+0.13*(\text{Leaf}-\text{M}n)\\ +0.28*(\text{LeafCu})+0.65*(\text{Leaf}-\text{Z}n)-0.006*\text{RLWC}\\ -0.15*\text{LWUE}+1.29*Pn-0.0024*gs+11.05\text{Tr};\\ (p<0.05;{\text{R}}^2=0.956;\text{RMSE}0.9825\%)\end{array}$$

3.8 Fruit nutrients composition

The concentration of different nutrients in fruits is considered crucial for both plant health and nutritional quality. The analysis of fruit nutrient composition showed that all nutrients except P, Mn, and Cu were significantly affected by different treatments involving SDI and DI (Figure 8). The highest concentration of N was observed with treatment T₃, followed by T₄, T₂, T_1, T₆, and T5 in decreasing order. Treatment T₃ also showed the highest K concentration, followed by T₅ and T₆. Ca levels were relatively low across all treatments. Like macronutrients, noticeable differences in the concentration of micronutrients in response to irrigation treatments were observed. The concentration of Fe and Mn in fruits was maximum with T₃ compared to the rest of the treatments. On the other hand, Zn concentration was highest with treatments T₁ and T_2. SDI treatments maintained optimum soil moisture content, thereby increasing root activity and nutrient uptake, which ultimately increased the leaf nutrient content and improved yield and quality. Optimizing irrigation practices, therefore, played a pivotal role.

Figure 8

Figure 8. Comparative response of SDI versus DI treatments on fruit nutrients composition (Pooled mean of two harvesting seasons).

4 Conclusion

Irrigation water is a precious commodity in citriculture that demands innovative research. Our studies in this direction showed that SDI outperformed DI in terms of quality production of Nagpur mandarin, coupled with improvement in soil fertility and leaf nutrient composition, with an edge in nutritional quality of fruits. SDI ensured higher WP and NUE, suggesting far better utilization efficiency of important inputs like water and nutrients. These studies are expected to provide better insights by scheduling nutrients such as N, K, and Zn during the fruit developmental growth stage, coupled with their partitioning and mobilization/remobilization, to develop a demand-driven fertilization strategy.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.

Author contributions

DM: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Validation, Visualization, Writing – original draft, Writing – review & editing. AS: Data curation, Funding acquisition, Methodology, Project administration, Resources, Validation, Visualization, Writing – original draft, Writing – review & editing. AT: Data curation, Supervision, Writing – review & editing. NM: Investigation, Supervision, Writing – review & editing. PJ: Resources, Software, Visualization, Writing – review & editing. AU: Data curation, Formal Analysis, Resources, Validation, Visualization, Writing – review & editing. CP: Data curation, Formal Analysis, Investigation, Resources, Validation, Visualization, Writing – review & editing.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This work was supported by the Indian Council of Agriculture Research, New Delhi through Central Citrus Research Institute, Nagpur.

Acknowledgments

The authors express their appreciation to ICAR-Central Citrus Research Institute for full support of the project.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that no Generative AI was used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fhort.2025.1552283/full#supplementary-material

References

Abd El-Naby S. K. M., Ahmed Mohamed A. A., and El-Naggar Y. I. M. (2019). Effect of melatonin, GA₃ and NAA on vegetative growth, yield and quality ofo ‘canino’ apricot fruits. Acta Sci. Pol. Hortorum Cultus 18, 167–174. doi: 10.24326/asphc.2019.3.16

Crossref Full Text | Google Scholar

Abo-Taleb A. S., Noaman F. V., and Sari El-Deen S. (1998). Growth of pomegranate transplants as affected by different regimes. Ann. Agric. Sci. 36, 1073–1091.

Google Scholar

Allen R. G., Pereira L. S., Raes D., and Smith M. (1998). Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56 Vol. 300 (Rome, Italy: FAO).

Google Scholar

Ardenti F., Abalos D., Capra F., Lommi M., Maris S. C., Perego A., et al. (2022). Matching crop row and dripline distance in subsurface drip irrigation increases yield and mitigates N₂O emissions. Field Crops Res. 289, 108732. doi: 10.1016/j.fcr.2022.108732

Crossref Full Text | Google Scholar

Association of Official Analytical Chemists (AOAC) (1995). Official method of analysis, 16th Ed. Ed. Williams S. (Arlington, VA: AOAC International).

Google Scholar

Autkar V. N., Kolte S. O., and Bagade T. R. (1988). Distribution of active rooting zones in Nagpur mandarin and estimates of water requirement for Vertisols of Maharashtra. Ann. Pl. Physiol. 2, 219–222.

Google Scholar

Ayars J. E., Fulton A., and Taylor B. (2015). Subsurface drip irrigation in California: Here to Stay? Agric. Water Manage. 157, 39–47. doi: 10.1016/j.agwat.2015.01.001

Crossref Full Text | Google Scholar

Ayars J. E., Johnsons R. S., Phene C. J., Trout T. J., Clark D. A., and Mead R. M. (2003). Water use by drip irrigated late season Peaches. Irri. Sci. 22, 187–194. doi: 10.1007/s00271-003-0084-4

Crossref Full Text | Google Scholar

Bhantana P. and Lazarovitch N. (2010). Evapotranspiration, crop coefficient and growth of two young pomegranate varieties under salt stress. Agric. Water Manage. 97, 715–722. doi: 10.1016/j.agwat.2009.12.016

Crossref Full Text | Google Scholar

Bhargava B. S. (2002). Leaf analysis for nutrient diagnosis, recommendation and management in fruits crops. J. Indian Soc Soil Sci. 50, 352–373.

Google Scholar

Bonachela S., Orgaz F. O., Villalobos F. J., and Fereres E. (2001). Soil evaporation from drip irrigated olive orchards. Irri. Sci. 20, 65–71. doi: 10.1007/s002710000030

Crossref Full Text | Google Scholar

Cahn M. and Hutmacher R. (2024). Subsurface drip irrigation. In Micro-irrigation for Crop Production (Amsterdam: Elsevier Science), 257–301.

Google Scholar

Camp C. R. (1998). Subsurface drip irrigation: A review. Tran. ASAE. 41, 1353. doi: 10.13031/2013.17309

Crossref Full Text | Google Scholar

Camp C. R. and Lamm F. R. (2003). Irrigation systems: Subsurface drip. In: Encyclopaedia of Water Science (New York, N.Y: Marcel Dekker), 560–564.

Google Scholar

Consoli S., Stagno, Roccuzzo F., Cirelli G., and Intrigliolo L. (2014). Sustainable management of limited water resources in a young orange orchard. Agricul. Water Manage. 132, 60–68. doi: 10.1016/j.agwat.2013.10.006

Crossref Full Text | Google Scholar

Dzikiti S., Verreynne J. S., Stuckens J., Strever A., Verstraeten W. W., Swennen R., et al. (2010). Determining the water status of Satsuma mandarin trees (Citrus unshiu Marcovitch) using spectral indices and by combining hyperspectral and physiological data. Agri. Forest Meteorol. 150, 369–379. doi: 10.1016/j.agrformet.2009.11.011

Crossref Full Text | Google Scholar

Fares A. and Alva A. K. (1999). Estimation of citrus evapotranspiration by soil water mass. Soil Sci. 164, 302–310. doi: 10.1097/00010694-199905000-00002

Crossref Full Text | Google Scholar

Franco L., Giardina G., Tuker J., Motisi A., and Provenzano G. (2019). Subsurface drip irrigation and ICT for the innovative irrigation water management: application to a citrus crop (C. reticulataTardivo di Ciaculli). IX Int. Sympo. Irri. Horti. Crops 1335, 453–460.

Google Scholar

Gao Y., Yang L., Shen X., Li X., Sun J., Duan A., et al. (2014). Winter wheat with subsurface drip irrigation (SDI): Crop coefficients, water-use estimates, and effects of SDI on grain yield and water use efficiency. Agric. Water Manage. 146, 1–10. doi: 10.1016/j.agwat.2014.07.010

Crossref Full Text | Google Scholar

Garcia Sanchez F., Syvertsen J. P., Gimeno V., Botia P., and Perez-Perez J. G. (2007). Response to flooding and drought stress by two citrus root stock seedlings with different water use efficiency. Physiol. Plant 130, 532–542. doi: 10.1111/j.1399-3054.2007.00925.x

Crossref Full Text | Google Scholar

Gorantiwar S. D., Meshram D. T., and Mittal H. K. (2011). Water requirement of pomegranate (Punicagranatum L.) for Ahmednagar district of Maharashtra state. Indian J. Agromet. 13, 123–127.

Google Scholar

Howell T. A. (2001). Enhancing water use efficiency in irrigated agriculture. Agron. J. 93, 281–289. doi: 10.2134/agronj2001.932281x

Crossref Full Text | Google Scholar

Huchche A. D., Srivastava A. K., Ram L., and Singh S. (1999). “Nagpur mandarin orchard efficiency in central India. Hi-tech citrus management,” in Proceedings of International Symposium on Citriculture. (Nagpur, Maharashtra, India: National Research Centre for Citrus), 24–28.

Google Scholar

Huchche A. D., Srivastava A. K., Ram L., and Singh. S. (2001). Statistical basis of production sustainability of citrus orchards. Trop. Agric. 90, 4.

Google Scholar

Ibrahim A. M. and Abd El-Samad G. A. (2009). Effect of different irrigation regimes and partial substitution of N-mineral by organic manures on water use, growth and productivity of pomegranate trees. Eur. J. Sci. Res. 38, 199–218.

Google Scholar

Iyengar B. R. and Shivananda T. N. (1990). Root-activity in sweet orange (Citrus sinensis) during different seasons. Indian J. Agric. Sci. 60, 605–608.

Google Scholar

Jamshidi S., Zand-Parsa S., Kamgar-Haghighi A. A., Shahsavar A. R., and Niyogi D. (2020). Evapotranspiration, crop coefficients, and physiological responses of citrus trees in semi-arid climatic conditions. Agric. WaterManage. 227, 105838. doi: 10.1016/j.agwat.2019.105838

Crossref Full Text | Google Scholar

Kadam D. (2020). Effect of pan evapo-replenishment based micro irrigation and fertigation on growth, yield and water use efficiency of Nagpur Mandarin in Central India. Life Sci. Leaflets. 127, 22–37.

Google Scholar

Kisekka I., Kati W. M., Mickael D. D., and Jonthan H. C. (2010). Evapotranspiration based irrigation for agriculture: crop coefficient of some commercial crops in Florida (Florida, USA: IFAS Extension University of Florida).

Google Scholar

Kuchanwar O. D., Chopde N., and Deshmukh S. (2017). Soil fertility and nutritional factors contributing towards citrus decline in central India. Ann. Pl. Soil Res. 19, 319–323.

Google Scholar

Ladaniya M. S., Marathe R. A., Murkute A. A., Huchche A. D., Das A. K., Goerge A., and Kolwadkar. J. (2021). Response of Nagpur mandarin (Citrus reticulata Blanco) to high density planting systems. Sci. Rep. 11, 10845. doi: 10.1038/s41598-021-89221-4, PMID: 34035326

PubMed Abstract | Crossref Full Text | Google Scholar

Latief A., Parvaze S., Parvaze S., and Habib Kanth R. (2017). Reference croevapotranspiration and crop water requirement of Apple (Malus Pumila) in Kashmir Valley. J. Agromet. 19, 262–264. doi: 10.54386/jam.v19i3.668

Crossref Full Text | Google Scholar

Leskovar D. I., Ward J. C., Sprague R. W., and Meiri A. (2001). Yield, quality, and water use efficiency of muskmelon are affected by irrigation and transplanting versus direct seeding. Hortic. Sci. 36, 286–291. doi: 10.21273/HORTSCI.36.2.286

Crossref Full Text | Google Scholar

Martinez J. and Reca J. (2014). Water use efficiency of surface drip irrigation versus an alternative subsurface drip irrigation method. J. Irri. Drain. Engg. 140, 04014030. doi: 10.1061/(ASCE)IR.1943-4774.0000745

Crossref Full Text | Google Scholar

Martínez-Gimeno M. A., Bonet L., Provenzano G., Badal E., Intrigliolo D. S., and Ballester C. (2018). Assessment of yield and water productivity of clementine trees under surface and subsurface drip irrigation. Agric. Water Manage. 206, 209–216. doi: 10.1016/j.agwat.2018.05.011

Crossref Full Text | Google Scholar

Meshram D. T., Gorantiwar S. D., Singh N. V., and Babu K. D. (2019). Response of micro-Irrigation Systems on growth, yield and WUE of Pomegranate (Punica granatum L.) in Semi-Arid Regions of India. Sci. Hort. 246, 686–692. doi: 10.1016/j.scienta.2018.11.033

Crossref Full Text | Google Scholar

Muhammad T., Zhou B., Puig-Bargu J., Ding C., Li S., Manan I., et al. (2022). Assessment of emitter clogging with multiple fouling and root intrusion in sub-surface drip irrigation during 5-year sugarcane growth. Agric. Water Manage. 274, 107981. doi: 10.1016/j.agwat.2022.107981

Crossref Full Text | Google Scholar

Panigrahi P. (2023a). Scheduling irrigation for improving water productivity and fruit quality in citrus (cv. Nagpur mandarin) under a dry tropical climate. Irri. Sci. 41, 511–520. doi: 10.1007/s00271-023-00847-w

Crossref Full Text | Google Scholar

Panigrahi P. (2023b). Impact of deficit irrigation on citrus production under a sub-humid climate: a case study. Water Supply 23, 1177–1188. doi: 10.2166/ws.2023.074

Crossref Full Text | Google Scholar

Panigrahi P., Sharma R. K., Hasan M., and Parihar S. S. (2014). Deficit irrigation scheduling and yield prediction of ‘Kinnow’mandarin (Citrus reticulate Blanco) in a semiarid region. Agric. Water Manage 140, 48–60. doi: 10.1016/j.agwat.2014.03.018

Crossref Full Text | Google Scholar

Panigrahi P., Shirgure P. S., Srivastava A. K., and Singh S. (2008). “Water management in citrus,” in Handbook of Citrus. Eds. Shyam S., Srivastava A. K., and Rao C. N. (NRC for Citrus, Nagpur), 16–17.

Google Scholar

Panigrahi P. and Srivastava A. K. (2016). Effective management of irrigation water in citrus orchards under a water scarce hot sub-humid region. Sci. Hortic. 210, 6–13. doi: 10.1016/j.scienta.2016.07.008

Crossref Full Text | Google Scholar

Panigrahi P. and Srivastava A. K. (2017). Water and nutrient management effects on water use and yield of drip irrigated citrus in vertisol under a sub-humid region. J. Integr. Agric. 16, 1184–1194. doi: 10.1016/S2095-3119(16)61500-9

Crossref Full Text | Google Scholar

Panigrahi P., Srivastava A. K., and Huchche A. D. (2012a). Effects of drip irrigation regimes and basin irrigation on Nagpur mandarin agronomical and physiological performance. Agric. Water Manage. 104, 79–88. doi: 10.1016/j.agwat.2011.11.018

Crossref Full Text | Google Scholar

Panigrahi P., Srivastava A. K., Huchche A. D., and Singh S. (2012b). Plant nutrition in response to drip versus basin irrigation in young ‘Nagpur’mandarin on inceptisol. J. Pl. Nutri. 35, 215–224. doi: 10.1080/01904167.2012.636124

Crossref Full Text | Google Scholar

Pereira L. S., Allen R. G., Smith M., and Raes D. (2015). Crop evapotranspiration estimation with FAO56: Past and future. Agric. Water Manage. 147, 4–20. doi: 10.1016/j.agwat.2014.07.031

Crossref Full Text | Google Scholar

Petillo M. G. and Castel J. R. (2007). Water balance and crop coefficient estimation of citrus orchard in Uruguay. Spanish J. Agric. Res. 5, 232–243. doi: 10.5424/sjar/2007052-243

Crossref Full Text | Google Scholar

Phene C. J. (1995). “The sustainability and potential of subsurface drip irrigation,” in Proceedings of the Fifth International Micro Irrigation Congress. (Orlando, FL, USA), 359–336.

Google Scholar

Pimpale A. R., Kharche V. K., Panchbhai D. M., and Wadatkar S. B. (2022). Performance of Nagpur mandarin (Citrus reticulata Blanco) under different levels of fertigation and soil applications. J. Soil Water Conser. 21, 101–106. doi: 10.5958/2455-7145.2022.00012.1

Crossref Full Text | Google Scholar

Provenzano G., Rodriguez-Sinobas L., and Roldán-Cañas J. (2014). Irrigated agriculture: Water resources management for a sustainable environment. Biosyst. Engg. 128, 1–3. doi: 10.1016/j.biosystemseng.2014.10.008

Crossref Full Text | Google Scholar

Puig-Sirera A., Provenzano G., Goonzalez-Altozano P., Intrigliolo D. S., and Rallo G. (2021). Irrigation water saving strategies in Citrus orchards: Analysis of the combine effects of timing and severity of soil water deficit. Agric. Water Manage. 248, 106773. doi: 10.1016/jagwt.2021.106773

Crossref Full Text | Google Scholar

Ranganna S. (2001). Handbook of analysis and quality control for fruits and vegetable products (New Delhi: Tata Mcgraw Hill), 860–861.

Google Scholar

Robles J. M., Botía P., and Pérez-Pérez J. G. (2016). Subsurface drip irrigation affects trunk diameter fluctuations in lemon trees, in comparison with surface drip irrigation. Agric. Water Manage. 165, 11–21. doi: 10.1016/j.agwat.2015.11.008

Crossref Full Text | Google Scholar

Shirgure P. S. and Srivastava A. K. (2013). Optimizing the potassium (K) dose of fertigation for the Nagpur mandarin (Citrus reticulate Blanco). Agric. Adv. 2, 242–249.

Google Scholar

Shirgure P., Srivastava A. K., Huchche A. D., and Patil P. (2016). Interactive effect of irrigation schedules and fertigation levels on fruit yield, quality and plant nutrition of Nagpur. Indian J. Agric. Sci. 86, 1509–1514.

Google Scholar

Shirgure P. S., Srivastava A. K., and Singh S. (2001). Effect of pan evaporation based irrigation scheduling on yield and quality of drip irrigated Nagpur mandarin (Citrus reticulata). Indian J. Agric. Sci. 71, 264–266.

Google Scholar

Shirgure P. S., Srivastava A. K., Singh S., and Pimpale A. R. (2004). Drip irrigation scheduling, growth, yield and quality of acid lime (Citrus aurantifolia). Indian J. @ Agricul. Sci. 74, 92–94.

Google Scholar

Singh D. K. and Rajput T. B. S. (2007). Response of lateral placement depths of subsurface drip irrigation on okra (Abelmoschusesculentus). Int.J. Pl. Prod. 1, 73–84.

Google Scholar

Sinobas L. R., Rodríguez M. G., and Lee T. S. (2012). A review of subsurface drip irrigation and its management. Water Quality Soil Mang. Irrig. Crops., 171–194. doi: 10.5772/30702

Crossref Full Text | Google Scholar

Sravani G. C., Singh L., and Bakshi M. (2020). Performance evaluation of drip irrigation (Fertigation) in citrus cultivation: a review. J. Pharmac. Phytoch. 9, 2557–2560. doi: 10.22271/phyto.2020.v9.i5aj.12726

Crossref Full Text | Google Scholar

Srivastava A. K. (2013). Nutrient management in Nagpur mandarin: frontierdevelopments. Agric. Adv. 2, 1–14.

Google Scholar

Srivastava A. K., Kohli R. R., and Huchche A. D. (1998). Relationship of leaf K and forms of soil K at different growth stages of Nagpur mandarin. J. Indian Soc Soil Sci. 46, 245–248.

Google Scholar

Srivastava A. K. and Singh S. (2009). Citrus decline: Soil fertility and plant nutrition. J. Pl. Nutri. 32, 197–145. doi: 10.1080/01904160802592706

Crossref Full Text | Google Scholar

Turner N. C. (1988). Measurement of plant water status by pressure chamber technique. Irri. Sci. 9, 289–308. doi: 10.1007/BF00296704

Crossref Full Text | Google Scholar

Wang Y., Liu N., Zhang J., Jin Y., Zhang J., and Wang Z. (2024). Effects of aeration on hydraulic and anti-clogging performance of subsurface drip irrigation emitters. Irrig. Sci. 45, 1–17. doi: 10.1007/s00271-024-00953-3

Crossref Full Text | Google Scholar

Wilkie J. D., Sedgley M., and Olsen T. (2008). Regulation of floral initiation in horticultural trees. J. Exp. Bot. 59, 3215–3228. doi: 10.1093/jxb/ern188, PMID: 18653697

PubMed Abstract | Crossref Full Text | Google Scholar

Yao C., Zhang L., Wu P., Liu Y., Cai Y., and Zhou W. (2021). Clogging formation and an anti-clogging method in subsurface irrigation system with porous ceramic emitter. Agric. Water Manage. 250, 106770. doi: 10.1016/j.agwat.2021.1016770

Crossref Full Text | Google Scholar

Zhang H., Wang D., Ayars J. E., and Phene C. J. (2017). Biophysical response of young pomegranate trees to surface and sub-surface drip irrigation and deficit irrigation. Irrig. Sci. 35, 425–435. doi: 10.1007/s00271-017-0551-y

Crossref Full Text | Google Scholar