Rationale behind the integrated use of irrigation and fertilization in optimizing citrus production on Indian Inceptisol

1 Introduction

Citrus (production of 159 × 10⁶ Mg from an area of 10 × 10⁶ ha with a productivity of 16 Mg ha⁻¹) is regarded as the world’s 210th most traded product with a total trade value of 16 × 10⁷ US dollars and a total growers value stretching over 1,090 × 10⁷ US dollars with fruit processing and fresh consumption valued at 986 × 10⁶ and 103 × 10⁶ US dollars, respectively (FAO, 2025). The future growth of the citrus industry will be marked not only by a simple increase in yield, but sustainability in production pattern via improved input use efficiency (IUE) is widely viewed as a major challenge. These challenges assume a much greater significance in the light of emerging issues like multiple nutrient deficiencies, imbalanced fertilization-induced leaching of nutrients, and erratic irrigation, all individually or collectively marking unwarranted inconsistency in production, and finally dictate the unit cost of citrus production (Srivastava and Shirgure, 2018; Stagno et al., 2024).

Previous studies (Qin et al., 2016) on water use efficiency (WUE) and nutrient use efficiency (NUE) using 1,009 yield observations from 55 studies in citrus showed median WUE ranging from 2.5 to 5.0 kg m⁻³, median NUE from 150 to 350 kg kg⁻¹, and median citrus yield of 30 to 60 Mg ha⁻¹, thereby leading to the recommendation of different irrigation and fertilization strategies (Panigrahi and Srivastava, 2017; Santini et al., 2025). These observations provided some clues to increase citrus production through the combined use of WUE and NUE (Stagno et al., 2024). While comparing the response of split application of nitrogen with drip irrigation versus single broadcast application under flood irrigation, Martinez-Alicantara et al. (2012) observed 12% higher NUE with former treatment in 8-year-old sweet orange (Citrus sinensis Osbeck cv. Navelina grafted on Carrizo citrange) on a Typic Xerofluvent soil, with up to 60% fertilizer reduction via drip fertigation without compromising fruit quality (Ma et al., 2022), although fruit quality becomes quite pivotal where fruits are sold as fresh fruits, especially in loose jacket mandarins unlike sweet oranges, most traded as juice. Nagpur mandarin (Citrus reticulata Blanco) grafted on rough lemon rootstock (Citrus jambhiri Lush) is one of the premium Indian citrus cultivars grown extensively on alkaline calcareous smectite-rich soils. These soils taxonomically typify Entisols, Inceptisols, and Vertisols depending oil depth and smectite clay distribution under predominantly hot subhumid tropical climate (Srivastava and Singh, 2008).

In majority of commercial citrus growing regions across the world, flood/basin irrigation is still a stand-alone option coupled with broadcast application of fertilizers (Quinones et al., 2007; Farag et al., 2025). Flood irrigation recorded lower (68.0%) total ¹⁵N recovery in the foliage compared to drip irrigation (71.0%), while whole tree N-use efficiency was significantly higher under high fertilization drip irrigation (75.1%) than under low fertilization flood irrigation (62.7%), according to studies carried out by Quinones et al. (2007). With the continuous flow of research, information about different fertigation strategies continues to take center stage in improving production, productivity, and fruit quality (Shirgure et al., 2016) with emphasis on WUE and NUE (Qin et al., 2016). Amid these developments, uniform doses of fertilizers and irrigation as blanket recommendations continue to be the most preferred choice (Shirgure, 2026). However, such practices are frequently associated with issues like excessive leaching of nutrients (Scholberg and Morgan, 2012), lower WUE–NUE, high production cost (Martinez-Alicantara et al., 2012), and disturbance in soil ecology-driven imbalance in soil fertility (Sorgonà and Abenavoli, 2012).

Of late, application of geo-spatial tools have helped in mapping soil fertility variation and delineating the MZs (Srivastava et al., 2010). This has further aided in fixing the fertilization and irrigation schedules (Naqvi et al., 2022) for rationalizing fertilizer-water use while maximizing productivity and fruit quality (Meshram et al., 2025). In this background, in order to develop a rationale for the integrated use of irrigation and fertilization, we aimed at (i) identifying soil fertility variation-based MZs using tree canopy metrics, (ii) studying the agronomic response of differential fertilization and irrigation doses, and (iii) computing the savings on nutrients and irrigation over corresponding blanket doses conducted over four fruiting seasons in Nagpur mandarin grown on black clay soil.

2 Materials and methods

2.1 Experimental setup

The field experiment was conducted during 2018–2022 at the Experimental Farm (21° 09′ 01″ N latitude and 79° 01′ 27″ E longitude, 346 m altitude) of ICAR-Central Citrus Research Centre, Nagpur, Maharashtra (India) practicing winter bloom (locally known as Ambia bahar). The experimental site was characterized by clay texture (sand 28.2%, silt 23.1%, and clay 48.7%), soil pH 7.4, EC 0.24 dS m⁻¹, and organic carbon 0.67. The experimental plot consisted of 144 Nagpur mandarin plants (4 years old) planted at 6.0 m apart either row-to-row or plant-to-plant (planting density of 277 plants ha⁻¹), representing hot subhumid tropical climate of central India. The soil was taxonomically defined as Typic Ustochrept, with smectite-rich clay mineralogy. All the 144 plants were considered as georeferenced points of soil fertility evaluation, varying in initial plant available nutrients (mg kg⁻¹): KMnO₄-N 109.3–134.6 (mean 123.6), Olen-P 7.1–11.8 (mean 10.2), NH₄OAc-K 118.4–176.3 (mean 167.3), DTPA-Fe 8.1–13.6 (mean 12.3), DTPA-Mn 6.8–10.8 (mean 9.6), DTPA-Cu 0.72–1.23 (mean 0.84), and DTPA-Zn 0.56–0.80 (mean 0.64).

The canopy-based variogram was developed using all spatial data by GIS Version 3.5 following the procedure as suggested by Mann et al. (2011) and compared the initial variogram with final variogram to arrive changes in production pattern across three MZs.

The treatments were formulated based on measuring the variation in tree canopy using GPS-QGIS Version 3.5 (Global Positioning System-Quantum Geographical Information System) combined tools for developing canopy-based variogram (1.30–9.64 m^3,with a CV of 22.3%–34.8%) as decision support (Mann et al., 2011). The median value (as stable metric to map spatial variability) of canopy volume was determined as 4.2 m³ to have normal distribution of canopy points and delineated three contrastingly different MZs as a manageable spatial grid, viz., high production management zone (HPMZ) (canopy volume more than 5 m³), medium production management zone (MPMZ) (canopy volume 3–5 m³), and low production management zone (LPMZ) (canopy volume less than 3 m³) (Figure 1). Based on spatial variation in canopy volume, three modules of fertilization were fixed as 100%(LPMZ, 600g N-200g P₂O₅-300g K₂O tree^-1 year^-1), 80% (MPMZ, 480g N-160g P₂O₅-240g K₂O tree^-1 year^-1), and 60% (HPMZ , 360g N-120g P₂O₅ -180g K₂O tree^-1 year^-1) of RDF. The RDF was previously worked out as 600g N-200g P₂O₅-300g K₂O tree^-1 year^-1 for 7-10-year-old mature Nagpur mandarin trees. Likewise, variable rates of irrigation were fixed at 100%, 90%, and 80% of irrigation requirement (IR) for LPMZ (81–102 L day⁻¹ tree⁻¹), MPMZ (73–92 L day⁻¹ tree⁻¹), and HPMZ (65–82 L day⁻¹ tree⁻¹), respectively, on an average. These three MZs were treated as three treatments: T₁ (HPMZ)—60% RDF + 80% IR, T₂ (MPMZ)—80% RDF + 90% IR, and T₃ (LPMZ)—100% RDF + 100% IR. Uniform doses of micronutrients as foliar spray carrying 0.50% FeSO₄ + 0.50% MnSO₄ + 0.50% ZnSO₄ were applied thrice, with the first spray at the fruit set stage, while second and third sprays were undertaken during the fruit development growth stage at a 45-day interval.

2.2 Imposition of treatments

Daily weather data were collected from Agro-met Observatory located at the same experimental farm. The average mean temperature, humidity, wind speed, rainfall, and evaporation rate were collected during the course of the study. The actual plant water requirement was estimated using reference crop evapotranspiration (ET_r) on daily basis of the Penman–Monteith method (Allen et al., 1998). The phenological stagewise crop coefficient (K_c) for citrus crop was taken into consideration, and accordingly, the crop evapotranspiration (IR_NM) of Nagpur mandarin was estimated. The average tree canopy spread of Nagpur mandarin under study was worked out as 1.6 m. The average daily ET_r values revealed that ET_r was highest in May and lowest in the month of September. The average ET_r value of 4-year studies was observed as 1,925.90 mm. The daily minimum and maximum ET_r values ranged from 2.89 to 9.40 mm.

The area under the plant canopy wetted at 100% wetting area as whole shaded area was required to be drip irrigated. The mandarin crop IR_NM was estimated with 100% water requirement in L day⁻¹ plant⁻¹. This was considered as 100% IR level in LPMZ. The IR in HPMZ and MPMZ was estimated at 80% IR and 90% IR, respectively. Accordingly, the irrigation was scheduled through drip irrigation to different MZs. The quantity of IR and monthly water schedules (excluding rainy months) alongside fertigation schedules adopted for their recommended doses is further presented (Table 1).

Table 1. Schedule of fertilizers and irrigation as per recommendation adopted as in phenological growth stages of Nagpur mandarin.

RDF: 600 g; N: 200 g; P₂O₅: 300 g; K₂O: 200 g; FeSO₄: 200 g; MnSO₄: 200 g; ZnSO₄: 50 g; Borax tree⁻¹ year⁻¹ was treated as 100% RDF. Using this dose as baseline, fertilization treatments involving 80% RDF and 60% RDF were adjusted.

The IR of trees during the peak summer month of May was observed as 165.0 L day⁻¹. The quantity of IR applied under HPMZ, MPMZ, and LPMZ on the basis of 48 plants was computed as 73.38, 82.55, and 91.72 × 10³ L day⁻¹, respectively, with IR_NM varying during the corresponding period varying between 2.53 and 5.59 mm day⁻¹. The mandarin plants were irrigated with the drip system (8 L h⁻¹ drippers, 3 plant⁻¹). Twin arrangements of water and nutrients were made using the drip system and fertilizer injection units. The fertigation is administered using a venturi-based liquid fertilizer injector (Maxjate), with an adopted fertilization schedule (Table 1).

2.3 Irrigation requirement (IR_NM)

The ET_r of the daily reference crop (the amount of water that vegetation transpires during growth, amount held in plant tissue, and the amount of moisture that evaporates from soil and surface of vegetation) was used to determine the amount of water required by Nagpur mandarin. Daily water need (IR_NM) of Nagpur mandarin was computed for the standard meteorological week period using the formula: IRNM=ETr×Kc×A×WAIE, where IR_NM stands for water requirement in L day⁻¹ tree⁻¹, ET_r is reference crop evapotranspiration in mm, K_c is crop coefficient in fraction, WA is wetted area in fraction, A is the area occupied by each tree in m², and IE is the irrigation efficiency of drip irrigation system in fraction. Similarly, irrigation time (h) was calculated using the equation IT = IR_NM/DC, where IT, IR, and DC stand for irrigation time (h), water requirement (L day⁻¹ tree⁻¹), and dripper discharge (L h⁻¹), respectively.

2.4 Estimation of reference crop evapotranspiration (ET_r, mm)

Reference crop evapotranspiration does not take crop traits or soil conditions into account; rather, it represents the evaporative power of the atmosphere at a particular location and time of year. Therefore, using the Penman–Monteith method, the daily ET_r was calculated using the daily climatic data for the period of January to December. In many different locations and climates, the Penman–Monteith technique has a high probability of accurately forecasting ET_r (Allen et al., 1998) using the formula:

ETr=0.408Δ(Rn-G)+γ(900T+273)U2(es-ea)Δ+γ(1+0.34u2), where ET_r stands for reference crop evapotranspiration (mm day⁻¹), G is the soil heat flux density (MJ m⁻² day⁻¹), R_n is the net radiation (MJ m⁻² day⁻¹), T is the mean daily air temperature (°C), γ is the psychometric constant (kPa °C⁻¹), Δ is the slope of saturation vapor pressure function (kPa °C⁻¹), e_s is the saturation vapor pressure at air temperature T (kPa), e_a is the actual vapor pressure at dew point température (kPa), and u₂ is the average daily wind speed at 2 m height (m s⁻¹).

2.5 Crop coefficient (K_c)

Using the shaded area techniques, the crop coefficient (K_c) values have been determined for several crop phenophases. The equation developed for deciduous fruit crops (Ayars et al., 2003; Gorantiwar et al., 2011) was used to derive the crop coefficient, using the equation: Kc=0.014x+0.08, where K_c and x stand for crop coefficient and percentage of shaded area, respectively. Water demand (L day⁻¹ tree⁻¹) for Nagpur mandarin was estimated using other factors, namely, crop coefficient (K_c), pan coefficient (K_p), wetted area (WA), and irrigation efficiency (IE) (Kisekka et al., 2010; Gorantiwar et al., 2011). The K_c curve was constructed against different stages of crop growth for any time during the growing season.

2.6 Wetted area

Wetted area factor is the proportion of the effective root zone with respect to the total area. The canopy area is the shaded area at solar noon hour and was measured weekly for all the trees under experimentation. Wetted area was then computed (Gorantiwar et al., 2011) using the formula: WA = SA/A, where SA and A denote shaded area in m² and area occupied by each tree in m², respectively. This computation enabled us to develop phenophase-based K_c and wetted area values.

2.7 Computation of WUE and NUE

The yield and daily water requirement were determined from the observed data, and the WUE was computed using the formula WUE (kg m⁻³) = Yield (kg)/IR_NM (m³) according to the procedure suggested by Poveda-Bautista et al. (2021). Likewise, NUE was calculated as fruit yield (kg tree⁻¹) divided by the amount of fertilizer applied (kg tree⁻¹) as outlined by Srivastava and Malhotra (2017).

2.8 Observations

The observations during the course of the study consisted of tree spread and plant height expressed as canopy volume using the following formula: canopy volume = 0.524 HD², where H and D represent plant height and spread, respectively (Castle, 1983). Fruit yield was measured by taking yield data of all trees by multiplying the weight of 100 fruits/tree with total number of fruits tree⁻¹. As many as 200 fruits/treatment were subjected to analysis of fruit size distribution with the help of a Vernier caliper (Model: 530-122).

All the fruit quality parameters, viz., total soluble solids, juice acidity, and fruit juice content, were analyzed following the procedural steps as highlighted by Ranganna (2001).

2.9 Analysis of collected samples

Volumetric soil moisture content at field capacity (FC) and the permanent wilting point (PWP) were determined using the pressure plate method (Soil Moisture Equipment Corp Pvt., USA, Model:1500F2) as 31.78% and 20.45%, respectively; moreover, the available water holding of soil is 15.94 cm m⁻¹ depth of soil.

The skirt-belt soil samples (0–20 cm depth coinciding with zone of maximum feeder root concentration) of each plant were collected, air-dried, and sieved through a 2-mm sieve to obtain a homogeneous soil sample. Likewise, 5- to 7-month-old index leaf samples representing the second, third, and fourth leaf from non-fruiting terminals were collected (Srivastava et al., 1994) and subjected to thorough cleaning followed by shade air-drying and grinding to obtain homogeneous leaf samples (Huchche et al., 2001). The soil samples hence obtained were analyzed for KMnO₄-N using the alkaline permanganate distillation method (Subbiah and Asija, 1956), colorimetric NaHCO₃-extractable Olsen-P (Page et al., 1982), neutral NH₄OAc extractable-K (Page et al., 1982) using a flame photometer (Model: Systronics 2410), and DTPA-extractable micronutrients (Fe, Mn, Cu, and Zn) using an atomic absorption spectrophotometer (Model: GBC 908) as per procedures suggested by Lindsay and Norvell (1978). Likewise, leaf samples were initially digested in diacid mixture of H₂SO₄ and HClO₄ in 1:4 ratio and subjected to analysis of various macronutrients and micronutrients as per standard procedures (Chapman and Pratt, 1962).

2.10 Statistical analysis

The data in the present study were statistically analyzed for CV and significant differences between the means of the treatment at p = 0.05, according to Tukey’s test using the SPSS 25.0.0.1 software package. The correlation matrix (using PROC CORR) was worked out to determine the strength of the linear relationship between plant available nutrients and fruit yield, within different MZs and with pooled response.

3 Results and discussion

3.1 Response on canopy volume

Improvement in canopy volume provides a consequential opportunity of larger photosynthetic area for fruit bearing. Trees with larger canopy and bearing volumes produce more fruit than smaller-canopy trees. In the present study, the mean plant canopy volume after first fruiting season was observed as 6.25, 5.45, and 4.11 m³, respectively, which correspondingly changed to 10.61, 10.00, and 8.55 m³ following imposition of variable rates of IR and RDF for four fruiting seasons (Table 2). Thus, variable fertigation treatments were associated with a net increase in tree canopy volume of 4.36, 4.55, and 4.44 m³ under HPMZ, MPMZ, and LPMZ, respectively, providing useful insights into consistent but differential development of tree canopy volume, regardless of MZs, either within or across MZs. These observations were further evident from temporal changes in CV measured on pooled data analysis, either within MZs (11.4%–18.1%) or across MZs (17.1%–20.2%), displaying the effectiveness of variable mode of fertilizers and irrigation water in developing an orchard with uniform canopy, a driver of improved orchard efficiency (OE) (Srivastava and Singh, 2008). In the first fruiting season, post-treatment response showed that average canopy volume of LPMZ accounted for 34.24% of canopy volume of HPMZ, later reduced to 19.41% by fourth year of fertigation treatment, indicating the progressive response of these treatments towards uniform development of canopy volume. Naqvi et al. (2022) in their preliminary results on response of canopy volume-based variable rate fertilization (VRF) revealed that stem girth performed significantly better than the plant height, with VRF outperforming uniform-rate fertilizer application. On the other hand, increasing the irrigation rate from 26.5% to 81% ET_c significantly enhanced the young citrus tree growth by increasing the citrus tree’s canopy volume (fourfold), tree height (29%), root life span (86%), and root length (twofold) on Arenic Alaquods. Thus, the application of 81% ET_c irrigation rate in commercial citrus groves was observed to be more efficient for sweet orange (C. sinensis Osbeck) trees varying from 2 to 4 years of age (Hamido and Morgan, 2020).

Table 2. Plant canopy growth response of Nagpur mandarin in response to four annual cycles of variable rates of fertilizers and irrigation across three MZs represented by HPMZ, MPMZ, and LPMZ (based on data of four fruiting seasons).

• HPMZ, MPMZ, and LPMZ stand for high production management zone, medium production management zone, and low production management zone, respectively.

• Figures in parentheses indicate their mean values of four fruiting seasons.

• Data within each MZ were generated on the basis of 27, 37, and 80 sampling points under HPMZ, MPMZ, and LPMZ, respectively.

• CV (coefficient of variation) was worked on the basis of pooled data of four fruiting seasons.

• SEM stands for standard error of the mean.

• Means separated by different letters within columns showed significant difference at p ≤ 0.05 as per Tukey test.

Comparing tree canopy-based variograms over space and time in response to integrated use of irrigation and fertilizers, significant response of variable fertigation across MZs was further observed (Figure 2a versus 2b). A marginal decrease in area of canopy volume (less than 3 m³) from an initial 38.2% to 32.4% following 4 years of treatments (a net decrease of 5.85%) was observed under LPMZ, while MPMZ (range of 3–5 m³) registered a substantial reduction in canopy volume from an initial 53.7% to 16.3% (a net decrease of 37.4%) after four fruiting seasons, showing a gradual shift in tree canopy across MZs. HPMZ (more than 5 m³) following treatment for four fruiting seasons by far produced the highest magnitude of improvements in canopy volume from an initial 8.1% to 51.3% (a net increase of 43.2%) occupied within this MZ. These responses on tree canopy volume suggested the differential crop response vis-a-vis variable rates of RDF and IR. Alva et al. (2008) through 5–6 years of field studies on coarse-textured Entisols demonstrated that variable fertigation proved more effective as compared to broadcast application of dry granular fertilizer with respect to improvements in tree canopy area of young trees, increased fruit as well as juice yield of sweet orange (C. sinensis Osbeck)-bearing and grapefruit (C. sinensis Osbeck × Citrus paradisi Macf.)-bearing trees, in addition to reduction in nitrate leaching to groundwater. Mann et al. (2011) used tree canopy volume to classify the citrus grove into five productivity zones labeled as very poor, poor, medium, good, and very good zones. They observed that although all the soil properties were strongly correlated with yield and were able to explain the productivity of the grove, citrus tree canopy volume was most strongly correlated (r = 0.85) with yield, explaining 73% of its variation. These studies showed that productivity of citrus orchard can be mapped using various attributes that directly or indirectly affect citrus production.

3.2 Response on fruit yield and quality

Improvement in canopy volume ensures higher fruit-bearing area due to increased partitioning of photosynthates as a function of elevated nutrient pool under the influence of optimum soil moisture (Panigrahi and Srivastava, 2017). However, the response on fruit yield is not always directly proportional to size of canopy volume, often described by OE, defined as the percentage of trees producing fruit yield more than mean orchard yield in relation to spatial variability in soil properties (Srivastava and Singh, 2008). In our study, LPMZ (less than 15 kg tree⁻¹) recorded an increase from 47.2% area after the first fruiting season to 57.8% following the fourth fruiting season, thereby causing an increase in area of 10.6% after four fruiting seasons (Figure 3a versus 3b). By contrast, MPMZ (15–30 kg tree⁻¹) experienced a reduction in area of 5.1% from 21.3% to 16.2% after treatment for four fruiting seasons. Similarly, HPMZ (more than 30 kg tree⁻¹) also responded with a net area reduction of 5.5% from 31.5% to 26.0% following four annual cycles of treatments. Hence, despite reduction in treatment response on canopy volume in LPMZ, fruit yield improvement emerged through this MZ only, while other MZs, viz., MPMZ and HPMZ, remained almost unresponsive to differential treatments. Such a response on fruit yield in relation to canopy volume not only indicated the responsiveness of integrated use of irrigation and fertilization, but also provided some additional clues about the better rationale of input use. Readjusting the nutrient use within an orchard using spatiotemporal variability therefore holds prime agronomic importance for improved OE. OE therefore takes into account the technical efficiency (ratio of output to production inputs), resource use efficiency (NUE and WUE), and physiological efficiency (ratio of canopy growth to fruit-bearing area), together associated with economic sustainability (Dhurve et al., 2025). OE hence, besides serving as an index of production sustainability, relates to the production efficiency of an orchard more realistically than the usual practice of mean orchard yield (Poveda-Bautista et al., 2021; Dzigbogia et al., 2025).

Analysis of yield-attributing parameters like number of fruits tree⁻¹ across three MZs showed the gradual reduction in difference between performances of LPMZ versus HPMZ (Table 3). With the first imposition of treatment response, LPMZ constituted for 25.0% of HPMZ, but later reduced to only 14.5% during the fourth round of variable fertigation treatment. Likewise, another yield-attributing parameter, fruit weight, was observed to be 15.1% higher under HPMZ over LPMZ following the first year of treatment, which later reduced to 13.7% during the fourth year of treatment due to the more rationalized distribution of production inputs. Fruit yield, on the other hand, showed equally varying responses of treatment, with HPMZ registering 31.71% higher yield over LPMZ, indicating yield-wise that these MZs still have a scope of improvement. However, CV varied very closely (12.6%–13.1% and 16.7%, respectively, within MZs and across MZs). Considering the cumulative fruit yield of four fruiting seasons (data not given), HPMZ recorded a maximum of 141.53 kg tree⁻¹ followed by 113.03 kg tree⁻¹ under MPMZ and 97.40 kg tree⁻¹ under LPMZ, thereby setting a yield difference of 45.13 kg tree⁻¹, regardless of MZs. These observations warrant a further scope of yield improvements across MZs, before each MZ function as a specially independent homogeneous production grid, despite only a marginal difference in canopy volume comparing HPMZ versus LPMZ, though the former was treated with 20% less IR coupled with 40% less RDF.

Table 3. Fruit yield and quality of Nagpur mandarin in response to variable rates of irrigation and fertilizers through four annual cycles of treatments across MZs represented by HPMZ, MPMZ, and LPMZ (data of four fruiting seasons).

• HPMZ, MPMZ, and LPMZ stand for high production management zone, medium production management zone, and low production management zone, respectively.

• Figures in parentheses indicate their mean values of four fruiting seasons.

• Data within each MZ were generated on the basis of 27, 37, and 80 sampling points under HPMZ, MPMZ, and LPMZ, respectively.

• CV (coefficient of variation) was worked on the basis of pooled data of four fruiting seasons.

• SEM stands for standard error of the mean.

• Means separated by different letters within columns showed significant difference at p ≤ 0.05 as per Tukey test.

An increase in fruit yield without desirable fruit quality is considered uneconomical. Important fruit quality parameters, viz., juice percent of fruits, juice TSS, and juice acidity, were observed to vary as 41.2%–44.8% (mean 43.7%), 9.3–10.5 °Brix (mean 9.9 °Brix), and 0.57%–0.85% (mean 0.69%), regardless of MZs (Table 2). Comparing the fruit quality development across MZs, LPMZ showed marginally higher juice content (0.90%) over HPMZ as per average response of treatments, while TSS showed no discernible changes between MZs. Juice acidity, on the other hand, registered 0.14% higher with LPMZ compared to HPMZ on average, indicating the more or less uniform development of fruit quality in response to variable rates of irrigation and fertilization combinations. Variation in CV, either within each of the three MZs or across MZs, further provided additional support to uniformity in the development of fruit quality attributes, more clustered around their mean values of the experimental plot, despite 20% less IR and 40% less RDF. Considering these results, Aguado et al. (2012) suggested that a moderate water stress applied during the maturity period is a sustainable strategy for saving water, increasing the irrigation productivity, and obtaining fruits with similar properties to those without deficit irrigation, while Zhang et al. (2026), in order to enhance temporal and spatial coverage, observed that citrus production systems exhibited an average yield of 40.4 Mg ha⁻¹, with corresponding total water productivity and total nitrogen productivity values of 4.0kgm⁻³ and 213kgkg⁻¹, respectively, with key quality parameters exhibiting marked regional variation, accrued through a meta-analysis of 1,208 observations from 78 studies in 11 citrus-producing countries.

Rationality of efficient input use heavily relies on the distribution of unform fruit size, coupled with optimum fruit quality, considering fruit size as the single most important factor in determining market returns. In our study, distribution on different grades of fruit size (A-grade more than 75 mm, B-grade 65–75 mm, and C-grade less than 65 mm) showed a clear-cut difference across three MZs in response to variable rates of irrigation and fertilizers (Figure 4). The HPMZ registered the maximum A-grade fruits, significantly followed by MPMZ and LPMZ in decreasing order. In contrast, MPMZ and LPMZ, despite no significant difference between them with regard to the distribution of B-grade fruits, showed a significantly superior response compared to LPMZ, while LPMZ registered significantly higher C-grade fruits over MPMZ and HPMZ, with the latter two MZs remaining significantly on par with each other. These observations are in alignment to uniformity in fruit size distribution across different MZs. Nagpur mandarin is mostly marketed as fresh fruit; such uniform grade size distribution has strong implications in dictating the economics of production response.

Achieving good-sized fruit is a complex process though, affected by a wide range of variables, viz., rootstock–scion combination, plant nutrition, irrigation, orchard management, and environmental factors. Such studies therefore call for an interactive management model for the optimization of citrus production and fruit size.

3.3 Use efficiency of applied inputs

Significant interactions between water and N-inputs in yield and NUE but not in WUE are reported, indicating that studies aiming at the optimization of water and N-inputs must consider interactions and optimize water and N-inputs simultaneously. They further suggested reducing over-optimal irrigation to optimal irrigation, coupled with increased citrus yield by 20%, WUE by 30%, and NUE by 15%. Similarly, reducing over-optimal N-fertilization to optimal N-fertilization increased the yield by 10%, WUE by 15%, and NUE by 40% (Qin et al., 2016). In our study, an integrated use of irrigation and fertilization after four fruiting seasons showed significant changes in WUE and NUE across MZs (Table 4). WUE was registered significantly higher under HPMZ (20% less IR) over either MPMZ (10% less IR) or LPMZ (100% IR), providing useful insights for further expanding the magnitude of response to IR treatments. Likewise, NUE was observed to be differentially expressed in response to treatments, with HPMZ in response to 80% IR combined with 60% RDF demonstrating 2.44-fold higher NUE (592.14 kg kg⁻¹) over LPMZ (242.59 kg kg⁻¹), which warrants an in-depth analysis about fixing the over-optimization of such important inputs. These responses must be viewed from a better available stock of nutrients under HPMZ compared to either MPMZ or LPMZ. Previously, Carr (2012), through a comprehensive review on water relation and IR of citrus, suggested yield response with 100% IR to the order 6–7 kg fresh fruit m⁻³, besides an increase in marketable-size fruits and improvements in fruit quality.

Table 4. Response on changes in WUE and NUE across MZs in response to variable rates of irrigation and fertilization after treatment of four fruiting seasons.

• HPMZ, MPMZ, and LPMZ stand for high production management zone, medium production management zone, and low production management zone, respectively.

• WUE and NUE stand for water use efficiency and nutrient use efficiency, respectively.

• N (nitrogen), P (phosphorous), and K (potassium).

• SEM stands for standard error of the mean.

Our studies showed differential response of MZs with regard to WUE and NUE through VRT mode. Such differential response of citrus is attributed to the efficient transport and assimilation of N, even with half N-fertilization, without any visible agronomic impact (García-Gómez and Carvajal, 2022). Such responses are further regulated by the proportion of N-investment, either in leaf biomass production (e.g., lemons) or in root biomass production (e.g., sweet oranges). However, greater yield and plant vigor correlated with N-investment in leaf biomass production (Dovis et al., 2016). These observations were further supported by the outcome of a meta-analysis showing that drip fertigation-mediated WUE and NUE improved by 9%–70% and 16%–29%, respectively (Singh and Singh, 2026).

3.4 Response on plant available nutrients

A significant response of variable rates of irrigation and fertilizers (Table 5) was observed on changes in available nutrient pool of soil according to native spatial gradients in soil fertility of the three MZs. The plant available AN, AP, and AK (mg kg⁻¹) were recorded as 119.4–212.4, 9.5–16.2, and 138.7–207.5, respectively, regardless of MZs (Table 5). However, on average, HPMZ (170.7 AN, 13.3 AP, and 192.8 mg kg⁻¹ AK) continues to record significantly higher available macronutrients than either MPMZ (164.2 AN, 10.2 AP, and 182.8 mg kg⁻¹ AK) or LPMZ (144.9 AN, 9.8 AP, and 162.9 mg kg⁻¹ AK), even after 4 years of continuous application of variable treatments, though the latter two MZs displayed a quite similar magnitude of response. These changes in soil fertility are an indication of ongoing reorientation in spatial soil fertility towards the development of a more homogeneous production grid. Considering response on the three macronutrients, variation in CV within MZs (14.1-17.1%, 12.2-14.8%, and 10.4-11.8%, respectively, in HPMZ, MPMZ, and LPMZ) was observed lower than across management zones (19.1, 16.8, and 15.0% for AN, AP, and AK, respectively). Variation in CV, though, was observed to be higher for AN, AP, and AK (mg kg⁻¹) across HPMZ compared to MPMZ, and LPMZ after four cycles of variable treatments, suggesting further course of differential response, though on a declining magnitude of response with each annual cycle of treatment.

Table 5. Changes in plant available supply of nutrients (mg kg⁻¹) in response to four annual cycles of variable rates of irrigation and fertilizers across MZs (based on pooled data of four fruiting seasons).

• HPMZ, MPMZ, and LPMZ stand for high production management zone, medium production management zone, and low production management zone, respectively.

• Figures in parentheses indicate their mean values of four fruiting seasons.

• Data within each management zone were generated on the basis of 27, 37, and 80 sampling points under HPMZ, MPMZ, and LPMZ, respectively.

• CV (coefficient of variation) was worked on the basis of pooled data of four fruiting seasons.

-SEM stands for standard error of the mean.

• AN, AP, AK, AI, AM, AZ, and AC denote KMnO₄-N, Olsen-P, NH₄0Ac-K, DTPA-Fe, DTPA-Mn, DTPA-Zn, and DTPA-Cu, respectively.

-Means separated by different alphabets within columns showed significant difference at p ≤ 0.05 as per Tukey Test

All the four premier micronutrients, viz., DTPA-Fe, DTPA-Mn, DTPA-Zn, and DTPA-Cu, displayed a variation of 10.3–16.1, 8.6–11.1, 0.80–1.40, and 1.3–1.5 mg kg⁻¹, respectively, on average, regardless of MZs (Table 5). Although the micronutrients were uniformly applied as foliar spray, considerable variations across MZs were still observed. DTPA-Fe showed a higher variation of 11.4% in LPMZ compared to 8.2%–9.4% in either MPMZ or HPMZ, while DTPA-Mn showed no such variation across MZs. DTPA-Zn showed a CV of 9.8% in LPMZ followed by 8.2% in MPMZ, and 7.6% in HPMZ, indicating that all the MZs after 4 years of variable rate application of inputs behaved like a homogeneous production grid. AC observed no significant difference between LPMZ and MPMZ for CV (10.0%–11.4%); however, HPMZ registered a slightly higher CV of 11.4%. HPMZ, despite being treated with 80% IR and 60% RDF, continued to maintain production sustainability over either MPMZ or LPMZ.

Improved changes in soil fertility following implementation of variable rates of treatments were further evident from a gradual decline in CV with each fruiting season either within management zones or across management zones, developing a more homogeneous orchard towards better NUE coupled with WUE. Every likelihood of such orchard site may not display any differential MZ-based performance vis-a-vis input use, after response of some more fruiting seasons. Colaco and Molin (2017), comparing the effects of VRF versus uniform rate applications for six long years of field experiment (two 25-ha citrus fields in Sao Paulo, Brazil), observed better soil fertility management and yield increase in Field 1, presenting higher variability regarding soil and topography, while Field 2, which was less variable regarding soil and topography, produced poorer yield response from VRF. However, either way, variable rate technology resulted in higher fertilizer agronomic efficiency in both fields. These observations provide strong clues about the better response of NPK fertilizers in increasing the fruit yield when applied site-specifically.

3.5 Correlation between soil fertility and fruit yield across MZs

In the preceding discussion, spatial changes in soil fertility were significantly observed either within or across MZs. Such changes in available pool of nutrients in soil were observed to be differentially correlated with fruit yield (Figures 5a–d), as per MZs and on pooled data analysis. In LPMZ (Figure 5a), fruit yield was correlated with AN (r = 0.94, p = 0.01), AP (r = 0.92, p = 0.01), AK (r = 0.97, p = 0.01), AI (r = 0.75, p = 0.01), AM (r = 0.82, p = 0.01), AZ (r = 0.67, p = 0.05), and AC (r = 0.77, p = 0.01). These correlation coefficients were different under MPMZ (Figure 5b), evident from the correlation of fruit yield with AN (r = 0.92, p = 0.01), AP (r = 0.93, p = 0.01), AK (r = 0.99, p = 0.01), AI (r = 0.56, NS), AM (r = 0.69, NS), AZ (r = 0.88, p = 0.05), and AC (r = 0.76, p = 0.05), indicating single MZ behaving independent to other MZs. HPMZ (Figure 5c) presents a different scenario, where fruit yield is correlated with AN (r = 0.99, p = 0.01), AP (r = 0.94, p = 0.05), AK (r = 0.94, p = 0.01), AI (r = 0.85, p = 0.01), AM (r = 0.86, p = 0.01), AZ (r = 0.75, p = 0.05), and AC (r = 0.72, p = 0.05). Regardless of MZs (Figure 5d), fruit yield was correlated with AN (r = 0.86, p = 0.01), AP (r = 0.65, p = 0.05), AK (r = 0.80, p = 0.01), AI (r = 0.67, p = 0.05), AM (r = 0.76, p = 0.05), AZ (r = 0.54, NZ), and AC (r = 0.66, p = 0.05), displaying the strong relationship of available pool of nutrients influencing the fruit yield. These observations suggested soil fertility optimization with MZs is a necessary change to optimize the orchard production. However, Luo et al. (2025) suggested that optimized fertilization did not affect nutrient uptake in pomelo trees, though nutrient accumulation (C, N, P, K, Ca, and Mg) in fruits increased, whereas the accumulation in the leaf and branch decreased with increasing fruit load, resulting in a shift in nutrient distribution between organs. Hence, fruit yield distribution will play an important role in deciphering the individual role of nutrients in influencing the yield.

The MZs were thus statistically delineated and experimentally validated through spatial data reduction and transforming a heterogeneous orchard into statistically homogeneous production grids for site-specific input application. The application of irrigation and fertilizers on a variable rate basis is hence inevitable in modern-day citriculture to harness spatiotemporal variability, much to the advantage of orchard productivity, especially when climate variability, regulatory nutrient management, water conservation policies, and declining water compound the challenges involved for irrigated crop production.

4 Conclusions

Improving WUE together with NUE is the basis of economized production sustainability of citrus orchards. In this regard, our study validated two cardinal issues, viz., (i) concept of delineation of MZs using canopy volume-based variograms and (ii) agronomic performance of MZs via the integrated use of irrigation and nutrients in VRT mode, evident from lower CV within MZs than between MZs, with regard to different soil–plant parameters. Despite no field control on the spatial independence of different MZs, data outcome on issues relating changes in soil fertility and quality response issues aided in redistributing IR and RDF more precisely compared to unzoned orchard. Such differential soil–crop response also brought transformative changes in developing an orchard with a higher OE, although the orchard size and a shorter period of experimentation were the limitations of our current study. However, despite such limitations, our studies put forth a conceptual basis for the rationalized use of irrigation and nutrients, without undertaking any further rezoning of MZs. Rationalizing input use through MZs hence provided strong clues for maximizing production by squeezing the field spatial variability as per MZs and treating them as a more homogeneous production unit within the field, instead of addressing the spatial variability of field at one go.

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Footnotes

Edited by: María Serrano, Miguel Hernández University of Elche, Spain

Reviewed by: Mridusmita Debnath, Indian Council of Agricultural Research (ICAR), India

Svetoslav Malchev, Agricultural Academy, Bulgaria