Irrigation Scheduling and Nutrient Management in Green Gram Cultivation: An Evaluation of Yield and Water Productivity, Soil Water-Nutrient Dynamics, Energy Budgeting and Profitability

Abstract

Green gram is an excellent pulse crop with high economic and nutritional values. Optimal water and nutrient application can boost yield, water productivity, nutrient use, energy indices, and economic gains. The field experiment comprising three irrigation schedules (one irrigation at pre-flowering, two irrigations at pre-flowering and pod formation, and three irrigations at branching, pre-flowering and pod formation stages) and four nutrient management (control, 100% recommended fertilizer (RDF), farmyard manure at 5 t ha⁻¹, and 100% RDF + FYM at 5 t ha⁻¹) was arranged in a split-plot design with three replications during the summer seasons of 2016 and 2017. Based on the principal component analysis technique and economic assessment of all the treatments studied, three irrigations at branching, pre-flowering and pod formation stages with the recommended fertilizer plus FYM resulted in the maximum growth, yield components, seed yield (784 kg ha⁻¹), stover yield (2288 kg ha⁻¹), highest NPK uptake, total output energy, net energy gain, greatest gross return (USD 749.7 ha⁻¹), net return (USD 331.7 ha⁻¹) and higher BCR (1.79). However, maximum crop water productivity and irrigation water productivity (0.55 and 0.96 kg m⁻³, respectively) were recorded with a single irrigation at pre-flowering stage in tandem with full recommended fertilizer and farmyard manure. A linear relationship was detected between seed yield and seasonal crop evapotranspiration (ETc) at different nutritional levels. Seasonal water consumtion of 178 mm corresponding to an irrigation amount of 170 mm with a yield of 620 kg ha⁻¹ and CWP of 0.36 kg m⁻³ was a balance for optimum seed yield, ETc and CWP. The 0–30 cm depth of the root zone profile contributed 80–84% while the 30–60 cm depth accounted for 16–20% of total soil moisture depletion. Based on the physical and financial outputs, a three-irrigation schedule coupled with the full recommended fertilizer and manure was identified as the best management strategy for green gram production and can thus be recommended to farmers of non-saline coastal soils in eastern India.

Introduction

Green gram (Vigna radiata L. Wilezek) is an ancient and well known short duration leguminous crop grown extensively in arid and semi-arid regions. This crop is a native of the Indian Peninsula and suitable for diverse climatic, soil and cultural conditions (Chavan et al., 2014; Mondal et al., 2018). India alone resides in 65% of the global cultivated area and contributes 54% of the total production of green gram (Praveena et al., 2018). The symbiotic associations with Rhizobium help green gram to fix atmospheric N to the tune of 130–160 kg ha⁻¹ (Islam et al., 2017) and improve the soil’s physical and chemical characteristics (Kunjammal & Sukumar, 2019). It is a rich source of proteins (25%), carbohydrate (60.4%), fat (1.3%), dietary fiber (4.0%), different minerals and vitamins with a low cholesterol level (Yadav et al., 2009) and is useful for healthy and balanced diet for humans and animals. Green gram is considered a low-input requirement crop and can be grown as a vegetable, pulse, forage, green manure or cover crop; and is well fitted in any crop rotation (Yadav & Singh, 2014). However, the actual productivity of green gram (474 kg ha⁻¹) is considerably lower than its potential yield. In India, the pulse is predominantly grown in poorly fertile soil as a rainfed crop and its potential yield is largely constrained by weather aberrations, infestation of diseases and pests, variable moisture stress signatures across the growth cycle, improper agronomic practices and nutrient management (Priyadharshini et al., 2021; Saravanan et al., 2013; Yadav et al., 2019).

The limitation of water is the most significant abiotic stress in the environment, inhibiting plant growth, crop productivity and quality (Shamshiri et al., 2011). Water acts as a solvent to regulate the absorption and transport of nutrients from soil to plant, leaf photosynthesis, transfer food materials from source to sink, and maintain cell turgidity including the division and growth of plant tissue cells and, stomatal opening and closure (Sosiawan et al., 2021). Maintaining an optimum soil moisture regime in the root zone increases soil nutrient availability and, their absorption through extended root growth. Lower soil moisture conditions, on the contrary, reduce root growth and, water absorption and lower the yield and quality of produce (Sarkar et al., 2020). The crop is particularly susceptible to water stress during the flowering and pod development stages (Halder et al., 2022) and markedly inhibits the photosynthesis rate, metabolic processes, root nodulation and nutrient uptake, resulting in low production (Dasila et al., 2016; Naresh et al., 2013) Water deficit condition also inhibit seed germination, root and shoot growth, root nodulation and ultimately pod yield (Khakshu et al., 2011; Saima et al., 2018). During the summer season (March to mid-June), the availability of irrigation water resources becomes limited and crop evapotranspiration demand is too high due to low rainfall and an elevating atmospheric temperature. In such water-stress and heat-stress adversaries, strategic growth stage-wise irrigation planning is more relevant to augment yield and water productivity (Filho et al., 2020; Sarkar et al., 2020).

Plant nutrition is another key factor determining crop growth, yield and quality of produce (Kundu et al., 2023; Sathyamoorthi et al., 2008). Mineral fertilizers provide readily available nutrients to plants and strongly influence crop yield. However, increasing costs of fertilizer inputs, growing soil health hazards, environmental problems, and above all, global energy crisis pose a great challenge for alternative cheaper sources of plant nutrients (Chaudhary et al., 2015). Locally available low-cost organic materials such as farmyard manure, cattle manure, poultry manure and compost can boost yield and improve physical and biological properties of the soil, besides adding macro- and micronutrients to the soil (Kundu et al., 2023; Mishra et al., 2016; Patra et al., 2024). The crop responds positively to a balanced dose of nutrient application, which can be moderated through the integrated use of chemical fertilizers and organic manures (Chemutai et al., 2018; Daramy et al., 2016). Proper nutrient management under less soil water availability can relieve the plant by resisting multiple stresses and up-scaling the efficiencies of applied inputs (Abedi et al., 2011; Senthilkumar et al., 2023).

Green gram is a promising and remunerative short duration crop in the humid coastal region of eastern India during the summer season due to its high nutritional and market values. Faulty irrigation and nutrient management practices are the major reasons for the reduction in crop yield. There is no concrete information available to address the twin issues of soil water-stress and nutritional problems on green gram in this delicate environment. Keeping all these points in view, the present study was contemplated with the objective of optimizing the scheduling of irrigation and nutrient requirements for maximum growth, yield components, yield, water productivity, energy tradeoff and the economics of summer green gram in this agro-climatic zone. We hypothesized that the strategic application of nutrients through fertilizer and organic manure along with irrigation scheduling would increase the uptake of nutrients and water use efficiency, thus improving crop growth, yield and financial profits.

Materials and Methods

Experimental Site

The field investigations were continuously conducted during the summer seasons (March to May) of 2016 and 2017 in a farmer’s field, South 24 Parganas district, West Bengal (22°40´ N latitude, and 88°18´ E longitude; 7.0 m above the mean sea level) under the coastal saline zone of eastern India. The experimental site is located in a humid sub-tropical region with a mean annual rainfall of 1560 mm, the most of which (> 75%) is received during June–September as south-west monsoon. The weekly mean maximum and minimum air temperatures varied from 32.5⁰C to 38.7⁰C and 15.5⁰C to 18.7⁰C, respectively. The groundwater table is below 6.5 m during the cropping period. The experimental soil (0–15 cm layer) was typically non-saline with a clay loam texture (Typic Endoaquept). The important physical and chemical properties of 0–15 cm soil layer are shown in Table 1.

Table 1 Important physical and chemical characteristics of the experimental soil (0–15 cm depth)

Experimental Treatments and Design

The experiment was comprised of three levels of irrigation based on crop growth stage viz. I₁: one irrigation at pre-flowering, I₂: two irrigations at pre-flowering and pod development, and I₃: three irrigations at branching, pre-flowering and pod development in main plots and four levels of nutrient viz. N₁: control (without fertilizer and manure), N₂: 100% recommended dose of fertilizer (RDF), N₃: farmyard manure (FYM) at 5 t ha⁻¹, and N₄: 100% RDF + FYM at 5 t ha⁻¹ in sub-plots. The treatments were set in a split plot design with 12 treatment combinations and replicated thrice.

Crop Management

A land area of 55.5 m × 20.0 m was prepared to fine tilth by repeated ploughing followed by laddering and partitioned into three equal blocks. Each block was divided into 12 sub-plots where 12 treatment combinations were randomly distributed. The size of the small plot was 3 m × 3 m and a buffer strip of 1.5 m width was provided around the plot to minimize moisture drifting from neighboring plot and eliminate the effects of conveyance losses from the irrigation channels. Two irrigation channels each of 1.0 m in width were arranged in between two replications and water was applied through these irrigation channels. About 5 t ha⁻¹ of farmyard manure (0.55% N, 0.28% P₂O₅, and 0.52% K₂O on dry weight basis) was incorporated in the desired plots just before the final land preparation and mixed thoroughly with soil. The standard basal recommended dose of fertilizer in the region viz., 20, 40 and 20 kg ha⁻¹ of N, P₂O₅ and K₂O as urea, single superphosphate and muriate of potash, respectively, was applied in the furrows of the selected plots. The bold and healthy seeds of green gram (cv. B-105) were inoculated with Rhizobium culture at 60 g kg⁻¹ seed before sowing to facilitate symbiotic biological N₂ fixation. The treated dried-seeds at a rate of 25 kg ha⁻¹ were sown in lines at a spacing of 30 cm × 10 cm in pre-opened furrows at 3–4 cm depth. The seed was sown in the first week of March and harvested in the third week of May in each experimental year. The seedlings were thinned 10–12 days after sowing (DAS) to maintain a uniform plant population for each plot. The standard plant protection measures and intercultural operations were uniformly adopted in each subplot.

Irrigation Scheduling

Immediately after seed sowing, light irrigation at a depth of 20 mm was applied to all plots to ensure better and uniform seed germination and establish similar irrigation regime. Watering was imposed at the branching (15–18 DAS), pre-flowering (28–30 DAS), and pod development (45–48 DAS) stages of plant growth during both seasons, each at a 50 cm depth, according to a pre-determined irrigation schedule (Halder et al., 2022). A Parshall flume was installed at the head of each plot to measure the amount of irrigation water applied. Rainwater harvested in the adjacent pond during monsoon season was of good quality (pH 7.3, EC 0.68 dSm⁻¹) and used as a source of water for irrigating the plants.

Computation of Seasonal Crop Evapotranspiration

Crop evapotranspiration (ETc) for green gram under different irrigation treatments during the entire growing period was estimated by the root zone soil water balance equation (Simsek et al., 2005):

$${\text{ETc}}={\text{I}}+{\text{P}}+\Delta {\text{SW}}-{\text{D}}+{\text{C}}-{\text{R}}$$

(1)

where, I is the irrigation water (mm), P is the precipitation (mm), ± ΔSW is the change in water storage in the root zone profile between sowing and harvest(mm), D is the deep percolation below the rooting depth (mm), C is the capillary upward flux from groundwater and R is the surface runoff (mm). In our study, the maximum rooting depth was considered 45 cm (Halder et al., 2022). R was eliminated by providing bund at a height of 50 cm around the subplot and controlled irrigation was applied. C was overlooked in the equation as the depth of the groundwater table was 3.5 m below the surface. A low amount of rainfall received during the growing period was used to meet the plant’s evapotranspirative demand and was considered to be effective rainfall (Re). The soil profile water deeper than 45 cm was accepted as the deep percolation (D), which was not considered for crop evapotranspiration calculation. The soil moisture data for each layer of the root zone profile in each irrigation treatment was used for computing the soil moisture depletion by the plants using the following formula (Dastane, 1972):

$$\Delta {\text{SW}}={\sum }_{i=1}^{n}\frac{{(\uptheta }_{{\text{i}}1}-{\uptheta }_{{\text{i}}2})}{100}\times {\upgamma }_{{\text{i}}}\times {{\text{H}}}_{{\text{I}}}$$

(2)

where d = soil moisture depletion in root zone (cm), ${\uptheta }_{{\text{i}}1}$= soil moisture content in the i^th layer of profile 24 h after irrigation (%, w/w), ${\uptheta }_{{\text{i}}2}$= soil moisture content in the i^th layer of profile before the next irrigation (%, w/w),${\upgamma }_{{\text{i}}}$= bulk density of the i^th layer (Mg m⁻³), ${{\text{H}}}_{{\text{i}}}$=soil thickness of the i^th layer (cm), and ${\sum }_{i=1}^{n}={\text{s}}$ ummation ‘n’ number of soil layers of the root zone. Thus, the above Eq. (2) was reduced to the following form:

$${\text{ETc}}={\text{I}}+{\text{R}}\pm \Delta {\text{SW}}$$

(3)

Estimation of Water Productivity

Water productivity is an index of the efficiency of water use to increase crop yield in agriculture. Crop water productivity was calculated as the ratio of green gram seed yield to seasonal crop evapotranspiration (Howell, 2000) as:

$${\text{CWP}}=\frac{\mathrm{Y }}{{\text{ETc}}}$$

(4)

where CWP is crop water productivity (kg m⁻³), Y is seed yield (kg ha⁻¹) and ETc is crop evapotranspiration (m³ ha⁻¹).

Irrigation water productivity was calculated as the ratio of green gram seed yield to seasonal irrigation water consumed by the crop (Stanhill, 1986) as:

$${\text{IWP}}=\frac{{\text{Y}}}{{\text{IW}}}$$

(5)

where IWP is irrigation water productivity (kg m⁻³), Y is seed yield (kg ha⁻¹) and IW is irrigation water applied (m³ ha⁻¹).

Water-Yield Relationship

The association between seed yield and seasonal crop evapotranspiration (ETc) for green gram plant was assessed through linear regression analysis. In this approach, seed yield was considered a dependent variable and plotted against ETc as an independent variable to establish a water-yield production functional model (Fig. 1).

Fig. 1

Relationship between seasonal crop evapotranspiration and crop water productivity in green gram cultivation (Each point representing an average value of 3 samples)

Determination of Plant Growth Characteristics and Yield Attributes

At maturity, ten plants from the middle rows of each sub-plot were randomly selected, tagged and harvested for recording different growth parameters viz. plant height, number of branches plant⁻¹, leaf area index (LAI) and yield attributes such as number of pods plant⁻¹, number of seeds pod⁻¹ and 1000-seed weight. Leaf area was measured using a portable leaf area meter (LICOR-3000C, USA). LAI was then calculated by using the following formula (Yoshida et al., 1976):

$$\text{LAI}=\frac{\mathrm{Total}\;\mathrm{leaf}\;\mathrm{area}\;\mathrm{of}\;\mathrm{plants} (\text{cm}^2)}{\mathrm{Ground}\;\mathrm{area}\;\mathrm{covered}\;\mathrm{by}\;\mathrm{plants} (\text{cm}^2)}$$

(6)

Harvest index (HI) was determined by the following equation (Donald & Hamblin, 1976).

$$\text{HI}=\frac{\mathrm{Economic}\;\mathrm{yield}\;(\mathrm{kg}\;\text{ha}^{-1})}{\mathrm{Biological}\;\mathrm{yield}\;(\mathrm{kg}\;\text{ha}^{-1})}\times100$$

(7)

Measurement of Soil Moisture Extraction

For periodic soil moisture studies, soil samples were drawn with the help of a screw augur from a depth of 0–15, 15–30, 30–45 and 45–60 cm of each sub-plot just before and 24 h after irrigation or rainfall, at 10 day-interval and, during sowing and harvesting. The soil moisture contents of the samples were measured by the thermo-gravimetric method. hese data were used for calculating the soil moisture depletion from the root zone profile using Eq. 2.

Chemical Analysis of Soils and Plants

Initial and post-harvest surface soil samples (0–15 cm depth) were collected, cleaned, air-dried, ground in agate mortar and passed through a 2 mm sieve before laboratory analysis. Particle size distribution was determined by using the hydrometer method (Bouyoucous 1962), pH and electrical conductivity in 1:2 soil to water suspensions (Jackson, 1973) and bulk density by the core sample method (Bodman, 1942). Soil organic carbon was estimated by dichromate oxidation following the wet digestion method (Walkley & Black, 1934). Cation exchange capacity (CEC) was determined by the neutral normal NH₄OAc method (Schollenberger & Simon, 1945). Available soil N was estimated by following the alkaline permanganate (KMnO₄) method (Subbiah & Asija, 1956). Available soil P was extracted with a 0.5 M NaHCO₃ solution buffered at pH 8.5 and extracted P was determined by the ascorbic acid blue color method (Olsen, 1954). Available soil K was extracted with a neutral normal NH₄OAc solution and filtrate was estimated using flame photometer (Knudsen et al., 1982). At harvest, treatment-wise composite soil samples (0–15 cm depth) were gathered, processed and analyzed for available N, P, and K following the above standard methods. The tagged above-ground plant materials at harvest were sequentially washed with tap water, dilute HCl and distilled water. The independent samples including pods and stovers were chopped into small pieces, air-dried in shade, dried in an air-oven at 70 °C for 24 h, ground to fine powder, screened through a 1-mm sieveand mixed homogeneously. The samples were digested in a ternary acid mixture of 10:4:1 (v/v) of HNO₃:HClO₄:H₂SO₄. The concentration of N, P and K in the acid extract was measured by the titration method, spectrophotometer and flame photometer, respectively (Jackson, 1973). Plant uptake of N, P and K was computed by multiplying the dry biomass yield with the respective nutrient concentration as follows:

$$\mathrm{Nutrient}\;\mathrm{uptake}\;\left(\mathrm{kg}\;\mathrm{ha}^{-1}\right)\;=\;\frac{\mathrm{Nutrient}\;\mathrm{concentration}\;\left(\%\right)\;x\;\mathrm{plant}\;\mathrm{dry}\;\mathrm{biomass}\;\mathrm{yield}\;\left(\mathrm{kg}\;\mathrm{ha}^{-1}\right)}{100}$$

(8)

Energy Budgeting

For estimation of energy budgeting in green gram cultivation, amounts of energy utilized against each input in cultivation (human labour from land preparation to harvest, power tiller, diesel, seeds, organic manures, chemical fertilizers, irrigation, pump, and agro-chemicals (insecticides and herbicides) were considered for calculation of total input energy. Common energy consumption for each treatment and energy consumption for irrigation and nutrient management treatment were calculated separately (Table S1-3). Total output energy was estimated by considering the economic produce of the crop i.e., seed and stover using their energy equivalents (Table 2). Net energy gain, energy use efficiency, energy productivity and specific energy were calculated using a standard formula as described bellow (Poddar et al., 2022):

Table 2 Energy equivalents of different inputs and agronomic practices in green gram cultivation

$$\mathrm{Net}\;\mathrm{energy}\;\mathrm{gain}\;(\mathrm{MJ}\;\mathrm{ha}^{-1})\;=\;\mathrm{Energy}\;\mathrm{output}\;-\;\mathrm{Energy}\;\mathrm{input}$$

(9)

$$\mathrm{Energy}\;\mathrm{use}\;\mathrm{efficiency}\;\left(\%\right)\;=\;\frac{\mathrm{Energy}\;\mathrm{output}\;\left(\mathrm{MJ}\;\mathrm{ha}^{-1}\right)}{\mathrm{Energy}\;\mathrm{input}\;\left(\mathrm{MJ}\;\mathrm{ha}^{-1}\right)}\times\;100$$

(10)

$$\mathrm{Energy}\;\mathrm{productivity}\;(\mathrm{kg}\;\mathrm{MJ}^{-1})\;=\;\frac{\mathrm{Seed}\;\mathrm{output}\;(\mathrm{kg}\;\mathrm{ha}^{-1})}{\mathrm{Energy}\;\mathrm{input}\;(\mathrm{MJ}\;\mathrm{ha}^{-1})}$$

(11)

$$\mathrm{Specific}\;\mathrm{energy}\;(\mathrm{MJ}\;\mathrm{kg}^{-1})=\frac{\mathrm{Energy}\;\mathrm{input}\;\left(\mathrm{MJ}\;\mathrm{ha}^{-1}\right)}{\mathrm{Seed}\;\mathrm{output}\;(\mathrm{kg}\;\mathrm{ha}^{-1})}$$

(12)

$$\mathrm{Energy}\;\mathrm{intensiveness}\;(\mathrm{MJ}\;\mathrm{kg}^{-1})\;=\;\frac{\mathrm{Energy}\;\mathrm{input}\;\left(\mathrm{MJ}\;\mathrm{ha}^{-1}\right)}{\mathrm{Cost}\;\mathrm{of}\;\mathrm{cultivation}\;(\mathrm{USD}\;\mathrm{ha}^{-1})}$$

(13)

Economics Analysis

The economic evaluation of each treatment was worked out through benefit–cost ratio (BCR) analysis. The cost of cultivation included the expenses towards land preparation, seed purchase, seed treatment, sowing, intercultural operation, organic manure and chemical fertilizers, agro-chemicals, irrigation water, harvesting and processing with application charges. The cost of cultivation as well as the gross return was worked out by accounting for the prevailing rates of different cultural operations and using the minimum support price of the outputs (seed and stover) produced during the period of investigation. The net return was estimated by subtracting the seasonal cost of cultivation from the gross return. The treatment-specific BCR was calculated using the formula as:

$$\mathrm{BCR}\;=\;\frac{\mathrm{Gross}\;\mathrm{return}\;\left(\mathrm{USD}\;\mathrm{ha}^{-1}\right)}{\mathrm{Cost}\;\mathrm{of}\;\mathrm{cultivation}\;\left(\mathrm{USD}\;\mathrm{ha}^{-1}\right)}$$

(14)

Statistical Analysis

All the observed treatment-wise and year-wise experimental data for different soil, water and plant variables were performed by analysis of variance for a split plot design using SAS version 9.2 (SAS Institute Inc., Cary NC, USA) statistical software. Statistical differences between the means of individual treatments and their interactions were assessed using Fisher’s least significant difference (LSD) test at 5% level of probability (Gomez & Gomez, 1984). Since the variations in data for two experimental years examined by Bartlett’s chi-square test were found to be homogeneous, and the interactive effects between irrigation and nutrition treatments were almost identical, two years’ data were pooled to arrive at a conclusion. Principle component analysis (PCA) (Jolliffe & Cadima, 2016) was performed in Origin Pro (version 2018) to investigate the similarities and patterns of variance of all the detected plant, soil, water and energy variables through their distances among themselves. Following this, a non-parametric evaluation of each practice was performed based on regression factor scores and ranks under the principal components (with eigen values > 1) to determine the best management practice. The practice having the highest score and hence the lowest rank sum will indicate the best management practice among all the practices examined.

Results

Growth and Yield Characteristics

Various levels of irrigation, nutrition and their interaction had differential effects on average growth and yield contributing parameters of the plants (Table 3). The curtailment of irrigation and nutrient supply markedly reduced all the growth and yield components. The highest plant height (45.57 cm), number of branches plant⁻¹ (8.12), LAI (3.23), number of pods plant⁻¹ (18.36) and number of seeds pod⁻¹ (8.66) were recorded in I₃, which varied significantly over I₂ and I₁. The lowest average corresponding parameters of 42.06 cm, 7.05, 3.02, 16.25 and 8.09, respectively were noticed in I₁. Test weight and harvest index were statistically non-significant; however, I₃ showed a higher value while I₁ recorded at lower value. Averaged over the years and irrigation levels, N₄ displayed the tallest plant (46.93 cm), the highest branches plant⁻¹ (8.42), LAI (3.28), pods plant⁻¹ (18.96), seeds pod⁻¹ (8.78) and harvest index (25.12%). The N₃ treatment exhibited moderate growth and yield variables. The effects between N₄ and N₂ on branches plant⁻¹, LAI and seeds pod⁻¹ were statistically at par. N₁ presented significantly the least plant variables. Seed test weight was non-significant, where N₄ noted heavier value and N₁ the lower value. Interactive relationship between irrigation and nutrition showed that increasing frequency of irrigation at all nutrition treatments resulted an increase in plant variables and effect was more distinguished under conjunct influences of fertilizer and manure than sole application (Table 3). The highest plant height (48.16 cm), branches plant⁻¹ (8.85), LAI (3.36), pods plant⁻¹ (20.14), seeds pod⁻¹ (9.08) and harvest index (25.52%) were found in I₃N₄ combination, being at par with I₃N₂ and I₂N₄ combinations. In contrast, the I₁N₁ combination detected the lowest corresponding plant variables.

Table 3 Effects of different irrigation frequencies and nutrient management on growth, yield attributes and yield of summer green gram (pooled data)

Seed and Stover Yield

Seed and stover yields were also significantly affected due to different irrigation and nutrition treatments and their combinations (Table 3). Among irrigation treatments, I₃ showed the greatest seed yield (651 kg ha⁻¹) and stover yield (2078 kg ha⁻¹), being superior to I₂ and I₁. Seed yield enhancement in I₃ was 14.21% and 5.68% compared to I₁ and I₂, respectively. Similarly, among nutritional treatments, N₄ produced the maximum seed yield (734 kg ha⁻¹) and stover yield (2186 kg ha⁻¹), which significantly differed over the other treatments. The N₂ and N₃ treatments also varied significantly from one another. Seed yield increase in N₄ was 51.97, 7.15 and 34.19% as compared to N₁, N₂ and N₃, respectively. Control N₁ recorded significantly lowest seed and stover yield. Interactive relationships between irrigation and nutrition showed the maximum seed yield (784 kg ha⁻¹) and stover yield (2288 kg ha⁻¹) in the I₃N₄ combination, both of which were at par with I₃N₂ and I₂N₄ combinations. The minimum seed (461 kg ha⁻¹) and stover (1620 kg ha⁻¹) yields were obtained in I₁N₁ combination.

Seasonal Crop Evapotranspiration and Water Productivity

Average irrigation water amount for treatments I₁, I₂, and I₃ including a common irrigation of 20 mm was 70, 120 and 170 mm, respectively (Table 4). Average amount of Re for each treatment was 14.6 mm, while ΔSW value were 36.4, 35.6 and 34.6 mm for I₁, I₂ and I₃, respectively. Thus, the average seasonal ETc for green gram under I₁, I₂ and I₃ was 121.0, 170.2 and 219.2 mm, respectively. Both CWP and IWP were found to increase with a decrease in irrigation level. The mean maximum CWP and IWP of 0.47 and 0.81 kg m⁻³, respectively were observed in I₁ than in I₂ and I₃ with respective values of 0.36 and 0.51 kg m⁻³ and 0.30 and 0.38 kg m⁻³, respectively. Likewise, N₂ and N₄ resulted in maximum CWP and IWP of 0.42 and 0.64 kg m⁻³and of 0.45 and 0.68 kg m⁻³, respectively as compared to N₁ and N₃. The association between irrigation and nutrition revealed that the maximum CWP and IWP of 0.55 and 0.96 kg m⁻³, respectively were found in the I₁N₄ combination, while the minimum corresponding values of 0.23 and 0.30 kg m⁻³, respectively were recorded in I₃N₁ combination.

Table 4 Effects of different irrigation frequencies and nutrient management on water balance components, seasonal crop evapotranspiration (ETc), crop water productivity (CWP) and irrigation water productivity (IWP) of summer green gram (pooled data)

Water-Yield Relationship

The predicted association between seed yield (y) and seasonal crop evapotranspiration (ETc) for green gram was a linear and good fit between y and ETc as given bellow,

$${\text{y}}=0.8193\mathrm{ ETc}+472.95 \left({{\text{R}}}^{2}=0.98\right)$$

(15)

The coefficient of determination (R²) had a statistically significant value.

Soil Profile Moisture Extraction

Maximum soil moisture depletion by the green gram plant under different irrigation and nutrient treatments was found in the surface soil (0–15 cm layer) and it decreased with depth (Table 5). Among irrigation regimes, relatively higher moisture extraction was recorded in 0–15 cm and 15–30 cm soil depths with higher levels of irrigation at I₂ and I₃; while soil moisture extraction from 30–45 cm and 45–60 cm soil layers was greater with a lower level of irrigation at I₁. On average, upper soil layers (0–30 cm depth) contributed about 80–84%, while deeper layers (30–60 cm depth) accounted for 16–20% of total soil moisture extraction. Almost the same pattern of soil moisture extraction was noticed for different nutritional treatments, but in an irregular or asymmetrical direction.

Table 5 Soil moisture extraction pattern in the root zone profile for summer green gram as influenced by different irrigation frequencies and nutrient management (pooled data of 2-year)

Post-Harvest Soil Nutrient Status and Plant Nutrient Uptake

The available N, P and K status in the soil at harvest was significantly affected by the different irrigation regimes and nutritional levels (Table 6). The lower frequency of irrigation (I₁) resulted in higher N, P and K availability in the soil (85.58, 16.77 and 70.38 kg ha⁻¹, respectively); while the higher frequency of irrigation (I₃) rendered soil nutrient availability corresponding to 79.52, 14.64 and 60.96 kg ha⁻¹, respectively. Likewise, the higher level of nutrient application (N₄) resulted in maximum available N, P and K contents in the soil (97.30, 23.97 and 80.23 kg ha⁻¹, respectively). The N₂ and N₃ treatment, on the other hand, showed moderate availability of N, P and K in the soil as 78.47, 18.61 and 63.75 kg ha⁻¹ and 88.65, 11.13 and 66.31 kg ha⁻¹, respectively.

Table 6 Nutrient uptake by plants and available macronutrient contents in post-harvest soil (0–15 cm depth) as influenced by different irrigation frequencies and nutrient management (pooled data)

The interaction effects between irrigation frequency and fertilizer level on the available N, P and K status of harvested soil were also statistically significant (Table 6). The results indicated that at a particular nutrition level, the soil nutrients were reduced progressively in varying magnitudes with the concomitant increase in irrigation frequency. The treatment combination I₁N₄ displayed the maximum availability of N, P and K (100.52, 25.82 and 86.32 kg ha⁻¹, respectively) in the soil. On the contrary, the minimum N, P and K contents (63.15, 7.86 and 47.68 kg ha⁻¹, respectively) in the soil were observed in the I₃N₁ combination.

The effects of irrigation frequency, nutrition and I × N interaction were significant on average macronutrient uptake by plant (Table 6). Plant nutrient uptake consistently increased with an incremental irrigation regime. The significantly highest uptake of N, P and K in plant (47.16, 10.11 and 31.50 kg ha⁻¹, respectively) was recorded in I₃, while the significantly lowest uptake with respective value of 42.44, 8.67 and 21.40 kg ha⁻¹ was found in I₁. Similarly, among nutritional treatments, N₄ resulted in the highest accumulation of N, P and K in plants (49.71, 10.22 and 32.20 kg ha⁻¹, respectively) and it was statistically superior to those of N₂ and N₃. The significantly lowest uptake of N, P and K for plant with 41.07, 8.37 and 22.47 kg ha⁻¹, respectively was furnished in N₁. The interactive effects showed that at a certain level of nutrition, plant nutrient uptake increased to variable extents with an increase in irrigation level. However, the I₃N₄ combination displayed the maximum uptake of N, P and K in plants (52.12, 11.37 and 37.87 kg ha⁻¹, respectively), being superior to other treatment combinations. Conversely, the minimum N, P and K uptake of 38.14, 8.12 and 18.75 kg ha⁻¹, respectively was observed in the I₁N₁ combination.

Energy Budgeting

The imposition of various irrigation scheduling and nutrient management measures and their associations in the summer green gram showed a great variation in energy budgeting (Table 7). Total energy input derived from common farm operational input, and energy consumption for diverse irrigation and nutritional treatments differed significantly. Input energy was relatively higher in I₃ (13007.2 MJ ha⁻¹) which was 27.2–74.8% higher than in I₂ and I₁. In case of nutritional schedules, higher total energy consumption was found in N₄ (11879.6 MJ ha⁻¹) and it was 14.4–39.0% more than other nutrient management strategies. Energy output for different irrigation treatments was in order of I₃ > I₂ > I₁, while for different nutritional treatments, it was arranged in the sequence of N₄ > N₂ > N₃ > N₁. Net energy gain was almost identical for all irrigation regimes, whereas N₄ and N₂ exhibited similar values among nutritional treatments and both showed higher values than the other two treatments under study. Energy use efficiency (EUE) was greater in I₁ (428.1%) followed by I₂ (332.6%), both of which were considerably higher than I₃. For nutrient management interventions, EUE was reasonably higher in N₁ and N₂ and lower in N₄. Energy productivity (EP) was higher in I₁ (76.9 g MJ⁻¹) and lesser in I₃ (49.7 g MJ⁻¹). Among nutrient management, EP was highest in N₂ (68.4 g MJ⁻¹) followed by N₄ and lowest in N₃. Specific energy (SE) and energy intensiveness (EI) values were higher in I₃ (20.3 MJ kg⁻¹and 35.3 MJ US$⁻¹, respectively) under different irrigation treatments, whereas N₃ recorded higher value for SE and N₄ for EE among the nutrient management practices. Among the effects of interaction between I and N, total input and output energy consumption were highest in I₃N₄ and lowest in I₁N₁. Net energy gain was maximum under I₂N₄ followed by I₂N₄. Energy use efficiency was higher in I₁N₁ (468.8%) followed by I₁N₂ (442.7%) and lower in I₃N₃ (257.1%). Similarly, energy productivity was comparatively higher in treatment combinations that provided less irrigation and fertilization. SE and EI were relatively higher in treatment combinations supplemented with full fertilization and higher irrigation level of these, I₃N₃ resulted in higher values of 22.3 MJ kg⁻¹and 36.8 MJ US$⁻¹, respectively.

Table 7 Energy consumption through different irrigation frequencies and nutrient management practices in summer green gram production system (2-year pooled data)

The energy share of different inputs varied with treatment. The result showed that regardless of irrigation scheduling and nutrient management practices, diesel consumption accounted for the majority of total energy input (35.9–64.6%), followed by fertilizer and manure (0–36.2%), labor (9.7–23.4%) and irrigation (7.8–15.3%). The share of plant protection chemicals, seeds and machinery in the total energy input was less than 5% in almost all the treatments in the current study (Fig. 2).

Fig. 2

Percent sharing of total energy input under various irrigation scheduling and nutrient management in green gram cultivation

Principal Component Analysis

Principal component analysis (PCA) using all the detected soil, water, plant and energy variables as a result of different irrigation and nutrition management interventions generated16 principal components (PCs) of variations to three orthogonal components (with eigen values > 1). The first two PCs were extracted to represent about 91.97% of the total variation (Fig. 3, Table 8). The first principal component (PC1) accounted for the majority of the total variation (50.72%) and it was chiefly comprised of SY, PNU, PPU, PKU, TIE and TOE with significant loadings. The second principal component (PC2) represented about 41.25% of the total variation and it was mainly composed of CWP, IWP, SE and CWU. The summation of the ranking of all the adaptive practices based on the regression factor scores at each PC level showed that I₁N₄ followed by I₂N₄ and I₃N₄ coupling respectively achieved the highest score and the lowest rank sum as compared to the remaining treatment combinations (Table 8).

Fig. 3

Principal component bi-plot among the parameters studied under different irrigation frequencies and nutrient management in green gram (SY-Seed yield; CWU-Crop water use; CWP-Crop water productivity; IWP-Irrigation water productivity; PNU-Plant N uptake; PPU-Plant P uptake; PKU-Plant K uptake; SNA-Soil N availability; SPA-Soil P availability; SKA-Soil K availability; TIE-Total input energy; TOE-Total output energy; NEG-Net energy gain; EUE-Energy use efficiency; SE-Specific energy

Table 8 Regression factor scores and ranks of the different irrigation frequencies and nutrient management practices using principal component analysis for screening the best management practice

Economics

An analysis of the benefit–cost ratio (BCR) for evaluating the economic feasibility of different treatment combinations of irrigation and nutrient management on summer green gram is presented in Table 6. Maximum gross return (USD 749.7 ha⁻¹), net return (USD 331.7 ha⁻¹) and relatively higher BCR (1.79) were observed with the I₃N₄ combination. However, the highest BCR (1.81) with modest gross return USD 701.5 ha⁻¹) and net return (USD 313.9 ha⁻¹) was found in I₃N₂ combination. In contrast, application of different irrigation frequencies excluding nutrient fertilizer or manure addition displayed minimum gross and net monetary returns as well as BCR values.

Discussion

Maximum improvement of growth and yield attributes under higher scheduling of irrigation (I₃) might be attributed to sufficient availability and accessibility of water in the plant root zone for efficient absorption by extended root mass growth along the growth period. This could have favored the plant to avoid water stress, safeguard cells turgor pressure, stimulate quicker cell division and enlargement, accelerate greater metabolic processes and photosynthetic activity and transfer food materials from leaves to other plant organs, which led to vigorous vegetative growth (Halder et al., 2022; Sosiawan et al., 2021). Minimum plant variables under low water supply condition (I₁) were attributed to severe plant water stress as a result of arrested root growth and less water utilization from a relatively dry soil condition, which in turn causes reverse osmosis in plants, an inability to meet plant water requirements and ultimately a detrimental effect on growth and yield components (Chai et al., 2016; Kumari et al., 2018; Patra et al., 2022). The shortage or deficit irrigation water regime along the growth stages inhibits cell division and cell enlargement, reduces leaf expansion and LAI, lessens various plant physiological functions like slow rate of transpiration and stomatal opening as an adaptation strategy for lower water consumption for plants, a decrease in CO₂ fixation and nutrient uptake rendering low photosynthesis efficiency and other biochemical reactions, all of which led to restricted growth and yield constituents (Hegab et al., 2014; Nemeskeri & Helyes, 2019; Poddar et al., 2022). Reduced LAI because of shrinking leaf number and smaller leaf area under deficit irrigation without external nutrient supply was related to extreme water and nutrient stresses in plants, causing a decline in photosynthetic area, decreased food material storage in leaves and reduced seed production (Gebregwergis et al., 2016). Our findings are in conformity with those reports obtained by Athnere et al. (2020) on green gram, Singh et al. (2018) on black gram-French bean cropping sequence and Yadav and Singh (2014) on pulses. Similarly, balanced application of nutrient, combining chemical fertilizers with releasing easily available nutrients and organic manure with gradual but slowly nutrients release including growth-promoting substances, is essential to ensure an optimal nutritional environment in rhizosphere during the growing period. This favorable condition encourages the plant to develop an enlarged root system for efficient absorption of nutrients and water, its transportation to leaves for greater rates of photosynthesis, the partitioning of food materials to sink and promoting cell division and expansion that led to vigorous vegetative growth and yield-related components (Tsegaye et al., 2016). Conversely, independent application of nutrients through mineral fertilizers or organic manure was not sufficient to replenish the nutritional demand of plants over a longer period, resulting in stunted plant growth and depressed yield parameters (Khawas et al., 2020). When compared to the recommended chemical fertilization, organic manuring alone exhibited the lowest plant growth, yield variables, and yield because it was unable to meet the plant’s nutrient requirements throughout the growing period due to its slow nutrient-releasing properties. The synergistic effect of irrigation and nutrition (Table 2) showed the inevitability for maintenance of higher irrigation and plenty of nutrition regimes through easily available fertilizers nutrients and slowly available manurial nutrients (I₃N₄) in the rooting depth, which could alleviate plant physiological needs along the growing period for achieving the greatest plant growth and development. Least plant growth and yield attributes under severe deficit irrigation regime with zero nutrient supply extraneously (I₁N₁) might be attributed to the limitation of water and nutrients for plant use, especially during the vegetative and pod development periods, which resulted in a drastic reduction of vegetative growth.

Increased seed and stover yield of green gram plants under high level of irrigation scheduling (I₃) could be explained by the stimulating and synergistic effects of vegetative growth and yield components of plants. Higher physiological and metabolic activities and better plant growth in response to adequate soil water availability and its access to plant might have generated more photosynthates in leaves and its efficient transport from source to sink, which helps increase the production of yield contributors like the number of pods plant⁻¹ and number of seeds pod⁻¹ and consequently seed yield. The enhancement of green gram yield with increments of irrigation was consistent with the findings of Athnere et al. (2020). Reduced seed yield under low irrigation frequency (I₁) was a continuation of severe plant water stress and failure to replenish plant evapotranspiration demand, which causes many injurious effects on plants such as stomatal closure, repressed transpiration rate, reduced CO₂ fixation, decreased nutrient and water absorption by restricted root mass, slow photosynthesis rate, poor assimilates production and partitioning of food materials into seeds (Smith, 2011; Nemeskéri and Helyes 2019). The decline in seed yield with the concomitant decrease in irrigation frequency was reported earlier by Dasila et al. (2016). Likewise, the improvement of seed yield with the conjoint application of fertilizers and organic manure (N₄) was probably due to a balanced nutrient supply throughout the growing period, which assisted an increase in LAI and encouraged more assimilates production due to a higher rate of photosynthesis (Kumar & Sahu, 2013; Poddar et al., 2022; Sridhar et al., 2020). Maximum seed and stover yield under treatment receiving three irrigation at branching, pre-flowering and pod formation stages coupled with the application of 100% RDF and FYM (I₃N₄) was particularly due to the higher availability of nutrients and moisture in the root zone and their increased uptake by the plants across the growth period (Dasila et al., 2016). Least seed and stover yields under I₁N₁ combination could be due to constant plant water-stress accompanied by nutrient constraints throughout the growing period, which proved harmful for a healthy plant growth and development for production purposes (Halder et al., 2022).

The change in root zone water storage (ΔSW) by the plant indicated that dry soil moisture condition under I₁ treatment contributed higher soil water depletion than relatively wet soil moisture conditions under I₂ and I₃ treatments. The minimal distinction in soil water content in different layers of the root zone profile under varied irrigation conditions could be attributed to the plants’ maximum abstraction of water according to their availability in the soil for production as well as their losses through evaporation and deep percolation during the growing period, almost reaching the initial water status in the soil profile. Similarly, conjunctive use of nutrients through chemical fertilizers and organic manure (N₄) was found to deplete more soil water, presumably used for nutrient solubilization, as compared to individual treatment providing either only recommended fertilizers (N₂), organic manure (N₃) or zero nutrient addition (N₁). Maximum CWP and IWP under low irrigation frequency with I₁ could be due to proportional increase in seed yield with full use of applied water by the plant for production purposes (Fig. 1). On the contrarily, minimum CWP and IWP under moderate (I₂) and high irrigation frequency (I₃) might be due to greater loss of water via soil evaporation and deep percolation that resulted in an unproductive and economic water consumption for disproportional yield enhancement (Sarkar et al., 2020). Relatively the higher CWP and IWP under soil fertilization (N₂) as well as soil fertilization in conjunction with manuring (N₄) indicated the usefulness of chemical fertilizers and their integration with organic manure for efficient utilization of irrigation water as well as total water for increasing seed yield. The variability of water productivity with respect to various irrigation and nutrition couplings showed that the highest CWP and IWP were associated with high deficit irrigation complemented with a higher level of nutrition (I₁N₄) in comparison to an unstressed irrigation regime with zero nutrient addition (I₃N₁). The probable reasons were the efficient usages of both water and nutrients by the plants for achieving maximum yield under conditions of restricted soil evaporation and leaching of water and nutrients below the root zone.

Seasonal water consumption by plants against variable irrigation water supply is the decisive factor for achieving a targeted yield. The linear bonding of grain yield (y) to seasonal crop evapotranspiration (ETc) indicates that the plant yield increased consistently with an incremental ETc. In water limiting areas, this linkage becomes unjustifiable to recommend because water productivity has been given more priority than achieving the highest or target yield. Accordingly, two curves of seed yield and CWP with seasonal ETc were drawn to estimate the optimum ETc and CWP with a compromise of yield (Fig. 1). The junction of two curves showed that the optimum seasonal ETc was 178 mm corresponding to an irrigation amount of 170 mm with a seed yield of 620 kg ha⁻¹ and a CWP of 0.36 kg m⁻³. Thus, this equation can serve as a guideline for potential yield decision related to limited or scarce irrigation water condition.

The associations between seed yield and seasonal ETc under various nutritional levels were statistically significant with high R² values ranging between 0.958 to 0.992 (Fig. 4). The yield was found to increase linearly with the increments of ETc, but yield response to water application also varied greatly for different nutritional levels. The significant linear relationship between seed yields and seasonal ETc under diverse nutritional levels implies the essentiality of a specific nutritional composition and rate according to the availability of irrigation water to harvest a good yield. In our case,a combined application of recommended fertilizer and organic manure (N₄) followed by a recommended fertilizer (N₂) at each irrigation level was found to be more suitable to maximize yield than a sole application of FYM (N₃).These mathematical approaches will be helpful in providing the guidelines for green gram cultivation under various stresses of water and nutrients, which can be used judiciously by applying the desired frequency of irrigation in tandem with a particular nutritional level to obtain the target yield.

Fig. 4

Relationship between seed yield and seasonal crop evapotranspiration under different nutritional levels in green gram cultivation (Each point representing an average value of 6 samples)

The large variability in soil moisture extraction patterns in different layers of the profile was the combined effects of the differences in soil moisture availability under various irrigation regimes adopted the magnitudes of root mass growth and its extension, the quantum of water absorption by the plant roots and the climatic condition. Adequate soil moisture in the upper soil layers (0–30 cm depth) encouraged the root mass to flourish and resulted in a higher amount of moisture absorption from the surface (Goswami, 2011). Prolonged soil moisture stress under a low frequency irrigation schedule might have stimulated the plant roots to penetrate in the deeper layers of the soil profile for extracting soil moisture to meet the crop evapo-transpiration demand (Patra et al., 2021). Under soil moisture stress, a higher amount of soil moisture extraction from the deeper soil layers (45–60 cm) under treatment N₄ indicates that mineral fertilization with organic manuring might have favored more elongation and spread of plant roots to absorb more moisture from the deeper soil layers to replenish the crop evapo-transpiration need. Likewise the greater moisture extraction from the surface soil (0–15 cm depth) under N₄ (fertilization and manuring) and N₂ (fertilization) treatments as compared to N₁ (control) and N₃ (manuring) treatments was most likely the result of massive root growth and extension, causing relatively a higher moisture absorption by plants from this layer, which mitigate plant water requirements.

Higher uptake of N, P and K in plants in response to a higher frequency of irrigation at I₃ might be due to greater soil water availability in the root zone throughout the growing period, causing an increase in mineralization, solubilization and mobilization of macronutrients to plants (Padbhushan & Kumar, 2014). On the contrary, water-stress conditions due to low irrigation frequency (I₁) resulted in lesser nutrient and water availability in soil, inhibited root growth, low nutrients uptake by plant, poor photosynthate production and consequent migration from leaves to other plant organs (Halder et al., 2022). These results are in conformity with the findings of Swetha and Hussain (2017). Similarly, the highest accumulation of N, P and K in plants under N₄ treatment was primarily due to abundant soil nutrients availability and consequent utilization by plants through the growth stages. The addition of organic manure could improve the soil physico-chemical and hydro-physical conditions, enhancing more soil nutrient absorption (Yanthan et al., 2010). Application of only chemical fertilizer (N₂) or organic manure (N₃) into soil was impractical to mitigate plants’ optimum nutritional requirements during the growing period, causing low plant nutrients uptake. Maximum nutrients uptake in plots receiving a higher amount of irrigation water and nutrients (I₃N₄) was possibly due to greater availability of water and nutrients in the soil, higher absorption of nutrients by proliferated root mass, and translocation of assimilate produced. Whereas, the lower nutrient uptake under treatment I₁N₁ was probably due to acute soil moisture and nutrients stress conditions, which resulted in stunted root growth, least uptake of water and nutrients and a restricted allowance of assimilate from leaves to sink (Halder et al., 2022).

The lower amount of available nutrients under a higher level of irrigation (I₃) was possibly due to the solubilization of the native and applied nutrients in the soil, rendering efficient absorption by the root system and plant utilization, leading to less available nutrients in the soil. Similarly, exhaustion of native soil available nutrient reserves under control N₁ treatment over time was due to crop removal.. This depletion in other treatments was, however, compensated with the addition of mineral fertilizer or organic manure or both. However, the maximum amount of available nutrients under the I₁N₄ combination in the post-harvest soil could be due to acute soil water-stress coupled with high nutrient supplementation through fertilizer and manure, thereby rendering a greater portion of nutrients insoluble, unavailable and thus, utilizable to the plants.

Amounts of energy used from land preparation until harvesting for any crop cultivation differ according to the requirements and it has a greater influence on energy expenditure and energy productivity (Firrisa et al., 2014; Poddar et al., 2022; Tuti et al., 2012). Identification of a suitable irrigation and nutrient management schedule in green gram cultivation is a necessity, which is closely related to optimal input and labour management practices for converting maximum energy inputs into better yields as outputs (Jat et al., 2020; Kachroo et al., 2012). It is quite evident that among the different inputs, diesel, fertilizer and labour were the dominant energy-consuming inputs under this experiment and thus to improve the EUE, appropriate management of these components is very much essential (Yadav et al., 2017). The principal component analysis displayed in the PCA bi-plot (Fig. 3) revealed the inter relationships among diverse irrigation and nutrient treatments induced seed yields and various soil, water, plant and energy variables. The figure clearly indicates that all three irrigation-nutrient management practices (I₁N₄, I₂N₄ and I₃N₄) were found equally good among all the practices explored. The superiority of these treatments over the other practices was mainly associated with the highest CWU, CWP, IWP, plant N, P and K uptake and energy components like total output energy (TOE), total input energy (TIE) and specific energy (SE) that mutually influenced the seed yield enhancement of green gram. The summation of the ranking of all the practices tested (Table 8) also confirmed the results. The PCA identified those treatments that could deliver maximum seed yield with minimal input energy exploitation and maximum output energy gains. The beneficial effect of organic manure with inorganic fertilizers supplementation enhanced yield and EUE over sole mineral fertilizer application as reported by Doran and Parkin (1994) and Das et al. (2014). Tzanakakis et al. (2012) noticed differences in output and input energy as a result of various agronomic practices that caused alterations in net energy gain and energy productivity, specifically due to variations in yields, which reflect the environmental and economic sustainability in crop production. Brar et al. (2015) in rice and Nasseri (2019) in sprinkler irrigated wheat under conservation agriculture proved that lowering irrigation scheduling can improve the EUE and SE (Table 9).

Table 9 Economics of summer green gram production under varied irrigation frequencies and nutrient management practices (2-year pooled data)

Economic appraisal in relation to irrigation and nutrition coupling revealed that the seasonal cost of production increased with increasing costs for irrigation, fertilizers and organic manure inputs. Gross return, net return and BCR values increased substantially due to an increase in seed and stover yield as a result of increasing application of irrigation amounts and nutritional inputs. These results have provided fermers a lot of opportunities to in enhance marketable yield and monetary gain according to the availability and mobilization of variable inputs resources for green gram production. For instance, implementation of a single irrigation at pre-flowering supplemented with full RDF (I₁N₂) was found satisfactory for achieving modest economic returns under conditions of limited supply of irrigation water and adequate availability of chemical fertilizers. Similarly, the strategy of three irrigations scheduled at branching, pre-flowering and pod formation stages with the combined use of 100% RDF and FYM (I₃N₄) was emerged as the best treatment combination for accomplishing highest marketable yield and economic benefits under conditions of assured availability of irrigation water and inorganic and organic sources of nutrients. Our results support the study carried out by Halder et al. (2022), who observed a large variation in economics due to flexible irrigation schedule and nutrients application in green gram plant.

Conclusions

Proper scheduling of irrigation and nutritional supplementation are the deciding factors for enhancing the yield, water productivity, energy indices and economic gains of summer green gram grown in the adverse coastal agro-ecosystem. Based on principal component analysis and economic assessment of adapted different irrigation and nutrient management practices, it was found that three irrigations scheduling approaches at branching, pre-flowering and pod formation stage complemented with 20–40-20 kg NPK ha⁻¹ plus 5 t ha⁻¹ of farmyard manure was identified as the suitable coupling of irrigation and nutrition for achieving the maximum growth, yield components, seed yield, stover yield, economic benefits and macronutrients uptake with higher total output energy (TOE) and net energy gain (NEG) in green gram and hence can be recommended to farmers of the resources-scarce coastal agro-ecological region of eastern India or similar geographical conditions around the world. The imposition of moderate and severe deficit irrigation regimes during the growing period resulted in reduced water consumption and had negative impact on plant variables, yields and energy output values. Under severe water limiting condition, one irrigation at pre-flowering stage in conjunction with higher level of balanced nutrient application through inorganic fertilizers and organic sources,, could achieve maximum water productivity. The study also established the amount of seasonal water consumed corresponding to the irrigation amount that could provide an optimum balance between seed yield and water productivity in the plant.