Optimizing water use efficiency of sprouting broccoli through irrigation and nitrogen management under drip irrigation in semi-arid condition

Jitendra Rajput; Man Singh; K Lal; Manoj Khanna; A Sarangi; J Mukherjee; Shrawan Singh; P K Sahoo; Dimple; Kiruthiga Balakrishnan

doi:10.1038/s41598-026-39272-2

. 2026 Mar 24;16:14844. doi: 10.1038/s41598-026-39272-2

Optimizing water use efficiency of sprouting broccoli through irrigation and nitrogen management under drip irrigation in semi-arid condition

Jitendra Rajput ^1,^2,^✉, Man Singh ¹, K Lal ¹, Manoj Khanna ¹, A Sarangi ¹, J Mukherjee ³, Shrawan Singh ⁴, P K Sahoo ², Dimple ⁵, Kiruthiga Balakrishnan ¹

PMCID: PMC13168641 PMID: 41876569

Abstract

Broccoli is a high-value vegetable, and its potential production can be achieved with appropriate irrigation and nutrient management strategies, as adequate moisture and nitrogen availability in the crop root zone are essential for an optimal yield. Also, irrigation and nitrogen levels have significant effects on broccoli yield, biomass production, and water productivity. Therefore, field experiment was conducted to assess the response of broccoli (Brassica oleracea L. var. italica Plenck) cultivar Palam Samridhi at the research farm, Water Technology Centre, Indian Council of Agricultural Research-Indian Agricultural Research Institute (ICAR-IARI), New Delhi, India, during Rabi seasons in 2016–17 and 2017–18. The field experiment was laid out in split plot design of experiment methods with three irrigation levels viz., full irrigation (100% of field capacity (FC) (I1), 75% of FC (I2), and 50% of FC (I3) and four nitrogen levels viz., 50% of recommended dose of nitrogen (RDF) (N1), 75% RDF (N2), 100% RDF (N3), and 125% RDF (N4). The crop growth and yield attributes were analyzed statistically at 5% level of significance. The results revealed that the plant growth parameters viz., plant height, number of leaves and leaf area index did not show a statistically significant variation at 30 days of transplanting (DAT), however, all these three were found to be significant at 60 DAT and at harvesting of the crop. It was found that the irrigation and nitrogen levels had significantly influenced the broccoli head length, head diameter and head weight. As compared to irrigation treatment I3, the auxiliary shoot and head yield was increased by 47.87 and 16.89% in 2016–17, and 46.49 and 15.50% in 2017–18 in full irrigation level (I1). In both 2016–17 and 2017–18 crop seasons, increasing the nitrogen level from N1 to N4 resulted in significant improvements in auxiliary shoot and central head yield. Total yield and harvest index showed statistically significant variability under irrigation and nitrogen levels. The water use efficiency (WUE) and irrigation water productivity (WP) were increased by 20.32 and 19.86% by increasing nitrogen level from N1 to N4, and 9.86 and 54.79% by increasing the irrigation level from I3 to I1, respectively in 2016–17. Similarly, in 2017–18, the WUE and WP increased by 19.20 and 20.35% by increasing the nitrogen from N1 to N4, and 12.11 and 44.75%, by increasing irrigation level from I3 to I1, respectively. The study revealed that all the crop growth and yield attributes were found to be best under I1N4 treatment. Irrigation levels effect was more than the nitrogen levels effect on the crop growth and yield parameters. The interaction effects of the irrigation and nitrogen levels on yield was found non-significant (5%). The water use efficiency was found to be the best in I2N4 treatment. The study found that proper irrigation and nitrogen management significantly improve WUE in broccoli cultivation. The I2N4 treatment resulted in the highest WUE, demonstrating that deficit irrigation with increased nitrogen supply can improve water productivity.

Keywords: Water use efficiency, Economic water productivity, Drip fertigation, Cost of cultivation, Auxiliary shoots

Subject terms: Environmental sciences, Plant sciences

Introduction

Water is an essential resource for sustenance of life. It is a renewable resource yet limited, thus careful development, conservation, and exploitation is needed for its sustainable development¹. Water is a limiting factor in agricultural production. This fact is intensified in regions where water is scarce like arid and semi-arid regions. In these regions, properly managing irrigation is fundamental for sustainable production². Efficient and precise water use by irrigation systems is becoming increasingly important, especially in arid and semi-arid regions with limited water resources. Therefore, irrigation scheduling is important in the crop cultivation thus has a direct effect on water use efficiency (WUE)^3,4. Irrigation scheduling requires an understanding of the pattern of plant water use, which is affected by factors such as weather, growth stage and canopy wetness⁵.

In India, the net irrigated area during 2021–22 was 77.9 million hectares (Annual Report, 2021–22, Govt. of India). Some agricultural techniques have made it possible to optimize irrigation management, from drip irrigation systems to regulated deficit irrigation strategies able to maintain yields with lower irrigation volumes^6–8. Irrigation management is critical for the row crops, horticulture and vegetables among others. Vegetables plays a vital purpose not only in providing a balanced diet, nutrient rich, and as a cash crop, but also contribute in improving the farmer’s economic status⁹. There is need to increase production of vegetables to meet our domestic consumption as well as to increase the export potential of vegetables from India¹⁰. In India, area under vegetable cultivation occupies 9.49 million hectares (mha) with total production of 167.05 MT having a productivity of 17.60 tonnes per hectare¹⁰.

Broccoli (Brassica oleracea L. var. italica) is a widely cultivated dark green winter vegetable of the family Brassicaceae and a member of the cole crop group, valued for its rich nutritional profile containing glucosinolates and other dietary compounds with anticancer properties^11–13. It’s consumption has increased due to its distinct flavor and well-known health advantages, and since it can be used in both fresh and processed versions, it is regarded as a dual-purpose vegetable that closely resembles cauliflower, a different cultivar of the same species^14,15. The global production of broccoli was 71.45 million tonnes (MT) in 2017 with a share of 48% in China and 12% in India¹⁶. It is rich source of nutrition containing dietary fiber (2.6%), protein (3.3%), fat (3.3%), carbohydrate (5.5%), vitamin A, B and C, antioxidants, phytochemicals and minerals like P, Ca, Mg, Mn, Fe, Zn and Se (Andrés et al.¹⁵^,¹⁷). It requires frequent and light supply of irrigation and chemical fertilization to avoid water and nutrient deficiencies for attaining maximum production^18,19. The abundant or limited supply of these resource inputs at any stage of the growth cycle impacts crop growth and productivity²⁰.

Broccoli is grown under irrigation conditions and optimal nitrogen application is a determining factor for its optimal productivity and quality²¹, Erdem et al.²²). Nitrogen (N) is a widely used plant nutrient for crop production as it is essential for the growth and development of vegetative parts of the plant²³. The excess application of N fertilizer and excess irrigation after fertilizer application can lead to significant nitrate leaching out of the root zone²⁴. Thus, injudicious use of N fertilizer and irrigation may lead to environmental degradation. Broccoli is a rapidly growing crop that takes up little nitrogen in its first 40 days of growth, and most nitrogen accumulation may occur during the final 30 to 50 days preceding harvest²⁵. It is highly responsive to N fertilizer application and excessive N inputs can cause decrease of quality^26,27. Therefore, ensuring optimum nitrogen in the broccoli crop is important for higher yield and water use productivity. Surface irrigation is the prominent method of water application for the irrigation of crops, which has very low efficiency and productivity compared to micro irrigation techniques²⁸. Precise water management through micro-irrigation/drip irrigation has shown numerous benefits in terms of irrigation water savings, increases in yield and quality, and nutrient use efficiency in horticulture and vegetable crops²⁹. Drip irrigation is one of the efficient methods of irrigation having about 90% water use efficiency^30,31. Water saving from the drip irrigation system varied from 12 to 84% for different crops besides increasing the production of crops³². Shareef et al.³³ reported that the drip irrigation method saved 56.4% water and gave 22% more yield as compared to that of furrow irrigation method. Zhang et al.³⁴ studied that grain yield of spring maize and economic return did not significantly change in response to a 10% decrease in irrigation level while water use efficiency was increased by 4.61 to 6.66%. Drip irrigation in broccoli under varying irrigation regimes with surface mulch resulted in the saving of 21.2% to 52.7% water compared to surface irrigation which would bring 17.1 to 53.3% additional area under irrigation¹⁷. Excessive irrigation, on the other hand, causes loss of water and nutrients through deep percolation, reducing yield besides causing groundwater pollution³⁵. Crop parameters viz., plant height, dry matter production, yield, and its attributes were found to be maximum under drip irrigation over conventional irrigation in Broccoli³⁶. Irrigation management is a complex activity influenced by many factors closely related to the soil–plant–atmosphere continuum. Other factors are also important in deciding the irrigation criteria, such as the quality of the irrigation water or the crops’ nutrition. Drip irrigation with fertigation provides an efficient and cost-effective way to supply water and nutrients to crops³⁷. Fertigation enables the application of soluble fertilizers and other chemicals along with irrigation water uniformly and more efficiently³⁸. Moreover, drip irrigation systems are also adaptable to fertilize injection into the distribution system during the whole growing season because the water is applied directly to the plant root zone²². Improving water efficiency and minimizing agricultural pollutant emissions while maintaining crop yield and quality are therefore important challenges modern agriculture facing today. Improvement in drip irrigation systems and optimization of nitrogen fertilizer use is an effective strategy to solve this paradox^39,40.

With the advancements in the technologies, the application of nitrogen and irrigation water can be controlled and scheduled whenever required in drip irrigation system. The amount of crop biomass and leaf area index (LAI), increased as the N rate increased^41,42. Better crop growth of broccoli was obtained by one common surface irrigation at the time of transplanting and rest of the irrigations scheduled through drip at 0.8 potential evaporation fraction (PEF) at an alternate day along with the application of 80% RDF (96- 48–00 kg NPK ha-1) through fertigation in six equal splits at weekly interval started from a week after transplanting of broccoli¹⁷. Several other authors reported that the broccoli crop growth and yield attributes are influenced by different nitrogen levels and irrigation regimes^43,44. Broccoli is a high-value vegetable crop, but there has been little research into how irrigation and nitrogen management affect its development and resource efficiency. This is especially true in semiarid areas, where soil moisture deficiencies and nutrient imbalances are typical agricultural production concerns. Most of the study to date has focused on how irrigation or nitrogen influences broccoli growth on its own. There has been little investigation into how employing both together affects key growth parameters, biomass buildup, yield qualities, and water usage efficiency in broccoli growing. Given the increased emphasis on precision water-nutrient management to improve production and protect water resources, understanding these interactions in the field is critical for developing site-specific recommendations. Therefore, this study was conducted to address this shortcoming by meticulously examining the impact of various irrigation and nitrogen levels on broccoli growth characteristics, yield components, and water usage efficiency in semi-arid climate using drip irrigation technique.

Materials and methods

Study area description

ICAR-Indian Agricultural Research Institute (IARI) is situated in Delhi between the latitudes of 28°37′22″ and 38°39′05″ N and longitudes of 77°8′45″ and 77°10′24″ E at an average elevation of 228.61 m above the mean sea level. A map of IARI, showing the location of experimental site at Water Technology Centre farm is presented in Fig. 1. The Fig. 1 was prepared using the ArcGIS 10.2 version (https://appsforms.esri.com/products/download/). The climate of Delhi is categorized as semi-arid sub-tropical with hot and dry summer and cold winter and falls under the agro-climate zone of “Trans-Gangetic Plains” in the Agro-eco-region- IV⁴⁵. Summer months i.e., May and June are the hottest with the maximum temperature ranging between 41 and 46 °C while temperature falls to its lowest during January with minimum temperature ranging between 4 and 7 °C. The mean open pan evaporation reaches as high as 12.88 mm and as low as 0.6 mm per day during the months of June and January, respectively. The mean annual rainfall based on 100 years record (1915–2015) is 712 mm. About 80% of the annual rainfall is received during monsoon (June–September) and some rainfall is also received during winter season (December-March). The average relative humidity varied from 34.1 to 97.9% and wind speed from 0.45 to 3.96 m/s. The required weather data (minimum and maximum temperature, rainfall, sunshine hours, wind speed, etc.) for estimation of reference evapotranspiration was acquired from Institute Observatory, Division of Agricultural Physics, ICAR-IARI. New Delhi. Daily values of weather parameters during the crop growth months are displayed in Figs. 2 and 3 for 2016–17 and 2017–18 seasons, respectively.

Fig. 1 — Location map of the field experimental site.

Fig. 2 — Daily weather data during November to February in 2016–17 crop season.

Fig. 3 — Daily weather data during November to February in 2016–17 crop season.

Basic properties of soil at experiment site

Soil samples were collected from different layers i.e., 0–15 cm, 15–30 cm, and 30–45 cm, using soil auger to characterize the soil from experimental site 1 and 2. These samples were analyzed in laboratory for determining the physical properties of soil such as particle size distribution, soil texture, bulk density, field capacity, permanent wilting point, hydraulic conductivity and available nitrogen, phosphorous and potassium etc. Details of the methodology of soil analysis is given in Table 1.

Table 1.

Methodology for soil analysis.

Parameters	Methodology/Instruments	References
Soil texture	Hydrometer	⁴⁶
Bulk density	Gravimetric (core)	⁴⁷
Hydraulic Conductivity	Constant head permeameter	⁴⁷
Field capacity	Pressure plate apparatus (1/3 bar)	⁴⁸
Permanent wilting point	Pressure plate apparatus (15 bar)	⁴⁸
Nitrogen	KEL PLUS semi auto analyzer, Kjeldahl method	⁴⁹
Phosphorus	Olsen’s method and Colorimeter	⁵⁰
Potassium	Flame Photometer	⁵¹

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The soil of the experimental site was deep, well-drained sandy loam comprising of 54% sand, 20% silt and 27% clay for 0–15 cm depth. The average sand, silt and clay percentage in the 0–45 cm depth soil was 51.67%, 20.67%, and 27.67%, respectively. The bulk density of soil for the 0–45 cm depth was determined as 1.53 g cm^–3. The soil layer wise soil properties for the experimental site are displayed in the Table 2. The soil texture map using the USDA Soil Texture diagram was drawn and is displayed in the Fig. 4.

Table 2.

Physical properties of soil at the experimental site.

Depth (cm)	Particle size distribution			Textural class	Bulk density (gm cm^–3)	Field capacity (%)	Wilting point (%)
Depth (cm)	Sand (%)	Silt (%)	Clay (%)	Textural class	Bulk density (gm cm^–3)	Field capacity (%)	Wilting point (%)
0–15	73	15	12	Sandy loam	1.34	17.95	6.27
15–30	60	18	22	Sandy Clay loam	1.36	23.25	6.68
30–45	56	21	23	Sandy Clay loam	1.40	24.31	9.88

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Fig. 4 — USDA triangle based soil texture classification.

Initial soil samples were collected before planting from different depths and available Nitrogen (N), Phosphorus (P) and Potassium (K) were determined by using standard laboratory methods. The available N was estimated by alkaline KMnO4 method⁵² and available P content in soil was estimated by Olsen’s method⁵⁰. Whereas available K was determined using neutral normal ammonium acetate extraction method and flame photometry as described by Jackson⁴⁷ and expressed in kg/ha. Available N, P and K in different soil depths are presented in Table 3 for experimental site.

Table 3.

Available N, P and K in soil before transplanting of broccoli.

Soil depth (cm)	Available nitrogen (kg/ha)	Available phosphorus (kg/ha)	Available potassium (kg/ha)
0–15	86.35	24.32	125.65
15–30	78.68	18.23	95.65
30–45	72.62	12.58	86.24

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Design and installation of drip irrigation system

An online drip fertigation system was designed and laid on the experimental plots for broccoli at both the experimental sites. The control head of the system consisted of sand filter, screen filter, flow control valve, pressure gauges etc. The system was connected to fertigation pump for the application of fertilizers. A PVC sub main line (50 mm outer diameter, 6.0 kg/cm² working pressure class was laid for the experimental area. Lateral lines of LDPE (16 mm diameter) were taken out from the sub main line for the irrigation of broccoli crop. The lateral lines were spaced at 45 cm interval. The online drippers with rated discharge rate of 4 l/h were installed on the laterals. The spacing between the drippers was kept 45 cm as desired in the experiment. Each lateral line was provided with flow control valve at the start of the line to achieve specific irrigation and fertigation operation for each treatment. A separate movable set up of lateral lines having drippers was made for applying nitrogen levels to treatment plots.

Design and layout of field experiment

Field experiments were conducted with Palam Samridhi variety of broccoli in the year 2016–17 and 2017–18, respectively. One month old healthy seedlings of broccoli were transplanted on 15 November in 2016–17 and 11 November in 2017–18, respectively at the experimental site. The field experiment was executed with twelve treatments and three replications. The field experiment was conducted on a total area of 525.3 m² with individual plot sizes of 3.5 m × 2.1 m. Broccoli (Brassica oleracea L. var. italica) variety Palam Samridhi was selected as the test crop. The crop was transplanted with a uniform spacing of 45 cm × 45 cm between plants and rows. Nitrogen fertilizer was supplied through urea, with four application levels: 62.5 kg N/ha (N1), 94 kg N/ha (N2), 125 kg N/ha (N3), and 156 kg N/ha (N4). Irrigation was applied under three regimes—full irrigation at 100% field capacity (I1), 75% field capacity (I2), and 50% field capacity (I3). The experiment was designed with a total of 12 treatments, replicated three times to ensure statistical reliability. The recommended dose of fertilizers (RDF) were 125 kg/ha of N, 60 kg/ha of P, and 40 kg/ha of K. Application of single super phosphate (SSP) and muriate of potash (MOP) (P&K) as basal dose as per RDF. Details of the treatments is given in Table 4. Layout of the field experiment site is displayed in Fig. 5.

Table 4.

Treatment details for the experimental site.

Treatment	Combination	Description
T1	I1N1	Full irrigation (I1) with 50% (N1) nitrogen of RDF
T2	I1N2	Full irrigation with 75% (N2) nitrogen of RDF
T3	I1N3	Full irrigation with 100% (N3) nitrogen of RDN
T4	I1N4	Full irrigation with 125% (N4) nitrogen of RDF
T5	I2N1	75% irrigation (I2) of with 50% nitrogen of RDF
T6	I2N2	75% irrigation with 75% nitrogen of RDF
T7	I2N3	75% irrigation with 100% nitrogen of RDF
T8	I2N4	75% irrigation with 125% nitrogen of RDF
T9	I3N1	50% irrigation (I3) with 50% nitrogen of RDF
T10	I3N2	50% irrigation with 75% nitrogen of RDF
T11	I3N3	50% irrigation with 100% nitrogen of RDF
T12	I3N4	50% irrigation with 125% nitrogen of RDF

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Fig. 5 — Layout of the experiment field.

Palam Samridhi seeds were sown 150 g in nursery with 10 cm distance between two adjacent rows. Watering and weeding carried out as needed. Sowing was carried out manually on 14^th and 10^thOctober in 2016 and 2017, respectively.

Irrigation and fertigation schedule

The estimation of water requirement of a crop is one of the basic needs for crop planning on the farm. Total water used from the field preparation to harvesting of the crop was considered in computing water requirement. Water requirement is calculated by sum of irrigation requirement, effective rainfall and soil profile contribution. Effective Rainfall was calculated with the help of USDA Soil Conservation Service formula in CROPWAT 8. The quantity of irrigation water for each treatment was calculated based on the soil moisture content before irrigation and root zone depth of the plant using the Eq. 1:

where,

SMD = Soil moisture deficit (mm).

Ө_FC = Soil moisture content at field capacity (%).

Ө_I = Soil water content before irrigation (%).

D = Depth of root development (mm).

Bd = Bulk density of the particular soil layer (g cm^–3).

MAD = Management allowable Depletion (%).

f: Coefficient for each irrigation treatment levels in the experiment.

The coefficient of each treatment f (I1) = 1 (Full irrigation), f (I2) = 0.75 (75% of FC), and f (I3) = 0.50 (50% of FC), were used for different treatments to estimate the quantity of irrigation water. In the irrigation treatments (50 and 75% of FC), irrigation was scheduled on the same day as that of the fully irrigation treatment plots, but the irrigation depths were reduced to 25% and 50% of the full irrigation for I2 and I3 treatments, respectively.

Fertigation schedule

Treatment-specific nitrogen fertiliser requirements were determined depending on plot size. The full nitrogen dose was given via fertigation in four equal splits at 15-day intervals, with urea as the nitrogen source after adequate dilution. The fertigation regimen included applications throughout the vegetative stage (15 days after transplanting, DAT), head initiation stage (30 DAT), head enlargement stage (45 DAT), and pre-mature stage (60 DAT).

Field observations

Field observations included soil and plant sampling according to pre-decided schedule and yield and its attributes recording. A detail description of the procedures adopted for these parameters are presented in the following paragraphs. To determine uniformity in application of water, it is necessary to evaluate emitter discharge uniformity and system performance. The emission uniformity of water application was carried out before the start of the season during both the seasons i.e., 2016–17 and 2017–18. The 32 drippers were randomly selected from head, middle and tail end of the drip laterals. The dripper outflow of each emitter was collected in the cans for known duration. The emission uniformity (EU) of the dripper flow rates and coefficient of variation (CV) of the dripper discharge were calculated using following Eqs. 2 & 3:

where, SD_q is standard deviation of the dripper discharge (lph), q_aveg is the average dripper flow rate (lph), and q_lq is the average of lowest one-fourth of the drippers discharge (lph).

Five plants were chosen randomly from the middle rows of each unit plot, except for curd yields, which were recorded plot-wise. Data were collected for various parameters, including plant growth, yield attributes, and overall yields, to evaluate the impact of different treatments in this experiment. Measurements of plant height, number of leaves per plant, leaf length, and leaf breadth were taken at 30, 60 after transplanting (DAT) and at harvest. All other yield-related characteristics and yield parameters were recorded during and after harvest. Plant Height: Plant height was assessed at 30 days after transplanting (DAT), 60 DAT, and at harvest by measuring the distance in centimetres from the ground level to the tip of the longest leaf of five randomly selected plants. The average value of these measurements was subsequently calculated. Number of leaves per plant was counted from five randomly selected plants at30 days after transplanting (DAT), 60 DAT and at harvest and their mean value was calculated.

Central Head Diameter: The diameter of five randomly selected central head was measured at the time of harvesting and average was determined. The numbers of secondary heads were counted on five randomly selected plants per plot and average number of secondary heads for the plant was calculated. The weight of central head was recorded on randomly selected five plants from each plot and mean value was worked out and was expressed in grams. Yield data for both the main head and secondary heads were collected from all the plants in the plot in kilograms. Subsequently, the yield per hectare was calculated based on this data and expressed in metric tons per hectare (t/ha).

Harvest Index (HI): The harvest index was computed by taking the ratio of curd weight per plant to the total plant weight in a specific area, and it was expressed as a percentage.

Water use efficiency

Water use efficiency (WUE) is an operationalized concept for resource use efficiency (as defined above) and is a common metric used to assess ratio of plant production to water consumed⁵³. The ratio of crop yield or carbon uptake to water consumed or used are common indicators of WUE, which are assessed over spatial scales ranging from the leaf to the globe and at time scales from seconds to years⁵⁴.

Irrigation water productivity

In the context of crop production, irrigation water productivity is characterized as the ratio between the crop yield and the depth of irrigation water applied in the field⁵⁵.

Economic water productivity

Economic water productivity (EWP) refers to the ratio between outputs and inputs in monetary terms⁵⁶. With reference to the formula, while some authors consider the gross margin as the numerator, some others use the net margin (particularly in those cases where crops need huge initial investments) or the profits⁵⁷. The economic water productivity (EWP) is here obtained by dividing the net benefit by the irrigation water used:

Statistical analysis

The data collected from various treatments in both the field and laboratory were subjected to statistical analysis using the method proposed by Panse and Sukhatme (2000). The significance of treatment variations was tested using the F test⁵⁸, where the calculated F value was compared to the tabulated value. If the F test showed significance, the standard error of mean and critical differences was computed to determine the superiority of one entry over others. To establish the critical difference at a 5% level of significance⁵⁹, reference tables were consulted.

Results

Drip irrigation system (DIS) performance

Accurate irrigation application is crucial for the optimal performance of a drip irrigation system. Coefficient of variation (CV) and emission uniformity (EU) were used for performance parameters of the DIS in 2016–17 and 2017–18 (Table 5). The DIS was operated at pressure of 1.5 kg/cm² during both the cropping seasons. In 2016–17, the CV of drip discharge rate was 0.075. Similarly, the EU was 92.2%in 2016–17. A similar trend of CV and EU was observed in 2017–18 crop season. The CV and EU were 0.065 and 95%, respectively. These values indicate the variability in flow rates among the emitters. Generally, low CV suggests good system performance throughout the cropping season. Although the CV of the system in this case was slightly higher than the recommended values, it still fell within an acceptable range.

Table 5.

Performance evaluation of DIS.

Crop seasons	Performance criteria
Crop seasons	CV	EU (%)
2016–17	0.075	92.2
2017–18	0.065	95.0

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The EU values recorded for both cropping seasons exceeded 90%. According to Pitts⁶⁰ an EU greater than 90% implies excellent functioning of the drip system. Therefore, based on the EU values obtained, the system performed well in terms of delivering water uniformly to the crops. However, upon analysing (Table 5), it was observed that as the CV value decreased in the year 2017–18, the EU value increased compared to the 2016–17 crop seasons for start of the season and at the end. This is due to that the new lateral and dripper system were installed in 2017–18 crop season. The decrease in EU suggests decrease in the uniformity of water distribution within the system. It is likely that the water quality, containing certain contaminants or particles, led to the clogging of emitters and affected their performance for older DIS components. In summary, the trickle irrigation system exhibited acceptable performance based on the CV and EU values. Although the CV was slightly higher than the recommended range, it did not significantly affect the overall performance.

Crop water use (CWU)

The CWU was computed by soil moisture depletion method separately for each treatment. It was observed that the CWU for broccoli crop varied among different irrigation treatments and the effect of the nitrogen levels was insignificant for both the crop seasons. The CWU was obtained highest for the full irrigation (I1) treatment as no water stress was allowed and the irrigation was scheduled when the soil moisture level reached to the manageable allowable depletion (MAD) point and filled the crop root zone till the field capacity (FC). The irrigation in the deficit irrigation treatments (I2 and I3) was scheduled at the same time with the reduced amount as per the prescribed treatments criteria. The I2 treatment plots were give irrigation to fill the crop root zone up to 75% of FC and I3 for 50% of FC. The maximum value of CWU was 145.2 mm in 2016–17 and 143.6 mm in 2017–18. Likewise, the lowest value of CWU was obtained in I3 irrigation level with values of 105.0 and 92.5 mm, respectively in 2016–17 and 2017–18 crop seasons. The variability in the CWU is attributed to the weather conditions prevailed during the crop seasons. The seasonal CWU in I1 level was 38.1% and 55.3% higher compared to the I3 irrigation level in 2016–17 and 2017–18, respectively. Similarly, the CWU in I1 level was higher by 11.5 and 25.0% compared to the I2 level in 2016–17 and 2017–18, respectively. Irrigation levels wise the CWU is displayed in Fig. 6.

Fig. 6 — Actual crop water use of broccoli during 2016–17 and 2017–18 crop seasons.

The plant height of the tagged plants was measured at 30 DAT, 60 DAT, and at harvest, and the observations are displayed in Table 6. The effect of irrigation levels revealed that the maximum plant height was 47.08 cm and 48.41 cm under irrigation I1 level in 2016–17 and 2017–18 crop seasons at the harvest stage. Irrigation level I2 and I3 recorded plant height of 45.54 and 41.84 cm, respectively in 2016–17 and 46.51 and 42.09 cm in 2017–18, respectively. The maximum plant height under I1 level is attributed to the optimum moisture content availability in the crop root zone which resulted into conducive plant growth and development. While in I2 and I3 levels, the soil moisture level in the crop root zone was not brought to FC, thus lesser soil moisture availability between the irrigation events. The plant height was minimum under nitrogen level N1 which was obtained to be 42.19 and 43.29 cm in 2016–17 and 2017–18 crop seasons, respectively at the harvest stage. Similarly, the maximum plant height was found for N4 level i.e., 47.62 and 48.87 cm in 2016–17 and 2017–18, respectively. The nitrogen level positively affects the plant height to certain extent; thus, the best results obtained with the high nitrogen level. The results of the plant height measured at different time during the crop seasons are displayed in the Table 6. It was found that the irrigation as well as nitrogen levels had significantly affected plant height. The interactive effects of the irrigation and nitrogen levels results revealed that the maximum plant height of 50.1 cm and 54.4 cm was recorded in I1N4 treatment in 2016–17 and 2017–18, respectively. Further, the maximum plant height of 47.0 and 51.9 cm was found in I1N3 treatment in 2016–17 and 2017–18 crop seasons, respectively. The lowest plant height was recorded in I3N1 and I3N2 treatments at 30 DAT, 60 DAT and harvest in both the crop seasons. It is remarkable that the plant height was greatly influenced by the irrigation and nitrogen levels. At 30 DAT, the treatments did not show any significant different in the plant height. However, at 60 DAT and at harvest, the plant height was found staticallysignificant by individual effects of irrigation and nitrogen levels. However, the interaction effect did not show any significant variation among the treatments. Dunken test was used for multiple mean comparison. It was found that the irrigation and nitrogen levels significantly impacted the plant height.

Table 6.

Effects of irrigation and nitrogen levels on the plant height at 30 DAT, 60 DAT and at harvest.

Treatments	Plant Height, cm
	2016–17			2017–18
	30 DAT	60 DAT	At harvest	30 DAT	60 DAT	At harvest
I₁	22.33^a	45.33^c	47.08^b	22.91^a	46.55^b	48.41^b
I₂	21.53^a	44.14^b	45.54^b	22.49^a	45.57^b	46.51^b
I₃	19.81^a	41.20^a	41.84^a	20.65^a	42.50^a	42.09^a
CV (%)	5.24	5.70	7.76	6.94	6.08	6.81
SEm +	3.85	0.70	0.84	1.710	0.79	0.90
CD(P = 0.05)	NS	3.88	1.24	6.72	3.09	3.54
N₁	20.50^a	41.09^b	42.19^b	21.38^a	42.30^c	43.29^c
N₂	20.96^a	43.02^b	43.99^ab	21.44^a	44.22^bc	44.91^bc
N₃	21.31^a	44.17^a	45.47^ab	22.20^a	45.58^ab	46.43^ab
N₄	22.12^a	45.94^a	47.62^a	23.03^a	47.39^a	48.87^a
CV (%)	3.82	6.37	5.23	23.64	5.20	5.12
SEm +	3.24	0.92	0.78	1.73	0.83	0.72
CD(P = 0.05)	NS	2.75	2.32	5.15	2.47	2.14
Interaction
SEm +	5.62	1.60	1.35	3.00	1.44	1.25
CD(P = 0.05)	NS	NS	NS	NS	NS	NS
SEm +	6.21	1.42	1.54	3.12	1.47	1.41
CD(P = 0.05)	NS	NS	NS	NS	NS	NS

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Values followed by the same superscript letter in the same column are not significantly different (P < 0.05), SEm + -standard error (mean), NS-non-significant.

The fully developed leaves were counted at 30, 60 DAT and at harvest stage of the broccoli. The average number of leaves recorded for each treatment at different interval is presented in Table 7. Irrigation levels had influenced the number of leaves per plant significantly. The maximum number of leaves per plant recorded were 23.69 and 23.76 in 2016–17 and 2017–18 at the harvest stage for irrigation level I1. Similarly, under irrigation level I2, the number of leaves per plant recorded were 20.72 and 22.37 in 2016–17 and 2017–18, respectively at the harvest stage of the broccoli. However, irrigation level I3 recorded the minimum number of leaves plant and the number of leaves per plant were 19.80 and 21.53 in 2016–17 and 2017–18, respectively (Table 7). Also, the statistical analysis revealed that number of leaves were non-significantly differed at 30 DAT, however, at 60 DAT and at harvest stage, the number of leaves per plot were statistically different in irrigation levels at significance interval.

Table 7.

Effect of irrigation and nitrogen levels on the number of leaves at 30 DAT, 60 DAT and at harvest.

Treatments	No. of leaves per plant
	2016–17			2017–18
	30 DAT	60 DAT	At harvest	30 DAT	60 DAT	At harvest
I₁	9.19^a	22.47^c	23.69^c	9.86^a	24.01^b	23.76^b
I₂	8.61^a	20.72^b	22.27^b	9.29^a	22.82^b	22.37^a
I₃	8.32^a	18.62^a	19.80^a	8.99^a	20.09^a	21.53^a
CV (%)	9.54	11.57	11.73	8.85	11.14	5.10
SEm +	0.24	0.69	0.74	0.24	0.72	0.33
CD(P = 0.05)	NS	2.70	2.91	0.94	2.82	1.30
N₁	8.42^a	19.58^b	21.14^a	9.10^a	21.02^b	21.76^b
N₂	8.57^a	20.09^ab	21.69^a	9.24^a	21.81^ab	22.05^ab
N₃	8.75^a	20.94^ab	22.21^a	9.43^a	22.70^ab	22.70^ab
N₄	9.08^a	21.79^a	22.64^a	9.75^a	23.69^a	23.72^a
CV (%)	9.22	7.69	5.00	8.55	7.10	5.22
SEm +	0.27	0.53	0.37	0.27	0.53	0.39
CD(P = 0.05)	NS	1.57	1.09	0.79	1.57	1.17
Interaction
SEm +	0.46	0.91	0.63	0.46	0.91	0.68
CD(P = 0.05)	NS	NS	NS	NS	NS	NS
SEm +	0.47	1.05	0.92	0.47	1.07	0.68
CD(P = 0.05)	NS	NS	NS	NS	NS	NS

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The nitrogen levels had significantly influenced the number of plant leaves. The effect of nitrogen levels was found non-significant on the number of leaves per plant at 30 DAT. However, it was significantly affected the number of leaves per plant at 60 DAT and at harvest stage. The maximum number leaves per plant recorded with nitrogen level N4 were 22.64 and 23.72 in 2016–17 and 2017–18, respectively at harvest stage. Similarly, minimum number of leaves per plant recorded were 21.02 and 21.76 in 2016–17 and 2017–18, respectively. Thus, the highest and lowest number of leaves per plant were found in N4 and N1 nitrogen levels, respectively. The interactive effect of irrigation and nitrogen together was found non-significant at 5% level of significance. The highest number of leaves were recorded in I1N4 treatment at 30, 60 DAT and at harvest. Number of leaves per plant in I1N4 treatment at 60 DAT were 24.2 and 25.8 in 2016–17 and 2017–18, respectively, and at the harvest stage, the number of leaves per plant in the I1N4 treatment were 24.6 and 26.2 in 2016–17 and 2017–18 crop seasons, respectively. The lowest number of leaves per plant was recorded in I3N1 treatment at 30, 60 DAT and harvest. The number of leaves per plant were 18.3 and 18.9 at 60 DAT in 2016–17 and 2017–18 in I3N1 treatment. At the harvest time, the number of leaves per plant in the I3N1 treatment were 22.2 and 22.4 in 2016–17 and 2017–18, respectively.

The LAI measurements were taken at three specific stages i.e., 30 DAT, 60 DAT, and during the harvest period. It responded positively to the irrigation levels and was found maximum and minimum in irrigation levels I1 and I3, respectively. The LAI was found lowest at the time of transplanting, subsequently increased up to end of plant development stage. The LAI reached the maximum value during the head enlargement stage and then declined gradually at the end of the crop season. Statistical analysis revealed that the LAI was not significant at 30 DAT and was found significantly different at 60 DAT and at harvest stage (Table 8). The maximum LAI of 3.56 and 3.68 at 60 DAT in 2016–17 and 2017–18, respectively, was obtained in I1 level. Likewise, at harvest, the LAI was 3.76 and 3.84 in 2016–17 and 2017–18, respectively at I1 level. At Irrigation level I3 recorded the lowest value of LAI (2.62 and 2.71 at 60 DAT and harvest in 2016–17 and 2017–18, respectively). The LAI responded positively to the nitrogen levels. The LAI at 60 DAT and at harvest at the N1 level was 2.85 and 3.0 in 2016–17 and 2017–18, respectively. At the harvest time, the LAI was 3.05 and 3.16 in 2016–17 and 2017–18, respectively under N1 level. The higher values of LAI were obtained in N4 nitrogen level. In the growing season of 2016–17, the LAI measured 3.25 at 60 days after planting (DAT) and 3.55 at harvest time. Subsequently, in the following year (2017–18) and under the N4 level of treatment, the LAI recorded values of 3.47 at 60 DAT and 3.70 at harvest time. The effects of irrigation and nitrogen levels on the LAI is presented in Table 8. The effect of nitrogen levels on the LAI was not statistically significant at 30 DAT, however, at 60 DAT and harvest, the LAI was significantly different. The interactive effects of irrigation and nitrogen levels on the LAI was found non-significant. The maximum value LAI was 1.80, 3.77, and 3.89 at 30 DAT, 60 DAT and at harvest found in I1N4 treatment. The lowest value of the LAI was 1.56, 2.72 and 2.25 at 30 DAT, 60 DAT and at harvest found in I3N1 treatment.

Table 8.

Effects of irrigation and nitrogen levels on LAI at 30 DAT, 60 DAT and at harvest.

Treatments	LAI
	2016–17			2017–18
	30 DAT	60 DAT	At harvest	30 DAT	60 DAT	At harvest
I₁	1.76^a	3.56^c	3.76^b	1.78^a	3.68^b	3.84^b
I₂	1.69^a	3.24^b	3.70^a	1.70^a	3.47^a	3.74^a
I₃	1.62^a	2.35^a	2.47^a	2.62^a	2.60^a	2.71^a
CV (%)	12.21	7.92	5.70	7.39	10.59	17.68
SEm +	0.06	0.07	0.05	0.04	0.10	0.18
CD(P = 0.05)	NS	0.29	0.21	NS	0.39	0.69
N₁	1.64^a	2.85^a	3.05^a	1.65^a	3.0^c	3.16^c
N₂	1.67^a	3.00^ab	3.25^ab	1.67^a	3.19^bc	3.35^bc
N₃	1.72^a	3.11^bc	3.39^bc	1.72^a	3.34^ab	3.52^ab
N₄	1.74^a	3.25^cd	3.55^d	1.75^a	3.47^a	3.70^a
CV (%)	8.34	7.22	5.48	7.59	7.52	5.17
SEm +	0.05	0.07	0.06	0.04	0.08	0.06
CD(P = 0.05)	NS	0.22	0.18	NS	0.24	0.18
Interaction
SEm +	0.08	0.13	0.10	0.07	0.14	0.10
CD(P = 0.05)	NS	NS	NS	NS	NS	NS
SEm +	0.09	0.13	0.11	0.07	0.16	0.20
CD(P = 0.05)	NS	NS	NS	NS	NS	NS

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Yield attributes

The effects of irrigation and nitrogen levels on head weight, head length and head diameter are shown in Table 9. Irrigation and nitrogen levels had positively impacted all the yield attributes (Table 9). The head weight, length and diameter were found highest in I1 irrigation level. While, the lowest values of these attributes were found lowest in I3 irrigation level. The maximum values of these attributes were 297.47 g, 13.99 cm, and 16.02 cm for head weight, length and diameter, respectively in 2016–17. In 2017–18, the maximum values for head weight, length, and diameter were 303.13 g, 14.45 cm, and 16.18 cm, respectively in 2017–18. The minimum values of head weight, length and diameter were 235.83 g, 11.98 cm, and 12.76 cm, respectively in 2016–17 and 245.00, 12.35 cm, and 12.87 cm, respectively, for the same in 2017–18. Nitrogen levels also head positively affected the yield attributes and the highest values of the attributes were founded in nitrogen level N4. The lowest values of head weight, length and diameter were recorded in N1 nitrogen level in both the crop seasons. The lowest values of these attributes were243.36 g, 12.51 cm, and 13.46 cm in 2016–17, and 253.66 g, 12.88 cm, and 13.57 cm, respectively in 2017–18 for head weight, length and diameter. The maximum values of head weight, length, and diameter were 286.24 g, 13.69 cm and 15.34 cm in 2016–17 and 293.46 g, 14.16 cm and 15.51 cm, respectively in 2017–18 for the same. The interaction effect of irrigation and nitrogen treatments on the yield attributes was found insignificant. The maximum value of the head weight, length, and diameter were 315 g, 14.62 cm and 16.7 cm in 2016–17 in I1N4 treatment while in 2017–18, it was 323 g, 15.1 cm and 13.8 cm, respectively. This may be attributed to the sufficient availability of soil moisture and nitrogen during the crop season. The minimum value of the head weight, length, and diameter were 208 g, 11.38 cm and 11.5 cm in 2016–17 in I3N1 treatment while in 2017–18, 226 g, 11.7 cm and 11.6 cm, respectively.

Table 9.

Effects of irrigation and nitrogen levels on broccoli yield attributes in 2016–17 and 2017–18.

Treatments	Broccoli yield attributes
	2016–17			2017–18
	Head weight (g)	Head length (cm)	Head diameter (cm)	Head weight (g)	Head length (cm)	Head diameter (cm))
I₁	297.47^c	13.99^c	16.02^c	303.13^c	14.45^c	16.18^c
I₂	270.70^b	13.32^b	14.49^b	276.52^b	13.76^b	14.64^b
I₃	235.83^a	11.98^a	12.76^a	245.00^a	12.35^a	12.87^a
CV (%)	6.61	5.45	5.38	6.23	6.50	6.08
SEm +	5.11	0.21	0.22	4.95	0.25	0.26
CD(P = 0.05)	20.07	0.81	0.88	19.42	1.00	1.00
N₁	243.36^c	12.51^c	13.46^c	253.66^c	12.88^b	13.57^c
N₂	268.40^b	12.88^b	14.13^b	269.40^bc	13.28^ab	14.27^bc
N₃	274.00^ab	13.31^ab	14.77^ab	283.01^ab	13.75^ab	14.90^ab
N₄	286.24^a	13.69^a	15.34^a	293.46^a	14.16^a	15.51^a
CV (%)	6.08	5.42	5.15	7.11	5.45	5.17
SEm +	5.43	0.24	0.25	6.52	0.25	0.25
CD(P = 0.05)	16.14	0.70	0.74	19.37	0.73	0.75
Interaction
SEm +	9.41	0.41	0.43	11.29	0.41	0.43
CD(P = 0.05)	NS	NS	NS	NS	NS	NS
SEm +	9.62	0.41	0.43	10.96	0.41	0.43
CD(P = 0.05)	NS	NS	NS	NS	NS	NS

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Results revealed that central head yield, number of auxiliary shoots and auxiliary shoots yield was significantly influenced by application of different irrigation levels during both the years. The maximum central head yield of 15.17 and 16.02 t/ha was obtained at irrigation regime I1 in 2016–17 and 2017–18, respectively, and minimum central head yield of 11.53 and 12.02 t/ha was obtained at irrigation regime I3 in 2016–17 and 2017–18, respectively. The analysis of Table 10 data unequivocally demonstrates the application of N4 nitrogen level significantly enhanced the central head yield. It increased the central head yield per hectare by 17% over N1 level. The maximum central head yield of 14.41 and 15.11 t/ha was obtained at nitrogen levelN4, and minimum at nitrogen level N1 (12.32 and 13.20 t/ha) in 2016–17 and 2017–18, respectively. Interactive effects of different levels of irrigation and nitrogen levels on central head yield during both years were found to be non-significant (Table 10). The maximum central head yield during the 2016–17 and 2017–18 was found in I1N4 treatment with values of 15.9 and 16.7 t/ha, respectively, and minimum in I3N1treatment with values of 10.2 and 10.7 t/ha, respectively. The data revealed that irrespective of nitrogen levels, the increasing level of irrigation levels up to I1 brought significant improvement in average central head yield of sprouting broccoli. Similarly, irrespective of irrigation levels, the higher nitrogen levels plot also recorded significant higher central head yield compared to the lower nitrogen level plots.

Table 10.

Effects of irrigation and nitrogen levels on central head yield, number of auxiliary shoots per plant and auxiliary shoots yield in 2016–17 and 2017–18.

	2016–17			2017–18
	Central head yield (t/ha)	No. of auxiliary shoots (nos.)	Auxiliary shoots yield (t/ha)	Central head yield (t/ha)	No. of auxiliary shoots (nos.)	Auxiliary shoots yield (t/ha)
I₁	15.17^c	4.34^c	3.12^c	16.02^c	4.68^c	3.31^c
I₂	13.45^b	3.13^b	2.48^b	14.49^b	3.49^b	2.61^b
I₃	11.53^a	2.39^a	1.98^a	12.02^a	2.44^a	2.13^a
CV (%)	8.04	14.05	6.28	8.59	10.61	6.20
SEm +	0.31	0.13	0.05	0.35	0.11	0.08
CD(P = 0.05)	1.22	0.52	0.18	1.38	0.43	0.32
N₁	12.32^c	2.67^b	2.00^d	13.20^c	2.81^b	2.15^d
N₂	13.06^b	2.97^b	2.35^c	13.86^bc	3.15^b	2.50^c
N₃	13.75^ab	3.62^a	2.80^b	14.55^ab	3.88^a	2.93^b
N₄	14.41^a	3.88^a	2.96^a	15.11^a	4.30^a	3.16^a
CV (%)	6.99	8.19	6.33	5.87	8.80	5.91
SEm +	0.31	0.09	0.05	0.28	0.10	0.05
CD(P = 0.05)	0.93	0.27	0.16	0.82	0.31	0.15
Interaction
SEm +	0.54	0.16	0.09	0.48	0.18	0.09
CD(P = 0.05)	NS	NS	NS	NS	NS	NS
SEm +	0.56	0.19	0.09	0.54	0.19	0.11
CD(P = 0.05)	NS	NS	NS	NS	NS	NS

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The Number of auxiliary shoots per plant (NASPP) of sprouting broccoli was differentially influenced by application of different irrigation levels during both the years. The maximum NASPP of were recorded at irrigation regime I1 which was 81.58 and 38.65 per cent higher in comparison to I3 and I2 irrigation levels, respectively in 2016–17 (Table 10). Likewise, in 2017–18, maximum NASPP of4.68 was obtained at irrigation regime I1 which was 91.80 and 34.09 per cent higher in comparison to I3 and I2 irrigation levels, respectively. The data shown in the Table 10 indicated that application of nitrogen levels significantly enhanced NASPP. The NASPP was increased by 45.31% by increasing nitrogen level from N1 to N4 in 2016–17 and by 53.02% in 2017–18 by increasing nitrogen level from N1 to N4. Interactive effect of different levels of irrigation levels and nitrogen on NASPP was found non-significant. The data revealed that irrespective of nitrogen level, the increasing level of irrigation levels up to I1 brought significant improvement in NASPP. Similarly, irrespective of irrigation levels, the increasing nitrogen levels recorded significantly higher NASPP preceding levels. In general, the combined application of I1N4 remained significantly higher over rest of the treatments.

The ASY of sprouting broccoli was differentially influenced by application of different irrigation levels during both the years (Figs. 7, 8). The maximum ASY of 3.12 t/ha was obtained at irrigation regime I1 which was 57.57 and 25.80 per cent higher in comparison to I3 and I2 irrigation levels, respectively in 2016–17. Correspondingly, in 2017–18, maximum ASY of 3.31 t/ha was obtained at irrigation regime I1 which was 55.39 and 26.81 per cent higher in comparison to I3 and I2 irrigation levels, respectively. The data presented in the Table 10 indicated that application of nitrogen levels significantly enhanced the ASY of sprouting broccoli. The auxiliary shoot yield was increased by 48% by increasing nitrogen level N1 to N4 in 2016–17 and 46.97% in 2017–18. Interactive effect of different levels of irrigation and nitrogen levels on ASY was found non-significant. The data suggest that irrespective of nitrogen level, the increasing level of irrigation levels up to I1 brought significant improvement in ASY of sprouting broccoli. Similarly, irrespective of irrigation levels, the increasing nitrogen levels recorded significantly higher ASY over preceding levels. In general, the combined application of I1N4treatment remained considerably higher over the rest of the treatments. The auxiliary shoots yield’s percentage of the central head yield is displayed in Figs. 7 and 8 for 2016–17 and 2017–18, respectively. It can be viewed from the Figs. 7 and 8 that the treatment I1N4 gave highest values as 22.5 and 24.3% in 2016–17 and 2017–18, respectively. It was observed that the lowest auxiliary shoots yield as percentage of central head yield found in I3N1 treatment with values of 14.2 and 15.7% in 2016–17 and 2017–18, respectively. The results indicate that the percentage of auxiliary shoots yield with rest to the central head yield was influenced by the irrigation and nitrogen levels positively. The higher values were recorded in the treatments with full irrigation and highest nitrogen levels. Figure 9 display central head (a) and auxiliary shoots (b), respectively.

Fig. 7 — Auxiliary shoots yield’s percentage of central head yield during *Rabi* 2016–17.

Fig. 8 — Auxiliary shoots yield’s percentage of central head yield during *Rabi* 2017–18.

Fig. 9 — Central head and auxiliary shoots of sprouting broccoli.

Total yield of sprouting broccoli was significantly influenced by application of different irrigation levels during both the years as shown in Table 11. The maximum total yield of 19.12 t/ha was obtained at I1 irrigation level which was 29.27 and 12.67 per cent higher in comparison to I3 and I2 irrigation levels, respectively in 2016–17. Similarly, in 2017–18, 36.60 and 12.97 per cent higher yield in comparison to I3 and I2 irrigation levels, respectively, was obtained in I1 irrigation level. It is evident from data presented in the Table 11 that application of nitrogen levels N2, N3 and N4 significantly enhanced the total of sprouting broccoli as compared toN1. It increased the total yield to the extent of 6.74, 13.94 and 19.53%, at N2, N3, and N4, respectively over N1 in 2017–18. Similarly, N increased the total yield to the extent of 6.51, 13.81 and 19.53%, at N2, N3, and N4, respectively over N1 in 2016–17. Interaction effect of different levels of irrigation and nitrogen levels on total yield during both the years was non-significant. The data revealed that irrespective of nitrogen levels, the increasing level of irrigation levels up to I1 brought significant improvement in total yield of sprouting broccoli. Similarly, irrespective of irrigation levels, the increasing nitrogen level also recorded significantly higher total yield than other treatments. The biomass yield of sprouting broccoli was significantly influenced by application of different irrigation levels in both seasons as displayed in Table 11. The maximum biomass production of 56.05 t/ha was obtained at I1 irrigation level which was 22.21 and 6.68 per cent higher in comparison to I3 and I2 irrigation levels, respectively in 2016–17. Correspondingly, in 2017–18, the maximum biomass yield of 58.95 t/ha was obtained at I1 irrigation level which was 20.07 and 17.41 per cent higher in comparison to I3 and I2 irrigation levels, respectively.

Table 11.

Effects on irrigation and nitrogen levels on total yield, biomass yield and harvest index of sprouting broccoli.

Treatments	2016–17			2017–18
Treatments	Total yield (t/ha)	Biomass yield (t/ha)	Harvest index (%)	Total yield (t/ha)	Biomass Yield (t/ha)	Harvest Index (%)
I₁	19.12^c	56.05^c	32.65^b	19.33^c	58.92^c	32.83^b
I₂	16.97^b	52.54^b	30.32^a	17.11^b	55.29^b	30.99^a
I₃	14.29^a	45.86^a	29.53^a	14.73^a	47.09^a	30.09^a
CV (%)	5.71	8.77	6.92	7.82	10.77	5.70
SEm +	0.28	1.30	0.62	0.38	1.67	0.52
CD(P = 0.05)	1.10	5.11	2.42	1.50	6.57	2.02
N₁	15.35^c	48.98^b	29.17^c	15.41^d	50.98^b	30.19^c
N₂	16.35^b	51.12^ab	30.03^b	16.45^c	53.38^ab	30.53^b
N₃	17.47^a	52.14^ab	31.72^a	17.56^b	54.77^ab	31.93^a
N₄	18.27^a	53.70^a	32.42^a	18.42^a	55.95^a	32.58^a
CV (%)	5.01	8.43	5.23	5.2	6.76	5.12
SEm +	0.28	1.45	0.54	0.29	1.21	0.53
CD(P = 0.05)	0.84	4.30	1.60	0.87	3.60	1.59
Interaction
SEm +	0.49	2.50	0.93	0.51	2.10	0.93
CD(P = 0.05)	NS	NS	NS	NS	NS	NS
SEm +	0.57	2.53	1.01	0.52	2.47	0.95
CD(P = 0.05)	NS	NS	NS	NS	NS	NS

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It is clear from results presented in the Table 11 that application of nitrogen levels N2, N3 and N4 significantly enhanced the biomass yield as compared with N1level. It increased the biomass yield by 4.36, 6.45 and 9.63%, at N2, N3, and N4, respectively over N1 in 2016–17. Similarly, N application increased the total yield to the extent of 4.70, 7.43 and 9.74%, at N2, N3, and N4, respectively over N1 in 2016–17. Interaction effect of different levels of irrigation levels and nitrogen on biomass yield during both the years was non-significant. The data revealed that irrespective of nitrogen levels, the increasing level of irrigation levels up to I1 had improved the biomass yield. Similarly, irrespective of irrigation levels, the increasing nitrogen level also recorded significantly enhancement in the biomass production over preceding level.

The HI responded positively to the irrigation levels during both the crop seasons. The results depicted in Table 11 indicates that the maximum HI of 32.65% was obtained at irrigation regime I1 which was 10.56 and 7.68 per cent higher in comparison to I3 and I2 irrigation levels, respectively in 2016–17. Likewise, in 2017–18, maximum HI of 32.83% was obtained at irrigation regime I1 which was 9.10 and 3.0 per cent higher in comparison to I3 and I2 irrigation levels, respectively. Besides that, application of nitrogen levels significantly enhanced the HI. The HI was increased by 11.14% by increasing nitrogen level N1 to N4 in 2016–17 and 7.91% in 2017–18 (Table 11). Interactive effect of different levels of irrigation and nitrogen on HI was found non-significant. The data revealed that irrespective of nitrogen level, the increasing level of irrigation levels up to I1 increased HI. Similarly, irrespective of irrigation levels, the increasing nitrogen levels recorded significantly higher HI over the preceding levels. In general, the combined application of I1N4 remained significantly higher over rest of the treatments.

Water use efficiency (WUE), irrigation water productivity (IWP) and economic irrigation water productivity (EWP)

The results of irrigation and nitrogen levels effects on the broccoli WUE are presented in Table 12. The data indicated that the maximum WUE of 140.82 kg/ha-mm was obtained at irrigation regime I2 which was 14.28 and 4.06 per cent higher in comparison to I1 and I3 irrigation levels, respectively in 2016–17. Likewise, in 2017–18, the maximum WUE of 142.30 kg/ha-mm was obtained at irrigation regime I2 which was 5.72 and 18.8 per cent higher in comparison to I1 and I3 irrigation levels, respectively. The results shown in the Table 12 indicates that nitrogen levels significantly enhanced the WUE. It was increased by 6.97% by increasing nitrogen level from N1 to N4 in 2016–17 and 6.56% in 2017–18. Interactive effect of different levels of irrigation levels and nitrogen on WUE was found non-significant. Irrespective of irrigation levels, the increasing nitrogen levels recorded significantly higher WUE over the preceding levels (increasing the nitrogen levels). However, the irrigation level I2 gave higher WUE than the I1 and I3 in both the crop seasons. Overall, I2N4 treatment performed well and resulted into higher WUE (155.20 and 156.20 kg/ha-mm in 2016–17 and 2017–18, respectively) in both the crop seasons.

Table 12.

WUE, IWP and EWP of broccoli under varying irrigation and nitrogen levels.

Treatments	2016–17			2017–18
Treatments	WUE (kg/ha-mm)	IWP (kg/m³)	EWP (Rs./ha)	WUE (kg/ha-6 m)	IWP (kg/m³)	EWP (Rs./ha)
I₁	123.17^b	13.99^c	401.45^c	134.57^b	12.29^c	368.68^c
I₂	140.82^a	16.55^b	466.63^b	142.30^a	14.48^b	434.21^b
I₃	135.31^a	21.65^a	593.42^a	120.03^a	17.79^a	533.79^a
CV (%)	7.20	5.38	6.39	9.00	8.76	8.79
SEm +	2.77	0.27	8.98	3.44	0.38	11.30
CD(P = 0.05)	10.86	1.06	35.26	13.50	1.47	44.37
N₁	120.53^d	15.70^d	434.64^c	120.36^b	13.42^c	402.43^c
N₂	128.92^c	16.88^c	471.96^b	128.26^b	14.41^b	432.10^b
N₃	137.93^b	18.04^b	508.46^a	137.11^a	15.44^a	463.17^a
N₄	145.01^a	18.98^a	533.61^a	143.47^a	16.15^a	484.53^a
CV (%)	5.20	4.72	5.34	5.63	5.19	4.71
SEm +	2.31	0.27	8.67	2.48	0.26	7.00
CD(P = 0.05)	6.86	0.81	25.77	7.37	0.76	20.79
Inetraction
SEm +	4.00	0.47	15.03	4.30	0.45	12.12
CD(P = 0.05)	NS	NS	NS	NS	NS	NS
SEm +	4.43	0.49	15.81	5.07	0.54	15.42
CD(P = 0.05)	NS	NS	NS	NS	NS	NS

Open in a new tab

The IWP of each treatment have been calculated and its results were presented in Table 12. The results depicted in Table 12 indicated that the maximum IWP of 21.65 kg/m3 was obtained at irrigation regime I3 which was 54.75 and 30.81 per cent higher in comparison to I1 and I2 irrigation levels, respectively, in 2016–17. Likewise, in 2017–18, maximum IWP of 17.79 kg/m3 was obtained at irrigation regime I3 which was 44.75 and 22.85 per cent higher in comparison to I1 and I2 irrigation levels, respectively in 2016–17. Besides that, increased nitrogen levels significantly enhanced the IWP (Table 12). The EWP was increased by 20.89% by increasing nitrogen level N1 to N4 in 2016–17 and 20.34% in 2017–18. Interactive effect of different levels of irrigation levels and nitrogen on IWP was found non-significant. Increasing nitrogen levels recorded significantly higher IWP over the preceding levels. In general, the combined application of I3N4 remained significantly higher over rest of the treatments.

The EWP was calculated and presented in Table 12. The results depicted that the maximum EWP of 593.42 Rs/ha was obtained at irrigation regime I3 which was 47.81 and 30.81 per cent higher in comparison to I1 and I2 irrigation levels, respectively, in 2016–17. Likewise, in 2017–18, maximum EWP of 533.79 Rs. /ha was obtained at irrigation regime I3 which was 44.78 and 22.93 per cent higher in comparison to I1 and I2 irrigation levels, respectively. Moreover, the results given in the Table 12 indicates that nitrogen levels significantly enhanced the EWP. The EPW was increased by 20.33% by increasing nitrogen level N1 to N4 in 2016–17 and 20.34% in 2017–18. Interactive effect of different levels of irrigation levels and nitrogen on EWP was found non-significant. Increasing nitrogen levels recorded significantly higher IWP over the preceding levels. However, increasing irrigation amount resulted in to decrease in EWP. In general, the I3N4 treatment remained significantly higher over rest of the treatments.

Discussion

Precise application of irrigation is essential for ensuring the efficient functioning of a drip irrigation system. To evaluate the drip system performance, two important parameters viz., coefficient of variation (CV) and emission uniformity (EU) were utilized. In 2016–17, the CV of the dripper flow rates was measured at 0.075 and the EU was obtained as 92.2%. The lesser value of the EU and CV in 2016–17 is due to old age of the installed irrigation system. A similar trend of CV and EU was observed in 2017–18 crop season with vales of 0.065 and 95%, respectively. The improvement in the EU and CV in 2017–18 is due to installation of new set of laterals in the 2017–18. Generally, a low CV suggests good system performance throughout the cropping season. Martinez et al.⁶¹ assessed the drip irrigation system performance and Christiansen’s coefficient of uniformity (CU), EU, and CV ranged from 97.5 to 98.5, 95.9 to 97.7, and 0.02 to 0.04 and reported that the operating pressure heads do not have a statistically significant effect (α = 5%) on the CU, EU, and CV of the system. Similar findings were reported by Patil et al.⁶² for inline drip irrigation system and reported that CV varied between 0.059 and 0.091 in 2008 and 2009, respectively. Additionally, EU values exceeded 90.0% in both cropping seasons, which is considered excellent. These low CV and high EU values indicate that the system performed well throughout the cropping season. The laboratory experiment on irrigation and fertigation uniformity was conducted using the Non-axisymmetric Venturi Injector and urea⁶³. A field experiment was conducted to evaluate the performance of drip system with five operating pressures viz. I1 (0.4 kg/ cm²), I2 (0.6 kg/cm²), I3 (0.8 kg/cm²), I4 (1.0 kg/cm²), I5 (1.2 kg/cm²). It was observed that the average discharge of drippers was 1.08 lph, 1.24 lph, 1.50 lph, 1.62 lph and 1.74 lph and emission uniformity was 80.55%, 84.89%, 86.30%, 88.88% and 90.80 in each treatment respectively and coefficient of variation was observed 0.12, 0.13, 0.12, 0.11, and 0.09⁶⁴. Baydar et al.⁶⁵ evaluated the performance of the drip irrigation systems installed in 18 different nectarines (Prunus persica var. nucipersica orchards in the Tarsus Plain in the Mediterranean region from 2017 through 2018 and found that the CU varied between 81 and 98%; DU changed from 82 to 97%; EU 61–92%; absolute emission uniformity (EUa ranged between 93 and 98%. They concluded that the CU, DU, and EU values were acceptable, the variations in emitter flow rates and pressures were not acceptable.

The effects of irrigation and nitrogen levels on the broccoli growth parameters and yield attributes was analyzed statistically. Results indicated that the effects of irrigation and nitrogen levels on broccoli growth and yield parameters followed the similar trend in both the years. The maximum values were observed in 2017–18 compared to 2016–18 in all treatments. Number of leaves per plant, LAI, number of auxiliary shoot and yield, harvest index, biomass, head length, head diameter and head weight were significantly influenced by irrigation and nitrogen levels. The results revealed that the number of leaves per plant, plant height and LAI measured at the 30 DAT found non-significant. It was evident from the results that treatment I1N4 exhibited significantly maximum plant height, number of leaves, LAI, head weight, head diameter, head length, central and auxiliary head yield, biomass and HI. However, the treatment I1N3 had performed on par with the I1N4 treatment. The WUE was obtained maximum in I2N4 treatment and I2N3 treatment was on par with I2N4. Thus, in the water scarcity situation, the I2N3 treatment should be a viable solution to achieve high WUE. In contrast, the IWP and EWP were obtained higher in I2N4 and I3N4 treatments, respectively. The findings clearly demonstrated that full irrigation treatments assured no water stress and the crop achieved full crop growth and resulted into higher crop yield.

The plant height showed improvement with the increased irrigation amount. This could be due to fact that plant growth is vigorous when adequate soil moisture was available in the crop root zone. Similar findings were reported by Hussain et al.⁶⁶ for broccoli. In the current study, we observed that the LAI is influenced by the irrigation levels. This is confirmed by Gariya et al.⁶⁷ who found that the restrictions in the irrigation water amount reduced the LAI of Broccoli significantly. The growth parameters were found to be affected by the irrigation and nitrogen levels. This results are confirmed by findings reported by Singh,⁶⁸, which indicated that increased growth in Broccoli with higher nitrogen supply could be attributed to the rise in photosynthetic and assimilation rates, consequently leading to a noticeable increase in plant height. Thentu et al.⁶⁹ reported that the drip irrigation at 0.8 ET_c showed significantly higher plant height, LAI, number of leaves per plant, head diameter, and marketable head yield (17.82 t/ha) of broccoli. In our study, the maximum yield obtained was 20.53 t/ha in full irrigation and 125% nitrogen treatment. This variation in the broccoli yield compared to the previous studies could be due to climatic condition, cultivar and date of transplanting.

Naik et al.³⁶ had also reported similar findings for broccoli under net house condition. They concluded that the full irrigation with 125% N resulted in the highest broccoli yield of 22.02 t/ha. We also found the similar respond of the broccoli crop to the irrigation and nitrogen levels applications. However, the yield reported in our study was lesser than that Naik et al.³⁶, reported values which could be due to variations in climatic condition and the variety. Our results were also in line with Erdemetal²². for broccoli crop under field condition which concluded that applying irrigation levels till field capacity resulted in to the better crop growth and yield attributes. Spehia et al.⁷⁰ indicated that employing drip irrigation and nitrogen fertilizer resulted in higher yields compared to the control treatment. However, the most economically optimal yield with the best quality was achieved when using 70% N of RDF in combination with 60% ET_c as the irrigation treatment. Thompson et al.⁷¹, also reported similar findings, which indicated that nitrogen status influenced the growth and yield parameters of broccoli significantly and the effects of irrigation level was lesser compared to the nitrogen levels. However, we found that the irrigation levels effect was more dominant as compared to the nitrogen levels.

Soil moisture availability and distribution in the crop root zone controls the nitrogen and other nutrients availability to the plants. Thus, under full irrigation treatment the translocation of the nutrients was better which has resulted into the better crop growth and higher yields. Chand et al.⁷², studied the effect of fertigation on growth and yield attributes of broccoli variety Fiesta and study revealed that broccoli yield under 100% RDF resulted into yield of 25.6 t/ha. The broccoli yield obtained in our study was less as compared to what Chand et al.⁷², have reported. This variation could be due to broccoli variety, planting geometry, soil type, irrigation levels, nitrogen levels, and climatic condition.

The WUE, IWP and EWP had been influenced by the irrigation and nitrogen levels. The WUE had decreased with the increased in the irrigation levels. However, the nitrogen levels had positively influenced the WUE. Highest WUE was recorded in the I2N3 treatment and lowest in I1N1 treatment. The IWP was found maximum under I3N4 treatment and lowest in I1N1. The EWP showed similar trend as of IWP and was recorded best in I3N4 treatment Similar findings were reported for broccoli WUE by Erdem et al.²². The IWP of broccoli exhibited significant sensitivity to variations in applied irrigation water and effective rainfall, primarily impacting the yield obtained. As the amount of irrigation water supplied increased, IWP showed a gradual decline. Our results are in harmony with Patra et al.¹⁷, who reported that broccoli IWP significantly decreased due to increase in the irrigation water. Subhash,⁷³ reported, the treatment combination I2F2 (0.8 ET_c and 100% RDF) was found significantly superior in terms of attaining maximum yield of broccoli (5.46 t/ha) and the maximum water use efficiency (2.04 q/ha.cm) was found in treatment combination I1F2 (0.6 ET_c and 100% RD). Overall, the findings of the current study revealed significant information on how the broccoli responses to the irrigation levels and nitrogen availability. Such information may be useful for planning crop management strategies under input resources limitations.

Conclusions

The crop growth and yield attributes were analyzed statistically. The results revealed that, the plant growth parameters viz., plant height, number of leaves and leaf area index (LAI) did not show significant variation at 30 days of transplanting (DAT). However, plant height, number of leaves per plant and LAI were significant at 60 DAT and at harvesting of the crop. It was found that the irrigation and nitrogen levels had significantly influenced the broccoli head length, head diameter and head weight. In comparison to irrigation treatment I3, the auxiliary shoot and head yield was increased by 47.87 & 16.89% in 2016–17, and 46.49 and15.50% in 2017–18 under full irrigation (I1). In both the 2016–17 and 2017–18 seasons, increasing the nitrogen level from N1 to N4 resulted in significant improvements in auxiliary shoot and central head yield. Total yield and harvest index (HI) showed statistically significant variability under irrigation and nitrogen levels. Total yield was increased by 19.59 and 19.09% in 2016–17 and 2017–18, respectively by increasing nitrogen level from N1 to N4 and HI increased by 11.14 and 7.09%, in 2016–17 and 2017–18, respectively under the influence of the nitrogen levels. In similar way, by increasing the irrigation level from I1 to I3 had increased the total yield and HI by22.66 and 9.56% in 2016–17 and 26.83 and 8.34%, in 2017–18, respectively. The water use efficiency (WUE) and irrigation water productivity (WP) were increased by 20.32 and 19.86% by increasing nitrogen level from N1 to N4, and 9.86 and 54.79% by increasing the irrigation level from I3 to I1 (full irrigation), respectively in 2016–17. Similarly, in 2017–18, the WUE and WP had increased by 19.20 and 20.35% by increasing the nitrogen from N1 to N4, and 12.11 and 44.75%, by increasing irrigation level from I3 to I1, respectively. The cost economics was also influenced by the irrigation and nitrogen levels and the BC ratio ranged from 1.68 to 2.89 in 2016–17 and 1.54 to 3.01 in 2017–18. It was concluded that crop growth and yield were significantly affected under the different irrigation regimes. The study shows that nitrogen levels and irrigation regimes are important for the growth, and productivity of sprouting broccoli in semi-arid regions. Plant growth attributes, head development, total yield, and water use efficiency all improved considerably when there were sufficient moisture and a balanced nitrogen supply. The findings indicate that employing trickle irrigation to maintain optimal soil moisture levels, in conjunction with adequate nitrogen supplementation, can enhance crop growth and increase profitability. Optimizing water and nutrient management is crucial for maximizing broccoli yield and sustaining its cultivation in water-scarce environments.

Acknowledgements

Authors are thankful to the ICAR-IARI, New Delhi for facilitating experimentation facility. Research was supported by the lndian Council of Agricultural Research, Department of Agricultural Research and Education, Government of lndia.

Author contributions

Jitendra Rajput, Man Singh: conceptualized, methodology selection and carried formal analysis, field experimentation, writing original draft. K. Lal, M. Khanna, A. Sarangi, J. Mukherjee, Shrawan Singh, P. K. Sahoo, Dimple, Kiruthiga Balakrishnan: over view of the manuscript and methodology improvement, reviewing and editing. All the authors have contributed significantly to this research work. All authors have read and approved the final manuscript.

Funding

No external fund was received to conduct this work.

Data availability

Data may be made available from the corresponding author (Jitendra Rajput) on reasonable request.

Declarations

Competing interests

The authors declare that they have no competing interests.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

Data may be made available from the corresponding author (Jitendra Rajput) on reasonable request.

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PERMALINK

Optimizing water use efficiency of sprouting broccoli through irrigation and nitrogen management under drip irrigation in semi-arid condition

Jitendra Rajput

Man Singh

K Lal

Manoj Khanna

A Sarangi

J Mukherjee

Shrawan Singh

P K Sahoo

Dimple

Kiruthiga Balakrishnan

Abstract

Introduction

Materials and methods

Study area description

Fig. 1.

Fig. 2.

Fig. 3.

Basic properties of soil at experiment site

Table 1.

Table 2.

Fig. 4.

Table 3.

Design and installation of drip irrigation system

Design and layout of field experiment

Table 4.

Fig. 5.

Irrigation and fertigation schedule

Fertigation schedule

Field observations

Water use efficiency

Irrigation water productivity

Economic water productivity

Statistical analysis

Results

Drip irrigation system (DIS) performance

Table 5.

Crop water use (CWU)

Fig. 6.

Table 6.

Table 7.

Table 8.

Yield attributes

Table 9.

Table 10.

Fig. 7.

Fig. 8.

Fig. 9.

Table 11.

Water use efficiency (WUE), irrigation water productivity (IWP) and economic irrigation water productivity (EWP)

Table 12.

Discussion

Conclusions

Acknowledgements

Author contributions

Funding

Data availability

Declarations

Competing interests

Consent to participate

Consent for publication

Footnotes

References

Associated Data

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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