1. Introduction
Among different vegetables, green pea is an important vegetable being consumed in India. Green pea is a highly nutritive value vegetable crop. This crop is mostly grown during winter season in India. Improper irrigation scheduling with deficit quantity of water has led to water stress in pea production, causing a decrease in the evapotranspiration, growth parameters and pea yield [
1]. Harmful effects of improper water management, such asover application of water, deficit application of water, effect of water on soil salinity, water stress, and lack of research to determine the optimum quantity of water in pea production, have been reported by many researchers [
1,
2,
3]. An irrigation moisture regimen for green pea crop is crucial to optimize yield and avoid over-watering. Estimation of soil moisture is the key for irrigation scheduling [
4]. The use of soil moisture sensing devices for irrigation scheduling has ensured a low-volume and high-frequency irrigation supply for different vegetable crops. This has overcome the difficulty in employing more laborers as required in case of the traditionally followed surface irrigation system [
5]. Few studies have identified the effect of fertilizer levels through drip fertigation methods in green pea production. Application of morefertilizer compared to the recommended dose of fertilizer hasalso been studied by many researchers. However, there is a void in the study of drip fertigation in the cultivation of green pea.
Different varieties of green pea with varying irrigation levels, viz., 50%, 75%, 100%, and 125% ETc were tested under the drip irrigation and found a decrease in the productivity of pea at an excess and deficit irrigation treatments [
3]. Green pod yield decreased with an increase in drought conditions, occurringat the vegetative and flowering stages [
6]. Hence, sufficient moisture content and proper irrigation scheduling should be ensured for better green pea production.
The effects of irrigation on soil moisture distribution under different soil matric potential need to be studied. Tensiometers are highly accurate instruments and most appropriate tools for measuring the soil matric potential [
7,
8,
9]. The irrigation controller activated the operation of drip irrigation system when the soil moisture potential was below the specified lower limit, and deactivated the irrigation when the soil moisture was above the specified threshold limit [
10]. Irrigation at 85% field capacity based on the tensiometer data, and 75% of the recommended fertilizer dose to green pea, increased the yield by 20% and saved water by 16 to 35% under an automated drip irrigation system [
11]. Irrigation at different soil matric potential ranges of −20kPa, −40 kPa, and −60 kPa in guava was experimented, and it was found that irrigation at −40 kpawas suited for the better growth of guava plantation [
12].
Irrigation scheduling based on real time soil moisture measurement improves yield and water-use efficiency of crops, rather than irrigation scheduling based on climatological approach. Research works on real time soil moisture-based irrigation scheduling in green pea are scant. Thus, the present study was carried out to determine the irrigation scheduling based on real time soil moisture measurement and to evaluate the outcome of growth, yield, and water use-efficiency of green pea under drip irrigation in a sandy loam Inceptisol under semi-arid conditions.
2. Materials and Methods
2.1. Experimental Details
A field experiment was conducted in the experimental plots of Water Technology Centre, Indian Agricultural Research Institute (IARI), New Delhi, during 2020–2021. The weather conditions in New Delhi arecategorized as semi-arid and sub-tropical climate. The salinity of irrigation water was low with EC value of 0.22 dS/m and SAR value of 7.8. Soil properties of the experimental site were studied. The textual composition of the soil was 16:27:57 clay:silt:sand in the top layer of 0–20 cm depth. The RETC model was used to predict the soil hydraulic properties of the experimental field. The values obtained for the soil textural property (57:27:16% of sand:silt:clay) and bulk density (1.43 g.cm−3) from the model were = 0.053; = 0.401; = 0.019; n = 1.448, and Ks Saturated Hydraulic conductivity = 29.45 cm/day. Soil moisture was estimated at different levels of soil matric potential and soil moisture characteristics curve was derived for the experimental field. The soil moisture for different soil matric potential was fitted in Retention curve (RETC) Model and a graph was plotted for assessing the relationship between the observed and fitted values. The observed data were found to be in convergence with the soil water RETention Curve (RETC) modeled data with coefficient of determination (R2) value of 0.994,** which is significant at p < 0.01 level. The field capacity was 24.35%, while the permanent wilting point was 9.15% based on the volumetric basis in the sandy loam Inceptisol where the study was conducted.
2.2. Field Preparation and Seed Sowing
The size of experimental area was 28 m × 30 m. Drip laterals with inline emitter of capacity of 2 L per hour were laid on the raised beds. The green pea seeds (Pusa shree variety) were sown on raised beds of 75 cm size with furrow size of 15 cm with spacing of 30 cm × 10 cm. Pusa shree seeds are produced at Indian Agricultural Research Institute, New Delhi, India. A set of 12 treatments comprising of 4 irrigation treatments and 3 fertigation treatments were tested with 3 replications in a split-plot design. Irrigation levels were superimposed to the main plots, while the fertigation levels were superimposed to the subplots.
2.3. Soil Matric Potential Measurement
The soil matric potential was measured using tensiometers with a digital pressure transducer during the entire crop growth period. A tensiometermeasures the vacuum pressure of soil at the depth it is installed. Tensiometers (Irrometers, size: 30 cm; output: 0.5–5 V DC) were installed in each irrigation level treatments at a depth of 20 cm, which is the effective root zone depth of green pea crop. The digital pressure transducer acted as an electronic sensor to convert the mechanical pressure into an electric signal, and facilitates digital recording of the soil matric potential data continuously. The digital pressure transducer transferred the soil matric potential data to the irrigation controller, which automatically switch on and off the motor and solenoid valves (24 VAC solenoid and flow range 0.7–150 lpm) for irrigation. The data were recorded at hourly interval during a day.
2.4. Details of Irrigation and Fertigation Levels
For proper irrigation scheduling, four matric potential ranges were considered. The soil matric potential limit was programmed in the irrigation control unit. The four irrigation levels, i.e., I1: −20 kPa, I2: −30 kPa, I3: −35 kPa; and I4: −40 kPa were assigned as irrigation levels in the controller unit, and three fertigation levels 120%, 100%, and 80% of recommended NPK fertilizer dose (RDF) were established in drip irrigated plots.Soil moisture content data were observed in the oven dry method for different matric potential ranges and the soil moisture characteristics curve was derived. The soil matric potential at the pressure range of −20 kPa was equivalent to 20% of Maximum Allowable Depletion (MAD) in the experimental field. Similarly, −30 kPa, −35 kPa, and −40 kPa soil matric potential were equivalent to 30% of MAD, 40% of MAD, and 50% of MAD. When the soil moisture reached the specified soil matric potential, irrigation began automatically.
The uniformity distribution of water under drip irrigation was 93.15% and 91.85% before and after experimental season, respectively. The total volume of application of water differed for each of the 4 irrigation treatments. When the soil matric potential reached the specified limit, automatic activation of irrigation took place. The tensiometer sensors were placed at a depth of 20 cm. Since the hydraulic conductivity of soil is 1.4 cm/h, it took several hours to reach the tensiometer sensor tip. In order to enable the gaining of specified soil moisture potential data in the tensiometer, the irrigation time of operation was limited. The irrigation quantity was determined based on the soil moisture content present in each irrigation treatment as given in Equation (1):
where I is the irrigation quantity (mm) to be given; A is the surface area (m
2) of each plot; FC is the Field capacity (m
3/m
3);
is the volumetric water content before irrigation; and Z is the effective root zone depth (mm). Based on the depth of water determined under each treatment, the time of operation was controlled after activation of irrigation.Fertigation was given in five split doses on 20th day, 35th day, 50th day, 65th day, and 80th day of sowing through venturi device.
2.5. Plant Growth and Yield Parameters
Different parameters, viz., plant height, number of branches per plant, leaf area index (LAI), number of pods per plant, 100 pods weight, number of seeds per pods, and total soluble solid (TSS) content () of green pea under each treatment were observed. Five plants in each treatment were tagged randomly for observations. The leaf area index per plant was measured from each treatment (five plants per replication) using LAI 2000 LI-COR Plant canopy analyzer. The total green pea pod yield was calculated in all the treatments.
2.6. Water-Use Efficiency
The water-use efficiency (WUE) was determined for each irrigation treatment as given in Equation (2):
2.7. Statistical Analysis
Analysis of variance (ANOVA) of data was carried out in order to determine the effects of main plot treatments, sub-plot treatments, and their interactions using the data generated based on split plot design in the study [
13]. Based on Snedecor’s F-test, the effects of treatments were tested at
p < 0.05 and
p < 0.01 levels of significance [
14]. Estimates of correlation between different parameters were derived and tested at
p < 0.05 and
p < 0.01 levels of significance. Regression model of yield through different variables was calibrated to assess the effects of variables on pea yield [
15]. The prediction of yield was assessed based on the coefficient of determination (R
2) and standard error of mean (SEM) of predicted yield derived based on a regression model.
4. Conclusions
A study was conducted to test the effects of different combinations of irrigation and fertilizer levels, viz., irrigation at different soil matric potential threshold levels of −20 kPa, −30 kPa, −35 kPa, and −40 kPa; and fertilizer with 120%, 100%, and 80% of recommended fertilizer dose (40:60:50 kg/ha of NPK) on green pea yield during 2020–2021. The volumetric moisture content at different SMP threshold levels was observed throughout the crop growing season. Maximum pod yield (17.9 t/ha) and maximum water-use efficiency (11 kg/m3) were obtained in I2 (−30 kPa) SMP with 120% RDF treatment. Descriptive statistics of different plant growth parameters and water-use efficiency were derived, and an assessment of variability in the data due to varying irrigation and fertigation treatments wasmade. Significant correlations were found to exist between different parameters observed at different levels of irrigation and fertilizer application. Two regression models of green pea yield through different combinations of parameters were calibrated to predict the yield and assess the effects of parameters on yield. The models were assessed based on coefficient of determination (R2) and standard error of mean (SEM) of predicted yield. The models gave significant R2 in the range of 0.962* to 0.987** for prediction of yield. The study indicated that under −30 kPa SMP threshold irrigation treatment, the soil moisture regime from top layer to the depth of 20 cm was always at 80% of field capacity, which enhanced the plant growth and yield with significantly higher moisture content and good aeration in the effective root zone. Based on the findings made in the study, it is recommended that the superior treatment could be adopted by farmers for attaining maximum fresh and dry pea yield, and water-use efficiency under similar soil and agro-climatic conditions. This would greatly benefit the farmers to improve their livelihood under similar soil and agro-climatic conditions in any part of the world.