Productivity and Soil Moisture Optimization for Vegetable Crops in drylands: Reduced Runoff Farming with Sensor-Based Irrigation Solutions

doi:10.21203/rs.3.rs-5301497/v1

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Productivity and Soil Moisture Optimization for Vegetable Crops in drylands: Reduced Runoff Farming with Sensor-Based Irrigation Solutions

https://doi.org/10.21203/rs.3.rs-5301497/v1

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A polyhouse experiment (2 year) was conducted at AICRP for Dryland Agriculture during 2021/22 and 2022/23, to evaluate the impact of sensor driven irrigation levels on yield, yield attributes, irrigation efficiency and economic returns of broccoli, capsicum, pole bean and cherry tomato. The experiments were laid out separately in RCBD with six replications for each crop and wireless soil moisture sensors were installed with the purpose for collecting real time soil moisture content and controlling the irrigation levels (75, 50 and 25% ASM) via smart phone and surface irrigation as control. The two years study found that, sensor irrigation scheduling at 75% ASM recorded significantly higher average yield of broccoli (26.05 t ha^− 1), capsicum (48.59 t ha^− 1), pole bean (37.08 t ha^− 1) and cherry tomato (42.02 t ha^− 1). The mean irrigation production efficiency of broccoli (84.98 kg/ha-mm), capsicum (102.25 kg/ha-mm), pole bean (114.27 kg/ha-mm) and cherry tomato (88.41 kg/ha-mm) was higher at 75% ASM. Regression analysis revealed the polynomial relationship between the average quantity of water applied and yield of broccoli, capsicum, pole bean and cherry tomato. This relationship, with R² values ranging from 0.73 to 0.83, can be effectively utilized to optimize irrigation water distribution among the crops both individually and collectively. Scheduling of irrigation at 75% ASM, resulted substantially higher net returns of Rs. 6,55,899 ha^− 1 for broccoli, Rs. 9,69,689 ha^− 1 for capsicum, Rs. 11,07,535 ha^− 1 for pole bean and Rs. 5,79,865 ha^− 1 for cherry tomato.

Available soil moisture

broccoli

capsicum

pole bean and cherry tomato

Wireless sensors

The use of wireless soil moisture sensor facilitates the real-time soil moisture assessment, which makes the irrigation system more precise and efficient.

The Randomized Complete Block Design RCBD experiments were conducted separately with six replications for each of four vegetable crops and wireless soil moisture sensors were installed to meticulously maintain irrigation levels (75, 50 and 25% ASM).

The irrigation scheduling at 75% ASM outperformed all other irrigation treatment across tested crops , broccoli, capsicum, pole beans and cherry tomato in terms of overall production and water use efficiency.

Regression analysis demonstrated a polynomial relationship between the average quantity of water applied and yield of broccoli, capsicum, pole bean and cherry tomato (R = 0.73 to 0.83), which can be used for distribution of irrigation water within and across the crops.

The findings of the study suggest that a sensor-driven irrigation system is a valuable tool for the management of rooftop harvested water and its efficient use.

Polyhouse vegetable production has surged in popularity due to its ability to provide higher yield, lower pests and superior produce quality, even under challenging climatic conditions. Controlled environment in poly house optimizes plant growth and yield, making them a preferred choice for farmers seeking continuous vegetable cultivation (Meena et al., 2022). Although initial investment and labour requirements are higher, studies showed that the economic benefits outweigh the costs (Suresh et al., 2022). Crops such as cucumber, capsicum and tomato thrive in polyhouses, offering significantly higher yield and prices compared to open-field conditions (Rani et al., 2022). This showcases the potential for increased productivity and profitability of protective vegetable cultivation.

Nevertheless, at present irrigation practices employed in polyhouses often rely on traditional methods, such as surface irrigation, which can lead to water stress or overwatering. Water stress is a crucial issue, causing significant losses in vegetable production, particularly when compounded by other stressors like heat or nutrient deficiencies (Adil and Quraishi, 2023). In contrast, sensor-based drip irrigation has the potential to enhance water use efficiency by 70–90% (Attri et al., 2022) and boost crop productivity in polyhouses (Niaz and Tang, 2022; Ningoji et al., 2023). For instance, studies focused on capsicum crops have revealed that sensor-driven irrigation strategies can reduce water consumption by up to 25% while maintaining or even surpassing crop yields (Raju et al., 2020 and Ningoji et al., 2023). Furthermore, irrigation scheduling based on soil moisture presents a compelling solution for the efficient utilization of water in irrigated agriculture (Dobriyal et al., 2012). In this context, the importance of soil water thresholds in optimizing crop performance needs emphasis (Thompson et al., 2007). These thresholds represent the minimum soil moisture levels below which plants experience water stress and begin to decline. This in due course impacts the plants’ physiological functions of the crops (Shock et al., 2011). Thus, identifying and categorizing the appropriate soil water threshold for different crop-soil systems is crucial more efficient irrigation management (Bianchi et al.,2017 and Hubbell et al., 2003).

Sensor-based irrigation systems, play a pivotal role in modern agriculture. By leveraging IoT technology, these systems enable real-time monitoring of crucial parameters such as soil moisture levels continuously this data-driven approach facilitates precise irrigation scheduling, leading to increased crop yield and effective water conservation (Padma et al., 2023). Moreover, the integration of soil moisture sensors with protective cultivation systems allows the farmers to remotely oversee and manage irrigation processes, facilitating proactive decision-making and sustainable resource management (Suprava et al., 2023).

Building upon the advancements in sensor-based irrigation, the integration of traditional water management practices is essential. Runoff farming, a hydro-agricultural strategy practice common in arid and semi-arid regions, effectively captures and utilizes surface runoff for crop cultivation during periods of water scarcity (Louise et al., 2021 and Zhanassyl et al., 2023). Further, the research demonstrated that rainwater harvesting can significantly increase cultivated land utilization, crop water productivity and gross returns up to 60–100% in certain regions (Panwar et al., 2023 and Piemontese et al., 2020).

This approach is particularly valuable in India, where water scarcity is a major challenge. Through the integration of sensor-driven irrigation with rainwater harvesting, water management in polyhouse for vegetables can be significantly simplified at reduced cost and higher efficiency. Building upon this theme, the present study aims to examine the yield advantage and water saving potential of sensor-driven irrigation for vegetables under reduced runoff farming.

2.1 Experimental Site

A polyhouse experiment was conducted at AICRP on Dryland Agriculture, GKVK, University of Agricultural Sciences (UAS), Bangalore, India, during the kharif 2021-22 and 2022-23. The experimental site was located in Agro Climatic Zone V (Eastern Dry Zone) of Karnataka, at an altitude of 930 m above mean sea level (MSL). The soil at the experimental station is red sandy loam, consisting of 53.4 per cent coarse sand, 14.8 per cent fine sand, 16.6 per cent silt and 15.2 per cent clay. Soil analysis revealed a lower organic carbon content (0.48%) and available nitrogen (252.86 kg ha^− 1) medium levels of available phosphorus (49.31 kg ha^− 1) and potassium (166.04 kg ha^− 1) with a slightly acidic pH (5.26) and EC of 0.14 dS m^− 1.

The experiment for broccoli, capsicum, pole bean and cherry tomato were laid out separately in a randomized block design with six replications. Each plot had an area of 11.4 m² and was separated by 30 cm path between plots. The soil was prepared by applying basal dose of N: P: K and incorporating Trichoderma harzianum, Pseudomonas fluorescens and FYM. The crops and varieties used in the study are listed in Table 1. Seedlings were raised in sterilized cocopeat trays and transplanted to the polyhouse at 21–25 days (broccoli and cherry tomato) and 30–35 days (capsicum) old. Pole beans were directly sown in the polyhouse (Table 1).

Table 1

Crop and variety details
Crop	Sowing date		Variety	Spacing	RDF (kg ha^− 1)
Crop	Season-1	Season-2	Variety	Spacing	RDF (kg ha^− 1)
Broccoli	13-8-2021	8-8-2022	Saaki	30 cm _X 30 cm	120:80:60
Capsicum			Indira	45 cm _X 45 cm	160: 60: 30
Pole bean			Selection − 9	30 cm _X 30 cm	40: 50: 40
Cherry tomato			Zyrconita	45 cm _X 45 cm	100: 75: 75

2.2 Rain water harvesting and irrigation scheduling

The experiment harvested rain water from polyhouse roof top of an area of 1500 m² and stored in tank having 7,20,000 liters capacity. During 2021-22 and 2022-23, water harvested was 1992.6 and 2216.0 m³, respectively. The harvested water was used to irrigate broccoli, capsicum, pole bean and cherry tomato crops in the experimental site. The drip lateral line laid for each bed and inline emitters with discharge rate of 2 L h^− 1 were spaced at 30 cm intervals on the lateral drip. To accurately monitor soil moisture levels in real-time, Cultyvate Hydro Sense moisture sensors were employed. These sensors operate on the principle of electrical conductivity, wherein the soil's water content is determined by measuring its resistance to electrical current. Specifically, dry soil exhibits higher resistance due to limited water, while wet soil being a better conductor, registers lower resistance and these sensors trigger the irrigation system when soil moisture falls below preset thresholds (Table 2), ensuring timely watering until the desired moisture level is achieved.

Table 2

Field capacity and irrigation frequency for each irrigation regimes
Irrigation regimes	Vegetative stage		Irrigation frequency
Irrigation regimes	FC (Vol %)	ASM (Vol%)	Season-1	Season-2
I₁: 75% ASM	27.5%	24.5%	41	41
I₂: 50% ASM	27.5%	21.5%	25	27
I₃: 25% ASM	27.5%	18.5%	17	17
I₄: Surface irrigation	-	-	19	19

2.3 Observations on yield and yield attributes

Broccoli, capsicum, pole bean and cherry tomato were harvested from 6th - Nov to 15th - Dec, 25th - Sep to 29th - Nov, 8th - Sep to 16th - Nov and 11th - Oct to 3th - Jan, respectively during 2021-22. Similarly, during 2022-23, the harvest periods were spread between 12th Oct to 9th Jan 2023 for the same crops across irrigation. Yield data were collected from five randomly selected plants on each treatment. Mean curd weight for broccoli; mean fruit weight (g) for capsicum; mean pod weight for pole bean; and mean no. of fruit cluster for cherry tomato was recoded with standard protocol.

2.4 Data Calculation

Fruit yield per plant (g plant^-1) is calculated based on the total fruit weight harvested from selected plant in each treatment. Fruit yield (t ha^-1) was computed by multiplying the number of fruits per plant by the average fruit weight. The water use efficiency of crops was calculated according to equation given by (Zotarelli et al., 2011).

WUE ( kg/ha-mm) = Total fruit yield (kg)/Total water used (ha-mm)

2.5 Economic analysis

In order to assess the economic viability of the drip irrigation system under variable irrigation, both fixed and operating costs were included. Total cost, gross returns and net returns were estimated on the following assumptions: Depreciation = 33%, useful life of pump and engine = 20 years, useful life of drip irrigation system = 10 years, interest rate = 14%, repair and maintenance = 4%, no. of crops/year = 2. The fixed cost was calculated including water development (borehole, pump and engine) and drip irrigation systems (low density polyethylene pipe for main, sub-mains and laterals, filters, fertilizer unit, pressure gauges, control valves, water meter, drippers and other accessories). The operating cost was calculated including labour (installation, irrigation, planting, weeding, cultivation, fertilizer application, spraying and harvesting etc), land preparation, seeds, fertilizers, chemicals (insecticides and pesticides) and water pumping (diesel and oil). The gross returns for different irrigation levels was calculated, taking into consideration the marketable yield and current wholesale prices of broccoli (Rs. 40/kg), capsicum (Rs. 25/kg), pole bean (Rs.60/kg) and cherry tomato (Rs. 30/kg). Subsequently, the net returns for broccoli, capsicum, pole beanand cherry tomato at different irrigation levels was calculated, considering total cost (Fixed + operating) and gross retuturns (Rana et al. 2014).

Gross returns = [Curd/pod/ /fruit yield x market price] (Rs./ha)

Net returns = Gross returns – total cost of cultivation (Rs./ha)

2.6 Statistical analysis

Experimental data was subjected to Fisher’s Analysis of Variance (ANOVA) method. Difference between mean values of irrigation treatments were studied using ‘F’ and ‘t’ test for least significant difference at 0.05 significance level. The mean values of each treatment were designated by letters (a, b, c) which represent the significance degree of the difference between the means. Means represented by two letters in common indicate that the difference is not significant or weakly significant (Gomez and Gomez, 1984).

3.1 Yield and water use efficiency of vegetable crops

3.1.1 Broccoli

Irrigation regimes significantly (P < 0.05) impacted broccoli yield and yield per plant. Scheduling irrigation at 75% ASM maximized both overall yield (26.05 t ha⁻¹) and yield per plant (1444 g), surpassing all other treatments. While 50% ASM produced comparable yield to surface irrigation (23.96 and 23.47 t ha⁻¹, respectively) and both significantly outperformed the 25% ASM treatment (18.71 t ha⁻¹). Correspondingly, yield per plant followed a similar trend, with 50% ASM and surface irrigation closely trailing the 75% ASM, while 25% ASM exhibited the lower yield per plant (996 g).

Water use efficiency was higher for plot receiving 75% ASM irrigation treatment (84.98 kg/ha-mm), surpassing all other treatments, which achieved the more significant water savings (48.95%) compared to surface irrigation. Although both 50 and 25% ASM reduced water consumption (39.59 and 34.59%, respectively), the latter incurred a substantial yield penalty of 14.38%.

Table 3

Impact of different irrigation levels on broccoli yield and water use efficiency
Treatment	Average quantity of water applied (mm)			Mean curd yield (t ha^− 1)
Treatment	2021-22	2022-23	Pooled	2021-22	2022-23	Pooled
75% ASM	329.23	319.94	324.59	25.80 ± 0.84^a	26.30 ± 1.37^a	26.05 ± 1.90^a
50% ASM	380.12	388.04	384.08	23.41 ± 1.05^b	24.52 ± 1.27^b	23.96 ± 0.90^b
25% ASM	426.31	405.42	415.87	18.45 ± 0.96^c	18.96 ± 1.02^c	18.71 ± 0.80^c
Surface irrigation	635.67	643.93	639.80	23.52 ± 1.39^b	23.43 ± 1.65^b	23.47 ± 0.86^b
	Mean yield per plant (g)			WUE (kg/ha-mm)
75% ASM	2021-22	2022-23	Pooled	2021-22	2022-23	Pooled
50% ASM	1372 ± 58.42^a	1515 ± 56.42^a	1444 ± 149.60^a	78.36^a	91.60^a	84.98^a
25% ASM	1204 ± 62.93^b	1442 ± 66.62^a	1323 ± 151.09^b	61.59^b	63.20^b	62.40^b
Surface irrigation	990 ± 22.87^c	1003 ± 41.45^c	996 ± 171.55^d	43.28^c	46.80^c	45.04^c
75% ASM	1129 ± 46.64^b	1332 ± 52.56^b	1230 ± 197.80^c	37.00^d	31.70^d	34.35^d

3.1.2 Capsicum

Sensor driven irrigation regime at 75% ASM led to higher capsicum yield (48.59 t ha^− 1), which was significantly greater (P < 0.05) compared with 25% ASM and surface irrigation treatment (19.06 and 10.94%, respectively). 50% ASM recorded comparable yield (45.29 t ha^− 1) with both 75% ASM and surface irrigation (43.80 t ha^− 1). Conversely, 25% ASM resulted in significantly lower yield (40.81 t ha^− 1) than all other treatments. This trend was also reflected in yield per plant, with 75% ASM producing significantly higher yield per plant (1424 g), followed by 50% ASM (1312 g) and surface irrigation (1214 g), whereas 25% ASM resulted in lower yield per plant (1067 g).

Table 4

Impact of different irrigation regimes on capsicum yield and water use efficiency
Treatment	Average quantity of water applied (mm)			Average fruit yield (t ha^− 1)
Treatment	2021-22	2022-23	Pooled	2021-22	2022-23	Pooled
75% ASM	499.12	453.20	476.16	49.06 ± 2.03^a	48.11 ± 2.25^a	48.59 ± 1.22^a
50% ASM	552.63	552.63	552.63	45.60 ± 1.71^b	44.98 ± 1.97^ab	45.29 ± 1.29^ab
25% ASM	603.21	573.66	588.44	41.70 ± 1.78^c	39.92 ± 2.29^c	40.81 ± 1.39^c
Surface irrigation	907.89	919.70	913.80	43.43b ± 1.49^c	44.16 ± 1.84^b	43.80 ± 2.14^c
	Mean yield per plant (g)			WUE (kg/ha-mm)
	2021-22	2022-23	Pooled	2021-22	2022-23	Pooled
75% ASM	1467 ± 48.86^a	1380 ± ^a	1424 ± 153.66^a	98.29^a	106.20^a	102.25^a
50% ASM	1355 ± 55.72^b	1268 ± ^b	1312 ± 154.29^b	82.51^b	81.40^b	81.96b
25% ASM	1110 ± 58.02^d	1224 ± ^b	1067 ± 190.66^d	69.13^c	69.60^c	69.37c
Surface irrigation	1226 ± 28.32^c	1202 ± ^b	1214 ± 181.37^c	47.84^d	48.00^d	47.92d

Water use efficiency ranked as follows: 75% ASM (102.25 kg/ha-mm) > 50% ASM (81.96 kg/ha-mm) > 25% ASM (69.37 kg/ha-mm) > surface irrigation (47.92 kg/ha-mm). This trend correlates with the fact that 75% ASM used less water (476.16 mm) compared all other treatments, resulting in significant water saving of 47.66% compared to surface irrigation. The 50% ASM treatment also achieved notable water savings of 39.25%. Similarly, the 25% ASM treatment showed moderate water savings of 35.31%. However, this reduction in water usage led to an average yield loss of 18.64 per cent in 25% ASM compared to surface irrigation.

3.1.3 Pole bean

The irrigation schedule at 75% ASM observed higher average pole bean yield (37.08 t ha^− 1) and per plant yield (1241 g), demonstrating a statistically (P < 0.05) noteworthy disparity in comparison to other treatments. Irrigation at 50% ASM (31.78 t ha^− 1) and surface irrigation (29.37 t ha^− 1) recorded comparable yield. Conversely, 25% ASM resulted significantly lower yield (31.78 t ha^− 1) than all other treatments. This trend was also mirrored in yield per plant, 50% ASM and surface irrigation (1089 and 1037 g, respectively), recorded comparable yield per plant, whereas 25% ASM resulted significantly lower yield per plant of 892 g.

Water use efficiency was higher for 75% ASM (114.27 kg/ha-mm), followed by 50% ASM (82.76 kg/ha-mm), 25% ASM (63.77 kg/ha-mm) and surface irrigation (45.93 kg/ha-mm). Treatment 75% ASM used less water (324.59 mm) compared all other treatments, resulting in 48.95% water saving than surface irrigation. Further, the 50 and 25% ASM treatment recorded notable 39.59 and 34.59% water saving, respectively. But later treatment observed a significant yield reduction (26.25%).

Table 5

Impact of different irrigation levels on pole beans yield and water use efficiency
Treatment	Average quantity of water applied (mm)			Average pod yield (t ha^− 1)
Treatment	2021-22	2022-23	Pooled	2021-22	2022-23	Pooled
75% ASM	329.23	319.94	324.59	37.41 ± 1.61^a	36.76 ± 1.98^a	37.08 ± 1.47^a
50% ASM	380.12	388.04	384.08	31.21 ± 1.51^b	32.35 ± 2.21^b	31.78 ± 0.98^b
25% ASM	426.31	405.42	415.87	25.89 ± 1.48^c	27.07 ± 1.09^c	26.48 ± 1.17^d
Surface irrigation	635.67	643.93	639.80	29.40 ± 1.58^b	29.34 ± 1.78^c	29.37 ± 1.84^c
Treatment	Mean yield per plant (g)			WUE (kg/ha-mm)
Treatment	2021-22	2022-23	Pooled	2021-22	2022-23	Pooled
75% ASM	1200 ± 35.49^a	1282 ± ^a	1241 ± 174.47^a	113.63^a	114.90^a	114.27^a
50% ASM	1068 ± 41.86^b	1110 ± ^b	1089 ± 173.89^b	82.11^b	83.40^b	82.76^b
25% ASM	874 ± 42.92^c	910 ± ^c	892 ± 194.07^c	60.73^c	66.80^c	63.77^c
Surface irrigation	1009 ± 39.82^b	1065 ± ^b	1037 ± 181.51^b	46.25^d	45.60^d	45.93^d

3.1.4 Cherry tomato

Data presented in Table 6 demonstrate that yield and WUE of cherry tomato plants were affected significantly by irrigation regimes. Sensor-based irrigation at 75% ASM resulted insignificant increased (P < 0.05) cherry tomato yield (42.02 t ha^− 1). Subsequently, 50% ASM (38.63 t ha^− 1) and surface irrigation (36.33 t ha^− 1). In contrast, 25% ASM treatment was significantly recorded lower yield (35.44 t ha^− 1) than all other treatments. This trend was also evident in the yield per plant, with 75% ASM demonstrating a significantly higher yield per plant of 3161 g, followed by 50% ASM (2851 g) and comparable yield for surface irrigation (2716 g). On the other hand, 25% ASM showed significantly lower yield per plant of 2546 g.

Table 6

Impact of different irrigation levels on cherry tomato yield and water use efficiency
Treatment	Average quantity of water applied (mm)			Mean marketable yield (t/ha)
Treatment	2021-22	2022-23	Pooled	2021-22	2022-23	Pooled
75% ASM	499.12	453.2	476.16	42.53 ± 1.90^a	41.50 ± 2.94^a	42.02 ± 1.61^a
50% ASM	552.63	552.63	552.63	40.33 ± 1.71^b	36.92 ± 2.23^b	38.63 ± 1.35^b
25% ASM	603.21	573.66	588.44	37.81 ± 1.18^b	33.08 ± 1.53^c	35.44 ± 1.71^c
Surface irrigation	907.89	919.70	913.80	38.50 ± 1.31^b	34.17 ± 1.66^c	36.33 ± 1.76^c
Treatment	Mean yield per plant (g)			WUE (kg/ha-mm)
Treatment	2021-22	2022-23	Pooled	2021-22	2022-23	Pooled
75% ASM	3055 ± 145.31^a	3266 ± 150.88^a	3161 ± 140.47^a	85.21^a	91.6^a	88.41^a
50% ASM	2818 ± 93.88^b	2884 ± 119.17^b	2851 ± 173.42^b	72.98^b	66.8^b	69.89^b
25% ASM	2508 ± 90.49^c	2584 ± 101.99^c	2546 ± 156.21^c	62.68^c	57.7^c	60.19^c
Surface irrigation	2644 ± 122.14^bc	2789 ± 126.89^bc	2716 ± 148.74^bc	42.41^d	37.2^d	39.81^d

Water use efficiency was higher for 75% ASM (88.41 kg/ha-mm) and lower for surface irrigation (39.81 kg/ha-mm). This treatment also achieved greater water saving (47.66%) compared to surface irrigation. While, 50 and 25% ASM resulted in significant water savings (39.25 and 35.31%, respectively). Nonetheless, the later compromised yield by 2.51%.

3.2 Impact of irrigation regimes on yield components of vegetable crops

The Fig. 2a illustrates the impact of different irrigation regimes on broccoli curd weight across 2021 and 2022. In both the years, 75% ASM consistently yielded the significantly (P < 0.05) higher broccoli curd weight (444.45 and 448.62 g, respectively), followed by 50% ASM (347.29 and 346.12 g, respectively). Notably, 25% ASM and surface irrigation resulted in significantly lower curd weight. Similarly (Fig. 2b), 75% ASM consistently produced higher capsicum fruit yield (130.80 and 124.60 g, respectively) which was on par with the 50% ASM (124.20 and 116.20 g, respectively). Conversely, 25% ASM (110.4 and 104.20, respectively) resulted in significantly lower average fruit weight.

The bar graph (Fig. 3c) presented the average pod weight for different irrigation across 2021-22 and 2022-23. The results indicated significant (P < 0.05) impact of irrigation method on pod weight of pole bean. In both the years, 75% ASM steadily produced significantly higher pod weight (9.04 and 9.14 g, respectively), followed by 50% ASM (8.13 and 8.52, respectively) and surface irrigation (7.08 and 7.41 g, respectively). Conversely, 25% ASM resulted in notably lower pod weight (5.88 and 6.75, respectively).

The bar graph Fig. 4d indicate the number of fruit clusters per plant across different irrigation treatments for the years 2021-22 and 2022-23. The results indicated that, significant impact (P < 0.05) of irrigation regimes on number of fruit cluster. During 2021, 75% ASM produced higher number of fruit clusters (44.53 and 46.08, respectively), followed by 50% ASM (37.41 and 38.71, respectively). While, 25% ASM recorded lower number of fruit clusters (31.30 and 32.39, respectively). However, the performance of surface irrigation varied across the years.

3.3 Relationship between yield and water applied

Effect of depletion of soil moisture from field capacity to 75, 50 and 25% ASM were studied for broccoli, capsicum, pole bean and cherry tomato and its effect on yield was recorded. The relationship between curd/fruit/pod yield and average quantity of water applied for broccoli, capsicum, pole bean and cherry tomato is shown in Fig. 3a-d. The average quantity of water applied for broccoli and pole bean was ranged from 324.59 to 415.87 mm, while 476.16 to 588.44 mm for capsicum and cherry tomato across 75, 50 and 25% ASM treatments. Correspondingly, marketable yield ranged from 18.96 to 26.05 t ha^− 1 for broccoli, 40.81 to 48.59 t ha^− 1 for capsicum, 26.48 to 37.08 t ha^− 1 for pole bean and 35.44 to 42.02 t ha^− 1 for cherry tomato. The yield and water applied to the crop followed a polynomial relationship, with significant R² value for broccoli (R² = 0.86), capsicum (R² = 0.78), pole bean (R² = 0.80) and cherry tomato (R² = 0.82).

Further, developed regression equations were employed to estimate the yield for promised economic returns i.e. optimal water required to achieve a break-even yield (defined as the yield necessary to offset cultivation costs) and corresponding water requirements were determined as follows: For broccoli, 20 t ha^− 1 is required to meet out cost of cultivation for that 357.52 mm water is required. Similarly for capsicum (40 t ha^− 1, 515.60 mm), pole bean (25 t ha^− 1, 341.37 mm) and cherry tomato (34 t ha^− 1, 495.51 mm). The hypothesis behind this relationship establishment is to mitigate financial risks associated with higher capital investment in agriculture, safeguarding them from potential financial crisis and to help the farmers to make the informed decision.

3.4 Economic returns

The superior profit can only be obtained with the best combination of irrigation regimes and crop. Hence, the accounted cost of cultivation, gross returns, net returns and B: C ratio of different combinations of irrigation regimes and vegetable crops was calculated and presented in Table 7. When irrigated at 75% ASM, all four crops viz., broccoli, capsicum, pole bean and cherry tomato resulted in higher net returns (Rs. 589111, 1076787, 1359417 and 418630 ha^− 1, respectively) and B: C ratio (1.68, 1.86, 2.38 and 1.37, respectively) followed by 50% ASM compared to surface irrigation. Scheduling of irrigation at 25% ASM resulted in lower net returns (Rs. 297311, 754737, 867617 and 76580 ha^− 1, respectively) and B: C ratio (1.35, 1.60, 1.89 and 1.07, respectively) in comparison to surface irrigation.

The higher net returns and B: C ratio was mainly attributed to higher fruit yield in all four crops at 75% ASM (26.05, 48.59, 37.08 and 42.02 t ha^− 1, respectively) and lower operational cost. The poor economics observed with irrigation schedule at 25% ASM was mainly attributed to lower crop yield (18.71, 40.81, 26.48 and 35.44 t ha^− 1, respectively).

4.1 Optimal Irrigation Strategies

Optimizing water productivity in dryland conditions is crucial for sustainable agriculture, where efficient water use is essential (Shravan et al., 2024). Higher average water savings were observed at 75% ASM for broccoli (9.69%), capsicum (12.96%), pole beans (27.24%) and cherry tomato (10.47%), indicating the potential for efficient water usage at this level. While, the 50% ASM treatment also resulted in substantial water saving (39.13 to 40.21% for the same crops) compared to surface irrigation. Higher water requirement in the latter treatment attributed to significant water losses through percolation, runoff and evaporation (Dibyendu et al., 2023). In contrast, sensor-based irrigation, by precisely delivering water based on soil moisture condition might minimized these losses. The 25% ASM treatment, although achieved considerable water saving (32.94 to 33.56%) incurred significant yield penalties (Table 3 to 6). The findings emphasize that, while water conservation is a crucial objective, it should not be prioritized at the expense of crop productivity, should consider trade-offs between water savings and crop productivity when selecting the optimal irrigation level (Tejero et al., 2022 and El Chami et al., 2023).

Table 7

Assessing the economic viability of different irrigation levels under different vegetable species
Treatments	Cost of cultivation (Rs/ha)			Net returns (Rs/ha)			B: C ratio
Treatments	Season-1	Season-2	Pooled	Season-1	Season-2	Pooled	Season-1	Season-2	Pooled
C₁ = Broccoli
75% ASM	861529	864249	862889	580471	597751	589111	1.67	1.69	1.68
50% ASM	861129	863849	862489	497271	538951	518111	1.58	1.62	1.60
25% ASM	859929	862649	861289	288471	306151	297311	1.34	1.35	1.35
Surface irrigation	881529	884249	882889	457271	450951	454111	1.52	1.51	1.51
C₂ = Capsicum
75% ASM	1248323	1256803	1252563	1095277	1058297	1076787	1.88	1.84	1.86
50% ASM	1247923	1256403	1252163	963677	936597	950137	1.77	1.75	1.76
25% ASM	1246723	1255203	1250963	785677	723797	754737	1.63	1.58	1.60
Surface irrigation	1267123	1275603	1271363	884877	898297	891587	1.70	1.70	1.70
C₃ = Pole beans
75% ASM	980503	982663	981583	1373497	1345337	1359417	2.40	2.37	2.38
50% ASM	980103	982263	981183	1108897	1152337	1130617	2.13	2.17	2.15
25% ASM	978903	981063	979983	845097	890137	867617	1.86	1.91	1.89
Surface irrigation	999303	1001463	1000383	969197	964637	966917	1.97	1.96	1.97
C₄ = Cherry tomato
75% ASM	1124875	1133315	1129095	435725	401535	418630	1.39	1.35	1.37
50% ASM	1124475	1132915	1128695	334425	240735	287580	1.30	1.21	1.25
25% ASM	1123275	1131715	1127495	239925	-86765	76580	1.21	0.92	1.07
Surface irrigation	1143675	1152115	1147895	246225	129535	187880	1.22	1.11	1.16

Figure 3 demonstrates a polynomial relationship between irrigation and yield across the crops (broccoli, capsicum, pole bean and cherry tomato). This curve delineates the minimum and maximum attainable yield under varying irrigation regimes. The analysis revealed that, minimal water input results in minimal yield. As irrigation increases, yield exhibits a quadratic growth pattern. However, after crossing of irrigation threshold, yield diminishes and further increases would influence yield negatively. In real word, water and yield equation would be used for allocating irrigation water within and between the crops (Patra et al., 2022).

4.2 Stress Impact on Yield and Yield Attributes

Optimizing water productivity in dryland condition is crucial for sustainable agriculture, where efficient water use is essential. The results of the study indicated that, 75% ASM irrigation scheduling led to significant yield increase in all the crops compared to traditional surface irrigation practice. Broccoli showed a yield increase of 10.99 per cent, capsicum 13.21 per cent, pole bean 26.25 per cent and cherry tomato 15.66 per cent with 75% ASM. Further, study suggests that, providing a higher quantity of irrigation at once did not lead to increased yield in vegetable crops as observed in surface irrigation. This lack of yield improvement is attributed to the substantial stress experienced by the crop during the intervals between irrigation, it is evident in 50% ASM and even more noticeable in 25% ASM and surface irrigation (Table 3–6). By addressing the stress experienced by crops between irrigation interval, farmers can tailor their irrigation schedules to better support plant growth and maximize yield in vegetable crops (Lee et al., 2023). In support of the present result, previous study had shown that frequent irrigation could maintain a higher average water-filled pore space in the upper soil layer as compared with the lower frequency scheduling (Abalos et al., 2014; Musa et al., 2019 and Rane et al., 2022).

4.3 Economic Viability of Sensor Driven Irrigation Regimes

Adopting optimized irrigation scheduling and efficient irrigation method has the advantage in increasing WUE and save irrigation water. But for farmers, the economic returns is most important issue to be considered. Study conducted by Paredes et al., 2014; Bakhsh et al., 2015 and Rodrigues et al., 2013 indicated that, although drip and other precision irrigation method have higher WUE and yield as compared with the traditional irrigation method with same irrigation amount, the higher investment in the equipment off-set the benefit. To offset the higher initial cost and achieve economic viability, studies of Playán and Mateos, 2006; Rodrigues et al., 2013 suggested the strategies like increasing crop prices, implementing water pricing and providing government subsidies.

In contrast, our study challenges the conventional belief by demonstrating higher gross returns, net returns and benefit-cost ratio for broccoli, capsicum, pole bean at 75% ASM across the crops (Table 7). The economic success of 75% ASM was primarily attributed to significantly higher fruit yield compared to surface irrigation and unlike previous studies, our cost accounting methodology focused on depreciation (33% of total cost) instead of full cost of structure establishment, it helps to isolating the impact of irrigation management on profitability.

The study demonstrated that, implementing sensor-based irrigation scheduling in vegetable crops such as broccoli, capsicum, pole bean, and cherry tomato can significantly enhance yield, yield attributes, water use efficiency (WUE) and economically viabile. Specifically, the 75% ASM irrigation regime showed notable improvements of 10.99, 13.21, 26.25 and 15.66 per cent higher yield in broccoli, capsicum, pole bean and cherry tomato compared to traditional surface irrigation method. Overall, the results of this study provide compelling evidence for the adoption of sensor-based irrigation at 75% ASM as a sustainable and efficient approach in cultivating broccoli, capsicum, pole bean and cherry tomato in this particular experimental site, further balancing water conservation efforts with maintaining satisfactory crop yield is essential for ensuring long-term agricultural sustainability and food security, highlighting the need for further research and technological advancements in irrigation practices.

Credit authorship contribution statement

Vinutha, B B: Writing – review & editing, Writing – original draft, Methodology, Investigation, Data curation, Conceptualization. M. N. Thimmegowda: Writing – review & editing, Methodology, Formal analysis, Data curation, Conceptualization. Vasanthi, B. G: Writing – review & editing, Validation, Supervision, Methodology, Conceptualization. Mudalagiriyappa : Editing, Validation, Supervision, Methodology, Conceptualization. Mallikarjunagowda, A. P: Review & editing, Validation, Supervision, Methodology, Conceptualization. K. N. Geetha: Supervision, Methodology, Conceptualization. Lingaraju huggi : Editing, Validation. Data presentation. Santhosh ningoji: Writing – review & editing, Validation, Supervision, Methodology, Conceptualization.

Declaration of Conflict of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Author Contribution

Credit authorship contribution statementVinutha, B B: Writing – review & editing, Writing – original draft, Methodology, Investigation, Data curation, Conceptualization. M. N. Thimmegowda: Writing – review & editing, Methodology, Formal analysis, Data curation, Conceptualization. Vasanthi, B. G: Writing – review & editing, Validation, Supervision, Methodology, Conceptualization. Mudalagiriyappa : Editing, Validation, Supervision, Methodology, Conceptualization. Mallikarjunagowda, A. P: Review & editing, Validation, Supervision, Methodology, Conceptualization. K. N. Geetha: Supervision, Methodology, Conceptualization. Lingaraju huggi : Editing, Validation. Data presentation. Santhosh ningoji: Writing – review & editing, Validation, Supervision, Methodology, Conceptualization.

Acknowledgements

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data Availability

Data will be made available on request.

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No competing interests reported.

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Productivity and Soil Moisture Optimization for Vegetable Crops in drylands: Reduced Runoff Farming with Sensor-Based Irrigation Solutions

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Abstract

Figures

Highlights

1. Introduction

2. Materials and Methods

2.1 Experimental Site

2.2 Rain water harvesting and irrigation scheduling

2.3 Observations on yield and yield attributes

2.4 Data Calculation

2.5 Economic analysis

2.6 Statistical analysis

3. Result

3.1 Yield and water use efficiency of vegetable crops

3.1.1 Broccoli

3.1.2 Capsicum

3.1.3 Pole bean

3.1.4 Cherry tomato

3.3 Relationship between yield and water applied

3.4 Economic returns

4. Discussion

4.1 Optimal Irrigation Strategies

4.2 Stress Impact on Yield and Yield Attributes

4.3 Economic Viability of Sensor Driven Irrigation Regimes

5. Conclusion

Declarations

Declaration of Conflict of Interest

Author Contribution

Acknowledgements

Data Availability

References

Additional Declarations

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