Root morphological responses of Chilli (Capsicum annuum L.) to smart irrigation scheduling and drip irrigation systems

Introduction

India is increasingly facing a looming crisis of freshwater scarcity, driven by rapid population growth, climate variability, declining groundwater tables, and competing demands from agriculture, industry, and domestic sectors. Agriculture alone accounts for nearly 80-85% of the total water consumption in the country (Bigas, 2012), especially irrigated agriculture consumes more than two-thirds of the total freshwater on the planet, yet a significant proportion of this water is lost due to inefficient irrigation practices and poor scheduling. This issue causes substantial conflict in freshwater allocation between agriculture and other economic sectors (Chai et al., 2016) [1] . In semi-arid and arid regions, erratic rainfall patterns and prolonged dry spells further aggravate the situation, making water a critical limiting factor for crop productivity. Under such circumstances, improving water use efficiency through advanced irrigation methods and precise scheduling has become imperative for sustaining agricultural production. Efficient irrigation management plays a crucial role in improving crop growth, yield, and water use efficiency. Among various irrigation methods, drip irrigation (surface and subsurface drip) have emerged as promising solutions due to its ability to deliver water directly to the root zone (Simonne et al., 2012) [2] , thereby reducing evaporation losses, improving soil physical properties, crop productivity (Bader et al., 2010 and Elhindi, 2012) [3] and water use efficiency (Jones, 2004 and Bhattarai et al., 2008; Kaur and Brar, 2016) [5,4] . Subsurface drip irrigation (SDI), in particular, offers additional advantages such as minimizes surface evaporation, reduces weed growth (Kanda et al., 2019; Buser et al., 2025) [7,6] and enhances soil moisture uniformity in the root zone (Lamm and Trooien, 2003; Mchugh et al., 2008). These benefits contribute to improved root development (Guo et al., 2016) [8] and nutrient uptake efficiency. Scheduling irrigation is important for achieving crop-specific water requirements which would help to achieve targeted yield without the wastage of water (Leib et al., 2003) [9] . In recent years, advancements in precision agriculture have led to the development of sensor-based irrigation scheduling techniques which enables real-time monitoring and precise irrigation application based on crop demand (Jones, 2004; Thompson et al., 2007) [5,10] . These technologies, often integrated with artificial intelligence and automation, help optimize irrigation scheduling, reduce water wastage, and enhance crop performance under varying environmental conditions.

Chilli (Capsicum annuum L.) is one of the most important crops of tropical and subtropical regions. It is valued not only for its culinary uses but also for its nutritional and economic significance, being a rich source of vitamins A and C, capsaicin, and antioxidants. India is one of the leading producers and exporters of chilli, contributing significantly to global spice trade and farmer income (Devi et al., 2017). However, chilli productivity is often constrained by inefficient water management practices, particularly under semi-arid conditions where water scarcity and erratic rainfall prevail. Root system architecture plays a pivotal role in determining plant productivity (Sonia et al., 2023; Rahnama et al., 2024; Singh et al., 2024) [12,11,13] , as it governs the uptake of water and nutrients from the soil. Traits such as root length, root volume, and root dry weight are critical indicators of plant health and resource acquisition capacity (Lynch, 2013) [14] . Improved root growth enhances drought tolerance, nutrient absorption, and overall plant vigour, ultimately leading to higher yields. Keeping the above points in view, it is proposed to study the response of chilli root traits under different irrigation scheduling approaches and drip irrigation systems.

Material and Methods

An investigation was performed in chilli crop during rabi 2022-23 and 2023-24 on the sandy loam soils of Water Technology Farm Fields, College of Agriculture, Professor Jayashankar Telangana Agricultural University, Rajendranagar, Hyderabad, Telangana, India. The experimental site is situated at an altitude of 549 m above mean sea level with geographical coordinates of 17 0 19'28'' N latitude and 78 0 24'32'' E longitude. The soil bulk density was recorded as 1.41 g cm -3 at 0-15 and 1.45 g cm -3 at 15-30 cm layers. The study was executed as split plot design with drip irrigation methods (I) as main plot factor and irrigation scheduling approaches (S) as subplot factor. The levels examined under main plot factor were surface drip irrigation (I 1 ) and subsurface drip irrigation (I 2 ), and subplot factor were soil moisture sensor-based irrigation triggering (S 1 ), plant water stress sensor-based irrigation triggering (S 2 ), ET sensor-based irrigation triggering (S 3 ) and irrigation scheduling at 1.0 E pan by manual (control) (S 4 ). The gross plot dimensions for each plot is 6.0 m x 7.2 m. The laterals for both surface and subsurface drip methods were laid at 1.2 m apart and 0.15 m depth below soil surface for subsurface drip. Capacitance soil moisture sensor (SW-10) was used for soil moisture based automatic irrigation triggering with 16.8 and 23.6% soil moisture as lower and higher threshold values. The canopy (NTC-10K) and ambient temperature sensors were used for the irrigation triggering under plant water stress system with 0.3 and 0 crop water stress index as the lower and higher threshold values. Evapotranspiration system was coupled with solar radiation (EMC-01) and ambient temperature sensors which triggers the irrigation at 1.0 ET c . Irrigation was scheduled in the control treatment on every alternate day on the basis of pan evaporation data received from USWB class A open pan evaporimeter situated at Meteorological Observatory, Agro-Climatic Research Centre, Agricultural Research Institute, Rajendranagar. The root parameters such as root length (cm), root dry weight (g plant -1 ) and root volume (cm 3 ) were recorded at first and last picking of chilli by carefully uprooting three randomly selected plants and the mean values were computed. Root volume was determined following the method suggested by Sattelmacher (1987) [15] . The data collected during the experimental period for various parameters were analyzed statistically using the analysis of variance (ANOVA) technique appropriate for a split-plot design.

Results and Discussion

The data furnished in the Table 1 indicated that the root length of chilli at first and last pickings was significantly influenced by drip irrigation methods and irrigation scheduling approaches. On contrary, the interaction between these two factors could not exert any signifi-cant effect on root length at first and last pickings during both the years of study. At first picking, in between drip irrigation methods, significantly higher root length was registered with subsurface drip (34.9 and 35.8 cm) and was 5.7 and 5.9% higher than surface drip during 2022-23 and 2023-24 respectively. At last picking, during both the years, significantly higher root length was recorded under subsurface drip (66.2 and 66.9 cm) over surface drip respectively. The mean of both the years also adhered to the similar pattern of superiority during first and last pickings. The reason for the higher root length in subsurface drip irrigation could be attributed to the efficient utilization of available soil moisture due to less exposure of surface wetted soil to sun as well as optimized uptake of nutrients without losses promotes the robust and extensive root system crucial for plant productivity and resilience to environmental stresses. The above results coincide with the conclusions drawn by Rehan (2005) [16] in tomato and Elsharkawy et al. (2015) [17] in common bean. During first year, among irrigation scheduling approaches, significantly higher root length was observed under ET sensor based irrigation triggering (36.0 and 69.5 cm) than other sensor based irrigation triggering and remained at par with irrigation scheduling at 1.0 Epan by manual (35.3 and 67.6 cm). On the other hand, during second year, ET sensor based irrigation triggering registered significantly higher root length (36.8 and 70.3 cm) than other treatments, and comparable with irrigation scheduling at 1.0 Epan by manual (36.1 and 68.2 cm) at first and last pickings, respectively. The mean of two crop seasons also followed the same line of trend at both the pickings. The results of lower root length under surface drip were in accordance with the findings of Nisha (2007) [18] in ladies finger who reported that high infiltration rates and more atmospheric interactions at the soil surface resulted in low moisture availability eventually effecting root length.

and volume (cm 3 ) Significant influence of drip irrigation methods and irrigation scheduling approaches on root dry weight (g plant -1 ) and volume (cm 3 ) was opined from the data furnished in Table 2 and Fig. 1 at first and last pickings of chilli crop. Between the drip irrigation methods, at first picking, subsurface drip recorded significantly higher root dry weight (24.06 and 24.81 g) and root volume (27.85 and 28.74 cm 3 ) over surface drip during first and second year of study respectively. An overview of the data, revealed that at last picking also, the higher root dry weight (42.30 and 43.29 g) and root volume (54.59 and 55.19 cm 3 ) was registered with subsurface drip irrigation than surface drip during both the years of study respectively. The mean values at first and last pickings also adhered to the similar trend as above. The reason for the improved root dry weight could be ascribed to development of dense and extensive root system absorb more water and nutrient from the deeper layers of soil leading to increased root dry weight. These results are in accordance with the findings of Neama et al. (2014) [19] in eggplant and Abdelhady et al. (2017) [20] in tomato. These results collaborate with the findings of Dashtgol et al. (2023) [21] who reported that roots under subsurface drip system grown extensively and occupied larger volume of soil compared to conventional irrigation methods. Among irrigation scheduling approaches, at first picking, ET sensor based irrigation triggering registered significantly higher dry weight of roots (25.55 and 26.36 g) and root volume (29.56 and 30.42 cm 3 ) over other sensor based irrigation triggering treatments, but was on par with irrigation scheduling at 1.0 Epan by manual (25.12 and 25.69 g) and root volume (28.90 and 29.80 cm 3 ) during both the years of study respectively. At last picking, during first and second years of study, significantly higher root dry weight (45.05 and 46.05 g) and volume (57.50 and 58.22 cm 3 ) were recorded in ET sensor based irrigation triggering and over other treatments and was comparable with irrigation scheduling at 1.0 Epan by manual (43.27 and 44.20 g) and (55.61 and 56.36 cm 3 ) respectively. Increased root volume could be attributed to continuous frequent supply of irrigation water based on crop evapotranspiration losses provided ample amount of water to the crop resulted in higher volume to access and absorb more nutrients from the soil and ultimately supported the crop overall growth and development. The mean values at first and last picking also adhered to the similar trend as above. On contrary to the above, root dry weight and volume at first and last pickings were not significantly influenced by the interaction of drip irrigation methods and irrigation scheduling approaches during both the years of study respectively.

Conclusion

The above study concluded that improved root traits under subsurface drip between drip irrigation methods and ET sensor based irrigation scheduling among irrigation scheduling approaches were attributed to efficient soil moisture distribution, reduced evaporation losses, and enhanced nutrient uptake. Overall, irrigation scheduling using AI based sensors and subsurface drip proved to be an efficient strategy for promoting root growth and optimizing water use in chilli cultivation, thereby improving the crop productivity.

Authors have declared that no competing interests exist

Acknowledgement

AcknowledgementsThe authors express their sincere gratitude to Professor Jayashankar Telangana Agricultural University, Department of Agronomy, Water Technology Centre for providing facilities, continuous guidance and technical assistance for the study. Support received from NICRA, Navariti Innovations is duly acknowledged.

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21. How to cite this article: Bhavitha K, Pasha ML, Prakash TR, Ramulu V, Revathi P, Rajaiah P, et al. Root morphological responses of Chilli (Capsicum annuum L.) to smart irrigation scheduling and drip irrigation systems. International Journal of Research in Agronomy. 2026;9(9):1546-1550. doi: https://www.d oi.org/10.33545/2618060X.2026.v9.i9u.6726