Drip Irrigation Research Articles

Drip irrigation and mulching reduce weed interference and improve water productivity of spring maize

Abstract Drip irrigation and mulching were tested to minimize unproductive water loss through evaporation and weed interference. A field experiment was conducted during spring season of 2020 and 2021 in split plot design with three replications. The study includes six treatment combinations of drip irrigation methods (surface drip and subsurface drip irrigation) and mulching (black plastic, paddy straw and no mulch) along with one conventional furrow irrigation without mulching (as control) in main plots. Four weed control treatments (atrazine 1000 g a.i./ha as pre-emergence, two hand weedings at 30 and 60 days after sowing [DAS], weed free and weedy for whole crop growth period) were kept in the subplots. The combination of drip irrigation and mulches significantly enhanced leaf area index and crop biomass at 60 DAS than furrow irrigation. Integration of subsurface drip irrigation with plastic mulching resulted in the lowest weed density and biomass among main plots. Drip irrigation coupled with plastic and straw mulching resulted in 86 and 50% reduction in weed density and biomass, respectively, as compared to no mulching. Integration of subsurface drip with paddy straw mulch and black plastic mulch resulted in 17.1 and 15.5% higher maize grain yield, respectively, as compared to furrow irrigation. The highest irrigation water productivity (3.58 kg/m3) was observed in combination of subsurface drip and paddy straw mulch followed by combination of subsurface drip and black plastic mulch (3.51 kg/m3). Overall, straw mulching in drip irrigation system proved economical in terms of maize productivity.

Strategy of Development of Sustainable Red Chili Agribusiness Areas in North Sumatra Province

The development of sustainable red chili agribusiness areas in North Sumatra Province is a challenge that involves various aspects, including cultivation technology, farmer institutions, supply chain efficiency, government policy support, and environmental sustainability. This study aims to identify and formulate a sustainable red chili agribusiness development strategy that can increase farmers' productivity and welfare without sacrificing the environment. Through a qualitative approach, data were collected from in-depth interviews with farmers, agribusiness stakeholders, as well as a review of government policies related to sustainable agriculture. The results show that the application of precision agriculture technology, such as drip irrigation systems and integrated pest management, can significantly increase productivity while preserving natural resources. Strengthening farmer institutions, such as cooperatives, is also very important in coordinating production, improving market access, and stabilizing farmers' income. In addition, supply chain efficiency through the construction of storage and distribution centers will reduce post-harvest losses and price fluctuations, while government policy support is needed in the provision of agricultural infrastructure and stable price regulation. In conclusion, the sustainable development model of red chili agribusiness areas must include synergies between environmentally friendly technologies, strong institutions, efficient supply chains, and proactive policy support. With this model, red chili agribusiness areas in North Sumatra can develop sustainably, contribute to food security, and improve farmers' welfare.

Intermittent Drip Irrigation Soil Wet Front Prediction Model and Effective Water Storage Analysis

The depth and width of drip infiltration play a critical role in designing effective irrigation strategies. However, existing models primarily focus on continuous irrigation and fail to predict wetting patterns under intermittent drip irrigation. This study developed an infiltration model to estimate soil moisture depth and width under intermittent drip irrigation and identified strategies that enhance effective water storage. Indoor soil box simulations were conducted, with continuous drip irrigation as the control. Results showed that intermittent irrigation increased infiltration width and reduced depth, maximizing water storage efficiency. We recommend adopting an intermittent irrigation system with 1.5 h of irrigation followed by a 0.5 h interval, repeated four times. This system increased effective water storage by up to 16.23% compared to continuous irrigation. The proposed method is suitable for sandy loam farmland in southern Xinjiang and can significantly improve water use efficiency in arid regions.

Nitrogen cycling and associated grey water footprint in croplands under different irrigation practices

The pervasive application of nitrogen (N) fertilisers in agriculture poses a significant threat to freshwater ecosystems. The grey water footprint (GWF) is a measure of the volume of freshwater required to assimilate the relative water pollutants resulted from N leaching-runoff processes. The responses of N cycling and associated regional GWF to crop production under different irrigation methods have not been investigated. In this study, the N leaching-runoff rate, N₂O, and NH₃ emissions in croplands and the associated GWF per unit crop yield (UGWF) and annual total GWF (TGWF) of winter wheat-summer maize crop rotations across the Hebei, Shandong, and Henan Region (HSHR) of China were simulated and evaluated at the prefecture level over the 2004–2020 period. Four irrigation scenarios were modelled: furrow, sprinkler, surface drip, and subsurface drip irrigation. The results showed significant spatial and temporal heterogeneity in N leaching-runoff rates and emissions of N2O, and NH3. The N leaching-runoff rates for winter wheat and summer maize ranged from 3.9% to 10.9% and 15.0%–32.3%, respectively. With changing different irrigation methods, N leaching-runoff rates exhibited greater sensitivity than N₂O and NH₃ emissions; this sensitivity was more pronounced in winter wheat than in summer maize. Findings reveal that N pollution is influenced not only by irrigation methods but also by weather conditions, crop-specific rooting characteristics, sowing time, and soil texture. Winter wheat UGWF under the four irrigation methods mostly followed the order of furrow > sprinkler irrigation > surface drip irrigation > subsurface drip irrigation. Conversely, for summer maize, subsurface drip irrigation resulted in a higher UGWF compared to furrow irrigation, mainly due to increased water percolation thus higher N leaching-runoff rate in near-saturated soil with higher rainfall. This result reveals the potential conflict between water-saving irrigation and N pollution control. This analysis underscores the necessity of recognising the combined effects of agricultural management practices on water conservation and environmental safeguarding.

Integrating Automated Drip Irrigation and Organic Matter to Improve Enzymatic Performance and Yield of Water Efficient Chilli in Karst Region

Integrating Automated Drip Irrigation and Organic Matter to Improve Enzymatic Performance and Yield of Water Efficient Chilli in Karst Region

Biofilm growth Dynamics in a Micro-Irrigation with Reclaimed Wastewater in the Field scale.

The dripper clogging due to the development of biofilm can reduce the benefits of micro-irrigation technology implementation using reclaimed wastewater. The narrow cross-section and labyrinth geometry of the dripper channel enhance the fouling mechanisms. The aim of this study was to evaluate the water distribution and biofouling of drip irrigation systems at the field scale during irrigation with treated wastewater. Six 100 m lines of commercial pipes with two pressure-compensating dripper types (flow rate, Q, of 0.65 L.h-1 and 1.5 L.h-1, respectively) were monitored for four months. Different zones along the pipes were selected to evaluate the influence of hydrodynamical conditions (Reynolds number = 5400 to 0) on biofouling. Destructive methods involving the biofilm extraction by mechanical means, showed little biofilm development without significant differences in dry and organic matter content in function of the sampling location along the pipe or dripper flow rate (Q0.65 and Q1.5). These results were confirmed by non-destructive methods, such as optical coherence tomography, that nevertheless showed that biofouling concerned 15-20% of the total dripper labyrinth volume. Total organic carbon monitoring and its composition (by three-dimensional excitation and emission matrix fluorescence microscopy) showed that the biofilm did not significantly influence the organic matter nature. Our results indicated that the biological activity and biofilm development in irrigation systems were more affected by the environmental conditions, particularly water temperature, rather than flow conditions. This confirmed that treated wastewater with low organic content can be used in micro-irrigation systems without significant loss of efficiency, even in conditions requiring intensive irrigation, such as the Mediterranean climate.

Optimizing Irrigation and Nitrogen Application to Enhance Millet Yield, Improve Water and Nitrogen Use Efficiency and Reduce Inorganic Nitrogen Accumulation in Northeast China

Enhancing irrigation and nitrogen fertilizer application has become a vital strategy for ensuring food security in the face of population growth and resource scarcity. A 2-year experiment was conducted to determine to investigate the effects of different irrigation lower limits and nitrogen fertilizer application amounts on millet growth, yield, water use efficiency (WUE), N utilization, and inorganic nitrogen accumulation in the soil in 2021 and 2022. The experiment was designed with four irrigation lower limits, corresponding to 50%, 60%, 70%, and 80% of the field capacity (FC), referred to as I50, I60, I70, and I80. Four nitrogen fertilizer application were also included: 0, 50, 100, and 150 kg·hm−2 (designated as F00, F50, F100, and F150), resulting in a total of 16 treatments. Binary quadratic regression equations were established to optimize the irrigation and nitrogen application. The results demonstrated that the plant height, stem diameter, leaf area index, aboveground biomass, yield, spike diameter, spike length, spike weight, WUE, and nitrogen agronomic efficiency for millet initially increased before subsequently decreasing as the irrigation lower limit and nitrogen fertilizer application increased. Their maximum values were observed in the I70F100. However, the nitrogen partial factor productivity (PFPN) exhibited a gradual decline with increasing nitrogen application, reaching its peak at F50. Additionally, PFPN displayed a pattern of initial increase followed by a decrease with rising irrigation lower limits. The accumulation of NO3−-N and NH4+-N in the 0~60 cm soil layer increased with the increase of nitrogen fertilizer application in both years, while they tended to decrease as the irrigation lower limit increased. An optimal irrigation lower limit of 64% FC to 74% FC and nitrogen fertilizer application of 80 to 100 kg ha−1 was recommended for millet based on the regression equation. The findings of this study offer a theoretical foundation and technical guidance for developing a drip irrigation and fertilizer application for millet cultivation in Northeast China.

Methodology for validating autonomous and direct photovoltaic pumping in drip irrigation

Methodology for validating autonomous and direct photovoltaic pumping in drip irrigation

Precision drip Irrigation System and Foliar Application of Biostimulant and Fertilizers Containing Micronutrients Optimize Photochemical Efficiency and Grain Yield of Maize (Zea mays L)

Abstract Asymmetric drought propagation and depletion of soil nutrients threaten cereal crop productivity worldwide, calling for the application of validated agronomic practices to curtail their effect on crop production. This study evaluated the effect of precision drip irrigation, biostimulant, and micronutrients application on photochemical efficiency and yield of maize.An experiment laid in a randomized complete block design with irrigation and water stress was established in 2022 and 2023 growing seasons at the experimental area of the University of Debrecen. Other treatments included T1 (non-microbial biostimulant from plant origin), T2 (zinc based chemical fertilizer), T3 (boron and molybdenum based chemical fertilizer), and T4 (control). Data was collected on steady-state fluorescence (F’), maximal fluorescence (Fm’), quantum photosynthetic yield or efficiency of photosystem II (ΦPSII or Y(II)), electron transport rate (ETR), and grain yield and yield components. Precision drip irrigation significantly optimized ΦPSII, ETR, cob weight, number of seeds per cob, weight of 1000 seeds and grain yield. The biostimulant and micronutrients optimized Fm’, ΦPSII, and ETR at VT and R2 growth stages. Regardless of the water management regime, T1, T2 and T3 seasonally optimized grain yield. Between water management regimes, biostimulant had the highest yield optimization effect under precision drip irrigation in the season with elevated water stress.Optimum photochemical efficiency and grain yield is achievable through precision drip irrigation, biostimulant, and micronutrient application. However, further research involving 2–3 application times at critical stages of maize under precision drip irrigation and/or combined application of these treatments at season specific precision drip irrigation is required.

Phosphorus Supply Under Micro-Nano Bubble Water Drip Irrigation Enhances Maize Yield and Phosphorus Use Efficiency

This study aimed to explore the combined effects of micro-nano bubble water drip irrigation and different phosphorus (P) application rates (P0: 0 kg·hm−2; P1: 86 kg·hm−2; P2: 172 kg·hm−2; P3: 258 kg·hm−2) on maize growth, soil phosphorus dynamics, and phosphorus use efficiency to optimize irrigation and P fertilizer use efficiency. Through a field column experiment, the impact of micro-nano bubble water drip irrigation on maize plant height, stem diameter, leaf SPAD values, biomass, and yield was evaluated. The results showed that (1) irrigation methods significantly affected maize growth indicators such as plant height, stem diameter, and root dry weight. Micro-nano bubble water drip irrigation consistently promoted growth during all growth stages, especially under higher P application. (2) P application significantly increased the dry weight and P concentration in maize roots, stems, leaves, ears, and grains. Under micro-nano bubble water drip irrigation, the P concentrations in roots and grains increased by 59.28% to 92.59%. (3) Micro-nano bubble water drip irrigation significantly enhanced P uptake efficiency, partial factor productivity of P, and agronomic P use efficiency. Particularly under P1 and P2 treatments, the increases were 134.91% and 45.42%, respectively. Although the effect on apparent P recovery efficiency was relatively small, micro-nano bubble water drip irrigation still improved P utilization under moderate P levels. (4) Structural equation modeling indicated that P supply under micro-nano bubble water drip irrigation primarily regulated alkaline protease and alkaline phosphatase, enhancing soil P availability, which in turn promoted maize P accumulation and increased yield. In conclusion, this study demonstrated that the combination of micro-nano bubble water drip irrigation and appropriate P application can effectively promote maize growth and nutrient utilization, providing a theoretical basis for optimizing irrigation and fertilization strategies in maize production.

Simulation and Evaluation of Spring Maize Growth Under Drip Irrigation with Fully Biodegradable Film Mulching Based on the DSSAT Model

Fully biodegradable mulch film enhances temperature and moisture retention during the early stages of maize growth while naturally degrading in the later stages, providing an environmentally friendly alternative to conventional plastic mulch films. However, there is no consensus on its impact on maize growth and yield. The present study utilized field test data from spring maize covered with fully biodegradable mulch film in the Xiliaohe Plain, aiming to improve the Decision Support System for Agrotechnology Transfer (DSSAT) model while focusing on soil temperature, irrigation, rainfall, and evapotranspiration. The parameters of the DSSAT model were calibrated and validated using field test data from 2016 to 2018. The improved DSSAT model accurately simulated the maize growth process under various induction periods of fully biodegradable mulch film. The simulation accuracy of this model was as follows: MRE < 10%, nRMSE < 12%, and R2 ≥ 0.80. Moreover, the yield of spring corn covered with fully biodegradable mulch film was predicted using meteorological data from 2019 to 2023. This study suggests that regions such as the Xiliaohe Plain, which share climatic conditions, should opt for fully biodegradable mulch film with an induction period of approximately 80 days to ensure high yields across different hydrological years.

Reducing flow heterogeneity in drip irrigation networks using genetic algorithms

AbstractIrrigation based on non‐compensated drip emitters is extremely common in agriculture, especially due to its simplicity, robustness and competitive cost. Nevertheless, because of friction losses in the pipe, together with irregular terrain, these systems often suffer from uneven water distribution in the drip emitters, which not only results in inefficient use of water resources but also might lead to inadequate irrigation in certain parts of the field. This work proposes to design the topology of the irrigation network to compensate for these discharge differences. To this end, a graph‐based mathematical model is developed to determine the discharge flows at different emitters for any network topology. This model is employed to formulate an integer nonlinear optimization problem, for which a messy genetic algorithm is proposed. The methodology is validated on an example problem, which is based on a rectangular agricultural crop of 49 fruit trees. The results revealed a 70% reduction in the coefficient of variation of the irrigation discharge rates, which was employed as a metric of irrigation uniformity. This caused a 75% reduction in the water excess. The results demonstrate that the uniformity can be improved simply by choosing a proper connectivity layout to build the pipe network.

Effect of center-pivot and subsurface drip irrigation systems on growth and evapotranspiration of volunteer corn in corn, soybean, and sorghum

Abstract Volunteer corn (Zea mays L.) is a competitive weed in corn-based cropping systems. Scientific literature does not exist about the water use of volunteer corn grown in different crops and irrigation systems. The objectives of this study were to characterize the growth and evapotranspiration (ETa) of volunteer corn in corn, soybean [Glycine max (L). Merr.], and sorghum [Sorghum bicolor (L.) Moench] under center-pivot irrigation (CPI) and subsurface drip irrigation (SDI) systems. Field experiments were conducted in south-central Nebraska in 2021 and 2022. Soil moisture sensors were installed at depths of 0 to 0.30, 0.30 to 0.60, and 0.60 to 0.90 m to track soil water balance and quantify seasonal total ETa. Corn was the most competitive, as volunteer corn had the lowest biomass, leaf area, and plant height compared with the fallow. Soybean was the least competitive with volunteer corn, as the plant height, biomass, and leaf area of volunteer corn in soybean were similar to fallow at 15, 30, 45, and 60 d after transplanting (DATr). Averaged across crop treatments, irrigation type did not affect volunteer corn growth at 15 to 45 DATr. Soil water depletion and ETa were similar across crop treatments with and without volunteer corn, as water was not a limiting factor in this study. The ETa of volunteer corn was the highest in soybean (623 mm), followed by sorghum (622 mm), and corn (617 mm) under CPI. The SDI had higher irrigation efficiency, because without affecting crop yield, it had 3%, 6%, and 8% lower ETa in soybean (605 mm), sorghum (585 mm), and corn (571 mm), respectively. Although soil water use did not differ with volunteer corn infestation, a soybean yield loss of 27% was observed, which suggests that volunteer corn may not compete for moisture under fully irrigated conditions; however, it can impact the crop yield potential due to competition for factors other than soil moisture.

Climate-Resilient Horticulture: Adapting to Climate Change through Innovative Practices and Technologies

Climate change is a serious threat to horticulture as it will increase temperatures, cause unpredictable rainfall patterns, and extreme weather events that will affect crop yields, quality, and overall agricultural sustainability. Climate-resilient horticulture uses cutting-edge technologies and equipment to overcome these obstacles. Developing crop varieties that are resistant to heat, drought, and salt is a key strategy to make plants more resilient to adverse conditions. To conserve water and guarantee a constant supply of moisture, advanced water management techniques such as drip irrigation and rainwater collection are crucial. Climate adaptation is aided by soil management techniques that increase soil fertility and health, such as conservation tillage and organic amendments. Technological advancements such as precision horticulture enable farmers to optimize inputs and reduce waste, while controlled environment agriculture (CEA) systems, including greenhouses, hydroponics, allow cultivation of crops in regulated environments, reducing external climate impacts. Additionally, genetic engineering and biotechnology offer promising solutions to develop crops with increased climate resilience. Despite the potential of these innovations, challenges such as high costs, knowledge barriers, and policy constraints must be addressed for widespread adoption. This summary underscores the need to integrate these strategies to secure the future of horticulture in a changing climate, ultimately contributing to global food security and agricultural sustainability.

Machine learning‐driven microwave imaging for soil moisture estimation near leaky pipe

AbstractCharacterizing soil moisture around drip irrigation pipes is crucial for precise and optimized farming. Machine learning (ML) approaches are particularly suitable for this task as they can reduce uncertainties caused by soil conditions and the drip pipe positions, using features extracted from relevant datasets. This letter addresses local moisture detection in the vicinity of dripping pipes using a portable microwave imaging system. The employed ML approach is fed with two dimensional images generated using back projection as a radar‐based algorithm and the Born approximation as an inverse scattering method, based on spatio‐temporal (collected data at various positions over the soil surface and at different time points.) measurements at various frequencies. The study investigates the performance of K‐nearest neighbour (KNN) and convolutional neural networks (CNN) algorithms for moisture classification based on these imaging techniques. We also explore the potential of KNN and CNN for moisture estimation around the plant roots and in the presence of pebbles. In general, CNN outperforms KNN in moisture content detection from microwave data, especially after applying imaging algorithms. A combination of CNN as the ML approach and the back projection algorithm to provide training data, yielded accuracy more than other models for moisture content estimation. Also, the practical results demonstrate that our method can detect soil moisture with an estimation error of less than 10%.

Monitoring and Modelling Fluxes of Water and Nutrients to Surface Drainage Network From Irrigated Agricultural Fields in a Hydraulically Reclaimed Coastal Area

ABSTRACTThis paper describes the application of a physically based agrohydrological model (named FLOWS), coupled with a kinematic wave approach model (named KWV) for water and solute runoff routing, for interpreting the fate of water and nutrients coming from cultivated fields to surface drainage network located in ‘Piana del Sassu’ in the Arborea plain, a hydraulically reclaimed area with shallow groundwater. Modelling was supported by a large complex database on soil, groundwater and surface drainage water, which was used for establishing the boundary conditions for simulations, as well as for calibrating and validating the model. The model FLOWS provided the water and nutrient fluxes to the surface water, which were passed to the KWV model for their routing along the elementary fields in the experimental area and from these to the ditches and finally to the drainage channel. The modelling approach effectively predicted the water and solute distribution along the soil profile, as well as the losses of water and nutrients to the surface water. The results showed a significant amount of water and dissolved nutrients to flow quickly from the soil uppermost layer to the surface drainage network during both the irrigation season and during rainfall events. During irrigation applications, losses were mostly due to rainfall intensity exceeding the maximum infiltration velocity of the shallow soil layer in the case of sprinkler irrigation and to subsurface lateral drainage in the case of exceeding irrigation water provided by drip irrigation. This makes the Sassu plain a significant contributor of nutrients (nitrate and phosphorus) to the surface water. Consequently, even though the agricultural activities might not be an important issue for the groundwater vulnerability, the management of water and nutrients should be significantly improved to avoid ecohydrological threats to the important coastal water bodies present in the area.

Climate Risk Management among Smallholder Farmers: A Comparative Analysis of Flood-prone Alappuzha and Drought-affected Gondia in India

Climate change poses significant risks to agricultural systems worldwide, particularly in developing countries where smallholder farmers have limited adaptive capacity. This study examines the socioeconomic and demographic factors influencing climate risk management practices among smallholder farmers in two contrasting regions of India: the flood-prone Alappuzha district in Kerala and the drought-affected Gondia district in Maharashtra. Using data collected from 150 rice farmers, the research analyzes economic, social, technical, and physical dimensions shaping adaptive responses. Key findings reveal significant regional disparities in adaptive strategies. Alappuzha farmers exhibit greater resilience due to higher incomes, better compensation mechanisms, and stronger community networks. Their strategies primarily involve strengthening bunds, improving drainage infrastructure, adopting flood-resistant rice varieties, and relying on formal credit for support. Conversely, Gondia farmers face lower adaptive capacity, driven by limited access to credit, inadequate compensation, and weaker institutional support. Their climate risk management approaches include drip irrigation, planting drought-resistant crop varieties, and pursuing income diversification to reduce vulnerability. Education levels, access to insurance, and the use of localized weather information also play crucial roles in shaping adaptive capacity across both regions. The study underscores the need for targeted interventions to strengthen institutional support, expand educational programs, facilitate community networks, and improve access to localized weather information to enhance agricultural resilience to climate risks. These findings provide practical policy recommendations aimed at addressing region-specific challenges and leveraging local strengths to bolster adaptive capacity.

Optimizing Growth of Melon (Cucumis melo L. cv. Madesta) in Nutrient Film Technique and Drip Irrigation Hydroponics with Varied Substrates

Hydroponic systems offer a promising solution for urban farming and the utilization of unproductive land. Successful implementation, however, requires careful optimization to select the most effective hydroponic system tailored to specific plants and environmental conditions. This study aims to compare the growth and physiological responses of Madesta melons (Cucumis melo L. cv. Madesta) cultivated using the nutrient film technique (NFT) and drip irrigation system (DIS) with variations in growth media. The Madesta melon seeds underwent a two-week germination phase in coco peat media, followed by transplanting into NFT and DIS setups utilizing diverse growth media, including rice husk, rice husk mixed with compost, and compost only. Over four weeks post-cultivation, assessments were conducted on key growth metrics such as leaf count, leaf diameter, plant height, and stem diameter. Plant physiological responses were also analyzed, encompassing chlorophyll and nitrogen levels, along with the mineral composition within leaves and fruits. Results revealed that the DIS cultivation outperformed the NFT in terms of growth outcomes. Among the varied media combinations, the rice husk and compost blend supported growth most effectively. Notably, no significant differences were observed in leaf and fruit nitrogen content between the DIS and NFT systems, and the overall mineral content of the media remained relatively stable before and after the cultivation period. Mineral content analysis revealed calcium as the predominant element in the leaves, while potassium emerged as the most abundant mineral in the fruits. This research sheds light on the potential of hydroponic systems, specifically the DIS method, for enhancing melon cultivation, emphasizing the importance of selecting appropriate growth media to maximize plant growth.

The Application of Smart Drip Irrigation System for Precision Farming

Managing water resources in urban areas is relatively expensive due to the costs of electricity and water distribution from wells and water companies. Therefore, water resource management for urban agricultural purposes needs to be made efficient, such as through smart irrigation technologies, one of which is the drip irrigation system that engages soil moisture sensors and the Internet of Things (IoT) to control the amount of distributed water. This study aims to apply and evaluate the performance of a drip irrigation system based on soil moisture sensors and IoT in urban agriculture. The results showed that the distribution uniformity in the system was identified at fair levels, with a Coefficient of Uniformity (CU) of 90.15% and 86.58%, respectively. Furthermore, our study also found that the IoT-assisted drip irrigation system that engaged a Deep Neural Networks (DNN) model to meet the water requirement led to better peanut yield than the irrigation system based on soil moisture as a control.

Assessment of Farmer’s Attitude towards Drip and Traditional Irrigation System in Junagadh District, Gujarat

Cotton (Gossypium herbaceum) is a leading natural fiber crop that is cultivated for its soft, fluffy staple fiber. Drip irrigation is a method of irrigation wherein water is carried to the plant under low pressure, through small diameter plastic pipes and delivered at the root zone drop by drop through an emitting device. The research was conducted in Junagadh district, utilizing a multistage sampling technique. A total of 160 farmers comprising 80 drip irrigation user and 80 conventional irrigation system users were surveyed. Likert’s scale with three point scale was used to analyse attitude towards the drip and traditional irrigation system by using 10 statements for each respondents. The farmers in the research area having positive attitudes toward the drip irrigationmethods as positive trend with value 66.25% was observed among drip respondents. This positive attitude reflects the behavioural intention towards the acceptance of irrigation systems. The study also revealed that 48.75% of the non-adopter farmers had the positive attitude toward the traditional irrigation methods whereas 51.25% of them had a negative and neutral attitude.