Efficient Design of Drip Irrigation System using Water and Fertilizer Application Uniformity at Different Operating Pressures in a Semi‐Arid Region of India

Kumar, Mukesh; Kumar, Rohitashw; Rajput, T. B. S.; Patel, Neelam

doi:10.1002/ird.2108

Cited by 9 publications

(8 citation statements)

References 18 publications

(14 reference statements)

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“…e water quality values showed that the groundwater (IT 2 ) was saline (EC iw 1.5 to 3.0 dS.m −1 , pH < 8.0, and SAR < 10.0). e uniformity of the drip irrigation system was satisfactory and in line with the past study [34] which reported the highest uniformity coefficient (99.2%). e distribution uniformity (97.4%) was found under the drip irrigation at a pressure of 1.5 kg cm −2 using a 25 m long lateral line in New Delhi, India.…”

Section: Crop Yield and Water Use Efficiencysupporting

confidence: 87%

Saline Water Impact on Water Use Efficiency of Bitter Melon (Momordica charantia L) Using Drip Irrigation

Soomro

Shaikh

Andriyas

et al. 2022

Advances in Agriculture

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Water shortage is a real problem in many parts of the world and finding alternative solutions such as the application of saline water in cropping systems is highly appreciated. Research on drip irrigation and soil salinity is still inadequate, and their effect on crop yield and water use efficiency (WUE) is a huge challenge for small farmers. The present study was conducted in Malir, a semiarid region in the Sindh province of Pakistan. The purpose was to estimate the effects of two different qualities of irrigation water including fresh quality water (IT1 0.56 dS m−1) and saline groundwater (IT2 2.89 dS m−1) on WUE using drip irrigation technology in 2018–19. The experimental design was complete randomized block design (RCBD) with two treatments of irrigation: (1) freshwater (IT1) with 0.56 dS m−1 electrical conductivity and (2) saline water (IT2) with 2.89 dS m−1 electrical conductivity. The average biomass and crop yield under IT1 were 10.2 t.ha−1 and 7.4 t.ha−1, respectively, and were found higher than those under IT2 (7.3 t−1 and 4.2 t.ha−1, respectively). Hence, both the treatments remained equally effective in season 1 as compared to season 2 ( p ≤ 0.05 ). The WUE of bitter melon under IT1 was 1.60 and 1.56 kg.m−3 in seasons 1 and 2, respectively, and was higher than those under IT2 which were observed 1.21 and 1.07 kg.m−3 in seasons 1 and 2, respectively.

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Section: Crop Yield and Water Use Efficiencysupporting

confidence: 87%

Saline Water Impact on Water Use Efficiency of Bitter Melon (Momordica charantia L) Using Drip Irrigation

Soomro

Shaikh

Andriyas

et al. 2022

Advances in Agriculture

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“…The socioeconomic impacts of rainfall are significant, from physical damage in floods to disruptions in transport networks [3,9]. Simultaneously, India is challenged with increasing population and climate change, which have threatened the present freshwater need for irrigation and drinking [10][11][12][13]. To overcome the challenges of water scarcity and the deterioration of cultivable land, modeling rainfall-runoff plays an important role.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Statistical-Machine Learning Approach for Runoff Prediction

et al. 2022

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Nowadays, great attention has been attributed to the study of runoff and its fluctuation over space and time. There is a crucial need for a good soil and water management system to overcome the challenges of water scarcity and other natural adverse events like floods and landslides, among others. Rainfall–runoff (R-R) modeling is an appropriate approach for runoff prediction, making it possible to take preventive measures to avoid damage caused by natural hazards such as floods. In the present study, several data-driven models, namely, multiple linear regression (MLR), multiple adaptive regression splines (MARS), support vector machine (SVM), and random forest (RF), were used for rainfall–runoff prediction of the Gola watershed, located in the south-eastern part of the Uttarakhand. The rainfall–runoff model analysis was conducted using daily rainfall and runoff data for 12 years (2009 to 2020) of the Gola watershed. The first 80% of the complete data was used to train the model, and the remaining 20% was used for the testing period. The performance of the models was evaluated based on the coefficient of determination (R2), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and percent bias (PBAIS) indices. In addition to the numerical comparison, the models were evaluated. Their performances were evaluated based on graphical plotting, i.e., time-series line diagram, scatter plot, violin plot, relative error plot, and Taylor diagram (TD). The comparison results revealed that the four heuristic methods gave higher accuracy than the MLR model. Among the machine learning models, the RF (RMSE (m3/s), R2, NSE, and PBIAS (%) = 6.31, 0.96, 0.94, and −0.20 during the training period, respectively, and 5.53, 0.95, 0.92, and −0.20 during the testing period, respectively) surpassed the MARS, SVM, and the MLR models in forecasting daily runoff for all cases studied. The RF model outperformed in all four models’ training and testing periods. It can be summarized that the RF model is best-in-class and delivers a strong potential for the runoff prediction of the Gola watershed.

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“…Irrischeduler was developed to indicate the time for irrigation based on the soil moisture level. 12 If tea lacks nutrients, it will influence the yield and quality of the tea, and even the production. When tea leaves lack nutrients, it is difficult for experts to confirm the diagnosis if the symptoms are not clear or complicated.…”

Section: Introductionmentioning

confidence: 99%

“…Irrischeduler was developed to indicate the time for irrigation based on the soil moisture level. 12…”

Section: Introductionmentioning

confidence: 99%

Research on water and fertilizer irrigation system of tea plantation

Jia

Huang

Wang

et al. 2019

International Journal of Distributed Sensor Networks

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In this article, water and fertilizer irrigation system of tea plantation was developed to ensure stable yield, quality of tea, and appropriate irrigation and fertilization. Wireless sensor network node and gateway node are designed and deployed in the tea plantation for collecting soil moisture in real time. Each of the sensor nodes was composed of a STM32F103ZET6 microprocessor and a CC2420 transceiver module. Microcontroller S3C2410X is the kernel of hardware platform in the gateway nodes. All data were transmitted by wireless sensor network. The images of the tea leaves are collected by the high-speed camera loaded onto the unmanned aerial vehicle for analysis of the tea deficiency. The monitoring system is mainly used to display the humidity information about each node, the switch status of the water pump, the battery valve, and so on. It is convenient for user to manage the tea plantation. System configuration and parameter modification can be configured through the monitoring system. The dynamic time warping algorithm is used to judge the abnormal situation of the system. Diamond deployment is used in the network, and the networking experiments were conducted comparing with random deployment. Result showed that both time delay and congestion of network increased as the network scale varied from 5, 10, and 15 to 20 nodes. The topology stabilization time gets prolonged simultaneously. Packet loss rate decreased while data transmission interval varied from 10, 20, and 30 to 40 s. Packet loss rate values in diamond deployment are lower than those in random deployment. In order to improve the accuracy of tea deficiency detection, data fusion technology was adopted. A block-based histogram is employed and similarity distance is adopted to confirm the diagnosis of absence of one or more nutrients of tea. The image information and the spectral information are acquired. The principal component factor is extracted and input into the back propagation neural network to judge the quality of the tea. The number of principal component factors of image information and spectral information is set to six and three; the overall recognition rate reached 97.8%. Therefore, the level of the system abnormality can be determined by the dynamic time warping distance. Test results indicate that it is possible to accurately determine whether tea is deficient and accurately determine whether the system data acquisition is abnormal.

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Efficient Design of Drip Irrigation System using Water and Fertilizer Application Uniformity at Different Operating Pressures in a Semi‐Arid Region of India

Cited by 9 publications

References 18 publications

Saline Water Impact on Water Use Efficiency of Bitter Melon (Momordica charantia L) Using Drip Irrigation

Saline Water Impact on Water Use Efficiency of Bitter Melon (Momordica charantia L) Using Drip Irrigation

An Integrated Statistical-Machine Learning Approach for Runoff Prediction

Research on water and fertilizer irrigation system of tea plantation

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