Effect of deficit irrigation, phosphorous inoculation and cycocel spray on root growth, seed cotton yield and water productivity of drip irrigated cotton in arid environment

Rao, S. S.; Tanwar, S. P. S.; Regar, P. L.

doi:10.1016/j.agwat.2016.02.008

Cited by 54 publications

(23 citation statements)

References 41 publications

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“…From a modelling (crop simulation models) perspective, the germination is the very starting point to initiate the calculation of the growing degree days (GDD) accumulation, which is very important when known well, to properly estimate the whole seasonal phenology for best modelling (growth and production) outputs [4]. Because of the precipitation scarcity [5][6][7], supplementary irrigation is applied to wheat during spring to secure wheat water demands. Thus, the irrigation events take place between March and April, during the wheat vegetative cycle, which ends at heading/start of flowering.…”

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confidence: 99%

Sentinel-1 Data for Winter Wheat Phenology Monitoring and Mapping

et al. 2019

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The ability of Synthetic Aperture Radar (SAR) Sentinel-1 data to detect the main wheat phenological phases was investigated in the Bekaa plain of Lebanon. Accordingly, the temporal variation of Sentinel-1 (S1) signal was analyzed as a function of the phenological phases’ dates observed in situ (germination; heading and soft dough), and harvesting. Results showed that S1 data, unlike the Normalized Difference Vegetation Index (NDVI) data, were able to estimate the dates of theses phenological phases due to significant variations in S1 temporal series at the dates of germination, heading, soft dough, and harvesting. Particularly, the ratio VV/VH at low incidence angle (32–34°) was able to detect the germination and harvesting dates. VV polarization at low incidence angle (32–34°) was able to detect the heading phase, while VH polarization at high incidence angle (43–45°) was better than that at low incidence angle (32–34°), in detecting the soft dough phase. An automated approach for main wheat phenological phases’ determination was then developed on the western part of the Bekaa plain. This approach modelled the S1 SAR temporal series by smoothing and fitting the temporal series with Gaussian functions (up to three Gaussians) allowing thus to automatically detect the main wheat phenological phases from the sum of these Gaussians. To test its robustness, the automated method was applied on the northern part of the Bekaa plain, in which winter wheat is harvested usually earlier because of the different weather conditions. The Root Mean Square Error (RMSE) of the estimation of the phenological phases’ dates was 2.9 days for germination, 5.5 days for heading, 5.1 days soft dough, 3.0 days for West Bekaa’s harvesting, and 4.5 days for North Bekaa’s harvesting. In addition, a slight underestimation was observed for germination and heading of West Bekaa (−0.2 and −1.1 days, respectively) while an overestimation was observed for soft dough of West Bekaa and harvesting for both West and North Bekaa (3.1, 0.6, and 3.6 days, respectively). These results are encouraging, and thus prove that S1 data are powerful as a tool for crop monitoring, to serve enhanced crop management and production handling.

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confidence: 99%

Sentinel-1 Data for Winter Wheat Phenology Monitoring and Mapping

et al. 2019

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“…The frame diagram is shown in Figure 1, in which PHY and MAC adopt the IEEE802.15.4 protocol standard and the NWK and APL frameworks are designed by the ZigBee alliance [5,6].…”

Section: B Zigbee Protocol Frameworkmentioning

confidence: 99%

Simulation of the Core Technology of a Greenhouse-Monitoring System Based on a Wireless Sensor Network

2016

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Abstract-To solve the topology structure of a greenhouse environment-monitoring system and the ductility of joints, this study presents a design of a greenhouse-monitoring system based on the ZigBee wireless sensor network (WSN). The hardware and software designs of the network node are provided, and the process of the ZigBee network coordinator is elucidated. The system uses the microcontroller unit PIC18F4620CC2420 wireless transceiver module to send and receive data. Data from temperature and humidity sensors are collected using an inter-integrated circuit bus through the ZigBee network transmission to the monitoring platform. Test results show that the system has the advantages of having a simple structure, flexible nodes, and low power consumption. It can effectively monitor the temperature and humidity in a wireless environment. WSNs considerably help in greenhouse environment monitoring. The use of advanced technology to control greenhouse temperature and humidity can satisfy the optimum growth environment of greenhouse crops and effectively improve the yield and quality of crops. The application of WSNs in greenhouse monitoring is significant to the development of modern and precision agriculture in China.

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“…In the subsoil, furrow irrigation resulted in longer roots than buried drip irrigation or SDI. In a study by Rao et al [15], furrow irrigation resulted in higher rooting depths (30.1 cm) and lower root spread (42.3 cm) and root dry mass (16.1 g plant −1 ) compared with drip irrigation (root depth of 25.0 cm, root spread of 46.9 cm and root dry mass of 17.5 g plant −1 ) in an arid sub-tropical region in India. factor restricting agricultural development on the NCP [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…Direct-seeded cotton exhibits a typical taproot system, whereas transplanted cotton roots spread outward in the shape of a claw with approximately 2-3 dominant lateral roots [10]. Several studies examining cotton roots under different irrigation methods have focused on direct-seeded cotton roots [11][12][13][14][15][16][17]. Hulugalle et al [11] investigated the fine root (<2 mm diameter) production and mortality of cotton in furrow irrigation systems in Australia, and noted that cotton could not tolerate waterlogged conditions, so most of the fine roots were located in the top 0.5 m of the soil.…”

Section: Introductionmentioning

confidence: 99%

Root Development of Transplanted Cotton and Simulation of Soil Water Movement under Different Irrigation Methods

Zhang

Liu

Sun

et al. 2017

Water

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Abstract:Winter wheat and cotton are the main crops grown on the North China Plain (NCP). Cotton is often transplanted after the winter wheat harvest to solve the competition for cultivated land between winter wheat and cotton, and to ensure that both crops can be harvested on the NCP. However, the root system of transplanted cotton is distorted due to the restrictions of the seedling aperture disk before transplanting. Therefore, the investigation of the deformed root distribution and water uptake in transplanted cotton is essential for simulating soil water movement under different irrigation methods. Thus, a field experiment and a simulation study were conducted during 2013-2015 to explore the deformed roots of transplanted cotton and soil water movement using border irrigation (BI) and surface drip irrigation (SDI). The results showed that SDI was conducive to root growth in the shallow root zone (0-30 cm), and that BI was conducive to root growth in the deeper root zone (below 30 cm). SDI is well suited for producing the optimal soil water distribution pattern for the deformed root system of transplanted cotton, and the root system was more developed under SDI than under BI. Comparisons between experimental data and model simulations showed that the HYDRUS-2D model described the soil water content (SWC) under different irrigation methods well, with root mean square errors (RMSEs) of 0.023 and 0.029 cm 3 cm −3 and model efficiencies (EFs) of 0.68 and 0.59 for BI and SDI, respectively. Our findings will be very useful for designing an optimal irrigation plan for BI and SDI in transplanted cotton fields, and for promoting the wider use of this planting pattern for cotton transplantation.

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Effect of deficit irrigation, phosphorous inoculation and cycocel spray on root growth, seed cotton yield and water productivity of drip irrigated cotton in arid environment

Cited by 54 publications

References 41 publications

Sentinel-1 Data for Winter Wheat Phenology Monitoring and Mapping

Sentinel-1 Data for Winter Wheat Phenology Monitoring and Mapping

Simulation of the Core Technology of a Greenhouse-Monitoring System Based on a Wireless Sensor Network

Root Development of Transplanted Cotton and Simulation of Soil Water Movement under Different Irrigation Methods

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