Effect of planting density on yield and architecture suitability of groundnut (Arachis hypogaea) varieties

A field experiment was conducted during rainy (kharif) seasons of 2018 and 2019 at the research farm of Sri Venkateswara Agricultural College (Acharya N. G. Ranga Agricultural University), Tirupati, Andhra Pradesh, to study the effect of planting density on yield and architecture suitability of groundnut (Arachis hypogaea L.) varieties. The experiment included 4 sowing densities (D1, 33.3 plants/m2; D2, 50 plants/m2; D3, 66.6 plants/m2 and D4, 100 plants/m2) and 3 genotypes with varying architecture (G1, Kadiri 6-erect; G2, Kadiri 9-decumbent 2; and G3, Dharani-decumbent 3). The results showed that across planting densities, Dharani and Kadiri 9 genotypes showed higher architectural traits, structural carbohydrates and kernel yield compared to the Kadiri 6. A significant positive correlation was detected between the lodging percentage and both plant height (r = 0.88**) and internodal length (r = 0.61*). Significant negative correlations, were identified between lodging percentage and several parameters, including leaf thickness (r = -0.92**), specific leaf weight (r = -0.93**), stem diameter (r = -0.79**), specific stem weight (r = -0.97**), number of branches (r = -0.72**), cellulose content (r = -0.80**), and lignin content (r = -0.79**). These findings indicate that the decumbent architecture is optimal for achieving groundnut lodging resistance and kernel yield in high-density planting systems

1D-2D MODELING OF URBAN FLOODS AND RISK MAP GENERATION FOR THE PART OF HYDERABAD CITY

Space for water is now becoming guiding principle of urban planning because urban flooding is the major problem facing by most of the cities in India. Urban development in developing countries like India usually occurs with high population concentrating in small areas, with poor drainage conditions. People occupy floodplain areas in low flood years and when larger flood occurs it causes high damage. The origin for urban floods is floodplains encroachment and unplanned drainage systems. Complexities in the urban environment and drainage infrastructure have an inherent influence on surface runoff. This runoff generates urban flooding which poses challenges to modeling urban flood hazard and risk. As like in river flooding satellite images are not available for unban flooding scenario. So better modelling provides minimizing loss of life and property. The present study focuses on recognizing the highly effected areas which are liable to flooding when extreme rainfall occurs for part of Hyderabad city (Zone XIII). The entire Hyderabad city is divided into 16 zones and each zone having details of existing drain network. A coupled 1D-2D flood modelling approach is used to identify flood prone areas and develop flood inundation and flood risk maps. 1D model for pilot area is developed using storm water management model (SWMM) and coupled with 2D PCSWMM. A web based GIS platform INPPINS is used to geo reference the existing network details and exported to 1D SWMM model. The model is simulated for extreme flood event occurred in past. The simulation run results identifies overflowing drainage nodes and flood inundation maps and risk maps prepared. The flood risk maps identify the low lying areas which need immediate attention in case of emergency. The overflowing nodes suggest the need of improvement of drainage in the area to safely dispose of the storm water and minimize the flooding

Optimal Design of Intermittent Water Distribution Network Considering Network Resilience and Equity in Water Supply

In urban areas of developing countries, due to industrialization and population growth, water demand has been increasing significantly, thereby increasing stress on the existing water distribution systems (WDSs). Under these circumstances, maintaining equity in the allocation of water becomes a significant challenge. When building an intermittent water distribution system, it is important to provide a minimum level of supply that is acceptable as well as water supply equity. A non-dominated sorting genetic algorithm (NSGA-II) is employed for the optimal design of an intermittent water distribution network (WDN). Network resilience is taken as a measure of reliability (In), while the uniformity coefficient (CU) is taken as a measure of equity in the water supply. Maximizing network resilience, uniformity coefficient, and minimization of cost of the network are considered as the objectives in the multi-objective optimization model. Pressure-driven analysis (PDA) is used for the hydraulic simulation of the network. The NSGA-II model is applied and demonstrated over two water distribution networks taken from the literature. The results indicate that reliability and equity in WDNs can be accomplished to a reasonable extent with minimal cost

Influence of Organic Manures and Organic Sprays on Productivity and Economics of Summer Greengram [Vigna radiata (L.) Wilczek]

A field experiment was conducted to investigate the influence of organic fertilizers and organic sprays on the productivity and economic parameters of summer greengram (Vigna radiata) cultivation. The experiment was designed as a split-plot design with three replications. The primary plots encompassed four organic fertilizer treatments: Control (M1), Farm yard manure (M2), Vermicompost (M3), and Poultry manure (M4). The sub-plots included three organic spray treatments: Control (S1), Panchagavya (S2), and Jeevamrutha (S3). The results of the experiment revealed that the combination of poultry manure as the organic fertilizer in conjunction with the application of Panchagavya spray had the most significant impact on both seed yield (779 kg ha-1) and haulm yield (1909 kg ha-1) for summer greengram cultivation. Furthermore, this specific combination demonstrated notably higher gross returns (₹48,648 ha-1), net returns (₹30,125 ha-1), and a favourable B C ratio of 2.60

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Not AvailableBackground. The chlorophyll content is susceptible to deficit moisture stress and may affect the plant yield. Leaf chlorophyll content is directly related to tolerance and higher productivity under deficit moisture stress (WS). The SPAD meter is an excellent tool for rapid analysis of crop chlorophyll content. Therefore, establishing a relationship between leaf chlorophyll content and seed yield is crucial in sesame, particularly under deficit moisture stress. Methods. Seeds of 37 sesame genotypes with checks were used in this study. These genotypes were mostly landraces, adapted to different agro-ecological zones in India. The selected genotypes were evaluated under well water (WW) and deficit moisture stress (WS) conditions. The SPAD readings were recorded ten (10) times each at every seven days intervals from the juvenile/first bud (30 35 days after sowing) to ripening/ physiological maturity (95 100 days after sowing) stage. This study aimed to identify the association between leaf SPAD readings (recorded at 7-days interval) and seed yield of sesame genotypes. Results. The analysis of variance revealed the presence of significant variation in SPAD readings due to treatment (WW and WS), genotypes, and their interaction effects. The SPAD readings at all stages were positively correlated with seed yield in both WW and WS. High values of correlation coefficients were observed at 52 (r: 0.672) and 59 (r: 0.655) DAS under WS; whereas at 59 (r: 0.960), 66 (r: 0.972) and 73 (r: 0.974) DAS under WW at one percent significance level (p<0:01), which coincided with the mid-bloom stage of the sesame crop. The best-fit multiple regression model revealed that the dependence of sesame seed yield is significantly influenced by SPAD reading at 52 DAS under WS and 59 to 73 DAS under WW. Both these models provide a good fit with the chi-squared test, which compares the predicted and observed yield.Not Availabl

DroneScale: Drone load estimation via remote passive RF sensing

Drones have carried weapons, drugs, explosives and illegal packages in the recent past, raising strong concerns from public authorities. While existing drone monitoring systems only focus on detecting drone presence, localizing or fingerprinting the drone, there is a lack of a solution for estimating the additional load carried by a drone. In this paper, we present a novel passive RF system, namely DroneScale, to monitor the wireless signals transmitted by commercial drones and then confirm their models and loads. Our key technical contribution is a proposed technique to passively capture vibration at high resolution (i.e., 1Hz vibration) from afar, which was not possible before. We prototype DroneScale using COTS RF components and illustrate that it can monitor the body vibration of a drone at the targeted resolution. In addition, we develop learning algorithms to extract the physical vibration of the drone from the transmitted signal to infer the model of a drone and the load carried by it. We evaluate the DroneScale system using 5 different drone models, which carry external loads of up to 400g. The experimental results show that the system is able to estimate the external load of a drone with an average accuracy of 96.27%. We also analyze the sensitivity of the system with different load placements with respect to the drone's body, flight modes, and distances up to 200 meters

DroneScale: Drone load estimation via remote passive RF sensing

Drones have carried weapons, drugs, explosives and illegal packages in the recent past, raising strong concerns from public authorities. While existing drone monitoring systems only focus on detecting drone presence, localizing or fingerprinting the drone, there is a lack of a solution for estimating the additional load carried by a drone. In this paper, we present a novel passive RF system, namely DroneScale, to monitor the wireless signals transmitted by commercial drones and then confirm their models and loads. Our key technical contribution is a proposed technique to passively capture vibration at high resolution (i.e., 1Hz vibration) from afar, which was not possible before. We prototype DroneScale using COTS RF components and illustrate that it can monitor the body vibration of a drone at the targeted resolution. In addition, we develop learning algorithms to extract the physical vibration of the drone from the transmitted signal to infer the model of a drone and the load carried by it. We evaluate the DroneScale system using 5 different drone models, which carry external loads of up to 400g. The experimental results show that the system is able to estimate the external load of a drone with an average accuracy of 96.27%. We also analyze the sensitivity of the system with different load placements with respect to the drone's body, flight modes, and distances up to 200 meters