Low stress mechanical properties of fabrics woven from bamboo viscose blended yarns

This paper presents the low stress mechanical properties of plain fabrics woven from cotton, bamboo viscose and cotton-bamboo viscose blended yarns. Three blends (100 % cotton, 50:50 cotton-bamboo and 100 % bamboo) were used to produce three yarn counts (20, 25 and 30 Ne). Each of these yarns was used to make fabrics with different pick densities (50, 60 and 70 picks per inch). It was found that bending rigidity, bending hysteresis, shear rigidity, shear hysteresis and compressibility is lower for bamboo fabrics as compared to those of 100 % cotton fabrics. On the other hand, extensibility, tensile energy and compressional resilience are higher for 100 % bamboo fabrics than 100 % cotton fabrics. Higher pick density increases linearity of load-elongation curve, bending rigidity, shear rigidity and compressional resilience. Shear and bending rigidities show very good correlation with the respective hysteresis values.     

An outbreak of pine lappet moth, Kunugia latipennis, in mid-altitude hills of Meghalaya state, India

The pine lappet moth, Kunugia latipennis Walker (Lepidoptera: Lasiocampidae), is the major insect pest of pines found in the northeastern hilly region of India. Recently, an outbreak of lappet moths was observed in the mid-altitude hills of Meghalaya during May?CJune 2011. In this report, attempts are made to describe a recent outbreak of K. latipennis in the region. Females were found to be highly fecund and have the potential to cause severe damage to the pine forest. Incubation and pupal period were found to be 8?±?0.2?days and 16.1?±?0.3?days, respectively. However, many natural regulatory factors were observed during the course of time, which had substantial impact on their survival: these include mainly extreme weather fluctuations and natural enemies. Moths were found to be positively phototrophic; therefore, light traps could be the best option to manage the outbreak.     

Evapotranspiration data assimilation with genetic algorithms and SWAP model for on-demand irrigation

Evapotranspiration (ET) is one of the indicators of water use efficiency. Periodic information of ET based on remote sensing is useful for an on-demand irrigation (ODI) management. The main objective of this paper was to develop an ET data assimilation scheme to optimize the parameters of an agro-hydrology model for ODI scheduling. The soil, water, atmosphere, and plant (SWAP) simulation model has been utilized for this purpose. We computed remote sensing-based ET for a wheat field in the Sirsa Irrigation Circle, Haryana, in India using 18 cloud-free moderate resolution imaging spectroradiometer images taken between December 2001 and April 2002. The surface energy balance algorithm for land (SEBAL) was used for this purpose. Because ET estimates from SEBAL provide information on the surface soil moisture state, they were treated as observations to estimate unknown parameters of the SWAP model via a stochastic data assimilation (genetic algorithm) approach. The SWAP parameters were optimized by minimizing the residuals between SEBAL and SWAP model-based ET values. The optimized parameters were used as input to SWAP to estimate soil water balance for ODI scheduling. The results showed that the selected parameters (i.e. sowing, harvesting, and irrigation scheduling dates) were successfully estimated with the data assimilation methodology. The SWAP model produced reasonable states of water balance by assimilating ET observations. The root mean square of error was 0.755 and 2.132 cm³/cm³ for 0–15 and 15–30 cm soil depths the same layers, respectively. With optimized parameters for ODI, SWAP predicted higher yield and water use efficiency than traditional farmer’s irrigation criteria. The data assimilation methodology produced can be considered as an operational tool at the field scale to schedule irrigation or predict irrigation requirements from remote sensing-based ET.