We studied the effect of four weeding regimes (weed free, one manual weeding, one manual weeding + atrazine, and a weedy check) on larval density and leaf defoliation in four pear millet genotypes by the larvae of Oriental armyworm, Mythimna separata. Data were also recorded on the extent of larval parasitism under different weeding regimes, and the parasitoids involved. The leaf damage and larval densities were lower in weed free plots as compared to the weedy plots. This was also reflected in grain yield, as maximum grain yield was recorded in weed-free plots as compared to the weedy plots. Seven parasitoids (Cotesia ruficrus, Metopius rufus, Sturmiopsis inferens, Palexorista solemnis, P. laxa, Carcelia sp., and the entomopathogenic nematode Neoplectana sp. were recorded from M. separata larvae, of which M. rufus, Carcelia sp., and Neoplectana sp. were the most abundant. Parasitism by M. rufus was greater in plots with a weed cover and least in weed-free plots, while parasitsm by Carcelia sp. was lower in plots with one hand weeding than in weedy plots. Numerically, parasitism by Neopletana sp. was low in plots treated with atrazine, and maximum in plots weeded manually. Therefore, the minimum level of weeding, which does not affect the crop adversely should be undertaken to promote the biological control of M. separata in pearl millet.
Light is one of the major factors influencing crop‐weed balance. The influence of shading on the growth, leaf area, dry matter production, and seed production of some common weeds of the semiarid tropics was investigated in a field study at ICRISAT Research Center near Hyderabad, India, during the 1978 and 1979 rainy seasons. Different shade treatments were achieved by erecting rectangular bamboo frames over each plot. The weeds studied were Cyperus rotundus L., Digitaria ciliaris (Retz.) Koeler, Celosia argentea L., Acanthospermum hispidum DC., Tridax procumbens L., Dactyloctenium aegyptium (L.) P. Beauv., and Amaranthus viridis L. At 90% shading plant height was reduced to 30% of the control with Celosia and Tridax. Digitaria and Dactyloctenium showed 70 to 80% reductions in leaf area index (LAI) at 90% shading. Dry matter production in Digitaria, Dactyloctenium, and Acanthospermum was reduced up to 80% at higher shading levels. In Cyperus, the effect of decreasing light levels on plant height was not pronounced, but the LAI and dry matter production were severely affected. Photosynthetic photon flux density (PPFD) measurements showed a negative correlation between levels of shading and the seed production in these weeds.
The study indicated that by manipulating crop canopies to create desired shading, substantial weed suppression could be achieved. The agronomic significance of these results is discussed in the light of the weed‐suppressing capacity of crop canopies in different cropping systems of the semiarid tropics that may complement other control measures to obtain better weed management.
The Capital Asset Pricing Model (henceforth, CAPM) is considered an extensively used technique to approximate asset pricing in the field of finance. The CAPM holds the power to explicate stock movements by means of its sole factor that is beta co-efficient. This study focuses on the application of rolling regression and cross-sectional regression techniques on Indian BSE 30 stocks. The study examines the risk-return analysis by using this modern technique. The applicability of these techniques is being viewed in changing business environments. These techniques help to find the effect of selected variables on average stock returns. A rolling regression study rolls the data for changing the windows for every 3-month period for three years. The study modifies the model with and without intercept values. This has been applied to the monthly prices of 30 BSE stocks. The study period is from January 2009 to December 2018. The study revealed that beta is a good predictor for analyzing stock returns, but not the intercept values in the developed model. On the other hand, applying cross-section regression accepts the null hypothesis. α, β, β2 ≠ 0. Therefore, a researcher is faced with the task of finding limitations of each methodology and bringing the best output in the model.
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