Ovipositional preference of potato tuber moth and its damage to different genotypes of potato in free choice condition

Potato tuber moth is a serious pests of potato which cause qualitative as well as quantitative loss on tubers at stores. Major control mechanism is to use chemical pesticide but this pose great hazard risk to the growers and consumers. Therefore this study evaluated tubers of ten potato genotypes viz. CIP 394600.52, CIP 393371.164, Khumal Ujjawal, PRP 296667.2, CIP 393385.39, CIP 395112.32, PRP 226567.2, PRP 0165667.6, CIP 393371.159, and Khumal Upahar against potato tuber moths for their ovipositional preferences and damage potential with nine replication in the laboratory. Number of deposited eggs for four days at eye and outside the tubers on skin, number of tunnel and tunnel length was measured. Least percentage of egg laid eye was least in genotype CIP 394600.52, CIP 393371.164 and variety Khumal Ujjawal respectively. The least number of total eggs laid on eyes was on genotype CIP 394600.52 9 (2.33±1.00) followed by variety Khumal Ujjwal. Although genotype CIP 393385.39 and Khumal Ujjwal was among the most preferred (6.00±2.45) genotype for oviposition, average number of tunnels and average total tunnel length remained very low. Factors such as physical, nutritional, chemical or genetical which may be involved inducing resistance mechanism thus should also be studied and verified

Plant protection measures to promote organic farming in Nepal: prospects and challenges

Organic farming is a production system that relies on ecosystem management rather than external agricultural inputs to sustain the health of soils, ecosystem and organisms. This needs enough organic plant protection measures and biological fertilizers by eliminating synthetic pesticides and fertilizers. An attempt was made to review current plant protection measures for organic farming in Nepal. Though some insect pests and diseases are very hard to control without the use of chemical pesticides, this is high time to produce agriculture products organically. There is ample prospect of organic production in Nepal utilizing traditional knowledge of Nepalese farmers and existing agri-biodiversity. The paper is focused on best utilization of local natural resources, indigenous knowledge and bio-control agents for plant protection in organic agriculture. The information related to organic plant protection measures are collected from various sources and are grouped. The authors have listed technologies on organic plant protection measures in Nepal and made some suggestions to improve the organic farming of the country

Microbial Diversity in Freshwater and Marine Environment

Water covers seven tenths of the Earth's surface and occupies an estimated total volume of 1,386,000,000 cubic kilometers (km3). Of all the water found on Earth, 97% is marine. Maximum of this water is at a temperature of 2 to 3°C and devoid of light; 62% is under high pressure (>100 atm). Microscopic phytoplankton and associated bacteria generate a complex food web that can extend over long distances and extreme depths. The marine environment looks so vast that it will not be able to be exaggerated by pollution; however, in coastal areas human activities are increasingly disrupting microbial processes and damaging water quality.Nepal Journal of Biotechnology. Dec. 2015 Vol. 3, No. 1: 68-7

Evaluation of chemical pesticides for the management of Top Borer (Scirpophaga excerptalis Walker) in sugarcane

An experiment was conducted at research field of National Sugarcane Research Program, Jeetpur, Nepal in 2014 and 2016, to evaluate the efficacy of chemical insecticides against sugarcane top borer (Scirpophaga excerptalis Walker). Nine different treatments viz. Chlorantraniliprole 0.4 G, Cartap hydrochloride 4 G, Fipronil 0.3 G, Carbofuran 3 G (standard check) as soil application and foliar spray of Chlorantraniliprole 18.5 SC, Thiodicarp 75 WP, Spinosad 45 SC, Chlorpyrifos 20 EC (standard check) and one untreated check (control) were used in randomized complete block design with three replications. The top borer susceptible genotype, Co 0238 was planted on February and single application of these insecticides was done on July at brood stage against top borers. The lowest 10.65 and 12.43, 13.68, 14.61, 14.15 percentage of top borer damage was found in foliar application of Chlorantraniliprole @ 35g a.i. /ha followed by Spinosad @ 125g a.i. /ha and soil application of Cartap hydrochloride @ 1500g a.i /ha and foliar application of Thiodicarp @ 1500g a.i. /ha and Fipronil @ 100g a.i. /ha. The infestation percentage reduction over control was found highest in Chlorantraniliprole (69.40%) followed by Spinosad (64.29%) treated plots. Furthermore, the cane yield was highest in Chlorantraniliprole (92.30 mt/ha) and Spinosad (90.06 mt/ha) treated plots than that of other insecticide treated plots. The number of millable canes and cane diameter in the plots among the treatment was found non-significant. Based on the infestation reduction rate, foliar application of the chemical insecticide (Chlorantraniliprole 18.5 SC and Spinosad 45 SC) could be better option for chemical management of sugarcane top borer. &nbsp

Elastic Step DQN: A novel multi-step algorithm to alleviate overestimation in Deep QNetworks

Deep Q-Networks algorithm (DQN) was the first reinforcement learning algorithm using deep neural network to successfully surpass human level performance in a number of Atari learning environments. However, divergent and unstable behaviour have been long standing issues in DQNs. The unstable behaviour is often characterised by overestimation in the $Q$-values, commonly referred to as the overestimation bias. To address the overestimation bias and the divergent behaviour, a number of heuristic extensions have been proposed. Notably, multi-step updates have been shown to drastically reduce unstable behaviour while improving agent's training performance. However, agents are often highly sensitive to the selection of the multi-step update horizon ($n$), and our empirical experiments show that a poorly chosen static value for $n$ can in many cases lead to worse performance than single-step DQN. Inspired by the success of $n$-step DQN and the effects that multi-step updates have on overestimation bias, this paper proposes a new algorithm that we call `Elastic Step DQN' (ES-DQN). It dynamically varies the step size horizon in multi-step updates based on the similarity of states visited. Our empirical evaluation shows that ES-DQN out-performs $n$-step with fixed $n$ updates, Double DQN and Average DQN in several OpenAI Gym environments while at the same time alleviating the overestimation bias