Land fragmentation and its implications for productivity: evidence from Southern India

In developing economies land reform, in particular land redistribution has occupied a central role in debates about poverty — particularly chronic poverty — alleviation in rural areas. Even if it were accepted that land redistribution could alleviate poverty the enthusiasm for such redistribution needs to be tempered with consideration of the potential efficiency effects of land fragmentation. The fragmentation of land holdings could rise with land fragmentation. In turn, land fragmentation could lead to sub-optimal usage of factor inputs and thus to lower overall returns to land. The factors contributing to this could be losses due to extra travel time, wasted space along borders, inadequate monitoring, and the inability to use certain types of machinery such as harvesters. Fragmentation of land is widespread in India and it is believed that fragmented nature of land holdings may play a major role in explaining low levels of agricultural productivity. Despite substantial rise in yields India ranks 34th in yields for sugarcane, 57th for cotton, 118th for pulses, and, 51st for rice although India is a leading producer of each of these crops in aggregate terms. Further, there is evidence of inefficient use of resources in agriculture and the resulting increases in costs, e.g., 25 times more water/tonne of output is being used to irrigate Cotton in India than in Egypt. In response to the perceived adverse effects of land fragmentation the then Finance Minister allocated Rs. 5 million over a period of five years, as an incentive for land consolidation, in his 2000 budget speech. However, the Planning Commission of India has indicated a near complete failure on this front. To date, however, there has been no systematic attempt at quantifying the effects of land fragmentation and understanding the channels through which these effects operate. The present paper attempts to fill this void. In this paper, we undertake a detailed assessment of the consequences of land fragmentation using a unique panel data set from Southern India, with comprehensive information on all landholding households in two contiguous villages over a five-year period. In particular, we examine whether technical efficiency of farm production is significantly related to farm size, whether yield is importantly impacted by the degree of fragmentation as measured by the number of plots, average plot size, and an index of fragmentation,2 and whether such fragmentation impacts upon labor allocations. We then use stochastic production function methods to measure the degree of technical efficiency and relate this to the degree of land fragmentation. Our results show clearly that land fragmentation has a significant adverse effect on land productivity. The plan of this paper is a follows. In section II we review the literature on this issue whereas section III discusses the data asset. Section IV details the methodology and estimation procedure, section V presents the results and section VI concludes

Bioremediation of crude oil contaminated tea plantation soil using two Pseudomonas aeruginosa strains AS 03 and NA 108

Crude oil contamination of soil is a major concern for tea industry in Assam, India. Crude oil is a persistent organic contaminant which alters soil physical and biochemical characteristics and makes tea plants more susceptible against crude oil contamination. Therefore, two native bacterial strains designated as AS 03 and NA 108 having crude oil degradation ability was isolated from crude oil contaminated soil. Isolates were evaluated for reduction of crude oil phytotoxicity and soil bioremediation. Biochemical and 16s ribosomal ribonucleic acid (rRNA) analysis confirmed that the bacterial strains belong to Pseudomonas aeruginosa. Under in vitro evaluation, it was found that both the strain could tolerate crude oil up to 40% (v/v). However, structural changes including morphology, difference in number of colonies were found in the presence of hydrocarbon in both AS 03 and NA 108. Also, an improvement in growth of bacterized tea plants was observed compared to non-bacterized plants grown in crude oil contaminated soil. The cumulative increment in height was 5 to 42%, compared to non-bacterized plants and with significantly higher root and shoot dry biomass accumulation. Soil treatment with both AS 03 and NA 108 improved soil quality including organic carbon, conductivity, pH and degradation of total petroleum hydrocarbon (TPH) of the contaminated soil. These findings suggest that the tested bacteria can be exploited for bioremediation of crude oil contaminated soil in the geographical region of Assam.Keywords: Pseudomonas, tea plant, total petroleum hydrocarbon, crude oilAfrican Journal of Biotechnology Vol. 12(19), pp. 2600-261

A Novel Graphical Lasso based approach towards Segmentation Analysis in Energy Game-Theoretic Frameworks

Energy game-theoretic frameworks have emerged to be a successful strategy to encourage energy efficient behavior in large scale by leveraging human-in-the-loop strategy. A number of such frameworks have been introduced over the years which formulate the energy saving process as a competitive game with appropriate incentives for energy efficient players. However, prior works involve an incentive design mechanism which is dependent on knowledge of utility functions for all the players in the game, which is hard to compute especially when the number of players is high, common in energy game-theoretic frameworks. Our research proposes that the utilities of players in such a framework can be grouped together to a relatively small number of clusters, and the clusters can then be targeted with tailored incentives. The key to above segmentation analysis is to learn the features leading to human decision making towards energy usage in competitive environments. We propose a novel graphical lasso based approach to perform such segmentation, by studying the feature correlations in a real-world energy social game dataset. To further improve the explainability of the model, we perform causality study using grangers causality. Proposed segmentation analysis results in characteristic clusters demonstrating different energy usage behaviors. We also present avenues to implement intelligent incentive design using proposed segmentation method.Comment: Proceedings of the Special Session on Machine Learning in Energy Application, International Conference on Machine Learning and Applications (ICMLA) 2019. arXiv admin note: text overlap with arXiv:1810.1053

Role of Dietary Components in Modulating Hypertension

Hypertension is a major health issue, particularly in medically underserved populations that may suffer from poor health literacy, poverty, and limited access to healthcare resources. Management of the disease reduces the risk of adverse outcomes, such as cardiovascular or cerebrovascular events, vision impairment due to retinal damage, and renal failure. In addition to pharmacological therapy, lifestyle modifications such as diet and exercise are effective in managing hypertension. Current diet guidelines include the DASH diet, a low-fat and low-sodium diet that encourages high consumption of fruits and vegetables. While the diet is effective in controlling hypertension, adherence to the diet is poor and there are few applicable dietary alternatives, which is an issue that can arise from poor health literacy in at-risk populations. The purpose of this review is to outline the effect of specific dietary components, both positive and negative, when formulating a dietary approach to hypertension management that ultimately aims to improve patient adherence to the treatment, and achieve better control of hypertension

Exploring the Natural Preservation Potential of Aqueous Guava Leaf Extracts on Pangasius Pangasius: An Experimental Study

Fish, being a significant biological indicator in water, serves as a valuable food source once harvested. However, the high protein content in fish makes it an ideal medium for microorganisms, which can lead to spoilage. In areas without access to freezers or ice, preserving fish becomes a challenge. The objective of this study is to investigate the efficacy of Psidium guajava (guava) leaves as a natural preservation method for Pangasius pangasius fish. Key parameters, including gills, eye, texture, odor, and mucilage, were used to evaluate fish quality. Leaf methanolic extract was applied at doses of 0%, 20%, 40%, 60%, and 80%. Data were collected between 1 and 3 days after storage. Results showed that fish quality declined and began to deteriorate after 2 days of storage, particularly in the control treatment (0% extract). However, the fish samples treated with doses of 60% and 80% experienced relatively good quality over the course of 2 days. Although some spoilage occurred in these samples, they remained suitable for consumption. In contrast, fish samples treated with other doses exhibited complete spoilage and were no longer consumable. In conclusion, guava leaf extracts offer a promising alternative for fish preservation

Machine Learning for Smart and Energy-Efficient Buildings

Energy consumption in buildings, both residential and commercial, accounts for approximately 40% of all energy usage in the U.S., and similar numbers are being reported from countries around the world. This significant amount of energy is used to maintain a comfortable, secure, and productive environment for the occupants. So, it is crucial that the energy consumption in buildings must be optimized, all the while maintaining satisfactory levels of occupant comfort, health, and safety. Recently, Machine Learning has been proven to be an invaluable tool in deriving important insights from data and optimizing various systems. In this work, we review the ways in which machine learning has been leveraged to make buildings smart and energy-efficient. For the convenience of readers, we provide a brief introduction of several machine learning paradigms and the components and functioning of each smart building system we cover. Finally, we discuss challenges faced while implementing machine learning algorithms in smart buildings and provide future avenues for research at the intersection of smart buildings and machine learning

Clobetasol propionate cream 0.025%: a topical therapeutic for dermatological disorders

Due to the anti-inflammatory and vasodilator effects of topical corticosteroids, they help in treating atopic eczema, psoriasis, chronic hand eczema, and localized vitiligo, among other dermatological diseases. Clobetasol propionate (CP) is the most popular topical medication used to treat plaque psoriasis. It has anti-inflammatory, antimitotic, antipruritic, and immunosuppressive characteristics. The USFDA has approved CP 0.025% cream for the treatment of moderate-to-severe psoriasis in adults. Propylene glycol, short-chain alcohols, and sorbitol-based emulsifiers are all recognized contact allergens, and the formulation has exhibited hypoallergenic effects. CP 0.025% is an effective and safe agent due to its high active ingredient penetration and minimal systemic absorption. The clinical experience of employing CP 0.025% cream in diverse dermatologic disorders is discussed in this case series, with a focus on its efficacy and safety