Maheswarappa H.P.,AICRP on Palms |
Maheswarappa H.P.,Central Plantation Crops Research Institute |
Thomas G.V.,CPCRI |
Thomas G.V.,Central Plantation Crops Research Institute |
And 6 more authors.
Indian Journal of Agronomy | Year: 2014
A long-term field investigation was carried out during 2001 to 2010 at Vittal (Karnataka) in a 22 year old coconut garden under laterite soil to study the impact of inorganic fertilizer substitutions by vermicompost (VC) on productivity and profitability of coconut (Cocos nucifera L.). The treatments, viz. recommended inorganic fertilizer (500 g N, 320 g P and 1200 g K/palm/year), 25% of N in the form of VC (9.6 kg/palm) + 75% of NPK, 50% N in the form of VC (19.2 kg/palm) + 50% of NPK, 75% in the form of VC (28.8 kg/palm) + 25% NPK and 100% N in the form of VC alone (38.5 kg/palm) were imposed in randomized block design. Annual leaf production did not differ significantly among the treatments; however, integrated treatments resulted in higher number of leaves (12 no.). Six years pooled data on nut yield indicated that, application of vermicompost in combination with inorganic fertilizer either at 25% of N + 75% NPK (64.5 nuts/palm/year) or 50% of N + 50% NPK (66.2 nuts/palm/year) resulted in significantly higher nut yield. There was improvement in the nutrient status of coconut leaves with integrated nutrient management practices compared to inorganic or organic manure alone application. The soil organic carbon build up was observed with application of 50% N or more in the form of vermicompost compared to the other treatments. Microbial population in respect of fungi and phosphate solubilizes were higher when vermicompost was applied. © 2014, Indian Society of Agronomy. All rights reserved.
Thomas R.J.,Central Plantation Crops Research Institute |
Rajesh M.K.,CPCRI |
Kalavathi S.,Central Plantation Crops Research Institute |
Krishnakumar V.,Central Plantation Crops Research Institute |
And 3 more authors.
Indian Journal of Genetics and Plant Breeding | Year: 2013
Root (wilt) disease is a major constraint to coconut production in Kerala State. Conserving ecotypes with resistance or tolerance to the disease on a community basis is essential to sustain coconut production in the root (wilt) disease prevalent areas. Three communities'viz., Pathiyoor and Devikulangara (Alappuzha District) and Thodiyoor (Kollam District) were selected and a survey was conducted with the participation of stakeholders, to characterize the local coconut ecotypes. Six ecotypes comprising of four talls and two dwarfs were identified and morphological data revealed that the local 'Jappanan' ecotype closely resembled Evoor Green Tall ecotype. Simple Sequence Repeat (SSR) analysis in 90 selected coconut palms representing the six ecotypes using 14 markers indicated that the observed heterozygosity was higher in tall ecotypes (0.179-0.365) compared to the dwarfs (0.03-0.07). Lower values for observed heterozygosity compared to the expected heterozygosity in tall ecotypes are indications of genetic basis for disease resistance observed in diseasefree mother palms. Molecular characterization helped in identifying diverse coconut ecotypes having application in production of vigorous hybrids. In the dendrogram constructed using nut character data, three of the tall ecotypes (Green Tall, Brown Tall and Brick Red Tall) clustered together whereas 'Jappanan' clustered separately. Mantel's correlation test using the ZT software revealed significant correlation (0.96) between the SSR data and morphological data.
Hemalatha N.,St Aloysius College |
Brendon V.F.,St Aloysius College |
Shihab M.M.,St Aloysius College |
Procedia Computer Science | Year: 2015
Ethylene response factor (ERF) constitutes one of the most important gene families which are related to environmental responses and tolerancein plants. ERF genes are defined by the domain AP2/ERF, which comprises approximately 60 amino acids and are involved in DNA binding. Development of computational tools using machine learning tools will definitely enhance rice genome annotation. Machine learning algorithm involves construction and study of systems that can learn from data, rather than follow only explicitly programmed instructions. This study primarily emphasizes on the development of prediction tool, ERFPred, for drought responsive protein ERF in rice using machine learning algorithms. We have used fourteen different feature extraction methods including amino acid features, dipeptide, tripeptide, hybrid methods and exchange group features. Using, Random Forest classifier, we have obtained a precision rate of 100% for the ERFPred tool. To prove that species specific tool is better than an All plant tool, a general tool for plants, two different approaches were used and validated. The results obtained were also further compared with sequence similarity search tool, PSI-BLAST. © 2015 The Authors.