IMPACT OF CROP LEVEL AND HANG TIME ON THE COMPOSITION OF FOUR WINE GRAPE CULTIVARS FROM THE NIAGARA REGION

This study analyzed the use of two viticultural practices: “crop level” (half crop; HC, and full crop; FC) and “hang times”, and their impact on the composition of four grape cultivars; Pinot gris, Riesling, Cabernet Franc and Cabernet Sauvignon from the Niagara Region and wine volatile composition by GC-MS. It was hypothesized that keeping a full crop with a longer hang time would have a greater impact on wine quality than reducing the crop level. In all cultivars, a reduction of crop level induced reductions in yield, clusters per vine and crop load, with increases in Brix. Extended hang time also increased Brix related to desiccation. The climatic conditions at harvest had an impact on hang time effects. The GC-MS analysis detected the presence of 30 volatile components in the wine, with different odour activity values. Harvest time had a positive impact than crop reduction in almost all compounds.

Statement of John O'Connor Damaged Wheat Crop

Statement (handwritten, 3 pages) in which John O’Connor states that his wheat crop of 1834 was damaged. A fence was also down which resulted in his wheat crop being destroyed by cattle and pigs. The defendants had to pay the plaintiff for damages. S. D. Woodruff was the arbitrator in this case, Aug. 1835.

Chart of station 2, crop sections of the old back ditch on the south side of the feeder, station 45, station 118 and the total length from the culvert to lot no. 5

Chart of station 2, crop sections of the old back ditch on the south side of the feeder, station 45, station 118 and the total length from the culvert to lot no. 5. This is signed by Fred Holmes, April 13, 1857.

Morphophysiological traits in crop improvement case study-sorghum [by] Ratikanta Maiti; ed. [by] Pedro A. Wesche-Ebeling, Manuel Rojas Garcidueñas [and] Paul R. Earl

UANL

Diversité des champignons endophytes mycorhiziens et de classe II chez le pois chiche, et influence du génotype de la plante

réalisé en cotutelle avec la Faculté des Sciences de Tunis, Université Tunis El Manar.

Drought Climatology of Indo-Gangetic Region of India Using Remote Sensing and Crop Growth Simulation Models

Department of Atmospheric Sciences, Cochin University of Science and Technology

Algal blooms and zooplankton standing crop along the southwest coast of India

Dept. of Marine Biology, Microbiology and Biochemistry,CUSAT

Biochemical effects of the pesticide Chlorpyrifos on the fish Oreochromis mossambicus (Peters)

Man uses a variety of synthetic material for his comfortable materialistic life. Thus human interactions may become harmful for various terrestrial and aquatic lives. This is by contaminating their habitat and by becoming a threat to organisms itself. Thus the application and dispersal of several organic pollutants can lead to the development of several mutated forms of the species when exposed to sublethal concentrations of the pollutants. Otherwise, a decrease in number or extinction of these exposed species from earth's face may happen. Pesticides, we use for the benefit of crop yield, but its persistence may become havoc to non-target organism. Pesticides reaching a reservoir can subsequently enter the higher trophic levels. Organophosphorus compounds have replaced all other pesticides, due to its acute toxicity and non-persistent nature.Hence the present study has concentrated on the toxicity of the largest market-selling and multipurpose pesticide, chlorpyrifos on the commonly edible aquatic organism, fish. The euryhaline cichlid Oreochromis mossambicus was selected as animal model. The study has concentrated on investigating biochemical parameters like tissue-specific enzymes, antioxidant and lipid-peroxidation parameters, haematological and histological observations and pesticide residue analysis.Major findings of this work have indicated the possibility of aquatic toxicity to the fish on exposure to the insecticide chlorpyrifos. The insecticide was found as effective to induce structural alteration, depletion in protein content, decrease in different metabolic enzyme levels and to progress lipid peroxidation on a prolonged exposure of 21 days. The ion-transport mechanism was found to be adversely affected. Electrophoretic analysis revealed the disappearance of several protein bands after 21days of exposure to chlorpyrifos. Residue, analysis by gas chromatography explored the levels of chlorpyrifos retaining on the edible tissue portions during exposure period of 21days and also on a recovery period of 10 days.

Mean Squared Residue Based Biclustering Algorithms for the Analysis of Gene Expression Data

Computational Biology is the research are that contributes to the analysis of biological data through the development of algorithms which will address significant research problems.The data from molecular biology includes DNA,RNA ,Protein and Gene expression data.Gene Expression Data provides the expression level of genes under different conditions.Gene expression is the process of transcribing the DNA sequence of a gene into mRNA sequences which in turn are later translated into proteins.The number of copies of mRNA produced is called the expression level of a gene.Gene expression data is organized in the form of a matrix. Rows in the matrix represent genes and columns in the matrix represent experimental conditions.Experimental conditions can be different tissue types or time points.Entries in the gene expression matrix are real values.Through the analysis of gene expression data it is possible to determine the behavioral patterns of genes such as similarity of their behavior,nature of their interaction,their respective contribution to the same pathways and so on. Similar expression patterns are exhibited by the genes participating in the same biological process.These patterns have immense relevance and application in bioinformatics and clinical research.Theses patterns are used in the medical domain for aid in more accurate diagnosis,prognosis,treatment planning.drug discovery and protein network analysis.To identify various patterns from gene expression data,data mining techniques are essential.Clustering is an important data mining technique for the analysis of gene expression data.To overcome the problems associated with clustering,biclustering is introduced.Biclustering refers to simultaneous clustering of both rows and columns of a data matrix. Clustering is a global whereas biclustering is a local model.Discovering local expression patterns is essential for identfying many genetic pathways that are not apparent otherwise.It is therefore necessary to move beyond the clustering paradigm towards developing approaches which are capable of discovering local patterns in gene expression data.A biclusters is a submatrix of the gene expression data matrix.The rows and columns in the submatrix need not be contiguous as in the gene expression data matrix.Biclusters are not disjoint.Computation of biclusters is costly because one will have to consider all the combinations of columans and rows in order to find out all the biclusters.The search space for the biclustering problem is 2 m+n where m and n are the number of genes and conditions respectively.Usually m+n is more than 3000.The biclustering problem is NP-hard.Biclustering is a powerful analytical tool for the biologist.The research reported in this thesis addresses the problem of biclustering.Ten algorithms are developed for the identification of coherent biclusters from gene expression data.All these algorithms are making use of a measure called mean squared residue to search for biclusters.The objective here is to identify the biclusters of maximum size with the mean squared residue lower than a given threshold. All these algorithms begin the search from tightly coregulated submatrices called the seeds.These seeds are generated by K-Means clustering algorithm.The algorithms developed can be classified as constraint based,greedy and metaheuristic.Constarint based algorithms uses one or more of the various constaints namely the MSR threshold and the MSR difference threshold.The greedy approach makes a locally optimal choice at each stage with the objective of finding the global optimum.In metaheuristic approaches particle Swarm Optimization(PSO) and variants of Greedy Randomized Adaptive Search Procedure(GRASP) are used for the identification of biclusters.These algorithms are implemented on the Yeast and Lymphoma datasets.Biologically relevant and statistically significant biclusters are identified by all these algorithms which are validated by Gene Ontology database.All these algorithms are compared with some other biclustering algorithms.Algorithms developed in this work overcome some of the problems associated with the already existing algorithms.With the help of some of the algorithms which are developed in this work biclusters with very high row variance,which is higher than the row variance of any other algorithm using mean squared residue, are identified from both Yeast and Lymphoma data sets.Such biclusters which make significant change in the expression level are highly relevant biologically.

Impact of Climatic Variability and Drought on Crop Yields in Kerala

In the present investigation, the impacts of the variability of the climatic parameters on the yields of major crops grown in the State are analyzed. In particular, the effects of rainfall variability on the water balances of the different regions in the State have been studied. Through this analysis the drought climatology of the region has been studied along with an overview of the climatic shifts involved in individual years. The relationship between weather parameters and crop yields over the State has been analyzed with case studies of two crops- coconut and paddy. Crop-weather models for forecasting coconut and paddy yields have been developed, which could be used for planning purposes

An agroclimatic assessment of the crop growth potential of south india

Usually, under rainfed conditions the growing period exists in the humid months. Hence, for agricultural planning knowledge about the variabilities of the duration of the humid seasons are very much needed. The crucial problem affecting agriculture is the persistency in receiving a specific amount of rainfall during a short period. Agricultural operations and decision making are highly dependent on the probability of receiving given amounts of rainfall; such periods should match the water requirements of different phenological phases of the crops. While prolonged dry periods during sensitive phases are detrimental to their growth and lower the yields, excess of rainfall causes soil erosion and loss of soil nutrients. These factors point to the importance of evaluation of wet and dry spells. In this study the weekly rainfall data have been analysed to estimate the probability of wet and dry periods at all selected stations of each agroclimatic zone and the crop growth potentials of the growing seasons have been analysed. The thesis consists of six Chapters.

Wireless Sensor Networks for Paddy Field Crop Monitoring Application in Kuttanad

The evolution of wireless sensor network technology has enabled us to develop advanced systems for real time monitoring. In the present scenario wireless sensor networks are increasingly being used for precision agriculture. The advantages of using wireless sensor networks in agriculture are distributed data collection and monitoring, monitor and control of climate, irrigation and nutrient supply. Hence decreasing the cost of production and increasing the efficiency of production.This paper describes the application of wireless sensor network for crop monitoring in the paddy fields of kuttand, a region of Kerala, the southern state of India.

A novel Sigma–Delta based parallel analogue-to-residue converter

Animportant step in the residue number system(RNS) based signal processing is the conversion of signal into residue domain. Many implementations of this conversion have been proposed for various goals, and one of the implementations is by a direct conversion from an analogue input. A novel approach for analogue-to-residue conversion is proposed in this research using the most popular Sigma–Delta analogue-to-digital converter (SD-ADC). In this approach, the front end is the same as in traditional SD-ADC that uses Sigma–Delta (SD) modulator with appropriate dynamic range, but the filtering is doneby a filter implemented usingRNSarithmetic. Hence, the natural output of the filter is an RNS representation of the input signal. The resolution, conversion speed, hardware complexity and cost of implementation of the proposed SD based analogue-to-residue converter are compared with the existing analogue-to-residue converters based on Nyquist rate ADCs

Modelling of global crop production and resulting N2O emissions

Landwirtschaft spielt eine zentrale Rolle im Erdsystem. Sie trägt durch die Emission von CO2, CH4 und N2O zum Treibhauseffekt bei, kann Bodendegradation und Eutrophierung verursachen, regionale Wasserkreisläufe verändern und wird außerdem stark vom Klimawandel betroffen sein. Da all diese Prozesse durch die zugrunde liegenden Nährstoff- und Wasserflüsse eng miteinander verknüpft sind, sollten sie in einem konsistenten Modellansatz betrachtet werden. Dennoch haben Datenmangel und ungenügendes Prozessverständnis dies bis vor kurzem auf der globalen Skala verhindert. In dieser Arbeit wird die erste Version eines solchen konsistenten globalen Modellansatzes präsentiert, wobei der Schwerpunkt auf der Simulation landwirtschaftlicher Erträge und den resultierenden N2O-Emissionen liegt. Der Grund für diese Schwerpunktsetzung liegt darin, dass die korrekte Abbildung des Pflanzenwachstums eine essentielle Voraussetzung für die Simulation aller anderen Prozesse ist. Des weiteren sind aktuelle und potentielle landwirtschaftliche Erträge wichtige treibende Kräfte für Landnutzungsänderungen und werden stark vom Klimawandel betroffen sein. Den zweiten Schwerpunkt bildet die Abschätzung landwirtschaftlicher N2O-Emissionen, da bislang kein prozessbasiertes N2O-Modell auf der globalen Skala eingesetzt wurde. Als Grundlage für die globale Modellierung wurde das bestehende Agrarökosystemmodell Daycent gewählt. Neben der Schaffung der Simulationsumgebung wurden zunächst die benötigten globalen Datensätze für Bodenparameter, Klima und landwirtschaftliche Bewirtschaftung zusammengestellt. Da für Pflanzzeitpunkte bislang keine globale Datenbasis zur Verfügung steht, und diese sich mit dem Klimawandel ändern werden, wurde eine Routine zur Berechnung von Pflanzzeitpunkten entwickelt. Die Ergebnisse zeigen eine gute Übereinstimmung mit Anbaukalendern der FAO, die für einige Feldfrüchte und Länder verfügbar sind. Danach wurde das Daycent-Modell für die Ertragsberechnung von Weizen, Reis, Mais, Soja, Hirse, Hülsenfrüchten, Kartoffel, Cassava und Baumwolle parametrisiert und kalibriert. Die Simulationsergebnisse zeigen, dass Daycent die wichtigsten Klima-, Boden- und Bewirtschaftungseffekte auf die Ertragsbildung korrekt abbildet. Berechnete Länderdurchschnitte stimmen gut mit Daten der FAO überein (R2 = 0.66 für Weizen, Reis und Mais; R2 = 0.32 für Soja), und räumliche Ertragsmuster entsprechen weitgehend der beobachteten Verteilung von Feldfrüchten und subnationalen Statistiken. Vor der Modellierung landwirtschaftlicher N2O-Emissionen mit dem Daycent-Modell stand eine statistische Analyse von N2O-und NO-Emissionsmessungen aus natürlichen und landwirtschaftlichen Ökosystemen. Die als signifikant identifizierten Parameter für N2O (Düngemenge, Bodenkohlenstoffgehalt, Boden-pH, Textur, Feldfrucht, Düngersorte) und NO (Düngemenge, Bodenstickstoffgehalt, Klima) entsprechen weitgehend den Ergebnissen einer früheren Analyse. Für Emissionen aus Böden unter natürlicher Vegetation, für die es bislang keine solche statistische Untersuchung gab, haben Bodenkohlenstoffgehalt, Boden-pH, Lagerungsdichte, Drainierung und Vegetationstyp einen signifikanten Einfluss auf die N2O-Emissionen, während NO-Emissionen signifikant von Bodenkohlenstoffgehalt und Vegetationstyp abhängen. Basierend auf den daraus entwickelten statistischen Modellen betragen die globalen Emissionen aus Ackerböden 3.3 Tg N/y für N2O, und 1.4 Tg N/y für NO. Solche statistischen Modelle sind nützlich, um Abschätzungen und Unsicherheitsbereiche von N2O- und NO-Emissionen basierend auf einer Vielzahl von Messungen zu berechnen. Die Dynamik des Bodenstickstoffs, insbesondere beeinflusst durch Pflanzenwachstum, Klimawandel und Landnutzungsänderung, kann allerdings nur durch die Anwendung von prozessorientierten Modellen berücksichtigt werden. Zur Modellierung von N2O-Emissionen mit dem Daycent-Modell wurde zunächst dessen Spurengasmodul durch eine detailliertere Berechnung von Nitrifikation und Denitrifikation und die Berücksichtigung von Frost-Auftau-Emissionen weiterentwickelt. Diese überarbeitete Modellversion wurde dann an N2O-Emissionsmessungen unter verschiedenen Klimaten und Feldfrüchten getestet. Sowohl die Dynamik als auch die Gesamtsummen der N2O-Emissionen werden befriedigend abgebildet, wobei die Modelleffizienz für monatliche Mittelwerte zwischen 0.1 und 0.66 für die meisten Standorte liegt. Basierend auf der überarbeiteten Modellversion wurden die N2O-Emissionen für die zuvor parametrisierten Feldfrüchte berechnet. Emissionsraten und feldfruchtspezifische Unterschiede stimmen weitgehend mit Literaturangaben überein. Düngemittelinduzierte Emissionen, die momentan vom IPCC mit 1.25 +/- 1% der eingesetzten Düngemenge abgeschätzt werden, reichen von 0.77% (Reis) bis 2.76% (Mais). Die Summe der berechneten Emissionen aus landwirtschaftlichen Böden beträgt für die Mitte der 1990er Jahre 2.1 Tg N2O-N/y, was mit den Abschätzungen aus anderen Studien übereinstimmt.

Efficient phosphorus application strategies for increased crop production in sub-Saharan West Africa

Comparable data are lacking from the range of environments found in sub-Saharan West Africa to draw more general conclusions about the relative merits of locally available rockphosphate (RockP) in alleviating phosphorus (P) constraints to crop growth. To fill this gap, a multi-factorial field experiment was conducted over 4 years at eight locations in Niger, Burkina Faso and Togo. These ranged in annual rainfall from 510 to 1300 mm. Crops grown were pearl millet (Pennisetum glaucum L.), sorghum (Sorghum bicolor (L.) Moench) and maize (Zea mays L.) either continuously or in rotation with cowpea (Vigna unguiculata Walp.) and groundnut (Arachis hypogaea L.). Crops were subjected to six P fertiliser treatments comprising RockP and soluble P at different rates and combined with 0 and 60 kg N ha^-1. For legumes, time trend analyses showed P-induced total dry matter (TDM) increases between 28 and 72% only with groundnut. Similarly, rotation-induced raises in cereal TDM compared to cereal monoculture were only observed with groundnut. For cereals, at the same rate of application, RockP was comparable to single superphosphate (SSP) only at two millet sites with topsoil pH-KCl <4.2 and annual average rainfall >600 mm. Across the eight sites NPK placement at 0.4 g P per hill raised average cereal yields between 26 and 220%. This was confirmed in 119 on-farm trials revealing P placement as a promising strategy to overcome P deficiency as the regionally most growth limiting nutrient constraint to cereals.