iii) To analyze the changing pattern of landuse and croppingpattern.
Review of literature
Some of the previous studies on agricultural production in the colonial period deal with undivided India , some deal with British India [2,4], and others deal with areas of contemporary India , but very few investigate the case for areas of contemporary Pakistan and Bangladesh in a way comparable with that for India. If we restrict to Punjab and Bengal, there are a few good studies with comparative perspectives between Indian Punjab and Pakistan Punjab and between West Bengal and East Bengal (Bangladesh) [1,5]. However, the coverage of these studies is limited those investigating the pre-1947 period did not adjust for the boundary changes, while those comparing the areas corresponding to the current international borders investigated the post-1947 period only. Although it is true that the state of Pakistan did not exist before 1947 and the state of Bangladesh did not exist before 1971, investigating agricultural production trends for "fictitious" Pakistan before 1947 and "fictitious" Bangladesh before 1971 would give us valuable insights, since farming is carried out on land, which is immovable by definition.
Both landusepattern and cropping considerably in TamilNadu state. state, the share of net sown area been continuously declined from around 36 per cent in 2013-14. had declined from 79.57 lakh ha 2013-14, nearly 26 per cent of the five decades. Among the major has also registered a declining in the state, as the crop occupies cropped area. It is assumed technological factors are crucial pattern as well as croppingpattern. Nadu, specifically delta region, pattern due to inadequate rainfall Cuddalore, Villupuram, Nagapattinam, Thanjavur are the major delta
Climatic descriptions based on averages might be suitable for stations where the climate for each of the individual years follows the average climatic pattern. However, this generalization is not often true because of uncertainties inherent in rainfall patterns. The presentation of rainfall data in the form of simple arithmetic averages therefore provides a very general understanding for a generalized application. Many agricultural operations revolve around the probability of receiving given amounts of rainfall. Markov chain probability is one of the models that have been found suitable to describe the probability daily and weekly rainfall distribution. In the first order Markov chain, the probability of an event that would occur on any single day depends only on the conditions during the preceding day and is independent of events of further preceding days. The model calculates the initial probabilities of getting a dry spell / wet spell in a given standard meteorological week. The calculation of conditional probabilities provides the information on the dry spell followed by dry spell or wet spell vice versa. The probabilities of rainfall can be used for a number of agricultural planning purposes such as land-use planning, choice of crops, cropping system and assessing level of risk spatially. Hence, the mapping rainfall probability characteristics could greatly help the transfer of farming systems technology to the field, which forms the basis for the present study. In the present chapter, the daily rainfall data for the 28 years (1980 –2007) have been collected for 18 stations of in and around the study area and converted into weekly totals as meteorological standard weeks.
the classifications and obtained from LISS 111 data were considered. The agricultural land was 10983.37 hec (109.83sq.km), built-up land was 2974.24hec (29.74sq.km), fallow land was 796.22hec (7.96sq.km), mixed plantation was 3386.28hec (33.86sq.km), scrub land was 644.22hec (6.44sq.km), without scrub land was 568.61hec (5.69sq.km), and water bodies were 457.07hec (4.57sq.km) respectively, out of the total land extent. During 2009 the aerial distribution of the landuseland cover have been presented in the Table 4 and Fig. 2.2. Madukkur block the imageries for the year 2009 based on the LISS 111 data were taken into account. The agricultural lands were 9052.02hec (90.52sq.km), built-up land was 2372.07hec (23.72sq.km), fallow land was 876.89hec (8.77sq.km), mixed plantation was 3693.53hec (36.94sq.km), river sand was 356.65hec (3.57sq.km), scrub land was 423.43hec (4.23sq.km), water bodies were 447.14hec (4.47sq.km), and without scrub land was 423.43hec (4.23sq.km) of the total land extent. The detailed changes have been analyzed in the change detection part. During 2009 the aerial distribution of the landuseland cover have been presented in the Table 2 and Fig 2.1. Peravurani block the imageries for the year 2009 based on the classifications were made in the LISS 111 data. It was formed the agricultural land covering 13806.69hec (138.07sq.km), built-up land was 2877.36hec (28.77sq.km), fallow land was 984.01hec (9.84sq.km), mixed plantation was 2520.88hec (25.21sq.km), river sand was 396.98hec (3.97sq.km), scrub land was 962.16hec (9.62sq.km), water bodies were 245.76hec (2.46sq.km), and without scrub land was 918.16hec (9.18sq.km) respectively out of the total land extent. During 2009 the aerial distribution of the landuseland cover have been presented in the Table 3 and Fig. 2.2. Pattukkottai block the imageries for the year 2009 based on the classifications used in the LISS 111 data structure, were considered. The agricultural land was 17800.55hec (178.01sq.km), built-up land was 3418.20hec (34.18sq.km), fallow land was 1039.60hec (10.40sq.km), mixed plantation was 2786.45hec (27.86sq.km), river sand was 287.89 hec (2.88sq.km), scrub land was 1002.58hec (10.03sq.km), water bodies were 1014.55hec (10.15sq.km), without scrub land was 1094.56hec (10.95sq.km), aquaculture pond and salt pan 2005.37hec (20.05sq.km) mangroves were 1719.44hec (17.19sq.km), and marshy land was 290.01hec (2.90 sq.km). During 2009 the aerial distribution of the landuseland cover have been
changes in monsoon. It is natural, when a land in the neighbour hood of township was going waste, due to density of people some land is converted probably for housing. In 2005 compared to 2015 the fallow land (16.12%) had been increased in this block. The fallow land is in high concentration in north to south particularly in the villages name kandiyur, mannarsamuthiran, maharajapuram, maruvur, sathanur, Thennancheri, Thiruvaiyaru, Semmankudi, Rajendiram, valappakkudi, varahur, Thulasenthira. In 2015 Fallow land occupied an area of (24.66 sq.km). This was increased in this year. The increase was in the villages found in the eastern side of suyampatchanallur, vadukakkudi, Avikkarai, Aachanur, karur, Kuzhimathur, Naducauvery, rayampettai, suysmpatchanallur, Nagathi, punavasal, Sathanur, Thennancheri, Tiruchchatturai, vellamperambur, Punavasal, Keelatiruppanthuruthi, Kalyanapuramsethi, karupur, Konerirajapuram, Manakkarambai, Mealtiruppanturuthi, Peramur, Semmankudi, Thenperambur, Thiruvalampozhil. The fallow land was increased due to changes of monsoon over a long period. Two cropping system have been brought down into a single crop system in the field. This fallow land must have been the area on the Eastern side of settlement and house plots in the neighbourhood of the major developing town like revenue village. From 2005 to 2015 fallow land had over all changes ie 16.12%.
Evaluation of urban expansion and its use play a vital role in effective urban management in terms of providing water supply, storm water drainage, sewerage and solid waste collection. In recent years, the significance of spatial data technologies, especially the application of remotely sensed data, has increased and geographical information systems (GIS) have been widely used. This study investigates the urbanization process in terms of landuse, built up density and sprawl using remotely sensed images of Thanjavur City, located in TamilNadu State of India, as a case study and (GIS). The changes in the landuse were analyzed from a topographical map of 1970, images from a ETM+ EarthSat 1999 and IRS P6, 2006. The results revealed significant changes in landuse and proportion of high, medium and low density built up area. Further, it has been identified that in the study area dominates the leapfrog sprawl rather than low density and ribbon sprawl.
vector in the representative pixels and the mean vector in each signature for classifying the multiple classes based on the Euclidean distance. The clustered image consists of different feature classes is introduced to nearby neighbour resembling (kernel window size 33) analysis for replacing the disintegrated pixels to the most common pixel of feature class. The result of classified images is categorized into twelve feature classes based on the USGS-LULC classification system at level II category. The level II system is probably comprises major LULC features for compilation and mapping at regional scale. Therefore, among the four level (Level I-IV) category of this system, the level II classification system framework is considered more suitable for categorizing the LULC features from the classified image. In this level II system, the clear definition of LULC features helps to differentiate the many classes belongs to common LULC family and is intentionally provides attributes of the LULC features. For example, based on distinct LULC feature definition, the agriculture land (Level I) is classified to cropland and plantation area in the study area using level II system. The result of LULC map (1:10000) is cross verified with the state level landuse and land cover map (scale 1:50000) for 2011-2012 published by Bhuvan (Indian Geoportal website) and the result shows the spatial distribution of LULC features are relatively same as the referenced map, however, statistical measurement of both maps are not cross-verified in this case. Finally, the LULC layers for the 2 years 2000 and 2017 are separately prepared for geo-database with the attributes include class name,
Fig.4. shows the landuse/land cover in 2008 image. Table. II. illustrates the changes in landuse in 8 years from 2000. The maximum changes occurred in Barren Rocky. The 9.87% area of Barren Rocky had reduced in 2008. Crop Land and Dense Forest were increased to 1.52 km 2 , 7.68 km 2, and 2.09 km 2 areas respectively.
The present study was conducted in Kerala because it has maximum gap between production and requirement of food grains as compared to other states of India. In this study croppingpattern is defined as the proportion of area under different crops at a point of time. Crops selected for study were classified as food crops and non food crops. Recent (from 2001-to-2012-13) change of growth and instability analysis of crops is done in this study. Change in Landusepattern was studied in terms of increase or decrease in area under different landuse categories of the state. To measure the change in croppingpattern Secondary data on area, production and productivity of major crops grown in Kerala was collected from publications of Govt like farm guide and web sites like http://www.ecostat.kerala. gov.in. Twelve crops were selected for study which accounted for more than 80% of cropped area. The study was restricted to principal crops with the assumption that the excluded crops do not affect croppingpattern and in turn do not vitiate the main conclusions of the study.
Disease surveillance in livestock is essential to know the quick status of animal diseases in a region for taking appropriate action. Geographical information system (GIS) is an effective tool to understand and identify the areas of disease prevalence in reference to the livestock. The study area records for the highest number of exotic breeds and contributes to the milk production of the State in a major way and for this reason disease surveillance is much required. Major outbreaks with attacks and deaths for each block were mapped based on mortality and morbidity analysis in Arch GIS 9.3. The major diseased pruned area (Site 1) was analyzed at the village level to identify the underlying cause for the disease spread and thematic maps were derived.
Nagapattinam is one of the coastal district of TamilNadu; in the eastern coast of Bay of Bengal. The study area forms the tail end area of the Cauvery river basin and kollidam in the north. The study area extents between 79 0 38‘30‖E to 79 0 49 ‘ 0‖E and 10 0 23‘30 ‖ N to 10 0 41 ‘ 0 ‖ N, it stretches from river Coleroon in the north. The study area falls in the survey of India top sheets 58 N/14 with a total area of about 205.12 km 2 (Figure.1) Agriculture is the main activity in the study area. Paddy, Sugarcane, Coconut, Blanton, vast gardens of mango and plantain tree and other verdant vegetations are the major crops. The normal annual rainfall of the area of 1500mm. The geological formation of Nagappattinam district is made up of recent deposits and the major area is occupied by the Alluvial deposits (Fig.2). The recent formations have a very thick lateritic cap consisting of impure lime stones and sand stones of silt, clay calcareous and argillaceous variety, in the coast. The sand stones are covered by a thin layer of wind brown sandy clays, unconsolidated sand, clay bound sands and mottled clays with lignite seams. In the east, the alluvial deposits of the river Cauvery and its tributaries lie over the tertiary sand stone. They
Survey of rice growing blocks in Kanyakumari District was carried out during 2008-09, 2009-10 and 2010-11 to establish the stem borer incidence and damage per cent in the farmers field. The maximum incidence was observed in Agasteeswaram, Thovalai and Thuckalay blocks and minimum incidence in Thuckalay, Killiyoor and Thiruvattar blocks during 2008-09, 2009-10 and 2010-11, respectively. The distribution of stem borer complex was also assessed for three years in two rice cultivars viz., TPS 3 and CR 1009 and the data revealed that the yellow stem borer, Scirpophaga incertulas was found to be dominant in Kanyakumari District. The field experiments revealed that chlorantraniliprole 0.4 GR was proved to be the best among all the tested insecticides with reduced stem borer infestation and recorded higher yield. Neem oil and Trichogramma japonicum exerted minimum reduction percentage of stem borer. However, they were found to be superior over untreated control which can be included as a component in Integrated Pest Management programme.
Northeast monsoon season (October to November) is the major period of rainfall for TamilNadu accounting for nearly half (48 per cent) of the annual rainfall and which is commen in the Tiruchirappalli district too. This season is much more important for TamilNadu rather than the southwest monsoon, as the TamilNadu State receives the bulk of its rainfall during this season. As a matter of fact, much of the agricultural rhythm in TamilNadu corresponds to the northeast monsoon. The northeast monsoon rainfall is invariably associated with the cyclonic depressions formed in the Bay of Bengal. As such, the distribution of rainfall closely follows the cyclonic tracks. The cyclonic depressions originate in the Bay of Bengal around the Andaman and Nicobar Islands and move in a westerly direction affecting the entire coast from the Thanjavur delta to Chennai. In general, the amount of rainfall is the highest on the coast and decreases westward. The amount of rainfall is low in the southeastern parts due to the reduced effect of the depressions.
During 1989-2000, the conversion pressure was mainly onto Permanent water sources and wet land as it shows the maximum loss. But in case of 2000-2014, the pressure transferred to croplands and planted trees as most of the bare lands had already been converted to built-up areas or cultivated land (Figure 5). Urbanization in the study area has been very rapid on other land covers with discontinuous patches which resulted in diversi- fied and uneven expansion. Inadequate housing, unplanned and haphazard development, ubiquitous urban po- verty, absence of proper landuse policy, inequity of lands, pitiable coordination among responsible organiza- tions and absence of reliable information on the current landuse practice, all are contributing to the urban sprawling which is leading to the unconceivable emergence of slums and squatters. The environment of slum is extremely unhygienic as they are located at sites such as solid waste dumps, open drains and sewers, embank- ment and often along the rail line. In addition, the people living in slums are extremely vulnerable to natural ha- zards such as floods. Thus the accelerated growth of slum population fosters to the loss of expensive wetlands, planted trees cover, and also negatively affecting both human and physical environments. On the contrary, the ever-increasing urban population and its poverty result in overexploitation of natural resources to a level which is no longer sustainable for future.
Kubendran and Vanniarajan (2005) founded that, the change in consumption pattern is due to changes in food habits. If income and urbanization increase among consumers, the percentage of income spent on consumption increases. The urban consumers prefer mostly branded products compared to rural consumers. The most significant factors influencing buying decisions were acceptability, quality, regular supply, door delivery and the mode of payment. Ramasamy (2005) studied consumer behaviour towards instant food products in Madurai, the second largest city in TamilNadu and observed that consumers do build opinion about a brand on the basis of which various product features play an important role in decision making process. A large number of respondents (78.00%) laid emphasis on quality and 76.00 per cent on price which is an important factor, while 64.00 per cent of the respondents attached importance to the image of the manufacturer and 50.00 per cent considered packaging as an important factor and an equal percentage (50.00%) felt longer self life influenced them.
the electronic resources resources; this may be due to the fact that all the universities under study are connected to internet and at the same time they spend most of their time in their difference offices. It was also discovered that low bandwidth is the main problem encountered by the majority of the users; this supported the view of Manda (2006) that low bandwidth is the major constraint to faculty members' use of electronic resources in Tanzania.
Landuse means the surface utilization of all developed and vacant land on a specific point at a given time and space. This change may be due to two most probable reasons. Firstly, the requirements of the society may be the cause for bringing change in the landuse. Secondly, the technological impact also promotes changes that an individual as well as the society is able to maximize the advantage. “Landuse leads one back to the village farm and the farmer to the fields, garden, pastures, fallow land, fores and to the isolated farms lead as geography deals with spatial relationship between these aspects and planning (T.V. Freeman 1968)”. It is due to the landuse changes to meet the variable demands of the land by the society in its new ways and conditions of life. A Geographical Analysis on General LandUsePattern in Thiruvarur District of Tamilnadu for the years between 2010 and 2015. Out of the total geographical area, 64% to 77% area occupies under Net area sown. Forest is identified only in Thiruthiraipoondi Taluk without any change.
LANDUSE/LAND COVER CHANGE ANALYSIS, CENTRAL TAMILNADU COAST C.Pandiammal , Associate Professor in Geography , Govt Arts College(Auto), Karur-5 Dr.A.Ilanthirayan, Assistant Professor in Geography, Govt Arts College(Auto), Karur-5 Dr.P.H.Anand , Head & Associate Professor in Geography, Govt Arts College(A) for Men,
Agricultural lands have decreased considerably because of human interference. It is necessary, before implementing any sort of landuse practices in the study area in future by considering the existing socio-economic scenario. It has expected that the findings of the investigation will undoubtedly be useful to planners and local bodies to implement suitable landuse plans in the watershed, thereby achieving eco-preservation and enabling the restoration of degraded land units to the maximum possible extent. Local people should aware of the consequences of conversion of paddy fields. Land and water management activities should conducted only after detailed landuse planning, sand mining from rivers should be regulated and further expansion of agricultural plantation at the expense of other crops. Remote sensing was quite useful for landuse and land cover mapping. It was found that main impact of random growth of settlement is on the surrounding agriculture land and land with scrub. Under utilization of potential land, increased population, and land conversion are the major driving forces for the change in landuse during the 39 years. The overall accuracy of the present land cover study is 85%. Based, on the analysis of changes in landuse/land cover some of the remedial measures are suggested, which are essential for optimum and sustainable utilization of land resources and prevention of further undesirable and deteriorated changes in landuse. Crop rotation could help to improve the land potential and to avoid poor yield. Based on the soil suitability fruit trees could be planted to improve the economy of the people.
India is an agriculturally predominant country where about 75 percent of the population is dependent on it. Since the breakthrough in agricultural production technology, farm credit comprising of about 60 percent short-term credit and 40 percent term credit played a crucial role in stepping up agricultural production and employment in India. NPAs are classified under three categories-Substandard, Doubtful and Loss. The present study is an attempt to examine the short- term agricultural credit in an agriculturally important district of TamilNadu. The study is based on primary data collected from 120, 50 marginal and 70 small randomly selected from 6 villages in kurunjipadi block in Cuddalore district. The data pertain to the agricultural year 2012-2013. To analysis was attempted to examine the requirement, availability and short-term agricultural credit from different lending agencies. The credit included the interest charges paid by borrower cultivators, opportunity value of time spent in getting a loan. The short-term loans, the marginal and small farmers obtained seeds, irrigation and fertilizer alone and not for the purchase of tractor and land. The repayment is high in non-institutional banks as compared to institutional banks.The government has to encourage the farmers to repay the loans by offering some discounts/Zero interest. For the farmers those repay regularly must have given some priority in sanction of more loans without delay.