Potential of soil resources of Coconut Research Station, Aliyarnagar, Tamil Nadu, India for agro-technology generation

Potential of soil resources of Coconut Research Station, Aliyarnagar of Tamil Nadu Agricultural University and one of the Centers of ICAR-AICRP (Palms), was assessed by soil profile examination and spatial variability mapping. Three soil profiles were examined, one each in A, B and C blocks of the farm, soils were studied horizon wise samples were collected, and fertility parameters were analyzed. Spatial variability of primary nutrients was mapped employing GIS techniques. Soil profile examination revealed the presence of canker nodules in the lower horizons and the depth of the soil was not a constraint for the cultivation of perennial crops. The texture of the soil varied from loamy sand to sandy clay loam. pH was alkaline and electrical conductivity was less than 2 dSm-1. The content of KMnO4-N was low, and Olsen P, NNNH4OAc-K and organic carbon were medium. Land capability class was IIIew and was highly suitable (S1) for coconut, moderately suitable (S2) for cocoa and marginally suitable (S3) for pepper. The soil taxonomic class is fine-loamy mixed, isohyperthermic Fluventic/Typic Haplustepts. Rock outcrops were noticed over 5 per cent of the area. Top soil erosion and seepage problems resulting in temporary water logging are the major fertility constraints associated with this farm. Scrupulous application of organic manures, split application of fertilizers, providing trenches in areas of water logging, etc., are the strategies to overcome the constraints, which are existing in the farm

Mapping of coconut growing areas in Tamil Nadu, India using remote sensing and GIS

Importance of remotely sensed data for inventorying, mapping, monitoring and for the management and development planning for the optimum utilization of natural resources has been well established. Though, a lot of applications have been attempted using remote sensing tool, mapping of coconut growing areas has not been attempted at a regional level. Hence, this study was envisaged to map the coconut growing areas in Tamil Nadu, India using Survey of India Toposheet grid (1:50,000 scale) and Digital Globe data. The temporal window of these datasets ranged from March 2012 to June 2014. The data sets have a spatial resolution of 41 cm. It has been observed that Coimbatore has largest area under coconut among all districts of Tamil Nadu, followed by Tiruppur, Thanjavur and Dindigul. In terms of percentage of coconut area to the total geographical area of the district, Tiruppur, leads the list, followed by Kanyakumari, Coimbatore and Thanjavur. On comparing the area obtained by this study with the area as per Coconut Development Board using a paired t-test, a p-value of 0.005 was obtained and hence, there is no significant difference between the two. Hence, it can be said that geospatial technologies like remote sensing and geographical information system are the best tools for accurate assessment and spatial data creation for crop mapping and area assessment

Mapping and classification of crops using high resolution satellite image

In the present study an attempt was made to perform land use land cover classification at Level-III in order to discriminate and map individual crops. IRS Resources at 2 LISS IV sensor imagery (5.0 m spatial resolution) of September 2014 was utilized for the study. A hybrid classification approach of unsupervised classification followed by supervised classification was adopted to identify and map the crop area in Kodumudi block, Erode district of Tamil Nadu. Signature evaluation was carried out to study the class separability and through cross tabulation and the accuracy was assessed by error matrix. The signature separability analysis to classify various land cover classes indicated that the class viz., waterbody, settlement, sandy area and fallow land were better and for vegetation sub-classes viz., individual crops were poor, which means classification of individual crops was a challenge. The overall accuracy with three different algorithms varied from 56 to 65 per cent and this low accuracy was due to the problem in discriminating the tonal variation and spectral pattern of individual crops in the study area. Thus, classification of vegetation categories into individual crops using LISS IV data resulted in moderate classification accuracy in areas with multiple cropping

Agricultural drought monitoring in Tamil Nadu in India using Satellite-based multi vegetation indices

Drought being an insidious hazard, is considered to have one of the most complex phenomenons. The proposed study identifies remote sensing-based indices that could act as a proxy indicator in monitoring agricultural drought over Tamil Nadu's region India. The satellite data products were downloaded from 2000 to 2013 from MODIS, GLDAS – NOAH, and TRMM. The intensity of agricultural drought was studied using indices viz., NDVI, NDWI, NMDI, and NDDI. The satellite-derived spectral indices include raw, scaled, and combined indices. Comparing satellite-derived indices with in-situ rainfall data and 1-month SPI data was performed to identify exceptional drought to no drought conditions for September month. The additive combination of NDDI showed a positive correlation of 0.25 with rainfall and 0.23 with SPI, while the scaled NDDI and raw NDDI were negatively correlated with rainfall and SPI. Similar cases were noticed with raw LST and raw NMDI. Indices viz., LST, NDVI, and NDWI performed well; however, it was clear that NDWI performed better than NDVI while LST was crucial in deciding NDVI coverage over the study area. These results showed that no single index could be put forward to detect agricultural drought accurately; however, an additive combination of indices could be a successful proxy to vegetation stress identification.

IMPACT OF TSUNAMI 2004 IN COASTAL VILLAGES OF NAGAPATTINAM DISTRICT, INDIA

ABSTRACTA quake-triggered tsunami lashed the Nagapattinam coast of southern India on December 26, 2004 at around 9.00 am (IST). The tsunami caused heavy damage to houses, tourist resorts, fishing boats, prawn culture ponds, soil and crops, and consequently affected the livelihood of large numbers of the coastal communities. The study was carried out in the Tsunami affected villages in the coastal Nagapattinam with the help of remote sensing and geographical information science tools. Through the use of the IRS 1D PAN and LISS 3 merged data and quick bird images, it was found that 1,320 ha of agricultural and non-agricultural lands were affected by the tsunami. The lands were affected by soil erosion, salt deposition, water logging and other deposited sediments and debris. The maximum run-up height of 6.1 m and the maximum seawater inundation distance of 2.2 km were observed at Vadakkupoyyur village in coastal Nagapattinam.Pre and Post Tsunami survey on soil quality showed an increase in pH and EC values, irrespectiveof distance from the sea. The water reaction was found to be in alkaline range (> 8.00) in most of the -1wells. Salinity levels are greater than 4 dS m in all the wells except the ring well. The effect of summer rainfall on soil and water quality showed the dilution of soluble salts. Pumping of water has reduced the salinity levels in the well water samples and as well as in the open ponds. Following the 2004 event, it has become apparent to know the relative tsunami hazard for this coastal Nagapattinam. So, the Tsunami hazard maps are generated using a geographical information systems (GIS) approach and the results showed 20.6 per cent, 63.7 per cent and 15.2 per cent of the study area fall under high hazard, medium hazard and low hazard category respectively

Monitoring vegetation dynamics using multi-temporal Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) images of Tamil Nadu

Vegetation indices serve as an essential tool in monitoring variations in vegetation. The vegetation indices used often, viz., normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were computed from MODIS vegetation index products. The present study aimed to monitor vegetation's seasonal dynamics by using time series NDVI and EVI indices in Tamil Nadu from 2011 to 2021. Two products characterize the global range of vegetation states and processes more effectively. The data sources were processed and the values of NDVI and EVI were extracted using ArcGIS software. There was a significant difference in vegetation intensity and status of vegetation over time, with NDVI having a larger value than EVI, indicating that biomass intensity varies over time in Tamil Nadu. Among the land cover classes, the deciduous forest showed the highest mean values for NDVI (0.83) and EVI (0.38), followed by cropland mean values of NDVI (0.71) and EVI (0.31) and the lowest NDVI (0.68) and EVI (0.29) was recorded in the scrubland. The study demonstrated that vegetation indices extracted from MODIS offered valuable information on vegetation status and condition at a short temporal time period

Optimization of Spray Fluid for Herbicide Application for Drones in Irrigated Maize (Zea mays L.)

A field experiment was conducted in a randomized complete block design to screen the optimum spray fluid of herbicide application for drone based on visual toxicity and weed control efficiency in maize (Zea mays L.) during the summer season (March 2021) at eastern block farms of Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu. Three herbicide treatments namely Atrazine, Tembotrione and 2, 4-D with recommended dosages, 75% and 125% as pre-emergence herbicides applied on 3 days after, early post-emergence herbicides applied on 15 days after sowing and post-emergence applied on 25 days after sowing respectively. Totally thirty treatments with different spray fluids such as 500, 400, 300, 200, 100, 80, 60, 40, 30 L ha-1 and 20 L ha-1 were replicated three times. The study revealed that T5- Recommended dosage of pre emergence Atrazine – early post emergence Tembotrione – post emergence 2, 4-D (spray fluid 100 L of water ha-1),T6- Recommended dosage of pre emergence Atrazine – early post emergence Tembotrione–post emergence 2, 4-D (spray fluid 80 L of water ha-1), T7- Recommended dosage of pre emergence Atrazine – early post emergence Tembotrione – post emergence 2, 4-D (spray fluid 60 L of water ha-1) and T8- Recommended dosage of pre emergence Atrazine – early post emergence Tembotrione – post emergence 2, 4-D (spray fluid 40 L of water ha-1) produced the best results with respect to phytotoxicity and weed control efficiency. Based on the results it was concluded that the application of spray fluid 80 L ha-1 was optimum for herbicide application through drones with recommended dosage pre emergence Atrazine 1 kg a.i ha-1 on 3 days after sowing – early post emergence Tembotrione 120 g a.i ha-1 on 15-20 days after sowing - post emergence 2, 4-D 1 kg a.i ha-1 on 30 - 35 days after sowing

Crop Diversification Assessment in Tank Ayacut Areas of Lower Palar Sub-Basin of Chengalpattu District, Tamil Nadu, India Using Geo-Spatial Techniques

For the assessment of crop diversification in the major tank Ayacut area of the Lower Palar sub-basin in Chengalpattu district of Tamil Nadu, research works were carried out using Sentinel 2 optical data by relating with ground truth data, to identify the crops in pixel-based classification and further classified the crops using Random Forest machine learning algorithms. The total area estimated under crop classification was 15767.97 and 28818.17 ha respectively for the summer seasons of 2018 and 2021. Since, the summer season experiences high crop diversification. The water spread area and water volume of tanks estimated were 612.31 and 1177.89 ha and 6,39,248 and 14,06,056 m3 respectively for 2018 and 2021. The accuracy assessment of ground truth points by confusion matrix reveals an overall classification accuracy of 96.8% (2018) and 94.9 % (2021) with kappa scores of 0.96 and 0.94 respectively. The crop diversification assessments were estimated using the Simpson Index of Diversity and values of 0.63 and 0.68 were accounted for in 2018 and 2021 respectively. The diversified pattern of crops is significantly correlated with tank water availability which increased the cropping area in 2021 as confirmed by the Crop Diversification factor

Generating Soil Parent Material Environmental Covariates Using Sentinel – 2A Images for Delineating Soil Attributes

Soil mapping procedures typically involve the combination of possible soil-forming SCORPAN factors. Among the factors, parent materials/ mineralogy has been considered important for the soil classification besides the Organisms (O) and Relief (R). Inclusion of the parent material covariate for the Digital soil mapping involves implication through geological maps, spectral derivatives and predictive modelling. In this study, the most prominent parent materials identified were derived using the spectral indices formulated based on the Sentinel – 2A multispectral information. While considering the coarse spatial resolution constraints of the existing Landsat -8 bands that may limit certain applications, Sentinel-2 images were used for the indices derivation. The generated mineral maps can support the digital soil mapping of the soil attributes at different spatial scales