Assessment of Climate Induced Soil Salinity Conditions of Gosaba Island, West Bengal and Its Influence on Local Livelihood

Author(s):  
Anwesha Haldar ◽  
Ajay Debnath
2019 ◽  
Vol 24 (2) ◽  
pp. 192-209 ◽  
Author(s):  
Sanghamitra Adak ◽  
Arindam Roy ◽  
Priyanka Das ◽  
Abhishek Mukherjee ◽  
Sonali Sengupta ◽  
...  

Proceedings ◽  
2020 ◽  
Vol 36 (1) ◽  
pp. 146
Author(s):  
Sukamal Sarkar ◽  
Donald S Gaydon ◽  
Koushik Brahmachari ◽  
Manoj Kumar Nanda ◽  
Argha Ghosh ◽  
...  

Due to seasonal dry-season salt accumulation in the coastal saline zone (CSZ) of West Bengal, India, the cultivation of winter crops (following summer rice) is rare. To address this issue, field experiments were conducted over two years (2016–18) in the CSZ to study the feasibility of cropping system intensification through incorporating grass pea into the dominant rice-fallow rotation. The experiment was conducted in strip plot design with two factors namely, Factor A: Six dates of rice sowing (at one-week intervals—2nd week of June to 3rd week of July) and Factor B: Two land situations (Medium-upland and Medium-lowland). The experiment was simulated using APSIM (Agricultural Production Systems sIMulator) utilizing the APSIM-SWIM water balance module to understand the mechanisms of seasonal soil salinity dynamics and the associated crop responses. The results suggest that irrespective of land situation, early sown rice (2nd week of June) produces higher dry matter and yield compared to late sown crops. This early rice sowing also facilitated better subsequent grass pea performance, by avoiding the worst of the salinity build-up and drought stress later in the winter. The model performed well in simulating the observed rice and grass pea yields (R2 = 0.97 with low bias (slope, α = 0.93, intercept, β = 149 kg ha−1), RMSE = 558 kg ha−1). It may be concluded that ASPIM-SWIM is an effective tool to understand, assess and predict the complex bio-physical mechanisms of ground water and soil salinity dynamics in rice-pulse-based cropping systems of CSZ of West Bengal.


Author(s):  
Tonmoy Sengupta ◽  
B. K. Bandyopadhyay ◽  
Sudipta Tripathi

The delta region of the river Ganges spreads over India (West Bengal) and Bangladesh is popularly known as Sundarbans. Crop productivity of the region is very poor. Agricultural lands of the region are mostly saline and low-lying with drainage congestion due to presence of brackish groundwater table at shallow depth and flat topography. In recent years, improvement in productivity of these lands was witnessed by farmers when the elevation of lowlands was increased through land shaping. In the present study changes in the salinity status of soil were due to raise of land elevation through land shaping was investigated for two years and 3 seasons (winter, summer and late summer). It was found that there was a considerable decrease in soil salinity due to increase in elevation of lowlands through land shaping. The salinity of original lowlands was about 200% higher than the raised lands or uplands made through land shaping. Salinity of rhizosphere soil decreased with depth and there was a strong seasonal variation of soil salinity. At all soil depths soil salinity increased as the dry season progressed from winter to late summer through summer and soil salinity was highest in the surface (0-10 cm). The depth to groundwater table and the groundwater salinity also showed strong seasonal variation and were maximum in late summer season. The drainage condition of soil improved with increasing in land elevation.


Planta Medica ◽  
2007 ◽  
Vol 73 (09) ◽  
Author(s):  
M Gangopadhyay ◽  
R Bhattacharya ◽  
D Chakraborty ◽  
S Bhattacharya ◽  
A Mitra ◽  
...  

2020 ◽  
Vol 12 (1) ◽  
pp. 60-69 ◽  
Author(s):  
Pijush Basak

The South West Monsoon rainfall data of the meteorological subdivision number 6 of India enclosing Gangetic West Bengal is shown to be decomposable into eight empirical time series, namely Intrinsic Mode Functions. This leads one to identify the first empirical mode as a nonlinear part and the remaining modes as the linear part of the data. The nonlinear part is modeled with the technique Neural Network based Generalized Regression Neural Network model technique whereas the linear part is sensibly modeled through simple regression method. The different Intrinsic modes as verified are well connected with relevant atmospheric features, namely, El Nino, Quasi-biennial Oscillation, Sunspot cycle and others. It is observed that the proposed model explains around 75% of inter annual variability (IAV) of the rainfall series of Gangetic West Bengal. The model is efficient in statistical forecasting of South West Monsoon rainfall in the region as verified from independent part of the real data. The statistical forecasts of SWM rainfall for GWB for the years 2012 and 2013 are108.71 cm and 126.21 cm respectively, where as corresponding to the actual rainfall of 93.19 cm 115.20 cm respectively which are within one standard deviation of mean rainfall.


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