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... is clearly seen that power tiller, power thresher, and other costly machinery were present at an optimum level because of the good financial condition in South Surma. (Table 3). This study also narrated that foot pump was used when LLP did not cover the hilly and yard crop for irrigation. ...Context 2
... per unit cultivated area refers to the number of machinery used in the study area. Table 3 shows that Osmani nagar was used for the highest number of modern machinery but tractor are existing 0.016 per square kilometer area whereas 0.175 in Fenchuganj. In this study, we investigated that reaper and combine harvester were not familiar in the rural areas of Sylhet region. ...Context 3
... is clearly seen that power tiller, power thresher, and other costly machinery were present at an optimum level because of the good financial condition in South Surma. (Table 3). This study also narrated that foot pump was used when LLP did not cover the hilly and yard crop for irrigation. ...Context 4
... per unit cultivated area refers to the number of machinery used in the study area. Table 3 shows that Osmani nagar was used for the highest number of modern machinery but tractor are existing 0.016 per square kilometer area whereas 0.175 in Fenchuganj. In this study, we investigated that reaper and combine harvester were not familiar in the rural areas of Sylhet region. ...Similar publications
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Citations
... The magnitude of hydraulic conductivity represents that the agricultural soil type is mostly clay and silty clay. Some researchers also reported that this area's soil type is poor permeable and contains clay and silty clay (Shamsudduha 2011;Zafor et al. 2017;Al Mamun et al. 2019). However, Ahmed et al. (2007) reported that this region's soil type is sandy loam to clay loam. ...
The
knowledge of soil hydraulic properties is indispensable to solve many soil and water management problems related to agriculture, ecology, and environmental issues in water. This research aimed to generate the saturated hydraulic conductivity (Ks) information of topsoil from soil class and terrain attributes (elevation, roughness, slope, terrain ruggedness index (TRI), topographic position index (TPI), and flow direction) using multiple linear regression (MLR) and support vector regression (SVR) techniques to develop a spatial distribution map of Ks for an agricultural landscape. Sixty-five cores of soil sample (diameter 5 cm and length 10 cm) were collected from the topsoil layer (0–10 cm) from the agricultural field in different locations (Upazilas) of the Sylhet region (3452 km2) in Bangladesh and conducted laboratory tests following Darcy’s constant head method. The topsoil was clay or silt clay, having very low Ks values. The mean of Ks for the agricultural soils is 1.70 × 10–06 cm/s and varied significantly (p < 0.01) among different Upazilas (sub-district). To generate the topsoil layer’s Ks values, the developed MLR model (with R2 = 0.598 and RMSE = 1.12 × 10–06 cm/s) and SVR model (with training \({R}_{\mathrm{train}}^{2}=0.64\), RMSE = 1.22 × 10–06 cm/s and NSE = 0.58 and testing \({R}_{\mathrm{test}}^{2}=0.73\), RMSE = 8.19 × 10–07 cm/s, NSE = 0.71) were found suitable compared to simple interpolation methods. The SVR model performed better in the modeling process than the MLR based on the goodness of fits parameter. However, the SVR model underestimates the higher Ks values in both training and testing stages. In contrast, the MLR model was found to be more balanced. Finally, the spatial variability map of Ks for the topsoil layer can be generated from soil texture information and terrain attributes for facilitating agro-hydrological model development in a data-limited area.
