The agricultural potential of Bangladesh’s coastal region has been threatened by the impact of climate change. Pulse crops with high nutritional value and low production costs such as green gram constitute an important component of a healthy and accessible diet for the country. In order to optimize the production of this important staple, this research aims to promote climate-smart agriculture by optimizing the identification of the appropriate land. The objective of this research is to investigate, estimate, and identify the suitable land areas for green gram production based on the topography, climate, and soil characteristics in the coastal region of Bangladesh. The methodology of the study included a Geographic Information System (GIS) and the Multicriteria Decision-Making approach: the Analytical Hierarchy Process (AHP). Datasets were collected and prepared using Landsat 8 imagery, the Center for Hydrometeorology and Remote Sensing (CHRS) data portal and the Bangladesh Agricultural Research Council. All the datasets were processed into raster images and then reclassified into four classes: Highly Suitable (S1), Moderately Suitable (S2), Marginally Suitable (S3), and Not Suitable. Then, the AHP results were applied to produce a final green gram suitability map with four classes of suitability. The results of the study found that 12% of the coastal area (344,619.5 ha) is highly suitable for green gram production, while the majority of the land area (82.3% of the area) shows moderately suitable (S2) land. The sensitivity analysis results show that 3.3%, 63.4%, 28.0%, and 1.2% of the study area are S1, S2, S3, and NS, respectively. It is also found that the highly suitable land area belongs mostly to the southeastern part of the country. The result of this study can be utilized by policymakers to adopt a proper green gram production strategy, providing special agricultural incentive policies in the highly suitable area as a provision for the increased food production of the country.

Soil salinity is a negative impact of climate change, and it is a significant problem for the coastal region of Bangladesh, which has been increasing in the last four decades. The issue of soil salinity substantially limits the agricultural crop production in coastal areas. Therefore, a soil salinity assessment is essential for proper land-use planning in agricultural crop production. This research was carried out to determine the soil salinity area with different salinity levels in Barguna Sadar Upazila (sub-district). The remote sensing technique, which is a potentially quick yet effective method for the soil salinity estimation in data-scarce conditions, was applied. The methodology employed the Landsat 8 OLI dataset along with nine soil salinity indices to develop a soil salinity map. The maps were from Soil Resource Development Institute (SRDI), and low NDVI value (−0.01 to 0.48) was produced using satellite images illustrate the extent of the soil salinity for the study area. However, nine linear regressions, which were made between the pixel value of the satellite-based generated map and ground truth soil salinity data, that is, the EC value, indicate a maximum R² value for the salinity index SI 7 = G × R/B, representing a value of 0.022. This minimal R² value indicates a negligible relationship between the ground EC value and the pixel value of the salinity index generated map, inferring that the indices are not sufficient to assess the soil salinity. Nonetheless, this research’s findings offer a guide for researchers to investigate alternative geospatial approaches for this geophysical condition.

Soil Organic Carbon (SOC) is a determining factor of soil health and agricultural crop productivity. The SOC level has generally declined since European settlement in Australia, primarily due to the clearing of native vegetation for agricultural purposes. To enhance soil health and crop yield, it is necessary to determine the attainable SOC potential and site-specific soil constraints that limit SOC build-up. To achieve this, we applied a Boundary Line Analysis (BLA) model to a dataset consisting of 1,782 soil sample sites, collected from 72 farms across the grain-growing regions of Australia. Laboratory measured soil properties, including clay content, electrical conductivity (EC), cation exchange capacity (CEC), pH, exchangeable sodium percentage (ESP), and silt+clay from both the topsoil (0–30 cm) and subsoil (30–60 cm) were used as predictors in two separate BLA model to assess attainable SOC levels in the topsoil (0–30 cm), representing topsoil-driven and subsoil-driven constraints, respectively. Upper-envelope functions between SOC and each predictor variable were derived using a locally estimated scatterplot smoothing (LOESS) model in the BLA framework. A separate BLA was performed for different rainfall regions across Australia to reflect differences in attainable SOC. Digital soil maps for each soil property for a case study farm in the Wimmera, Victoria, were used to illustrate how the BLA can be used to identify limiting factors at the within-field level. Next, we applied the developed BLA models to that case study farm to determine the attainable SOC at the farm scale across six key soil properties. The results of the entire regional BLA model revealed that the attainable SOC in the topsoil varied from 0.98 % (mean minimum) to 1.39 % (mean maximum), in contrast to a mean actual (measured) SOC of 0.68 % (SD = 0.37; IQR = 0.45–0.82) across the region. This signifies a sequestration potential of 0.3–0.71 % in the uppermost soil layer, depending on rainfall zones and management practices. In addition, digital maps of the case study farm generated by applying BLA-derived models to spatial layers of soil predictors determine the attainable SOC, show its spatial pattern, and identify the most limiting factors across soil depths. The resulting map also illustrates the potential SOC gain after ameliorating the constraints. This BLA model is reproducible and can be readily applied across the 72 individual farms in Australia (if they have Digital Soil Maps) to quantify attainable SOC and identify site-specific soil constraints. Thus, providing a rapid and effective decision-support tool for farmers and farm managers to implement site-specific soil and agronomic management, thereby improving soil carbon levels.

Soil Organic Carbon (SOC) is a determining factor of soil health and agricultural crop productivity. The SOC level has generally declined since European settlement in Australia, primarily due to the clearing of native vegetation for agricultural purposes. To enhance soil health and crop yield, it is necessary to determine the attainable SOC potential and site-specific soil constraints that limit SOC build-up. To achieve this, we applied a Boundary Line Analysis (BLA) model to a dataset consisting of 1,782 soil sample sites, collected from 72 farms across the grain-growing regions of Australia. Laboratory measured soil properties, including clay content, electrical conductivity (EC), cation exchange capacity (CEC), pH, exchangeable sodium percentage (ESP), and silt+clay from both the topsoil (0–30 cm) and subsoil (30–60 cm) were used as predictors in two separate BLA model to assess attainable SOC levels in the topsoil (0–30 cm), representing topsoil-driven and subsoil-driven constraints, respectively. Upper-envelope functions between SOC and each predictor variable were derived using a locally estimated scatterplot smoothing (LOESS) model in the BLA framework. A separate BLA was performed for different rainfall regions across Australia to reflect differences in attainable SOC. Digital soil maps for each soil property for a case study farm in the Wimmera, Victoria, were used to illustrate how the BLA can be used to identify limiting factors at the within-field level. Next, we applied the developed BLA models to that case study farm to determine the attainable SOC at the farm scale across six key soil properties. The results of the entire regional BLA model revealed that the attainable SOC in the topsoil varied from 0.98 % (mean minimum) to 1.39 % (mean maximum), in contrast to a mean actual (measured) SOC of 0.68 % (SD = 0.37; IQR = 0.45–0.82) across the region. This signifies a sequestration potential of 0.3–0.71 % in the uppermost soil layer, depending on rainfall zones and management practices. In addition, digital maps of the case study farm generated by applying BLA-derived models to spatial layers of soil predictors determine the attainable SOC, show its spatial pattern, and identify the most limiting factors across soil depths. The resulting map also illustrates the potential SOC gain after ameliorating the constraints. This BLA model is reproducible and can be readily applied across the 72 individual farms in Australia (if they have Digital Soil Maps) to quantify attainable SOC and identify site-specific soil constraints. Thus, providing a rapid and effective decision-support tool for farmers and farm managers to implement site-specific soil and agronomic management, thereby improving soil carbon levels.