scholarly journals Land Suitability Assessment for Maize (Rabi) Cultivation in Cox’s Bazaar Sadar Upazila, Cox’s Bazaar, Bangladesh

2018 ◽  
Vol 44 (1) ◽  
pp. 35-51
Author(s):  
Abdul Hoque ◽  
Khandaker Tanvir Hossain
Keyword(s):  
Soil Depth ◽  
Winter Season ◽  
Land Suitability ◽  
Soil Reaction ◽  
Climate Conditions ◽  
Suitability Assessment ◽  
Present Study Area ◽  
Maize Cultivation ◽  
Land Suitability Assessment ◽  
Geomorphic Units

Based on the various agro-edaphic and agro-climatic characteristics, the suitability of maize cultivation during winter season has been determined in Cox’s Bazaar Sadar Upazila. For this purpose, under the existing climate conditions, considered agro-edaphic factors of various geomorphic units are soil permeability, effective soil depth, available soil moisture, soil reaction (pH), soil salinity, slope etc. Long-term climate attributes of the study area were used to determine the overall climate suitability classes, and the combined land suitability classes for maize (rabi) cultivation have been determined through the adjustment of the agro-edaphic and agro-climatic suitability criteria. Thus, recognized combined land suitability classes for maize (rabi) cultivation in the present study area are ‘highly suitable’, ‘suitable’, and ‘moderately suitable’. A total of 896 hectares land has been found as ‘highly suitable’ for maize cultivation in Cox’s Bazaar Sadar Upazila while approximately 4403 and 11,000 hectares have been identified as ‘suitable’ and ‘moderately suitable’, respectively. Asiat. Soc. Bangladesh, Sci. 44(1): 35-51, June 2018

scholarly journals Land Suitability Assessment and Agricultural Production Sustainability Using Machine Learning Models

Agronomy ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 573 ◽  
Author(s):  
Ruhollah Taghizadeh-Mehrjardi ◽  
Kamal Nabiollahi ◽  
Leila Rasoli ◽  
Ruth Kerry ◽  
Thomas Scholten
Keyword(s):  
Machine Learning ◽  
Agricultural Land ◽  
Soil Depth ◽  
Traditional Approach ◽  
Soil Samples ◽  
Land Suitability ◽  
Agricultural System ◽  
Climate Data ◽  
Suitability Assessment ◽  
Land Suitability Assessment

Land suitability assessment is essential for increasing production and planning a sustainable agricultural system, but such information is commonly scarce in the semi-arid regions of Iran. Therefore, our aim is to assess land suitability for two main crops (i.e., rain-fed wheat and barley) based on the Food and Agriculture Organization (FAO) “land suitability assessment framework” for 65 km2 of agricultural land in Kurdistan province, Iran. Soil samples were collected from genetic layers of 100 soil profiles and the physical-chemical properties of the soil samples were analyzed. Topography and climate data were also recorded. After calculating the land suitability classes for the two crops, they were mapped using machine learning (ML) and traditional approaches. The maps predicted by the two approaches revealed notable differences. For example, in the case of rain-fed wheat, results showed the higher accuracy of ML-based land suitability maps compared to the maps obtained by traditional approach. Furthermore, the findings indicated that the areas with classes of N2 (≈18%↑) and S3 (≈28%↑) were higher and area with the class N1 (≈24%↓) was less predicted in the traditional approach compared to the ML-based approach. The major limitations of the study area were rainfall at the flowering stage, severe slopes, shallow soil depth, high pH, and large gravel content. Therefore, to increase production and create a sustainable agricultural system, land improvement operations are suggested.

scholarly journals Agricultural Land Suitability Assessment Using Satellite Remote Sensing-Derived Soil-Vegetation Indices

Land ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 223
Author(s):  
Rubaiya Binte Mostafiz ◽  
Ryozo Noguchi ◽  
Tofael Ahamed
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Agricultural Land ◽  
Expert Knowledge ◽  
Vegetation Indices ◽  
Land Suitability ◽  
Assessment System ◽  
Yield Estimation ◽  
Suitability Assessment ◽  
Land Suitability Assessment

Satellite remote sensing technologies have a high potential in applications for evaluating land conditions and can facilitate optimized planning for agricultural sectors. However, misinformed land selection decisions limit crop yields and increase production-related costs to farmers. Therefore, the purpose of this research was to develop a land suitability assessment system using satellite remote sensing-derived soil-vegetation indicators. A multicriteria decision analysis was conducted by integrating weighted linear combinations and fuzzy multicriteria analyses in a GIS platform for suitability assessment using the following eight criteria: elevation, slope, and LST vegetation indices (SAVI, ARVI, SARVI, MSAVI, and OSAVI). The relative priorities of the indicators were identified using a fuzzy expert system. Furthermore, the results of the land suitability assessment were evaluated by ground truthed yield data. In addition, a yield estimation method was developed using indices representing influential factors. The analysis utilizing equal weights showed that 43% of the land (1832 km2) was highly suitable, 41% of the land (1747 km2) was moderately suitable, and 10% of the land (426 km2) was marginally suitable for improved yield productions. Alternatively, expert knowledge was also considered, along with references, when using the fuzzy membership function; as a result, 48% of the land (2045 km2) was identified as being highly suitable; 39% of the land (2045 km2) was identified as being moderately suitable, and 7% of the land (298 km2) was identified as being marginally suitable. Additionally, 6% (256 km2) of the land was described as not suitable by both methods. Moreover, the yield estimation using SAVI (R2 = 77.3%), ARVI (R2 = 68.9%), SARVI (R2 = 71.1%), MSAVI (R2 = 74.5%) and OSAVI (R2 = 81.2%) showed a good predictive ability. Furthermore, the combined model using these five indices reported the highest accuracy (R2 = 0.839); this model was then applied to develop yield prediction maps for the corresponding years (2017–2020). This research suggests that satellite remote sensing methods in GIS platforms are an effective and convenient way for agricultural land-use planners and land policy makers to select suitable cultivable land areas with potential for increased agricultural production.

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