Agricultural land suitability assessment for agricultural productivity based on GIS modeling and multi-criteria decision analysis: the case of Tekirdağ province

2021 ◽  
Vol 194 (1) ◽  
Author(s):  
Emre Ozsahin ◽  
Mehmet Ozdes
Keyword(s):  
Decision Analysis ◽  
Agricultural Land ◽  
Agricultural Productivity ◽  
Land Suitability ◽  
Gis Modeling ◽  
Multi Criteria Decision Analysis ◽  
Suitability Assessment ◽  
Land Suitability Assessment

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.

The Use of Artificial Neural Networks in a Geographical Information System for Agricultural Land-Suitability Assessment

1994 ◽  
Vol 26 (2) ◽  
pp. 265-284 ◽  
Author(s):  
F Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Classification ◽  
Agricultural Land ◽  
Assessment Methods ◽  
Land Suitability ◽  
Geographical Information ◽  
Suitability Assessment ◽  
Artificial Neural ◽  
Land Suitability Assessment

Agricultural land-suitability assessment involves the analysis of a large variety and amount of physiographic data. Geographical information systems (GISs) may facilitate suitability assessment in data collection. To generate accurate results from the data, appropriate suitability-assessment methods are required. However, the assessment methods which can currently be used with GISs, such as that developed by the United Nations Food and Agriculture Organization and the statistical pattern—classification method, have limitations which may lead to inaccurate assessment. An artificial neural network is an effective tool for pattern analysis. A neural network allows decision rules of greater complexity to be applied in pattern classification. By formulating the land-suitability-assessment problem into a pattern—classification problem, neural networks can be used to achieve results of greater accuracy. In this paper, a neural-network-based method for land-suitability assessment is discussed, and a set of neural networks is described. The integration between the neural networks and a GIS is addressed, and some experimental results are presented and analyzed.

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.

Soil informatics for agricultural land suitability assessment in Seoni district, Madhya Pradesh, India

2015 ◽  
Vol 49 (4) ◽  
pp. 315
Author(s):  
B.P. Bhaskar ◽  
S.V. Bobade ◽  
S.S. Gaikwad ◽  
Dipak Sarkar ◽  
S.G. Anantwar ◽  
...  
Keyword(s):  
Agricultural Land ◽  
Madhya Pradesh ◽  
Land Suitability ◽  
Suitability Assessment ◽  
Land Suitability Assessment

scholarly journals Assessment of Potential Land Suitability for Tea (Camellia sinensis (L.) O. Kuntze) in Sri Lanka Using a GIS-Based Multi-Criteria Approach

Agriculture ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. 148 ◽  
Author(s):  
Sadeeka Layomi Jayasinghe ◽  
Lalit Kumar ◽  
Janaki Sandamali
Keyword(s):  
Land Use ◽  
Sri Lanka ◽  
Agricultural Land ◽  
Land Suitability ◽  
Suitability Assessment ◽  
Land Use Efficiency ◽  
Tea Production ◽  
Land Suitability Assessment ◽  
Environmental Limits ◽  
Potential Land

The potential land suitability assessment for tea is a crucial step in determining the environmental limits of sustainable tea production. The aim of this study was to assess land suitability to determine suitable agricultural land for tea crops in Sri Lanka. Climatic, topographical and soil factors assumed to influence land use were assembled and the weights of their respective contributions to land suitability for tea were assessed using the Analytical Hierarchical Process (AHP) and the Decision-Making Trail and Evaluation Laboratory (DEMATEL) model. Subsequently, all the factors were integrated to generate the potential land suitability map. The results showed that the largest part of the land in Sri Lanka was occupied by low suitability class (42.1%) and 28.5% registered an unsuitable land cover. Furthermore, 12.4% was moderately suitable, 13.9% was highly suitable and 2.5% was very highly suitable for tea cultivation. The highest proportion of “very highly suitable” areas were recorded in the Nuwara Eliya District, which accounted for 29.50% of the highest category. The model validation results showed that 92.46% of the combined “highly suitable” and “very highly suitable” modelled classes are actual current tea-growing areas, showing the overall robustness of this model and the weightings applied. This result is significant in that it provides effective approaches to enhance land-use efficiency and better management of tea production.

scholarly journals Can We Use Machine Learning for Agricultural Land Suitability Assessment?

Agronomy ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 703
Author(s):  
Anders Bjørn Møller ◽  
Vera Leatitia Mulder ◽  
Gerard B. M. Heuvelink ◽  
Niels Mark Jacobsen ◽  
Mogens Humlekrog Greve
Keyword(s):  
Machine Learning ◽  
Agricultural Land ◽  
Mechanistic Model ◽  
Land Suitability ◽  
Learning Models ◽  
Specialty Crops ◽  
Suitability Assessment ◽  
Land Suitability Assessment ◽  
Ecological Suitability ◽  
Machine Learning Models

It is vital for farmers to know if their land is suitable for the crops that they plan to grow. An increasing number of studies have used machine learning models based on land use data as an efficient means for mapping land suitability. This approach relies on the assumption that farmers grow their crops in the best-suited areas, but no studies have systematically tested this assumption. We aimed to test the assumption for specialty crops in Denmark. First, we mapped suitability for 41 specialty crops using machine learning. Then, we compared the predicted land suitabilities with the mechanistic model ECOCROP (Ecological Crop Requirements). The results showed that there was little agreement between the suitabilities based on machine learning and ECOCROP. Therefore, we argue that the methods represent different phenomena, which we label as socioeconomic suitability and ecological suitability, respectively. In most cases, machine learning predicts socioeconomic suitability, but the ambiguity of the term land suitability can lead to misinterpretation. Therefore, we highlight the need for increasing awareness of this distinction as a way forward for agricultural land suitability assessment.

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