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 Potential land and technology recommendation for cacao in Papua, Indonesia

2021 ◽  
Vol 306 ◽  
pp. 04008
Author(s):  
Afrizal Malik ◽  
Widia Siska ◽  
Heppy Suci Wulanningtyas ◽  
Merlin K. Rumbarar ◽  
Adhe Poppy Wira Etikha ◽  
...  
Keyword(s):  
Land Use ◽  
Geographic Information Systems ◽  
Soil Fertility ◽  
Soil Type ◽  
Land Suitability ◽  
Technological Innovations ◽  
Site Specific ◽  
Suitability Assessment ◽  
Land Suitability Assessment ◽  
Potential Land

Cacao is a primary commodity in the Keerom Regency, but production has been decreasing in recent years. Cacao cultivation on unsuitable land and without cutting-edge technology would impede efforts to increase its productivity. The study aimed to evaluate land suitability for cacao in Keerom Regency, Papua, and to suggest site-specific technologies. The study was carried out in the Keerom Regency of Papua in 2015. Land suitability assessment was carried out by matching data on land characteristics (climate, slope, soil type, and soil fertility) with land use requirements for cacao cultivation. The data was analyzed using geographic information systems. The result showed that 78.077 ha (8.60%) of the studied area were highly suitable for cacao cultivation, 123.645 ha (13.63%) was moderately suitable, and 389.603 ha (42.94%) was marginally suitable. About 316.082 ha (34,83%) of the studied area was classified as not-suitable. The recommendation technology for increasing cacao yields were fertilization, pruning, shade management, and individual terraces. Cultivation on suitable land and the application of technological innovations are expected to increase cacao production in Keerom Regency, Papua. The findings of this study could be used as a reference for policymakers to establish cacao development in the future.

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.

scholarly journals Land suitability assessment for improved land use planning in selected watersheds of Haryana

2021 ◽  
Vol 42 (2) ◽  
pp. 285-294
Author(s):  
J. Sahoo ◽  
◽  
mr. Dinesh ◽  
A. Dass ◽  
M.A. Bhat ◽  
...  
Keyword(s):  
Land Use ◽  
Land Use Planning ◽  
Arid Region ◽  
Crop Production ◽  
Land Suitability ◽  
Available N ◽  
Suitability Assessment ◽  
Land Suitability Assessment ◽  
Semi Arid Region ◽  
Semi Arid

Aim: The current study aimed to evaluate the capacity and suitability of land for semi-arid region of Haryana in selected watersheds to identify the major limitations of crop production. Methodology: The study was carried out in Bhiwani district of Haryana in 2017 where eight typical pedons (P) representing four micro-watersheds viz., Motipura (P1 and P2), Sainiwas (P3 and P4), Jhumpa (P5 and P6), Budhsheli (P7 and P8) were studied. Results: The studied pedons were neutral to alkaline in reaction (pH 6.87-9.10), non-saline (EC 0.02-0.21 dS m-1) and low in organic carbon (OC) (0.06-0.27%). Available N, P, K and S were low (42.00-189.00 kg ha-1), low to medium (4.20-17.10 kg ha-1), low to high (62.20-326.50 kg ha-1) and low (0.40-19.20 mg kg-1) in the studied pedons, respectively. Soils were deficient in available Fe and Zn but marginal to sufficient in available Mn and Cu. Interpretation: Soils were classified as IIsf and IIItsf and S3s and N1s according to LCC and irrigation suitability, respectively. The pedons were found suitable (S1) to moderately suitable (S2) for guar (cluster bean), oilseeds (mustard), moderately suitable (S2) to marginally suitable (S3) for pearl millet, gram and forestry, and marginally suitable (S3) for cotton and wheat. Key words: Land suitability, Nutrients, Pedon, Semiarid, Watershed

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.

Land suitability assessment in the Namoi Valley of Australia, using a continuous model

Soil Research ◽  
2001 ◽  
Vol 39 (2) ◽  
pp. 273 ◽  
Author(s):  
J. Triantafilis ◽  
W. T. Ward ◽  
A. B. McBratney
Keyword(s):  
Land Use ◽  
Continuous Model ◽  
Land Suitability ◽  
Land Evaluation ◽  
Fuzzy Approach ◽  
Suitability Assessment ◽  
Suitability Maps ◽  
Punctual Kriging ◽  
Land Suitability Assessment ◽  
Membership Score

In an agricultural context, land evaluation is assessment for a specified kind of land utilisation. The final result of agricultural evaluation is a map, which partitions the landscapes into suitable and unsuitable areas for a particular land-use of interest. However, this approach may not represent the continuity of land. Land suitability could be better expressed by a fuzzy approach. In this paper a fuzzy methodology is used to evaluate land suitability in the Edgeroi district for various crops including barley, dryland cotton, oats, pasture, soybean, sorghum, sunflower, and wheat. This is achieved using a membership function to derive a land-suitability membership score ranging from non-suitable (i.e. 0) to suitable (i.e. 1). We express this as continuous land suitability maps using punctual kriging. An expression for overall land suitability (i.e. its versatility) and its capacity with respect to suitability to particular rotations is introduced to highlight the most productive units of soil.

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