Combination of fuzzy-AHP and GIS techniques in land suitability assessment for wheat (Triticum aestivum) cultivation

Kiliç, Orhan; Ersayın, Kemal; Günal, Hikmet; Khalofah, Ahlam; Alsubeie, Moodi Saham

doi:10.1016/j.sjbs.2021.12.050

Cited by 50 publications

(19 citation statements)

References 50 publications

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“…Accordingly, a systematic methodology has been introduced to assess the suitability of FCAM in hilly terrain by using a machine learning approach. In addition, compared with traditional methods that weight the indications and criteria based on a single perspective (e.g., the experts’ evaluation, AHP) ( Bozdağ et al., 2016 ; Han et al., 2021 ; Kilic et al., 2022 ), this approach has a clear advantage in that it adjusts the weights according to dynamic changes in the environment. This can improve the effectiveness and feasibility of the weighting, enhancing the capacity of local decision-makers to determine the suitability of FCAM.…”

Section: Discussionmentioning

confidence: 99%

Assessing farmland suitability for agricultural machinery in land consolidation schemes in hilly terrain in China: A machine learning approach

Heng

Ma²,

Liu³

et al. 2023

Front. Plant Sci.

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Identifying available farmland suitable for agricultural machinery is the most promising way of optimizing agricultural production and increasing agricultural mechanization. Farmland consolidation suitable for agricultural machinery (FCAM) is implemented as an effective tool for increasing sustainable production and mechanized agriculture. By using the machine learning approach, this study assesses the suitability of farmland for agricultural machinery in land consolidation schemes based on four parameters, i.e., natural resource endowment, accessibility of agricultural machinery, socioeconomic level, and ecological limitations. And based on “suitability” and “potential improvement in farmland productivity”, we classified land into four zones: the priority consolidation zone, the moderate consolidation zone, the comprehensive consolidation zone, and the reserve consolidation zone. The results showed that most of the farmland (76.41%) was either basically or moderately suitable for FCAM. Although slope was often an indicator that land was suitable for agricultural machinery, other factors, such as the inferior accessibility of tractor roads, continuous depopulation, and ecological fragility, contributed greatly to reducing the overall suitability of land for FCAM. Moreover, it was estimated that the potential productivity of farmland would be increased by 720.8 kg/ha if FCAM were implemented. Four zones constituted a useful basis for determining the implementation sequence and differentiating strategies for FCAM schemes. Consequently, this zoning has been an effective solution for implementing FCAM schemes. However, the successful implementation of FCAM schemes, and the achievement a modern and sustainable agriculture system, will require some additional strategies, such as strengthening farmland ecosystem protection and promoting R&D into agricultural machinery suitable for hilly terrain, as well as more financial support.

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Section: Discussionmentioning

confidence: 99%

Assessing farmland suitability for agricultural machinery in land consolidation schemes in hilly terrain in China: A machine learning approach

Heng

Ma²,

Liu³

et al. 2023

Front. Plant Sci.

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“…Soil chemical characteristics such as soil pH, EC, SAR, CaCO 3 , nutrients were preferred by researchers in many studies due to their effect on nutrient availability, root growth and fertility (Karaca et Kılıc et al 2022). Intensive use of chemical fertilizers, especially nitrogen fertilizers, for agricultural production in the study area (İbrikçi et al 2015) highlights soil pH as the core soil properties responsible for nutrient availabilities in the area (Zurovec et al 2021).…”

Section: Soil Sampling and Analysesmentioning

confidence: 99%

“…The spatial dependency used to express the extent of spatial changes within the study area indicated strong, moderate or weak degrees of spatial dependence when it is less than 25%, between 25 and 75% and higher than 75%, respectively (Cambardella et al 1994). The spatial dependence of SQI had a strong degree an indication of an ongoing similarity between samples even over long distances (Kılıc et al 2022). In addition, the SQI of arable and horticultural land had a normal distribution and low CV in the study area (Table 4).…”

Section: Soil Quality Assessment and Spatial Variability Of Sqimentioning

confidence: 99%

Effect of long-term different land use on soil quality in an Entic Chromoxerert

Acar

Çelik

Budak

et al. 2022

Preprint

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The differences in land use can potentially affect soil quality. In this respect, this study sought to assess the long-term effect of horticulture and arable lands on the soil quality of the clayey Arıklı soils (Entic Chromoxerert). Physical, chemical, biological and fertility indicators of the soil were analyzed to determine the soil quality index (SQI) using the Fuzzy Analytical Hierarchy Process (FAHP) and Geographical Information System (GIS) for a 225 ha area of the Arıklı soil series. The physical quality received the highest weight in the study. The highest weights in physical, chemical, biological and fertility quality were obtained in AWC, pH, OC and P indicators, respectively. SQI in arable lands ranged from 38.60 to 68.91%, with a mean of 51.77%. Compared to arable, SQI in horticulture varied within a narrower range and had a higher mean SQI (59.87%). In this study, 74.2% of the area had a low SQI and 25.8% had medium SQI. Conventional soil tillage and burning of stubble residues in the arable lands had a statistically negative effect on SQI. Compared to arable, in horticulture BD, OC, AWC, MBC, BGEA and K had better scores thus improving SQI. The results concluded that conventional soil tillage practice should be abandoned and reduced or no-till practices should be adopted.

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“…To date, fuzzy combined with MODM for land suitability assessment has not been widely adopted. Most researchers combined fuzzy and MADM such as AHP (Keshavarzi et al, 2020;Kılıc et al, 2022;Mosadeghi et al, 2015;Nasery et al, 2021;Paul & Ghosh, 2022;Sengupta et al, 2022;Zalhaf et al, 2021) due to the simple application and easy implementation. However, in fuzzy-MADM, the weight of the indicator is usually determined subjectively by the researcher or in accordance with expert opinions.…”

Section: Introductionmentioning

confidence: 99%

Land suitability assessment for agricultural crops in Enrekang, Indonesia: combination of principal component analysis and fuzzy methods

Sappe

Baja

Neswati

et al. 2022

Sains Tanah J. Soil Sci. Agroclimatology

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<p>Land suitability assessment is essential for the efficient use of diminishing fertile agricultural land. Assessment parameters include soil texture, pH, the sum of basic cations, base saturation, cation exchange capacity, organic carbon, soil depth, slope, and mean annual temperature and precipitation data. Results showed that 76.28% and 23.26% of the total area were optimally and moderately suitable for coffee growth, respectively; 9.6% and 90% were optimally and moderately suitable for cocoa growth, respectively; 1.98%, 78.74%, and 19.26% were optimally, moderately, and marginally suitable for clove growth, respectively; and 6.68%, 86.89%, and 6.41% was optimally, moderately, and marginally suitable for pepper growth, respectively. The final land suitability index (LSI) was strongly influenced by the threshold values used by the researcher and the quality of the land indicator itself. Plant threshold values differed due to variations in plant recruitment. The main limiting factors were mean annual temperature <26°C, acidic soil pH, and low CEC. This study showed that the fuzzy method is ideal for converting the numerical data of various magnitudes into membership function values and representing land suitability. The principal component analysis is an effective method to determine the weights of multiple factors in a systematic and objective manner. The linearity test found a correlation between LSI and production with f = 0.00, indicating that the applied model can predict agricultural production and is applicable to other agricultural land management.</p>

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Combination of fuzzy-AHP and GIS techniques in land suitability assessment for wheat (Triticum aestivum) cultivation

Cited by 50 publications

References 50 publications

Assessing farmland suitability for agricultural machinery in land consolidation schemes in hilly terrain in China: A machine learning approach

Assessing farmland suitability for agricultural machinery in land consolidation schemes in hilly terrain in China: A machine learning approach

Effect of long-term different land use on soil quality in an Entic Chromoxerert

Land suitability assessment for agricultural crops in Enrekang, Indonesia: combination of principal component analysis and fuzzy methods

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