GIS-based fuzzy membership model for crop-land suitability analysis

Ahamed, Tofael; Rao, K. Gopal; Murthy, J. S. R.

doi:10.1016/s0308-521x(99)00036-0

Cited by 196 publications

(50 citation statements)

References 5 publications

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“…Fuzzy sets methodology in land evaluation has raised a lot of interest among researchers, since this method gives a more realistic output in comparison with the Boolean approach (Burrough, 1989;Tang et al, 1991;Burrough et al, 1992;Van Ranst et al, 1996 andNisar Ahmed et al, 2000). McBratney and Odeh (1997) discussed the application of fuzzy sets in soil science.…”

Section: Introductionmentioning

confidence: 99%

Soil suitability evaluation for crop selection using fuzzy sets methodology

Sharififar

Ghorbani

Sarmadian

2016

AAS

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In this study appraisal of four different agricultural land evaluation methods including the so-called Storie method, square root method, maximum limitation method and fuzzy sets method, was done. The study was performed in Bastam region, located in Semnan province at the north east of Iran. Three crops including tomato, wheat and potato were assessed for the purpose of this research. Soil characteristics assessed were rooting depth, CaCo 3, organic carbon content, clay content, pH and slope gradient. Statistical analyses were done at significance levels of α = 0.1 and α = 0.05. Results of regression between land indices, calculated through the four methods, with observed yields of the crops, showed that the regression were significant in fuzzy sets method for all of the assessed crops at p = 0.05 but not significant in maximum limitation method for any of the crops. The Storie and square root methods also showed a significant correlation with wheat yield at p = 0.1. This study was a demonstrative test of fuzzy sets theory in land suitability evaluation for agricultural uses, which revealed that this methodology is the most correct method in given circumstances.Key words: fuzzy sets, land evaluation, land use, soil classification, crop growth conditions, soil suitability, crop selection IZVLEČEK UPORABA METODOLOGIJE MEHKIH MNOŽIC PRI OCENI PRIMERNOSTI TAL ZA RAZLIČNE POLJŠČINEV raziskavi so bile ocenjene štiri metode vrednotenja kmetijskih zemljišč vključujoč Storijevo metodo, metodo kvadratnega korena, metodo maksimalne omejitve in metodo mehkih množic. Raziskava je potekala na območju Bastama, v provinci Semnan, v severovzhodnem delu Irana. Primernost zemljišč je bila ovrednotena za tri poljščine, paradižnik, pšenico in krompir. Ocenjene so bile naslednje lastnosti tal: globina koreninjenja, vsebnosti CaCO 3, organskega ogljika in gline, pH in naklon zemljišča. Statistične analize so bile narejene pri stopnji značilnosti α = 0,1 in α = 0.05. Rezultati regresijske analize za odvisnost pridelkov izbranih polščin od indeksov primernosti zemljišč, izračunanih po zgoraj omenjenih metodah je pokazala, da je statistično značilna samo odvisnost pridelka od indeksa primernosti zemljišč dobljenega z metodo mehkih množic" Storijeva metoda in metoda kvadratnega korena sta pokazali značilno odvisnost prideleka pšenice od indeksa primernosti zemljišč pri α = 0,1. Raziskava je bila demonstracijski preskus primernosti uporabe metode mehkih množic pri vrednotenju zemljišč v kmetijski rabi. Ugotovljeno je bilo, da je bila ta metodologija v danih okoliščinah najprimernejša.Ključne besede: mehke množice, vrednotenje zemljišč, raba tal, klasifikacija tal, razmere za rast poljščin, primernost tal, izbira poljščin

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Section: Introductionmentioning

confidence: 99%

Soil suitability evaluation for crop selection using fuzzy sets methodology

Sharififar

Ghorbani

Sarmadian

2016

AAS

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“…Alongside this pairwise comparison and the calculated weights, a consistency ratio is also calculated, which is an indicator of the quality of the subjective assessments made by the experts involved [5,11,27]. If the calculated CR is below the set threshold of 0.1, then the pairwise comparisons are relatively consistent and fit for use in the AHP.…”

Section: Intensity Rating Definitionmentioning

confidence: 99%

Evaluation of Deterministic and Complex Analytical Hierarchy Process Methods for Agricultural Land Suitability Analysis in a Changing Climate

Romeijn

Faggian

Diogo

et al. 2016

IJGI

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Land suitability analysis is employed to evaluate the appropriateness of land for a particular purpose whilst integrating both qualitative and quantitative inputs, which can be continuous in nature. However, in agricultural modelling there is often a disregard of this contiguous aspect. Therefore, some parametric procedures for suitability analysis compartmentalise units into defined membership classes. This imposition of crisp boundaries neglects the continuous formations found throughout nature and overlooks differences and inherent uncertainties found in the modelling. This research will compare two approaches to suitability analysis over three differing methods. The primary approach will use an Analytical Hierarchy Process (AHP), while the other approach will use a Fuzzy AHP over two methods; Fitted Fuzzy AHP and Nested Fuzzy AHP. Secondary to this, each method will be assessed into how it behaves in a climate change scenario to understand and highlight the role of uncertainties in model conceptualisation and structure. Outputs and comparisons between each method, in relation to area, proportion of membership classes and spatial representation, showed that fuzzy modelling techniques detailed a more robust and continuous output. In particular the Nested Fuzzy AHP was concluded to be more pertinent, as it incorporated complex modelling techniques, as well as the initial AHP framework. Through this comparison and assessment of model behaviour, an evaluation of each methods predictive capacity and relevance for decision-making purposes in agricultural applications is gained.

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“…The LSE is a prerequisite in achieving optimum utilization of the available land resources, while preserving highly suitable lands with high yields [24,25]. It collects and processes in mathematical equations climatic and other physical parameters that affect crop yields [26].…”

Section: Crop Yield Simulationmentioning

confidence: 99%

Integrating a Spatially Explicit Tradeoff Analysis for Sustainable Land Use Optimal Allocation

Zhang

Yang

et al. 2014

Sustainability

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To achieve agricultural sustainability, basic land planning would allocate more areas for specific land uses with high ecosystem service values. However, this has always failed because of its low economic interest. Paying farmers would be effective for its implementation. To overcome previous studies' limitation in spatial display, we developed a novel approach with a spatially explicit land use plan under payment policy. It integrates the tradeoff analysis into a traditional land use optimal allocation system. The land allocation system generates an origin land use scenario. Our method analyzes farmers' tradeoffs in changing crops by adopting payment policy. Finally, the origin land use map is reallocated. The newly reclaimed region of Yili, China is studied as a representative area. Our tool established a tradeoff curve indicating the adoption proportion of farmers changing other land use types to clover, which is a specific crop with high ecosystem service values, at different payment prices. Areas adopting the payment policy were identified, and corresponding spatial distribution maps of land use re-allocation were generated for the tradeoff curve. Sensitivity analysis validated the robustness of our model. Results demonstrated that our method can provide more spatial and economic information for sustainable land use planning. OPEN ACCESSSustainability 2014, 6 8910

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GIS-based fuzzy membership model for crop-land suitability analysis

Cited by 196 publications

References 5 publications

Soil suitability evaluation for crop selection using fuzzy sets methodology

Soil suitability evaluation for crop selection using fuzzy sets methodology

Evaluation of Deterministic and Complex Analytical Hierarchy Process Methods for Agricultural Land Suitability Analysis in a Changing Climate

Integrating a Spatially Explicit Tradeoff Analysis for Sustainable Land Use Optimal Allocation

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