Land Suitability Prediction Modelling for Agricultural Crops Using Machine Learning Approach: A Case Study of Khuzestan Province, Iran

Authors: Saba Gachpaz, Hamid Reza Heidari

Abstract:

The sharp increase in population growth leads to more pressure on agricultural areas to satisfy the food supply. This necessitates increased resource consumption and underscores the importance of addressing sustainable agriculture development along with other environmental considerations. Land-use management is a crucial factor in obtaining optimum productivity. Machine learning is a widely used technique in the agricultural sector, from yield prediction to customer behavior. This method focuses on learning and provides patterns and correlations from our data set. In this study, nine physical control factors, namely, soil classification, electrical conductivity, normalized difference water index (NDWI), groundwater level, elevation, annual precipitation, pH of water, annual mean temperature, and slope in the alluvial plain in Khuzestan (an agricultural hotspot in Iran) are used to decide the best agricultural land use for both rainfed and irrigated agriculture for 10 different crops. For this purpose, each variable was imported into Arc GIS, and a raster layer was obtained. In the next level, by using training samples, all layers were imported into the python environment. A random forest model was applied, and the weight of each variable was specified. In the final step, results were visualized using a digital elevation model, and the importance of all factors for each one of the crops was obtained. Our results show that despite 62% of the study area being allocated to agricultural purposes, only 42.9% of these areas can be defined as a suitable class for cultivation purposes.

Keywords: Land suitability, machine learning, random forest, sustainable agriculture.

References:

[1] Li, H., et al. (2020). "The framework of an agricultural land-use decision support system based on ecological environmental constraints." Science of the Total Environment 717.
[2] Elaalem, M. and A. Comber (2010). Land Evaluation Techniques Comparing Fuzzy AHP with TOPSIS methods. 13th AGILE International Conference on Geographic Information Science 2010. Guimarães, Portugal.
[3] Valizadeh, N. and D. Hayati (2021). "Development and validation of an index to measure agricultural sustainability." Journal of cleaner production 280.
[4] T.N., P. (2003). Land suitability analysis for agricultural crops: A fuzzy multicriteria decision making approach Netherlands international institute for geo-information and earth observation master degree.
[5] Baja, S., et al. (2006). "Fuzzy Modelling of Environmental Suitability Index for Rural Land Use Systems: An Assessment Using A GIS." Division of Geography, School of Geosciences, The University of Sydney.
[6] Liu, Z., et al. (2019). "Toward sustainable crop production in China: An energy-based evaluation." Journal of cleaner production 206: 11-26.
[7] Girma, R. and A. Moges (2015). "GIS Based Physical Land Suitability Evaluation for Crop Production in Eastern Ethiopia: A Case Study in Jello Watershed." Agrotechnology 5(1).
[8] Taghizadeh-Mehrjardi, R., et al. (2020). "Land Suitability Assessment and Agricultural Production Sustainability Using Machine Learning Models." agronomy 10(4).
[9] Mustafa, A. A., et al. (2011). "Land Suitability Analysis for Different Crops: A Multi Criteria Decision Making Approach using Remote Sensing and GIS." Researcher 3(12).
[10] Gebre, S. L., et al. (2021). "Multi-criteria decision making methods to address rural land allocation problems: A systematic review." International Soil and Water Conservation Research.
[11] Abd El-Hameed, A. H., et al. (2013). "Land suitability classification of some Qalubiya soils." Annals of Agric. Sci., Moshtohor 51(2).
[12] Zhai, Z., et al. (2020). "Decision support systems for agriculture 4.0: Survey and challenges." Computers and Electronics in Agriculture 170.
[13] AL-Taani, A., et al. (2021). "Land suitability evaluation for agricultural use using GIS and remote sensing techniques: The case study of Ma’an Governorate, Jordan." The Egyptian Journal of Remote Sensing and Space Sciences 24(1): 109-117.
[14] Akbari, M., et al. (2019). "Evaluating land suitability for spatial planning in arid regions of eastern Iran using fuzzy logic and multi-criteria analysis." Ecological Indicators 98: 587-598.
[15] Sharma, R., et al. (2020). "A systematic literature review on machine learning applications for sustainable agriculture supply chain performance." Computer and operations research 119.
[16] Rahmati, O., et al. (2019). "Land subsidence modelling using tree-based machine learning algorithms." Science of the total environment 672.
[17] Komlavi Akpotia, et al. (2019). "Agricultural land suitability analysis: State-of-the-art and outlooks for integration of climate change analysis." agriculture systems 173: 172-208.
[18] Habibie, M. I., et al. (2020). "Development of micro-level classifiers from land suitability analysis for drought-prone areas in Indonesia." Remote Sensing Applications: Society and Environment 20.
[19] Mollaloa, A., et al. (2018). "Machine learning approaches in GIS-based ecological modeling of the sand fly Phlebotomus papatasi, a vector of zoonotic cutaneous leishmaniasis in Golestan province, Iran." Acta Tropica 188: 187-194.
[20] Feyisa, G. L., et al. (2020). "Characterizing and mapping cropping patterns in a complex agro-ecosystem: An iterative participatory mapping procedure using machine learning algorithms and MODIS vegetation indices." Computers and Electronics in Agriculture 175.
[21] Pilevar, A. R., et al. (2020). "Integrated fuzzy, AHP and GIS techniques for land suitability assessment in semi-arid regions for wheat and maize farming." Ecological Indicators 110.
[22] Karamidehkordi, E. (2010). "A Country Report: Challenges Facing Iranian Agriculture and Natural Resource Management in the Twenty-First Century." Human Ecology 38(2): 295-303.
[23] Ashraf, S., et al. (2021). "Anthropogenic drought dominates groundwater depletion in Iran." Scientific Reports 11(1): 9135.
[24] Masroor, M., et al. (2021). "The impact of drought conditions on groundwater potential in Godavari Middle Sub-Basin, India using analytical hierarchy process and random forest machine learning algorithm." Groundwater for Sustainable Development 13.
[25] Pant, J., et al. (2021). "Analysis of agricultural crop yield prediction using statistical techniques of machine learning." Materials Today: Proceedings.
[26] Serrano, J., et al. (2019). "Evaluation of Normalized Difference Water Index as a Tool for Monitoring Pasture Seasonal and Inter-Annual Variability in a Mediterranean Agro-Silvo-Pastoral System." Water 11(62).
[27] Ali, M. I., et al. (2019). "Detection of Changes in Surface Water Bodies Urban Area with NDWI and MNDWI Methods." international journal of advanced science engineering information technology 9(3): 946-951.
[28] FAO (2015). World reference base for soil resources 2014, International soil classification system for naming soils and creating legends for soil maps. Italy, Rome, Food and Agriculture Organization of the United Nations.
[29] FAO (1976). "A framework for land evaluation " FAO soils bulletin 32.