Crop-Suitability Analysis Using the Analytic Hierarchy Process and Geospatial Techniques for Cereal Production in North India

Abstract

Identification of cropland suitability is obligatory to adapting to the increased food needs driven by population expansion, environmental contamination, and climate change. Given this, the present study was conducted to assess cereal crops’ suitability in India’s Haryana state by integrating Analytic Hierarchy Process (AHP) and geographic information system (GIS) technique. Multiple factors were considered for this study, such as rainfall, temperature, soil texture, drainage density, pH, organic carbon, electrical conductivity, and slope. The AHP technique was utilized to decide the weights of each individual parameter using experts’ opinions. The crop-suitability model was developed using the model builder module in ArcGIS 10.8, and each input parameter was reclassified as per the optimum crop-growth requirement and overlaid utilizing the reclassify tool and weighted overlay analysis. The crop suitability classes were estimated as highly suitable, S1 (6%); moderately suitable, S2 (71%); and marginally suitable, S3 (23%) for the calculated arable land for the wheat crop. Similarly, the crop suitability class of rice S2 (28%); S3 (72%), for sorghum S1 (28%); S2 (71%); S3 (1%), for maize S2 (85%); S3 (15%) and for pearl millet S1 (60%); S2 (40%) were also estimated. The study has observed that, as per the soil physico-chemical characteristics and climate, the area is moderately fertile. Therefore, agricultural production can be improved by cultivating the crop in highly and moderately suitable zones. Diversification of marginally suitable regions for crops other than that for which it is not suitable can be taken up, which will also ensure the income security of marginal farmers.

Crop suitability analysis by adopting geo-spatial algorithm: a case study of Sirajganj district (flood-prone area) in Bangladesh

“…It may be used to determine if the final result is resilient to minor modifications in the input data (Newham, Norton, Prosser, Croke, & Jakeman, 2003; Ticehurst, Cresswell, & Jakeman, 2003; Zoras, Triantafyllou, & Hurley, 2007). The three most popular methods for analyzing the sensitivity of a set of factors are modifying the factors' values, their relative importance and their weights (Shaloo et al. , 2022).…”

“…It may be used to determine if the final result is resilient to minor modifications in the input data (Newham, Norton, Prosser, Croke, & Jakeman, 2003;Ticehurst, Cresswell, & Jakeman, 2003;Zoras, Triantafyllou, & Hurley, 2007). The three most popular methods for analyzing the sensitivity of a set of factors are modifying the factors' values, their relative importance and their weights (Shaloo et al, 2022) weights of the factors using the "what-if" technique, sensitivity analysis has been performed in this study. Final maps, which show the spatial variations in suitability class, were created for each LUT after each factor was given equal weights by weighted overlay analysis.…”

Pearl Millet Crop Biophysical Parameter Retrieval From Space Borne Polarimetric SAR Data Using Machine Learning

“…The recent expansion in the Earth Observation systems pave better way of retrieving information than conventional survey (Rawat & Kumar, 2015). Especially in the context of agriculture, operational monitoring of crop using optical data has been well‐established for crop acreage, crop production forecasting with missions like CAPE (Crop Production and Acreage Estimation) (Ray et al., 2014), crop suitability (S. Kumar et al., 2013; Shaloo et al., 2022), risk assessment (Kiran Kumar, 2016) etc., through various Indian Missions like IRS, Resourcesat, other EO missions Sentinel‐1,2, LANDSAT. However, the availability of data during overcast or cloudy conditions during Kharif season is limited.…”

Agricultural land suitability assessment with GIS-based multi-criteria decision analysis and geostatistical approach in semi-arid regions

“…AHP is based on pairwise comparisons of factors according to their importance using a comparison scale on the decision hierarchy (Ramamurthy et al, 2020;Everest et al, 2021). In the evaluation of different ecological characteristics in land suitability analysis, the integration of GIS and AHP provides spatial analysis of the data and rational results according to the preferences of the decision maker (Pilevar et al, 2020;Şenol et al, 2020;Shaloo et al, 2022).…”