Combining SWOT analysis and neutrosophic cognitive maps for multi-criteria decision making: a case study of organic agriculture in India

Soft Computing https://doi.org/10.1007/s00500-023-08097-w APPLICATION OF SOFT COMPUTING Combining SWOT analysis and neutrosophic cognitive maps for multi-criteria decision making: a case study of organic agriculture in India Jagan Obbineni1 · Ilanthenral Kandasamy2 · W. B. Vasantha2 · Florentin Smarandache3 Accepted: 23 March 2023 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023 Abstract The conventional agricultural system heavily depends on chemicals and inorganic fertilizers, which cause environmental issues. Organic agriculture impacts 6 of the 17 Sustainable Developmental Goals (SDGs) of the United Nations. Strategies to develop organic agriculture have used SWOT and MCDM techniques for analysis. However, the examination of the influence of one strategy over the other strategies has yet to be investigated. This paper proposes a model that combines the existing SWOT analysis with neutrosophic cognitive maps (NCM) models to analyze interconnections among the various strategies obtained from SWOT. This research deploys the proposed SWOT–NCM model to analyze the case study of developing organic farming in Tamil Nadu, India. It offers insights into the strategy’s influence over other strategies so that the best is given maximum importance while implementing organic farming. The framework captures the interconnections and ranks the strategies by order of influence, providing fresh insights by taking the farmers’ perspective while working with the strategies from the SWOT analysis to model an NCM. A comparative analysis of this SWOT–NCM model with other MCDM models that use SWOT to analyze the agriculture problem, and a sensitivity analysis of the proposed model, is performed. According to our study, the best possible strategy to encourage organic farming is minimum support price (MSP) and centralized procurement. This proposed model can analyze other MCDM problems that use SWOT analysis. Keywords Sustainable organic farming · SDGs · SWOT · Neutrosophy · Neutrosophic cognitive maps (NCMs) · Multi-criteria decision making 1 Introduction B Ilanthenral Kandasamy Jagan Obbineni W. B. Vasantha Florentin Smarandache 1 VIT School of Agricultural Innovations and Advanced Learning (VAIAL), VIT, Tiruvalam Rd, Vellore, Tamil Nadu 632014, India 2 School of Computer Science and Engineering (SCOPE), VIT, Tiruvalam Rd, Vellore, Tamil Nadu 632014, India 3 Department of Mathematics, University of New Mexico, 705 Gurley Avenue, Gallup, NM 87301, USA The existing global agricultural system relies on chemicals and fertilizers that have boosted productivity (Tsvetkov et al. 2018), but over the years, it has created a significant imbalance in nature. There have been detrimental effects on the environment, and society (Udeigwe et al. 2015), ranging from soil degradation (Liu et al. 2018), water pollution (Cai et al. 2018), biodiversity loss (Wintermantel et al. 2019; Shakoor et al. 2018) and harmful effects on human health (Nicolopoulou-Stamati et al. 2016). The United Nations, in its 2030 Agenda (Palmer 2015) (United Nations General Assembly, 2015), listed 17 Sustainable Development Goals (SDGs) for sustainable development for all without leaving anyone behind. Nearly six goals are connected directly to food production, consumption and environmental issues (SDGs 2, 3, 6, 13, 14 and 15 listed in Fig. 1a). Since organic agriculturalist refrains from using agrochemicals and produce in harmony with nature, it 123 J. Obbineni et al. Fig. 1 a Sustainable organic farming and its impact: The impact of organic farming on food production and consumption influences SDG 2(Zero Hunger) and SDG 3 (Good Health and Well-Being). Its impact on the environment indirectly influences SDG 6 (Clean Water and San- itation), SDG 13 (Climate Action), SDG 14 (Life Below Water) and SDG 15 (Life on Land). b The area of organic farming in hectares (ha) in Tamil Nadu during the past 6 years (2016–2021) is natural that organic farming is a feasible solution (Smith and Lampkin 2018). As a global community that is seriously involved in achieving the SDGs by 2030, we must shift to organic farming, which is more sustainable. According to the surveys conducted by the Research Institute of Organic Agriculture, nearly only 1.4% of the world’s farmland is under organic farming. Organic farmlands have increased in China and Argentina, and it has decreased in Ukraine and Iran. Although there is no significant expansion in organic farmland, the tendency to prefer organic products has increased due to consumers making an informed choice in purchasing organic products (Bryła 2016; JarczokGuzy 2018). Thus, organic farming development contributes to the goals of sustainable agricultural development and aids in meeting consumers’ demand for organic products in local and global markets. The annual report 2020–21 issued by the Indian Government1 states that nearly 54.6% of the Indian workforce is involved in the agricultural sector, and it accounts for nearly 18% of Gross Value Added (GVA). Also, 60% of the land area is agricultural farmland. Hence, it is well established that agriculture plays a vital role in India’s economy. Currently, in India, only 4.34 million hectares are under organic farming, which is lower than the 2015–16 total area of 5.71 million hectares and only 3.1% of the 140 million hectares of the net cultivable land area. In Tamil Nadu, it is estimated that an area of 41,619 hectares is under organic cultivation, which is about 0.8% of the net sown area in the state.2 Figure 1b shows the trend in Tamil Nadu for the past five years. Despite efforts to promote organic agriculture, the progress in this direction is plodding, and the state has shown a meager increase in area under organic cultivation year on year. Hence, it is crucial to determine and select the most promising techniques or strategies for developing organic farming based on a comprehensive list of various factors. Several researchers have studied the various strategies for organic farming development. Implementation of innovative technologies (Ferreira et al. 2020), support for scientific research methods (Tsvetkov et al. 2018), governmental support and subsidies to farmers (Adams Inkoom 2017), creating awareness among customers and farmers (Aghasafari et al. 2020) and management of organic farming constraints and various modifications to the regulatory standards (Brzezina et al. 2017), were some of the strategies that researchers suggested. Other investigations suggested green marketing (Aceleanu 2016), the establishment of organic certification institutions (Adebiyi 2014) and trade policies (KhezriNejhad Gharaei and Bakhshoudeh 2014). These studies use qualitative and quantitative techniques such as statistical analysis, mathematical modeling for regression, equations and summary information. They do not consider all the other factors influencing organic agribusiness in a region, (...truncated)