Nutrient Variability Mapping and Demarcating Management Zones by Employing Fuzzy Clustering in Southern Coastal Region of Tamil Nadu, India

Declining soil organic carbon, inappropriate, imbalanced fertilizer application and intensive cropping patterns without replenishing soil nutrients has led to soil degradation and environmental contamination in various agroecosystems, which has led to a negative impact on humans, animals and aquatic ecosystem [1]. It has also led to diminishing soil fertility and crop productivity loss across the entire Indian geography [2]. The soils thus exhibit multi-nutrient deficiencies [3]. Being fertile alluvial land with sufficient moisture availability under a tropical climate, extensive cultivation of a more intensive nature is common in the Tamirabarani River basin of Southern India where the study area is located. Intensive cultivation without appropriate management as per site condition leads to soil fertility imbalance and deficiency of nutrients which, in turn, causes crop yield decline. Therefore, understanding the state of the soil is vital for sustainable agricultural production. Soil nutrient variability is one of the prime factors affecting crop growth and yield of crop. Soil surveys facilitate implementing appropriate management practices by providing holistic information on soil characteristics. Soil-specific management practices enhance land productivity and sustain environmental health.

Soil nutrient status is highly variable due to topography, soil type, vegetation, climate and different cultivation practices. Neglecting this variation, fertilizer is often applied in a blanket application that may lead to excessive or insufficient input application. Excess fertilizer application should be avoided to improve the crop yield with a less detrimental environmental impact. Hence, to manage the spatial variability, fertilizer dosage for a desired targeted yield has to be calibrated [4,5]. Therefore, facts about the distribution of nutrients are essential for efficient nutrient management and to achieve sustainable crop production [6,7]. An efficient technique for proper understanding of the variations in soil properties assigned within fields for designating homogeneous management zones should be established [8]. Delineating management zones with homogenous soil properties is the most identified technique for precise soil fertility management. However, to demarcate homogenous soil zones, assessment of the inherent soil nutrient status of an area is vital, as this aids sustainable management of crops. Several researchers utilized soil nutrient data to identify management zones [9,10,11,12].

Numerous techniques and tools were employed to define the management zones. Typically, low-cost sensing data—such as electrical conductivity data [13] and remotely sensed data [14]—and digital elevation models [15,16], topographical and soil maps [17], crop yield [18,19], nutrient index methods [20] and geostatistics-based nutrient management zone methodology [21] were used conventionally for demarcating management zones for appropriate management. These methods help to identify critical nutrients that limit crop productivity [22]. To divide a land into potential zones for crop management, Speranza et al. [23] employed yield data, whereas Shukla and Sharma [24] used fuzzy clustering of soil parameters.

Principal component analysis (PCA) minimizes the data redundancy and aids in grouping data through clustering process [25,26]. Amongst other approaches, fuzzy clustering was utilized by several researchers to identify alike management zones [27,28,29]. These are the two major steps involved in demarcating soil management zones.

Among the soil properties, the CV values for available Zn, Cu, Mn, SOC and EC were greater than those for available K, P, N and Fe content. High variations in soil micronutrients may be attributed to the depletion of micronutrients as a result of nutrient mining [50]. The soil parameters showed high spatial variation within the study region and the need for appropriate nutrient management, according to spatial variability, to optimize crop management. Significant correlations existed between the majority of the soil properties. The SOC is considered as an important property that influences availability of nutrients. Metwally et al. [51] reported positive correlation between SOC and N and P. Correlation studies on soil properties revealed that PCA is the ideal tool for figuring out the primary causes of variability in data.

The best-fit semivariogram model was spherical. Additionally, researchers observed that spherical models are well suited to represent major soil parameters [31,52]. The spatial variability map of soil properties pH, EC, K and Cu were highly accurate as compared to other soil properties. The finding demonstrated that there is spatial autocorrelation in the soil parameters. It is attributed to environmental factors such as closeness to the river Tamirabarani, farming systems, fertilization and management practices implemented for farming [53]. pH, SOC, Fe, Zn, Cu and Mn had modest geographical dependence, but EC, K, P and N recorded large geographical dependence. Strong spatial dependency for EC, N, P and K is attributed to closeness to the sea coast and prevailing climatic conditions, whereas moderate spatial dependence of the soil properties is ascribed to the intrinsic soil characteristics, differences in farming techniques and soil fertilization. The spatial distribution maps showed high variations of soil nutrients, which are ascribed to land use and management. The PCA aggregated ten variables into three PCs to account for gross spatial variability in these properties. The technique used to delineate management zones solely considered the available nutrients and spatial information in the PCA.

The management zone map indicates four fertility management zones, as shown in Figure 6. ANOVA was performed to evaluate the combined effect of PCA and the fuzzy cluster algorithm in delineating management zones. The four MZs that were produced were distinct from one another. A similar approach was adopted by other researchers [25,28]. The low status of N may be ascribed to the mining of nutrients due to the continuous cultivation of paddy without replenishing it with organic matter. The eastern study area is affected by seawater intrusion through the Bay of Bengal and may supplement the soil with K. Except MZ1, K deficiency was less widespread in the research area [31].