Madras Agricultural Journal
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Research Article | Open Access | Peer Review

Exploring the Spatial Variability in Soil Macronutrients (NPK) of Coconut Research Station, Aliyar Nagar employing Geospatial Techniques

C. Sudhalakshmi R. Kumaraperumal K. Arulmozhiselvan N. Shoba
Volume : 104
Issue: March(1-3)
Pages: 49 - 53
DOI:
Downloads: 0
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Abstract


Knowledge on the soil variability of research stations is imperative for the assessment of land capability for specific land use and for the correlation of experimental data generated in various research trials. A study was undertaken in Coconut Research Station, Aliyar Nagar to characterize the spatial variability of available macronutrients (nitrogen (N), phosphorus (P) and potassium (K)) and to compute their respective nutrient indices. Two hundred and fifty eight geo-referenced soil samples were collected from the surface (0-15 cm) and subsurface (15-30 cm) layers of A, B and C blocks of the farm. The farm soil is predominantly sandy textured belonging to the taxonomic class Typic / Fluventic Ustropept. KMnO - N, Olsen P and 1NNH OAC K were estimated adopting the standard procedures. GIS aided fertility maps were prepared employing Arc-GIS software and nutrient indices were computed. Acute deficiencies of nitrogen and moderate deficiencies of phosphorus and potassium were observed in the soils. Sandy texture of the soil, intensive irrigation systems, paucity of organic matter, low base exchange status and kaolinitic clay mineral with poor potassium binding sites are the potential causes for nutrient deficiencies. Enhancing the organic matter load through residue recycling, split application of N and K fertilizers and water saving irrigation systems can reverse the above effect.

DOI
Pages
49 - 53
Creative Commons
Copyright
© The Author(s), 2025. Published by Madras Agricultural Students' Union in Madras Agricultural Journal (MAJ). This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited by the user.

Keywords


Fertility mapping GIS Nutrient index Spatial variability

Introduction


Soil, representing a continuum of diversified genetic processes and being one of the biggest natural heritages of mankind, deserves greater consideration than merely as a medium of plant growth. Plethora of evidence has accumulated stating that soil properties vary across farm fields, causing spatial variability in crop yields (Rockstrom et al., 1999; Gaston et al., 2001).

Conventional blanket use of fertilizer not only reduces nutrient use efficiency but also causes nutrient imbalances in soil, resulting in decreased crop yields (Ladha et al., 2005). Soil fertility evaluation in researcher-managed plots, wherein technologies emerge, requires a unified approach, and the results can be successful only if the evaluation is a continuous process.

There are various techniques for soil fertility evaluation, of which soil testing is the most widely accepted tool in the world (Havlin et al., 2010). Spatially and temporally dynamic soil management practices are important factors in a successful site-specific management strategy. For studies on the spatial distribution pattern of soil properties, techniques such as conventional statistics and geostatistics are widely applied (Liu et al., 2006).

The advent of information technology has provided tools like the Global Positioning System (GPS) and Geographical Information System (GIS), which help in collecting a systematic set of geo-referenced samples and generating spatial data about the distribution of nutrients (Sharma, 2004).

At Coconut Research Station, Aliyar Nagar, research on crops of varying phenology and variable nutrient demands, viz., coconut, groundnut, paddy, cocoa, pepper, tomato, and brinjal, are in vogue. Knowledge of the native soil fertility of the farm is imperative for efficient nutrient management and for correlating research findings generated through various research trials.

Hence, the study was framed with the main objective to explore, document, and map the spatial variability of soil macronutrients (N, P, and K) in Coconut Research Station, Aliyar Nagar.


 

Methodology


Coconut Research Station, Aliyar Nagar, is located in the Western Zone of Tamil Nadu at 10°28’ N latitude and 76°58’ E longitude at an altitude of 288 m. The annual precipitation of the region in which the station is located is 802 mm. The mean maximum and minimum temperatures are 34°C and 24°C, respectively.

The station has a total area of 21.7 ha with a cultivable area of 18.7 ha. The soils are sandy loam to sandy clay loam in texture and are taxonomically classified as Typic / Fluventic Ustropept. The major and predominant geological formations of Aliyar consist of granite gneiss, biotite gneiss, and charnockite.

The station comprises A, B, and C blocks. Geo-referenced surface (0-15 cm) and subsurface (15-30 cm) soil samples were collected from:

  • A Block: 14 fields
  • B Block: 17 fields
  • C Block: 12 fields

Samples were taken at three locations (L1, L2, and L3) within each field at a rate of six samples per field, totaling 258 samples (Figure 1).

Figure 1. Soil sampling locations of Coconut Research Station, Aliyar Nagar

The pH of the soil samples was neutral in reaction, and electrical conductivity was harmless throughout the farm. The soils are moderately calcareous with free CaCO₃ ranging from 3-15%.

Available nitrogen, phosphorus, and potassium were estimated as per the standard procedures outlined by:

  • Subbiah and Asija (1956) - Nitrogen
  • Olsen et al. (1954) - Phosphorus
  • Stanford and English (1949) - Potassium

The farm map was overlaid over QuickBird Satellite imagery (Figure 2) and digitized. A database file consisting of X and Y coordinates was created in Microsoft Excel. A shape file showing the sampling locations was created using ArcGIS software. The database file was then joined to the point data.

Thematic maps on available nutrient status (N, P, and K) were generated by categorizing the fertility status as ‘low’, ‘medium’, and ‘high’ using kriging.

Nutrient Index Calculation

Nutrient index is used to represent the soils under low, medium, and high categories. The nutrient indices of the soil samples were calculated employing the following formula:


Nutrient 
index = {(1 x number of samples in low status) + (2 x number of samples in medium status) + (3 x number of samples in high status)}/ 
Total number of samples.

Nutrient index value of less than 1.67 is considered as low, 1.67 to 2.45 as medium and more than 2.45 as high.

 

Results Discussion


KMnO₄ – N

The available nitrogen status of the farm soils ranged from 113 to 389 kg ha⁻¹ with a mean content of 233.6 kg ha⁻¹. The general trend noticed was that the soil samples were low to medium in available nitrogen status.

In the surface soil, 84% of the soil samples recorded low status, and 16% of the samples recorded high status of available nitrogen. In the sub-surface, about 92% of the samples had low status, 8% of the soil samples medium, and not even a single sample of the 258 had recorded high status of KMnO₄ - N.

In the surface samples of the fields A2, A3, A4, and A5, the status was medium as noted in the map, which is mainly because of in situ incorporation of groundnut haulms resulting in the subsequent build-up of organic matter, henceforth increasing the available nitrogen content of the soil (Figure 1).

Figure 1. Soil sampling locations of Coconut Research Station, Aliyar Nagar

Figure 2. Farm map overlaid over Quick Bird Satellite Imagery

Figure 3. Spatial variability of KMnO4 - N (kg ha-1) in the surface and subsurface samples of CRS, Aliyar Nagar

Figure 4. Spatial variability of Olsen -P (kg ha-1) in the surface and subsurface samples of CRS, Aliyar Nagar

Figure 5. Spatial variability of 1NNH4O-Ac (kg ha-1) in the surface and subsurface samples of CRS, Aliyar Nagar

Table 1. Nutrient index and interpretative groupings of the farm soils of CRS, Aliyar Nagar

The soils of the Western Ghats are reported to be poor in potassium fertility. The farm belongs to the region which is highly weathered and rich in kaolinitic clay mineral which has hardly any binding site for potassium (Venkatesan and Murugesan, 2006). The data further draws support from the poor cation exchange capacity of the soils of the farm which is 1-3 cmol (p⁺) kg⁻¹ soil. Hence, split application of potassic fertilizers and redefining potassium recommendations can alone go a long way in increasing the exchangeable pool of potassium in the sandy textured soils of the farm.

To conclude, nitrogen and phosphorus indices were low irrespective of the depth of sampling, whilst potassium index was medium in both the surface and subsurface layers of the farm. Sandy texture of the soils of the farm, flood irrigation systems, low water table, paucity of organic matter due to rapid mineralization, poor base exchange status, and kaolinitic clay mineral with poor potassium binding sites are the reasons staging nutrient deficiencies.

Revisiting fertilizer recommendations for sandy textured soils, building up the organic matter content through residue recycling, split application of N and K fertilizers, and water-saving irrigation systems hold great promise in improving the soil health of the farm.

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