Description:FIELD OF THE INVENTION
The present invention relates to soil testing and fertility assessment, particularly to a novel method for determining optimal soil sampling size and defining homogeneous zones within a field. The invention enhances soil nutrient management through a statistically sound approach, ensuring accurate soil sampling and effective fertilizer recommendations.
BACKGROUND OF THE INVENTION
Soil testing is essential for assessing soil fertility, recommending fertilizers, and identifying suitable soil amendments. The process involves soil sample collection, testing, interpretation, and fertilizer recommendation. The accuracy of soil testing largely depends on the representativeness of the collected soil samples. Unrepresentative samples can lead to either over-application or under-application of fertilizers, resulting in economic losses and potential environmental harm. In soil sample collection, determining where to collect samples (sampling spots) and how many samples to collect (sampling size) is influenced by field variability. This variability can be characterized by factors such as soil texture, color, slope, and stoniness, which are key determinants of soil fertility variability. Variations in soil fertility create heterogeneity within fields, complicating effective nutrient management. Conventional site-specific nutrient management (SSNM) approaches, which aim for precision nutrient application, often fail to address the challenges posed by high variability and heterogeneity in soil fertility. Therefore, diagnosing soil fertility variability and heterogeneity is crucial. This diagnosis helps in delineating soil sampling zones, determining appropriate sampling densities, and adopting an improved SSNM approach. Such measures ensure higher profitability, reduced environmental pollution, and optimized fertilizer use. good format
Currently, there is no universally accepted method for determining the optimal soil sampling size for collection and testing. Soil samples are generally collected based on field characteristics such as texture, stoniness, color, and slope. According to the guidelines of the Soil Health Card Scheme, soil samples are to be collected from a 2.5-hectare area in irrigated regions and a 10-hectare area in rainfed regions. However, these recommendations have been widely criticized by soil scientists for their inability to account for soil nutrient variability within fields effectively.
In practice, it is often suggested to collect 8–16 soil samples per hectare to minimize heterogeneity, but this guideline lacks a robust statistical foundation. Within descriptive statistics, the maximum curvature method is available to determine sampling size. This method utilizes the coefficient of variation (CV) to analyze optimal homogenous zones or plot sizes for soil sampling. However, the coefficient of variation, while useful, is not an absolute measure of field variability, limiting its applicability in soil sampling size determination. To address these challenges, a statistically sound method is needed to accurately define soil sampling size, ensuring effective representation of field variability. This will enable more precise soil nutrient management and contribute to improved agricultural productivity and sustainability.
1. Guideline for implantation of soil health card scheme. 2015. Govt. of India
2. Masood, M. A and Raza, I. 2012. Estimation of optimum field plot size and shape in paddy yield trial. American-Eurasian Journal of Scientific Research.7(6), 264-269.
COMPARISON
1. Spatial variance provides an analysis of absolute spatial heterogeneity, offering a precise understanding of field variability.
2. It is more reliable than the coefficient of variation, especially when datasets include zero, negative, or near-zero values, where the coefficient of variation becomes invalid or misleading.
3. Spatial variance can identify missing or erroneous values in the dataset, ensuring accurate and dependable results.
4. Both the values in the dataset and the spatial distances between data points are utilized in the calculation of spatial variance, making it a comprehensive method for assessing variability.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
The present invention provides a novel statistical method for soil sampling that effectively accounts for soil fertility variability and heterogeneity. By utilizing spatial variance analysis, the method divides a field into homogeneous zones, ensuring optimal soil sampling size for accurate fertility assessment.
The method involves subdividing a given field into multiple small subplots and collecting soil samples from each subplot using GPS coordinates. Soil samples are analyzed for key fertility parameters, including pH, electrical conductivity, organic carbon, and available macronutrients and micronutrients. The analyzed data is then converted into a geospatial format using GIS software for further processing.
The invention introduces a systematic approach for subplot combination, ensuring comprehensive coverage of field variability. By progressively merging subplots into larger units, the method calculates spatial variance for different plot sizes, enabling the identification of the optimal homogeneous zones. This approach addresses the challenges of soil fertility heterogeneity by providing a statistically valid determination of soil sampling size, ensuring precision in nutrient application.
Through the use of geostatistical tools such as Kriging, the method determines the spatial distribution of soil parameters and establishes an optimum soil sampling framework. The process results in the delineation of field-specific homogeneous zones, allowing for precise soil sampling and targeted fertilizer recommendations.
By improving the accuracy of soil fertility assessment, this invention minimizes nutrient mismanagement, reduces environmental pollution, and enhances agricultural productivity. The method is applicable across diverse soil types and cropping systems, making it a valuable tool for precision farming and sustainable agriculture.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
Soil fertility variation and heterogeneity within a field pose significant challenges to effective soil sampling and testing. This novel statistical method addresses these issues by identifying soil fertility variability and dividing the field into homogeneous zones. The number of homogeneous zones corresponds to the required number of soil samples, ensuring representative sampling for accurate soil analysis.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: FLOW CHART FOR ANALYSIS OF SPATIAL VARIANCE BY GEOSTATISTICAL METHOD
FIGURE 2: PLOTTING OF CURVE
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The proposed invention introduces a statistically sound methodology for soil sampling by assessing field heterogeneity through spatial variance analysis. The method involves several key steps, including subplot division, soil sampling, geospatial data processing, subplot combination, spatial variance analysis, and determination of homogeneous zones.
The process begins by dividing a one-hectare field into 200 subplots, each covering 50 square meters. Soil samples are collected from each subplot using GPS to ensure precise location tracking. The collected samples undergo laboratory analysis for soil parameters such as pH, electrical conductivity, organic carbon, available macronutrients (nitrogen, phosphorus, potassium, sulfur), and essential micronutrients (iron, manganese, zinc, copper). The results are compiled into a CSV file and imported into GIS software for geospatial processing.
In GIS, the data is mapped and reprojected to a standardized coordinate reference system. The subplots are then combined into progressively larger units following structured row and column configurations. Spatial variance is calculated for each new unit using geostatistical methods, ensuring a comprehensive analysis of field variability. The process continues until the entire field is assessed, enabling the identification of optimal homogeneous zones.
Using spatial variance analysis, the method determines the point of maximum curvature on a variance-plot size curve. This critical point represents the optimal soil sampling size, ensuring that each homogeneous zone accurately reflects field fertility. The final outcome is a precise delineation of sampling zones, allowing for site-specific nutrient management.
Soil fertility variation and heterogeneity within a field pose significant challenges to effective soil sampling and testing. This novel statistical method addresses these issues by identifying soil fertility variability and dividing the field into homogeneous zones. The number of homogeneous zones corresponds to the required number of soil samples, ensuring representative sampling for accurate soil analysis.
The stepwise procedure for developing homogeneous zones is outlined below:
Step 1: Subplot Division
• Divide a 1-hectare area into 200 subplots, with each subplot covering 50 m².
(1ha =10,000 m2;10,000÷200=50 m2)
Step 2: Soil Sampling
• Collect soil samples from each of the 200 subplots using GPS to record latitude and longitude coordinates.
Step 3: Soil Parameter Analysis
• Analyze soil samples for the following parameters:
o pH, Electrical Conductivity (EC), Organic Carbon (OC)
o Available Nitrogen (AN), Available Phosphorus (AP), Available Potassium (AK), Available Sulphur (AS)
o Iron (Fe), Manganese (Mn), Zinc (Zn), Copper (Cu)
• Organize the results in a tabular format in Excel and save the file in CSV format.
ID No. Latitude
(X) Longitude (Y) pH EC
dS m 2 OC
(%) AN
Kg ha-1 AP
Kg ha-1 AK
Kg ha-1 AS
ppm Fe
ppm Mn
ppm Zn
ppm Cu
ppm


Step 4: Data Conversion in QGIS
1. Open QGIS software and import the CSV file as a delimited text layer:
o Navigate to Layer → Add Layer → Add Delimited Text Layer.
o Set File Format to CSV, and map columns to their respective fields (e.g., latitude and longitude).
o Define the geometry CRS as EPSG: 4326-WGS 84.
o Add the layer to the project and reproject it to UTM Zone 43N using the Data Management Tool in the Vector menu.
2. Save the output as a shapefile for further analysis.
Step 5: Subplot Combination for New Units
• To analyze spatial variance effectively, subplots are combined into new units using different row and column configurations. This process ensures all rows and columns are included in the analysis, without leaving any subplot unaccounted for. The procedure is as follows:
• Initial Setup:
 For a 1-hectare area divided into 200 subplots, there will be 20 rows and 10 columns (200 subplots = 20 × 10).
 The minimum area for calculating spatial variance is set at 1000 m².
• Forming New Units:
 Start with the smallest possible new unit, which is 20 subplots, and experiment with different row × column combinations. Examples include:
 20×1 (20 rows, 1 column)
 2×10 (2 rows, 10 columns)
 4×5 (4 rows, 5 columns)
 5×4 (5 rows, 4 columns)
 Each combination of rows and columns will have a different spatial variance, which needs to be calculated.
• Progressively Larger Units:
 Continue forming larger units by increasing the number of subplots combined, up to a maximum of 200 subplots. For instance, units can be formed for 40, 60, 80 subplots, and so on.
• Handling Non-Divisible Units:
 For subplot numbers that cannot be evenly divided into rows and columns (e.g., 110, 130, 150, 170, 190), group the subplots as a single unit. Any remaining subplots can be grouped into another unit.
• Determine Spatial Variance:
 Calculate the spatial variance for each newly formed unit using geostatistical methods.
Step 6: Spatial Variance Analysis
1. Open the reprojected shapefile in ArcGIS.
2. Use the Geostatistical Analyst Tool to perform spatial variance analysis:
o Select Kriging/Cokriging in the Geostatistical Wizard and apply Ordinary Kriging.
o Input data, select a soil parameter (e.g., pH), and check for normality; apply transformations if necessary.
o Generate a semivariogram and record geostatistical parameters: Range (distance), Sill (Spatial variance), and Nugget (error).
3. Repeat the analysis for all soil parameters and subplot combinations.
Step 7: Determination of Homogeneous Zones
• Plot a curve between spatial variance (y-axis) and plot size (x-axis).
• Identify the point of maximum curvature, which represents the optimum plot size for homogeneous zones.
o For instance, if the optimum size is 2000 m², divide the field into 5 homogeneous zones for sampling: 10,000 m2 ÷ 2000 m2 = 5 zones
• Collect one soil sample from each homogeneous zone.
This methodology ensures the identification of field heterogeneity, optimal soil sampling density, and reliable soil fertility assessment, contributing to precision nutrient management for sustainable agriculture.

Rows Columns
1 2 3 4 5 6 7 8 9 10
1 1 21 41 61 81 101 121 141 161 181
2 2 22 42 62 82 102 122 142 162 182
3 3 23 43 63 83 103 123 143 163 183
4 4 24 44 64 84 104 124 144 164 184
5 5 25 45 65 85 105 125 145 165 185
6 6 26 46 66 86 106 126 146 166 186
7 7 27 47 67 87 107 127 147 167 187
8 8 28 48 68 88 108 128 148 168 188
9 9 29 49 69 89 109 129 149 169 189
10 10 30 50 70 90 110 130 150 170 190
11 11 31 51 71 91 111 131 151 171 191
12 12 32 52 72 92 112 132 152 172 192
13 13 33 53 73 93 113 133 153 173 193
14 14 34 54 74 94 114 134 154 174 194
15 15 35 55 75 95 115 135 155 175 195
16 16 36 56 76 96 116 136 156 176 196
17 17 37 57 77 97 117 137 157 177 197
18 18 38 58 78 98 118 138 158 178 198
19 19 39 59 79 99 119 139 159 179 199
20 20 40 60 80 100 120 140 160 180 200
Table 1. How to divide one ha land into numbers subplot

Combination
Row × Column Basic Unit Plot Size (m2) Spatial Variance
2×10 20 1000 X1
20×1 20 1000 X2
4×5 20 1000 X3
5×4 20 1000 X4
3×10 30 1500 X5
15×2 30 1500 X6
10×3 30 1500 X7
5×6 30 1500 X8
6×5 30 1500 X9
20×2 40 2000 X10
Combination
Row × Column Basic Unit Plot Size (m2) Spatial Variance
4×10 40 2000 X11
10×4 40 2000 X12
8×5 40 2000 X13
5×8 40 2000 X14
10×5 50 2500 X15
5×10 50 2500 X16
6×10 60 3000 X17
10×6 60 3000 X18
12×5 60 3000 X19
15×4 60 3000 X20
20×3 60 3000 X21
7×10 70 3500 X22
14×5 70 3500 X23
8×10 80 4000 X24
20×4 80 4000 X25
16×5 80 4000 X26
9×10 90 4500 X27
10×9 90 4500 X28
15×6 90 4500 X29
18×5 90 4500 X30
10×10 100 5000 X31
20×5 100 5000 X32
110 points 110 5500 X33
20×6 120 6000 X34
15×8 120 6000 X35
130 points 130 6500
X36
20×7 140 7000 X37
150 points 150 7500 X38
20×8 160 8000 X39
170 170 8500 X40
20×9 180 9000 X41
190 points 190 9500 X42
20×10 200 10000 X43

Table 2. Various possible combination of different subplots into one unit to analyze spatial variance
Heterogeneity or variability within a field is assessed using two key metrics: the coefficient of variation (CV) and spatial variance. The coefficient of variation is a relative measure of variability, while spatial variance is an absolute measure that captures the total variability across a spatial area. The proposed statistical method is based on spatial variance, providing a more robust and comprehensive approach to analyzing field variability. By measuring the total variability across a defined spatial area, spatial variance offers greater precision in identifying heterogeneity, making it a reliable foundation for developing optimal soil sampling strategies.
 
, Claims:1. A system for soil sampling and fertility assessment comprising:
A field division module for subdividing a field into multiple subplots;
A soil sampling unit for collecting soil samples from the subplots using GPS coordinates;
A laboratory analysis unit for testing soil parameters including pH, electrical conductivity, organic carbon, macronutrients, and micronutrients;
A geospatial processing module configured to analyze soil sample data using GIS tools;
A spatial variance analysis module for determining field variability and delineating homogeneous zones.
2. The system as claimed in claim 1, wherein the geospatial processing module imports soil data into GIS software and reprojects it to a standardized coordinate reference system.
3. The system as claimed in claim 1, wherein the soil sampling unit ensures representativeness by utilizing GPS for precise sample location tracking.
4. The system as claimed in claim 1, wherein the spatial variance analysis module calculates field variability through geostatistical methods, including Kriging.
5. The system as claimed in claim 1, wherein the optimal homogeneous zones are determined by analyzing the point of maximum curvature in a variance-plot size curve.
6. The system as claimed in claim 1, wherein the laboratory analysis unit provides nutrient content assessments for precise fertilizer recommendations.
7. A method for determining optimal soil sampling size, comprising the steps of:
o Subdividing a field into multiple subplots.
o Collecting soil samples from each subplot with GPS tracking.
o Analyzing soil samples for nutrient content.
o Converting analyzed data into a geospatial format.
o Combining subplots into progressively larger units for spatial variance calculations.
o Determining homogeneous zones based on variance-plot size analysis.
8. The method as claimed in claim 7, wherein the step of geospatial processing involves mapping soil parameters using GIS software.
9. The method as claimed in claim 7, wherein the step of spatial variance analysis is conducted using geostatistical techniques, including Kriging.
10. The method as claimed in claim 7, wherein the determination of homogeneous zones ensures site-specific nutrient management and optimal fertilizer application.