# Using the farmers’ knowledge on soils improves local digital soil mapping products: A case study in South India

Philippe Lagacherie, S Dharumarajan, R Vasundhara

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Philippe Lagacherie, S Dharumarajan, R Vasundhara. Using the farmers’ knowledge on soils improves local digital soil mapping products: A case study in South India. Geoderma, 2025, 464, pp.117609. 10.1016/j.geoderma.2025.117609. hal-05456905

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# Using the farmers’ knowledge on soils improves local digital soil mapping products: A case study in South India

Philippe Lagacherie<sup>a,b,*</sup> <sup>IDI</sup>, S. Dharumarajan<sup>c, ID</sup>, R. Vasundhara<sup>c</sup>

## ARTICLE INFO

Handling Editor: Jingyi Huang

### Keywords:

- Digital soil mapping

- Participative study

- Distance covariates

- Random forest

- South India

## ABSTRACT

As ethnopedologists have observed, farmers have accumulated valuable knowledge of local spatial variations in soils, which they are currently mobilising for their own use. Such knowledge could complement the soil databases that provide the usual soil inputs of Digital Soil Mapping (DSM) approaches. This paper presents a novel participatory DSM approach that uses the collective knowledge of farmers living in a small area (Gopalapura village, South India) as input to DSM models that predict soil texture classes, depth and stoniness.

The collection of farmers’ soil knowledge involved two steps. First, a local soil typology was created by a group of 27 farmers. Secondly, the farmers were asked to locate each soil type within the area of interest. The output was a set of locations labelled with a soil type, which was used as additional input for training a random forest in two ways: i) as an alternative soil input dataset, and ii) as a new set of covariates by calculating topography-oriented buffer distances from the farmers’ soil types. In order to evaluate these new soil data and their potential contribution to improving the performance of the DSM, the localisations of farmer soil types, as well as the predicted maps of texture, depth and stoniness classes, were compared with a very detailed (1:8,000) soil map.

The results showed fairly good agreement of soil class determinations between the soil map and the farmer’s soil information. However, using the farmer soil types as an alternative soil input to train a random forest gave poor prediction performances. Conversely, adding topography-oriented distances to farmer soil types to the usual set of covariates significantly improved the predictions regardless of the soil property tested and the density of soil sampling.

The hybridisation of digital soil mapping and ethnopedological approaches seems promising both for improving the quality of local soil maps and for ensuring a better appropriation of such maps by future local users.

## 1. Introduction

A prerequisite for sustainable management of the soil resource is an accurate knowledge of the soil cover, including variations in all soil properties that may be relevant for decision making. Despite their fine resolution, it has been shown (Lemercier et al., 2022, Rossiter et al., 2022) that the Digital Soil Mapping (DSM) products now available at global or national scales do not provide sufficiently accurate representations of local soil patterns for soil management decisions at the local level. To overcome this problem, it is necessary to consider new Digital Soil Mapping approaches that would focus on the real areas that decision makers should deal with, such as municipal areas, watersheds or soil

districts (Wadoux et al., 2024).

Downscaling the application of DSM to these smaller governance areas would allow for greater local involvement in the mapping process. Well-designed participatory approaches (Rossiter et al., 2015; Wadoux & McBratney, 2023) could replace soil experts, who are often unavailable for collecting additional knowledge on local soil patterns. Such participatory approaches would also guarantee better appropriation of the survey outcomes.

In this perspective, two different approaches can be envisaged for the collection of local soil information. The first is to have citizens contribute to the collection of data according to a protocol predefined by scientists (e.g. Filippelli et al., 2018). Such an approach can provide soil

<footnote><sup>a</sup> LISAH, University Montpellier, Agro Paris Tech, INRAE, IRD, Institut Agro Montpellier, Montpellier, France</footnote>

<footnote><sup>b</sup> Indo-French Cell for Water Sciences, Indian Institute of Science, ICWaR, Bangalore, India</footnote>

<footnote><sup>c</sup> ICAR-National Bureau of Soil Survey and Land Use Planning, Bangalore, India</footnote>

<footnote>* Corresponding author.
E-mail address: philippe.lagacherie@inrae.fr (P. Lagacherie).</footnote>
scientists with directly usable soil data, but is limited in the quality and quantity of information collected if the protocol is too vague or too complex, respectively. Consequently, as in the example above, much work remains to be done by the scientific staff.

A second possible approach is to elicit the soil knowledge of local farmers. Indeed, they already observe their land (Rossiter et al., 2015) and are aware of the value of soil spatial information and the need to improve it for their own use. Ethnopedology is a hybrid discipline, nourished by both natural and social sciences, that studies the soil and land knowledge systems of rural populations, from the most traditional to the most modern (Barrera-Bassols and Zinck, 2003; Perez-Rodriguez et al., 2023). Among a wide variety of topics, ethnopedological research has developed approaches to formalise local soil knowledge into classification schemes (Barrera-Bassols and Zinck, 2003; Richelle et al., 2018). However, relating such classifications to the classical soil information usually collected in soil surveys does not seem easy, which still makes it difficult to use the farmer soil information as input for our DSM models.

This paper presents a novel participatory approach that uses the collective knowledge of farmers living in a small area (Gopalapura village, South India) as input to DSM models predicting soil texture, depth and stoniness classes. The farmers’ soil knowledge was expressed as sets of plots with identified soil classes, previously defined in a participatory workshop. This information was then used to construct new covariates expressing the relative position of each site from the plots with known farmer soil types.

## 2. Materials and methods

### 2.1. The study area

The study was carried out in the area of Gopalapura, a small village located in the Deccan Plateau of South India (Fig. 1), and covering 19.12 km2 (11°43'30" to 11°47'30" N Latitude and 76°33'30" to 76°37'30" E longitude). The village is located in the Berambadi catchment, which is part of the Kabini Critical Zone Observatory (CZO) (Sekhar et al., 2016). The elevation within the Gopalapura area ranges from 845 to 1019 m above mean sea level. The annual rainfall (2005–2018) is 993 mm, of which about 55 % falls during the southwest monsoon (June to September) and 25 % during the northeast monsoon (October to December). According to this rainfall distribution, agricultural practices are organised around two main cropping seasons: Kharif (May-August) and Rabi (September-December) (Sharma et al., 2018). A large variety of crops are cultivated in small farms of, on average, 1.2 ha including a limited set of small plots of, on average, 0.4 ha (Robert et al., 2017).

The soil cover of this area is organised following a topographical soil sequence between the residual reliefs and the drainage channels (see Fig. 8 in Gunnell and Bourgeon, 1997). This sequence displays the following profiles (modified from section 3.2.2. in Gunnell and Bourgeon, 1997): “Thin, immature soils (Lithic Ustorthents, Ustropepts) occur in the upper, steeper slopes. Lower down, rauch of the glacis, of 2.5 km length, is covered by fersiallitic soils of a rich, deep red colour (Paleustalfs, Rhodustalfs) (Soil Survey Staff, 1999) developed on wash pediments of granitic origin, followed either by immature soils (Typic Ustropepts) developed on coarse red colluvium, or by black soils (Vertic

<figure><img src="image_3.png" /><figcaption>Fig. 1. a) location of karnataka (india), b) location of chamrajnagar district in karnataka, c) location of study area in chamrajnagar district and d) study area with field boundary.</figcaption></figure>
Ustroepepts) on finer slope material at the distal part of the wash pediment".

## 2.2. Datasets

### 2.2.1. Soil data

The soil data used in this study were all obtained from a very detailed soil map overlapping the Gopalapura area (Fig. 2). This map was prepared by the ICAR-NBSS&LUP following the protocol described in the field guide (Natarajan et al., 2016). The map was constructed from a base map at 1:8000 scale, including image interpretation for geology and landforms. The soil classes were characterised by 85 soil profile observations and analyses (density of 4.5/ km²).

The delineated mapping units found in Gopalapura correspond to different soil phases. They are represented on the map by symbols consisting of a combination of letters and/or numbers. For example, the map unit HDRcC2g1 is a phase of the Hundipur (HDR) series, the fourth lower case letter (c) indicates the texture of the surface soil, the fifth capital letter (C) indicates the slope of the land, the sixth number (2) indicates the degree of soil erosion occurring in the map unit and the last symbol (g1) indicates the stoniness. All these symbols refer to predefined ordinal classes of soil characteristics. The soil depth, texture and stoniness were measured in field and then classified into standard classes for interpretation as per field guide (Natarajan et al., 2016).

For the purposes of this study, three maps of soil property classes were derived: soil texture map, soil depth map and soil stoniness map. The legends of these maps are provided in Table 1.

<table><caption>**Table 1**
The soil property classes of the Gopalapura soil map.</caption><thead><tr><td>Soil depth</td><td>texture</td><td>Stoniness</td></tr></thead><tbody><tr><td>Shallow, 25–50 cm</td><td>Loamy sand</td><td>Non stony (&lt;15 %)Stony</td></tr><tr><td>Moderately shallow, 50–75 cm</td><td>Sandy loam</td><td>(15–35 %)Very Stony</td></tr><tr><td>Moderately deep, 75–100 cm</td><td>Sandy clay loam</td><td>(35–60 %)Extremely Stony</td></tr><tr><td>Deep, 100–150 cm</td><td>Sandy clayClay</td><td>(60–80 %)</td></tr><tr><td>Very deep, &gt; 150 cm</td><td></td><td></td></tr></tbody></table>

### 2.2.2. Soil covariates

In Digital Soil Mapping, soil properties are predicted from a set of spatial data that are available over the whole study area, namely the soil covariates. In the following, we provide a brief description of the covariates listed in Table 2.

Seventeen relief covariates were calculated by using Saga-GIS 2.3.1 version (as per the methods described in SAGA-GIS documentation) from a Digital elevation model (DEM) with 30 m resolution that was obtained from Shuttle Radar Topography Mission (SRTM) data and processed using ArcGIS10 data management toolbox. The three climate covariates were obtained from the WorldClim 2 database (total rainfall and average temperature) (Fick and Hijmans, 2017) and from Landsat data (land surface temperature) (https://earthexplorer.usgs.gov). The MODIS vegetation Indices of 16-day composite products at 250 m resolution (NDVI and EVI) were retrieved from the NASA Earth-data (https://earthdata.nasa.gov/). In addition, 10 bands of sentinel-2 data (Date of acquisition: 24th April 2017) were used as covariates (https://dataspace.copernicus.eu).

Finally, these covariates were interpolated onto the set of parcel

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Detailed soil map of Gopalapura area (Hegde et al., 2019).</figcaption></figure>
<table><caption>**Table 2**
Covariates used for DSM model.</caption><thead><tr><th>Source</th><th>name</th><th>Description</th></tr></thead><tbody><tr><td rowspan="10">SRTM DEM<br>(30 m)</td><td>elevation</td><td>Elevation</td></tr><tr><td>Slope</td><td>Local Slope gradient</td></tr><tr><td>Aspect</td><td>Local Slope Aspect</td></tr><tr><td>Stream Power index</td><td>measure of the erosive power of flowing water, calculated based on slope and contributing area (Moore et al., 1991)</td></tr><tr><td>Terrain</td><td>highlights the amount of elevation</td></tr><tr><td>Ruggedness index</td><td>difference between adjacent cells of a digital elevation model (Riley et al., 1999)</td></tr><tr><td>Profile Curvature</td><td>Curvature parallel to slope direction</td></tr><tr><td>Plan Curvature</td><td>Curvature perpendicular to slope direction</td></tr><tr><td>MRVBF</td><td>Multi Resolution Valley Bottom Flatness Index (Gallant and Dowling, 2003),</td></tr><tr><td>MRRTF</td><td>Multi Resolution ridge Top Flatness Index (Gallant and Dowling, 2003),</td></tr><tr><td rowspan="2">TPI</td><td>Topographical Position Index (mean difference of elevation with neighbouring pixels) (Guisan et al., 1999)</td></tr><tr><td>Topographical Wetness Index (Sorensen et al., 2006): Ln a/ tan b, with a the upstream area and b the slope gradient</td></tr><tr><td>Valley depth</td><td>Indicates the depth of valley</td></tr><tr><td>Slope height</td><td>height relative to the surrounding area</td></tr><tr><td>LS factor</td><td>Slope length (L) and slope steepness (S)</td></tr><tr><td>Relative slope</td><td>The steepness of the terrain at a particular location relative to its surrounding area</td></tr><tr><td>Channel Network Distance</td><td>Flow path distance from each cell to the nearest channel</td></tr><tr><td>Channel Network Base Level</td><td>Height of each cell above its nearest channel along the flow path.</td></tr><tr><td>Valley depth index</td><td>Relative position of a cell between the valley bottom and surrounding ridges</td></tr><tr><td>Analytical hill shade</td><td>The angle between the surface and the incoming light beams</td></tr><tr><td>CIT-index</td><td>The Channel Initiation Threshold index (Montgomery and Foufoula-Georgiou, 1993)</td></tr><tr><td>Landsat-8</td><td>LST</td><td>Land surface temperature</td></tr><tr><td>MODIS (MOD13Q1)</td><td>NDVI</td><td>Normalised Difference Vegetation index</td></tr><tr><td>MODIS (MOD13Q1)</td><td>EVI</td><td>Enhanced vegetation index</td></tr><tr><td>Sentinel 2</td><td>Bands 2 to 8A and 11, 12</td><td>Different bands from Visible to NIR (10–20 m resolution)</td></tr><tr><td rowspan="2">WorldClim 2</td><td>Precipitation</td><td>Total annual rainfall</td></tr><tr><td>Average temperature</td><td>Annual average temperature</td></tr></tbody></table>

centroid for further modelling (see 2.5. below) using a bilinear algorithm.

## 2.3. Eliciting the soil farmer knowledge

A participatory approach was used to elicit the soil knowledge of the Gopalapura farmers. It took the form of two consecutive workshops held at the village school on 1 and 2 December 2022. These workshops brought together 27 farmers cultivating plots at different locations in the Gopalapura area. Farmers were invited to participate in kind, with no requirement for pre-selection or attendance at a training session before the workshops.

### 2.3.1. Workshop 1: Building a farmer soil typology

This workshop followed the methodology developed in a previous study (Lagacherie et al., 2021). After a reminder of the purpose of the study and a presentation of the aims and procedures of the workshop, the discussions began with the farmers freely expressing their views on the different types of soil (or 'land') that they could distinguish on their farms. As the different farmers spoke, the different soil types suggested were recorded on a document for all to see (Fig. 3, left). For each soil

type, the discussions converged on an agreed description that included both morphological and functional characteristics (see Table 3 below).

#### 2.3.1.1. Workshop 2: Localizing the soil farmer types.
In this workshop, the farmers were asked to locate the soil types they had previously defined. To do this, the farmers were provided with base maps showing an aerial view of the Gopalapura area, completed with plot boundaries and plot numbers. They were asked to locate the soil types by placing pins of different colors, each color corresponding to one of the farmers' soil types (Fig. 3, right). As the plots were very small (section 2.1), the farmer soil types were mostly (88 %) assigned to whole plots.

### 2.4. Deriving soil covariates from farmer soil type locations

The approach for constructing soil covariates from farmers' soil type locations collected in participatory workshops was inspired by the Random Forest for Spatial data (RFsp) approach (Hengl et al., 2018), which introduced geographical covariates to account for proximity to observation locations. Such geographic covariates were expressed as buffer distances to sites or groups of sites, which were added to the usual sets of covariates used as input to a Random Forest algorithm. Following Lagacherie et al. (1995), at each parcel centroids, we calculated a set of topography-oriented distances from a farmer's soil type, defined as the distance to the centroid of the nearest plot assigned to a given identified farmer's soil type along one of the following three directions: uphill, downhill, and at similar elevation (Fig. 4). These directions were defined as "strictly superior to $(z_x + z_0)$", "strictly inferior to $(z_x - z_0)$" and "comprised between $(z_x - z_0)$ and $(z_x + z_0)$", respectively, where $z_x$ is the elevation at grid node x and $z_0$ is an elevation tolerance that takes into account the accuracy of the digital elevation model and the characteristics of the local relief. If no parcel belonging to a given type of farmer could be found in a given direction, a large value (9999 m) was assigned to the distance for that direction.

These new geographical covariates were calculated by a custom-written R script using the proxy package (Meyer and Buchta, 2022) and the following geographical data inputs: the locations of the farmers' soil types, the DEM and the set of parcel centroids. Finally, the new set of soil covariates consisted of 3 x n distance grids, where n is the number of farmer soil types.

### 2.5. Digital soil mapping of ordinal soil classes

A random forest algorithm was used to provide the prediction functions for depth, stoniness and texture classes. The first two soil properties to be predicted were expressed in the soil map as ordinal variables (depth classes and stoniness) with known underlying continuous variables (depth and stone content). The latter, soil texture, is not normally expressed by ordinal classes. However, in this particular case, the soil texture classes could be considered as ordinal because they were ordered according to the clay content (Table 1), which was the underlying continuous variable.

Following the recommendation of Hornung (2019), we converted the three soil properties into continuous variables by assigning the midpoint of the intervals defining their ordinal classes. This allowed a classical regression tree algorithm to be used to construct the prediction functions. Therefore, a quantile forest algorithm (Meinshausen, 2006) was applied using the R package ranger (Wright and Ziegler, 2017). Finally, the predicted distributions of the continuous variables were transformed back to the distributions of the original ordinal package using a specific R script.

### 2.6. The experiment

The aim of this experiment was twofold: i) to assess the reliability of the farmers' soil types on the classical soil survey determinations and ii)
<figure><img src="image_5.png" /><figcaption>Fig. 3. The two consecutive workshops to elicit farmers’ soil knowledge: building a soil typology (left) and localizing the farmer soil types (right).</figcaption></figure>

to assess the benefit of adding the farmers’ soil type localisations to the set of input data used to predict soil depth, texture and stoniness.

### 2.6.1. Reliability of farmers’ soil types

The texture, depth and stoniness classes, as considered in the detailed soil map (Fig. 2), were assigned to the farmers’ soil types based on the qualitative descriptions collected during the participatory workshop (see Table 3 below). In addition, the texture, depth and stoniness classes were collected from the detailed soil map at each location where a farmer’s soil type was documented. The farmer soil types and map soil class assignments were then compared. The plots where discrepancies occurred were systematically visited to determine whether or not the discrepancies were caused by a mapping error. Furthermore, the farmers’ soil types were matched up with the USDA’s great soil groups (Soil survey staff, 1999), as shown on the soil map.

To evaluate the added-value of mobilising farmers again to reduce the observed discrepancies, they were presented to the farmers in a third workshop, followed by individual interviews. Based on these discussions, an alternative version of the farmers’ locations was produced, as an alternative to the initial one.

### 2.6.2. Evaluation of DSM model performances

To assess the benefit of adding farmer input (2.5.), four different DSM models were compared for their ability to predict texture, depth and stoniness classes for the Gopalapura village area: i) a baseline model trained without any farmer data, ii) a model trained with only the soil farmer type locations that predicted soil farmer types from which were derived the soil class predictions by considering the most represented soil property class within the predicted soil type iii) two models trained with the farmer geographical covariates derived either from the initial farmer type locations or from the farmer type locations corrected after the third workshop (see previous section).

For the baseline model and the two models that used farmer geographical covariates, soil class maps derived from the detailed soil map (Fig. 2) were used as the sources of data to provide both Random Forest calibration sets and evaluation sets. This allowed a wide range of DSM model calibration and evaluation scenarios to be explored and the significance of differences in DSM model performance to be assessed. To ensure that such soil maps represented well the real spatial distributions of the soil property classes, their purities were evaluated from an additional field sampling of 57 locations (Supplementary material S1), at which depth, texture and stoniness classes were determined and compared with the soil class given by the map. This sampling design aimed to provide an even spatial coverage of the study area. The DSM

model calibration/evaluation scenarios were built as follows:

The initial set of 1145 plots was first divided into a 'master' calibration set and an evaluation set, representing 70 % and 30 % of the plots respectively. The evaluation set was selected once using Constrained Latin hypercube sampling (Minasny and McBratney, 2006) using all the candidate covariates. This set was used to evaluate all the DSM models.

The different scenarios of the model calibration sets were all derived from the master calibration dataset. Different numbers of plots with known property classes were considered (50, 75, 100, 125, 150, 200, 300, 400). For each number, the sampling of plots was repeated 200 times. As for the evaluation dataset, the sampling of all calibration datasets was performed using a Latin hypercube method. The random forest algorithm for ordinal soil class described in section 2.5. was run for each calibration dataset using the default hyperparameters (mtry, min node size and sample fraction) provided in the ranger package.

The prediction performances of the DSM models were evaluated by computing the prediction accuracy. Accuracy was calculated as the proportion (in %) of the validation sites whose soil property class matches the highest probable predicted soil property class as derived from the predicted soil class distribution.

## 3. Results

### 3.1. Preliminary results: Purities of soil maps

The soil class map, which was used to calibrate and evaluate the DSM models, was estimated from a set of 57 locations evenly distributed across the study area (see supplementary material S1). Comparisons between the observed soil property classes at these locations and the classes provided by the soil property maps derived from the detailed soil map resulted in purity levels of 86 %, 84 % and 89 % for the depth, texture and stoniness maps, respectively. These high purity levels confirm the high precision of this detailed soil map and its ability to serve as ground truth for our DSM experiment.

### 3.2. Farmer workshop data

Ten farmer soil types were identified during the first farmer workshop (Table 3). The farmer soil types were named and described by some of their morphological characteristics (texture, depth, stoniness, ...). The descriptions were complemented by a number of soil behavior criteria, such as their potential for specific crops and their response to fertilisation and irrigation, which was probably what the farmers were
<table><caption>**Table 3**
The farmer soil types obtained after the workshop 1 and the number of locations obtained after workshop 2 (last column).</caption><thead><tr><th>soil type</th><th>Characteristics</th><th>Behaviour</th><th>Occurrences<br>Nb of parcels</th></tr></thead><tbody><tr><td colspan="4">R : Red soils</td></tr><tr><td>R1. Red sandy soils</td><td>30-45cm depth, occurs in midlands</td><td>Less fertile, frequent irrigation requires in 3-4 days. Yield to low to moderate</td><td>33</td></tr><tr><td>R2. Red gravelly soils<br>a) Small gravels</td><td rowspan="2">45-90 cm, occurs in uplands</td><td rowspan="2">Moderate fertility, frequent irrigation required</td><td>3</td></tr><tr><td>b) Big gravels</td><td>2</td></tr><tr><td>R3. Deep red soils</td><td>stoneless, deep to very deep soils (2m depth) occurs in lowlands and midlands</td><td>Smooth inter-cultivation, crop growth is good and good for all the crops and weed growth is high</td><td>23</td></tr><tr><td>R4. Red calcareous soil</td><td>white color</td><td>product quality is good, fertilizer need to apply and water is not required much</td><td>0</td></tr><tr><td>R5. White sandy mixed soils</td><td>shallow soils</td><td>More water required, groundnut, mustard quality is good</td><td>0</td></tr><tr><td colspan="4">B. Black soils</td></tr><tr><td>B1. Sandy black soils</td><td>Organic rich soils (leaves, stics decomposed)</td><td>Less fertilizer requirement, less water requirement, quality of product cabbage, cucumber</td><td>11</td></tr><tr><td>B2. Good mixed soils</td><td>loose and friable</td><td>Intercultivation operation is easy</td><td>38</td></tr><tr><td>B3. Black clayey soils</td><td>6-7 feet depth</td><td>work flow hard, weekly once irrigation, extent of area is less, crop choice limitation and low fertilizer requirement</td><td>1</td></tr><tr><td>B4. Black calcareous soils</td><td>white calcareous</td><td>used to wash cloths, adding tank silt is common to get yield</td><td>2</td></tr></tbody></table>

most concerned about in their daily work.

113 locations of farmers' soil types were collected during workshop 2 in the central part of the study area (Fig. 5). Most of the locations (98) concerned four soil types (R1, R3, B1 and B2). Two farmer soil types were not found in the study area. Finally, the rare farmer soil types R2, B3 and B4 were assigned to their most similar types, R1, B2 and B2 respectively.

### 3.3. Farmer and soil survey data comparisons

The distributions of depth, texture and stoniness classes of the set of localised farmer soil types (sky blue in Fig. 6) were fairly similar in shape, but showed marked differences from those of the whole study area. Indeed, the soils of the parcels with localized farmer soil types

appeared deeper, more clayey and less stony.

The farmer soil types were characterised by intersecting the plots with the local farmer soil types using the depth, texture and stoniness classes provided by the detailed soil map. The resulting confusion matrices (Table 4) showed that, as expected from their descriptions (Table 3), the farmer soil classes were fairly well differentiated with respect to these three soil properties. However, some discrepancies could be identified, characterised by a significant number of unexpected assignments (orange boxes in Table 3). From the discussions with the farmers during the third participatory workshop, it appeared that the discrepancies in depth and texture class assignments were caused by misclassifications of some R3 soil types in R1, whereas the anomalies in stoniness classes were caused by misclassification of some R2 soil types in R1. Such misclassifications resulted from the farmers' local judgments
<figure><img src="image_6.png" /><figcaption>Fig. 4. Definition of topography-oriented distances from farmer soil types.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 5. Localisations of farmers’ soil types as provided by the farmers in the second participative Workshop.</figcaption></figure>

of texture and stone content, which differed from those of the soil surveyors.

Apart from these discrepancies, it should be noted that each farmer soil types included several soil property classes (Table 4), which meant that the farmer classification was less precise than the soil surveyor one. Finally, using the farmer soil types as predictors of the soil property class by considering the dominant soil property class resulted in prediction

accuracies of 67 %, 58 % and 87 % for the depth, texture and stoniness classes respectively.

Farmer soil types assignments were corrected by removing the discrepancies identified above. After modifying three soil depth class assignments from the soil maps during the field visit to plots with assignment discrepancies, the corrected soil farmer type localisations resulted in better prediction accuracies of 79 %, 60 % and 99 % for the
<figure><img src="image_8.png" /><figcaption>Fig. 6. Distributions of soil property classes in the study area (“all”) and within the set of plots with localized farmers’ soil types (“farmer”).</figcaption></figure>

**Table 4**

Within farmer soil types distributions of soil property classes provided by the soil map. The unexpected soil property classes for a given soil farmer type are indicated by orange boxes.

<table><thead><tr><th rowspan="2">Farm. Soil types</th><th colspan="5">Soil depth classes (cm)</th></tr><tr><th>25-50</th><th>50-75</th><th>75-100</th><th>100-150</th><th>&gt;150</th></tr></thead><tbody><tr><td>R1</td><td>10</td><td>4</td><td>9</td><td>2</td><td>8</td></tr><tr><td>R2</td><td>0</td><td>2</td><td>3</td><td>0</td><td>0</td></tr><tr><td>R3</td><td>0</td><td>1</td><td>0</td><td>10</td><td>12</td></tr><tr><td>B1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>10</td></tr><tr><td>B2</td><td>0</td><td>0</td><td>0</td><td>1</td><td>40</td></tr></tbody></table>

<table><thead><tr><th rowspan="2">Farm. Soil types</th><th colspan="4">Texture classes</th></tr><tr><th>Sandy loam</th><th>Sandy Clay loam</th><th>Sandy Clay</th><th>Clay</th></tr></thead><tbody><tr><td>R1</td><td>7</td><td>9</td><td>17</td><td>0</td></tr><tr><td>R2</td><td>1</td><td>4</td><td>0</td><td>0</td></tr><tr><td>R3</td><td>1</td><td>9</td><td>13</td><td>0</td></tr><tr><td>B1</td><td>0</td><td>1</td><td>3</td><td>7</td></tr><tr><td>B2</td><td>0</td><td>7</td><td>10</td><td>24</td></tr></tbody></table>

<table><thead><tr><th rowspan="2">Farm. Soil types</th><th colspan="3">Stoniness Classes</th></tr><tr><th>&lt; 15%</th><th>15-35%</th><th>35-60%</th></tr></thead><tbody><tr><td>R1</td><td>21</td><td>11</td><td>1</td></tr><tr><td>R2</td><td>2</td><td>3</td><td>0</td></tr><tr><td>R3</td><td>22</td><td>1</td><td>0</td></tr><tr><td>B1</td><td>11</td><td>0</td><td>0</td></tr><tr><td>B2</td><td>41</td><td>0</td><td>0</td></tr></tbody></table>

**Table 5**

Within farmer soil types distributions into USDA soil great groups.

<table><thead><tr><th rowspan="2">Farm. Soil types</th><th colspan="5">USDA great groups</th></tr><tr><th>Haplustalf</th><th>Paleustalf</th><th>Rhodustalf</th><th>Haplustepts</th><th>Ustorthents</th></tr></thead><tbody><tr><td>R1</td><td>4</td><td>9</td><td>15</td><td>3</td><td>2</td></tr><tr><td>R2</td><td>2</td><td>0</td><td>2</td><td>0</td><td>1</td></tr><tr><td>R3</td><td>0</td><td>8</td><td>7</td><td>5</td><td>3</td></tr><tr><td>B1</td><td>0</td><td>1</td><td>1</td><td>9</td><td>0</td></tr><tr><td>B2</td><td>0</td><td>3</td><td>3</td><td>22</td><td>1</td></tr></tbody></table>
depth, texture and stoniness classes, respectively.

In addition to the previous analysis, we investigated the alignment between the farmer soil classification and the USDA's classification of soil groups, as shown on the soil map. Table 5 shows that the farmer soil types only partially matched the great soil groups. While a good match was observed between the black soils (B1 and B2) and the Haplustepts groups, the red soils (R1, R2 and R3) belonged to different groups and even orders.

### 3.4. Prediction performances

The performances for predicting soil classes were systematically higher when using the covariates derived from the farmer soil type locations, whatever the considered soil properties and the numbers of samples (Fig. 7). Furthermore, all these increases of performances were found significant using a Student's t-test ($p < 0.001$) with the notable exception of the sparsest sets of known plots (50) when associated with non-corrected farmer soil types. However, the gains of performances were more important when predicting textural classes than when predicting stoniness and depth classes. Using the corrected locations instead of the initial one did not bring any significant improvement of the prediction performances except for the sparsest sets of known plots. Finally, all the predictions evoked before largely outperformed the ones derived from the predicted map of farmer soil types obtained by training a random forest from the farmer's soil type locations without any observed plots (dotted lines on Fig. 7).

### 3.5. Mapping example

Fig. 8 (right) shows an example of a soil property map produced using a DSM model with soil covariates derived from farmers' localisations of soil types. This example is a soil texture map created using 150 soil observations and the uncorrected farmer localisations.

Comparisons with the soil map obtained from a field survey revealed that, in this example, the pattern of texture class variations was predicted fairly accurately, although there was some confusion between

neighbouring soil texture classes (accuracy = 54%).

The ranking of soil covariate importance obtained by the permutation test provided by the Ranger package (Altmann et al., 2010) revealed that six out of the fifteen most important Random Forest covariates in this example were derived from farmers' localisations of soil types. This ranking did not reveal any predominance of a given soil type or slope direction.

## 4. Discussion

### 4.1. The value of soil farmer knowledge for use in DSM

In this study, we explored the use of farmers' soil knowledge as a new input for local applications of digital soil mapping. Such soil knowledge has received much attention in the past in the context of ethnopedology (Barrera-Bassols and Zinck, 2003; Perez-Rodriguez et al., 2023) and has already been used for soil mapping (Ortíz Solorio and Guttierez Castorena, 2022). However, to the best of our knowledge, this is the first time that farmers' knowledge has been used in a digital soil mapping approach.

Although, according to the principles of ethnopedology, the farmers had complete freedom to express their soil knowledge, the soil data retrieved from this knowledge showed a fairly good agreement with the ones of a classical detailed soil map (Table 4). Such a result was in good agreement with Lleverino-Gonzalez et al. (2000), who measured an accuracy of 76% for the farmers' location of soil classes in Mexico. The use of such farmer soil type locations as DSM inputs therefore does not appear to be much worse than the common use of legacy soil data, the uncertainties of which have recently been assessed (Lagacherie et al., 2024). Such a satisfactory result was probably favoured by gathering the farmers together for workshops instead of interviewing them individually, thereby limiting inconsistencies in their responses. We also experimented the benefit of an a posteriori control of the farmer soil locations which permitted to correct some mis-interpretations and increase their accuracy. As recommended for legacy soil data (Lagacherie et al., 2024), such control sampling should be systematically performed

<figure><img src="image_9.png" /><figcaption>Fig. 7. Accuracy of soil property class predictions across spatial densities, covariates set and soil properties.</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>**Fig. 8.** Soil texture maps: obtained from a detailed soil survey (left) and from a DSM model incorporating 150 soil observations and non-corrected localisations of soil types from soil farmers (right).</figcaption></figure>

in the future campaigns for collecting such locations of farmers’ soil types.

From a quantitative point of view, we collected 113 locations of farmers’ soil types, representing only 10 % of the total study area. Although this can be considered a good result in terms of the investment made (two two-hour workshops with 27 farmers), the information collected in this way was not sufficient to map the study area, as eth-nopedologists usually do (Perez-Rodriguez et al., 2023). The data collected was also too small to be representative of the whole area, as shown by the comparisons of distributions of soil property classes in Fig. 6. It should also be noted that three farmer soil types listed in the first workshop were not found in the second workshop. This probably shows that the selected farmer panels did not perfectly match the area to be mapped. In the future, more attention should be paid to a better selection and involvement of the farmer panels in order to increase the size of the areas documented with such data, without compromising the good quality of the data mentioned above. To this end, an on-line tool for collecting the locations of farmers’ soil types looks promising, provided that the farmers’ soil typology is well assimilated by the target group of farmers in the region to be mapped.

Finally, it should be noted that the study was conducted in an area of intensive agriculture comprising small farms and fields. Here, farmers have in-depth knowledge of their soil types. However, these favourable conditions cannot be reproduced everywhere, particularly in agro-systems where individual growers manage thousands of hectares and are not familiar with soil conditions across all their fields. Therefore, this approach will have to be applied in other contexts to determine its potential applications and limitations.

## 4.2. How to integrate soil farmer knowledge in DSM?

The simplest and cheapest way to use farmers’ knowledge to train a DSM model would have been to use it directly as a substitute for classical soil observations to train DSM models for each soil property class to be mapped. However, in our case, both the imperfect discriminations of the soil property classes provided by the soil farmer types (Table 4) and the too small number of farmer soil type locations did not allow to obtain satisfactory results (Fig. 7, dotted lines). In the future, larger sets of such farmers’ soil types locations will need to be tested as DSM inputs to determine the minimum number of sites required to obtain satisfactory DSM products.

Alternatively, farmer soil type locations were used as input to calculate a set of new soil covariates for a DSM model trained with classical soil observations. These covariates were constructed following the principle of “buffer distances to sampling locations” proposed by Hengl et al. (2018). However, following Lagacherie et al. (1995), we refined this principle by calculating this distance from a group of locations – those with the same farmer soil type – and by distinguishing different distances with respect to different directions along the slope – uphill, downhill and at the same altitude-. This refinement allowed to take into account the specific spatial distribution of soils along topo-sequences observed in our study region (Gunnell and Bourgeon, 1997). This significantly improved the predictions, regardless of soil properties and sampling densities (Fig. 7). Such a new approach to defining buffer distances has a great potential for application, as spatial distributions of soils along topo-sequences are very common throughout the world (see a recent review by Omokaro, 2023). The same approach can also be applied to the non-exhaustive soil covariates often provided by remote sensing of soil properties (Lagacherie and Gomez, 2018), beyond the farmer soil type locations considered in this paper. However,
such applications should be limited to local digital soil mapping where soil topo-sequences can be treated separately, which becomes impossible when considering larger scales (Lagacherie, 2025).

### 4.3. Ethnopedology and DSM: A promising association

This study showed that it is possible to combine two distant sub-disciplines of soil science – ethnopedology and digital soil mapping – to achieve a common mapping objective. By applying an ethnopedological approach, new input data was provided to Digital Soil Mapping at a moderate cost, increasing the quality of the maps produced (Fig. 7). The benefits of ethnopedology could be easily extended to Digital Soil Assessment mapping (Kidd et al., 2015, Rabot et al., 2022) by also taking into account the considerable knowledge of soil farmers on soil behavior, as illustrated in this study (Table 2). Indeed, such knowledge should be used to modulate at the local scale soil function scores and thresholds, which are usually determined from broader scale expertise and literature (Vogel et al., 2019) and may be flawed by specific local contexts.

On the other hand, the integration of DSM techniques into ethnopedological approaches should also benefit the latter. In fact, DSM's ability to produce a complete soil map from a series of punctual soil observations allows it to cope with incomplete sets of farmers' soil determinations, thus relaxing the requirement for a complete characterization of the mapped area by the farmers. In addition, provisional maps can be produced as the farmer's soil survey progresses, allowing the farmer to identify mapping errors and correct the map in an iterative process.

Finally, we have shown that, in a small region, a soil map can be developed by combining two different sources of soil knowledge, that of the local farmer and that of the soil surveyor. Such an association is a guarantee for a better appropriation and memory of the soil maps produced.

## 5. Conclusions

The main lessons to retain after this study are the following:

* The knowledge of the local farmers about local soil variations and a detailed soil map were found to be in fairly good agreement.

* The use of topography-oriented buffer distances from farmers' soil class locations as new soil covariates was found to be the most efficient way of incorporating farmers' soil knowledge to improve DSM predictions. The accuracy of predicted soil classes increased significantly, regardless of the soil classes considered and the sampling density.

* The hybridisation of digital soil mapping and ethnopedological approaches seems promising both for improving the quality of local soil maps and for ensuring a better appropriation of such maps by future local users.

### CRediT authorship contribution statement

**Philippe Lagacherie:** Writing – review & editing, Writing – original draft, Validation, Methodology, Investigation, Conceptualization. **S. Dharumarajan:** Writing – review & editing, Validation, Resources, Methodology, Investigation, Data curation. **R. Vasundhara:** Writing – review & editing, Resources, Methodology, Investigation, Data curation.

### Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Philippe Lagacherie reports was provided by French National Institute for Agricultural Research INRAE. If there are other authors, they declare that they have no known competing financial interests or personal

relationships that could have appeared to influence the work reported in this paper.

### Acknowledgements

We would like to thank the 27 farmers from Gopalapura (South India) who gave us their time and shared their soil knowledge during three workshops. We especially thank one of them, P. Sthanthamurthy for selecting the farmer panel and organizing the workshops. This work was achieved within the Indo-French Cell for Water Sciences at the Indian Institute of Science, Bangalore.

## Appendix A. Supplementary material

Supplementary data to this article can be found online at https://doi.org/10.1016/j.geoderma.2025.117609.

### Data availability

The authors do not have permission to share data.

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