Introduction

Rich in calcium carbonate in the form of crusts and nodules, calcareous soils provide serious problems for farming because they affect water circulation, fertility, and soil structure1. These soils are widespread in China and many other countries, where they align with important agricultural areas. While advancements in farming methods, such as improved fertilization and pest management, have helped, these soils continue to be a barrier to maintaining healthy soil and sustainable crop production. Issues like soil degradation, erosion, and salinization make the problem even worse, especially in areas dominated by calcareous soils. This makes it essential to study and manage these soils carefully2,3,4,5,6. To overcome these challenges, we need a clear and effective approach to evaluate agricultural potential in such difficult environments.

The FAO framework (1976) and ALES7, may be termed as traditional land classification systems which looks primarily on the soil characteristics assessment of agricultural potentiality. Although, they can be considered as beneficial, they tend to not to take into account a more other relevant aspects, including the environment, economy and social conditions, which can pose as critical variables in determining the farming potential of any land. This is particularly so in an agricultural context of calcareous soils where for example, infrastructure, land use and the local socio-economics affect the success of farming greatly.

The Land Evaluation and Site Assessment (LESA) approach provides a solution for it in a more practical way. It merges Land Evaluation (LE), which seeks to understand the soil attributes, with Site Assessment (SA), which encompasses much more factors, such as environmental, economic, and social conditions8. As a result of the combination of these components, the LESA system is concerned with providing the most objective and practical approach to land assessment in various contexts where assessment can be quite difficult9,10,11.

For example, the non-soil factors are proximity to water sources, accessibility for agricultural machinery, and socio-economic considerations. To suit its environment, the LESA approach is flexible, as its SA component addresses such factors as urbanization and infrastructure improvement, whereas the LE component uses more stable features such as soil characteristics12,13. This two-component structure adds reliability and relevance of land capability evaluation and rating and therefore, supports the use of LESA in sound decision making regarding agricultural land use. On the other hand, the ability to implement such innovative strategies as the LESA program self-assessment among the calcareous soils has not been fully explored in the research. The uniqueness of these soils’ physical and chemical properties requires a combination of the LESA’s integrative evaluation framework and spatial analysis techniques of ArcGIS. The adaptability of the LESA assessment procedures through the inclusion of ArcGIS gives an opportunity to change their internal logic and structure with respect to the properties and socio-economic environments of the calcareous soils regions thus enhancing the accuracy and usefulness of the outcomes. Integrations of this nature have been shown by previous studies to enhance the accuracy and efficiency of land evaluation processes and provide useful information on sound planning for land resources especially sustainable ones8,14.

This research addresses a significant gap in the literature by adapting and localizing the Agricultural Land Use Suitability Evaluation Methodology with Spatial Analysis within an ArcGIS framework. The goal is to assess and visually represent the agricultural potential of calcareous soils in a watershed-scale region of China. The proposed approach is designed to tackle the unique challenges posed by the physical and chemical characteristics of calcareous soils, as well as the socio-economic and environmental context of land use in these areas. By providing a more detailed and contextualized evaluation of agricultural potential, this study aims to support decision-makers in addressing the complex challenges of agricultural development and environmental protection in regions with calcareous soils.

Materials and methods

Studied region

This research was fullfilled in China with an area of 570.5 ha, located between latitudes 27° 49’ 42.74’’ to 27° 51’ 25.19’’ and longitudes 116° 26’ 44.55’’ to 116° 28’ 10.55’’ (Fig. 1). The region has a diverse elevation range, varying from 238 to 1198 m. According to the Soil Taxonomy classification system (USDA, 2010), the main soil types are Inceptisols, Mollisols, and Entisols. Inceptisols have minimal horizon development, Mollisols are determined based on their thick, dark, and fertile horizon, while Entisols have no profile development. The parent material in this region is rich in calcium carbonate, significantly influencing soil properties and agricultural potential. The agricultural landscape is primarily dominated by the cultivation of cereal grains, corn, and alfalfa, crops that are well-suited to the local soil and climatic conditions. These crops are important part of the region’s agricultural output, which are planted based on soil types and nutrient capacity. Calcium carbonate has a vital role in improving soil structure and fertility15.

Fig. 1
figure 1

A screenshot of Google Earth-map of the studied sites in China16 and sampled soil distribution.

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Climatic conditions

The study area experiences a wide range of temperatures over the course of a year, ranging from 6.2 °C to 30.0 °C, with an average temperature of 18.8 °C. There are different climatic conditions, which greatly affect farming practices and crop selection. The relatively large mean annual rainfall of 184.8 mm is associated with high water tables. However, rainfall patterns indicate soil erosion and water management challenges. In general, heavy rainfall has resulted in faster runoff and increased risk of flooding, necessitating the adoption of more efficient water management strategies, which not only require more efficient use of water but also to reduce the risks associated with this extreme weather in the region.

Sampled soils

Soil samples were taken from 91 locations within the topsoil layer (0–30 cm) using a random sampling method, and these were analyzed for various physicochemical parameters.

The analyses included:

(a) Organic Carbon (OC) content, determined using the oxidation method17;

(b) Calcium Carbonate Equivalents (CCE), measured through the titration method18;

(c) soil pH, assessed in a CaCl2 (0.01 M) extract;

(d) Electrical Conductivity (EC), measured in the saturated soil extract; and.

(e) Particle size distribution, including sand, silt, and clay, determined using the hydrometer method19.

Table 1 details the measured soil attributes, with the identified soil texture classes in this study including clay loam, sandy loam, silty loam, loam, clay, and silty clay loam. Furthermore, maps were generated for soil textural fractions, CCE, OC, and pH, offering critical insights into the spatial distribution of these properties. This mapping is crucial for understanding the variability in soil characteristics across the study area and for making informed decisions about land use and management.

Table 1 The descriptive statistics of measured soil parameters.
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Spatial analyzing

The Ordinary Kriging model is a widely recognized spatial method for effectively generating thematic maps of soil properties3. This geostatistical technique predicts soil attributes at unmeasured locations by leveraging the spatial correlation among observed data points. The process begins with the calculation of the experimental semi-variogram, which quantifies the degree to which soil property values change over distance. The semi-variogram is computed as follows:

$$\gamma\left(h\right)=\frac{1}{2 N\left(h\right)}\sum_{i=1}^n{\left[Z\left({x}_{i}\right)-Z({x}_{i}+h)\right]}^{2}$$
(1)

In this equation, Υ(h) represents the partial choice of village value at a particular distance, Z(x_i) denotes the observed soil quality value at location xi and h is the distance between two data points between two and how the variance decreases once the experimental Once the semi-variogram is calculated, it is modeled into a continuous function representing the observed data exactly the same. Semi-variograms can be modeled using different functions, the most common being Gaussian, Exponential, and Spherical models3. The main semi-variogram parameters are Nugget (variance at zero distance), Sill (outside the semi-variogram layer), and Range (distance of the semi-variogram reaching the sill)3, Modeled semi -variance kriging -It is the basis for the projection process. Ordinary kriging uses this function to spatially, predict soil properties in unsampled areas by calculating a weighted average of nearby observed parameters Weights are assigned according to spatial correlations described by quasi-variance diagrams are correct, and ensure that adjacent data points get predicted so much more affected than far they are:

$$\widehat{Z}\left({x}_{0}\right)=\sum_{i=1}^{n}Wi\:\left({x}_{0}\right)Z\left({x}_{i}\right)$$
(2)

Where Wi are the weights assigned to each observed value and \(\:\widehat{Z}\left({x}_{i}\right)\)is the predicted soil property value at the un-sampled location. Previous studies such as Piccini et al. (2014), Li (2010), and Eldeiry and Garcia (2010), have demonstrated that Ordinary Kriging provides sufficient accuracy for most soil properties20,21,22.

LESA model

The LESA model is a land capability classification tool designed to assist farmers and land managers in assessing the agricultural potential of their land. This model offers a systematic approach to land evaluation, customized to local conditions9, by incorporating the expertise of a local committee. In this study, the LESA model was refined and adapted with the input of 15 local agricultural experts who formed a dedicated committee. These experts, with extensive experience and deep knowledge of the region’s agricultural potential and limitations, played a pivotal role in tailoring the model.

At this session, experts identified key factors affecting agricultural viability in the region. Each item was discussed in detail to ensure a comprehensive and relevant understanding of local conditions. Each expert assessed the importance of the items based on his professional judgment and field experience. The ratings were then summed to obtain consensus weights. In addition, the selected factors and their weights were confirmed by field tests and comparative analysis using historical agricultural performance data in the region. This iteration ensured that the LESA model accurately reflected the specific conditions in the study area and was based on expert opinion and empirical evidence.

The support of local experts was important for several key reasons. First, in selecting factors, the committee identified important factors affecting land potential at the local level, incorporating broader factors such as soil type and socio-economic environmental conditions They are the. Finally, within the scale of the items, the committee developed a customized scale for each item, reflecting the range of conditions observed in the study area This scale established a solid framework for quantification and comparison suitable arable land types. These scales established a robust framework for quantifying and comparing the agricultural suitability of different land parcels.

By incorporating the expertise and insights of the local committee, the revised LESA model provides a more accurate and contextual assessment of soil potential. This optimal approach facilitates this informed and efficient land use planning and decision-making, thus enhancing sustainable agricultural development in the region Knowledge through this process access to it enables better quality allocation, better crop selection, and ultimately higher agricultural productivity. The LESA approach is calculated as follows9:

(i) LE part.

In this section, classifications such as soil productivity index, prime farmland, and land capability are utilized to evaluate the agricultural potential of the land. The land capability classification system ranks land from Class I, which has the fewest limitations, to Class VIII, which has the most severe limitations, taking into account factors such as soil type, climate, and topography. The soil productivity index evaluates soil quality by considering fertility, texture, structure, organic matter content, and pH levels, thereby identifying areas with high agricultural potential. Prime farmland classification pinpoints lands that are most suitable for agriculture, based on their optimal physical and chemical properties. Together, these evaluations offer a comprehensive assessment, providing essential guidance for sustainable land use planning and agricultural management.

(1) Classification of land capability.

The FAO method classifies land into categories ranging from Class I to Class VIII, based on its suitability for agricultural use. Each class represents the land’s potential for sustainable agriculture, along with the specific limitations that must be managed. The classification categories are as follows:

Class I: Land with very few limitations, suitable for a wide range of crops with minimal management.

Class II: Land with some limitations that require moderate conservation practices.

Class III: Land with significant limitations needing careful management and conservation practices.

Class IV: Land with severe limitations, suitable for some crops but requiring very careful management.

Class V: Land generally unsuitable for cultivation but can be used for grazing, forestry, or wildlife habitat with appropriate management.

Class VI: Land with very severe limitations, unsuitable for cultivation, and limited use for grazing or forestry.

Class VII: Land with extremely severe limitations, primarily used for grazing, forestry, or wildlife habitat under very restricted conditions.

Class VIII: Land unsuitable for agriculture or commercial forestry, best used for recreation, wildlife habitat, or conservation purposes.

(2) Soil productivity index.

This region is primarily dedicated to producing agronomic crops such as corn, wheat, and alfalfa. The Soil Productivity Index (SPI) for this area was calculated using Eq. (3), which is based on expected yields under optimal management practices23. The SPI assigns values ranging from 0 to 100, reflecting the soil’s potential yield capacity. Leveraging the expertise of local agricultural managers, target yields for key crops in the region have been established: alfalfa at approximately 5.2 tons per hectare, wheat at about 57 tons per hectare, and forage corn at around 6.1 tons per hectare. These figures highlight the region’s agricultural productivity potential, providing a benchmark for assessing soil quality and guiding management practices to optimize crop production.

$$\:SPI=\left[(Corn\:yield/62)+(Wheat\:yield/5.5)+(Alfalfa\:yield/4.5)\right]\times\:100$$
(3)

(3) Classification of prime farmland.

The area is categorized into six distinct classes based on their suitability for agricultural use and various conditions. These classes are:

  1. 1)

    prime farmland: land that is optimal for agricultural production with no significant limitations

  2. 2)

    important farmland: land that is very good for agricultural production but May have minor limitations

  3. 3)

    Prime Farmland if Draining: Land that becomes prime for agricultural use if adequate drainage measures are implemented,

  4. 4)

    Prime Farmland if something done for Flooding Protection: Land suitable for agriculture if it is safeguarded against flooding or if flooding is infrequent,

  5. 5)

    Prime Farmland if something done for Flooding Protection During the agronomy Season: Land that requires both flood protection and drainage during the growing season to be suitable for prime agricultural use, and.

  6. 6)

    Not-Prime Farmland: Land that does not meet the criteria for prime farmland due to significant limitations.

(ii) SA part.

The SA Section9, was meticulously developed by the LESA committee, developed on their extensive insights, knowledge, and experience. This section thoughtfully integrates non-soil-related data, which is organized into three primary subsections:

SA (1): Influencing non-soil related features on agricultural productions.

In this category of SA factors, the LESA committee highlighted the significance of the area and land use of neighboring fields in shaping agricultural productivity (Table 2). The sub-factors considered include:

Compatibility of Surrounding Land Use: This sub-factor evaluates the influence of adjacent land uses on agricultural activities. Compatible land uses, such as other agricultural areas or open spaces, can bolster and enhance farming productivity. In contrast, incompatible land uses, such as industrial or urban developments, may lead to conflicts and diminish the efficiency and effectiveness of farming operations.

Agricultural Land Area within 1.5 Miles: This sub-factor measures the proportion of land dedicated to agriculture within a 1.5-mile radius. A higher percentage of agricultural land in this vicinity suggests a strong agricultural community and infrastructure, which can significantly boost the viability and sustainability of farming operations.

Availability of Support Services: This sub-factor examines the accessibility of essential agricultural support services, including equipment suppliers, repair facilities, extension services, and markets. The presence of these services is vital for effective farm management and can have a substantial impact on the productivity and profitability of agricultural operations.

Table 2 Scores considered for SA (1).
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SA (2): Pressures of development on farming.

In this category of SA factors, the LESA committee identified key infrastructure elements—road networks, drainage, and irrigation systems—as essential for agricultural productivity in the region (Table 3). These components are critical for ensuring efficient farm operations, market accessibility, and effective water management. The selected sub-factors include:

  • Distance to Urban Feeder Highway: This sub-factor evaluates the proximity of farmland to major highways that connect rural areas to urban centers. Closer proximity to these highways enhances the transportation of agricultural products to markets, reducing both costs and transit time, thereby improving market access for farmers.

  • Distance to Public Water: This sub-factor assesses the accessibility of public water sources necessary for irrigation and other agricultural uses. Proximity to reliable water sources is crucial for sustaining crop health and ensuring consistent agricultural productivity, particularly in areas prone to drought or with irregular rainfall.

  • Distance to Public Road: This sub-factor measures the distance from farmland to public roads, which are essential for the daily movement of goods, services, and labor. Efficient road access improves the overall effectiveness of farm operations by facilitating the timely delivery of inputs and the swift transportation of produce to markets.

  • Distance to Public Drainage Systems: This sub-factor considers the proximity to public drainage systems, which are vital for managing excess water and preventing field waterlogging. Effective drainage systems are key to protecting crops from water damage, maintaining soil health, and preventing yield losses due to excessive moisture.

Table 3 Scores considered for SA (2).
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SA (3): The values of public on farming activities.

Based on the insights of the LESA committee, the following sub-factors were incorporated to assess a site’s suitability for agricultural production, taking into account environmental and cultural considerations (Table 4):

  • Proximity to Historic Buildings: This sub-factor measures the distance between farmland and historic buildings or cultural landmarks. The proximity to such sites can impact land use decisions due to preservation regulations and potential opportunities for tourism. Additionally, historic buildings can enhance the cultural value of the land, making them an important factor in agricultural planning.

  • Proximity to Wetlands and Riparian Areas: This sub-factor evaluates the closeness of farmland to wetlands and riparian zones, which are vital for maintaining biodiversity, water quality, and ecosystem services. Farms near these sensitive areas require careful management to avoid negative environmental impacts. However, these areas can also offer benefits to agriculture, such as natural water filtration and flood mitigation.

  • Environmentally Sensitive Areas: This sub-factor assesses the location of farmland relative to environmentally sensitive areas, including habitats for endangered species, protected natural areas, and regions prone to erosion or other environmental risks. Agricultural practices near these areas must comply with strict regulations to protect ecological integrity and prevent environmental degradation. Recognizing and respecting environmentally sensitive areas is crucial for sustainable land management and conservation efforts.

Table 4 Scores considered for SA (3).
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(iii) Input Factors Weights to the LESA.

The LESA committee, composed of 15 agricultural experts, was crucial in developing the weighting factors for the LESA model, blending both soil-related and non-soil-related data to assess land suitability for agriculture (Table 5). Drawing on their vast experience, the committee incorporated soil-related data to indirectly measure the environmental and economic aspects of farming, highlighting the importance of soil quality in sustainable agriculture. As a result, the land capability classification factor was given the highest weight in the LE portion of the model, underscoring its significance in agricultural productivity. Simultaneously, the committee recognized the impact of non-soil-related factors, assigning a weight of 0.3 to SA-1, which includes socio-economic conditions, environmental impact, and land use compatibility. These factors are critical in understanding the broader agricultural context, influencing both sustainability and profitability. Furthermore, the committee emphasized the importance of infrastructure, prioritizing SA-2 factors such as irrigation systems, sewer systems, and road networks, which are essential for effective farm operations. The careful balance between soil-related and non-soil-related factors ensures a comprehensive evaluation of agricultural land. This holistic approach allows for informed decision-making that supports sustainable agricultural development. The LESA model thus serves as a robust tool for guiding land use planning and agricultural policy.

Table 5 Various input data weight to the LESA.
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The weights defined in Table 5 are then applied to calculate the LESA score using a specific Eq. (4). This equation integrates the weighted values of various factors explained LE and SA parts to provide a clear and quantifiable measure of the land’s suitability for agricultural use:

$$\:LESA\:score=\sum_{i=1}^{n}{W}_{i}{\mu\:}_{i}\left(x\right)$$
(4)

Where \(\:{\mu\:}_{i}\left(x\right)\) represents the points assigned to each factor, and Wi is the weight assigned for each factor, ensuring that that \(\:{W}_{i}\in\:\left[0.\:1\right]\), \(\:\sum_{i=1}^{n}{W}_{i}=1\).

Model performance

To assess the accuracy and performance of the developed models, we utilized several statistical indices. These indices included Mean Error (ME), Root Mean Square Error (RMSE), and the Coefficient of Determination (R2):

Mean Error (ME): This index measures the average deviation between predicted and observed values, providing a clear indication of the model’s bias or systematic error. By analyzing this deviation, the index helps determine whether the model consistently overestimates or underestimates the actual values, providing a crucial measure of its accuracy.

$$\:ME=\frac{1}{N}\sum_{i=1}^{N}(\widehat{{P}_{i}}-{P}_{i})$$
(5)

Root Mean Square Error (RMSE): This index assesses the model’s prediction accuracy by calculating the square root of the average squared differences between the predicted and observed values. By focusing on the squared differences, it ensures that both small and large errors are appropriately accounted for, making it a sensitive measure of overall prediction accuracy. The result provides a comprehensive understanding of the model’s error magnitude, reflecting how closely the model’s predictions align with actual observations. It is computed as:

$$\:RMSE={\left[\sum_{i=1}^{N}(\widehat{{P}_{i}}{-{P}_{i})}^{2}/N\right]}^{0.5}$$
(6)

Coefficient of Determination (R2): This index assesses how much of the variance in the observed data can be explained or predicted by the independent variables in the model. It is a key measure of the model’s goodness of fit, reflecting how accurately the model represents the actual data, expressed as:

$$\:{R}^{2}=1-\left[\sum_{i=1}^{N}({{P}_{i}-\widehat{{P}_{i}})}^{2}\Bigg/\left(\sum_{i=1}^{N}({{P}_{i})}^{2}-\frac{{\left(\sum_{i=1}^{N}{P}_{i}\right)}^{2}}{N}\right)\right]$$
(7)

where, Pi is measured data, N is the sampled point and \(\:\widehat{{P}_{\text{i}}}\) is predicted data.

Results and discussion

The soil features

The parameters for soil feature mapping in ArcGIS are described in detail in Table 6, where the corresponding map is given in Fig. 2. These parameters were chosen in order to capture the geological features changes in location accuracy. Quasi-variance diagrams with heterogeneous characteristics were identified as the most appropriate models, and spherical, Gaussian, and exponential quasi-variations were the most accurate to represent these factors in the models tested, and to establish direction a it depends on the spatial variation of this soil type is emphasized. The C0/Sil ratio was used to assess site dependence, and Cambardella et al. (1994)24, and the author. This study found that soil properties such as calcium carbonate equivalent (CCE), depth, silt, clay, gravel exhibit a moderate site dependence, with values ​​ranging from 0.25 to 0.75 This moderate reliability can be attributed to factors such as management practices, including field and fertilization, and its elevation changes in the study area Accordingly in contrast, soil electrical conductivity (EC), pH, and sand content showed significant spatial dependence, with coefficients less than 0.25, indicating a strong influence of local conditions den Besides, changes in organic carbon (OC) values ​​were found to affect temperature-dependent microbial activity) and he wrote. This decrease in microbial activity contributes to the observed spatial variation in OC concentrations. To further examine heterogeneity, a k factor representing the proportion of the major to minor range was used. The K factor, as recommended by researcher exceeded one in all studies of the soil properties studied, indicating large directional variability.

Table 7 gives the results of the Ordinary Kriging (OK) to map the soil-related factors. Figure 2 shows the predicted maps of these soil properties. These maps offer valuable insights into the complex patterns of soil characteristics, essential for informed agricultural planning and land management. These maps not only indicates the spatial variability of soil characteristics but also provide insights into how this pattern influences crop suitability. For instance, areas with higher pH (Fig. 2a) and CCE (Fig. 2c) might be more suited for crops tolerant to calcareous conditions, while area with higher OC (Fig. 2d) and moderate clay (Fig. 2e) could provide crops requiring fertile, well-aggregated soils. Conversely, areas with high gravel content (Fig. 2h) or shallow soil depth (Fig. 2i) might face restrictions for deep-rooted crops and might be better for rangeland or agroforestry practices. By understanding these spatial distributions, stakeholders can make more precise decisions regarding crop selection, fertilization, and other management practices. The use of OK ensures reliable predictions, making these maps crucial tools for optimizing agricultural productivity and sustainability.

Fig. 2
figure 2

The maps of soil properties including a pH, b electrical conductivity, c calcium carbonate equivalent, d soil organic carbon, e clay, f silt, g sand, h gravel and i depth parameters.

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Table 6 Semi-variograms analysis results for measured soil factors.
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Table 7 The OK (ordinary Kriging) applying results for this area.
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Land capability evaluation

LE part

Figure 3 provides a comprehensive breakdown of the subsections within the LE part, including the Soil Productivity Index (SPI), classification of prime farmland, and classification of land capability. These classifications are essential for understanding how various soil characteristics impact agricultural productivity across the region. Specifically, soil salinity and texture are critical in significantly influencing crop yields. The land capability subsection evaluates these influences by categorizing soils into classes ranging from I to III, where higher classes indicate greater limitations for agricultural use. As shown in Table 8, 39.05% of the area have Class I, 60.82% into Class II, and only 0.12% into Class III, indicating the diverse agricultural suitability within the area. Figure 3also shows that soils in the northern and eastern parts of the area are mainly Class I, indicating minimal restrictions for crop farming. These areas are characterized by loam and clay loam soils, making them ideal for high agricultural productivity. The importance of soil texture for identifying agricultural success has been reported by Ostovari et al. (2019) and Kazemi et al. (2016)4,28, along with the expert opinions of the LESA committee. In addition, the classification of prime farmland and the SPI has an important role in the assessment of the region’s agricultural potential. Table 8 also presents the results for primary agricultural land and SPI classification, while Fig. 3 illustrates these classifications in the study area. The SPI map shows high levels of agricultural initiatives in the southwestern, eastern and northern regions, aimed at improving crop yields. Moreover, the eastern and northeastern parts of the region are identified as prime farmlands, where strategic investments in resources and energy could substantially boost agricultural production. The detailed mapping and classification provided in this study offer valuable insights for optimizing agricultural practices and land management strategies. By understanding the spatial distribution of soil quality, capability, and productivity, stakeholders can make informed decisions that support sustainable and efficient land use. This comprehensive analysis not only highlights areas of high agricultural potential but also identifies regions where targeted interventions could enhance productivity. Ultimately, these insights contribute to long-term agricultural sustainability, by promoting effective management of the region’s soil resources to support continued agricultural success.

Fig. 3
figure 3

The maps of LE part such as a classification of land capability, b soil productivity index, and c classification of prime farmland.

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Table 8 Scores of LE components in the studied area.
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SA factors

The SA factors in the LESA method encompass non-soil characteristics that significantly impact agricultural productivity. Figure 4 illustrates the spatial distribution of various SA factors, while Table 9 provides a detailed analysis of the corresponding results. The SA (1) factors assess the influence of non-soil-related parameters on agricultural production, focusing on three key sub-factors identified by the committee: area characteristics, agricultural support services, and their importance for commercial farming activities. The SA (2) factors address developmental pressures that affect agricultural production, such as urban feeder highways, road networks, and critical infrastructure like irrigation and drainage systems. These elements are vital for establishing a sustainable agricultural foundation. Additionally, the SA (3) factors, also informed by committee input, consider environmental and cultural influences, including the presence of wetlands and historic buildings, which can pose significant threats to farming activities.

Fig. 4
figure 4

The maps of SA part such as a SA (1), b SA (2) and c SA (3).

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Similar studies further support the importance of incorporating soil properties in geophysical research. For example, Dang and Sugumaran (2005) used non-land data such as development potential and agricultural output to assess land in the USA as well as Hoobler et al. (2003) refined the LESA method to integrate features such as locations near highways, drains, and historic buildings. These approaches emphasize the important role of non-land factors in ensuring the sustainability of agricultural practices by providing a comprehensive analysis of land potential. However, it is important to note that the weighting of SA factors can vary significantly across different geographic scales, introducing potential discrepancies in LESA results. For instance, in smaller regions with limited infrastructure, the influence of SA (2) factors like road networks may be more pronounced, whereas in larger, well-connected areas, SA (1) factors such as access to agricultural support services might carry greater weight. To address this limitation, future LESA applications should consider scale-dependent weighting schemes that are adaptable to the specific characteristics of the study area. Incorporating multi-scale validation or sensitivity analysis could further enhance the replicability and robustness of the LESA methodology in diverse geographic contexts. By including these diverse elements, the LESA method offers a more holistic evaluation, supporting informed decision-making and the long-term viability of agricultural lands.

Table 9 Scores of SA part in the studied area.
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Employing LESA system

The LESA scores were determined by applying a linear additive model that weights various factors explained LE and SA parts, as detailed in Eq. (4). Figure 5 presents the map of land potential generated using the LESA methodology, which categorizes land capability for cropping systems based on the LESA committee’s guidelines. These categories are defined as “>80 points” (best), “60–80 points” (good), “40–60 points” (marginal), and “<40 points” (not suitable). According to the distribution of these classifications, as shown in Table 10, 7.59% of the region is classified as “marginal,” 74.96% as “good,” and 17.45% as “best” for crop farming. Higher LESA scores indicate greater suitability for crop production, which is clearly reflected in the land capability map (Fig. 5). The northwestern and southeastern parts of the region exhibit higher LESA scores, indicating better land potential for agriculture, while the central, eastern, and western areas show lower scores. This variation correlates with the differences observed in the LE and SA components (Figs. 3 and 4), suggesting that regions with higher LESA scores benefit from superior soil quality and favorable non-agricultural conditions.

Fig. 5
figure 5

Land potential map by employing LESA method (>80 points: best, 60–80 points: good, 40–60 points: marginal, and <40 points: not suitable).

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Various studies have emphasized the need to evaluate soil capacity assessment methods to different field conditions such as the studied area13,25,26,27,28, Large fields; located in China Using LESA method to identify, classify these lands and identify farmland consolidation and development efforts To integrate geopotential data from LESA with Geographic Information System (GIS) technology a, it improves the efficiency of the decision-making process, making it faster, more innovative and more versatile3,11. However, it is important to acknowledge the limitations of the LESA method, in particular, it cannot adequately account for dynamic land-use changes over time, such as urban expansion, cropping pattern trends, rotation socioeconomically or these variables can affect the greater earth potential It assumes a steady state. Moreover, although the linear additive model is effective for various LE and SA factors, it does not account for possible nonlinear interactions between these factors, which may affect the accuracy of the results in complex systems can increase variation.

By leveraging LESA scores and GIS tools, agricultural planners can make informed decisions that optimize land use, improve crop yields, and support sustainable farming practices across different regions.

Table 10 The LESA system scores and areas.
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Conclusion

This study examines the use of the Land Evaluation and Site Assessment (LESA) method, developed by the NRCS, in combination with ArcGIS to assess land capability at a watershed scale in China. The LESA method integrates two key components: Land Evaluation (LE), focusing on soil productivity, prime farmland classification, and land capability, and Site Assessment (SA), which evaluates support services, developmental pressures, and public values like environmental and cultural factors. Using the FAO land capability classification system, the study found that 39.05% of the area is Class I, 60.82% is Class II, and 0.12% is Class III for crop farming. Incorporating GIS analysis with the LESA method further categorized 7.59% of the area as “marginal,” 74.96% as “good,” and 17.45% as “best” for agricultural activities. The integration of LESA with GIS provides an approach by combining soil and non-soil factors to optimize land use. This methodology enhances agricultural productivity and sustainability, enabling stakeholders to make informed decisions that balance environmental, socio-economic, and agricultural needs. It supports preserving high-quality farmland and strategically utilizing resources in diverse areas, ensuring sustainable agricultural practices and long-term viability. Future research should focus on incorporating advanced techniques, such as machine learning, could also refine LESA’s predictive capabilities and improve its responsiveness to complex agricultural challenges.