Abstract
Cornell Comprehensive Assessment of Soil Health (CASH) approach was used to assess soil health status in EL Koudia, a representative site of rainfed agricultural perimeters of the semiarid Northwest Morocco. The main objective was to assess the soil’s potential to promote crop productivity while enforcing environmental sustainability in the study site. We examined 11 soil health indicators encompassing physical, chemical and biological soil properties, including bulk density (Bd), available water capacity (AWC), wet aggregate stability (WAS), pH, active carbon (ActC), organic matter (OM), soil protein index (SPI), soil respiration (Resp), phosphorus (P), potassium (K), and micronutrients (Mg, Mn, Fe, and Zn). Results showed that El Koudia’s soil revealed a non-compacted structure (Bd= 1.41g/cm3) and a significant variability of AWC (0.17g/g) and WAS (12.3, 9.6, and 9 for 0-5cm, 10-15cm, and 5-10cm layers respectively). Chemical indicators showed few differences related to depth, except for pH, P, and Zn in topsoil. Biological indicators varied significantly along soil depth, indicating richer and more active microbial communities in the upper layers. Overall, according to Cornell CASH scoring, EL Koudia soils reveal significant limitations in WAS, SPI and pH, likely stemming from prolonged intensive tillage and continuous monocropping system, and thus raising the necessity of more efficient soil management strategies. With an overall score of 48, EL Koudia soils fall into the medium range indicating room for improvement. Through this study, the CASH of EL Koudia site provides opportunities to deepen our understanding of soil quality, pinpoint targeted management interventions, and monitor soil health evolution.
Introduction
Soil is a vital, invaluable, and non-renewable resource that plays a critical role in food security and ecological balance (, ). Its degradation or loss is a quasi-permanent process that cannot be reversed within a human lifespan, as an inch of topsoil takes over a century to form (, ). Moreover, the increasing demand for food production, both in quality and quantity, is driving anthropogenic pressures on agricultural soils. This phenomenon, combined to global environmental challenges, is affecting approximatively one-third of the word’s soils in terms of organic matter loss, salinization, compaction, erosion, and other soil degradation manifestations (–). It is therefore essential to protect and value soils, both for their crucial roles in food security and sustaining vital ecosystem services (9).
In Morocco, the agricultural sector is a key pillar of economic growth, generating nearly 15% of GDP, employing around 40% of active population and supporting rural livelihoods and national development. Still, Moroccan agriculture does nevertheless face several challenges with 40% of agricultural land suffering from severe soil erosion, (10); salinization affecting an estimated area of 700,000 ha, of which 500,000 ha is irrigated (11); as well as many other degradation forms such as soil compaction, loss of organic matter due to intensive tillage; deforestation and low vegetation cover. Given the scale of this degradation trends and their impact on soil functions, it appears essential to adopt comprehensive approaches and indicators, not only to assess the losses but also to measure soil resilience through its health and functionality.
Soil health (SH), defined by FAO (12) as “its ability to function as a living system”, is essential to ensure long-term food security of the continuously growing population. It defines the soil as a vital ecosystem which must be appropriately managed to restore and maintain its ability to optimally function (13). Doran and Parkin (14) and Bini et al. (15) further elucidate that SH sustains both biological productivity and environmental quality, while promoting plant and animal health. This concept has gained increasing interest among scientists, farmers and policy makers, mainly during the last few decades (9). As well, the agricultural sector has significantly shifted its understanding of soil health, recognizing the importance of biological and physical factors alongside conventional chemical properties especially, since physical and biological soil degradation started to spread widely. The need to develop holistic assessment approaches has driven both researchers and farmers to improve the assessment and management of soil biological and physical function (16). Consequently, the concept of soil health nowadays involves the integration of the soil’s physical, biological, and chemical components (17, 18).
Several efforts are currently made to protect and maintain soil functions for future use. Acknowledging the need to sustain, restore and build healthy soils for enduring agricultural production is gaining more place, and there was some advancement in this area, still considerable work is required to be done. The Cornell Comprehensive Assessment of Soil Health (CASH) methodology, on which this study is based, was established to evaluate soil health through physical, chemical, and biological properties, enabling an understanding of soil processes and the identification of particular limitations that might influence soil management. The CASH methodology is the subject of several previous research works across various contexts. As a case in point, Doran and Parkin (14) used CASH scoring to evaluate soil quality in agricultural systems and emphasize the importance of sustainable management, while Yu and Zhao (19) assessed soil health under varying land-use systems and highlighted the impact of management practices on soil quality. As well, Pérez et al. (20) used CASH to assess the impacts of organic amendments on soil health, which showcased its use in promoting sustainable agriculture. Identifying appropriate management strategies not only fulfil environmental sustainability objectives, but it also enhances current research efforts on sustainable agriculture and ecosystem services. This is achievable by pinpointing specific constraints within soil systems, as applied through the CASH approach, which provides a diagnostic framework for the physical and chemical as well as biological components of soil and offers a straightforward pathway towards appropriate management practices while ensuring environmental sustainability (17, 21).
Beside its role in agricultural productivity and ecosystem functioning, soil health is increasingly recognized as a key component of food security and human health through the soil-crop-food pathway (22). Healthy soils enhance soil structure and stability, maintain nutrient availability, support soil biodiversity, sequester carbon and mitigate climate change effects, regulate water dynamics (23, 24). Conversely, ongoing soil degradation, particularly through erosion, poor water retention, organic matter loss, nutrients depletion and reduced microbial activity decrease crop production and may diminish crop nutritional quality and exacerbate malnutrition worldwide particularly in semi-arid environment (22). Together, these constraints can compromise human well-being by decreasing food availability, reducing nutritional crop quality, and ultimately, limiting long-term agricultural sustainability. For this reason, maintaining and restoring soil health through sustainable management practices is essential not only for environmental conservation and crop resilience, but also for supporting long-term food security and public health sustainability.
The aim of this research is to develop a complete evaluation and an effective oversight of the soil health at EL Koudia research station in Northwest Morocco, using CASH approach. The specific objectives were to: (i) develop a baseline assessment of the physical, chemical and biological soil indicators of the site, (ii) identify soil constraints and properties influenced by management practices or environmental factors; and (iii) provide management recommendations to improve soil functioning and sustainability. This baseline evaluation was also intended to support future investigations on the effects of different tillage systems, including conventional tillage, reduced tillage, and no-tillage systems on soil health in semi-arid agricultural systems.
2 Materials and methods
2.1 presentation of the research site
The research was carried out on a 2.3ha study site in El Koudia experimental station, a research facility of the National Institute of Agricultural Research (INRA) located at 30 km Southwest of Rabat, in the Northeast coastal area of Morocco (Figure 1). The geographic coordinates of the site are 6° 57’ 14.3¨ W and 33° 47’ 21.3¨ N (25). The region is under semiarid Mediterranean climate, with hot and dry summers and mild winters. The mean annual temperature is 18 °C and the mean annual rainfall is 460 mm. The dominant cropping system in the study zone is wheat monocropping, with an average tillage depth of 20 cm.
Figure 1
2.2 Soil sampling and analysis
The soil type of the study site is non-calcareous Chromic Luvisol (25). Soil sampling was conducted in autumn (November), prior to seeding period. A regular 3 x 3 grid sampling design (Figure 2) was established within the 2.3 ha experimental field (270 m x 85 m) to ensure representative spatial coverage of the study area. The field consisted of nine experimental blocks arranged in three rows and three columns. Each block measured 67m x 21m, with 4m buffer strips between adjacent rows to avoid interference. One sampling point was located at the center of each block, resulting in nine sampling locations distributed uniformly across the field. At each sampling point, soil samples were collected at three depth intervals (0-5, 5-10, 10–15 cm), yielding a total of 27 soil samples (reflecting the shallow, little−developed soil profile at the site). This sampling design was adopted to capture the spatial variability of soil properties while providing a reliable baseline assessment of soil health. Subsequently, soil samples were air-dried, crushed and sieved to 8 mm, 2mm and 0.2 mm, then stored in boxes for further analysis. Part of the soil samples were kept in a cooler during field collection, then transferred to a cold room in the laboratory to preserve soil biological indicators and not to interfere with the biological analyses carried out subsequently. Soil testing was performed in two laboratories: The Soil Health Laboratory the School of Integrative Plant Science at Cornell University in Ithaca, NY (USA) and the soil Physics Laboratory at INRA-Rabat (Morocco). Additionally, nine bulk density samples were taken in the field using 10 cm deep cylinders without soil disturbance. The analysis and processing of physical, chemical, and biological soil health indicators were carried out in line with the CASH protocol (21), encompassing the following aspects:
Figure 2
2.2.1 Physical indicators
Particle size distribution analysis was carried out using the rapid quantitative method developed by Kettler et al. (26). This method is based on the dispersion of soil mineral particles (sand, clay, and silt) through a 3% sodium hexametaphosphate solution. Sequential steps involving sieving and sedimentation of the soil solution were employed to segregate distinct particle sizes.
Bulk density (Bd) is a key indicator of soil compaction and reflects the soil’s physical health, including its structural support, water movement, solute transport, and aeration. Bd was measured using the mass-volume ratio before and after dried at 105 °C for 48 hours, as outlined by Grossman and Reinsch (27).
Wet Aggregate Stability (WAS) serves as a reliable indication for assessing both physical and biological health of soil. Strong aggregate stability helps averting crusting, runoff, and erosion while promoting aeration, water infiltration rates, water retention, and the health of roots and microorganism’s health (28–31). WAS was measured using the Cornell Rainfall Simulator, where a simulated 12.5 mm rainfall event is applied during 5 min at the sample placed on 0.25 mm sieve. WAS is then calculated by subtracting the weight of the dried sieved particles from the initial weight of the soil before rainfall exposure (21, 32).
Available water capacity (AWC) is an important parameter that indicates the amount of water that soil can store for crop utilization, which affects crop productivity in water-stressed conditions. AWC is determined based on water content at field capacity (θfc) and at permanent wilting point (θpwp). Soil samples are saturated and equilibrated to – 10 kPa (θfc) and -1500 kPa (θpwp) pressures using ceramic plates in air pressure chambers (33, 34).
2.2.2 Chemical indicators
Soil acidity (pH) was measured in a 1:1 water/soil ratio. The considered soil nutrients (P, K, Mg, Fe, Mn and Zn) were extracted using ammonium acetate at pH 4.8 and measured by Inductively Coupled Plasma Optical Emission Spectroscopy (21, 35, 36). All nutrient contents were calculated per mass of soil (mg/kg).
2.2.3 Biological indicators
Soil organic Matter (SOM) is a key indicator of soil health. It significantly affects water retention, soil aggregate stabilization, ionic exchange capacity and nutrient cycling. SOM also acts as a long-term reservoir for nutrients. SOM content was determined by measuring mass loss on ignition in a muffle furnace at 500 °C for two hours. The percentage loss is then multiplied by a 0.7 and adjusted by substracting 0.23 (21, 25).
Active Carbon (ActC) is the accessible form of organic carbon. It is an energy source for soil microorganisms, thus a good indicator of soil biological health. It is often more sensible to management changes than the total organic matter content. ActC was measured by adding potassium permanganate solution (KMnO4) to the soil to oxidize the ActC, then measuring the absorbance at 550 nm using a hand-held colorimeter (Hach, Loveland, CO) (37).
Autoclaved Citrate Extractable (ACE) Soil protein Index (SPI): protein content is highly correlated to the overall soil health thanks to the information it holds on the soil biological and chemical health index, particularly the quality of soil organic matter. SPI represents the nitrogen (N) pool bound to soil organic matter that can be mineralized by microbial activity for plant uptake. It was measured by extracting proteins from soil through several centrifugation and autoclaving steps using 0.02 M sodium citrate at pH 7. To determine the soil protein concentration, we used a bicinchoninic acid assay a standard curve of bovine serum albunim (38, 39).
Soil respiration (Resp) indicates the state of the soil biological soil community. It reflects both the abundance and activity of microbial life. This biological activity is crucial for recycling nutrients from soil organic matter, particularly converting nitrogen into different forms, and breaking down organic residues. It also influences key physical properties like OM accumulation and aggregate formation and stabilization. Resp was measured by trapping and quantifying carbon dioxide (CO2) emitted by soil microorganisms during a four-day incubation in a sealed chamber with a KOH trap solution. An electrical conductivity meter measures the CO2 released (35, 40).
2.3 Scoring system
Cornell CASH approach uses scoring functions to convert the results of each soil health indicator into interpretive scores via a curve that attributes scores ranging from 0 to 100 corresponding to the measured values. Each measurement is compared to a standardized value following the approach developed by Andrews et al. (41). A Gaussian distribution function, as suggested by Arshad and Martin (42), is applied for each indicator Equation 1:
(35)
Where µ and σ are the mean and standard deviation of samples within the Cornell Soil Health Lab database. The integral of the Equation 1 provides the cumulative normal distribution function (CND), which is then multiplied by 100 to serve as a soil health scoring function (35, 43). The curve’s shape determines the scoring interpretation: « More is better » for normal shape, « Optimum range » for a linear shape, and « less is better » for other shapes (21, 44).
Chemical indicators allocate elevated scores for values within the optimal range for most soils. Scores decrease as the difference between the measured value and optimal value increases. Conversely, physical and biological indicators often award higher scores for higher measured values, except for indicators like bulk density that score higher for lower values. While some indicators correlate strongly with soil texture, many require distinct scoring functions for coarse, medium, and fine texture.
These functions were developed based on the distribution of measured indicator values in regional soils with similar textures (43). The scoring system is as follows:
- Below 20%: Very low level (red), reflecting a constraint.
- 20-40%: Low level (Orange), indicating low functioning.
- 40-60%: Suboptimal functioning (yellow).
- 60-80%: Excellent functioning (light green).
- Above 80%: Optimal or near-optimal functioning (dark green).
Micronutrients evaluation involves assessing deficiency or sufficiency, reported as a score from the average of four subscores for Mg, Fe, Mn, and Zn. Each micronutrient value (measured in ppm) receives a subscore of 100 (if the value is optimal) or a subscore of 0 (if the value is deficient/excessive). The mean of these subscores is then converted into a final score (0, 4, 11, 56, or 100) according to an empirical scoring defined by Cornell CASH: an average of 100 corresponds to a score of 100 (excellent, colored dark green). If a micronutrient is deficient or excessive, the average subscore is 75, resulting in an overall score of 56 (moderate, colored yellow). If two, three or all four micronutrients are deficient or excessive, the mean subscore becomes 50, 25 or 0, respectively, giving overall scores of 11, 4, or 0 (poor, colored red). The values 4, 11, and 56 therefore reflect, in a hierarchical and empirical way, the number of problematic micronutrients and the overall level of soil adequacy (Table 1: A & B) (35).
Table 1
| A. Micronutrient | Subscore (ppm) | |
|---|---|---|
| 0 | 100 | |
| Magnesium | < 33 | ≥ 33 |
| Iron | > 25 | ≤ 25 |
| Manganese | > 50 | ≤ 50 |
| Zinc | < 0.25 | ≥ 0.25 |
| B. Mean of micronutrient subscores | Overall micronutrient score | |
| 100 (all adequate) | 100 | |
| 75 (3 of 4) | 56 | |
| 50 (2 of 4) | 11 | |
| 25 (1 of 4) | 4 | |
| 0 (0 of 4) | 0 | |
(A) Optimal value ranges (ppm) for micronutrients for all soil samples. Individual micronutrient subscores can be either 0 (sub-optimal) or 100 (optimal), based on the values. (B) Overall micronutrient score using the mean of the four subscores (35).
Once all indicators have been scored and color-coded appropriately, the overall soil health score in CASH is obtained as a statistically weighted combination (derived from its database) of the physical, chemical and biological functional scores, normalized to a final value between 0 and 100 (21). This score provides insight into the soil’s overall health status. However, the key is identifying specific soil processes facing constraints or operating sub-optimally, allowing targeted management strategies to address these issues.
In the present study, the overall soil health scores were obtained directly from the Cornell CASH report. Individual indicators were transformed into standardized scores using the CASH scoring functions, and the final overall score for each depth was generated automatically by the CASH assessment system. No additional weighting, statistical adjustment, or manual calculations were performed by the authors.
2.4 Statistical analysis
Descriptive statistics (Max, Min, Mean and Std) were used to characterize soil properties. A one-way ANOVA was performed to test the effect of soil depth (0–5, 5–10, and 10–15 cm) on each soil property, with depth treated as a fixed factor. The statistical model was specified as Yij=μ+di+ϵij, where μ is the overall mean, di is the effect of depth I, and ϵi the residual error. The experimental unit was the soil sample, with nine independent replicates per depth obtained from a 3 x 3 sampling grid. The assumptions of normality and homogeneity of variances were checked visually using residual plots and Q–Q plots, no major violations were detected. Means were compared using Bonferroni-adjusted post-hoc tests.
A Pearson correlation analysis was conducted to assess pairwise relationships between all soil variables. Given the large number of pairwise comparisons, this analysis is presented as exploratory. Emphasis was placed on the strongest and biologically meaningful associations.
All statistical analyses were conducted using GENSTAT software (18th edition).
3 Results
Table 2 summarizes soil health indicator statistics by depth. The soil at the El Koudia site exhibits a balanced texture with a high percentage of sand (57%) across all depths, indicating a sandy loam texture at 0–5 cm and sandy clay loam at deeper layers.
Table 2
| Indicator | N | 0–5 cm | 5–10 cm | 10–15 cm | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Min | Max | Mean | Std dev | Min | Max | Mean | Std dev | Min | Max | Mean | Std dev | ||
| % Sand | 9 | 50 | 60 | 56.89a | 2.934 | 55 | 58 | 56.89a | 0.93 | 56 | 59 | 57a | 1.5 |
| % Silt | 9 | 21 | 24 | 22.11b | 0.928 | 20 | 24 | 21.22a | 1.48 | 20 | 24 | 21.67ab | 1.32 |
| % Clay | 9 | 17 | 25 | 19.56a | 2.297 | 17 | 21 | 20.22a | 1.30 | 19 | 22 | 20.11a | 1.45 |
| AWC (g/g) | 9 | 0.15 | 0.2 | 0.176a | 0.021 | 0.14 | 0.21 | 0.174a | 0.02 | 0.11 | 0.2 | 0.164a | 0.03 |
| WAS (%) | 9 | 8.4 | 19.7 | 12.31b | 3.706 | 5.9 | 14.6 | 9.644a | 2.96 | 6.8 | 14.4 | 9.144a | 2.30 |
| pH | 9 | 6.8 | 8.1 | 7.933a | 0.427 | 6.8 | 8.2 | 8ab | 0.46 | 7.3 | 8.3 | 8.067b | 0.30 |
| P (ppm) | 9 | 10.2 | 162.2 | 39.77b | 47.87 | 8.3 | 121.8 | 29.31ab | 35.81 | 6 | 71.8 | 19.57a | 21.4 |
| K (ppm) | 9 | 294.6 | 1028 | 464.4a | 217.1 | 281.5 | 1237 | 454.3a | 299.1 | 243.7 | 2325 | 646.2a | 709.6 |
| Mg (ppm) | 9 | 76.9 | 140.8 | 108.1a | 26.07 | 79.6 | 142.7 | 112.0a | 22.15 | 83.2 | 157.3 | 110.8a | 24.18 |
| Fe (ppm) | 9 | 0.2 | 0.4 | 0.311a | 0.060 | 0.2 | 0.5 | 0.333a | 0.0 | 0.2 | 0.5 | 0.311a | 0.093 |
| Mn (ppm) | 9 | 16.9 | 49.1 | 25.87a | 11.79 | 16.8 | 75.4 | 26.18a | 18.82 | 13.6 | 73.8 | 23.39a | 19.13 |
| Zn (ppm) | 9 | 0.2 | 0.4 | 0.289b | 0.0928 | 0.2 | 0.4 | 0.244ab | 0.0726 | 0.1 | 0.3 | 0.2a | 0.05 |
| OM (%) | 9 | 2.5 | 3.3 | 2.9b | 0.3 | 2.4 | 3.1 | 2.844b | 0.24 | 2.2 | 3 | 2.689a | 0.252 |
| Prot (mg/g) | 9 | 3.8 | 4.8 | 4.433b | 0.316 | 3.8 | 5.3 | 4.411b | 0.470 | 2.6 | 4.7 | 3.867a | 0.689 |
| Resp (mgCO2/g) | 9 | 0.4 | 0.7 | 0.544b | 0.101 | 0.4 | 0.6 | 0.5ab | 0.0866 | 0.3 | 0.6 | 0.433a | 0.1 |
| ActC (mg/kg-1) | 9 | 431 | 523 | 478b | 32.02 | 393 | 550 | 474.8b | 44.43 | 326 | 484 | 420.9a | 45.22 |
Min, Max, Mean and standard deviation of soil health indicators measured at El Koudia site at site at three depths (0-5, 5–10 and 10–15 cm).
AWC, Available water capacity; WAS, Water aggregate stability; P, Phosphorus; K, Potassium; Mg, Magnesium; Fe, Iron; Mn, Manganese; Zn, Zinc; OM, Organic matter; Prot, ACE protein index; Resp, Soil respiration; ActC, Active carbon; N, Number of independent sampling locations within each depth; Min, Minimum; Max, Maximum; Std dev, Standard deviation. Mean followed by different letters within a row are significantly different according to Bonferroni–adjusted post hoc comparisons following one-way ANOVA (p<0.05). Mean sharing at least one letter are not significantly different.
Values followed by different letters within the same row are significantly different at p<0.05; values sharing the same letter, or sharing a common letter such as “ab,” are not significantly different.
El Koudia site is classified as a Chromic Luvisol (25). ANOVA indicates a significant difference (P = 0.022) in silt content across depths, while sand or clay content remain stable. The sandy loam texture at the surface, which transitions to sandy clay loam at depth, shows a gradual increase in clay, characteristic of clayey illuvial horizons that are characteristic of Luvisols (45). Besides, sandy loams offer good drainage and aeration but lower water/nutrients retention, common in arid agricultural regions. The soil texture is an inherent property rarely changed by management practices. Consequently, it is not considered as a direct soil health indicator. However, it serves as valuable ground information in interpreting other measured indicators and determining suitable management practices.
Soil bulk density (Bd) is a physical parameter that varies with soil texture. At the El Koudia site, the average Bd in the surface layer (0–10 cm) is 1.41g/cm3. Despite the texture variability (from sandy loam to sandy clay loam) and in accordance with established benchmarks (Table 3), the bulk density indicates moderate compaction level across these combined textures which does not restrict plant and root growth and water infiltration (48, 49).
Table 3
| Soil type | Dry density, ρb (g/cm3) |
|---|---|
| Sand | 1.52 |
| Sandy loam | 1.44 |
| Loam | 1.36 |
| Silt loam | 1.28 |
| Clay loam | 1.28 |
| Clay | 1.20 |
Available water capacity exhibited minimal variation across the profile, remaining close to 0.19 g/g at all sampled depths, with a slight increase in the 0–5 cm and 5–10 cm layers (Table 1) which is not statistically significant. Regardless of textural changes, the stable value of the available water retention capacity is likely due to compensating effects of clay content at depth and organic matter at the surface. In contrast, water aggregate stability showed a highly significant difference among the three depths (P = 0.003), as confirmed by the Bonferroni test (Table 2). The greatest value was recorded in the 0–5 cm layer (12.3b), compared to the 0–5 cm (9.6a) and 10–15 cm layers (9.1a). Stronger stability is typically associated with higher organic matter content and microbial activity in the sandy loam layer. However, lower one reflects fewer stable aggregates in the sandy clay loam horizons, due to decreased biological activity and dominance of mineral aggregation mechanisms.
Statistical analysis revealed that most soil chemical indicators exhibited no significant depth-related differences, except for pH, phosphorus and zinc (Table 2). pH increased slightly but significantly with depths (from 7.9a to 8.1b), shifting with the alkaline category. It promotes the availability of nutrients such as magnesium (from 108a to 110.8a ppm) and potassium (from 464a to 646.2a), as these elements are more soluble in this pH range, iron (0.3a ppm) and manganese (from 25.9a to 23.4a) remain of low contents due to limited solubility at pH above 7 (Figure 3). On the other hand, phosphorus content exhibited a decreasing pattern. It is more abundant near the surface (39.8ppm) and less available (19.6 ppm) deeper due to root activity, plant uptake and possibly leaching. Similarly, zinc (less absorbed in alkaline pH) recorded highly significant differences across depths, with the highest value reported in the 0–5 cm layer.
Figure 3
Biological indicators displayed very highly significant differences (P<0.001) between soil depths, particularly for the ACE protein index and active carbon (Table 2), with the highest values at the upper layers (0–5 cm and 5–10 cm). Likewise, organic matter and soil respiration also exhibited highly significant depth-related differences (P = 0.004 and P = 0.026, respectively), with high levels at the upper layers. Average OM content ranged from 2.7% to 2.9%, indicating moderate fertility. These surface-rich biological parameters collectively suggest an abundant and active microbial community, concentrated where organic inputs from roots and residues are more present.
4 Discussion
Pearson correlation analysis results illustrate significant correlations among the measured physical, chemical, and biological soil health indicators (Table 4). A strong negative correlation between sand and clay content was reported (R=-0.70***), indicating that soils richer in sand have less clay content, consistent with the study site’s texture (25). This balance influences soil physical properties like texture, water retention and nutrient availability. Similarly, active carbon (ActC) and organic matter (OM) showed significant negative relationships with silt (R=-0.704***, R= -0.614*** respectively), more likely owed to the fact that small silt increases reduce the dominant sand fraction thus diluting the stock of labile particulate OM and ActC. In these coarse soils, with poor in OM, silt offers less protection than clay, promoting faster decomposition (51). The strongest correlation was marked between soil OM and ActC, with a coefficient of R = 0.78 (P<0.001). This highlights that management-induced changes in soil OM occur early and much more quickly, particularly in its labile content (ActC), our findings align with those reported by Graham et al. (52), Salari Nik et al. (53). Soils richer in OM generally have higher active carbon pools, indicating good soil biological health. Likewise, water aggregate stability strongly correlated with organic matter (R = 0.603***). This relationship is well known, as organic matter acts as a binding agent that stabilizes soil aggregates, thereby improving soil texture and resistance to erosion, consistent with the findings of Tisdall and Oades, (54). The positive link to soil respiration (R = 0.508*), a vital biological indicator, suggests that microbial processes play a key role in aggregate formation and stability, consistent with the findings of Raich and Tufekciogul, (55), who highlighted the importance of microbial activity in enhancing soil structure. Similarly, correlation coefficients among all biological indicators (OM, Active Carbone, Protein and Respiration) ranged from 0.38 and 0.77, suggesting that these biological processes tend to be jointly enhanced. These findings are in agreement with those of Schindelbeck et al. (36). Likewise, WAS exhibited a strong correlation with Magnesium (R = 0.69***) and moderate correlation with Zinc (R = 0.58*). These relationships underline that essential nutrients (Mg, Zn) and biological activity are integral to forming and maintaining stable soil aggregates. Iron revealed strong negative correlations with Magnesium (R= -0.56**) and pH (R= -0.68**). The negative correlation with pH reflects reduced iron solubility and availability at higher pH levels. This relationship with Mg may indicate competitive ion interactions or differences in soil mineralogy affecting nutrient dynamics. For phosphorus, it showed a strong positive correlation with zinc (R = 0.64***). This suggests a close linkage between micronutrient (Zn) availability and phosphorus, potentially due to co-occurrence in soil minerals or similar adsorption/desorption behaviors.
Table 4
| % Sand | % Clay | % Silt | Prot | ActC | WAS | AWC | Fe | Mg | Mn | OM | P | K | Resp | Zn | pH | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| % Sand | – | |||||||||||||||
| % Clay | -0.70*** | – | ||||||||||||||
| % Silt | -0.42* | -0.29 | – | |||||||||||||
| Prot | 0.20 | -0.14 | -0.21 | – | ||||||||||||
| ActC | 0.21 | 0.17 | -0.61*** | 0.63*** | – | |||||||||||
| WAS | 0.30 | -0.11 | -0.24 | -0.004 | 0.32 | – | ||||||||||
| AWC | -0.01 | -0.16 | 0.20 | -0.30 | -0.19 | 0.22 | – | |||||||||
| Fe | -0.32 | -0.02 | 0.41* | 0.24 | -0.19 | -0.39* | 0.38* | – | ||||||||
| Mg | 0.27 | 0.11 | -0.49 | -0.19 | 0.17 | 0.69*** | 0.05 | -0.56** | – | |||||||
| Mn | 0.18 | -0.15 | 0.03 | 0.17 | -0.02 | 0.13 | -0.03 | -0.18 | 0.11 | – | ||||||
| OM | 0.47** | 0.01 | -0.70*** | 0.39* | 0.78*** | 0.60*** | -0.08 | -0.41* | 0.55** | 0.01 | – | |||||
| P | 0.32 | -0.26 | -0.02 | 0.09 | 0.09 | 0.27 | 0.01 | -0.34 | 0.15 | 0.74*** | 0.04 | – | ||||
| K | 0.36 | -0.09 | -0.27 | 0.14 | -0.07 | -0.003 | -0.26 | -0.30 | 0.09 | 0.45* | 0.05 | 0.43* | – | |||
| Resp | -0.06 | 0.22 | -0.19 | 0.06 | 0.42* | 0.51* | 0.12 | -0.27 | 0.36 | -0.14 | 0.41* | -0.09 | -0.21 | – | ||
| Zn | 0.15 | -0.21 | 0.07 | 0.04 | 0.19 | 0.58* | 0.21 | -0.19 | 0.30 | 0.47* | 0.15 | 0.64*** | 0.05 | 0.32 | – | |
| pH | 0.21 | 0.16 | -0.49** | -0.45* | 0.19 | 0.31 | -0.14 | -0.68*** | 0.44* | -0.17 | 0.31 | 0.16 | 0.19 | 0.16 | 0.17 | – |
Pearson correlations for soil health indicators (0–15 cm) at El Koudia site.
*, **, *** Significant at the 0.05, 0.01, and 0.001 probability levels, respectively.
Prot, Soil Protein; ActC, Active Carbone; WAS, Water Aggregate Stability; AWC, Available Water Capacity; Fe, Iron; Mg, Magnesium; Mn, Manganeze; Zn, Zinc; OM, Organic Matter; P, Phosphorus; K, Potassium; Resp, Respiration.
4.1 Soil health interpretations for El Koudia soil site through scores
Table 5A illustrates scores with color coded for physical, chemical, and biological soil indicators, along with the overall quality score for El Koudia site. The Cornell assessment revealed major constraints in soil aggregate stability, soil protein index, and pH, with all scores falling below 20% (indicated in red). This indicates potential environmental risks. The constraints do seemingly originate from long-term intensive tillage and continuous mono-cropping practices.
Table 5
| A | Rating | |||
|---|---|---|---|---|
| Indicator | Scoring approch | 0–5 cm | 5–10 cm | 10–15 cm |
| WAS (%) | CND | 13 | 11 | 10 |
| AWC (g/g) | CND | 59 | 58 | 53 |
| OM (%) | CND | 63 | 56 | 55 |
| Prot (mg/g) | CND | 19 | 19 | 15 |
| Resp (mgCO2/g) | CND | 43 | 36 | 29 |
| ActC (mg/kg-1) | CND | 50 | 48 | 38 |
| pH | Suff | 22 | 16 | 16 |
| P (ppm) | Suff | 54 | 77 | 81 |
| K (ppm) | Suff | 100 | 100 | 100 |
| Mg (110.8; 108.1; 112) ppm Fe (0.31; 0.31; 0.33) ppm Mn (39.1; 25.9; 26.2) ppm Zn (0.3; 0.2; 0.24) ppm | Suff Suff Suff Dif | 56 | 56 | 56 |
| Overall quality score | 48 | 50 | 49 | 45 |
(A) Soil health indicators value with scores and scoring approach according to Havlin et al. (56).
CND, Cumulative normal distribution; Suff, Sufficiency; Dif, Dificiency; CND numbers are scores (0-100) and sufficiency/deficiency numbers are mean measured values and interpreted based on Havlin et al. (56). Micronutrient score according to the CASH framework: 100 = 4 adequate micronutrients; 56 = 3 of 4 adequate; 11 = 2 of 4 adequate; 4 = 1 of 4 adequate; 0 = no adequate micronutrients.
Table 5B
| B. Indicator | Constraints | Short-Term Management Recommendations | Long-Term Management Recommendations |
|---|---|---|---|
| WAS | Aeration, Infiltration, Rooting, Crusting, Sealing, Erosion, Surface runof | • Integrate fresh organic substances • Employ cover/rotation crops with deep-rooted drought-tolerant cover crops • Integrate mulch, manure and green manure • Maintain residue cover to reduce evaporation and improve infiltration | • Minimize tillage practices • Rotate with forage crops and mycorrhizal hosts • Apply surface mulching • Adopt water conservation practices to enhance soil moisture Haut du formulaire |
| Protein | OM Quality, Organic N Storage, N Mineralization | • Supply Nitrogen-rich organic inputs (low C:N ratio) such as manure, • Retain crop residues to reduce rapid organic matter mineralization • Grow legumes or grass legume mixtures • Inoculate legume with Rhizobia and monitor nodulation | • Minimize tilling • Alternate with forage legume sods • Monitor the C:N ratio of inputs • Employ cover crop and introduce fresh manure • Monitor pH and manage alkalinity to optimize nutrients availability and biological activity |
| pH | High pH: Toxicity, Nutrient Availability | • Cease lime or wood ash applications • Adopt soil test-based fertilization | • Conduct soil testing annually • Employ higher % ammonium or urea • Increase OM amendments to improve micronutrient availability |
Major soil health indicators constraints and both short and long term management recommendation.
Research shows that strong aggregate stability helps prevent problems like crusting, erosion, and runoff (30, 31). It also facilitates aeration, improves infiltration, and supports water storage (57–59). Moreover, it improves seed emergence, germination, and root and microbial health (43, 59). Consequently, the poor aggregates stability observed in our study can exacerbate both physical and biological soil health issues, leading to increased erosion. Similarly, the low soil protein rating is strongly linked to prolonged tillage practices (60). Since protein content is associated with soil aggregation, any destruction of soil aggregates from disturbance does negatively affect protein levels.
With regards to pH levels that are deviating significantly from the optimal range (6<pH<7), we may encounter reduced plant nutrient availability, hindered growth and limited crop yield quality. Hence, managing pH levels, soil protein and aggregate stability remains an urgent priority for both short- and long-term management strategies, as these factors could significantly affect the soil functionality under the current management practices. Micronutrient levels are sufficient for Magnesium (108.1<Mg<112 ppm), Iron (0.31<Fe<0.33 ppm), and Manganese (25.9<Mn<39.1 ppm), while Zinc (0.2<Zn<0.3 ppm) remains lacking. These micronutrients received low scores, indicated in yellow (Table 5A), due to limited absorption, especially for iron, zinc and manganese. This is mostly because of the soil’s elevated alkalinity (Figure 3). A similar issue was observed with phosphorus in the surface layer (0–5 cm) that overshadowed its higher content at deeper layers (5–10 and 10–15 cm, light green color) (Figure 4).
Figure 4
These findings indicate that while minor elements function at an average level, management practices should be driven toward enhancing this aspect, as it currently shows suboptimal performance. Soil management efforts should aim to improve this functionality while addressing other identified soil constraints. In addition, taking that our study site benefits from proper fertilization during the sowing period, the crop management should focus on maintaining this condition during the cropping cycle.
Biological indicators, including OM, ActC, and soil respiration (Figure 4), show notably high scores in the surface layer (0–5 cm). This richness is mainly attributed to biological activities that enrich the upper soil horizon, where OM is more labile (17). Active carbon, as the labile component of soil OM, serves as a food source for the microbial community and responds to management changes more quickly than total organic matter. This responsiveness explains the improvements in both ActC and respiration (indicated in yellow color), which tend to be improved jointly (37). This process enhances overall soil resilience, highlighting the need for management strategies that preserve this functionality while addressing other soil constraints. Regarding the available water capacity, results indicate a suboptimal score (yellow color) across all three depths with the highest rating at the upper (59). This aligns with the OM results, as increased OM content directly enhances soil water retention.
The overall quality score, reflecting the soil average health status of the health status of the El Koudia site, is 48 (Table 5A, Figure 4), placing it in the medium range (yellow color). This indicates potential for soil health improvement. If the identified constraints are not addressed in future management planning, there is a risk of decreasing yields and sustainability over time. Therefore, it is crucial to implement the management recommendation listed for both the short and long term (Table 5B) to reduce current constraints, improve soil functionality, and maintain optimal soil performance.
This study adds to the limited literature on integrated soil health assessment in semi-arid North African systems. Although CASH and similar frameworks have been widely used elsewhere (humid subtropical area (Brazil, 35); Jharkhand-India, 17), their application in Moroccan dryland conditions remains scarce. This work therefore provides an initial contextualized assessment that may help guide future in similar environments. The Cornell CASH scoring functions provided a standardized and internationally recognized framework for integrating physical, chemical, and biological soil health indicators. Their application enabled a comprehensive baseline assessment of soil functioning at the El Koudia and facilitated the identification of the main soil health constraints. As these scoring functions were developed from datasets representing diverse agroecological conditions, indicators such as pH and biological properties should be interpreted within the specific, climatic, edaphic, and management context of the study site. Nevertheless, the CASH framework remains a valuable tool for evaluating soil health status and supporting management decisions, particularly where locally calibrated soil health assessment systems are not yet available.
Our assessment of soil health in El Koudia, a semi-arid rainfed agricultural area in northwestern Morocco, extends beyond a simple evaluation of soil quality and productivity, as soil functioning play a key role in the crop-soil-food-security-human health continuum. In this context, the observed limitations in wet aggregate stability, soil protein index, and pH, together with variation in biological activity and micronutrient availability, indicate reduced soil resilience and important management constraints. These conditions may contribute to lower crop productivity, less stable food production, and reduced nutritional quality of harvested crops. Although agricultural soils supply most of the calories and are the primary source of essential nutrients for human nutrition, including micronutrients such as zinc, iron, magnesium, manganese, etc., low biological activity, poor aggregate stability, nutrients unbalances, and micronutrients deficiencies can indirectly affect food security and dietary quality, particularly in vulnerable semi-arid farming systems where yields are highly dependent on water and nutrient dynamics (61). Therefore, the CASH results from El Koudia are relevant to human health in an indirect but meaningful way, as they highlight potential soil health constraints that may compromise long-term agricultural sustainability, crops nutritional quality, and the capacity of farming systems to support healthy livelihoods under increasing climatic and management pressures (62, 63).
5 Conclusions
This study aimed to evaluate 11 physical, chemical, and biological soil indicators to investigate the soil health status of the semi-arid El Koudia site. Using these indicators to evaluate soil health improves our understanding of soil quality, helps identifying specific management actions, and allows to track improvements over time. Physical indicators indicate a non- compacted soil that promotes plant growth, while aggregate stability and available water capacity vary significantly. Whereas, chemical indicators displayed no notable changes with depth, except for pH, P, and Zn. Biological indicators demonstrate significant differences along the layers, highlighting richness at the upper layers, which are characterized by an active microbial community. Pearson correlation analysis reveals that coefficients among biological indicators (OM, Protein, Respiration, and ActC) highlight mutual enhancement of biological processes.
Based on Cornell soil health assessment, the soil of the study site encounters major limitations in soil aggregate stability, soil protein, and pH. These constraints are consistent with the reported history of intensive tillage and continuous mono-cropping at the site. Managing these constraints is important for short-and long-term decision-making, as they could significantly impact current soil functionality. With an overall quality score of 48, the El Koudia site falls within the medium range suggesting room for improvement in soil health. However, unless we overcome the recognised constraints in future management, crop yield and sustainability can be affected. In order to address these problems and optimize soil functions, short and long-term management recommendations should be implemented, with a particular recommendation towards the three pillars of conservation agriculture namely minimum soil disturbance, crop rotation and inclusion of crop residues. This approach strives to maintain optimal soil performance and solve the identified constraints. By using these techniques, the site can move towards a higher soil health status, ensuring sustainable productivity and environmental protection.
Statements
Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Author contributions
ZM: Data curation, Validation, Formal analysis, Methodology, Conceptualization, Writing – review & editing, Investigation, Writing – original draft. NB: Visualization, Writing – review & editing. SB: Writing – review & editing, Methodology, Visualization, Formal analysis. RM: Writing – review & editing, Investigation. IL: Visualization, Writing – review & editing. HY: Data curation, Conceptualization, Writing – review & editing. SC: Writing – review & editing, Formal analysis. FB: Writing – review & editing. RZ: Supervision, Methodology, Writing – review & editing.
Funding
The author(s) declared that financial support was received for this work and/or its publication. The publication of this work was supported by the Faculty of Science, Ibn Tofail University, Kenitra, Morocco.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
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Summary
Keywords
CASH scoring, El Koudia, Morocco, soil health, sustainability, tillage
Citation
El Mouridi Z, Brhadda N, Bennani S, Mrabet R, Lembaid I, Yachou H, Charradi S, Bentata F and Ziri R (2026) Baseline soil health assessment using the Cornell CASH framework in a semi-arid Luvisol of Northwestern Morocco: constraints and management implications. Front. Soil Sci. 6:1864554. doi: 10.3389/fsoil.2026.1864554
Received
24 April 2026
Revised
12 June 2026
Accepted
17 June 2026
Published
13 July 2026
Volume
6 - 2026
Edited by
José A. González-Pérez, Spanish National Research Council (CSIC), Spain
Reviewed by
Mounaim Halim El Jalil, Mohammed V University, Morocco
Michael Akaninyene Okon, Federal University of Technology Owerri School of Agriculture and Agricultural Technology, Nigeria
Updates
Copyright
© 2026 El Mouridi, Brhadda, Bennani, Mrabet, Lembaid, Yachou, Charradi, Bentata and Ziri.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Zineb El Mouridi, zineb.elmouridi@inra.ma
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.