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Article

Spatiotemporal Dynamics of Soil and Soil Organic Carbon Losses via Water Erosion in Coffee Cultivation in Tropical Regions

by
Derielsen Brandão Santana
1,
Guilherme Henrique Expedito Lense
1,
Guilherme da Silva Rios
1,
Raissa Eduarda da Silva Archanjo
1,
Mariana Raniero
1,
Aleksander Brandão Santana
2,
Felipe Gomes Rubira
1,
Joaquim Ernesto Bernardes Ayer
3 and
Ronaldo Luiz Mincato
1,*
1
Institute of Natural Sciences, Federal University of Alfenas, R. Gabriel Monteiro da Silva 700, Alfenas 37130-001, MG, Brazil
2
Department of Food and Drugs, School of Pharmaceutical Sciences, Federal University of Alfenas, R. Gabriel Monteiro da Silva 700, Alfenas 37130-001, MG, Brazil
3
Department of Chemistry, University Center of Paulínia, R. Maria Vilac 121, Paulínia 13140-000, SP, Brazil
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(3), 821; https://doi.org/10.3390/su17030821
Submission received: 17 October 2024 / Revised: 17 January 2025 / Accepted: 19 January 2025 / Published: 21 January 2025
(This article belongs to the Section Sustainable Agriculture)
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Abstract

:
Water erosion has severe impacts on soil and the carbon cycle. In tropical regions, it is significantly influenced by rainfall, soil erodibility, rapid changes in land use and land cover (LULC), and agricultural management practices. Understanding the dynamics of water erosion is essential for implementing precise land degradation control. This study aimed to estimate soil and soil organic carbon (SOC) losses due to water erosion over five years in a coffee-producing area in Brazil using the revised universal soil loss equation (RUSLE). The results revealed that average soil losses in coffee plantation areas ranged from 1.77 to 1.80 Mg ha−1 yr−1, classified as very low. Total and potential soil loss ranged from 2184.60 to 6657.14 Mg ha−1, a 305% difference, demonstrating the efficiency of vegetative cover (C factor) and conservation practices (P factor) in reducing soil loss rates. SOC losses were less than 200 kg ha−1 yr−1, with averages of 17.67 and 13.00 kg ha−1 yr−1 in coffee areas. In conclusion, agricultural management practices, such as the presence of native vegetation, maintaining vegetative cover in coffee rows, contour planting, and improving agronomic techniques, are essential for reducing soil and SOC losses, even in scenarios of biennial alternation in coffee production. Thus, sustainable agricultural management plays a crucial role in mitigating water erosion, maintaining productivity, and addressing climate change.

1. Introduction

The adverse impacts of soil degradation raise significant concerns. One-third of the world’s soils are degraded by erosion [1]. There are estimated annual losses of 75 Pg on arable lands [2], leading to severe environmental and socioeconomic damages [3]. Approximately 80% of arable lands face moderate to severe erosion [4].
Erosion is a natural process but is exacerbated by anthropic activities, such as native forest conversion to arable lands and unsustainable agricultural management systems [5]. The primary consequence of water erosion is the loss of soil, soil organic carbon (SOC), soil organic matter (SOM), and the depletion of nutrients, phosphorus (P), and pesticides [6]. Water erosion also disrupts soil–atmosphere interactions, reduces agricultural productivity, and causes sedimentation, eutrophication, and pollution of water bodies, thereby threatening global food and nutritional security [7].
In tropical areas, erosion is accelerated by intense rainfall [8], greater soil erodibility [9], and faster changes in land use and land cover (LULC) [10]. In Brazil, the annual soil losses in 2002 were estimated at 822.6 million Mg, with a cost of USD 6 billion [11]. In the same year, the costs associated with fertilizer losses range from USD 18.15 to USD 107.76 per hectare per year [12]. In the São Paulo state, the average estimated rate of soil loss is 30 Mg ha−1 yr−1 [8]. The cumulative effects over time lead to significant soil degradation and a loss of productivity [10].
Brazil holds the largest amount of cultivable land (around 850 million ha) and currently (2024) is the world’s leading coffee producer [13] due to climatic conditions. There are approximately 330 thousand rural properties dedicated to coffee cultivation, 78% of which are family-owned farms where coffee serves as the primary source of income [14]. Despite its socioeconomic significance, much debate surrounds the environmental impacts of the coffee production model, which is historically based on the deforestation of the Atlantic Forest and the Cerrado, full-sun monoculture, and intensive soil use, and is adapted from sugarcane cultivation techniques [15]. These factors may aggravate the deleterious impacts of water erosion, particularly soil and SOC losses, increasing greenhouse gas emissions and depleting the soil’s nutrient content and productive capacity [16]. Given this situation, adopting sustainable agricultural management is essential, especially in the Minas Gerais state, which is responsible for approximately 50% of Brazil’s coffee production [14].
SOC is often used as a key indicator of soil quality because it reflects anthropic actions [17]. Essential elements in preserving and increasing SOC stocks include agricultural management practices and C input via fertilization [18]. These practices can enhance SOC reservoirs and mitigate deficits arising from water erosion [19]. Thus, the main source of SOC comes from SOM, which contains most of the essential nutrients, such as N and P [18]. Furthermore, increasing SOM and SOC levels in tropical agricultural lands would help achieve the goals of the “4/1000 Initiative: Soils for Food Security and Climate”, an international initiative launched during COP21 in 2015, with the goal of increasing SOC by 0.4% per year, that is, 4 per thousand (4/1000), which aims to ensure that agriculture plays its part in combating climate change and food security by more than 40 countries [20]. However, there is a lack of research addressing the spatiotemporal dynamics of soil and SOC losses associated with LULC changes and agricultural management practices for tropical regions, especially given the context of climate change [21].
Estimating and spatializing soil and SOC losses is essential for long-term water erosion mitigation [22]. Mathematical models utilizing geographic information systems (GISs) are widely employed for water erosion assessment, offering alternatives to traditional empirical methods, which are labor-intensive, expensive, and require several years of analysis [23]. These models efficiently estimate soil and SOC losses and evaluate the sediment delivery ratio (SDR), such as the revised universal soil loss equation (RUSLE), which is applied worldwide [24,25]. It can be implemented in GIS softwares, such as ArcGIS 10.3 [26] and InVEST 3.13 (integrated valuation of ecosystem services and tradeoffs) [27]. This study aimed to estimate the spatial losses of soil and SOC in an area with different LULC types that are predominantly dedicated to coffee production in southeastern Brazil between 2017 and 2022. For this purpose, we utilized the RUSLE in ArcGIS and InVEST. The aim was to assess the temporal influence of LULC, LULC changes, and agricultural management practices on water erosion, analyze within the context of sustainable coffee production, and make comparisons with other areas.

2. Materials and Methods

2.1. Study Area and Description

The study area was the Conquista Farm, owned by Ipanema Coffees (Ipanema Agrícola S.A.), located in the Alfenas Municipality, Minas Gerais state, Brazil. The local climate is humid subtropical (Cwb), according to the Köppen classification [28], characterized by dry winters and mild summers, with an average annual temperature and precipitation of 21.2 °C and 1500–1750 mm, respectively. The geological substrate consists of gneisses, overlaid by quaternary soil coverings, comprising unconsolidated fluvial deposits of gravel, sand, and clay [29].
The area is characterized by Red Latosol [30], corresponding to Ferralsol in the World Reference Base for Soil Resources (WRB) [31], and, to a lesser extent, indiscriminate floodplain soils (IFSs) in sediment deposition areas (Figure 1A). The Latosols of this area originate from crystalline rocks, such as granites and gneisses, commonly found in mountainous regions and/or plateaus. Their color is reddish-brown due to the higher iron content. The predominant texture is clayey, which provides good water and nutrient retention capacity. However, these soils exhibit low natural fertility due to intense leaching, meaning they lose many soluble nutrients through rainfall. The pH is often acidic, necessitating correction through the application of lime [30].
The altitude ranges from 760 to 890 m, with an average slope ranging between 3 and 8%, suitable for coffee production (Figure 1B). The slope gradients range from flat (0–3%) to strongly undulating (>20–45%) (Figure 1C).
The Conquista Farm encompasses 2045.90 ha, with over 60% dedicated to coffee cultivation. Coffee plots range from 1.85 to 79.85 ha and are distributed according to the variety, with Acaiá, Paraíso 2, and Mundo Novo predominating. The number of bags (bag = 60 kg) harvested varies from 22 to 95 per ha, with an average productivity of 45 per year with conventional tillage, monocultural systems, and a full-sun model. Several conservation practices are employed during production: (i) growing coffee seedlings in a vivarium; (ii) selecting the ideal seeds for planting based on the soil composition and topographical features; (iii) instituting a fertilization regime guided by annual soil and foliar analysis with continuous monitoring of weed, disease, and pest populations; (iv) integrating agronomic technological advances throughout the production process; (v) using drip-irrigation systems; (vi) promoting vegetation cover between coffee rows, mainly Brachiaria species; (vii) using organic fertilizers with slow-release formulations; (viii) preserving remnants of native vegetation and carrying out routine pruning; and (ix) harvesting coffee beans at the ideal stage of ripeness.

2.2. Methodology

To achieve our goals, we implemented a methodological approach in different time series, in 2017, 2019, and 2022, which (i) employed the RUSLE to estimate the soil and SOC losses; (ii) validated the soil loss data between the sediment yield observed and the SDR; and (iii) evaluated the changes in the SOM and SOC contents in coffee cultivation utilizing fieldwork collection samples covering depths from 0 to 20 cm, as shown in the flowchart in Figure 2. The choice of these years was due to a lack of information for some soil samples in 2021; therefore, 2022 was selected.
The SDR tool is part of the InVEST 3.13 software [27] and utilizes the RUSLE [24] to estimate the total and maximum soil losses coupled with other methodologies [32,33]. The RUSLE considers five multiplied parameters, as shown in Equation (1):
A = R × K × L S × C × P
where A is the average annual soil loss (Mg ha−1 yr−1); R is the rainfall erosivity factor (MJ mm ha−1 h−1 yr−1); K is the soil erodibility factor (Mg h MJ−1 mm−1); LS is the topographic factor expressing the relationship between the slope length (L) and slope (S) (dimensionless); C is the factor for land use and cover (dimensionless); and P is the factor for conservation practices (dimensionless).
We calculated the maximum soil loss, which is the potential total soil loss in the original land cover, with a lack of agricultural management practices via Equation (2) [27]:
  A = R × K × L S
InVEST 3.13 requires specific input data to utilize the SDR tool. These data were initially generated in ArcGIS 10.3 [26] and included the DEM, erosivity, erodibility, land cover, biological data table (C and P factors), watershed area or specific area, threshold flow accumulation value, maximum SDR value, Borselli connectivity index (IC0 parameter), and maximum L value. The input data in the raster and vector formats represent quantitative maps with information correlating to the numerical values linked to each pixel.
We initially acquired the DEM from the L-band image of the Alos PALSAR satellite [34] at a resolution of 30 m, but it was resampled to 12.5 m. We obtained the erosivity factor (R) for the southern region of Minas Gerais [35]. Subsequently, we determined the erodibility factor (K) in two stages: Firstly, we generated a map of soil classes based on the soil map of Minas Gerais [36]. Secondly, we calculated the erodibility of Latosols [37], excluding areas of sedimentary deposits near bodies of water (30 m). Following this, we extracted the LULC data from MapBiomas Collection 8 of 2023 [38] for Alfenas in 2017, 2019, and 2022 (Figure 3A–C). These years were selected based on the availability of data from the farm. After this, we conducted field checks to mitigate errors associated with the LULC classification (Table 1).
The biological data table establishes the correlation between the LULC classes and the values of the C and P factors of the RUSLE. These factors represent the impact of land use and cover (C) and conservation practices (P) on water erosion. We obtained the C and P values from the specialized literature (Table 2).
We used the following default values in InVEST: 1000 for the threshold flow accumulation parameter [27]; 2 and 0.5 for Borselli’s K and IC0 parameters, respectively [32]; 0.8 for the maximum SDR value [33]; and 122 for the maximum L value [24,46]. Consequently, we generated maps representing the total and maximum soil losses as the LULC for 2017, 2019, and 2022.

2.3. Sediment Delivery Ratio (SDR)

We examined the correlation between the actual sediment yield and the estimated SDR to validate the soil loss using the RUSLE. The sediment yield represents the total eroded soil generated in a watershed, while the SDR signifies the fraction of eroded material transported to water bodies [47].
We utilized hydrosedimentological station number 61,661,010 operated by the Instituto Mineiro de Gestão das Águas (IGAM) (the station available for the period) to calculate the sediment production observed in the field. We computed the fluxes for 2017, 2019, and 2022 based on monthly data from the monitoring bulletins of the Furnas UHE reservoir provided by the Agência Nacional de Águas e Saneamento Básico (ANA) [48]. Subsequently, we developed the curve relating to the total transported sediments and the water flow for the set of fluxes for 2017, 2019, and 2022 and compared the sediments observed with those estimated with InVEST.

2.4. SOM and SOC Models

Unlike the soil losses obtained with InVEST, the SOC and SOM contents were spatialized and calculated in ArcGIS for each coffee sample. Here, 46 soil samples were collected from coffee cultivation, 1 for each coffee plot, every January in 2017, 2019, and 2022. These samples weighed approximately 600 g and were collected at a depth of 0 to 20 cm (Figure 3A–C). The SOM was determined by Cooxupé laboratories using the dry quantification methodology in a muffle furnace via incineration [30]. The SOC content was calculated by multiplying the SOM content by the van Bemmelen constant of 0.58 [49,50]. After, we calculated the average SOC and SOM contents in two intervals: from 2017 to 2019 and from 2020 to 2022. This approach aimed to reduce the impact of variations in the results due to sampling and analysis issues.
For each collected sample, a location point was recorded. Each point was then assigned a row in the ArcGIS attribute table with the information “SOM content” for each year. We spatialized this SOM in a raster format via ordinary kriging interpolation using the Geostatistical Wizard in ArcGIS 10.8 for all areas because coffee plantations (including coffee plants and bare soil) occupy approximately 80% of the area, distributed throughout the entire region, thus facilitating interpolation. The SOC losses were derived by multiplying the SOM values by the soil losses [51] in raster format.

3. Results

3.1. RUSLE Factors and Soil Losses

The R, K, and LS factors of the RUSLE are depicted in Figure 4A, 4B, and 4C, respectively. The C factor in 2017, 2019, and 2022 is illustrated in Figure 4D, 4E, and 4F, and the P factor in Figure 4G, 4H, and 4I, respectively.
The R values ranged from 7028.44 to 7118.39 MJ mm ha−1 h−1 yr−1. These values were considered medium–high according to a national erosivity study [52] and high according to a global erosivity study [53]. The highest R values were found in tropical areas [54] due to rainfall rates. Rainfall erosivity significantly affects the soil loss in agricultural lands in these locations.
The K value for Latosols was 0.02 Mg h MJ−1 mm−1, classified as moderate [55]. Latosols are naturally deep, more resistant to water erosion, well drained, and clay-textured [56].
The LS values ranged from 0 to 29.20, with 57.25% of the area below an average of 5.40 on the less steep slopes. An increase in the LS enhances the speed of runoff and soil loss rates [57].
The values of C and P in the coffee plantation were low, indicating good canopy coverage and efficient agricultural management practices. We observed the lowest C and P values in native forests, while the highest were found in bare soil at the coffee rows and access roads, where vegetation cover is less protective against soil degradation. Additionally, given the high R values, maintaining low C and P values is essential for mitigating soil losses [24]. The total, potential, and average soil losses, categorized by LULC class, are presented in Figure 5, Figure 6 and Figure 7.
The difference between total soil loss and potential soil loss is presented in Table 3.
The average soil loss is presented in Figure 7.
We observed the highest average soil losses in bare soil and the lowest in native forests, consistent with the pattern of total soil loss. Total soil losses ranged from 7236.97 to 7368.03 Mg yr−1, while total potential soil loss ranged from 13,227.84 to 13,665.37 Mg yr−1. Potential soil losses in coffee plantations without conservation practices increased from 2346.91 to 6657.15 Mg yr−1. This indicates that, with the maximum C and P factors in this LULC category, soil losses would be significantly higher. This finding highlights the effectiveness of conservation management practices in reducing P values and the presence of Brachiaria in coffee rows to reduce the C factor, mitigating water erosion.
Regarding water erosion and eroded material, Figure 8 illustrates the sediment curve relative to water discharge.
Table 4 shows the SDR estimated with the InVEST software and the observed values.
The variation ranged from 4% to 12%. A variation below 20% is considered acceptable, indicating the results’ accuracy [58]. The spatial distribution of the soil losses is illustrated in Figure 9.

3.2. Soil Organic Matter (SOM) and Carbon (SOC) Losses

The weighted average SOM content ranged from 2.0 to 4.7% between 2017 and 2022 (Figure 10). The data for SOC losses are presented in Table 5.
The SOM content is within the range typically observed in Cerrado soils, approximately 5%, which is considered high [59]. In tropical regions, SOM levels are generally lower, typically ranging from 1% to 3%, due to climatic conditions such as high temperatures and humidity that accelerate organic matter decomposition and carbon mineralization. However, when considering all soil types globally, the average SOM content generally ranges between 2% and 4% [60].
The SOM variation might be attributed to (i) the different management practices for each coffee plot, such as weeding, pruning, fertilization [61], spacing, and the presence of brachiaria grass between coffee rows [62]; (ii) the heavy mechanization starting in 2019, especially with sweeping in the coffee plantation; (iii) the proximity to the reservoir, as higher soil moisture levels tend to accelerate organic matter decomposition [63]; and (iv) the decrease in rainfall volume recorded between 2015 and 2020 [64].

4. Discussion

4.1. Soil Losses

Figure 9 illustrates the difference between estimated soil loss and soil loss in the absence of conservation practices. The average soil losses in coffee cultivation areas ranged from 1.77 to 1.80 Mg ha−1 yr−1, which is consistent with findings from nearby sub-basins reporting a maximum soil loss of 2.91 Mg ha−1 yr−1 [65,66,67,68]. These rates were considerably lower than those observed in another study [69], which reported values of 31.11, 32.83, and 23.9 Mg ha−1 yr−1 in regions with Latosol of recent agricultural expansion in the Brazilian Cerrado. This difference can be attributed to the following: (i) the difficulty of implementing conservation practices due to the large area size; (ii) the use of conventional tillage; (iii) high LS factor; (iv) agricultural expansion associated with deforestation and bared soil; and (v) the high erodibility, which reached a value of 0.72 Mg h MJ−1 mm−1, and cover management practices, which reached a value of 0.96.
The less steep terrain, combined with the conservation agricultural practices in coffee cultivation at Conquista, provided soil protection against precipitation, particularly due to (i) the maintenance of vegetative residues between coffee rows for part of the year, which attenuated surface runoff velocity and increased the concentration of soil organic matter (SOM) at the soil surface; (ii) planting along contour lines, facilitating agricultural management and reducing the dispersion of vegetative residues; (iii) fertilization aimed at promoting a denser and healthier vegetative cover; (iv) vegetative strips on slopes and along the edges of rural roads; (v) controlled machinery traffic, particularly during the harvest season; (vi) irrigation management to prevent excess water in the soil; and (vii) continuous monitoring of productivity [70].
The average soil losses in coffee cultivation remained virtually constant from 2017 to 2022. However, despite the low soil loss rates, they need to be reduced to levels comparable to those observed in native forests, ranging from 0.26 to 0.32 Mg ha−1 yr−1, in order to ensure the long-term sustainability of agricultural systems [1]. Although these losses are currently stable, they are not yet associated with a reduction in productivity, which is a positive sign. In tropical areas, due to the high rainfall, soil erodibility, and rapid changes in land use and land cover, reducing soil losses should be progressively prioritized. As evidenced in Figure 8, the agricultural practices adopted, combined with the maintenance of native vegetation, are essential in this regard, not only to mitigate soil losses but also to maintain and increase SOC stocks. The importance of vegetation cover in mitigating soil losses, particularly of carbon (C) and P, which are directly influenced by human activities and exhibit more rapid changes, is illustrated by the disparity between the soil and potential soil losses (Figure 5 and Figure 6).
Studies on erosion and phosphorus have concluded that more than 50% of the global loss of this element in agriculture can be attributed to soil degradation, particularly water erosion [6]. Water erosion releases phosphorus bound to soil minerals in agricultural lands into water bodies, adversely affecting aquatic ecosystems. Thus, studies on phosphorus content in water can be combined with research on water erosion and soil loss. Another issue, especially in Latin America, is the inefficient management of organic phosphorus, which is linked to geological reserves. Therefore, reducing soil erosion is essential for maintaining phosphorus stocks [6].
The minimal variations in average soil losses and the absence of reduction in crop productivity over time may demonstrate the relevance of agricultural practices in the context of the biennial nature of coffee. Moreover, the lower the losses of SOC, the lower the cost associated with replenishing SOM through fertilization. However, it is necessary to diversify management approaches with alternating strategies from year to year, increasingly aiming at reducing such rates and enhancing carbon sequestration.
We observed the highest average rates of soil loss in bare soil areas, corresponding to access roads between coffee rows and rural roads. The values ranged from 12.23 to 12.30 Mg ha−1 yr−1. These values were lower than those reported in the Rio Grande Basin [71] and in steep areas in Colombia, both exceeding 100 Mg ha−1 yr−1 [72]. This difference may be attributed to (i) the bare soil being predominantly located in coffee rows, reducing sediment transport via runoff; (ii) the increased SOM due to fertilization and maintenance of vegetative residues that fall onto the soil during part of the year until harvesting; and (iii) the lower C value.
The average soil losses in eucalyptus ranged from 1.80 to 2.06 Mg ha−1 yr−1, surpassing the values reported in Rio Grande do Sul (0.12 to 0.81 Mg ha−1 yr−1) [41] and in Bahia (1.46 Mg ha−1 yr−1) [73]; nevertheless, they were lower than the results in deforested areas in the Brazilian Cerrado (33 to 38 Mg ha−1 yr−1) [69]. Depending on the management practices employed, eucalyptus cultivation can intensify soil losses due to (i) the shading created by the vegetative canopy, hindering the growth of other species and diminishing soil aggregation and structure [74], and (ii) its shorter cultivation cycle of 6 years compared with that for coffee, resulting in reduced vegetation cover for prolonged periods.
We observed the lowest average soil losses in native forests, ranging from 0.26 to 0.32 Mg ha−1 yr−1, lower than the values for forests in Paraná state (1.78 to 6.68 Mg ha−1 yr−1) [75]. These rates were similar for nearby sub-basins (0.06 Mg ha−1 yr−1) [67]. Preserving vegetative cover is essential for reducing soil erosion, as it enhances water infiltration, reduces runoff, and mitigates sediment release and transport [68]. Furthermore, restoring native vegetation contributes to soil loss reduction [67]. Vegetation provides additional benefits by increasing soil moisture and SOM levels and delivering ecosystem services, such as promoting the presence of pollinating insects, thereby optimizing overall production [74].
For pasture and other crop classes, the average soil losses ranged from 1.07 to 2.08 Mg ha−1 yr−1, classified as very low [48] due to the predominance of flat to gently undulating terrain. Pastures offer more efficient soil protection than eucalyptus.

4.2. Soil Organic Carbon (SOC) Losses

Although there was minimal variation in the average soil loss, the average SOC losses decreased from 17.67 to 13.00 kg ha−1 yr−1. Most areas recorded values below the average during this period. This variation can be attributed to the decrease in total soil losses from 2019 to 2022 (Figure 5 and Figure 6). These average SOC losses were lower than the values estimated for Ethiopian agricultural lands (14.4–32.8 kg ha−1 yr−1) [22] and in experimental farm studies in the USA (117–358 kg ha−1 yr−1) [74]. Lower SOC losses indicate (i) reduced interference in the global SOC cycle; (ii) lower costs associated with fertilizer replenishment; and (iii) higher agricultural soil quality [75].
Controlling water erosion can improve soil carbon sequestration [76], especially on steep terrain. Thus, despite low rates of SOC loss, the long-term implications include restricting carbon sequestration and damaging soil quality [77]. The SOC losses observed in our study were lower in a comparative analysis of the SOC losses due to water erosion under varied conditions (i.e., climate, slope, soil, and management) with a wide range of SOC loss values from 1000 to 3000 kg ha−1 yr−1 for uncultivated soils in very humid regions to less than 15 kg ha−1 yr−1 for forests and other dense vegetation cover types [78].
According to Table 5, areas with SOC losses below 50 kg ha−1 yr−1 are predominant. These losses can be considered low [77], but there is still potential for improvement. Losses exceeding these values are concentrated in coffee planting rows and rural roads, proportional to soil losses. These areas require the most intervention, not only to reduce SOC deficits but also to preserve stock levels through measures such as maintaining native vegetation, reducing the intensity of coffee sweeping practices, promoting crop diversification, and incorporating Brachiaria species.
SOC losses were associated with soil losses, as shown in Figure 5, Figure 6 and Figure 7. The total soil loss was greater in 2019, which resulted in a higher average SOC loss as well. In 2022, both losses were lower. Therefore, reducing soil loss can contribute to reducing SOC loss; however, further studies are necessary to corroborate this finding. Various sustainable management practices are employed at Conquista, such as adopting contour farming, applying green manure, and maintaining vegetative cover between coffee rows. These practices improve the soil structure, increase its water retention capacity, and reduce dependence on chemical fertilizers and pesticides, increasing SOC stocks [77]. The average soil and SOC losses also decreased during the study period by the expansion of native forest areas, next to 14%, as evidenced in Table 1. Restoring vegetation, particularly when involving diverse plant species and sustainable management practices, can increase SOC and nitrogen (N) stocks [79]. Despite the lack of data on SOC in native forests, a reduction in soil losses leads to increased SOC fixation, which reduces transport and minimizes deposition in water bodies [76]. Studies on coffee cultivation demonstrate a reduction in soil loss ranging from 7% to 35% with the adoption of effective strategies for mitigating soil erosion, such as increasing vegetation cover and implementing soil conservation practices [80].
The analysis of the impact of agricultural management on SOC levels in tropical climates represents a challenge [18] and requires comprehensive assessment. Practices such as fertilization, composting, no-till, and contour farming—along with agroecological management, which promotes plant diversity—can increase SOC rates in tropical cultivation areas [81] due to enhanced microbial activity. This, perhaps, could be the next goal for future studies in the area.
In the context of coffee production, understanding how the environment reacts to anthropic actions on soils is fundamental for assessing ecosystem services. Thus, integrating agricultural systems with management techniques that control water erosion rates; optimize the application of nitrogen fertilizers; improve soil biological, physical, and chemical attributes; increase water retention; and enhance SOM, SOC, and N stocks are fundamental to minimizing climate change impacts [82].
Even with the limitations related to the calibration of factors for specific regional applicabilities [83], the models’ inability to capture highly complex landscape interactions [84], the low availability of reliable long-term data, and the lack of information to corroborate in situ validation [85], the RUSLE has proven useful for spatializing water erosion and estimating soil and SOC losses [81]. However, the RUSLE’s simplicity allows its application in areas where data for more complex models are scarce, and it is widely used, contributing to the formation of an increasingly robust database and improving the accuracy for different regions [86].
Soil loss studies are highly relevant not only for coffee cultivation but also for other crops. The findings from these studies can contribute, for example, to improving sustainability, productivity, and environmental health; these include the following: (i) Erosion control. Since coffee is typically grown in steep or sloped areas, it is more susceptible to erosion. Therefore, maintaining cover crops along planting rows can mitigate soil loss and be applied to other crops, such as grains, fruits, and vegetables. (ii) Reducing the use of fertilizers, as the maintenance of soil organic matter decreases the need for manual soil amendments. (iii) The necessity of using monitoring techniques to assess the spatial distribution of erosion, quantify soil loss, and evaluate soil quality attributes. (iv) Providing a database that offers a better understanding of how climate change affects erosion and the subsequent degradation of soil, particularly in tropical countries.
It is essential to adapt agricultural systems to these warming conditions and extreme weather events [21]. Improved agronomic practices result in SOC increases that can exceed 0.4% per year [87]. This will only be possible with more access to information for farmers, aiming to adopt sustainable practices [88] that prioritize reducing disturbances to the soil, stabilizing and maintaining SOC stocks [89], and combining production and sustainability for a growing population. Brazil is highly dependent on agricultural soils. Given this, the pressure on the agricultural production system will increase [89]. In this context, the study of the Conquista Farm is a good example of the relevance of vegetative cover and conservationist practices in reducing water erosion while maintaining agricultural production and tackling climate change.

5. Conclusions

This study assessed and analyzed the spatiotemporal dynamics of soil and SOC losses due to water erosion in tropical coffee-growing areas in Brazil. Vegetative cover has been crucial in minimizing soil losses, with native forest areas exhibiting the lowest rates and bare soils exhibiting the highest. Therefore, the preservation of native vegetation and the maintenance of vegetation cover along coffee rows, as in sustainable agricultural management practices, have the potential to reduce soil and SOC loss rates in tropical coffee cultivation over a five-year period. However, long-term continued reductions in these rates are necessary.
It is valid to state that modeling soil carbon dynamics faces limitations, particularly in regions with diverse soil types, land uses, climatic conditions, and agricultural management practices. Despite these challenges, studies in this field are crucial for providing a broader understanding of soil carbon trends with speed and efficiency. However, to enhance the reliability of such models, the integration of high-resolution remote sensing data, the development of a robust region-specific database, and the application of machine learning techniques are essential. Furthermore, combining modeling efforts with strategic field measurements for validation enables more informed decision-making for climate change mitigation and sustainable land management. This is particularly impactful when such information reaches land managers and policymakers in an effective manner.
The difference between total and potential soil losses was approximately 305%. This difference can be attributed to the role of ground cover and the agricultural management practices adopted—the maintenance of native forest areas and the presence of ground vegetation along planting rows—which mitigate the values of C and P.
Between 2020 and 2022, the rates of SOC loss remained below average and decreased with the expansion of native forest areas despite the biennial nature of coffee production and LULC changes. To address food security challenges, it is essential to enhance agricultural management practices aimed at increasing SOC stocks and mitigating the impacts of climate change. Future LULC scenario studies can promote agricultural and environmental sustainability and assist managers in understanding the impacts on ecosystem services.

Author Contributions

Conceptualization, D.B.S., J.E.B.A. and R.L.M.; methodology, D.B.S., M.R. and A.B.S.; software, G.H.E.L. and G.d.S.R.; formal analysis, R.E.d.S.A.; resources, R.L.M.; writing—original draft preparation, D.B.S.; writing—review and editing, F.G.R., J.E.B.A. and R.L.M.; supervision, J.E.B.A. and R.L.M.; funding acquisition, R.L.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by IPANEMA AGRÍCOLA S.A. (grant number 23087.002795/2022-05 in the Diário Oficial da União (4 May 2022). The APC was funded by Ronaldo Luiz Mincato. This study was partially funded by CAPES (financial code: 001).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data will be available upon request to the authors.

Acknowledgments

The authors thank Ipanema Agrícola S.A. for the scholarship to the first author and for authorizing this study in the area; FAPEMIG (Fundação de Amparo à Pesquisa do Estado de Minas Gerais) (Foundation for Research Support of the State of Minas Gerais) for the scholarship to the second author; and CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) (Coordination for the Improvement of Higher Education Personnel) for the scholarship to the third, fourth, and sixth authors. All individuals included in this section have consented to the acknowledgement.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Conquista Farm location map. (A) Soil classes; (B) digital elevation model (DEM); (C) slope. IFS = indiscriminate floodplain soil.
Figure 1. Conquista Farm location map. (A) Soil classes; (B) digital elevation model (DEM); (C) slope. IFS = indiscriminate floodplain soil.
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Figure 2. Flowchart of methodological procedures.
Figure 2. Flowchart of methodological procedures.
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Figure 3. LULC maps and soil sampling of Conquista Farm for (A) 2017, (B) 2019, (C) 2022.
Figure 3. LULC maps and soil sampling of Conquista Farm for (A) 2017, (B) 2019, (C) 2022.
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Figure 4. RUSLE factor maps: (A) erosivity factor R; (B) erodibility factor K; (C) topographic factor LS; (D) C factor in 2017; (E) C factor in 2019; (F) C factor in 2022; (G) P factor in 2017; (H) P factor in 2019; (I) P factor in 2022.
Figure 4. RUSLE factor maps: (A) erosivity factor R; (B) erodibility factor K; (C) topographic factor LS; (D) C factor in 2017; (E) C factor in 2019; (F) C factor in 2022; (G) P factor in 2017; (H) P factor in 2019; (I) P factor in 2022.
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Figure 5. Total soil loss categorized by LULC class in 2017, 2019, and 2022.
Figure 5. Total soil loss categorized by LULC class in 2017, 2019, and 2022.
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Figure 6. Total potential soil loss categorized by LULC class in 2017, 2019, and 2022.
Figure 6. Total potential soil loss categorized by LULC class in 2017, 2019, and 2022.
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Figure 7. Average soil loss categorized by LULC class in 2017, 2019, and 2022.
Figure 7. Average soil loss categorized by LULC class in 2017, 2019, and 2022.
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Figure 8. Water discharge–sediment transport curve.
Figure 8. Water discharge–sediment transport curve.
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Figure 9. Spatial distribution of soil losses: (A) 2017, (B) 2019, (C) 2022. Total potential soil losses: (D) 2017, (E) 2019, (F) 2022.
Figure 9. Spatial distribution of soil losses: (A) 2017, (B) 2019, (C) 2022. Total potential soil losses: (D) 2017, (E) 2019, (F) 2022.
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Figure 10. Spatial distribution of SOM levels: (A) period 1, (B) period 2. Spatial distribution of SOC losses: (C) period 1, (D) period 2. Values above and below average SOC losses: (E) period 1, (F) period 2. Period 1 = 2017–2019; period 2 = 2020–2022.
Figure 10. Spatial distribution of SOM levels: (A) period 1, (B) period 2. Spatial distribution of SOC losses: (C) period 1, (D) period 2. Values above and below average SOC losses: (E) period 1, (F) period 2. Period 1 = 2017–2019; period 2 = 2020–2022.
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Table 1. LULC areas (ha) and percentage in 2017, 2019, and 2022.
Table 1. LULC areas (ha) and percentage in 2017, 2019, and 2022.
ClassYear
201720192022
ha%ha%ha%
Coffee1325.9464.811288.4062.981234.2460.33
Eucalyptus24.231.1940.081.9661.983.03
Facilities4.500.224.700.233.690.18
Native forest135.936.64151.417.40170.368.33
* Other crops84.934.1585.824.1983.664.09
Pasture89.664.3892.294.51110.115.38
Water bodies5.530.275.530.276.330.31
Bare soil375.1818.34377.6718.46375.5318.35
Total2045.901002045.901002045.90100
* Miscellany of agriculture and pasture.
Table 2. Correspondence of the C and P value factors and sources.
Table 2. Correspondence of the C and P value factors and sources.
LULCC FactorSource C FactorP Factor [39]
Coffee0.086[40]0.350
Eucalyptus0.121[41]0.560
Native forest0.001[42]0.200
Other crops0.096[43]0.350
Pasture0.061[44]0.350
Bare soil0.600 *[45]1.000
* For the bare soil, we adopted a C value of 0.6, according to the similarity with Ethiopia.
Table 3. Differences between total soil loss and potential soil loss.
Table 3. Differences between total soil loss and potential soil loss.
2017 YearTotal Soil Loss (Mg yr−1)Total Potential Soil Loss (Mg yr−1)Difference (Mg yr−1)Difference (%)
Coffee234666574310283
Eucalyptus4710255217
Native forest426119145
Other crops17026190153
Pasture9516064167
Bare soil458864231834139
Sum729013,6656374187
2019 YearTotal Soil Loss  (Mg yr−1)Total Potential Soil Loss  (Mg yr−1)Difference (Mg yr−1)Difference (%)
Coffee231964214102276
Eucalyptus8214360173
Native forest395516142
Other crops16925384149
Pasture11220087177
Bare soil464565031858140
Sum736813,5776209184
2022 YearTotal Soil Loss  (Mg yr−1)Total Potential Soil Loss  (Mg yr−1)Difference (Mg yr−1)Difference (%)
Coffee218459693785273
Eucalyptus111269158241
Native forest546410117
Other crops174277103159
Pasture12322299180
Bare soil458864231834139
Sum723613,2275990182
Table 4. Estimated and observed SDR values for the years 2017, 2019, and 2022.
Table 4. Estimated and observed SDR values for the years 2017, 2019, and 2022.
YearEstimated SDR ( M g   h a − 1 )Observed SDR ( M g   h a − 1 ) Variation (%)
20170.280.294
20190.300.3412
20220.250.2811
Table 5. Differences in SOC losses in two periods.
Table 5. Differences in SOC losses in two periods.
SOC LossPeriod 1 *
Area (ha)		% of the AreaArea (ha)Period 2 **
% of the Area		Difference (%) Period 2–Period 1
0–5729.7735.67780.3038.142.47
>5–10229.3411.21247.9612.120.91
>10–15260.4412.73274.9613.440.71
>15–2596.974.74158.147.732.99
>25–50343.5016.79283.9713.88−2.91
>50–10083.884.10135.236.612.51
>100–200199.679.76112.525.50−4.26
>200102.295.0052.782.58−2.42
2045.901002045.9100100
* Period 1 = 2017–2019; ** Period 2 = 2020–2022.
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Santana, D.B.; Lense, G.H.E.; Rios, G.d.S.; Archanjo, R.E.d.S.; Raniero, M.; Santana, A.B.; Rubira, F.G.; Ayer, J.E.B.; Mincato, R.L. Spatiotemporal Dynamics of Soil and Soil Organic Carbon Losses via Water Erosion in Coffee Cultivation in Tropical Regions. Sustainability 2025, 17, 821. https://doi.org/10.3390/su17030821

AMA Style

Santana DB, Lense GHE, Rios GdS, Archanjo REdS, Raniero M, Santana AB, Rubira FG, Ayer JEB, Mincato RL. Spatiotemporal Dynamics of Soil and Soil Organic Carbon Losses via Water Erosion in Coffee Cultivation in Tropical Regions. Sustainability. 2025; 17(3):821. https://doi.org/10.3390/su17030821

Chicago/Turabian Style

Santana, Derielsen Brandão, Guilherme Henrique Expedito Lense, Guilherme da Silva Rios, Raissa Eduarda da Silva Archanjo, Mariana Raniero, Aleksander Brandão Santana, Felipe Gomes Rubira, Joaquim Ernesto Bernardes Ayer, and Ronaldo Luiz Mincato. 2025. "Spatiotemporal Dynamics of Soil and Soil Organic Carbon Losses via Water Erosion in Coffee Cultivation in Tropical Regions" Sustainability 17, no. 3: 821. https://doi.org/10.3390/su17030821

APA Style

Santana, D. B., Lense, G. H. E., Rios, G. d. S., Archanjo, R. E. d. S., Raniero, M., Santana, A. B., Rubira, F. G., Ayer, J. E. B., & Mincato, R. L. (2025). Spatiotemporal Dynamics of Soil and Soil Organic Carbon Losses via Water Erosion in Coffee Cultivation in Tropical Regions. Sustainability, 17(3), 821. https://doi.org/10.3390/su17030821

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