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Landscape pattern analysis using GIS and remote sensing to diagnose soil erosion and nutrient availability in two agroecological zones of Southern Mali

Landscape pattern analysis using GIS and remote sensing to diagnose soil erosion and nutrient... Background Soil is a basic natural resource for the existence of life on earth, and its health is a major concern for rural livelihoods. Poor soil health is directly associated with reduced agricultural land productivity in many sub- Saharan countries, such as Mali. Agricultural land is subjected to immense degradation and the loss of important soil nutrients due to soil erosion. The objective of the study was to diagnose the spatial distribution of soil erosion and soil nutrient variations under different land use in two agroecological zones of Southern Mali using the Geographical Information System (GIS) software, the empirically derived relationship of the Revised Universal Soil Loss Equation, in-situ soil data measurement and satellite products. The soil erosion effect on agricultural land productivity was dis- cussed to highlight the usefulness of soil and water conservation practices in Southern Mali. Results The results of the land use and land cover change analysis from 2015 to 2019 revealed significant area reductions in water bodies, bare land, and savanna woodland for the benefit of increased natural vegetation and agricultural land. There was significant variation in the annual soil loss under the different land use conditions. Despite recordings of the lowest soil erosion rates in the majority of the landscape (71%) as a result of field-based soil and water conservation practices, the highest rates of erosion were seen in agricultural fields, resulting in a reduction in agricultural land area and a loss of nutrients that are useful for plant growth. Spatial nutrient modelling and map- ping revealed a high deficiency and significant variations (p < 0.05) in nitrogen (N), phosphorus (P), potassium (K), and carbon (C) in all land use and land cover types for the two agroecologies. Conclusions The study highlighted the inadequacies of existing field-based soil and water conservation practices to reduce soil erosion and improve landscape management practices. The findings of the study can inform land man- agement planners and other development actors to strategize and prioritize landscape-based intervention practices and protect catchment areas from severe erosion for the enhanced productivity of agricultural fields. Keywords Soil erosion, Revised Universal Soil Loss Equation, Land use, Soil nutrient, Landscape, Southern Mali *Correspondence: International Crops Research Institute for the Semi-Arid Tropics (ICRISAT- Birhanu Zemadim Birhanu Tanzania), PO Box 34441, Dar es Salaam, Tanzania Z.Birhanu@Cgiar.Org; birhanusek@gmail.com Bamako, Mali Laboratoire d’Optique, de Spectroscopie et des Sciences de l’atmosphère (LOSSA), Université des Sciences de Techniques et de Technologie de Bamako, BPE 3206 Bamako, Mali International Crops Research Institute for the Semi-Arid Tropics (ICRISAT- Mali), Bamako, Mali © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Sanogo et al. Agriculture & Food Security (2023) 12:4 Page 2 of 11 accumulated runoff from farms, grazing areas, or bush - Background lands. Landscape-level information about the processes Landscape patterns determine the physiographic charac- of soil erosion, water infiltration, and the associated loss teristics that affect the rate of soil loss from agricultural of valuable nutrients is often missing in most studies due fields. Land use and land cover play a major role in the to either insufficient data or methods of estimation [10]. soil nutrient cycle, specifically regarding the sources and The objective of the study was, therefore, to diagnose the sinks of carbon [1]. Studying their changes at any scale is spatial distribution of soil erosion and soil nutrient varia- critical to assess the impact of anthropogenic and natu- tions under different land use in two agroecological zones ral changes on soil properties. Remote sensing data have of Southern Mali using the Geographical Information been widely used for monitoring land use and land cover System (GIS) software, the empirically derived relation- changes, and their use in combination with ground meas- ship of the Revised Universal Soil Loss Equation, in-situ urements provides a highly accurate view of the Earth’s soil data measurement, and satellite products. Specifi - components, such as landscapes and hydrospheres [2]. cally, the study examined (i) land use changes between Land use and land cover have detrimental effects on run - two different agroecologies over time, (ii) spatial factors off and soil physical and chemical properties and hence that influenced soil erosion and nutrient loss, (c) varia - significantly impact soil organic and macronutrients that tions in soil nutrients under different land use practices, are useful for plant growth [3, 4]. Land use and land cover and (d) usefulness of soil and water conservation prac- also have an impact on the rate of soil erosion [5]. Studies tices on agricultural land productivity. [6–9] have highlighted an increase in soil erosion rates in areas, where natural vegetation has been converted into Methods farms, settlements, and grasslands over time [10]. Exces- Study area sive and uncontrolled erosion leads to the loss of impor- The study was conducted in the two agroecological tant soil nutrients, such as nitrogen (N), phosphorus (P), zones of Southern Mali (Bougouni and Koutiala districts) and potassium (K), and consequently a decline in poten- (Fig.  1). The total land area of Bougouni district is esti - tial crop yield at the plot and farm levels [6]. mated as 20,028 km with a population of 458,546, and Mali is a country with a high rate of population growth Koutiala district has an area of 8,740 k m and a popula- (2.9% in 2021 as per the World Bank Group report, while tion of 580,453 [13]. The ecosystem of Southern Mali is the data in sub-Saharan Africa was 2.6%). The southern best defined as a Sudano-Guinean savanna [11] and an region of Mali is the most populated area in the coun- agricultural system characterized by rainfed, small-scale try exerting high pressure on land due to the physical crop-livestock, and agro-pastoral farming systems [12]. expansion of urban and agricultural fields. This growth The study area receives high rainfall in the range of 800 has resulted in an increased soil erosion rate over time to 1200  mm and is considered the breadbasket of Mali [5]. The semiarid region of Southern Mali is character - [13]. In contrast, 34% of Mali’s poor residents and 45% ized by intensive agricultural practices, land degrada- of Mali’s food-poor residents live in the region [14, 15]. tion, and extreme climatic variability. Traditional and less The major soil types in the study area are well-developed, mechanical agricultural practices are applied to mono- weakly leached, ferruginous sand through loamy coarse cultures, intercropping, tillage, and agroforestry in most sand to sandy clay loams and coarse textured. The sand agroecological farm fields and landscapes. Maize (Zea −1 content ranged from 250 to 890  kg  ha , with an aver- mays), sorghum (Sorghum bicolor), cotton (Gossypium −1 age of 647  kg  ha . For silt, the range was from 40 to spp), groundnut (Arachis hypogaea), and millet (Penni- −1 −1 620 kg  ha , with 221.1 kg  ha on average, while the clay setum glaucum) are the main crops cultivated by apply- −1 content ranged from 50 to 330  kg  ha , with an average ing a combination of manure and inorganic fertilizers. −1 of 131.1 kg  ha . The long-term (1970–2018) average However, soil erosion has been a major problem affect - monthly maximum and minimum air temperatures in the ing agricultural productivity, as it affects the whole land - two districts are 33 and 22  °C in Bougouni and 34 and scape, and interventions to mitigate it at the farm level 23 °C in Koutiala, respectively [16]. seldom have an effect. Until recently, sustainable land management practices Data and data sources in most parts of Mali focused on reducing runoff and Data for land use/cover and the RUSLE model soil loss at the plot or farm level through soil and water Satellite-based information derived from Landsat 8 conservation (SWC) practices, such as contour bunding images was used to produce land use and land cover [7]. Although important in its application at the plot or maps, and spatially derived crop management factors farm level, the efficiency of contour bundling is limited were used in the empirical model of the Revised Univer- in addressing landscape degradation and the loss of crop sal Soil Loss Equation (RUSLE) to estimate the annual productivity. Excessive soil erosion is usually caused by S anogo et al. Agriculture & Food Security (2023) 12:4 Page 3 of 11 Fig. 1 Map showing the study area in the two districts of Bougouni and Koutiala (Sikasso region), Southern Mali soil erosion loss. In addition, the digital elevation model data from the African soil profile (https:// data. isric. org/ (DEM) was derived from Landsat 8 images to determine geone twork/ srv/ eng/ catal og. searc h#/ search). In 2015, the empirical parameters of the RUSLE. soil data were collected under the Africa RISING pro- Rainfall data from 2000 to 2019 were collected from ject from 350 sampling sites in 10 villages of the Bou- national meteorological stations in both the Bougouni gouni and Koutiala districts of Southern Mali (Table 1). and the Koutiala districts. To obtain the spatial coverage Sampling sites from each village were selected through of rainfall data, the study used gridded monthly precipi- a stratified random sampling technique. The strata tation data at a 1-km spatial resolution by the climatol- included geographical location, availability of field- ogy at high resolution for the Earth’s land surface areas based soil and water conservation practices, mainly [17]. CHELSA climatological data have a higher accuracy contour bunding (CB), food crop type and mixture, nat- in predictions of precipitation patterns than that of many ural bush and/or grazing land, agroforestry or presence other products [17]. CHELSA products are in a geo- of forestland, presence of termitarium and oxen kraal- graphic coordinate system referenced to the World Geo- ing sites). From each village, a minimum of 29 com- detic System 1984 (WGS84) horizontal datum, with the posite soil samples were collected from depths of 0 to horizontal coordinates expressed in decimal degrees. The 15 cm with distribution across the strata. On a farmer’s data were in GeoTiff format, which can be viewed using field, samples were taken at five locations across two GIS software. diagonal transects, and the coordinates at the midpoint were determined using hand-held Garmin GPS. Stand- ard laboratory soil tests were employed to evaluate the Data for soil nutrients fertility status of the soils, thereby mapping the erod- The in-situ soil data were archived from a project ibility and impact of land use on soil nutrients; carbon named the “Africa Research In Sustainable Intensifica - (C), nitrogen (N), phosphorus (P), and potassium (K) tion for the Next Generation (Africa RISING)” data- analyses were performed. base for Mali. The data were used to validate gridded Sanogo et al. Agriculture & Food Security (2023) 12:4 Page 4 of 11 Identification of factors that influence soil erosion Table 1 Soil sample collection in ten villages in the districts of Bougouni and Koutiala and nutrient loss Rainfall erosivity factor (R) Village Longitude Latitude Population MAR* N** The rainfall erosivity factor (R) explains the variations in (mm) rainfall intensity at different locations within the land - Madina 11.35 − 7.66 1582 1137 31 scape that can cause soil erosion. The R values for each Dieba 11.51 − 7.931 1121 1139 29 agroecological zone were calculated using the CHELSA Sibirila 11.43 − 7.77 929 1138 29 database and gridded data from 1979 to 2016 using Flola 11.42 − 7.64 465 1102 29 Eq. 4. The formula has been used in many parts of Africa Nampossela 12.33 − 5.34 2443 813 29 [19]: N’golonianasso 12.43 − 5.70 4383 849 30 R factor =−8.12 + 0.562 ∗ P M’pessoba 12.67 − 5.71 9862 800 33 (4) Sirakele 12.52 − 5.47 4502 818 34 where R is the rainfall erosivity factor in MJ mm Kani 12.25 − 5.19 2488 944 33 −1 −1 −1 ha  h  year , and P is the mean annual rainfall in mm. Zanzoni 12.61 − 5.57 3463 842 31 Total 308 Soil erodibility factor (K) MAR (Mean Annual Rainfall) Soil detachment and transport caused by the impact of ** N (Number of soil sampling locations) raindrops and surface flow are represented by the K fac - tor. Soil data on texture, structure, organic matter, and Data analysis permeability were used to estimate the K factor as per Land use changes between two different landscapes Eq.  5 [20]. Data available in the Africa RISING database over time for Mali were used to validate the gridded data from the Land use changes between two different periods were African soil profile (https:// data. isric. org/ geone twork/ evaluated based on percentage changes. A series of srv/ eng/ catal og. search) and to adequately determine the processes from data acquisition, data pre-processing, soil erodibility factor (K). supervised classification, and post-classification were performed. Pre-processing was the first step conducted % sand+% slit on Landsat 8 images [18]. Geometric correction, image % clay (5) K factor = enhancement, and topographical correction were per- 100 formed for data pre-processing. The raw data were projected to the UTM WGS-84 projection system for Slope length (L) and slope (S) factors supervised classification. An accuracy assessment was Slope steepness and slope length (LS-factor) were deter- performed using ArcGIS software to determine the mined from a DEM. The Spatial Analyst Toolbox and kappa coefficient of agreement (Eqs. 1–3): the Map Algebra Raster Calculator in the ArcGIS envi- P − P ronment were used to generating multiple slope maps o c KA = (1) and flow accumulation as well as to calculate and gen - 1 − P erate the topographic factor map. The flow accumula - tion was calculated from the Spatial Analyst Hydrology toolset of ArcMap in the ArcGIS environment. The slope P = P o ii (2) of the study area as a percentage was calculated by the i=1 Slope tools in the Spatial Analyst Surface toolset of Arc- Map from the DEM of the districts. The DEM data were P = (P ∗ P ) c +i downloaded from https:// lpdaa csvc. cr. usgs. gov/ appee i+ (3) i=1 ars/ downl oad/ 4d436 aca- c5be- 4011- 981f- 1e602 0f 37c79. These data were used to determine and map the slope where r is the number of rows in the error matrix, P is ii length and slope gradient of the topographic factors in the proportion of pixels in row i and column I, P is the i+ the study area. The determination of the LS factor was proportion of the marginal total of row i, and P is the +i performed using Eq. 6: proportion of the marginal total of column i. S anogo et al. Agriculture & Food Security (2023) 12:4 Page 5 of 11 Table 3 Conservation practice factor (P) resolution LS = Pow [flow accumulation] ∗ 22.1, 0.4 Slope (%) Contouring Strip cropping Terracing (6) [slope of DEM] 0–7 0.55 0.27 0.10 ∗ Pow sin ∗ 1.4 0.0896, 1.4 7–11.3 0.60 0.30 0.12 11.3–17.6 0.80 0.40 0.16 where flow accumulation denotes the accumulated 17.6–26.8 0.90 0.45 0.18 upslope contributing area for a given cell, and LS is the > 26.8 1.0 0.50 0.20 combined slope length and slope steepness factor. Crop management factor (C) the annual soil loss rate of each cell using the “raster cal- The crop management factor (C) was used to reflect the culator”. In the present study, soil erosion was estimated effect of cropping practices on erosion control in agricul - using the RUSLE (Eq.  4) based on the raster calculator tural lands and vegetation covers. The C-factor is defined technique in ArcGIS 10.7. The five factors (in the raster as the ratio of soil loss from land cropped under specific layer) used in the RUSLE model are erodibility (K), slope conditions to the loss from clean-tilled, continuous fal- length (L), slope steepness (S), cropping system (C), and low. In the present study, the C values were determined conservation practice (P): based on the values presented in Table  2. Land use and cover maps were converted into polygons to estimate the A = R × K × (L × S) × C × P (7) spatial values of the C-factor based on the specific land where A is the annual rate of soil loss use [9]. −1 −1 (ton ha  yr ), and R is the annual rainfall erosivity fac- −1  h−1 −1 tor (MJ mm ha  yr ). K  is expressed in tons ha yr. Conservation practice factor (P‑factor) −1 −1 −1 ha  MJ  mm , and L, S, C, and P are dimensionless. In conservation practice, the P-factor values range from 0 The five-factor layers used in the RUSLE were pro - to 1 depending on the land use/cover types. The highest duced from various data sources with different spatial value is assigned to areas with no conservation practice resolutions. Thus, all factor layers were previously spa - or with a high slope, while the minimum values cor- tialized and re-gridded to the same spatial resolution as respond to built-up land and plantation area with strip shown in the flow chart (Fig. 2). and contour or small sloped areas. The P-factor values in Table  3 were based on the land cultivation method and Statistical analysis slope [21]. The spatial values of the P-factor were deter - A T test was applied to compare the means of the sam- mined from a DEM and GIS technique. ples in different years. Variations in the annual soil loss and soil nutrient loss among different land use types were Revised Universal Soil Loss Equation (RUSLE) evaluated at the 5% significance level in the two different The RUSLE was developed by Renard et  al. [22] based on the modifications of the USLE [23]. There have been many studies on soil erosion worldwide [8, 9], and they have used RUSLE to predict annual soil loss, with results revealing that land use has an impact on the rate of soil erosion  [24–26]. The RUSLE model uses five different raster layers processed by overlay analysis to generate Table 2 Attribute values of vegetation cover (C-factor) [9] Class C‑factor Built-up land 0.003 Savanna woodland 0.004 Bare land 1 Waterbody 0 Fig. 2 Flow chart for the determination of annual soil loss Agriculture 0.4 Natural vegetation 0.025 Sanogo et al. Agriculture & Food Security (2023) 12:4 Page 6 of 11 agroecological zones. The spatial difference ratio was and 33. The value of the crop support practice factor (P) used to detect land use change occurring between the ranged between 0.55 and 1. two studied agroecologies. Spatial pattern of the annual soil loss Results Table  6 shows the variation in the mean soil loss under Land use and land cover change different land uses across the two agroecologies. In Land use and land cover maps were classified into six Bougouni, in 2015, a higher mean value of soil loss was classes, namely, agricultural land (AL), bare land (BL), observed in bare land followed by agricultural land. Simi- water (W), settlements (S), savanna woodland (SW), lar observations were made in the second period (2019), and natural vegetation (NV). Percentage change analy- but agricultural land was more vulnerable than bare land. ses showed an increase in NV by 1151 km at a rate of Similarly, in Koutiala, agricultural land was more vulner- 2 2 230 km per year, and AL increased by 1600 k m at a rate able to land degradation in 2019. Considering the mean of 320 km per year for Bougouni. This increase was a values of soil erosion loss, it was observed that the rate of result of the introduction of new agricultural practices land degradation vulnerability was greater in many of the that converted forest areas into cashew plantations (com- agriculture fields. mercial trees). Settlement areas increased at a rate of Statistical analysis between the mean values indicated a 0.5 km /year, and in other land uses, SW and bare land significant difference in annual soil loss among the differ - showed a significant decline. In a similar mode, AL and ent land use types across locations for different periods NV areas increased over the studied period in Koutiala (p < 0.05) (Table  7). The soil erosion loss in SW and the (Table  4). Land use and land cover change detection vegetation were not significant across the two agroecolo - analysis between the two agroecologies showed that NV gies (P > 0.05). Significant soil erosion loss (p < 0.05) was and agriculture areas increased at a higher rate in Bou- observed across locations for agriculture, settlements, gouni than in Koutiala. Bougouni was more favourable to and bare land in the two periods. The relationship for environmental development than Koutiala. However, the bare land was stronger than that for agriculture (Table 7). anthropogenic effects of agriculture and deforestation As shown in Table  8, the magnitude of soil erosion played major roles in land use changes in Bougouni. loss was classified into five categories ranging from low to severe. The results revealed that a low rate of soil ero - Spatial factors influencing soil erosion and nutrient loss sion occurred in many agricultural fields in both districts. Figure 3 and Table 5 show the variations in the spatial fac- Over the period from 2015 to 2019, a decline in the soil tors in the two agroecologies. The R values were higher erosion rate was observed in all classes except for the in Bougouni than in Koutiala. The K values showed that ‘moderate rate’. This decline in erosion rates could be erodibility was greater in Koutiala than in Bougouni, explained by the introduction of different cultural prac - implying that soil in Bougouni resists erosion better than tices, such as contour bunding, afforestation, stone lines, that in Koutiala (Fig.  4). The spatially mapped LS factors and Zai, introduced by different projects in the two dis - resulted in LS values ranging between 1.4 and 58 for Bou- tricts [6, 7, 12]. gouni, while in Koutiala, the values ranged between 1.4 Table 4 Land use and land cover in 2015 and 2019 Land use and Bougouni Koutiala land cover Area (2015) Area (2019) Change (%) Area (2015) Area (2019) Change (%) 2 2 2 2 Km % Km % Km % Km % Settlements 44.0901 0.22 45.5643 0.23 0.01 66.79 0.70 68.68 0.72 0.02 Water 154.127 0.78 141.683 0.72 − 0.06 116.0976 1.21 16.30 0.17 − 1.04 SW* 2152.59 10.91 827.492 4.19 − 6.72 393.0814 4.09 205.65 2.14 − 1.95 Bare land 1788.99 9.06 373.638 1.89 − 7.17 561.5779 5.85 337.61 3.52 − 2.33 NV** 11,677.10 59.17 12,828.92 65.00 5.83 5137.06 53.49 5,633.94 58.66 5.17 Agriculture 3919.44 19.86 5519.02 27.96 8.10 3329.41 34.67 3,342.37 34.80 0.13 SW Savannah Woodland NV** Natural Vegetation S anogo et al. Agriculture & Food Security (2023) 12:4 Page 7 of 11 Fig. 3 Spatial pattern a R Factor, b K Factor, c LS Factor, d P Factor, in Bougouni variation in soil nutrients under different land use types. Table 5 Spatial factors influencing soil erosion and nutrient loss The results revealed that there were significant varia - District/ R K LS P C tions in N, P, K, and C across different land use catego - Agroecology ries in both the Bougouni and the Koutiala agroecologies Bougouni 683–180 0.073–0.015 1.4–58 0.55–1 Refer Table 2 (P < 0.05). Koutiala 477–226 1.4–33 1.4–33 0.55–1 Refer Table 2 A comparison between soil nutrients in the two agro- −1 −1 −1 R = Rainfall Erosivity in MJmmha  h y , K = Soil Erodibility (K) t.ha.h. ecologies highlighted that Bougouni had a higher con- −1 −1 −1 ha  MJ  mm , LS = Slope Length (L) and Slope (S) Factors, P = Crop Support centration of nitrogen and a higher carbon content than Practice Factor, C = Cropping Management Factor Koutiala. The variance between these two districts was dependent on the land use types. Across the three land uses (agriculture, water, and bare land), phosphorus was Spatial variation in C, N, P, and K under different land uses higher in Bougouni than in Koutiala, but it was the oppo- Nitrogen (N), phosphorus (P), and potassium (K) are site for the other land uses (settlements, natural vegeta- among the soil nutrients that are important for plant tion, and savanna woodland) (Table 9). growth and development. Nitrogen (N) is a macronutri- ent that is required by crops in large quantities, and its Discussion deficiency restricts crop production in many agricul - Soil erosion is the main driver of land degradation in tural fields. Plants need the same amount of potassium many sub-Saharan agricultural fields. The study areas and nitrogen for growth. Phosphorus (P) is required by in Southern Mali are mostly covered by natural vegeta- plants to increase yield. Soil organic carbon (C) improves tion and agricultural fields. Over time, agricultural and soil’s physical and chemical properties. Table 9 shows the Sanogo et al. Agriculture & Food Security (2023) 12:4 Page 8 of 11 Fig. 4 Spatial pattern a R Factor, b K Factor, c LS Factor, d P Factor, in Koutiala Table 6 Mean values of annual soil erosion loss under different land uses in t/ha/year Landuse Bougouni Koutiala Erosion rate land cover 2015 2019 2015 2019 Bougouni Koutiala Mean Std Mean Std Mean Std Mean Std Mean Mean Agriculture 7.8 2.3 3.7 2.28 7.22 2.43 5.14 3.71 − 4.1 − 2.08 Settlements 0.21 1.09 2.61 3.24 0.04 0.01 1.38 2.46 2.4 1.34 S W 0.08 0.06 1.21 3.91 0.10 0.16 0.94 2.05 1.13 0.84 Bare soil 17.53 5.82 2.87 3.16 14.90 8.91 3.11 4.14 − 14.66 − 11.78 NV 0.42 0.1 0.79 1.6 1.82 4.19 0.38 0.09 0.37 − 1.43 Water 0.00 0.00 0.15 0.71 0.01 0.06 0.23 0.54 0.15 0.22 SW Savannah Woodland, NV Natural Vegetation Table 7 Comparison between mean soil erosion rates Period Statistic Agriculture Settlements SW NV Bare land 2015 p value 6.07e-05*** 2.2e-16*** 0.1013 0.1862 1.38e-06*** 2019 p value 0.000219* 0.003478* 0.4961 0.4133 0.007281** SW Savannah Woodland, NV Natural Vegetation; ***Significant across locations, **Significant between the years, *Significant across land uses S anogo et al. Agriculture & Food Security (2023) 12:4 Page 9 of 11 Table 8 Land area in the percentage of soil erosion loss rate for 2015 and 2019 in two agroecologies Soil erosion rate Bougouni Koutiala (t/ha/year) Area (2015) (%) Area (2019) Change Area (2015) Area (2019) Change (%) (%) (%) Low (< 5) 71.70 69.67 − 2.04 64.29 65.45 1.16 Moderate (5–11) 17.02 26.64 9.62 26.74 26.80 0.06 High (11–25) 10.67 3.43 − 7.24 8.35 6.57 − 1.78 Very High (25–50) 0.54 0.23 − 0.30 0.54 1.12 0.58 Severe > 50 0.07 0.03 − 0.05 0.08 0.06 − 0.02 Table 9 Mean values and standard deviation of soil nutrient properties for different land uses in two agroecologies Land use Land Soil nutrient properties cover Bougouni* Koutiala* Nitrogen (mg/ Phosphorus Potassium Carbon (mg/ Nitrogen Phosphorus Potassium Carbon kg) (ppm) cmol + /kg kg) (mg/kg) (ppm) cmol + /kg (mg/kg) Agriculture 591.82 ± 52.96 407.24 ± 84.44 102.93 ± 14.71 8.36 ± 2.67 479.04 ± 65.14 399.25 ± 117.19 101.13 ± 8.20 4.98 ± 1.38 Settlements 629.69 ± 69.48 469.34 ± 140.5 93.52 ± 13.13 6.78 ± 0.83 517.196 ± 47.11 520.88 ± 189.29 107.04 ± 8.59 6.14 ± 1.56 Water 796.75 ± 175.9 425.86 ± 99.49 108.42 ± 24.44 13.65 ± 4.95 675.97 ± 112.83 424.02 ± 87.73 114.98 ± 7.14 8.55 ± 2.18 SW 679.71 ± 75.45 376.87 ± 70.42 102.42 ± 12.22 9.88 ± 2.62 534.06 ± 69.95 418.64 ± 63.07 109.07 ± 9.46 5.68 ± 1.33 Bare land 682.77 ± 93.96 450.49 ± 128.9 104.15 ± 13.85 10.43 ± 2.66 515.087 ± 67.34 419.55 ± 91.63 103.40 ± 10.11 5.34 ± 1.22 NV 694.68 ± 62.34 385.95 ± 90.50 100.24 ± 13.41 9.30 ± 1.71 516.60 ± 66.00 411.19 ± 63.30 106.56 ± 8.50 5.01 ± 1.34 P Value 1.96E-49 2.34E-12 4.62E-10 1.06E-60 4.08E-84 3.09E-18 7.37E-34 7.13E-76 Sample Size (N) = 112 for Bougouni and 122 for Koutiala, SW = Savanna Woodland, NV = Natural Vegetation settlement areas have increased due to urban population Soil erosion vulnerability was reduced by the appli- growth and the need for more agricultural fields [27]. For cation of soil and water conservation (SWC) practices. instance, the increase in cashew plantations in Bougouni The major SWC practices in the study area that have has led to the expansion of agriculture, leading to an demonstrated a reduction in annual soil loss and an increase in fallow areas. The decrease in water bodies was increase in farm-level soil nutrients are stone bunds, mainly due to gold mining activities and drought created ridges, contour bunding, and tree plantations [6]. by rainfall variability in a few landscapes. Land uses have In this study, the highest values of N, K, and C were an important impact on the soil’s chemical and physical recorded in water bodies, and a greater amount of P properties [28]. Previous studies have highlighted the was noted in settlement areas with P in vegetation cov- influence of land use on soil management practices [29– ers (natural vegetation and savanna woodland). These 31]. In addition, the importance of soil and water conser- findings corroborate well with previous assessments of vation in controlling soil erosion has been demonstrated soil fertility variation in different land uses and man - by many studies conducted in the region [6, 7]. agement practices [27, 28, 33]. The findings of this study revealed the presence of The economy of Mali is mainly based on rainfed agri - low erosion rates in large landscapes in the studied culture, and hence, any depletion in soil nutrients may areas, as established elsewhere [1, 27, 32]. Despite the cause food insecurity and poverty for smallholder farm- low magnitude of runoff from the larger part of the ing systems. As soil erosion has a significant impact on Southern Mali landscapes, the runoff generated from agricultural productivity, land management practices many agricultural fields is severe and is the cause of should be considered a priority by decision-makers for land degradation as well as the loss of important soil agricultural-led economic growth. The current land nutrients. When forest and natural vegetation areas are management practice in Mali may be the reason for the the targets of human-induced activities, the rate of vul- varying erosion risk rather than natural conditions, as nerability to erosion increases over time [6]. explained by Rousseva et al. [35]. Based on the findings Sanogo et al. Agriculture & Food Security (2023) 12:4 Page 10 of 11 AL Agricultural Land of this study and recommendations from previous SW Savanna Woodland studies [6, 7, 34], soil nutrient loss could be controlled NV Natural Vegetation by applying appropriate soil and water conservation DEM Digital Elevation Model Africa RISING A frica Research in Sustainable Intensification for the practices integrated with good agronomic practices. Next Generation The most common SWC practices recommended in CB Contour Bunding the study area include a combination of construct- C Organic carbon GPS Global Positioning System ing contour bunding, planting trees, stone bunds, and terracing. This study calls for increased efforts by all Acknowledgements stakeholders to implement the needed intervention at This work was supported by the Africa Research in Sustainable Intensification for the Next Generation (Africa RISING) project in Mali. The authors are grateful the landscape scale and boost agricultural productivity for the financial support provided by the United States Agency for Interna- in the region. tional Development (USAID) through the International Institute of Tropical Agriculture (IITA). The authors are also thankful to the International Science Programme (ISP/IPPS) through the Laboratoire d’Optique de Spectroscopie et Conclusion des Sciences de l’Atmosphère (LOSSA) at the Faculty of Sciences and Technol- Through spatial modelling and empirical relations, soil ogy of Bamako for the use of the institute’s spatial data and GIS and remote loss was estimated for 2015 and 2019 using soil loss sensing facilities. parameters and the RUSLE. The results revealed that Author contributions the dominant portion of land use and land cover was KS and BZB developed the outline and wrote the different sections of the characterized by erosion risks leading to land degrada- manuscript. SS and AB contributed to the scientific content and review of the findings. All authors have read and accepted the final version of the manu- tion. High deficiencies in soil nutrients (N, P, K, and C) script. All authors read and approved the final manuscript. were observed in the landscapes. Many of these nutri- ents were found in the water bodies as a result of the Author’s information Mr. Karamoko Sanogo is a Ph.D. student at the Université des Sciences de increased rate of soil erosion from the landscape over Techniques et de Technologie de Bamako of Mali. He is also a scientific officer time, thus leaving only a small amount on the agricul- at the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT ) tural fields. The implementation of a few SWC and res - in Mali. He works on climate change, remote sensing, and soil and water management. Dr. Birhanu Zemadim Birhanu is the Global Theme Leader for toration practices, such as contour bunding and cashew Landscapes, Soil Fertility, and Water Management at ICRISAT. His research area plantation, seemed to contribute to reduced soil ero- includes landscape-based integrated management of land and water. He also sion risk over the studied period (2015 to 2019). works on validating and scaling agricultural and natural resource manage- ment technologies and practices from farm to landscape scale in a participa- The results of this study are useful in providing tory and integrated watershed approach. Dr. Souleymane Sanogo is an Associ- guided information about the importance of controlling ate Professor, and specialist in Meteorology and climate sciences at the Physics soil erosion at the landscape level and retaining impor- Department of the University of Sciences, Techniques, and Technologies of Bamako (USTTB). His study area includes climate modelling and integrated tant plant growth parameters in agricultural fields. assessments of climate impacts on water, agriculture, energy, and land. Profes- Mapping soil erosion risk at the landscape level helps sor Abdramane Ba is the coordinator of the Laboratory of Optics Spectroscopy identify vulnerable areas and protect them at a priority and Atmospheric Science (LOSSA) at the Physics Department of the University of Sciences, Techniques, and Technologies of Bamako (USTTB). level. Land planners, government extension services, and local NGOs can, therefore, use the results of this Funding study to develop informed natural resource conserva- USAID, AID-BFS-G-11-00002 From USAID (Prime Sponsor),Birhanu Zemadim Birhanu. tion strategies and prioritize intervention measures to protect soil loss from many agricultural farms. This Availability of data and materials study strongly recommends scaling up the implementa- Not applicable. tion of SWC practices at the landscape scale and inte- grating other sustainable land management practices, Declarations such as afforestation and crop management strategies, Ethics approval and consent to participate for the health of the ecosystem and improved agricul- Not applicable. tural productivity. Consent for publication Not applicable. Abbreviations Competing interests GIS Geographic Information System All authors declare they have no competing interests. RUSLE R evised Universal Soil Loss Equation SE Soil Erosion SWC Soil and Water Conservation Received: 10 January 2022 Accepted: 13 January 2023 N Nitrogen P Phosphorus K Potassium S anogo et al. Agriculture & Food Security (2023) 12:4 Page 11 of 11 References 19. Hurni H. Erosion productivity conservation systems in Ethiopia. In: Pro- 1. Kim C. 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Landscape pattern analysis using GIS and remote sensing to diagnose soil erosion and nutrient availability in two agroecological zones of Southern Mali

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Abstract

Background Soil is a basic natural resource for the existence of life on earth, and its health is a major concern for rural livelihoods. Poor soil health is directly associated with reduced agricultural land productivity in many sub- Saharan countries, such as Mali. Agricultural land is subjected to immense degradation and the loss of important soil nutrients due to soil erosion. The objective of the study was to diagnose the spatial distribution of soil erosion and soil nutrient variations under different land use in two agroecological zones of Southern Mali using the Geographical Information System (GIS) software, the empirically derived relationship of the Revised Universal Soil Loss Equation, in-situ soil data measurement and satellite products. The soil erosion effect on agricultural land productivity was dis- cussed to highlight the usefulness of soil and water conservation practices in Southern Mali. Results The results of the land use and land cover change analysis from 2015 to 2019 revealed significant area reductions in water bodies, bare land, and savanna woodland for the benefit of increased natural vegetation and agricultural land. There was significant variation in the annual soil loss under the different land use conditions. Despite recordings of the lowest soil erosion rates in the majority of the landscape (71%) as a result of field-based soil and water conservation practices, the highest rates of erosion were seen in agricultural fields, resulting in a reduction in agricultural land area and a loss of nutrients that are useful for plant growth. Spatial nutrient modelling and map- ping revealed a high deficiency and significant variations (p < 0.05) in nitrogen (N), phosphorus (P), potassium (K), and carbon (C) in all land use and land cover types for the two agroecologies. Conclusions The study highlighted the inadequacies of existing field-based soil and water conservation practices to reduce soil erosion and improve landscape management practices. The findings of the study can inform land man- agement planners and other development actors to strategize and prioritize landscape-based intervention practices and protect catchment areas from severe erosion for the enhanced productivity of agricultural fields. Keywords Soil erosion, Revised Universal Soil Loss Equation, Land use, Soil nutrient, Landscape, Southern Mali *Correspondence: International Crops Research Institute for the Semi-Arid Tropics (ICRISAT- Birhanu Zemadim Birhanu Tanzania), PO Box 34441, Dar es Salaam, Tanzania Z.Birhanu@Cgiar.Org; birhanusek@gmail.com Bamako, Mali Laboratoire d’Optique, de Spectroscopie et des Sciences de l’atmosphère (LOSSA), Université des Sciences de Techniques et de Technologie de Bamako, BPE 3206 Bamako, Mali International Crops Research Institute for the Semi-Arid Tropics (ICRISAT- Mali), Bamako, Mali © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Sanogo et al. Agriculture & Food Security (2023) 12:4 Page 2 of 11 accumulated runoff from farms, grazing areas, or bush - Background lands. Landscape-level information about the processes Landscape patterns determine the physiographic charac- of soil erosion, water infiltration, and the associated loss teristics that affect the rate of soil loss from agricultural of valuable nutrients is often missing in most studies due fields. Land use and land cover play a major role in the to either insufficient data or methods of estimation [10]. soil nutrient cycle, specifically regarding the sources and The objective of the study was, therefore, to diagnose the sinks of carbon [1]. Studying their changes at any scale is spatial distribution of soil erosion and soil nutrient varia- critical to assess the impact of anthropogenic and natu- tions under different land use in two agroecological zones ral changes on soil properties. Remote sensing data have of Southern Mali using the Geographical Information been widely used for monitoring land use and land cover System (GIS) software, the empirically derived relation- changes, and their use in combination with ground meas- ship of the Revised Universal Soil Loss Equation, in-situ urements provides a highly accurate view of the Earth’s soil data measurement, and satellite products. Specifi - components, such as landscapes and hydrospheres [2]. cally, the study examined (i) land use changes between Land use and land cover have detrimental effects on run - two different agroecologies over time, (ii) spatial factors off and soil physical and chemical properties and hence that influenced soil erosion and nutrient loss, (c) varia - significantly impact soil organic and macronutrients that tions in soil nutrients under different land use practices, are useful for plant growth [3, 4]. Land use and land cover and (d) usefulness of soil and water conservation prac- also have an impact on the rate of soil erosion [5]. Studies tices on agricultural land productivity. [6–9] have highlighted an increase in soil erosion rates in areas, where natural vegetation has been converted into Methods farms, settlements, and grasslands over time [10]. Exces- Study area sive and uncontrolled erosion leads to the loss of impor- The study was conducted in the two agroecological tant soil nutrients, such as nitrogen (N), phosphorus (P), zones of Southern Mali (Bougouni and Koutiala districts) and potassium (K), and consequently a decline in poten- (Fig.  1). The total land area of Bougouni district is esti - tial crop yield at the plot and farm levels [6]. mated as 20,028 km with a population of 458,546, and Mali is a country with a high rate of population growth Koutiala district has an area of 8,740 k m and a popula- (2.9% in 2021 as per the World Bank Group report, while tion of 580,453 [13]. The ecosystem of Southern Mali is the data in sub-Saharan Africa was 2.6%). The southern best defined as a Sudano-Guinean savanna [11] and an region of Mali is the most populated area in the coun- agricultural system characterized by rainfed, small-scale try exerting high pressure on land due to the physical crop-livestock, and agro-pastoral farming systems [12]. expansion of urban and agricultural fields. This growth The study area receives high rainfall in the range of 800 has resulted in an increased soil erosion rate over time to 1200  mm and is considered the breadbasket of Mali [5]. The semiarid region of Southern Mali is character - [13]. In contrast, 34% of Mali’s poor residents and 45% ized by intensive agricultural practices, land degrada- of Mali’s food-poor residents live in the region [14, 15]. tion, and extreme climatic variability. Traditional and less The major soil types in the study area are well-developed, mechanical agricultural practices are applied to mono- weakly leached, ferruginous sand through loamy coarse cultures, intercropping, tillage, and agroforestry in most sand to sandy clay loams and coarse textured. The sand agroecological farm fields and landscapes. Maize (Zea −1 content ranged from 250 to 890  kg  ha , with an aver- mays), sorghum (Sorghum bicolor), cotton (Gossypium −1 age of 647  kg  ha . For silt, the range was from 40 to spp), groundnut (Arachis hypogaea), and millet (Penni- −1 −1 620 kg  ha , with 221.1 kg  ha on average, while the clay setum glaucum) are the main crops cultivated by apply- −1 content ranged from 50 to 330  kg  ha , with an average ing a combination of manure and inorganic fertilizers. −1 of 131.1 kg  ha . The long-term (1970–2018) average However, soil erosion has been a major problem affect - monthly maximum and minimum air temperatures in the ing agricultural productivity, as it affects the whole land - two districts are 33 and 22  °C in Bougouni and 34 and scape, and interventions to mitigate it at the farm level 23 °C in Koutiala, respectively [16]. seldom have an effect. Until recently, sustainable land management practices Data and data sources in most parts of Mali focused on reducing runoff and Data for land use/cover and the RUSLE model soil loss at the plot or farm level through soil and water Satellite-based information derived from Landsat 8 conservation (SWC) practices, such as contour bunding images was used to produce land use and land cover [7]. Although important in its application at the plot or maps, and spatially derived crop management factors farm level, the efficiency of contour bundling is limited were used in the empirical model of the Revised Univer- in addressing landscape degradation and the loss of crop sal Soil Loss Equation (RUSLE) to estimate the annual productivity. Excessive soil erosion is usually caused by S anogo et al. Agriculture & Food Security (2023) 12:4 Page 3 of 11 Fig. 1 Map showing the study area in the two districts of Bougouni and Koutiala (Sikasso region), Southern Mali soil erosion loss. In addition, the digital elevation model data from the African soil profile (https:// data. isric. org/ (DEM) was derived from Landsat 8 images to determine geone twork/ srv/ eng/ catal og. searc h#/ search). In 2015, the empirical parameters of the RUSLE. soil data were collected under the Africa RISING pro- Rainfall data from 2000 to 2019 were collected from ject from 350 sampling sites in 10 villages of the Bou- national meteorological stations in both the Bougouni gouni and Koutiala districts of Southern Mali (Table 1). and the Koutiala districts. To obtain the spatial coverage Sampling sites from each village were selected through of rainfall data, the study used gridded monthly precipi- a stratified random sampling technique. The strata tation data at a 1-km spatial resolution by the climatol- included geographical location, availability of field- ogy at high resolution for the Earth’s land surface areas based soil and water conservation practices, mainly [17]. CHELSA climatological data have a higher accuracy contour bunding (CB), food crop type and mixture, nat- in predictions of precipitation patterns than that of many ural bush and/or grazing land, agroforestry or presence other products [17]. CHELSA products are in a geo- of forestland, presence of termitarium and oxen kraal- graphic coordinate system referenced to the World Geo- ing sites). From each village, a minimum of 29 com- detic System 1984 (WGS84) horizontal datum, with the posite soil samples were collected from depths of 0 to horizontal coordinates expressed in decimal degrees. The 15 cm with distribution across the strata. On a farmer’s data were in GeoTiff format, which can be viewed using field, samples were taken at five locations across two GIS software. diagonal transects, and the coordinates at the midpoint were determined using hand-held Garmin GPS. Stand- ard laboratory soil tests were employed to evaluate the Data for soil nutrients fertility status of the soils, thereby mapping the erod- The in-situ soil data were archived from a project ibility and impact of land use on soil nutrients; carbon named the “Africa Research In Sustainable Intensifica - (C), nitrogen (N), phosphorus (P), and potassium (K) tion for the Next Generation (Africa RISING)” data- analyses were performed. base for Mali. The data were used to validate gridded Sanogo et al. Agriculture & Food Security (2023) 12:4 Page 4 of 11 Identification of factors that influence soil erosion Table 1 Soil sample collection in ten villages in the districts of Bougouni and Koutiala and nutrient loss Rainfall erosivity factor (R) Village Longitude Latitude Population MAR* N** The rainfall erosivity factor (R) explains the variations in (mm) rainfall intensity at different locations within the land - Madina 11.35 − 7.66 1582 1137 31 scape that can cause soil erosion. The R values for each Dieba 11.51 − 7.931 1121 1139 29 agroecological zone were calculated using the CHELSA Sibirila 11.43 − 7.77 929 1138 29 database and gridded data from 1979 to 2016 using Flola 11.42 − 7.64 465 1102 29 Eq. 4. The formula has been used in many parts of Africa Nampossela 12.33 − 5.34 2443 813 29 [19]: N’golonianasso 12.43 − 5.70 4383 849 30 R factor =−8.12 + 0.562 ∗ P M’pessoba 12.67 − 5.71 9862 800 33 (4) Sirakele 12.52 − 5.47 4502 818 34 where R is the rainfall erosivity factor in MJ mm Kani 12.25 − 5.19 2488 944 33 −1 −1 −1 ha  h  year , and P is the mean annual rainfall in mm. Zanzoni 12.61 − 5.57 3463 842 31 Total 308 Soil erodibility factor (K) MAR (Mean Annual Rainfall) Soil detachment and transport caused by the impact of ** N (Number of soil sampling locations) raindrops and surface flow are represented by the K fac - tor. Soil data on texture, structure, organic matter, and Data analysis permeability were used to estimate the K factor as per Land use changes between two different landscapes Eq.  5 [20]. Data available in the Africa RISING database over time for Mali were used to validate the gridded data from the Land use changes between two different periods were African soil profile (https:// data. isric. org/ geone twork/ evaluated based on percentage changes. A series of srv/ eng/ catal og. search) and to adequately determine the processes from data acquisition, data pre-processing, soil erodibility factor (K). supervised classification, and post-classification were performed. Pre-processing was the first step conducted % sand+% slit on Landsat 8 images [18]. Geometric correction, image % clay (5) K factor = enhancement, and topographical correction were per- 100 formed for data pre-processing. The raw data were projected to the UTM WGS-84 projection system for Slope length (L) and slope (S) factors supervised classification. An accuracy assessment was Slope steepness and slope length (LS-factor) were deter- performed using ArcGIS software to determine the mined from a DEM. The Spatial Analyst Toolbox and kappa coefficient of agreement (Eqs. 1–3): the Map Algebra Raster Calculator in the ArcGIS envi- P − P ronment were used to generating multiple slope maps o c KA = (1) and flow accumulation as well as to calculate and gen - 1 − P erate the topographic factor map. The flow accumula - tion was calculated from the Spatial Analyst Hydrology toolset of ArcMap in the ArcGIS environment. The slope P = P o ii (2) of the study area as a percentage was calculated by the i=1 Slope tools in the Spatial Analyst Surface toolset of Arc- Map from the DEM of the districts. The DEM data were P = (P ∗ P ) c +i downloaded from https:// lpdaa csvc. cr. usgs. gov/ appee i+ (3) i=1 ars/ downl oad/ 4d436 aca- c5be- 4011- 981f- 1e602 0f 37c79. These data were used to determine and map the slope where r is the number of rows in the error matrix, P is ii length and slope gradient of the topographic factors in the proportion of pixels in row i and column I, P is the i+ the study area. The determination of the LS factor was proportion of the marginal total of row i, and P is the +i performed using Eq. 6: proportion of the marginal total of column i. S anogo et al. Agriculture & Food Security (2023) 12:4 Page 5 of 11 Table 3 Conservation practice factor (P) resolution LS = Pow [flow accumulation] ∗ 22.1, 0.4 Slope (%) Contouring Strip cropping Terracing (6) [slope of DEM] 0–7 0.55 0.27 0.10 ∗ Pow sin ∗ 1.4 0.0896, 1.4 7–11.3 0.60 0.30 0.12 11.3–17.6 0.80 0.40 0.16 where flow accumulation denotes the accumulated 17.6–26.8 0.90 0.45 0.18 upslope contributing area for a given cell, and LS is the > 26.8 1.0 0.50 0.20 combined slope length and slope steepness factor. Crop management factor (C) the annual soil loss rate of each cell using the “raster cal- The crop management factor (C) was used to reflect the culator”. In the present study, soil erosion was estimated effect of cropping practices on erosion control in agricul - using the RUSLE (Eq.  4) based on the raster calculator tural lands and vegetation covers. The C-factor is defined technique in ArcGIS 10.7. The five factors (in the raster as the ratio of soil loss from land cropped under specific layer) used in the RUSLE model are erodibility (K), slope conditions to the loss from clean-tilled, continuous fal- length (L), slope steepness (S), cropping system (C), and low. In the present study, the C values were determined conservation practice (P): based on the values presented in Table  2. Land use and cover maps were converted into polygons to estimate the A = R × K × (L × S) × C × P (7) spatial values of the C-factor based on the specific land where A is the annual rate of soil loss use [9]. −1 −1 (ton ha  yr ), and R is the annual rainfall erosivity fac- −1  h−1 −1 tor (MJ mm ha  yr ). K  is expressed in tons ha yr. Conservation practice factor (P‑factor) −1 −1 −1 ha  MJ  mm , and L, S, C, and P are dimensionless. In conservation practice, the P-factor values range from 0 The five-factor layers used in the RUSLE were pro - to 1 depending on the land use/cover types. The highest duced from various data sources with different spatial value is assigned to areas with no conservation practice resolutions. Thus, all factor layers were previously spa - or with a high slope, while the minimum values cor- tialized and re-gridded to the same spatial resolution as respond to built-up land and plantation area with strip shown in the flow chart (Fig. 2). and contour or small sloped areas. The P-factor values in Table  3 were based on the land cultivation method and Statistical analysis slope [21]. The spatial values of the P-factor were deter - A T test was applied to compare the means of the sam- mined from a DEM and GIS technique. ples in different years. Variations in the annual soil loss and soil nutrient loss among different land use types were Revised Universal Soil Loss Equation (RUSLE) evaluated at the 5% significance level in the two different The RUSLE was developed by Renard et  al. [22] based on the modifications of the USLE [23]. There have been many studies on soil erosion worldwide [8, 9], and they have used RUSLE to predict annual soil loss, with results revealing that land use has an impact on the rate of soil erosion  [24–26]. The RUSLE model uses five different raster layers processed by overlay analysis to generate Table 2 Attribute values of vegetation cover (C-factor) [9] Class C‑factor Built-up land 0.003 Savanna woodland 0.004 Bare land 1 Waterbody 0 Fig. 2 Flow chart for the determination of annual soil loss Agriculture 0.4 Natural vegetation 0.025 Sanogo et al. Agriculture & Food Security (2023) 12:4 Page 6 of 11 agroecological zones. The spatial difference ratio was and 33. The value of the crop support practice factor (P) used to detect land use change occurring between the ranged between 0.55 and 1. two studied agroecologies. Spatial pattern of the annual soil loss Results Table  6 shows the variation in the mean soil loss under Land use and land cover change different land uses across the two agroecologies. In Land use and land cover maps were classified into six Bougouni, in 2015, a higher mean value of soil loss was classes, namely, agricultural land (AL), bare land (BL), observed in bare land followed by agricultural land. Simi- water (W), settlements (S), savanna woodland (SW), lar observations were made in the second period (2019), and natural vegetation (NV). Percentage change analy- but agricultural land was more vulnerable than bare land. ses showed an increase in NV by 1151 km at a rate of Similarly, in Koutiala, agricultural land was more vulner- 2 2 230 km per year, and AL increased by 1600 k m at a rate able to land degradation in 2019. Considering the mean of 320 km per year for Bougouni. This increase was a values of soil erosion loss, it was observed that the rate of result of the introduction of new agricultural practices land degradation vulnerability was greater in many of the that converted forest areas into cashew plantations (com- agriculture fields. mercial trees). Settlement areas increased at a rate of Statistical analysis between the mean values indicated a 0.5 km /year, and in other land uses, SW and bare land significant difference in annual soil loss among the differ - showed a significant decline. In a similar mode, AL and ent land use types across locations for different periods NV areas increased over the studied period in Koutiala (p < 0.05) (Table  7). The soil erosion loss in SW and the (Table  4). Land use and land cover change detection vegetation were not significant across the two agroecolo - analysis between the two agroecologies showed that NV gies (P > 0.05). Significant soil erosion loss (p < 0.05) was and agriculture areas increased at a higher rate in Bou- observed across locations for agriculture, settlements, gouni than in Koutiala. Bougouni was more favourable to and bare land in the two periods. The relationship for environmental development than Koutiala. However, the bare land was stronger than that for agriculture (Table 7). anthropogenic effects of agriculture and deforestation As shown in Table  8, the magnitude of soil erosion played major roles in land use changes in Bougouni. loss was classified into five categories ranging from low to severe. The results revealed that a low rate of soil ero - Spatial factors influencing soil erosion and nutrient loss sion occurred in many agricultural fields in both districts. Figure 3 and Table 5 show the variations in the spatial fac- Over the period from 2015 to 2019, a decline in the soil tors in the two agroecologies. The R values were higher erosion rate was observed in all classes except for the in Bougouni than in Koutiala. The K values showed that ‘moderate rate’. This decline in erosion rates could be erodibility was greater in Koutiala than in Bougouni, explained by the introduction of different cultural prac - implying that soil in Bougouni resists erosion better than tices, such as contour bunding, afforestation, stone lines, that in Koutiala (Fig.  4). The spatially mapped LS factors and Zai, introduced by different projects in the two dis - resulted in LS values ranging between 1.4 and 58 for Bou- tricts [6, 7, 12]. gouni, while in Koutiala, the values ranged between 1.4 Table 4 Land use and land cover in 2015 and 2019 Land use and Bougouni Koutiala land cover Area (2015) Area (2019) Change (%) Area (2015) Area (2019) Change (%) 2 2 2 2 Km % Km % Km % Km % Settlements 44.0901 0.22 45.5643 0.23 0.01 66.79 0.70 68.68 0.72 0.02 Water 154.127 0.78 141.683 0.72 − 0.06 116.0976 1.21 16.30 0.17 − 1.04 SW* 2152.59 10.91 827.492 4.19 − 6.72 393.0814 4.09 205.65 2.14 − 1.95 Bare land 1788.99 9.06 373.638 1.89 − 7.17 561.5779 5.85 337.61 3.52 − 2.33 NV** 11,677.10 59.17 12,828.92 65.00 5.83 5137.06 53.49 5,633.94 58.66 5.17 Agriculture 3919.44 19.86 5519.02 27.96 8.10 3329.41 34.67 3,342.37 34.80 0.13 SW Savannah Woodland NV** Natural Vegetation S anogo et al. Agriculture & Food Security (2023) 12:4 Page 7 of 11 Fig. 3 Spatial pattern a R Factor, b K Factor, c LS Factor, d P Factor, in Bougouni variation in soil nutrients under different land use types. Table 5 Spatial factors influencing soil erosion and nutrient loss The results revealed that there were significant varia - District/ R K LS P C tions in N, P, K, and C across different land use catego - Agroecology ries in both the Bougouni and the Koutiala agroecologies Bougouni 683–180 0.073–0.015 1.4–58 0.55–1 Refer Table 2 (P < 0.05). Koutiala 477–226 1.4–33 1.4–33 0.55–1 Refer Table 2 A comparison between soil nutrients in the two agro- −1 −1 −1 R = Rainfall Erosivity in MJmmha  h y , K = Soil Erodibility (K) t.ha.h. ecologies highlighted that Bougouni had a higher con- −1 −1 −1 ha  MJ  mm , LS = Slope Length (L) and Slope (S) Factors, P = Crop Support centration of nitrogen and a higher carbon content than Practice Factor, C = Cropping Management Factor Koutiala. The variance between these two districts was dependent on the land use types. Across the three land uses (agriculture, water, and bare land), phosphorus was Spatial variation in C, N, P, and K under different land uses higher in Bougouni than in Koutiala, but it was the oppo- Nitrogen (N), phosphorus (P), and potassium (K) are site for the other land uses (settlements, natural vegeta- among the soil nutrients that are important for plant tion, and savanna woodland) (Table 9). growth and development. Nitrogen (N) is a macronutri- ent that is required by crops in large quantities, and its Discussion deficiency restricts crop production in many agricul - Soil erosion is the main driver of land degradation in tural fields. Plants need the same amount of potassium many sub-Saharan agricultural fields. The study areas and nitrogen for growth. Phosphorus (P) is required by in Southern Mali are mostly covered by natural vegeta- plants to increase yield. Soil organic carbon (C) improves tion and agricultural fields. Over time, agricultural and soil’s physical and chemical properties. Table 9 shows the Sanogo et al. Agriculture & Food Security (2023) 12:4 Page 8 of 11 Fig. 4 Spatial pattern a R Factor, b K Factor, c LS Factor, d P Factor, in Koutiala Table 6 Mean values of annual soil erosion loss under different land uses in t/ha/year Landuse Bougouni Koutiala Erosion rate land cover 2015 2019 2015 2019 Bougouni Koutiala Mean Std Mean Std Mean Std Mean Std Mean Mean Agriculture 7.8 2.3 3.7 2.28 7.22 2.43 5.14 3.71 − 4.1 − 2.08 Settlements 0.21 1.09 2.61 3.24 0.04 0.01 1.38 2.46 2.4 1.34 S W 0.08 0.06 1.21 3.91 0.10 0.16 0.94 2.05 1.13 0.84 Bare soil 17.53 5.82 2.87 3.16 14.90 8.91 3.11 4.14 − 14.66 − 11.78 NV 0.42 0.1 0.79 1.6 1.82 4.19 0.38 0.09 0.37 − 1.43 Water 0.00 0.00 0.15 0.71 0.01 0.06 0.23 0.54 0.15 0.22 SW Savannah Woodland, NV Natural Vegetation Table 7 Comparison between mean soil erosion rates Period Statistic Agriculture Settlements SW NV Bare land 2015 p value 6.07e-05*** 2.2e-16*** 0.1013 0.1862 1.38e-06*** 2019 p value 0.000219* 0.003478* 0.4961 0.4133 0.007281** SW Savannah Woodland, NV Natural Vegetation; ***Significant across locations, **Significant between the years, *Significant across land uses S anogo et al. Agriculture & Food Security (2023) 12:4 Page 9 of 11 Table 8 Land area in the percentage of soil erosion loss rate for 2015 and 2019 in two agroecologies Soil erosion rate Bougouni Koutiala (t/ha/year) Area (2015) (%) Area (2019) Change Area (2015) Area (2019) Change (%) (%) (%) Low (< 5) 71.70 69.67 − 2.04 64.29 65.45 1.16 Moderate (5–11) 17.02 26.64 9.62 26.74 26.80 0.06 High (11–25) 10.67 3.43 − 7.24 8.35 6.57 − 1.78 Very High (25–50) 0.54 0.23 − 0.30 0.54 1.12 0.58 Severe > 50 0.07 0.03 − 0.05 0.08 0.06 − 0.02 Table 9 Mean values and standard deviation of soil nutrient properties for different land uses in two agroecologies Land use Land Soil nutrient properties cover Bougouni* Koutiala* Nitrogen (mg/ Phosphorus Potassium Carbon (mg/ Nitrogen Phosphorus Potassium Carbon kg) (ppm) cmol + /kg kg) (mg/kg) (ppm) cmol + /kg (mg/kg) Agriculture 591.82 ± 52.96 407.24 ± 84.44 102.93 ± 14.71 8.36 ± 2.67 479.04 ± 65.14 399.25 ± 117.19 101.13 ± 8.20 4.98 ± 1.38 Settlements 629.69 ± 69.48 469.34 ± 140.5 93.52 ± 13.13 6.78 ± 0.83 517.196 ± 47.11 520.88 ± 189.29 107.04 ± 8.59 6.14 ± 1.56 Water 796.75 ± 175.9 425.86 ± 99.49 108.42 ± 24.44 13.65 ± 4.95 675.97 ± 112.83 424.02 ± 87.73 114.98 ± 7.14 8.55 ± 2.18 SW 679.71 ± 75.45 376.87 ± 70.42 102.42 ± 12.22 9.88 ± 2.62 534.06 ± 69.95 418.64 ± 63.07 109.07 ± 9.46 5.68 ± 1.33 Bare land 682.77 ± 93.96 450.49 ± 128.9 104.15 ± 13.85 10.43 ± 2.66 515.087 ± 67.34 419.55 ± 91.63 103.40 ± 10.11 5.34 ± 1.22 NV 694.68 ± 62.34 385.95 ± 90.50 100.24 ± 13.41 9.30 ± 1.71 516.60 ± 66.00 411.19 ± 63.30 106.56 ± 8.50 5.01 ± 1.34 P Value 1.96E-49 2.34E-12 4.62E-10 1.06E-60 4.08E-84 3.09E-18 7.37E-34 7.13E-76 Sample Size (N) = 112 for Bougouni and 122 for Koutiala, SW = Savanna Woodland, NV = Natural Vegetation settlement areas have increased due to urban population Soil erosion vulnerability was reduced by the appli- growth and the need for more agricultural fields [27]. For cation of soil and water conservation (SWC) practices. instance, the increase in cashew plantations in Bougouni The major SWC practices in the study area that have has led to the expansion of agriculture, leading to an demonstrated a reduction in annual soil loss and an increase in fallow areas. The decrease in water bodies was increase in farm-level soil nutrients are stone bunds, mainly due to gold mining activities and drought created ridges, contour bunding, and tree plantations [6]. by rainfall variability in a few landscapes. Land uses have In this study, the highest values of N, K, and C were an important impact on the soil’s chemical and physical recorded in water bodies, and a greater amount of P properties [28]. Previous studies have highlighted the was noted in settlement areas with P in vegetation cov- influence of land use on soil management practices [29– ers (natural vegetation and savanna woodland). These 31]. In addition, the importance of soil and water conser- findings corroborate well with previous assessments of vation in controlling soil erosion has been demonstrated soil fertility variation in different land uses and man - by many studies conducted in the region [6, 7]. agement practices [27, 28, 33]. The findings of this study revealed the presence of The economy of Mali is mainly based on rainfed agri - low erosion rates in large landscapes in the studied culture, and hence, any depletion in soil nutrients may areas, as established elsewhere [1, 27, 32]. Despite the cause food insecurity and poverty for smallholder farm- low magnitude of runoff from the larger part of the ing systems. As soil erosion has a significant impact on Southern Mali landscapes, the runoff generated from agricultural productivity, land management practices many agricultural fields is severe and is the cause of should be considered a priority by decision-makers for land degradation as well as the loss of important soil agricultural-led economic growth. The current land nutrients. When forest and natural vegetation areas are management practice in Mali may be the reason for the the targets of human-induced activities, the rate of vul- varying erosion risk rather than natural conditions, as nerability to erosion increases over time [6]. explained by Rousseva et al. [35]. Based on the findings Sanogo et al. Agriculture & Food Security (2023) 12:4 Page 10 of 11 AL Agricultural Land of this study and recommendations from previous SW Savanna Woodland studies [6, 7, 34], soil nutrient loss could be controlled NV Natural Vegetation by applying appropriate soil and water conservation DEM Digital Elevation Model Africa RISING A frica Research in Sustainable Intensification for the practices integrated with good agronomic practices. Next Generation The most common SWC practices recommended in CB Contour Bunding the study area include a combination of construct- C Organic carbon GPS Global Positioning System ing contour bunding, planting trees, stone bunds, and terracing. This study calls for increased efforts by all Acknowledgements stakeholders to implement the needed intervention at This work was supported by the Africa Research in Sustainable Intensification for the Next Generation (Africa RISING) project in Mali. The authors are grateful the landscape scale and boost agricultural productivity for the financial support provided by the United States Agency for Interna- in the region. tional Development (USAID) through the International Institute of Tropical Agriculture (IITA). The authors are also thankful to the International Science Programme (ISP/IPPS) through the Laboratoire d’Optique de Spectroscopie et Conclusion des Sciences de l’Atmosphère (LOSSA) at the Faculty of Sciences and Technol- Through spatial modelling and empirical relations, soil ogy of Bamako for the use of the institute’s spatial data and GIS and remote loss was estimated for 2015 and 2019 using soil loss sensing facilities. parameters and the RUSLE. The results revealed that Author contributions the dominant portion of land use and land cover was KS and BZB developed the outline and wrote the different sections of the characterized by erosion risks leading to land degrada- manuscript. SS and AB contributed to the scientific content and review of the findings. All authors have read and accepted the final version of the manu- tion. High deficiencies in soil nutrients (N, P, K, and C) script. All authors read and approved the final manuscript. were observed in the landscapes. Many of these nutri- ents were found in the water bodies as a result of the Author’s information Mr. Karamoko Sanogo is a Ph.D. student at the Université des Sciences de increased rate of soil erosion from the landscape over Techniques et de Technologie de Bamako of Mali. He is also a scientific officer time, thus leaving only a small amount on the agricul- at the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT ) tural fields. The implementation of a few SWC and res - in Mali. He works on climate change, remote sensing, and soil and water management. Dr. Birhanu Zemadim Birhanu is the Global Theme Leader for toration practices, such as contour bunding and cashew Landscapes, Soil Fertility, and Water Management at ICRISAT. His research area plantation, seemed to contribute to reduced soil ero- includes landscape-based integrated management of land and water. He also sion risk over the studied period (2015 to 2019). works on validating and scaling agricultural and natural resource manage- ment technologies and practices from farm to landscape scale in a participa- The results of this study are useful in providing tory and integrated watershed approach. Dr. Souleymane Sanogo is an Associ- guided information about the importance of controlling ate Professor, and specialist in Meteorology and climate sciences at the Physics soil erosion at the landscape level and retaining impor- Department of the University of Sciences, Techniques, and Technologies of Bamako (USTTB). His study area includes climate modelling and integrated tant plant growth parameters in agricultural fields. assessments of climate impacts on water, agriculture, energy, and land. Profes- Mapping soil erosion risk at the landscape level helps sor Abdramane Ba is the coordinator of the Laboratory of Optics Spectroscopy identify vulnerable areas and protect them at a priority and Atmospheric Science (LOSSA) at the Physics Department of the University of Sciences, Techniques, and Technologies of Bamako (USTTB). level. Land planners, government extension services, and local NGOs can, therefore, use the results of this Funding study to develop informed natural resource conserva- USAID, AID-BFS-G-11-00002 From USAID (Prime Sponsor),Birhanu Zemadim Birhanu. tion strategies and prioritize intervention measures to protect soil loss from many agricultural farms. This Availability of data and materials study strongly recommends scaling up the implementa- Not applicable. tion of SWC practices at the landscape scale and inte- grating other sustainable land management practices, Declarations such as afforestation and crop management strategies, Ethics approval and consent to participate for the health of the ecosystem and improved agricul- Not applicable. tural productivity. Consent for publication Not applicable. Abbreviations Competing interests GIS Geographic Information System All authors declare they have no competing interests. RUSLE R evised Universal Soil Loss Equation SE Soil Erosion SWC Soil and Water Conservation Received: 10 January 2022 Accepted: 13 January 2023 N Nitrogen P Phosphorus K Potassium S anogo et al. Agriculture & Food Security (2023) 12:4 Page 11 of 11 References 19. Hurni H. Erosion productivity conservation systems in Ethiopia. In: Pro- 1. Kim C. 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Journal

Agriculture & Food Security – Springer Journals

Published: Mar 15, 2023

Keywords: Soil erosion; Revised Universal Soil Loss Equation; Land use; Soil nutrient; Landscape; Southern Mali

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