Use of soil spectral reflectance to estimate texture and fertility affected by land management practices in Ethiopian tropical highland.

As classical soil analysis is time-consuming and expensive, there is a growing demand for visible, near-infrared, and short-wave infrared (Vis-NIR-SWIR, wavelength 350-2500 nm) spectroscopy to predict soil properties. The objectives of this study were to investigate the effects of soil bunds on key soil properties and to develop regression models based on the Vis-NIR-SWIR spectral reflectance of soils in Aba Gerima, Ethiopia. Soil samples were collected from the 0-30 cm soil layer in 48 experimental teff (Eragrostis tef) plots and analysed for soil texture, pH, organic carbon (OC), total nitrogen (TN), available phosphorus (av. P), and potassium (av. K). We measured reflectance from air-dried, ground, and sieved soils with a FieldSpec 4 Spectroradiometer. We used regression models to identify and predict soil properties, as assessed by the coefficient of determination (R2), root mean square error (RMSE), bias, and ratio of performance to deviation (RPD). The results showed high variability (CV ≥ 35%) and substantial variation (P < 0.05 to P < 0.001) in soil texture, OC, and av. P in the catchment. Soil reflectance was lower from bunded plots. The pre-processing techniques, including multiplicative scatter correction, median filter, and Gaussian filter for OC, clay, and sand, respectively were used to transform the soil reflectance. Statistical results were: R2 = 0.71, RPD = 8.13 and bias = 0.12 for OC; R2 = 0.93, RPD = 2.21, bias = 0.94 for clay; and R2 = 0.85 with RPD = 7.54 and bias = 0.0.31 for sand with validation dataset. However, care is essential before applying the models to other regions. In conclusion, the findings of this study suggest spectroradiometry can supplement classical soil analysis. However, more research is needed to increase the prediction performance of Vis-NIR-SWIR reflectance spectroscopy to advance soil management interventions.

Introduction

Ethiopia has a wide spatial diversity of soil properties [1]. Improved sustainable land management (SLM) practices including soil bunding may alter key soil qualities [2,3] and control soil loss [4,5], and thereby soil functions [6,7]. SLM activities influence soil physicochemical properties and minimize soil degradation while improving yields [8,9] and they improve people’s livelihoods [10]. However, livelihoods are at risk because of soil fertility depletion [11]. Hence, increasing crop production to feed the population is a challenge.

Understanding soil quality requires characterization and assessing spatial variation of soil properties related to crop growth and development [12,13]. Information on soil fertility is needed for site-specific crop and fertilizer management [14,15]. Crop production and yield, plant indicators, and soil texture and colour are widely used to measure soil fertility [16]. The lack of an up-to-date soil database impedes government efforts to boost agricultural production in Ethiopia. Most soil guidelines are not site-specific and soil fertility interventions tend to be blanket recommendations [17,18]. Failing to use soil data can result in nutrient depletion [19], compaction, flooding, and low crop yields [20]. Thus, detailed soil data are essential.

Knowledge of soil resources is essential for developing effective land-use planning and implementing SLM practices. Little information is available for the Upper Blue Nile Basin of Ethiopia, where soils and land-use types patterns fluctuate within small distances [21,22]. Reasons include unavailability and inflated costs of soil laboratory tests, particularly for many soil samples gathered over time and in large areas.

To determine soil properties, Ethiopia has relied on costly and time-consuming traditional analytical methods. However, it faces a crucial difficulty in using these methods owing to the high cost of chemicals and the poor performance of laboratories. As a result, rapid sampling and analysis of soil properties in the field and laboratory are unavailable.

Laboratory soil spectrometry in the visible, near-infrared, and short-wave infrared (Vis-NIR-SWIR, 350–2500 nm) range provides an option for physical and chemical soil studies [23]. Contents of soil OC [24,25], clay [26], TN [27], and soil texture [28] have been well estimated from Vis-NIR-SWIR spectra.

Land-use and management strategies highly influence soil properties [8,29,30]. Understanding variations in soil properties across fields is critical for assessing crop growth and development restrictions related to soil nutrients and to proposing corrective steps for optimal development and effective land-use management [8]. SLM activities, including soil bunds, should address increased human demands and maintain environmental sustainability [31]. Bunds are slope-side embankments made of soil, stone, or a combination of the two. Soil eroded between two bunds is dumped behind the lower bund, which is then lifted to create a bench terrace is formed [32]. Bunds improve soil fertility by reducing runoff and soil loss [33,34].

Furthermore, implementing effective soil bunding can be beneficial in restoring degraded soil quality and functions while ensuring sustainable production. Consequently, understanding how soil properties change as a result of various land management practices is vital to proposing optimal management practices. This research would also help farmers and local planners in developing successful land conservation strategies.

Soil erosion and depleted soil productivity have been major issues in the Aba Gerima catchment, resulting in low crop yields. It would also be useful to replace traditional laboratory soil analysis with more accurate and low-cost methods. Spectroradiometry performed well in measuring soil carbon in different agroecologies, soil forms, and land management activities of Ethiopia [34], but spectroradiometric evidence of the effects of soil bunding on soil characteristics is lacking in the catchment, Ethiopia, and Africa as a whole. Thus, the objectives of this study were to develop regression models from Vis-NIR-SWIR spectral reflectance data and assess the effects of soil bunding on soil texture, pH, OC, TN, av. P, and av. K by using spectroradiometric evidence.

Materials and methods

Description of the study area

The study was carried out at Aba Gerima (11°39′0″N– 11°40′30″N and 37°29′30″E– 37°31′30″E), a tropical highland of Ethiopia (Fig 1B). By the Köppen–Geiger classification, the site represents midland, [35], with altitudes varying from 1,900 to 2,000 m above sea level.

Fig 1

Locations of study catchment and soil sampling plots: (a) Ethiopia, (b) Aba Gerima land-use/land-cover (LULC) types, and (c) Soil sampling plots.

Records from 1994 to 2021 at nearby meteorological stations show that the study area receives an average annual rainfall of 1,076 to 1,953 mm and has an average monthly maximum temperature of 27.0°C and an average monthly minimum temperature of 12.6°C (Fig 2; S1 Table). The main rainfall occurs from June to August, and the rest of the year is dry [36].

Fig 2

Long-term (1994–2021) monthly rainfall (RF), maximum temperature (Tmax), and minimum temperature (Tmin) in the study area.

Methods

Soil sampling approaches

Cultivated lands were considered in 2019 according to land-use/land-cover information [37]. The catchment having an area of 426 hectare was categorized into three topographic classes, namely gently (2%–5%), moderately (5%–10%), and strongly sloping (10%–15%). There were 24 plots without soil bunds (WB) and 24 plots with soil bunds (SB) reinforced with grass and stone. The soil bunds were built all over the catchment [38,39]. The stone bunds and soil bunds are commonly used on steep slopes (Fig 3A) and moderate slopes (Fig 3B), respectively. The bunds are five years old, have bottom width of 0.8 m and a height of 0.5 m, as described by [40]. Bahir Dar University permitted the work, field site access, and soil sampling in the catchment. Forty-eight representative soil-sampling plots were identified, with a minimum size of 40 m × 40 m (1600 m2) each. All plots were intentionally distributed (Fig 1E). Each plot was geo-referenced with a handheld GPS device (GPSMAP64, Garmin, Olathe, Kansas, USA). In each plot, five soil samples were collected at the top (0–30 cm depth) with an Edelman auger and mixed well in a bucket to make a 1-kg composite soil sample.

Fig 3

Treated with stone-faced soil bunds (a) and with soil bunds (b) at the upper and lower slopes in Aba Gerima catchment.

Physicochemical soil analysis

Composite soil samples (n = 48) were air-dried, ground, and sieved to 2 mm. Later, they were analysed for soil texture, pH, OC, TN, av. P, and av. K at Amhara Design and Supervision Works Enterprise. After the annihilation of organic matter (OM) and soil dispersion, soil texture (sand, silt, and clay) was determined by the hydrometer method [41]. Silt and clay were determined from hydrometer readings after 40 s and clay particles in suspension after 2 h, and the percentage of sand was estimated subtracting by 100 from (clay (%) + sand (%)). Finally, we calculated soil textural classes using the textural triangle of the USDA system [42,43].

Soil pH was determined potentiometrically with a digital pH meter in a 1:2.5 (soil:water) supernatant suspension [44]. Into 100-mL beakers, we poured 10 g of air-dried soil and 25 mL of purified water, stirred it for 1 min with a glass rod and allowed it to equilibrate for 1 h before we measured the pH of the supernatant. Soil OC content was analysed with the wet digestion method, which entails digesting the OC with potassium dichromate in a sulphuric acid solution [45].

Soil TN was determined by the Kjeldahl process, which involves oxidizing OM with condensed sulfuric acid and converting the organic N into ammonia. We weighed 1 g of air-dried soil (<0.5 mm sieve) into a digestion tube; added 2 g of catalyst mixture and a couple of carborundum boiling stones, then stirred the mixture; added 7 mL of concentrated H2SO4; and digested the mixture on a block digester preheated to 300°C until the digest was white. After it cooled, we added 50 mL of distilled water, transferred the digest into macro Kjeldahl flasks, and rinsed it with distilled water. We weighed 20 mL of boric acid into a receiver flask, added two drops of indicator solution, and placed the flask under the condenser. Then 75 mL of 40% NaOH was carefully squeezed down the neck of each distillation flask containing the digest, and the mixture was gently stirred. The digests were placed in Kjeldahl distillation flasks, fitted to the appropriate holders, and heated to begin the distillation. The receiver flask was removed when about 80 mL of distillate was obtained. The solution in the receiver flask was stirred with a magnetic stirrer bar and titrated with 0.1 N H2SO4 from green to pink.

Available P was quantified by the Bray II approach, shaking the soil sample with 0.3 N ammonium fluoride in 0.1 N hydrochloric acid, as described by [46]. The av. P was then measured by spectrophotometer [47]. Available K was analysed by extracting the soil sample with Morgan’s solution and measured with a flame photometer [48].

Soil spectra collection and pre-processing

Each air-dried and ground soil sample (Fig 4D) was placed on a table covered with black geo-membrane. The soil reflectance data in the Vis-NIR-SWIR (350–2500 nm) range were collected with an ASD FieldSpec 4 spectroradiometer (Analytical Spectral Devices [ASD] Inc., Boulder, CO, USA; Fig 4D). Reflectance was measured between 10:30 and 11:00 in direct sunlight. The field of view was set at 25°, and the distance between the trigger of the spectroradiometer’s fibre optic cable and the soil specimen was held at 10 cm for all observations. The spectroradiometer was recalibrated against a white Spectralon (Labsphere Inc., North Sutton, NH, USA) every 10 min.

Fig 4

Soil spectral measurement: (a) soil samples, (b) FieldSpec Pro spectroradiometer with battery, (c) a close-up of fiber optic cable, and (d) soil reflectance measurement with the sensor.

The radiometer was placed on a table at 1 m above the ground. To avoid unwanted scattering, the table was covered with black geo-membrane. Each scan took 22 s, and a reading was done on each sample. The reflected spectra were recorded and processed in Remote Sensing 3 v.6.4, View Spec Pro v.6.2 software (ASD Inc.). The pre-processing techniques, such as multiplicative scatter correction, median filter, and Gaussian filter for OC, clay, and sand, respectively were used to transform the spectrum using licensed Unscrambler v.10.5 software (CAMO, Inc., Oslo, Norway). The analysis omitted the noisy spectral regions between 1,340–1,459 nm 1,802–1,971 nm, and 2402–2500 nm [49,50] before spectral modeling.

Data analysis

Before analysis, we verified the soil dataset (n = 48) for normality assumption by the skewness, kurtosis, and Shapiro–Wilk tests (P < 0.05). The data were tested by Pearson’s linear correlation analysis in SAS v. 9.4 and SPSS v. 24.0 (SPSS Inc., Chicago, IL, USA) software.

We analysed variance and regression in SAS v. 9.4 and separated the means by the least significant difference test (P < 0.05). The datasets were divided randomly into 65% of datasets for calibration (31 samples) and 35% of datasets for validation (17 samples). We calculated the coefficient of determination (R2; [51]), root mean square error (RMSE), bias, and ratio of performance to deviation (RPD) [52] (McDowell et al., 2012).) for calibration (31 soil samples) and validation (17 soil samples) as: where n is the number of observations, ŷ is the predicted value, ӯ is the mean observed value, y is the observed (measured) value, and SD is the standard deviation of observed values.

When R2 approaches 1, RPD > 2 RMSE and bias approaches 0, a model improves as a predictor and becomes more efficient [53]. According to [54], models with RPD > 2 are considered “excellent” models with RPD ranging from 1.4 to 2 are considered “acceptable,” and models with RPD < 1.4 are considered “poor.”

Results and discussion

General statistics for measured soil properties

Soil OC, TN, and av. P had high variability (CV ≥ 35%), but soil pH had low variability (CV ≤ 15%) ([55]; Table 1). The high variability in soil properties might be due to soil erosion [56]. The low variability of soil pH corroborates findings of pH varies slightly [6].

Table 1

Descriptive statistics and Shapiro–Wilk probability test of soil parameters in Aba Gerima catchment, Blue Nile basin.

StatisticSoil parameter	Mean (μ) ± SEM	SD (σ)	CV (%)	Minimum	Maximum	Skewness	Kurtosis	Shapiro–WilkP-value
pH	5.6 ± 0.04	0.25	4.52	4.7	6.01	−1	1.94	0.01
Sand (%)	27.6 ± 1.97	13.6	49.28	5	66	0.88	0.74	0.03
Silt (%)	28.2 ± 1.03	7.11	25.21	13	41	−0.44	−0.48	0.12
Clay (%)	44.3 ± 2.56	17.7	39.95	11	80	−0.05	−0.74	0.43
OC (%)	1.65 ± 0.13	0.92	55.52	0.479	4.9	1.51	2.75	<0.001
log OC (%)	0.161 ± 0.03	0.23	139.75	−0.32	0.69	0.1	−0.16	0.99
TN (%)	0.15 ± 0.01	0.08	49.87	0.05	0.44	1.54	3.36	<0.001
log TN (%)	−0.86 ± 0.03	0.2	−23.49	−1.3	−0.36	0.12	−0.04	0.98
Av. P (ppm)	12.2 ± 1.11	7.67	62.87	4.07	38	1.98	3.92	<0.001
Log Av. P (ppm)	1.02 ± 0.03	0.23	22.25	0.61	1.58	0.59	0.31	0.08
Av. K (g/kg)	104 ± 3.78	26.2	25.19	41.8	150	−0.51	0.03	0.14

CV (%) = σ/μ × 100, where CV = coefficient of variation; σ = standard deviation (SD), μ = mean; SEM, standard error of the mean; OC, organic carbon; TN, total nitrogen; Log, logarithmic; av. P, available phosphorus; av. K, available potassium.

The skewness, kurtosis, and Shapiro–Wilk (P < 0.05) tests indicated that soil texture, pH, and av. K (Table 1; S1–S3 Figs) met the assumption of homogeneity of variance [57]. However, soil OC, TN, and av. P tended to be logarithmically distributed owing to their positive skewness and slightly asymmetrical distribution (Figs 5 and S1). Similar results are presented in the literature [58].

Fig 5

Pearson’s correlation matrix among soil variables in the 0–30 cm soil layer.

Correlation values are colour-coded. OC, organic carbon; TN, total nitrogen; av. P, available phosphorus; av. K, available potassium. Asterisks indicate significant differences: *P < 0.05 and **P < 0.01.

Correlation and regression analyses

Soil pH exhibited a weak relationship with av. K and clay content with av. P (Fig 4; [59]). Soil OC had no relation with av. P or av. K. Similarly (except pH vs. av. K), soil pH and OC did not correlate with K or P contents in Ethiopia [60], Morocco [61], and China [62]. However, the increased soil OC, pH, and clay content might contribute to higher av. P and av. K contents. As the strong positive correlation between soil OC and TN indicated collinearity, we eliminated TN from the variance and spectral analyses. In contrast, [63] found a close relationship between soil OC and TN, as most of the TN was bound in the soil OM.

Influences of soil bunding on changes in soil properties

Soil texture, OC, and av. P contents varied substantially (P < 0.05 to P < 0.001) between bunded and non-bunded plots in the three slope classes (Table 2). Soil pH in the Aba Gerima catchment varied from strongly (<5.5) to moderately acidic (5.6–6.5) [64]. In bunded plots, higher soil pH values could be attributed to clay and OM, which retain more basic cations. Lower pH values obtained at non-bunded plots might be due to the inappropriate use of ammonium-based fertilizers and pesticides [65], increased leaching of basic cations, and nitrification ([34,66]. Consequently, the soils of the study area could be affected by acidity problems. Thus, soil pH is a key parameter to monitor the influences of SLM practices on soil quality and crop growth in the region.

Table 2

Analysis of variance in the effect of bunding on soil properties in Aba Gerima.

Soil parametersTreatments		pH	Sand (%)	Silt (%)	Clay (%)	OC (%)	Av. P (ppm)	Av. K(mg kg–1)
Bunded plots(n = 24)	S1B	5.62a	18.75c	20.25c	61.00b	2.30b	13.72b	116.55a
	S2B	5.72a	22.38ac	31.00ab	46.63ab	1.51ab	10.23ab	97.35a
	S3B	5.60a	18.88c	25.75ac	55.38b	1.57ab	11.03b	106.71a
Non-bunded plots(n = 24)	S1W	5.54a	33.25ab	31.25ab	35.50ac	1.22ac	10.41b	106.73a
	S2W	5.63a	35.50b	33.25b	31.25c	1.54ab	13.03b	101.30a
	S3W	5.52a	36.75b	27.50ab	35.75ac	0.90c	6.37a	94.61a
Mean		5.61	27.58	28.17	44.25	1.45	10.5	103.88
CV (%)		4.61	42.84	21.05	32.98	123.4	20.6	25.65
LSD		0.26	11.92	5.98	14.73	0.2	0.21	26.89
Significance		ns	**	***	***	***	*	ns

OC, soil organic carbon; av. P, available phosphorus; av. K, available potassium. S1, 2%–5%; S2, 5%–10%; S3, 10%–15%; B, soil bund reinforced with stone and grass; W, without soil bund; CV, coefficient of variation; LSD, least significant difference; ns, not significant. n = 48. Values followed by the same letter are not significantly different.

The Aba Gerima catchment’s soils range from clayey to sandy loam [S4 Fig; 41]. The dominance of silt and clay particles in bunded plots in all three slope classes (S1B, S2B, S3B; Table 2) could be due to the control of soil erosion by bunding. Conversely, the dominance of sand in non-bunded plots on strong slopes (S2W and S3W) might be related to the erosion of finer soil particles [67]. Similarly, bunded soils have higher silt and clay content and lower sand content than non-bunded soils [68,69].

Contents of OC (excluding S1B), av. P, and av. K were low [64], lower than values reported in the same catchment [6] and the Uwite Catchment, Ethiopia [70]. However, the OC concentration of the soil in S1B was within the recommended range for plant growth.

Low levels of OC and av. K could be ascribed to higher rates of erosion due to rainfall, inappropriate cultivation, removal of crop remains and animal dung, a high rate of mineralization through increased temperature, and leaching [71,72]. The highest contents of clay (61%), OC (2.30%), av. P (13.72 ppm), and av. K (116.55 mg/kg) were found in bunded plots on gentle slopes (S1B), possibly due to the accumulation of fine soil particles and available nutrients. These findings support reports that bunding improves soil fertility [6,73] and indicate that it improved clay and silt accumulation, soil OC, av. P, and av. K contents 5 years after implementation.

The pH, OC, av. P, and av. K values were lower than critical levels, explaining reduced soil quality in the Aba Gerima catchment. Thus, enhancing the quality of Ethiopian soils requires increasing soil OM content through the implementation of SLM methods and biomass accumulation [8,74].

Reflectance characteristics of soils

The mean spectral reflectance of the 48 soil samples tended to increase between 350 and 1100 nm (visible and near-infrared regions) (Fig 6). The lower reflectance of soils from bunded plots could be due to higher soil OM content and smaller particle size [75,76] or to intensive mineral fertilization, higher microbial activity, and lower soil pH [77]. As evidenced by the absorption peaks in Fig 6 [78,79], it could potentially be attributable to the Fe-OH or Mg-OH in the soils.

Fig 6

Soil reflectance spectra with and without bunding.

The soils of the catchment vary from clayey to sandy loam soils (S2 Fig). Reflectance was highest from sandy loam soils throughout the spectrum, lowest from clay soils at 400–1000 nm and loam soils at 1000–2400 nm (Fig 7). As a result, smaller soil particles have higher reflectance. This finding is consistent the results [80] that sandy soils had higher reflectance and clay soils had lower reflectance. As particle size decreases, multiple scattering increases, thus increasing reflectance [81]. Furthermore, the soil spectral reflectance curve showed different trends at different wavelengths, rising rapidly at 400–600 nm and more steadily at 800–2450 nm.

Fig 7

Influence of soil texture on soil spectral signatures.

Soils with higher OC content are darker and have lower spectral reflectance than soils with lower OC content (Fig 8) [82]. The presence of OM strongly influences soil reflectance, which decreases as OM content increases [83]. Similarly, as soil moisture increases, the reflectance of incoming visible light falls consistently, making soils look darker [84]. In comparison to dark soils, red soils have less OM and more iron oxides. As a result, soils rich in iron have greater reflectance than soils rich in soil OC [85].

Fig 8

Effects of soil organic carbon content on soil spectral reflectance.

Modeling of soil properties

Fig 9 shows the results of the PLSR analysis calibrated to predict OC, clay, and sand content using pre-processed soil reflectance. For each visible band (400–700 nm), near-infrared band (701–1300 nm), and short-wave infrared band (1301–2500 nm), the red box shows the high loading and influential wavelengths associated with soil properties Higher loading values could indicate which portions of the wavelengths are the most influential in the calibration.

Fig 9

Loading (contribution) of relevant wavelength for modeling soil properties.

(a) Clay, (b) Sand, and (c) organic carbon contents.

In the visible range, wavelengths with the highest correlation loading values are 355 and 570 nm for organic carbon, and 568 and 570 nm for clay content; 568 and 570 nm for sand content prediction. Thus, the visible spectrum was the most influential in the OC, clay, and sand prediction models. This behavior could owing be to the soil color, which is dominated by free iron oxides [86,87]. The authors [88] also reported 480–600 nm and 720–820 nm as key spectral regions for the OC prediction model.

In the NIR region, the band found at 845 and 850 nm for organic carbon and clay and 1290 nm for sand can be related to the chromophorous components mainly hematite and goethite [89] and the OC content [90]. Furthermore, high correlation loading values were observed at 1592 and 1595 nm for clay and organic carbon content detection at 2293 and 2300 nm. This response could be due to clay, soil water content, and OM content [91,92]. In general, different wavelengths were discovered to be significant for the different soil properties.

As [93] indicated combining the optimum spectral bands was recommended for soil OC detection. The equation for the optimal band combination equations to predict soil OC with the highest R2 (0.92) and the lowest RMSE (0.27) was: For clay content, the best fit (R2 = 0.86, RMSE = 0.60) was: For sand, the best fit (R2 = 0.94, RMSE = 0.74) was: Based on the regression coefficients, the most influential wavelengths for OC, clay, and sand content prediction were λ850, λ1592, and λ1295 nm, respectively.

We plotted the measured soil properties (%) against the predicted soil properties (%) to validate the model (Fig 10). The simple regression analyses were used the pre-processed dataset (S2 Table) to test the accuracy of prediction of the soil properties. For clay content, the R2, bias, and RPDfor validation dataset were (0.93, 0.94, and 2.21, respectively (Fig 10), better than values reported by 0.70 [94], [95] (0.64), and 0.66 [96], and more accurately than values reported in the literature: R2 = 0.73 with RMSE = 5.40 [28], R2 = 0.83 with RMSE = 0.34 [97], R2 = 0.62 with RMSE = 2.06 [98], and R2 = 0.71–84 [99].

Fig 10

Scatterplots of the model validation results for soil organic carbon (a), clay content (b), and sand content (c). R2, coefficient of determination and RPD, ratio of performance to deviation.

For sand content, we achieved R = 0.94 with RMSE = 0.74 for calibration and R2 = 0.85 with bias = 0.31 and RPD = 7.54 for validation (Fig 10). The sand model was considered excellent according to the R, RPD threshold values [54] (Chang et al. (2001), acceptable bias levels [53] (Bellon-Maurel et al., 2010). Similar values were reported: R2 = 0.80 with RMSE = 3.28 [28], R2 = 0.81 with RMSE = 3.84 [26], R2 = 0.90 with RMSE = 11.66 [98], and R2 = 0.56 to 0.71 [99]. Our predictions of sand content were more accurate than in the literature: R2 = 0.76 with RMSE = 0.92 [97] and R2 = 0.77 [100]. This information could be used to develop and monitor soil management scenarios [26].

For soil OC, we achieved R2 = 0.92 with RMSE = 0.27 for calibration and R2 of 0.71 with bias = 0.12 and RPD = 8.13; bias = 0.12 for validation (Fig 10), which are considered good by R2 threshold values of [101], fair by the RPD threshold values [54] (Chang et al. (2001), and acceptable by bias values [53].

The OC model’s performance is similar to earlier findings: R2 = 0.84–0.93 [102], R2 = 0.63–0.90 with RMSE = 6.40–0.78 [103], R2 = 0.91 [104], R2 = 0.85 with RMSE = 3.77 [105], R2 = 0.57–0.7 [106], R2 = 0.77–0.83 [107,108]; and R2 = 0.764 with RMSE = 0.344 for validation [109,110]. However [111,112], found lower prediction performance (R values varying from 0.57 to 0.73 and RPD values ranging from 1.80 to 1.93) for soil OC models. In general, regression models based on Vis-NIR-SWIR reflectance spectroscopy could be used to predict soil properties in the study area.

Conclusions

Soil bunding in the Aba Gerima catchment, Ethiopia, positively influenced clay, silt, OC, and av. K contents of soils. Soil physicochemical properties (texture, OC, and av. P) varied widely between bunded and non-bunded plots in different slope classes. In contrast, soil pH and contents of clay, silt, sand, OC, av. P, and av. K were all higher in soils on lower slopes than on higher slopes. These findings suggest that site-specific information could inform land management interventions, such as soil bunding, for sustainable soil management. The reflectance of soils from bunded plots was lower, which improved soil fertility.

This study looked at how Vis-NIR-SWIR (350–2500 nm) soil spectral data could be used to determine some soil physical and chemical parameters. The use of regressive functions to estimate the calibrated soil attributes with their pretreatment spectral reflectance data was proposed due to high correlation loading and regression coefficients. Regression models were developed for predicting the clay, sand, and OC contents with acceptable accuracy (R2 > 0.70, RPD > 2, and bias values < 0).

Our findings suggest that Vis-NIR-SWIR reflectance spectroscopy might be utilized for soil characterization, evaluation, and monitoring in a quick and non-destructive manner. The findings have implications for spatial management and monitoring of soil physicochemical properties across the catchment. Soil prediction models based on spectroradiometry will help land-users and policymakers by contributing to the development of sustainable and site-specific soil management strategies.

As the tempo-spatial variation of soil properties between regions would influence the accuracy of the estimation model, caution should be vital before applying the models to other areas. Nonetheless, further research is required to identify which portions of the spectrum contribute to the models, improve the predictive capacity of Vis-NIR-SWIR spectroscopy, support land management interventions, and explore their effects on other soil parameters such as biological properties.

Descriptive summary and Normality test of soil organic carbon (OC), total nitrogen (TN), and available phosphorus (av. P) in Aba Gerima catchment.

SE, standard error; SD, standard deviation; CV = coefficient of variation; min, minimum; max, maximum.

(JPG)

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Descriptive summary and Normality test of soil pH, sand (%), silt (%), and clay (%) in Aba Gerima catchment.

SE, standard error; SD, standard deviation; CV = coefficient of variation; min, minimum; max, maximum.

(JPG)

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Descriptive summary and Normality test of logOC (%), logTN (%), logav.P (ppm), and av.K (mg/Kg) in Aba Gerima catchment.

Log, logarithmically transformed; OC, organic carbon; TN, total nitrogen; av. K, available phosphorus; available potassium, min, minimum and max, maximum.

(JPG)

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Soil textural classes in Aba Gerima catchment.

(JPG)

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Climatic data of Aba Gerima catchment.

(XLSX)

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Pre-processed soil spectral data for selected wavelengths of study catchment.

(XLSX)

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28 Apr 2022

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We look forward to receiving your revised manuscript.

Kind regards,

Chun Liu

Academic Editor

PLOS ONE

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Whilst you may use any professional scientific editing service of your choice, PLOS has partnered with both American Journal Experts (AJE) and Editage to provide discounted services to PLOS authors. Both organizations have experience helping authors meet PLOS guidelines and can provide language editing, translation, manuscript formatting, and figure formatting to ensure your manuscript meets our submission guidelines. To take advantage of our partnership with AJE, visit the AJE website (http://aje.com/go/plos) for a 15% discount off AJE services. To take advantage of our partnership with Editage, visit the Editage website (www.editage.com) and enter referral code PLOSEDIT for a 15% discount off Editage services.  If the PLOS editorial team finds any language issues in text that either AJE or Editage has edited, the service provider will re-edit the text for free.

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The name of the colleague or the details of the professional service that edited your manuscript

A copy of your manuscript showing your changes by either highlighting them or using track changes (uploaded as a *supporting information* file)

A clean copy of the edited manuscript (uploaded as the new *manuscript* file).

3. In your Methods section, please provide additional information regarding the permits you obtained for the work. Please ensure you have included the full name of the authority that approved the field site access and, if no permits were required, a brief statement explaining why.

4. We note that Figure 1 in your submission contain map/satellite images which may be copyrighted. All PLOS content is published under the Creative Commons Attribution License (CC BY 4.0), which means that the manuscript, images, and Supporting Information files will be freely available online, and any third party is permitted to access, download, copy, distribute, and use these materials in any way, even commercially, with proper attribution. For these reasons, we cannot publish previously copyrighted maps or satellite images created using proprietary data, such as Google software (Google Maps, Street View, and Earth). For more information, see our copyright guidelines: http://journals.plos.org/plosone/s/licenses-and-copyright.

We require you to either (1) present written permission from the copyright holder to publish these figures specifically under the CC BY 4.0 license, or (2) remove the figures from your submission:

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We recommend that you contact the original copyright holder with the Content Permission Form (http://journals.plos.org/plosone/s/file?id=7c09/content-permission-form.pdf) and the following text:

“I request permission for the open-access journal PLOS ONE to publish XXX under the Creative Commons Attribution License (CCAL) CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). Please be aware that this license allows unrestricted use and distribution, even commercially, by third parties. Please reply and provide explicit written permission to publish XXX under a CC BY license and complete the attached form.”

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The following resources for replacing copyrighted map figures may be helpful:

USGS National Map Viewer (public domain): http://viewer.nationalmap.gov/viewer/

The Gateway to Astronaut Photography of Earth (public domain): http://eol.jsc.nasa.gov/sseop/clickmap/

Maps at the CIA (public domain): https://www.cia.gov/library/publications/the-world-factbook/index.html and https://www.cia.gov/library/publications/cia-maps-publications/index.html

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Natural Earth (public domain): http://www.naturalearthdata.com/.

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In your revised cover letter, please address the following prompts:

a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially sensitive information, data are owned by a third-party organization, etc.) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent.

b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories.

We will update your Data Availability statement on your behalf to reflect the information you provide.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: No

Reviewer #2: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: No

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

Reviewer #2: No

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This paper presents the use of Vis-NIR spectrometer to measure soil fertility parameters for some experimental sites in Ethiopia. While interesting, unfortunately the study was conducted on a small number of samples, even if the prediction is meaningful it does not have any meaning or scientific contribution. The relationship between spectra index and soil properties are dubious and there is no independent validation of the data. As such, this paper has serious flaws in the analysis and provided no real value.

Reviewer #2: The authors are presenting an interesting research topic addressing current needs for understanding soil fertility status which play important role in addressing food security and climate change challenges. The choice of technique to analyze soil is laudable especially when large areas are targeted.

However, the authors fell to explain the end-to-end scientific workflow for the procedure. All spectroscopic datasets are known to contain some level of redundant information in form of noise which should be filtered prior to modeling. This step was not explained.

Another important lacking is how the simple linear regression models were created. Which spectral bands were used? How were they selected?

The choice of Nash- Sutcliffe Efficiency is mostly used in simulation models. The authors should justify why it was used. Other metrics commonly used and acceptable in spectroscopy studies like bias, RPD should be considered.

Closely related to this aspect of metric is about selection of calibration (training set) and validation (testing sets) from the 48 spectra acquired.

I recommend further work to be done on this paper to improve on it is general quality.

I highlight below specific areas which requires amendment.

L127 .. well mixed…should be … mixed well…

L129 correct repeated word ‘at’.

Determination of Sand content from the difference as authors suggests on line 137 should be written clearly.

L173, mean spectra should not be obtained before checking for quality of spectra to screen bad spectra. Authors should outline what measures were taken to avoid including a bad spectrum (‘noisy’ replicate among the five into the averaged).

L180 to 183 – The requirement for assumption of normality is not clear. Why was this necessary? Was this done on the spectral dataset or on the physicochemical data?

L302 to 303 is not clear; it should be rewritten.

L310 to L315 gives model summary statistics for R-squared as a range instead of a single value for the calibration data set. Why is this?

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #1: No

Reviewer #2: Yes: ok

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

18 May 2022

Date: May 17, 2022

Rebuttal letter

PONE-D-22-07890

We are happy about the academic editor and the reviewers’ comments, which strengthen the current version of the manuscript “Use of soil spectral reflectance to estimate texture and fertility affected by land management practices in Ethiopian tropical highland”. In addition, our supreme sincere gratitude goes to you and the reviewers who devote their valuable time to bring our manuscript to a competent paper.

We have provided a one by one reply to all concerns and comments given below. We thank you for your consideration of this resubmission and look forward to your response.

Best regards,

Gizachew Ayalew Tiruneh (on behalf of all co-authors)

Lecturer in Debre Tabor University

Ph.D. Fellow in soil science, Bahir Dar University

Email: tiruneh1972@gmail.com

RESPONSE TO ALL COMMENTS

Dear editor and reviewers, thank you so much for taking your valuable time to elevate the quality of our manuscript. We do hope that the editor’s and Reviewer’s concerns will be addressed.

Editor comments:

Comment 1: A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

Response: We addressed the concerns provided by the editor and reviewers and uploaded a file labeled “Response to Reviewers”.

Comment 2: A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

Response: We tried to highlight our revised paper with tracked changes. We uploaded this as a separate file labeled 'Tracked changes'.

Comment 3: An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

Response: We revised our manuscript without tracked changes. We uploaded this as a separate file labeled 'Manuscript'.

Comments 4: If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter.

Response: We have not made any changes to financial disclosure.

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

Comment 1: Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf.

Response: Thank you. We tried to follow the PLOS ONE's style requirements throughout the manuscript.

Comment 2: We suggest you thoroughly copyedit your manuscript for language usage, spelling, and grammar. If you do not know anyone who can help you do this, you may wish to consider employing a professional scientific editing service.

Whilst you may use any professional scientific editing service of your choice, PLOS has partnered with both American Journal Experts (AJE) and Editage to provide discounted services to PLOS authors. Both organizations have experience helping authors meet PLOS guidelines and can provide language editing, translation, manuscript formatting, and figure formatting to ensure your manuscript meets our submission guidelines. To take advantage of our partnership with AJE, visit the AJE website (http://aje.com/go/plos) for a 15% discount off AJE services. To take advantage of our partnership with Editage, visit the Editage website (www.editage.com) and enter referral code PLOSEDIT for a 15% discount off Editage services. If the PLOS editorial team finds any language issues in text that either AJE or Editage has edited, the service provider will re-edit the text for free.

Response: Thank you for your advice. We have thoroughly revised our manuscript with the help of Grammarly (premium), ELSS editing service, and licensed iThenticate software (as attached), and we do hope that the reviewers concerns will be addressed.

Comment 3: Upon resubmission, please provide the following:

The name of the colleague or the details of the professional service that edited your manuscript. A copy of your manuscript showing your changes by either highlighting them or using track changes (uploaded as a *supporting information* file)

A clean copy of the edited manuscript (uploaded as the new *manuscript* file).

Response: The details of the colleague:

Name Tiringo Yilak Alemayeh, Place: Debre Tabor University, Work: Senior lecturer, and email address: tiringoy4@gmail.com

Name José Miguel Reichert, Place: Universidade Federal de Santa Maria (UFSM), Work: Professor of Soil Science and email address: reichert@ufsm.br

English language editing professional service was also obtained as you refer in the attached document. We tried to highlight our revised paper with tracked changes uploaded as a *supporting information* file.

Comment 4: In your Methods section, please provide additional information regarding the permits you obtained for the work. Please ensure you have included the full name of the authority that approved the field site access and, if no permits were required, a brief statement explaining why.

Response: Bahir Dar University officially approved the study, field site access, and soil sampling in the Aba Gerima catchment (the details are found from ethics statement from attached document).

Comment 5: We note that Figure 1 in your submission contains map/satellite images which may be copyrighted. All PLOS content is published under the Creative Commons Attribution License (CC BY 4.0), which means that the manuscript, images, and Supporting Information files will be freely available online, and any third party is permitted to access, download, copy, distribute, and use these materials in any way, even commercially, with proper attribution. For these reasons, we cannot publish previously copyrighted maps or satellite images created using proprietary data, such as Google software (Google Maps, Street View, and Earth). For more information, see our copyright guidelines: http://journals.plos.org/plosone/s/licenses-and-copyright.

We require you to either (1) present written permission from the copyright holder to publish these figures specifically under the CC BY 4.0 license, or (2) remove the figures from your submission:

a) You may seek permission from the original copyright holder of Figure 1 to publish the content specifically under the CC BY 4.0 license.

We recommend that you contact the original copyright holder with the Content Permission Form (http://journals.plos.org/plosone/s/file?id=7c09/content-permission-form.pdf) and the following text:

“I request permission for the open-access journal PLOS ONE to publish XXX under the Creative Commons Attribution License (CCAL) CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). Please be aware that this license allows unrestricted use and distribution, even commercially, by third parties. Please reply and provide explicit written permission to publish XXX under a CC BY license and complete the attached form.”

Please upload the completed Content Permission Form or other proof of granted permissions as an "Other" file with your submission.

In the figure caption of the copyrighted figure, please include the following text: “Reprinted from [ref] under a CC BY license, with permission from [name of publisher], original copyright [original copyright year].”

b) If you are unable to obtain permission from the original copyright holder to publish these figures under the CC BY 4.0 license or if the copyright holder’s requirements are incompatible with the CC BY 4.0 license, please either i) remove the figure or ii) supply a replacement figure that complies with the CC BY 4.0 license. Please check copyright information on all replacement figures and update the figure caption with source information. If applicable, please specify in the figure caption text when a figure is similar but not identical to the original image and is therefore for illustrative purposes only.

The following resources for replacing copyrighted map figures may be helpful:

USGS National Map Viewer (public domain): http://viewer.nationalmap.gov/viewer/

The Gateway to Astronaut Photography of Earth (public domain): http://eol.jsc.nasa.gov/sseop/clickmap/

Maps at the CIA (public domain): https://www.cia.gov/library/publications/the-world-factbook/index.html and https://www.cia.gov/library/publications/cia-maps-publications/index.html

NASA Earth Observatory (public domain): http://earthobservatory.nasa.gov/

Landsat: http://landsat.visibleearth.nasa.gov/

USGS EROS (Earth Resources Observatory and Science (EROS) Center) (public domain): http://eros.usgs.gov/#

Natural Earth (public domain): http://www.naturalearthdata.com/.

Figure 1 study area description and sampling distribution

Response: Thank you for your suggestion. In Response, we replaced the Figure 1 with the new one.

Comment 6: Please ensure that you refer to Figures 5, 10 and 11 in your text as, if accepted, production will need this reference to link the reader to the figure.

Response: Thank you for your suggestion, and we cited Figure 5 in the text and removed Figures 10 and 11 from the revised manuscript.

Comment 7: Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information.

Response: Thank you for your advice, and we added Supporting Information files at the end of the revised manuscript with their captions.

Comment 8: We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For more information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions.

In your revised cover letter, please address the following prompts:

a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially sensitive information, data are owned by a third-party organization, etc.) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent.

b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories.

We will update your Data Availability statement on your behalf to reflect the information you provide.

[Note: HTML markup is below. Please do not edit.]

Response: Data required for this study are within the manuscript and/or supplementary files.

Reviewer comments:

Comment 1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: No

Reviewer #2: Partly

________________________________________Response: Thank you. We appreciate your valuable comments. We tried to address the comments and incorporated them in the revised manuscript. We hope that this revised version will be satisfying.

Comment 2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: No

________________________________________

Response: Thank you. We have gone thoroughly the revised manuscript, and hopefully that the reviewers will be satisfied.

Comment 3: Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

________________________________________

Response: Thank you. Data required for this study are within the manuscript and/or supplementary files.

Comment 4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

Reviewer #2: No

________________________________________Response: Thank you for your advice. We have thoroughly revised our manuscript with the help of Grammarly (premium), ELSS editing service, and licensed iThenticate software (as attached documents), and we do hope that the reviewers concerns will be addressed.

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This paper presents the use of Vis-NIR spectrometer to measure soil fertility parameters for some experimental sites in Ethiopia. While interesting, unfortunately the study was conducted on a small number of samples, even if the prediction is meaningful it does not have any meaning or scientific contribution. The relationship between spectra index and soil properties are dubious and there is no independent validation of the data. As such, this paper has serious flaws in the analysis and provided no real value.

Response: Thank you for your concern and advice. We deleted the issue of relationship between spectral index and soil properties from the revised manuscript.

Reviewer #2: The authors are presenting an interesting research topic addressing current needs for understanding soil fertility status which play important role in addressing food security and climate change challenges. The choice of technique to analyze soil is laudable especially when large areas are targeted.

However, the authors fell to explain the end-to-end scientific workflow for the procedure. All spectroscopic datasets are known to contain some level of redundant information in form of noise which should be filtered prior to modeling. This step was not explained.

Another important lacking is how the simple linear regression models were created. Which spectral bands were used? How were they selected?

The choice of Nash- Sutcliffe Efficiency is mostly used in simulation models. The authors should justify why it was used. Other metrics commonly used and acceptable in spectroscopy studies like bias, RPD should be considered.

Closely related to this aspect of metric is about selection of calibration (training set) and validation (testing sets) from the 48 spectra acquired.

I recommend further work to be done on this paper to improve on it is general quality.

________________________________________ Response: Thank you for your kind suggestions. The pre-processing techniques such as multiplicative scatter correction for OC, median filter for clay, and Gaussian filter for sand were used to reduce “noise” from the raw spectrum before modeling of soil parameters. Besides, redundant spectral information was minimized through selecting wavelengths with high loading values and regression coefficients at each visible, near-infrared, and short-wave infrared spectrum for the studied soil parameter using licensed Unscrambler software. We removed Nash-Sutcliffe Efficiency and newly added metrics such as bias, RPD to validate the independent validation dataset (17 soil samples) from the 48 spectra. We hope that the reviewers concerns will be addressed.

Reviewer #2: I highlight below specific areas which requires amendment.

L127 .. well mixed…should be … mixed well…

L129 correct repeated word ‘at’.

Determination of Sand content from the difference as authors suggests on line 137 should be written clearly.

L173, mean spectra should not be obtained before checking for quality of spectra to screen bad spectra. Authors should outline what measures were taken to avoid including a bad spectrum (‘noisy’ replicate among the five into the averaged).

L180 to 183 – The requirement for assumption of normality is not clear. Why was this necessary? Was this done on the spectral dataset or on the physicochemical data?

L302 to 303 is not clear; it should be rewritten.

L310 to L315 gives model summary statistics for R-squared as a range instead of a single value for the calibration data set. Why is this?

________________________________________Response: Thank you for the comments. We tried to incorporate the above comments in the revised manuscript. We hope that this revised version will be satisfying.

Comment: While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

Response: Thank you. We have used PACE with this submission, so this should be right.

Please note that once again, thank you very much. Your comments are greatly appreciated.

Best regards,

Gizachew Ayalew Tiruneh (on behalf of all co-authors)

Lecturer in Debre Tabor University

Ph.D. Fellow in soil science, Bahir Dar University, Email: tiruneh1972@gmail.com

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

15 Jun 2022

Use of soil spectral reflectance to estimate texture and fertility affected by land management practices in Ethiopian tropical highland

PONE-D-22-07890R1

Dear Dr. Tiruneh,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Chun Liu

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: The authors have addressed review comments adequately.

For future work an approach that does not use linear models should be tested to ensure multicollinearities from highly correlated variables is addressed.

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Reviewer #2: Yes: Dr. Andrew M. Sila

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12 Jul 2022

PONE-D-22-07890R1

Use of soil spectral reflectance to estimate texture and fertility affected by land management practices in Ethiopian tropical highland

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