Volume 9, Number 3 (April 2022):3545-3560, doi:10.15243/jdmlm.2022.093.3545 ISSN: 2339-076X (p); 2502-2458 (e), www.jdmlm.ub.ac.id
Open Access 3545 Research Article
Evaluating the changes of Ultisol chemical properties and fertility characteristics due to animal manure amelioration
Heru Bagus Pulunggono1*, Vira Widya Kartika2, Desi Nadalia1, Lina Lathifah Nurazizah3, Moh Zulfajrin2
1 Department of Soil Science and Land Resources, Faculty of Agriculture, IPB University,16680, West Java, Indonesia
2 Graduate Program of Soil Science and Land Resources Departement, Faculty of Agriculture, IPB University,16680, West Java, Indonesia
3 Graduate Program of Agronomy and Horticulture Department, Faculty of Agriculture, IPB University,16680, West Java, Indonesia
*corresponding author: [email protected]
Abstract Article history:
Received 31 January 2022 Accepted 5 March 2022 Published 1 April 2022
Amending Ultisols using organic matter encourages a paramount improvement in its chemistry and fertility characteristics. This study was aimed to evaluate the changes in soil chemical properties due to the animal manure amelioration in Ultisol in the Jasinga, Bogor, West Java, using classical and advanced statistical methods. Composite soil samples were collected then incubated with three types of animal manure (cow, chicken, and goat) and four rate levels (0, 2.5, 5, and 7.5% of dry weight). The dynamics of eleven soil variables (pH, organic C, total N, cation exchange complex/CEC, base saturation/BS, and exchangeable Al, H, Ca, Mg, K, and Na) were observed four times (0, 2, 4, and 6 weeks). Basic cation saturation ratio/BCSR and sufficiency level of available nutrients/SLAN soil fertility approaches were applied. Modeling comparison was done among multiple linear regression/MLR, machine learning/ML (tree regression/TR, random forest/RF, gradient boosting machine/GBM), and deep learning/DL (multilayer perceptron/MLP). Most of the soil chemical and fertility parameters exhibited strong relation among three applied factors. Generally, their values failed to reach the BCSR’s ideal soil and national SLAN’s sufficiency criteria; oppositely, they were categorized as sufficient based on the global SLAN approach. Multivariate analysis revealed the similarity among manure type and rate, whereas incubation time showed the opposite trend. MLR usage was convenient in modeling BS, pH H2O, and Al saturation.
Meanwhile, CEC modeling requires more sophisticated methods. This study highlighted the possible improvement of Ultisol chemical properties and fertility characteristics by amending it with a higher rate and low C/N ratio of animal manure, and using ML to capture non-linear relationships in soil.
Keywords:
BCSR-SLAN incubation time machine learning rate
soil amendment
To cite this article: Pulunggono, H.B., Kartika, V.W., Nadalia, D., Nurazizah, L.L. and Zulfajrin, M. 2022. Evaluating the changes of Ultisol chemical properties and fertility characteristics due to animal manure amelioration. Journal of Degraded and Mining Lands Management 9(3):3545-3560, doi:10.15243/jdmlm.2022.093.3545.
Introduction
Podzolic (Ultisols), an acidic and advanced weathered soil, is widely distributed in Indonesia. This type of soil is spread in Kalimantan, Papua, Sumatra,
Sulawesi, and Java, covering about 50.4 million hectares or 29.05% of the total soil order (Puslitanak 2000). The extensification of agricultural areas (e.g., rice and corn estates, oil palm plantations) require more arable lands, facing Ultisols as their critical
Open Access 3546 constraints. Ultisols commonly have low base
saturation, acidic soil reactions, and elevated Al3+
saturations. However, a distinct Ultisol is found in the Jasinga area, Bogor, West Java, with extremely high aluminum saturation. A high aluminum saturation could cause severe toxicity to the plant. Al3+
competition potentially enhances the desorption and leaching process of nutrient cations from the soil exchange complex, also hampering their absorption in the plant root area (Singh et al., 2017; Jaiswal et al., 2018; Zhao and Shen, 2018). Low pH exacerbates that effect by increasing the micronutrients/trace elements availability (e.g., Fe, Mn, Cu, and Zn), which is potentially toxic for plants. This condition, combined with other soil properties, may lead to nutrient deficiency, resulting in limited plant growth and development and a decline of plant productivity (Bojórquez-Quintal et al., 2017).
Animal manure as an organic matter source plays a vital role in improving soil properties, the quality that is crucial in mitigating the adverse effect of chemical fertilizer and combating land degradation. Animal manure application demonstrated positive effects on Ultisols physical, chemical, and biological properties, particularly in alleviating soil acidity and Al toxicity (Zhou et al., 2013; Ngo et al., 2014; Ch’ng et al., 2015;
Peng et al., 2016; Shi et al., 2019; Ye et al., 2019), which also had advantageous effects on plant growth and development (Masmoudi et al., 2018; Pandey et al., 2021). Furthermore, Bogor regency of West Java province were the regions that possessed a relatively high number of animal farms (i.e., chicken, goat, and cow farms; Statistics Indonesia of West Java, 2021), excreting huge volumes of fecal material in total. In this area, the utilization of excessive manure is considered an advantage regarding its high potential for soil amelioration. However, the negligence would result in a detrimental effect since the raw faecal material may harbor dangerous microbial communities that potentially pollute the environment and infect humans and animals (Manyi-Loh et al., 2016;
Manikandan et al., 2020).
Many research papers have demonstrated the dynamic of soil fertility components during manure applications (e.g., Masmoudi et al., 2018; Muktamar et al., 2020; Liu et al., 2021). Unfortunately, very little attention was pinpointed to the classical approach of soil fertility interpretation, particularly for assessing the soil, fertilization, or amelioration in the tropical region. The basic cation saturation ratio/BCSR and sufficiency level of available nutrients/SLAN were the two major approaches developed to evaluate the soil fertility status (Chaganti and Culman, 2017). The BCSR approach held on “the balancing theory,” which promoted an ideal ratio between three essential soil cations (Ca, Mg, and K) to improve soil health and nutrient availability (Albrecht 1975; Zimmer, 2017).
On the other hand, the SLAN approach was established based on “the law of diminishing returns.”
Based on the SLAN perspective, the soil nutrients
certainly possessed their particular critical levels. The crop would likely respond while the magnitude of fertilization conducted below that level. Both approaches were showed significant discrepancies and became debates, leading to the disregarding of BCSR from the soil scientists community. Some researchers, government agencies, and soil scientists favored the SLAN approach concerning its reliable scope, economic scale, and yield advantages (Kopittke and Menzies 2007; Anda, 2012; Chaganti and Culman, 2017). Albeit its lack of scientific evidence, there are widespread practices of BCSR and its derivatives to the farmer and consultants (Brock et al., 2020; Culman et al., 2021), along with the growing interest to study their efficacy (Souza et al., 2016; Chaganti et al., 2021) and continuing research using BCSR to evaluate cation imbalance in soil (Anda, 2012; Bonomelli et al., 2019;
Roobroeck et al., 2021).
For decades, linear regression/LR (or multiple linear regression/MLR) has dominated the soil science field, extensively utilized in quantifying, interpreting, and predicting various soil data and models (e.g., Sharma et al., 2015; Rossiter, 2018). Advancing with global trends, the application of ML and DL in soil science studies have also been growing and gained great interest (Liakos et al., 2018; Rossiter, 2018;
Padarian et al., 2020). Unfortunately, it is difficult to find studies that focus on the assessment of manure application in marginal soil using MLR, ML and DL algorithms, particularly in tropical regions.
Considering the rarity of publications covering the issue and some contradicting facts, assessing ML and DL performance, their comparison with MLR, and how the soil properties interrelationships in marginal soil as affected by manure amelioration can be challenging at once, exciting tasks. In a comparison study between portable X-ray fluorescence/pXRF and laboratory analyses, Rawal et al. (2019) reported that the R2 (observed vs predicted) of regression tree/RT and random forest/RF surpassed MLR in predicting CEC and BS in subtropical soils. A similar result (using pXRF) was also found by Teixeira et al. (2020) in predicting CEC, BS, and Al saturation of tropical soils. Both authors denoted non-linear relationships between predicted soil properties and the covariates/predictors variables, possibly suffering MLR performance. Seyedmohammadi et al. (2016) exploited the opportunity of DL technique to significantly increase CEC prediction using clay and organic C compared to MLR, albeit they mentioned the possibility of linear relationships between CEC with both covariates. Differently, other researchers (Sharma et al., 2014; Sharma et al., 2015) demonstrated that high R2 (0.8 to 0.9) could emerge from MLR equations when predicting CEC and pH in subtropical soils, although, not performed ML or DL algorithms in their studies.
This paper studied both classical and advanced interpretations of manure amelioration in Ultisol, a marginal and degraded soil, which is rarely explored
Open Access 3547 by soil scientists. The objective of this paper is to
evaluate the changes in soil chemical properties due to the animal manure amelioration in Ultisol using various statistical methods, two soil fertility approaches, and modeling. Based on that, there are four hypotheses developed in this study: (1) adding different types, rates, and incubation times of animal manures in Ultisol might improve soil health by raising CEC, BS, and pH, as well as lowering Al saturation;
(2) the differences in manure type, rate, and incubation time would affect Ultisol in terms of the SLAN soil fertility approach; otherwise, a similar design will not reach the ideal level of nutrient saturation or ratio based on the BCSR method; (3) ML and DL could exceed MLR performance since the dataset consisted of a relatively dynamic soil condition due to manure amelioration; and (4) the manure type, rate, and incubation time influence the CEC, BS, pH, and Al saturation, regardless of the models.
Materials and Methods Study location
This research was conducted in the Greenhouse of Cikabayan Experimental Station, IPB University Campus Dramaga, from February to August 2020.
Selected Ultisol as the source of soil material was collected under acacia (Accacia mangium) local people plantation in Jasinga, Bogor (6°28'01.0"S;
106°28'31.7"E). The Ultisol site is located at 115.4 masl, experiencing a humid tropical climate with the mean annual precipitation being 3,710 mm and an average annual temperature of 25.93 °C. Based on the field observation, the Ultisol’ parent material was felsic claystone. The Ultisol’ physical and chemical properties are presented in Table 1. As shown in Table 1, Ultisol at the study site had low and very low C, N, and exchangeable bases, as well as elevated Al saturation.
Table 1. Soil physical and chemical properties.
Soil Properties Value Status
Physical Properties Texture
Sand (%) 11.26
Clay
Silt (%) 7.69
Clay (%) 81.05
Chemical Properties
C (%) 1.74 L
N (%) 0.17 L
C/N 10.20 L
CEC (me/100 g) 38.28 H
ECEC (me/100 g) 17.77 -
Clay to CEC ratio (molc/kg clay) 0.47 MC
Exchangeable base (me/100g)
Na 0.28 L
K 0.24 L
Ca 1.93 VL
Mg 0.52 L
Base saturation (%) 7.90 VL
Individual cation saturation (%)
Na 1.58 -
K 1.35 -
Ca 10.86 -
Mg 2.93 -
H 4.95 -
Al 78.33 VH
Exchangeable H (me/100 g) 0.88 -
Exchangeable Al (me/100 g) 13.92 -
pH (H2O) 4.25 SAc
pH (KCl) 3.44 -
Note: The status and value of the chemical properties were referred on the Indonesian Soil Research Institute MC: mixture of clay mineral, SAc: strong acidity, L: low, VL: very low, H: high, VH: very high.
Manure incubation
Ultisol samples (±2 kg) were compositely collected using hoe at a depth of 0-20 cm. The soil samples were dried and weighed equally 1000 g, then treated with
animal manures, consisting of cow, chicken, and goat manures. Manure physical and chemical properties are shown in Table 2. The manure rate (based on the dry weight of manure) for each sample was 0, 2.5, 5, and
Open Access 3548 7.5 % of the dry weight of soil. According to the
treatment, the soil and manure samples were mixed, then inserted in a vessel (d = 24 cm; h = 27 cm), and incubated for six weeks with water content maintained around 75% of field capacity. During the incubation process, the soil subsamples were taken compositely (0, 2, 4, and 6 weeks) and then analyzed for chemical analysis-similar to initial chemical analysis. The soil samples were air-dried for 24 hours before the analysis then sieved using a 2 mm sieve.
Table 2. Manure physical and chemical properties.
Manure Properties Cow Chicken Goat Physical Properties
Water Content (%) 73.01 60.64 80.47 Chemical Properties
C (%) 31.98 41.51 42.20
N (%) 2.08 1.37 2.49
C/N 15 30 17
pH 7.18 7.51 8.40
Exchangeable bases (me/100g)
Na 0.53 0.87 1.36
K 1.71 1.44 1.92
Ca 1.58 1.71 2.11
Mg 0.95 0.66 0.88
Laboratory measurement
Soil analyses were carried out in the Soil Chemistry and Fertility Laboratory, Soil Science and Land Resources Department, Faculty of Agriculture, IPB University. The initial soil examination for texture using pipette method. Soil reaction was determined using soil to water mixture of 1:1. Organic carbon was extracted using the Walkey and Black method. Total N was extracted using the Kjeldahl method.
Exchangeable aluminum was extracted using KCl 1 N.
Cation base, and cation exchangeable capacity/CEC were extracted using an ammonium acetate (NH4OAc) solution pH 7.0. The final solutions of Ca and Mg were measured using a Shimadzu AA-6300 atomic absorption spectrophotometer. Meanwhile, K and Na were measured using a flame emission spectrophotometer.
The effective CEC (ECEC) was obtained by summating all base and acid cations. Clay to CEC ratio/CCR was calculated by dividing CEC by clay percentage. Since the observed soil pH was acidic, the ECEC was applied as the CEC reference for calculating the base and individual saturation. The base saturation is calculated as the summation of entire base cations (K, Na, Ca, and Mg) divided by ECEC was then presented as a percent. The saturation of individual base and acid cations, expressed as percentages, is calculated from individual base cations divided by ECEC.
Data analysis and interpretation
Data listing was done using Microsoft Excel. The statistical analyses were conducted through R statistical software in the Rstudio environment (R Core Team, 2021). Variance analysis was employed using the ANOVA type III method due to the incorporation of interaction. Tukey’s honest significant test (HSD) with a 95% confidence interval was chosen as a posthoc test. Both variance analysis and posthoc tests were executed using the R base stat package. Pearson correlation was performed between the entire continuous variables. Multiple linear regression/MLR was applied among the entire treatments/factors (type, rate, and incubation time) plotted as categorical effects, whereas eleven continuous variables (pH H2O, pH KCl, organic C, total N, CEC, and exchangeable Ca, Mg, K, Na, H, and Al) were assigned as responses.
Principal component analysis (PCA) as the multivariate analysis was performed to reveal the underlying relationships between the treatments/factors and variables, executed using R FactoMineR (Husson et al., 2020), and factoextra (Kassambara and Mundt, 2020) packages.
Soil chemical properties were firstly interpreted referring to the Technical Guidelines for Chemical Analysis of Soil, Plants, and Fertilizers; Soil Research Institute (Eviati and Sulaeman, 2009). Secondly, the classical soil fertility approaches were applied, consisting of (1) the base cation saturation ratio/BCSR approach (Kopittke and Menzies 2007; Chaganti and Culman 2017); and (2) global and regional criteria of the sufficiency level of available nutrients/SLAN (Aitken and Scott 1999; Bruce 1999; Gourley 1999;
Eviati and Sulaeman, 2009). Both approaches were used for interpreting the dynamic of soil fertility at every treatment and level.
Modeling CEC, BS, pH, and Al saturation
Mechanistic models consisting of CEC, BS, pH, and Al saturation as affected by manure type, rate, and incubation time were assembled to assess in-depth relationships among some soil chemical and fertility properties. The models also accounted for some covariates of soil properties that related to the modeled outcome. The constructed formulas were:
CEC ~ Type+Rate+Time+Org C+Exch Al+Exch H+Exch Ca+Exch Mg+Exch K+Exch Na
BS ~ Type+Rate+Time+CEC+Exch Al+Exch H+Exch Ca+Exch Mg+Exch K+Exch Na
pH H2O ~ Type+Rate+Time+BS+Exch Al+Exch H+Exch Ca+Exch Mg+Exch K+Exch Na+Org C
Sat Al ~ Type+Rate+Time+pH H2O+BS+Exch H+Exch Ca+Exch Mg+Exch K+Exch Na+Org C
Open Access 3549 Before any statistical analyses were applied, all
datasets (CEC, BS, pH, and Al Saturation) were randomized and divided into training (70%) and validation (30%) datasets while preserving the randomization and splitting process for reproducibility. The numerical covariates were normalized concerning the min-max normalization methods, avoiding the domination of larger values, preserving the original relationship, and keeping the data at the same scale. The models were tested and tuned through grid search passed in the caret package (Kuhn et al., 2020) with 10-fold repeated cross- validation using the training dataset.
Algorithm compared
Multiple Linear Regression/MLR. MLR is regarded as a standard statistical method in interpreting and predicting data and models in many fields, notably when dealing with numerical variables. MLR represents linear function with ordinary least square/OLS algorithm. OLS estimates the relationships between one or more independent variables and a dependent variable, minimizing the sum of squared residuals (Montgomery et al., 2012). In R, MLR was executed using the lm function in the R base stat package.
Tree Regression/TR. Developed by Breiman et al.
(1984), TR or CART (Classification and Regression Tree) was among the widely used supervised ML technique (Speybroeck, 2012; Padarian et al., 2020).
TR develops a single decision tree, recursively partitioning the data into two regions, thus minimizing the overall sums of squares error. The TR algorithm will consider all covariates in the splitting process and choose the best covariates with the most considerable reduction in deviance. The splitting process is terminated after reaching several stopping criteria (Breiman et al., 1984). To gain optimal tree depth and avoid overfitting due to a very deep and complex tree, the models were pruned (or tuned) using cost complexity/cp, minsplit, and maxdepth hyperparameters. The rpart package (Therneau et al., 2022) was used to perform TR algorithm in this study.
Random Forest/RF. RF is one of the most popular ML techniques based on ensemble learning (Padarian et al., 2020). Unlike TR which builds a single tree using all covariates and observations of the training sample, RF algorithm performs random resampling and selects the specific covariates and observations to build multiple deep and fully grown trees. Then, the individual tree outputs were averaged to gain the final prediction. These techniques (namely: bootstrap aggregation/bagging and random subspace methods) significantly alleviate model overfitting, reduce the dominance of variables with a large samples size and boost the predictive power (Breiman, 2001; Ho, 1998).
This study executed RF through randomForest and ranger packages (Liaw and Wiener, 2018; Wright et
al., 2021). The ranger package was utilized to minimize computation time during the grid search of RF’ hyperparameters, while randomForest-package was performed to the final RF model. In this study, RF algorithm was optimized using ntree, mtry, and nodesize hyperparameters.
Gradient Boosting Machine/GBM. Gradient boosting machine/GBM or gradient boosting tree/GBT is currently the most popular ML technique. GBM and its derivatives (e.g., XGBoost) often generates strong predictive results across many research fields and ML competitions (Chen and Guestrin, 2016; Nielsen, 2016). Similar to RF, the GBM algorithm is developed based on ensemble learning trees. However, GBM use boosting with gradient descent methods which construct an ensemble of shallow tree learners (considered weak learners), successively grown and sequentially trained from previous information.
Through these methods, every new learner will improve and optimize the current error, hence, adjusting their predictive ability (Friedman, 2001).
The gbm package used in this study performs stochastic gradient descent method that incorporates random sampling to the gradient descent, allowing GBM algorithm to compute efficiently in minimalizing the loss function (Greenwell et al., 2020; Ridgeway, 2020). n.trees, interaction.depth, shrinkage, n.minobsinnode, and bag.fraction hyperparameters were adjusted to gain an optimum GBM prediction.
Multi-layer Perceptron/MLP. Inspired by fascinating human brain networks, MLP works in a similar manner, using multiple layers of input and output units interconnected with several hidden layers. As a kind of artificial neural network/ANN, a simple computational unit in MLP is called neurons, consisting of a set of input, weights, and activation functions. In MLP, the covariates or predictors were fed into multiple neurons. The input values were randomly weighted, bias introduced, then summed using the transfer function. After reaching a particular threshold, summed net input triggered the activation function to generate output information. The output information from one neuron will be passed as input to other connected neurons from other layers. In the output layer, the resulting predicted output is then compared to the expected output using the performance function.
The MLP algorithm will minimize their differences by propagating back the error through the network and updating the weight bias. This technique, called back- propagation, is able to repeatedly train the entire neural network, giving MLP a remarkable learning performance (Beysolow II, 2017). This study applied a feedforward ANN/MLP trained with stochastic gradient descent using resilient back-propagation and weight backtracking (neuralnet-package; Fritsch et a., 2019) as a DL method. Hidden layers and threshold hyperparameters were adjusted specified to each model. The performance function was calculated using
Open Access 3550 the sum of square error (SSE), whereas the logistic
function was chosen as the activation function.
Model evaluation and variable importances retrieval The entire models were evaluated using root mean square error/RMSE that utilized the validation dataset.
The coefficient of determination (R2) and p-value was computed from predicted/modeled and actual observed values. Furthermore, variable importances were generated separately from each algorithm. Sum of decreases in impurity, the percent increase in mean square error/MSE, and the largest average decrease in MSE were used to achieve variable importance of TR, RF, and GBM, respectively. The important variable in MLP was retrieved through the connection weights algorithm (Olden et al., 2004). For comparison reasons, only the weight magnitude/absolute value was preserved, dropping their direction.
Results and Discussion
Effect of manure addition on soil chemical properties The changes of several soil chemical parameters during the incubation of three types of animal manures are presented in Table 1. According to the analysis of variance, almost all of the observed soil chemical characteristics were strongly affected by the differences in incubation time and manure rate. There were significant differences between the incubation time on pH H2O, pH KCl, total N, exchangeable acid and base cations, CEC, BS (P<0.001), and organic C (P<0.01). Some parameters (e.g., pH H2O, pH KCl, organic C, exchangeable bases, and base saturation) showed statistically high values and concentrations at the first two weeks, which decreased at the next four and six weeks. The increment in incubation time seemingly exhibited an inconsistent effect on the soil
chemical parameters also likely possessed a non-linear pattern (Table 3 and Figure 1), capturing the ongoing process of organic matter decomposition. Table 3 denotes remarkable differences between rates on pH KCl, organic C, exchangeable Ca, Mg, K, and Al (P<0.001), pH H2O (P<0.01), and total N (P<0.05).
Under the highest manure rate (7.5% o dry weight of soil), seven variables were gained their highest value (pH H2O and BS) and concentrations (organic C, total N, and exchangeable base cations). The rate effect on the soil parameters was considered linear, including on exchangeable Al (e.g., Figure 1). The increase of manure rate would decrease exchangeable Al, oppositely, increase BS and exchangeable base cations. Moreover, the manure rate of 5% dry weight of soil generated the statistically lower exchangeable Al compared to 0 and 2.5% of dry weight of soils.
Significant differences were denoted between manure type on organic C, exchangeable Ca, Mg, and K (P<0.001), CEC, pH KCl, and exchangeable Na (P<0.01), demonstrating that the difference in manure type was chiefly affected seven of eleven observed characteristics. Among the three types of manure, cow manure was the most promising source, as indicated by the highest statistical average over nine soil chemical variables, such as pH KCl, organic C, BS, CEC, ECEC, and all of the exchangeable bases. However, the diversifying attempt of manure source did not improve pH H2O, total N, and exchangeable acid cations in Ultisol. This finding was in contrast with Muktamar et al. (2020), who found that chicken and cattle manures addition could help significantly lower exchangeable Al in Ultisol. Several researchers also found that chicken manure was an effective ameliorant than other animal manures (Alade et al., 2019;
Muktamar et al., 2020). This was possibly related to the high CN ratio of chicken manure compared to other manure sources used in this study (Table 2).
Figure 1. Multiple linear regression standardized effect plots for time, rate, and manure type (remarks: responses exceeding the dotted line are considered statistically significant).
Open Access 3551 Table 3. Effect of different treatments (type, rate, and incubation time) of manure amelioration on Ultisol’ chemical properties.
Variables Type Rate Incubation Time
Goat Chiken Cow 1 2 3 4 0 2 4 6
pH H2O 4.24 4.22 4.22 4.31 a 4.14 b 4.17 ab 4.27 ab 5.01 a 4.29 b 3.77 c 3.82 c
pH KCl 3.48 a 3.46 ab 3.45 b 3.44 c 3.45 bc 3.47 ab 3.49 a 3.83 a 3.61 b 3.20 c 3.21 c Organic C 2.14 a 1.95 b 1.79 c 1.72 c 1.86 b 2.06 a 2.20 a 2.09 a 1.93 b 1.93 b 1.89 b
Total N 0.19 0.19 0.19 0.17 b 0.18 ab 0.20 ab 0.22 a 0.16 b 0.19 ab 0.22 a 0.21 a
Exch. Cations
K 0.83 a 0.48 b 0.22 c 0.28 b 0.38 b 0.69 a 0.69 a 0.35 b 0.74 a 0.59 a 0.36 b Ca 3.01 a 2.54 b 2.34 b 1.93 b 2.34 b 2.95 a 3.31 a 3.06 b 3.69 a 2.21 c 1.58 d Mg 1.13 a 0.84 b 0.63 c 0.52 b 0.69 b 1.02 a 1.24 a 1.16 a 1.18 a 0.84 b 0.28 c Na 0.48 ab 0.51 a 0.32 b 0.28 b 0.32 b 0.54 a 0.60 a 0.74 a 0.58 a 0.32 b 0.11 b
H 0.81 0.81 0.69 1.02 0.91 0.90 0.92 0.47 b 0.63 b 2.24 a 0.40 b
Al 12.65 12.55 12.85 14.10 a 13.74 a 12.07 b 10.81 b 11.53 b 10.64 b 14.68 a 13.88 a
BS 28.69 a 24.60 b 21.05 c 16.82 d 20.60 c 28.72 b 32.99 a 30.12 b 35.25 a 19.25 c 14.51 d
CEC 39.35 a 39.18 ab 37.87 b 38.53 38.82 38.46 39.39 40.32 a 37.95 b 37.69 b 39.24 ab
ECEC 19.03 a 17.84 ab 17.32 b 18.13 18.38 18.16 17.57 17.31 b 17.46 b 20.88 a 16.60 b
Note: The variance analyses were grouped based on factors and variables. Means that do not share a letter are significantly different according to Tukey HSD test.
Table 4. Pearson correlation among the entire observed variables.
pH H2O pH KCl Org C Total N Exch Al Exch H CEC Exch Ca Exch Mg Exch K
pH KCl 0.858**
Org C 0.225** 0.272**
Total N -0.380** -0.328** -0.009
Exch Al -0.362** -0.491** -0.314** 0.196*
Exch H -0.392** -0.425** -0.096 0.155 0.341**
CEC 0.172* 0.196* 0.214** -0.023 -0.144 -0.200*
Exch Ca 0.395** 0.570** 0.405** -0.054 -0.501** -0.148 0.014
Exch Mg 0.389** 0.532** 0.499** 0.002 -0.370** -0.029 0.094 0.771**
Exch K -0.077 0.072 0.484** 0.122 -0.235** 0.049 0.067 0.474** 0.606**
Exch Na 0.446** 0.567** 0.375** -0.077 -0.358** -0.123 0.095 0.550** 0.622** 0.291**
Note: asterisk (*) and double asterisks (**) indicated statistically significant on α5% and α1%, respectively
Open Access 3552 Figure 2. Soil chemical’s PCA biplots consisted of score and loading plots grouped by (a) manure type, (b) rate,
and (c) incubation time.
Results of principal component analysis (PCA; Figure 2) revealed two PCs were obtained in the measured parameters accounted for 56.1% of the total variance (PC1=38.7%, PC2=17.4%). Differently with the analysis of variance, the manure type and rate did not possess enough strength to provide the difference on the observation samples, as shown by overlapped circles. The resemblance among the manure types is due to the relatively similar mineralization process that occurs in the chicken manure and both cow and goat manures. However, there was a difference in the observation points grouped by incubation time. The grouping based on the manure incubation time resulted in three groups, which gradually increased from the third to the first quadrant. Moreover, the observation points did not differ in the last two weeks. These results indicate that the manure mineralization process slowed and possibly reached a stable state at four and six weeks after being incubated with Ultisol.
Effect of manure addition on soil fertility
According to “the balancing theory” of BCSR (Liebhardt, 1981; Chaganti and Culman, 2017), the cation saturation for ideal soil was postulated as 60 to 75% for Ca, 10 to 20% for Mg, and 2 to 5% for K, transformed to Ca:Mg ratio of 6.5:1, Ca:K of 13:1, and Mg:K of 2:1. Without amelioration (Table 1), Ultisol at the study site suffered from an imbalance of cation concentration; as indicated by most of the exchangeable cation saturations fell below the ideal ratio. However, Mg:K was considered to reach the ideal ratio in the soil. From the BCSR perspective, this condition requires proper soil management, asserting the appropriate fertilization is done until exchangeable cations attain the ideal ratio. Similar to this finding, other researchers have reported the cation imbalances in tropical soils as assessed by the BCSR method (Anda, 2012; Meena, 2021). Otherwise, some soils derived from nutrient-rich parent materials were developed higher cations saturation, regardless of their climate origin, either in tropical (Kasno et al., 2021;
Roobroeck e al., 2021) or subtropical regions, resulting in the ideal cation ratios that meet the favorable condition for plant growth and development.
The Ultisol amended with different types, rates, and incubation times exhibited significant differences in Ca, Mg, and K saturations (Table 6). However, the amelioration based on that factors failed to reach the ideal level of Ca and Mg in the soil as suggested by BCSR. Similar imbalances were also recorded on the Ca:Mg and Ca:K ratios. The amendment of animal manure successfully increased most of K saturation above the ideal level, except for cow manure and the lowest manure rate. Furthermore, the Mg:K ratio also exhibited a high value exceeding its ideal level in the soil. In general, the condition indicates that the type difference, maximum rate of 7.5% dry weight of soil, and six weeks of incubation time were not sufficient in ameliorating Ultisol in terms of the BCSR approach.
Increasing Ca and Mg using calcite, dolomite, and kieserite combination may help gain their ideal saturation levels, as well as improve other soil chemical properties such as inclining pH and alleviating micronutrient toxicity. Without including a K source, the fertilization and amelioration would also cause very low K saturation compared to Ca and Mg and a high Ca:K and Mg:K ratios. Imbalance fertilization would affect the cation interaction in soil;
in this case, the cation competition would repel K from soil and root exchange complexes, generating plant K deficiency and soil K leaching. In order to reach the ideal cation saturation and ratio based on the BCSR soil fertility approach, further research is required using a combination of animal manure rate with other types of fertilizer consisting of Ca, Mg and K.
Stood on “the law of diminishing returns”
(Kopittke and Menzies 2007), general/global CCDS/SLAN approach on soil fertility assessment based on Australian soil classified nutrients sufficiencies ranging from 0.5 to 1.5 me/100g for Ca, 0.2 to 0.3 me/100g for Mg, and 0.2 to 0.5 me/100g for K (Aitken and Scott, 1999; Bruce, 1999; Gourley, 1999). Nationally, the Indonesian threshold for cation sufficiency level varied from 6.0 to 10.0 me/100g of Ca, 1.1 to 2.0 me/100g of Mg, and 0.4 to 0.5 me/100g of K, and 0.4 to 0.7 me/100g of Na (classified as
“medium” using ISRI’s Guidelines; Eviati and Sulaeman, 2009).
Open Access 3553 Table 6. Effect of different treatments (type, rate, and incubation time) of manure amelioration on Ultisol’ fertility characteristics.
Variables Manure Type Rate Incubation Time
Goat Chiken Cow 1 2 3 4 0 2 4 6
Saturation (%)
K 4.78 a 2.75 b 1.28 c 1.60 b 2.17 b 3.98 a 4.00 a 2.05 b 4.25 a 3.39 a 2.06 b
Ca 17.40 a 14.68 b 13.52 b 11.14 b 13.51 b 17.03 a 19.12 a 17.65 b 21.32 a 12.73 c 9.09 d
Mg 6.50 a 4.84 b 3.66 c 3.00 b 3.97 b 5.86 a 7.15 a 6.67 a 6.82 a 4.88 b 1.62 c Na 2.77 a 2.93 ab 1.87 b 1.63 b 1.87 b 3.13 a 3.47 a 4.30 a 3.35 a 1.83 b 0.62 b
H 5.37 5.37 5.45 5.89 5.25 5.17 5.28 2.71 b 3.62 b 12.95 a 2.31 b
Al 73.00 72.42 74.16 81.39 a 79.30 a 69.66 b 62.42 b 66.53 b 61.40 b 84.75 a 80.09 a
Ca:Mg 3.67 4.50 4.54 5.08 a 4.25 ab 3.84 b 3.78 b 2.93 b 3.49 b 3.34 b 7.19 a
Ca:K 6.48 b 8.06 b 11.25 a 8.96 9.82 7.25 8.37 11.97 a 7.73 b 7.32 b 7.37 b
Mg:K 2.12 2.58 2.78 2.36 2.84 2.14 2.62 4.05 a 2.26 b 2.54 b 1.12 c
Note: The variance analyses were grouped based on factors and variables. Means that do not share a letter are significantly different according to Tukey HSD test.
Open Access 3554 Without amending manure (Table 1), Ultisol at the
study site is classified as sufficient regarding the general SLAN criteria. However, the entire cations were categorized as insufficient based on Indonesian criteria. Similar to the BCSR approach, fertilization or amelioration must be conducted to avoid soil nutrient mining.
Based on the general/global SLAN soil fertility perspective, amending Ultisol with three different treatments, as mentioned earlier, successfully maintained the sufficiency of Ca, Mg, and K in soil (Table 6). However, according to national SLAN criteria, a contrary result was found, particularly for exchangeable Ca. Moreover, higher manure rates (5.0 and 7.5% of dry weight of soil) were considered sufficient to reach the critical level of exchangeable Mg and K in Ultisol. A low level of exchangeable Ca observed in the soil during organic tissue
decomposition might be caused by Ca immobilization in saprophytic fungal hypha (Figure 5; Dauer and Perakis, 2014; Ferreira et al., 2016), which possibly restricts its exchangeable form. Since several researchers reported an opposite trend for a specific manure type (Ranjbar and Jalali, 2012), further research is needed to clarify the general pattern of nutrient dynamics during manure amelioration in soil.
Similar to the previous analysis (Figure 2), overlapped circles on multivariate PCA at Figure 3 revealed the manure type and rate incapability to reach differences among many observation samples. However, the incubation time could extricate the observation samples to gain differences, as shown by the gradual movement of the less overlapped circles (0, 2, and 4 weeks). A possible, stable state on animal manure decomposition in Ultisol is shown by overlapped circles in the last four and six weeks.
Figure 3. Soil fertility’s PCA biplots consisted of score and loading plots grouped by (a) manure type, (b) rate, and (c) incubation time.
Modeling CEC, BS, pH, and Al saturation
Besides the aforementioned classical approach in interpreting the manure-affected soil data, this study demonstrated the importance of using ML as an alternate technique. According to Figure 4, the MLR outperformed ML and DL at BS and Al saturation models in terms of RMSE perspective. In ML family, RF and GBM exhibited stable performances and lower RMSE than MLR and DL, particularly in modeling CEC and pH. MLP was the worst algorithm compared to MLR and DL, even though the hidden layers were already increased until reaching near the maximum number allowed (similar with or 2/3 of the input layer
number) during the tuning process. This condition is possibly due to the vanishing gradient as a consequence of the hidden layers increment or stalling and trapping at the local minima, which is indicated that MLP utilization requires many upgrades with a more complex algorithm (Feng et al., 2019). As suggested by the previous researchers (Rawal et al., 2019; Teixeira et al., 2020), CEC and pH may have a non-linear relationship with other soil chemical properties. When plotted against observation data (Figure 5), the predicted value of MLR generated a fairly good R2 and p-value compared to ML and DL, particularly in BS, pH H2O, and Al saturation models.
Figure 4. RMSE (expressed as percentages) comparison among MLR, TR, RF, GBM, and MLP based on CEC, BS, pH H2O, and Al saturation modeling.
3.36 1.30
0.25 6.35
2.86 4.82 0.23
14.47
3.02 1.71 0.22
10.75
3.08 1.73 0.30
9.07
38.03 23.38 3.27
68.96
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
CEC BS pH H2O Al Sat
RMSE
MLR TR RF GBM MLP
Open Access 3555 This indicated that some soil parameters had linear
relationships, as reported by previous researchers (Sharma et al., 2014; Seyedmohammadi et al., 2016;
Adam et al., 2021). However, this study showed similar results with Teixeira et al. (2020) and Rawal et al. (2019), who demonstrated a constrained use of MLR in predicting CEC. Furthermore, TR and RF exhibited low to moderate R2 and p-values in modeling
CEC and pH H2O, respectively, surpassing MLR and MLP. Since the predictive ability of the entire CEC models was lower than expected, this study suggested the incorporation of related variables, such as clay content. Sharma et al. (2015) demonstrated that the involvement of clay content and organic C as covariates could boost the model performance compared to the elemental-built CEC model.
Figure 5. Comparative validation among (1) MLR, (2) TR, (3) RF, (4) GBM, and (5) MLP based on (a) CEC, (b) BS, (c) pH H2O, and (d) Al saturation modeling. Remarks: Norm represents a normalized value that is used for
feeding the neural network.
The exchange complex in Ultisol in the tropical region is largely provided by clay fraction rather than organic material due to a low organic matter content (e.g., Armanto, 2019; Table 1). The clay mineral of tropical Ultisol commonly is kaolinitic, having a low CEC to clay ratio (Ch’ng et al., 2015). This study amended
manure to a more mixed mineralogies of Ultisol (Table 1), which possibly had non-linear relationships with the covariates. The increase in CEC by increasing organic material fraction in the soil is already included in the model (i.e., type, rate, time, organic-C). Without generalizing all soil studies, this study found that the
Open Access 3556 linear method is still convenient to predict BS, pH H2O
and Al saturation in Ultisol as affected by manure type, rate, and incubation time. Moreover, modeling CEC requires more sophisticated approaches, in this case, assigning related covariates and utilizing ML algorithms. Figures 6 and 7 depicted the fourth hypothesis partially that amending Ultisol with animal
manures could affect its properties. Generally, the incubation time was the most influencing factor governing the entire soil parameters in manured Ultisol compared to other manure treatments and all covariates. Furthermore, incubation time possessed notable relative importance than other covariates in pH H2O and Al saturation models.
Figure 6. Variable importance (expressed as % relative importance) comparison among (1) MLR, (2) TR, (3) RF, (4) GBM, and (5) MLP based on (a) CEC, (b) BS, (c) pH H2O, and (d) Al saturation modeling.
0 20 40
Rate Exch_K Exch_Na Exch_H Type Exch_Al Exch_Mg Exch_Ca C_Org Time
0 10 20 30
CEC Type Exch_H Rate Exch_K Exch_Na Time Exch_Al Exch_Mg
Exch_Ca 1b
0 20 40 60
Type Exch_Al Exch_Ca Exch_Na Rate
Time 1c
0 20 40
Type Exch_K Exch_Na Rate Time
BS 1d
0 10 20 30
Rate Exch_Ca Time Exch_Na Type Exch_K Exch_Al Exch_Mg Exch_H
C_Org 2a
0 20 40
Exch_H CEC Type Time Rate Exch_K Exch_Na Exch_Al Exch_Mg
Exch_Ca 2b
0 20 40 60
C_Org Rate Exch_K Exch_Ca Exch_Al
Time 2c
0 10 20 30
Rate Type Exch_H Exch_Na Exch_Ca
BS 2d
0 10 20 30
Exch_Al Rate C_Org Exch_H Type Time Exch_Ca Exch_Mg Exch_Na
Exch_K 3a
0 10 20 30
CEC Type Exch_H Rate Time Exch_Na Exch_K Exch_Al Exch_Mg
Exch_Ca 3b
0 20 40 60
Type Exch_Al C_Org Exch_K Exch_Mg BS Exch_Na Rate Exch_H Exch_Ca
Time 3c
0 20 40 60 80 Type
C_Org Exch_Al Exch_K Rate Exch_Mg BS Exch_Na Exch_Ca Exch_H
Time 3d
0 5 10 15 20
Type Time Rate Exch_Na Exch_Ca Exch_K Exch_Mg Exch_Al C_Org
Exch_H 4a
0 20 40 60
Type Time Rate Exch_H CEC Exch_K Exch_Na Exch_Al Exch_Mg
Exch_Ca 4b
0 10 20 30 40 Type
BS Rate Exch_Na C_Org Exch_K Exch_Al Exch_Ca Exch_Mg Exch_H
Time 4c
0 10 20 30
Time Type Rate Exch_Na Exch_Ca Exch_K pH_H2O Exch_Mg C_Org Exch_H
BS 4d
0 50 100
Exch_H Rate Exch_K Exch_Na Type Exch_Mg Time C_Org Exch_Al
Exch_Ca 5a
0 10 20 30
Rate Time Type_1 CEC Exch_H Exch_K Exch_Mg Exch_Na Exch_Al
Exch_Ca 5b
0 10 20 30
Type_1 Exch_Al Exch_Mg Exch_Ca C_Org Exch_Na Rate Exch_K BS Exch_H
Time 5c
0 10 20 30
Exch_K pH_H2O Exch_Na C_Org Exch_Mg Rate Time Exch_Ca Type_1 Exch_H
BS 5d
1a
Open Access 3557 Figure 7. Relative variable importance among the entire models and their averaged value.
These findings agreed with earlier classical approaches, such as analysis of variance and multivariate analyses (Table 3; Figures 4 and 5). As mentioned in the previous studies (Huang et al., 2017;
Muktamar et al., 2020; Wongsaroj et al., 2021), the variability of manure source and quantity affects the biochemical process in Ultisols was supposed to cause an imbalance in decomposition and nutrient release.
However, it was not observed in this study, as shown by reduced controls of manure type and rate over CEC, BS, pH H2O, and Al saturation. Hence, an extended observation period must be concerned, or more matured manure is used to generate compelling power of both treatments.
Conclusion
The combination of many statistical methods in evaluating the effects of animal manure type, rate, and incubation time on Ultisol revealed various results.
Most of them exhibited strong relation among three applied factors with many soil chemistry and fertility parameters. Divergently, general relationships based on multivariate analysis denoted that amending Ultisol with different manure types (cow, chicken, and goat) and rate (0-7.5% of the dry weight of soil) might possess lower strength than the incubation time.
Generally, ameliorating Ultisol based on the entire factors failed to reach the BCSR’s ideal soil, except for K saturation and Mg:K ratio. Similar patterns were observed based on the national SLAN’s sufficiency criteria, except for goat manure and higher rate.
However, the global SLAN approach has relatively unconstrained criteria, allowing all soil fertility parameters from the entire factor to gain sufficiency.
Advanced modeling used in this study demonstrated the capability of ML to model and predict soil properties, as well as supported the findings of the classical linear approach. This study encourages the usage of MLR as a convenient method to model BS, pH H2O, and Al saturation in Ultisol as affected by manure type, rate, and incubation time. In
this case, CEC modeling requires more sophisticated approaches, which require covariates involvement and ML utilization.
Acknowledgement
The authors would like to thank the Department of Soil Science and Land Resources, Faculty of Agriculture, IPB University for their help in using the laboratory for soil analysis.
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