OIL PALM BIOENERGY CROPPING: THE CHANGE of SOIL QUALITY

OIL PALM BIOENERGY CROPPING: THE CHANGE of SOIL QUALITY

Abstract— Soil quality changes according to the use and management as well as specific inherent quality of soil. This research was conducted to study the changes of soil quality of corn field to oil palm plantation in Ultisols and Oxisols. The location, Panyipatan District Tanah Laut Regency South Kalimantan Province was determined based on climate, soil, and plants using stratified purposive sampling procedure. Soil quality was evaluated quantitatively. The soil quality index (SQI) was calculated using SMAF which consists of three stage procedures, i.e.  soil indicators selection to build minimum data set (MDS); soil quality indicators interpretation using curve-fitting model, (Curve Expert Version 3.1; 6.1.3) which measured indicators of MDS were transformed to scores; and integration of soil quality indicators to calculate SQI. The MDS of soil quality indicators of study sites included bulk density (BD), available water content (AWC), soil pH, phosphate (P-available), soil organic carbon (SOC), and aggregate stability (AGG). The results showed that SQI values of bioenergy cropping were decreased, from 71; 74.40; and 70.05 of corn field SQI to 64; 66; and 62 of bioenergy cropping filed SQI.

 Keywords— Soil quality; Soil quality changes; Corn feed plant; and Oil palm bioenergy cropping.

I.      Introduction

The concept of soil quality has emerged since the 1990s (Karlen et al., 1997). The concept of soil quality emphasizes that the soil serves not only for crop production but also for the function of ecosystem services. Maintaining soil productivity is not only for plants production and reduce soil degradation including erosion problems, but also for maintenance of the environment. Definition of soil quality, which is inferred from some definitions, is the soil's capacity to function to maintain biodiversity in and above ground; maintain continuity of cycles of water, air, and nutrients; maintain the productivity of animals and plants; support the health and well-being (Karlen et al., 1997; USDA-NRCS, 2004; 2009; 2010)..

SQ has both inherent and dynamic quality. SQ assessment related mostly with dynamic SQ, which is SQ changes that depending on how it is managed and soil inherent itself.  Over time, together with treatments, SQ could improve, stable, or decline. Therefore, SQ is necessitated to be monitored frequently. SQ cannot be measured directly. It needed indicators to be evaluated. Assessment SQ indicators should be done simultaneously, for the SQ is an integration of soil physical, chemical, and biological quality. Not all of soil parameters can act as SQ indicators. Result of selection attributes of soil into a data packet is called the minimum data set (MDS). Integration of the overall indicator of MDS is called soil quality index (SQI). MDS is defined as a limited number of biological, chemical, and physical indicators that together give an overall measure of SQ, (USDA-NSCR, 2009). The objectives of this study are to examining the SQ dynamic due to land use change in upland corn fields changed to oil palm cropping system.

II.   MATERIAL AND METHOD

A.    Materials

Location of the study, Panyipatan District Tanah Laut Regency South Kalimantan Province was determined based on the purpose of research and site-specific factors, such as climate, soil, and plants in accordance with stratified purposive sampling procedures. Using stratified purposive sampling, it was chosen Ultisols and Oxisols which consist of three nearly level upland locations stratified based on the differences of soil subgroup, Tipyc Hapludoxs (TH), Plinthic Kandiudults (PK), and Plinthic Hapludoxs (PH) in corn field of Panyipatan District Tanah Laut Regency South Kalimantan Province. But then, after 10 years the corn filed was changed to oil palm cropping (Figure 2).

 

Corn field	Oil palm bioenergy cropping

Fig. 1  Corn field changed to oil palm bioenergy cropping in Bumi Asih, Payimpatan.

 

To see the SQ, soil were sampling in those two land use areas and were analyzed in the laboratory according to standard procedures (Kertonegoro et al., (1998), Lambert et al., (1993), and Chemical Laboratories Staff (1995). Soil moisture content (WC, %): is the soil mass before and after drying, measured using gravimetric method. Available water capacity (AWC, %): is a picture of the amount of water stored by soil, which is available for plants. AWC is the amount of water stored between the permanent wilting point and field capacity. Texture is the fraction of particles of sand, silt and clay, obtained using Bouyoucos hydrometer method. Bulk density (BD, g cm-3), is the ratio between the mass of soil by volume. Water-filled pore space (WFPS %) was calculated using the assumption of soil particle density of 2.65 g cm-3 and BD and soil water content, WC gravimetry. The chemical composition of soil, a standard analysis to measure levels of pH, and nutrients. Level measurements are interpreted in terms of sufficiency or excess, not for a specific plant. Soil acidity (soil pH), obtained by calculating the pH of soil in suspension (H2O 1: 5) was measured using a pH meter. N-total (%), calculated using micro-Kjeldahl method. Soil organic carbon (SOC,%), determined based on the amount of oxidized C , P –total( mg 100 g -1) and K –total ( mg 100 g -1) is determined by using 25% HCl. P available, Pbray (ppm) extracted under the influence of soil pH, by Bray method. Determination of exchangeable cations (me %), Ca-exch, Mg- exch, K-exch, and Na-exch, measured in extracts of neutral ammonium acetate (NH 4 Ac pH 7) with flamefotometer (K and Na) and AAS (Ca and Mg). Soil organic matter (SOM, %): calculated based on levels of C, assuming carbon C amounted to 48-58% of the total weight of soil organic matter. According to Gregorich et al. (1997) total organic C and N is also a rough measurement of soil biological attributes.

A.    Methods

1)            Determining MDS:  The MDS is determined through intercepting the indicators resulted from two different methods, those are statistical step by step data reduction (Brejda et al., 2000a;200b) and expert opinion of SMAF (Table 1). Statistical analysis was performed using UNIVARIATE, GLM, and PFA of SAS 9.1.3 (SAS inst. Inc, 2003). Since SQ is an integration of soil physical, chemical, and biological characteristics, the soil attributes are highly correlated. Therefore, determining SQ indicators may be achieved by examining soil attributes simultaneously. Multivariate statistical analysis is considered accurate because provides techniques for simultaneously analyzing correlated variables (Brejda et al., 2000a). In this study, multivariate analysis of variance (MANOVA) was first used to determine whether there were significant management effects on the soil variables.  Wilk’s lambda and F statistic were examined to test the (null) hypothesis of no overall treatment effect.  Variables with p value < 0.06 and CV < 40 were retained for further factor analysis used Principal factor analysis (PFA).  The soil variables retained was examined to test the (null) hypothesis of no common factors. Factors with eigenvalue > 1 were retained for interpretation using rotation to maximize the relationship among interdependent soil variables (Brejda et al., 2000b). (Andrews et al., 20014). The SMAF follows three basic steps, those are indicators selection, indicator interpretation, and integration to SQ index value. In this step, indicator selection, SMAF uses a series of decision rules to generate MDS. The selection criteria of decision rules are the management goals associated soil functions, like maximize productivity, waste recycling, or environmental protection; and site-specific factors, like region or crop sensitivity (Table 1).

TABLE I
POTENTIAL MANAGEMENT GOALS AND ASSOCIATED SOIL FUNCTIONS AND SOIL QUALITY INDICATORS

Source: Modified Table 1 and Table 2 of Andrews et al., 2004; †  Winhold et al., 2009.

Management goals	Supporting soil function	Indicator
Productivity     Waste recycling     Environmental protection	Nutrient cycling	P, pH, and TOC
	Water retention	AWC, BD, pH, and TOC
	Physical stability and support	BD, AGG, and TOC
	Filtering and buffering	-
	Resistance and resilience	TOC
	Biodiversity and habitat	TOC  and WFPS†

 

These Table 1 rules serve as an expert system to select appropriate SQ indicators (Bellocchi et al., 2002; Andrews et al., 2004).  The user is asked to select four to eight indicators consist of at least one indicator from each function. In this study only six indicators are selected (Makalew et al., 2010; Makalew, 2011).

The indicators resulted from these two methods were then intersected to determine the MDS of the study area.

2)            Indicator interpretation: Interpretation of indicators includes transformation of each MDS indicator to unit less value using nonlinear scoring curve (Karlen and Scott, 1994; Andrews et al., 2004). It is assumed that the relationship between a given indicator and the soil function(s) it represents hold relatively constant among system. This relationship determines the shape of curve or the algorithm equation of an indicator.  The curve shapes (Figure 2) include upper asymptotic sigmoid curve (more-is better), lower asymptote (less-is-better), and mid-point optima (Gaussian function). The curve shapes were determined by the literature review and consensus of collaborating researches.

 

Curve of more-is-better	Curve of less-is-better	Curva of mid-point-optima

Fig. 2 Curve non linear, standard scoring function

                         Source: Gugino et al. (2009).

The algorithms were constructed using a curve fitting program, Curve Expert V. 1.3; V6.1.3. Bulk density (BD) is assigned a less-is-better function because of the inhibitory effect on root growth and porosity (Grossman et al., 2001). Available water capacity (AWC) is more-is-better function based on its roles of water availability for crop requirement, nutrient solubility, and biological activity (Gregory et al., 2000). Total organic carbon (TOC) is more-is-better function based on its roles in soil fertility, water partitioning, and structural stability (Herrick and Wander, 1998).

 

TABLE II

ALGORITHM AND LOGIC STATEMENTS FOR INTERPRETATION OF MDS INDICATORS

Indicator	Scoring Algorithm	Fixed parameter	Site specific factor
BD	Y = a- b*exp (-c xd)	a=0,094	b,c,d= ƒ(texture, mineralogy)
AWC	y= a + b* cos (c*x + d)	a=0,477;  b=0,52675; c= 6,87765	d= ƒ(texture,OM)
TOC	y = a / 1+b * exp –c * TOC	a=1; b=50,1	c=ƒ(iOM, texture, climate)
pH	y = a * exp ((-(x-b)2) / 2*c2)	a=1,0	b,c= ƒ(crop)
Test P	Y = (a*b + c*(soil P * test factor d) / (b + (soil P * test factor)d)	a=9,26.106;  c=1,0; d=3.06	b=ƒ(crop,TOC, texture,test P)

*Fixed parameters are those parameters in the algorithm that do not change when CurveExpert data processing. BD=bulk density (g.cm-3); AWC= available water capacity (g.g-1); pH= (-log H+); TOC=   total organic Carbon (%); Test P=Pbray1 (mg.kg-1)

Source: Andrews et al., 2004.

 

Variation of mid-point optimum or Gaussian function was used for soil pH based on crop sensitivity and effects on nutrient availability (Smitt and Doran, 1996).  The curve of soil P (Phosphor), according to Maynard and Pierzynski (Andrews et al., 2004) is mid-point optimum based on crop response and environmental risk. The SMAF assumed that the expected range for each indicator will vary according to site-specific controlling factors, such as climate or inherent soil properties. Each SMAF scoring curve consist of an algorithm or logic statement (e.g. if, then, else) in Table 2. The algorithms are quantitative relationships between empirical values of measured indicators and normalized scores, reflecting the performance of ecosystem service(s) or soil function(s).  An indicator score of 1 represents the highest potential function for that system.

 

1)             Integration into an idex: The SMAF integrates all indicator scores. This value is considered to be an overall assessment of SQ, reflecting management practice effects on soil functions. The equation (1) of soil quality index (SQI) is as follows,

 

                          (1)

 

 

 

The SQI is soil quality ndex, S represents the score indicator value and n represents the number of indicators in the MDS

I.      RESULT AND DISCUSSION

A.    Soil Characteristics and Site-Specific Factors

The result of site description shows that the soil study sites consist of Oxisols and Ultisols with soil slope is 0-3%, dominated by kaolinite clays formed in igneous intrusive ultramaphic rock (serpentinite) and clay rocks (CSARD, 2001). In general, the effects of temperature and high rainfall, have undergone a process of further development and included in the category that have low fertility class. The thickness was very deep (> 120 cm) with low to moderate permeability, and specific capacity of soil to hold cations is very weak (Oxisols with CEC <16 meq 100 g -1 (NH4Oac, pH = 7). High Fe content also indicates that this land has experienced a long period in its formation. With the the process clay illuviation, Ultisols formed has a pH < 5. As a result of very fast humus mineralization process, this soil has low soil organic matter content and low base saturation (Sunarminto, 2000; van Wambeke, 1974). Other important features that appear in both the soil formation processes are that the formation of gravel plintite. May be due to the slow movement of water in a relatively long time in the soil profile (there are wet - dry alternately). The high-level weathering, high iron conditions, and limitations of drainage conditions support the formation plintite. This whole process, with the percentage of clay change according to depth, making the study sites have subgroup Typic Hapludox Oxisols Order, Ultisols Order Kandiudult Plinthic subgroup, and subgroup Plinthic Hapludox Oxisols Order that all three sub-groups has fine-textured, soils reaction acid to very acid, low CEC, and low organic matter.

Based on soil functions associated with the land development sites, it was determined 22 biophysical attributes of soil for corn land consisting of physical, chemical, and biological soil attributes, namely the variable texture (Sand (%), silt (%), clay (%) , bulk density (g cm-3), available water capacity, AWC 1 / 3 bar -15 bar (%), CEC (cmol (+) kg-1), pH (H2O 1: 5), P2O5 (HCl 25 %). K2O (HCl 25%), Pbray1 (ppm), K-exch. (cmol (+) kg-1), Nattk (cmol (+) kg-1), Ca-exch. (cmol (+) kg-1), Mg-exch. (cmol (+) kg-1), total cations (cmol (+) kg-1), Al +3 (cmol (+) kg-1), Fe +3 (ppm), soil organic carbon (SOC) (%) N total, (%), N-NO3 (ppm), and variable N-NH4 + (ppm). Furthermore, of the 22 attributes of biophysical soil 17 variables were included in the dynamic nature of soil namely bulk density (g cm-3), available water capacity, AWC 1 / 3 bar -15 bar (%), pH (H2O 1: 5), P2O5 (HCl 25%). K2O (HCl 25%), Pbray1 (ppm), K-exch. (cmol (+) kg -1), Na-exch. (cmol (+) kg-1), Ca exch. (cmol (+) kg-1), Mg-exch. (cmol (+) kg-1), total cations (cmol (+) kg-1), Al +3 (cmol (+) kg-1), Fe +3 (ppm), soil organic carbon (SOC) (%), N total, (%), N-NO3 (ppm), and variable N-NH4 + (ppm) . Variables significantly influenced by the environment is bulk density (g cm-3), available water capacity, AWC 1 / 3 bar -15 bar (%), pH (H2O 1: 5), P2O5 (HCl 25%), K2O (HCl 25 %), Pbray1 (ppm), Ca-exch. (cmol (+) kg-1), Mg-exch. (cmol (+) kg-1), total cations (cmol (+) kg-1), soil organic carbon (SOC) (%) , and variable N total (%).

A.    Statistical Factor Analysis

The correlation matrix, mean values, standard deviation the eigenvalues resulted from PROC FACTOR (not shown).  In general, the correlations among the soil variables are moderate positive, no high frequency of correlation showed.  There are four eigenvalues which together account for 78.14 % of the standardized variance.  Four factor retained on the basis of the eigenvalue-greater-than-one-rule is rotated (equamax) (Table 4).

 

TABLE IV

ROTATED FACTOR LOADINGS AND COMMUNALITIES OF 11 SOIL VARIABLE RETAINED:                 SOIL P, AWC, TOC, BD, PBRAY, PH, CA, TOTAL CATION, K, MG, AND N

Factor	Factor1	Factor2	Factor3	Factor4	Commu-nalities
Eigen-value	2.221365	1.946628	1.914142	1.102425
P	0.78716	0.07635	0.26847	0.10431	0.708413
AWC	0.70139	0.09426	0.19087	-0.42472	0.717644
TOC	0.59549	0.25395	-0.37194	0.27453	0.632814
BD	-0.75234	-0.16482	0.05546	-0.11908	0.610432
Pbray	0.04395	0.75074	-0.15652	0.36077	0.720198
pH	0.13713	0.73395	0.37234	-0.08670	0.703643
Ca	0.23640	0.72302	-0.30747	-0.02020	0.673590
Total cation	0.30809	0.43826	0.41362	-0.23515	0.513365
K	0.06706	0.02911	0.80041	0.05200	0.648697
Mg	-0.03058	0.14087	-0.76655	0.33151	0.718283
N	0.11045	0.04934	-0.08494	0.71808	0.537481

 

Based on Table 4, the four factors and soil attributes comprised can be described in Table 5.

TABLE V

SQ FACTORS AND THE SOIL ATTRIBUTES THAT COMPRISE THESE FACTORS

Factor 1	Factor 2	Factor 3	Factor 4
P (soil)	Pbray (Fertility)	K (Fertility)	N (Organic matter)
P	Pbray1	K	N
AWC	Soil pH	Exch. Mg
TOC	Exch. Ca
BD

 

The PFA result showed that there were four factors of soil quality with their components, i.e. soil development factor with composition of Ptotal (%), AWC (%), SOC (%) BD (g cm-3); soil fertility factor with composition of Pbray (ppm), pH, Ca-exch. (cmol (+) kg-1); soil fertility factor with composition of K (%), Mg-exch. (cmol (+) kg-1 ); soil organic matetr factor consists of N (%).

The indicators decided to be included in MDS (SMAF) are BD, AWC, TOC, soil pH, and soil Pbray1.  Combining the two methods, the MDS indicators offering follows Table 6.

 

TABLE VI

 MINIMUM DATA SET  BASED ON MANAGEMENT GOAL AND SITE-PECIFIC FACTOR (SMAF) AND FACTOR ANALYSIS OF PFA

Soil function	Productivity	Environmental protection	Indicator
Nutrient cyclle	√	√	pH, P, N, K
Water retention	√	√	AWC, BD, pH
Physical stability and support	√	√	BD, pH
Filtering and buffering	-	√	BD, soil P, TOC
Resistence and resilience	√	√	TOC

 

BD related to physical stability and support for plants, affects root growth and porosity.  AWC related to ability of soil to retain water and make it sufficiently available for plant uses. AWC is also used to develop water budgets, predict droughtiness, design and operate irrigation systems, design drainage systems, protect water resources, and predict yields (USDA-NRCS, 2008). Organic matter affects AWC by increasing soil’s ability to hold water.

At field capacity, organic matter has a higher water holding capacity than a similar volume of mineral soil. At the permanent wilting point the water held by organic matter is also higher. Organic matter can improve soil structure and aggregate stability, resulting in increased pore size and volume. Soil pH influences the solubility of nutrients and affects the activity of micro-organisms responsible for breaking down organic matter and most chemical transformations in the soil. Soil pH thus affects the availability of soil nutrients.

The MDS indicators observed, indicator scored, and SQI measured with Equation (1) of corn field and oil palm bioenergy cropping of Bumi Asih Panyimpatan could be seen in Table 7 and Figure 5.

 

TABLE VII

SOIL QUALITY MEASURED, SOIL QUALITY SCORED, AND SOIL QUALITY INDEX (SQI) OF CORN FIELD

Indicator	CLTH		CLPK		CLPH
	Observed	Scored	Observed	Scored	Observed	Scored
BD	1.08	0.98	1.20	0.79	1.23	0.72
AWC	15.00	0.66	9.78	0.49	12.71	0.59
TOC	2.55	0.70	2.21	1.00	1.88	0.41
pH	4.46	0.77	4.19	0.57	4.33	0.62
Pbray1	6.39	0.61	6.45	0.62	4.52	0.37
WFPS	0.43	0.75	0,44	0.79	0,39	0,70
Soil Quality Index (equation 1)		75		71		57

 

TABLE VIII

SOIL QUALITY MEASURED, SOIL QUALITY SCORED, AND SOIL QUALITY INDEX (SQI) OF CORN FIELD CHANGED TO OIL PALM PLANTATION

Indicator	Oil Palm		Oil Palm		Corn
	Observed	Scored	Observed	Scored	Observed	Scored
BD	1.00	0.99	1.00	0.99	1.00	0.99
TOC	1,57	0.27	1.80	0.37	1.88	0.40
pH	4.3	0.60	4.3	0.60	3.9	0.48
Pbray1	5.40	0.50	6.08	0.59	5.60	0.53
WFPS	0.43	0.82	0.40	0.76	0.36	0.68
Soil Quality Index (Equation 1)		64		66		62

 

Table 7 and Figure 4 showed that SQI decreases after the change of land use from corn field to oil palm bioenergy cropping from 75 and 71 to 66 and 64; whilst without changing the land use, continues corn, the SQ seems increases from 57 to 62.

 

Fig. 3 Soil Quality Index (SQI) of six different lands used for Corn and Oil Palm.

 

The changes could be initiated with decreasing of organic matter, pH, and soil P. In other area of corn field, the SQ increases. The increasing of soil pH and soil P make the SQI better.

I.      CONCLUSION

SQ of corn field decreased after changed to oil palm bioenergy cropping; but increased in long time corn field land. SMAf could be best tool to assess SQ quantitatively An easy way to comply with the conference paper formatting requirements is to use this document as a template and simply type your text into it.

ACKNOWLEDGMENT

The Author would like to thank to the Government of South Kalimantan Province for the fud research, the Director of General of Higher Education of Indonesia (funded under Fundamental Research Fund), and the Rector and the Principal of Research Institution of Lambung Mangkurat University Indonesia

 

 

 

 

B.    References

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