Kasetsart J. (Nat. Sci.) 41 : 8-14 (2007) Predicting Phosphorus Buffer Coefficients of Thai Flooded Rice Soils Using Soil Properties Najphak Hongthana, Tasnee Attanandana* and Jongruk Chanchareonsook ABSTRACT To quantify a more precise phosphorus (P) requirement prediction by the phosphorus decision support system (PDSS), the most important factor in PDSS equation, the phosphorus buffer coefficient (PBC) was studied. The use of clay content as a predictor of PBC prediction might not be suitable when applied with Thai flooded rice soils. Ten flooded rice soils were collected to study the PBC estimated by Bray-2 (PBC Bray-2 ), Mehlich-1 (PBC Mehlich-1 ) and iron oxide-impregnated filter paper method or Pitest (PBC Pi-test ). Multiple regression was used to study the relationship between PBC and soil properties which involved P sorption process. The results indicated that PBC Bray-2 correlated with organic matter and iron determined by citrate-bicarbonate-dithionite method (Fe d ) (adjr 2 =0.87) whereas PBC Mehlich-1 correlated with Fe d (adjr 2 =0.73). Unlike those two chemical extractions, PBC Pi-test correlated with iron determined by ammonium oxalate ph 3 in darkness (Fe o ), aluminum determined by citrate-bicarbonatedithionite method (Al d ) and soil ph (adjr 2 =0.93). However, the study suggested that the PBC should be estimated by Pi-test so that the model would allow more accurate PBC prediction. Key words: phosphorus buffer coefficient, phosphorus sorption, flooded rice soil, iron oxide-impregnated filter paper method INTRODUCTION In Thailand, phosphorus (P) fertilizer recommendations given to farmers are very general. The phosphorus decision support system (PDSS) developed by Yost et al. (1992) is adopted to establish the site-specific P fertilizer recommendation so that the cost of crop production and the environmental impact are reduced. Several soil factors are considered in P prediction process. However, uncertainty analysis indicated the largest source of uncertainty in prediction was the phosphorus buffer coefficient (PBC), an increase of extractable P per unit of applied P (Yost et al., 1992). PBC is crucial in that it is used to calculate how much P fertilizer must be added to keep the extractable P at a level sufficient for desired yield. At present, PBC is predicted by equation that relates PBC with soil clay content percentage as proposed by Cox (1994). The use of clay to predict PBC has been suggested for soils of similar mineralogy. Nevertheless, it has not been satisfactory for soils with a wide range of soil mineralogy but relatively Department of Soil Science, Faculty of Agriculture, Kasetsart University, Bangkok 10900, Thailand * Corresponding author, e-mail: agrtna@yahoo.com
Kasetsart J. (Nat. Sci.) 41(5) 9 high clay content such as flooded rice soils of Thailand. Although Wang et al. (2000) proposed the inclusion of soil physical properties such as P sorption site density and reactive mass so that the PBC prediction would be more effective, the procedure used in such analysis is costly as well as impractical for measuring in routine laboratory. Consequently, the study of simpler approach is required. Since the extractable P used in all variables in the P prediction equation is needed to come from the same soil P testing method, estimation of PBC by numerous methods would provide the prospective users with several alternatives. In Thailand, Bray-2 extraction is widely used to estimate available P. The second most popular extractant also used in Kasetsart University is Mehlich-1, a dilute double acid extractant. Both of these methods have been found to correlate well with total P uptake of maize (Attanandana et al., 1999). A major disadvantage in using extracting solutions, however, is that they may mobilize not only plant-available P but also some otherwise stable and non-labile soil P. A new and innovative method that does not have the limitation of extracting solution is iron oxideimpregnated filter paper method (Pi-test) (Sissingh, 1983). The Pi-test is unique in this respect because the iron oxide-impregnated filter paper does not react with soil but act as a sink for soil solution P. More recently, Hosseinpur and Ghanee (2006) found that the amount of P extracted by Pi-test significantly correlated with P uptake, unlike those extracted by Olsen, Cowell, Mehlich-1, 0.01 M CaCl 2, AB-DTPA and 0.1 M HCl. The objective of this study is, therefore, to relate the PBC estimated by Mehlich-1, Bray-2 and Pi-test with soil properties which involve P sorption process using stepwise multiple regression so that the user will be provided with certain options. MATERIALS AND METHODS Ten flooded rice soils, representing the major flooded rice soils of Thailand, were selected for these experiments. They were Chachoengsao (Cc), Bang Nam Prieo (Bp), Rangsit (Rs), Nakhon Pathom (Np), Nan (Na), Phan (Ph), Doem Bang (Db), Sena (Se), Saraburi (Sb), and Si Thon (St) soil series. Surface soil samples (0-10 cm depth) were collected from each location in Thailand, airdried, ground to pass a 2-mm sieve, and thoroughly mixed. Samples of each were analyzed for soil properties (Table 1). Phosphorus buffer coefficients of flooded rice soils Each soil sample was amended with potassium dihydrogen phosphate (KH 2 PO 4 ) at the rate of 0, 25, 50, 100, 200, and 400 mg P kg -1. The soil mixtures were then flooded with 25 ml of deionized water and incubated at 30 C for 2 weeks. The extractable P was analyzed by three methods: Bray-2, Mehlich-1 and Pi-test. Iron oxide-impregnated filter paper method (Pi-test) FeO paper preparation: The 5.5-cm disc of ash free, hard filter paper Whatman no.50 (2.7 µm pore openings) as proposed by Myers et al. (1995) was immersed in acidified 0.65 M FeCl 3 for 5 min (Chardon, 2000). Let the paper drip dry at room temperature for 1 hr. The paper was then treated with NH 3 vapor through a bath containing 5% NH 4 OH until the paper color changed from yellow to brown (Chardon et al., 1996). Since NH 4 Cl could lower ph level, causing the increase of P concentration in soil solution, the FeO paper was washed with deionized water several times to get rid of NH 4 Cl. After being air-dried, the FeO paper was kept in the closed pack. Shaking soil suspension with FeO paper: One gram of soil was shaken with 40 ml of 0.01
10 Kasetsart J. (Nat. Sci.) 41(5) M CaCl 2 solution. One FeO paper protected by polyethylene screen (0.1 mm opening) in a fixed position was shaken with soil suspension on a reciprocating shaker at a speed of 130 excursions/ min for 16 hrs. The paper was taken out and thoroughly rinsed with deionized water to remove adhering soil particles. Determination of P extracted by FeO paper: The FeO with adsorbed P was dissolved by shaking in 40 ml of 0.1 M H 2 SO 4 for 1 hr. Note: Since the soil mixtures were flooded with deionized water, the concentration of extracting solution used in each soil P testing was adjusted to be consistent with the method as described. All the extracted P solutions were determined using the method of Murphy and Riley (1962). The PBC was the slope of linear regression equation plotted between added P and extractable P. RESULTS AND DISCUSSION Ten flooded rice soils were Inceptisols and Alfisols, predominantly acidic with soil ph values ranging from 4.2 to 6.5. According to Table 2, the PBC was different among soil series and soil P testing methods. Across soil series, Db and St soil series provided the higher PBC, regardless of soil P testing methods. Possibly, Db and St had the small amount of clay content (17.1% and 7.1%, respectively) so they offered less capacity to adsorb P. Of all soil P testing methods, PBC Bray-2 provided the higher values than PBC Mehlich-1 and PBC Pi-test. Unlike the chemical extractions, the value of PBC Pi-test was quite different from PBC Bray-2 and PBC Mehlich-1. Predicting PBC using soil properties Stepwise multiple linear regression analysis was used to select the soil properties Table 1 Soil properties. Soil properties Soil series Bp Rs Np Cc Ph Na Sb Se Db St Texture 1 C C L L C C C C SL SL Clay 1 (%) 54.9 40.9 20.9 20.9 50.9 50.9 55.1 59.1 17.1 7.1 ph 2 4.2 4.2 5.7 5.3 4.4 5.5 6.0 4.6 6.5 4.9 OM 3 (g kg -1 ) 20 17 34 25 26 18 42 42 14 3 CEC 4 (cmol c kg -1 ) 18.3 21.0 27.0 21.8 13.6 9.6 24.6 21.3 4.8 1.9 P 5 (mg kg -1 ) 5.8 15.8 25.0 25.5 2.1 22.5 32.7 22.3 7.0 1.5 P 6 (mg kg -1 ) 2.3 5.4 2.8 3.6 0.5 5.2 2.5 3.0 3.6 0.8 P 7 (mg kg -1 ) 0.8 2.2 7.3 5.2 1.5 7.7 10.9 5.4 2.2 1.6 Fe 8 d (g kg -1 ) 14.73 8.81 9.75 8.89 11.50 11.19 7.41 9.88 3.79 1.95 Fe 9 o (g kg -1 ) 5.51 5.55 6.99 6.20 7.58 7.35 6.22 9.89 1.14 0.56 Al 8 d (g kg -1 ) 0.86 0.81 0.43 0.47 0.30 0.13 0.56 0.03 0.27 0.05 Al 9 o (g kg -1 ) 1.27 1.39 1.26 0.92 0.62 0.39 1.53 0.72 0.42 0.20 1 hydrometer method 2 glass electrode (soil: water 1:1) 3 Walkley and Black titration (Walkley and Black, 1934) 4 NH 4 OAc, ph 7, replacement method 5 Bray-2 (0.1 M HCl+0.03 M NH 4 F, soil-to-solution 1:5, 1 minute of shaking time) 6 Mehlich-1 (0.05 M HCl+0.0125 M H 2 SO 4, soil-to-solution 1:10, 5 min of shaking time) 7 Pi-test (Myers et al., 1995; Chardon et al., 1996; Chardon, 2000) 8 citrate-bicarbonate-dithionite method (Loeppert and Inskeep, 1996) 9 ammonium oxalate ph 3 in darkness (Loeppert and Inskeep, 1996)
Kasetsart J. (Nat. Sci.) 41(5) 11 which best related to PBC estimated by three different methods. The predictive models were summarized in Table 3. Statistical models of PBC estimated by three different methods showed that there were different independent variables, namely, organic matter (OM), iron determined by citratebicarbonate-dithionite method (Fe d ), iron determined by ammonium oxalate ph 3 in darkness (Fe o ), aluminum determined by citratebicarbonate-dithionite method (Al d ) and soil ph (Table 3). The difference of independent variables in each model was attributed to the effect of soil P testing methods. For PBC Bray-2, OM and Fe d could explain 87% of variability in PBC Bray-2 (model 1) while Fe d alone could explain 73% of variability in PBC Mehlich-1 (model 2). Unlike model (1) and model (2), the inclusion of Al d, Fe o and soil ph could explain 93 % of variability in PBC Pi-test but the explanation of these variables in model (3) were more reasonable according to the reason given below. To best describe, all independent variables in models were categorized into two groups. The first was the Fe and Al effect. As the Fe and Al increased, more soil P was sorbed thus decreasing PBC. The latter was the effect of soil ph. Havlin et al. (2005) reported that soil ph was the most important factor in P sorption. At low ph values, P sorption was largely from reaction with Fe/Al oxide and precipitation as AlPO 4 and FePO 4 while the activity of Fe/Al oxides decreased and P precipitated as secondary minerals of Ca-P or was adsorbed to surfaces of CaCO 3 at higher ph. Likewise, Curtin and Syers (2001) found that the decrease of Olsen-P per unit of increased soil ph could range from 3 to 7 mg kg -1. As a result, PBC decreased with soil ph increase. Also it was not uncommon that there were different forms of Fe and Al occurring in each model. Usually the citrate-bicarbonate-dithionite and the ammonium oxalate extraction were thought to be selective solution for crystalline and amorphous form of Fe, respectively. Bertsch and Table 2 The phosphorus buffer coefficients of the ten flooded rice soils. Soil series Phosphorus buffer coefficient Bray-2 Mehlich-1 Pi-test Saraburi (Sb) 0.9479 0.7513 0.0704 Nakhon Pathom (Np) 0.7917 0.4109 0.0714 Sena (Se) 0.8147 0.6152 0.0765 Chachoengsao (Cc) 0.7493 0.4916 0.0858 Phan (Ph) 0.7077 0.4194 0.1044 Nan (Na) 0.7649 0.5500 0.1347 Rangsit (Rs) 0.6920 0.3735 0.3309 Bang Nam Prieo (Bp) 0.5441 0.3029 0.3386 Doem Bang (Db) 0.9222 0.8577 0.5006 Si Thon (St) 0.9545 0.8810 0.8169 Table 3 Models and coefficients describing the PBC prediction of flooded rice soils. Statistical model adjr 2 (1) PBC Bray-2 = 1.00 + 0.004 OM - 0.04 Fe d 0.87** (2) PBC Mehlich-1 = 0.99 0.05 Fe d 0.73** (3) PBC Pi-test = 1.51 0.13 ph 0.09 Fe o 0.19 Al d 0.93** **significant at the 0.01 level
12 Kasetsart J. (Nat. Sci.) 41(5) Bloom (1996) reported that many times both extractions were assumed to analyze both forms of Al as well. Wiriyakitnateekul et al. (2005) reported that the variability in P sorption by Thai soils could be explained by a combination of Fe d, Fe o, Al d and Al o. However Al d alone was almost as effective in predicting the P sorption capacity of Thai soils. In case of Thai paddy soils, Cholitkul (1968) reported that Fe-P was higher than other forms (approximately 30-40% of total phosphate forms). Zhang et al. (2003) have also confirmed that P availability of flooded rice soils was largely controlled by the transformation of Fe, from crystalline to amorphous form. Khalid et al. (1977), Willett et al. (1978) and Shahandeh et al. (1994) found a close relationship between P sorbed and Fe o, indicating that poorly crystalline and amorphous form of Fe played a primary role in P sorption of flooded soils. The greater surface area of amorphous Fe was responsible for more soil P sorption. Based on this information, Pi-test showed a promising soil P testing method in PBC prediction of flooded rice soils because Fe o variable was merely appeared in model (3). Soil properties were substituted into model (1), (2) and (3) to predict the PBC Bray-2, PBC Mehlich-1 and PBC Pi-test, respectively. The predicted PBC was plotted against observed PBC to study the relationship using 1:1 relationship (Figure 1). The relationship depicted in Figure 1 showed that the variation among extractants is sensitive to soil differences. The extractants rank Bray 2 least sensitive, Mehlich 1 more sensitive and Pi strip, the most sensitive as indicated by the range in PBC observed. The various extractants differed to the extent that PBC could be predicted from the observed values, for which the graph shows that the Pi-strip method predicted the closest. The two chemical extractants produced estimates of the sorption coefficient (the PBC), however, the Pi-strip test is, in effect, a desorption coefficient and is thus more likely to simulate actual dynamics of release of nutrient P to plants. CONCLUSION PBC of flooded rice soils estimated by Bray-2, Mehlich-1 and Pi-test depended on different independent variables, Fe o, Fe d, Al d and soil ph. This was attributed to the effect of soil P testing methods. The study suggested that PBC should be estimated by Pi-test because this method is the estimation of desorption coefficient which is the information of P release to plants. 1 Bray 2 Mehlich1 Pi-test 0.8 predicted PBC 0.6 0.4 0.2 0 0.0 0.2 0.4 0.6 0.8 1.0 observed PBC 0.0 0.2 0.4 0.6 0.8 1.0 observed PBC 0.0 0.2 0.4 0.6 0.8 1.0 observed PBC Figure 1 The observed PBC plotted against predicted PBC. The straight line represents 1:1 relationship.
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