Full Length Research Paper
ABSTRACT
In East Africa, research has indicated that N, P and soil acidity are the major production constraints to common bean production. The optimum pH for bean production in tropical soils ranges from 5.8 to 6.5. But in Uganda, 23% of beans are grown in soils with pH below 5.0. Research conducted on common bean production is mainly about the major nutrients and information about lime requirements to address soil acidity in different soils is patchy. A study was carried out to determine the lime requirements for Phaseolus vulgaris L. production in Cambisols and Umbrisols and this was based on their low soil pH and Ca levels. The lime requirement was determined using titration method and titration curves for each soil type established by titrating 30 g soil in 60 mL 0.01 M CaCl2 (1:2) with 3 mL 0.022 MCa(OH)2 per addition. Results indicate that to raise pH from 5.02 to 6.5, the Cambisol (“Limyufumyufu”) requires 6.1 tonnes of Ca(OH)2 per hectare, while the Umbrisol(“Luyinjayinja”) requires 5.4 tonnes of Ca(OH)2 per hectare to raise pH from 5.26 to 6.5. There is need to address soil acidity in Cambisol and Umbrisol through liming using the lime requirement equations determined in this study. In order to provide growers and farmers with more options for such acid soils,however, plant breeding programs should select or develop germplasm tolerant to Al toxicity and/ or low soil available phosphorus as well.
Key words: Phaseolus vulgaris L., titration, Al toxicity, Cambisol, Umbrisol.
INTRODUCTION
Common bean (Phaseolus vulgaris L.) isestimated to be the second most important source of dietary protein and the third most important source of calories (FAOSTAT, 2012). Common bean is considered a low status food, often referred to as “meat of the poor” (Katungi, 2009) with the annual per capita consumption being higher among low-income people who cannot afford animal protein (Beebe et al., 2013).
Uganda’s bean production is common in the central, eastern and western regions (Sibikoet al., 2013). In the Lake Victoria Crescent agroecological zone, beans are mainly grown on three soil types locally classified as: ‘Liddugavu’’ (Phaoezem, Hapludoll), ‘‘Limyufumyufu’’ (Cambisol, Kandiudalf) and ‘Luyinjayinja’’ (Umbrisol, Hapludoll) (Tenywaet al., 2014). The latter two soils are locally known as having the “lunnyo” condition, which according to local, indigenous knowledge suggests multiple factors limit bean (P. vulgaris, L.) production (Fungo et al., 2010).
Smallholder farmers encounter multiple constraints such as pests and diseases (Beebe et al., 2013), low labour productivity and unreliable climatic conditions (Birachi et al., 2012). Soil related constraints account for about 30% of the widely acknowledged ‘yield gap’ (Folmer et al., 1998; Kapkiyai et al., 1999) that threatens food security.
Among soil related constraints, low extractable phosphorus, nitrogen, and high soil acidity associated with aluminium and manganese toxicity (Lunze et al., 2012) are the major soil fertility problems associated with the “lunnyo” soils. Soil pH strongly influences the availability of nutrients in the soil, the activities of soil microorganisms, plant growth and yield (Anderson et al., 2013). The optimum pH for bean production in tropical soils ranges from 5.8 to 6.5 (Edmeades et al., 2012). However, most of the soils in Sub Saharan Africa are acidic and possess high phosphorus-fixing capacities (Nziguheba, 2007).
The major options for improving soil fertility include use of wood ash, crop residues and manures, but they vary widely in quantity and quality (Ebanyat, 2009). Inorganic fertilizers are highly nutrient concentrated, but at times they give no yield response when applied where soil acidity is severe (Fageria and Baligar, 2008).The lime requirement to address soil acidity in different soils is not known. Therefore, this study was conducted to determine the lime requirements for bean production in the extensive local soils “Limyufumyufu” and “Luyinjayinja”.
MATERIALS AND METHODS
Study soil collection and preparation
The two soils known to have the “lunnyo” condition were collected from farmers’ fields in two representative communities (Mukungwe and Lwankoni) in Masaka district located in Central Uganda at 31.7361°E latitude and 0.34111°S longitude). These soils were selected for study based on a series of farmer meetings from three communities, which indicated that these two soils are important, farmer-recognized soil series for common bean production. In addition, the farmers indicated that bean production on the two soils was problematic and therefore were considered to have the “lunnyo” condition or characteristic. Soil analysis results (Table 1). indicate that, indeed, the two soils are low in soil pH and the Limyufumyufu is low in soil Ca. KCl-extractable Al, a measure of level of toxic aluminium, was high in the Limyufumyufu suggesting another probable reason that Al-sensitive bean (P. vulgaris, L.) was known by farmers to grow poorly in this soil. The two soil types(Limyufumyufu and Luyinjayinja) classified in the FAO Mapping Legend as Cambisol and Umbrisol, respectively). They are classified in the US Soil Taxonomy as “TypicKandiudalfs” and “TypicHapludolls”, which, for the former suggests highly weathered and the latter, less weathered status, respectively. Soil samples were obtained in a zig-zag pattern at ten locations within each field, from a depth of 0 to 15 cm.
A composite sample of about 25 kg soil was obtained from an area of approximately 50 m x 100 m for each soil. Soil was taken for laboratory analyses. Soil samples were air dried in a dust free area, and crushed with a mortar and pestle to pass a 2-mm sieve.
Laboratory analyses for soil samples
Total soil organic carbon was determined by dry combustion method using total organic carbon analyzer(American Society for Testing and Materials, 1994).Extractable P was determined using the Olsen method (Kuo, 1996). Exchangeable K+, and Na+ were determined using a flame photometer, while Ca2+ and Mg2+ were determined using an atomic absorbance spectrometer. The exchangeable cations were extracted from the samples by shaking for 16 to 24 h with 100 ml 2 MNaClfor 0.5 to 2.5 cmol/kg of exchangeable cations (Clark, 1965). Soil pH was measured in a 1:2.5 soil to water ratio using a pH electrode. The Kjeldahl method was used to determine total N (Bremner, 1965). Micronutrients were extracted in the Mehlich 3 extractant solution (Mehlich, 1984). The micronutrients Cu, Mn and Zn were measured by atomic absorption, while boron was measured using a colorimetric method (Berger and Truog 1939).Soil texture was determined using the hydrometer method (Bouyoucos, 1936).
Titration method of determining the lime requirement in the laboratory
The lime requirement was determined in the laboratory at Makerere University using titration method as follows: The pH meter was calibrated with standard pH 4.00 and 7.00 buffers before each titration, soil pH measurements and titrations were performed in a soil/0.01 M CaCl2 (1:2) suspension while being stirred. Titration curves for each soil type were established by titrating 30 g soil in 60 mL 0.01 M CaCl2 (1:2) with 3 mL 0.022 M Ca(OH)2 per addition (Kisselet al., 2010; Barouch as et al., 2013). The time interval to achieve an ion-exchange balance between 0.022 M Ca(OH)2 additions was 30 min based on the research results of Liu et al. (2004) and Weaver et al. (2004). The soil suspension was stirred continuously by a magnetic stirrer during titration and the pH was measured at the end of each time interval. Increments (3-mL aliquots) of 0.022 M Ca(OH)2 were added until the pH reached 6.5. All determinations were performed in triplicate. After each measurement, the electrodes were rinsed with distilled water to avoid cross contamination from sample to sample. For the evaluation of the Al3+ in the extracts of the 1 mol L-1KCl solution, 1:10 (v/v) soil/solution ratio (McLean, 1965), Titrimetric method(standard method), according to the routine methodology adapted from McLean (1965) was used. Primarily, the exchangeable acidity (Al3+ + H+ tit) is determined by titration of 25 mL KCl extract with 0.025 mol L-1NaOH, using 1 g L-1 phenolphthalein as an indicator (titration from colorless to pink). Then, the concentration of Al3+was obtained by back-titration of the same KCl extract, previously used, after the acidification with a drop of HCl and addition of 40 g L-1NaF, with 0.025 mol L-1HCl (titration from pink to colorless).
Statistical analysis
Determining the pH resulting from lime (Ca(OH)2) application
The first step in the incubation procedure was to determine the soil pH that resulted from the increasing amounts of added Ca(OH)2. This was determined by plotting the pH resulting from the application of Ca(OH)2 in the CaCl2 suspension on the y-axis and the amounts of added Ca(OH)2 on the x-axis (Figure 1).
Determining the amount of Ca(OH)2 needed to attain a specified soil pH
The incubation data were then re-plotted as the amount of Ca(OH)2 on the y-axis and the soil pH on the x-axis to properly predict the amounts of Ca(OH)2 needed to attain a specific soil pH (in CaCl2) suspension. This step is sometimes called a “Calibration” and differs from simply re-arranging the regression equation from the plot of soil pH on the y-axis and the amounts of Ca(OH)2 on the x-axis as described above. This “calibration” step is needed because a regression equation cannot be simply re-arranged as if it were a standard algebraic equation. For example, one cannot simply re-arrange a regression equation in the above paragraph to predict the amounts of Ca(OH)2 needed for a specific soil pH. After plotting the amounts of Ca(OH)2 on the y-axis and soil pH on the x-axis, a regression equation was fitted to the data to enable the prediction of the amounts of Ca(OH)2 needed to attain a specified soil pH (Figure 2). The estimate of lime requirement is then the difference in the amounts of Ca(OH)2 between the two predictions and is given in Table 2. This replotting and fitting a regression equation to the data is required because a regression equation is a prediction of the dependent variable assuming no errors in the predictor variable (Gelman and Hill, 2009).Various target soil pH values were then selected as possible target values (5.0, 5.5, 6.0, and 6.5, Edmeades et al., 2012). The corresponding amounts of Ca(OH)2 required based on the laboratory incubations for the two soils were then tabulated (Table 2) based on target crop pH requirements after the time allocated for the neutralization reaction to occur.
RESULTS AND DISCUSSION
Soil pH increase with applied Ca(OH)2
The change in soil pH that occurred with lime application is shown in Figure 1. The amount of Ca(OH)2 needed to increase soil pH increases in a nonlinear manner as shown by the quadratic curves that were fit to the data. This nonlinear response to lime applications probably reflects the presence of additional buffering compounds on the soil surfaces that result in smaller increases in pH as higher pH’s are obtained. This is not unusual in highly buffered variable charge soils in which the CEC increases as soil pH increases (Uehara and Gilman, 1981).
In order to obtain estimates of lime requirement to attain various target soil pH’s, the data shown in Figure 1 are replotted in order to obtain regression estimates of the amount of lime needed to attain various target pH’s. We note that it is not valid to take the regression equations fit in Figure 1 and re-arrange them to estimate lime requirement. This occurs because regression equations are fit on the assumptions that there are no errors in the predictor variable and thus regression equations are not the same as a typical algebraic equation with which re-arrangement is valid. Figure 2 shows the replotted data and again illustrate that as the target soil pH increases the amount of lime needed further increases, resulting in the curvilinear relationship between lime applied and the resultant soil pH. It is important to note that this highly buffered behavior of the Ugandan soils differs from the soils in the Liu et al.(2004, 2005) and the Barouchas (2013) papers where the increase in soil pH was linear for the lower levels of applied lime. Consequently, the recommendation of Liu(2005) to use a one-point incubation curve to determine lime requirement does not hold for these two Ugandan soils. Consequently, these data suggest that a lime incubation curve needs to be determined for each of these soil groups, which then can be used to estimate lime requirements to attain various target pH’s depending on the desired crop. This is illustrated in Table 2.
For comparison, we have also calculated the lime requirement using Liu et al. (2005)’s recommendation single lime addition method (Table 2) rows 3 and 4. As expected, the lime requirement estimates from single addition underestimate the lime requirement obtained from an incubation curve that spans the range of soil pH 5.0 to 6.5.
Estimates of lime requirement
The practical determination of lime requirements for these soils is not concluded with these estimates; rather, it is only the beginning of practical estimates. These are only estimates of lime requirement based on controlled laboratory conditions. Practical estimates need field verification under realistic conditions of use of a locally available liming material, whose chemical quality relative to the 100% Calcium Carbonate Equivalency of Ca(OH)2 must be determined. In addition, the physical quality of the agricultural limestone must also be determined, since lime that does not pass a 0.15-mm sieve lower possibility of dissolving and thus of lower effectiveness. In addition to these variables that strongly affect lime reactivity, the soil and crop management practices such as amounts and form of nitrogen also strongly affect soil pH in practical situations. Examples abound of the strongly acidifying effects of ammoniacal fertilizers on lowering soil pH (Chao et al.,2014). Other factors will affect how frequently soil pH should be monitored. As suggested above, if the lime contains some larger particles these will require a longer time to react, thus affecting the effective residual effectiveness of the limestone. Consequently, soil pH should be monitored over time to ensure crop growth is not limited.
Physico-chemical properties of the two soils used in the LRS
The two soil types were sandy clay loam textural class and they were quite acidic. The Limyufumyufu had a pH of 5.02 while the Luyinjayinjainitial pH was 5.26 (Table 1). Considering the critical levels of K, Mg and Ca (1.15, 3.12 and 0.68 cmolc/kg, respectively), these nutrients were above the critical levels as indicated in Table 1. The Limyufumyufu soil had 33.9% higher Cation Exchange Capacity (CEC) compared to Luyinjayinja. The Cambisol had a much higher exchangeable Aluminium content, which may have resulted in sharply reduced bean growth.
Physico-chemical properties of the selected soils used in the LRS
The initial pH of the two soil types was below the critical level for bean growth which ranges from 5.8 to 6.5 (Table 1) (Edmeades et al., 2012). The availability of phosphorus is also influenced by the pH, which is readily available at a pH (H2O) of 6 to 7 (Plessis et al., 2002). This is in agreement with the nutrient omission study results, which indicate that phosphorus is the most limiting nutrient in the three soils where beans are mainly grown in Lake Victoria Crescent (Kyomuhendo et al., 2018).
Soil pH in 0.01 MCaCl2 were depressed at all levels of Ca(OH)2 addition in all soils, a common effect due to displacement of Al3+ and H+ from increased soil solution of Ca2+, and due to elimination of the junction potential effect (Bloom, 2000). Fageria and Baligar (2008) noted that Effective Cation Exchange Capacity (ECEC) is an important parameter for predicting fertility behavior of agricultural soils, and in their study, ECEC increased significantly (P < 0.01) with increasing pH in aquadratic, nonlinear response.
At low pH values, Al3+ is the predominant exchangeable cation on clay minerals. As the pH is raised, the Al3+ hydrolyzes, freeing the exchange sites for Ca2+, and results in an increase in the ECEC (Kisinyo et al., 2013). This may be one of the reasons the Limyufumyufu soil requires more lime to achieve suitable pH levels for P. vulgaris bean.
Titration curves for the two soil types used in the lime requirement study (LRS)
Cambisol had a higher lime requirement than Umbrisol (Figure 1). This can be attributed to the differences in the initial pH where Cambisol was more acidic than Umbrisol (Table 1). According to Edmeades et al. (2012), the initial pH of the soil is the major factor determining the quantity of lime required to raise pH either to 5.8 or 6.5, a range of pH for bean production.
The higher lime requirement for Cambisol than Umbrisol can be attributed to differences in terms of exchangeable cations where by Ca2+, Mg2+, and K+ contents were lower in Cambisol than Umbrisol (Table 1). This is in agreement with results by Fageria and Baligar (2008) who reported that soils with high fertility in terms of exchangeable Ca2+, Mg2+, and K+ require less lime than do those with lower soil fertility. When Ca2+, Mg2+, and K+ contents are higher, a lower lime rate is required, because of higher levels of these basic cations in the soil, meaning relatively higher base saturation and higher pH than with lower levels of these cations (Fageria and Baligar, 2008).
Anderson et al. (2013) reported that in soils with a negligible orno exchangeable A1, the pH did not change by liming meaning that most of the base added had been consumed by deprotonation of hydroxyl groups of organic matter and on clay mineral surfaces (Guadalix and Pardo, 1994).
CONCLUSIONS
As indicated above, these laboratory incubations are only relatively quick estimates of lime requirement. Clearly, longer term studies with locally available liming materials that typically vary in Calcium Carbonate Effectiveness (%CCE) and also vary in particle size analysis need to be conducted. Furthermore, local crop management practices such as rates and types of nitrogen fertilizers need to be assessed in field studies.
Nonetheless, these results suggest that major growth limitations due to soil acidity merit further study and field testing in order to more fully utilize such acid soils with food production potential.
CONFLICT OF INTERESTS
The authors have not declared any conflict of interests.
REFERENCES
|
American Society for Testing and Materials(1994).Standard Test Method for Total Organic Carbon in Water.D2579-93.Annual Book of ASTM Standards.American Soc. Testing and Materials, Philadelphia, Pa. |
|
|
Anderson NP, Hart JM, Sullivan DM, Christensen NW, HorneckDA,Pirell GJ (2013). Applying lime to raise soil pH for crop production.Western Oregon State University. |
|
|
Barouchas PE, Moustakas N, Liopa-Tsakalidi A (2013). A rapidprocedure to calculate lime requirements based on single titration with base. Archives of Agronomy and Soil Science 59(2):317-325. |
|
|
Beebe Rao MI, Blair WM, Acosta-Gallegos AJ (2013).Phenotyping common beans for Adaptation to drought. Africa Crop Science 4(35):1-20. |
|
|
Berger KC, Truog E (1939).Boron determination in soils and plants. Industrial and Engineering Chemistry Analytical Edition 11:540-545. |
|
|
Birachi EA, Ochieng J, Wozemba D, Ruraduma C, Niyuhire MC,Ochieng D (2012). Factors influencing smallholder farmers' bean production and supply to market in Burundi. African Crop Science Journal 19(4):335-342. |
|
|
Bloom PR (2000). Soil pH and pH buffering. P.B333-B352. In M summer (ed.) Handbook of soil science. CRC press, Boca Raton, FL. |
|
|
Bouyoucos GJ (1936). Directions for Making Mechanical Analysis of Soils by the Hydrometer Method. Soil Science 4:225-228. |
|
|
BremnerJH (1965). Total nitrogen. In: Black C A, Evans D D, White J L., Ensminger L E, Clark F E,DinauerR C (eds.). Methods of Soil Analysis.Part 2. ASA, Madison, Wisc., USA. pp. 1149-1178. |
|
|
Chao Z, Zhingang L, Jianfeng Y, Huan Y, Yan S, Hongliang T, Russell Y, Huasong W (2014). Acid soil is associated with reduced yield, root growth, and nutrient uptake in black pepper (Piper nigrum, L.). Agricultural Sciences 5:466-473. |
|
|
Clark JS(1965). Extraction of exchangeable cations from Wyoming bentonite suspensions.Canadian Journal of Soil Science 45:39-48. |
|
|
Ebanyat P (2009).A road to food? Efficacy of nutrient management options targeted to heterogeneous soilscapes in the Teso farming system Uganda. PhD Thesis. Wageningen University, Wageningen. |
|
|
Edmeades DC, Wheeler DM, Waller JE (2012). Comparison of methods for determining lime requirements of New Zealand soils. New Zealand Journal of Agricultural Research 28:1. |
|
|
Fageria NK,BaligarVC (2008). Ameliorating Soil Acidity of Tropical Oxisols by Liming For Sustainable Crop Production. Advances in Agronomy, P 99. |
|
|
FAOSTAT(2012). Commodity Balances - Crops Primary Equivalent. |
|
|
Folmer ECR, Geurts PMH, Francisco JR(1998). Assessment of soil fertility depletion in Mozambique. Agriculture, Ecosystems and Environment 71:159-167. |
|
|
FungoB, Grunwald S,Tenywa MM, Vaniauwe B,Nkedi-Kizza P (2010).Lunnyo Soils in the Lake Victoria Basin of Uganda - Link to Toposequence and Soil. Online Journal of Earth Sciences 4(2):63-71. |
|
|
Gelman A, Hill J(2009). Data Analysis Using Regression and Multilevel Hierarchical Models.Cambridge University Press. New York. |
|
|
Guadalix ME,Pardo MT (1994).Effects of liming onsoilpropertiesandP solubility in some SpanishAndosols. Elsevier Science B.V., Amsterdam Geoderma 63:53-61. |
|
|
Kapkiyai JJ, Karnja NK, QureshiJN, Smithson PC,Woomer PL (1999).Soil organic matter and nutrient dynamics in a Kenyan Nitisol under long-term fertilizer and organic input management. Soil Biology and Biochemistry 31:1773-1782. |
|
|
Katungi E, Farrow A, Chianu J, Sperling L, Beebe S (2009). Common bean in Eastern and Southern Africa: a situation and outlook analysis. International Centre for Tropical Agriculture, 61. |
|
|
Kisinyo PO, Othieno CO, Gudu SO, Okalebo JR, Opala PA, Maghanga JK,Ng'etich WK, Agalo JJ, Opile RW, Kisinyo JA, Ogola BO (2013). Phosphorus Sorption and Lime Requirements of Maize Growing Acid Soils of Kenya. Sustainable Agriculture Research 2(2):116-123. |
|
|
Kissel DE, Bogart GA, Miguel L, CabreraAthens GA (2010). Systems and method for predicting the lime requirement in soils.Patent N0.US 7,679,058 B2. |
|
|
Kuo S (1996). Phosphorus. In: Sparks, D.L. (Ed.). Methods of soil analysis. Part3:ChemicalMethods.ASABookSeries,American Society of Agronomy Inc., Madison, WI 5:869-919. |
|
|
Kyomuhendo P, Tenywa MM, Kyebogola S, Semalulu O, Lenssen A, Yost, R, MazurR, GOETTSCH LH(2018).Limiting Nutrients for Bean Production on Contrasting Soils of Lake Victoria Crescent of Uganda. African Crop Science Journal 26(4)5430-554. |
|
|
Liu M, Kissel DE, Vendrell PF Cabrera ML(2004). Soil Lime Requirement by Direct Titration with Calcium Hydroxide. Soil Science Society of America Journal 68:1228-1233. |
|
|
Liu M, Kissel DE, Cabrera ML, Vendrel PF (2005). Soil Lime Requirement by Direct Titration with a Single Addition of Calcium Hydroxide. Soil Science Society of America 69:522-530. |
|
|
Lunze L, Abang MM, Buruchara R, Ugen MA, Nabahungu NL, Rachier GO, Ngongo M,Rao I (2012). Integrated soil fertility management in bean-based cropping systems of Eastern, Central and Southern Africa.Soil fertility improvement and integrated nutrient management -A global perspective, Dr. Joann Whalen (Ed.). ISBN: 978-953-307-945-5. |
|
|
Mclean EO(1965).Aluminum. In: Black, C.A. (Ed.) Methods of soil analysis:Part 2.Chemical methods. Madison: ASA, pp.978-998. |
|
|
Mehlich A (1984). Mehlich 3 soil test extractant: A modification of the Mehlich 2 extractant. Communications in Soil Science and Plant Analysis 15(12):14091416 |
|
|
Nziguheba G (2007). Overcoming phosphorus deficiency in soils of eastern Africa: Recent advances and challenges. In: A. Bationo, B. Wasswa, J. Kihara, & J. Kimetu (Eds.), Advances in integrated soil fertility management in Sub-Saharan Africa: Challenges and opportunities (149-160p). Netherland: Springer. |
|
|
Plessis MJ, Fourie D, Liebenberg AJ, Liebenberg MV,Zyl CJ (2002). Drybean Production. Resource Centre, Directorate Agricultural Information Services, Pretoria, 0001 South Africa. |
|
|
Sibiko KW, Ayuya OI, Gido EO,MwangiJK (2013). An analysis of economic efficiency in bean production : evidence from eastern Uganda. Journal of Economics and Sustainable Development 4(13):1-10. |
|
|
Tenywa MM, Semalulu O, Luswata CK, Kyomuhendo P, Namakula J, Abbott E, Yost R,Kabango F (2014). Farmer Decision-Making Strategies for Improved Soil Fertility Management in Maize-Bean Production Systems.Focus Group Interviews with Bean Farmers in Masaka and Rakai District, Uganda.(Unpublished Report). Project S02.1-Annual Report for 2013-14. October 2014. |
|
|
Uehara G, Gillman G (1981). The mineralogy, chemistry, and physics of tropical soils with variable charge clays. Westview Press, Inc., Boulder, Colorado. |
|
|
Weaver AR, KisselDE, Chen F, West LT, Adkins W, Rickman D,Luvall JC (2004). Mapping soil pH buffering capacity of selected fields in the coastal plain. Soil Science Society of America Journal 68:662-668p. |
|
Copyright © 2024 Author(s) retain the copyright of this article.
This article is published under the terms of the Creative Commons Attribution License 4.0
