RESEARCH COMMUNICATIONS 16. Phillips, J. J., Handbook of Training Evaluation and Measurement Methods, Gulf, Houston, TX, 1991. 17. Kirkpatrick, D. L., Techniques for evaluating training programs. J. Am. Soc. Train. Dev., 1959, 13, 11–12. 18. Anita, P. B., Becky, F. A. and Mavin, M., Supervisor–Team Training: Issues in Evaluation, University of Berkeley, USA, 2006. 19. Fullard, F., A model to evaluate effectiveness of enterprise training programmes, Int. Enterpreneurship Manage. J., 2006, 3, 263– 276. 20. Phillips, J., ROI: The search for best practices. Train. Dev., 1996, 50, 42–46. 21. Stoel, D., The evaluation heavy weight match. Train. Dev., 2004, 58, 46–48. 22. Alliger, G. and Janak, E., Kirkpatrick’s levels of training criteria: thirty years later. Pers. Psychol., 1989, 42, 331–342. 23. Alliger, G., Tannenbaum, S. Bennett, W., Traver, H. and Shotland, A., A meta analysis of the relations among training criteria. Pers. Psychol., 1997, 50, 341–358. 24. Bates, R., A critical analysis of evaluation practice: the Kirkpatrick model and the principle of beneficence. Eval. Program Plann., 2004, 27, 341–347. 25. Holton, E. F. The flawed four-level evaluation model. Human Resour. Dev. Q., 1996, 7, 5–21. 26. Swanson, R. A. and Holton, E. F., Foundations of Human Resource Development, Berrett-Koehler Publishers, San Francisco, CA, 2001. 27. Newby, T., Training Evaluation Handbook, Pfeiffer, San Diego, CA, 1993. 28. Pulley, M., Navigating the evaluation rapids. Train. Dev., 1994, 48, 19–24. 29. Clark, D., Instructional system development-Evaluation phase. http://www.nwlink.com/~Donclark/hrd/sat6.html#introevaluate, 2007. 30. Johnson et al., Law Enforcement Training in Southeast Asia: A Theory Driven Evaluation. Police Pract. Res., 2006, 7, 195–215. 31. Wiessner et al., Constructing knowledge in leadership training programmes. Community Coll. Rev., 2007, 35, 88–112. 32. Report, Directorate of Publications and Information on Agriculture, ICAR, New Delhi, 1997.
Received 11 August 2008; revised accepted 26 November 2008
Soil erosion limits for Lakshadweep Archipelago Debashis Mandal* and K. P. Tripathi Central Soil and Water Conservation Research and Training Institute, 218, Kaulagarh Road, Dehradun 248 195, India
Soil loss tolerance limits (T value) define the soil loss amounts that are tolerable to maintain, continuously and economically, the sustainability of soil productivity. Within these limits, soil erosion and soil formation processes are in equilibrium. The Lakshadweep Islands is prone to soil erosion and about 20 running kilometre seashore line is being subjected to severe *For correspondence. (e-mail:
[email protected]) 276
erosion. The unique land and soils of the Lakshadweep Coral Islands require careful management to protect the fragile ecosystem. Soils of ten inhabited islands of Lakshadweep were studied in detail to assign T values, for suggesting a conservation plan. The T value for the whole Archipelago varied between 7.5 and 12.5 t ha–1 yr–1. The spatial delineation of soils with respect to T value can facilitate the management of these valuable resources and prevent their degradation. Keywords: Conservation plan, soil erosion, soil loss tolerance, soil sustainability. SOIL is an essential natural resource, which is available in limited quantities. Soil functions are mainly in crop production and as a filtering agent indispensable for the maintenance of water quality. In tropical agro-ecosystems, soil erosion is the main land-degradation process, especially if land use is intense1. Soil erosion can reduce crop productivity, due either to physical degradation or nutrient depletion2. Soil erosion is also an environmental hazard. In this case, the impacts are called off-farm, while silting and pollution of water resources are the major consequences3. Erosion limits have to be defined in order to keep these impacts at acceptable levels. Soil loss tolerance is the maximum rate of annual soil erosion that may occur and still permit a high level of crop productivity to be obtained economically and indefinitely4. The T value is also sometimes called ‘permissible soil loss’. Within these limits, soil erosion and soil formation processes are in equilibrium. Soil loss tolerance depends on the soil type. In very deep and homogenous soils, the effects of erosion will be less pronounced than in shallow soils encountered on highlands of semiarid zones or highly weathered soils whose nutrient storage and availability depend largely on the organic matter of the surface layer5. Determination of soil tolerance is intended to compare the expected soil loss with the soil loss tolerance. If soil loss is less than or equal to the soil loss tolerance, soil loss can be still permitted. The maximum soil loss tolerance for tropical regions5 is 25 t ha–1 yr–1. A commonly used soil loss tolerance rate is 5–12 t ha–1 yr–1 for shallow to deep soils6,7. However, the current used rates for tolerable soil loss are far too high for fragile tropical soils with low levels of fertility5,7. It has also been indicated that tolerance values for tropical soils have not yet been formulated at the international level5. Established annual soil loss tolerance limits7–9 vary between 0.2 and 11 t ha–1 yr–1. It is important to mention here that soil formation is a positive feedback process, i.e. the product of the process accelerates the production of the product. Therefore soils have to be kept in place to make more of them. The estimated rate of soil loss from farmland has a disastrous consequence for food production. Further, each harvesting removes the plant nutrients from the soil. In a selfCURRENT SCIENCE, VOL. 96, NO. 2, 25 JANUARY 2009
RESEARCH COMMUNICATIONS sustaining, small ecosystem, these plant nutrients are essentially recycled back into the soil. Based on the information10 for similar soils, it is estimated that about 40,000 t of soil is being eroded every year due to water erosion from this Archipelago. Decline in soil and land productivity, fragile ecosystems and land degradation are of great concern to this territory. T values commonly serve as objectives for conservation and farm planning. These objectives assist in identification of cropping sequences and management systems that will maximize production and sustain long-term productivity. Conservation objectives for soil loss tolerance are based on maintaining a suitable seedbed and nutrient supply in the surface soil, maintaining an adequate depth and quality of the rooting zone, and minimizing unfavourable changes in water availability throughout the soil. A single T value is assigned to each identified soil series on soil survey map. Considerable loss in productivity is likely to occur on most soils if they erode for several centuries at the present soil erosion levels11. Soil erosion continues to be a major concern for the development of sustainable agricultural management systems. The Lakshadweep Islands is prone to soil erosion and about 20 running kilometre seashore line is being subjected to severe erosion12. In the present investigation, we have hypothesized that sitespecific, adjusted T values are needed to be defined for different soil types in the Lakshadweep Archipelago. The Lakshadweep Islands is scattered (10–400 km) in the Arabian Sea at about 200–400 km away from the Malabar coast. It lies between 8°–12°N lat. and 72°–74°E long. and covers an area of 32 km2. The climate of the Lakshadweep Islands is moderate, temperature ranges from 27°C to 35°C, and relative humidity from 70 to 76%. Total rainfall in the district is approximately 1600 mm/yr. The entire cultivable area of the district is thickly planted with coconut trees. The soils of Lakshadweep are red loamy and sandy in texture formed by the disintegration of coral limestone underlined with pebbles of different shapes and sizes, and deficient in macro and micronutrients essential for plant growth. It is generally poor in the level of nitrogen and potassium and fairly good in regard to phosphorus. The soils of the islands, on the other hand, are highly calcareous and sandy in nature (Udipsamments). The soils are alkaline (pH up to 8.5), structureless and poor in available nitrogen and potassium. Published information on land and soils of the islands was studied in detail, including the spatially delineated map of the region13,14. Bulk density and saturated hydraulic conductivity (HC) data were derived using appropriate pedotransfer function (SSWATER software). Soil erodibility factor (K-factor) was estimated with the help of standard nomogram4. Based on the differences in soil depth, drainage condition, texture, gravelliness and chemical properties, the soils of the islands have been grouped into ten soil series14. The methodology for estimating soil loss tolerCURRENT SCIENCE, VOL. 96, NO. 2, 25 JANUARY 2009
ance by biophysical approach has been described by Mandal et al.15. Appropriate scoring methods were used to transform indicators into dimensionless scores. Model parameters are presented in Table 1. For example, saturated HC, presented as a ordinal form consisting of five categorical classes (Table 1), takes an asymmetric left function or ‘more is better’, because the resistance to erosion will be better, as the infiltration level increases. A weighted additive model (eq. (1)) as described below was used to determine the state of the soil. Potential indicators were assigned weights according to their relative importance. The primary function of the soil with respect to erodibility is to accommodate water entry into the soil matrix, i.e. infiltration16. Based on this rationale, the highest weight of 0.35 was assigned to HC. The next most important indicator, resisting physical degradation (K-factor) was assigned 0.25. Bulk density was assumed to complement HC, and was assigned a weight of 0.10. The remaining 0.30 was assigned to organic carbon content (0.15) and pH (0.15). In this approach, the ability of soil to sustain plant growth was assumed to be less important than the process contributing to water entry and transport or erodibility. Rating with respect to each soil function was multiplied with the respective weighted value and these were added together. Sum of all the weighted parameters of soil functions indicated the state of the soil (Q) under each soil mapping unit as indicated below: Q = qwewwe + qwtwwt + qrpdwrpd + qrbdwrbd + qspgwspg,
(1)
where qwe is the rating for accommodating water entry (HC), qwt the rating for accommodating water transport (bulk density), qrpd the rating for accommodating rate of physical degradation (K-factor), qrbd the rating for accommodating rate of biological degradation (organic carbon content), qspg the rating for sustaining plant growth (pH) and w is the weight with respect to each function. Soil mapping units were grouped into three soil groups I (Q < 0.33), II (Q = 0.33–0.66) and III (Q > 0.66), based on the aggregated score (Q) as obtained in eq. (1). This indicates that a soil mapping unit falling in the soil group III performs soil functions in an optimum manner to resist erosive forces and thus may allow higher erosion levels than other soil groups. A general guideline of T matrix developed at the Iowa State University Statistical Laboratory17 was used to arrive at the soil loss tolerance values for each soil mapping unit (Table 2), based on the soil group of each soil mapping unit and the soil depth. The soils are light textured, predominantly sandy or loamy sand and occasionally sandy loam. All soils are underlain by impervious coral limestone. The physical and chemical properties of the soils are presented in Table 3. The minimum dataset of soil required for this model was decided based on primary functions of the soil with regard to resistance to water erosion. These functions 277
RESEARCH COMMUNICATIONS Table 1.
Categorical ranking of soil attributes used to convert soil properties into dimensionless standard scores Ordinal/categorical ranking
Soil attributes
1
Saturated hydraulic conductivity (cm h–1) Bulk density (Mg m–3) K-factor Organic carbon (%) pH
2
3
4
5
Model function used
5.0 (1.0)
More is better
1.5 (1.0) 6.5–7.5 (1.0)
Less is better Less is better More is better Optimum at pH 6.5–7.5
Values in parenthesis are converted scores between 0 to 1.
Table 2.
Estimation of T value based on the matrix of soil depth and aggregated score Annual soil loss tolerance (t ha–1)
Soil depth (cm) 0–25 25–50 50–100 100–150 >150
Group I (Q < 0.33)
Group II (Q = 0.33–0.66)
Group III (Q > 0.66)
2.5 2.5 5.0 7.5 10.0
2.5 5.0 7.5 10.0 12.5
5.0 7.5 10.0 10.0 12.5
and indicators were derived from the sensitivity analysis of the Water Erosion Prediction Project18, which includes infiltration rate, bulk density, organic carbon, K-factor and pH. Hydraulic conductivity values ranged from 2.86 to 7.9 cm h–1. Among the soils, Andrott-3 series had significantly lower value than the others, providing strong evidence that run-off from this site is maximum. The highest value of infiltration (7.9 cm h–1) in the Chetlat-1 series was due to its skeletal textural nature. Bulk density ranged from 1.57 to 1.71 Mg m–3. Higher range of bulk density is due to the sandy and coarse nature of the soils. Kalapeni-2 and Andrott-1 had lower soil bulk density than the other sites because of less compaction. K-factor ranged from 0.04 to 0.13. Kalapeni-2 and Andrott-3 had lower K-factor as result of their greater organic carbon content. Organic carbon content ranged from 0.77 to 4.78%. Low organic carbon content was commonly observed in the coastal line deposition of Chetlat-1 series. The pH varied between 7.9 and 8.5 for the whole region. The soil pH of Kalapeni-1 series was more favourable for crop cultivation, particularly for surface soils. All these soil factors were converted into standard scores (Table 4) using the appropriate scoring curves. The converted values of infiltration rate varied between 0.5 and 1.0. Although there were significant differences among the observed values of infiltration rate, none of them would be considered detrimental for water accommodation and transportation within the soil system, except the Andrott-1 series. Scores of bulk density value ranged from 0.2 to 0.3. Our study showed that bulk density is reflected by fundamen278
tal edaphic characteristics and is the most sensitive property for soil assessment of this region, owing to its very low score. The converted values of organic carbon fluctuated within 0.8–1.0, with only a marginal difference among most soil series, except Chetlat-1 and Kavaratti soils. Similar trend was observed in case of the K-factor. It is evident from the results that organic carbon and K-factor are directly related. Measure of soil performance (Q) was calculated quantitatively by aggregating the score for each soil parameter obtained by multiplying the rating with the respective weightage (eq. (1)). According to this model, sum of the weighted indicators determined the level of resistance to erosion offered by a particular soil. T values were then assigned to each soil series based on the T matrix (Table 2) and are presented in Table 4. Within the same soil group, T values varied with the change in soil depth. This result showed that Kalapeni-3 series with an additive aggregate (Q) of 0.81 has the highest capacity of structural and functional integrity to the driving force of water erosion. Organic carbon and pH did make a big difference among the various soils. Since T value depends on many factors, it varied from one set of conditions to another. Andrott-1 and Kavaratti series had a total aggregated score of 0.62 and 0.65 respectively, and qualified as soil group II. All other soils had aggregated scores more than 0.66 (varied from 0.71 to 81) and qualified as soil group III. From the matrix of soil depth vs soil group (Table 2), it is evident that as the soil depth exceeded 150 cm, the T value was 12.5 t ha–1 yr–1 and therefore, soils of the Chetlat-1 series had T value of 12.5 t ha–1 yr–1. Soils under Kavaratti, Kadmat, Kalapeni-2, Chetlat-2 and Andrott-1 had a T value of 7.5 t ha–1 yr–1. This is because of either limitations in soil depth or soil group. The remaining soils, although qualified as soil group III, had a T value of 10.0 t ha–1 yr–1 due to limitation in soil depth. These variations of soil groups and T values were mostly due to different levels of the most sensitive indicators, viz. hydraulic conductivity and organic carbon content. According to our estimation maximum area (36.55% TGA) of the Archipelago was assigned a T value of 7.5 t ha–1 yr–1, followed by 12.5 t ha–1 yr–1 (32.5% TGA) and 10.0 t ha–1 yr–1 CURRENT SCIENCE, VOL. 96, NO. 2, 25 JANUARY 2009
RESEARCH COMMUNICATIONS Table 3.
Physical and chemical properties of ten representative soil series
Soil series
Soil classification
Area (ha)
Bulk density (mg m–3)
Infiltration rate (cm h–1)
Organic carbon (%)
pH
K-factor
Chetlat-1 Kavaratti Andrott-2 Kalapeni-1 Kadamat Kalapeni-2 Andrott-3 Chetlat-2 Kalapeni-3 Andrott-1
Typic Ustipsamments Typic Ustipsamments Typic Ustipsamments Typic Ustipsamments Lithic Ustipsamments Typic Ustorthents Typic Ustorthents Lithic Ustorthents Typic Tropopsamments Typic troporhents
809 326 506 151 55 96 74 88 41 346
1.71 1.64 1.63 1.63 1.63 1.57 1.58 1.59 1.64 1.57
7.9 4.5 4.13 4.05 4.13 4.13 2.86 2.90 4.91 3.18
0.77 0.97 1.34 2.50 2.06 4.78 3.58 2.90 2.40 1.24
8.5 8.4 8.2 7.9 8.3 8.0 8.0 8.1 8.1 8.2
0.13 0.12 0.12 0.08 0.06 0.04 0.04 0.06 0.09 0.08
Table 4.
Converted values on 0–1 scale (unit-less), aggregated score (Q), soil depth, soil groups and assigned T values for different soil series
Soil series
Bulk density
Infiltration rate
Organic carbon
pH
K-factor
Aggregated score (Q)
Soil group
Depth (cm)
T value (t ha–1 yr–1)
Chetlat-1 Kavaratti Andrott-2 Kalapeni-1 Kadamat Kalapeni-2 Andrott-3 Chetlat-2 Kalapeni-3 Andrott-1
0.2 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.2 0.3
1.0 0.8 0.8 0.8 0.8 0.8 0.5 0.5 0.8 0.5
0.5 0.5 0.8 1.0 1.0 1.0 1.0 1.0 1.0 1.0
0.5 0.6 0.7 0.9 0.6 0.8 0.8 0.7 0.7 0.7
0.8 0.8 0.8 1.0 1.0 1.0 1.0 1.0 1.0 0.8
0.72 0.65 0.72 0.83 0.79 0.83 0.72 0.71 0.81 0.62
3 2 3 3 3 3 3 3 3 2
>150 100–150 76–100 50–75 10–40 115–150 80–94 27–30 76–100 60–100
12.5 7.5 10.0 10.0 7.5 7.5 10.0 7.5 10.0 7.5
(30.97% TGA). The spatial distribution of T values in five large islands of the Lakshadweep is presented in Figure 1. Soils loss tolerance limits of 10 inhabited islands of Lakshadweep were determined to understand the need for soil conservation plans. T value for the whole Archipelago varied between 7.5 and 12.5 t ha–1 yr–1. It has been estimated that with the prevailing farming practices the topsoil loss from farmland is on average 5–10 times the food production, i.e. on the higher side for poor countries like India19. Assignment of site-specific T values will help understand the vulnerability of some soils in this region. So the most important concern in this region is to protect the productive soil to a level of sustained production. This is possible by bringing the soil loss within acceptable limits (T value) as suggested in this communication. The spatial delineation of soils with respect to T values can facilitate the management of these valuable resources and prevent their degradation.
Figure 1. Spatial distribution of T values in five inhabited islands of Lakshadweep. CURRENT SCIENCE, VOL. 96, NO. 2, 25 JANUARY 2009
1. Lal, R., Soil erosion and land degradation: the global risks. Adv. Soil Sci., 1990, 7, 129–172. 2. Larson, W. E. et al. (eds), In Proceedings of Soil Erosion and Productivity Workshop, Minnesota, 1990, p. 142. 3. Clark, E. H., Haverkamp, J. A. and Chapman, W., Eroding Sous. The Off-farm Impacts, The Conservation Foundation, Washington DC, 1985, pp. 1–252. 4. Wischmeier, W. H. and Smith, D. D., Predicting Rainfall Erosion Losses – A Guide to Conservation Planning, US Department of 279
RESEARCH COMMUNICATIONS
5. 6.
7.
8.
9. 10.
11.
12.
13.
14. 15.
16.
17.
18.
19.
Agriculture, Agricultural Handbook 537, Washington DC, 1978, p. 85. Ringo, D. E., Assessment of erosion in the Turasha catchment in the lake Naivasha area, Kenya, M Sc thesis, Enschede, 1999. Crosson, P., Soil erosion in developing countries: Amounts, consequences and policies. In Paper presented at the University of Wisconsin, Department of Agricultural Economics, 15 November 1983. Lal, R., Soil Erosion in the Humid Tropics with Particular Reference to Agricultural Land Development and Soil Management, IAHS Publication 140, Wallingford, UK, 1983, pp. 221–239. Hurni, H., Land degradation, famines and resources scenarios. In World Soil Erosion and Conservation (ed. Pimental, D.), Cambridge University Press, Cambridge, 1993, pp. 27–62. Hudson, N., Soil Conservation, BT Batsford Ltd, London, 1986. Singh, G., Babu, R., Narain, P., Bhushan, L. S. and Abrol, I. P., Soil erosion rates in India. J. Soil Water Conserv., 1992, 47, 97– 99. McCormack, D. E., Young, K. K. and Kimberlin, L. W., Current criteria for determining soil loss tolerance. In Determinants of Soil Loss Tolerance (eds Schmidt, B. L. et al.), ASA Special Publication No. 45, 1982, pp. 95–111. FAO, Fertilizer use by crops in India. Land and Plant Nutrition Management Services, Land and Water Development Division, Food and Agriculture Organization of the United Nations, Rome, 2005, p. 59. Krishnan, P., Naidu, L. G. K., Shamsuddin, V. M., Raghu Mohan, Sehgal, J. and Velayutham, M., Soils of Lakshadweep for Optimizing Land Use, National Bureau of Soil Survey and Land Use Planning, Nagpur, 1997, Publ. 70, p. 60. Krishnan, P. et al., Land, soil and land use of Lakshadweep Coral Islands. J. Indian Soc. Soil Sci., 2004, 52, 226–231. Mandal, D., Dadhwal, K. S., Khola, O. P. S. and Dhayni, B. L., Adjusted T values for conservation planning in Northwest Himalayas of India. J. Soil Water Conserv., 2006, 61, 391–397. Karlen, D. L. and Sott, D. E., A framework for evaluating physical and chemical indicators of soil quality. In Defining Soil Quality for a Sustainable Environment (eds Doran, J. W. et al.), SSSA Special Publication No. 35, SSSA, Madison, WI, 1994, pp. 53–72. Natural Resources Conservation Service, National Soil Survey Handbook – Title 430-VI, 1999; http://www.statlab.iastate.edu/ soils/nssh/ Nearing, M. A., Deer-Ascough, L. and Laflen, J. M., Sensitivity analysis of the WEPP hillslope profile erosion model. Trans. Am. Soc. Agric. Eng., 1990, 33, 839–849. Rajamani, V., Farmland geology – an emerging field in sustainability science. Curr. Sci., 2002, 83, 557–559.
ACKNOWLEDGEMENTS. We thank Dr V. N. Sharda, Director, Central Soil and Water Conservation Research and Training Institute, Dehradun for providing the necessary support to conduct this study. We also thank M. P. Juyal (T 2-T 3) for help with drawing the figures.
Received 18 December 2007; revised accepted 26 November 2008
The new digital Ah index of geomagnetic activity at Alibag and other stations D. Martini1,3,*, K. Mursula2 and S. Alex1 1
Indian Institute of Geomagnetism, New Panvel, Navi Mumbai 410 218, India 2 Department of Physical Sciences, P. O. Box 3000, University of Oulu, Oulu 90014, Finland 3 Permanent address: GGRI-Sopron, P. O. Box 5, 9401, Hungary
The geomagnetic activity carries important information about various parameters of the near-earth space and the solar magnetic activity on short as well as on longterm scales. In short or moderately long-term studies, the Kp/Ap index is a widely used reliable measure of geomagnetic activity. On the long-term scale, the aa index has, till recently, been the only index offering a sufficiently long registration for centennial studies. However, the long-term robustness of aa index was seriously questioned, and the early registrations until the first decades of the last century are not available in digital form in sufficiently high resolution. Therefore, the aa index cannot be verified, nor its probable error be reassuringly quantified. Here we further verify the incorrectness of aa, using a recently introduced digital measure of the geomagnetic activity, the Ah index calculated at the Indian Alibag station. Global Ah from a number of long-running stations can be used to reliably extend the Ap series by roughly 30 years, allowing the study of geomagnetic activity for more than a century at the three-hourly resolution. Local Ah indices can also be calculated at any latitudinal region. Keywords: Digital index, geomagnetic activity and indices, long-term variations. GEOMAGNETIC activity is an important parameter to study not only the physical processes in near-earth space, but also to understand and quantify the long-term development of the solar magnetic activity. The heliospheric magnetic field is known to be the main modulator of cosmic rays and a significant cause of geomagnetic activity. One of the most important questions in solar–terrestrial science is whether and by how much has the sun increased its magnetic activity during the last century. Based on the longest uniform measure of geomagnetic activity available at that time, the K-based aa index, Lockwood et al.1 concluded that the strength of the heliospheric field has more than doubled in the last hundred years. Although this result is in a fairly good agreement with other results based on studies of cosmogenic isotopes (e.g., Usoskin et al.2) and a theoretical model presented by Solanki et al.3, the aa index – being the only independent proxy of longterm solar magnetic activity – still plays a crucial role to conclude a doubling of the heliospheric magnetic field. *For correspondence. (e-mail:
[email protected])
280
CURRENT SCIENCE, VOL. 96, NO. 2, 25 JANUARY 2009
