Large‐scale deforestation for plantation agriculture in the hill country of Sri Lanka and its impacts

Wickramagamage, P.

doi:10.1002/(sici)1099-1085(19981030)12:13/14<2015::aid-hyp716>3.3.co;2-v

Cited by 10 publications

(20 citation statements)

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“…The entire Kalu Ganga Basin is situated in the highest rainfall region of the Wet Zone. Decline of rainfall at Nuwara Eliya has also been reported in many other studies as well (Ranatunge 1988;Madduma and Kuruppuarachchi 1988;Chandrapala 1996;Domroes 1996;Wickramagamage 1998;Madduma and Wickramagamage 2004;Jayathilaka et al 2011). The phenomenon of increasing SWM rainfall in the Southwestern lowlands (Colombo) and decreasing trends at high elevations (Nuwara Eliya at 2300m) were attributed by Kayane et al (1998) to possible lowering of condensation altitude of SWM air masses heavily laden with moisture derived from the warm Indian Ocean.…”

Section: Introductionsupporting

confidence: 78%

“…Past studies on the long-term trends of rainfall in Sri Lanka covering various time periods from 1870 to 2007 have shown spatially and temporally variable trends, both positive and negative (Wickramagamage 1998;Malmgren et al 2003;Madduma and Wickramagamage 2004;Schaefer 1998;Senevirathna et al 1997;Suppiah 1986Suppiah , 1996Eriyagama and Smakhtin 2006). Some of these positive trends are in the lowlands and negative trends in the the highlands (Kayane et al 1998;Wickramagamage 1998;Madduma and Wickramagamage 2004;Herath and Ratnayake 2004).…”

Section: Introductionmentioning

confidence: 99%

“…Some of these positive trends are in the lowlands and negative trends in the the highlands (Kayane et al 1998;Wickramagamage 1998;Madduma and Wickramagamage 2004;Herath and Ratnayake 2004). Madduma and Wickramagamage (2004) showed using mean annual rainfall values of two periods, 1900-1929 and 1964-1993, that the lowest rainfall (904-1553 mm) region was expanding.…”

Section: Introductionmentioning

confidence: 99%

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Spatial and temporal variation of rainfall trends of Sri Lanka

Wickramagamage

2015

Theor Appl Climatol

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This study was based on daily rainfall data of 48 stations distributed over the entire island covering a 30-year period from 1981 to 2010. Data analysis was done to identify the spatial pattern of rainfall trends. The methods employed in data analysis are linear regression and interpolation by Universal Kriging and Radial Basis function. The slope of linear regression curves of 48 stations was used in interpolation. The regression coefficients show spatially and seasonally variable positive and negative trends of annual and seasonal rainfall. About half of the mean annual pentad series show negative trends, while the rest shows positive trends. By contrast, the rainfall trends of the Southwest Monsoon (SWM) season are predominantly negative throughout the country. The first phase of the Northeast Monsoon (NEM1) displays downward trends everywhere, with the exception of the Southeastern coastal area. The strongest negative trends were found in the Northeast and in the Central Highlands. The second phase (NEM2) is mostly positive, except in the Northeast. The Inter-Monsoon (IM) periods have predominantly upward trends almost everywhere, but still the trends in some parts of the Highlands and Northeast are negative. The long-term data at Watawala Nuwara Eliya and Sandringham show a consistent decline in the rainfall over the last 100 years, particularly during the SWM. There seems to be a faster decline in the rainfall in the last 3 decades. These trends are consistent with the observations in India. It is generally accepted that there has been changes in the circulation pattern. Weakening of the SWM circulation parameters caused by global warming appears to be the main causes of recent changes. Effect of the Asian Brown Cloud may also play a role in these changes.

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Section: Introductionsupporting

confidence: 78%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatial and temporal variation of rainfall trends of Sri Lanka

Wickramagamage

2015

Theor Appl Climatol

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“…1). This terrain was forested until the early nineteenth century, when the land was cleared at a regional scale, to make way for coffee and tea plantations (Wickramagamage, 1998). Subsequently, with the decline of tea production, some of the abandoned lands, characterized by steep slopes were converted to seasonal crops.…”

Section: Introductionmentioning

confidence: 99%

Quantifying sheet erosion in agricultural highlands of Sri Lanka by tracking grain-size distributions

Hewawasam

Illangasinghe

2015

Anthropocene

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The reduction of productivity in arable lands of the tropical highlands through intensified soil erosion is a major reason for food scarcity in many developing countries. Identifying soil erosion hot spots in highland agricultural croplands and monitoring the effectiveness of soil conservation are crucial for improving land management practices. This paper develops a low cost and efficient method to quantify the rate of soil loss caused by splash and sheet erosion, in-situ at the plot scale. This method assumes that textural differences between the topsoil and the underlying soil within the plot are due to erosion of the top layer. Grain size analyses conducted in agricultural soils of the Upper Uma Oya Catchment (UUOC) of Sri Lanka provided data to quantify splash or sheet erosion and deposition. Net soil loss from the plot was calculated based on rates of area weighted soil loss and deposition. The net splash and sheet erosion rate in the plot was 20 t ha-1 yr-1 , which is comparable with previous soil erosion results obtained in the area (25 t ha-1 yr-1) at similar temporal and spatial scale and under the same agricultural use. This study suggests that approximately half of the total erosion in the UUOC area results from splash and sheet erosion, whereas the rest is driven by concentrated flow through rill and gully erosion. This method is potentially transferable to other croplands as a robust approach for rapid measurement of splash and sheet erosion.

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“…Further, Jayathilake et al, (2012) using mean annual rainfall of 65 stations in the south-western part of the country observed a shrinking rainfall pattern during 1980-1992 and 1993-2007. This trend was not limited to the low country but was also observed in Nuwara Eliya in the upcountry (Chandrapala, 1996, Domroes, 1996, Wickramagamage, 1998).…”

Section: Graphical Presentation Of Observed Meteorological Variablesmentioning

confidence: 52%

Weather variability and coconut production in Sri Lanka: state-contingent analysis

Jayalath¹

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Weather variability presents an external impediment that increases the uncertainty in crop production potential specially for perennial crops under rain-fed cultivation. In particular, increasing rainfall variability that has accompany climate change in the tropics is increasing production uncertainty exposing farmers to a range of risks. Producer behavior under uncertainty is theoretically advanced in Chambers and Quiggin (2000) with the development of the state-contingent approach. This theory advocates farmers manage uncertainty by allocating inputs into different states of nature. This will enhance farmers' capacity to choose variable production technologies to manage uncertainty associated with unexpected events as state-contingent production technologies are naturally more flexible. This thesis examines the application of a state-contingent model for coconut production to assess the impact of a changing climate on coconut production with a view to assess adaptation options for a production system involving long-lived crops. The analysis of production of perennial crops encounters the treatment of long gestation periods, gradual increase in yield over an extended maturation period and yield stabilization following maturity. In considering optimal resource allocation from an agricultural production system perspective, a) the nature of resource constraints, b) the appropriateness of production technology and c) decision makers managerial behaviour needs to be considered together. The analysis in this thesis integrates these three aspects, with a special focus on managing uncertainty in coconut production in Sri Lanka. Climate is a primary determinant of crop choice and weather variability is the major factor influencing agricultural productivity and uncertainty in production outcomes. To examine the impact of changes in climatological variables, rainfall and temperature, on coconut production, a production time series over 1950-2014 was used to identify variations and trends over time and their statistical significance. The graphical and statistical analyses undertaken proved useful in gaining a more complete understanding of how the production environment is changing in the short run as well as over the long run. It offered a sound basis to further consider effective adaptation options in agricultural production, especially long-lived assets such as coconuts, in response to changing states of climate, representing the natural environment in which production takes place. It was revealed that while climate change impacts are observable over longer time horizons, understanding the nature of such changes may allow farmers to adapt to more observable variations in weather over narrow time periods.

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Large‐scale deforestation for plantation agriculture in the hill country of Sri Lanka and its impacts

Cited by 10 publications

References 0 publications

Spatial and temporal variation of rainfall trends of Sri Lanka

Spatial and temporal variation of rainfall trends of Sri Lanka

Quantifying sheet erosion in agricultural highlands of Sri Lanka by tracking grain-size distributions

Weather variability and coconut production in Sri Lanka: state-contingent analysis

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