Climate Change Impacts On Rice Farming Systems in North ... - AgMIP

1 downloads 0 Views 951KB Size Report
Thoburn, Carolyn Mutter, Jim Jones, Cynthia Rosenzweig, Jerry Hatfield and others at AgMIP has been essential and unstinted. The support of our Resource ...
Climate Change Impacts On Rice Farming Systems in North Western Sri Lanka Lareef Zubair, S.P. Nissanka, S.Ariyawansha, A.P.Keerthipala, B.R.V. Punyawardhene, V. Ralapanawe, W.M.W. Weerakoon,. K.D.N.Weerasinghe, P. Wickramagamage, P. Agalawatte, S.C. Chandrasekara, A.L.C.DeSilva, C.M. Navaratne, J. Gunaratna, D.I. Herath, R.M. Herath, A. Karunaratne, S. Ratnayake, P. Samaratunga, K.Sanmuganathan, J. Vishwanathan E. Wijekoon, Z. Yahiya. ARP - Daniel Wallach

Sri Lanka AgMIP team at Launch Workshop in Colombo.

1. Introduction Locations of meteorological stations and studied farming

systems (Centered at 07.52N, 80.43E).

3. Climate Analysis

Current (Black line and stars) and future (box-and-whiskers) monthly and seasonal temperature (top left) and precipitation (bottom left) for Batalagoda in the mid-20th Century. Corresponding annual and seasonal estimates are at right.

Current (square) and future (letter key for GCM’s) mean Yala (left) and Maha (right) estimates for rainfall and temperature.

The climatology of rainfall for Batalagoda in Kurunegala depicts bimodal rain peaks from April to June and from October to December. Rice growing seasons (Yala and Maha) commence with these rainy seasons. The climate projection is for the midcentury (2040-2069) based on 20 GCMs under higher concentrations (RCP8.5). The future projections following protocols of AgMIP shows an increase in temperature by 1.5-2.8 ⁰C for Yala and 1.1 to 2.4 ⁰C for Maha and an increase in rainfall by -0.5 – 2 mm/day during Yala and -0.1 to 2.6 mm/day during Maha. The rainfall in the future period is similar to the historical period except for September to December when the models project a significant increase.

5. Crop Analysis (Yield Simulations) Goals -To understand the impact of climate on current farming systems under climate change, with and without adaptation, crop simulations were undertaken for each system. Inputs: Weather data: from observations and GCMs; Fertilizer and water application: gathered through the farm survey, Soil: Parameters were estimated from the relevant fields. Results: Yields simulated in DSSAT using DOME input files and AgMIP IT tools are shown. The yield based on two GCMs lead to lower yields than the baseline by (