SEARCA – FAO Project “The Impacts of El Nino Southern Oscillation (ENSO) Events on Rice Production, Area and Yield in Sri Lanka”

Climatic fluctuations have a profound influence on the cultivation of crops such as rice, which is a staple food in Sri Lanka while other cereals such as maize and millet are consumed in more limited quantities. The El Niño / Southern Oscillation (ENSO) phenomenon is a primary mode of climatic variability that affects Sri Lanka. Despite advances in the capacity to predict the evolution of the ENSO phenomenon and advances in understanding the influence of ENSO on rainfall in tropical regions such as Sri Lanka, there has been limited use of climate predictions for agricultural decision-making. In this study, we analyze the relationship of Sri Lankan rice production, harvested area and yield with rainfall and ENSO, for the “Maha” (October to March) and “Yala” (April to September) cultivation seasons based on data from 1950-2009. Maha is the primary growing season as it garners much more rainfall; the area under cultivation during Yala is about half of that during Maha. Farmers decide on the extent they cultivate during Yala based on a variety of factors including market conditions, subsidies, available stored irrigation water and gamble on upcoming rainfall. We have used correlation analysis, regression analysis and composite analysis to evaluate the association between rice production, area harvested, yield, ENSO and rainfall, for entire Sri Lanka and separately for three regions as described below. Previous work has divided Sri Lanka into four regions based on seasonality of rainfall climatology. The “Northern” and “Southern” regions are away from the mountains and are largely not influenced by topographic effects. The southern half of Sri Lanka has an anchor shaped mountain with a North-South ridge that rises to 2.5 km. The Eastern and Western sides of the ridge are identified as two regions as well. We have previously studied the climatic characteristics of these regions. Rice is cultivated primarily in the lowland areas but there are also regions of cultivation at elevations reaching 1300m. Statistics on aspects of rice production are available for 25 districts – these 25 districts can be allocated among the 4 climatic regions with significant mismatch only in the Southern which is the smallest region. We undertook a cluster analysis of rice production variations of the 25 districts – the results from this exercise too supported a regionalization similar to the climatic regions except in the Southern region. Based on these results, we have reduced the four climatic regions to three by eliminating the small Southern region the remaining regions are termed Northern, South-Western, and Eastern. The Production (P), Harvested Area (A) and Yield (Y) data for each of these regions and for “All Sri Lanka” were estimated. The trends in these data which are longer-lived than the typical ENSO-cycle were identified using a 5 point moving average of the data and removed – further analysis were undertaken based on the normalized anomalies. We have repeated this work with detrending undertaken based on 3 order polynomial fit and based on a first-order differencing scheme. The results are largely consistent with that described below. Historical monthly rainfall was estimated for All-Sri Lanka and for each of the climate regions based on an average of quality controlled observations from a fairly well distributed network of synoptic rainfall stations. The sea surface temperature based index of NINO3.4 was used as the ENSO index at a monthly time-step. Previous work has shown that rainfall in the first half of “Maha” season (October to December) has a consistent positive correlation with NINO 3.4. This relationship is of the opposite sign from that for the correlation between ENSO and rainfall from January to March. Similarly, the rainfall from July to August shows consistent and significant relationship with ENSO. A year-byyear analysis of rainfall climatology under El Nino, Neutral and La Nina conditions supports the above interpretation (Figures 13 (a-d)). For the analysis for Maha, we have used the rainfall during the planting season which is from October to December (OND) as it provides the strongest correlations with Maha PAY data. For the “Yala” season (April to September), we have considered rainfall in both May-August (MJJA) and June to August (JJA) separately. Usually, the rice crop that is chosen for Yala has a shorter growing season of 3-4 months and farmers who do not have assured irrigation await the first seasonal rains in April or May before deciding on the extent of rice cultivation. The OND rainfall correlates with ENSO and normalized anomalies of Maha season’s harvested area and production with a significance of 1% level for entire Sri Lanka. ENSO indices for the Maha season correlates with normalized anomalies of production and yield of this season at 5% level. During Yala, the JJA rainfall shows a strong correlation with ENSO indices (1% significant level). ENSO indices during Yala correlate with normalized anomalies of harvested area and production at 5% significant level. At the regional level, there are variations to these relationships. During the Maha season, the ENSO/OND rainfall relationships are strong for all regions, but the level of significance for Northern region is relatively lower. Additionally, during Maha ENSO indices shows strong relationships with normalized anomalies of seasonal yield and production in Northern and Eastern regions. The relationships between OND rainfall and normalized anomalies of Maha season’s PAY are strong only in the Northern region. During Yala, ENSO indices correlates with JJA rainfall strongly except in Northern region. In addition to JJA rainfall, average of ENSO index for Yala correlates strongly with normalized anomalies of harvested area and production in Eastern region. This relationship is weaker in the Northern and South-Western regions. This work shows that there is modest predictability of Production, Area and Yield based on ENSO indices – with skill depending on the season and region. Predictions of ENSO indices are available 3-6 months in advance and these may be used in conjunction with the above relationships to provide advance assessment. Planting season rainfall may be predicted with enhanced skill, and this too can inform a prediction system. However, other predictors based on the Indian Ocean and Pacific Ocean sea surfaces should be brought in to augment the skill.