Table 2 - uploaded by Arjun Vysakh
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Average and percentage change in paddy yield of first crop in weak, moderate and strong El Niño episodes in autumn
Source publication
Monthly rainfall data for the 14 districts of Kerala from 1985 to 2014 were used in the present analysis to compute the annual and seasonal rainfall. IITM's Sub divisional monthly rainfall data from 1951-2014 has been also used to get a clear picture of the El Niño effect on rainfall variability in Kerala. District-wise area, production and product...
Contexts in source publication
Context 1
... Journal of Agricultural Sciences 9(5) Yield Thiruvananthapuram 6421 7271 13 5852 -9 6359 -1 Kollam 5588 7271 30 4702 -16 5549 -1 Pathanamthitta 5412 5515 2 3863 -29 6047 12 Alappuzha 5556 6606 19 ...
Context 2
... rainfall and high temperature during El Niño could affect the rice productivity. Percentage change in rice productivity of first crop (Virippu; April-May to September- October) in the weak, moderate and strong years is shown in (Table 2). So the variation in south west monsoon rainfall affects the autumn crop yield to a large extent. ...
Citations
... Several studies were undertaken to link the ISMR, droughts and El Niño and its impact of food grain production over India (Selvaraju, 2003;Shweta Saini and Gulati, 2014;Gadgil and Francis, 2016;Bhatla et al., 2019). However, there are only few/limited studies which try to analyse the El Niño effect on agricultural productivity and that also at the macro level (Rao et al., 2011;Prasad et al., 2014;Patel et al., 2014;Ajithkumar and Vysakh, 2018;Dakhore et al., 2020;Bhuvaneswari et al., 2013). These studies indicated that there is large scale variability of impact over India, particularly impact at district level, which is the smallest administrative unit available to get the availability of yield data and also the smallest administrative unit for planning. ...
The spatial and temporal variability of Indian Summer Monsoon Rainfall (ISMR) during El Niño years (2002, 2004 & 2009) and its impact on district-wise yield of four important kharif crops viz., rice, maize, pearlmillet and sorghum over India were analysed. The number of districts fall under different rainfall classes are different in normal and El Niño years and found that there is a small shifting of spine curve towards left, which indicate the number of districts under lower rainfall classes increased in El Niño years. As far as National level analysis indicated that the yield of these major kharif crops grown steadily during the study period with higher rate of 43 kg/ha in maize and lower rate of 6.2 kg/ha in sorghum due to technological advancement. Identified high and medium vulnerable districts with respect to above crops in El Niño years based on yield changes > −10 per cent compared to average of previous and succeeding years. 77 and 36 districts identified as highly vulnerable with respect to rice and sorghum, respectively. Whereas 36 and 65 districts in the case of sorghum and maize, respectively. Thus, the results of this study provide an opportunity to policy makers/ researchers/other stakeholders to prepare short and long term contingency plan to cope up with the situation, if there is any forecast of El Niño in future.
... This means that extreme climate events will greatly impact the decline in planting area and production due to agricultural land affected by flooding and drought and even attacks by Plant Pest Organisms. This is also revealed from the research results [25,26,27,28,29,30,31] which states that extreme climate events and rainfall variability greatly affect agricultural production. ...
Indonesia has a large tropical archipelago with a complex and dynamic climate. Climate change has a significant impact especially in the agriculture sector where the magnitude of such impact varies from one location to another. Information about the magnitude of the impact and its monitoring in each location is still limited. The research objective was to determine the key locations for Indonesia's climate variability based on SST Niño 3.4 index in El-Niño and La-Niña conditions. More than 4000 rain-stations data were used in this analysis. Key locations were determined based on a strong and significant correlation between rainfall and SST Niño 3.4 index under El-Niño and La-Niña conditions with a lag of 1 to 4 months. Based on the analysis results, 6 rain stations could be used as key locations on El-Niño and 5 rain stations on La-Nina condition. Key locations can be used as priority locations for impact analysis and monitoring of the impact of extreme climate, especially in the agricultural sector.
