Current Science

Manju Zacharias ¹, S. Naresh Kumar ¹, S. D. Singh ¹, D. N. Swaroopa Rani ¹, P. K. Aggarwal ¹

Affiliations
1 Centre for Environment Science and Climate Resilient Agriculture, Indian Agricultural Research Institute, New Delhi 110 012, IN

Abstract

Regional climate models (RCMs) are considered to be more useful than general circulation models for assessing impacts of climate change scenarios in agriculture. In this communication, the climatic outputs of an RCM-PRECIS (providing regional climates for impact studies) model were analysed by comparing its baseline simulation daily weather data on temperature and precipitation patterns with the observed weather for the corresponding period (1960-1990) in order to find out the bias in the model. Results showed that model could simulate the mean weather parameters on an aggregated scale, but could not satisfactorily represent spatio-temporal variations. There exists a bias towards higher precipitation along with more intense warm and cold events in the baseline simulation. In order to quantify the impacts of the PRECIS model biasness in baseline simulations on crop performance, rice (kharif season) and wheat (rabi season) yields were simulated using the observed weather and the PRECIS baseline weather for several locations representing the Indo-Gangetic Plains. With more extreme weather parameters in the baseline simulated data, the grain yields of rice and wheat were reduced, even causing wheat crop failure in several years as against none observed. The results indicated that using PRECIS baseline daily weather may cause bias in crop performance assessments. Since the bias in baseline will be carried forward in the assessment of future climatic impacts, there is a need to develop more reliable regional climate scenarios for the Indian region.

Keywords

Climate Change, Crop Yield, Impact Assessment, Regional Climate Models.

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S. Naresh Kumar ¹, Anuja ¹, Md. Rashid ¹, S. K. Bandyopadhyay ¹, Rabindra Padaria ², Manoj Khanna ³

Affiliations
1 Centre for Environment and Climate Resilient Agriculture, Indian Agricultural Research Institute, New Delhi 110 012, IN
2 Divison of Agricultural Extension, Water Technology Centre, Indian Agricultural Research Institute, New Delhi 110 012, IN
3 Water Technology Centre, Indian Agricultural Research Institute, New Delhi 110 012, IN

Abstract

Adaptation strategies that can minimize the negative effects of climatic risk were implemented in over 2000 farms in 12 villages of Mewat district in Haryana, India. Detailed household (HH) level data from 120 farm families for two periods (prior to intervention and end of project period) indicated: (i) agricultural profit of adapted farmers was more than that of non-adapted farmers in all strata according to the difference in difference model; (ii) non-adapted farmers in <4 acre groups have to either alter the existing agricultural practices to reduce management cost and increase profit or incur additional cost for adaptation; (iii) large farmers may have to rationalize their management investments for gaining more profits; (iv) the profit is not directly proportional to the cost of adaptation, if any, among different strata of farmers; (v) agricultural income alone cannot sustain small and marginal farm (<4 acre) families, however with adaptation a self-sustaining agriculture could be achieved; (vi) suitable adaptation can reduce the cost of farm operations, and increase agricultural profits as well as adaptive capacity to climatic risks; (vii) additional cost is not always required for adaptation, and rationalizing agricultural expenditure is essential to adapt to climatic risks. At community level differential costs of adaptation and profits are likely. Policies for incentivizing these 'responsive adaptation' costs for small and marginal farmers would be required. However, investments may be required for establishing permanent agricultural-infrastructure for managing water and agricultural produce in order to sustain agricultural profitability.

Keywords

Adaptation Cost, Agricultural Profit, Climate Change, Farm Family, Land Holding.

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