Climate-change and agriculture - an indian perspective

In this paper, we examine the major predictions made so far regarding the nature of climate change and its impacts on our region in the light of the known errors of the set of models and the observations over this century. The major predictions of the climate models about the impact of increased concentration of greenhouse gases ave at variance with the observations over the Indian region during the last century characterized by such increases and global warming. It is important to note that as far as the Indian region is concerned, the impact of year-to-year variation of the monsoon will continue to be dominant over longer period changes even in the presence of global warming. Recent studies have also brought out the uncertainties in the yields simulated by crop models. It is suggested that a deeper understanding of the links between climate and agricultural productivity is essential for generating reliable predictions of impact of climate change. Such an insight is also required for identifying cropping patterns and management practices which are tailored for sustained maximum yield in the face of the vagaries of the monsoon.

Climate change and agricultrue-An Indian perspective

In this paper, we examine the major predictions made so far regarding the nature of climate change and its impacts on our region in the light of the known errors of the set of models and the observations over this century. The major predictions of the climate models about the impact of increased concentration of greenhouse gases ave at variance with the observations over the Indian region during the last century characterized by such increases and global warming. It is important to note that as far as the Indian region is concerned, the impact of year-to-year variation of the monsoon will continue to be dominant over longer period changes even in the presence of global warming. Recent studies have also brought out the uncertainties in the yields simulated by crop models. It is suggested that a deeper understanding of the links between climate and agricultural productivity is essential for generating reliable predictions of impact of climate change. Such an insight is also required for identifying cropping patterns and management practices which are tailored for sustained maximum yield in the face of the vagaries of the monsoon.

Gene expression programming-fuzzy logic method for crop type classification

Crop type classification using remote sensing data plays a vital role in planning cultivation activities and for optimal usage of the available fertile land. Thus a reliable and precise classification of agricultural crops can help improve agricultural productivity. Hence in this paper a gene expression programming based fuzzy logic approach for multiclass crop classification using Multispectral satellite image is proposed. The purpose of this work is to utilize the optimization capabilities of GEP for tuning the fuzzy membership functions. The capabilities of GEP as a classifier is also studied. The proposed method is compared to Bayesian and Maximum likelihood classifier in terms of performance evaluation. From the results we can conclude that the proposed method is effective for classification.

Fingerprinting environmental conditions and related stress using stable isotopic composition of rice (Oryza sativa L.) grain organic matter

Sea level rise (SLR) is a primary factor responsible for inundation of low-lying coastal regions across the world, which in turn governs the agricultural productivity. In this study, rice (Oryza sativa L.) cultivated seasonally in the Kuttanad Wetland, a SLR prone region on the southwest coast of India, were analysed for oxygen, hydrogen and carbon isotopic ratios (delta O-18, delta H-2 and delta C-13) to distinguish the seasonal environmental conditions prevalent during rice cultivation. The region receives high rainfall during the wet season which promotes large supply of fresh water to the local water bodies via the rivers. In contrast, during the dry season reduced river discharge favours sea water incursion which adversely affects the rice cultivation. The water for rice cultivation is derived from regional water bodies that are characterised by seasonal salinity variation which co-varies with the delta O-18 and delta H-2 values. Rice cultivated during the wet and the dry season bears the isotopic imprints of this water. We explored the utility of a mechanistic model to quantify the contribution of two prominent factors, namely relative humidity and source water composition in governing the seasonal variation in oxygen isotopic composition of rice grain OM. delta C-13 values of rice grain OM were used to deduce the stress level by estimating the intrinsic water use efficiency (WUEi) of the crop during the two seasons. 1.3 times higher WUE, was exhibited by the same genotype during the dry season. The approach can be extended to other low lying coastal agro-ecosystems to infer the growth conditions of cultivated crops and can further be utilised for retrieving paleo-environmental information from well preserved archaeological plant remains. (c) 2015 Elsevier Ltd. All rights reserved.