Climate change plays an important role in agricultural production. Agricultural productivity is highly affected by a number of factors including precipitation, temperature. This paper examines the relationship between the yield of two major rice crops (e.g., Kharif and Rabi) and three main climate variables (e.g., maximum temperature, minimum temperature and rainfall). The dynamic relationships among the variables considered for observing the impact of climate change on rice yields are examined based on Vector Autoregression (VAR) model with the use of Granger causality test, impulse response functions and variance decomposition for the data. Maximum and Minimum temperature have significant effects, meanwhile rainfall has negative impact on Kharif rice yield. Adverse effects on Rabi rice yield are observed by maximum temperature and rainfall, whereas minimum temperature affect yield positively. Appropriate adaptive techniques are recommended to overcome this emerging hazard of climate change on rice production.

Segmentation is an important step in many medical imaging applications and a variety of image segmentation techniques do exist. Of them, a group of segmentation algorithms is based on the clustering concepts. In our research, we have intended to devise efficient variants of Fuzzy C-Means (FCM) clustering towards effective segmentation of medical images. The enhanced variants of FCM clustering are to be devised in a way to effectively segment noisy medical images. The medical images generally are bound to contain noise while acquisition. So, the algorithms devised for medical image segmentation must be robust to noise for achieving desirable segmentation results. The existing variants of FCM-based algorithms, segment images without considering the spatial information, which makes it sensitive to noise. We proposed the algorithm, which incorporate spatial information into FCM, have shown considerable resilience to noise, yet with increased noise levels in images, these approaches have not performed exceptionally well. In the proposed research, the input noisy medical images are employed to a denoising algorithm with the help of effective denoising algorithm prior to segmentation. Moreover, the proposed approach will improve upon the existing variants of FCM-based segmentation algorithms by integrating the spatial neighborhood information present in the images for better segmentation. The spatial neighborhood information of the images will be determined using a factor that represents the spatial influence of the neighboring pixels on the current pixel. The employed factor works on the assumption that the membership degree of a pixel to a cluster is greatly influenced by the membership of its neighborhood pixels. Subsequently, the denoised images will be segmented using the designed variants of FCM. The proposed segmentation approach will be robust to noisy images even at increased levels of noise, thereby enabling effective segmentation of noisy medical images.