S. Naresh Kumar

Simulation of leaf blast infection in tropical rice agro-ecology under climate change scenario

Assessing disease risk has become an important component in the development of climate change adaptation strategies. Here, the infection ability of leaf blast (Magnaporthe oryzae) was modeled based on the epidemiological parameters of minimum (Tmin), optimum (Topt), and maximum (Tmax) temperatures for sporulation and lesion development. An infection ability response curve was used to assess the impact of rising temperature on the disease. The simulated spatial pattern of the infection ability index (IAI) corresponded with observed leaf blast occurrence in Indo-Gangetic plains (IGP). The IAI for leaf blast is projected to increase during the winter season (December–March) in 2020 (2010–2039) and 2050 (2040–2069) climate scenarios due to temperature rise, particularly in lower latitudes. However, during monsoon season (July–October), the IAI is projected to remain unchanged or even reduce across the IGP. The results show that the response curve may be successfully used to assess the impact of climate change on leaf blast in rice. The model could be further extended with a crop model to assess yield loss.

Elevated CO2 and Temperature Affect Leaf Anatomical Characteristics in Coconut (Cocos nucifera L.)

Climate change impacts on plants are mainly through elevated carbon dioxide [CO2] and air temperature [T] apart from rainfall variability. Plant responses to external factors include anatomical changes, which are relatively less studied as compared to physiological responses.

Improving Crop Adaptations to Climate Change: Contextualizing the Strategy

Climatic stresses have been affecting agricultural productivity and thereby present a major challenge for the food and nutritional security. The frequency and magnitude of these stresses are projected to increase and impact the crop yields at global level as well as in India. Genetic adaptation is identified as the most crucial factor for improving productivity in future climates. Contextualization of genetic improvement for changing climates is essential to improve the crop productivity as well as to conserve the natural resources. Serious reorientation of breeding efforts is required for a comprehensive genetic improvement programme that should address the challenges of changing climates and growing demand for food and nutritional quality. The approaches to be deployed for crop improvement should include characterization of projected climatic stresses, entire germplasm with projected climatic variability as background, utilization of entire genetic diversity and deploying multipronged approaches for genetic improvement. This chapter is aimed to contextualize the issues and approaches for breeding climate resilient varieties.

Coconut and Areca Nut

The plantation crops, owing to their perennial nature, live through the cycles of several types of stresses. Abiotic stresses such as droughts, dry spells, high and low temperatures and floods significantly affect the growth, development and yield of the plantations. At seedling stage, the high light intensity stress causes seedling mortality if no protective measurements are taken. Studies indicated that plantation crops such as coconut impart drought tolerance through morphological, anatomical, physiological, biochemical and molecular mechanisms. The tolerant genotypes with better revival capacity are also identified. Though genotypic improvement through breeding is important, it is more important to improve the population by incorporating the progeny of identified in situ drought-tolerant and high-yielding palms. This should hasten the selection process for abiotic stress tolerance as well as for high yield as the in situ drought-tolerant palms have withstood the naturally occurring stresses in their life cycle. Apart from genetic improvement, the agronomic management becomes very important in plantation crops for sustaining the yield. Therefore, the soil moisture conservation measures and drip irrigation become very important. In this chapter, these issues are discussed in detail with research evidences with special reference to coconut.

Comparative Study of Classification Techniques for Weather Data

Data mining techniques are widely used to analyze the large amount of data. Classification is an important technique which classifies data of various real world applications. This paper aims to compare the performance of classification algorithms for weather data using Waikato Environment for Knowledge Analysis (WEKA). Performance analysis done using cross fold and training set method. The best algorithm found was J48 Decision Tree classifier with highest accuracy and minimum error as compared to others.