Training Needs of Horticulture Extension Personnel in Jammu Region of J&K State, India

Effective extension work depends upon competent and well-trained extension personnel. Horticulture extension personnel (Horticulture Development Officers and Horticulture Technicians) occupy the focal position in transfer of technologies to the orchardists in Jammu and Kashmir. Given this a study entitled “Training needs of horticulture extension personnel in Jammu region of Jammu and Kashmir” was undertaken. Data was collected from 200 horticulture extension personnel (30 horticulture development officers and 170 horticulture technicians) working at gross root level in all ten districts of Jammu region. Training need important score was categories in to three categories viz. least important, important, most important by using mean ± S.D technique. The finding reveals that horticulture development officer and horticulture technicians’ categories Pests /disease identification and their control measures as most important training areas in technical skills where time and methods of planting was placed as least important. Similarly in case of communication skills demonstration technique was rated highest important training need area and script writing as least important. Motivation technique and programme planning were also categories as most important training need areas of supervisory skills by horticulture development officers. Two factors viz. trainings attended and information utilization sources were significantly affecting the training needs of horticulture extension personnel.

Training Needs of Horticulture Extension Personnel in Jammu Region of J&K State, India

Effective extension work depends upon competent and well-trained extension personnel. Horticulture extension personnel (Horticulture Development Officers and Horticulture Technicians) occupy the focal position in transfer of technologies to the orchardists in Jammu and Kashmir. Given this a study entitled “Training needs of horticulture extension personnel in Jammu region of Jammu and Kashmir” was undertaken. Data was collected from 200 horticulture extension personnel (30 horticulture development officers and 170 horticulture technicians) working at gross root level in all ten districts of Jammu region. Training need important score was categories in to three categories viz. least important, important, most important by using mean ± S.D technique. The finding reveals that horticulture development officer and horticulture technicians’ categories Pests /disease identification and their control measures as most important training areas in technical skills where time and methods of planting was placed as least important. Similarly in case of communication skills demonstration technique was rated highest important training need area and script writing as least important. Motivation technique and programme planning were also categories as most important training need areas of supervisory skills by horticulture development officers. Two factors viz. trainings attended and information utilization sources were significantly affecting the training needs of horticulture extension personnel.

Changing Farmers' Perception towards Sustainable Horticulture: A Case Study of Extension Education in Farming Community in Yogyakarta, Indonesia

Applying sustainable horticulture as an innovation in The Special Region of Yogyakarta (DIY) Indonesia can be a commendable example in agricultural extension education. Previous research has revealed that understanding farmers' perceptions of innovation is essential for appropriate interventions to change their behavior. In DIY, the surveys were conducted in 2016 with 257 males and 93 females of farmers groups member from 21 villages in Sleman, Bantul, and Kulonprogo Regency. The objective of the survey was to determine the effects of farmer's internal factors on the perception of ecological, social economy, and ethical (ESE) urgency as a component of sustainable horticulture practices. The findings from the ecological, social, and ethical dimensions among the farming community in DIY indicated that, directly and indirectly, the farmers can acknowledge and practice sustainable horticulture. However, this was altering several factors, most notably, motivation and the prospect of increased income. The important thing in extension work was motivation, and a major motivating factor was the possibility of increased agricultural income. This study suggests that extension education of achieving horticultural sustainability in DIY should be based on the motivation of farmers and thoughtfulness of their basic needs especially needs to have higher income.

Verb Metaphoric Extension Under Semantic Strain

This paper explores the processes underlying verb metaphoric extension. Work on metaphor processing has largely focused on noun metaphor, despite evidence that verb metaphor is more common (e.g., Krennmayr, 2011). Across three experiments, we tested the hypothesis that verb metaphoric extensions arise when a verb-noun pairing results in semantic strain. Experiment 1 showed that verbs are more likely than nouns to alter their meaning under semantic strain (the verb mutability effect). Participants paraphrased simple intransitive sentences like The motor complained (sample paraphrase: The engine revved loudly). We developed a novel methodology of using word2vec to assess the degree of semantic change that occurred from initial sentence to paraphrase for both nouns and verbs. Experiment 2 demonstrated that the verb mutability effect was chiefly due to online meaning adjustments, rather than to differences in polysemy between nouns and verbs. In Experiment 3, we replicated the word2vec results with an assessment using human subjects. The results also showed that nouns and verbs change meaning in qualitatively different ways, with verbs more likely to change meaning metaphorically, and nouns more likely to change meaning taxonomically or metonymically. These findings bear on the origin and processing of verb metaphors and provide a link between online sentence processing and diachronic change over language evolution.

Farmers' mental models of soil fertility in a semi-arid area of Kenya

Effective knowledge exchange between farmers and other stakeholders, such as agricultural extensionists and soil scientists, is essential for increasing opportunities for sustainable soil fertility management. To achieve this, it is necessary to understand local farmers’ conceptualisation of soil fertility. This study visualizes farmers’ perceptions of soil fertility as mental models, in order to explore the expansion of their soil knowledge and the extent of their comprehension of the relationship between soil properties that are seen and measured and soil processes. Aggregated mental models of fertile and low fertility soils were created from data collected from 59 farmer interviews at two villages in Kitui County, Kenya. The share of respondents of each concept were shown to analyse the knowledge gaps among farmers and between villages. The mental models revealed that farmers recognize the important roles of soil texture, water availability and farm management in soil fertility. Their knowledge related to their lived experience of the actual productivity of soils, which resulted in a strongly different perspective of fertile and low fertility soil. The differences of perception between the villages were also recognized as the result of differences in land availability. Although the farmers who mentioned soil processes were very few, farmers had the potential to integrate further soil scientific knowledge. Consequently, using the mental model approach to visualize farmers’ perceptions produced benefits by clarifying understanding of farmers’ knowledge and identifying gaps where soil science and extension work could help to expand farmers’ knowledge.

Big Data Framework Using Spark Architecture for Dose Optimization Based on Deep Learning in Medical Imaging

Deep learning and machine learning provide more consistent tools and powerful functions for recognition, classification, reconstruction, noise reduction, quantification and segmentation in biomedical image analysis. Some breakthroughs. Recently, some applications of deep learning and machine learning for low-dose optimization in computed tomography have been developed. Due to reconstruction and processing technology, it has become crucial to develop architectures and/or methods based on deep learning algorithms to minimize radiation during computed tomography scan inspections. This chapter is an extension work done by Alla et al. in 2020 and explain that work very well. This chapter introduces the deep learning for computed tomography scan low-dose optimization, shows examples described in the literature, briefly discusses new methods for computed tomography scan image processing, and provides conclusions. We propose a pipeline for low-dose computed tomography scan image reconstruction based on the literature. Our proposed pipeline relies on deep learning and big data technology using Spark Framework. We will discuss with the pipeline proposed in the literature to finally derive the efficiency and importance of our pipeline. A big data architecture using computed tomography images for low-dose optimization is proposed. The proposed architecture relies on deep learning and allows us to develop effective and appropriate methods to process dose optimization with computed tomography scan images. The real realization of the image denoising pipeline shows us that we can reduce the radiation dose and use the pipeline we recommend to improve the quality of the captured image.