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Design thinking a modern human centered problem-solving concept that can be used in the face of complex problems is expected to create meaningful innovations after internalizing the problems of farmers and associating them with Climate Smart Agricultural Practices which is offering array of solutions to enable farmers mitigate the problems of Climate Change. The study aims to identify factors influencing Design Thinking ability among undergraduate students of agriculture in modelling Climate Smart Agricultural Practices. This inquiry was conducted at three Colleges of Agriculture under Central Agricultural University, Imphal, in which B.Sc. (Agriculture) degree is provided. Total of 28 final year students have been randomly selected as respondents for the study. Nine independent variable viz., Age, Gender; OGPA; Competency on ICT; Creative Problem Solving; Ability to Visualize Abstract Ideas; Memory Retention, Aptitude & Reading Behavior; and one dependent variable viz. Operationalization of Design Thinking were considered in the scientific inquiry. The binary logistic regression model was significant at p = 0.037 with AIC & BIC values of 40.86 & 54.18, respectively indicating moderate acceptable explanations of independent variables on outcome of dependent variable. With the subsequent Cox & Snell R2 and Nagelkerke R2 values of 0.471 and 0.629, revealed that the independent variables interacted and explained the significant outcome of the dependent variable in the range of 47.10 to 62.90%. The model identified; Age; Gender; OGPA and Creative Problem Solving had significant effect on Design Thinking. The study suggested that strategies need to be focused on development of students’ Aptitude; Competency on ICT, Memory Retention; Ability to Visualize Abstract Ideas and Reading Behavior.

journal of pharmacognosy and phytochemistry

Factors influencing design thinking ability in modelling climate smart agricultural practices by under-graduate agriculture students of central agricultural university Imphal: a binary logistic regression modeling