RESUMEN
The current study has mapped the impact of changes in different climatic parameters on the productivity of major crops cultivated in India like cereal, pulses, and oilseed crops. The vulnerability of crops to different climatic conditions like exposure, sensitivity, and adaptive indicators along with its different components and agribusiness has been studied. The study uses data collected over the past six decades from 1960 to 2020. Analytical tools such as the Tobit regression model and Principal Component Analysis were used for the investigation which has shown that among climatic parameters, an increase in temperature along with huge variations in rainfall and consistent increase in CO2 emissions have had a negative impact by reducing crop productivity, particularly cereals (26 percent) and oilseed (35 percent). Among various factors, adaptive factors such as cropping intensity, agricultural machinery, and livestock density in combination with sensitivity factors such as average operational land holding size and productivity of cereals, and exposure indicators like Kharif (June-September) temperature, heavy rainfall, and rate of change in maximum and minimum Rabi (October-February) temperature have contributed significantly in increasing crop vulnerability. The agribusiness model needs to be more inclusive. It should pay attention to small and remote farmers, and provide them with inclusive finance that can facilitate the adoption of climate-smart financial innovations, serve the underserved segments, and help them reach the target of a sustainable and inclusive agribusiness model. Though the social, technological, and economic initiatives can enhance the adaptive capacity of farmers, political measures still have a major role to play in providing a healthy climate for agriculture in India through tailored adaptive approaches like the adoption of craft climate adaptation program, dilating the irrigation coverage and location-centric management options. Hence, multidisciplinary and holistic approaches are worth emphasizing for evaluating the future impacts of change in climate on Indian agriculture.
RESUMEN
BACKGROUND: The exploration of clinically relevant information in the free text of electronic health records (EHRs) holds the potential to positively impact clinical practice as well as knowledge regarding Crohn disease (CD), an inflammatory bowel disease that may affect any segment of the gastrointestinal tract. The EHRead technology, a clinical natural language processing (cNLP) system, was designed to detect and extract clinical information from narratives in the clinical notes contained in EHRs. OBJECTIVE: The aim of this study is to validate the performance of the EHRead technology in identifying information of patients with CD. METHODS: We used the EHRead technology to explore and extract CD-related clinical information from EHRs. To validate this tool, we compared the output of the EHRead technology with a manually curated gold standard to assess the quality of our cNLP system in detecting records containing any reference to CD and its related variables. RESULTS: The validation metrics for the main variable (CD) were a precision of 0.88, a recall of 0.98, and an F1 score of 0.93. Regarding the secondary variables, we obtained a precision of 0.91, a recall of 0.71, and an F1 score of 0.80 for CD flare, while for the variable vedolizumab (treatment), a precision, recall, and F1 score of 0.86, 0.94, and 0.90 were obtained, respectively. CONCLUSIONS: This evaluation demonstrates the ability of the EHRead technology to identify patients with CD and their related variables from the free text of EHRs. To the best of our knowledge, this study is the first to use a cNLP system for the identification of CD in EHRs written in Spanish.
