9 extractive question-and-answer pairs built from Assessing the impact of climate change on agricultural productivity in Farah Province, Afghanistan – A case study | Annals of Agricultural Research, published by epubs.icar.org.in. Source-evidenced topics include climate change, agricultural productivity, farah province, afghanistan, sustainable farming methods, financial resources, farming methods, sustainable farming. Every answer is a verbatim span of text the source prints, and each row carries the passage it sits in, its offset in that passage, the source quote, the page and the location in the document, so any row can be checked against the original. Question types in the export: 6 factual, 2 list, 1 summary. 3 of the 9 pairs (33.3%) ask explanatory questions; the remaining 6 are factual or table-based questions. 100.00% of rows pass the corpus quality gate.
This dataset contains nine question-and-answer pairs derived from a study on the effects of climate change on agricultural productivity in Farah Province, Afghanistan. The research identifies key factors influencing productivity, such as farmers' awareness, sustainable farming methods, access to financial resources, and farming experience. Statistical methods like multiple linear regression and Cronbach’s alpha test were used to analyze data from 385 randomly selected individuals. The findings suggest that these factors significantly impact agricultural outcomes and offer strategies to mitigate climate change effects.
Browse more agriculture datasets, including other records from the same source when available.
Explore Agriculture datasetsAgronomic zinc biofortification of potato (Solanum tuberosum) through soil and foliar zinc sulphate application in Rajasthan | The Indian Journal of Agricultural Sciences - Question and Answer Dataset
Agriculture · 6 rows
Assessment on therapeutic value and diversity analysis in traditional rice (Oryza sativa) landraces of Tamil Nadu | The Indian Journal of Agricultural Sciences - Question and Answer Dataset
Agriculture · 13 rows
Assessment of avoidable yield losses due to insect pests and diseases in wheat (Triticum aestivum) | The Indian Journal of Agricultural Sciences - Question and Answer Dataset
Agriculture · 12 rows
Bhat (Black soybean):A sustainable choice for hill farming | Indian Farming - Question and Answer Dataset
Health · 6 rows
ICAR Annual Report 2023-24 - Question and Answer
Agriculture · 590 rows
ICAR Annual Report 2023-24 — Question and Answer Dataset
Agriculture · 590 rows
Bio-smart technologies for increasing shelf life and postharvest disease management in fruits for distant and export markets | Indian Horticulture - Question and Answer Dataset
Agriculture · 5 rows
Agricultural growth and its impact on gross state value added: An econometric analysis of major crop sectors in Haryana | The Indian Journal of Agricultural Sciences - Question and Answer Dataset
Agriculture · 9 rows
Use the API URL with your free DD token in notebooks, scripts, and pipelines.
https://www.desidata.in/api/datasets/epubs-icar-org-in-bfbcd646af3d84698bc6ed89638d345d/downloadDataset downloads are free. For Python or API downloads, sign in once and create a free DD token; set it as DD_TOKEN or save it in your notebook's secrets. Requests are linked to your account so your download history and counts stay accurate.
# One-time install: pip install desidata
# Set DD_TOKEN in your environment first (create a free token in Profile & settings).
import desidata
df = desidata.load("epubs-icar-org-in-bfbcd646af3d84698bc6ed89638d345d")
df.head()First 9 of 9 rows
| question | answer | context | answer_start | question_type | knowledge_quality_score | source_document | source_quote | source_page | source_location | confidence | validation_status |
|---|---|---|---|---|---|---|---|---|---|---|---|
| What does the research suggest to improve agricultural productivity and reduce the negative effects of climate change? | increasing farmers’ awareness, using sustainable farming methods, providing access to financial resources, and leveraging farmers’ experience | The F-statistic was 78.451 with a significance level of p-value = 0.000, indicating the significance of the regression model. The t-tests for awareness of climate change (t = 4.205, p-value = 0.000), sustainable farming methods (t = 3.753, p-value = 0.000), access to financial resources (t = 3.112, p-value = 0.002), and farmers’ experience (t = 2.014, p-value = 0.045) showed that all these factors significantly affect agricultural productivity. the Cronbach’s alpha test showed a value of 0.876, indicating high reliability of the research instrument. the findings of this research suggest that increasing farmers’ awareness, using sustainable farming methods, providing access to financial resources, and leveraging farmers’ experience can help improve agricultural productivity and reduce the negative effects of climate change. | 599 | summary | 0.970 | https://epubs.icar.org.in/index.php/AAR/article/view/172667 | the findings of this research suggest that increasing farmers’ awareness, using sustainable farming methods, providing access to financial resources, and leveraging farmers’ experience can help improve agricultural productivity and reduce the negative effects of climate change. | p[0] | 0.700 | valid | |
| What factors were examined to analyze the impact of climate change on agricultural productivity? | farmers’ awareness of climate change, sustainable farming methods, access to financial resources, and farmers’ experience | This research investigates the impact of climate change on agricultural productivity. To analyze this impact, factors such as farmers’ awareness of climate change, sustainable farming methods, access to financial resources, and farmers’ experience were examined. The statistical population of this research included farmers and agricultural experts in the target region, from whom 385 individuals were randomly selected. Statistical methods such as multiple linear regression, Cronbach’s alpha test, Kolmogorov-Smirnov test, and ttests and F-tests were used for data analysis. The results of the multiple linear regression showed that 61.2% of the changes in agricultural productivity are explained by the variables of awareness of climate change, the use of sustainable farming methods, access to financial resources, and farming experience (R2=0.612). | 126 | list | 0.820 | https://epubs.icar.org.in/index.php/AAR/article/view/172667 | To analyze this impact, factors such as farmers’ awareness of climate change, sustainable farming methods, access to financial resources, and farmers’ experience were examined. | ||||
| What statistical methods were used for data analysis in the research? | multiple linear regression, Cronbach’s alpha test, Kolmogorov-Smirnov test, and ttests and F-tests | This research investigates the impact of climate change on agricultural productivity. To analyze this impact, factors such as farmers’ awareness of climate change, sustainable farming methods, access to financial resources, and farmers’ experience were examined. The statistical population of this research included farmers and agricultural experts in the target region, from whom 385 individuals were randomly selected. Statistical methods such as multiple linear regression, Cronbach’s alpha test, Kolmogorov-Smirnov test, and ttests and F-tests were used for data analysis. The results of the multiple linear regression showed that 61.2% of the changes in agricultural productivity are explained by the variables of awareness of climate change, the use of sustainable farming methods, access to financial resources, and farming experience (R2=0.612). | 449 | list | 0.820 | https://epubs.icar.org.in/index.php/AAR/article/view/172667 | Statistical methods such as multiple linear regression, Cronbach’s alpha test, Kolmogorov-Smirnov test, and ttests and F-tests were used for data analysis. | ||||
| What was the significance level of the F-statistic in the regression model? | p-value = 0.000 | The F-statistic was 78.451 with a significance level of p-value = 0.000, indicating the significance of the regression model. The t-tests for awareness of climate change (t = 4.205, p-value = 0.000), sustainable farming methods (t = 3.753, p-value = 0.000), access to financial resources (t = 3.112, p-value = 0.002), and farmers’ experience (t = 2.014, p-value = 0.045) showed that all these factors significantly affect agricultural productivity. the Cronbach’s alpha test showed a value of 0.876, indicating high reliability of the research instrument. the findings of this research suggest that increasing farmers’ awareness, using sustainable farming methods, providing access to financial resources, and leveraging farmers’ experience can help improve agricultural productivity and reduce the negative effects of climate change. | 56 | factual | 0.490 | https://epubs.icar.org.in/index.php/AAR/article/view/172667 | The F-statistic was 78.451 with a significance level of p-value = 0.000, indicating the significance of the regression model. | p[0] | 0.700 | ||
| What percentage of changes in agricultural productivity are explained by the studied variables? | 61.2% | This research investigates the impact of climate change on agricultural productivity. To analyze this impact, factors such as farmers’ awareness of climate change, sustainable farming methods, access to financial resources, and farmers’ experience were examined. The statistical population of this research included farmers and agricultural experts in the target region, from whom 385 individuals were randomly selected. Statistical methods such as multiple linear regression, Cronbach’s alpha test, Kolmogorov-Smirnov test, and ttests and F-tests were used for data analysis. The results of the multiple linear regression showed that 61.2% of the changes in agricultural productivity are explained by the variables of awareness of climate change, the use of sustainable farming methods, access to financial resources, and farming experience (R2=0.612). | 635 | factual | 0.370 | https://epubs.icar.org.in/index.php/AAR/article/view/172667 | The results of the multiple linear regression showed that 61.2% of the changes in agricultural productivity are explained by the variables of awareness of climate change, the use of sustainable farming methods, access to financial resources, and farming experience (R2=0.612). | ||||
| What was the F-statistic value in the regression model? | 78.451 | The F-statistic was 78.451 with a significance level of p-value = 0.000, indicating the significance of the regression model. The t-tests for awareness of climate change (t = 4.205, p-value = 0.000), sustainable farming methods (t = 3.753, p-value = 0.000), access to financial resources (t = 3.112, p-value = 0.002), and farmers’ experience (t = 2.014, p-value = 0.045) showed that all these factors significantly affect agricultural productivity. the Cronbach’s alpha test showed a value of 0.876, indicating high reliability of the research instrument. the findings of this research suggest that increasing farmers’ awareness, using sustainable farming methods, providing access to financial resources, and leveraging farmers’ experience can help improve agricultural productivity and reduce the negative effects of climate change. | 20 | factual | 0.370 | https://epubs.icar.org.in/index.php/AAR/article/view/172667 | The F-statistic was 78.451 with a significance level of p-value = 0.000, indicating the significance of the regression model. | p[0] | 0.700 | valid | |
| What was the p-value for the t-test on farmers’ experience? | 0.045 | The F-statistic was 78.451 with a significance level of p-value = 0.000, indicating the significance of the regression model. The t-tests for awareness of climate change (t = 4.205, p-value = 0.000), sustainable farming methods (t = 3.753, p-value = 0.000), access to financial resources (t = 3.112, p-value = 0.002), and farmers’ experience (t = 2.014, p-value = 0.045) showed that all these factors significantly affect agricultural productivity. the Cronbach’s alpha test showed a value of 0.876, indicating high reliability of the research instrument. the findings of this research suggest that increasing farmers’ awareness, using sustainable farming methods, providing access to financial resources, and leveraging farmers’ experience can help improve agricultural productivity and reduce the negative effects of climate change. | 364 | factual | 0.370 | https://epubs.icar.org.in/index.php/AAR/article/view/172667 | The t-tests for awareness of climate change (t = 4.205, p-value = 0.000), sustainable farming methods (t = 3.753, p-value = 0.000), access to financial resources (t = 3.112, p-value = 0.002), and farmers’ experience (t = 2.014, p-value = 0.045) showed that all these factors significantly affect agricultural productivity. | ||||
| What was the value of the Cronbach’s alpha test in the research? | 0.876 | The F-statistic was 78.451 with a significance level of p-value = 0.000, indicating the significance of the regression model. The t-tests for awareness of climate change (t = 4.205, p-value = 0.000), sustainable farming methods (t = 3.753, p-value = 0.000), access to financial resources (t = 3.112, p-value = 0.002), and farmers’ experience (t = 2.014, p-value = 0.045) showed that all these factors significantly affect agricultural productivity. the Cronbach’s alpha test showed a value of 0.876, indicating high reliability of the research instrument. the findings of this research suggest that increasing farmers’ awareness, using sustainable farming methods, providing access to financial resources, and leveraging farmers’ experience can help improve agricultural productivity and reduce the negative effects of climate change. | 493 | factual | 0.370 | https://epubs.icar.org.in/index.php/AAR/article/view/172667 | the Cronbach’s alpha test showed a value of 0.876, indicating high reliability of the research instrument. | p[0] | 0.700 | valid | |
| How many individuals were randomly selected? | 385 | To analyze this impact, factors such as farmers’ awareness of climate change, sustainable farming methods, access to financial resources, and farmers’ experience were examined. The statistical population of this research included farmers and agricultural experts in the target region, from whom 385 individuals were randomly selected. Statistical methods such as multiple linear regression, Cronbach’s alpha test, Kolmogorov-Smirnov test, and ttests and F-tests were used for data analysis. | 295 | factual | 0.330 | https://epubs.icar.org.in/index.php/AAR/article/view/172667 | The statistical population of this research included farmers and agricultural experts in the target region, from whom 385 individuals were randomly selected. | p[0] | 0.800 | valid |
Read straight from the file — download or use the API URL for the full dataset.
| p[0] |
| 0.700 |
| valid |
| p[0] |
| 0.700 |
| valid |
| valid |
| p[0] |
| 0.700 |
| valid |
| p[0] |
| 0.700 |
| valid |