AI Article Synopsis

  • The dataset provides insights into rice production practices from 8 Indian states, using interviews conducted with farmers during the 2018 rainy season.
  • The data, collected via the Open Data Kit on mobile devices, includes approximately 225 variables related to farming activities, like land preparation and crop yield, ensuring accurate data entry and minimizing errors.
  • This comprehensive dataset aims to fill a national data gap and supports evidence-based agricultural policy-making and technology adoption analyses.

Article Abstract

This dataset provides detailed information on rice production practices being applied by farmers during 2018 rainy season in India. Data was collected through computer-assisted personal interview of farmers using the digital platform Open Data Kit (ODK). The dataset,  = 8355, covers eight Indian states, viz., Andhra Pradesh, Bihar, Chhattisgarh, Haryana, Odisha, Punjab, Uttar Pradesh and West Bengal. Sampling frames were constructed separately for each district within states and farmers were selected randomly. The survey was deployed in 49 districts with a maximum of 210 interviews per district. The digital survey form was available on mobile phones of trained enumerators and was designed to minimize data entry errors. Each survey captured approximately 225 variables around rice production practices of farmers' largest plot starting with land preparation, establishment method, crop variety and planting time through to crop yield. Detailed modules captured fertilizer application, irrigation, weed management, biotic and abiotic stresses. Additional information was gathered on household demographics and marketing. Geo-points were recorded for each surveyed plot with an accuracy of <10 m. This dataset is generated to bridge a data-gap in the national system and generates information about the adoption of technologies, as well as enabling prediction and other analytics. It can potentially be the basis for evidence-based agriculture programming by policy makers.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9679526PMC
http://dx.doi.org/10.1016/j.dib.2022.108625DOI Listing

Publication Analysis

Top Keywords

rice production
12
production practices
12
practices applied
8
applied farmers
8
large survey
4
survey dataset
4
dataset rice
4
farmers
4
farmers largest
4
largest farm
4

Similar Publications

Estimating pesticide concentrations in paddy rice systems is challenging due to unique cultivation methods and water management practices. Various models, ranging from simple exposure calculators to complex scenario-dependent tools, have been developed globally to address this issue (PADDY, MED-Rice, RICEWQ, PFAM). In Brazil, pesticides are used in paddy rice production, and there is a potential risk of these compounds reaching waterbodies.

View Article and Find Full Text PDF

In July 2023, panicle and leaf blight-like symptoms were observed from the rice () variety, PVL03, in research field plots in Louisiana (Rayne, LA 70578, USA; 30.21330⁰ N, 92.37309⁰ W).

View Article and Find Full Text PDF

Exploiting the efficient Exo:Cas12i3-5M fusions for robust single and multiplex gene editing in rice.

J Integr Plant Biol

January 2025

State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences (ICS), Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100081, China.

The development of a single and multiplex gene editing system is highly desirable for either functional genomics or pyramiding beneficial alleles in crop improvement. CRISPR/Cas12i3, which belongs to the Class II Type V-I Cas system, has attracted extensive attention recently due to its smaller protein size and less restricted canonical "TTN" protospacer adjacent motif (PAM). However, due to its relatively lower editing efficiency, Cas12i3-mediated multiplex gene editing has not yet been documented in plants.

View Article and Find Full Text PDF

A measurement of the dijet production cross section is reported based on proton-proton collision data collected in 2016 at by the CMS experiment at the CERN LHC, corresponding to an integrated luminosity of up to 36.3 . Jets are reconstructed with the anti- algorithm for distance parameters of and 0.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!