Survey data on livelihoods and inputs and outputs of crop production in Quzhou county on the North China Plain.

Data Brief

State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China.

Published: April 2024

Farmers' decisions on crop choice, management practices, and livelihood strategies are essential to agricultural sustainability. This data article describes three datasets on crop production in Quzhou, a county in the central part of North China Plain. The three datasets cover different scales. The village dataset assembles basic data on all 342 villages of Quzhou county, including information on population, land area, crop grown, labour, irrigation and markets. Data was sourced from the yearbook data of 2017 and a village cadres survey in 2018. The village dataset was used to create a village typology from which 35 villages belonging to seven village types (five for each type) were selected for stratified random sampling to collect information on farm characteristics and cropping practices. We surveyed these 35 villages, interviewing fifteen farmer households per village (525 in total) in 2020. The interviewees represented two farm management models: smallholder farms and business farms. The resulting household dataset provides farm-level data, including demographic data of farming decision-makers and the number of household members, land use and machinery resources, crop production management practices, and government subsidies. The crop-level dataset was derived from the household survey and included input-output inventories for each crop grown during one year on each field greater than 1/30th ha (1/2 mu) on the 525 surveyed farms within a year. This dataset comprises information on cropping practices in 1352 fields. The three datasets provide a basis for analyses on cropping practices and sustainability attributes of farms and crops in a typical agricultural county on the North China Plain.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10964062PMC
http://dx.doi.org/10.1016/j.dib.2024.110269DOI Listing

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