scholarly journals Variations in village migration profiles in rural China: An analysis based on the Second National Agricultural Census data

2015 ◽  
Vol 24 (2) ◽  
pp. 160-186 ◽  
Author(s):  
Bin Wu ◽  
Bernadette Robinson ◽  
Wenjin Long
Keyword(s):  
Census Data ◽  
Rural China ◽  
Agricultural Census

scholarly journals Livestock intensification potential in Brazil based on agricultural census and satellite data analysis

2018 ◽  
Vol 53 (9) ◽  
pp. 1053-1060 ◽  
Author(s):  
Arielle Elias Arantes ◽  
Victor Rezende de Moreira Couto ◽  
Edson Eyji Sano ◽  
Laerte Guimarães Ferreira
Keyword(s):  
Primary Productivity ◽  
Carrying Capacity ◽  
Dry Matter ◽  
Census Data ◽  
Stocking Rate ◽  
Agricultural Census ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Stocking Rates

Abstract: The objective of this work was to evaluate the potential of livestock intensification in Brazil. Beef cattle stocking rates were estimated according to agricultural census data on livestock production in Brazilian municipalities. Pasture carrying capacity was obtained by combining moderate resolution imaging spectroradiometer (Modis) images of gross primary productivity and data on dry matter demand per animal unit (AU). Cattle stocking rate for Brazil, in 2014/2015, was 0.97 AU ha-1, and the carrying capacity was 3.60 AU ha-1; therefore, there is an average livestock intensification potential of 2.63 AU ha-1. The highest average intensification potential was observed for the Southern region (3.62 AU ha-1), and the lowest for the Northern (2.13 AU ha-1) and Northeastern regions (2.22 AU ha-1). It is possible to estimate cattle stocking rate, pasture carrying capacity, and potential of livestock intensification by integrating data on agricultural census and remote sensing.

A methodology for the analysis of the relationships between farms and their physical environment

2009 ◽  
Vol 148 (1) ◽  
pp. 101-116 ◽  
Author(s):  
J. A. RIVEIRO ◽  
M. F. MAREY-PÉREZ ◽  
E. R. DÍAZ-VARELA ◽  
C. J. ÁLVAREZ
Keyword(s):  
Spatial Distribution ◽  
Physical Environment ◽  
Census Data ◽  
Production Systems ◽  
Direct Interaction ◽  
Spatial Location ◽  
Agricultural Activity ◽  
Environment Factors ◽  
Agricultural Census ◽  
Heterogeneous Region

SUMMARYAgricultural activity involves direct interaction with the physical environment factors in the environmental context in which the activity is developed. Galicia, northwest Spain, is an environmentally heterogeneous region that encompasses territorial spaces with different levels of suitability for each agricultural activity. In barely 30 years, the production systems of the region have evolved from self-sufficiency to commercial production; however, the requirements of production for each differ greatly. During such a transformation, many farms have disappeared while other farms have conformed to the requirements of the current production systems without changing location. Decision-making in rural planning requires knowing the spatial distribution of farms, the evolution of farm distribution and the relationship between the spatial location of farms (associated with some specific environmental characteristics) and the production systems used.The current paper describes a methodology for analysing the spatial distribution of farms and for determining the relationships between the spatial location of farms and the values of the physical environment factors that are characteristic of each spatial location. The methodology has been developed by using Agricultural Census data and is exemplified through the analysis of two crops (wheat and potato) and one farming activity (dairy farming). Results show the location of farms and the production systems used, and reveal different degrees of fit to the physical environment factors considered.

scholarly journals Delayed Registration and Identifying the “Missing Girls” in China

2016 ◽  
Vol 228 ◽  
pp. 1018-1038 ◽  
Author(s):  
Yaojiang Shi ◽  
John James Kennedy
Keyword(s):  
Sex Ratio ◽  
Policy Implementation ◽  
Census Data ◽  
Rural China ◽  
Descriptive Statistics ◽  
Sex Ratio At Birth ◽  
Selective Abortion ◽  
Late Registration ◽  
Cultural Approach ◽  
Sex Selective Abortion

AbstractIn 2010, according to the sixth Chinese census, the sex ratio at birth (SRB) was 118 males for every 100 females. The global SRB average is about 105. Thus, the gap between 118 and 105 is made up of “missing girls.” Scholars present three main explanations for the skewed SRB statistic: sex-selective abortion, infanticide and delayed or late registration. Most studies take a demographic and cultural approach to explain the high SRB. However, we believe the story of the “missing girls” is also an administrative one and adopt the street-level bureaucrat theory of policy implementation to explain the pervasiveness of late registration in rural China. We use descriptive statistics derived from the 1990, 2000 and 2010 census data to identify the “missing girls.” We believe the combination of late registration and unreported births may point to a larger proportion of “missing girls” than previously reported from the SRB statistic.

scholarly journals Farms worldwide: 2020 and 2030 outlook

2021 ◽  
pp. 003072702110255
Author(s):  
Olaf Erenstein ◽  
Jordan Chamberlin ◽  
Kai Sonder
Keyword(s):  
Large Scale ◽  
Census Data ◽  
National Level ◽  
Farm Households ◽  
Distribution Data ◽  
Simple Rules ◽  
Agricultural Census ◽  
Policy Guidance ◽  
Development Objectives ◽  
Existing Data

Rural development objectives are often framed relative to a targeted number of beneficiary farms and farm households. Yet the data available on the number and distribution of the world’s farms has been surprisingly fragmented and coherent estimates of the number of farms in a given region for a given year have not been available. We take a set of simple rules to use existing data sources to generate a harmonized set of farm number estimates at the national level. We estimate there are 656 million farms globally in 2020, with a projected decline to 624 million farms globally by 2030. These estimates can be used to better inform policy and large-scale investment programming and design. We also articulate the need for further investments in basic agricultural census data, and outline an agenda for the generation of farm distribution data that would be most useful for further policy guidance.

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