PDF(Pubmed)
Abstract：
In this research, we build two food systems datasets in Mexico; The first one describes the structure of agricultural production units and the second one describes food security aspects of the rural population in these agricultural production units. We also build a third dataset, consisting of path diagrams and path coefficients (derived from Structural Equation Modeling) that relate the first dataset to the second dataset in the four most populated ecoregions of Mexico. The description of the path models and the insights they bring to the current state of food security in Mexican rural households are detailed in an associated article entitled \"Is food security primarily associated with smallholder agriculture or with commercial agriculture?: An approach to the case of Mexico using structural equation modeling\" (https://doi.org/10.1016/j.agsy.2021.103091). The agricultural variables (in the first dataset) include farm size, destination of the farmer\'s production, cultivation practice / water management, predominant source of income of the household, land tenure type, crop diversity, agricultural surface expansion, and the presence of forest cover. They are based on the primary data of the full, latest available agricultural census in Mexico and corresponding official land use / land cover data. The second dataset consists of four food security indicators designed and built for the first food security model in Mexico that incorporates food availability, food accessibility and food utilization aspects. They include the Food Self-sufficiency Index (the balance between food production and food consumption), the Food Access Index (inversely related to marginalization), the Entitlement to Public Health Care index, and the Undernutrition Infrequency index (related to hospital sickness records). We provide the path tables and diagrams that describe the links between the agricultural structure and food security. These diagrams provide the first nationwide statistical evidence for the prominent role of smallholder agriculture in rural food security at the national level and at ecoregion scale for a country of the global South. In order to further investigate the structure of the agricultural production units and their relationships with socio-economic, territorial and landscape data, artificial intelligence (i.e. data mining and machine learning) techniques could be performed on this compendium of datasets. The food security data may stir the development of more food security models in Mexico in relation to other drivers such as consumption habits and non-agricultural activities of rural households.
摘要：
在这项研究中,我们在墨西哥建立了两个粮食系统数据集；第一个描述了农业生产单位的结构，第二个描述了这些农业生产单位中农村人口的粮食安全方面。我们还建立了第三个数据集，由路径图和路径系数（从结构方程建模得出）组成，它们将墨西哥四个人口最多的生态区域中的第一个数据集与第二个数据集相关联。路径模型的描述及其对墨西哥农村家庭当前粮食安全状况的见解在题为“粮食安全主要与小农农业还是与商业农业有关？：使用结构方程模型的墨西哥案例的方法”（https://doi.org/10.1016/j。agsy.2021.103091）。农业变量（在第一个数据集中）包括农场大小，农民生产的目的地，种植实践/水管理，家庭的主要收入来源，土地权属类型，作物多样性,农业表面扩张，和森林覆盖的存在。它们基于完整的主要数据，墨西哥最新的农业普查和相应的官方土地利用/土地覆盖数据。第二个数据集包括为墨西哥第一个粮食安全模型设计和构建的四个粮食安全指标，该模型包含了粮食供应，食物可及性和食物利用方面。它们包括粮食自给自足指数(粮食生产和粮食消费之间的平衡)，食品获取指数(与边缘化成反比)，公共医疗保健权利指数，和营养不良频率指数（与医院疾病记录相关）。我们提供了描述农业结构与粮食安全之间联系的路径表和图。这些图表首次在全国范围内提供了统计证据，证明小农农业在国家一级和生态区规模的农村粮食安全中发挥了突出作用。为了进一步研究农业生产单元的结构及其与社会经济的关系,领土和景观数据，人工智能（即数据挖掘和机器学习）技术可以在这个数据集汇编上执行。粮食安全数据可能会激发墨西哥与其他驱动因素有关的更多粮食安全模型的发展，例如消费习惯和农村家庭的非农业活动。
