A dataset for soil organic carbon in agricultural systems for the Southeast Asia region.

The determination of changes in soil organic carbon (SOC) content under different cropping systems is necessary for policy development oriented towards soil conservation, C sequestration, and future C credit markets. The aim of this study was to generate an open SOC dataset resulting from a systematic literature search related to the agricultural systems for Southeast Asia. The dataset has 209 articles and 4341 observations on soils of cropping systems in this region from articles published between 1987 and 2023. This dataset included different management practices, land uses, soil sampling depth, and length of SOC content assessment. In addition, inherent features of crop production reported in the experiments were included in the dataset. This dataset can be applied to quantify and compare the impact of different land uses or management practices on SOC content, providing foundational knowledge towards identifying sustainable practices. Lastly, it is a useful guide for future regional SOC sequestration policies and the development of C credit markets.

Investigating Salmonella enterica, Escherichia coli, and Coliforms on Fresh Vegetables Sold in Informal Markets in Cambodia†.

ABSTRACT: Vegetables in Cambodia are commonly sold in informal markets lacking food safety standards and controls. Current data on microbial contamination of vegetables in Cambodian informal markets are limited. The purpose of this study was to investigate Salmonella enterica and indicator organisms (Escherichia coli and coliforms) on the surface of fresh vegetables sold in informal markets in Cambodia. Samples of loose-leaf lettuce, tomatoes, and cucumbers were collected from 104 vendors at four informal markets in Battambang and Siem Reap provinces during the rainy and dry seasons. Detection methods for S. enterica were adapted from the U.S. Food and Drug Administration's Bacteriological Analytical Manual. Coliform and E. coli populations were quantified by plating onto E. coli/coliform count plates. S. enterica was most prevalent on lettuce during the dry season (56.5%, 95% confidence interval [CI] [41.0, 70.8]) than during the rainy season (15.4%, 95% CI [7.5, 29.1]), whereas no significant seasonal differences were apparent for tomatoes and cucumbers. Regardless of season, levels of S. enterica were highest on lettuce (5.7 log CFU/g, 95% CI [5.5, 5.9]), relative to cucumbers (4.2 log CFU/g, 95% CI [3.8, 4.6]) and tomatoes (4.3 log CFU/g, 95% CI [4.1, 4.6]). For E. coli, prevalence was higher during the rainy season (34.0%, 95% CI [25.4, 43.8]) than during the dry season (9.1%, 95% CI [4.9, 16.5]), with the highest prevalence estimated on lettuce. Coliform levels on lettuce and tomatoes were greater during the rainy season (6.3 and 5.3 log CFU/g, 95% CI [5.7, 6.8] and [4.7, 5.8], respectively) than during the dry season (5.2 and 3.9 log CFU/g, 95% CI [4.7, 5.7] and [3.4, 4.4], respectively). These results indicate seasonal patterns for microbial prevalence in lettuce and an overall high level of bacterial contamination on raw vegetables sold in Cambodian informal markets.

Prevalence of Salmonella enterica Isolated from Food Contact and Nonfood Contact Surfaces in Cambodian Informal Markets.

ABSTRACT: The lack of hygiene and sanitation practices and insufficient infrastructure in Cambodian informal markets may increase the risk of food contamination, specifically raw vegetables, which in turn may increase the chances of contracting a foodborne disease. The aims of this study in informal markets in Cambodia were (i) to quantify the prevalence of Salmonella enterica based upon differences in season of the year (rainy versus dry), surface types (food contact surfaces versus nonfood contact surfaces), and location of vendors within the market (inside versus outside) and (ii) to characterize S. enterica serotype prevalence. A total of 310 samples were screened for S. enterica prevalence following the U.S. Department of Agriculture guidelines, and results were confirmed by PCR assay. Whole genome sequencing was used to determine the serotype for each isolate in silico using SeqSero 1.0 on draft genomes. A total of 78 samples were confirmed positive for S. enterica. During the dry season, S. enterica was more prevalent on food contact surfaces than on nonfood contact surfaces (estimated probability of detection [confidence interval]: 0.41 [0.25, 0.59] and 0.17 [0.08, 0.32], respectively; P = 0.002), but no differences were apparent in the rainy season. No differences in S. enterica prevalence were found based on location within the market (P = 0.61). Sixteen S. enterica serotypes were detected across multiple surfaces. The most common S. enterica serotypes were Rissen (18 isolates), Hvittingfoss (11), Corvallis (10), Krefeld (8), Weltevreden (6), and Altona (6). Accurate data on the prevalence of S. enterica in informal markets are crucial for the development of effective surveillance and implementation of suitable intervention strategies at the domestic level, thus preventing foodborne illness.

The Rural Household Multiple Indicator Survey, data from 13,310 farm households in 21 countries.

The Rural Household Multiple Indicator Survey (RHoMIS) is a standardized farm household survey approach which collects information on 758 variables covering household demographics, farm area, crops grown and their production, livestock holdings and their production, agricultural product use and variables underlying standard socio-economic and food security indicators such as the Probability of Poverty Index, the Household Food Insecurity Access Scale, and household dietary diversity. These variables are used to quantify more than 40 different indicators on farm and household characteristics, welfare, productivity, and economic performance. Between 2015 and the beginning of 2018, the survey instrument was applied in 21 countries in Central America, sub-Saharan Africa and Asia. The data presented here include the raw survey response data, the indicator calculation code, and the resulting indicator values. These data can be used to quantify on- and off-farm pathways to food security, diverse diets, and changes in poverty for rural smallholder farm households.