Inadequate food and nutrition affect human well-being, particularly for many poor subpopulations living in rural areas. The purpose of this research was to analyze the factors that determine the Household Dietary Diversity Score (HDDS) in the rural area of the Paute River Basin, Azuay Province, Ecuador. The sample size of 383 surveys was determined by a stratified random sampling method with proportional affixation. Dietary diversity was measured through the HDDS, with 12 food groups (cereals; roots and tubers; fruits; sugar/honey; meat and eggs; legumes or grains; vegetables; oils/fats; milk and dairy products; meats; miscellaneous; fish and shellfish) over a recall period of 7 days. A Poisson regression model was used to determine the relationship between the HDDS and sociodemographic variables. The results show that the average HDDS of food consumption is 10.89 foods. Of the analyzed food groups, the most consumed are cereals; roots and tubers; fruits; sugar/honey. In addition, the determinants that best explain the HDDS in the predictive model were housing size, household size, per capita food expenditure, area of cultivated land, level of education, and marital status of the head of household. The tools used in this research can be used to analyze food and nutrition security interventions. Furthermore, the results allow policymakers to identify applicable public policies in the fight against hunger.

The MAL1 treatment was effective in reducing both ammonia-N concentrations and methane emissions without depressing SS and SM fermentation, but more research is needed to formulate environmentally cleaner diets for ruminants. © 2016 Society of Chemical Industry.

Incubations were carried out with batch cultures of ruminal micro-organisms from sheep to analyse the influence of the N source on in vitro CH production. The two substrates were mixtures of maize starch and cellulose in proportions of 75:25 and 25:75 (STAR and CEL substrates, respectively), and the three nitrogen (N) sources were ammonia (NH Cl), casein (CA) and isolated soya bean protein (SP). Five isonitrogenous treatments were made by replacing non-protein-N (NPN) with CA or SP at levels of 0 (NPN), 50 (CA50 and SP50, respectively) and 100% (CA100 and SP100) of total N. All N treatments were applied at a rate of 35 mg of N/g of substrate organic matter and incubations lasted 16.5 h. With both proteins, N source × substrate interactions (p = 0.065 to 0.002) were detected for CH production and CH /total VFA ratio. The increases in CH production observed by replacing the NPN with protein-N were higher (p < 0.05) for STAR than for CEL substrate, but the opposite was observed for the increases in volatile fatty acid (VFA) production. As a consequence, replacing the NPN by increased levels of CA or SP led to linear increases (p < 0.05) in CH /total VFA ratio with STAR, whereas CH /total VFA ratio tended (p < 0.10) to be decreased with CEL substrate. Increasing the amount of both proteins decreased linearly (p < 0.05) ammonia-N concentrations, which may indicate an incorporation of amino acids and peptides into microbial protein without being first deaminated into ammonia-N. In incubations with the tested N sources as the only substrate, the fermentation of 1 mg of CA or SP produced 1.24 and 0.60 μmol of CH respectively. The results indicate the generation of CH from protein fermentation, and that the response of CH production to protein-N supply may differ with the basal substrate.

Eliminating food insecurity is one of humanity’s greatest global challenges. Thus, the purpose of this research was to analyze the factors that determine food insecurity in households in the rural area of the Paute River Basin, Azuay Province, Ecuador. Stratified sampling was used as the sampling method, with proportional affixation. Moreover, we employed the Latin American and Caribbean Household Food Security Measurement Scale (ELCSA). We estimated the main determinants of household food insecurity using two binomial logit models and one ordered logit model. For the analysis of the data, the respective statistical and econometric tests were employed. The results show that housing size and access to food security information are the most important determinants of food insecurity in the three predictive models applied in this research. This research contributes to the existing literature on food insecurity and provides important information for policymakers, especially regarding food insecurity in rural areas, which has profound economic and social implications.