Dietary diversity became a global concern in improving health conditions through the habit of food group consumption by adding health dimension to the issue of food calorie consumption. Access to nutritionally adequate and good quality diet is essential to human health, productivity and work output. However, despite the various concerns by governments all over the world on ensuring that every household can at least provide three square meals per day, food insecurity continues to be a major development problem across the globe. This study assessed the effect of COVID-19 pandemic lockdown on households’ dietary diversity in Jos Metropolis, Plateau State, Nigeria. A multistage sampling technique was used to select 265 households. Data was collected using well-structured questionnaire. The analytical techniques were; Descriptive statistics, Dietary Diversity index and Z-Statistics Test. Gender, age, household size, education, marital status, cooperative membership and access to credit were the socioeconomic characteristics described. Result indicate that 86% of the households had low food dietary diversity while 14% of the households had high food dietary diversity before and after the pandemic lockdown. Similarly, 18% of the households had low calorie consumption while 82% of the households had high dietary diversity before and after the pandemic lockdown. Cereals, legumes/grains, oils/fat, roots and tubers, sugar and honey and meats were the most common food consumed by the households daily before and after the COVID-19 pandemic lockdown. Result further indicate that the pandemic lockdown had effect on the dietary diversity and food consumption patterns of the urban households. It can be concluded that the understanding of the effect of the COVID-19 pandemic lockdown on dietary diversity and food consumption patterns of households is important in developing policy measures such as social safety nets, home feeding programmes, the school feeding programme, conditional cash transfers schemes and improved marketing channels that will help mitigate against households falling into food insecurity during similar pandemic in the future.

This study was carried out to investigate the effects of different sowing times on duration of tasselling, plant height, first ear height, ear length, ear diameter, number of ears per plant, fresh ear yield with husk, fresh ear yield unhusked, and marketable number of ears of different sweet corn (Zea mays L. saccharata Sturt.) varieties in Diyarbakır between the years of 2010-2012. The trial was conducted as split plots in randomised complete blocks with three replications. The main plots were sowing times, and the sub-plots were varieties. The trials were carried out at eight different sowing times (1 April, 15 April, 1 May, 15 May, 1 June, 15 June, 1 July and 15 July) with ‘Merit’, ‘Jubilee’, ‘Lumina’, ‘Vega’ and ‘Sakarya’ composite sweet corn varieties. From the variance analysis of the three-year combined average, the duration of tasselling, plant height, first ear height, ear length, ear diameter, number of ears per plant, fresh husked ear yield, fresh unhusked ear yield and marketable ear number were statistically significant for sowing times and varieties. According to the sowing time × variety interaction, except ears per plant and first ear height values, all other traits were significant. The fresh husked and unhusked ear yields varied between 8541.7-19396.8 kg ha-1 and 5065.0-13485.7 kg ha-1, respectively. The highest fresh husked and fresh unhusked ear yields were obtained from the 15 April sowing time of ‘Vega’ variety. The results state that optimal sowing dates for different varieties of sweet corn could be from 1 April to 1 May, and the most suitable variety to be planted is ‘Vega’ in Diyarbakır conditions.

This study examined the nexus between cluster farming and household dietary diversity among smallholder wheat farmers in Oromia region, Ethiopia. Three-stage sampling procedures were employed to gather data from 384 household heads on a cross-sectional survey that was carried out in June 2022. Descriptive statistics and a binary logistic regression model were used for data analysis. The average, minimum, and maximum household dietary diversity scores were 7.10, 1, and 11, respectively. A significant difference was observed: participants in cluster farming had 14% more dietary diversity than their counterparts. Sex, marital status, participation in wheat cluster farming, home gardening, off-farm income, number of crops grown, cooperative membership, and access to training determined household dietary diversity. The study indicates that dietary diversity in households is influenced by a variety of factors. While wheat cluster-based farming is heavily promoted, household food and nutrition security also need to consider other elements like home gardening, off-farm income, cooperative membership, and training access.

Models for the prediction of water loss (ML) and solid gain (SG) in osmotic dehydration process based on artificial neural network (ANN) perform better as compared to other models developed for osmotic dehydration, because these models mathematically correlate a wide quantity of processing variables with ML and SG. The main advantage of these models is that they are predictive rather than correlative, also these models can be easily implemented in a spreadsheet, and they are very useful and practical for process design and control. The aim of this work is to use a developed model based on ANN to predict outcomes in osmotic dehydration processes. Predictions were made with different process conditions and were validate by using experimental data reported in literature. Good predictions of ML (MRE 19%) and variable behavior for SG (MRE: 62%) were obtained. | Los modelos para predecir las cinéticas de pérdida de agua (ML) y ganancia de sólidos (SG) empleando redes neuronales artificiales (ANN) han demostrado tener mejor comportamiento que los demás modelos desarrollados debido a que correlacionan empíricamente gran cantidad de las variables con ML y SG. La principal ventaja de estos modelos es que son predictivos en vez de correlativos, además pueden implementarse fácilmente en una hoja de datos, y son muy útiles y prácticos para el diseño y control del proceso. El objetivo de este trabajo es emplear un modelo desarrollado con ANN para predecir resultados en procesos de deshidratación osmótica. Se hicieron predicciones de comportamiento con diferentes condiciones de proceso y se validaron con resultados experimentales presentados en la literatura. Se obtuvieron buenas predicciones de ML (MRE 19%) y un comportamiento variable para SG (MRE 62%).