Supporting the Bidding Decisions of Smallholder Farmers in Public Calls in Brazil

In Brazil, the National School Feeding Program (PNAE) seeks to contribute to the socio-economic development of smallholder farmers, prioritizing them in supplying their products for preparing daily meals in public schools. However, farmers face challenges in determining which school calls to bid for and the potential benefits from their participation, due to the multiple quantitative and qualitative decision criteria involved. This paper presents a novel Data Envelopment Analysis (DEA)-based method for bidding priority setting, to support the decision making. The model was applied for a case study in Brazil. The academic contribution lies in the innovation of using a Double-Frontier Slack-Based Measure (SBM) DEA model for Hierarchical Network systems, i.e., applied to multiple levels and followed by a tie-breaking method. The practical contribution lies in the decision support of farmers by presenting the results at three levels, the first of which is a ranking by the town or urban cluster priority, the second by the school, and the third by the products. Thus, using the rankings of calls, farmers can make informed decisions regarding the feasibility of bidding for each PNAE public call. At the same time, the objective rankings can alleviate friction and conflict within co-operatives during the decision-making process.

Simulating the Impact of Solar Energy on Pyramid and Stair Urban Blocks

Developing countries such as Iran are rapidly expanding, putting pressure on non-renewable energy resources. The building sector takes a major share of the total energy consumption of the country and is projected to increase further, resulting in the call for strategies to reduce energy use by improving the thermal performance of buildings. This study addresses the compelling need to provide optimum design guidelines for future apartment buildings in the city of Shiraz by investigating two urban cluster typologies, stair and pyramid, arranged in five orientations. The results showcase the ideal combination of 155° for the Pyramid typology, which contributes the least to the annual energy loads of the buildings.

745Probability-proportional-to-size cluster sampling as an effective methodology for large-scale participant recruitment

Background We demonstrate the utility of Probability-Proportional-to-Size Cluster Sampling (PPS-CS) to select participants/sites for large scale surveys. Methods Post ethical and administrative clearance, PPS-CS was carried-out to estimate the prevalence of ‘sublexical dyslexia’, a reading impairment in children from III-VII grades in Udupi district of India. Schools were regarded as clusters. By performing PPS-CS, calculated sample size of 1812 children was systematically recruited. Sampling frame: List of all 1256 schools in Udupi district was retrieved. Kannada medium schools were shortlisted, which yielded 128 schools later classified individually as urban or rural. Results Strength of schools was retrieved and cumulative strength (cs) was derived. With an average school size of 105 for rural and 98 for urban cluster, 18 schools were required to meet the sample size. Owing to a ratio of 3.5:1 of rural-to-urban students, 14 rural and 4 urban schools were selected. Ratio of ‘cs’ to ‘number-of-schools-required’ gave a sampling interval (SI) of 758 for rural, and 662 for urban cluster. A random number (R) was selected between one and SI. First school picked was that with cs > =R. Second was that with cs > =SI+R. The third was with cs > =(SI+R at II school)+SI. Progressively, 18 schools were identified. Conclusions With disproportionate sizes of clusters, PPS-CS ensured that selected participants reflect population estimates accordingly. Prevalence of sublexical dyslexia in Udupi district was therefore accurately estimated using PPS-CS. Key messages Large-scale studies on healthcare or businesses may be effectively carried-out using PPS-CS!

Hunger and Its Associated Factors in an Urban Cluster in the Western Brazilian Amazon: A Population-Based Study

Background: Hunger affects millions of people worldwide. In the current pandemic scenario of COVID-19, Brazil experienced an epidemic peak of hunger, amplifying existing pre-pandemic vulnerabilities, mainly in the Northern Region of the Country. The aim of the present study was to investigate the prevalence of food insecurity and its associated factors in homes with children under five years of age in an urban cluster of a municipality of the Western Brazilian Amazon. Methods: A household survey was conducted with a probabilistic sample of 557 children and their families. Food insecurity (FI) was determined using the Brazilian Food Insecurity Scale. Associations between variables were analyzed based on the prevalence ratio (PR) and respective 95% confidence intervals (CI) calculated through multiple Poisson regression analysis. Variables with a p -value <0.05 after adjustments were considered significantly associated with the outcome. Results: A high prevalence of food insecurity was found among the families in the study (76.47%, PR=1.90, CI: 1.36-2.67, p <0.001); 42.90% had moderate (PR=1.93, CI: 1.31-2.83, p <0.01) and severe FI (PR=1.41, CI: 1.10-1.83, p <0.02), which was associated with low family income, participation in governmental income transfer programs, and heads of households with less than seven years of schooling. Moreover, substantial frequencies of height deficit and overweight were found among the children. Conclusions: The high prevalence of hunger and food insecurity and its associated factors reflects the context of geographic isolation and social exclusion in which these families live, suggesting that a substantial portion of the population under five years of age had experienced episodes of hunger in the 90 days prior to the survey. The high prevalence of height deficit and overweight among the children reveals a scenario of epidemiological/nutritional polarization, requiring the formulation of specific public policies for this population.

How urbanisation alters the intensity of the urban heat island in a tropical African city

Due to the combined effects of urban growth and climate change, rapid urbanisation is particularly challenging in African cities. Areas that will house a large proportion of the urban population in the future coincide with where natural hazards are expected to occur, and where hazard risk management institutions, knowledge, and capacity are often lacking. One of the challenges posed by rapid urbanisation is the Urban Heat Island (UHI) effect, whereby urban areas are warmer than the surrounding rural areas. This study investigates urbanisation patterns and alterations in surface UHI (SUHI) intensity for the Kampala urban cluster, Uganda. Analyses show that between 1995 and 2017, Kampala underwent extensive changes to its urban built-up area. From the centre of the city to adjoining non-built up areas in all directions, the urban land cover increased from 12,133 ha in 1995 to 25,389 ha in 2016. The area of SUHI intensity in Kampala expanded significantly over the 15-year period of study, expanding from 22,910 ha in 2003 to 27,900 ha in 2016, while the annual daytime SUHI of 2.2°C in 2003 had decreased to 1.9°C by 2017. Although SUHI intensity decreased in some parts of the city, elsewhere it increased, suggesting that urbanisation does not always lead to a deterioration of environmental conditions. We postulate that urban development may therefore not necessarily create an undesirable impact on local climate if it is properly managed. Rapidly growing cities in Africa and elsewhere should ensure that the dynamics of their development are directed towards mitigating potentially harmful environmental impacts, such as UHI effect through careful planning that considers both bluespaces and greenspaces.

Differentiated Effects of Urbanization on Precipitation in South China

In this paper, precipitation data from the Tropical Rainfall Measuring Mission (TRMM), together with atmospheric reanalysis data, are employed to identify warm-season precipitation (1998–2014) changes and their association with rapid urbanization in south China. Three urban clusters (Chenyu, Yangtze Delta, and Fujian Guangdong coast) are focused. The results reveal that, for the inland Chengyu urban cluster, a lack of precipitation trend is likely due to insignificant trends in convective available potential energy (CAPE) and total column water vapor (TCWV). They are likely resulted from a reduced local moisture recycling in urban areas, balanced by an increased evapotranspiration of rural areas, together with a stable advection of water vapor input. For the Yangtze River Delta urban cluster, a negative trend in precipitation is associated with a slightly decreased CAPE and an increased TCWV, but is very likely related to urbanization induced an increased planetary boundary layer (PBL) and reduced land surface evaporation. For the Fujian Guangdong coast urban cluster, a marked positive precipitation trend is well explained by positive trends in CAPE and TCWV. The increased precipitation likely benefits from enhanced moisture recycling due to improved vegetation cover in rural areas, and enhanced advection moisture inputs due to urbanization along the coast. These results suggest urbanization effects on precipitation vary with regional conditions. In the coastal area, urbanization enhances sea breezes, which may benefit precipitation if sea breezes go along with the prevailing moisture. In inland area, urbanization likely leads to a warmer-dryer climate if large-scale land cover keeps stationary.