Report on consumer acceptability tests of NARITA hybrids in Tanzania and Uganda

Consumer acceptability tests of NARITA hybrids were conducted with a total of 572 randomly selected men and women farmers from 5 sites in different agro-ecological zones in Tanzania and Uganda (Maruku, Mitalula and Moshi in Tanzania; Kawanda and Mbarara in Uganda). Evaluations were done between July and November 2018 under the project ‘Improvement of banana for smallholder farmers in the Great Lakes Region of Africa’. At each site, focus group discussions (FGDs) were first conducted with different age groups: young women, young men (35 years) to ascertain the main products households make using cooking banana cultivars and the preparation method. The most important product was then prepared in each site - steamed matooke in both Uganda sites and boiled fingers in all Tanzania sites. On a given day, about 100 farmers were each provided with coded samples of four NARITA hybrids plus one local check and asked to rate each sample on a 5-point hedonic scale for the following attributes: colour, aroma, texture in hand, taste, mouthfeel and overall acceptability. This report provides results that can help inform the selection of the best NARITAs to take on-farm and subsequent varietal release

Development and evaluation of neural network models to estimate daily solar radiation at Córdoba, Argentina Desenvolvimento e avaliação de modelos de redes neurais para estimação da irradiação solar diária em Córdoba, Argentina

The objective of this work was to develop neural network models of backpropagation type to estimate solar radiation based on extraterrestrial radiation data, daily temperature range, precipitation, cloudiness and relative sunshine duration. Data from Córdoba, Argentina, were used for development and validation. The behaviour and adjustment between values observed and estimates obtained by neural networks for different combinations of input were assessed. These estimations showed root mean square error between 3.15 and 3.88 MJ m-2 d-1 . The latter corresponds to the model that calculates radiation using only precipitation and daily temperature range. In all models, results show good adjustment to seasonal solar radiation. These results allow inferring the adequate performance and pertinence of this methodology to estimate complex phenomena, such as solar radiation.<br>O objetivo deste trabalho foi desenvolver modelos de redes neuronais, do tipo retropropagação, para a estimação da irradiação solar, a partir de dados de irradiação solar extraterrestre, amplitude térmica, precipitação, nebulosidade e razão de insolação. O treinamento e a validação foram realizados com dados correspondentes a Córdoba, Argentina. O comportamento e ajuste entre os valores observados e os estimados pelas redes foram avaliados para diferentes combinações das variáveis de entrada, que apresentaram valores do erro quadrático médio entre 3,15 e 3,88 MJ m-2 d-1 . Este último valor corresponde ao modelo que calcula a irradiação somente utilizando precipitação e amplitude térmica diária. Os resultados exibem em todos os modelos um ajuste apropriado ao comportamento sazonal da irradiação solar e permitem concluir a pertinência e o adequado desempenho desse método para estimar fenômenos complexos como a irradiação solar