Food production to meet human demand has been a challenge to society. Nowadays, one of the main sources of feeding is soybean. Considering agriculture food crops, soybean is sixth by production volume and the fourth by both production area and economic value. The grain can be used directly to human consumption, but it is highly used as a source of protein for animal production that corresponds 75% of the total, or as oil and derived food products. Brazil and the US are the most important players responsible for more than 70% of world production. Therefore, a reliable forecasting is essential for decision-makers to plan adequate policies to this important commodity and to establish the necessary logistical resources. In this sense, this study aims to predict soybean harvest area, yield, and production using Artificial Neural Networks (ANN) and compare with classical methods of Time Series Analysis. To this end, we collected data from a time series (1961–2016) regarding soybean production in Brazil. The results reveal that ANN is the best approach to predict soybean harvest area and production while classical linear function remains more effective to predict soybean yield. Moreover, ANN presents as a reliable model to predict time series and can help the stakeholders to anticipate the world soybean offer.

Resumo: Os produtores de milho precisam decidir sobre qual a melhor alternativa para comercialização do produto: vender antes da colheita, no momento da colheita, no mercado futuro ou armazenar para vender na entressafra. Entretanto, essa nem sempre é uma tarefa fácil já que depende de diversas variáveis que afetam diretamente essa decisão. Neste estudo, foram analisados dados da produção de milho internacional e brasileira e buscou-se identificar qual a melhor estratégia de comercialização de milho para os produtores de Mato Grosso do Sul (MS) considerando quatro variáveis de decisão em conjunto: logística, preço, produtividade e disponibilidade de produto. Essa análise multicritério foi realizada adotando-se a metodologia do Analytic Hierarchy Process (AHP) com o uso do software Expert Choice® v. 11. Os resultados indicaram que a melhor decisão para o produtor de milho de MS seria vender o produto na entressafra e também apontaram que a logística foi o critério de maior peso na tomada de decisão (0,467).Palavras-chaves: AHP; Produção de milho; Logística de grãos; Mato Grosso do Sul. Abstract:Corn producers need to find the best alternative to sell the production; selling before harvest, during the harvest, in the future market or store to sell in the off-season period. However, this is not an easy task because the crop is affected by several variables. In this study, we analyzed international and Brazilian corn production data to find out the best strategy of corn commercialization for Mato Grosso do Sul State (Brazil) producers.

Background: In this article we share our experience of creating a digital pathology (DP) supraregional germ cell tumour service, including full digitisation of the central laboratory. Methods: DP infrastructure (Philips) was deployed across our hospital network to allow full central digitisation with partial digitisation of two peripheral sites in the supraregional testis germ cell tumour network. We used a survey-based approach to capture the quantitative and qualitative experiences of the multidisciplinary teams involved. Results: The deployment enabled case sharing for the purposes of diagnostic reporting, second opinion, and supraregional review. DP was seen as a positive step forward for the departments involved, and for the wider germ cell tumour network, and was completed without significant issues. Whilst there were challenges, the transition to DP was regarded as worthwhile, and examples of benefits to patients are already recognised. Conclusion: Pathology networks, including highly specialised services, such as in this study, are ideally suited to be digitised. We highlight many of the benefits but also the challenges that must be overcome for such clinical transformation. Overall, from the survey, the change was seen as universally positive for our service and highlights the importance of engagement of the whole team to achieve success.

Soybean is one of the main sources of protein directly and indirectly in human nutrition, and it is highly dependent on logistics to connect country growers and international markets. Although recent studies deal with the impact of logistics on international trade, this impact in agricultural commodities is still an open research question. Moreover, these studies usually do not consider the influence of all components of the logistics on trade. This paper, therefore, aims at identifying the role of logistics performance in soybean exports among Argentina, Brazil, the US and their trading partners from 2012 to 2018. Using an extended gravity model, we examine whether the indicators of the World Bank Logistics Performance Index (LPI), adopted as a proxy of logistics efficiency, are an important determinant of bilateral soybean trade facilitation. The results lead to the conclusion that it is necessary to analyze the LPI throughout its indicators because they may affect trade differently. The novelty of this article is to provide an analysis of the impact of different logistics aspects on commodity trade, more specifically in the soybean case. Finally, regarding the model results, logistics infrastructure has a positive and significant correlation with soybean trade as supposed in most of the literature.