The study was aimed to evaluate the effect of successive applications of increasing biochar rates along with other mineral fertilizers and poultry manure on onion nutrition and yield. The experiment consisted of 2 (agricultural year) × 11 (treatments) factorial combinations over two cropping years (2017 and 2018). Thus, 11 treatments were tested as follows: C = control; MF = mineral fertilizer; PM = poultry manure; B1+ MF = 1 Mg ha⁻¹ of biochar+MF; B1+ PM = 1 Mg ha⁻¹ of biochar+PM; B2+ MF = 2 Mg ha⁻¹ of biochar+MF; B2+ PM = 2 Mg ha⁻¹ of biochar+PM; B4+ MF = 4 Mg ha⁻¹ of biochar+MF; B4+ PM = 4 Mg ha⁻¹ of biochar+PM; B10+ MF = 10 Mg ha⁻¹ of biochar+MF; B10+ PM = 10 Mg ha⁻¹ of biochar+PM. The highest onion yield (39.9 Mg ha⁻¹) was obtained in 2017 with the biochar rate of 5.9 Mg ha⁻¹ while an increase of 308 kg ha⁻¹ in onion yield was found for each Mg ha⁻¹ of biochar added to soil in 2018. The use of biochar combined with mineral fertilizers increased onion yield; and the use of biochar combined with poultry manure improved soil fertility status.

Artificial neural networks (ANN) are computational models inspired by the neural systems of living beings capable of learning from examples and using them to solve problems such as non-linear prediction, and pattern recognition, in addition to several other applications. In this study, ANN were used to predict the value of the area under the disease progress curve (AUDPC) for the tomato late blight pathosystem. The AUDPC is widely used by epidemiologic studies of polycyclic diseases, especially those regarding quantitative resistance of genotypes. However, a series of six evaluations over time is necessary to obtain the final area value for this pathosystem. This study aimed to investigate the utilization of ANN to construct an AUDPC in the tomato late blight pathosystem, using a reduced number of severity evaluations. For this, four independent experiments were performed giving a total of 1836 plants infected with Phytophthora infestans pathogen. They were assessed every three days, comprised six opportunities and AUDPC calculations were performed by the conventional method. After the ANN were created it was possible to predict the AUDPC with correlations of 0.97 and 0.84 when compared to conventional methods, using 50 % and 67 % of the genotype evaluations, respectively. When using the ANN created in an experiment to predict the AUDPC of the other experiments the average correlation was 0.94, with two evaluations, 0.96, with three evaluations, between the predicted values of the ANN and they were observed in six evaluations. We present in this study a new paradigm for the use of AUDPC information in tomato experiments faced with P. infestans. This new proposed paradigm might be adapted to different pathosystems.

O objetivo deste trabalho foi estimar curvas de progresso de requeima, em genótipos de tomateiro, e identificar grupos de genótipos resistentes à doença. Foram avaliados 25 híbridos de tomateiro, originados de cruzamentos entre quatro variedades comerciais, um acesso do Banco de Germoplasma de Hortaliças (BGH), da Universidade Federal de Viçosa (UFV), e cinco linhagens F8 (Solanum lycopersicum x Solanum habrochaites), estas últimas selecionadas como fonte de resistência à requeima. As plantas foram inoculadas com uma mistura de esporângios de Phytophthora infestans e, em seguida, foram realizadas seis avaliações quanto à severidade de requeima, a intervalos de três dias. Ajustou-se o modelo exponencial aos dados de percentagem de severidade de requeima, e as estimativas obtidas quanto à incidência inicial da doença (y o) e taxa de progresso da doença (r) foram submetidas à análise de variância multivariada (Manova). As médias dessas estimativas, para cada genótipo, foram submetidas à análise de agrupamento. Observou-se um número ótimo de oito grupos distintos, o que possibilitou identificar genótipos resistentes e suscetíveis. Os híbridos experimentais Ikram x 73 A, Nemo-Netta x 133 A, Ikram x 163 A e Nemo-Netta x 163 A apresentaram a menor taxa de progresso de requeima e, portanto, maior resistência à doença.

In Brazil, the first genetically modified (GM) crop was released in 1998, and it is estimated that 84, 78, and 50% of crop areas containing soybean, corn, and cotton, respectively, were transgenic in 2012. This intense and rapid adoption rate confirms that the choice to use technology has been the main factor in developing national agriculture. Thus, this review focuses on understanding these dynamics in the context of farmers, trade relations, and legislation. To accomplish this goal, a survey was conducted using the database of the National Cultivar Registry and the National Service for Plant Variety Protection of the Ministry of Agriculture, Livestock and Supply [Ministério da Agricultura, Pecuária e Abastecimento (MAPA)] between 1998 and October 13, 2013. To date, 36 events have been released: five for soybeans, 18 for corn, 12 for cotton, and one for beans. From these events, 1395 cultivars have been developed and registered: 582 for soybean, 783 for corn and 30 for cotton. Monsanto owns 73.05% of the technologies used to develop these cultivars, while the Dow AgroScience - DuPont partnership and Syngenta have 16.34 and 4.37% ownership, respectively. Thus, the provision of transgenic seeds by these companies is an oligopoly supported by legislation. Moreover, there has been a rapid replacement of conventional crops by GM crops, whose technologies belong almost exclusively to four multinational companies, with the major ownership by Monsanto. These results reflect a warning to the government of the increased dependence on multinational corporations for key agricultural commodities.