Search

Cultivated maize (Zea mays L.) is the oldest and one of the most important crop species in the world. Changing climatic conditions in recent years, warm weather, expansion of acreage and intensification of maize cultivation have resulted in an increase in the threat posed by diseases caused by, among others, Fusarium fungi. Breeding success in all plant species is determined by access to starting materials with possible high genetic diversity also in terms of disease resistance. Identification of parental combinations that produce offspring that are high-yielding and resistant to Fusarium, among other diseases, is one of the costliest steps in breeding programs. We used maize lines which, as a result of five-year field observations, were divided into resistant and susceptible to F. verticillioides. It is known that resistance to fusarium is a trait strongly dependent on environmental conditions. Due to the fact that the years of observation of the degree of infestation were hot and dry, the resistance of some lines could result from favorable environmental conditions. In view of the above, the aim of this study was to analyze the genetic basis of the resistance of these lines and to correlate molecular analyses with field observations. Comprehensive field and molecular analyses will allow the selection of reference lines that will be resistant to fusarium in the field and, at the same time, will have pyramidized resistance genes. Such lines can be used for crossbreeding to obtain fusarium-resistant varieties. In addition, an attempt was made to develop Multiplex PCR conditions for faster identification of the analyzed markers. As a result of the analyses, it was found that the resistance of the studied maize lines was correlated with the number of molecular markers identified in them. Both field and laboratory analyses have shown that the best line that can be used for crossbreeding as a source of fusarium resistance genes is the line number 25. It has a resistance level of 8–9 on the nine-point COBORU scale. In this line, as a result of molecular analyses, 10 out of 12 markers were identified (SSR 85, Bngl 1063, Bngl 1740, Umc 2082, Bngl 1621, Umc 2059, Umc 2013, SSR 93, SSR 105, STS 03) related to fusarium resistance genes, which may be the reason for such a high resistance to this pathogen. Similarly, 9 markers were identified for line number 35 (SSR 85, Bngl 1063, Bngl 1740, Umc 2082, Bngl 1621, Umc 2059, Umc 2013, SSR 93, STS 03). This line, however, was characterized by a slightly lower resistance at the level of 7–8. Line 254 turned out to be the least resistant, as the resistance was at the level of 4–5, and the number of identified molecular markers was 5. Lines numbered 25 and 35 can be successfully used as a source of fusarium resistance genes.

Genotype and weather conditions play crucial roles in determining the volume and stability of a soybean yield. The aim of this study was to identify the key meteorological factors affecting the harvest date (model M_HARV) and yield of the soybean variety Augusta (model M_YIELD) using a neural network sensitivity analysis. The dates of the start of flowering and maturity, the yield data, the average daily temperatures and precipitation were collected, and the Selyaninov hydrothermal coefficients were calculated during a fifteen-year study (2005–2020 growing seasons). During the experiment, highly variable weather conditions occurred, strongly modifying the course of phenological phases in soybean and the achieved seed yield of Augusta cultivar. The harvesting of mature soybean seeds took place between 131 and 156 days after sowing, while the harvested yield ranged from 0.6 t·ha−1 to 2.6 t·ha−1. The sensitivity analysis of the MLP neural network made it possible to identify the factors which had the greatest impact on the tested dependent variables among all the analyzed factors. It was revealed that the variables assigned ranks 1 and 2 in the sensitivity analysis of the neural network forming the M_HARV model were total rainfall in the first decade of June and the first decade of August. The variables with the highest impact on the Augusta soybean seed yield (model M_YIELD) were the mean daily air temperature in the second decade of May and the Seljaninov coefficient values calculated for the sowing–flowering date period.