This study determined the changes in soil nutrients, organic carbon, organic matter and heavy metals during the early development stages of maize fertilized with different sources and doses. Also, this study confirmed that the used biosolid in the formulation of organomineral fertilizer makes greater efficacy than the effects promoted by mineral or filter cake organomineral fertilizers already used in agricultural. The experiment was done in a greenhouse at the Federal University of de Uberlândia - Campus Umuarama. The experimental design was randomized block design, as a 2 x 5 + 2 factorial, containing two sources of organomineral fertilizer consisting of organic residues (biosolid and filter cake), five doses of organomineral fertilizer (60, 80, 100, 120 and 140% of P2O5 recommendation for maize crop), a positive control with mineral fertilization (100%) and a negative one with no fertilization with four replications. The soil pH and contents of phosphorus, potassium, calcium, magnesium base saturation, base sum, micronutrients, organic carbon, organic matter and heavy metals were determined subsequently after plant removal, 65 days after sowing. The organomineral fertilizers based on biosolid and filter cake yielded increases in phosphorus, boron and manganese in the soil, especially the biosolid source. Regardless of the fertilizer organic matter source, soil phosphorus increased linearly with increasing doses of the sources. Doses equal or below the recommendation for maize (100% P2O5) were less effective in supplying potassium. The organomineral sources did not add heavy metals to the soil. Fertilizers based on biosolid can supply nutrients, such as phosphorus, boron and manganese at high doses, replacing mineral fertilization, while maintaining and improving soil fertility with no contamination by heavy metals.

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In the current world scenario of agriculture it is necessary to find technologies to reach high productivity that are effective in providing soil conditioning improvement for the plants. Thus, this study analyzed the efficacy of organomineral fertilizers formulated with different organic matter sources on growth factors of maize. The experiment was done in a greenhouse at Campus Umuarama, Federal University of Uberlândia-MG, in a randomized block design, as a 2 x 5 + 2 factorial, containing two sources of organic matter, biosolid and filter cake, five doses of P2O5 recommendation for the crop and two additional treatments: mineral fertilization (100% of P2O5 of the recommended dose) and a control (no fertilizer), with four replications. Plant height, stalk diameter, and chlorophyll a and b were determined at 35 days after sowing (DAS); plant height, stalk diameter, shoot and root fresh and dry matter were determined at 65 DAS. The organomineral sources with biosolid and filter cake yielded greater height and diameter at 35 DAS than those of mineral fertilization. Regardless of the fertilizer organic matter source, stalk diameter at 35 DAS presented linear increase with increasing doses of the fertilizer sources. The use of fertilizer based on biosolid resulted in greater shoot fresh matter than the filter cake source, regardless of dose applied. In general, greater results were obtained for root fresh and dry matter at the greater fertilizer doses in comparison with the mineral fertilization. Organomineral fertilizers can be a viable alternative for partial or total substitution of mineral fertilization, since similar or better results were found for maize growth characteristics.

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The objective of this study was to analyze the genetic diversity among colored cotton fiber genotypes using technological characteristics of the fiber, seed cotton yield and percentage of fiber, to identify potential parents with high performance. The experiment was conducted in the Brazilian city of Uberlândia, Minas Gerais, during the 2016/2017 harvest. Twelve colored fiber genotypes were used in a randomized block design. The characteristics that were evaluated included micronaire, maturation, fiber length, length uniformity, short fiber index, fiber resistance, elongation, seed cotton yield and percentage of fiber. Genetic divergence was estimated using the Mahalanobis generalized matrix with Unweighted Pair Group with Arithmetic Mean (UPGMA) and the Tocher method. The Singh method was used to evaluate the relative contributions of the characteristics in the divergence. In detecting divergence, fiber length and maturation were observed to have contributed the most. In order to obtain segregant populations with greater genetic variability and greater productive potential, hybridizations between UFUJP-17 and UFUJP-16 with commercial cultivars could be promising. Crossing UFUJP-16 with commercial controls would have a higher chance of success of producing superior fiber quality.

Cotton is one of the main agricultural products produced in Brazil. With such a high demand in the market, it is necessary that the cotton cultivars present high productivity and fiber quality. In order to favor the expression of the potential of the genotypes, the cultivation must occur in climatic conditions that provide good development of the plants, being the sowing time a primordial factor for the good performance of the cotton plant. In order to establish an ideal sowing season for different cotton genotypes, the present study aimed to evaluate the best sowing season of cotton genotypes for the environment of Uberlândia (Minas Gerais State), aiming at productivity and fiber quality. The experiment was carried out in field conditions, in the 2016/2017 harvest in the experimental area located at Fazenda Capim Branco, in the city of Uberlândia, Minas Gerais State. A randomized complete block design (DBC) with four replications in a 4x7 factorial scheme was used: 4/12 sowing dates: 05/12, 19/12, 30/12, 13/01 and 7 genotypes. 5 strains of the breeding program of the Federal University of Uberlândia (UFU) and 2 commercial cultivars. The evaluated characteristics were: seed cotton yield, feather yield, micronaire index, maturity index, fiber length, uniformity of length, short fibers, resistance and elongation. It was concluded that the best sowing season for a high productivity was the one performed on 12/05/16, with emphasis on the UFUJP-Z genotype. For fiber quality, UFUJP-C showed the best results at the 12/19/16 sowing season.

Soybean is one of the most important sources of protein and vegetable oil in the world. Given its increasing demand, the increment in yield has only been possible due to investments in research and production technology, especially in breeding programs. One of the main factors influencing soybean yield is the seed morphology; however, its analyses are hampered by the lack of efficient computational approaches with not only accurate results, but also a high flexibility to user preferences and needs. In this context, the present work provides a methodological framework for: (i) seed segmentation in soybean images; (ii) seed morphological evaluation; and (iii) image-based prediction of the hundred-seed weight trait. We used genotypes from a partial diallel cross design, which aimed at obtaining genotypes with high agronomic performance. In addition to the measurement of the hundred-seed weight, we collected RGB images of seeds of each plot. For image segmentation, we created an in-house image processing pipeline, which enabled a full morphological seed evaluation. For predicting the hundred-seed weight, we compared different machine learning algorithms using as input the morphological characteristics obtained, and also features from state-of-the-art convolutional neural network (CNN) architectures. The image segmentation methodology showed to be highly efficient, as more than 98% of the seeds in the images were correctly identified. Even if the seeds were close, the segmentation strategy could separate them into independent image components. In addition to supplying a highly accurate decision support system for soybean breeders, we verified the morphological phenotyping adaptability in other plant species, fully assessing the pipeline generalization. We consider the use of this methodology highly advantageous, as the method is entirely based on widely used morphological operations, which results in an easy implementation and low computational costs. Using these morphological measures, we could estimate machine learning models for predicting the hundred-seed weight, achieving considerable predictive accuracy. The same results were observed for CNN-obtained features, showing the efficiency of the morphological measurements as feature extractors. The possibility of obtaining seed morphological characteristics provides a valuable tool for the continuous and efficient development of new soybean cultivars in breeding programs aimed at long-term genetic gain. Additionally, through a faster seed image acquisition workflow, with less chance of errors and low cost, it is also possible to make predictions of important soybean characteristics. The work conducted has the potential to help future research and the industry to develop automated phenotyping tools, incorporating the proposed analytical workflows.