Abstract in English:

ABSTRACT. Heterogeneity among experimental units can introduce experimental errors, necessitating the use of techniques that enhance statistical inferences to address this issue. One effective approach is determining the optimal plot size, which can reduce experimental error. While frequentist methods are commonly employed for this purpose, Bayesian approaches offer distinct advantages. Therefore, our objective was to estimate the optimal plot size for chickpea experiments using the Bayesian approach and compare the results with those from the frequentist approach. We conducted two control experiments (with no treatments) involving eight cultivation rows, each spanning seven meters in length, with 50 cm spacing between rows and 10 cm spacing between plants. We evaluated the central six rows, totaling 60 plants per cultivation row. At the end of the growth cycle, we assessed seed count, seed weight, harvest index, and shoot dry mass. Data collection was conducted at the individual plant level. We determined the optimal number of plots using both the frequentist approach (modified maximum curvature method) and Bayesian approach, employing informative and uninformative prior distributions. The optimal plot size varied depending on the specific experiments and the variables under analysis. However, there was consensus in the estimation of the optimal experimental plot size between the two approaches. We recommend using 15 plants as the optimal plot size for chickpea cultivation.

Abstract in English:

ABSTRACT. Brazil is the third-largest producer of fruits in the world, with yellow passion fruit (Passiflora edulis) as one of its main crops. This species requires cross-pollination, with Xylocopa bees being the primary pollinators due to its self-incompatibility. The presence of Solanum lycocarpum close to passion fruit orchards can serve, due their flowers, as an attraction for pollinating bees, improving the production of the crop. Thus, the objectives of this study were to: i) investigate whether the planting of S. lycocarpum close to the passion fruit orchard increased the richness and abundance of bees; ii) evaluate whether the implementation of trap nests in the passion fruit orchard and close to S. lycocarpum attracted bees to nests in this location; iii) record the time of greatest foraging activity of Xylocopa frontalis in the passion fruit orchard; and iv) quantify the number of natural nests of X. frontalis in the passion fruit orchard during the experimental period. A total of 48 seedlings of S. lycocarpum were planted and divided into two rows along the eastern border of the yellow passion fruit crop. Bees were recorded for 15 minutes each hour from 8:30 am to 3:30 pm. During the remaining 45 minutes, the posts were inspected for natural bee nests. The frequency, abundance, species richness, and indices of Shannon-Winner, Simpson, and Pielou equitability indices were assessed. The most common species were X. frontalis, Apis mellifera, and Trigona spinipes. The flowering of S. lycocarpum near passion fruit orchards positively influenced the increase in the number of nests and species richness of pollinating bees, indicating that this plant, close to passion fruit orchards, can contribute to fruit production. Finally, we showed the preferred times for bee foraging and the period in which insecticide application should be avoided.

Abstract in English:

ABSTRACT. Soil organisms are vital for soil quality and can indicate environmental conditions. This study aimed to understand the diversity of soil fauna and its connection to plant residue decomposition and maize grain yield across various locations and crop seasons in a subtropical setting. We conducted experiments in Frederico Westphalen, Santa Maria, and São Vicente do Sul, Rio Grande do Sul State, Brazil, during two crop seasons in 2020/2021, totalling six experiments. We assessed parameters such as plant residue decomposition rate, soil fauna abundance, and grain yield. Results showed significant variations in decomposition rate, fauna abundance, and diversity measures (Shannon, Margalef, and Pielou indices, plus relative frequency) across environments. Four taxonomic groups comprised over 80% of collected individuals, with Araneae and Coleoptera showing more than half of relative frequency Our analysis revealed that areas with higher grain yields had faster decomposition rates, suggesting they fostered greater organism activity and nutrient cycling, indicating their potential as soil quality indicators.

Abstract in English:

ABSTRACT. Environmental conditions significantly impact the performance of sweet potato genotypes, necessitating the study of genotype x environment (GE) interactions to select genotypes adaptable to varying cultivation conditions. This study aimed to assess GE interactions in sweet potatoes for animal feed and identify high-performance genotypes suitable for different seasons. We conducted two tests during the Brazilian winter of 2019 and summer of 2020. Employing a partially balanced triple lattice experimental design with 100 treatments (92 sweet potato genotypes and eight controls) and three replications, we measured vine green matter yield (VGMY), percentage vine dry matter (PVDM), vine dry matter yield (VDMY), percentage of root dry matter (PRDM), and roots dry matter yield (RDMY). We ranked genotypes, highlighting the best performers for individual and combined seasons. Significant differences in VGMY, PRDM, and RDMY were observed for GE interaction. VGMY, VDMY, and PRDM favored the summer season, while PVDM and RDMY performed better in the winter season. Genotypes 2018-31-713, 2018-72-1438, 2018-31-666, 2018-12-252, 2018-19-461, 2018-19-389, 2018-38-946, 2018-31-689, and 2018-37-864 proved most suitable for VGMY and VDMY across growing seasons. Genotypes 2018-28-514, 2018-15-268, and 2018-19-443 demonstrated potential in percentage vine dry matter. Genotypes 2018-31-666, 2018-72-1438, and 2018-15-277 are recommended for PRDM in both seasons. Genotypes 2018-19-464, 2018-28-556, 2018-55-1154, 2018-28-543, 2018-53-1038, 2018-72-1432, and 2018-19-443 exhibited greater potential for RDMY, making them ideal for animal feed in both growing seasons.

Abstract in English:

ABSTRACT. Artificial neural networks (ANNs) are powerful nonparametric tools for estimating genomic breeding values (GEBVs) in genetic breeding. One significant advantage of ANNs is their ability to make predictions without requiring prior assumptions about data distribution or the relationship between genotype and phenotype. However, ANNs come with a high computational cost, and their predictions may be underestimated when including all molecular markers. This study proposes a two-step genomic prediction procedure using ANNs to address these challenges. Initially, molecular markers were selected either directly through Multivariate Adaptive Regression Splines (MARS) or indirectly based on their importance, identified through Boosting, considering the top 5, 20, and 50% of markers with the highest significance. Subsequently, the selected markers were employed for genomic prediction using ANNs. This approach was applied to two simulated traits: one with ten trait-controlling loci and heritability of 0.4 (Scenario SC1) and the other with 100 trait-controlling loci and a heritability of 0.2 (Scenario SC2). Comparisons were made between ANN predictions using marker selection and those without any marker selection. Reducing the number of markers proved to be an efficient strategy, resulting in improved accuracy, reduced mean squared error (MSE), and shorter adjustment times. The best ANN predictions were obtained with ten markers selected by MARS in SC1, and the top 5% most relevant markers selected using Boosting in SC2. As a result, in SC1, predictions using MARS achieved over a 31% increase in accuracy and a 90% reduction in MSE. In SC2, predictions using Boosting resulted in more than a 15% increase in accuracy and an 83% reduction in MSE. For both scenarios, computational time was up to ten times shorter with marker selection. Overall, the two-step prediction procedure emerged as an effective strategy for enhancing the computational and predictive performance of ANN models.

Abstract in English:

ABSTRACT. Limited information is available regarding the genetic inheritance of ornamental traits in peppers (Capsicum spp.), which is crucial for enhancing these plants for ornamental purposes in breeding programs. This study aimed to elucidate the genetic inheritance of ornamental traits in segregating populations of pepper plants (C. annuum L.) from distinct parents and to characterize them based on their flowering and fruiting cycles. The selected parents, UNI01 and UNI05, were sourced from the active germplasm bank of Universidade Estadual de Montes Claros, Janaúba, Minas Gerais State, Brazil. The experiment took place in a greenhouse, involving manual hybridization between UNI01 and UNI05 to obtain seeds of segregating populations, which included F1, RC1, RC2, F2, and F3 generations. Qualitative traits assessed included flower corolla color, immature fruit color, and shapes of longitudinal and transversal fruit sections. Quantitative traits encompassed mean flowering and fruiting days. We employed chi-square tests (χ²) to evaluate segregation patterns. The descriptor "corolla color" exhibited codominance, with a white corolla and purple borders linked to heterozygous genotypes. Dominant inheritance controlled the color of immature fruits, primarily purple. Genetic inheritance for transversal and longitudinal fruit shapes remained unexplained in the tested segregations. Parental and segregating generations displayed similar flowering and fruiting cycles. These results provide valuable insights for future breeding programs aimed at using this species for ornamental purposes.

Abstract in English:

ABSTRACT. Utilizing selection indices is an effective strategy for the simultaneous evaluation of multiple traits in kale breeding programs. This approach allows for the selection of kale genotypes that exhibit enhanced productivity and adaptability by combining desirable attributes for the crop. In this study, we employed a mixed model approach in combination with various selection indices to estimate selection gains and recommend the most suitable index for kale breeding. The experiment was conducted at the Center of Development and Technology Transfer, Federal University of Lavras, Ijaci, MG. Thirty-four experimental genotypes were assessed in a randomized block design with three replicates, featuring four plants per plot. We evaluated several traits, including total leaf yield, number of leaves, average leaf mass, number of sprouts and chlorophyll content. Data analysis was performed at both the plot average level and the average quantity of the five harvests. Statistical analysis of mixed models confirmed the presence of genetic variability among kale genotypes. We examined the Smith and Hazel, Mulamba and Mock, Z-index, and FAI-BLUP indices. Smith and Hazel, Mulamba and Mock, as well as Z-index, were found unsuitable for leafy kale selection in breeding programs. The FAI-BLUP index demonstrated superior performance, aligning with the specific objectives of the kale breeding program and offering desirable gains. Therefore, we recommend the use of the FAI-BLUP index in kale breeding programs.

Abstract in English:

ABSTRACT. Developing new cultivars, particularly in perennial species like Coffea arabica, can be a time-consuming process. Employing molecular markers in genome-wide selection (GWS) for predicting genetic values offers an alternative to accelerate this process. However, implementing GWS typically involves genotyping many markers for both training and candidate individuals, which can increase the total genotyping cost for the breeding program. Therefore, this study aimed to assess the feasibility of using low-density marker panels to predict the genetic merit of C. arabica for a range of desirable agronomic traits. For this purpose, GWS analyses were performed using the G-BLUP method with panels of varying marker densities, selected based on marker effect magnitude. The results indicate that employing lower-density panels might be advantageous for this species' improvement. Models based on these panels yielded accurate predictions for various traits and demonstrated high agreement in terms of selected individuals compared to more complex models.

Abstract in English:

ABSTRACT. Nutrient leaching is a common issue in sandy soils. The use of hydrogel polymers can mitigate this problem by enhancing soil water retention. This study aims to assess the effect of hydrogel polymer application on nutrient leaching in sugarcane-cultivated soil and its impact on plant growth over a 196-day cycle. Parameters examined include soil water retention (%), nutrient leaching (N, P, K, Ca, Mg, and S) analyzed through the water collected after natural drainage, as well as various plant growth parameters such as stem height and diameter, and fresh and dry stem and leaf mass. The highest soil water retention was observed in treatments with 1.5 and 2.0 g kg-1 of hydrogel polymer. Regarding nutrient leaching, the treatments with 1.5 and 2.0 g kg-1 of hydrogel polymer exhibited the lowest values, resulting in reductions of over 85% for all accumulated nutrients leached by the end of the crop cycle. The application of hydrogel, especially at higher doses, also enhanced sugarcane growth, notably increasing fresh stem mass. These results suggest that hydrogel polymers could serve as a sustainable solution for controlling nutrient leaching in sugarcane cultivation, contributing to the sustainable development of agriculture and environmental preservation.

Abstract in English:

ABSTRACT. The nodulation of common beans occurs continuously until the flowering stage, followed by nodule senescence. However, reports have indicated the potential for late nodulation in this species, contributing to increased grain production. This study aimed to evaluate the occurrence of late nodulation in common beans and its contribution to plant growth. Experiments were carried out by testing two inoculation strategies: rhizobial inoculation (1) in different sections of the root system and (2) at different phenological stages. Plants were harvested at flowering and the beginning of pod filling. When the first strategy was applied, both inoculation on the seeds and throughout the pot volume resulted in greater nodulation compared to the uninoculated control. However, shoot biomass accumulation remained unaffected. When the second strategy was applied, supplementary inoculation at different stages did not improve nodulation or plant growth compared to seed inoculation. We conclude that neither method promoted effective late nodulation of common beans and that seed inoculation was sufficient to promote good vegetative development of common beans.

Abstract in English:

ABSTRACT. The use of integrated agricultural production systems has been expanded due to the multiple functions they perform. Although soil structural studies have elucidated the relationship of these systems with plant development, adjustments are needed to incorporate the diversity of management systems employed. Thus, the hypothesis of this study was that integrated cropping systems mitigate soil structural degradation and increase the agronomic performance of crops. The objectives of this study were to evaluate the biological soil loosening potential containing paiaguás grass and the effectiveness of integrated systems in promoting the agronomic performance of soybean plants and to model the least limiting water range (LLWR) considering the adopted management regimes. An experiment was performed based on a randomized block and split-plot design. In the plots, the traffic intensity (0, 2, 10, and 30 passes of an agricultural tractor. In the subplots, soybean cultivation was performed under the three management systems (simple: monoculture grains; integrated: intercropping between grass and grains; and pasture in monoculture). The following soil physical quality indicators were determined: bulk density (Bd) and LLWR; these indicators are related to phenological development attributes and soybean productivity. The integrated agricultural production systems promoted biological soil loosening and improved soybean yield. The use of Paiaguás grass in monoculture enhanced edaphic benefits and enabled greater grain production compared to grain monocropping. The least limiting water range was an efficient parameter for modeling the physical behavior of the soil, and the application of the LLWR was improved by considering penetration resistance reference values specific to each management system. Our results highlight the soundness of using the LLWR in evaluating the response of soybean to physical changes in soil due to compaction, and the reference values for penetration resistance contribute to greater accuracy in the LLWR and the physical diagnosis of soil properties.

Abstract in English:

ABSTRACT. Studies on modeling the growth of annual crops are typically conducted for economically significant crops like soybeans, corn, and wheat. Conversely, there has been limited exploration of annual forage crops, despite their substantial importance, as they can help address forage supply shortages during periods of low production for perennial tropical forages. This study aimed to parameterize the APSIM-Oats model for simulating the growth of black oats (Avena strigosa Schreb cv. IAPAR 61 Ibiporã) cultivated for forage purposes and managed under a cut-and-carry system. Two experiments were conducted in 2018 and 2019 in Piracicaba, São Paulo State, Brazil, encompassing both irrigated and non-irrigated plots. Various productive, biometric, and soil moisture variables were monitored throughout the crop cycles. Parameters were manually calibrated through a trial-and-error process until the estimates closely matched the observed data. Model evaluation involved comparing observed and simulated data using statistical indices. The most favorable results were obtained for live biomass, leaf mass, and stem mass (with modeling efficiency exceeding 0.55 in the rainfed system and surpassing 0.34 for the irrigated system). Estimates of soil water content exhibited better accuracy for shallower soil layers (0 to 0.30 m). The calibration of the APSIM-Oats model for black oats yielded satisfactory estimates for live biomass under rainfed conditions. The simulations in this study represent an initial step in modeling the growth of black oats.

Abstract in English:

ABSTRACT. In tropical and subtropical regions, nitrogen (N) is often limited and significantly impacts corn production costs. In this context, bio-inputs have been used to reduce N and water supplied to plants. This study assesses the impact of varying N levels, Azospirillum brasilense seed inoculation, and biostimulant use on the agronomic performance of summer-grown green corn (Zea mays L.) across three growing seasons (2017/2018, 2018/2019, and 2019/2020). Five N-fertilizer levels (0, 30, 60, 90, and 120 kg N ha-1), two A. brasilense seed inoculation levels (0 and 100 mL ha-1), and two biostimulant doses (0.0 and 1.0 L ha-1) were evaluated in a completely randomized block design, arranged in a 5 x 2 x 2 factorial scheme, with four replications. Results showed that A. brasilense seed treatment did not increase ear yield or kernel protein content (PROT) and reduced PROT when combined with the two highest N levels. The application of biostimulant increased ear weight by 5.08% in the 2018/2019 growing season, leading to an increase in PROT. However, the use of inoculant and biostimulant did not reduce the amount of N-fertilizer applied to green corn plants.

Abstract in English:

ABSTRACT. This study aimed to optimize both agronomically and economically leaf green mass productivity of kale and its agronomic components when fertilized with equivalent biomass amounts of the hairy woodrose (Merremia aegyptia L.) and roostertree (Calotropis procera Ait.) spontaneous species from the Caatinga biome in two cropping seasons. The experimental design was in randomized blocks with five treatments and five replications. The treatments consisted of equivalent biomass amounts of hairy woodrose and roostertree at doses of 16, 29, 42, 55, and 68 ton ha-1, on a dry basis. In each experiment, a treatment without fertilization (control) and a treatment with chemical fertilization were used. The maximum optimized physical efficiencies of the kale commercial leaf productivity and number of leaf packets per square meter were 16.92 ton ha-1 and 6.97, respectively, when the amounts of the green manure biomass of 56.41 and 48.63 ton ha-1 were incorporated into the soil. The optimized maximum net income of 47,841.44 BRL ha-1 and rate of return of 2.47 reals for each real invested were obtained when the amounts of the green manure biomass were 53.26 and 64.31 ton ha-1 added to the soil. The use of M. aegyptia and C. procera biomass as green manure is a viable technology for kale producers in monocropping in a semi-arid environment.

Abstract in English:

ABSTRACT. Soil bulk density (BD) serves as a crucial physical property for characterizing soils and assessing the quality of their management systems. Various methods, including the Core, Clod, and Jolly balance (JBM) methods, are employed for BD measurement. However, these methods can yield significantly different measurements due to analytical errors. This study aims to assess the accuracy of these methods in a clayey Oxisol under different management conditions, while also identifying primary experimental errors in BD determination and strategies for their mitigation. Different statistical approaches were employed to analyze the impacts of sample volume, paraffin temperature, and management systems on BD determination methods. Method accuracy exhibited variation among management systems, particularly notable in secondary forest (SF) areas. In these areas, Core-based BD measurements were 37% lower than those obtained by the Clod and JBM methods. This disparity can be attributed to the higher macroporosity observed in SF, leading to greater sample volume loss and smaller volumes analyzed by the Clod and JBM. A correlation between paraffin temperature for sample coating and clod volume was observed, with paraffin temperature affecting BD measurements only in clods larger than 69.9 cm3. The paraffin temperature inducing the lowest mean error for larger clods was 92°C. For clods smaller than 69.9 cm3, BD measurement errors arose due to inadequate sample volume. Representative elementary volume was identified as a means to mitigate BD overvaluation by the Clod method. A volume of 99 cm3 proved effective in reducing mean BD errors to 5%, making it suitable for both field sampling and laboratory analytical procedures.