Garlic cultivation holds significant importance in Brazil. However, research on garlic crops is currently limited and outdated, highlighting the necessity for new studies, particularly focused on enhancing weed management and optimizing the use of agricultural inputs. This study aimed to assess the extent of yield losses attributed to weed interference and the specific periods during which weed interference impacts garlic crop yields in the state of Santa Catarina, Brazil. The experiments were conducted in Curitibanos, Santa Catarina State, Brazil, during 2019 and 2020, using the garlic cultivars Chonan and Ito, respectively. A randomized block experimental design was employed, consisting of 16 treatment groups with four replications. The design incorporated a 2×8 factorial arrangement, involving two weed management strategies (coexistence and control) and eight crop-weed coexistence periods (0, 15, 30, 45, 60, 90, 120, and 130 days after crop emergence – DAE). The study evaluated the weed community and recorded data on the number of bulbs, bulb diameter, as well as total and commercial bulb yields of garlic crops. The results indicated average losses of 24.3% in total bulb yield and 28.9% in commercial bulb yield. Critical periods for effective weed control were identified as 6 to 126 DAE in 2019, 5 to 126 DAE in 2020 for total yield, 3 to 126 DAE in 2019, and 5 to 126 DAE in 2020 for commercial bulb yield. Consequently, it is imperative to maintain garlic crops weed-free throughout most of their growth cycle.

One of the domains of genetic enhancement that has extensively employed both simulation and authentic data is Biometrics. Selecting efficient models for the Genome-Wide Selection (GWS) process using molecular markers (SNPs) presents several challenges. Among these challenges is the effective identification of the optimal model for fitting a given dataset. To contribute to this endeavor, this paper's primary objective is to assess the predictive accuracy of nine (9) distinct models, each following different paradigms within the realm of Biometrics. The data employed in this study were generated through simulation, encompassing the primary issues encountered in this field of research, including high dimensionality, nonlinearity, and multicollinearity. As the primary findings, notable observations include the enhancement of predictive efficiency as data noise decreases, the predominance of the tree paradigm (for low noise levels, BOO), and the efficacy of the neural network paradigm (for high noise levels, RBF).

It is common to observe conventional methods for estimating soybean crop yields, making the process slow and susceptible to human error. Therefore, the objective was to develop a model based on deep learning to estimate soybean yield using digital images obtained through a smartphone. To do this, the ability of the proposed model to correctly classify pods that have different numbers of grains, count the number of pods and grains, and then estimate the soybean crop yield was analyzed. As part of the study, two types of image acquisition were performed for the same plant. Image acquisition 1 (IA1) included capturing the images of the entire plant, pods, leaves, and branches. Image acquisition 2 (IA2) included capturing the images of the pods removed from the plant and deposited in a white container. In both acquisition methods, two soybean cultivars, TMG 7063 Ipro and TMG 7363 RR, were used. In total, combining samples from both cultivars, 495 images were captured, with each image corresponding to a sample (plant) obtained through methods AI1 and AI2. With these images, the total number of pods in the entire dataset was 46,385 pods. For the training and validation of the model, the data was divided into subsets of training, validation, and testing, representing, respectively, 80, 10, and 10% of the total dataset. In general, when using the data from IA2, the model presented errors of 7.50 and 5.32% for pods and grains, respectively. These values are considerably lower than when the model used the IA1 data, where it presented errors of 34.69 and 35.25% for pod and grain counts, respectively. Therefore, the data used from IA2 provide better results to the model.

Rich in oil and protein, canola is a significantly promising crop and is widely explored as an alternative to grain cultivation in the winter period. Recently, canola oil has been largely consumed due to a variety of health benefits and low production costs. Nonetheless, impasses such as determining the correct sowing date and optimizing harvesting methods make it difficult to expand cultivation. Accordingly, the purpose of the study was to evaluate the performance of two canola hybrids on three sowing dates and distinct harvesting procedures. The study was conducted in Cachoeira do Sul, Rio Grande do Sul, Brazil, on three sowing dates (2019, 2020, and 2021), with two hybrids (Hyola 433 and Hyola 575 CL). The methods of direct harvesting, direct harvesting with the application of adjuvant, cut-row, and cut-row with the application of adjuvant were evaluated. The main yield components were determined, in addition to biochemical parameters, such as oil and protein content and determination of oil quality (acid, peroxide, iodine, extinction, and saponification indices). Appropriately, this study indicated that canola is significantly sensitive to climatic conditions, mainly to temperature and sowing time, reducing grain and biomass yield in late sowing and resulting in serious harvest losses. Oil and grain protein contents were affected by climatic conditions. Nevertheless, the protein content was not affected by late sowing. Canola oil presented significant chemical conditions, with a correlation between the crops with higher oil yield and better oil quality.

Proper potassium (K) fertilization management is crucial for optimizing yield and minimizing environmental impacts in colored fiber cotton cultivars, particularly in semiarid regions. This study aimed to assess the efficiency of K use by colored cotton cultivars under K fertilization in a semiarid region. Two experiments were conducted in 2019 and 2021, representing two agricultural seasons. The experimental design employed randomized blocks with split plots and four replications. The main plots consisted of five randomized K doses (0, 60, 120, 180, and 240 kg ha-1 of K2O), while subplots included four cultivars of colored cotton (BRS Rubi, BRS Safira, BRS Topázio, and BRS Verde). The results revealed that BRS Rubi exhibited superior agronomic efficiency with a dose of 60 kg ha-1 of K2O in both agricultural seasons. The maximum efficiency of K use by colored cotton cultivars was achieved with a dose of 240 kg ha-1 of K2O in the semiarid region of Brazil. The cultivars BRS Rubi and BRS Topázio demonstrated the highest use efficiency. Furthermore, BRS Topázio displayed the highest K accumulation in plant shoots during both seasons.

Nonlinear regression models represent an alternative way to describe plant growth. In this study, we aimed to model the growth of linseed using four methods for data collection (longitudinal, mean, random, and cross-sectional) and fitting the logistic and Von Bertalanffy nonlinear regression models. The data came from experiments conducted between 2014 and 2020 in the municipality of Curitibanos, Santa Catarina, Brazil. The study had a randomized block design, with experimental units consisting of six lines, 5.0 m long and 3.0 m wide, containing the varieties and cultivars of linseed with four replicates. We performed weekly assessments of the number of secondary stems and plant height and measured total dry mass fortnightly. After tabulation, the data were analyzed using the four methods, and the logistic and Von Bertalanffy models were fitted. The logistic model for the plant height variable exhibited the best performance using the longitudinal, mean, and cross-sectional methods. It was an alternative approach that reduced the time and labor required to conduct the experiment.

Agroforestry systems may show low CO2 efflux, and CO2 efflux contributes to sustainability. This work aimed to evaluate the soil CO2 efflux in coffee plantations cultivated in agroforestry and full-sun systems during the winter in high-altitude tropical climate regions. The work was carried out at three family farms (RO, GI, and PA) in Minas Gerais, Brazil. Two treatments were established: coffee with and without trees, and 20 sampling spots for soil and gases. The air and soil temperatures in the agroforestry systems were lower than in the full-sun systems. The soil moisture content in agroforestry systems was higher than full-sun only on the GI. Except for the agroforestry systems in PA, all the other systems showed an increase in CO2 efflux with increasing soil moisture. This increase was more pronounced in agroforestry systems (RO), followed by full sun (RO). On the GI farm, this correlation was lower in the agroforestry system. Soil CO2 efflux was positively correlated with soil temperature and negatively correlated with total nitrogen, labile carbon and total organic carbon. Therefore, despite the microclimate stability promoted by the agroforestry systems in the winter, no decrease in the soil CO2 efflux was observed when compared to full sun systems.

Lima bean cultivation has not received proper national and regional attention by research agencies, impairing the knowledge of the agronomic characteristics and its potential, information that is essential for the selection and launching of superior genotypes. The objective of this work was to characterize the seeds of 17 lima bean genotypes produced by family farmers in Paraíba State. The characterization was based on the descriptors for Phaseolus lunatus. A completely randomized design was used with 15 replicates. Data were submitted to analysis of variance with a subsequent grouping of means by the Scott-Knott test at 1% of probability. For analysis of the genetic diversity, the Tocher method was used based on the generalized Mahalanobis distance. There is variability among the genotypes. According to the means test, genotype 10 has a higher seed length, width, and weight. Considering the diversity among the groups and aiming to select light-colored and higher means of the evaluated traits, the selection of genotypes 8, 13, 4, and 10 is recommended to establish a breeding program that satisfies both the consumer and the producer.

Downy mildew is a major problem for grape growers, as this disease is difficult to control. Synthetic fungicides are used to treat downy mildew with handheld backpack and tractor sprayer applications, with high chemical exposure by operators. As important tools for maximising yield, application technologies must be studied to optimise control efficiency. The objective of this study was to evaluate the efficiency of fungicide spray application using Remotely Piloted Aircraft (RPA) for the control of downy mildew in grapevine, with different spray volumes. The study was divided into two experiments using 4 vine lines, 10 blocks and 5 treatments with different mixture volumes: Experiment 1 with RPA application of 5 mixture volumes - 0, 22, 44, 66, and 88 L ha−1; Experiment 2 with RPA application of 3 mixture volumes - 44, 66, and 88 L ha−1 and a backpack application of one mixture volume - 800 L ha−1. Coverage percentage, droplet density and volume median diameter (VMD) were evaluated. Downy mildew severity on grapevine leaves was assessed using visual analysis and a diagrammatic scale. The application of 44 L ha−1 provided the greatest coverage and droplet density in the upper and middle strata; however, the backpack application had a better droplet distribution than the RPA application. Treatments of 44 L ha−1 with RPA and backpack application (800 L ha−1) provided the best disease control. In the trellis system, RPA application must be improved because of the low coverage in the lower parts of the plant, and further studies with different spray nozzles and application heights are needed.