Igor Lima Bretas

Animal excreta represent one of the primary pathways for nutrient return to pastures. We investigated the spatial distribution pattern of dung pads during seven grazing cycles over two years and the nutrient concentration in forage and cattle dung in different systems in a subhumid tropical region. The treatments were: (I) brachiaria grass (Brachiaria decumbens syn. Urochloa decumbens Stapf R. D. Webster) + gliricidia (Gliricidia sepium (Jacq.) Kunth ex. Walp.; B+G); (II) brachiaria grass + sabia (Mimosa caesalpiniifolia Benth.; B+S); [...] ABSTRACT Animal excreta represent one of the primary pathways for nutrient return to pastures. We investigated the spatial distribution pattern of dung pads during seven grazing cycles over two years and the nutrient concentration in forage and cattle dung in different systems in a subhumid tropical region. The treatments were: (I) brachiaria grass (Brachiaria decumbens syn. Urochloa decumbens Stapf R. D. Webster) + gliricidia (Gliricidia sepium (Jacq.) Kunth ex. Walp.; B+G); (II) brachiaria grass + sabia (Mimosa caesalpiniifolia Benth.; B+S);

Grasslands are important terrestrial ecosystems that provide feed for livestock, support soil conservation and nutrient cycling, and store soil carbon (C). In many grazed grasslands, restricted nitrogen (N) inputs from fertilizers and minimal legume presence may constrain belowground biomass accumulation and soil C storage because of limited N. Our objective was to compare root–rhizome biomass and composition, root accumulation, and soil organic C (SOC) stocks for three grazed, year‐round forage systems based on bahiagrass ( Paspalum notatum Flügge). Systems included a bahiagrass monoculture during summer, overseeded with cool‐season grasses for winter, and receiving 224 kg N ha ⁻¹ year ⁻¹ (BG‐N [bahiagrass during the warm season overseeded with ryegrass‐oats mixtures during the cool season, with 224 kg N ha ⁻¹ divided equally in cool and warm seasons]); a bahiagrass monoculture during summer, overseeded with a cool‐season grass–legume mixture for winter, and receiving 34 kg N ha ⁻¹ year ⁻¹ (BG‐CL [bahiagrass (no N‐fertilized) during the warm season overseeded with ryegrass–oats–clovers mixture (34 kg N ha ⁻¹ ) during the cool season]); and a bahiagrass–rhizoma peanut ( Arachis glabrata Benth.) mixture during summer, overseeded with a cool‐season grass–legume mixture for winter, and receiving 34 kg N ha ⁻¹ year ⁻¹ (BG‐RP [bahiagrass and rhizoma peanut (no N‐fertilized) during the warm season overseeded with ryegrass–oats–clovers mixture (34 kg N ha ⁻¹ ) during the cool season]). This study assessed belowground responses and estimated decomposition dynamics during 2 years in a long‐term grazing system. During summer, BG‐RP had greater root–rhizome biomass (9770 kg OM ha ⁻¹ [where OM stands for organic matter]) than BG‐N (6230 kg OM ha ⁻¹ ) and BG‐CL (7320 kg OM ha ⁻¹ ). Greater root–rhizome mass in BG‐RP increased N (35%) and C (38%) stocks compared with BG‐N, but root–rhizome accumulation rate was not different among forage systems (14.5–15.9 and 15.6–22.6 kg OM ha ⁻¹ day ⁻¹ during cool and warm seasons, respectively). Nine years after imposing forage systems, SOC stocks (0–90 cm) had increased and were similar (104 Mg ha ⁻¹ ) across forage systems. Integrating legumes with limited N fertilizer application for 9 years resulted in similar SOC as heavily N‐fertilized, grass‐only systems.

Corn ( Zea mays ) is a globally important crop to produce grain and silage for livestock systems. However, corn generally requires high levels of nitrogen (N) fertilizer. Introducing sunn hemp ( Crotalaria juncea L.; SH), an annual legume, is a potential option for reducing N inputs. This study aimed to evaluate initial insights of herbage accumulation (HA), nitrogen biological fixation, and nitrogen use efficiency (NUE) using SH intercropped with corn in two locations, Marianna, FL, and Marshfield, WI. Treatments were SH monoculture, corn without N‐fertilizer (Corn − N), N‐fertilized corn (Corn + N), corn and SH planted on the same day (Corn + SH0), and corn and SH planted 4 weeks later (Corn + SH4). Corn + N had greater HA at both sites. The treatment SH fixed 87 kg N ha ⁻¹ and had a greater %N derived from the atmosphere (%Ndfa) in Marianna (71.4%). N yield was greater for Corn + N in both sites, and no difference was found between the intercropped treatments. Corn + SH0 in Marianna and Corn + SH4 in Marshfield resulted in great NUE (72% and 69%, respectively). The data indicate that intercropping corn and SH can enhance NUE. However, intercropping corn and SH reduces the overall HA, which requires further evaluation to promote producer adoption of these systems.

Estimating the botanical composition in mixed pastures is challenging due to resource‐intensive sampling methods overlooking grassland spatial variability. We aimed to (I) classify bahiagrass ( Paspalum notatum Flügge) and rhizoma peanut ( Arachis glabrata Benth.) in grass–legume pastures using aerial multispectral imagery and machine learning, (II) estimate the proportion of grass and legumes on an area basis, and (III) estimate the botanical composition on a dry matter basis to guide grazing management. Unmanned aerial vehicle‐based images were taken in the early and late warm seasons of 2023 and 2024. Field campaigns were conducted for ground‐truth species labeling ( n = 554) across nine paddocks under three treatments (fertilized grass, unfertilized grass, and grass–legume mixed) using a high‐precision positioning system. Ninety‐six points were harvested and manually sorted for ground truth botanical composition estimation on a dry matter basis. The classification dataset was split into training and testing using three different approaches to assess spatial generalization performance. A random forest (RF) was employed as the base model, and a convolutional neural network (CNN) was developed using fivefold cross‐validation on the training set. Our CNN model outperformed the RF model, achieving an average cross‐validated overall accuracy of 93% and 88% for grass–legume classification on the grid‐based and repeated spatial hold‐out testing set. Combining land cover classification, compressed height, and vegetation indices showed promising potential to predict total biomass ( R ² = 0.85) and grass biomass ( R ² = 0.83) in bahiagrass–rhizoma peanut pastures using stepwise multiple linear regression. This approach also enabled the estimation of legume proportion by difference.

Pasture systems can significantly impact soil carbon (C) and nitrogen (N) mineralization by influencing organic inputs, root turnover, and microbial processes. This study evaluated soil biological activity (SBA), soil C, and N mineralization in long‐term N‐fertilized grass or grass–legume grazing systems. Soil samples (0‐ to 10‐cm depth) were collected in 2022 from 10‐year grazing systems at the University of Florida's North Florida Research and Education Center in Marianna, FL. Three grazing systems were evaluated: N‐fertilized bahiagrass ( Paspalum notatum Flüggé) during summer and overseeded with N‐fertilized ryegrass ( Lolium multiflorum Lam ) and oat ( Avena sativa L.) during winter (BGN); unfertilized bahiagrass overseeded with ryegrass, oat, and a mixture of clovers ( Trifolium spp.; BG); and bahiagrass‐rhizoma peanut (RP; Arachis glabrata Benth.) mixture overseeded with the same ryegrass–oat–clover mixture as for BG (BGRP). A 24‐day soil incubation was conducted to evaluate SBA, C mineralization, inorganic N, microbial biomass carbon (MBC), C and N stocks, and δ ¹³ C from CO ₂ . On average, BGN and BG had 12% greater SBA than BGRP. Carbon and N stocks were 32% greater in BGN compared with BGRP, although the proportion of total C mineralized did not differ among treatments. Nitrification was 34% greater in BGN than in BGRP. These results indicate a tradeoff between reduced synthetic N inputs and lower soil C and N accumulation in grass–legume systems. However, despite lower C and N stocks, the BGRP system maintained comparable relative C mineralization and exhibited lower inorganic N availability, suggesting a lower nitrogen loss risk.

Precision agriculture, including sensors and artificial intelligence, is transforming agricultural monitoring. This study developed predictive models for fresh and dry forage mass, canopy height, forage density, dry matter (%DM), and crude protein (%CP) concentrations in signalgrass [Urochloa decumbens (Stapf) R.D. Webster] pastures using machine learning and UAV-based multispectral imagery. The experiment was conducted at the Federal University of Viçosa (2019–2020), applying nitrogen doses after each harvest to promote variability. Multiple Linear Regression (MLR), Support Vector Regressor (SVR), and Random Forest Regressor (RFR) models were trained with multispectral and meteorological data. The best results were obtained for fresh forage mass with RFR (R² = 0.82, RMSE = 2894.10 kg ha⁻¹), dry forage mass with SVR (R² = 0.68, RMSE = 719.87 kg ha⁻¹), and dry matter concentration with MLR (R² = 0.64, RMSE = 3.83%). Forage density showed moderate performance (R² = 0.56), while canopy height demonstrated limited accuracy (R² = 0.44). Crude protein was not adequately predicted by any model, highlighting multispectral sensor limitations and suggesting hyperspectral sensors usage. Results demonstrate the applicability of remote sensing combined with machine learning in forage management, but indicate the need to expand temporal and spatial data variability and integrate different sensor types to increase model robustness.

Understanding cattle grazing behaviour is essential for optimising pasture management and improving animal performance. This study assessed the grazing behaviour and landscape preferences of steers grazing bahiagrass (BG) monoculture, mixed pastures of BG and rhizoma peanut (BGRP), and BG with nitrogen fertilisation (BGN) using Global Positioning System (GPS) tracking. Grazing behaviour was analysed across two periods (July–August and September–October) over the warm seasons of 2023 and 2024 to determine differences in movement patterns, grazing time, and herbage utilisation. There was a significant interaction between treatment and period for the distance walked by steers. Overall, the BGRP treatment exhibited 21% less movement during period 1, while steers grazing BG walked 16% more during period 2 compared to the other treatments. Animals in the BGRP treatment exhibited lower movement speeds and reduced grazing time, while increasing resting time compared to other treatments. On average, animals spent approximately 21% of the day in shaded areas. Within the mixed pastures, animals spent more time in the grass‐legume strips than in the grass‐only strips, despite the latter occupying a larger proportion of the area. Additionally, herbage mass and accumulation varied across treatments, with BGN pastures maintaining greater herbage mass than BG. These findings highlight the effect of pasture composition on grazing behaviour, demonstrating that cattle in mixed pastures travel less and exhibit different foraging strategies compared to those in monocultures. The study underscores the importance of integrating GPS tracking in pasture research to optimise grazing management and improve efficiency in mixed‐species pastures.