No abstract

The objective of this experiment was to examine effects of adding 2 exogenous fibrolytic enzymes (EFE) to the total mixed ration (TMR) on the performance of lactating dairy cows (experiment 1) and the kinetics of ruminal degradation of the diet (experiment 2). Twelve EFE had been screened in a series of in vitro assays that identified the most potent EFE and their optimal doses for increasing the digestibility of bermudagrass. In experiment 1, 66 Holstein cows (21±5 d in milk) were grouped by previous milk production and parity (45 multiparous and 21 primiparous) and assigned randomly to 1 of the following 3 treatments: (1) control (CON, untreated), (2) Xylanase Plus [2A, 1mL/kg of TMR dry matter (DM); Dyadic International, Jupiter, FL], and (3) a 75:25 (vol/vol) mixture of Cellulase Plus and Xylanase Plus EFE (3A, 3.4mL/kg of TMR DM; Dyadic International). The EFE were sprayed twice daily onto a TMR (10% bermudagrass silage, 35% corn silage, 5% alfalfa-orchardgrass hay mixture, and 50% concentrates; DM basis) and fed for a 14-d training and covariate period and a 70-d measurement period. Experiment 2 aimed to examine the in situ DM ruminal degradability and ruminal fermentation measurements of the diets fed in experiment 1. Three ruminally fistulated lactating Holstein cows were assigned to the diets. The experiment had a 3×3 Latin square design with 23-d periods. In experiment 1, application of 2A increased intakes (kg/d) of DM (23.5 vs. 22.6), organic matter (21.9 vs. 20.9), and crude protein (3.9 vs. 3.7) and tended to increase yields (kg/d) of fat-corrected milk (41.8 vs. 40.7) and milk fat (1.48 vs. 1.44). In particular, 2A increased milk yield (kg/d) during wk 3 (41.2 vs. 39.8, tendency), 6 (41.9 vs. 40.1), and 7 (42.1 vs. 40.4), whereas 3A increased milk yield (kg/d) during wk 6 (41.5 vs. 40.1, tendency), 8 (41.8 vs. 40.0), and 9 (40.9 vs. 39.5, tendency). In experiment 2, EFE treatment did not affect ruminal DM degradation kinetics or ruminal pH, ammonia-N, and volatile fatty acid concentration. Application of 2A to the bermudagrass-based TMR increased DM intake and milk production, implying that this EFE could be used to increase the performance of lactating dairy cows fed diets containing up to 10% bermudagrass.

No abstract

This study aimed to modify a webcam by replacing its near-infrared (NIR) blocking filter to a low-cost red, green and blue (RGB) filter for obtaining NIR images and to evaluate its performance in two agricultural applications. First, the sensitivity of the webcam to differentiate normalized difference vegetation index (NDVI) levels through five nitrogen (N) doses applied to the Batatais grass (Paspalum notatum Flugge) was verified. Second, images from maize crops were processed using different vegetation indices, and thresholding methods with the aim of determining the best method for segmenting crop canopy from the soil. Results showed that the webcam sensor was capable of detecting the effect of N doses through different NDVI values at 7 and 21 days after N application. In the second application, the use of thresholding methods, such as Otsu, Manual, and Bayes when previously processed by vegetation indices showed satisfactory accuracy (up to 73.3%) in separating the crop canopy from the soil.

No abstract

The aim of this study was to quantify the spatio-temporal changes in land use/ cover (LULC), as well as analyze landscape patterns over a 20-year period (1995 - 2015) in the Catolé watershed, northern Minas Gerais State, using landscape metrics. The LULC maps were obtained using Landsat 5 and 8 data (Processing level 1) through supervised classification using the maximum likelihood classifier. Seven thematic classes were identified: dense vegetation, sparse vegetation, riparian vegetation, cropland, planted forest, bare soil, and water. From the LULC maps, classes related to the natural landscape (dense, sparse, and riparian vegetation) were grouped into forest patches, which was then ordered by size: very small (< 5 ha); small (5 - 10 ha); medium (10 - 100 ha); large (100 ha); and a general class (no distinction of patch size). Then, metrics of area, size and density, edge, shape, proximity and core area were calculated. The dense vegetation portion of the study area decreased considerably within a given time, while the portion of cropland and bare soil increased. Overall, in the Catolé river basin, the total area of natural vegetation decreased by 3,273 hectares (4.62%). Landscape metrics analysis exhibited a reduction in the number of very small patches, although the study area was still considered as fragmented. Moreover, a maximum edge distance of 50 m is suggested for conducting studies involving core area metrics in the Catolé watershed, as values above this distance would eliminate the very small patches.

No abstract

Coffee ripeness monitoring is a key indicator for defining the moment of starting the harvest, especially because the coffee quality is related to the fruit ripeness degree. The most used method to define the start of harvesting is by visual inspection, which is time-consuming, labor-intensive, and does not provide information on the entire area. There is a lack of new techniques or alternative methodologies to provide faster measurements that can support harvest planning. Based on that, this study aimed at developing a vegetation index (VI) for coffee ripeness monitoring using aerial imagery. For this, an experiment was set up in five arabica coffee fields in Minas Gerais State, Brazil. During the coffee ripeness stage, four flights were carried out to acquire spectral information on the crop canopy using two quadcopters, one equipped with a five-band multispectral camera and another with an RGB (Red, Green, Blue) camera. Prior to the flights, manual counts of the percentage of unripe fruits were carried out using irregular sampling grids on each day for validation purposes. After image acquisition, the coffee ripeness index (CRI) and other five VIs were obtained. The CRI was developed combining reflectance from the red band and from a ground-based red target placed on the study area. The effectiveness of the CRI was compared under different analyses with traditional VIs. The CRI showed a higher sensitivity to discriminate coffee plants ready for harvest from not-ready for harvest in all coffee fields. Furthermore, the highest R2 and lowest RMSE values for estimating the coffee ripeness were also presented by the CRI (R2: 0.70; 12.42%), whereas the other VIs showed R2 and RMSE values ranging from 0.22 to 0.67 and from 13.28 to 16.50, respectively. Finally, the study demonstrated that the time-consuming fieldwork can be replaced by the methodology based on VIs.