Fragment size of corn silage according to the dry matter and forage harvester adjustments

In Brazil, the best results in milk production are found in the state of Paraná. Such results are reached through genetic selection of the animals and management of their diets, in which whole plant corn silage is widely used. Aiming the silage quality, it was evaluated the influence of dry matter content of the corn culture as forage and the harvester adjustments on the fragment size of whole plant corn silage. The fragment size of two corn hybrids silage (SPEED and 2B688) was evaluated using a 5x3 factorial, with 4 repetitions. The first factor was the harvest time of the plants (105, 108, 112, 118, and 123 days after sowing (DAS)), which determines the forage dry matter (DM) content. The second factor was the harvester adjustments (2, 6.5 and 11mm of theoretical fragment length (TFL)). The DM content did not affect the average fragment size of 2B688. For SPEED, however, the real fragment size decreased as the maturation of plants increased. The conclusion is that the DM content and harvester adjustments can affect the real fragment sizes, according to different plant genotypes. The alterations of the harvester adjustments resulted in different fragment sizes, however, it were different from those indicated by the manufacturer.

Identification of croplands of winter cereals in Rio Grande do Sul state, Brazil, through unsupervised classification of normalized difference vegetation index images

This study aimed to propose methods to identify croplands cultivated with winter cereals in the northern region of Rio Grande do Sul State, Brazil. Thus, temporal profiles of Normalized Difference Vegetation Index (NDVI) from MODIS sensor, from April to December of the 2000 to 2008, were analyzed. Firstly, crop masks were elaborated by subtracting the minimum NDVI image (April to May) from the maximum NDVI image (June to October). Then, an unsupervised classification of NDVI images was carried out (Isodata), considering the crop mask areas. According to the results, crop masks allowed the identification of pixels with greatest green biomass variation. This variation might be associated or not with winter cereals areas established to grain production. The unsupervised classification generated classes in which NDVI temporal profiles were associated with water bodies, pastures, winter cereals for grain production and for soil cover. Temporal NDVI profiles of the class winter cereals for grain production were in agree with crop patterns in the region (developmental stage, management standard and sowing dates). Therefore, unsupervised classification based on crop masks allows distinguishing and monitoring winter cereal crops, which were similar in terms of morphology and phenology.