Development of tropical grassland biomass prediction model based on UAV RGB images

B.L.P. Ramos¹*, A.A. Seixas¹, L.M.G. Nascimento¹, D.L.S. Dias⁴, J.M.S. Amorim¹, O.L. Lemos³ and M.S. Pedreira²

¹University State Southwestern Bahia, Postgraduate Program in Animal Science,
BR 415, Itapetinga – BA, Brazil
²University State Southwestern Bahia, Department of Plant Science and Animal Science, Road of Good Will, km 04, Vitória da Conquista – BA, Brazil
³University State Southwestern Bahia, Department of Agricultural and Soil Engineering, Road of Good Will, km 04, Vitória da Conquista – BA, Brazil
⁴University State of Feira de Santana, Department of Biological Sciences, Av. Transnordestina, Feira de Santana – BA, Brazil
*Correspondence: agro.barbara@outlook.com

Abstract:

The objective of this study is to assess the predictive potential of indices derived from RGB images captured by a camera mounted on a remotely piloted vehicle (RPV) to estimate the fresh and dry forage yield of grasses from the Urochloa genus. The experiment was conducted between December 2021 and January 2023, involving four cultivars of the Urochloa genus (U. brizantha cv. Braúna, U. brizantha cv. Paiaguás, U. hybrid cv. Camello, and U. decumbens cv. Basilisk), with flights conducted at two heights (20 and 100 metres). The values of the Green Leaf Index (GLI) and Digital Vegetation Model (DVM) extracted were correlated with the yields of fresh (FFY), dry forage yield (DFY), dry matter content (DM), and crude protein (CP). The results showed that DVM exhibited greater efficiency in estimating DM and CP at a flight altitude of 20 m. In contrast, GLI proved more efficient in estimating FFY and DFY at 100 m altitude, suggesting the potential for combining DVM and GLI to develop predictive models. The RGB images obtained via RPV have potential for estimating forage productivity and quality, expanding the possibilities of pasture management techniques.

Key words:

digital vegetation model, drone, green leaf index, pasture, production and consumption charts

Meat chemical composition of pasture pure lambs and crossbreeds

J. Vecvagars, D. Bārzdiņa and D. Kairiša*

Latvia University of Life Sciences and Technologies, Faculty of Agriculture, Institute of Agrobiotechnology, Liela street 2, LV – 3001 Jelgava, Latvia
*Correspondence: daina.kairisa@llu.lv

Abstract:

Increased customer interest of safe, healthy and environmentally friendly food consumption promote development of sheep farming industry in Latvia. Aim of the paper – explain of different pasture-fattened sheep breed and their crosses lamb meat composition traits. A study of pasture fattened lamb meat chemical composition was carried out from year 2013 to 2017. Fattening lambs were kept in breeding rams control station ‘Klimpas’ (57°50’58.8’’N 25°19’39.6’’E) pasture array. Lambs were slaughtered in a certified slaughterhouse, but analysis of meat chemical composition were conducted in laboratory of Institute of Food Safety, Animal Health and Environment (BIOR). For the analysis of the meat chemical composition were used up to 1 kg heavy Quadriceps femoris muscle samples. In meat were analysed following elements of its chemical composition: dry matter, protein, fat, minerals, pH, cholesterol and unsaturated fatty acids. Data analysis shows that the lambs before slaughter ranged in age from 5 to 8 months. Lamb meat obtained from the study groups had a significantly different total amount of dry matter and fat. The lowest total fat, but the highest ash content was obtained in the lamb meat of the extensive breed group. The lowest total fat and the highest ash content were obtained in the lamb meat from the extensive breed group. In meat obtained a small (in individual samples < 0.10%) cis-10-pentadecenoic acid, cis-11-eicosenoic acid and myristoleic acid content. Of unsaturated fatty acids in lamb meat were represented higher amount of oleic acid, linoleic acid and elaidic acid.

Key words: