The spatial and temporal variability of crop parameters are fundamental in precision agriculture. Remote sensing of crop canopy can provide important indications on the growth variability and help understand the complex factors influencing crop yield. Plant biomass is considered an important parameter for crop management and yield estimation, especially for grassland and cover crops. A recent approach introduced to model crop biomass consists in the use of RGB (red, green, blue) stereo images acquired from unmanned aerial vehicles (UAV) coupled with photogrammetric softwares to predict biomass through plant height (PHT) information. In this study, we generated prediction models for fresh (FBM) and dry biomass (DBM) of black oat crop based on multi-temporal UAV RGB imaging. Flight missions were carried during the growing season to obtain crop surface models (CSMs), with an additional flight before sowing to generate a digital terrain model (DTM). During each mission, 30 plots with a size of 0.25 m² were distributed across the field to carry ground measurements of PHT and biomass. Furthermore, estimation models were established based on PHT derived from CSMs and field measurements, which were later used to build prediction maps of FBM and DBM. The study demonstrates that UAV RGB imaging can precisely estimate canopy height (R2 = 0.68–0.92, RMSE = 0.019–0.037 m) during the growing period. FBM and DBM models using PHT derived from UAV imaging yielded R2 values between 0.69 and 0.94 when analyzing each mission individually, with best results during the flowering stage (R2 = 0.92–0.94). Robust models using datasets from different growth stages were built and tested using cross-validation, resulting in R2 values of 0.52 for FBM and 0.84 for DBM. Prediction maps of FBM and DBM yield were obtained using calibrated models applied to CSMs, resulting in a feasible way to illustrate the spatial and temporal variability of biomass. Altogether the results of the study demonstrate that UAV RGB imaging can be a useful tool to predict and explore the spatial and temporal variability of black oat biomass, with potential use in precision farming.

A exploração desordenada dos recursos naturais, o uso inadequado dos solos, atrelado ao uso abusivo de fertilizantes, corretivos e agrotóxicos vem provocando inúmeros problemas ambientais, principalmente em áreas de nascentes e várzeas, comprometendo a qualidade e a quantidade dos recursos hídricos. Estudos em nível de microbacia hidrográfica podem trazer informações importantes para o correto manejo e adequação dos problemas encontrados. Com este intuito, o objetivo do presente trabalho foi realizar o estudo do uso e ocupação do solo, bem como a definição das características morfométricas da microbacia do rio Guarani, localizada na área rural do município de Quedas do Iguaçu - PR. Para isto, realizou-se a elaboração de quatro diferentes mapas, sendo eles: mapa da rede de drenagem, mapa da área de preservação permanente (APP), mapa de uso e ocupação do solo e o mapa do relevo da microbacia. Realizou-se também a análise morfométrica da microbacia. Destaca-se a existência de irregularidades na microbacia em estudo que com o manejo adequado podem ser corrigidos no intuito de prezar pela qualidade da água e na contensão de possíveis processos erosivos.

Maize silage is the main conserved roughage used in animal feed in Brazil and improving its quality has great relevance. The aim of this experiment was to evaluate the characteristics of maize silage, containing different percentages of soybean biomass. In this way, different percentages of soybean green biomass added to maize ensilage (0, 10, 20, 30 and 40%-experiment 1 and 0, 10, 20, 30, 40 and 50% in experiment 2) were evaluated. Experiments were laid out as a completely randomized. Variables were submitted to analysis of variance and when it present significance was applied regression analysis. Silage ashes increased as soybean biomass increased. Regarding to the neutral and acid detergent fiber and the amount of total digestible nutrients, there was no effect of the treatments. At experiment 1, silage crude protein increased from 7.5 to 12.6% from sole maize silage to the silage with 39.2% of soybean dry biomass, which represent an increase of 67.24%. At experiment 2, it increased from 6.77 to 12.09%, which represent 78.58% more protein at the treatment with 50% of soybean green biomass (41% dry matter of soybean) in relation to the sole maize silage. At experiment 2, for every 1% increase in soybean dry matter biomass addition, there was an increase of 0.1% of maize silage crude protein. The addition of soybean biomass to corn silage increases the ashes and crude protein content of silage.

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