A adubação nitrogenada é essencial para o aumento de produção de forrageiras, porém a fonte de nitrogênio (N) e fatores como o manejo podem ocasionar elevadas perdas de amônia (NH3) por volatilização. O presente trabalho foi conduzido com objetivo de quantificar as perdas de N por volatilização de NH3 em fontes nitrogenadas em pastagem. O experimento foi desenvolvido na Universidade Estadual de Mato Grosso do Sul (UEMS), Unidade Universitária de Cassilândia-MS, utilizando o delineamento experimental em blocos ao acaso com quatro tratamentos (ureia; ureia revestida com polímero; ureia com inibidor de urease; e nitrato de amônia) e cinco repetições. Em todos os tratamentos foi aplicada a dose equivalente a 100 kg ha-1 de N. Para a quantificação da volatilização de N-NH3 foram utilizados absorvedores de espumas, colocados à 1,0 cm da superfície do solo. As perdas de N-NH3 por volatilização foram menores quando utilizou-se ureia com inibidor de urease [N-(n-butil) tiofosfórico triamida] e ureia com polímero, quando comparadas ao uso da ureia convencional. A ureia com NBPT retardou o pico de volatilização em dois dias em relação à ureia convencional.
Using spectral data to quantify nitrogen (N), phosphorus (P), and potassium (K) contents in soybean plants can help breeding programs develop fertilizer-efficient genotypes. Employing machine learning (ML) techniques to classify these genotypes according to their nutritional content makes the analyses performed in the programs even faster and more reliable. Thus, the objective of this study was to find the best ML algorithm(s) and input configurations in the classification of soybean genotypes for higher N, P, and K leaf contents. A total of 103 F2 soybean populations were evaluated in a randomized block design with two repetitions. At 60 days after emergence (DAE), spectral images were collected using a Sensefly eBee RTK fixed-wing remotely piloted aircraft (RPA) with autonomous take-off, flight plan, and landing control. The eBee was equipped with the Parrot Sequoia multispectral sensor. Reflectance values were obtained in the following spectral bands (SBs): red (660 nm), green (550 nm), NIR (735 nm), and red-edge (790 nm), which were used to calculate the vegetation index (VIs): normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), green normalized difference vegetation index (GNDVI), soil-adjusted vegetation index (SAVI), modified soil-adjusted vegetation index (MSAVI), modified chlorophyll absorption in reflectance index (MCARI), enhanced vegetation index (EVI), and simplified canopy chlorophyll content index (SCCCI). At the same time of the flight, leaves were collected in each experimental unit to obtain the leaf contents of N, P, and K. The data were submitted to a Pearson correlation analysis. Subsequently, a principal component analysis was performed together with the k-means algorithm to define two clusters: one whose genotypes have high leaf contents and another whose genotypes have low leaf contents. Boxplots were generated for each cluster according to the content of each nutrient within the groups formed, seeking to identify which set of genotypes has higher nutrient contents. Afterward, the data were submitted to machine learning analysis using the following algorithms: decision tree algorithms J48 and REPTree, random forest (RF), artificial neural network (ANN), support vector machine (SVM), and logistic regression (LR, used as control). The clusters were used as output variables of the classification models used. The spectral data were used as input variables for the models, and three different configurations were tested: using SB only, using VIs only, and using SBs+VIs. The J48 and SVM algorithms had the best performance in classifying soybean genotypes. The best input configuration for the algorithms was using the spectral bands as input.
The objective of this work was to evaluate soil biomass and microbial activity and soybean yield under different limestone and gypsum doses and different cover crops. The experiment was carried out in the experimental area of the Fundação de Apoio a Pesquisa Agropecuária de Chapadão, on a Dystrophic Red Latosol, using cultivar Desafio. The experiment consisted of a randomized blocks design, in a split-plot factorial scheme (3x4x3), with three replications. Plots consisted of three gypsum doses: control (without gypsum), recommended dose (2.3 Mg ha-1), and double dose (4.6 Mg ha-1). Subplots consisted of four limestone doses (2, 4, and 6 Mg ha-1) and the control (without limestone). Each block had three different cover crops: Brachiaria, Millet, and allow. The values obtained with the test revealed that brachiaria had better basal respiration in the absence of gypsum. Conversely, millet had better basal respiration in with the gypsum dose. Basal respiration, using brachiaria as cover crop, was higher at the dose of 2700 kg ha-1 of limestone. However, for the fallow and the millet, basal respiration was higher when using the highest limestone dose of 6000 kg ha-1. The variable microbial biomass showed differences between cover crops only in the absence of gypsum. Brachiaria and fallow presented the highest mean for microbial biomass. The use of millet as a cover crop together with gypsum doses increased the microbial biomass. The variables mass of 100 grains and grain yield had higher mean at the limestone dose of 6000 kg ha-1 .
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