RESUMO -A área foliar possui correlação entre as atividades fotossintéticas e de transpirações das espécies vegetais, uma vez que esta reflete a capacidade da planta em interceptar as radiações e efetuar as trocas gasosas. Dessa forma, torna-se um importante indicativo da produtividade das culturas agrícolas. Tendo em vista a escassez de trabalhos sobre a estimativa da área foliar do abacaxizeiro, torna-se objetivo deste trabalho identificar equações para a determinação da área foliar do abacaxizeiro cv Vitória utilizando relações alométricas das plantas. Foram utilizadas 120 plantas de abacaxizeiro, coletadas aleatoriamente no momento da indução floral artificial, que ocorreu aos 270 dias após o plantio. Foram mensurados altura (h) e número de folhas (NF), comprimento (C) e largura (L) da folha "D" e o produto destas duas últimas variáveis (CxL). Os dados foram submetidos à análise de regressão e selecionou-se a equação que melhor se ajustou às correlações. A validação dos modelos utilizou 60 novas plantas, e os valores obtidos foram avaliados por meio do coeficiente de determinação (R 2 ), correlação de Pearson (r), erro médio (EA), erro médio absoluto (ERA) e raiz do quadrado médio do erro (RQME). O modelo que utilizou o produto das dimensões lineares (AF=19,298*(C x L)-559,9*) mostrou-se o mais adequado para a estimativa da área foliar do abacaxizeiro, devido aos baixos erros encontrados, alta correlação e fácil mensuração. Termos para indexação: abacaxi, dimensões lineares, biometria. LEAF AREA ESTIMATIVE OF PINEAPPLE (CV. VITORIA) USING ALLOMETRIC RELATIONSHIPSABSTRACT -The plant leaf area has correlation between the photosynthesis activity and plant transpiration, once reflect the plant capacity to intercept the radiation. Therefore can be an important indicative of productivity in agricultural crops. Front of the scarcity of studies about pineapple leaf area, this study aimed identify equations to determine the pineapple leaf area (cv Vitoria) by alometric relationships. 120 pineapples plants were used and collected at the moment of flower induction, 270 days after transplanting. Height (h) and number of leaves (NF), length (C) and width (L) of "D" leaf were measured. The product of length and width (CxL) was considered as an additional variable. The data was analyzed with regression analysis and was chose the equation that presented the best correlation. The model validation used 60 new plants and the results was evaluated by coefficient of determination (R2), Pearson correlation (r), mean error (EA), absolute mean error (ERA) and the root mean square error (RQME). The product of length and width like variable (AF=19,298*(CXL)-559,9*) was the best to determine the leaf area in the pineapple, with low errors, high correlation and facility on the measurement.

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The performances of six time-domain reflectometry (TDR) and frequency-domain reflectometry (FDR) type soil moisture sensors were investigated for measuring volumetric soil-water content ( v) in two different soil types. Soilspecific calibration equations were developed for each sensor using calibrated neutron probe-measured  v. Sensors were also investigated for their performance response in measuring  v to changes in soil temperature. The performance of all sensors was significantly different (P<0.05) than the neutron probe-measured  v , with the same sensor also exhibiting variation between soils. In the silt loam soil, the 5TE sensor had the lowest root mean squared error (RMSE) of 0.041 m 3 /m 3 , indicating the best performance among all sensors investigated. The performance ranking of the other sensors from high performance to low was: TDR300 (High Clay Mode), CS616 (H) and 10HS, SM150, TDR300 (Standard Mode), and CS616 (V) (H: horizontal installation and V: vertical installation). In the loamy sand, the CS616 (H) performed best with an RMSE of 0.014 m 3 /m 3 and the performance ranking of other sensors was: 5TE, CS616 (V), TDR300 (S), SM150, and 10HS. When  v was near or above field capacity, the performance error of most sensors increased. Most sensors exhibited a linear response to increase in soil temperature. Most sensors exhibited substantial sensitivity to changes in soil temperature and the  v response of the same sensor to high vs. normal soil temperatures differed significantly between the soils. All sensors underestimated  v in high temperature range in both soils. The ranking order of the magnitude of change in  v in response to 1°C increase in soil temperature (from the lowest to the greatest impact of soil temperature on sensor performance) in silt loam soil was: SM150, 5TE, TDR300 (S), 10HS, CS620, CS616 (H), and CS616 (V). The ranking order from lower to higher sensitivity to soil temperature changes in loamy sand was: 10HS, CS616 (H), 5TE, CS616 (V), SM150, and TDR300 (S). When the data from all sensors and soils are pooled, the overall average of change in  v for a 1°C increase in soil temperature was 0.21 m 3 /m 3 in silt loam soil and-0.052 m 3 /m 3 in loamy sand. When all TDR-and FDR-type sensors were pooled separately for both soils, the average change in  v for a 1°C increase in soil temperature for the TDR-and FDR-type sensors was 0.1918 and-0.0273 m 3 /m 3 , respectively, indicating that overall TDRtype sensors are more sensitive to soil temperature changes than FDR-type sensors when measuring  v .

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Leaf area is a component of crop growth and yield prediction models. Few studies have used the structure from motion (SfM) algorithm, which is based on the principles of traditional stereophotogrammetry, to obtain the leaf area index (LAI). Thus, the objective of this study was to follow the evolution of the LAI and percentage of land cover (%COV) in coffee plants, using pre-established equations and plant measurements obtained from generated 3D point clouds, combined with the application of the SfM algorithm to digital images recorded by a camera coupled to a UAV (unmanned aerial vehicle). The experiment was conducted in a coffee plantation located in southeastern Brazil. A rotary wing UAV containing a conventional camera was used. The images were collected once per month for 12 months. Image processing was performed using PhotoScan software. Regression analysis and spatial analysis were performed using R and GeoDa software, respectively. The resulting %COV data had R 2 and RMSE values of 89% and 3.41, respectively, while those for LAI had R 2 and RMSE of 88% and 0.47, respectively. Significant %COV results were obtained in the months of January, February and March of 2018. There was significant autocorrelation for the LAI values from January to May 2018, with most blocks in the central and centerwest regions presenting LAI values > 3.0. It was possible to monitor the temporal and spatial behavior of the LAI and %COV, allowing for the conclusion that this methodology generated results that are consistent with the literature.

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. As aplicações ocorreram em intervalos de 20 dias utilizando-se um sistema de irrigação localizada com gotejadores de vazão nominal de 4 L h -1 espaçados em 0,40 m. As doses de vinhaça aplicadas foram calculadas, seguindo recomendações da norma CETESB P4.231, de modo que o tratamento T4 suprisse a demanda por potássio do abacaxizeiro. A coleta da solução foi realizada por meio de extratores de cápsulas porosas após 30; 120 e 210 dias do início da aplicação da vinhaça e verificou-se que o aumento dos volumes de níveis aplicados resultou em maiores valores de Condutividade elétrica do solo (CEs) e concentrações de potássio (K) e nitrato (NO 3-), com maiores valores obtidos na camada de 0,20 m. Os resultados revelaram que o pH diminuiu à medida em que se aumentavam os volumes de vinhaça aplicados.Palavras-chave: Efluentes. Reuso de água. Lixiviação.

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