The importance of understanding spatial variability of soils is connected to crop management planning. This understanding makes it possible to treat soil not as a uniform, but a variable entity, and it enables site-specific management to increase production efficiency, which is the target of precision agriculture. Questions remain as the optimum soil sampling interval needed to make site-specific fertilizer recommendations in Brazil. The objectives of this study were: i) to evaluate the spatial variability of the main attributes that influence fertilization recommendations, using georeferenced soil samples arranged in grid patterns of different resolutions; ii) to compare the spatial maps generated with those obtained with the standard sampling of 1 sample ha -1 , in order to verify the appropriateness of the spatial resolution. The attributes evaluated were phosphorus (P), potassium (K), organic matter (OM), base saturation (V%) and clay. Soil samples were collected in a 100 × 100 m georeferenced grid. Thinning was performed in order to create a grid with one sample every 2.07, 2.88, 3.75 and 7.20 ha. Geostatistical techniques, such as semivariogram and interpolation using kriging, were used to analyze the attributes at the different grid resolutions. This analysis was performed with the Vesper software package. The maps created by this method were compared using the kappa statistics. Additionally, correlation graphs were drawn by plotting the observed values against the estimated values using cross-validation. P, K and V%, a finer sampling resolution than the one using 1 sample ha -1 is required, while for OM and clay coarser resolutions of one sample every two and three hectares, respectively, may be acceptable. Key words: soil attributes, grid sampling, kriging, cross-validation, kappa statistic Tamanho ideal em grades de amostragem de solos para aplicação em taxa variável em manejo localizado RESUMO: A importância de compreender a variabilidade espacial do solo está conectada ao planejamento do manejo das culturas. Este entendimento faz com que seja possível tratar o solo não como uma entidade uniforme, mas variável, e permite o gerenciamento de sítios específicos para aumentar a eficiência de produção, que é o objetivo da agricultura de precisão. Questões relacionadas com a otimização do intervalo de amostragem do solo se faz necessário para a realização das recomendações de adubações no Brasil. Os objetivos deste estudo foram: i) avaliar a variabilidade espacial dos principais atributos que influenciam as recomendações de adubação, usando amostras de solos georreferenciadas dispostas em padrões de grades de diferentes resoluções; ii) comparar os mapas espaciais gerados com o mapa padrão obtido com amostragem de 1 amostra ha -1 , a fim de verificar a adequação da resolução espacial. Os atributos avaliados foram fósforo (P), potássio (K), matéria orgânica (MO), saturação por bases (V%) e argila. As amostras de solos foram coletadas numa grade de 100 × 100 m e georreferenciadas. Um desbaste foi ...

Soybean production both in Brazil and globally has regularly been threatened by drought periods. The use of infrared thermography to evaluate the canopy's temperature and its relationship with plant water status constitutes an important tool for agricultural monitoring. However, studies regarding the water status of soybean plants through unmanned aerial vehicle (UAV)-based thermal imaging are yet to be reported. Thus, the present study aimed to evaluate the water status of soybean plants submitted to different water conditions via thermal images obtained through an UAV thermal infrared camera. The field experiment was undertaken at the National Soybean Research Centre (Embrapa Soja, a branch of the Brazilian Agricultural Research Corporation) in a randomized complete block design, with four replicates. The following water conditions were evaluated: irrigated (IRR, receiving rainfall and irrigation when necessary, and with a soil water matric potential between −0.03 MPa and −0.05 MPa), non-irrigated (NIRR, receiving only rainfall), and water deficit (or drought stress) induced at the vegetative (DSV) and reproductive (DSR) stages. Water deficit was induced using rainout shelters. Soil moisture and weather data were monitored daily. Thermal images were obtained on twelve dates, half in 2016-2017 and half during the 2017-2018 crop seasons, through a thermal infrared camera (DIY-Thermocam) sensitive to temperatures ranging from -40°C to 200°C, with 0.5 °C accuracy and 14-bit radiometric resolution. Images in RAW format (160 pixels x120 pixels) were obtained at 125 m above ground level. They were then processed and calibrated by acquiring a correction factor resulting from the effect of atmospheric attenuation. The canopy temperature was evaluated in relation to that of air temperature and through the Normalized Relative Canopy Temperature (NRCT). Atmospheric attenuation was positively correlated to flight altitude, so that image correction eliminated such an effect. The thermal behaviour of soybean plants was directly correlated to soil water availability and atmospheric vapour pressure deficit, with differences ranging from 0.2 °C to 7.2°C. Plants grown at lower soil moisture conditions had higher temperatures, which were

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Monitoring of soybean genotypes is important because of intellectual property over seed technology, better management over seed genetics, and more efficient strategies for its agricultural production process. This paper aims at spectrally classifying soybean genotypes submitted to diverse water availability levels at different phenological stages using leaf-based hyperspectral reflectance. Leaf reflectance spectra were collected using a hyperspectral proximal sensor. Two experiments were conducted as field trials: one experiment was at Embrapa Soja in the 2016/2017, 2017/2018, and 2018/2019 cropping seasons, where ten soybean genotypes were grown under four water conditions; and another experiment was in the experimental farm of Unoeste University in the 2018/2019 cropping season, where nine soybean genotypes were evaluated. The spectral data collected was divided into nine spectral datasets, comprising single and multiple cropping seasons (from 2016 to 2019), and two contrasting crop-growing environments. Principal component analysis, applied as an indicator of the explained variance of the reflectance spectra among genotypes within each spectral dataset, explained over 94% of the spectral variance in the first three principal components. Linear discriminant analysis, used to obtain a model of classification of each reflectance spectra of soybean leaves into each soybean genotype, achieved accuracy between 61% and 100% in the calibration procedure and between 50% and 100% in the validation procedure. Misclassification was observed only between genotypes from the same genetic background. The results demonstrated the great potential of the spectral classification of soybean genotypes at leaf-scale, regardless of the phenological stages or water status to which plants were submitted.

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