Resumo -O objetivo deste trabalho foi avaliar a acurácia na caracterização da variabilidade espacial de fósforo e potássio disponíveis no solo, pelo uso de diferentes dimensões de malhas amostrais, bem como a similaridade dos mapas temáticos gerados. O estudo foi conduzido em área de Latossolo Vermelho de 41,96 ha, em Boa Vista das Missões, RS. A amostragem de solo foi realizada na camada de 0,00-0,10 m, tendo-se utilizado sete dimensões de malha amostral: 50x50, 75x75, 100x100, 125x125, 150x150, 175x175 e 200x200 m. Os dados de P e K foram submetidos às análises de estatística descritiva e de geoestatística, e a similaridade dos mapas temáticos gerados foi analisada pelo coeficiente de desvio relativo e pela matriz de correlação de Pearson. A redução da dimensão da malha amostral aumenta a acurácia na caracterização da variabilidade espacial de P e K e, consequentemente, a qualidade das informações geradas por meio dos mapas temáticos. Malhas amostrais ≤100x100 m são recomendadas para planos de amostragem de solo adotados nas áreas de agricultura de precisão no Estado do Rio Grande do Sul.Termos para indexação: adubação à taxa variada, agricultura de precisão, amostragem georreferenciada de solo, fertilidade do solo, similaridade de mapas temáticos. Sampling grid size for characterization of the spatial variability of phosphorus and potassium in an OxisolAbstract -The objective of this work was to evaluate the accuracy in the characterization of the spatial variability of soil phosphorus and potassium, using different sampling grid sizes, as well as the similarity of the thematic maps generated. The study was carried out in a 41.96 ha Oxisol area in the municipality of Boa Vista das Missões, in the state of Rio Grande do Sul, Brazil. Soil sampling was done at the 0.00-0.10 m layer, using seven sampling grid sizes: 50x50, 75x75, 100x100, 125x125, 150x150, 175x175, and 200x200 m. P and K data were subjected to descriptive statistics and geostatistical analyses, and the similarity of the thematic maps generated was analyzed by the coefficient of relative deviation and Pearson's correlation matrix. The reduction in the size of the sampling grid increases the accuracy in the characterization of the spatial variability of P and K and, consequently, the information generated by the thematic maps. Sampling grid sizes ≤100x100 m are recommended for soil sampling plans adopted in precision agriculture areas in the state of Rio Grande do Sul, Brazil.Index terms: variable-rate fertilization, precision agriculture, georeferenced soil sampling, soil fertility, similarity of thematic maps. IntroduçãoA agricultura de precisão (AP) é um avanço tecnológico relativamente recente de gerenciamento do sistema solo-planta-atmosfera, baseada nos princípios da caracterização da variabilidade espacial e da gestão de informações, que engloba fatores de produção e produtividade das culturas (Montanari et al., 2012). Entre as ferramentas da AP, a amostragem georreferenciada de solo por meio de malhas regulares, para caracterizar a variabil...

RESUMO Em decorrência da instabilidade da produtividade das principais culturas associada ao déficit hídrico, tem se tornado cada vez mais frequente a necessidade do uso de tecnologias como a irrigação e a agricultura de precisão (AP 2010/2011 and 2011/2012, in an area of 35ha managed under notill and center-pivot

The total or partial removal of sugarcane (Saccharum spp. L.) straw for bioenergy production may deplete soil quality and consequently affect negatively crop yield. Plants with lower yield potential may present lower concentration of leaf-tissue nutrients, which in turn changes light reflectance of canopy in different wavelengths. Therefore, vegetation indexes, such as the normalized difference vegetation index (NDVI) associated with concentration of leaf-tissue nutrients could be a useful tool for monitoring potential sugarcane yield changes under straw management. Two sites in São Paulo state, Brazil were utilized to evaluate the potential of NDVI for monitoring sugarcane yield changes imposed by different straw removal rates. The treatments were established with 0%, 25%, 50%, and 100% straw removal. The data used for the NDVI calculation was obtained using satellite images (CBERS-4) and hyperspectral sensor (FieldSpec Spectroradiometer, Malvern Panalytical, Almelo, Netherlands). Besides sugarcane yield, the concentration of the leaf-tissue nutrients (N, P, K, Ca, and S) were also determined. The NDVI efficiently predicted sugarcane yield under different rates of straw removal, with the highest performance achieved with NDVI derived from satellite images than hyperspectral sensor. In addition, leaf-tissue N and P concentrations were also important parameters to compose the prediction models of sugarcane yield. A prediction model approach based on data of NDVI and leaf-tissue nutrient concentrations may help the Brazilian sugarcane sector to monitor crop yield changes in areas intensively managed for bioenergy production.

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The utilization of Normalized Difference Vegetation Index (NDVI) data obtained through satellite images can technically improve the process of delimiting management zones (MZ) for annual crops, resulting in socioeconomic and environmental benefits. The aim of this study was to compare delimited MZ, using crop productivity data, with delimited MZ using the NDVI obtained from satellite images in areas under a no-tillage system. The study was carried out in three areas located in the state of Rio Grande do Sul, Brazil. Three crop productivity maps, from 2009 to 2015, were used for each area, whereby the NDVI was calculated for each crop productivity map using images from the Landsat series of satellites. Descriptive and geostatistical analysis were conducted to determine the productivity and NDVI data. The MZ were then delimited using the fuzzy c-means algorithm. Spearman's correlation matrix was used to compare the methodologies used for delimiting the MZ. The MZ based on NDVI calculated from the satellite images correlated with the MZ based on crop productivity data (0.48 < r < 0.61), suggesting that the NDVI can replace or be complementary to productivity data in delimiting MZ for annual cropping systems.

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In sub-tropical Brazil, the wheat (Triticum aestivum L.) crop requires identification of pending constraints as premise for grain yield (GY) increases. In this light, spatial variation of soil properties and their relationship with GY were investigated in a case study, where the delineation of homogeneous zones could lead to site-specific management in view of crop improvement. In 2012, twelve chemical and physical soil attributes, GY and the three yield components (spikes per square meters, grains per spike, grain weight) were geo-referentially assessed in a 50×50 m grid in a 4.7 ha wheat field. GY exhibited a modest mean (2.61 Mg ha -1 ), associated with a noticeable variation (CV, 17.4%). A multiple stepwise regression of soil carbon (C) and pH explained a high share of GY variation (R², 0.83**). Maps of C, pH and GY obtained through inverse distance weighting showed the spatial trends of the three traits. C and pH clustering delineated three homogeneous zones at respective low, intermediate and high levels of C, pH, and also GY, setting the premise for a differential management of crop inputs. In particular, a significant part (21.8%) of field surface featured very low GY (2.05 Mg ha -1 ); thus substantial yield increase could be envisaged through targeted supply of organic amendments (soil C, 14.1 g dm -3 ), and especially lime (soil pH, 4.92). A larger field portion (54%) showed intermediate GY (2.65 Mg ha -1 ), C (15.3 g dm -3 ) and pH (5.23), deserving a lesser degree of amelioration. The remaining 24.2% of field surface exhibited the highest GY (3.16 Mg ha -1 ), C (17.2 g dm -3 ) and pH (5.46). Based on the difference between GY registered in the low vs. high zone, overcoming soil constraints could be credited with a remarkable (>50%) yield increase, although further years of wheat cropping would be needed to prove the consistency of the two temporally stable soil traits, C and pH, as yield determinants. Nevertheless, this case study addressing a world area that features very different conditions from wheat grown in temperate regions shows good prospects for variable application of crop inputs in the frame of precision agriculture techniques.

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