Abstract in English:ABSTRACT The apparent soil electrical conductivity (ECa) is an attribute commonly used for the characterization of the spatial variability of soil, but its determination by handheld sensors consumes considerable time and labor. The reduction in the number of sampling points allows minimize them but can result in increased uncertainty of interpolated maps. Thus, the goal of this study was to identify the best spacing and number of ECa measurements, to guarantee the quality of maps generated in three vineyards. The ECa values were obtained using a handheld sensor in different sampling grids. The data were submitted to descriptive statistical and geostatistical analyses. The relative deviation and Kappa coefficient of agreement were used to assess the similarity of generated maps. The reduction in the number of points and increase in the size of the sampling grid reduced the quality of maps and this reduction was greater when the spacing increased in the direction of the terrain slope. A minimum limit of 100 sampling points should be considered for the sampling planning to generate ECa spatial distribution maps, with a more conservative approach when regarding the increase in spacing in the direction of the terrain slope.
Abstract in English:ABSTRACT Vegetation indices (VIs) are quantitative measures used to describe the distribution and spatial variability of the vegetation cover of natural or cultivated areas. The aim of this study was to delimit homogeneous zones (HZs) of different VIs using geostatistics and multivariate analysis in order to identify vegetation patterns in Cabernet Franc and Cabernet Sauvignon vineyards. The evaluation was performed in two vineyards in the municipality of Espírito Santo do Pinhal in the state of São Paulo, Brazil. Reflectance (ρ) was measured at three wavelengths of the electromagnetic spectrum (670, 730, and 780 nm) at canopy height in georeferenced points along planting rows using the Crop Circle ACS-430 active sensor. Nine VIs were calculated based on the ratios between the ρ values. Geostatistical data analysis allowed the spatial prediction of VIs by ordinary kriging interpolation. Principal component analysis and fuzzy k-means clustering were applied for HZs delimitation and the optimal number of zones was defined according to cluster validity functions. Despite the variations of the VIs spatial distribution patterns, the multivariate analysis resulted in a representative categorization of the grapevine vegetative vigor and delimitation of HZs for this characteristic. This was validated according to the observed significant differences between VIs.
Abstract in English:ABSTRACT Surface temperature (Ts) is a determining factor to obtain energy balance parameters, being relevant to understand the influence of this variable on the estimation of evapotranspiration. Thus, the objective of this study was to simulate errors in Ts estimation to verify the consequences of actual evapotranspiration (ETa) estimated by the SAFER (Simple Algorithm for Evapotranspiration Retrieving) model. For this, an image of the Landsat-8 satellite was used to induce errors from 0.2K to 10K in the variable Ts, allowing verifying the consequences in the ETa data. After the estimations of Ts and ETa, the quantitative consequences and dynamics of Ts impact on the ETa data were verified along the different land uses in the study area. The results showed that the precise estimation of Ts is essential to obtain ETa accurately. The image of ETa errors presented the highest relative errors on the surface with exposed soils and with high Ts values. However, the highest residuals of ETa images occurred on the surfaces with milder Ts and higher evapotranspiration rates (irrigated surfaces).
Abstract in English:ABSTRACT Active optical sensors have been widely used for the spatial and temporal monitoring of peanut culture because they are accurate, non-destructive methods for rapidly obtaining data. The objective of this study was to determine the optimal stage of crop growth for collecting sensor readings based on correlations between quality indicators. In addition, we compared vegetation indices (Normalized Difference Vegetation Index [NDVI], Normalized Difference Red-Edge Index, [NDRE], and Inverse Ratio Vegetation Index, [IRVI]) by monitoring temporal variability in the peanut crop in order to determine which of them obtained the best reading quality throughout the process. The experiment was performed on the 2016/17 crop in the agricultural area of the municipality of Dumont in the state of São Paulo, Brazil. The experimental design was based on the basic assumptions of statistical quality control and contained 63 sample points in a 30 × 30 m grid. The parameters were evaluated at 30, 45, 60, 75, and 119 days after sowing (DAS) using proximal sensing with GreenSeeker and OptRX sensors. We found that 45 and 60 DAS were the optimal times for monitoring peanut crop variability. For spatiotemporal monitoring of the culture with control charts, NDRE showed the best readings throughout the process when compared to NDVI and IRVI.
Abstract in English:ABSTRACT Tree crops, such as Arabica coffee (Coffea arabica L.), present enormous technical challenges in terms of pesticide application. The correct deposition and distribution of the active ingredient throughout the aerial part of these plants depends on knowledge of the canopy volume, but manually determining this volume is time consuming and imprecise. The objectives of this study were to develop a method to determine the vegetation volume of coffee crops from digital images captured by camera onboard unmanned aerial vehicles and to compare this approach with traditional vegetation volume estimation (tree row volume (TRV) method). Manual measurements of the canopy volume of four coffee cultivation areas were compared with data obtained using the method presented in this paper. It was concluded that the vegetation volume of coffee trees, a highly important variable in defining pesticide application techniques (in addition to other uses), could be determined in a practical and precise way by digitally processing the images captured by unmanned aerial vehicles. The method is fast and permits the assessment of large areas. Furthermore, estimates based on this method and the traditional TRV method were not significantly different.
Abstract in English:ABSTRACT Cacao is a species of great economic and social importance. Expanding the area grown with this crop has been limited by its climatic requirements. Agroclimatic zoning for agricultural sector and creation of land suitability maps by fuzzy logic contribute to such production expansion. In this sense, this study aimed to develop rainfall zoning for cacao in Bahia state using the fuzzy logic method. The used data came from rainfall historical series of 519 meteorological stations distributed throughout the state. Geostatistical analyses were used to quantify the spatial dependence degree of studied variable and kriging was used to develop maps representing mean monthly rainfall. These maps were submitted to continuous classification by fuzzy mapping for identification of high-risk areas for cocoa growing based on rainfall. Based on the fuzzy method, the southern mesoregion of Bahia state presented the highest rainfall uniformity, suggesting that this area is more suitable for cocoa growing.
Abstract in English:ABSTRACT In the study of spatial variability of soil attributes, it is essential to define a sampling plan with adequate sample size. This study aimed to evaluate, through simulated data, the influence of parameters of the geostatistical model and sampling configuration on the optimization process, and resize and reduce the sample size of a sampling configuration of a commercial area composed of 102 points. For this, an optimization process called genetic algorithm (GA) was used to optimize the efficiency of the geostatistical model estimation based on the Fisher information matrix. The simulated data evidenced that the variation of the nugget effect or practical range did not significantly alter the sample size. GA was efficient in reducing the sample size, determining for soil chemical attributes a sample size between 30 and 40 points (29.41 to 39.22% of the initial sampling grid). The presence of spatial dependence was observed for all soil chemical attributes in the two sampling configurations (initial and optimized). The optimized sampling configuration evidenced an increase in trend intensity in the north direction and a more efficient estimation of parameters of the linear spatial regression model.
Abstract in English:ABSTRACT A common agricultural problem in many regions of Brazil is maize lodging, as a consequence of strong winds and rain which impacts on crop growth and yield. However, collecting data using ground-based, manual field measurement methods is inefficient. An emerging tool is the Remotely Piloted Aircraft System (RPAS), capable of delivering spatial data with high resolution and flexible periodicity. In this study, the potential to detect the maize lodging using crop surface models derived from RPAS was assessed. Our RPA-based approach uses a quantitative threshold to determine lodging percentage. The threshold values of plant height, used to detect the occurrence of lodging, were based on fixed and variable values. The validation of percentage lodging was performed using the RGB orthomosaic. The derived lodging estimates showed a very high correlation to the reference data. High correlations were observed for the fixed threshold at 60% (R2 = 0.93, RSME = 8.72%) and the variable thresholds, Jenks natural breaks and iso-clusters (R2 = 0.92, RSME = 8.89% and R2= 0.92, RSME = 9%, respectively). This study demonstrated the potential of the use of this technique, reducing the subjectivity of ground-based evaluation and the laborious traditional technique of lodging inference.
Abstract in English:ABSTRACT Variation in the spatial distribution of leaf chlorophyll content associated with the progression of the phenological cycle of crops may occur in cultivated areas as a result of the variability of environmental conditions and of the intrinsic properties of the plants. The objective of the present study was to model the trend in variation and assess the temporal stability of index of chlorophyll a, b, and total chlorophyll (Chla, Chlb, and Chlt, respectively), and to characterize the spatial distribution of Chlt index in grapevine cv. ‘Chardonnay’. The assessments consisted of in situ measurements made with a portable meter in a commercial vineyard located in the municipality of Espírito Santo do Pinhal, state of São Paulo, Brazil, in the period between flowering and fruit maturation. Descriptive statistics were applied to the indices and regression models were fitted to ascertain the relationship of their mean variation with time. The temporal stability of Chlt index was estimated using Spearman's rank correlation analysis and thematic maps were created using geostatistical analysis and spatial estimation by ordinary kriging. The Chlb and Chlt indices were non-linearly associated with cycle progression and their decrease after the start of maturation was estimated. The temporal stability of the Chlt index was low and variation in its spatial distribution was observed over the assessed period.
Abstract in English:ABSTRACT Data interpolation is widely required in precision agriculture. However, its effectiveness is a function of the characteristics of the dataset, being necessary for the evaluation of several parameters. This study aimed to identify how the spatial interpolators, Kriging, and Inverse Distance Weighting, are influenced by the degree of spatial dependence of the variables analyzed and the number of neighbors considered in the interpolation process (sampling neighborhood). Soil samples were collected from three sugarcane fields. By the optimization process, we verified that the sampling neighborhood influences the accuracy of interpolations, but there is not a standard recommendation to follow. Thus, the best sampling neighborhood must ever be optimized for each case when preparing fertilizer prescription maps. Evaluating the performance of interpolations is always important to infer the reliability of the prescription maps, since no index that measures the degree of spatial dependence is effective. Because high prediction errors can occur when spatial dependence is poorly modeled, one cannot expect crop response with the continuous application of fertilizers in variable rates. Thus, work with homogeneous soil zones can be an interesting palliative approach. This study guides precision agriculture practitioners on some points that should be carefully considered in the data interpolation process for generating fertilizer prescription maps.
Abstract in English:ABSTRACT The application of nitrogen (N) fertilizer is complex and expensive, so its correct management has financial and environmental benefits. The use of optical proximity sensors is a promising technique. However, the movement of the agricultural machinery or of the person carrying the sensor will result in height differences and/or different tilt and twist angles with respect to the canopy. We considered whether these variations would affect the reflectance measurement. In this study, we took normalized difference vegetation index (NDVI) readings of a wheat canopy, to which 90 kg ha-1 of urea had been applied in stage 5, and observed the NDVI in stages 6, 8 and 10.5. We also tested soybeans, to which 90 kg ha-1 of urea had been applied in stage R1, and took NDVI readings in stages R2 and R5. Our goal was to study the effects of the position of an active reflectance sensor (GreenSeeker) on the NDVI index at different heights and at different angles to the canopy. We observed that the height of the sensor affected the NDVI depending on the stage of the plant and that angles up to 15° of the sensor did not directly affect the readings.
Abstract in English:ABSTRACT Spatial variability evaluation of qualitative attributes can be used as an excellent strategy to design forms of intervention that result in better crop profitability for some agricultural crops, for example, sugarcane. Based on the assumption that qualitative attributes of sugarcane present spatial variability and their distributions along the stems are uniform in different varieties, this study aimed to evaluate the distributions of the qualitative parameters of different sugarcane varieties and the spatial variability of these attributes in a commercial field. Samples of nine varieties were collected for laboratory quality analysis, and the Brix parameter was analyzed by a digital refractometer. The analysis of variance, the Tukey test, and geoestatistcs were the statistical analyses applied to the dataset. The maps were generated using 91 sample results from the laboratory analysis of the 16.6 ha field. It was found that, in the harvest period, there was no significant difference in Brix content along the sugarcane stems. Therefore, we can conclude that the Brix content along the sugarcane stems does not change in the harvesting period, and the ideal sampling size to better represent the factors affecting sugarcane qualitative attributes is six points per hectare.
Abstract in English:ABSTRACT The use of new technologies to meet the demands of the agricultural market is increasing; however, technical information on application is scarce for some areas of knowledge, including irrigation management. The objective of this study is to evaluate an automatic irrigation system with capacitance sensors connected to a local wireless network for the semiautomatic management of irrigation in tomato crops compared with a manual control system based on time-domain reflectometry (TDR)-type sensors. The experiments were carried out in a protected environment, and the seedlings were transplanted following surface drip lines. The study adopted a completely randomized block design consisting of two treatments and 12 repetitions, totaling 24 subplots. The evaluated treatments were an irrigation management system with TDR sensors and a manually-programmed controller, and an irrigation management system with capacitance sensors and a semiautomatically-programmed controller connected to a digital assistant. Quantitative and qualitative parameters as well as the green and dry matter production were evaluated in each treatment. The results indicated that both sensors were effective in managing irrigation in tomato crops. Furthermore, both systems were accurate, and the Alexa® digital assistant was efficient in programming the GreenIQ® semiautomatic system by voice commands.
Abstract in English:ABSTRACT Soil fertility attributes have different scales and forms of spatial and temporal variations in agricultural fields. Adequate spatiotemporal characterization of these attributes is fundamental to the successful development of strategies for variable rate application of fertilizers, enabling the classic benefits of precision agriculture (PA). Studies on Brazilian soil have shown that at least 1 sample ha-1 is required for the reliable mapping of key fertility attributes. However, this sampling density is difficult owing to the operational challenges of sample collection and the cost of laboratory analyses. Given this limitation, soil sensors have emerged as a practical and complementary technique for obtaining information on soil attributes, at high spatial density, without the production of chemical residues and at a reduced cost. Scientists worldwide have devoted their attention to the development and application of sensor systems for this purpose. The concept of proximal soil sensing (PSS) was established in 2011 and involves the application of soil sensors directly on the field. PSS techniques involve different disciplines, such as instrumentation, data science, geostatistics, and predictive modeling. The integration of these different disciplines has allowed successful sensor application for the spatial diagnosis of soil fertility attributes. The present work aimed to present a bibliographic review of the concepts involved and main techniques used in soil sensing to predict fertility attributes. We sought to present a broad view of the challenges, advances, and perspectives of sensor application in Brazilian tropical soils in the context of PA.