Nutrient extraction and exportation by common bean cultivars under different fertilization levels: I - macronutrients

The use of cultivars with a higher yield potential and the adoption of new technology have achieved high grain yields in common bean, which probably changed the demand for nutrients in this crop. However, there is almost no information about the periods of the cycle in which nutrients are most demanded at which quantities by the main cultivars. The objective of this study was to evaluate the macronutrient extraction and exportation by the common bean cultivars Pérola and IAC Alvorada, under different levels of NPK fertilization, on a dystroferric Red Nitosol, in Botucatu, São Paulo State, Brazil. The experiment was arranged in a randomized complete block (split plot) design with four replications. The plots consisted of six treatments based on a 2 x 3 factorial model, represented by two cultivars and three NPK levels (PD0 - 'Pérola' without fertilization, PD1 - 'Pérola' with 50 % of recommended fertilization, PD2 - 'Pérola' with 100 % of recommended fertilization, AD0 - 'IAC Alvorada' without fertilization, AD1 - 'IAC Alvorada' with 50 % of recommended fertilization, and AD2 - 'IAC Alvorada' with 100 % of recommended fertilization) and subplots sampled seven times during the cycle. At higher levels of NPK fertilization, the grain yield and macronutrient extraction and exportation of both cultivars were higher, but without statistical differences. Macronutrient absorption was higher in the treatments with 100 % of recommended NPK fertilization (average amounts per hectare: 140 kg N, 16.5 kg P, 120 kg K, 69 kg Ca, 17.9 kg Mg, and 16.3 kg S). Regardless of the treatment, the demand for N, P, K, Ca, and Mg was highest from 45 to 55 days after emergence (DAE), i.e., in the R7 stage (pod formation), while the highest S absorption rates were concentrated between 55 and 65 DAE. More than 70 % of P, between 58 and 69 % of N, 40 and 52 % of S, 40 and 48 % of K, and 35 and 45 % of Mg absorbed during the cycle was exported with grains, whereas less than 15 % of Ca was exported.

Nutrient extraction and exportation by common bean cultivars under different fertilization levels: II - micronutrients

Where the level of agricultural technology is higher, common bean cultivars with a higher yield potential possibly require greater amounts of micronutrients. In Brazil however, there is a lack of information about the micronutrient extraction and exportation by the main grown cultivars. The objective of this study was to evaluate micronutrient (B, Cu, Fe, Mn, and Zn) extraction and exportation by common bean cultivars Pérola and IAC Alvorada, under different levels of NPK fertilization, on a dystroferric Red Nitosol, in Botucatu, São Paulo State, Brazil. The experiment was arranged in a randomized complete block (split plot) design with four replications. The plots consisted of six treatments based on a 2 x 3 factorial model, represented by two cultivars and three NPK levels (PD0 - 'Pérola' without fertilization, PD1 - 'Pérola' with 50 % of recommended fertilization, PD2 - 'Pérola' with 100 % of recommended fertilization, AD0 - 'IAC Alvorada' without fertilization, AD1 - 'IAC Alvorada' with 50 % of recommended fertilization, and AD2 - 'IAC Alvorada' with 100 % of recommended fertilization) and subplots sampled seven times during the cycle. Higher levels of NPK fertilization increased micronutrient extraction by both cultivars, and treatments with 100 % of recommended NPK fertilization extracted on average 167 g B, 58 g Cu, 1,405 g Fe, 1,213 g Mn and 211 g Zn per hectare. Regardless of the treatment, the highest demand period for B, Cu, Fe, Mn and Zn in both cultivars occurred at the R7 stage (pod formation), i.e. 42 to 55 days after emergence (DAE). The amount of B, Cu, Fe, Mn and Zn exported depended mainly on the level of NPK fertilization used, with values per hectare ranging from 38 to 90 g of B, 12 to 26 g of Cu, 222 to 568 g of Fe 234 to 467 g of Mn, and 40 to 96 g of Zn.

Spatial estimation of foliar phosphorus in different species of the genus Coffea based on soil properties

Information underlying analyses of coffee fertilization systems should consider both the soil and the nutritional status of plants. This study investigated the spatial relationship between phosphorus (P) levels in coffee plant tissues and soil chemical and physical properties. The study was performed using two arabica and one canephora coffee variety. Sampling grids were established in the areas, and the points georeferenced. The assessed properties of the soil were levels of available phosphorus (P-Mehlich), remaining phosphorus (P-rem) and particle size, and of the plant tissue, phosphorus levels (foliar P). The data were subjected to descriptive statistical analysis, correlation analysis, cluster analysis, and probability tests. Geostatistical and trend analyses were only performed for pairs of variables with significant linear correlation. The spatial variability for foliar P content was high for the variety Catuai and medium for the other evaluated plants. Unlike P-Mehlich, the variability in P-rem of the soil indicated the nutritional status of this nutrient in the plant.

Spatial statistical analysis and selection of genotypes in plant breeding

The objective of this study was to evaluate the efficiency of spatial statistical analysis in the selection of genotypes in a plant breeding program and, particularly, to demonstrate the benefits of the approach when experimental observations are not spatially independent. The basic material of this study was a yield trial of soybean lines, with five check varieties (of fixed effect) and 110 test lines (of random effects), in an augmented block design. The spatial analysis used a random field linear model (RFML), with a covariance function estimated from the residuals of the analysis considering independent errors. Results showed a residual autocorrelation of significant magnitude and extension (range), which allowed a better discrimination among genotypes (increase of the power of statistical tests, reduction in the standard errors of estimates and predictors, and a greater amplitude of predictor values) when the spatial analysis was applied. Furthermore, the spatial analysis led to a different ranking of the genetic materials, in comparison with the non-spatial analysis, and a selection less influenced by local variation effects was obtained.

Prediction of soil orders with high spatial resolution: response of different classifiers to sampling density

The objective of this work was to evaluate sampling density on the prediction accuracy of soil orders, with high spatial resolution, in a viticultural zone of Serra Gaúcha, Southern Brazil. A digital elevation model (DEM), a cartographic base, a conventional soil map, and the Idrisi software were used. Seven predictor variables were calculated and read along with soil classes in randomly distributed points, with sampling densities of 0.5, 1, 1.5, 2, and 4 points per hectare. Data were used to train a decision tree (Gini) and three artificial neural networks: adaptive resonance theory, fuzzy ARTMap; self‑organizing map, SOM; and multi‑layer perceptron, MLP. Estimated maps were compared with the conventional soil map to calculate omission and commission errors, overall accuracy, and quantity and allocation disagreement. The decision tree was less sensitive to sampling density and had the highest accuracy and consistence. The SOM was the less sensitive and most consistent network. The MLP had a critical minimum and showed high inconsistency, whereas fuzzy ARTMap was more sensitive and less accurate. Results indicate that sampling densities used in conventional soil surveys can serve as a reference to predict soil orders in Serra Gaúcha.

Spatial variability of the rainfall erosive potential in the State of Mato Grosso do Sul, Brazil

Information about rainfall erosivity is important during soil and water conservation planning. Thus, the spatial variability of rainfall erosivity of the state Mato Grosso do Sul was analyzed using ordinary kriging interpolation. For this, three pluviograph stations were used to obtain the regression equations between the erosivity index and the rainfall coefficient EI30. The equations obtained were applied to 109 pluviometric stations, resulting in EI30 values. These values were analyzed from geostatistical technique, which can be divided into: descriptive statistics, adjust to semivariogram, cross-validation process and implementation of ordinary kriging to generate the erosivity map.Highest erosivity values were found in central and northeast regions of the State, while the lowest values were observed in the southern region. In addition, high annual precipitation values not necessarily produce higher erosivity values.

Spatial autocorrelation of ndvi and gvi indices derived from landsat/tm images for soybean crops in the western of the state of Paraná in 2004/2005 crop season

This research aims at studying spatial autocorrelation of Landsat/TM based on normalized difference vegetation index (NDVI) and green vegetation index (GVI) of soybean of the western region of the State of Paraná. The images were collected during the 2004/2005 crop season. The data were grouped into five vegetation index classes of equal amplitude, to create a temporal map of soybean within the crop cycle. Moran I and Local Indicators of Spatial Autocorrelation (LISA) indices were applied to study the spatial correlation at the global and local levels, respectively. According to these indices, it was possible to understand the municipality-based profiles of tillage as well as to identify different sowing periods, providing important information to producers who use soybean yield data in their planning.

Spatial statistics applied to soybean production data from Paraná State for 2003-04 to 2009-10 crop-years

In the current study, we performed a soybean production spatial distribution analysis in Paraná State. Seven crop-year data, from 2003-04 to 2009-10, obtained from the Paraná Department of Agriculture and Supply (SEAB) were used to develop a Boxmap for each crop-year, show soybean production throughout this time interval. Moran's index was used to measure spatial autocorrelation among municipalities at an aggregate level, while LISA index local correlation. For each index, different contiguity matrix and order were used and there was a significance level study. As a result, we have showed spatial relationship among cities regarding the production, which allowed the indication of high and low production clusters. Finally, identifying main soybean-producing cities, what may provide supply chain members with information to strengthen the crop production in Paraná.

Spatial variability of apparent electrical conductivity and soil properties in a coffee production field

Precision agriculture based on the physical and chemical properties of soil requires dense sampling to determine the spatial variability of these properties. This dense sampling is often expensive and time-consuming. One technique used to reduce sample numbers involves defining management zones based on information collected in the field. Some researchers have demonstrated the importance of soil electrical variables in defining management zones. The objective of this study was to evaluate the relationship between the spatial variability of the apparent electrical conductivity and the soil properties in the coffee production of mountain regions. Spatial variability maps were generated using a geostatistical method. Based on the spatial variability results, a correlation analysis, using bivariate Moran's index, was done to evaluate the relationship between the apparent electrical conductivity and soil properties. The maps of potassium (K) and remaining phosphorus (P-rem) were the closest to the spatial variability pattern of the apparent electrical conductivity.

Identification, assessment and ordering of concepts for agro-energy planning and possibilities of spatial analysis

ABSTRACT The present study aims to present the main concepts of the sugarcane straw to energy planning. Throughout the study, the subject is contextualized highlighting broader aspects of sustainability, which is considered the main driver towards agro-energy modernization. Concerning sugarcane straw, we first evaluated its availability regarding technical and economic aspects, and then it summarized the straw production chain for energy supply purposes. As a proposal to support agro-energy planning, it is presented some spatial tools that have been barely used in the Brazilian energy planning context so far. Therefore, working on straw to electricity associated with supply chain basis, we developed a conceptual model to spatially assess this bioenergy system. Using the model proposed, it is described the whole supply chain at state level, which accounted the potential of a single mill to explore straw, as well as main costs associated with straw acquisition, investments on the straw recovery routes and electricity transmission. Bearing these concepts in mind, it is fully believed that spatial analysis can bring important information for agro-energy action plans.

Methodological approaches to planar and volumetric scintigraphic imaging of small volume targets with high spatial resolution and sensitivity

Single-photon emission computed tomography (SPECT) is a non-invasive imaging technique, which provides information reporting the functional states of tissues. SPECT imaging has been used as a diagnostic tool in several human disorders and can be used in animal models of diseases for physiopathological, genomic and drug discovery studies. However, most of the experimental models used in research involve rodents, which are at least one order of magnitude smaller in linear dimensions than man. Consequently, images of targets obtained with conventional gamma-cameras and collimators have poor spatial resolution and statistical quality. We review the methodological approaches developed in recent years in order to obtain images of small targets with good spatial resolution and sensitivity. Multipinhole, coded mask- and slit-based collimators are presented as alternative approaches to improve image quality. In combination with appropriate decoding algorithms, these collimators permit a significant reduction of the time needed to register the projections used to make 3-D representations of the volumetric distribution of target’s radiotracers. Simultaneously, they can be used to minimize artifacts and blurring arising when single pinhole collimators are used. Representation images are presented, which illustrate the use of these collimators. We also comment on the use of coded masks to attain tomographic resolution with a single projection, as discussed by some investigators since their introduction to obtain near-field images. We conclude this review by showing that the use of appropriate hardware and software tools adapted to conventional gamma-cameras can be of great help in obtaining relevant functional information in experiments using small animals.

Effect of blanching time on selective mineral elements extraction from the spinach substitute (Tetragonia expansa) commonly used in Brazil

The true spinach (Spinacia oleracea) does not grow well in warm climates and for that reason is not commercialized in Brazil. Instead, a spinach substitute (Tetragonia expansa), originally from New Zealand, is widely used in the country. There is scant information on the mineral profile and none on the soluble mineral fraction of this vegetable. The solubility of a mineral is one of the important factors for its absorption. For this reason, the calcium, magnesium, iron, manganese, copper, zinc, potassium, and sodium soluble fractions in the raw spinach substitute were determined and the effect of blanching times on the solubility of these minerals was investigated. Blanching times of 1, 5, and 15 minutes were employed. The magnesium, manganese, potassium, and sodium soluble fractions increased sizably with shorter blanching time. Longer blanching time (15 minutes) caused large losses of minerals. The soluble mineral fractions can contribute poorly to diet in terms of potassium, magnesium, manganese, and zinc. The spinach substitute cannot be considered a dietary source of calcium, iron and copper due to the insolubility of these minerals in the vegetable, possibly caused by the large oxalate content.