Resumo em Inglês:

ABSTRACT Pilot-scale test stations make it possible to obtain reliable and comparable results applicable to full–scale systems by conforming to specified proportional limits. Therefore, in this study, normal and frictional pressures were evaluated in a pilot-scale test station composed of a slender cylinder silo using maize, a free-flowing product, as the stored product. Temporal effects were analyzed and verified during filling, static, and discharge conditions. The maximum normal and frictional pressures were also evaluated. The results were compared with ISO 11697: 1995. During filling, accommodation peaks occurred only in the α: 30° hopper. In general, normal pressures were higher for the flat bottom whereas higher frictional pressures occurred for the 30° hopper. The maximum experimental pressures (normal and frictional) were lower than those provided by ISO 11697. Therefore, it is concluded that the coefficients used in the ISO standard are sufficient to promote safety in silo projects.

Resumo em Inglês:

ABSTRACT Infrared thermography (IR) is a non-invasive tool with potential to indicate changes in the animal’s thermal conditions in response to the thermally stressful environment. The objective of this study was to evaluate the application of IR to estimate the rectal temperature of crossbred goats of the Boer breed. Six male crossbred goats of the Boer breed were distributed in a completely randomized design and submitted to temperatures of 26, 30 and 34 °C. Rectal temperature (RT) and thermograms data were collected from animals at each air temperature evaluated. In the thermograms, the temperatures of the ocular globe (PT), head (HT), shoulder (ST), hindquarter (HQ) and maximum infrared (IRMax) of the animals’ surfaces were collected, the latter being observed in the lower region of the animals’ eyes, at all air temperatures evaluated. The correlation of PT, HT, ST, HQ and IRMax data with the RT was evaluated through the Pearson coefficient analysis and the concordance using Bland-Altman diagrams. With the exception of the IRMax surface temperature, the others were adequate for the accurate estimation of RT, with PT standing out for presenting the highest correlation coefficient with RT (r = 0.951) and estimation errors varying in the range of ± 0.27 °C.

Resumo em Inglês:

ABSTRACT Phosphorus deficiency can lead to serious reductions in crop yield and quality. Rapid and accurate determination of phosphorus stress status in vegetables is essential for ensuring the appropriate application of water and fertiliser, improving the yield and quality of vegetables, avoiding the waste of resources and serious non-point pollution source caused by the abuse of chemical fertilisers, and promoting green production and sustainability of facility agriculture. Chemical analysis and spectral or visual imaging methods for detection of phosphorus nutritional status are not conducive in facility production owing to their low accuracy and damage to plants. Owing to its penetration and fingerprint characteristics, terahertz time-domain spectroscopy (THz-TDS) can distinguish differences in internal components caused by excess phosphorus nutrients and changes in nucleic acids, nuclear proteins, phospholipids, and other macromolecules; thus, it could potentially be used to evaluate the nutritional status of crops. In this study, an innovative THz-TDS-based method was used to detect the nutritional status of phosphorus in lettuce. Lettuce was grown with different phosphorus levels using soilless cultivation and different nutrient solutions. Based on the standard formula of Yamasaki nutrient solution, the phosphorus content in the nutrient solutions was reduced or increased by 20%, 60%, 100%, and 150%, and lettuce samples exposed to each of these phosphorus concentrations were collected. Terahertz spectra are highly sensitive to water; thus, the lettuce samples were freeze-dried to minimise the effect of water and maintain their original quality and bioactivity. The spectra of lettuce were recorded using a TS7400 THz-TDS system; noise and interference were eliminated via normalisation based on Savitzky-Golay smoothing. The correction and validation sets were divided using sample set partitioning based on the joint x-y distance (SPXY). The stability competitive adaptive reweighted algorithm, iterative retention information variable algorithm, and interval combination optimisation algorithm were used to select the terahertz characteristic wavelength, and the successive projection algorithm was then used for secondary optimisation. Finally, a THz-TDS model of lettuce phosphorus was established using the partial least squares method with five principal component variables. The coefficient of determination of the model reached 0.7005, and the root-mean-square error of the predictions was 0.003273, indicating that this method has a high prediction accuracy.

Resumo em Inglês:

ABSTRACT This study aimed to evaluate the spatial dependence between agrometeorological variables and upland cotton yield the microregion of Cariri Oriental, Paraíba state (Brazil), using weighted spatial modeling system. In this study, we used the historical agricultural production data from the Brazilian Institute of Geography and Statistics (IBGE), observed rainfall data from the Executive Water Management Agency of Paraíba State (AESA), and air temperature averages estimated by the Estima_T software. Spatial regression models (classical - CR, autoregressive - SARM, and spatial error - SEM) were used to correlate the dependent variable (upland cotton yield) with covariates (agrometeorological variables). Fitted model parameters were estimated using the Maximum Likelihood method. Model performance was evaluated based on coefficient of determination (R2), maximum likelihood function logarithm (AIC), and spatial residue analysis. Moreover, an exploratory spatial analysis allowed us to verify spatial autocorrelation between upland cotton yield and agrometeorological elements, using statistical tools such as Moran’s index I.

Resumo em Inglês:

ABSTRACT Mastering accurate spatial planting and distribution status of the crops is significantly important for the nation to guide the agricultural production and formulate agricultural policies from a macro perspective. In this paper, the Landsat-8 OLI satellite images were taken as the data sources. And as for the nine crop types within the study area, such as the wheat, rice, and other crops, three classification methods of the random forest classification (RFC), the support vector machine (SVM), and the maximum likelihood classification (MLC) were applied in extracting the planting area of winter wheat in Wushi County of Xinjiang Uygur Autonomous Region. It can be seen from the results that, general classification accuracy of MLC, SVM, and RFC are respectively 80.58%, 87.95%, and 95.96%, while their Kappa coefficients are respectively 0.61, 0.76, and 0.86. The RFC method shows higher classification accuracy that those of MLC and SVM methods. The principal component analysis (PCA) was carried out on the original 7-band image to extract the first 4 principal components and calculate the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), wide dynamic range vegetation index (WDRVI), and normalized difference water index (NDWI). Meanwhile, the 6 additional auxiliary feature bands were superimposed on the original 7-band images to carry out reclassification, through which, the general accuracy of MLC increased by 3 percent while its Kappa coefficient increased by 0.06; the SVM general accuracy increased by 3.02 percent while its Kappa coefficient increased by 0.13; and the general accuracy of the RFC increased by 0.85 percent while its Kappa coefficient increased by 0.02. This indicates that, the adding of auxiliary information can improve the crop classification and identification ability and accuracy. Based on the comprehensive evaluation, the classification method of random forest is proved to have better performance in winter wheat identification.

Resumo em Inglês:

ABSTRACT Obtaining knowledge about the distribution of spatial variability of soil properties is crucial to the proper site-specific management. One way to improve the quality of soil mapping is by using auxiliary information (covariate). The objective of this study was to test whether remote and proximal sensing data can assist in soil properties mapping through geostatistical prediction. We worked in an experimental area cultivated with sugarcane located in Sao Paulo State, Brazil, and selected five soil properties: organic matter, CEC, base saturation, K and P availability. Two covariates often used to express soil variation were chosen, one obtained by remote sensing (SWIR2 band) and the other by proximal sensing (apparent soil electrical conductivity – ECa). These covariates were individually and together used in geostatistical interpolation method (kriging with external drift). We found that ECa is a more promising covariate than SWIR2 band from orbital imaging. Such proximal sensing can identify the soil short-range spatial variability. However, when the soil property variability is well explained by the sampling procedure, multivariate geostatistical methods may not improve the mapping accuracy.