Disponibilidade de potássio em solos para capim-braquiária cultivado em vasos

Raij, Bernardo van; Quaggio, J. A.

doi:10.1590/s0006-87051984000200023

Cited by 11 publications

(8 citation statements)

References 6 publications

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“…The contents of clay, sand, OM, CEC, pH, V, ex-P, ex-K, ex-Ca, and ex-Mg were determined in a commercial laboratory of soil fertility analyses. Methods described by Van Raij et al [ 57 ] were applied for soil analyses. Extractable nutrients (ex-P, ex-K, ex-Ca, and ex-Mg) were determined using ion exchange resin extraction.…”

Section: Methodsmentioning

confidence: 99%

Combined Use of Vis-NIR and XRF Sensors for Tropical Soil Fertility Analysis: Assessing Different Data Fusion Approaches

Tavares

Molin

Javadi

et al. 2020

Sensors

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Visible and near infrared (vis-NIR) diffuse reflectance and X-ray fluorescence (XRF) sensors are promising proximal soil sensing (PSS) tools for predicting soil key fertility attributes. This work aimed at assessing the performance of the individual and combined use of vis-NIR and XRF sensors to predict clay, organic matter (OM), cation exchange capacity (CEC), pH, base saturation (V), and extractable (ex-) nutrients (ex-P, ex-K, ex-Ca, and ex-Mg) in Brazilian tropical soils. Individual models using the data of each sensor alone were calibrated using multiple linear regressions (MLR) for the XRF data, and partial least squares (PLS) regressions for the vis-NIR data. Six data fusion approaches were evaluated and compared against individual models using relative improvement (RI). The data fusion approaches included (i) two spectra fusion approaches, which simply combined the data of both sensors in a merged dataset, followed by support vector machine (SF-SVM) and PLS (SF-PLS) regression analysis; (ii) two model averaging approaches using the Granger and Ramanathan (GR) method; and (iii) two data fusion methods based on least squares (LS) modeling. For the GR and LS approaches, two different combinations of inputs were used for MLR. The GR2 and LS2 used the prediction of individual sensors, whereas the GR3 and LS3 used the individual sensors prediction plus the SF-PLS prediction. The individual vis-NIR models showed the best results for clay and OM prediction (RPD ≥ 2.61), while the individual XRF models exhibited the best predictive models for CEC, V, ex-K, ex-Ca, and ex-Mg (RPD ≥ 2.57). For eight out of nine soil attributes studied (clay, CEC, pH, V, ex-P, ex-K, ex-Ca, and ex-Mg), the combined use of vis-NIR and XRF sensors using at least one of the six data fusion approaches improved the accuracy of the predictions (with RI ranging from 1 to 21%). In general, the LS3 model averaging approach stood out as the data fusion method with the greatest number of attributes with positive RI (six attributes; namely, clay, CEC, pH, ex-P, ex-K, and ex-Mg). Meanwhile, no single approach was capable of exploiting the synergism between sensors for all attributes of interest, suggesting that the selection of the best data fusion approach should be attribute-specific. The results presented in this work evidenced the complementarity of XRF and vis-NIR sensors to predict fertility attributes in tropical soils, and encourage further research to find a generalized method of data fusion of both sensors data.

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Section: Methodsmentioning

confidence: 99%

Combined Use of Vis-NIR and XRF Sensors for Tropical Soil Fertility Analysis: Assessing Different Data Fusion Approaches

Tavares

Molin

Javadi

et al. 2020

Sensors

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“…Before establishing the plots, we obtained 20 subsamples of soil from the 0–0.20 m layer of each site, which we combined into 250-cm 3 samples for chemical analysis (van Raij et al, 2001 ). P was extracted with Amberlite IRA-400 ion-exchange resin (20–50 mesh), quantified by colorimetry by the ascorbic acid method, and reported as mg dm −3 .…”

Section: Methodsmentioning

confidence: 99%

“…Tropical soils are highly weathered and have low natural fertility, such that fertilization is required to achieve high crop yields (van Raij et al, 2001 ; van Raij, 2011 ). Phosphorus (P) is the nutrient that most limits crop productivity in tropical soils (Novais and Smyth, 1999 ; Salcedo, 2006 ), especially in Brazil (Roy et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Phosphorus Over-Fertilization and Nutrient Misbalance of Irrigated Tomato Crops in Brazil

et al. 2017

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Over the past 20 years, the use of center-pivot irrigation has increased tomato (Solanum lycopersicum L.) yields in Brazil from 42 Mg ha−1 to more than 80 Mg ha−1. In the absence of field trials to support fertilizer recommendations, substantial amounts of phosphorus (P) have been applied to crops. Additional P dosing has been based on an equilibrated nutrient P budget adjusted for low-P fertilizer-use efficiency in high-P fixing tropical soils. To document nutrient requirements and prevent over-fertilization, tissue samples and crop yield data can be acquired through crop surveys and fertilizer trials. Nevertheless, most tissue diagnostic methods pose numerical difficulties that can be avoided by using the nutrient balance concept. The objectives of this study were to model the response of irrigated tomato crops to P fertilization in low- and high-P soils and to provide tissue diagnostic models for high crop yield. Three P trials, arranged in a randomized block design with six P treatments (0–437 kg P ha−1) and three or four replications, were established on a low-P soil in 2013 and high-P soils in 2013 and 2014, totaling 66 plots in all. Together with crop yield data, 65 tissue samples were collected from tomato farms. We found no significant yield response to P fertilization, despite large differences in soil-test P (coefficient of variation, 24%). High- and low-yield classes (cutoff: 91 Mg fruits ha−1) were classified by balance models with 78–81% accuracy using logit and Cate–Nelson partitioning models. The critical Mahalanobis distance for the partition was 5.31. Tomato yields were apparently not limited by P but were limited by calcium. There was no evidence that P fertilization should differ between center-pivot-irrigated and rain-fed crops. Use of the P budget method to arrive at the P requirement for tomato crops proved to be fallacious, as several nutrients should be rebalanced in Brazilian tomato cropping systems.

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“…Soil samples were characterized by clayey texture, low variability of clay content (between 345 and 511 g dm −3 ) and high variability of exchangeable nutrients. According to the local fertility interpretation [25], the level of ex-P content oscillates between low to medium; and the level of ex-K, ex-Ca and ex-Mg content varies between medium to very high. These samples are also characterized by a significant correlation between available and pseudo total contents for all nutrients.…”

Section: Resultsmentioning

confidence: 99%

Simplifying Sample Preparation for Soil Fertility Analysis by X-ray Fluorescence Spectrometry

Tavares

Nunes

Alves

et al. 2019

Sensors

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Portable X-ray fluorescence (pXRF) sensors allow one to collect digital data in a practical and environmentally friendly way, as a complementary method to traditional laboratory analyses. This work aimed to assess the performance of a pXRF sensor to predict exchangeable nutrients in soil samples by using two contrasting strategies of sample preparation: pressed pellets and loose powder (<2 mm). Pellets were prepared using soil and a cellulose binder at 10% w w−1 followed by grinding for 20 min. Sample homogeneity was probed by X-ray fluorescence microanalysis. Exchangeable nutrients were assessed by pXRF furnished with a Rh X-ray tube and silicon drift detector. The calibration models were obtained using 58 soil samples and leave-one-out cross-validation. The predictive capabilities of the models were appropriate for both exchangeable K (ex-K) and Ca (ex-Ca) determinations with R2 ≥ 0.76 and RPIQ > 2.5. Although XRF analysis of pressed pellets allowed a slight gain in performance over loose powder samples for the prediction of ex-K and ex-Ca, satisfactory performances were also obtained with loose powders, which require minimal sample preparation. The prediction models with local samples showed promising results and encourage more detailed investigations for the application of pXRF in tropical soils.

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Disponibilidade de potássio em solos para capim-braquiária cultivado em vasos

Cited by 11 publications

References 6 publications

Combined Use of Vis-NIR and XRF Sensors for Tropical Soil Fertility Analysis: Assessing Different Data Fusion Approaches

Combined Use of Vis-NIR and XRF Sensors for Tropical Soil Fertility Analysis: Assessing Different Data Fusion Approaches

Phosphorus Over-Fertilization and Nutrient Misbalance of Irrigated Tomato Crops in Brazil

Simplifying Sample Preparation for Soil Fertility Analysis by X-ray Fluorescence Spectrometry

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