Spectral data of tropical soils using dry-chemistry techniques (VNIR, XRF, and LIBS): A dataset for soil fertility prediction.

Proximal soil sensing technologies, such as visible and near infrared diffuse reflectance spectroscopy (VNIR), X-ray fluorescence spectroscopy (XRF), and laser-induced breakdown spectroscopy (LIBS), are dry-chemistry techniques that enable rapid and environmentally friendly soil fertility analyses. The application of XRF and LIBS sensors in an individual or combined manner for soil fertility prediction is quite recent, especially in tropical soils. The shared dataset presents spectral data of VNIR, XRF, and LIBS sensors, even as the characterization of key soil fertility attributes (clay, organic matter, cation exchange capacity, pH, base saturation, and exchangeable P, K, Ca, and Mg) of 102 soil samples. The samples were obtained from two Brazilian agricultural areas and have a wide variation of chemical and textural attributes. This is a pioneer dataset of tropical soils, with potential to be reused for comparative studies with other datasets, e.g., comparing the performance of sensors, instrumental conditions, and/or predictive models on different soil types, soil origin, concentration range, and agricultural practices. Moreover, it can also be applied to compose soil spectral libraries that use spectral data collected under similar instrumental conditions.

Sensor Fusion with NARX Neural Network to Predict the Mass Flow in a Sugarcane Harvester.

Measuring the mass flow of sugarcane in real-time is essential for harvester automation and crop monitoring. Data integration from multiple sensors should be an alternative to receive more reliable, accurate, and valuable predictions than data delivered by a single sensor. In this sense, the objective was to evaluate if the fusion of different sensors installed in a sugarcane harvester improves the mass flow prediction accuracy. A harvester was experimentally instrumented, and neural network models integrated sensor data along the harvester to perform the self-calibration of these sensors and estimate the mass flow. Nonlinear autoregressive networks with exogenous input (NARX) and multiple linear regression (MLR) models were compared to predict the mass flow. The prediction with the NARX showed a significant superiority over MLR. MLR decreases the estimated mass flow variability in the harvester. NARX with multi-sensor data has an RMSE of 0.3 kg s-1, representing a MAPE of 0.7%. The fusion of sensor signals improves prediction accuracy, with higher performance than studies with approaches that used a single sensor. The mass flow approach with multiple sensors is a potential approach to replace conventional yield monitors. The system generates accurate data with high sample density within sugarcane rows.

Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy.

Proximal sensing for assessing sugarcane quality information during harvest can be affected by various factors, including the type of sample preparation. The objective of this study was to determine the best sugarcane sample type and analyze the spectral response for the prediction of quality parameters of sugarcane from visible and near-infrared (vis-NIR) spectroscopy. The sampling and spectral data acquisition were performed during the analysis of samples by conventional methods in a sugar mill laboratory. Samples of billets were collected and four modes of scanning and sample preparation were evaluated: outer-surface ('skin') (SS), cross-sectional scanning (CSS), defibrated cane (DF), and raw juice (RJ) to analyze the parameters soluble solids content (Brix), saccharose (Pol), fibre, pol of cane and total recoverable sugars (TRS). Predictive models based on Partial Least Square Regression (PLSR) were built with the vis-NIR spectral measurements. There was no significant difference (p-value > 0.05) between the accuracy SS and CSS samples compared to DF and RJ samples for all prediction models. However, DF samples presented the best predictive performance values for the main sugarcane quality parameters, and required only minimal sample preparation. The results contribute to advancing the development of on-board quality monitoring in sugarcane, indicating better sampling strategies.

Spatial distribution of sorption and desorption process of 14C-radiolabelled hexazinone and tebuthiuron in tropical soil.

The understanding of the interaction between soil physicochemical attributes and herbicide behavior is fundamental for optimizing the efficient use of PRE-emergence herbicides in a more sustainable approach. However, it is still a poorly studied area within precision agriculture. Thus, the objective of this research was to evaluate the correlation of soil physicochemical attributes with the sorption and desorption processes of hexazinone and tebuthiuron to support application maps considering the field level variability. Soil samples from an agricultural area had their physicochemical attributes analyzed and were submitted to sorption and desorption studies of 14C-tebuthiuron and 14C-hexazinone using the batch equilibrium method. The values of sorption and desorption apparent coefficients (Kd), sorption and desorption percentage and bioavailability were correlated with soil attributes by Pearson's correlation. The Kd values of tebuthiuron and hexazinone sorption ranged from 1.2 to 2.9 mL g-1 and 0.4-0.6 mL g-1, respectively. For desorption of tebuthiuron and hexazinone, Kd values ranged from 3.4 to 4.4 mL g-1 and 2.6-3.0 mL g-1, respectively. A positive correlation among clay content, soil organic matter (OM), and tebuthiuron and hexazinone sorption Kd values were found. Both herbicides had variable retention according to geographic position in the area. The recommendation of application of PRE herbicides, such as tebuthiuron and hexazinone, observing the physicochemical attributes of the soil is an alternative to increase efficiency in weed control and decrease the risk of environmental contamination.

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

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.

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

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.

Energy flows in lowland soybean production system in Brazil / Fluxos de energia em sistema de produção de soja em várzea no Brasil

ABSTRACT: Soybean is the main product of Brazilian agribusiness, both production and income. Considering the increase in food and energy demand and the search for more sustainable production systems, this study aimed to analyze inputs and energy use of a possible area of expansion of soybean production: a system under sub irrigation management located in a lowland area of Cerrado biome, northern region of Brazil. Its environmental performance was compared to other Brazilian locations among them traditionally soybean producers. The evaluation and comparison was made through material and energy flow tools in order to determine the inputs embodied per area, as well as energy demand, availability and efficiency in the analyzed production system. Energy demand (IE) and energy availability (OE) of the analyzed production system were 7.6 and 57.1 GJ ha-1, respectively. Energy balance (EB) was 49,5 GJ ha-1, energy return over investment (EROI) was 7.5 and embodied energy in grains (EE) was 2,2 MJ kg-1, respectively. Highest energy consumption was due to the use of fertilizers, fuel and herbicide. The system is energy efficient, since it provides more energy than demands, and efficient when compared to usual production systems in other regions, however it is highly dependent on non-renewable energy.

RESUMO: A soja é o principal produto do agronegócio Brasileiro, em volume e geração de renda. Considerando o aumento da demanda por alimentos e energia, bem como a busca por sistemas de produção mais sustentáveis, o presente estudo teve como objetivo analisar o uso de energia oriunda de insumos agrícolas em área de possível expansão de produção de soja: sistema de produção sob subirrigação em área de várzea no Cerrado, região Norte do Brasil. Seu desempenho ambiental foi comparado a outros locais no Brasil, entre os quais regiões tradicionalmente produtores de soja. A avaliação e comparação foram feitas por meio do uso de ferramentas de fluxo de materiais e energia, a fim de determinar a quantidade de insumos utilizados por área, bem como a demanda, disponibilidade e eficiência do uso de energia no sistema de produção avaliado. A demanda (IE) e disponibilidade (OE) de energia foram de 7.6 e 57.1 GJ ha-1, respectivamente. O balanço energético (BE), o retorno de energia sobre o investimento (EROI) e a energia incorporada dos grãos (EE) foram 49.5 GJ ha-1, 7.5 e 2.2 MJ kg-1, respectivamente. O maior consumo de energia foi devido à utilização de fertilizantes, herbicidas e combustível. O sistema analisado é eficiente no uso da energia, uma vez que fornece mais energia do que é demandado, e eficiente quando comparado a sistemas de produção usuais em outras regiões, embora seja altamente dependente de energia de origem não-renovável.

Avaliação comparativa da distribuição transversal de adubos sólidos aplicados em cobertura em culturas anuais instaladas / Transversal distribution comparison of solid fertilizers applied over annual crops

Os distribuidores centrífugos predominam na aplicação de produtos sólidos na agricultura, por apresentarem grande capacidade de campo operacional e pela grande amplitude de dosagens que permitem aplicar. Ensaios para a caracterização do seu desempenho são realizados sem qualquer impedimento físico (como a presença de plantas), durante o trajeto parabólico de queda das partículas dofertilizante até o solo. O objetivo do presente trabalho foi avaliar comparativamente a distribuição transversal de adubos sólidos aplicados em cobertura nas culturas de milho, soja e algodão. Foram utilizados distribuidores de adubos e corretivos do tipo centrífugo. As avaliações foram desenvolvidas de acordo com a Norma ASAE S341.3/99. Os ensaios de distribuição transversal foram constituídos em alinhar lado a lado, no campo, de forma transversal, coletores nas entrelinhas das culturas instaladas, possibilitando a pesagem do material depositado e posterior avaliação dos resultados. Pode-se concluir que a distribuição transversal de fertilizantes sólidos aplicados em cobertura e a lanço em culturas já instaladas de milho e algodão é afetada pela altura das plantas, ou seja, pelo estádio fenológico em que a cultura se encontra, interferindo diretamente na largura efetiva de aplicação. Já a distribuição ransversal de fertilizantes sólidos aplicados em cobertura na cultura da soja não foi afetada pelas plantas. Assim, recomenda-se que a avaliação da largura efetiva das faixas de aplicação a lanço de fertilizantes sólidos em cobertura nas culturas de milho e algodão seja realizada no interior dessas culturas.

Centrifugal spreaders dominate the application of solid materials in agriculture offering expressive operational field capacity and extended range of applied rates. Field tests for characterization of theirperformance are conducted without any physical obstacles (such as the presence of plants) during the parabolic trajectory of the falling particles of fertilizer to the soil. The purpose of this study was to comparatively evaluate the transverse distribution of solid fertilizers applied on cropped corn, soybeans and cotton. Evaluations of the spreaders were designed according to ASAE S341.3/99 Standard. Tests consisted in aligning side by side collectors in-between the cropped rows andweighting the material deposited. The results showed that transverse distribution of solid fertilizers applied over the cotton and corn crops is affected by the crop height, interfering directly on the effective width of the spreader application, which was not observedin the soybean crop, once the fertilizer application is done when the crop was still below the collector's height. The results suggest that evaluation of effective width of the spreaders application need to be done under real crop environment.