ICP-OES analysis of total As and Cd in Columbian Oryza sativa L. rice.

Arsenic (As) and cadmium (Cd) are considered toxic elements, even at trace levels. Their accurate quantification in crops can be complex at low levels and due to interference with other elements. The aim of this work was to develop and validate an analytical method for As and Cd quantification in rice stem and grains from the production systems "Irrigated Rice Ecosystems" (IRE) and "Rainfed Rice Ecosystems" (RRE) in Colombia. Mineralisation was carried out by acid digestion using an open system with a heating plate. Metal detection was performed by inductively coupled plasma optical emission spectrometry (ICP-OES). Method adjustment, calibration, and validation were performed in accordance with AOAC standards, considering sensitivity, precision, accuracy, and selectivity parameters. The obtained method was applied to quantify levels in 259 rice stem and 443 grain samples from IRE and RRE.

Espectroscopía visible y del infrarrojo cercano para el análisis de nutrientes en tejido vegetal de caña de azúcar para producción de panela

La espectroscopía de reflectancia en el infrarrojo cercano (NIRS) es una tecnología rápida, multiparamétrica, amigable con el ambiente, de bajo costo y gran exactitud, para el análisis de diversos componentes en alimentos, en suelo y en agricultura. El objetivo del presente estudio fue construir modelos de calibración NIRS, para la predicción de nutrientes en tejido vegetal de caña de azúcar, para producción de panela, cultivada en la región de la Hoya del río Suárez. Un total de 416 muestras de tejido fueron escaneadas en el segmento espectral Vis-NIR. El análisis quimiométrico, se realizó con el software WinISI V4.10, aplicando la regresión de mínimos cuadrados parciales modificados, junto a una validación cruzada. Se evaluaron cuatro modelos con diferentes tratamientos matemáticos y el rendimiento de las calibraciones, se hizo por medio de la validación externa, analizando las medidas de bondad de ajuste, como el coeficiente de determinación de la predicción, el error estándar de la predicción ajustado por el sesgo y la desviación predictiva residual. Los resultados muestran que el modelo de calibración para N presentó el mayor poder predictivo. Para macronutrientes, las calibraciones, con mayor poder predictivo, fueron P y K y para micronutrientes, el modelo para B, mientras que para Cu presentó el más bajo poder predictivo. Se encontraron modelos adecuados para la predicción de los contenidos de N, Ca y P; para los demás nutrientes, se recomienda ampliar el conjunto de calibración.

Near Infrared Reflectance Spectroscopy (NIRS) is a fast, multiparametric, environmentally friendly, low-cost, and highly accurate technology for the analysis of components in food, soil, and agriculture. The purpose of this study was to generate NIRS calibration models for the prediction of nutrients in plant tissue of sugarcane to panela production cultivated in the Hoya del Río Suárez region. A total of 416 tissue samples were scanned in Vis-NIR spectral segment. Chemometric analysis was performed with the WinISI V4.10 software applying modified partial least squares regression with cross-validation. Four models with different mathematical treatments were evaluated, and the performance of calibrations was made through external validation analyzing the goodness-of-fit measures as prediction determination coefficient, standard error of the bias-adjusted prediction, and residual predictive deviation. The results showed that the calibration model for N had the highest predictive power. For macronutrients, the calibrations with the best predictive power were for P and K, and micronutrients for B, while Cu presented the lowest predictive power. Adequate models were found for the prediction of N, Ca, and P. In the case of the other nutrients, it is recommended to expand the calibration set.

Use of near-infrared reflectance spectroscopy on feces to estimate digestibility and dry matter intake of dietary nutritional characteristics under grazing conditions in Colombian creole steers.

Digestibility and intake are parameters difficult and expensive to estimate under grazing conditions; therefore, the aim of this study was to develop near-infrared reflectance spectroscopy (NIRS) calibrations applied to feces (F-NIRS) and evaluate their accuracy to predict dry matter digestibility (DMD) and dry matter intake (DMI) of Colombian creole cattle. Five digestibility trials using creole steers were conducted; indigestible neutral detergent fiber (iNDF) was used as internal marker and Cr2O3 and TiO2 as external markers. A total of 249 forage and 396 fecal samples from individual animals were collected, dried, and grinded for conventional chemical analysis. For spectral analysis, fecal samples were pooled across collection periods (77 samples). Chemometric analysis was performed using WinISI V4.10 software applying the modified partial least squares method. Cross-validation was performed to avoid overfitting the models. The goodness-of-fit statistics considered were the coefficient of determination in cross-validation and prediction sets (R2cv and r2, respectively) and the ratio performance deviation (RPD). Fecal NIRS calibrations developed for forage and supplement DMD showed a satisfactory fit (R2cv =0.87 and RPD=2.77 and R2cv=0.92 and RPD=3.50, respectively). The accuracy of fecal output equations using chromium (Cr) and titanium (Ti) was similar in terms of R2cv (0.92) and RPD (3.63 vs. 3.57). Total DMI equations using Ti performed better compared to Cr (R2cv = 0.82 vs. 0.78; RPD=2.41 vs. 2.17, respectively). The F-NIRS models were validated using a completely independent set of fecal samples showing a moderate fit (r2>0.8 and RPD>2.0). This study showed that F-NIRS is a feasible tool to predict DMD and DMI of creole steers under grazing conditions. However, previous to socialization, this requires an improvement in accuracy of the calibrated equations related to grazing animals in different production contexts.

Improving the accuracy of beef cattle methane inventories in Latin America and Caribbean countries.

On-farm methane (CH4) emissions need to be estimated accurately so that the mitigation effect of recommended practices can be accounted for. In the present study prediction equations for enteric CH4 have been developed in lieu of expensive animal measurement approaches. Our objectives were to: (1) compile a dataset from individual beef cattle data for the Latin America and Caribbean (LAC) region; (2) determine main predictors of CH4 emission variables; (3) develop and cross-validate prediction models according to dietary forage content (DFC); and (4) compare the predictive ability of these newly-developed models with extant equations reported in literature, including those currently used for CH4 inventories in LAC countries. After outlier's screening, 1100 beef cattle observations from 55 studies were kept in the final dataset (∼ 50 % of the original dataset). Mixed-effects models were fitted with a random effect of study. The whole dataset was split according to DFC into a subset for all-forage (DFC = 100 %), high-forage (94 % ≥ DFC ≥ 54 %), and low-forage (50 % ≥ DFC) diets. Feed intake and average daily gain (ADG) were the main predictors of CH4 emission (g d-1), whereas this was feeding level [dry matter intake (DMI) as % of body weight] for CH4 yield (g kg-1 DMI). The newly-developed models were more accurate than IPCC Tier 2 equations for all subsets. Simple and multiple regression models including ADG were accurate and a feasible option to predict CH4 emission when data on feed intake are not available. Methane yield was not well predicted by any extant equation in contrast to the newly-developed models. The present study delivered new models that may be alternatives for the IPCC Tier 2 equations to improve CH4 prediction for beef cattle in inventories of LAC countries based either on more or less readily available data.

Prediction of enteric methane production and yield in dairy cattle using a Latin America and Caribbean database.

Successful mitigation efforts entail accurate estimation of on-farm emission and prediction models can be an alternative to current laborious and costly in vivo CH4 measurement techniques. This study aimed to: (1) collate a database of individual dairy cattle CH4 emission data from studies conducted in the Latin America and Caribbean (LAC) region; (2) identify key variables for predicting CH4 production (g d-1) and yield [g kg-1 of dry matter intake (DMI)]; (3) develop and cross-validate these newly-developed models; and (4) compare models' predictive ability with equations currently used to support national greenhouse gas (GHG) inventories. A total of 42 studies including 1327 individual dairy cattle records were collated. After removing outliers, the final database retained 34 studies and 610 animal records. Production and yield of CH4 were predicted by fitting mixed-effects models with a random effect of study. Evaluation of developed models and fourteen extant equations was assessed on all-data, confined, and grazing cows subsets. Feed intake was the most important predictor of CH4 production. Our best-developed CH4 production models outperformed Tier 2 equations from the Intergovernmental Panel on Climate Change (IPCC) in the all-data and grazing subsets, whereas they had similar performance for confined animals. Developed CH4 production models that include milk yield can be accurate and useful when feed intake is missing. Some extant equations had similar predictive performance to our best-developed models and can be an option for predicting CH4 production from LAC dairy cows. Extant equations were not accurate in predicting CH4 yield. The use of the newly-developed models rather than extant equations based on energy conversion factors, as applied by the IPCC, can substantially improve the accuracy of GHG inventories in LAC countries.

Effect of Origanum chemotypes on broiler intestinal bacteria.

Essential oils have been proposed as alternatives to antibiotic use in food animal production. This study evaluated 3 chemotypes of the Origanum genus, containing varying amounts of secondary metabolites carvacrol, thymol, and sabinene, in the broiler chicken diet. Aerial parts of Origanum vulgare L. (OL), O. vulgare L. ssp. hirtum (OH), and O. majorana (OM) were collected from a greenhouse located in the high altitude Sabana de Bogotá (Savanna of Bogotá) and O. vulgare L. ssp. hirtum (OG) produced and ground in Greece. Oregano essential oils (OEO) from these plants were obtained by steam distillation and analyzed by gas chromatography coupled to a mass spectrometer. Six treatments were evaluated: 200 mg/kg of OEO from OH, OL, and OM, 50 mg/kg of OEO from OG, 500 mg/kg of chlortetracycline, and without additives. Broiler chicks were maintained at 2,600 m above sea level, placed in brooder cages under a completely randomized design. Template DNA was isolated from duodenal, jejunal, ileal, and cecal contents in each group and bacterial 16S rDNA patterns were analyzed by denaturing gradient gel electrophoresis. Dendrograms of denaturing gradient gel electrophoresis band patterns revealed 2 main clusters, OEO-treated chicks and nontreated control chicks, in each intestinal segment. Band patterns from different gut compartments revealed major bacterial population shifts in the foregut (duodenum, jejunum, and ileum) compared with the hindgut (cecum and colon) at all ages evaluated (P < 0.05). The OEO groups showed less shift (62.7% similarity coefficient) between these 2 compartments versus the control groups (53.7% similarity coefficient). A reduction of 59% in mortality from ascites was seen in additive-supplemented groups compared with the control group. This study represents the first work to evaluate the effects of the 3 main chemotypes of Origanum genus in broilers.

Colostral antibody-mediated and cell-mediated immunity contributes to innate and antigen-specific immunity in piglets.

Immunoglobulins and immune cells are critical components of colostral immunity; however, their transfer to and function in the neonate, especially maternal lymphocytes, is unclear. Cell-mediated and antibody-mediated immunity in sow blood and colostrum and piglet blood before (PS) and after (AS) suckling were assessed to investigate transfer and function of maternal immunity in the piglet. CD4, CD8, and γδ lymphocytes were found in sow blood and colostrum and piglet blood PS and AS; each had a unique T lymphocyte profile. Immunoglobulins were detected in sow blood, colostrum, and in piglet blood AS; the immunoglobulin profile of piglet serum AS mimicked that of sow serum. These results suggest selectivity in lymphocyte concentration into colostrum and subsequent lymphocyte transfer into the neonate, but that immunoglobulin transfer is unimpeded. Assessment of colostral natural killer activity and antigen-specific proliferation revealed that colostral cells are capable of influencing the innate and specific immune response of neonatal pigs.