Ajuste a un modelo matemático, comparación de las curvas de crecimiento y características morfológicas de cuatro Urochloas de una colección in vivo establecida en Antioquia, Colombia

The aim of this study was to adjust the growth curves of four Urochloas using a mathematical model that allows the inter comparison. The variables of height (cm), air temperature (°C), precipitation (mm) and pasture age (days) were evaluated. The growth of each species was analysed in the statistical software R-Project. The growth data of the species were adjusted with the use of the quadratic model yt = β0 + β1 t - β3 t2, with coefficients of determination (>0.9) and a value of p<0.001. As a result, Urochloa brizantha cv Piatá presented an R² = 0.9859 and was the species most positively influenced in its growth by the effect of age (days), followed by U. decumbens, U. mutica and U. brizantha cv Toledo, the latter with the least adjustment. Precipitation influenced the growth rate of U. mutica while temperature had no influence on the growth of the species. The curves were fitted to quadratic models, which indicates that its growth can be compared and predicted over time, which facilitates its management and optimal use.El objetivo del estudio fue ajustar las curvas de crecimiento de cuatro Urochloas mediante un modelo matemático que permita la comparación entre ellas. Se tuvieron en cuenta las variables de altura (cm), temperatura (°C), precipitación (mm) y edad del pasto (días). El crecimiento de cada especie se analizó en el software estadístico R-Project. Se ajustaron los datos de crecimiento de las especies con el uso del modelo cuadrático yt = β0 +β1 t -β3 t2, con coeficientes de determinación (>0.9) y un valor de p<0.001. Como resultado, Urochloa brizantha cv Piatá presentó un R² = 0.9859 y fue la especie más influenciada positivamente en su crecimiento por el efecto de la edad (días), seguido por U. decumbens, U. mutica y U. brizantha cv Toledo, esta última con el menor ajuste. La precipitación influyó en la tasa de crecimiento de la U. mutica mientras que la temperatura no tuvo influencia en el crecimiento de las especies. Las curvas se ajustaron a modelos cuadráticos, lo que indica que su crecimiento puede compararse y predecirse a lo largo del tiempo, lo que facilita su manejo y óptimo aprovechamiento

Cation Exchange Capacity in Grazing Systems and a Case Study for Quantification by Hyperspectral Imaging

This chapter provides an overview of cation exchange capacity (CEC) and its importance as an indicator of soil fertility, particularly in the assessment of grassland quality. The limitations of traditional methods are highlighted, and the need to explore more agile approaches to grassland quality assessment is emphasized. The increasing use of hyperspectral information (HSI) as an accurate tool for measuring soil properties, which promotes more effective and sustainable rangeland management, is further explored. This provides data on soil fertility and forage quality, enabling more accurate decisions. The benefits and challenges of using HSI data to estimate CEC and its potential to improve pasture and forage production will also be examined. HSI technology allows information to be collected and analyzed from reflected light at different wavelengths, providing a clear understanding of soil physical and chemical properties. In addition, a case study illustrating the estimation of CIC using hyperspectral cameras in the department of Antioquia, Colombia, is presented. The chapter emphasizes the relevance of this topic in the rangeland context and concludes with a future outlook that anticipates a change in the management and understanding of grazing systems

Use of Machine Learning Models for Prediction of Organic Carbon and Nitrogen in Soil from Hyperspectral Imagery in Laboratory

Organic carbon and total nitrogen are essential nutrients for plant growth. The presence of these nutrients at acceptable levels can create an optimal environment for the development of crops of interest. The application of spectroscopic techniques and the use of machine learning algorithms have made it possible to calibrate models capable of predicting the number of elements present in the soil. One of these techniques is hyperspectral imaging, which captures portions of the electromagnetic spectrum where the materials present in the soil can be differentiated due to the vibrations of chemical bonds. The objective of this research is to use statistical models to predict OC and N in soils from hyperspectral images. Transformations were applied to spectral and chemical data and the models used were Random Forest (RF) and Support Vector Machine (SVM). To select the best model, the values of the coefficient of determination (R2), root mean square error of prediction (RMSEP), and the ratio of performance to deviation (RPD) were considered. For OC, the values found for the RF model were an R2 of 0.87, an RMSEP of 0.10, and an RPD of 6.74; the SVM model presented an R2 of 0.92, an RMSEP of 0.20, and an RPD of 3.56. For the variable N, the values found for the RF model were an R2 of 0.79, an RMSEP of 0.03, and an RPD of 5.44; for the SVM model, they were an R2 of 0.87, an RMSEP of 0.08, and an RPD of 2.76. The RF model showed a better fit for both variables. The SVM model also produced acceptable results. The results show that machine learning models are a good alternative for analysing soil-related variables

Determination of Grass Quality Using Spectroscopy: Advances and Perspectives

Spectroscopy is a promising technique for determining nutrients in grasses and may be a valuable tool for future research. This chapter reviews research carried out in recent years, focusing on determining the quality of grasses using spectroscopy techniques, specifically, spectrophotometry. The chemical methods used to determine the nutritional quality of grasses produce chemical residues, are time-consuming, and are costly to use when analyzing large crop extensions. Spectroscopy is a non-destructive technique that can establish the nutritional quality of grass easily and accurately. This chapter aims to describe the techniques focused on the use of spectroscopy and machine learning models to predict and determine the quality of grasses. A bibliographic review was conducted and recent research articles were selected that showed spectroscopic techniques applied to grasses. Different methods and results focusing on the quality of the grasses were compiled. In general, this review showed that the most commonly used spectroscopic method is near-infrared analysis. Spectroscopy is a very effective tool that opens the way to new types of technologies that can be applied to obtain results in determining the quality of pastures, leaving behind the use of traditional methods that represent higher costs and disadvantages compared to traditional methods based on precision agriculture

Health-status outcomes with invasive or conservative care in coronary disease

BACKGROUND In the ISCHEMIA trial, an invasive strategy with angiographic assessment and revascularization did not reduce clinical events among patients with stable ischemic heart disease and moderate or severe ischemia. A secondary objective of the trial was to assess angina-related health status among these patients. METHODS We assessed angina-related symptoms, function, and quality of life with the Seattle Angina Questionnaire (SAQ) at randomization, at months 1.5, 3, and 6, and every 6 months thereafter in participants who had been randomly assigned to an invasive treatment strategy (2295 participants) or a conservative strategy (2322). Mixed-effects cumulative probability models within a Bayesian framework were used to estimate differences between the treatment groups. The primary outcome of this health-status analysis was the SAQ summary score (scores range from 0 to 100, with higher scores indicating better health status). All analyses were performed in the overall population and according to baseline angina frequency. RESULTS At baseline, 35% of patients reported having no angina in the previous month. SAQ summary scores increased in both treatment groups, with increases at 3, 12, and 36 months that were 4.1 points (95% credible interval, 3.2 to 5.0), 4.2 points (95% credible interval, 3.3 to 5.1), and 2.9 points (95% credible interval, 2.2 to 3.7) higher with the invasive strategy than with the conservative strategy. Differences were larger among participants who had more frequent angina at baseline (8.5 vs. 0.1 points at 3 months and 5.3 vs. 1.2 points at 36 months among participants with daily or weekly angina as compared with no angina). CONCLUSIONS In the overall trial population with moderate or severe ischemia, which included 35% of participants without angina at baseline, patients randomly assigned to the invasive strategy had greater improvement in angina-related health status than those assigned to the conservative strategy. The modest mean differences favoring the invasive strategy in the overall group reflected minimal differences among asymptomatic patients and larger differences among patients who had had angina at baseline

Initial invasive or conservative strategy for stable coronary disease

BACKGROUND Among patients with stable coronary disease and moderate or severe ischemia, whether clinical outcomes are better in those who receive an invasive intervention plus medical therapy than in those who receive medical therapy alone is uncertain. METHODS We randomly assigned 5179 patients with moderate or severe ischemia to an initial invasive strategy (angiography and revascularization when feasible) and medical therapy or to an initial conservative strategy of medical therapy alone and angiography if medical therapy failed. The primary outcome was a composite of death from cardiovascular causes, myocardial infarction, or hospitalization for unstable angina, heart failure, or resuscitated cardiac arrest. A key secondary outcome was death from cardiovascular causes or myocardial infarction. RESULTS Over a median of 3.2 years, 318 primary outcome events occurred in the invasive-strategy group and 352 occurred in the conservative-strategy group. At 6 months, the cumulative event rate was 5.3% in the invasive-strategy group and 3.4% in the conservative-strategy group (difference, 1.9 percentage points; 95% confidence interval [CI], 0.8 to 3.0); at 5 years, the cumulative event rate was 16.4% and 18.2%, respectively (difference, 121.8 percentage points; 95% CI, 124.7 to 1.0). Results were similar with respect to the key secondary outcome. The incidence of the primary outcome was sensitive to the definition of myocardial infarction; a secondary analysis yielded more procedural myocardial infarctions of uncertain clinical importance. There were 145 deaths in the invasive-strategy group and 144 deaths in the conservative-strategy group (hazard ratio, 1.05; 95% CI, 0.83 to 1.32). CONCLUSIONS Among patients with stable coronary disease and moderate or severe ischemia, we did not find evidence that an initial invasive strategy, as compared with an initial conservative strategy, reduced the risk of ischemic cardiovascular events or death from any cause over a median of 3.2 years. The trial findings were sensitive to the definition of myocardial infarction that was used