Frosty pod rot disease of cacao (FPR), caused by the fungus Moniliophthora roreri, has severely impacted the production of cocoa in Latin America since its discovery. Prior to the 1950s, FPR was known only from Colombia and Ecuador. However, beginning in the 1970s, its geographical range has dramatically expanded throughout most of the cacao‐producing regions of the Americas. The origin of the pathogen remains unknown. In this study, we evaluated the genetic diversity of M. roreri from areas spanning, as much as possible, its current geographical range using simple‐sequence repeat markers and a publicly available single‐nucleotide polymorphism data set. Two hotspots of genetic diversity were found: coastal Ecuador and the inter‐Andean Magdalena Valley of Colombia, neither of which correspond to the Amazonian origin of the host. However, both areas were early centres of intense cultivation of cacao. Our results indicate that M. roreri was introduced into both areas from its centre of origin, where intensive cacao cultivation probably led to the increase of inoculum and further dissemination of the disease. Current invasions can be traced to two genotypes responsible for all known instances of the pathogen in Central America, the Caribbean, Peru and Bolivia. We also report for the first time M. roreri in Maynas (Peruvian Amazon), which is probably the result of a recent introduction from Colombia.
A common issue in forest management is related to yield projection for stands at young ages. This study aimed to evaluate the Clutter model and artificial neural networks for projecting eucalypt stands production from early ages, using different data arrangements. In order to do this, the changes in the number of measurement intervals used as input in the Clutter model and artificial neural networks (ANNs) are tested. The Clutter model was fitted considering two sets of data: usual, with inventory measurements (I) paired at intervals each year (I1–I2, I2–I3, …, In–In+1); and modified, with measurements paired at all possible age intervals (I1–I2, I1–I3, …, I2–I3, I2–I4, …, In–In+1). The ANN was trained with the modified dataset plus soil type and geographic coordinates as input variables. The yield projections were made up to the final ages of 6 and 7 years from all possible initial ages (2, 3, 4, 5, or 6 years). The methods are evaluated using the relative error (RE%), bias, correlation coefficient (ryŷ), and relative root mean square error (RMSE%). The ANN was accurate in all cases, with RMSE% from 8.07 to 14.29%, while the Clutter model with the modified dataset had values from 7.95 to 23.61%. Furthermore, with ANN, the errors were evenly distributed over the initial projection ages. This study found that ANN had the best performance for stand volume projection surpassing the Clutter model regardless of the initial or final age of projection.
The Guazuma crinita Mart. is a dominant species of great economic importance for the inhabitants of the Peruvian Amazon, standing out for its rapid growth and being harvested at an early age. Understanding its vertical growth is a challenge that researchers have continued to study using different hypsometric modeling techniques. Currently, machine learning techniques, especially artificial neural networks, have revolutionized modeling for forest management, obtaining more accurate predictions; it is because we understand that it is of the utmost importance to adapt, evaluate and apply these methods in this species for large areas. The objective of this study was to build and evaluate the efficiency of the use of a deep neural network for the prediction of the total height of Guazuma crinita Mart. from a large-scale continuous forest inventory. To do this, we explore different configurations of the hidden layer hyperparameters and define the variables according to the function HT = f(x) where HT is the total height as the output variable and x is the input variable(s). Under this criterion, we established three HT relationships: based on the diameter at breast height (DBH), (i) HT = f(DBH); based on DBH and Age, (ii) HT = f(DBH, Age) and based on DBH, Age and Agroclimatic variables, (iii) HT = f(DBH, Age, Agroclimatology), respectively. In total, 24 different configuration models were established for each function, concluding that the deep artificial neural network technique presents a satisfactory performance for the predictions of the total height of Guazuma crinita Mart. for modeling large areas, being the function based on DBH, Age and agroclimatic variables, with a performance validation of RMSE = 0.70, MAE = 0.50, bias% = −0.09 and VAR = 0.49, showed better accuracy than the others.
Optimizing tree spacing in a forest plantation is one of the main management techniques to improve stand quality and productivity. Its influence on growth from an early age is an important matter for forest management. This study aims to evaluate the effect of tree spacing on early growth rate and yield over time in Eucalyptus grandis × Eucalyptus camaldulensis hybrids. The data were obtained from an experiment in Itamarandiba, Minas Gerais, Brazil. The plots were composed of five planting spacing (3.00 m × 0.50 m, 3.00 m × 1.00 m, 3.00 m × 1.50 m, 3.00 m × 2.00 m, and 3.00 m × 3.00 m) measured at the ages of 7, 12, 24, 36, 48, 61, 77, 85, and 102 months. Growth and yield were analyzed by fitting the Gompertz model and a baseline exponential model up to 36 months of age to evaluate the influence of early growth on the harvest age. A Pearson correlation matrix was also generated to find out the relationship between the mean annual increment in the respective treatments during the studied period. It was observed that a positive correlation in the average annual increase in the 3 × 2 and 3 × 3 spacings. It was verified that tree spacing influenced the yielded wood volume and the optimal harvest age. The early growth rate influences the optimal harvest, which may explain a possible loss of yield during the productive cycle of the forest stand.
<span class="fontstyle0">La estimación precisa del volumen del vuelo forestal hace posible estimar un valor monetario correcto. El objetivo de este estudio fue valorar el vuelo forestal del genero </span><span class="fontstyle2">Pinus </span><span class="fontstyle0">en el Parque Forestal Aylambo de la Universidad Nacional de Cajamarca, aplicando el modelo volumétrico de Schumacher y Hall (1993); con la finalidad de comparar la valoración económica con la valoración económica ajustada de la plantación, y conocer el valor actual del vuelo forestal. Se realizó un inventario al 100%. El modelo volumétrico fue ajustado utilizando regresión lineal, a través del método de mínimos cuadrados. Presentando la especie </span><em><span class="fontstyle2">Pinus patula </span></em><span class="fontstyle0">un R</span><span class="fontstyle0">2 </span><span class="fontstyle0">de 78,95% y R</span><span class="fontstyle0">2ajustado </span><span class="fontstyle0">de 78,83% y para la especie de </span><em><span class="fontstyle2">Pinus radiata </span></em><span class="fontstyle0">un R</span><span class="fontstyle0">2 </span><span class="fontstyle0">de 85,22% y R</span><span class="fontstyle0">2 ajustado </span><span class="fontstyle0">de 84,9%. Para estimar el volumen ajustado en m</span><span class="fontstyle0">3</span><span class="fontstyle0">, en razón al diámetro a la altura del pecho (DAP) y la altura comercial (Hc) fueron tomados en metros; los modelos determinados son: LnV = -0,9347984 + 2,00002362*LnDAP + 1,0015779*LnHc para </span><em><span class="fontstyle2">P. patula </span></em><span class="fontstyle0">y LnV = 0,35264369 + 1,9401478*LnDAP + 1,2038246*LnHc para </span><em><span class="fontstyle2">P. radiata</span></em><span class="fontstyle0">. El valor del vuelo forestal de la plantación fue de 4351,11 nuevos soles y el valor ajustado de 4344,71 nuevos soles.</span>
<p>El presente estudio fue realizado con los objetivos de regular la producción forestal con pronósticos de modelación Clutter y Redes Neuronales Artificiales (RNA) en plantaciones de eucalipto (híbridos de Eucalyptus urophylla x Eucalyptus grandis), localizado en la región Centro Oeste del Estado de Minas Gerais, Brasil. De las tablas de producción se generaron procesos de regulación forestal en formato de programación lineal utilizando el software RPF 2.0®, con el fin de maximizar el ingreso líquido futuro para 22 años, 2016 – 2038. Se generaron dos planos de manejo, obteniendo variables de prescripción, edad, rotación, volumen, costos, demanda. Para la aplicación del modelo de programación lineal se consideró un horizonte de planeamiento de 22 periodos, tasa de interés de 11,5% y una edad regulatoria de 7 años con 0% de variación permitida, considerando una sola rotación y una variación de producción entre 100 000 m3 y 60 000 m3. Se concluye que los dos planos de proyección tienen influencia en la regulación forestal, destacando la técnica de RNA como una alternativa más real y confiable para regular una plantación.</p>
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