Palm oil has become one of the most consumed vegetable oils in the world, and it is a key element in profitable global value chains. In Costa Rica, oil palm cultivation is one of the three crops with the largest occupied agricultural area. The objective of this study was to explain and predict yield in safe time lags for production management by using free-access satellite images. To this end, machine learning methods were performed to a 20-year data set of an oil palm plantation located in the Central Pacific Region of Costa Rica and the corresponding vegetation indices obtained from LANDSAT satellite images. Since the best correlations corresponded to a one-year time lag, the predictive models Random Forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO), Extreme Gradient Boosting (XGBoost), Recursive Partitioning and Regression Trees (RPART), and Neural Network (NN) were built for a Time-lag 1. These models were applied to all genetic material and to the predominant variety (AVROS) separately. While NN showed the best performance for multispecies information (r2 = 0.8139, NSE = 0.8131, RMSE = 0.3437, MAE = 0.2605), RF showed a better fit for AVROS (r2 = 0.8214, NSE = 0.8020, RMSE = 0.3452, MAE = 0.2669). The most relevant vegetation indices (NDMI, MSI) are related to water in the plant. The study also determined that data distribution must be considered for the prediction and evaluation of the oil palm yield in the area under study. The estimation methods of this study provide information on the identification of important variables (NDMI) to characterize palm oil yield. Additionally, it generates a scenario with acceptable uncertainties on the yield forecast one year in advance. This information is of direct interest to the oil palm industry.
En Costa Rica el caudal ambiental se calcula como un 10% del aforo caudal medio del río. Este porcentaje ha carecido de fundamento científico en relación con las condiciones fluviales aptas para preservar los ecosistemas presentes en los cauces. Las concesiones de agua para los diferentes usos han provocado un impacto en disponibilidad de agua para algunos lugares y épocas del año y en la calidad del recurso transformando las condiciones fisicoquímicas y ecológicas de los cauces. Del sistema fluvial del río Birrís, se concesiona un 20% para uso consuntivo como producción agrícola, humano y riego, y el restante se utiliza para la generación hidroeléctrica. Debido a la baja calidad de sus aguas a la fecha no existen especies indicadoras lo que imposibilita el uso de enfoques que tomen en cuenta los ecosistemas para generar curvas de preferencia. El presente trabajo utilizó un enfoque hidrológico para determinar el caudal ambiental del río Birrís usando la metodología de curva de duración de caudales. Se basó en el análisis de series de datos de clima obtenidas por 10 años y series de caudales diarios de un periodo de tres años. Se realizaron tres aforos de biota y flujo en tres puntos distribuidos a lo largo del cauce principal distribuidos en un periodo de un año. Finalmente, se determinó el caudal ambiental para un régimen de flujo basado en valores medios mensuales.
The coNSEquences of climate change have challenged researchers to generate models and projections to understand climate behavior under different scenarios. In Costa Rica, as in other countries, climate-change (CC) models and projections are essential to make decisions about the management of natural resources, mainly water. To understand climate change’s impact on hydraulic parameters such as velocity, depth, and river surface area, we studied the Pejibaye river basin, located in Jiménez in Cartago, Costa Rica. This watershed is characterized by having more than 90% of its surface area covered by forest. We used the precipitation and temperature data from meteorological stations (2000 to 2009) and climate-change scenarios (2000–2099) to predict the response of the basin in different periods. First, we calibrated (NSE = 0.77) and validated (NSE = 0.81) the HBV hydrological model using ten years of daily data from 2000 to 2009. The climate-change data (2000–2099) were incorporated into the calibrated HBV model. This allowed us to determine the impact of CC on the basin water regime for the periods 2040–2059 (CCS1) and 2080–2099 (CCS2). The IBER mathematical model was used to determine the changes in the hydraulic variables of the river flow. For the CCS1, we determined a 10.9% decrease in mean velocity and a 0.1-meter decrease in depth, while for CCS2, the effect will be an 11.3% reduction in mean velocity and a 0.14-meter decrease in depth. The largest decreases in river surface area per kilometer will occur in May (1710 m2) for CCS1 and April (2250 m2) for CCS2.
When using numerical models for hydraulic simulations of rivers, calibration is key to be able to reflect accurately the interaction of water flow in the channel and to make it resemble what is observed. In this study, a calibrated two-dimensional hydraulic model was created for two control paths located in the Ahogados and Tempisquito rivers. Paths were analyzed morphologically from a grain-size analysis and the different roughness coefficients were calculated through a numerical model using the empirical equations known as “Strickler-type” as a first approximation and later adjustment by a factor obtained from comparing the observed and simulated data. It was identified that both paths are mountain rivers with beds of coarse material, mostly boulders (cobble gravel) and pebble gravel. Calibrated roughness coefficients were determined with an error percentage between the area of the pattern formed by the simulated and observed of less than 10%, and new empirical equations adjusted to the characteristics of the riverbeds were formulated.
Climate change is a variation in the normal behavior of the climate. These variations and their effects will be seen in the coming years, the most imminent being anomalous fluctuations in atmospheric temperature and precipitation. This scenario is counterproductive for agricultural production. This study evaluated the effect of climate change on oil palm production for conditions in the Central Pacific of Costa Rica, in three simulation scenarios: the baseline between the years 2000 and 2019, a first climate change scenario from 2040 to 2059 (CCS1), and a second one from 2080 to 2099 (CCS2), using the modeling framework APSIM, and the necessary water requirements were established as an adaptive measure for the crop with the irrigation module. A decrease in annual precipitation of 5.55% and 7.86% and an increase in the average temperature of 1.73 °C and 3.31 °C were identified, generating a decrease in production yields of 7.86% and 37.86%, concerning the Baseline, in CCS1 and CCS2, respectively. Irrigation made it possible to adapt the available water conditions in the soil to maintain the baseline yields of the oil palm crop for the proposed climate change scenarios.
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