Exploring Product Diversification: the Case of Contract and Non-contract Farmers in the Philippine Cavendish Banana Value Chain

Uncertainties arising from market fluctuations limit choices of banana famers under contracts. However, they can opt not to renew their contracts with multi-national firms to sell to spot market or diversify. This paper examines optimal portfolio of Cavendish banana products of contract and non-contract farmers under uncertainty. We explore the effect of diversification by including banana flour from rejects aside from fresh banana. Constrained M-estimation of parameters and robust portfolio optimization results show that (1) non-contract farms benefit more from diversifying compared to contract farms; and (2) prices are higher for non-contract farms but profits are lower compared to contract farms

A Comparison Between Conventional and High-Priority Bus Services in Davao City, Southern Philippines

The increasing demand for transport system in Davao City has paved the way for the proposal of two transportation options, namely, the conventional bus service (CBS) and high-priority bus service (HPBS). A necessary step for transport policy making is quantitatively determining the differences between these transport options. In this study, we compare the projected performances of CBS and HPBS in terms of their expected load factor and passenger waiting time at chosen stations in Davao City. Our assessment is based on the data gathered about the existing public transport system at eight stations in the Mintal area during the morning rush hours. A single server batch service queueing model was adopted in this study to approximate the passenger waiting time at a station for each transport option. Passenger arrivals are fitted to a Poisson distribution using least square methods yielding a headway/service time of 3 min and service frequency during the observation period is 40. Our results show that though the load factor of CBS is higher than the HPBS, it fails to meet the passenger demands at some stations, which resulted in increased passenger waiting time

Economic Impact of Climate Determinants on Rice Farmlands in Davao Region, Philippines

Agriculture contributes largely to the economic growth of developing countries such as the Philippines. However, it is highly dependent on climate. The impending changes in climatic variables thus pose questions about its economic impacts on agricultural crops such as rice. There has been no study yet on the quantified positive or negative impact of changing weather patterns on the rice farmlands of Davao Region, Southern Philippines. Thus, this study used the Ricardian model in estimating the marginal effect of significant weather variables on the net revenue per hectare of rice farmlands in Davao Region. Farm net revenue per hectare was regressed on socio-demographic variables and on weather variables that affect rice growth, namely: rainfall, air temperature, air humidity, and wind speed. Primary survey on 44 rice farm households was conducted in 2015 for the socio-demographic variables and the computation of the farm net revenue per hectare, while secondary data for 2015 on the weather variables were obtained from the Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA) website. Results showed that air temperature and air humidity significantly affect the net revenue per hectare. Specifically, when air temperature increases beyond 27.03 °C, farm net revenue per hectare decreases. Farm net revenue also decreases when air humidity goes lower than 84.66%. Currently, the average air temperature and air humidity in the Davao Region are 26.75 °C and 85.18%, respectively. A unit increase in air temperature from the current average value reduces farm net revenue by PhP 116,420.50 per hectare, while a unit increase in air humidity raises farm net income by PhP 8,168.00 per hectare. This study recommends further educating people, particularly rice farmers, on mitigating the effects of changing weather conditions. Strategies and policies are crucial in order for farmers to adapt to these changing conditions

Measuring the Economic Impact of Weather Determinants on Aquaculture in the Davao Region, Philippines

Aquaculture is an important determinant of economic growth in the Davao Region in the Southern Philippines because it contributes towards increasing employment and agribusiness development opportunities. However, the industry faces various issues that affect farm productivity. One of these challenges pertains to welfare effects emanating from changing patterns of weather variables. Hence, this research aimed to measure the impact of weather determinants on aquaculture production’s net income based on surveyed farms in the Davao Region. Based on the Ricardian approach, our econometric model specifies the dependent variable as net income (PhP/ha) and this is a function of weather variables such as precipitation, humidity, and agro-climatic and socio-demographic variables. From the results, weather and agro-climatic variability have statistically significant effects on aquaculture’s net income. More specifically, a unit increase in the standard deviation of rain value increases net income by PhP12,730. However, an increase in one standard deviation of average air humidity decreased net income by approximately PhP2,940. Finally, unit increases in the standard deviation of soil moisture and soil temperature translate to increases in net income by approximately PhP16,150 and PhP16,170. Thus, given the results, strategies that would enable aquaculture farmers to mitigate and adapt to changing weather conditions should be implemented. Also, weather stations should be updated and upgraded in order to provide accurate readings and forecasts so that aquaculture farmers’ decision making will be improved with regards to their farm practices

Cytological effects of oregano, Coleus amboinicus lour (Dicotyldonae : Lamiales) leaf extract on human leukocyte chromosomes cultured in vitro

LIMPOCO„ANNA GUIA 0. University of the Philippines Los Banos, BS Biology, MARCH 1997. Cytological Effects of oregano, Coleus amboinicus Lour (Dicotyldonae:Lamiales) Leaf Extract on Human Leukocyte Chromosomes Cultured in vitro. Adviser Ma. Genaleen Q. Diaz Leukocytes from two male volunteers were separately treated with 1,3, and 5 percent leaf extract of oregano, Coleus amboinicus Lour. Increased concentrations of leaf extract resulted to a decrease in mitotic index. Statistical analysis showed significant differences among the mitotic indices of the different treatments at 59,6 level of significance (13% in the control is reduced to 6.3%). The percentage of normal cells in the control, 1%, 3%, and 5% oregano leaf extract concentrations are not significantly different from one another. But, the percentage of cells with aberrations treated with 1%, 3%, and 5% oregano leaf extract concentrations is significantly different from the control. Statistical analysis of the frequencies of aberrations showed no significant differences among the treatments for gaps, significant difference at high concentration (5%) for loose sister chromatid pairs. But for breaks significant difference among treatments were observed. In all the treatments, breaks had the highest frequency followed by gaps and lastly loose sister chromatid pairs. Results showed that the oregano leaf extract is a probable chromosome breaking agent

Linear mixed modelling of federated data when only the mean, covariance, and sample size are available

In medical research, individual-level patient data provide invaluable information, but the patients' right to confidentiality remains of utmost priority. This poses a huge challenge when estimating statistical models such as linear mixed models, which is an extension of linear regression models that can account for potential heterogeneity whenever data come from different data providers. Federated learning algorithms tackle this hurdle by estimating parameters without retrieving individual-level data. Instead, iterative communication of parameter estimate updates between the data providers and analyst is required. In this paper, we propose an alternative framework to federated learning algorithms for fitting linear mixed models. Specifically, our approach only requires the mean, covariance, and sample size of multiple covariates from different data providers once. Using the principle of statistical sufficiency within the framework of likelihood as theoretical support, this proposed framework achieves estimates identical to those derived from actual individual-level data. We demonstrate this approach through real data on 15 068 patient records from 70 clinics at the Children's Hospital of Pennsylvania (CHOP). Assuming that each clinic only shares summary statistics once, we model the COVID-19 PCR test cycle threshold as a function of patient information. Simplicity, communication efficiency, and wider scope of implementation in any statistical software distinguish our approach from existing strategies in the literature

Linear mixed modeling of federated data when only the mean, covariance, and sample size are available

Abstract: In medical research, individual-level patient data provide invaluable information, but the patients' right to confidentiality remains of utmost priority. This poses a huge challenge when estimating statistical models such as a linear mixed model, which is an extension of linear regression models that can account for potential heterogeneity whenever data come from different data providers. Federated learning tackles this hurdle by estimating parameters without retrieving individual-level data. Instead, iterative communication of parameter estimate updates between the data providers and analysts is required. In this article, we propose an alternative framework to federated learning for fitting linear mixed models. Specifically, our approach only requires the mean, covariance, and sample size of multiple covariates from different data providers once. Using the principle of statistical sufficiency within the likelihood framework as theoretical support, this proposed strategy achieves estimates identical to those derived from actual individual-level data. We demonstrate this approach through real data on 15 068 patient records from 70 clinics at the Children's Hospital of Pennsylvania. Assuming that each clinic only shares summary statistics once, we model the COVID-19 polymerase chain reaction test cycle threshold as a function of patient information. Simplicity, communication efficiency, generalisability, and wider scope of implementation in any statistical software distinguish our approach from existing strategies in the literature

Detection of Nitrogen in Layer-by-Layer Polymeric Films by Energy Dispersive X-Ray Spectroscopy

Scanning electron microscopy - energy dispersive X-ray spectrometry (SEM-EDS) is an elemental analysis technique widely used in various fields to identify any element in the periodic table except H, He and Li. It can be a quick way to assess the response of sensing films before deposition on sensing devices. Sensing films are usually organic thin films, but quantitative analysis of light elements and thin films is not recommended for SEM-EDS due to its limitations. In this study, SEM-EDS analysis of nitrogen in layer-by-layer polymeric thin film was optimized. The films were analyzed containing nitrogen in the form of nitrate counterions or as part of the repeat unit of the polymer. The build-up of the layer was verified by thickness measurement using atomic force microscopy. The results show that the limit for nitrogen concentration detection using nitrates was 2% by mass. Below this concentration, nitrogen content had no quantifiable response in either calculated nitrogen concentration by standardless correction methods or intensity of N Kα X-ray line. However, by adding nitrate ions to a film that already contains nitrogen in its structure the concentration was raised to 13.75%. In the range of 9.63 to 13.75%, a nonlinear response was observed using calculated nitrogen concentration while the response was linear with intensity of N Kα