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This paper aims at providing the evidence about how carbon sequestration in terrestrial ecosystems could contribute to the decrease of atmospheric CO₂ rates through the adoption of appropriate cropping systems such as agroforestry.

Stratified randomly selected plots were used to collect data on tree diameter at breast height (DBH). Composite soil samples were collected from three soil depths for soil carbon analysis. Above ground biomass estimation was made using an allometric equation. The spectral signature of each plot was extracted to study the statistical relationship between carbon stock and selected vegetation indices.

There was a significant difference in vegetation and soil carbon stocks among the different land use/land cover types (P < 0.05). The potential carbon stock was highest in the vegetation found in sparsely cultivated land (13.13 ± 1.84 tons ha⁻¹) and in soil in bushland (19.21 ± 3.79 tons ha⁻¹). Carbon sequestration potential of the study area significantly increased (+127174.5 tons CO₂e) as a result of conversion of intensively cultivated agricultural lands to agroforestry systems. The amount of sequestered carbon was found to be dependent on species diversity, tree density and tree size. The vegetation indices had a better correlation with soil and total carbon.

The paper has addressed an important aspect in curbing greenhouse gases in integrated land systems. The paper brings a new empirical insight of carbon sequestration potentials of agroforestry systems with a focus on drylands.

The purpose of this paper is to introduce an approach in measuring the shielding gas flow within laser powder bed fusion (L-PBF) machines under near-process conditions (regarding oxygen content and shielding gas flow).

The measurements are made sequentially using a hot-wire anemometer. After a short introduction into the measurement technique, the system which places the measurement probe within the machine is described. Finally, the measured shielding gas flow of a commercial L-PBF machine is presented.

An approach to measure the shielding gas flow within SLM machines has been developed and successfully tested. The use of a thermal anemometer along with an automated probe-placement system enables the space-resolved measurement of the flow speed and its turbulence.

The used single-normal (SN) hot-wire anemometer does not provide the flow vectors’ orientation. Using a probe with two or three hot-films and an improved placement system will provide more information about the flow and less disturbance to it.

A measurement system which allows the measurement of the shielding gas flow within commercial L-PBF machines is presented. This enables the correlation of the shielding gas flow with the resulting parts’ quality.

This paper aims to propose a hybrid-forecasting model for long-term tourism demand forecasting. As such, it attempts to model the tourism demand in the Philippines, which is a relatively underrepresented area in the literature, despite its tourism sector’s growing economic progress.

A hybrid support vector regression (SVR) – seasonal autoregressive integrated moving averages (SARIMA) model is proposed to model the seasonal, linear and nonlinear components of the tourism demand in a destination country. The paper further proposes the use of multiple criteria decision-making (MCDM) approaches in selecting the best forecasting model among a set of considered models. As such, a preference ranking organization method for enrichment of evaluations (PROMETHEE) II is used to rank the considered forecasting models.

The proposed hybrid SVR-SARIMA model is the best performing model among a set of considered models in this paper using performance criteria that evaluate the errors of magnitude, directionality and trend change, of a forecasting model. Moreover, the use of the MCDM approach is found to be a relevant and prospective approach in selecting the best forecasting model among a set of models.

The novelty of this paper lies in several aspects. First, this paper pioneers the demonstration of the SVR-SARIMA model’s capability in forecasting long-term tourism demand. Second, this paper is the first to have proposed and demonstrated the use of an MCDM approach for performing model selection in forecasting. Finally, this paper is one of the very few papers to provide lenses on the current status of Philippine tourism demand.

This paper aims to demonstrate methods that sustainability-conscious brands can use to include their primary producers in the measurement and reporting of the environment and sustainability performance of their supply chains. It explores three questions: How can farm businesses provide information required in sustainability reporting? What are the challenges and opportunities experienced in preparing and presenting the information? What future research and policy instruments might be needed to resolve these issues.

This study identifies and describes methods to provide the farm-level information needed for environmental performance and sustainability reporting frameworks. It demonstrates them by compiling natural capital accounts and environmental performance information for two wool producers in the grassy woodland biome of Eastern Australia; the contrasting history and management of these producers would be expected to result in different environmental performances.

The authors demonstrated an approach to NC accounting that is suitable for including primary producers in environmental performance reporting of supply chains and that can communicate whether individual producers are sustaining, improving or degrading their NC. Measurements suitable for informing farm management and for the estimation of supply chain performance can simultaneously produce information useful for aggregation to regional and national assessments.

The methods used should assist sustainability-conscious supply chains to more accurately assess the environmental performance of their primary producers and to use these assessments in selective sourcing strategies to improve supply chain performance. Empirical measures of environmental performance and natural capital have the potential to enable evaluation of the effectiveness of sustainability accounting frameworks in inducing businesses to reduce their environmental impacts and improve the condition of the natural capital they depend on.

Two significant social implications exist for the inclusion of primary producers in the sustainability and environmental performance reporting of supply chains. Firstly, it presently takes considerable time and expense for producers to prepare this information. Governments and members of the supply chain should acknowledge the value of this information to their organisations and consider sharing some of the cost of its preparation with primary producers. Secondly, the “additionality” requirement commonly present in existing frameworks may perversely exclude already high-performing producers from being recognised. The methods proposed in this paper provide a way to resolve this.

To the best of the authors’ knowledge, this research is the first to describe detailed methods of collecting data for natural capital accounting and environmental performance reporting for individual farms and the first to compile the information and present it in a manner coherent with the Kering EP&L and the UN SEEA EA. The authors believe that this will make a significant contribution to the development of fair and standardised ways of measuring individual farm performance and the performance of food, beverage and apparel supply chains.

Present a method to impute missing data from a chaotic time series, in this case lightning prediction data, and then use that completed dataset to create lightning prediction forecasts.

Using the technique of spatiotemporal kriging to estimate data that is autocorrelated but in space and time. Using the estimated data in an imputation methodology completes a dataset used in lightning prediction.

The techniques provided prove robust to the chaotic nature of the data, and the resulting time series displays evidence of smoothing while also preserving the signal of interest for lightning prediction.

The research is limited to the data collected in support of weather prediction work through the 45th Weather Squadron of the United States Air Force.

These methods are important due to the increasing reliance on sensor systems. These systems often provide incomplete and chaotic data, which must be used despite collection limitations. This work establishes a viable data imputation methodology.

Improved lightning prediction, as with any improved prediction methods for natural weather events, can save lives and resources due to timely, cautious behaviors as a result of the predictions.

Based on the authors’ knowledge, this is a novel application of these imputation methods and the forecasting methods.