HRMARS is well established journal publisher based in Pakistan. It is publishing prominent journals in social sciences since 2011.

This study focuses on identifying and analyzing spending trend profiles and developing the per capita consumption models to forecast the fresh agro-food per capita consumption in Malaysia. Previous published works have looked at statistical and machine learning methods to forecast the demand of agro-food such as ARIMA and SVM methods. However, ordinary least squares (OLS) and neural network (NN) methods have shown better results in modelling time series data. For that reason, the primary objective of this study is to model and forecast the consumption per capita (PCC) of several selected fresh agro-food commodities in Malaysia using the OLS and NN methods. The secondary objectives of the paper include investigating the performance of OLS against NNs with three different topologies, discussing the correlation between Malaysia GDP per capita and the agro-food commodity PCC, and finally assessing whether the PCC data are increasing over time or decreasing over time and whether the trend in either direction is statistically significant by using the Mann–Kendall statistical test. Based on the results of the agro-food consumption per capita (PCC) forecasting, several critical agro-food commodities are also identified in this work. The material of the study consists of the per capita consumption of thirty-three (33) agro-food items that can be categorized into rice, livestock, vegetables, fisheries, and fruits, total gross domestic product (GDP) per capita, and the total population of Malaysia between 2010 and 2017. Based on the results obtained, the neural network () model was found to produce the lowest total MSE of 17.95, for all 33 fresh agro-food investigated in this study. Several agro-food commodities have been identified as having significant positive (e.g., rice, spinach, cabbage, celery cabbage, eggplant, cucumber, poultry, lamb, squid, tuna, star fruit, jackfruit, durian, sweet corn, and coconut) or negative (e.g., pork, mackerel, papaya, guava, mangosteen, pineapple, banana, rambutan, and watermelon) trends using the Mann–Kendall trend test. This study also demonstrated that the production of critical agro-food commodities (e.g., rice, chili, cabbage, celery cabbage, poultry (chicken/duck), beef, lamb, crab, mango, and coconut) should be improved to ensure self-sufficiency ratios (SSRs) of more than 100% to accommodate the increased projected consumption in Malaysia by the year 2025. This paper concludes that neural network methods produce better prediction, and future works include forecasting agro-food demand based on other independent variables such as weather conditions, disease outbreak, and stock market trends. There is a need to explore further the capability of ensemble models or hybrid models based on deep learning methods using multi-source data, as these have been shown to improve the performance of the base model. With these ensemble models combined with multi-source data, a more comprehensive analysis of the PCC can be obtained.

The increased fuelwood demand and commercialization activities in Nigeria over the years have incurred various sustainability issues and concerns along the fuelwood value chain. This study employed value chain analysis to examine the sustainability of the fuelwood value chain in Yobe, Nigeria, in relation to economic, environmental, and social aspects. From the analysis, it was found that fuelwood business activities were conducted by registered licensed actors (formally) and informally by non-licensed actors who do not register with the government agency that controls forest-related activities. Profit was not equally distributed along the value chain, whereby 65% of the total profits were reaped by the transporters. Natural forests were the main source of fuelwood, with 18 tree species being preferred and commonly traded by both formal and informal chain actors. Women’s participation in the formal fuelwood value chain was limited and mainly concentrated in the informal chain. The findings of this study can inform policymakers to enhance the sustainability of fuelwood production and trade along the value chain, especially in terms of profit sharing, exploitation of forest resources, and gender equality.

As a staple food and a key component of livestock feed, the growing demand for maize in Indonesia has spurred the expansion of hybrid maize cultivation. However, despite advancements in seed technology and government initiatives to boost maize production, farmers in rural areas continue to face obstacles in accessing high-quality seeds. This study explores the influence of the marketing mix—encompassing product, price, promotion, and distribution—alongside personal factors on farmers’ purchasing decisions for hybrid maize seeds in Soppeng District. Utilizing structural equation modeling (SEM) and survey data from 100 respondents, the findings indicate that product quality and price are the most critical determinants, with farmers prioritizing seed performance and affordability. Distribution also plays a vital role in rural areas, ensuring that farmers can readily access high-quality seeds. At the same time, personal factors such as farming experience and income significantly shape purchasing behavior. Notably, promotional efforts appear to have a limited impact, suggesting that traditional marketing approaches may not be the most effective in this context. Seed companies should focus on product development, refine pricing strategies, and strengthen distribution networks to enhance market penetration. In parallel, policymakers can facilitate access to agricultural credit, invest in rural infrastructure, and promote farmer education programs to improve purchasing power and awareness. Ultimately, adapting marketing strategies to align with local economic and cultural conditions can drive greater adoption of hybrid seeds, boost agricultural productivity, and contribute to the long-term sustainability of rural farming communities.

Global climate change puts pressure on agricultural systems, which in turn exacerbate global warming. Climate-smart agriculture (CSA) aims to address the dual challenges by enhancing productivity, resilience, and mitigation. However, existing research remains largely isolated within either natural or social sciences, limiting its potential to address complex agricultural issues. This review bridges the gap with a bibliometric analysis of 997 Web of Science documents (2013–2023) using CiteSpace and VOSviewer. It aims to reveal cross-disciplinary challenges and opportunities by analyzing keywords clusters and citation bursts, based on publication trends, categories, journals, authors, affiliations, and geographic distributions. The findings include: (1) From horizontal keywords clusters: CSA adoption in Southern Africa cluster is hindered by multiple barriers, while inclusive and collaborative approach offers opportunities. No-tillage cluster faces inconsistent yield impacts. Climate adaptation struggles with resource, governance, and continuity challenges while pro-poor economic integration offers opportunities. Greenhouse gas cluster presents contradictory findings with opportunities through studying in mechanisms driving GHG fluxes, and the challenges and opportunities in heat stress cluster are in balancing temperature extremes. (2) From temporal keywords citation bursts: early research focus on policy integration and agricultural system dynamics with challenges in cost-benefit uncertainties and policy scalability, while later on, incorporating women into CSA design provides key opportunities for inclusive development. In the latest stage, methane emissions from paddy fields have become a major issue. These insights lay a foundation for addressing the intertwined challenges of climate change and agricultural sustainability. Graphical abstract

The focus of this study was to classify actual buying behavior of edible bird’s nest (EBN) products into relatively homogenous groups for the purpose of market segmentation. Determining the profile of actual customers could provide direct characterization of products that should be displayed on the shelf. The data were collected by quota sampling with 312 buyers participated in the survey. Three types of cluster models (K-means, Kohonen, and two-step cluster) were estimated and compared with in order to identify an optimum number of clusters and capture group with a similar pattern. Foremost, a two-step method was chosen for analysis as it provides better average silhouette and well-defined of group segments than the other two cluster methods. The results provide specific characterization of buyers that will be useful to build a target list of potential customers and develop new offerings to meet the new needs of customers for EBN products in Malaysia.