Current issue No 4/2024, December | Agris on-line Papers in Economics and Informatics

Digital Marketing Strategy Development for Recovery Ecotourism Visit After COVID-19 Pandemic: A Comparison Study on BJBR and Kampung Blekok Mangrove Ecotourism, Indonesia

Zainal Abidin Virda Faradila Rahmad Siti Ngayesah Ab Hamid Asyifa Anandya Pudji Purwanti Dwi Sofiati Mentari Puspa Wardani Supriadi Supriyadi DOI: 10.7160/aol.2024.160401
No 4/2024, December
pp. 3 - 17

ABSTRACT

Ecotourism is a form of tourism that can help overcome the problem of low welfare of local communities. The digital cultural transformation that occurred in the era of revolution 4.0, especially during the COVID-19 pandemic, was able to change the entire cycle of the tourism ecosystem. Besides, several ecotourism experiences have experienced a significant decline in tourist visits, including Bee Jay Bakau Resort and Kampung Blekok ecotourism. This research aims to develop a digital marketing strategy for ecotourism at Bee Jay Bakau Resort and Kampung Blekok to restore visitation levels after the COVID-19 pandemic. This research uses a qualitative approach with SWOT analysis and is quantitatively calculated using the Quantitative Strategic Planning Matrix (QSPM). Data were collected through interviews with ecotourism managers including marketing employees, and HRD managers; visitors, local communities, Tourism Awareness Groups, and the Environmental Service (DLH). Also, direct observation of the ecotourism conditions studied, documentation, and literature studies. The research results show that the strengths and opportunities of BJBR and Kampung Blekok are greater than the weaknesses and threats, so the strategy formulated is aggressive (growth-oriented strategy). The strategic priority lies in optimizing the use of information technology and social media as promotional media, especially the frequency of promotions. The strategy used is none other than to increase the value of ecotourism as a form of growth so that it can compete with other ecotourism in returning the level of tourist visits after the COVID-19 pandemic.

KEYWORDS

SWOT analysis, QSPM, social media, mangrove ecotourism, digital marketing.

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Impact of Livelihood Diversification on the Economic Performance of Rural Households in Nasarawa State, Nigeria

Roseline Ojoromiwa Anyachukwudinarum Waheed Mobolaji Ashagidigbi Jimoh Atanda Afolabi Olumide Akinrinola DOI: 10.7160/aol.2024.160402
No 4/2024, December
pp. 19 - 32

ABSTRACT

This study examined the impact of livelihood diversification on the economic performance of rural households in Nasarawa state, Nigeria. Multistage sampling procedure was used to select 390 respondents. Endogenous switching regression model was employed to carry out the impact analysis of diversified agricultural and non-agricultural activities on rural households’ economic performance of which income, poverty gap, and severity were indicators. The empirical findings revealed that rural household’s age, gender, level of education, access to market, membership of cooperatives, access to public transport and rural-urban seasonal migration significantly influenced income, while gender, level of education, household size, access to farmland, access to market, membership of cooperative and entrepreneurial skills significantly influenced rural households’ poverty gap and severity. Improved income of rural households in the study area promotes agricultural activities which is the mainstay of their economy. In conclusion, livelihood diversification improves living standard and reduces poverty for rural families and their communities.

KEYWORDS

Livelihood diversification, economic performance, rural households.

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Image-Based Solutions for Precision Food Loss Evaluation

Adrián Csordás DOI: 10.7160/aol.2024.160403
No 4/2024, December
pp. 33 - 43

ABSTRACT

The high amount of food loss and waste significantly challenges the sustainable development. The agriculture needs rapid and fundamental transformation to enhance its efficient and sustainabile operation. However, to measure precisely the effect of the new policies and practices is also difficult. The present study analyses the applied methods’ data sources, as one of the key factors regarding the effective estimation of food loss and waste. By conducting a systematic literature review using the PRISMA approach, a lack of scientific focus was found related to the new data collection methods. Based on the selected articles reasonably slight amount joined the application of image processing to food loss estimation related purposes. The reviewed studies principally used the image-based solutions for the prevention and reduction of on-farm food loss. This recognition lighted up the application of image processing in agriculture, but only the thematic map analysis revealed the privileged status of ”plant disease detection” within the studied area. The results suggest the possibility of applying image-based data sources to quantify food loss. Even though the limitations of agricultural image processing, the application of new data sources, and methods could considerably improve the accuracy of food loss and waste quantification in addition to the operation on farm level in short term.

KEYWORDS

Computer vision, sustainable development, data collection, smart farming, innovation, digitalisation.

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Renewable Energy Intentions in Indonesian Agriproduct Purchasing: Exploring Product Quality, Customer Orientation, Perceived Environmental Knowledge, and Farmers’ Knowledge with a Moderation Effect

Evi Feronika Elbaar Masliani DOI: 10.7160/aol.2024.160404
No 4/2024, December
pp. 45 - 67

ABSTRACT

The smallholder farmers come across various constraints in cultivation of agriproducts and face number of challenges in marketing the agrifoods in Indonesia that assists in sustaining the market position. This research effort entails the product quality, customer orientation, farmers’ knowledge and perceived environment knowledge with moderation role of intention to use renewable energy to explain the purchase decisions of agriproducts locally-produced in Indonesia. The study was quantitative in nature and sample of 308 respondents of customers of agriproducts in different regions of Indonesia was collected that depicted interesting results. The results show that hypothesis H1, H2, H4 and H5 reported statistically significant, but hypothesis H3 was rejected. The moderation effect of intention to use renewable energy reported between product quality, customer orientation, perceived environment knowledge and purchase decision, and no moderation effect was reported between farmers’ knowledge and purchase decisions. The study suggested to devise the effective marketing initiatives for agriproducts specifically to ensure the quality, customers’ feedback, and needs to focus on enhancing the knowledge of farmers towards adoption of innovative initiatives for implementation of renewable energy.

KEYWORDS

Renewable energy, product quality, customer orientation, farmers’ knowledge, innovation and purchase decision.

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Does Monetary Policy Stabilise Food Inflation in Hungary?

Imre Fertő Tibor Bareith DOI: 10.7160/aol.2024.160405
No 4/2024, December
pp. 69 - 77

ABSTRACT

This study examines the relationship between monetary policy and food price inflation in Hungary from January 2007 to December 2023 using the Nonlinear Autoregressive Distributed Lag (NARDL) model. Our analysis reveals that although the short-term impact of monetary policy on food prices is minimal, there is a notable long-term effect where implementing tighter monetary measures increases food price inflation over time. Policymakers must take a nuanced approach when dealing with food price shocks, considering both monetary and fiscal interventions. Our research highlights the significance of combining monetary policy actions with specific fiscal strategies and structural changes in the agriculture to reduce the negative effects of food inflation and protect the well-being of vulnarable populations.

KEYWORDS

Food prices, inflation, monetary policy, Nonlinear ARDL, asymmetry.

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Application of Quality Management System in the Research Process: A Case Study for Plant Phenotyping Research

Alexander Galba Eva Kánská Vojtěch Mikeš Jiří Vaněk Jan Jarolímek DOI: 10.7160/aol.2024.160406
No 4/2024, December
pp. 79 - 86

ABSTRACT

Phenomics research, driven by advancements in imaging and image processing, enables high-throughput measurements of plant traits, providing insights into growth, tissue development, and biochemical states. However, data accuracy is critical to reliable outcomes, especially in complex methods like 3D reconstruction and hyperspectral imaging. This study demonstrates the role of Quality Management Systems (QMS) in enhancing the research process in plant phenotyping. The study emphasizes the importance of a robust data quality assurance pipeline, focusing on error identification and improving data labeling processes through semi-automation. Root Cause Analysis (RCA) was employed to address discrepancies in annotated datasets and identify critical issues, such as misalignment in experimental protocols and operational errors, including the misplacement of irrigation hoses during data collection. Corrective actions, such as photo documentation and procedural revisions, significantly improved data quality. Additionally, algorithmic support streamlined the annotation process, increasing efficiency and data reliability. This integrated approach underscores the necessity of quality control in research, especially for geographically distributed teams working under variable conditions, and highlights the broader applicability of QMS in optimizing research outputs.

KEYWORDS

Quality management system, data quality, plant phenotyping, research process, root cause analysis, data labeling process.

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Hybrid Approaches for Irrigation Optimization Based on Weather Forecast: a Study on Reference Evapotranspiration Prediction in Beni Mellal

Hamza Jdi Khalid El Moutaouakil Noureddine Falih Karim Doumi DOI: 10.7160/aol.2024.160407
No 4/2024, December
pp. 87 - 97

ABSTRACT

Accurate prediction of Reference Evapotranspiration (ET0) is vital for optimizing irrigation, thereby facilitating efficient water management and agricultural planning. This study compares three distinct methods for predicting ET0 using the FAO Penman-Monteith (FAO-PM), leveraging daily weather data collected over a span of 38 years, from 1984 to 2022. The first approach involves predicting ET0 directly based on actual ET0 values, while the second hybrid approach uses Recurrent Neural Networks (RNN) to predict Net Radiation, Temperature, Wind speed, and Dew Point Temperature. These predicted values are then utilized in the FAO-PM equation to calculate ET0 (RNN-FAO-PM). The third approach is another hybrid method that combines RNN for predicting the weather parameters, followed by the application of a well-trained Random Forest (RF) model that uses the predicted weather parameters as features to predict ET0 (RNN-RF). The performance of each method is evaluated using various metrics, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R2) values for both training and testing datasets. The results of this study reveal that the hybrid approaches demonstrate comparable performance for long-term prediction of ET0 of the period Spanning from 2020 to 2022 (3 years). These hybrid approaches slightly outperform the RNN method when applied solely on the ET0 time series. This finding contributes to the research in the area of water resource management, specifically in the context of irrigation optimization. It provides valuable insights that can inform agricultural decision-making in the Beni Mellal region, enabling more efficient and effective use of water resources for irrigation purposes.

KEYWORDS

Reference evapotranspiration forecast, irrigation optimization, deep learning, weather forecast.

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Risk Optimality and, Subscription and Subscription Intensity of Weather Index Insurance: Application of T-MOTAD and Negative Binomial Double Hurdle Model

Augustine Koufie Henry de-Graft Acquah Samuel Kwesi Ndzebah Dadzie DOI: 10.7160/aol.2024.160408
No 4/2024, December
pp. 99 - 108

ABSTRACT

This study sought to analyze risk efficient income and examine its effect on subscription and subscription intensity of weather index insurance (WII). Data was obtained from food crop farmers who were randomly sampled from the Upper West Region; and further, the T-MOTAD and Negative binomial hurdle model were estimated to arrive at the study findings. The study fills methodological gap by estimating the negative binomial hurdle and Zero-inflated negative binomial models as advancement of the Poisson regression model. Further, AIC, BIC, Log-likelihood, rootogram as well as the Vuong test were employed to ascertain the empirical superiorities of the estimated models to the data set. Results show that the risk efficient plans’ incomes of GHS9403.42 ($854.08) and GHS9835.10 ($893.29) are higher than that of the income of GHS7412.97 ($673.29) from the farmer’s optimal plans. Also, about two-third of farmers have subscribed to the weather index insurance in the study area; for intensity of subscription, 0.39ha on average out of every hectare of land cultivated is covered with the weather index insurance. The negative binomial hurdle model showed empirical superiority for the fit of the data set. The farmer’s decision to subscribe and their subscription intensity of the weather index insurance are significantly influence by age, sex, farm size, experience, education, insurance prompt payment, extension service, credit access and risk efficient income. It is recommended that farmers should adopt the risk efficient plan to earn higher income to be able to afford WII premium, as this will increase their subscription intensity.

KEYWORDS

Risk optimality, Subscription intensity, weather index insurance, T-MOTAD, Negative binomial double hurdle model, food crops, Upper West region of Ghana.

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Challenges and Trends in Agricultural Employment: The Case of Hungary

Zalán Márk Maró Áron József Borda Jeremiás Máté Balogh DOI: 10.7160/aol.2024.160409
No 4/2024, December
pp. 109 - 128

ABSTRACT

The agriculture and food industry faces many challenges, including a shortage of skilled and seasonal workers, low productivity, and a demographic shift towards an ageing agricultural population. The agricultural productivity and efficiency of Central and Eastern European countries, including Hungary, are relatively low compared to that of Western Europe. This study explores the complex landscape of agricultural employment in Hungary by analysing its situation and challenges that are in line with international standards. Using national- and company-level data, the study applies an analytical framework comprising descriptive statistics and a non-parametric Kruskal-Wallis test to explore patterns and trends in the sector’s performance. In Hungary, more than 70% of farm managers are over 45 years old. Furthermore, despite the increase in the number of people with an agricultural education, around 150,000 farms still rely on experience-based management. We identify statistically verifiable and notable differences in the investigated indicators (sales revenue in proportion to number of employees, wage efficiency, personnel expenses per capita, assets value per capita) according to the founding period (pre-1989, 1989-2004, post-2004). The study concludes by arguing for generational change, better agricultural education and emphasis on the concentration of skills and capital within families as a sustainable solution, thereby addressing the complex challenges of the agricultural labour market and creating flexibility in the sector by attracting younger and educated people.

KEYWORDS

Agricultural employment, rural employment labour, migrant labour, employment problems, labour productivity, efficiency, Kruskal-Wallis tests, Hungary.

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Effect of Farm Size on the Structure of Crop Production

Anna Žovincová DOI: 10.7160/aol.2024.160410
No 4/2024, December
pp. 129 - 140

ABSTRACT

The study deals with the statistical analysis of crop production structure concerning farm size. Given the large-scale nature of Czech agriculture and the deepening structural imbalance, this is a topical issue. Firstly, the trends in the area of sown crops between 1993–2023 and their expected development between 2024–2025 were assessed. Subsequently, the weighted data of conventional farms focused on field crop production operating in the Czech Republic were analysed using the Kruskal–Wallis test. With the exception of peas, the share of crops grown depends on the size of the farm. There are statistically significant differences, mainly between small and very large farms and between small and large farms. At the same time, it is clear that in the long term, there has been a significant decline in the area sown to potatoes, rye, barley, and forage, which are crops that account for a higher proportion of the harvested area structure on small holdings.

KEYWORDS

Agricultural policy, Czech Republic, field crops, nonparametric methods, time series.

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