Agricultural Employment Research Articles

Do climate change, access to electricity and renewable energy consumption matter in aquaculture production in Africa?

AbstractThe contribution of aquaculture to economic development cannot be overemphasized. Several factors affect aquaculture production. However, few studies document the impact of climate change, electricity and other macroeconomic covariates on aquaculture production in Africa. The present research's objective is to examine the impact of rainfall, temperature, access to electricity, per capita national income, renewable energy consumption, employment in agriculture, and carbon emissions on aquaculture production in 32 African countries. We rely on panel data using rigorous estimation techniques involving the fixed effects and method of moment quantile regression approaches. Our fixed effects model result indicates that an increase in temperature reduces aquaculture production, while renewable energy consumption, net per capita income, employment in agriculture, and carbon emissions increase aquaculture production. Similarly, the 75th and 90th quantiles of the method of moment quantile regression results indicated that both rainfall and temperature decrease aquaculture production, while renewable energy consumption, employment in agriculture, and carbon emissions increase aquaculture production. The study provides useful insight into the climate, economic, and technological factors that might increase or decrease aquaculture production in Africa and similar contexts. Overall, our findings are important for improving aquaculture production in Africa, particularly with the current low production relative to the global north. The results have implications on the current and future climate action that calls for increased reliance on renewable energy and targeting improvements in agricultural employment, and net national income. Additional efforts are needed to achieve carbon neutrality while ensuring sustainable aquaculture production in Africa.

Digital Technology: A Game Changer in Vegetable Cultivation

Vegetables are important constituents of Indian agriculture and nutritional security due to their short duration, high yielding capacity, nutritional richness, economic viability and ability to generate on farm and off farm employment. Increasing per capita income, health consciousness, urbanisation, increasing working women, shifting of farmers to high value vegetables are also important ingredients for fuelling vegetable growth in India. But we are still not getting the required /capita vegetables/day.so the answer to increasing the production of various vegetables employing various digital technologies is required. Growing high yielding varieties/hybrids and applying new production methods can significantly increase yield in most vegetables. The application of digital technologies to integrate agricultural output from the paddock to the customer is known as digital agriculture. These technologies can give the agricultural business the tools and data it needs to make better decisions and increase productivity). Information and communication technology (ICTs) and Artificial intelligence (AI) are the two key technologies used in digital agriculture. Information and communication technology (ICT) can be defined as a collection of technologies that aid or support the storage, processing, or distribution of data/information.Despite a big, well-educated, well-trained, and well-organized agricultural extension workforce, over 60% of farmers in the country remain unreached, with no extension organisation serving them. Radio and television are the primary sources of information. e- Extension, Agrisnet, Digital green, e- Sagu, Agmarknet, iKisan, Village Knowledge Centers, Kisan Call Centers, SMS Portal/mKisan portal are some of the e-Agriculture efforts in India. Furthermore, information and communication technology (ICT) aid in the timely completion of decisions, as well as the discovery of the best solutions and efficient systems for water management and irrigation in order to maximise production Artificial intelligence is assisting several agricultural sectors in increasing output and efficiency. In every industry, AI technologies are help in overcoming traditional hurdles. AI intervention in agriculture is assisting farmers in increasing their farming efficiency while reducing harmful environmental impacts. The agriculture business has embraced AI with open arms in order to improve overall results.AI is changing the way food is produced, resulting in a 20% reduction in agriculture sector emissions. Incorporating AI technology into agriculture, aids in the control and management of any unwelcome natural occurrence. It consists of robotic process automation, a machine learning platform, and sensors, among other things. Harvesting, weed management, pest and disease monitoring, irrigation control, and predictive agricultural analytics are all possible with these. Artificial intelligence and information and communication technologies (ICT) have the potential to be a technological revolution and a boom in agriculture, helping to feed the world's growing population.

Harvesting sustainability: assessing Industry 4.0 in agri-food supply chains

PurposeThe agri-food industry is experiencing a revolutionary shift due to the introduction of Industry 4.0 technologies to improve efficiency, transparency and sustainability. The importance of agri-food supply chains (AFSC) in promoting sustainability is expanding as the globe struggles with issues including resource scarcity, climate change and population growth. In order to better understand how Industry 4.0 might improve sustainability in a world that is changing quickly, this work aims to focus on identifying various sustainability assessment factors influencing AFSC to increase overall sustainability, minimize resource consumption, cut waste and streamline operations.Design/methodology/approachImportant sustainability assessment factors are identified from the past academic literature and are then validated using the fuzzy-Delphi method. A method called decision-making trial and evaluation laboratory (DEMATEL) is used to examine and analyze structural models with complex causal linkages. The results are then validated using sensitivity analysis.FindingsThe factors that emerged as the highest ranked for evaluating the sustainability of Industry 4.0 in AFSC are market competitiveness, and knowledge and skill development, followed by resource efficiency. Industry 4.0 technologies are essential for increasing the marketability of agricultural products because of the major implications of market competitiveness. The significance of knowledge and skill development draws attention to Industry 4.0’s contribution to the promotion of chances for farmers and agricultural employees to increase their capability.Practical implicationsBy outlining the nexus between Industry 4.0 technologies and sustainability, the study presents a comprehensive framework that would be relevant for researchers, policymakers and industry stakeholders who want to leverage Industry 4.0 technology to build more sustainable AFSC in the future. The study findings can help the farmers or producers make sensible choices that adhere to sustainability standards and guarantee long-term financial viability.Originality/valueThe originality of this work lies in the identification of sustainability assessment factors especially for AFSC in the era of digitalization which has not been discussed previously.

IMPACT OF DEMOGRAPHIC FACTORS ON ECONOMIC ACTIVITY IN BULGARIA'S RURAL TERRITORIES

Bulgaria has one of the fastest rates of population decline in the EU and the world. In 2021, the value is observed to be 21.6% lower compared to the value recorded in 2001. Changes in rural areas are even more dynamic and have a negative impact on their development opportunities. This paper aims to examine and analyse the economic activity of the rural population using statistical data obtained from population censuses. The analysis is mainly focused on the interdependencies between economic activity, employment in agriculture and their implications for the rural economy. Correlation and regression analysis were applied to test the research hypotheses. The summary of the obtained results shows that the coefficient of economic activity rises until 2020, but activity is uneven and at different rates in different regions, with the coefficient being lower in villages at the expense of economic activity in cities; • The unemployment rate in Bulgaria has gone through three stages: until 2013, it was in double digits, then it dropped to 4.2 (in 2021), and in 2019, an increase began, which varied greatly by region. The unemployment rate is significantly higher in the villages, with the most significant differences in the younger age groups; • The correlation analysis revealed a weak, positive, and insignificant relationship between the rural population and the coefficient of economic activity, as well as the presence of a medium, positive, and significant relationship between the rural population and the agricultural labour force; According to the regression analysis, Annual work unit has a positive and significant influence on the population in rural areas, indicating that agricultural specialization and automation are still in their early stages in Bulgaria, and the workforce in the industry is structure-determining Demographic processes have a negative impact on rural development. A cyclical pattern emerges wherein a declining local economy and depopulation coexist and mutually reinforce one another. A decline in demographic potential and a lack of human capital may limit investment flow. For these reasons, investing in human capital is regarded as a critical means of reversing unfavourable trends in demographic structures and processes in all countries affected by accelerated aging and depopulation. This includes improving the health-care system, education and other social services, as well as raising the living standards of rural inhabitants. To achieve this goal, regional demographic policies must be developed that take into account the demographic and socioeconomic characteristics of each region. Key words: demographic processes, economic activity, rural territories. JEL: J11, J43

Damping accumulated discrete MGM(1, m) power model and its application to forecasting agricultural output value share and employment share

PurposeThe damping accumulated discrete MGM(1, m) power model is proposed for the problem of forecasting the share of agricultural output value and the share of employment in China.Design/methodology/approachIn this study, the damping accumulated discrete MGM(1, m) power model was developed based on the idea of discrete modelling by introducing a damping accumulated generating operator and power index. The new model can better identify the non-linear characteristics existing between different factors in the multivariate system and can accurately describe and forecast the trend of changes between data series and each of them.FindingsThe validity and rationality of the new model are verified through numerical experiment. It is forecasted that in 2023, the share of agricultural output value in China will be 7.14% and the share of agricultural employment will be 21.98%, with an overall decreasing trend.Practical implicationsThe simultaneous decline in the share of agricultural output value and the share of employment is a common feature of countries that have achieved agricultural modernisation. Accurate forecasts of the share of agricultural output value and the share of employment can provide an important scientific basis for formulating appropriate agricultural development targets and policies in China.Originality/valueThe new model proposed in this study fully considers the importance of new information and has higher stability. The differential evolutionary algorithm was used to optimise the model parameters.

Spatial-temporal distribution pattern and driving factors of agricultural carbon sinks in Beijing-Tianjin-Hebei region from the perspective of carbon neutrality

AbstractDeveloping policies to reduce carbon emissions in agriculture is crucial for achieving the ‘dual carbon’ goal. Therefore, scientifically analysing the temporal and spatial distribution characteristics of agricultural carbon sinks and their driving factors holds paramount importance for the coordinated and integrated development of regional agriculture and the realization of sustainable development. Based on the perspective of carbon cycle in agricultural production, the measurement system of agricultural net carbon sink was established from the perspective of carbon sink/carbon source, this study conducted an analysis of the temporal and spatial variation characteristics as well as driving mechanisms of agricultural net carbon sinks. The findings are as follows: (1) The agricultural net carbon sink exhibited an increasing trend from 2009 to 2020, with favourable intensity and level. (2) Agricultural land use and livestock and poultry production constituted the primary sources of agricultural carbon emissions. Notably, agricultural carbon emissions demonstrated a decreasing trend during the study period. (3) The net agricultural carbon sink displayed local spatial aggregation, indicating significant regional differences. (4) Factors such as rural economic level, urbanization level and agricultural employment significantly promoted the net carbon sink. In contrast, the rural industrial structure, technical level and crop seeding had inhibitory effects. Therefore, it is imperative to promote the reduction of agricultural carbon emissions in the Beijing-Tianjin-Hebei region. This entails accelerating the construction of new agriculture and rural areas, facilitating industrial upgrading, promoting the development of low-carbon-sink regions into high-carbon-sink regions and actively fostering the coordinated and integrated development of regional agriculture.

Peran Sektor Pertanian Dalam Pertumbuhan Ekonomi Di Kawasan ASEAN

<p><em>ASEAN is an agricultural region that is abundant in natural products. In addition, this sector has contributed to economic growth in the ASEAN region through the availability of jobs and an increase in GDP. However, in recent years the productivity of the agricultural sector has shown a decline, which can affect economic growth. This study aims to determine the influence of the agricultural sector on economic growth in the ASEAN region. This study uses a quantitative method with panel data, namely annual data from 1997-2019 in 7 countries that are members of the ASEAN organization (Cambodia, Indonesia, Malaysia, Lao PDR, Philippines, Thailand, and Vietnam). Keywords: ASEAN, economic growth, agricultural sector. The data source for this research is annual data obtained from FAO and WDI. The analysis technique uses multiple linear regression tests using General Least Square Calculation (Cross Section SUR with Estimation Coefficient of Cross Section SUR (PSCE) with Eviews 9 software. Through this method, the result is that simultaneously all independent variables are significant in influencing the dependent variable. Or it can be explained that Agriculture Land, Employment in agriculture, and Agriculture, forestry, and fishing, value added have a significant effect on economic growth (GDP) in the ASEAN region.</em></p>

Agroeconomic Indexes and Big Data: Digital Marketing Analytics Implications for Enhanced Decision Making with Artificial Intelligence-Based Modeling

Agriculture firms face an array of struggles, most of which are financial; thus, the role of decision making is discerned as highly important. The agroeconomic indexes (AEIs) of Agriculture Employment Rate (AER), Chemical Product Price Index (CPPI), Farm Product Price Index (FPPI), and Machinery Equipment Price Index (MEPI) were selected as the basis of this study. This research aims to examine the connection between digital marketing analytics and the selected agroeconomic indexes while providing valuable insights into their decision-making process, with the utilization of AI (artificial intelligence) models. Thus, a dataset of website analytics was collected from five well-established agriculture firms, apart from the values of the referred indexes. By performing regression and correlation analyses, the index relationships with the agriculture firms’ digital marketing analytics were extracted and used for the deployment of the fuzzy cognitive mapping (FCM) and hybrid modeling (HM) processes, assisted by using artificial neural network (ANN) models. Through the above process, there is a strong connection between the agroeconomic indexes of AER, CPPI, FPPR, and MEPI and the metrics of branded traffic, social and search traffic sources, and paid and organic costs of agriculture firms. It is highlighted that agriculture firms, to better understand their sector’s employment rate and the volatility of farming, chemicals, and machine equipment prices for future investment strategies and better decision-making processes, should try to increase their investment in the preferred digital marketing analytics and AI applications.

Measuring the multiplier effect of regional tourism and its spatial distribution in Indonesia before and after the COVID-19

PurposeThe tourism multiplier effect (TME) is the total economic impact of tourism demand, representing the linkages between tourism and other businesses in an area. However, study about it is limited in Indonesia, especially at the provincial level and after the COVID-19 crisis. This study aims to estimate the TME in all provinces of Indonesia, test its differences in priority and non-priority areas before and after the COVID-19 crisis, analyze its spatial distribution and examine the determinant factor of TMEDesign/methodology/approachThis study applies an input-output model to measure the TME of all provinces in Indonesia, an independent sample t-test to examine the similarity of TME in priority and nonpriority areas, a paired sample t-test to examine the similarity of it before and after the COVID-19 crisis, and spatial analysis to check its spatial relationship.FindingsThe result shows that regional TME ranges from 1.25 to 2.05 in 2019, which changed slightly over time. The empirical result shows the TME difference before and after the COVID-19 crisis, and there is a spatial correlation in terms of TME with the hot spots are clustered in the eastern region of Indonesia, However, there was a slight change in the position of hot spots during the COVID-19 crisis. Moreover, the spatial model shows that value-added and employment in agriculture, manufacturing, trade and transportation affect the size of TME.Originality/valueThis study contributes to the academic literature by providing the first estimate of the TME at the provincial level in Indonesia, comparing the it in priority and non-priority areas before and after the COVID-19 crisis, and mapping its spatial distribution.

Circular economy and agricultural employment: a panel analysis of EU advanced and emerging economies

AbstractUsing data from 24 European advanced and emerging countries between 2000 and 2020, this paper examines the impacts of circular economy (CE) on agriculture employment (AE) and female employment patterns. Based on the availability of data, the study uses three CE indicators: generation of municipal waste (GMW) per capita, the recycling rate of municipal waste (RRMW), and patents related to recycling and secondary raw materials (PRSRM). To investigate the relationship between CE and AE, as well as CE and female AE, panel cointegration analysis and Granger causality tests are conducted. The results reveal that whereas RRMW, PRSRM and AE are in equilibrium in the long run, increasing waste lowers female attraction to the sector. The study therefore suggests that developing and expanding waste management initiatives and innovation are critical to the growth of agricultural employment and the closing of existing gender gaps. Despite concerns that rising recycling practices may negatively impact employment, our empirical results indicate that CE will create opportunities and increase employment levels. The evidence suggests that promoting CE in agriculture is economically, ecologically, and socially beneficial.

A dynamic linkage between greenhouse gas (GHG) emissions and agricultural productivity: evidence from Ethiopia

The consequences of greenhouse gas emissions are a global problem and are felt most clearly in poor countries. Every sector in Ethiopia is affected by greenhouse gas emissions, but the productivity of the agricultural sector is particularly at risk. Although climate change is a long-term phenomenon, no in-depth macro-level studies have been conducted to guide discussion in this area. Therefore, the study fills this gap and carefully examines these impacts over time from 2019 to 2022 using the Vector Auto Regressive Model. Our results show that a 1% increase in fertilizer consumption, agricultural land, nitrous oxide (N2O) emissions, rural population, and area devoted to grain production results in a 0.28, 2.09, 15.92, 5.33, and 1.31 percent increase in agricultural yield in the long-run, respectively. A negative relationship was found between agricultural employment, agricultural methane emissions (CH4), carbon dioxide emissions (CO2), and agricultural productivity with a significance level of 5%. This means that under a black box condition, a one percent increase in agricultural employment, CH4, and CO2emissions in the country in the long run will lead to a decrease in agricultural productivity by 5.82, 17.11, and 2.75 percent respectively, as we also found that all regressors except technology adoption had an elastic relationship with agricultural productivity. The short-term error correction estimates show that the coefficient of the “speed of adjustment” term for the expected productivity equation is both statistically significant and negative. The value of the coefficient term of −0.744 shows that an adjustment of 74.4% is made each year to converge the long-run equilibrium level. Therefore, Ethiopia needs to take measures that keep the economy away from sectors that produce a lot of carbon. These must be coordinated at a global level to achieve social change towards a fair and environmentally sustainable future and to increase agricultural productivity.

Tractor acquisition and agricultural performance among Nigerian farmers: Evidence from Co-integration Modeling technique

Given the envisioned significance of the agricultural sector in Nigeria, which scholars have argued has the potential to promote pro-poor growth and poverty alleviation. However, questions have been raised about using and accessing modern agricultural machinery for farm cultivation. In this regard, this study uses a co-integration modelling technique to investigate the impact of tractor acquisition on agricultural performance. Two equations were established through the normalisation processes to proxy agricultural performance: the agricultural output and employment equations. Firstly, the analysis procedure requires a preliminary test to determine the statistical properties of the series used in this study; evidence from the outcome indicates that the series are not stationarity at level form and are integrated of order one. Secondly, based on the outcome of the preliminary result, the error correction model approach to co-integration was adopted, which helps to capture the short-term dynamics and long-term equilibrium of the effect of tractor acquisition on agricultural output and employment. The findings from the agricultural output equation show that credit to the agricultural sector, land cultivated for agricultural purposes, and tractor acquisition are statistically significant contributors to agricultural output. The agricultural employment equation shows that credit to agricultural purposes, land cultivated for agriculture and rainfall have a positive long-term relationship with employment. At the same time, tractor acquisition has a negative impact on agricultural employment in the long run. In the short run, exchange rate, rainfall, and tractor acquisition positively correlate with agricultural employment. In contrast, credit for agricultural purposes and land cultivated for agriculture negatively affects employment. Based on the findings, there is a need to strengthen policies that support access to credit, promote sustainable land use practices, and enhance water management. Also, policymakers should consider implementing measures that encourage the adoption of tractor technology while simultaneously addressing potential challenges related to job displacement.

The relationship of employment in the agricultural sector to electric power consumption, government final consumption, and the democracy index in Bangladesh

Agriculture stands as the cornerstone of Bangladesh’s rural economy, sustaining not only food production, but also serving as a primary source of employment for its rural populace. Modernising this sector holds the potential to bolster its expansion, and generate further employment opportunities, thereby fostering rural socio-economic development. The purpose of the study is to examine the impact of electric power consumption, government final consumption expenditure, and the democracy index on employment in the agricultural sector in Bangladesh. Employing a dynamic ARDL model, we examined the short and long run dynamics between agricultural employment and other independent variables, utilising data spanning the period from 1991 to 2022. The findings underscore the pivotal role played by electricity in the growth of employment in agriculture. The study illuminates the nuanced interplay between various factors, revealing that government expenditure positively influences the agricultural sector, while the democratic landscape exerts a negative influence. In light of these insights, the paper advocates for strategic policy measures, emphasising the critical need for the government to prioritise the development of the electric sector, recognising it as a linchpin for sustainable agricultural growth and overall socio-economic advancement. Simultaneously, the study highlights the imperative of fostering a conducive political environment, underscoring its direct correlation with positive changes in the agricultural sector. Through these concerted efforts, Bangladesh can pave the way for a more resilient and prosperous agricultural landscape, contributing significantly to its overarching socio-economic development.

Design of a neuro-fuzzy model for agricultural employment in Colombia using fuzzy clustering

<p>High levels of poverty in rural areas constitute one of the main challenges for developing countries. Since agricultural employment is the main source of income in these areas, the design of tools that simulate and help public policymakers will be remarkably useful. This work proposes the development of a model for agricultural employment in Colombia, considering input variables such as education, contract, and income, and the output is the amount of agricultural employment. Real data measured in Colombia are used for the design and adjustment of the model. To design the fuzzy system for an agricultural employment model, the methods employed are fuzzy C-means clustering and neuro-fuzzy systems. The systems were tested with different cluster configurations, and a fuzzy system was obtained with an adequate distribution of the fuzzy sets and the respective rules that relate the sets. It was observed that as the clusters increase, the adjustment function decreases. The implementation of neuro-fuzzy systems to model agricultural employment will allow public policymakers to generate guidelines that adjust to their political agendas with a lower degree of uncertainty.</p>

Digitalisation and sustainable employment as a factor of agricultural development

In recent years, the attention of researchers has focused on sustainable development issues in labour and employment. Of particular concern are the negative trends of unsustainable employment in the agricultural sector. The study aims to conduct a comparative assessment of the sustainability of digital and non-digital forms of employment in agriculture. The empirical basis was a nationwide sociological survey of citizens (N = 3,890) conducted in June – September 2023. The results revealed a difference in employment conditions between two segments of agricultural workers: ‘digital’ and ‘non-digital’. Those working in the digital segment are more likely to be satisfied with their working conditions, have higher labour income and more flexible working hours. The results obtained allow us to formulate the hypothesis that digital employment is more sustainable than non-digital employment. It is concluded that it is viable to implement a set of measures aimed at increasing the sustainability and attractiveness of work in agriculture through the transformation of working conditions, the system of professional training, attraction and retention of personnel. Digital workers can act as drivers of current and future transformations in agricultural industry, so it is essential to promote a systematic increase in the sustainability of their employment.

California has 882,000 farmworkers to fill 413,000 jobs

California's agricultural employers hired an average of 413,000 workers between 2018 and 2021 and reported an average of 882,000 farmworkers during these years, a ratio of 2.1 workers for each job. Average agricultural employment has been relatively stable since 2000, while the number of workers reported by agricultural employers declined. Despite concerns about farm labor shortages, the data show a relatively stable pool of farmworkers, including a rising share who are brought to farms by farm labor contractors.

Изменение отраслевой структуры занятости в Казахстане: тенденции и перспективы

The subject of the study is the processes of changing the sectoral structure of employment in Kazakhstan. The purpose of the work is to analyze these changes and forecast the structure of employment by economic activity in the medium term. The relevance of the study is due to the need to understand the intensive dynamics of the sectoral structure of employment and predict its changes under the influence of many different factors. The scientific novelty consists in identifying a weak relationship between changes in the structure of employment and the economic structure in Kazakhstan, which allows forecasting employment based on extrapolation of the prevailing trends. The research methods are: dynamic series analysis, comparative analysis, deduction, modeling. In the course of the study, the author obtained the following results: a weak relationship between changes in the structure of employment by economic activity and the structure of Kazakhstan's GDP was revealed; the main trends of changes in the share of employment of the main types of economic activity are revealed; a forecast assessment of the structure of employment by economic activity in the medium term is given. The author draws conclusions about a further reduction in the aggregate share of employment in the sectors of production of goods and an increase in employment in the service sector; about the outstripping growth of employment in activities with predominantly government financing; about the lagging growth of employment in the manufacturing industry and the continued decline in employment in agriculture in Kazakhstan.

An IoT and machine learning solutions for monitoring agricultural water quality: a robust framework

All living things, comprising animals, plants, and people require water to survive. The world is covered in water, just 1 percent of it is fresh and functional. The importance and value of freshwater have increased due to population growth and rising water demands. Approximately more than 70 percent of the world's freshwater is used for agriculture. Agricultural employees are the least productive, inefficient, and heavily subsidized water users in the world. They also utilize the most water overall. Irrigation consumes a considerable amount of water. The field's water supply needs to be safeguarded. A critical stage in estimating agricultural production is crop irrigation. The global shortage of fresh water is a serious issue, and it will only get worse in the years to come. Precision agriculture and intelligent irrigation are the only solutions that will solve the aforementioned issues. Smart irrigation systems and other modern technologies must be used to improve the quantity of high-quality water used for agricultural irrigation. Such a system has the potential to be quite accurate, but it requires data about the climate and water quality of the region where it will be used. This study examines the smart irrigation system using the Internet of Things (IoT) and cloud-based architecture. The water's temperature, pH, total dissolved solids (TDS), and turbidity are all measured by this device before the data is processed in a cloud using the range of machine learning (ML) approaches. Regarding water content limits, farmers are given accurate information. Farmers can increase production and water quality by using effective irrigation techniques. ML methods comprising support vector machines (SVM), random forests (RF), linear regression, Naive Bayes, and decision trees (DT) are used to categorize pre-processed data sets. Performance metrics like accuracy, precision, recall, and f1-score are used to calculate the performance of ML algorithms.

ІНТЕГРАЦІЙНІ ПРОЦЕСИ ТА ЇХ ЗНАЧЕННЯ ДЛЯ РОЗВИТКУ МАЛИХ ФОРМ ПІДПРИЄМНИЦТВА В АГРАРНОМУ БІЗНЕСІ

The emergence and development of large integrated formations in the domestic agro-industrial complex is a natural reflection of world trends. In the conditions of fierce competition on the market of agricultural goods and food, large integrated structures, using the advantages of specialization, concentration and cooperation, are able to achieve a high level of marketability and profitability of production in a short period of time. In conditions of weak state support, agro-industrial formations became the main investors of agricultural organizations and received comprehensive support and assistance in certain regions. The rate of development of the agricultural sector in such regions shows that large agro-industrial formations contribute to the growth of agricultural production, employment and incomes of the rural population. They are able to ensure a certain price parity between the agricultural sector and other sectors of the economy, reduce prices and make products more accessible to the population by eliminating intermediary structures. The development and improvement of the economic efficiency of various integrated formations and their prospects is a complex and contradictory process, since the current situation largely depends on the political, legal, socio-economic and other conditions in the country. The development of agro-industrial integration is a complex objective economic process. Agricultural enterprises enter into integration ties, seeking to mitigate the risk caused by the specifics of agricultural production. Agricultural enterprises also strive to ensure stable incomes thanks to the availability of a reliable raw material base and sales markets for their products. These prerequisites created the conditions and gave impetus to the revival, formation and development of integrated associations in agriculture and explored their significance for small forms of entrepreneurship. Therefore, the development of integration processes in the field of agro-industrial complex may be the most important condition for revitalizing the activity of agro-industrial complex organizations and stabilizing the region's economy. In this article, we conducted a theoretical review of the classification of integration processes. Scientific works and publications of domestic and foreign scientists and specialists in the field of integration processes on the investigated problem served as the theoretical basis of the research.

Japan’s agricultural policy towards food security improvement. Key strategies, initiatives and outcomes

Food security in Japan has been under observation of analytics in both Japan and abroad as it is one of the key national security issues. Leading academic researchers of this issue in Japan include Suzuki N., Takeya T., Yamamoto R. and Oguchi H. However, the main focus of their analysis is the social aspect. The English language literature provides more materials on food security problem, but the situation is changing faster than information flows across the border. It means that the most up-to-date resources for analysis of the topic are the documents of the Japanese official institutions. Based on the Global Food Security Index (which takes into account the issues of food affordability, availability, quality and natural resources resilience), Japan is considered to be 6th most self-sufficient country in the world. However, the Japanese government still sees the national food security issue as problematic for the country’s stability due to heavy dependency on agricultural import. To overcome the trend, Japan utilizes multiple mechanisms to eventually improve agricultural self-sufficiency. The main focus is kept on the following 4 critical levers – i) productivity improvement, ii) agricultural employment improvement, iii) demand shift towards traditional dietary and new markets opening and iv) SDG agenda. The research also provides a case study of technologies implemented in the agricultural field in Japan in order to improve the self-sufficiency level. Moreover, emphasis is made on the government policies to develop the local market and to build the new markets abroad for the domestic farmers’ production expansion.