Agricultural Land Prices Research Articles

Future land use maps for the Netherlands based on the Dutch One Health Shared Socio-economic Pathways.

To enable detailed study of a wide variety of future health challenges, we have created future land use maps for the Netherlands for 2050, based on the Dutch One Health Shared Socio-economic Pathways (SSPs). This was done using the DynaCLUE modelling framework. Future land use is based on altitude, soil properties, groundwater, salinity, flood risk, agricultural land price, distance to transport hubs and climate. We also account for anticipated demand for different land use types, historic land use changes and potential spatial restrictions. These land use maps can be used to model many different health risks to people, animals and the environment, such as disease, water quality and pollution. In addition, the Netherlands can serve as an example for other rapidly urbanising deltas where many of the health risks will be similar.

SUSTAINABLE DEVELOPMENT OF RURAL AREAS IN THE REPUBLIC OF MOLDOVA THROUGH INCREASING THE EFFICIENCY OF THE LAND RESOURCE USE IN THE AGRICULTURAL SECTOR

The evaluation of the agricultural land in the Republic of Moldova represents the main factor in the sustainable rural space development and it emphasizes economic criteria for the efficiency of land resources. The hedonic price in the sales-purchase transactions of parcels in agricultural land may be the scientific innovation and originality of the geographically weighted regression methodology with the spatial approach in the used econometric model. The price of agricultural land used parcels in Maiovca village, Ocnița district were evaluated by using the data from transactions registered in 2022 and the results of the geographically weighted regression stands up the suitable conclusions of the licensed assessors in the field. The autocorrelation coefficient value evaluated by SAR is ρ = 0.072 according to Cobb Douglas model confirms the high quality of the regression techniques carried out and the used spatial methodology can be implemented in cadastral practice for the taxation in the agrarian sector. The geographically weighted regression utility proposed in this methodology has advantage in research area because of the similar evaluation techniques carried out in the countries of the European Union that are adjacent to the Republic of Moldova.

Agricultural Land Market in Ukraine: Challenges of Trade Liberalization and Future Land Policy Reforms

After over 20 years of a strict moratorium on the turnover of agricultural land in Ukraine, an official market for this type of land has recently been established. The purpose of this study was to examine the functioning of the fundamental elements of this market from the moment of its introduction on 1 July 2021 until today, as well as to understand the possible directions for its further development. The analysis of the agricultural land market and the visualization of the obtained results were carried out using the PostgreSQL database, Python and SQL programming languages, and the QGIS and Metabase tools. It was found that the volume of the agricultural land market remains insignificant, as the total area of sold land during this period made up only 0.7% of the arable land area of Ukraine. Prices for land plots remained at the level of their normative monetary value and are still relatively low, which indicates the significant undercapitalization of agricultural land as a production factor. The stability of agricultural land prices under the influence of various factors also indicates their relative virtuality, which, taking into account the further lifting of restrictions on the size of land plots to be sold as of 1 January 2024, requires the use of appropriate land policy instruments in order to fully utilize the multipurpose role of land in the achievement of the Sustainable Development Goals, which should be based on a human-rights-based approach to rural development, food security, and land policy.

Improving the SNA: Alternative measures of output, input, income, and productivity

AbstractThe current System of National Accounts (SNA) Gross Domestic Product (GDP) concept does not measure the income generated by the production sector since it includes depreciation and excludes capital gains and losses on assets used in the production sector. The paper suggests an accounting framework that measures the income generated by the production sector of an economy and implements this measure using the Augmented Productivity Database (APDB) developed by Asian Productivity Organization and Keio University for China over the years 1970–2020. Real gross and real net income generated by the Chinese production sector are decomposed into explanatory factors including TFP growth using the framework suggested by Jorgenson and Diewert and Morrison. TFP growth is further decomposed into technical progress and inefficiency components using the nonparametric approach developed by Diewert and Fox. The APDB has estimates for the price and quantity of agricultural, industrial, commercial, and residential land used in China. The paper argues that changes in land use should be treated in the same manner as inventory change and added to the alternative output measures. It turns out that Jorgensonian user costs for land are frequently negative. The problems associated with negative user costs are discussed in the paper.

The causes of differences between the average agricultural land prices of hungarian counties

Currently, there are significant differences between the average per hectare agricultural land prices of Hungarian counties. In this study, I endeavour to identify the causes of the price differentials by using a regression model. The data employed in the analysis relate to the years 2013, 2016 and 2020, and cover all of the 19 counties. The average yields of the main arable crops and the per capita GDP values of counties have a vigorous positive effect on land prices. Much like in numerous pieces of earlier research, it is demonstrated that larger average farm size also increases the price of agricultural land. Land prices are considerably higher in lowland areas with a smaller density of settlements than in otherwise similar but hilly or montainous regions, where there are more settlements. During the 2010s, agricultural land prices in Hungary grew dynamically. The rise of per capita GDP, average farm size and farm output per unit area played an important part in this, but perhaps the most consequential factor was the rise of the yields of the chief arable crops. The spectacular growth trend of average yields is attributable, above all, to the advance of agrotechnology. Since the fall of communism, Hungarian agriculture has shifted more and more towards intensive cereal production. Apparently, technological development has enhanced the profitability of cereal production relative to the other branches of agriculture, and it has thus raised the values of plots located in areas most suitable for arable farming.

A terra rural e seu potencial de desenvolvimento imobiliário

The research focuses on the problem of rural land valuation, revealing the excessive income that the urbanization of rural land entails compared to the development of productive activity. The study refers to a context of an intermediate city with characteristics of aridity and seismicity, recognized as an “oasis city”. This is the case in the Metropolitan Area of San Juan, Argentina, where the phenomenon acquires representative qualities due to its intensity and characteristics. The study intends to identify the real estate valuation processes that affect the availability of agro-productive land. This is a descriptive and relational research design, based on a case study, including fieldwork, semi-structured interviews, and a survey of land supply prices. The research produces scientific evidence about the significant increase in the prices of agricultural land that emerges from its real estate development potential. In this way, the study carried out produces scientific evidence about the differential valuation of expanding areas and the selective behavior of investments. These processes explain the changes generated in land use and urban expansion on agro-productive land.

‘LAND as GOLD’: Commodification of Agricultural Land and its Implications in Andhra Pradesh

Agricultural-land sale prices in India exponentially increased during post-reforms period. This article studies operation of agricultural-land sale market and its implications in Andhra Pradesh during 2001–2017. It is based on primary data from six villages in three regions. The results show that sale prices of unit agricultural land have skyrocketed, and they are no way related to returns from land. The rich, non-cultivating households are increasingly buying agricultural land at higher prices for non-cultivating purposes and left fallow. They treat agricultural land as gold for store value, speculative trading, tax benefits, etc. The higher land sale prices along with depleting returns tempted distressed small farmers to sell their land. Consequently, cultivating small farmers are pushed to supply-side of land market as distressed sellers but not on the demand side of land market as buyers. Agricultural land is slowly but permanently going away from the hands of cultivator to noncultivators. Continuation of these trends has an adverse impact on equity and efficiency in agriculture land ownership in the state.

DETERMINANTS OF CHANGES IN AGRICULTURAL LAND PRICES AT REGIONAL LEVEL IN POLAND

The aim of the research was to identify factors determining changes in land prices and to indicate clusters of Polish voivodeships distinguished according to the adopted variables. Because of the specific features of the agricultural land market in Poland, the performed analyses accounted for the distinction in prices of agricultural land from the Agricultural Property Stock of the State Treasury and from private land trade. In view of the formal and legal regulations implemented in Poland, and the way changes in land prices are recorded, the analysis concerned the time period of 2013-2020, divided into two sub-periods: 2013-2016, and 2017-2020. One of the most significant considerations was the fact that there were two submarkets on the Polish agricultural land market, such as the farmland in private turnover and the farmland from the Agricultural Property Stock of the State Treasury. The highest differentiation of land prices between the voivodeships was due to the average monthly disposal income per capita from a family farm relative to the average monthly income per capita from being self-employed, and the total acreage of farmland sold from the State Treasury Stock.

The Most Expensive Agricultural Land Prices in Europe: An Economic Analysis of Tenerife, Canary Islands, Spain

Agriculture in the Canary Islands has greater limitations than in the rest of Spain due to the cultivation areas being geographically limited and the abrupt orography of the archipelago. As a result, in certain situations, tillage of the land is more complex and costs increase. This study focuses on the island of Tenerife and aims to identify the determining variables that directly affect the price of agricultural land, considering the type of crop. For this purpose, a survey was designed for farm managers on the island of Tenerife and, after analysing the responses, we focused on tubers, legumes, vineyards, bananas, and cereals. A multilinear regression model showed that the highest land price corresponds to those farms destined for banana production, with a value of 16.52 €/m2. The price of agricultural land on the island of Tenerife was found to be eight times higher than the European average. The main factors impacting this value are irrigation, the orography of the land, and the presence of farm buildings.

Analiza evoluției pieței funciare agricole prin prisma tranzacțiilor de vânzare-cumpărare

The agricultural land market, as a specific element of the real estate market, was reestablished in the Republic of Moldova by the end of the last century. During the development of the national agricultural land market, there were different fluctuation trends of the main indicators, which reflect the quantitative aspects of land development: the correlation between demand and supply, market price of agricultural land, sold-purchased land areas, etc. The most important indicator is the market price of agricultural land, which, under the conditions of free land market, is formed based on the correlation between supply and demand for land. The sale-purchase type of business predominates in the transaction structure of the land market. In this context, in order to investigate the issues related to the functioning of the agricultural land market, the number of sale-purchase transactions of agricultural land, the area of agricultural land subject to sale, as well as the average sale-purchase price of agricultural land have been analyzed.

Assessment of the Impact of Plastic Recycling on Agricultural Land Prices: A Case Study in Vietnam

Pollution in Minh Khai, Van Lam district is seriously contributed by plastic recycling plants. Therefore, this study evaluated the impact of plastic recycling pollution on agricultural land prices in Minh Khai, Van Lam district. Hedonic price analysis was employed in analyzing the data being collected. The results show that crop yield and quality as well as agricultural land prices in the area tend to decrease due to the impact of pollution from plastic recycling. The analysis also showed land prices are positively related to crop yields, soil quality and land size while distance to the highway and quality of the irrigation water are negatively related. Based on the hedonic price method, the research shows that the price of agricultural land increases by 0.97 VND/m2 (USD 0.042 per 1000m2) when away from the plastic recycling area. It is estimated that the value of agricultural land in the district damaged by pollution is about 79 billion VND (3.4 million USD). Therefore, local authorities should implement zoning of production into concentrated areas. Hence, it is possible to invest in pollution treatment and reduce negative impacts on the external environment.

THE PRICE OF AGRICULTURAL LAND IN BULGARIA – SELECTED FACTORS

The main goal of the report is to build a model to highlight the significant factors affecting the price of agricultural land in Bulgaria. Panel data for 9 years and 25 districts were used. Fixed-effect panel regression and stepwise regression were applied. The empirical results prove that the price of agricultural land in Bulgaria, for the considered period, is influenced by: the relative share of households with access to the Internet, the length of first-class roads and the average annual salary of employed persons. The existing differences in the variables by area also have a strong influence

Factors Influencing Farm-Land Value in the Czech Republic

Czech farm-land had enjoyed considerable growth in value from 2008 to 2019. This paper identifies the main determinants of agricultural land prices variations and how these variations are influenced by urbanization, lease payments, and subsidies. These three factors were thoroughly examined for the existence of a unit root using the Augmented Dickey Fuller Test. The long-run relationship between farm-land value and these elements was estimated using the co-integration approach, specifically the Johansen procedure. The estimation confirms the existence of this long-run relationship. Short-run fluctuation in agricultural land prices is explained using the Error Correction Model. As the most important variable in the short-run performance of Czech agricultural land prices, the authors identified the influence of urbanization. This information could be used to help regulators avoid potential agricultural land value instabilities and volatility in the market and hence would contribute to sustainable land use.

Os efeitos espaciais são fatores determinantes dos preços das terras agrícolas no Brasil

Abstract This study aims to determine whether spatial effects are determinants of agricultural land prices in Brazil. For this purpose, data on the value of the bare land in municipalities in Brazil for 2020, provided by the Federal Revenue Service, were used. Although this database has national coverage, the contiguous data necessary for the application of spatial econometric models allowed us to cover the central-south region of the country. An exploratory spatial data analysis was performed, and the spatial Durbin econometric model was applied. Based on our results, there is a strong spatial dependence on land price, manifested both in the dependent variable, spatially lagged, and through the spillover effects of the independent variables. In addition, it was found that the degree of urbanization of a municipality, the municipal gross domestic product per capita, the average size of properties, the agricultural productivity, and the area dedicated to soybean planting in a rural property are also important determining factors in the pricing of agricultural land. Accordingly, it is possible to conclude that spatial effects influence the determination of agricultural land prices in Brazil.

Spatial effects are determinants of agricultural land prices in Brazil

Resumo Este estudo tem como objetivo verificar se os efeitos espaciais são fatores determinantes dos preços das terras agrícolas no Brasil. Para isso, foram utilizados dados do valor da terra nua de municípios do Brasil, para o ano de 2020, disponibilizados pela Receita Federal. Embora a base de dados tenha cobertura nacional, os dados contíguos, necessários na aplicação dos modelos econométricos espaciais, permitiram abranger a região Centro-Sul do país. Foi realizada a análise exploratória de dados espaciais e aplicado o modelo econométrico Durbin espacial. Pelos resultados obtidos verifica-se a existência de forte dependência espacial no preço da terra, manifestada tanto na variável dependente, espacialmente defasada, quanto por meio de efeitos de transbordamento das variáveis independentes. Além disso, verificou-se que o grau de urbanização do município, o produto interno bruto per capita municipal, o tamanho médio das propriedades, a produtividade agrícola e a área dedicada ao plantio da soja, na propriedade rural, também são importantes fatores determinantes do preço das terras agrícolas. Conclui-se que efeitos espaciais têm influência na determinação dos preços das terras agrícolas no Brasil.

The Risk of Agricultural Land Abandonment as a Socioeconomic Challenge for the Development of Agriculture in the European Union

In this article, the risk of agricultural farmland abandonment was assessed with the use of a synthetic measure of socioeconomic problems as challenges to the quality of life in rural areas in the European Union. To determine the direction and dynamics of changes in farmland abandonment in the EU countries, variables based on EUROSTAT and FADN data were analyzed using Hellwig’s method, and data for 2010, 2013, 2016, and 2019 were compared. The EUROSTAT methodology for the agri-environmental indicator “risk of land abandonment” was adapted for the needs of this study. Agricultural land is abandoned for many reasons, including conversion to other uses, but also abandonment of farming. The results of the analysis indicate that the risk of farmland abandonment was highest in countries with difficult farming conditions, such as Greece, Spain, Portugal, Romania, and Finland. In turn, Belgium, Denmark, Germany, and the Netherlands, i.e., the most economically developed countries, were most resilient to this risk. An analysis of the factors that contribute to farmland abandonment demonstrated that the likelihood of this risk decreases with a rise in agricultural investments, farm income, population density, prices of agricultural land, road quality, and density. A high proportion of ageing farm owners was the only factor that increased the risk of agricultural land abandonment.

A decision support discussion to select location for vertical farm in Bahrain

During the 21st century, food security has become a significant concern due to the growing population and the Earth's diminishing resources. The Kingdom of Bahrain is one of the numerous countries that has been affected by urbanization, where the urban expansion was at the price of agricultural land. The paper introduces a rational for Vertical Farms that are sustainable solutions for 21st- century urbanization. It brings multiple benefits to Bahrain and its community in terms of food security, education, awareness, and health. It also supports its food security strategy and its 2030 vision. The study discusses the proposed project location in terms of the project’s needs as an urban vertical farm that aims to serve locals & residents of Bahrain by improving the quality and quantity of locally grown fruits and vegetables in an indoor controlled environment. The paper discussed various analyzed case studies of comparable type projects to understand better the project's purposes, spaces, technological needs, and other significant features. Then, introducing and discussing a decision support framework for a set of criteria that responds to the primary sustainability pillars have been established to identify the best potential site to assure project success.

Economic Value of Food Commodities and Labor Shift due to Rice Field Conversion in Batu City, Indonesia

Increasing tourist activities in Batu City makes agricultural land conversion unavoidable. This research aims to determining the lost economic value of food commodities due to converted agricultural land area and labor shift. The conversion area of agricultural land in all areas can be identified through Landsat image 8 ETM+ in 2017 and 2021. An overlay analysis using ArcGIS 10.2 software with 7 variables was then utilized to determine land capability and land suitability. Then, it was combined with the survey results of 334 land price points to determine the distribution of agricultural land prices in Batu City by using Kriging interpolation analysis. The results of the research show that the agricultural land area has decreased in the past 4 years by 169.96 Ha, with a lost economic value of approximately 5 billion Indonesian rupiah. The agricultural land price in Batu City is divided into 4 classifications. The results indicate that the agricultural land price is high, so it accelerates the agricultural land conversion. Therefore, the government should pay close attention to land conversion.

Business processes of digitalization of the agricultural land market in Ukraine

The article substantiates the business processes of soil use digitization in order to increase their quality and price in the conditions of the formation of the agricultural land market in Ukraine. For this purpose, environmentally appropriate, socially necessary and economically justified processes have been developed to create synergy in the balance of price and quality of agricultural land. It is proposed to use the ecological audit methodology for these reasons: expert assessment of the quality and price of land; attraction of “green” investments to restore land quality; using the digitization tool - e-calculator. The methodology for building the e-calculator is based on a system of possibilities: obtaining the maximum yield while balancing nutrients in the soil; soil quality improvement under the conditions of planning to obtain the planned harvest with minimal investments; increasing the content of nutrients in the soil to increase the price of agricultural land. The conducted calculations confirm that: preservation and reproduction of the groats fertility guarantees obtaining larger harvests in the future; increases the value of land as the main means of production; increases the investment attractiveness and liquidity of agricultural land. It is substantiated that the costs of carrying out agrochemical measures are profitable investments.

АРХІТЕКТУРА ПРОГРАМНОГО ЗАБЕЗПЕЧЕННЯ ДЛЯ ЗБОРУЙ АНАЛІЗУ ДАНИХ ПРО ВАРТІСТЬ ЗЕМЛІ СІЛЬСЬКОГОСПОДАРСЬКОГО ПРИЗНАЧЕННЯ З ВИКОРИСТАННЯМ МАШИННОГО НАВЧАННЯ

The work presents a general approach to the software architecture for data collection and analysis using machine learning methods to predict the price of agricultural land. A brief review of recently published literature is made. New research results using LightGBM, Fast Tree, and Fast Forest methods to predict land price are provided. The highlighted stages include data collection, storage, exploration, and transformation, aggregation, optimization, and the use of machine learning methods. It is proposed to use several software applications for the implementation of these stages. An architecture diagram of the interaction of such applications is provided. The use of inflation data and the impact of such data on forecast quality reviewed. In addition to the dataset collected from the auctions and classifieds sites (first data source), data available on the website of the State Service of Ukraine for Geodesy, Cartography, and Cadastre (second data source) were used. The dataset collected from first data source consist of 2123rows, collected from seconds data source contains 815529 (further filtered to less rows due to different types of contract, year, etc). List of datasets for which machine learning was performed has been modified: attribute about soil type, distances from big cities, inflation were added. Information from the data available on the website of the State Service of Ukraine for Geodesy, Cartography, and Cadastre was converted to dataset, and it was further filtered and utilized, ranging from 75843 to 102924rows. From the initial data set (D1), two additional ones were created: without data on land rent (D2) and with prices that take into account inflation (according to the National Bank of Ukraine) (D3). Experiments on removing the value of the regulatory monetary assessment of land reviewed. Also, for data source data was converted to four datasets. Dataset D4 – constructed using date filters for 2022 and 2023 for the "sale-purchase agreement" type. Rows without specified prices will be removed from the dataset. The total number of rows in dataset D4 is 102924. Dataset D5 – additionally apply a filter on the price per hectare of land, ranging from no less than 10000 UAH to no more than 150000 UAH. After this filtration, only 82241 parcels will remain in the dataset. Dataset D6 – expand dataset D5 with information about distances from the largest cities in Ukraine to the land. Dataset D7 – further expand dataset D6 with information about inflation. Thus, machine learning methods LightGBM, Fast Tree, and Fast Forest have been used for datasets collected from two different sources. If the cost prediction has an error of no more than 10%, it is considered correct. The result of the machine learning, depending on the algorithm and data set, for R2 up to 0.897 and percentage of correct predictions up to 91%. Research shows that as the number of rows in the initial dataset increases, the quality of prediction improves. As for second data source – it have slightly worser prediction results then first data source, but second data source usage have shown how fast dataset can be extended with new parameters (e.g. soil type, distances from cities, inflation) to significantly increase prediction quality. Another possible way to improve the quality of the forecast could be the integration of certain data sets on non-market factors affecting land value (e.g., a map of potential contamination by explosive objects). The summary suggests further steps to improve the quality of predictions.