Biofuel Growth: The Unintended Effects of the Ethanol Boom on Farmland Values

Global biofuel production has increased over tenfold since 2000 and numerous biofuel production plants have been built in the Great Plains region. We estimate the impact of biofuel plants on the agricultural economy with county-level data on the number of farm operators, net farm income (NFI), and agricultural land values during 1997–2017 for 798 counties in twelve Great Plains states. We find that biofuel production facilities have a small negative association with the number of farm operators, but positive associations with NFI and farmland values in the county where the biofuel plant is located and neighboring (adjacent) counties. Sustained positive associations of biofuel production with farm income and land values are likely to depend on the continuation of national and state government programs of ethanol blend mandates and tax credits. Diminished government bioenergy support could lead to declining farm income and land values in areas near plants.

With the acceleration of circulation of rural land, the situation of three property right division that property in land, land contract right and land management right throws out a challenge to the connotation and evaluation of the value of farm land that different property corresponds with. The connotation of the value of collective ownership of land is not only the value of production profit, social guarantee value, ecological value and potential value, but also the most complete land value. Although it is in line with the economic logic and the logic of property rights to divide land contracted management right into land contract right and land management right, it is still not clearly defined in the law, which is the primary and difficult question of the connotation and evaluation of land contract right and management right. The core of the value of land contract right are the right of identity and part of the right of usufruct, and the core of land management right is the value of part of the right of usufruct.Key words: Three Property Right Division; land contract management right; land contract right; land management right

This study is an evaluation of the relation between farm land values and the returns to farm land. The primary motivation for this study has been to explain the observed relation between farm land values and returns which has in some years been contradictory to the suggested theoretical relation between land values and returns. The primary objective of this study has been to examine the importance of growth of the returns to farm land in explaining the historical levels of farm land and its relationship with the returns to farm land. Data were collected on the values of cash rented farms and estimates of gross cash rents paid on those for 24 states for the period 1929-1980. Twelve states were selected for analysis. The 12 states were Ohio, Indiana, Illinois, Iowa, Missouri, Pennsylvania, Michigan, South Dakota, Minnesota, Wisconsin, North Dakota, and Oklahoma;The theoretical land value model was developed from the present value approach for determining values. The explanatory variables identified from this approach are the returns to farm land, the capitalization rate, the growth rate of the returns to farm land, the inflation rate, inflation rate, and taxes. Because of lack of data, taxes are not included in the empirical models;The empirical models to determine the association between the explanatory variables and farm land values were estimated by regression analysis. Two basic sets of regression were estimated; one using farm land value per acre as the dependent variable and one using the ratio of gross cash rent to farm land value as the dependent variable. The results of the empirical models indicate that gross cash rents are strongly related to farm land values. In general, the growth rate of gross cash rents did not perform as expected. The coefficient on nominal and real growth rates was significantly different from zero but did not have the correct sign. The coefficients on the lagged growth rates were found to be not significantly different from zero. The general rise in the price level did not appear to have a significant impact on the nominal or real value of farm land. The capitalization factor was found to be statistically significant in estimating the rent-to-value ratio;Granger causality tests were also applied to the data. Overall, the results did not support the hypothesis that gross cash rents caused, in the Granger sense, farm land values.

Current USDA forecasts indicate that US farms are entering a period of lower net farm income, following historical highs in 2012 and 2013. A sustained period of lower net farm income may lead to lower farmland values as returns to farmland decline, raising questions about whether specific sectors of the U.S. farm economy will become financially stressed in this environment. Using USDA’s 2013 Agricultural Resource Management Survey data (ARMS) with new imputed farm debt data we assess financial stress for both borrowers and lenders under scenarios of lower net farm income and land values. To accomplish this, we use both financial ratio analysis and a synthetic credit rating model. We then examine scenarios of a 25% drop in net farm income, and a 35% drop in land values. Our results suggest that the overall financial health of the U.S. farm economy is relatively strong, however some sectors remain vulnerable. We find that a drop of 35% in farmland values will negatively impact specific sectors, including: peanut, tobacco, and poultry farms; highly leveraged farms, farms with gross sales over 1 million dollars, and farmers who rent a majority of their farmland.

Nonclassical measurement error threatens the validity of empirical economic models. We examine the extent to which land value measures that are commonly used in studies of the US farmland market are subject to nonclassical measurement error. We consider differences in county‐level land values from two popular data sources: (1) self‐reported land value assessments from the 2017 US Census of Agriculture and (2) observed sales prices from agricultural land transactions (2013–2017). We find that the difference between these data sources, which we interpret as measurement error, is correlated with multiple determinants of land value. Farmland market characteristics, including market size, are negatively related to this difference, implying that more information in a market allows respondents to better update their priors about their land's value. Survey respondents are also slow to update their assessments in areas of rapid recent price changes. Surprisingly, we find measurement error in farmland value to be correlated with climate. To illustrate the empirical implications, we conduct a Ricardian analysis of agricultural climate change impacts and find that temperature, in particular, is differentially capitalized into self‐reported land value estimates and sales prices. Under certain climate specifications, using self‐reported land values generates climate damage estimates that are more pessimistic but ultimately not significantly different from those using sales prices. However, regional heterogeneity highlights where this difference is significant, namely the northern half of the eastern United States. More generally, our findings highlight important considerations for researchers relying on farmland values from alternative data sources.

Climate change impacts on farmland value in Bangladesh

Purpose – In recent years, prices for prime farmland have increased substantially, begging the question is the dramatic increase the result of a speculative bubble or consistent with market fundamentals with increases driven by increased global demand, low interest rates, and recent changes to US agricultural and energy policies. The purpose of this paper is to investigate the impacts of recent agricultural support policies and ethanol policies on farmland values and rental rates. Design/methodology/approach – Farm-level Agricultural Resource Management Survey data collected by the United States Department of Agriculture (USDA) between 1998 and 2008 as well as county-level data collected by the USDA, US Census Bureau, and Bureau of Economic Analysis are used to determine the impacts of recent agricultural support policies and ethanol policies on farmland values and rental rates, while controlling for parcel characteristics and urban pressure. Specifically, weighted ordinary least squares and two-stage least squares are used to investigate the impact of various governmental agricultural support policies, corn ethanol facilities location, and local corn ethanol production capacity on farmland values and rental rates. Findings – The results indicate that government payments, urban pressure, and the proximity of the parcel to an ethanol facility have a positive impact on both farmland values and rental rates. More specifically, parcels located in the same county as at least one corn ethanol facility are more valuable and command higher rental rates. In addition, county-level ethanol production capacity is positively associated with farmland values and rental rates. An inverse relationship between distance of the parcels from an ethanol facility and farmland values is also found; a similar result is found for rental rates. Research limitations/implications – The findings suggest that agricultural support payments and ethanol policies are capitalized into farmland values. These findings have important implications for the formulation of future farm policy. A limitation of the analyses is that farmland values are estimated by landowners; future research could utilize farmland transaction data to overcome potential biases generated by using landowner estimates. In addition, while our study period covers 11 years, future research could expand the time period further to analyze the effect of more recent agricultural and ethanol policies. Originality/value – This paper extends prior research pertaining to factors influencing farmland values and rental rates by also examining the proximity of the parcel to an operating ethanol facility using a unique data set.

Farmland values are influenced by productivity levels, prices of pertinent crops, farm incomes, urban sprawl, and external investment pressure. Since cropping systems in a given region are similar to adjacent regions and soil productivity indexes change slowly across regions, it was expected that farmland values are spatially clustered even at the state level. We tested spatial correlation on US state-level farmland values from 1950 to 2014. Spatial correlation was detected in farmland values and percent changes in farmland values. These results indicate that traditional analysis techniques that ignore values of neighboring states may be dominated by advanced spatial analysis. Evaluation of state-level farmland values provide appraisers with insights into how a shock to farmland values impact values in surrounding states. Future analyses will validate these results by examining available sub-state and county-level data.

The determinants of regional farmland values are evolving over time and the farmland value growth varies by regions. As a result, the new regional farmland valuation models need to be dynamically updated with the changes in public policy, input and output markets and regional environmental amenities. This study used a Hedonic pricing model to examine the key determinants that influence the farmland values in 26 counties of California across the seven regions based on the different crop varieties and county-level economic characteristics. Specifically, we analyzed the relationship between variables that are deemed to influence demand, supply and the agricultural land values. The estimation results show that the farmland value in California is mostly determined by the production, productivities and dollar returns to the tree nuts, citrus and wine grapes. Specifically, higher productivity and net returns contribute to the increase in the farmland values. Urban influence factors have been playing a critical role in affecting the overall farmland value. For example, each additional acre of land converted to urban use raises the farmland value by $0.89 per acre. In addition, high real estate earnings might lead to rising farmland values. However, high farm earnings per capita could lower the farmland value, which suggests a tendency of witching from pursuing economies of scale to pursuing high value-added crop production that needs less farmland. Finally, high per-capita GDP and high population density can increase the farmland values.

ECONOMISTS have historically been conditioned to a close relationship betwen the prices of farm products and land values. Land values soared with the increase of commodity prices during World War I, collapsed with prices in 1921, remained steady as prices remained steady in the 1920's, collapsed again with farm prices in the early 1930's, recovered slowly as farm prices increased in the late thirties and during World War II. In the lull between World War II and the Korean war, farm prices and land values moved upward together. Three decades of relatively close correlation between farm prices and land values verified the residual theory of rent, and economists were content that land values were positively related to movements in gross agricultural income. The converse of this, of course, is that if gross income remains steady, land values should remain steady. This has not been the course of events since the end of the Korean episode. In 1952, the U. S. Department of Agriculture estimate of realized gross income was $37.0 billion; in 1964 the estimate was $42.2 billion or an increase of 14 percent. Land values, on the other hand, stood at an index of 83 and 1952 and 131 in 1964, or an increase of more than 57 percent for the period.

PurposeConsistent and reliable data on farmland values is critical to assessing the overall financial health of agricultural producers. However, little is known about the idiosyncrasies and similarities of standard land value data sources – US Department of Agriculture (USDA), Federal Reserve Bank land value surveys, and transaction prices. The purpose of this paper is to determine the differences and similarities of land value movements from three land value data sources.Design/methodology/approachIn addition to Oklahoma transaction prices, two survey sources are considered: the USDA annual report and the quarterly Tenth District Survey of Agricultural Credit Conditions administered by the Federal Reserve Bank of Kansas City. The paper describes each data set and identifies differences in data sampling, collection, and reporting. Average values of Oklahoma farmland across data sources are examined. USDA estimates are regressed against quarterly Federal Reserve values across multiple states to determine the point in time represented by USDA estimates. Granger causality tests determine if Federal Reserve land value estimates anticipate movements in USDA land value estimates.FindingsIt is found that all three data sources are highly correlated, but transaction prices tend to be higher, especially for irrigated cropland and ranchland. USDA land values are reported as representing land values on January first, but instead they more closely represent first and second quarter land values according to a multi‐state comparison to changes in quarterly Federal Reserve land values. Given the finding that first quarter Federal Reserve Bank land values lead USDA land values and that they are published before the USDA release, Federal Reserve land values are a timely indicator of agricultural producers' financial position.Originality/valueNo previous research has addressed the topic of how various sources of agricultural land values compare.

The economic valuation of farmland traditionally focuses on market-based approaches, potentially undervaluing the fundamental role of natural attributes. This study aims to assess the economic value of farmland by explicitly integrating key natural attributes, including soil fertility, slope, and climatic conditions. This study develops a novel assessment framework integrating natural attributes to determine farmland economic value, with Hefei, China as the case study. The assessment combines three methods: the Comprehensive Farmland Quality Index to classify farmland by quality, the Thornthwaite-Mather model to estimate effective precipitation and water availability, and the soil productivity potential method to calculate baseline productive capacity. Together, these methods provide an objective, reproducible, and ecologically grounded basis for farmland valuation. The results reveal the economic value of farmland in Hefei. Key findings include: In 2021, the total area of farmland in Hefei was 481,500 hectares, with approximately 5% classified as first-grade land, 10% as second-grade land, 19% as third-grade land, 32% as fourth-grade land, and 33% as fifth-grade land. The estimated standard production potential per unit of farmland in 2021, from highest to lowest, was 10.28 t·hm−2, 9.94 t·hm−2, 9.31 t·hm−2, 8.91 t·hm−2, and 7.95 t·hm−2 for first- to fifth-grade land, respectively. Based on rice production inputs and outputs, the estimated economic value reflects a single-year, non-discounted theoretical output. The economic total value of farmland in Hefei in 2021 was calculated to be 1.363 billion yuan. Specifically, the economic value of first-grade farmland was 81 million yuan; second-grade farmland was 158 million yuan; third-grade farmland was 273 million yuan; fourth-grade farmland was 444 million yuan; and fifth-grade farmland was 407 million yuan. Among all districts of Hefei, the economic value of farmland in 2021 ranked from highest to lowest as follows: Chaohu, Feidong, Feixi, Lujiang, Shushan, Yaohai, Luyang, and Baohe.

We quantify the impacts climate change on New Zealand’s agriculture. We implement the Ricardian approach of land climate-pricing using QV data. We explore the nonlinear relationship between climate variables and farmland values while controlling for socio-economic and topographical-geographical features. Furthermore, we measure the persistence of drought using autoregressive (AR) model. We simulate future farmland values under climate change. Preliminary results show the heterogeneity in which rural land values are affected by climate depending on the land use category. The rural land value decreases with summer temperature among all land uses, while it increases with spring temperature. The cumulative impacts of soil moisture deficit in summer reduce farmland values.

'Vegetable Spirits' and Energy Policy

This study develops a comprehensive model of farmland value determination to analyze the effects of various economic variables such as net farm income, government payments, macroeconomic factors, and demographic conditions on farmland values for the counties along the Snake River valley in Idaho. Land values, net farm income, and population show considerable variation among the counties. Therefore, use of county level cross-sectional and time-series data helps to assess the impacts of various factors on land values more accurately. The empirical results show that net farm income, wheat yield, population, and credit availability have positive effects, and property tax rates, interest rates, and debt to asset ratio have negative effects on farmland values.