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Study of Cropland Values in Mississippi

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Publication Number: P3404

This publication analyzes the value of cropland in the state of Mississippi from 2015 to 2017. It focuses on identifying internal and external factors that affect land prices. Internal factors include characteristics of the cropland such as soil quality, acreage, and any improvements made to the land. External factors include location, regional income, and so on. This publication is based on actual land sales transactions from 2015 to 2017.

The United States Department of Agriculture (USDA) has estimated that farm real estate comprises over 83 percent of farm assets in 2019 (Economic Research Service, 2019). This means that changes in land markets can have a tremendous impact on the wealth of landowners. With approximately 10.9 million acres of farmland in Mississippi, landowners and land buyers must understand the factors that influence cropland values so that they can make informed decisions regarding selling and purchasing prices. Understanding what factors impact cropland values is also important since cropland values can have an effect on the ability of a landowner to secure credit. There have been many studies about cropland values and the factors that can affect them (Falk, 1991; Drescher, Henderson, & McNamara, 2001; Miranowski & Hammes, 1984), but no such study has been conducted in Mississippi. For these reasons, this publication analyzes cropland values and characteristics within the state.


This publication uses data from 345 land sales in Mississippi from 2015 to 2017. Different land types within each land sale, known as “sub-parcel” land types, are labeled in the sale records. The land types include information on various kinds of improvements such as precision leveling and irrigation methods. See Table 1 for more detailed definitions of the land types.

Soil characteristics for the land parcels are collected from the Soil Survey Geographic Database (SSURGO) under the direction of the National Cooperative Soil Survey. Table 2 shows the soil variables used and their respective definitions . Available water storage (AWS) is the volume of water that the soil has the ability to retain. “Wet depth” (which is the actual average depth to the nearest wet soil layer in a specific location) is used to estimate the effect of dryer soils on cropland values. Wet depth differs from AWS because factors such as topography can affect the depth to the nearest wet soil layer, whereas AWS is mainly affected by soil structure. The slope gradient of the land parcel has also been found in previous studies to affect cropland values (Borchers, Ifft, & Kuethe, 2014; Ervin & Mill, 1985). This publication will estimate its impact on Mississippi cropland values.

Table 1. Land use definitions.

Land type

Land description*


Irrigated crop I

Precision-leveled, flood-irrigated soils.


Irrigated crop II

Furrow-irrigated soils; best suited to cotton or corn.


Irrigated crop III

Furrow-irrigated soils; best suited to grain production.


Irrigated crop IV

Pivot-irrigated soils; best suited to cotton or corn.


Irrigated crop V

Pivot-irrigated soils; best suited to grain production.


Cropland I

Non-irrigated soils; best suited to cotton or corn.


Cropland II

Non-irrigated soils; best suited to grain production.


*Land descriptions are provided by the bank from which the data was sourced.

Table 2. Soil variable definitions.

Soil variable


Available water storage

Weighted average of the volume of water that the soil, to a depth of 25 centimeters, can store that is available to plants.


Weighted average of all the slope gradients in the land parcel.

Wet depth

Weighted average of a parcel’s shallowest depth to a wet soil layer throughout the year.


A “spatial error” regression model was used for this study. This model accounts for spatial correlation, which means that nearby land sales share similar characteristics. Such a model was used because tests concluded that spatial correlation existed in the dataset.

It was necessary to account for the effect of urban proximity on cropland values, which has been noted by previous studies (Stewart & Libby, 1998; Patton & McErlean, 2003). Sales within 10 miles of six high-population areas were deemed as “urban sales” to control for the additional value of being near urban areas. The six high-population areas were Gulfport, Hattiesburg, Jackson, Tupelo, Pascagoula, and Memphis, Tennessee.

The effect of county-level median per capita income, which has been determined by Huang et al. (2006) to be a significant factor in farmland values, was also considered in the modeling. Researchers found that median per capita income was an indicator of potential future development. As development occurs in an area, undeveloped land becomes scarcer, which could lead to increased value for cropland in the area.

The additional value of cropland being in the Delta region of the state was also considered. The Delta region begins in the northwestern corner of the state and extends to the southwestern corner. It adjoins the Mississippi River and is known for its rich soils. It is assumed that land buyers prefer cropland in the Delta due to increased access to markets, and possibly because of a perception that cropland in the Delta is of a higher quality than other cropland in the state.

Indicator variables for the year of sale of each parcel were also included to estimate the effect of any sudden crop price changes. There was little crop price movement during these years (2015–17), so we expected the results for the year of sale to be insignificant.

The value of each land type relative to the Cropland I type was estimated at the parcel value by isolating the dominant land type in each parcel. This means that the results show the added (or diminished) value of another dominant cropland type relative to Cropland I.


Cropland Types

Figures 1, 2, and 3 show the results of the analysis model. All variables that were not statistically significant are not included in the figures. It is important to realize that the values of the land type variables in Figure 1 do not provide an estimate of the total value of the land type, but rather the increase (or decrease) in value that the land type has over the Cropland I land type. For example, if Irrigated Cropland I (precision-leveled land) is the dominant cropland type within a parcel, the value of this land is expected to be approximately $1,113 per acre more than the value of Cropland I. For Irrigated Cropland II and Irrigated Cropland III, the additional values over Cropland I values are around $728 and $609 per acre, respectively. Irrigated Cropland IV is estimated to increase average cropland values by approximately $874 per acre if it is the dominant cropland type. Irrigated Cropland V was not significantly different than Cropland I.

The results also provide the premium placed on improvements such as precision leveling and flood irrigation. For example, Irrigated Cropland I increased cropland values approximately $385 per acre over Irrigated Cropland II if it was the dominant cropland type. The additional value of Irrigated Cropland I over Irrigated Cropland II is found by subtracting the value of Irrigated Cropland I over Cropland I ($1,113) by the value of Irrigated Cropland II over Cropland I ($728). The results indicate that precision-leveled, flood-irrigated land is valued more than unleveled, furrow-irrigated land and should be an additional consideration to those thinking about having cropland precision-leveled.

Irrigated Cropland IV (pivot-irrigated land) is estimated to be worth $265 per acre more than Irrigated Cropland III (furrow-irrigated land). However, there are few observations in our dataset for Irrigated Cropland IV, which could have affected the results for that variable. It was assumed that all furrow-irrigated lands would have a higher value than pivot-irrigated lands.

Cropland type results description in text.
Figure 1. Cropland type results.

Soil Characteristics

The results of the wet-depth variable in Figure 2 indicate that a 1-inch increase in the average depth to the nearest wet soil layer increases the average price of cropland by around $19.94 per acre. This means that cropland that is better drained is valued more than poorly drained cropland. The average slope gradient also significantly affected cropland values, with a 1 percent increase resulting in a $72.14 decrease in the average price per acre of cropland. The AWS variable was not statistically significant.

Soil characteristic results description in text.
Figure 2. Soil characteristic results.

Location, Parcel Size, and Income

Additional results in Figure 3 show that a 1-acre increase of cropland in a parcel increased the average price of cropland by over $0.39 per acre. This result could be explained by the increased scale of production that occurs with large parcels of cropland. If the sale was in the Delta region, the average cropland price was over $384 per acre more than non-Delta cropland. Urban areas had a significant positive impact on cropland values, as results estimated an approximately $541 per acre premium over rural cropland sales. Cropland near urban areas tends to have higher value because of its development value, so these estimated results were expected. Median per capita income of a county also had a small yet significant impact on cropland values, with a $100 increase in median per capita income resulting in an approximate $9 per acre increase in average cropland price.

Location, income, and size results description in text.
Figure 3. Location, income, and size results.


Landowners and people involved in land transactions need to understand the factors that affect cropland values. Spatial and urban affects can significantly impact cropland values and should not be ignored. Cropland near urban areas is estimated to have a higher market value than cropland in rural areas. Also, cropland in the Delta region has a higher market value than similar land in a non-Delta region.

The results indicate how different improvements are valued for a piece of land. Irrigation techniques, such as flood, furrow, and pivot irrigation, affect cropland values differently. Slope negatively affects cropland values, and depth to the nearest wet soil layer has a positive effect on cropland values.


Borchers, A., Ifft, J., & Kuethe, T. (2014). Linking the price of agricultural land to use values and amenities. American Journal of Agricultural Economics, 96(5), 1307–1320.

Drescher, K., Henderson, J. R., & McNamara, K. T. (2001). Farmland Price Determinants (No. 374-2016-19804).

Economic Research Service. (2019). Assets, Debt, and Wealth. Retrieved from

Ervin, D. E., & Mill, J. W. (1985). Agricultural land markets and soil erosion: Policy relevance and conceptual issues (No. 2141-2018-6525).

Falk, B. (1991). Formally testing the present value model of farmland prices. American Journal of Agricultural Economics, 73(1), 1–10.           

Huang, H., Miller, G. Y., Sherrick, B. J., & Gomez, M. I. (2006). Factors influencing Illinois farmland values. American Journal of Agricultural Economics, 88(2), 458–470.

Miranowski, J. A., & Hammes, B. D. (1984). Implicit prices of soil characteristics for farmland in Iowa. American Journal of Agricultural Economics, 66(5), 745–749.

Patton, M., & McErlean, S. (2003). Spatial effects within the agricultural land market in Northern Ireland. Journal of Agricultural Economics, 54(1), 35–54.

Stewart, P. A., & Libby, L. W. (1998). Determinants of farmland value: The case of DeKalb County, Illinois. Review of Agricultural Economics, 20(1), 80–95.

Publication 3404 (POD-11-19)

By Evan Gregory, Extension Associate, Agricultural Economics; Xiaofei Li, PhD, Assistant Professor, Agricultural Economics; Keith Coble, PhD, Professor and Head, Agricultural Economics; and Bryon Parman, PhD, Extension Ag Finance Specialist, North Dakota State University Extension.

Copyright 2019 by Mississippi State University. All rights reserved. This publication may be copied and distributed without alteration for nonprofit educational purposes provided that credit is given to the Mississippi State University Extension Service.

Produced by Agricultural Communications.

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Extension Service of Mississippi State University, cooperating with U.S. Department of Agriculture. Published in furtherance of Acts of Congress, May 8 and June 30, 1914. GARY B. JACKSON, Director

Department: Agricultural Economics
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Portrait of Mr. Evan James Gregory
Extension Associate I