Category: Farm Management

There are many ways that crop yield can be impacted throughout the growing season, including too much rain, not enough rain, wind, hail, insect pressure, herbicide drift, and even deer. Deer damage is routinely brought up in producer meetings as a major area of concern, especially for corn, cotton, and soybean production. Crop insurance indemnities can provide data on the prevalence of this issue. Wildlife indemnity payments have been increasing in southern states, but make up less than 1.5% of total insurance indemnity payments (Duncan et al., 2023). Additionally, wildlife indemnities includes damage caused by all animals, therefore the portion of wildlife payments that can be attributed to deer is unknown. However, there can be significant losses without an insurance payment, so indemnity payments don’t show the full damage picture. 

Surveys of producers typically show deer as a significantly worse problem compared to what is shown by insurance payments. Producers in Georgia reported that 19,535 acres were damaged by deer with losses of $153.85/ac (Mengak and Crosby, 2017). More broadly (Hand et al. 2024), respondents across the southeastern U.S. reported that 33-41% of cotton acres were affected by deer annually, with yield losses of 34-42%. Respondents considered deer to be the most significant pest to cotton, with damages of $152 million in 2023.

A survey was sent out to Mississippi row crop producers to determine the impact of deer^[1]. Producers reported yield losses in 12 different crops, with the majority of losses coming in corn, cotton, and soybeans. In total, 13 respondents reported damage in corn, 21 reported damage in cotton, and 90 reported damage in soybeans. Respondents reported damage occurring in 45 different counties in Mississippi. Economic loss was calculated given the reported yield loss and any replant costs. 

For corn, cotton, and soybeans, 17,830 total acres were reported to be affected by deer damage with a total economic impact of $4.6 million (Table 1). The acres damaged accounted for 17% of the total acres planted by the respondents. Soybeans were by far the most impacted, with 90 respondents reporting damages on 14,204 acres, of which 4,013 acres of soybeans had to be replanted. Total economic loss for soybeans was $3.68 million or $258.91/ac. Cotton had the second most acres impacted at 2,066 acres, with 597 acres being replanted. Total economic loss for cotton was $640,733 or $310.21/ac. Lastly, producers reported 1,561 acres of corn damaged with 171 acres of replant. Economic loss for corn was $294,109 or $188.46/ac. 

Producers were also asked a series of questions on what actions they took to reduce deer damage on their land. The most common method (48% of respondents) used hunting to control deer. This was followed by allowing other hunters on the land, 23%, and securing a deer depredation permit, 21% (Figure 1). Similar to producer surveys in other states, the results show that deer damage is a substantial issue for row crop producers. The results don’t show the full impact of deer damage, as not all producers filled out the survey. However, producers who were more severely affected by deer damage would be more likely to fill out the survey. The economic loss also depends on the year; if crop prices were higher, the economic loss would be greater and vice versa. Furthermore, there are other costs outside of yield and replant that impact producers from this issue, such as not planting the desired/most profitable crop, carcass disposal where applicable, and machinery downtime from flat tires. More work is needed in this area to determine the true impact of deer and to evaluate optimal mitigation techniques. 

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^[1] Funding for the survey was provided by the Mississippi Soybean Promotion Board.

Table 1. Reported Economic Loss Due to Deer Damage for Mississippi, 2024
Item	Corn	Cotton	Soybeans
Respondents	13	21	90
Acres Planted	9,222	9,507	84,243
Acres Damaged	1,561	2,066	14,204
Acres Replanted	171	597	4,013
Average Yield Loss	38 bu/ac	416 lbs/ac	24 bu/ac
Total Economic Loss	$294,109.90	$640,732.63	$3,677,496.10
Average Loss Per Acre	$188.46	$310.21	$258.91
Average Loss Per Respondent	$22,623.84	$30,511.08	$40,861.07

References

Duncan, H., Boyer, C., and Smith, A. (2023). Soybean Indemnity Payments for Wildlife Damage. Southern Ag Today3(29.1). July 17, 2023.

Mengak, M. and Crosby M. (2017). Farmers’ perceptions of white-tailed deer damage to row crops in 20 Georgia counties during 2016. University of Georgia Extension.

Hand, L.C., Roberts, P., and Taylor, S. (2024). Growers, consultants, and county agents perceive white-tailed deer to be the most economically impactful pest of Georgia cotton. Crop, Forage & Turfgrass Management. Volume 10, Issue 2. 

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Mills, Brian E., and Brianna Croft. “Deer Impact on Crop Producers: A Buck’s Buck Effect.” Southern Ag Today 5(22.1). May 26, 2025. Permalink

Pasture, Rangeland, and Forage (PRF) insurance has become a key risk management tool for ranchers and forage producers looking to protect themselves against the unpredictability of rainfall. However, like all insurance products, PRF comes with its own set of risks. In this article, we explore the risk associated with producer interval selection and its potential downsides and upsides.

A unique risk with PRF insurance is that rainfall during a particular two-month interval does not necessarily lead to forage growth during that interval. Rainfall is obviously crucial for forage production, but the impact of precipitation on forage is not instantaneous. Often, rain that occurs during one interval may contribute to forage growth in the following months more than the month in which the rain occurred.  Therefore, choosing a PRF interval that aligns directly with your critical forage production interval could potentially be a mismatch.

A downside of this timing mismatch is that a producer may not receive an indemnity payment when needed. For instance, if the insured interval experiences average rainfall but the interval prior had low precipitation or the rain came towards the end of an interval, the forage growth may still be insufficient. Unfortunately, since the payment is based strictly on the rainfall during the insured interval, producers might not receive any payout despite facing significant challenges. The chance of this outcome occurring is considered a False Negative Probability (FNP). False in the sense that the signal (rainfall) did not correspond with the underlying production need (forage production), and negative in that the outcome provided no protection when you needed it. 

On the flip side, this same mismatch can work in favor of producers. Suppose the insured interval experiences low rainfall, but the previous interval had good precipitation. In that case, sufficient forage growth can occur in the insured interval, and the insured can still receive an indemnity payment. The likelihood of the PRF policy providing a payment even when forage conditions are favorable is the False Positive Probability (FPP).

Figure 1 below illustrates this potential downside risk through the prevalence of FNPs in grids in Arkansas. These values were calculated by creating a forage/vegetation index to match the Rainfall Index used by the PRF program. Using Normalized Difference Vegetation Index (NDVI) values, we found the FNP percentages for each grid and each interval. Figure 1 highlights the June-July interval, telling us the percent chance that the forage/vegetation index would indicate a need for an indemnity based on the coverage level when the policy using the rainfall index has not been triggered (Keller & Saitone, 2022). This shows the prevalence of this issue and that producers in certain regions should be more wary of this type of risk. 

A unique risk with PRF insurance is tha

Figure 1: False Negative Probability Percentages in Arkansas Grids for the June-July Interval (1981-2023)

Inversely, Figure 2 presents the frequency of FPPs showing the upside risk. Reversing the methodology, these were calculated as the percent chance that the rainfall index indicates an indemnity should be issued based on the coverage level when the forage/vegetation index says an indemnity should not be issued. This scenario tends to be more prevalent, which is good for the policyholder. Certain grids exhibiting high FPPs also tend to show high FNPs, indicating they might frequently receive unwarranted payments while simultaneously facing situations where they do not receive payments when needed. This raises an issue with the producer, causing them to change how they manage their finances to protect themselves instead of the program doing so properly. 

Figure 2: False Positive Probability Percentages in Arkansas Grids for the June-July Interval (1981-2023)

While these figures only highlight the prevalence of FNP and FPP in Arkansas, these risks are inherent in PRF and are just as likely in the other southern states. To counter this risk, producers should consider not only the months when forage is most needed, but also the months when moisture and precipitation are most important. Using this information, they can choose their PRF intervals appropriately and reduce the risks involved in the program.

References

Keller, James B., and Tina L. Saitone. 2022. “Basis Risk in the Pasture, Rangeland, and Forage Insurance Program: Evidence from California.” American Journal of Agricultural Economics 104 (4): 1203–23. 

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Davis, Walker B., Lawson Connor, and Hunter Biram. “Analyzing the Upside and Downside Risk in PRF Policy Selection: Timing Mismatch.” Southern Ag Today 5(21.1). May 19, 2025. Permalink

As mentioned in previous Southern Ag Today (SAT) articles (Martinez and Ferguson 2022, Martienz 2023), monitoring Non-Real Estate Farm Debt provides insight into debt health. Last year, there were periods of drought and increased input prices for producers. At the time of this article, planting is on everyone’s mind (completed or about to start), producers are bailing hay, and all prices in every supply chain are working their way through tariffs. The most recent reports offer insights through the end of 2024. As a refresher, every commercial bank in the U.S. submits their quarterly Reports of Condition and Income, which are known as call reports. Within these call reports are totals of agricultural loans and the status (on time or late) of the loans. Figure 1 displays the total loan volume (yellow line) and loan volume for three late categories (30-89 days late, 90+ days late, non-accrual) for the last 16 quarters (4 years). The totals are for all the Southern Ag Today States. 

Through the end of 2024, non-accrual (blue line) loans continued to decrease, which is positive, and loans that are 90+ days late (grey line) remained relatively the same. Total loans (yellow line) are down from the previous quarter, which is expected due to seasonal trends. But, total loan debt is up 4.8% compared to 2023. The most concerning statistic is the loans that are 30-89 days late (orange line). At the end of 2024, debt that was 30-89 days late, was up 5.2% compared to the end of 2023 and the highest since Q1 of 2021. Q1 is seasonally the highest quarter for 30-89 days late loans, but given that it’s up from a year ago, the Q1 2025 reports will provide an indication of debt health in 2025 and moving forward.

From a sky high view, the call reports indicate that there are some possible caution signals for debt in the SAT states. Total non-current debt is approximately 1%, which is still relatively low. The next two quarters will provide answers if the signals are false alarms or true signals of concern. In the coming months, it is crucial that producers are mindful of their working capital and continue the positive production and risk management strategies they have implemented thus far. 

Figure 1. Non-Real Estate Farm Debt from 2021 Q1- 2024 Q4 

References

Martinez, Charley, and Haylee Ferguson . “Current Non-Real Estate Farm Debt“. Southern Ag Today 2(30.3). July 20, 2022. Permalink

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Martinez, Charley, and Parker Wyatt. “Current Non-Real Estate Farm Debt.” Southern Ag Today 5(20.1). May 12, 2025. Permalink

Limited labor availability has been a constant challenge for many American farmers. While labor shortages impacting the agricultural sector have been well documented by economists and highlighted by the popular press, the focus has been on the need for workers in labor-intensive sectors, like the production of fruits and vegetables. The emphasis on these industries is well deserved. According to data from the USDA, labor costs can be as high as 35% for fruit and nursery farmers, and their dire need for workers has been reflected in their growing reliance on foreign temporary workers coming under H-2A visas. Less attention has been paid to the labor demand of other producers, like animal sector farmers.  

To better understand the specific workforce needs of livestock farmers, we conducted a survey last year of cattle and dairy producers in Wisconsin, Georgia, and North Carolina. Unlike the case of specialty crop producers, labor inputs represent a significantly smaller share of total production costs for dairy and cattle farmers (Table 1). Almost half of the survey respondents (46.86%) indicated that labor accounted for less than 5% of their total costs. Moreover, almost 96% said labor costs were no more than 25% of their aggregate bills. Animal feed, land, transportation, and fuel likely account for a larger share of total costs. Although dairy and cattle farmers might not be relying on many workers in their current operations, we wanted to know more about their plans and if they were foreseeing a subsequent jump in their need for additional workers. Nearly 60% of farmers indicated they were planning to employ about the same number of workers going forward (Table 2). Only 4.31% said they would hire more people, but 27.59% were not sure about their short-term future labor demand. 

Our survey also asked farmers about their preferences towards potential modifications to the H-2A program. Under current rules, only growers of seasonal crops (commodities whose production processes take less than a year) can hire H-2A workers. This has been cited as a major limitation for dairy producers and farmers of agricultural commodities with operations running year-round. Likewise, the increasing minimum wages of H-2A laborers are a major concern among current and potential users of this program, and hiring gaps between sectors have been associated with wage disparities arising from such costs (Escalante et al., 2025). The Farm Workforce Modernization Act has been introduced in Congress multiple times but has not been passed. The proposed legislation would make several changes to the rules of the H-2A program. We shared some of the changes with respondents and asked them to indicate which was the most important for them. Interestingly, about a third cited having the government cover or subsidize housing costs (Table 3). Another third said that creating a yearly quota of H-2A workers allocated specifically to non-seasonal sectors like dairy (which would imply giving access to such farmers to the program) was their top choice. The proposed change chosen the least was legalizing currently undocumented workers (as only 5.88% of the sample selected this option).

Altogether, we document that labor represents a relatively small fraction of total costs for dairy and cattle producers. In addition, most farmers are not planning to hire many more workers in the foreseeable future. These findings suggest that access to labor is less of a constraint to animal sector producers compared to their fruit, vegetable, and indoor plant counterparts. However, the results also show that livestock producers would welcome updates to the H-2A program that would allow them to access this source of foreign legal agricultural workers. This is likely the case, as labor is still a crucial input for these farmers, given the complementarities between labor and capital. Even if labor costs represent a smaller share of their total production costs, dairy and cattle farms still need dependable workers to operate.   

Table 1. Labor Costs as a Percentage of Total Production Costs   

	Freq.	Percent	Cum.
Less than 5%	209	46.86	46.86
5% – 6%	29	6.50	53.36
7% – 10%	61	13.68	67.04
11% – 15%	65	14.57	81.61
16% – 20%	37	8.30	89.91
21% – 25%	26	5.83	95.74
26% +	19	4.26	100.00
Total	446	100.00

Table 2. Plans to Hire Foreign Workers in the Future 

	Freq.	Percent	Cum.
More	5	4.31	4.31
About the same	68	58.62	62.93
Less	11	9.48	72.41
Don’t know	32	27.59	100.00
Total	116	100.00

Table 3. Top Hypothetical Changes to the H-2A Program

	Freq.	Percent	Cum.
Modify rules for determination of minimum hiring wages for H-2A workers	21	6.50	6.50
Allowing H-2A workers to stay year-round	24	7.43	13.93
Allowing H-2A workers to work for multiple employers	52	16.10	30.03
Providing govt support to build and maintain workers’ living facilities	106	32.82	62.85
Legalizing undocumented farm workers	19	5.88	68.73
Creating yearly quota of workers’ visas for non-seasonal sectors like dairy	101	31.27	100.00
Total	323	100.00

References

Gutierrez-Li, A. (2024). Feeding America: How Immigrants Sustain US Agriculture. Research Paper. Center for the U.S. and Mexico. Baker Institute for Public Policy. Rice University.

Escalante, C., Gutierrez-Li, A., and Bhuiyan, N. (2025). Relating crop and livestock H-2A labor decisions to AEWR and sector wage gaps. Southern Ag. Today. Forthcoming.

Farm Labor. (2025). Economic Research Service. U.S. Department of Agriculture. Accessed online in April 2025.

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Gutierrez-Li, Alejandro, and Cesar Escalante. “Understanding the Labor Needs of Livestock Producers.” Southern Ag Today 5(19.1). May 5, 2025. Permalink

This article extends the regional and industry concentration analysis of H-2A patronage trends laid out in a previous Southern Ag Today article. Given the larger shares of the Southern region and crop industries in total H-2A employment figures, we offer some wage-based explanations for these patronage trends.  

H-2A employment decisions are anchored on the adverse effect wage rate (AEWR) principle, which was conceived to specifically revert any possible market anomaly when foreign workers are hired under the H-2A program. The Department of Labor (DOL) was tasked to issue a fixed wage rate (AEWR) to mitigate adverse effects on local labor market conditions that may be caused by the employment of underpaid alien workers. A current year’s AEWR is determined based on the results of the previous year’s Farm Labor Survey conducted by the U.S. Department of Agriculture (USDA) among farms with annual sales of $1,000 or more (USDA, 2023). For farm work not devoted to herding or production of livestock on the range (non-range occupations that comprise the bulk of H-2A employers),^[1] AEWRs are set at the state level and enforced to apply to all workers regardless of nationality. 

Figure 1 plots national average wages over a five-year period (2020-2024) for two farm work positions: farmworkers in crop, nursery, and greenhouse operations (usually accounting for more than 80% of all H-2A workers hired) and farmworkers in farms producing ranch and aquacultural products (which are positions held by about 4% of all H-2A workers). These wages are compared to the national average of state-level AEWRs.  An adjusted AEWR level is added to the analysis to account for discrepancies between labor remuneration packages offered to domestic and H-2A workers.  The latter not only receive wages conforming to the AEWR benchmark but are also provided with housing, transportation, meal allowances, and fringe benefits as mandated by the program.  The plots in Figure 1 indicate that crop, nursery, and greenhouse workers were consistently paid higher than H-2A workers in all years, while the adjusted H-2A wages only exceeded average livestock wages in 2023 and 2024.

The regional wage analyses provide some deviations from the earlier trends (Figure 1), which could be influenced by regional variations in demographic, structural, and economic conditions affecting H-2A employment decisions. Figures 2 and 3 present plots of the domestic wage-AEWR differentials using regional average field and livestock wages, respectively, over the same five-year period.  In these plots, a positive gap indicates a higher regional field/livestock wage than its average AEWR.  

In Figure 2 (field workers’ wages), the South region’s wage differential is positive only in 2022, while remaining negative in other years. The West, which is the second most popular regional H-2A employer, has consistently maintained a positive field wage-AEWR gap in all years. These trends indicate that while the West farms’ decisions to hire H-2A workers for field work may be motivated by wage considerations (where H-2A labor is cheaper than domestic labor), the South’s decision to hire more expensive H-2A field workers in certain years could have been driven by non-wage factors. Some analysts argue that the higher labor productivity of more expensive H-2A workers rationalizes some farms’ preference for these workers.

In Figure 3, the South posted slightly negative domestic livestock wage-AEWR differentials in 2020 and 2021; it maintained a positive gap for the rest of the period.  The West again maintained a positive gap during the entire period. These trends reveal some unique employment predicaments in livestock industries. Given that livestock farms in the country usually rely less on H-2A labor and would rather employ domestic residents, these decisions persist even when domestic livestock wages are higher than the adjusted AEWR.  Compared to crop farms, livestock farms are more inclined to seek workers and employ them for a longer tenure as their operations have longer business and production cycles.  These farms usually lure prospective workers with training offers that could upgrade their skills and job classification (from unskilled to better paying skilled positions).  A follow-up article will present more detailed evidence on livestock farms’ domestic and foreign labor hiring practices.

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Figure 1.  Adverse Effect Wage Rates (AEWRs) and Farmworkers’ Wages in Crop and Livestock Farms, U.S. Average, 2020-2024

Figure 2.  Gaps Between Adverse Effect Wage Rates (AEWRs) and Field Workers’ Wages, By Production Region, 2020-2024

Figure 3.  Gaps Between Adverse Effect Wage Rates (AEWRs) and Livestock Workers’ Wages, By Production Region, 2020-2024

^[1] Distinctions in AEWR-setting are made between range and non-range occupations. Non-range workers are employed under jobs with the following Standard Occupational Classification (SOC) titles:  graders and sorters of agricultural products; agricultural equipment operators; farmworkers and laborers in crop, nursery, and greenhouse; farmworkers in the farm, ranch, and aquacultural animals; packers and packagers (hand); and all other agricultural workers (Congressional Research Service, 2023).

References:

Calvin, L., P. Martin, and S. Simnitt. (2022). Adjusting to Higher Labor Costs in Selected U.S. Fresh Fruit and Vegetable Industries. EIB-235, Economic Research Service, U.S. Department of Agriculture, Washington, DC.

Congressional Research Service. (2023) Adverse Effect Wage Rate (AEWR) Methodology for Temporary Employment of H-2A Nonimmigrants in the United States. Washington DC.  Available online at https://crsreports.congress.gov | IF12408. Accessed on August 3, 2023.

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Escalante, Cesar L., and Alejandro Guitierrez-Li. “Relating Crop and Livestock H-2A Labor Decisions to AEWR and Sectoral Wage Gaps.” Southern Ag Today 5(18.1). April 28, 2025. Permalink