Category: Farm Management

The US poultry industry generates roughly 13.9 million tons of broiler litter (BL) annually. Broiler litter is a mixture of chicken feces, urine, bedding material (such as pine shavings, peanut hulls, or sawdust), spilled feed, and feathers. Most BL is applied on agricultural lands such as pastures and row crops as a soil amendment to improve the soil organic matter. 

Broiler litter typically contains 11 essential plant nutrients – nitrogen (N), phosphate (P₂O₅), potash (K₂O), calcium (Ca), magnesium (Mg), sulfur (S), copper (Cu), zinc (Zn), iron (Fe), manganese (Mn), and boron (B).  It is a valuable organic fertilizer for sustaining soil fertility and supporting plant growth. However, nutrient concentrations in BL are highly variable and depend on several factors such as bird age, type of ration fed, number of flocks between cleanouts, age of litter, amount and type of bedding materials, compositing method, litter pH, and moisture content. The nutrient content of litter may also vary from one poultry operation to another. A survey analyzing the nutrient content of BL samples collected from poultry houses across Alabama revealed significant variability in nutrient composition. This is consistent with the BL analysis from Kentucky in a previous Southern Ag Today article (here). Table 1 provides the range of nutrient concentrations in BL collected from seven different poultry farms supported by three different integrators (Pilgrim’s Pride, Tyson, and Ingram). The analysis revealed that N content in litter can be as high as 66 lb/ton and as low as 34 lb/ton, with a median value of 58 lb/ton. Similarly, the P₂O₅ content was found to vary between lows of 38 to highs of 59, with a median value of 42 lb/ton. K₂O was found to vary between 46 to 73 lb/ton with a median value of 52 lb/ton. Interestingly, the total carbon content of BL ranged from 260 to 609 lb/ton. Each ton of BL applied contributes a median of 528 lb of carbon to the soil. While the median values suggest a typical nutrient composition of broiler litter as 60-40-50, relying solely on these estimates can be costly for row crop growers purchasing litter as a substitute for commercial fertilizer—especially during periods of high fertilizer prices. To ensure accurate nutrient value and cost-effectiveness, it is strongly recommended that growers collect a representative sample of the litter and have it analyzed by a certified laboratory specializing in manure testing. This ensures they are getting the nutrient value they are paying for.

Growers using BL as fertilizer should be aware of the potential environmental risks associated with its application. For instance, applying litter annually for more than five consecutive years can lead to phosphorus accumulation in the soil, which may negatively impact water quality. Growers should watch for extreme phosphorus buildup by routinely testing their soils. Additionally, applying litter to fallow fields during the winter months should be avoided, as rainfall can cause nutrients to dissolve and either wash away or leach into the soil, reducing effectiveness, increasing environmental risk, and lowering the economic value of BL. The ideal time to apply litter is 10 days before spring green-up in the case of pasture and 10 days before planting a row crop. The nutrients in litter are available as both fast-release and slow-release. The fast-release components provide nutrients within a matter of 10 days, whereas the slow-release nutrients become available over months or even years. Most farmers should take advantage of the fast-release nutrients by synchronizing the litter application timing close to the timing of spring green-up.

Determining the value of BL compared to commercial fertilizer isn’t always easy. If you have values of N, P₂O₅, and K₂O, the calculation is straightforward. However, the availability of fertilizer materials may include products such as DAP (18-46-0).  In this case, use the value of N from a material that is N only (Urea) and subtract that value from the price of DAP. The remaining value is the price of P₂O₅ in DAP. Recent fertilizer prices in Alabama averaged $655/ton for Urea, $884/ton for DAP, and $509 for Potash (0-0-60). These prices give us a per unit value of N $0.71, P₂O₅ $0.68 and K₂O $0.42.  Using the median value of nutrient values in Table 1, the value of a ton of BL is $91.58. (N $41.18, P $28.56, K $21.84). If BL can be purchased, delivered, and spread for less than $91.58 per ton, it should be considered as a possible substitute for commercial fertilizer. Consider the micronutrients and carbon as a bonus towards soil fertility.

Table 1. Range of nutrient concentrations in broiler litter 

		As sampled or wet basis (lb/ton)
Sample #	moisture content (%)	N	P2O5	K2O	C	Ca	Mg	Al	B	Cu	Fe	Mn	S	Zn
1	29	34	39	46	260	42	10	5.0	0.1	1.1	6.4	0.7	52.3	0.7
2	18	53	59	73	527	46	12	0.3	0.1	1.4	0.3	0.9	12.2	0.9
3	20	66	40	52	609	23	8	0.2	0.3	0.3	0.2	0.8	9.3	0.6
4	18	58	42	60	540	40	10	0.3	0.1	0.3	0.2	1.0	23.4	0.8
5	20	65	38	54	528	46	10	0.4	0.1	0.3	0.2	0.9	24.4	0.8
6	27	54	47	48	521	34	9	0.4	1.4	0.5	0.4	1.0	12.9	0.7
7	25	60	43	50	570	27	8	0.2	0.3	0.4	0.1	0.8	9.3	0.6
Mean	23	56	44	55	508	37	9	1	0.3	1	1	1	21	1
Minimum	18	34	38	46	260	23	8	0	0.1	0.3	0.1	1	9	1
Maximum	29	66	59	73	609	46	12	5	1	1	6	1	52	1
Median	20	58	42	52	528	40	10	0.33	0.10	0.38	0.25	0.89	12.92	0.74

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Prasad, Rishi, Kent Standford, and Max Runge. “Broiler Litter as a Nutrient Source for Crop Production.” Southern Ag Today 5(24.1). June 9, 2025. Permalink

A decade ago, our friends & colleagues, Extension Economists across the Southern region, developed a comprehensive collection of articles in Surviving the Farm Economy Downturn.  Well… what is old is new again.  The issues addressed in that publication are all too relevant today.  With stagnant crop prices and elevated costs of production, the resulting thin margins in crop production make for a challenging economic environment, to say the least. Side note: it was this early collaboration that also marked the beginning of Southern Ag Today.

Back in February, we highlighted 5 key farm management strategies from the collection (see  Managing Through Tough Times).  Today, we’re focusing on one particular article discussing the borrower/lender relationship.  While most annual operating loan renewals are in place for the crop year, it’s a good time to emphasize the idea that the borrower/lender relationship should be ongoing throughout the year.  Key takeaways from Working With Your Ag Lender in Good Times and Bad:

Partnership

The dynamics of the borrower/lender relationship are unique. Much more than a simple customer transaction, both parties are dependent and literally invested in the business of the other.  As such, both should consider it a partnership and expect to work together.

Full Disclosure/Trust

A good partnership needs to be built on trust.  Both parties should be open about their business as it affects the other.  Borrowers should disclose any changes to original plans and/or other transactions that affect repayment capacity.  Lenders should fully disclose their processes, standards, credit decisions, and timing, which could affect the borrower’s access to capital and business operations.  

Communication

Communication should be continual.  Don’t leave your credit discussion to that once a year loan renewal process.  Both sides should be willing to have ongoing discussions about progress, ideas, successes, and challenges.  Importantly, don’t just engage in communication because you have something to say.  Start a conversation for the sake of what you need to hear.  

Know your business

One of the things that makes a borrower a good partner is that they know and can explain their own business very well.  A manager who is on top of their game builds confidence in the lender.  The same is true for making a lender a good partner.  Borrowers want lenders who are well-versed in the operations of their credit institution.

Know your partner’s business.

We all remember a Grandmother telling us, “Mind your own business.”  At some point, she probably also told you to “put yourself in the other person’s shoes.”  In this case, it is the business of both partners to put themselves in the other’s shoes.  Each should take the time to understand how the other operates, their incentives, their profit structure, and how they make decisions.  Listen and learn from each other, and… always listen to your Grandmother.

Check out the full article (pg. 38), as well as the other articles in Surviving the Farm Economy Downturn.  

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Klose, Steven, and Jordan Shockley. “Working With Your Ag Lender.” Southern Ag Today 5(23.1). June 2, 2025. Permalink

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