Carbon Footprint on Swine Deep Pit Finishing Farms

Carbon cycles can be as complex as we make them. In this case, I’m only going to focus on the manure portion of the carbon cycle; that is to say, I’m not looking at how diet and the crops are grown impact the carbon footprint, but only the manure management choices we make.

In terms of carbon, if we assume that the average weight of our pigs is 70 pounds and they excrete manure at a rate of the ASABE standard (0.375 kg VS/animal-day), with approximately 58% of the volatile solids being carbon, then every pig space will generate 80 kg of carbon in the manure every year. All we can do is control the form this carbon takes as we move it around.

 

Daily Haul

Although this system is minimally used in practice, it still gives us something to compare against. One quick note, I won’t be accounting for the amount of energy used to hauling the manure, only where that 80 kg of the carbon ends up. Most research suggests about 13% of the carbon in manure is stabilized in soil, which amounts to 10 kg. The other 70kg of carbon is converted into carbon dioxide, so this system generates about 257 kg CO₂ per pig.

 

Deep Pit Manure Storage

In a deep pit manure storage, the average is approximately 12.2 kg CH₄ generated per animal space per year. This is approximately 9.1 kg of carbon, but as methane, it is 25 times stronger greenhouse gas than carbon dioxide, which works out to 305 kg CO₂ equivalents. While the manure is stored in the pit, CO₂ is also generated and released; most manure-based biogas is approximately 60% methane and 40% carbon dioxide. Assuming this ratio, we will generate another 8 kg of CO₂, accounting for 2 kg of C.

 

We started with 80 kg of carbon and have converted 17 kg into gasses before application, leaving 63 kg of carbon in the manure. Again, 13% of this carbon will be stabilized in the soil, so approximately 8.2 kg, while the remaining 54.8 kg of carbon gets converted into CO₂, another 201 kg of CO₂.

 

Thus, in this system, 508 kg of CO₂ equivalents are generated per pig space per year. This is about double what we saw from the daily haul system because some carbon is converted into methane, a potent greenhouse gas. This provides our first insight into how to minimize our carbon footprint; reducing or eliminating methane emissions is critical.

 

Anaerobic Digestion of Manure

In this case, we will again, not be accounting for any energy that goes into moving manure for land application or getting it into the digester, but I will account for energy used in heating the anaerobic digester and for cleaning and compression of the generated methane to put it on in a natural gas pipeline.

 

Starting with our previous assumptions, pigs are 70 pounds. They excrete manure at a rate of the ASABE standard (0.375 kg VS/animal-day), with approximately 58% of the volatile solids being carbon giving the 80 kg of carbon per pig space per year. But in this case, we also need an estimated methane production potential, which I will estimate as 0.4 m³ CH₄/kg VS.

 

Anytime manure is stored anaerobically, some of the organic matter will break down and release methane and carbon dioxide. In an anaerobic digestion system, we want to minimize this time so more of the methane is captured in the digester. I assumed methane generated before manure collection and movement to a digester as 0.05 m³ CH₄/kg VS. This amounts to 4 kg CH₄ (3 kg of carbon, 75 kg CO₂ equivalents). Manure decomposition will also generate 2.6 kg CO₂ (0.7 kg C).

 

This leaves 0.35 m³ CH₄/kg VS of potential; I assumed the digester would be 75% efficient at converting potential into production. In the digester, we would hope to generate 21 kg CH₄ (16 kg C), which will all be combusted into CO₂ for power (58 kg CO₂); however, this means we don’t need to combust a fossil fuel for power, saving that CO₂ from being emitted, making this a negative emission of 58 kg CO₂. During digestion, we will generate 14 kg CO₂ (4 kg C).

 

The effluent from the digester needs to be stored, and as it is stored, more methane and CO₂ will be emitted. I assume that 10% of the remaining potential will be converted to methane. This is 0.7 kg CH₄ (0.5 kg C, 2 kg CO₂ equivalents) and 0.45 kg CO₂ (0.1 kg C).

 

These emissions leave us with 55 kg C in manure. Again, assuming 13% will stabilize in the soil (7 kg C) and the rest will become CO₂ (48 kg C, 176 kg CO₂).

 

Doing some math, we are at 212 kg CO₂ per pig space per year. I still need the energy to heat the digester and compress biogas. How much heat is needed is dependent on location, digester design, insulation value, and operation scale. As a best guess, I estimate this as 0.144 MMBtu per pig, with each MMBtu time 53 kg CO₂/MMbtu giving 7.6 kg CO₂. Cleaning and compressing the biogas from a pig space would take approximately 30 kWh, with every kWh generating about 0.38 kg of CO₂. Compression and cleaning of the biogas take 11.6 kg CO₂ gives a carbon balance of 231 kg CO₂ equivalents per pig space per year.

 

Implications:

What does this tell us? If we can find a way to encourage the adoption of anaerobic digestion systems, we can save around 277 kg CO₂ equivalents per pig space per year, a reduction of 55% compared to our baseline, and even slightly lower than the daily haul system. More importantly, we get that without wasting as much fertilizer value the manure would offer as daily haul systems will typically result in large amounts of nitrogen loss (next Scoop, we’ll look at energy in fertilizers and what that means for different manure systems).

Impact of Variability of Cattle Manure Application on Soil Nutrients and Crop Yield

 A study from 2011 and 2012 up in Saskatchewan looked at how the rate and uniformity of solid cattle manure application impacted crop yield under two fertilizer practices, manure application only and manure with supplemental urea fertilizer. This study is unique because it focuses on solid manure application uniformity with and without supplemental fertilizer. It used three different manure spreaders to give different manure application uniformities.

 

Manure rates tested were 0, 9, and 27 tons per acre, respectively, with three different coefficients of variation of manure application achieved (10, 50, and 110%). The low application rate supplied approximately 150 lb total N/acre, while the high rate supplied around 450 lb total N/acre. No statement on the fraction estimated to be plant available was provided, but it is fair to assume approximately 50%. On half of the plots, an additional 71 lb N/acre was applied using urea. They repeated two years growing oats in year one and barely in year two. Each year's yield was scaled to the percent of max for that year and then averaged.

Figure 1. Illustration of how solid cattle manure application rate (tons/acre) and uniformity (coefficient of variation) impacted crop yield (for oats and barley) as a function of maximum yield achieved.

 

Important notes and take-homes:

In all cases, the use of urea increased crop production, causing a significant increase (p = 0.0005) in yield. Solid manure can provide sufficient nitrogen to support crop nitrogen need on an annual basis. Still, in many cases, the higher carbon content of the manure causes early-season nitrogen tie-up, so while overall availability may be sufficient, there could still be periods of inadequate nitrogen supply.

 There were no statistical differences among treatments where urea was applied. These treatments all had sufficient nutrients to maximize yield. Urea on its own was sufficient to provide the nitrogen the crops needed.

 There was no statistical difference between manure application rates (p = 0.22). While we think more is often better, adding more manure didn't improve yield, presumably because while overall nitrogen supply was increased, a greater tie-up of nitrogen occurred with the greater manure application rates, causing a more seasonal deficiency.

 While no impact of application variability was seen on the urea applied plots, in the manure-only plots, application variability of 50% had more significant yields than 10% statistically but was no different than 110%. Generally, the data suggest improved uniformity increased yield at the higher application rate, where manure had to supply fertility.

How does uncertainty impact the manure nitrogen application rate you select?

 Selecting an appropriate nitrogen fertilizer rate is critical for optimizing profit from cornfields. Applying too little N reduces profit by reducing grain yield; too much N and you don’t get a return on the nitrogen you bought and can cause damage to the environment. In Iowa, most manure management plans are filled out using the yield goal method, with current university guidelines suggesting the use of the maximum return to nitrogen approach. If you are a long-time reader of this blog, you’ve probably seen both of these discussed before, so don’t worry, that isn’t the topic today. Instead, I’m focusing on uncertainties in the application and what that means for how we make decisions.

A lot of uncertainties exist when using manure as a fertilizer. Some examples include:

 · Nitrogen need of the crop (every growing season is a bit different)

· Spatial variation in nitrogen need to support crop production (because all soils aren’t the same)

· Nutrient content of the manure

· Nutrient variation from start to finish of manure application

· Application Rate Control and Variation in Application Rate

· Availability of the manure nitrogen to the crop

· Amount of nitrogen lost to volatilization

· Non-uniformity in application rate

 For now, I want to look at the manure application parts of this uncertainty and assume we know the crop response to nitrogen perfectly. How do all the variations and uncertainty impact the nitrogen application rate we should select? To answer this question, I first parameterized the yield response curve from the maximum return to nitrogen. The price of corn was set at $5.65 a bushel and nitrogen price at $0.40 a pound, which in a corn-soybean rotation gave an optimal nitrogen rate of 150 lb N/acre.

 The manure nitrogen content was set at 40 lbs N/1000 gallon, nitrogen availability at 95%, the nitrogen volatilization coefficient at 98%, and the desired application rate of 3706 gallons/acre calculated. A Monte Carlo simulation was then performed. For each variable that added uncertainty (manure N/content, Application rate, Volatilization coefficient, Nitrogen availability, and the knife-to-knife coefficient of variation), a normal distribution was constructed using the average value listed above and standard deviations of 2.75 lb N/1000 gallons, 250 gallons/acre, 0.01 % volatilization, and 0.05 % availability, respectively. Knife-to-knife variation varied between 0 and 100% were evaluated. I then performed 3500 simulations drawing randomly from the distributions I created to determine the nitrogen application rate for each knife (for distributions with natural limits, such as volatilization coefficient, no values over 100% were allowed).

 A lost value from application variability and uncertainty was calculated. If the actual amount of available N applied was greater than the MRTN rate of 150 lb N/acre, the value was set at the differences between the amount of N used minus 150 lb N/acre times a nitrogen price of $0.40 a pounds. If the nitrogen application rate was less than the MRTN rate, the value was set at the difference between corn yield at MRTN and the projected corn yield at the N rate applied times a corn price of $5.65 a bushel. The average loss in profit for all 400 knife simulations for each of the 3500 simulations was calculated, and then the average and standard deviation of the 3500 simulations were calculated.

A maximum return curve was calculated by taking the profit that would have been generated with perfect information (200 bu/acre x $5.65/bu – 150 lb N/acre x $0.40/lb N) minus the profit lost from uncertainty and application variability using the procedure listed above. Here we see an interesting trend – the uncertainty of ammonia volatilization and nitrogen availability and the variation in volumetric application rate and manure nitrogen content during the application, make it advisable to apply six pounds more available nitrogen per acre than if we didn’t have these variations. This occurs as the economics of nitrogen application is non-symmetrical, with the cost of being a pound short greater than being a pound heavy. Suppose we factor in any knife-to-knife application variability. The story gets more interesting, with the ideal application rate first increasing (until we reach a knife-to-knife application variability of about 40%, where the ideal rate is 167 pounds of N/acre, or 17 pounds/acre higher than the known nitrogen response curve we put in. Ideal nitrogen rate then decreasing to 137 lb available N/acre.

 

Figure 1. Impact of knife-to-knife variability of the effects of the maximum return to nitrogen for spring-applied swine manure to corn in a corn-soybean rotation. The ideal rate varies with our machinery variation.

 

But what about a fall application? As the MRTN curve is based on spring nitrogen applications, I added a term to the model to account for N-loss from fall to spring. For fall applications, I assumed an average of 15 lbs N/acre with a standard deviation of 15 lb N/acre and performed the same Monte Carlo simulation as above (but with the available N corrected for estimated nitrogen loss.

Figure 2. Impact of knife-to-knife variability on the impact of the maximum return to nitrogen for a fall and spring-applied swine manure to corn in a corn-soybean rotation and continuous corn rotations. The ideal rate varies with our machinery variation.

Almost all the curves looked the same. For example, the Maximum Return to Nitrogen in a continuous corn rotation was approximately 50 pounds higher in the continuous corn rotation than in the corn-soybean rotation, whether the manure was applied in the fall or the spring. The difference was impacted slightly by the knife-to-knife variability of the application equipment, but only slightly. Similarly, while the nitrogen loss from fall application was set at 15 lb N/acre, the fall application rates were on average 22 lb N/acre higher to hit the optimal rate.

So overall, where does this leave us. There is uncertainty and variability in every decision we make. The more confident we are about our equipment and manure, the closer our rate should be to the “true” MRTN. However, from an economic perspective, if there is uncertainty or variability in what we are doing, the right rate for us sneaks upward just slightly. This insurance N helps us in years we’d otherwise be short. And this is why we still talk about the 4Rs of right rate, right place, right timing, and the right type of fertilizer.

Manure Scoop: The Value of Real Time Nutrient Sensing

 

A few years ago, I became interested in the value of manure sampling and how obtaining good information helped us make better nutrient management decisions. I tried to use the theory of value of data to determine how much a manure sample was worth. Read a summary here.

 

Many factors cause variations in manure's average nutrient concentration: diet, housing type, manure storage type, environmental conditions, management techniques, and treatment practices. Just as critically, our ability to agitate and create a uniform, homogeneous mixture is often limited by our ability to stir manure storages.

 

A repeated sampling at five manure storages was used to assess the average, standard deviation, and coefficient of variability. The data were summarized as averages across the sampling data set to determine the variability in manure concentrations from each manure application event. Average manure nutrient concentrations were 28, 16, and 21 pounds of N, P, and K, respectively, per 1,000 gallons with standard deviations of 4, 7, and 3.

 

In determining the manure test's value, it is essential to understand how a farmer can use the information gained from the test results, i.e., how having this information alters the farmer's nutrient management and affects the farm profit. This is a complex topic, as almost limitless possibilities exist. This evaluation assumed the manure application method would be either injection or immediate incorporation to maximize N utilization. Additionally, we assumed best management practices for manure application timing were followed. As a result, the yield response to available N (defined here as the sum of ammonia N and organic N expected to mineralize in the first growing season) would be the same as the yield response to mineral N fertilizer. Finally, we limited crop rotation choices to continuous corn and corn-soybean rotations, as these represent the dominant rotations in the upper Midwestern U.S.

 

Our methodology was to estimate the profit that would have been made if the manure was assumed to have a "typical" nutrient composition and then to compare this to the profit generated if the actual nutrient composition was known. To make this evaluation, an economic model was developed as an Excel spreadsheet. The model compared the costs and revenue of corn production. Corn yield was calculated as the product of maximum yield and the estimated percent yield that was achieved.

 

For a corn-soybean rotation where the manure is going to corn, this means that the real-time nutrient correction for manure would be worth approximately $3.13 per acre. However, in a continuous corn rotation, which is more sensitive to nitrogen application in terms of crop response, it would be worth around $4.29 per acre. However, understanding just how big this variability is from load-to-load or pass-to-pass is critical for putting value to this technology.

Manure Nitrogen Availability from Manures and N Application Recommendations Around the Midwest

 

How much nutrient is there? While it seems a simple question, with manures where the only thing consistent about them is inconsistency, the answer isn't always easy. The place I like to start is what does 'availability' mean.

 I define availability as nitrogen present in a form able to be used. When talking about manures, we typically mean that this percent of the nutrient will cycle through a form that plants can use.

I'll use the term supply to specify how much is added; less the fractions are lost to volatilization, leaching, denitrification.

 Within the state of Iowa, our go-to document on the subject of nutrient availability is "Using Manure Nutrients for Crop Production," specifically table 1. A second correction is made for ammonia volatilization losses based on the type of manure (as this impacts the amount of nitrogen that is in the ammonium at the time of application) and the application method (as this influences how long the manure it's on the surface and is exposed for potential losses.

Table 1. Iowa Suggested Manure Nutrient Availability.

Manure Source	1st Year	2nd Year	3rd Year
Beef Cattle (solid or liquid)	30-50	10	5
Dairy (solid or liquid)	30-50	10	5
Liquid swine	90-100	0-10	0
Poultry	50-60	0-10	0

Table 2. Iowa Suggested Manure Nitrogen Volatilization Correction Factors.

Application Method	Incorporation	Volatilization Correction Factor
Direct Injection	-	0.98-1.00
Broadcast (liquid/solid)	Immediate Incorporation	0.95-0.99
Broadcast (liquid)	No Incorporation	0.75-0.90
Broadcast (solid)	No Incorporation	0.70-0.85
Irrigation	No Incorporation	0.60-0.75

Iowa nitrogen management recommendations either come from the Yield Goal Method or the Maximum Return to Nitrogen concept. In the Yield Goal Method. As a base case for comparison, I will look at deep-pit swine manure testing at 50 lbs/N per 1000 gallons. In Iowa, the average yield times 1.1 is 215 bushels/acre of corn and 56 bushels/acre of soybean. Assuming a nitrogen use factor of 1.2 lb N/expected bushel of corn and a soybean credit of 50 lb N/acre, nitrogen application rates would be 208 lb N/acre to the corn phase of a corn-soybean rotation and 258 lb N/acre in a continuous corn rotation. The Corn nitrogen calculator would be 140 lb N/acre in a corn-soybean rotation and 188 lb N/acre in a continuous corn rotation. I'll show a figure of these results in just a second, but I also wanted to compare them to two neighboring states, Illinois and Minnesota. Second-year N availability was estimated in the continuous corn rotation as what didn't mineralize the first year.

 Illinois uses the same nitrogen volatilization recommendations as Iowa (they come from Midwest Plan Service) and uses the Midwest plan service method to estimate nitrogen availability from the manure. For swine manure with approximately 70% of the nitrogen in the ammonia form and a 35% mineralization factor, 80% of the nitrogen will be available in the first year. The second-year availability for the continuous corn rotation is estimated to be half of the amount mineralized from the organic nitrogen fraction, which amounts to approximately 2 pounds. The desired Nitrogen application rate for Illinois is selected using MRTN, but rather than the optimum value, the maximum within a $1 profit is recommended.

Minnesota suggests that 150 plant available pounds of N per acre should be applied in a corn-soybean rotation and 195 in a continuous corn rotation. In Minnesota, they don't separate corrections for availability and loss but incorporate both into a correction factor. They also provide a second-year availability factor of 15% for swine manure.

What does this mean? I put together this figure of nitrogen application rate recommendations, two for Iowa (Yield Goal and MRTN), to compare the suggested rates for Minnesota and Illinois. Note this is only for nitrogen and doesn't consider any phosphorus limitations that may restrict manure application.

Figure 1. Summary of recommended manure application rates for the corn phase of a corn—soybean rotation. Swine manure with 50 lb N per 1000 gallons, 70% ammonium with error bars set based on high and low suggestions for nitrogen availability and volatilization losses suggested within each state.

 
Figure 2. Summary of recommended manure application rates for a continuous corn rotation. Swine manure with 50 lb N per 1000 gallons, 70% ammonium with error bars set based on high and low suggestions for nitrogen availability and volatilization losses suggested within each state.

 As we look at this data, I came home with a few takeaways. The first being, the factor ammonia loss with broadcast application in Minnesota is much higher than in Iowa and Illinois, based on the Midwest Plan Service Methods. Iowa and Illinois have volatilization factors of 15-30%, while Minnesota uses ~45%. The difference in volatilization assumption makes a substantial change in the recommended application rate.

The two recommendations for Iowa make an interesting comparison. When using the yield goal approach, Iowa's recommendations are similar to those provided with the Illinois method. In the case of continuous corn, the Yield Goal method for Iowa tends to be slightly higher, while in the corn-soybean rotation marginally lower, but overall, the results are similar.

When we look at the recommendations resulting from using the MRTN for Iowa, the results are more comparable to the Minnesota recommendations. In the corn-soybean phase, Iowa's MRNT recommendation would be lower thanks to both the lower mineralization suggestion and the slightly higher N application recommendation.

Overall, these results indicate that Iowa's approaches place us within the context of the surrounding states.