Erratum to: Greenhouse gas mitigation through dairy manure acidification
Bibliographic record
Abstract
V. Sokolov, A. VanderZaag, J. Habtewold, K. Dunfield, C. Wagner-Riddle, J. J. Venkiteswaran, & R. Gordon. (2019). J. Environ. Qual. 48:1435–1443. https://doi.org/10.2134/jeq2018.10.0355 The authors discovered some minor calculation errors in this article. The changes do not alter the main results and discussions, merely correct some values that might confuse the readers. We apologize for any inconvenience. Corrected values are shown in bold. Corrections to the affected paragraphs in the “Results and Discussion” section, as well as corrected Tables 2 and 3, Figure 4, and Supplemental Figure S2, are provided below. In addition, the fourth Core Idea should be changed to the following: • A range of yearly H2SO4 cost was estimated to be Can$11.3 to 33.9 cow−1. The cumulative N2O emissions over the entire study period (160 d) were 3.32, 0.82, and 1.40 g m−2, from control, medium pH, and low pH, respectively (Table 2). There was no statistical difference between treatments (P > 0.05). Le Riche et al. (2017) measured N2O emissions from manure with wood shavings as bedding, which formed a surface crust. They found N2O emissions of 11.5 g m−2, which is up to 10 times that of manure with sand bedding. The low emissions from manure with sand bedding are due to lack of crust formation on top of manure, which creates an environment for nitrification (VanderZaag et al., 2009; Le Riche et al., 2017). The absence of crust is a more influential factor than pH level on the emission of N2O. The daily average N2O fluxes were 0.021, 0.005, and 0.009 g m−2 d−1 from control, medium pH, and low pH tanks, respectively (Table 2). The control tanks showed increasing N2O fluxes from the start of July to mid-August, followed by a steep decline through to the end of the monitoring period (Supplemental Fig. S2). The acidified tanks did not show such trends, with the exception of the second low pH treatment replicate, which showed higher emissions than the other acidified tanks. Overall, the medium pH treatment reduced total CO2–eq emissions by 85%, whereas the low pH treatment reduced emissions by 88% (Table 3) compared with the nonacidified manure (control). For all treatments CH4 made up the majority of the total CO2–eq emissions. Control and medium pH treatments had 97% CH4 reductions. This is consistent with Le Riche et al. (2016, 2017), who reported that 98% of CO2–eq emissions were from CH4 for manure from the same farm. The remainder of the total emissions in the control and medium pH treatments were from direct and indirect N2O emissions, which were <4% of the total CO2–eq. In the low pH treatment, direct and indirect N2O emissions made up 12% of total CO2–eq emissions due to the lowered CH4 emissions (86%). As a preliminary assessment, the cost of acidification was calculated for use on a 150-cow dairy farm based on GHG emissions and manure production measured by Baldé et al. (2016). We assumed this using the medium pH acidification rate employed in this study (1.01 L 98% H2SO4 m−3 manure). The farm produced 25 m3 of manure per day with annual emissions of 33 Mg CH4 (Baldé et al., 2016), or 1122 Mg on a CO2–eq basis. Although only CH4 was measured by Baldé et al. (2016), we measured that >96% of CO2–eq emissions from liquid dairy manure are from CH4. The annual acid cost was calculated assuming this daily manure production over 6 mo. Since manure is usually spread in early spring and late fall, the manure stored over winter does not need acidification, since Canada has low CH4 emissions during the cold season. Therefore, only manure stored over the warm season would require any treatment. To account for possible price ranges, the cost of H2SO4 used was estimated at three price points, Can$200, 400, and 600 Mg−1 (Figure 4). The acid cost was estimated to be Can$1700, 3390, and 5090 yr−1 at rates of Can$200, 400, and 600 Mg−1, respectively. These rates would translate to Can$11.3, 22.6, and 33.9 cow−1, respectively, given that acidification occurs only during half the year when the weather is warm. Carbon credit earnings were calculated by assuming 85% reductions in GHG emissions, based on the medium pH treatment in this study. Three possible carbon credit rates were used, given that the current rate is Can$10 Mg−1 CO2–eq and is expected to increase to Can$30 Mg−1 CO2–eq by 2020, and then to Can$50 Mg−1 CO2–eq by 2022 (Environment and Climate Change Canada, 2017). The estimated earning at the three carbon credit rates, for 85% reduction from 33 Mg CH4, were calculated to be Can$9,990, 29,960, and 49,930 (Figure 4). Since many farms produce less CH4 due to management or environmental factors, we also calculated the three carbon credit rates for 85% reduction from 16 Mg CH4. These were calculated to be Can$4840, 14,530, and 24,210. Figure 4 shows the amount of savings at three acid prices for each carbon credit rate, given CH4 total production of 33 or 16 Mg. In most cases, the carbon credits were more than the cost of acid, with potential for the farmer to make profit of Can$1,450 to 48,230. Only in one scenario was there a cost of acid, which was Can$250 yr−1. Acidification of liquid dairy manure to pH 6.5 and 6.0 (compared with untreated manure with a pH of 7.0), in batch filled storage tanks, reduced overall GHG emissions by 85 and 88%, respectively. The largest contribution was from CH4, which was reduced by 87% and 89%, respectively. For NH3, there was a clear treatment effect, where emissions were reduced by 41% for the medium pH and 53% in the low pH compared with the nonacidified manure. A theoretical cost analysis using an example farm calculated that medium pH treatment would cost the farmer Can$1700 to 5090 yr−1 in acid but have the potential to be offset by carbon credits received of at least Can$4800. In the future, acidification needs to be tested at lower rates to find the best cost–benefit acidification rate, include H2S monitoring to ensure acidification is safe, as well as testing in gradually filled tanks, and on dairy farms to see the effects of acidification on a larger scale and with continuous filling.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.056 | 0.046 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".