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Atzori AS, Atamer Balkan B, Gallo A. Feedback thinking in dairy farm management: system dynamics modelling for herd dynamics. Animal 2023; 17 Suppl 5:100905. [PMID: 37558585 DOI: 10.1016/j.animal.2023.100905] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Revised: 06/28/2023] [Accepted: 06/29/2023] [Indexed: 08/11/2023] Open
Abstract
Systems perspectives and system dynamics have been widely used in decision-making for agricultural problems. However, their use in dairy farm management remains limited. This work demonstrates the use of systems approaches and feedback thinking in modelling for dairy farm management. The application of feedback thinking was illustrated with causal loop and stock-and-flow diagrams to disentangle the complexity of the relationship among farm elements. The study aimed to identify the dynamic processes of an intensive dairy farm by mapping the animal stocks (e.g., heifers, lactating cows, dry cows) with the final objective of anticipating the expected milk deliveries over a long time period. The project was conducted for a reference dairy farm that was intensively managed with a herd size of >2 500 cattle heads, which provided monthly farm records from Jan 2016 to Dec 2019. Model development steps included: (i) problem articulation with farm interviews and data analysis; (ii) the development of a dynamic hypothesis and a causal loop diagram; (iii) the development of a stock-and-flow cattle model describing ageing chains of heifers and cows and subsequent calibration of the model parameters; (iv) the evaluation of the model based on lactating cows and milk deliveries against farm historical records; and (v) the analysis of the model results. The model characterized the farm dynamics using three main feedback loops: one balancing loop of culling and two reinforcing loops of heifers' replacement and cows' pregnancy, pushing milk delivery. The model reproduced the historical oscillation patterns of lactating cows and milk deliveries with high accuracy (root mean square percentage error of 2.8 and 5.2% for the number of lactating cows and milk deliveries, respectively). The model was shown to be valid for its purpose, and applications of this model in dairy farm management can support decision-making practices for herd composition and milk delivery targets.
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Affiliation(s)
- A S Atzori
- Department of Agricultural Sciences, University of Sassari, Viale Italia 39, 07100 Sassari, Italy; System Dynamics Italian Chapter, Italy
| | - B Atamer Balkan
- Department of Agricultural Sciences, University of Sassari, Viale Italia 39, 07100 Sassari, Italy; System Dynamics Italian Chapter, Italy.
| | - A Gallo
- Department of Animal Science, Food and Nutrition (DIANA), Facoltà di Scienze Agrarie, Alimentari e Ambientali, Università Cattolica del Sacro Cuore, 29122 Piacenza, Italy; System Dynamics Italian Chapter, Italy
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Li M, Reed KF, Cabrera VE. A time series analysis of milk productivity in US dairy states. J Dairy Sci 2023; 106:6232-6248. [PMID: 37474368 DOI: 10.3168/jds.2022-22751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 02/28/2023] [Indexed: 07/22/2023]
Abstract
As US dairy cow production evolves, it is important to characterize trends and seasonal patterns to project amounts and fluctuations in milk and milk components by states or regions. Hence, this study aimed to (1) quantify historical trends and seasonal patterns of milk and milk components production associated with calving date by parities and states; (2) classify parities and states with similar trends and seasonal patterns into clusters; and (3) summarize the general pattern for each cluster for further application in simulation models. Our data set contained 9.18 million lactation records from 5.61 million Holstein cows distributed in 17 states during the period January 2006 to December 2016. Each record included a cow's total milk, fat, and protein yield during a lactation. We used time series decomposition to obtain each state's annual trend and seasonal pattern in milk productivity for each parity. Then, we classified states and parities with agglomerative hierarchical clustering into groups according to 2 methods: (1) dynamic time warping on the original time series and (2) Euclidean distance on extracted features of trend and seasonality from the decomposition. Results showed distinguishable trends and seasonality for all states and lactation numbers for all response variables. The clusters and cluster centroid pattern showed a general upward trend for all yields [energy-corrected milk (ECM), milk, fat, and protein] and a steady trend for fat and protein percent for all states except Texas. We also found a larger seasonality amplitude for all yields (ECM, milk, fat, and protein) from higher lactation numbers and a similar amplitude for fat and protein percent across lactation numbers. The results could be used for advising management decisions according to farm productivity goals. Furthermore, the trend and seasonality patterns could be used to adjust the production level in a specific state, year, and season for farm simulations to accurately project milk and milk components production.
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Affiliation(s)
- M Li
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI 53705
| | - K F Reed
- Department of Animal Science, Cornell University, Ithaca, NY 14850
| | - V E Cabrera
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI 53705.
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3
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Li M, Reed KF, Lauber MR, Fricke PM, Cabrera VE. A stochastic animal life cycle simulation model for a whole dairy farm system model: Assessing the value of combined heifer and lactating dairy cow reproductive management programs. J Dairy Sci 2023; 106:3246-3267. [PMID: 36907761 DOI: 10.3168/jds.2022-22396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 11/18/2022] [Indexed: 03/12/2023]
Abstract
This analysis introduces a stochastic herd simulation model and evaluates the estimated reproductive and economic performance of combinations of reproductive management programs for both heifers and lactating cows. The model simulates the growth, reproductive performance, production, and culling for individual animals and integrates individual animal outcomes to represent herd dynamics daily. The model has an extensible structure, allowing for future modification and expansion, and has been integrated into the Ruminant Farm Systems model, a holistic dairy farm simulation model. The herd simulation model was used to compare outcomes of 10 reproductive management scenarios based on common practices on US farms with combinations of estrous detection (ED) and artificial insemination (AI), synchronized estrous detection (synch-ED) and AI, timed AI (TAI, 5-d CIDR-Synch) programs for heifers; and ED, a combination of ED and TAI (ED-TAI, Presynch-Ovsynch), and TAI (Double-Ovsynch) with or without ED during the reinsemination period for lactating cows. The simulation was run for a 1,000-cow (milking and dry) herd for 7 yr, and we used the outcomes from the final year to evaluate results. The model accounted for incomes from milk, sold calves, and culled heifers and cows, as well as costs from breeding, AI, semen, pregnancy diagnosis, and calf, heifer, and cow feed. We found that the interaction between heifer and lactating dairy cow reproductive management programs influences herd economic performance primarily due to heifer rearing costs and replacement heifer supply. The greatest net return (NR) was achieved when combining heifer TAI and cow TAI without ED during the reinsemination period, whereas the lowest NR was obtained when combining heifer synch-ED with cow ED.
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Affiliation(s)
- M Li
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison 53705
| | - K F Reed
- Department of Animal Science, Cornell University, Ithaca, NY 14850
| | - M R Lauber
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison 53705
| | - P M Fricke
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison 53705
| | - V E Cabrera
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison 53705.
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Ros MBH, Godber OF, Olivo AJ, Reed KF, Ketterings QM. Key nitrogen and phosphorus performance indicators derived from farm-gate mass balances on dairies. J Dairy Sci 2023; 106:3268-3286. [PMID: 37002136 DOI: 10.3168/jds.2022-22297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 12/06/2022] [Indexed: 03/31/2023]
Abstract
Efficient management of N and P on dairy farms is critical for farm profitability and environmental stewardship. Annual farm-gate nutrient mass balance (NMB) assessments can be used to determine the nutrient-use efficiency of farms, set efficiency targets, and monitor the effect of management changes with minimal inputs required. In New York, feasible N and P balances have been developed as benchmarks for dairy farm NMB, alongside key performance indicators (KPI) that serve as predictors for high NMB. Here, 3 yr of NMB data from 47 farms were used to evaluate the main drivers of N and P balances and identify additional KPI. From the 141 farm records, 26% met both the feasible N balances per hectare and per megagram of milk produced. For P, 53% of the records met both benchmarks. Imports, rather than exports, drove NMB primarily by feed and fertilizer purchases, consistent with earlier findings. Linear regression analysis showed that a selection of KPI currently used, particularly animal density, nutrient-use efficiency, and the amount of home-grown feed, explained a large portion of variation in NMB. Heifer-to-cow ratio and the relative proportion of various forage crops may provide further insight into the drivers of feed and fertilizer imports and ultimately farm-gate NMB. This study provides avenues toward a better assessment of whole-farm nutrient management and means for farms to communicate progress to stakeholders and consumers.
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Affiliation(s)
- Mart B H Ros
- Department of Animal Science, Cornell University, Ithaca, NY 14853
| | - Olivia F Godber
- Department of Animal Science, Cornell University, Ithaca, NY 14853
| | - Agustin J Olivo
- Department of Animal Science, Cornell University, Ithaca, NY 14853
| | - Kristan F Reed
- Department of Animal Science, Cornell University, Ithaca, NY 14853
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Li M, Rosa GJM, Reed KF, Cabrera VE. Investigating the effect of temporal, geographic, and management factors on US Holstein lactation curve parameters. J Dairy Sci 2022; 105:7525-7538. [PMID: 35931477 DOI: 10.3168/jds.2022-21882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 05/10/2022] [Indexed: 11/19/2022]
Abstract
We fit the Wood's lactation model to an extensive database of test-day milk production records of US Holstein cows to obtain lactation-specific parameter estimates and investigated the effects of temporal, spatial, and management factors on lactation curve parameters and 305-d milk yield. Our approach included 2 steps as follows: (1) individual animal-parity parameter estimation with nonlinear least-squares optimization of the Wood's lactation curve parameters, and (2) mixed-effects model analysis of 8,595,413 sets of parameter estimates from individual lactation curves. Further, we conducted an analysis that included all parities and a separate analysis for first lactation heifers. Results showed that parity had the most significant effect on the scale (parameter a), the rate of decay (parameter c), and the 305-d milk yield. The month of calving had the largest effect on the rate of increase (parameter b) for models fit with data from all lactations. The calving month had the most significant effect on all lactation curve parameters for first lactation models. However, age at first calving, year, and milking frequency accounted for a higher proportion of the variance than month for first lactation 305-d milk yield. All parameter estimates and 305-d milk yield increased as parity increased; parameter a and 305-d milk yield rose, and parameters b and c decreased as year and milking frequency increased. Calving month estimates parameters a, b, c, and 305-d milk yield were the lowest values for September, May, June, and July, respectively. The results also indicated the random effects of herd and cow improved model fit. Lactation curve parameter estimates from the mixed-model analysis of individual lactation curve fits describe well US Holstein lactation curves according to temporal, spatial, and management factors.
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Affiliation(s)
- M Li
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison 53705
| | - G J M Rosa
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison 53705
| | - K F Reed
- Department of Animal Science, Cornell University, 272 Morrison Hall, Ithaca, NY 14850
| | - V E Cabrera
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison 53705.
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Tricarico J, de Haas Y, Hristov A, Kebreab E, Kurt T, Mitloehner F, Pitta D. Symposium review: Development of a funding program to support research on enteric methane mitigation from ruminants. J Dairy Sci 2022; 105:8535-8542. [DOI: 10.3168/jds.2021-21397] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 03/30/2022] [Indexed: 11/19/2022]
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Menendez HM, Brennan JR, Gaillard C, Ehlert K, Quintana J, Neethirajan S, Remus A, Jacobs M, Teixeira IAMA, Turner BL, Tedeschi LO. ASAS-NANP SYMPOSIUM: MATHEMATICAL MODELING IN ANIMAL NUTRITION: Opportunities and Challenges of Confined and Extensive Precision Livestock Production. J Anim Sci 2022; 100:6577180. [PMID: 35511692 PMCID: PMC9171331 DOI: 10.1093/jas/skac160] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 04/28/2022] [Indexed: 11/18/2022] Open
Abstract
Modern animal scientists, industry, and managers have never faced a more complex world. Precision livestock technologies have altered management in confined operations to meet production, environmental, and consumer goals. Applications of precision technologies have been limited in extensive systems such as rangelands due to lack of infrastructure, electrical power, communication, and durability. However, advancements in technology have helped to overcome many of these challenges. Investment in precision technologies is growing within the livestock sector, requiring the need to assess opportunities and challenges associated with implementation to enhance livestock production systems. In this review, precision livestock farming and digital livestock farming are explained in the context of a logical and iterative five-step process to successfully integrate precision livestock measurement and management tools, emphasizing the need for precision system models (PSMs). This five-step process acts as a guide to realize anticipated benefits from precision technologies and avoid unintended consequences. Consequently, the synthesis of precision livestock and modeling examples and key case studies help highlight past challenges and current opportunities within confined and extensive systems. Successfully developing PSM requires appropriate model(s) selection that aligns with desired management goals and precision technology capabilities. Therefore, it is imperative to consider the entire system to ensure that precision technology integration achieves desired goals while remaining economically and managerially sustainable. Achieving long-term success using precision technology requires the next generation of animal scientists to obtain additional skills to keep up with the rapid pace of technology innovation. Building workforce capacity and synergistic relationships between research, industry, and managers will be critical. As the process of precision technology adoption continues in more challenging and harsh, extensive systems, it is likely that confined operations will benefit from required advances in precision technology and PSMs, ultimately strengthening the benefits from precision technology to achieve short- and long-term goals.
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Affiliation(s)
- H M Menendez
- Department of Animal Science (Menendez, Brennan, Quintana); Department of Natural Resource Management (Ehlert); South Dakota State University, 711 N. Creek Drive, Rapid City, South Dakota, 57702, USA
| | - J R Brennan
- Department of Animal Science (Menendez, Brennan, Quintana); Department of Natural Resource Management (Ehlert); South Dakota State University, 711 N. Creek Drive, Rapid City, South Dakota, 57702, USA
| | - C Gaillard
- Institut Agro, PEGASE, INRAE, 35590 Saint Gilles, France
| | - K Ehlert
- Department of Animal Science (Menendez, Brennan, Quintana); Department of Natural Resource Management (Ehlert); South Dakota State University, 711 N. Creek Drive, Rapid City, South Dakota, 57702, USA
| | - J Quintana
- Department of Animal Science (Menendez, Brennan, Quintana); Department of Natural Resource Management (Ehlert); South Dakota State University, 711 N. Creek Drive, Rapid City, South Dakota, 57702, USA
| | - Suresh Neethirajan
- Farmworx, Adaptation Physiology, Animal Sciences Group, Wageningen University, 6700 AH, The Netherlands
| | - A Remus
- Sherbrooke Research and Development Centre, 2000 College Street, Sherbrooke, QC J1M 1Z3, Canada
| | - M Jacobs
- FR Analytics B.V., 7642 AP Wierden, The Netherlands
| | - I A M A Teixeira
- Department of Animal, Veterinary, and Food Sciences, University of Idaho, Twin Falls, ID 83301, USA
| | - B L Turner
- Department of Agriculture, Agribusiness, and Environmental Science, and King Ranch® Institute for Ranch Management, Texas A&M University-Kingsville, 700 University Blvd MSC 228, Kingsville, TX 78363, USA
| | - L O Tedeschi
- Department of Animal Science, Texas A&M University, College Station, TX 77843-2471, USA
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Li J, Kebreab E, You F, Fadel JG, Hansen TL, VanKerkhove C, Reed KF. The application of nonlinear programming on ration formulation for dairy cattle. J Dairy Sci 2022; 105:2180-2189. [PMID: 34998551 DOI: 10.3168/jds.2021-20817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 10/26/2021] [Indexed: 11/19/2022]
Abstract
The objective of this study was to compare the application of iterative linear programming (iteLP), sequential quadratic programming (SQP), and mixed-integer nonlinear programming-based deterministic global optimization (MINLP_DGO) on ration formulation for dairy cattle based on Nutrient Requirements of Dairy Cattle (NRC, 2001). Least-cost diets were formulated for lactating cows, dry cows, and heifers. Nutrient requirements including energy, protein, and minerals, along with other limitations on dry matter intake, neutral detergent fiber, and fat were considered as constraints. Five hundred simulations were conducted, with each simulation randomly selecting 3 roughages and 5 concentrates from the feed table in NRC (2001) as the feed resource for each of 3 animal groups. Among the 500 simulations for lactating cows, 57, 45, and 21 simulations did not yield a feasible solution when using iteLP, SQP, and MINLP_DGO, respectively. All the simulations for dry cows and heifers were feasible when using SQP and MINLP_DGO, but 49 and 11 infeasible simulations occurred when using iteLP for dry cows and heifers, respectively. The average ration costs per animal per day of the feasible solutions obtained by iteLP, SQP, and MINLP_DGO were $4.78 (±0.71), $4.45 (±0.65), and $4.44 (±0.65) for lactating cows; $2.39 (±0.52), $1.48 (±0.26), and $1.48 (±0.26) for dry cows; and $0.98 (±0.72), $0.97 (±0.15), and $0.91 (±0.14) for heifers, respectively. The average computation time of iteLP, SQP, and MINLP_DGO were 0.59 (±1.87) s, 1.15 (±0.62) s, and 58.69 (±68.45) s for lactating cows; 0.041 (±0.070) s, 0.76 (±0.37) s, and 14.84 (±39.09) s for dry cows; and 1.60 (±2.90) s, 0.51 (±0.19) s, and 16.45 (±45.56) s for heifers, respectively. In conclusion, iteLP had limited capability of formulating least-cost diets when nonlinearity existed in the constraints. Both SQP and MINLP_DGO handled the nonlinear constraints well, with SQP being faster, whereas MINLP_DGO was able to return a feasible solution under some situations where SQP could not.
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Affiliation(s)
- J Li
- Department of Animal Science, University of California, Davis 95616
| | - E Kebreab
- Department of Animal Science, University of California, Davis 95616
| | - Fengqi You
- Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY 14853
| | - J G Fadel
- Department of Animal Science, University of California, Davis 95616
| | - T L Hansen
- Department of Animal Science, Cornell University, Ithaca, NY 14853
| | - C VanKerkhove
- School of Operations Research and Information Engineering, Cornell University, Ithaca, NY 14853
| | - K F Reed
- Department of Animal Science, Cornell University, Ithaca, NY 14853.
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Dillon JA, Stackhouse-Lawson KR, Thoma GJ, Gunter SA, Rotz CA, Kebreab E, Riley DG, Tedeschi LO, Villalba J, Mitloehner F, Hristov AN, Archibeque SL, Ritten JP, Mueller ND. Current state of enteric methane and the carbon footprint of beef and dairy cattle in the United States. Anim Front 2021; 11:57-68. [PMID: 34513270 DOI: 10.1093/af/vfab043] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Affiliation(s)
- Jasmine A Dillon
- Department of Animal Sciences, Colorado State University, Fort Collins, CO, USA
| | | | - Greg J Thoma
- Ralph E. Martin Department of Chemical Engineering, University of Arkansas, Fayetteville, AR, USA
| | - Stacey A Gunter
- Southern Plains Range Research Station, USDA Agricultural Research Service, Woodward, OK, USA
| | - C Alan Rotz
- Pasture Systems and Watershed Management Research Unit, USDA Agricultural Research Service, University Park, PA, USA
| | - Ermias Kebreab
- Department of Animal Science, University of California-Davis, Davis, CA, USA
| | - David G Riley
- Department of Animal Science, Texas A&M University, College Station, TX, USA
| | - Luis O Tedeschi
- Department of Animal Science, Texas A&M University, College Station, TX, USA
| | - Juan Villalba
- Department of Wildland Resources, Utah State University, Logan, UT, USA
| | - Frank Mitloehner
- Department of Animal Science, University of California-Davis, Davis, CA, USA
| | - Alexander N Hristov
- Department of Animal Science, The Pennsylvania State University, University Park, PA, USA
| | - Shawn L Archibeque
- Department of Animal Sciences, Colorado State University, Fort Collins, CO, USA
| | - John P Ritten
- Department of Agricultural and Applied Economics, University of Wyoming, Laramie, WY, USA
| | - Nathaniel D Mueller
- Department of Ecosystem Science & Sustainability, Colorado State University, Fort Collins, CO, USA.,Department of Crop & Soil Sciences, Colorado State University, Fort Collins, CO, USA
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Sherman JF, Young EO, Jokela WE, Cavadini J. Impacts of low-disturbance dairy manure incorporation on ammonia and greenhouse gas fluxes in a corn silage-winter rye cover crop system. JOURNAL OF ENVIRONMENTAL QUALITY 2021; 50:836-846. [PMID: 33861473 DOI: 10.1002/jeq2.20228] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Accepted: 03/30/2021] [Indexed: 06/12/2023]
Abstract
Manure and fertilizer applications contribute to greenhouse gas (GHG) and ammonia (NH3 ) emissions. Losses of NH3 and nitrous oxide (N2 O) are an economic loss of nitrogen (N) to farms, and methane (CH4 ), N2 O, and carbon dioxide (CO2 ) are important GHGs. Few studies have examined the effects of low-disturbance manure incorporation (LDMI) on both NH3 and GHG fluxes. Here, NH3 , N2 O, CH4 , and CO2 fluxes in corn (Zea mays L.)-winter rye (Secale cereale L.) field plots were measured under fall LDMI (aerator/band, coulter injection, strip-till, sweep inject, surface/broadcast application, broadcast-disk) and spring-applied urea (134 kg N ha-1 ) treatments from 2013 to 2015 in central Wisconsin. Whereas broadcast lost 35.5% of applied ammonium-N (NH4 -N) as NH3 -N, strip-till inject and coulter inject lost 0.11 and 4.5% of applied NH4 -N as NH3 , respectively. Mean N2 O loss ranged from 2.7 to 3.6% of applied total N for LDMI, compared with 4.2% for urea and 2.6% for broadcast. Overall, greater CO2 fluxes for manure treatments contributed to larger cumulative GHG fluxes compared with fertilizer N. There were few significant treatment effects for CH4 (P > .10); however, fluxes were significantly correlated with changes in soil moisture and temperature. Results indicate that LDMI treatments significantly decreased NH3 loss but led to modest increases in N2 O and CO2 fluxes compared with broadcast and broadcast-disk manure incorporation. Tradeoffs between N conservation versus increased GHG fluxes for LDMI and other methods should be incorporated into nutrient management tools as part of assessing agri-environmental farm impacts.
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Affiliation(s)
- Jessica F Sherman
- USDA-ARS, Institute for Environmentally Integrated Dairy Management, 2615 Yellowstone Dr., Marshfield, WI, 54449, USA
| | - Eric O Young
- USDA-ARS, Institute for Environmentally Integrated Dairy Management, 2615 Yellowstone Dr., Marshfield, WI, 54449, USA
| | - William E Jokela
- Retired. USDA-ARS, Institute for Environmentally Integrated Dairy Management, 2615 Yellowstone Dr., Marshfield, WI, 54449, USA
| | - Jason Cavadini
- Marshfield Agricultural Research Station, Univ. of Wisconsin, M605 Drake Ave., Stratford, WI, 54484, USA
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The Ruminant Farm Systems Animal Module: A Biophysical Description of Animal Management. Animals (Basel) 2021; 11:ani11051373. [PMID: 34066009 PMCID: PMC8151839 DOI: 10.3390/ani11051373] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 05/07/2021] [Accepted: 05/08/2021] [Indexed: 12/13/2022] Open
Abstract
Dairy production is an important source of nutrients in the global food supply, but environmental impacts are increasingly a concern of consumers, scientists, and policy-makers. Many decisions must be integrated to support sustainable production-which can be achieved using a simulation model. We provide an example of the Ruminant Farm Systems (RuFaS) model to assess changes in the dairy system related to altered animal feed efficiency. RuFaS is a whole-system farm simulation model that simulates the individual animal life cycle, production, and environmental impacts. We added a stochastic animal-level parameter to represent individual animal feed efficiency as a result of reduced residual feed intake and compared High (intake = 94% of expected) and Very High (intake = 88% of expected) efficiency levels with a Baseline scenario (intake = 100% of expected). As expected, the simulated total feed intake was reduced by 6 and 12% for the High and Very High efficiency scenarios, and the expected impact of these improved efficiencies on the greenhouse gas emissions from enteric methane and manure storage was a decrease of 4.6 and 9.3%, respectively.
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12
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McKnight L, Ibeagha-Awemu E. Modeling of livestock systems to enhance efficiency. Anim Front 2019; 9:3-5. [PMID: 32002245 PMCID: PMC6951937 DOI: 10.1093/af/vfz011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
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