Impact of climate change on the nitrogen budget of a dairy farm in the Fraser Valley, British Columbia, Canada
Bibliographic record
Abstract
The dairy sector contributed $19.9 (CAD) billion to Canada's GDP in 2015, but the industry has come under increasing public scrutiny regarding its environmental and economic sustainability, particularly under climate change. The Fraser Valley region of British Columbia, Canada, a high-intensity dairy producing region, is projected to experience higher winter, spring and summer temperatures and increased precipitation, particularly in fall, with implications for nitrogen (N) cycling within agroecosystems, crop and livestock production. This study aimed to explore N flows in a Canadian dairy farm using a whole-farm partially process-based modelling approach to investigate the impacts of different climate and cropping scenarios on farm N inputs and outputs. This study used farm data and the Integrated Farm System Model (IFSM) to assess: 1) N flows in an intensive, high-producing dairy farm in the Lower Fraser Valley with and without a winter double crop; and 2) farm production and N losses under two future climate scenarios based on medium (RCP4.5) and high (RCP8.5) emission scenarios in the near future (NF, 2020–2045) and distant future (DF, 2050–2075). Across all scenarios, the N use efficiency of the farm (N exported in meat, milk and feed / N inputs) was between 31.4 % and 34.3 % (slightly higher with winter wheat ( Triticum aestivum L.)), indicating that about two-thirds of N imported as feed and fertilizer was lost to the environment or accumulated in the soil. In the NF and DF scenarios (without double crop), the largest increases related to manure NH 3 -N losses, which rose by 8.1 % (NF) and 19.4 % (DF) from housing; 15.7 % (NF) and 44.0 % (DF) from storage; and 3 % (NF) and 18.5 % (DF) following land application. Projected temperature increases also raised emissions from synthetic fertilizer. Other gaseous N emissions generally declined in the future, probably due to increased NH 3 -N losses, whereas leaching N losses increased slightly (0.5–1.6 %), probably due to higher projected summer and fall precipitation. The winter wheat double crop scenarios generally led to lower N losses via gaseous pathways and leaching/runoff compared with the baseline scenarios, attributable to more N capture by winter wheat. The apparent loss to surrounding water and air of at least two-thirds of the N imported to the farm highlights the urgent need for the implementation of a range of management strategies that can reduce overall N imports to the system and reduce losses of N inputs via volatilization, runoff and leaching. • Dairy N flows explored using the Integrated Farm System Model under future climate and double-cropping scenarios. • Across future climate scenarios, dairy farm N use efficiency was 31.4–34.3 %. • Farm N use efficiency was slightly higher under double-cropping with winter wheat. • Largest future increases in N losses from NH 3 (housing, manure storage/application). • Loss of two-thirds of imported N highlights need for strategies to lower N imports and emissions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".