Predicting Response of Potato and Barley to Climate Change in Maine Using the Crop Model DSSAT
Bibliographic record
Abstract
Climate change has the potential to impact yield and yield stability, and thus, sustainability in agriculture. Farmers are confronted seasonally with the challenges and unpredictability weather can bring. Current climate change projections anticipate an overall rise in temperature, precipitation and CO2 for the Northeast with weather increasing in variability in the forms of heatwaves, drought and heavy rain events. Using the computer simulation model DSSAT (Decision Support Systems for Agrotechnology), we aimed to assess the vulnerability and potential climate adaptation strategies for potato and barley in Maine. Chapter 1, “Assessing the Vulnerability of Potato and Barley to Climate Change using the Crop Model DSSAT”, encompasses the calibration and evaluation of the crop model DSSAT for two varieties each of potato (an early-season and late-season) and barley (a 2-row variety and 6-row variety) in Maine. The growth and development of each variety was assessed across numerous planting dates under a baseline weather scenario (1989-2018) and four future weather scenarios for 2050-2079, varying by emissions scenario and CO2 concentration. An additional assessment was conducted looking at yield stability under less variable and more variable weather. Following any necessary adjustments, model evaluations found the calibrated model to adequately simulate all four varieties under various management and growing conditions in the state. Subsequent simulations revealed that the late-season variety of potato and the 6-row barley variety may be more stable with climate change in Maine, while the early-season variety of potato may be more vulnerable, particularly with increased weather variability. The late-season variety of potato and both varieties of barley performed best with the earliest possible planting, while the early-season variety of potato performed better with late planting. Crop growth and development improved with climate change and projected elevated CO2 for all four varieties in terms of biomass and final yield. Crop quality could not be evaluated. Chapter 2, “Investigating Soil Health as a Climate Resilience Strategy for Potato and Barley in Maine Using the DSSAT Crop Model”, evaluates adaptive management strategies for potato and barley in Maine. Adaptive management strategies included improved soil health in a manure-based system (amended) and irrigation in a fertilizer-based system, both compared to a conventional fertilizer-based system (nonamended). Here, the model was evaluated for a set of data containing many rotations of potato and barley in a nonamended fertilizer-based system and an amended manure-based system. Following minor changes and a successful evaluation, simulations were conducted using a feasible planting date and the five weather scenarios from Chapter 1. Results found the irrigated system to perform best under all five weather scenarios for potato with the amended system a close second in production performance, while the performance of barley in the amended system was equal to that of the irrigated system. While irrigation may not be the most viable option for all Maine farmers, this study illustrated the importance soil health both now and in the future in improving or maintaining current crop production.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".