Essays on Cropping Choice, Carbon Sequestration, and the Productivity of Saskatchewan Farms
Bibliographic record
Abstract
In the face of escalating global climate change, the need to mitigate greenhouse gas emissions has never been more pressing. Agriculture is a major source of greenhouse gas emissions, but is also gaining recognition for its potential role to reduce atmospheric carbon dioxide by sequestering carbon in the soil. A recent French initiative called 4 per mille states that if farmers were to increase soil organic carbon (SOC) on their fields by 0.4% per year, that would be enough to offset all annual anthropogenic greenhouse gas emissions and stop further increases in atmospheric carbon dioxide. This initiative highlights that only a small percentage increase in SOC over a large area of land can yield substantial environmental benefits.Government and industry stakeholders are beginning to recognize the potential for SOC, and are in the early stages of developing carbon markets for SOC. But there are still important scientific and economic questions to be answered. These questions call for further research on how to accurately measure SOC, understand soil capacity for additional carbon sequestration, and how different farm management practices affect SOC and what their associated private and public benefits are. The topic of carbon markets has garnered significant attention due to recent government initiatives promoting climate-smart agriculture and private investment in carbon offset programs. In this dissertation, I bridge the gap between research conducted by soil scientists, who focus on modeling and sampling techniques to better measure SOC, and the work of economists, who concentrate on policy design and the valuation of environmental benefits. My methodology addresses the challenges of accurately measuring soil carbon and examines how these models can be used to inform policy design and efforts to establish sustainable carbon markets in agriculture.This dissertation examines, in three chapters, the relationship between carbon sequestration, farm management practices, and agri-environmental policy. I analyze the private and public benefits from carbon sequestration on farms in Saskatchewan from 1998 to 2019. To do this, I employ a novel field-level dataset from the Saskatchewan Crop Insurance Corporation (SCIC) that includes detailed information for each field in Saskatchewan: cropping choice, yield, fertilizer use, crop insurance coverage, and the number of seeded hectares. In Chapter 1, I develop a novel SOC prediction model that builds on the existing models developed specifically for Saskatchewan soils. I examine the case of carbon sequestration in the Saskatchewan prairies, which have experienced substantial increases in SOC over the past 30 years. I use several SOC prediction models to simulate the stock of SOC on fields over time and compare the results. I then compute the external social benefit from carbon sequestration on all insurable hectares by simulating various counterfactual scenarios in which 25%, 50%, and 75% of canola hectares are replaced by summer fallow, representing a reversion to tillage-based systems, and using various values for the social cost of carbon (SCC) ranging from 14 USD/Mg to 185 USD/Mg. During the period 1998–2019, this external social benefit ranges from 481 million to 6 billion USD. Projecting average carbon inputs 150 years into the future yields an estimated external social benefit ranging from 851 million to 30.2 billion USD for the same counterfactual comparisons.In Chapter 2, I use the SOC data simulated in Chapter 1 to determine the effects of increased SOC stocks on crop yield, productivity, and on-farm profit. I use a dynamic panel regression to estimate the shadow value of SOC and find that increased SOC stocks have a statistically significant and a positive affect on crop yields. Performing dynamic simulations, I compute the on-farm and external social benefits from carbon sequestration that are attributed to selecting a particular crop rotation, and compare these benefits across rotations. I find that adoption of the Canola-Spring Wheat-Peas-Spring Wheat crop rotation leads to a 27.5%, 8.2%, and 4.4% increase in long-term average profit in the brown, dark brown, and black & gray soil zones attributable to increased SOC over 32 years. On-farm benefits from increased SOC are lower for crop rotations with lower carbon sequestering capabilities, highlighting the long-term effects on farm profitability and productivity from selecting particular crop rotations. I compute the external social benefit from the adoption of Canola-Spring Wheat-Peas-Spring Wheat rotation relative to Spring Wheat-Fallow-Spring Wheat-Fallow on all insurable hectares in Saskatchewan from 2023 to 2055, and find benefits amounting to 108 billion CAD when employing a SCC of 185 USD/Mg. I find that the external social benefits from crop rotations that include canola rather than pulses or fallow are greater than the associated private benefits. Hence, selecting crop rotations that have greater SOC sequestration potential not only improves on-farm profitability over time, but also generates sizable environmental benefits.In Chapter 3, I assess the effectiveness of second-best policies aimed at increasing the stock of SOC by examining a hypothetical policy that subsidizes additional canola hectares differently for each soil zone in Saskatchewan. To analyze the effect of these subsidies, I develop a simulation model that includes on-farm acreage responses and a SOC state equation to measure changes in SOC stocks due to alterations in land-use. I find that a policy offering optimal subsidies specific to each soil zone for additional hectares of canola, implemented in 2019 and continuing indefinitely for all insured fields in Saskatchewan, generates an external social benefit worth 15.2 billion CAD when the subsidy is set to maximize the net external social benefit (NESB), and 30.4 billion CAD when it is set to maximize the change in total welfare. These benefits are calculated using acreage responses estimated by a nested logit model with field-level data from the SCIC, and an annual carbon rental rate computed using a SCC of 185 USD/Mg. The NESB is the public benefit from carbon sequestered by increasing canola hectares minus policy costs, while the change in welfare is the NESB plus the change in producer surplus. This chapter quantifies the potential environmental and social benefits from implementing second-best policies that aim to increase SOC stocks by subsidizing changes in cropping choices.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".