Navin Ramankutty

Crop production plays an important role in the Canadian Prairie economy. Crop yield models based on satellite data offer promise for obtaining consistent yield estimates across large regions. With the growing availability of observational yield data, mostly at coarser spatial scales, linear regression models have been a popular modeling tool in Canadian agriculture. Using historical crop yields from two spatial scales for key crops in the Canadian Prairies, this dissertation evaluates how to improve satellite-based yield estimation models by incorporating biophysical data and using new modeling algorithms.First, I developed linear regression models for the Canadian Prairies (Alberta, Saskatchewan, Manitoba) using satellite data for historical yield estimation using municipality scale data for model training. Vegetation indices derived from different satellite datasets (Terra-MODIS, Landsat 7&8, Sentinel-2) were tested. I found that a single vegetation index model could perform as well as a model trained using multiple different vegetation indices as long as the right vegetation index was identified for the purpose. This highlights the critical need to identify the target application and corresponding sensor-specific vegetation index/indices. Second, using the same municipality scale data, I investigated how the addition of weather data to a vegetation index-based model influences estimation accuracy. I also compared the performance of multiple linear regression to machine learning. I found that combining weather data with vegetation indices within a machine learning approach improved estimation accuracy by 3-5% across crops compared to linear regression using satellite data alone. Third, I developed a crop yield prediction model calibrated using sub-field-scale yields from one farm in the Canadian Prairies. I assessed the performance of machine and deep learning models and different combinations of satellite and biophysical data. I found that Random Forests using optimal inputs improved prediction accuracy by 7-8% across crops compared to linear regression using only vegetation indices. An application of the optimal model in within-season forecasting mode showed that end-of-season yield forecasts stabilized within three months of lead time for canola and wheat. The findings of this dissertation demonstrate the potential of additional biophysical inputs and non-linear machine learning models for improving crop yield estimation and forecasting.

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Agriculture in Canada and specifically the Prairies is a key provider of global calories but is vulnerable given it is mostly rainfed and is prone to droughts. In this dissertation we examine how growing conditions and productivity in the Canadian Prairies have changed and could shift in the future. Historical trends in yields and climate are examined to understand factors that have shaped production on the Prairies. We also delve into future predictions of climate change and their impact on yields as well as exploring shifts in key agroclimatic indices under a range of possible futures.First we compare and contrast yields in key field crops for both Canada and U.S. to understand spatial and temporal trends, including crops developed predominantly through private industry and through publicly funded programs. We find lower yield growths in publicly funded crops such as wheat, particularly in the U.S. This underscores the need for more targeted investments in underperforming areas and crops as well as strategies to continue increasing yields in the high-performing areas. Second, we estimated the historical impacts of climate change on yields in the Prairies. Examining trends in agroclimatic indices and developing statistical models for assessing climate impact, we find that historical temperature trends have had a negative effect on yields for most of the Palliser triangle in Alberta and Saskatchewan. Overall from 1980-2021 climate change has resulted in an accumulated net loss of -1.9MT of barley (mostly due to negative yield impacts in Alberta) while canola and spring wheat gained 1.27MT and 0.5MT respectively, compared to a counterfactual of no climate change.We then used these statistical models to estimate the impact of climate change scenarios on future Canadian Prairie crop yields. We found that by 2050 under an intermediate scenario (SSP2-4.5) the average yields of barley, canola and spring wheat could be reduced by 25%, 19% and 21% respectively. Our findings highlight that agriculture productivity in the Canadian Prairies has experienced significant change in the last 4 decades and there is a need to focus on regionally specific resilience and adaption planning to address climate change impacts.

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Food systems are at the epicenter of our environmental, equity and health crises. One way for addressing these interconnected challenges is the implementation of the human right to food. This research explores practical implications of explicitly including the right to food within state constitutions—a concept widely discussed in academia at a theoretical level but less examined empirically. Drawing on 30 countries that have recognized this right in their constitutions, this dissertation focuses on the strongest candidates: Mexico and Bolivia. This research developed a novel joined-up analytical framework, a comparative case study approach, and predominantly employs qualitative methods to evaluate the impact of a constitutional right to food on national-level legislation, policies, and court decisions.The findings reveal that the constitutional inclusion of the right to food is an effective way to advance the right’s realization. In Mexico and Bolivia the constitutional inclusion has triggered governmental actions to implement and extend the right to food beyond general constitutional principles. In Mexico, seven laws and five public policies have been enacted, accompanied by five court decisions addressing the right to food. Similarly, Bolivia has had four laws, six public policies, and three court decisions. Notably, in both countries, the three branches of government have exhibited a comprehensive understanding of the right to food. Governmental efforts to implement the right to food have considered key aspects of the right, including accountability and remedy, accessibility and availability, equity, environmental sustainability, health, procedural rights, and business responsibilities.However, despite these positive developments, legal gaps and systemic implementation challenges persist, hindering the potential of the right to food as an effective solution. Crucial right to food topics such as food loss and waste, protection for certain vulnerable populations, and social protection remain unaddressed. Furthermore, both countries face significant implementation challenges, which could explain the substantial gap between right to food promises and actual delivery. These findings highlight the need for ongoing scrutiny of human rights including right to food implementation research.

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Improving global food security while also reducing the environmental footprint of the food system is a major challenge. The responsibility for solving this is, however, not equal - there is large inequality in food consumption between the world's poorest and richest. The objective of this thesis is to estimate inequality in access to food between and within countries, and its implications for climate change and sustainable development goals.In Chapter 2, I developed a theoretically-grounded statistical model to estimate the relationship between average per capita income and food access. I then used this model to estimate within-country food access for income deciles in 135 countries. I found that, from 1961 to 2013, between-country inequality in food access declined by 48%, while within-country inequality increased by 42%. In Chapter 3, I built on these results to estimate inequality in GHG emissions from food consumption (production plus net imports). I then assessed the reductions required from the top emitters to achieve global climate targets. I found that, in 2012, between 40-45% of the world's population ate diets above a target per capita cap. In Chapter 4, I explored how future scenarios of income growth versus redistribution would meet sustainable development goals of poverty, hunger, inequality and climate. I found that both an exclusive income growth scenario and an exclusive income redistribution scenario are capable of drastically reducing hunger and extreme poverty compared to the control scenario, but both fail to eradicate them entirely by 2030. A combined income growth and redistribution scenario achieves three goals but is accompanied by a 53% increase in food consumption GHG emissions. Finally, I found that it is possible to use income growth targeting the poorest and the hungry to alleviate extreme poverty and hunger with only a small increase in GHG emissions and at a low cost.In conclusion, my research shows that there is a great disparity in access to food between and within countries. The pathways to close the gap in a timely manner must be ambitious and innovative because historic progress does not set a sufficient precedent.

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Predicting crop yield response to climate change is a topic of active research. A popular method involves building statistical models using historical climate and agricultural data, and then applying them on future climate projections for predicting crop yields. Using India as a case study, this dissertation examines these statistical models along two dimensions: the type of climate variables included, and the statistical techniques used. We also employ these models for predicting climate change impact on Indian crop yields till 2100.First, we examine the role of seasonal (e.g. total seasonal precipitation) versus subseasonal (e.g. precipitation over each crop growing stage) climate variables in explaining crop yields. We observe that even though adding extra climate variables does not always improve overall model accuracy, the proportion of yield variability explained by climate (versus non-climatic variables like geography and time) can increase significantly. This underscores the importance of combining physiological and statistical knowledge while choosing climate variables for statistical crop models. Second, we compare the well-known statistical method of OLS linear regression (LR) to a popular machine learning method called boosted regression trees (BRTs). While LR models were simpler to interpret, BRTs could uncover unexpected non-linear relationships and exhibited better yield prediction accuracy. Compared to LR, BRTs sometimes showed lower sensitivity to temperature variation. Higher flexibility of BRTs allowed them to identify obscure interactions between variables that could be missed by LR.We then use different climate variables and statistical techniques for building statistical models to predict climate change impact on India’s future crop yields. We found that nationally-averaged rice, wheat, and pearl millet yields could reduce by up to 3.4, 4.3, and 5.5 percent (respectively) by 2050 under the intermediate emissions scenario. Some parts of India may benefit from climate change, while other regions could face yield losses of up to 20 percent. Depending on the climate variables or statistical technique employed, we observe high variability in yield change predictions. We therefore suggest combining multiple models for estimating climate change impact on crop yields.

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Agriculture is at the centre of society’s most pressing sustainability challenges, including food insecurity, climate change, ecological degradation, and social inequity. Organic agriculture, when practiced according to an ethic grounded in ecology, health, fairness, and care, has been proposed as a remedy to these challenges. Building on a movement for an alternative to socially and ecologically exploitative food production, organic agriculture is now a multi-billion-dollar industry with established legal and regulatory frameworks around the world. While this growth could be seen as a success, empirical research has called into question the extent to which organic agriculture and market-oriented third-party certifications can foster sustainability transitions and has found that performance is often context dependent (e.g. depending on which practices are adopted). There remain significant gaps in knowledge about how organic agriculture is practiced in jurisdictions around the world relative to the sustainability-related principles on which it was founded, especially the principle of fairness. To address these gaps, I developed a mixed-method assessment grounded in a critical realist methodological approach to evaluate the contributions of organic agriculture to socio-ecological sustainability in Canada. I utilized both qualitative and quantitative methods—drawing from interviews with farmers, inspectors and organic policymakers, analysis of census data for farms across Canada, surveys of vegetable farmers in British Columbia and organic policy documents—to investigate how organic agriculture is shaped and enacted by organic community members at multiple scales. My analysis of organic standards in North America, along with census and survey data in Canada, provide strong evidence for higher levels of adoption of ecologically sustainable management in organic agriculture relative to all other farms. Yet, despite explicit attention to the principle of fairness in organic standards and among organic community actors, I found little evidence that organic agriculture in Canada is correlated with improved working conditions for farmworkers in practice. Across Mexico, the US and Canada, no organic standards contain any requirements related to social sustainability. At the same time, standards governance and community-led efforts toward integrating the principle of fairness into certification show potential to advance a more just and sustainable agriculture.

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Farms are becoming larger in high-income countries and smaller and more fragmented in low-income countries. This farm size transition has motivated recent international policy calls to combat inequities in the global food system and to promote more environmentally friendly production practices. This dissertation seeks to understand the impact of this transition on global food production, on the environment, and on small-scale farmers’ livelihoods. Each chapter aims to examine the underlying assumptions of these policy calls by testing the relationship between small-scale farms and key socio-ecological outcomes. Through creating a harmonized dataset across 55 countries, Chapter 2 estimates that 30-34% of the world’s food is produced by farms under 2 hectares in size and smaller farms producer a greater diversity of crop species than larger farms. Chapter 3’s meta-analysis synthesizes the past 50 years of empirical evidence on the relationships between farm size and several socio-ecological outcomes of farming systems (i.e., yield, biodiversity, resource-use efficiency, greenhouse gas emissions, and profitability). We found that smaller farms have higher yields and promote more non-crop biodiversity (across species at the farm and landscape scales) than larger farms. We found no relationship between farm size and resource-use efficiency, but smaller farms had a non-significant trend to lower greenhouse gas emissions per crop output than larger farms. Chapter 4 builds on this meta-analysis to investigate the relationship between farm size, productivity, and income using harmonized national sample surveys from 34 countries across the Global South. Our results highlight that while smaller farms are more productive than larger farms, they have lower per capita incomes. Critically, we also found that the current internationally agreed upon target of doubling small-scale farmer incomes is not aggressive enough to transition them out of poverty. Chapter 5 outlines pathways for the international community to identify and monitor small-scale food producers, which is a central hurdle to ensure governments keep their agreed upon commitments to support small-scale farmers. This dissertation makes several empirical contributions to the literature on the contribution of small farms to the global food system, and can help inform policy initiatives aimed to support small-scale farmers.

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