The ability to analyze rotations at the field scale across the entire Midwestern US allows us to ask how farmers optimize their rotations in complex economic and biophysical landscapes that include pressures to both simplify and diversify. Several biophysical and policy variables show statistically clear relationships with rotational complexity: high land capability, high rainfall during the growing season, and proximity to bio-fuel plants are all associated with rotational simplification. Given policy incentives, farmers often find that “corn on corn on dark dirt usually pencil out to be the way to go,” with farmers growing corn year after year when high quality soil is available. However, when that proverbial “dark dirt” is not available, calculations are not so simple. If growing conditions are sufficiently poor , these intensive corn systems may not be profitable, and farmers will have to rely more heavily on non-corn crops to maintain crop health and profitability in their fields.We see this dynamic at play with land capability in the present analysis. Despite—or rather because of— the fact that more diverse rotations improve soils, the most degrading cropping systems counter intuitively tend to occur on the highest quality land. Highly capable lands can be farmed intensively without dipping into a production “danger zone” in years with weather that is historically typical for the region, creating a pattern of land use that is likely to degrade these high quality lands in the long term and potentially jeopardize future yields, particularly in the face of climate change. Recent analyses show that enhanced drought tolerance and resilience for crops is one of the key benefits of diverse crop rotations. In the present analysis, mobile rolling shelving mean rainfall during the growing season correlates positively with rotational simplification. Farmers may therefore be employing crop rotation in areas of low rainfall to achieve production levels that will keep a farm solvent, as was seen with rotational complexity increases in Nebraska during a drought period from 1999 to 200773.

This trend is further accentuated by the negative interaction between land capability and rainfall variance in our analysis, where higher rainfall variability leads to even more diverse rotations on marginal lands.Proximity to bio-fuel plants, the main policy indicator in our model, showed a statistically clear trend towards rotational simplification, likely due to increased economic profits. Local corn prices increase by $0.06 – $0.12/bushel in the vicinity of a bio-fuel plant, amplifying incentives to grow corn more frequently. Wang and Ortiz Bobea were surprised not to find an impact of bio-fuel plant proximity on county-level frequencies of corn cropping in their own analysis, and the present analysis—done at a field rather than county scale—shows exactly this expected effect: corn-based rotations are simplified when in closer proximity to a bio-fuel plant. In the current economic and policy landscape, farmers are pushed to simplify rotations through more frequent corn cropping, especially in proximity to bio-fuel plants, while marginal soils and low rainfall pull fields towards more diverse rotations.RCI’s ability to classify rotational complexity across large regions at the field scale and with low computational cost opens doors to future analyses that explore the interplay between localized landscape conditions, management choices, and agricultural, environmental, and economic outcomes. We see a strong potential to employ this metric not only in new regions, but in analyses that address how results from field experiments with crop rotation may scale up to regional levels. We also note that the metric should be used with caution. For example, because RCI cannot recognize functional groups in crop sequences , it cannot capture the added benefits that diverse functional groups often add to a rotation. In addition, though RCI includes a perennial correction that avoids penalizing multiple consecutive years of perennials the metric likely still underestimates the benefits of perennials in rotations. RCI is neutral to the soil benefits of annuals vs. perennials, while in practice the year-round cover and crop species mixes that often accompany perennials may boost soil benefits beyond those of annuals. Consecutive years of perennials are uncommon in our study area , and we encourage caution before applying the metric to regions with a more substantial perennial presence.

We therefore recommend using RCI in studies that explore a wide range of cropping sequences where large differences in RCI are very likely to be meaningful, rather than as a tool to rank sequences that give similar scores. It is also important to note that, though the index can be applied to data of any sequence length, RCI values from different sequence lengths cannot be compared to each other; a rotation that results in a 2.2 from examining a six-year sequence will not be a 2.2 when examining a five or seven-year sequence. We also note that in using crop sequence as a proxy for crop rotation, RCI cannot fully capture the cyclical nature of true crop rotations. Because RCI examines a fixed number of years, it may “split up” identical rotations in ways that give slightly different scores or ABBAAB in a six-year sequence). As these discrepancies will decrease when longer sequences are considered, we recommend applying RCI to sequences that are as long or longer than the longest expected rotation in the study region.We hope to see RCI used in future analyses that extend beyond the Midwest; however, regional and historic patterns of crop production likely influence farmers’ rotational decisions and may render RCI scores calculated from disparate geographical regions difficult to interpret when called into direct comparison. We therefore see great promise in RCI as a rotational metric, and caution against applications that are overly narrow and overly broad .The time period chosen in this study, 2012 – 2017, coincides with the introduction of the Renewable Fuel Standard, or “bio-fuel mandate,” which took full effect in 2012. This policy mandates that 7.5 billion gallons of bio-fuel be blended with gasoline annually, and caused bio-fuel plants to open and local corn prices to soar across the Midwestern US. Now in 2021, there is significant political pressure both to maintain the bio-fuel mandate in its current state and to relax the standards, and new exemptions to the mandate have already caused several bio-fuel plants to close in the region. Given the link between bio-fuel plant proximity and rotational complexity, our analysis suggests that these closures, if continued, would likely be associated with an increase in mean RCI in the Midwestern US.

Using our current model, simulations of randomly closing 20 of the 198 bio-fuel plants in the region lead to an increase of 0.003 in average RCI in the region, driven by greater distance to the nearest bio-fuel plant. In turn, increasing average RCI by 0.003 represents, for instance, the equivalent of 41,000 ha of cropland switching from corn-soy rotations to the most diverse rotation possible . Rotational simplification near bio-fuel plants is a pertinent example of the influence that policy can have on farm management decisions and its landscape repercussions. Bio-fuel mandates are one of several policies, including crop insurance and research funding priorities, that currently maintain the profitability of corn production; however, these policies need not be the ones that define rotational landscapes, and increased funding for policies such as the Conservation Stewardship Program could better align farmers’ economic incentives with improved environmental health80 . When strong economic incentives encourage rotational simplification, our analysis suggests that it is more likely to occur on land with favorable biophysical conditions for corn growth. With our current policy structure, the highest quality lands in the Midwestern US therefore become the most prone to degradation through intensive management.As rainfall becomes more variable with changing climates, farmers around the world are contending with droughts that are increasing in both intensity and duration. For many farmers, restricted water use has become a constant and looming threat, forcing the agricultural sector to confront a key question: how can we adapt to water scarcity without jeopardizing farmer livelihoods? This question is particularly salient for California’s agricultural system, heavy duty industrial pallet racks which has become increasingly fragile in recent decades due to its dependence on a shifting and shrinking water supply. Changing climates have caused droughts that not only result in massive financial losses , but also raise major concerns for farmers’ ability to maintain continuity in their farming operations. Because California’s waters are over-allocated even in years of typical rainfall, the Sustainable Groundwater Management Act,which requires sustainable groundwater use by 2040, implies that irrigation will need to be discontinued on hundreds of thousands of cropland acres. In this backdrop, dry farming, a practice in which farmers grow crops with little to no irrigation, has quickly garnered interest from farmers and policy-makers around the state. While dry farming is an ancient practice with rich histories in many regions, perhaps most notably the Hopi people in Northeast Arizona, dry farming emerged more recently in California, with growers first marketing dry farm tomatoes as such in the Central Coast region in the early 1980’s. In a lineage that likely traces back to Italian and Spanish growers, dry farming on the Central Coast relies on winter rains to store water in soils that plants can then access throughout California’s rain-free summers, allowing farmers to grow produce with little to no external water inputs. As water-awareness gains public attention, dry farming has been increasingly mentioned as an important piece in California’s water resiliency puzzle, however, while some extension articles exist, no peer-reviewed research has been published to date on vegetable dry farming in California. We therefore assembled a group of six dry farming operations on the Central Coast to collaboratively identify and answer key management questions in the dry farm community.

Growers identified three main management questions that would benefit from further research: 1. Which depths of nutrients are most influential in determining fruit yield and quality? 2. Are AMF inoculants effective in this low-water system, and more broadly. 3. How can farmers best support high-functioning soil fungal communities to improve harvest outcomes? Growers were primarily concerned with fruit yield and quality, with blossom end rot prevention and overall fruit quality being of particular interest due to the water stress and high market value inherent to this system. Managing for yields and quality present a unique challenge in dry farm systems, as the surface soils that farmers typically target for fertility management in irrigated systems dry down quickly to a point where roots will likely have difficulty accessing nutrients and water. Because plants are likely to invest heavily in deeper roots as compared to irrigated crops, we hypothesized that nutrients deeper in the soil profile would be more instrumental in determining fruit yields and quality. As deficit irrigation and drought change microbial community composition in other agricultural and natural systems, we hypothesized that dry farm management would cause shifts in fungal communities in response to dry farm management, which could in turn improve tomato harvest outcomes. Beyond general shifts in fungal communities, farmers were specifically interested in arbuscular mycorrhizal fungi inoculants, which are increasingly available from commercial sellers. Recent research has shown that AMF can help plants tolerate water stress, and we therefore hypothesized that commercial AMF inoculants might be beneficial. We organized a season-long field experiment from early spring to late fall of 2021 to answer these questions, sampling soils and collecting harvest data from plots on seven dry farm tomato fields on the Central Coast. Each farmer managed the fields exactly as they normally would, with AMF inoculation being the only experimental manipulation. We sampled soils for nutrients and water content at four depths down to one meter throughout the growing season to determine which nutrient depths influenced harvest outcomes. We also took DNA samples from soils and roots in surface and subsurface dry farm soils, as well as nearby irrigated and non-cultivated soils, sequencing the ITS2region to analyze the fungal community to verify inoculation establishment and more broadly characterize soil fungal communities to see how fungal communities changed under dry farm management and determine whether these changes or the introduction of an inoculant influenced harvest outcomes. We then used Bayesian generalized linear mixed models to estimate the effects of nutrient depths and fungal community metrics on yield and fruit quality data from 10-20 weekly harvests on each field. Our results highlight a tension between managing nutrients for fruit yield and quality, while fungal community metrics show promise for increasing fruit quality.

Once the innovation is proven profitable and is locally available, its adoption may still be hampered by constraints facing SHF in accessing liquidity, risk-reducing instruments, information, and markets. These four categories of constraints have been extensively analyzed using in particular randomized control trials to identify their causal relations to adoption . These studies typically seek to identify ways of overcoming these constraints that could be implemented by governments, international organizations, NGOs, and benevolent agents such as philanthropic foundations and corporate social responsibility initiatives.Due to seasonality, especially under rainfed farming conditions which is where most of the lag in modernization currently prevails , there is a lack of correspondence between the timing of agricultural incomes and that of expenditures. As a consequence, the inter-temporal displacement of liquidity through credit and savings appears to be important for farmers to invest in new technologies, purchase inputs, optimize the timing of sales, buy consumption goods, and cover timely expenditures such as school fees. Financial services for SHFs appear to frequently be ill-designed for their purpose, expensive, excessively risky, and not easily available. Even when they have formal land titles, SHF are typically unwilling to put their land at risk as collateral with a commercial bank, thus acting as “risk constrained” . Microfinance products that effectively circumvent the collateral problem by relying on group lending and joint liability tend to be too expensive for the long agricultural cycles and have repayment conditions that are typically ill adapted to the timing of farmers’ capacity to pay . Availability of credit from formal sources, both commercial and non-profit, is consequently limited, weed dry rack and SHFs must either self-finance or rely on informal lenders with prohibitive interest rates. Hence, there would appear to exist a largely unresolved liquidity constraint on adoption originating on the supply side of the financial market. Yet, this is often not the main reason for low adoption which may be on the demand side.

Recent field experiments are providing evaluations of interventions aiming at relaxing the liquidity constraint on SHFs, with fertilizer the most commonly used indicator of technology adoption because of its ubiquitous recognition and yet massive underuse. While contexts and interventions vary for these experiments, they surprisingly tend to show that a liquidity constraint is not the reason why a majority of SHFs are under-investing in fertilizers. The main constraint may be instead lack of profitability in adopting fertilizers.A first category of experiments consists in providing unrestricted access to credit to a defined eligible population, as was done in Morocco , Mali , and Ethiopia . While interest rates in these studies were variously subsidized , uptake remained low: only 17% of eligible farmers took a loan in Morocco, 21% in Mali, and 36% in Ethiopia. Furthermore, farmers that did take a loan only used a small fraction of the liquidity to increase their expenditures on fertilizer or other agricultural inputs . Such credit displaces the equilibrium allocation of liquidity in favor of the targeted inputs, similarly to what a price discount would do. And yet, uptake remained low. In Malawi input credit for high-yielding maize and groundnuts was taken by 33% of the farmers . This low demand for credit thus seems to be reflective of a low demand for the inputs themselves. Low demand for fertilizer is exemplified in two rather extreme experiments. In Mali, Beaman et al. provided to another group of farmers a pure cash grant, rather than the credit described above. This only increased expenditures on fertilizer by 15%, in comparison with 11% with a credit that had to be paid for, showing that credit is not the major constraint to adoption. In Mozambique, a group of progressive and well positioned farmers, with good access to extension services and to input and output markets, were offered vouchers with a 75% discount on fertilizer price, and yet only 28% of the farmers redeemed their vouchers .

There are a few experiments that show the importance of well-tailored credit and savings products to support the adoption of profitable innovations by SHF. Two cases exhibit the importance of accounting for the seasonal distribution of farmer income. In Kenya, One Acre Fund offered harvest-time loan at 10% interest rate with repayment expected 9 months after harvest, collateralized with stored maize. The objective was to allow farmers to avoid selling their harvest at the time of the year where prices are lowest, postponing sale to the period of high prices. 63% of eligible farmers took the loan . A similar savings scheme through group-based grain storage was introduced in Kenya. Fifty eight percent of the farmers took-up the product, and were twice as likely to sell maize on the market . While these financial products contribute to the households’ welfare, and indirectly increase the return to agricultural production, there is no evidence that they induced higher adoption of fertilizer or other modern technologies. These experiments point to the existence of other constraints to fertilizer use. This can be lack of complementary inputs , excessively high risk, or high transaction costs in reaching markets that all make fertilizer use not profitable. These other constraints need to be jointly addressed with credit availability. An example is new financial products such as Risk Contingent Credit–where repayment is insured by an index insurance, where insurance serves as collateral for the loan, and where the insurance premium is paid with loan repayment at the end of the season–that have promise and are under experimentation . Another example is precision farming where soil testing allows to customize fertilizer recommendations to heterogenous local conditions and to design comprehensive technological packages . Conclusion is thus that, in spite of presumptions, credit availability is not the main constraint to fertilizer use for a large majority of SHF in SSA and SA. For them, low fertilizer use is mainly due to low profitability associated with physical, market, and institutional conditions.

Interventions to jointly secure the profitability of fertilizers and the availability of well-designed financial products for their own particular circumstances are necessary for large scale adoption to occur.Smallholder farmers are exposed to many risks that can put their livelihoods and assets in jeopardy and deter investment. Shocks include weather, plagues, prices, and health. As a consequence, SHF engage in shock-coping adjustments after an adverse event has occurred, and in risk-management strategies in anticipation of shocks difficult to cope with. Both responses are costly. Shock-coping includes dis-saving, emergency borrowing, sale of productive assets, emergency migration, use of child labor by taking children out of school, and postponement of consumption expenditures. Some of these responses can have long-term consequences, particularly when they imply decapitalization of assets, including child human capital and health . SHF also engage in risk-management practices. This includes preferring to use low return-low risk traditional technologies, holding large precautionary savings and productive assets that are biased toward liquidity , and engaging in income diversification at an efficiency cost . These costly strategies to deal with risk reduce the resources available for investment and technology adoption. Risk exposure also has a direct consequence on the adoption of technologies in reducing their expected return. Thinking of fertilizer, for example, dry racks for weed the possibly that drought or flood wipes out the harvest and hence the return to fertilizer discourages its application in the first place The obviously missing piece in the panoply of risk management and risk coping instruments used by smallholder farmers is insurance. Agricultural insurance is common in developed countries, although usually heavily subsidized. For SHF in developing countries, the administrative and implementation costs for agricultural insurance that requires verification of losses by an adjust or are too high to make it cost effective. Index insurance, where payments are triggered by a verifiable local index of rainfall or small area average yield has promise . Yet, take up has been very low, typically not exceeding 6 to 18% at market price for multiple reasons including basis risk, lack of trust, liquidity constraints, and limited salience of benefits The question for this paper, however, is whether insurance, when taken, induces farmers to adopt technology. Only a few studies in which the insurance uptake was sufficient permit this analysis. Experimental results for Ghana have shown that farmers that purchased insurance increased their use of chemical inputs by 24% . Mobarak and Rosenzweig offering a insurance service to farmers in India, show that it induced them to replace their use of traditional risk tolerant rice varieties by higher yielding varieties. Cai shows that a weather insurance policy in China induced tobacco farmers to increase their production of this risky but highly profitable crop by 16 percent and their borrowing by 29 percent. These experimental results suggest that if one could improve the design and marketing of the insurance product so that uptake could increase, technology adoption may follow. Promising avenues include products with reduced basis risk , financial training to help farmers better understand the value of insurance , group insurance on the presumption that managers have a better understanding of the product than farmers , and combining index insurance with other risk-reducing instruments, including social assistance for large shocks . Also promising is to reduce risk through resilient technology such as drought and flood tolerant seed varieties. SwarnaSub1, a superior rice technology with flood resilience properties, is appealing to farmers in flood-prone areas of India. Emerick et al. show that adoption of Swarna-Sub1 enhances agricultural productivity by crowding in modern inputs and cultivation practices , and increasing credit demand.

Finally, an innovative financial product introduced by BRAC in Bangladesh is contingent credit lines indexed on events such as flooding . This experiment shows that households given pre-approval to take a loan if they experienced flooding in their local area increased investment in risky production practices as part of their risk-management response. Since offering contingent credit lines has little ex-ante cost, the behavioral effect can be very large.A farmer’s decision to adopt a technology relies on his assessment of its value for himself. Hence beyond being aware of the existence of the technology, the farmer needs fairly complete information on the specificity of the technology, how to use it or adapt it, and how it would perform in his own context. To take some examples, the adoption of a new variety or management practice requires being aware and informed on their characteristics, associated best practices, and benefits. But even the adoption of fertilizers which are broadly familiar to most farmers, requires reliable information on their quality and the very specific quantity and timing of application that depends on local conditions. This learning process is particularly difficult due to the heterogeneity of contexts across farmers and across years . The traditional model of public or private sector extension agents faces multiple limitations. In addition to customization of the advice that should be delivered, the sheer number of smallholder farmers and their geographical dispersion limit what a public or private extension service can possibly achieve. In India, for example, fewer than 6% of the agricultural population reports having ever received information from extension services . In Uganda and Malawi, these numbers are higher but still imply receiving a service less than once a year . Given the multitude of smallholder farmers and the limited number of extension agents, extension services have typically focused their efforts on training “contact farmers” and expecting that these entry points in the farming community will circulate information in their social networks, inducing other farmers to adopt . The idea is to reinforce the process of learning and diffusion of technology from farmers experiencing for themselves to watching others experience the technology . Recent research has attempted to improve on this model by identifying optimum entry point farmers to maximize the subsequent diffusion of information and adoption. The theory of social networks gives useful clues as to which farmers to potentially select as contact farmers based on their position in the network . Other less theoretically based experiments compare the diffusion of the technology through contact farmers selected as “peer” farmers , large farmers, extension officer-designated farmers, community-designated farmers, members of women’s groups, etc. . Results do not converge to a general finding, suggesting that who is a better contact farmer is context specific. To be good entry points, contact farmers also need to be good experimenters, demonstrating the benefits of the new technology. Finding out who those good demonstrators are in not easy.

Around 50% of the land area is used for agricultural purposes and is characterized by tropical, dry, and temperate climates along with diverse ecosystems, land uses, and management practices. The region is densely populated, and per capita land availability in some countries is less than 0.1 ha and is continuously decreasing. The possibility of increasing crop area is limited. The region is undergoing rapid industrialization contributing to greater emission of GHGs. In addition, there is rapid degradation of soil quality with low SOM content due to fertility-mining practices . Lal reported a C sequestration potential of 7–10 Tg C yr1 and 18–35 Tg C yr1 from restoration of degraded land in India and South Asia, respectively. With the adoption of recommended management practices on the cropland of South Asia, SOC potential was estimated to be 11–22 Tg C yr1 . The underlying assumptions included were the implementation of appropriate policies to promote recommended management practices such as conservation agriculture , mulch farming, cover crops, integrated nutrient management with manuring and biological nitrogen fixation, weed dryers and water conservation and harvesting. Lal also reported a soil inorganic C sequestration potential of 19–27 Tg C yr 1 of secondary carbonates and 26–38 Tg C yr 1 of leaching of carbonates in the arid regions of South Asia. Using International Soil Reference and Information Centre Soil Grids 250 m and FAO GLC Share Land Cover database, Zomer et al. reported a C sequestration potential of 0.11–0.23 Pg C hr 1 in South Asia. Assuming that C sequestration continues for 20 years, the current soil C stock of 7.68 Pg is likely to increase to 9.87 or 12.18 Pg for medium and high sequestration scenarios, respectively.

Grace et al. , using IPCC methodology together with local data, calculated a sequestration potential of 44.1 Mt C over 20 years from the implementation of zero tillage practices in rice-wheat systems of India.SOC sequestration is a dynamic process, and the amount and duration of C storage depends on the pools and their cycling , the form of stabilization , and the physical location of the C in the soil . Rates of turnover of organic matter depend on soil properties such as clay content and nutrient status. Clay is one of the key carbon-capture materials and tends to bind organic matter in soil and helps to protect it from microbial breakdown . Yang et al. also showed that the quasi-irreversible sorption of high molecular-weight sugars within clay aggregates, inaccessible by the microbes is responsible for clay-C protection. In addition, temperature plays a crucial role, which is complex because of variation in the temperature sensitivities of different SOM fractions . The impact of temperature becomes more crucial with a rise in ambient temperature due to climate forcing, resulting in microbially-driven increases in decomposition. Therefore, there are limits to C sequestration which are not only biophysical but also include technical and economic barriers.Over time, SOC reaches a steady-state equilibrium, balancing C gains and losses. Since organic inputs vary in quality, quantity, and subsequent interactions with soil constituents and environment, the ability of a soil to retain C is not unlimited. Carbon saturation is often used to describe the maximum capacity of a soil to retain C as a stabilized fraction based on soil properties . Sanderman et al. opined that while the term ‘soil C saturation’ is conceptually and theoretically appealing, the results from some of the long-term experiments may not support it. For example, Blair et al. found that total C stocks increased linearly with input levels of up to 200 Mg dry weight ha 1 for 15 years, without showing any signs of saturating behavior. However, Stewart et al. found some evidence of saturation.

Likewise, Johnston et al. reported that the annual addition of farmyard manure in the Broadbalk long-term experiment at Rothamsted increased C over the 160-year period, but the higher increase in early years was followed by a slower increase in later years, arriving at a new equilibrium. The time taken for soil to reach a new equilibrium tends to vary not only between soils within a temperate or tropical environment but also between the environments. It has been suggested that SOC saturation depends on clay and silt content and that there is a critical C concentration below which a soil’s function is reduced . Nevertheless, most current SOC models assume first-order kinetics for the decomposition of various conceptual pools of organic matter , which means that equilibrium C stocks are linearly proportional to C inputs .Carbon stored in soils is non-permanent. With changes in land use and land management, soil loses C, which can only be maintained or increased with the continuous addition of C input. By changing agricultural management or land use, soil C is lost more rapidly than it accumulates . Soil clay plays an important role in retaining C. Agricultural soil with a 50% clay content requires >2.2 Mg C ha 1 annually to maintain a given C level, while agricultural soil containing a 30% clay content requires more than 6.5 Mg C ha 1 annually. In addition, the rate of C input must be higher at existing soil C levels to maintain a level stock of C in the soil . Microbial decomposition and mineralization to CO2 is the major outcome of organic C. Approximately 1–2% of crop residues are stabilized as humified SOM for a period that are composed of large complex macromolecules, carbohydrates, proteinaceous materials, and lipids. This could be 60–85% of the total SOM . However, this notion, which was based on chemical analysis of the extracted materials has been challenged, and recent understanding suggests that humic substances are marginally important . Based on direct high-resolution in situ observations with non-destructive techniques, it has been established that humic substances are rather simple, smaller biomolecules . Although Hayes and Swift however, strongly disagreed with these views. They presented a detailed account of decomposition processes leading to the formation of a range of products including soil humic substances with a degree of resistance to microbial degradation. The new thinking in SOM research suggests that the molecular structure of plant inputs and organic matter has a secondary role in determining C residence times over decades to millennia, and that C stability depends mainly on the biotic and abiotic environment . The biotic and abiotic factors along with dynamics of labile C pools are required to evaluate management, land use, and climate change effects on SOC changes and soil functionalities .

New findings suggest that microbial decomposition actually facilitates long-term C sequestration by maintaining C flow through the soil profile , and that infrequent tillage may not cause sufficient disruption of soil aggregates leading to C loss . Schmidt et al. proposed that a new generation of experiments and soil C models will be needed to make advances in our understanding of SOM and our responses to global warming.While many land and crop management practices are known to enhance SOC sequestration, benefit accrual is constrained by the existence of numerous adverse forces on the ground. Table 5 provides key adoption constraints to an effective SOC sequestration strategy, the existing practice, and their implications. There are major barriers for farmers to adopt SOC sequestration practices because of the trade-offs involved. For example, the removal of crop residues from the field for other uses such as fodder, fuel, and fencing are traditional practices for managing residues. Not only is this an economical option for farmers, but there is also a lack of knowledge and capacity which discourages the adoption of practices promoting SOC sequestration. Likewise, shifting to zero- or reduced-tillage requires altering farm implements/equipment and the substitution of conventional crop and weed-control methods. The adoption of practices to enhance SOC also involves additional costs and the risk of getting lower yields in the short term. Much remains unknown about SOC storage, so it is difficult to estimate total benefits and to know which soil management practices offer the most potential for a given soil type, climate, and crop.Not only does SOC sequestration involve economic and biological costs but there can also be environmental cost. When mismanaged, some management practices that are known to result in C sequestration and GHG mitigation risk losing SOC and/or enhancing GHG emissions. Notably, N fertilization, either from organic or inorganic source, drying cannabis has negative consequences when applied sub-optimally–used either insufficiently or excessively. On one hand, when applied in inadequate amounts over time, for example in Africa, then there is no or negligible soil C build up . On the other hand, when applied in excess, for example in China and India, then soil C decreases from enhanced decomposition, which increases N2O emission, NH3 volatilization, and/or NO3 leaching. No-till compared to conventional tillage is another example of a practice that is reported to result in higher N2O emissions . No-till adoption may also increase the use of herbicides and pesticides, potentially affecting the environment . Sub-optimal or excess organic amendment to soil can also have an adverse effect on grain yield from nutrient immobilization. A growing interest in bio-fuel, resulting in a competition for fixed C, could also be a threat to SOC sequestration , as the use of bio-fuel involves burning of C which originated recently from photosynthetic activity.Lal et al. proposed six soil C management strategies to increase SOC: minimum disturbance of soil, maintenance of permanent ground cover, intensification of nutrient recycling mechanisms, creation of a positive nutrient balance, enhancement of biodiversity, and reduction in losses of water and nutrients. These strategies are generally applicable in South Asia and could be achieved notably through conversion of degraded land to perennial vegetation, increasing the NPP of agricultural ecosystems, and converting conventional tillage to no-till farming opined that a C-management strategy should not only be able to increase SOC content, but also should have some potential for reducing GHG emissions. Carbon management practices are aimed at increasing the ecosystem C balance by adding more C into the soil , increasing below- and above-ground biomass , sequestering SOC , and also reducing C losses from the soil . In the eastern Indo-Gangetic Plains of India, CA management practices like zero tillage with partial residue retention in rice-wheat systems could increase SOC content by 4.7 Mg C ha 1 after seven years of practice . Avoidance of adverse land use, management strategies, and restoration of degraded land can help in maintaining SOC stocks in soil . Table 6 provides details of various management options for increasing soil SOC stocks and reducing GHG emissions.Proper land leveling is known to enhance input use efficiency, crop growth, and yield . In South Asia, the majority of agricultural lands are poorly leveled by traditional land-leveling practices . Precision land leveling is laser-assisted, and very fine leveling of land is achieved with the desired grade within 2 cm of its average micro elevation . PLL is known to lower GHG emission by improving water and N use efficiency . Under Indian conditions, PLL could reduce almost0.15 Mg of CO2-e ha 1 year 1 of GHG emissions due to less time spent for pumping irrigation water and decreased cultivation time . PLL is critical for efficient water use and for increasing water productivity, and improves crop productivity through better crop establishment practices . There has been 6–11% and 10–25% increases in wheat yields in Punjab, India due to PLL. The associated increase in NPP in terms of crop residues and below ground biomass can be a source of soil C if further managed properly.Loss of SOC is often attributed to the practice of tilling the soil. Adoption of zero or reduced tillage will enable SOC sequestration, and is believed to be one of the key global mitigation strategies of climate change. Zero tillage has been widely reported as a viable option in increasing the C storage in soils , although few have reported no change . Most of the cases where zero tillage showed SOC increase, were mostly sampled to a depth of 30 cm or less, thereby not-revealing changes down the profile. In limited studies, where soil sampling was beyond 30 cm, no apparent difference in SOC between conservation and conventional tillage was recorded . Soil aggregates are stabilized under reduced and zero tillage practice, which physically protect C from mineralization , however, the effect is realized over the long run .

In general, an influx and accumulation of fresh manure to a corral encourages methanogenesis and also enhances N2O and NH3 emissions with concentrated urine patches . In several studies, CH4 uptake occurred in corrals, specifically in late summer when soil was dry and in winter when soil was frozen or cold, thereby inhibiting methanogenesis . On the barn floor, aerobic and anaerobic conditions may also lead to relatively lower N2O emissions . In addition, CH4 emissions tend to be negatively correlated with heat stress in naturally ventilated dairy barns because of decreased animal activity . ΔN2O:ΔCH4 maxima were also highest during the summer for free stall barns, corrals, and crops. This may be explained by the higher air temperatures in animal housing areas and irrigation of croplands using the manure wastewater from the holding pond. Generally, the relative abundance of available N, whether as NH4 + or NO3 – , soil oxidation reduction potential, soil temperature, moisture, oxygen availability, pH, microbial communities, and degradable carbon sources impact N2O emissions. Manure application to cropland promotes N2O and NH3 losses . Direct N2O losses are generated from nitrification and denitrification reactions in the soil. NH3 volatilization is an indirect source of N2O when NH3 is volatilized from manure, for example, and re-deposited onto soil, where it is converted into N2O. Methane losses from manure application are relatively low because of carbon uptake by the soils under aerobic conditions . Higher N2O emissions generally occur in warmer and moist soils, drying weed which enhance denitrification and nitrification . Thus, during the summer, higher temperatures and moist conditions in the animal housing areas increased CH4 emissions and, to an even larger extent, N2O emissions.

We also observed higher N2O losses from manure effluent application during the summer, with relatively low CH4 emissions.Manure lagoons were characterized by considerably higher ΔN2O:ΔCH4 in autumn compared to winter and summer measurements. Aerobic conditions at the inlets of manure lagoons can lead to denitrification reactions performed by facultative anaerobes . Summer measurements are likely to have higher CH4 emissions relative to N2O emissions. Increasing air temperatures and wind speed commonly increase CH4 emissions since they affect microbial activity, diffusion, and convection of liquid manure storage . N2O emissions from denitrification are also impacted by similar factors, including warm temperatures, labile C, and anaerobic conditions . Other factors that influence N2O emissions from manure include redox potential, pH, and substrate concentration. Winter measurements were conducted a few days after a rainfall event, which may have increased CH4 emissions from the manure lagoons relative to N2O emissions. Methane emissions may increase after a rainfall event, given that it agitates the surfaces and increases ebullition rates of CH4 from super-saturated lagoon waters . Our study shows that manure lagoons had relatively higher CH4 emissions than N2O emissions during the summer, given warmer air temperatures, and winter months, following agitation of manure surface from rainfall events. The solid drying area and dry bedding of manure had higher ΔN2O:ΔCH4 values in winter relative to autumn measurements. Winter measurements were conducted only a few days after a rainfall event, which may have produced higher N2O emissions relative to CH4 emissions in the dry manure storage piles. In contrast, dry bedding had relatively highΔNH3:ΔCH4 values in the summer compared to autumn measurements. Higher air temperatures during the summer may have volatilized more NH3 relative to CH4 emissions. The solid drying area had the lowest ΔNH3:ΔCH4 values among all sources across seasons.

Higher ΔNH3:ΔCH4 values for the solid drying area were observed in the winter and spring relative to summer and autumn. Solid manure storage is heterogenous in aerobic and anaerobic composition depending on manure management practices. Nitrous oxide emissions from solid manure storage are positively related to total N content since it enhances nitrification and denitrification . N2O production is also positively related to the total carbon content because denitrifiers strongly rely on carbohydrates for energy . The heterogeneity of solid manure storage also affects the relative abundance of methanogens and methanotrophs in the substrate . Methane fluxes from solid manure systems are positively correlated with moisture, C/N ratio, NH4 + -N, and total organic carbon . High CH4 and NH3 emissions occur primarily at the early stage of decomposition of carbon and nitrogen sources from fresh manure . Methane fluxes increase with higher NH4 + since it inhibits CH4 oxidation via production of toxic hydroxylamine and nitrate from ammonium oxidation or competition for methane monooxygenase. Static solid manure piles are predominantly aerobic, but may form anaerobic areas if the proper moisture, density, and porosity is met. The anaerobic areas in the piles enhance CH4 emissions . In our study, N2O:CH4 enhancement ratios from dry bedding were primarily influenced by rainfall events that enhanced N2Oemissions during the winter measurements. In addition, NH3:CH4 enhancement ratios from dry bedding were primarily influenced by higher air temperatures that increased NH3 emissions during the summer. This study’s enhancement ratios were consistent with previous relevant studies . Our summer and autumn NH3:CH4 and N2O:CH4 enhancement ratios were higher than previously reported in literature and state inventories. Our winter NH3:CH4 enhancement ratios were lower compared to another California study conducted during the winter . This difference may be explained by the rainfall events presiding our measurements, which enhanced CH4 emissions more than NH3 emissions. Our work underscores the importance of seasonal measurements as enhancement ratios are greatly influenced by changes in environmental factors, such as temperature, rainfall, and wind speed. Enhancement ratios may be a useful tool to characterize and identify emission sources from dairy farms. As shown in this study, animal housing , wet manure management , dry manure management , silage piles, and cropland had distinct enhancement ratios. This tool could be particularly useful for source attribution of an emission plume in a region with multiple sources of CH4, NH3, and N2O emissions.

Seasonal information about enhancement ratios is also important as shown by the seasonal variability in enhancement ratios for different sources of emissions. Dairy management practices and physicochemical and meteorological factors greatly influenced the relative contributions of CH4, NH3, and N2O emissions.Manure lagoons contribute about 35% of California dairy farm CH4 emissions statewide . In these lagoons, organic-rich manure waste is stored as a liquid, creating anaerobic conditions that produce CH4 that is subsequently emitted to the atmosphere, much of it from the lagoon surface. However, our understanding of manure lagoon CH4 emissions is far from complete, complicating mitigation strategies for reducing or capturing CH4 . In addition, temporal and spatial variability complicate emission estimates, which depend on physicochemical and micrometeorological predictors. Physicochemical predictors include organic substrate availability, pH, oxidation-reduction potential , nutrients, electron acceptors, curing weed chemical oxygen demand . Micrometeorological factors include air and pond temperature, friction velocity, wind speed, and precipitation . As such, it is essential to quantify the magnitude and uncertainty associated with CH4 emissions from dairy manure lagoons specific to the location of interest. The processes that impact CH4 fluxes from manure lagoons are production, transport, and consumption. The large amounts of organic substrates found in liquid dairy manure under anaerobic conditions provide a conducive environment for methanogenesis and CH4 production. Acetoclastic methanogens and acetogenic and hydrolyzingmicroorganisms drive this methane fermentation process. Methanogenic substrates, such as H2, CO2, formate, and acetate, are generated as by-products by microorganisms in the dissolved and suspended solids found in the stored liquid manure . Total solids content in dairy slurry is an indicator of the volatile solids content, the biodegradable organic matter that may produce CH4 . Dairy slurry with high VS content tend to have higher CH4 production rates . Favorable conditions for methanogenesis include neutral pH, ORP below -200 mV, nutrients and depletion of electron acceptors such as NO3 – . The fraction of degradable organic matter greatly determines the amount of CH4 production in liquid manure and is expressed as biochemical or chemical oxygen demand . Higher BOD or COD tends to produce more CH4 . Methane oxidation can occur when there are low CH4 production rates under high oxygen conditions and a slow diffusion process . Slurry may form crusts as it contains more solids that can float to the lagoon’s surface. The crust layer may slow the diffusion of gases and provide a conducive environment for CH4 oxidation under aerobic conditions . The primary transport pathways for CH4 to reach the surface of manure lagoons are through diffusion, ebullition , and agitation events . Diffusion of CH4 occurs within the aqueous boundary layer orplant-mediated transport via aerenchymatous vegetation. Transport of dissolved gases through the aqueous boundary layer is generally a slow process that is dependent on the concentration gradient . Albeit uncommon in dairy manure lagoons, another potential CH4 pathway is through aerenchymatous vegetation, as is commonly found in wetlands and lakes .

Methane may also escape through ebullition when CH4 is produced at such a fast rate that it forms bubbles and passes through the substrate layer . Mechanical agitation, from such events as rainfall and high wind speed, may also release CH4 trapped in manure lagoons to the atmosphere . Wind speed and friction velocity affects near-surface turbulence, and subsequently influences ebullition and diffusion of gases . Increased turbulence of the lagoon surface emits more CH4 to the atmosphere . Temperature can influence diffusion and ebullition of CH4 fluxes from the lagoon surface at short time scales through changes in CH4 solubility, transfer of gas across the air–water interface, and thermal contraction and expansion of free-phase gas . Latent heat flux at diel scales serves as a proxy for CH4 volatilization as evaporation of water and CH4 emissions are driven by similar physical mechanisms and tend to positively covary . Methane production and oxidation rates are also impacted by the temperature effect on microbial metabolism and enzyme kinetics, with higher temperatures generally associated with higher CH4 production or oxidation rates. Furthermore, CH4 production is influenced indirectly by temperature through seasonal changes in substrate availability . These methods can be broadly separated into two categories: floating chambers and micrometeorological methods . Each of these approaches has its benefits and disadvantages. One of the main advantages of the eddy covariance method is its ability to measure long-term diurnal and temporal CH4 fluxes. It is relatively low-maintenance and time-efficient compared to other techniques. Like other micrometeorological methods, the eddy covariance technique also measures across large spatial scales without disturbing the ecosystem. However, there is an inherent uncertainty with CH4 emission estimates using micrometeorological methods since they are each based on unique assumptions about the micrometeorological transport of mass and energy and surface homogeneity . Another disadvantage of using the eddy covariance method is the inability to separate CH4 fluxes between different areas of the manure lagoon. Other micrometeorological methods, such as presented in Thiruvenkatachari et al., , where mobile atmospheric measurements were coupled with a dispersion model, and floating chambers could apportion CH4 emissions to different areas of the manure lagoon. Floating chambers are a cost-effective method to measure accurate direct CH4 emission rates from different regions of the manure lagoon. Some of the disadvantages of floating chambers include: it is labor-intensive; there is a risk of disturbing the observational environment; chambers capture only a snapshot of CH4 fluxes at a given point in time; and the sampling protocol needs to be carefully designed to avoid inaccurate estimates, such as large pressure differences between the inside of the chamber and ambient levels . In addition, floating chambers run the risk of over accumulation of CH4 within the chamber. This is especially a risk in manure lagoons where CH4 can reach high concentrations at fast rates. In California, there are 1,750,329 milk cows, of which 93% are in the Central Valley, wherein the predominant manure management includes storage of manure in lagoons . The California GHG inventory currently quantifies CH4 emissions from dairy manure management practices with emission factors based on several parameters, including cow population and demographics, average statewide manure management practices, and climate . However, these estimates are based on emission factors derived from few pilot and lab-scale studies outside of California . Consequently, current GHG inventory estimates are likely not representative of California’s climate and unique biogeography. In addition, the current inventory includes no temporal information on emissions at timescales shorter than 1 year. So far, there is not a clear consensus whether inventories are representative of emissions given a dearth of measurements. As such, a major obstacle to assessing emissions through field measurements and comparing them to inventories are the different timescales .

While the condition of some women, communities of color, and low-income communities, for example, has improved in some regards, such communities ultimately still experience the brunt of an unjust food system, particularly in terms of wealth, land access, access to positions of power, and degree of democratic influence. Thus, given both the racial/ethnic, gender, and economic inequities found, and the structural barriers to addressing such inequities found, this report also posits a couple long term strategies from which to envision a new life for the Farm Bill in particular, and food and agriculture policy in general. The first, for example, concerns Farm Bill programs that have the potential to be effective anti-poverty programs, such as SNAP. One approach could be overhauling such programs so that they stay beyond the influence of corporate interest groups and lobbying efforts. This, in essence, would require removing such programs from the Farm Bill, redesigning them primarily as anti-poverty and economic stimulus programs, and recovering, in part, their original potential. Another, for example, concerns the Farm Bill’s remaining titles that have somewhat improved the conditions of marginalized communities, such as its Rural Development programs. One approach could be keeping programs geared toward rural development within the Farm Bill while giving them a more central role, thus uplifting farmers as well the communities in which they live and work. Ultimately, given such short term and long term strategies, this report neither calls simply for minor reforms to the Farm Bill, nor calls for throwing it out and doing something different. Rather, it calls for a combination of both.The US Farm Bill reflects a prime opportunity to challenge corporate control and structural racialization from multiple angles: social, political, economic, commercial grow room and environmental. It also reflects a prime opportunity to address corporate control and structural racialization within multiple time frames and at multiple scales: from the scale of the food system to that of society itself.

Yet such attempts at structural change will have little traction unless such demands come from a very powerful social movement. That is, structural change requires a strong and united movement that is capable of organizing and mobilizing at the state and national level, and that ultimately aims to produce conditions required for food sovereignty, including food access, health equity, fair and living wages, land access, just immigration policy, restraints upon corporations, non-exploitative farm labor conditions, and environmental well-being, among others, in particular, and racial/ethnic, gender, and economic justice, more broadly. Such a movement would thus need to encompass grassroots and advocacy organizations that are anti-capitalist, new economy, anti-racist, and feminist, and that are oriented toward environmental justice, labor rights, immigration rights, food justice, climate justice, and human rights, among other strategies and goals. The food sovereignty movement itself already embodies much of this coalitionary work and is carried forth by a wide ranging group of organizations including, among others: La Via Campesina, The Network of Farmers and Agricultural Producers Organizations of West Africa , Eastern Africa Farmers Federation , Eastern and Southern Africa Farmers’ Forum, We Are the Solution, and other agrarian-based farmers’ movements; the International Planning Committee on Food Sovereignty; ATTAC; We Are the Solution; World March of Women; many food justice and rights-based movements; and indigenous peoples movements in North America and elsewhere that engage with the particular histories of colonialism in their respective regions. This movement necessarily calls for food systems change on the basis of entitlements, structural reforms to markets and property regimes, and class-based, redistributive demands for land, water and resources. Demands for food sovereignty are frequently anti-imperialist, anti-corporatist and/or anti-capitalist. In this framework for social, political, and economic change, the Farm Bill then is a barrier to true structural change, as it itself has become a pillar of neoliberalism, and has long impeded democratic influence with layers of committees. However, although the food sovereignty movement, broadly, is oriented towards a number of critical issues , there exists a gap that this report has aimed to address. 

That is, still lacking from the core of such efforts—particularly as they take shape in the United States—is an anti-racist critique that acknowledges and aims to address the underlying racial logic and history of not only the Farm Bill, but of all domains of life—social, political, economic, and environmental—including neoliberalism, and thus corporate control, itself. Such a movement must not be afraid to mark this racial logic and history as that of white supremacy, and its concomitant logics and histories as those of heteropatriarchy and colonialism and imperialism, visible, at the very least, in all the ways outlined in this report. In short, a just and democratic food system is not simply the end goal. Rather, it is also a strategic means to challenging the structures that impede the possibility of a just life for all peoples in all domains of life. Only when the agenda and work of the broad-based food sovereignty movement upholds a meta-narrative that takes into account wealth, race/ethnicity, and gender, can the struggle that low-income communities, communities of color, and women face with regard to the food system be connected to the struggles they face elsewhere—including labor, employment, health, housing, the school-to-prison pipeline, and police violence. Only then can such a movement truly strive for a just society that upholds the dignity for all peoples.To address such concerns, it is imperative for researchers to participate in UA actively, make findings accessible to participants, and/or involve community members in the research process.Local government officials should promote UA sites and ensure that information on UA is frequently updated. As discussed by Jackson et al. , who conducted an inventory of UA in Los Angeles County, online sources like Google Maps may be unreliable due to the “transitory status” of UA sites, which “are subject to sporadic vacancy of land, funding, and active volunteerism.” During this study, I had difficulty determining whether UA sites were still active or not due to the absence of information, outdated websites, or out of service phone numbers. I identified 14 UA sites that were either rebuilt, changed management, or ceased operations. As of April 2024, the city has not yet published an updated map. To aid the creation of UA sites, cities can incorporate pro-agriculture zoning codes and UA incentive programs. City zoning codes for Long Beach permit agricultural activities and allow UA within multi-family, commercial, and light industrial zones .

Long Beach’s Urban Agriculture Incentive Zone Program offers a property tax reduction to vacant lot owners who allow their property to be used for UA. The UAIZ program connects owners to local farmers and gardeners, who are required to use organic agriculture methods . Landowners are eligible to participate in the UAIZ program if their lot is between 0.10 to 3 acres in size, does not have habitable structures, and is not listed on the Department of Toxic Substance Control’s EnviroStor Database . However, UA leaders expressed concerns about the uncertainty of leasing land from the city and private owners. The UAIZ Program’s lease-term was a minimum of five years. After the term ends, landowners can decide not to renew the lease, forcing UA participants to move. UA leaders were also required to obtain administrative use permits from the city, which can be time-consuming. Cities could offer affordable, dry racking expedited permits for UA sites. The City of Escondido’s “Adopt-A-Lot” policy encouraged the use of vacant land for community gardens by offering a no-fee permit and waiving normal zoning requirements . Policymakers can foster the success of UA sites, especially those in low-income neighborhoods, through resources and funding. They can demonstrate support by providing in-kind donations, speaking at community events, and promoting UA in press releases and social media coverage. UA sites needed resources for capacity building, such as assistance with grant writing, fundraising, and hiring and training staff. Grants awarded by federal, state, county, and city departments were often UA sites’ largest source of funding. The State of California, for example, bolstered Ground Education’s ability to deliver gardening lessons to thousands of students. In 2022, the nonprofit was awarded $315,000 through the Expanded Learning Opportunities Program, which funds after-school and summer school enrichment programs that serve disadvantaged students . To conclude, the longevity of UA sites is determined by partnerships between multiple entities, including those at the policy-level. Policymakers possess the power to decide whether community members have the right to build and grow their UA sites. Even thriving UA sites are at risk of closure, if the land they exist on can be sold or repurposed at any time. Therefore, UA leaders call for policies and funding opportunities that incentivize the development and sustained operation of urban gardens and farms. Policymakers should consider UA as a long-term investment in their communities’ health for future generations.Agricultural practices impact and influence climate change and air quality, with an estimated 23% of total anthropogenic greenhouse gas emissions stemming from Agriculture, Forestry and Other Land Use . In the United States alone, livestock contributes an estimated 66% of total agricultural GHG emissions . The primary emissions stem from greenhouse gases, such as methane , nitrous oxide , carbon dioxide , as well as air pollutants, such as ammonia , a gas-phase precursor to fine particulate matter. CH4 is more efficient at trapping infrared radiation than CO2, with a lifetime of about 10 years in the troposphere and a global warming potential about 28 times that of CO2 on a 100-year scale . Since 2007, the global mole fraction of atmospheric CH4 has steadily increased from 1781 ppb to 1895 ppb. Meanwhile, the 13C/12C isotopic ratio of CH4 has shifted to more negative values, suggesting a shift towards more biogenic sources that may include an increase in agricultural sources . Atmospheric N2O levels are about 334 ppb and have increased by more than 20% since 1750, with a GWP 265 times that of CO2. The agricultural sector contributes an estimated 52% of anthropogenic N2O emissions . In addition, global emissions of NH3 have doubled in the last 70 years, and are expected to rise, posing a concern for poor air quality . In the United States, California leads the nation in dairy production, with 1.8 million milk cows and $6.5 billion in milk sales . In the last decade, the State of California has emerged as a leader in GHG reduction strategies. Under California’s Global Warming Solutions Act of 2006 [Assembly Bill 32 ], the State mandates that GHG emissions are reduced to 1990 levels by 2020 . Additionally, in 2016, Legislature passed SB 32, which directs the State to reduce GHG emissions 40% below 1990 levels by 2030, along with SB 1383, which directs efforts towards reducing short lived climate pollutants that have a strong climate forcing potential. The dairy sector contributes a substantial amount of CH4, N2O, and NH3 emissions, and as such are important to study to meet these requirements. For instance, dairy enteric fermentation and manure management, account for an estimated 27% and 25% of total CH4 emissions in the State inventory, respectively . Although N2O is not yet targeted by SB 1383, it is estimated that N2O emissions from manure management account for about 13% of the statewide total N2O . However, there is high uncertainty in emission estimates of CH4, N2O, and NH3 from dairy farms in California, in large part due to a dearth of measurements conducted at the facility level and across timescales. So far, there have only been two studies that have investigated on-farm seasonal CH4 emissions in California . In addition, there have only been two source attribution studies in California that have used isotopic signatures of CH4 that were conducted in Southern California. Another useful source attribution method is to use enhancement ratios, which are defined as ratios between enhancements of trace gases above atmospheric background mole fractions. There has only been one such study investigating dairy farms in California, but was limited to only one season during winter Anaerobic microbial breakdown of carbohydrates in the digestive tract of cattle produces about 30-40% of CH4 as a by-product . Ruminants, such as cattle, have large fermentative cavities in the beginning of the digestive tract that break down carbohydrates and plant cell walls, and form acetate, propionate, butyrate, succinate, H2, and CO2 through the Embden-Meyerhof-Parnas pathway . Methanogens then use the by-product H2 and reduce CO2 into CH4 .

One of the most significant changes in the US economy since the beginning of the 20th century is the national abandonment of farming as a household livelihood strategy. This “agricultural transition” is marked by a number of characteristics: the move away from farming by most Americans and the challenging conditions that remaining farmers experience; the decline in the number of farms and farm population; the growth of larger farms vis-à-vis acreage, sales, and real estate capitalization; and the gradual replacement of family with hired labor. The post-World War II period ushered in perhaps the most rapid transformation, particularly by way of New Deal interventions, and their reformulation and erosion over the next few decades. Between 1940 and 1980, for example, the farm population declined ten-fold, the farm numbers declined by more than half, acreage more than doubled, and real average sales increased six-fold. Farmers also experienced periodic crises during key moments within such long term structural change, such as those that took place during the 1980s and in the mid-1990s. Such shifts were linked to the polarization of production. For example, between 1939 and 1987, the market share of sales by the largest 5% of producers increased from 38.3% to 54.5%. Agricultural firms have expanded not just through vertical and horizontal consolidation, as outlined in Part I, they have also done so through production contracts, wherein a farmer raises or grows an agricultural product, including livestock, for such firms. While only about 8.9% of farms operated under production contract in 2012—up from 3% only a decade earlier—they produced 96% of all poultry, 43% of all hogs, and around 25% of all cattle. Contract farming carries with it numerous risks that compromise the long term well-being of producers themselves. Furthermore, vertical grow room most farms cannot fully employ or sustain families. To survive in farming, families have taken off-farm jobs. As of 2013, for example, 87% of farmers’ median household income came from non-farm sources.

The median farm income for operations that specialize in grains, rice, tobacco, cotton, or peanuts, 23% of income came from on-farm sources. Conversely, livestock operations, apart from dairy, have generally not had a positive income from farming. That is, without income garnered by way of off-farm sources, such operations would go negative. As outlined below, the complete lack of profitability of such operations, and the relatively great profitability of grain and other commodity crop operations, cannot be understood as separate from the racialized distribution of operation types, with white producers generally running more profitable grain and other commodity crop operations, and producers of color running less profitable livestock operations. Shifts in agricultural production were tied not only to the polarization of production but also to racial, gender, and economic polarization. For example, although Blacks were able to establish a foothold in southern agriculture post-Emancipation, rural Blacks were virtually uprooted from farming over the next several decades. In 1920, 14% of all US farmers were Black , and they owned over 16 million acres. By 1997, however, fewer than 20,000 were Black, and they owned only about 2 million acres. While white farmers were losing their farms during these decades as well, the rate that Black farmers lost their land has been estimated at two and a half to five times the rate of white-owned farm loss. Furthermore, although between 1920 and 2002, the number of US farms shrank—from 6.5 million to 2.1 million, or by 67%—the decline was especially steep among Black farmers. Specifically, between 1920 and 1997, the loss of US farms operated by Blacks dropped 98%, while the loss of US farms operated by whites dropped 65.8%. As outlined above, such shifts have been attributed to the general decline of small farms, land erosion, boll weevil infestations of cotton, New Deal farm programs geared toward white landowners, postwar cotton mechanization, repressive racial and ethnic relations, and the lure of jobs and relative safety in the North.

Remaining Black farmers were not only older and poorer than others, they also continued to disproportionately face structural discrimination with regard to land ownership and access to federal support, whether because of ineffectiveness, discrimination in implementation, poor design, lack of funding, or unintended shortcomings. The following section focuses on three sets of Farm Bill programs in particular and elaborates upon the history of each as they relate to racial and economic inequity, particularly in terms of income and wealth, access to program benefits, land access, access to positions of power, and degree of democratic influence.Farm Service Agency Lending Programs and the Farm Bill Discrimination by the USDA and FSA Loan Distribution Program is among the most significant causes of limited access to, and loss of, farmland by people of color. Specifically, lending program discrimination has undermined the economic capacity of farmers of color to anticipate and respond to rapid consolidation and specialization, such as limited capacity to adopt scientific and technological innovations in agricultural production, and greater vulnerability to price volatility.Toward this end, allegations of unlawful discrimination against farmers of color in the management and local administration of USDA lending programs—and the USDA’s limited response to such allegations—have been long-standing and well-documented. For example, in 1965, the US Commission on Civil Rights found evidence of discrimination in the USDA’s treatment of employees of color and in its program delivery. Furthermore, in the early 1970s, the USDA was found intentionally forcing farmers of color off their land through its loan practices. In 1982, the US Civil Rights Commission again found evidence of continued discrimination actively contributing to the decline in minority farm ownership. Despite such findings, in 1983, only one year later, President Reagan pushed for budget cuts that ultimately eliminated the USDA Office of Civil Rights, the primary body for addressing such claims of discrimination. 

Even after the USDA Office of Civil Rights was restored in 1996 during the Clinton Administration, discrimination in the lending programs continued for years. Although the USDA officially prohibits discrimination, the structure for the election of FSA county, area, and local committees that decide who receives loans and under what terms facilitates continued racial discrimination. Toward this end, a 1997 USDA Office of Civil Rights report observed that FSA county committees operate as closed networks and are disproportionately comprised of white men, noting that, in 1994, 94% of the county farm loan committees included no women or people of color. As of 2007, such trends continue, with just 90 Black committee members among a total 7,882 committee members around the country, slightly over 1%. Decades of discrimination and lack of access to such crucial positions have sparked several class-action lawsuits by women farmers and by various groups of farmers of color. Only recently has the Farm Bill attempted to address a major cause of racially discriminatory FSA lending program outcomes by targeting the lack of people of color within FSA committees. Specifically, it was not until a provision, Section 10708, in the 2002 Farm Bill that the com-position of FSA county, area, and local committees were required to be “representative of the agricultural producers within the area covered by the county, area, or local committee,” and to accept nominations from organizations representing the interests of socio-economically marginalized communities. Furthermore, a provision, Section 1615, of the 2008 Farm Bill required county or area committees that are themselves undergoing rapid consolidation to develop procedures to maintain representation of farmers of color on such committees. It was not until early 2012, however, that federal regulations were made consistent with legislative changes. Because of the historic discrimination against farmers of color, and other structural barriers to land ownership for people of color, cannabis racks the population of agricultural producers is already heavily skewed toward white men. Thus, such measures to guarantee FSA committees are representative of agricultural producers in any particular region fall short in their attempts to address the acutely historical causes and outcomes of structural racialization that have upheld white land ownership in particular.The second major channel among the Farm Bill and other federal food and agricultural policies that have played a historic and ongoing role in structural racialization is the Farm Bill’s commodity programs, which have undergirded white farmland ownership at the expense of farmland ownership by people of color. While the FSA lending programs have upheld white farmland ownership amidst increasing consolidation and specialization, the Farm Bill commodity programs uphold white farmland ownership by way of increasing consolidation and specialization. Specifically, increasing agricultural specialization and consolidation—due in part to federal agricultural policy and corporate control, and increased mechanization, fertilizer use, and genetic modification—have upheld white farmland ownership because of both the historic access to prime farmland afforded to white farmers as well as the commodity support programs that are most applicable to the crops grown on such farmland. Limited access to prime farmland, and thus limited access to commodity support programs in conjunction with limited access to federal lending programs as outlined above, has compromised the possibility of farmland ownership for people of color. Historically, people of color were not only excluded from land ownership, but when land ownership was in sight, access to the best farmland was largely out of reach. 

After Emancipation, for example, chronic indebtedness kept the primarily Black population of sharecroppers tied to the same land, neither able to resist the demands and directions of their employers nor able to accrue enough wealth to buy their own land. Although some were able to garner the financial means to break such predatory cycles of debt and purchase their own land, few Blacks could afford to achieve ownership of land with the richest soil, including the notorious “Black Belt” itself, between Georgia and Arkansas. Rather, most Black-owned farms were on more marginal lands in the upper and coastal South, where Black farmers often had to supplement the low yields and profits with sharecropping on more substantial white-owned lands or with outside labor. The best opportunities available to farmers of color, Black or otherwise, on such land tended and remain to be specialty crops and livestock. As of 2012, for example, 63.6% of Asian American farmers, compared to only 8.5% of white farmers, grew fruits and vegetables. Moreover, as of 2012, 46.8% of Black farmers, compared to 29.1% of white farmers, raised beef cattle. Conversely, as of 2012, white farmers grow 98.6% of all grain and oilseed crops. Furthermore, livestock and specialty crops, including fruits and vegetables, are not eligible for these commodity programs, leaving farmers of color with less government support. Specifically, the current agriculture funding structure, from research funding to crop subsidies, and to conservation programs, as will be outlined in Part IV, is heavily weighted to support the large-scale production of commodity crops—among them, wheat, corn, soybeans, and others—crops that are primarily grown by white farmers on the highest quality farmland. Thus, as a result, as of 2012, 40% of white farmers receive government payments while only 30% of Black farmers receive government payments. Furthermore, white farmers that do receive payments receive an average of $10,022 per farm, while Black farmers that receive payments receive an average of $5,509 per farm. Farmers of color, and new immigrant farmers in particular, often grow high-value, labor-intensive horticultural products on small plots of land, which also receive less government support. In 2012, small-scale farmers received an average of $5,003 per farm while large-scale farmers received an average of $47,732 per farm. Perhaps most significantly, as of 2012, 97.8% of all government payments are given to white farmers. According to a 2012 USDA Economic Research Service study, the distribution of commodity-related payments—including federal crop insurance indemnities—to US farmers has shifted toward larger farms as part of the trend of increasing consolidation of farming operations, ensuring that those who have historically benefited from exclusionary practices benefit further. Significantly, because the operators of larger farms generally have higher incomes than those of smaller farms, the shift of commodity-related payments to larger farms led to a shift of payments to higher income households. For example, in 1991, households with incomes over $54,940 received 50% of commodity payments, households with incomes greater than $115,000 received 25% of commodity payments, and households with incomes over $229,000 received 10% of commodity payments. Since then, the distribution of payments has increasingly favored higher income households: by 2009, households earning over $89,540 received 50% of commodity payments, households with incomes greater than $209,000 received 25% of commodity payments, and households with incomes of at least $425,000 received 10% of commodity payments.

As of 2012, 11.8% of executive and senior level officials and managers, and 21% of all first- and mid-level officials and managers were people of color, despite people of color comprising over 25% of the US population. Agricultural workers in particular experience ongoing and widespread violations of the limited protections afforded to them by federal law. This is oftentimes the result of competing producers aiming to drive down their costs by not complying with employment laws. Between 2010 and 2013, for example, among agricultural employers, the Department of Labor found 1,901 violations of the Fair Labor Standards Act , which sets the federal minimum wage, overtime pay, child labor rules, and payroll record keeping requirements. A 2009 survey of approximately 200 farmworkers paid by “piece-rate” in Marion County, Oregon, found that workers experienced extensive violations of the state’s minimum wage law. Almost 90% of workers surveyed reported that their “piece-rate” earnings frequently amounted to less than minimum wage, averaging less than $5.30 per hour—37% below hourly minimum wage. Furthermore, a 2013 survey of farmworkers in New Mexico found extremely low wages and high levels of wage theft: 67% of field workers surveyed were victim to wage theft within the year prior to the survey; 43% stated that they never received the minimum wage, and 95% said they were have never been paid for the time spent waiting each day in the field to begin working. The combination of employers’ exploitation of the immigration system, and workers’ low income, limited formal education, limited command of the English language, and undocumented status, greatly hinders farmworkers from seeking any retribution or recognition of their rights. For example, as of 2009, the National Agricultural Workers Survey found that 78% of all farmworkers were foreign born, with 75% born in Mexico; 42% of farmworkers surveyed were migrants, pipp rack with 35% of migrants having traveled between the United States and another country, primarily Mexico.

Furthermore, 44% said they couldn’t speak English “at all” and 26% said they could speak English only “a little”; and the median level of completed education was sixth grade, with a large group of farmworkers completing fourth to seventh grades. With limited legal aid, many agricultural workers fear that challenging the illegal and unfair practices of their employers will result in further abuses, jobs losses, and, ultimately, deportation. Worse yet, few attorneys are available to help poor agricultural workers, and federal legal aid programs are prohibited from representing undocumented immigrants. The exploitation of migrant agricultural workers begins long before they reach the United States, and this migration has largely been driven by US trade and foreign policy in Central and Latin America. Specifically, most agricultural workers are in the United States as part of the H-2A Temporary Agricultural Workers program, which allows US employers to bring foreign nationals to the United States to fill temporary or seasonal agricultural jobs. However, nearly all such employers rely on private recruiters to find available workers in their home countries and arrange their visas and transportation to the fields. US agricultural employers thrive and rely upon an immigration system and recruitment network that provides “cheap” labor , and, as such, this recruitment network outside US borders remains unregulated and highly exploitative. Among the most grievous of such practices, for example, is the collection of fees from workers as a prerequisite to being hired. Many growers are willfully ignorant of recruiters’ activities, despite recently revised regulations that require growers to promise that they have not received any such fees. With many potential workers striving to escape poor conditions in their respective homelands, there is much incentive for recruiters to charge “recruiting fees” for personal profit, leaving H-2A workers with a great deal of debt upon their arrival to the United States. While some have paid upwards of $11,000 for such opportunities to work, others have given the deed to their house or their car to recruiters as collateral so as to ensure “compliance” with the terms of their contract. Many fear for their physical safety and safety of their family members if they are not able to repay their debts.

Many farmworkers been deceived about their wages and working conditions , and, to make matters worse, many workers are tied to one employer and therefore have no choice but to work regardless of the low pay and abysmal working conditions of their employers. Ultimately, the H-2A program and US labor market creates conditions ripe for debt-peonage. Furthermore, although H-2A program regulations require employers to give job preference to qualified US workers, in practice the H-2A program ultimately puts US workers out of work given the seeming cost benefits of employing H-2A workers. Toward this end, employers go to great lengths to unlawfully exclude qualified US workers in favor of H-2A workers, many of whom have themselves migrated to the United States during prior seasons. For example, employers schedule interviews at inconvenient times or locations; hire too early in the season, lead workers to arrive for work when there is none; limit their hours in order to discourage them from continuing to work; use employment contracts that demand that workers forfeit their right to sue a grower for lost wages and/or other illegalities; and impose productivity quotas and other unrealistic work demands on employees. These practices greatly discourage US workers from applying to these jobs, which then allows employers to “legally” hire H-2A workers. Additionally, the profits reaped by large agricultural employers and by corporations at all levels of the food system not only come at the expense of the food system worker’s livelihoods and US job loss, but are also subsidized by taxpayers themselves. For example, Walmart, which sells 25% of all the groceries in the United States and is the largest employer in the US and world, has among the lowest wages across the retail industry. Walmart workers cost US taxpayers an estimated $6.2 billion in public assistance that would counteract the consequences of their low wages, including SNAP, Medicaid and subsidized housing. Because 58% of food system workers surveyed reported having no health care coverage, more than one-third of workers surveyed have used the emergency room for primary care, which taxpayers help cover. Finally, corporations like Walmart are able to determine wages and benefits for workers throughout their entire supply chain, given their massive procurement power and ability to dictate purchasing prices to its suppliers.

This pressure and influence forces suppliers to lower their worker’s wages, multiplying the number of workers robbed of fair and livable wages and taxpayer subsidization of corporate profits. In short, when food system workers require public assistance, the onus rests on taxpayers and the federal government, rather than on those that are responsible for creating these unhealthy outcomes—corporations. After over thirty years of liberal trade policies beginning in the late 1970s and early 1980s, many developing countries have been left with a great dependence on the global market for basic food and grains. Developing countries had yearly agricultural trade surpluses of $1 billion in the early 1970s. Yet by 2000, the food deficit in such countries had grown to $11 billion per year. At the height of the 2007–2008 global food price crisis, Low-Income Food Deficit Countries import bills reached over $38 billion for basic cereal grains. Such systemic vulnerability is, in part, a result of international finance institutions, structural adjustment, free trade agreements, and a broader divestment of the state from agricultural development. Furthermore, pipp racking system not only are overproduction and US food aid to blame, but also corporate actors use such international crises as opportunities to make additional calls for emergency aid coupled with further trade liberalization and increased investment in agricultural productivity. For example, although the 2014 Farm Bill authorizes $80 million annually for the Local and Regional Procurement Program, which encourages greater use of food that is locally or regionally grown for food aid, it pales in comparison to the $1.75 billion Food for Peace Title II through which United States Agency for International Development provides food assistance. Furthermore, foreign economies are undermined not only by such efforts that directly shuttle surplus and heavily subsidized commodities—produced for the benefit of corporate entities—to developing countries, but also by production support programs themselves, such as commodity payments or crop insurance. For example, a 2012 International Centre for Trade and Sustainable Development report found that the shift from direct payments to crop insurance support for farmers is likely to have far reaching effects on global trade and prices because of the anticipated change to cropping patterns. Specifically, the likelihood that the new programs will influence planting decisions is greatly enhanced because payments in all the new programs are calculated using actual planted acreage. Ultimately, if planting decisions are influenced enough, then program-induced changes in US crop acreage will be reflected in trade flows that have the potential to harm farmers in developing countries and cause fluctuations in global food prices. Academic Research and Development: One major way corporations profit and exert their control with regard to education, research, and development is their influence over academic research and development. Agricultural research in the United States is carried out primarily by three entities: the federal government, largely through the US Department of Agriculture; academia, primarily through land-grant universities; and the private sector. Over the past several decades, corporate interests have co-opted publicly-oriented agricultural research and land-grant university research efforts in particular. The federal government created land-grant universities in 1862 by deeding tracts of land to every state to pursue agricultural research to support agricultural production in the United States. Although public investments have maintained agricultural research since the creation of these universities, over recent decades public funding has stalled, prompting land-grant universities to appeal to agribusiness to remedy such financial shortcomings. Significantly, the landmark 1980 Bayh-Dole Act pushed universities to take this particularly entrepreneurial role, generating revenue through producing patents from which the private sector could profit. The Bayh-Dole Act, as part of the neoliberalization of science and academic research itself, prompted greater industry influence over land-grant research, as university research agendas became oriented toward the needs of corporate partners. Major agribusiness donors to land-grant universities across the United States, including Syngenta, Monsanto, PepsiCo, Nestle, Dow Agroscience, Chevron, DuPont and others, now push research carried out by faculty and students toward developments in bio-fuels, commodity crops research, genetically engineered foods, and other areas of interest. Land-grant universities today not only carry out corporate-directed research but also depend on agribusinesses to underwrite research grants, endow faculty chairs, sponsor departments, and finance the construction of new buildings. Even USDA research and USDA-funded research itself reflects corporate interests. The USDA spends roughly $2 billion per year on agricultural research, which goes toward funding USDA researchers and researchers at land-grant universities. This money, however, is largely directed toward a corporate-friendly industrial agriculture research agenda: the National Academy of Sciences found that USDA research prioritizes commodity crops, industrialized livestock production, technologies geared toward large-scale operations, and capital-intensive practices. The Farm Bill does not prioritize funding for more sustainable farming programs, with programs such as the Organic Agriculture Research and Education Initiative and Specialty Crop Research Initiative accounting for only 2% of the USDA’s research budget. Most research funding is directed toward commodity crops research. In 2010, for example, the USDA funded $204 million to research all varieties of fruits and vegetables, and spent $212 million to research just four commodity crops: corn, soybeans, wheat, and cotton. Seed Patents: Another major way private industry continues to profit and exert their influence vis-à-vis relations of education, research, and development, is seed research and patents. Since the early 1980s, the global seed industry has grown substantially and is now worth an estimated $44 billion and is expected to grow to an estimated $85 billion by 2018. The cumulative effect of seed legislation has facilitated the massive consolidation of corporate power, thus securing corporate control of one of the most crucial agricultural inputs. This history of seed legislation began shortly before the New Deal, beginning with the US Plant Patent Act of 1930 and continued with the 1970 Plant Variety Protection Act. Significantly, seed legislation did not move into the judicial system until the 1980 Supreme Court decision Diamond v. Chakrabarty, which laid the legal groundwork for the privatization and commodification of the genetics of seeds. 

Given that about 7.1% of pork with be produced Prop 12 rules , this implies about 0.54 million of 7.6 million sows in North America will be confined under California’s housing standards. California pork consumers will pay about $188 million annually to provide four square feet more per sow on average for about 540 thousand sows in North America. Thus, California buyers of covered pork will pay about $87 per square foot of additional housing space. The passage of Prop 12 in California by a significant majority indicates citizens’ interests in improving animal welfare. Regulations such as Prop 12 and its counterparts in other states such Local jurisdictions have increasingly imposed regulations on agricultural production processes within the jurisdiction to address issues associated with pollution, animal welfare, and farm worker health and well-being. Several papers have studied the impacts of such regulations, with the work summarized by Sumner . These regulations differ considerably in their impacts from those that restrict farm production practices for products sold within a local jurisdiction. The first type creates heterogeneous production costs and alters the comparative advantage of different production regions but generally does not affect downstream operations. This paper explores the economic implications of the latter group of regulations, with specific application to the impact of California Proposition 12 on the North American pork supply chain. Key innovations of the model are allowing heterogeneity in the costs of farms to meet the mandate and incorporating that a mandate in many cases will only apply to a portion of the output of the live animal. The model incorporates capital conversion for compliance at farms and variable production proportions between covered and non-covered pork in processing farm raw products into finished consumer products.

The model shows how these aspects interact and drive substantial price and quantity adjustments along vertically linked markets and across geographically different markets. Simulations show that, rolling benches for growing despite significant industry opposition to Prop 12, its mandates do not impose much negative total effect on hog producers in North America. Most firms that elect to comply with Prop 12’s mandates will increase profits, and losses to non-compliers are slight. Prop 12 causes moderately higher prices in California for covered pork products and generates a consumer welfare loss of about $188 million annually.Prop 12 will not make stall housing operations adopt California’s standards. These pork products will be diverted for the California market under Prop 12. Because California’s standards are stricter than typical group housing, breeding pigs confined in converting operations will have slightly more space than before. Prop 12 and, more generally, the regulations on products sold in local jurisdictions represent only one policy instrument to improve welfare for farm animals. To illustrate this point, I considered a simple alternative policy under which the California government would raise a general fund to directly subsidize farms that convert their housing practices. I showed that, for the same cost to California, this alternative policy could incentivize conversion of about three times as much sow housing to compliance with Prop 12 regulations as Prop 12 itself will achieve if it becomes fully implemented. This example illustrates that Prop 12 and, more broadly, regulations imposed at the point of purchase are likely not efficient ways to influence conventional farming practices.Google Surveys provides inferred respondent characteristics based on internet use rather than reported demographics by respondents to represent the general population of internet users. Google collects demographic information of their users when they make an account or while they use Google’s service. Google identifies general demographic information about websites when sufficient Google users visit those websites.

Given this demographic information specific to websites, Google infers visitors’ demographic information. Google Surveys cannot collect data from non-internet users, but the share of internet users in the U.S. population was about 91% in 2020 . Google Surveys cannot obtain responses from non-internet users, but it is not rare that responses from part of the population are not collected in survey studies. For example, survey studies often collected responses from only college students , people in a local region , and customers in a store . Several papers have found evidence that the inferred demographics of Google Surveys provided representative samples of the U.S. population and reliable estimation results . Table 5.3 reports respondent shares by demographics. The numbers in parentheses are the corresponding 95% confidence intervals. Table 5.3 reports the results by three different samples: a subsample including only respondents who selected “I don’t buy carrots,” a subsample including only respondents who did not select that option, and the full sample. Table 5.3 shows important patterns in the data about inferring demographics. Google inferred gender, age category and region based on search patterns and other information about the URL of the respondent computer. The shares of respondents without inferred demographic information was about 20% for gender and age, but negligible for region. The gender was not inferred for about 18% of respondents in the full sample. The age group was not inferred for about 20% of respondents. The geographical location was inferred for more than 99% of respondents. The shares of “not inferred” are slightly higher in the respondents selecting “I don’t buy carrots” than the other respondents. Table 5.4 excludes respondents for whom Google Surveys did not infer the full set of demographics, presumably because they did not have sufficient information on that respondent. The overall sample shares are similar to the U.S. population shares. The share selecting “I don’t buy carrots” is more male and younger than those who responded to the carrots purchase choices and the U.S. population. The gender pattern in the total sample is very similar to the U.S. population. The age range is slightly more middle aged with fewer 25-34 and fewer over 65. In addition, a smaller share of respondents is in the Northeast and South and more are in Midwest relative to the U.S. population. Given the findings of this section, I consider the following points in the model specification of Chapters 6 and 7. First, I include demographic variables as explanatory variables. Second, I use sampling weights based on demographic groups to make the sample represent the population.

Google Surveys also provide information about how long individual respondents elapsed between when the survey was opened and when it was completed. I explore the response time because several prior studies using online responses found that the inclusion of response times as a control in statistical estimation reduced random responses and standard errors of estimated parameters . The concern is that respondents that are too quick may not be actually reading the questions, and respondents that take too long were likely interrupted in their responses. Table 5.5 reports descriptive statistics on the response time by the WTP question types, the choice of “I don’t buy carrots,” and whether inferred demographics were provided. The table includes ten categories. Three features are common across the categories. First, the average response time is slightly less than 30 seconds for most categories. Second, the standard deviation within each category is high relative to the average for all the categories in the table. Third, the min and max values are substantially different from the average in each category. Three points are noticeable in comparison with categories. First, on average, respondents took about the same time for the Yes-No questions as the Multiple-Choice questions . Second, respondents choosing “I don’t buy carrots” tended to spend less than the other respondents. Third, on average, respondents without inferred demographics spent more than those with demographics. Based on the findings of this section, I consider the following model specification in Chapters 6 and 7. First, I include the response time as an explanatory variable in regressions. Second, I compare the models with and without outliers in response time. The outliers include both those with a very short response time and a very long response time because the relationship between response time and response reliability is possibly not linear . A very long response time possibly indicates insufficient attention to the survey because respondents often do multiple activities simultaneously on the internet.Specifically, corporate control refers to control of political and economic systems by corporations in order to influence trade regulations, tax rates, and wealth distribution, among other measures, vertical air solutions and to produce favorable environments for further corporate growth. Structural racialization refers to the set of practices, cultural norms, and institutional arrangements that are reflective of, and help to create and maintain, racialized outcomes in society, with communities of color faring worse than others in most situations. In this light, the production of racial/ethnic, gender, and economic inequity in the United States is more so a product of cumulative and structural forces than of individual actions or malicious intent on behalf of private or public actors. In order to challenge and eliminate corporate control and structural racialization in the United States, therefore, it is necessary to analyze the ways that public and private institutions are structured. It is also necessary to analyze how government programs are administered and operate in ways that reproduce outcomes that marginalize low-income communities, women, and communities of color in terms of health, wealth, land access, power, and degree of democratic influence. Additionally, as this report aims to do, it is crucial to analyze the genesis and formation of critical institutions and structures themselves.Therefore, the US Farm Bill—the flagship piece of food and agricultural legislation since its inception in 1933, which informs the heart of public and private policies that make up much of the US food system—is the subject of this report. This report is of particular importance now for two reasons. First, the Farm Bill will be under consideration again in 2019, yet there is no comprehensive critique of the Farm Bill that addresses its underlying contradictions, particularly with regard to racial/ethnic, gender, and economic inequity. Second, it is imperative that campaigns by grassroots, community, and advocacy organizations—generally most active during the period of Farm Bill negotiations in Congress—have enough time to gather adequate information and conduct in-depth analysis for targeted yet comprehensive policy change. As such, the timing of this report is also imperative for coalition-building efforts and the growth of an effective broad-based food sovereignty movement.Corporate consolidation and control have become central features of the US food system, and of the Farm Bill in particular. As of 2014, large-scale family-owned and non-family-owned operations account for 49.7% of the total value of production despite making up only 4.7% of all US farms. As of 2013, only 12 companies now account for almost 53% of ethanol production capacity and own 38% of all ethanol production plants. As of 2007, four corporations own 85% of the soybean processing industry, 82% of the beef packing industry, 63% of the pork packing industry, and manufacture about 50% of the milk. Only four corporations control 53% of US grocery retail, and roughly 500 companies control 70% of food choice globally. At every level of the food chain, from food production to food service, workers of color typically earn less than white workers. For example, a majority of farm workers who receive “piece rate” earnings , and many of whom are migrants from Mexico, frequently earn far less than minimum wage—an exploitative practice deeply tied to immigration policy, as elaborated upon below. On average, white food workers earn $25,024 a year while workers of color make $19,349 a year, with women of color, in particular, suffering the most. Furthermore, few people of color hold management positions in the food system, while white people hold almost three out of every four managerial positions. One result of this racial disparity in food system labor is that non-white workers experience a far greater degree of food insecurity than their white counterparts.Food insecurity in the US disproportionately affects low-income communities and communities of color, and these communities are over represented in the lowest-paying sectors of the labor market. For example, as of 2013, 14.3% of US households—17.5 million households, roughly 50 million persons—were food insecure. The report also found that the rates of food insecurity were substantially higher than the national average among Black and Latino/a households, households with incomes near or below the federal poverty line, and single parent households.

Finally, sowmortality showed a significant increase in t + 1 with one more sow death than during the baseline period . In general, the indicators confirm that a PRRS outbreak affected several production stages for an extended period of time .The decline in weaned pigs marketed in week t − 1, although statistically insignificant, as well as changes in some performance indicators , suggest that the outbreak may have started in week t − 1, one week before it was reported. We therefore developed an alternative estimate of production losses that can be compared to the estimated loss if the outbreak is assumed to begin in week t. Eliminating t − 1 from the preoutbreak period led to estimation of a slightly higher baseline and, as a result, to a higher estimate of PRRS losses. Nonetheless, the difference between this estimate and our primary estimate is very small. Our primary estimate is that PRRS reduced weaned pig production per farm by 7.4% on an annual basis, leading to a decrease in output value per sow year of $86.6, or $367,521 per farm year for an average sized farm. If instead we assume the outbreak began in t −1 , the estimated reduction in weaned pig production was 7.6%, or $88.8 less per sow year and an average revenue loss of $376,773 among the farms studied.We analyzed the impact of a PRRS outbreak on weaned pig production in a set of sow farms that are part of the same swine firm in the US. We estimated the time profile of disease effects, identifying the weekly changes in output relative to a pre-outbreak baseline. We find that PRRS caused a 7.4% decline in production value measured over a one-year period. Correspondingly, PRRS reduced production by 1.92 weaned pigs per sow when adjusted to an annual basis.

This decrease is substantially larger than the 1.44 decrease of weaned pigs per sow/year reported in another study . We note that total losses due to PRRS are likely to be greater than the revenue losses estimated in this study, how to cure cannabis fast as total losses must include cost increases associated with the disease, e.g., an increase in management expenses, bio-security investments, additional feed and veterinary inputs, plus a possible decrease in the weight or in the sales price of piglets . We found that weaned pig production declined in week t − 1, although statistically insignificant, as did several performance indicators. The data suggest that the average PRRS outbreak in this set of farms began at least one week before it was announced. This delay may be explained, at least in part, by the inability of producers to detect PRRS until animals begin to show explicit clinical signs, as well as the additional time needed to test and confirm the disease. The lag between the outbreak of disease and the appearance of clinical signs may be longer in farms using vaccination programs, as in our sample, where clinical signs may be subtle . It seems likely that some weaned pigs being shipped by these farms in week t − 1, when the disease was almost certainly present in these farms, but as yet unannounced, were infected with PRRS. The relatively slow identification of the disease means that animal movements out of infected premises must be a common source of disease spread. This is particularly important in sow farms that deliver wean pigs to different swine grower facilities each week. The rise in abortions was the strongest signal of PRRSV activity in our data. Increased surveillance, particularly to rising abortions, may allow farms to identify PRRS more quickly. Abortions were rising in the several weeks prior to the reporting of the outbreak in some of the farms in the sample.

Abortions rose significantly in t − 1 and then increased sharply in week t. The number of abortions declined rapidly and fairly monotonically following week t, with a slight uptick in weeks t + 10 to t + 13, and recovered to the baseline level by about week t + 20. Thus, to the extent that abortions are an indicator of an active virus in the sow herd, circulation of the virus appears to have ended about 20 weeks after it was reported. The uptick in weeks t + 10 to t + 13 suggests that the disease may have been infecting other susceptible cohorts of sows within the farms two to three months after the initial outbreak. The length of PRRS outbreaks, as well as their effects over time, is highly variable. For example, one study estimated effects of an outbreak during 12 weeks post detection , while another indicated that production of negative piglets was reached 27 weeks post infection . Our results demonstrate that PRRS has a negative effect on weaned pig production for a longer time than previously estimated. In our study, the estimated means of weaned pig production remained below the baseline throughout the 35 weeks that we are able to observe following the outbreak. Although the production of weaned pigs recovered to a level that is not significantly different from the baseline, we cannot definitively declare that there was no effect beyond week t + 35. Nonetheless, it appears that any continued effect is likely to be very small relative to the large effect occurring before week t + 35. We detected a consistent decrease in production until the 5th week after the outbreak report, followed by a non-monotonic recovery. All performance parameters followed a similar non-monotonic recovery pattern. Each indicator manifested a sharp worsening after the outbreak, followed by partial recovery and at least one mild period of deterioration. The dynamic up-and down impact of PRRS on weaned pig production was surprising. The precise causes are unclear, but the disease may spread more slowly and unevenly through the sow herd than anticipated, particularly on large units with multiple cohorts, in addition to possible incoming flows of replacement sows.

This effect might also explain the longer period of recovery in our study, versus another study that found production returned to the baseline in 16.5 weeks for cohorts vaccinated with an MLV and using herd closure as a control strategy . Other performance indicators provided consistent signals. Pre-weaning mortality increased sharply in weeks t − 1 to t + 1, declined to pre-outbreak levels by t + 10, and then oscillated about that level until about t + 24. Sow mortality increased in week t + 1 and remained above baseline levels until week t + 5. The increase in sow mortality could affect the age structure of the herd and consequently its production. Stillbirths increased until week t + 12, indicating that some infected sows carried damaged fetuses to birth. The number of stillbirths remained elevated through t + 36, suggesting that infected sows may have a higher probability of producing stillborn piglets for more than one pregnancy. The failure to conceive was followed by repeated services, which must have contributed to the lag in weaned pig production in later weeks. The numbers of pigs aborting or dying indicated that PRRS had its strongest effects on fetuses. PRRS kills relatively few sows and piglets, though the economic damage from sow mortality and/or their subsequently reduced productivity is important. Information regarding the strains of PRRS virus that affected each farm was not available for this study, as systematic sequencing of PRRS virus following outbreaks is still scarce. More than one strain might affect a given area, although in general genetic variation is more related to temporal rather than spatial variation . Using a sample of 16 farms may help capture the variability of PRRS outbreaks in the industry, assuming different strains may be affecting different farms. According to a number of studies, no vaccine prevents PRRS infection, but vaccination may reduce the risk of infection and may also reduce the intensity of outbreaks by reducing the amount of virus excreted by ill animals . Therefore, our results may show smaller damages than those that would be obtained for farms that do not vaccinate. Similarly, because the farms analyzed in this study belong to a firm with standardized protocols for disease management, our measure of PRRS’ impact could be smaller than would be measured on farms with poorer protocols. We developed and used a straightforward approach to quantify the dynamic effect of PRRS on weaned pig production within sow farms. We found that PRRS decreased weaned pig production for at least 35 weeks among the firms studied. The magnitude of PRRS’ impact, vertical growing weed as expressed in the duration and magnitude of the output decline, were both greater than anticipated. We found that recovery oscillated about a rising trend, i.e., recovery does not depict a clear monotonic increase in production, suggesting that farms suffered from a continuing circulation of the disease within the herd and/or a lingering effect on sows and piglets. Analysis of the underlying performance indicators provided additional insight regarding how PRRS affects farm output over time. Previous studies have utilized numerous assumptions to develop estimates of the total annualized losses to the swine industry due to PRRS . We have not attempted to replicate those studies. However, our results suggest PRRS may cause significantly higher losses on sow farms than has been estimated previously. Further, we believe that the losses identified in our farm sample are likely to be smaller than those on the average sow farm infected with PRRS. Nonetheless, we found substantial variation in performance among even a set of relatively standardized 16 farms. There is thus need for caution when using simple averages, as we often have done, rather than distributions across farms.Food companies have increasingly introduced products featuring farm practices as product attributes, with organic practices representing a leading example.

About 1,400 new organic products were introduced in 2009 and 3,000 in 2016 . To contribute to understanding the organic market, I explore econometrically buyer willingness to pay for carrots grown with organic practices relative to conventional carrots. I also export the demand for convenience and processing practices by exploring willingness to pay for fresh cut carrots relative to full sized carrots. Some food processing and marketing companies supply food products only from farm outputs produced with certain farm practices. For example, McDonalds and Walmart, have announced that within the next decade they will buy, use or sell only cage-free eggs . As of May 8, 2016, over 160 prominent food companies had announced that they will use only cage-free eggs, most by 2025 . Although not generally practiced by major retailers, many specialty markets and restaurants offer only or primarily organic food products.Governments also contribute to the demand shifts away from once conventional food products. For example, several U.S. states have introduced mandatory rules to eliminate conventional eggs from the in-state market. For example California and other states, including Massachusetts, Michigan, Oregon, and Washington, passed such laws . California has implemented mandatory cage-free housing for eggs consumed in California starting January 2022 as a part of the implementation of Proposition 12 . My model of government restrictions on food products that may be sold based on farm practices, which is applied to California’s Proposition 12 rules for pork products, shows how specific features of regulations affect market outcomes. Such product regulations may be imposed only on buyers within a specific jurisdiction but apply to farm practices outside that jurisdiction. Such regulations seem to be increasingly common and controversial, as reflected by the Hog industry challenge of Prop 12 before the U.S. Supreme Court . However, economists have not fully explored their impacts on prices and economic welfare, either within or beyond the regulating jurisdiction.The Prop 12 regulations on pork products allowed for sale in California specify mandates about how the breeding pigs are housed. The housing rules apply to sows that farrow pigs that produce pork to be sold to buyers in California. My model incorporates four empirical and regulatory features that determine economic impacts: California comprises about 9% of the market for North American pork; The regulations cover only some of the pork products from each hog. When a fraction of production becomes California compliant, the converting farms incurconversion costs and higher ongoing production costs; Segregation and traceability along the supply chain of hogs and pork destined for California is costly; and The quantity demanded for covered and non-covered pork products respond to relative prices, which are affected by costs of production, and pork demand may respond directly to the farm practice mandate.

These scenarios were then enacted using three Farm Personas. Much agricultural and environmental assessment literature utilize case studies and scenarios as a means to demonstrate problem areas, exemplify good practices, to provide guidance to the agricultural community, and to create public awareness. These materials provided supplements to the data and findings presented in Chapters 3 and 4 and were valuable substitutes for domain-expert guidance.The structure of each persona-scenario set, as articulated for the scenario-based evaluation, is outlined in Table 6.7 . The subsequent scenarios describe sustainable agricultural practices and environmental assessments that involve modeling farm components, activities, resources, and data. An overview table containing all persona-scenario sets is available in 6.8 .Scholars have published extensively on the multifunctional benefits of urban agriculture including: promoting urban sustainability, reducing air and water pollution, building social cohesion, promoting community health and nutrition, teaching food literacy, and creating radical economic spaces for resistance to the capitalist political economy and structural inequities embedded in the “neoliberal city” . Despite growing evidence of these diverse health, education, and environmental benefits of urban agriculture, these vibrant spaces of civic engagement remain undervalued by city policy makers and planners in the United States. Thriving urban farms and gardens are under constant threat of conversion to housing or other competing, vertical grow rack system higher-value land uses due to rising land values, and other city priorities.

This land use challenge and threat to urban farm land tenure is especially characteristic of U.S. cities like San Francisco, one of the most expensive land and housing markets in the country. Under the current urban agriculture paradigm in the U.S., food justice scholars and advocates either try to quantify and highlight the multiple benefits of UA  or pursue a critical theoretical approach, arguing that urban agriculture can yield unfavorable results if pursued without an equity lens, especially in cities with intense development pressures and gentrification concerns . A productivist focus is problematic, because, while urban agriculture can be an important component of community food security, its other social and ecological benefits are just as, and sometimes more, significant . In this article, we suggest that the current debates around “urban agriculture” in the U.S. often lead to an unhelpful comparison with rural farms regarding yield, productivity, economic viability, and ability to feed urban populations, most notably in the policy arena. Defined in these ways, the radical, transformative potential of urban food production spaces and their preservation often gets lost or pushed to the side in city planning decisions in metropolitan regions such as the San Francisco Bay Area, where the threat of displacement is ubiquitous given high levels of economic inequality and extreme lack of affordable land. In order to facilitate what scholars such as Anderson et al. 2018a refers to as the “agroecological transition,” already underway in many urban food ecosystems around the globe , we argue that applying an agroecological approach to inquiry and research into the diversity of sites, goals, and ways in which food is produced in cities can help enumerate the synergistic effects of urban food producers. This in turn encourages the realization of the transformative potential of urban farming, and an articulation of its value meriting protected space in urban regions. Urban agroecology is an evolving concept that includes the social-ecological and political dimensions as well as the science of ecologically sustainable food production .

UAE provides a more holistic framework than urban agriculture to assess how well urban food initiatives produce food and promote environmental literacy, community engagement, and ecosystem services. This paper presents a case study of 35 urban farms in San Francisco’s East Bay in which we investigated key questions related to mission, production , labor, financing, land tenure, and educational programming. Our results reveal a rich and diverse East Bay agroecosystem engaged in varying capacities to fundamentally transform the use of urban space and the regional food system by engaging the public in efforts to stabilize, improve, and sustainably scale urban food production and distribution. Yet, as in other cities across the country, urban farms face numerous threats to their existence, including land tenure, labor costs, development pressure, and other factors that threaten wider adoption of agroecological principles. We begin by comparing the concepts of UA and UAE in scholarship and practice, bringing in relevant literature and intellectual histories of each term and clarifying how we apply the term “agroecology” to our analysis. We pay particular attention to the important nonecological factors that the literature has identified as vital to agroecology, but seldomly documents . We then present findings from a survey of 35 diverse urban farm operations in the East Bay. We discuss the results, showing how an agroecological method of inquiry amplifies important aspects of urban food production spaces and identifies gaps in national urban agriculture policy circles. We conclude by positing unique characteristics of urban agroecology in need of further studies and action to create equitable, resilient and protected urban food systems.Agricultural policy in the United States is primarily concerned with yield, markets, monetary exchange, and rural development. The United States Department of Agriculture defines agricultural activities as those taking place on farms. Farms are defined as “any place from which $1,000 or more of agricultural products were produced and sold, or normally would have been sold, during the year” . Urban agriculture has been proliferating across the country in the last decade on both public and private lands, as both for-profit and nonprofit entities, with diverse goals, missions and practices largely centered on food justice priorities and re-localizing the food system.

Yet U.S. agriculture policy has been struggling to keep up. In 2016, the USDA published an Urban Agriculture Toolkit, which aims to provide aspiring farmers with the resources to start an urban farm including an overview of the startup costs, strategies for accessing land and capital, assessing soil quality and water availability, production and marketing, and safety and security . The 2018 U.S. Farm Bill provides a definition of urban agriculture to include the practices of aquaponics, hydroponics, vertical farming, and other indoor or controlled environment agriculture systems primarily geared towards commercial sales. In both the Toolkit and Farm Bill, non-profit, subsistence, and educational urban farming enterprises are not well integrated or included in the conceptualization of UA. While there are many definitions of urban agriculture in the literature from the simplest definition of “producing food in cities” to longer descriptions of UA such as that of the American Planning Association that incorporate school, pipp racks rooftop and community gardens “with a purpose extending beyond home consumption and education,” the focus of many UA definitions used in policy arenas continues to center around the production and sale of urban produced foods. Accordingly, food systems scholars have recognized that “Urban agriculture, [as defined], is like agriculture in general”, devoid of the many political, educational, and food justice dimensions that are prioritized by many U.S. urban farming efforts. Thus the social-political nature of farming, food production, and food sovereignty are not invoked by formal UA policy in the U.S. Many goals and activities common in urban food production, including education, nonmonetary forms of exchange, and gardening for subsistence are obscured by the productivist definitions and can be thus neglected in policy discussions. Furthermore, UA policy in the U.S. remains largely agnostic about the sustainability of production practices and their impact on the environment. While U.S. agriculture policy narrowly focuses on the production, distribution and marketing potential of UA, broader discussion of its activities and goals proliferate among food systems scholars from a range of fields including geography, urban planning, sociology, nutrition, and environmental studies. These scholars are quick to point out that UA is much more than production and marketing of food in the city, and includes important justice elements . In the Bay Area context, we continue to see the result of this dichotomy: thriving urban farms lose their leases , struggle to maintain profitability or even viability and encounter difficulties creating monetary value out of their social enterprises. In light of the ongoing challenge to secure longevity of UA in the United States, there is a need for an alternative framework through which food and farming justice advocates can better understand and articulate what UA is, and why it matters in cities.Agroecology is defined as “the application of ecological principles to the study, design and management of agroecosystems that are both productive and natural resource conserving,culturally sensitive, socially just and economically viable” , and presents itself as a viable alternative to productivist forms of agriculture.

Agroecology in its most expansive form coalesces the social, ecological, and political elements of growing food in a manner that directly confronts the dominant industrial food system paradigm, and explicitly seeks to “transform food and agriculture systems, addressing the root causes of problems in an integrated way and providing holistic and long-term solutions” . It is simultaneously a set of ecological farming practices and a method of inquiry, and, recently, a framework for urban policy making ; “a practice, a science and a social movement” . Agroecology has strong historical ties to the international peasant rights movement La Via Campesina’s food sovereignty concept, and a rural livelihoods approach to agriculture where knowledge is created through non-hegemonic forms of information exchange, i.e. farmer-to farmer networks . Mendez et al. describe the vast diversity of agroecological perspectives in the literature as “agroecologies” and encourage future work that is characterized by a transdisciplinary, participatory and action-oriented approach. In 2015, a global gathering of social movements convened at the International Forum of Agroecology in Selengue, Mali to define a common, grassroots vision for the concept, building on earlier gatherings in 2006 and 2007 to define food sovereignty and agrarian reform. The declaration represents the views of small scale food producers, landless rural workers, indigenous peoples and urban communities alike, affirming that “Agroecology is not a mere set of technologies or production practices” and that “Agroecology is political; it requires us to challenge and transform structures of power in society” . The declaration goes on to outline the bottom-up strategies being employed to build, defend and strengthen agroecology, including policies such as democratized planning processes, knowledge sharing, recognizing the central role of women, building local economies and alliances, protecting biodiversity and genetic resources, tackling and adapting to climate change, and fighting corporate cooptation of agroecology. Recently, scholars have begun exploring agroecology in the urban context. In 2017, scholars from around the world collaborated on an issue of the Urban Agriculture magazine titled “Urban Agroecology,” conceptualizing the field both in theory and through practical examples of city initiatives, urban policies, citizen activism, and social movements. In this compendium, Van Dyck et al. describe urban agroecology as “a stepping stone to collectively think and act upon food system knowledge production, access to healthy and culturally appropriate food, decent living conditions for food producers and the cultivation of living soils and biodiversity, all at once.” Drawing from examples across Europe, Africa, Latin America and Asia and the United States, the editors observe that urban agroecology “is a practice which – while it could be similar to many ‘urban agricultural’ initiatives born out of the desire to re-build community ties and sustainable food systems, has gone a step further: it has clearly positioned itself in ecological, social and political terms.” . Urban agroecology takes into account urban governance as a transformative process and follows from the re-emergence of food on the urban policy agenda in the past 5-10 years. However, it requires further conceptual development. Some common approaches in rural agroecology do not necessarily align with urban settings, where regenerative soil processes may require attention to industrial contamination. In other cases, the urban context provides “specific knowledge, resources and capacities which may be lacking in rural settings such as shorter direct marketing channels, greater possibility for producer-consumer relations,participatory approaches in labour mobilisation and certification, and initiatives in the area of solidarity economy” . Focusing on the social and political dimensions of agroecology, Altieri and others have explicitly applied the term “agroecology” to the urban context, calling for the union of urban and rural agrarian food justice and sovereignty struggles . Dehaene et al. speak directly to the revolutionary potential of an agroecological urban food system, building towards an “emancipatory society” with strong community health and justice outcomes.