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Rural transformation is accompanied by land concentration in medium farms in countries like Kenya , Ghana , Tanzania , and Zambia . These farms are typically mechanized and owned by well-educated urban-based professionals who can be effective agents for technology adoption. These various success stories show that using agriculture for development can be done, but has not yet been sufficient to overcome aggregate rising gaps in yields between SSA and the rest of the world.While there has been limited success with raising public expenditures on agriculture, there has been considerable progress with data collection and with rigorous experimentation on how to promote the modernization of agriculture. We consequently know a lot more today about how to use agriculture for development than we did ten years ago, even though this knowledge has most often not been put into practice in the desirable form and to the desirable degree. It is consequently important to start by reviewing what we have learned. The main argument that has been used in support of the need for a structural transformation as the mechanism to grow and reduce poverty is that there is a large labor productivity gap between agriculture and non-agriculture . An important observation, however, based on the LSMS-ISA dat for SSA is that while the gap in labor productivity per person per year between non-agriculture and agriculture is indeed large, the gap in labor productivity per hour worked is relatively small . In other words, weed drying rack when agricultural workers do work, their labor productivity is not very different from that of non-agricultural workers.

What this suggests is that there is a deficit in work opportunities for agricultural vs. non-agricultural workers that creates an income gap between the two categories of workers.Because households engage in a multiplicity of sectoral activities, the relevant contrast in labor productivity is not between agriculture and non-agriculture, but between rural and urban households, with rural households typically principally engaged in agriculture. Looking at labor calendars for rural and urban households in Malawi in Figure 1, we see that weekly household hours worked are not different for rural and urban households at peak labor time, which corresponds to the planting season in December and January . During the rest of the year, there are much less employment opportunities for rural than urban households, with the former working about half the time worked by urban households during the low season . Lack of labor smoothing across months can thus be a major cause of income differentials between rural and urban households. Measuring annual labor productivity as median household real consumption per capita, rural households are at 57% of individuals in urban households. When this is measured not per year but per hour worked, rural households are at 81% of individuals in urban households. With high urban unemployment in Malawi limiting the option of reducing rural poverty through permanent or seasonal rural-urban migration, this suggests that a key instrument for rural poverty reduction is to have less idle time for land and labor throughout the monthly calendar. For Bangladesh, Lagakos et al. proposed filling labor calendars for rural households through migration to cities during the lean season. When this option is not available due to high urban unemployment filling and smoothing labor calendars in rural areas becomes a key dimension of poverty reduction. This can involve employment both in agriculture with more diversified farming systems and in the local rural non-farm economy. This is the purpose of the agricultural and rural transformations that are important in redefining how to use agriculture for development.

Based on work done for the IFAD Rural Development Report led by Binswanger, for China by Huang , by BRAC on graduating the ultra-poor out of poverty , for the Gates Foundation by Boettiger et al. , and for the ATAI project , a strategy of using agriculture for development would involve the following five steps: Asset building, Green Revolution, Agricultural Transformation, Rural Transformation, and ultimately Structural Transformation as described in Table 1. We refer to this strategy as the Agriculture for Development sequence. Minimum asset endowments for SHF under the form of land, capital, health, knowledge and skills, and social capital are needed to initiate production for the market and participation in a value chain. This corresponds to minimum capital endowments to get started in production in farm household models such as Eswaran and Kotwal’s , and to asset thresholds to escape poverty traps in Barrett and Carter . The BRAC graduation model for the rural ultra-poor thus importantly starts with achieving minimum asset thresholds for households to engage in self-employment in agriculture , with rigorous impact evaluations demonstrating success in raising household consumption in five of six case countries. Evaluation with a randomized experiment of a BRAC credit program for landless workers and SHF in Bangladesh shows that loans can be used to achieve minimum asset endowments by renting land and selecting more favorable fixed rent over sharecropping contracts . The Green Revolution, whereby productivity growth is achieved in staple crops through the adoption and diffusion of high yielding variety seeds and fertilizers is the initial step in agricultural modernization. It has been actively pursued to achieve food security and is a learning ground for the subsequent transformations of agriculture and rural areas. It has been a major success of the Consultative Group in International Agricultural Research and is still an ongoing effort in Sub-Saharan Africa and Eastern India. A key objective of the Agricultural Transformation is to fill in rural households’ labor calendars over as much of the year as possible through multiple cropping — which typically requires water control to cultivate land in the dry season, the development of value chains for new crops, and contracting among agents in these value chains. An example is the introduction of short duration rice varieties in Bangladesh that frees the land for an additional crop, typically high value products such as potatoes and onions, between rainy season and dry season rice crops. This makes an important contribution to filling land and labor calendars and to reducing the length of the hungry season . Because the Agricultural Transformation implies diversification of farming systems, it is a key element of national food security strategies where diverse diets, including perishable goods such as fruits and vegetables, dairy products, and meats that are less traded than staple foods, are an important element of healthy diets . SHFs are engaged in value chains that define the way they relate to markets. Value chains for agricultural products link farmers backward to their input and technology suppliers and forward to intermediaries, processors, cannabis drying racks and ultimately consumers . Relations within value chains often take the form of contractual arrangements. Induced by income gains for consumers, urbanization, and globalization, there has been in recent years a rapid development of value chains not only for low-value staple food crops, but also for medium value traditional domestic consumption and export crops, and high-value non-traditional export crops.

Their structure can take a wide variety of forms in linking SHF to consumers, ranging from traditional spot markets to elaborate contract farming, productive alliances , and out-grower schemes . Contracts can be “resource-providing”, thus contributing to solve market and institutional failures for participating SHFs. A key objective of the Rural Transformation is to give access to smallholder households to sources of income beyond agriculture. In Ghana, income derived from the rural non-farm economy for rural households is about 40% of total income, a share that increases as land endowments fall . It is indeed the case that, with land limitations, smallholder households rarely exit poverty with agriculture alone. A rural transformation requires the development of land markets and of labor markets . This process will typically happen first in the more favorable areas where a rural non-farm economy linked to agriculture can develop through forward, backward, and final demand linkages. It corresponds to the Agriculture Demand-Led Industrialization strategy advocated by Adelman and Mellor that is actively pursued in countries such as Ethiopia and Rwanda, and through CAADP in much of Sub-Saharan Africa.In vast regions of Sub-Saharan Africa and South Asia, the unfolding of an Agriculture for Development sequence has been held back by multiple obstacles that originate in asset deficits, market failures, and institutional deficiencies . This results in constraints to adoption of new technologies and lack of development of inclusive value chains. These failures may result in lack of profitability of innovations for particular SHFs given their specific circumstances, lack of local availability of the innovations in spite of potential profitability, and constraints to adoption in spite of potential profitability and availability. These constraints concern most particularly lack of access to sources of liquidity such as credit and savings, risk and lack of access to risk-reducing instruments such as insurance and emergency credit lines, lack of access to information about the existence of new technology and how to use it, and lack of access to input and output markets due to high transaction costs such as poor infrastructure and collusion of traders in local markets. The Agriculture for Development sequence is thus particularly multidimensional and difficult to implement. There are basically two contrasted approaches to potentially overcoming the problems that obstruct an Agriculture for Development sequence. The first consists in focusing on particular groups of farmers and addressing each of the problems in their own shapes and forms that affect them in modernizing. We can label this a “supply-side” approach to modernization and transformations. It consists in securing the existence and profitability of innovations, ensuring their local availability, and then overcoming each of the four major constraints to demand and adoption through either better technology or through institutional innovations . The agents for this approach are principally public and social such as governments, development agencies, NGOs, and donors. The second consists in creating incentives for SHF to modernize by building value chains for the particular product, and managing vertical and horizontal coordination within the value chains to overcome the profitability-availability-constraints obstacles as they apply to inclusion and competitiveness of SHF in the value chain. This is a “demand-side” approach to modernization and transformations. It consists in creating the demand for innovations in order to establish SHF competitiveness within a value chain, and then securing the existence, availability, and conditions for adoption of innovations. The approach thus requires both value chain development and value chain inclusion of SHFs. In this case, the agents are principally private such as enterprises and producer organizations for contracting, and lead firms, multi-stakeholder platforms, and benevolent agents for coordination. The theory of change we use in this review paper is represented in Figure 2. Circumstances for unleashing an Agriculture for Development sequence include the national and international context and policies, deficits in access to assets, and market and government failures that affect SHF. Approaches to modernization can follow a supply-side or a demand-side approach, in each case with specific agents engaging in the corresponding activities. Desired outputs are productivity growth in staple foods and Agricultural and Rural Transformations; desired outcomes are growth and poverty reduction. In what follows, we review each of these approaches in turn. Both have been extensively used and analyzed, yet belong to somewhat separate traditions in spite of obvious complementarity.Technological innovation are first analyzed in experimental plots, usually for yield and resilience to specific shocks. But this does not tell us whether the innovation is likely to be adopted by SHF. Analysis of the adoption problem should start with verification that the innovation is indeed profitable for the intended SHF under their own circumstances, objectives, and capacities. Measuring profitability in farmers’ plots is however very difficult . There are data problems in observing family labor time and definitional problems in establishing the opportunity cost for family labor and self-provided inputs. Conditions also vary year-to-year due to weather conditions, with only short time series to observe how climate affects outcomes, made even more difficult to interpret with climate change. And there are many unobservable conditions and complementary factors that affect profitability and compromise the external validity of any measurement made at a particular time and place. An alternative approach is to verify profitability without measuring it. Some among the best endowed and best located farmers have to be able to make sustained use of the innovation for the innovation to have adoption potential by others under current market, policy, and complementary input conditions. This can be established by observation, experimentation, or simulation.

It presents a detailed account of management practices to enhance soil C storage and GHG mitigation, and a meta-analysis of published appraisals in the cropping systems of South Asia.Agriculture in South Asia is predominantly cereal-based, i.e. the cultivation of about 40 million hectares with multiple cereal crops or a single cereal crop, followed by a non-cereal crop such as legumes, vegetables, or potatoes, in an annual rotation . Rapid population growth and climate unpredictability in South Asia will increase the demand for food by at least 40% by 2050 . Meeting this projected need is doubly challenging, considering that 94% of the land suitable for farming is already under production and that 58% of agricultural areas face multiple hazards such as water shortage and extreme heat stress . It is anticipated that the current situation will worsen with climate change, which includes rising temperatures . The region is undergoing rapid economic growth, resulting in an increase in the emission of GHGs into the atmosphere. As of 2017, South Asia accounted for 7.5% of the world’s total CO2 emission from burning fossil fuels, of which India’s share was 6.6% and the remaining less than 1% was shared by seven other countries in the region . A large proportion of the total GHG emission from agriculture in South Asia comes from CH4 and N2O, cannabis drying racks representing 17% of the world’s total in 2017 with 179% increase since 1990 . India accounted for 11.8% and the other seven countries for the remaining 5.2% of total global CH4 and N2O emissions. Among the major sources of GHG emissions, rice cultivation is responsible for both CH4 and N2O emissions .

In South Asia, on a CO2-equivalent basis, rice cultivation and N fertilization are responsible for the largest emissions. Other sources of CH4 emissions include crop residue burning , and other sources of N2O emissions include the application of manure and crop residues to soils. Meeting the increased demand for food during the Green Revolution was associated with intensive cropping, soil management, and the use of agrochemicals, hence, resulted in the gradual loss of SOM . Although crop productivity has doubled or tripled during the last decades, negative impacts on the environment, biodiversity, soil, and air quality are common consequences . Conventional cultivation practices with exhaustive tillage and removal of crop residues by burning or for other uses in South Asia have not only resulted in nutrient and C losses but have also created a severe air pollution problem . About 2 million farmers in northwest India burn an estimated 23 million tons of rice residues every year . In some of the cities of northwest India, particulate air pollution in 2017 exceeded by more than five times the safe daily threshold limit, causing severe health problems both in rural and urban areas . Continuous tillage with the removal or burning of crop residues has also brought about the loss of SOM, resulting in a lower threshold, and adversely affecting soil functioning .The term “C sequestration” has been defined in many ways but broadly it is used to describe both natural and deliberate processes by which CO2 is either removed from the atmosphere or diverted from emission sources and stored in the terrestrial environment , oceans, and geological formations . It is the process of capture and long-term storage of CO2 in a stable state. This process can be direct or indirect, and can be biological, chemical, geological, or physical in nature. When inorganic CO2 is sequestered directly by plants through photosynthesis or through chemical reactions in the soil, this process is often called “C fixation”. Biological processes that occur in soils, wetlands, forests, oceans, and other ecosystems can store CO2, which is referred as “C sinks”. Bernoux et al. argued that since soils are associated with CH4 and N2O as well as with CO2 fluxes, the concept of “soil C sequestration” should not be limited to considerations of C storage or CO2 balance. All GHG fluxes must be computed at the plot level, or preferably at the level of the entire soil-plant pools of agroecosystems in C–CO2 or CO2-e, incorporating as many emission sources and sinks as possible for the entire soil-plant system.

These fluxes may originate from different ecosystem pools: solid or dissolved, organic or mineral. Bernoux et al. proposed that “soil C sequestration” or better, “soil-plant C sequestration”, should be considered as the result of the net balance of all GHGs, expressed in C–CO2 or CO2-e, computing all emission sources and sinks of a given agroecosystem in comparison to a reference agroecosystem, for a given period. Beyond its role in climate-change mitigation, SOM is not only a key component in nutrient cycling, but also influences a wide range of ecosystem services including water availability and quality and soil erodibility and is a source of energy for the soil biota that act as biological control agents for the pests and diseases of plants, livestock and even humans . SOM is most beneficial when it decays and releases energy and nutrients, and therefore its turnover is more important than the accrual of non-productive organic matter deposits . We propose that a definition of C sequestration should encompass not only the components of SOM in C storage and GHG mitigation, but also the characteristic dynamic turnover that results in labile pools essential for maintaining soil health. Therefore, there are two highly related aspects of C sequestration that aim to attain food security under a changing climate: reducing GHG emissions for mitigating climate change, and increasing soil C storage and linked C recycling for improving the efficient use of resources .Soils act both as a C sink and a C source . Eventually, the ability of a soil system to sequester C lies in the balance between net gains and net losses. Before the dramatic increase in C emissions during the industrial revolution, the global C cycle, or “C flux” was maintained at a near balance between uptake of CO2 and its release back into the atmosphere . Therefore, soil organic carbon can be characterized as a dynamic equilibrium between gains and losses. Practices that either increase gains or reduce losses can promote soil C sequestration. The soil C gain occurs largely from photosynthetically captured C and from the recycling of a part of the NPP as crop residues, including root biomass, rhizodepositions or manure/organic waste. The loss of soil C occurs largely from respiration by plants and the microbial decomposition and mineralization of organic residues to CO2 and CH4. In addition, soil erosion and photo degradation of surface litter are other important forms of C loss. Natural ecosystems are undisturbed and strike a balance of C gains over C losses, hence maintain greater C storage or C sinks. But the conversion of stable natural ecosystems to disturbed agricultural systems promotes soil C loss, converting soil from a net sink to a source of GHGs. It is interesting to note that globally, weed dry rack about 50% of vegetated land surface has been converted to agriculture . A recent estimate indicated that since the beginning of agriculture about 10–12 millennia ago, 456 Gt of C has been lost from the terrestrial biosphere . There are two components: from the prehistoric era to about 1750, the loss is estimated as 320 Gt; and from 1750 to the present era, there has been a further loss of 136 Gt. Another estimate reported the reduction of soil C by 128 Gt during the 10,000 years of cultivation .

On the other hand, Paustian et al. reported a soil C loss of 0.5 to >2 Mg C per hectare per year following the conversion of a natural ecosystem to cropland. This would result in the loss of 30–50% of the total C stock in the top 30 cm layer of topsoil until a new equilibrium was established. The large historic losses over a large time frame, and the fact that soil possesses two to three times more C storage capacity than the atmosphere, have led to a belief that soil has the potential to mitigate GHG emissions and climate change via sequestering soil C. During the last few decades, several researchers have published a range of estimates of soil C sequestration/C storage potential in agriculture. Based on 22 published studies, Fuss et al. reported global estimates of technical potential annual C sequestration rates ranging from 0.51 to 11.37 Gt of CO2 . A large range of reported estimates represented diverse agroecologies/systems , and management practices . The discrepancies in the areas assumed for extrapolation were reported to be the main reason for the large variation in the reported rates of SOC sequestration. In addition, variations in soil depths and the SOC equilibrium durations used for extrapolation cannot be ruled out. Nevertheless, based on the median values of minimums/maximums ranges, the best estimate of technical potential was 3.8 Gt CO2 yr 1 or 1.03 Gt C yr 1 . It is encouraging that a strong interest in this area is not limited to the scientific community only. Recently in the global C agenda for climate change mitigation and adaptation, soils have become a part through the initiation of three high level programmes . Firstly, in 2015, the French government launched the “4 per 1000” initiative at the 21st Conference of Parties of the United Nations Framework Convention on Climate Change as part of the Lima Paris Climate Agreement. The agreement recommended a voluntary plan of 4p1000 to sequester C in world soils at the rate of 0.4% or 4‰ annually . Secondly, at COP23 in 2018, the Koronivia workshops on agriculture were launched, giving emphasis on soils and SOC for climate-change mitigation. And finally in 2019, the FAO launched a program for the recarbonization of soils, called RECSOIL . In 4p1000 initiative, the value of 0.43% is based on the ratio of global anthropogenic C emissions and total SOC stock . Annual GHGs emissions from fossil C are estimated at 3.7 Gt per year and a global estimate of soil C stock of 860 Gt at 40 cm of soil depth. The value of 3.7 Gt C of emissions per year comes from the range of 2–5 Gt C estimated by Fuss et al. . For agricultural soils, Smith estimated the value of 0.45%, which is based on 1.3 Gt C of emissions per year and an agricultural SOC stock of 286 Gt C at 0–40 cm depth . For a 0–30 cm depth, the same annual sequestration potential would be equal to 0.53% of emissions and 0.56% of global and agricultural soil stocks . Considering the land area of the world as 149 million km2 , the average amount of C is calculated to be 161 tonnes of SOC per hectare, and 0.4% of this would be 0.6 tonnes of C per hectare per year. It has been argued that the initiative’s target of 4p1000 is highly ambitious, and important questions have been raised as to whether it is feasible to increase SOC stocks by 0.4% per year on average around the world . Soussana et al. and Rumpel et al. mentioned that 4p1000 initiative is indeed an aspirational goal with much uncertainty about what is achievable but aimed to promote concerted research and development programs on good soil management that could help mitigate climate change. They discussed various specific criticisms of the initiative in relation to biophysical, agronomic, and socioeconomic issues, and provided a more realistic scenario of what was possible and not possible. Subsequently, Amundson and Beaudeu further elaborated on the challenges and complexities involved in achieving this goal, and opined that adaptation may be more relevant than mitigation. They proposed the concept of “weather proofing soils” which would involve the development and promotion of improved soil C management approaches that are more adaptable. Recently, Amelung et al. suggested a soil-specific perspective on feasible C sequestration and some of its trade-offs. They also highlighted that crop land soils with large yield gap and/or large historic SOC losses have major potential for carbon sequestration. A greater need for local, reusable, and diversified knowledge on preservation and restoration of higher SOC stocks has been suggested . A few promising sustainable management options with higher SOC sequestration potential were identified for farmers in America .South Asia accounts for less than 5% of the world’s total land area and supports around 25% of the world’s population .

These source signatures were comparable across season, particularly from manure lagoons, and were always different from one another by at least ~8‰. Additionally, isotopic signatures from CH4 hotspots observed from remote mobile surveys were consistent with on-farm isotopic signatures and captured CH4 source areas. Our downwind observations revealed that enteric fermentation derived CH4 contributed from 0 to 93% of CH4 in plumes that varied with the amount of animal housing and lagoon in the emission footprint . Measurements of 13C of CH4 downwind of dairy farms may be a useful tool to monitor and quantify enteric:manure ratios with changes in mitigation . As shown in this study, isotopic signatures of CH4 downwind of dairy farms can be used to estimate the fraction of contributing sources, such as from manure lagoons and enteric fermentation source areas. We measured that the fraction of enteric CH4 to total CH4 from a mixed cluster of dairy farms ranged from 0.33 to 0.53 similar to model predictions of 0.5 for this region . Most CH4 mitigation strategies separately address CH4 emitted from enteric fermentation, such as through feed additives , or manure emissions by changing management techniques . As governing bodies undertake mitigation strategies to reduce CH4 emissions from enteric fermentation or dairy manure management, it is essential to verify mitigation effectiveness. In California, for example, numerous dairy farms have recently adopted or plan to install digesters in the near future to capture and convert CH4 from manure lagoons into fuel. Although digesters are designed to capture most CH4 emissions, studies have detected notable CH4 leaks from biogas plants . An important area of future research is to quantify the effect of mitigation strategies by comparing δ13CCH4 downwind of dairy farms before and after installation of digesters.

Isotopic signatures in this study agree with previous research showing that manure CH4 is more enriched in 13C than enteric CH4. Our on-farm measurements, however, weed dryers show that manure lagoon CH4 is relatively more enriched in 13C than previously reported in Southern California . Townsend-Small et al. reported a 13CCH4 range of -52.4‰ to -50.3‰ from manure bio-fuel from a manure digester facility and Viatte et al. reported 13C of CH4 of about -57‰ near manure lagoons. This may be explained by differences in CH4 generation processes and manure management differences between Southern California and San Joaquin Valley. Dairies in the San Joaquin Valley predominately use flush systems and store manure in lagoons, while Southern California dairies typically operate dry lots that forgo flushing manure from the feed lanes such that less manure is stored in anaerobic lagoons . Nevertheless, all California farms produce liquid manure from flushing solids in the milking parlor . Although Viatte et al. reported a more depleted 13C of CH4 of about -57‰ near manure lagoons compared to this study, they also observed an ~8‰ fractionation between enteric CH4 and manure CH4, consistent with our findings of isotopic fractionation between manure lagoons and enteric CH4 from free stall barns. There may also be differences in the stable carbon isotope composition of feed and differences in biogeochemical factors that play a key role in determining which microbial communities and pathways promote or inhibit CH4 generation from dairy manure management, and in turn affect the isotopic signature of CH4 emissions. These include pH, dissolved oxygen level, temperature, volatile fatty acids, chemical composition of the substrate, total nitrogen, and nutrient composition .Substrate depletion may also explain this variation, but additional measurements of δ 13C of volatile solids or CO2 concentrations would be needed to confirm isotopically fractionated substrates.

During acetate fermentation, CH4 and CO2 are commonly formed simultaneously. Reduction of CO2 may further transform the generated CO2 into CH4. In the influential study conducted by Whiticar et al. , CH4 generated from pure acetate fermentation resulted in δ 13C-CH4 ranging from -60 to -33‰, whereas CH4 from pure CO2 reduction had δ13C-CH4 values ranging from -110 to -60‰. However, bacterial oxidation in the substrate may affect these pathways before being emitted to the atmosphere, and consequently enrich 13C values of CH4. Measurements of δ2H-CH4 can provide information about partial oxidation since this process enriches δ13C-CH4 and δ2H-CH4 values . Possible explanations for the subtle differences of the manure isotopic signatures between seasons at the reference site may be influenced by changes in diet composition of the milking cows, substrate depletion, perterbations in the lagoon , or a combination of these factors. A future study examining δ 13C and δ2H of methane and δ 13C-CO2 from dairy manure lagoon waste is necessary to confirm the dominant processes contributing to the enriched δ 13CCH4 signatures from California dairy manure lagoons. Isotopic signatures of CH4 from enteric fermentation depend on the C isotopic ratio of foods, specifically with the proportion of plants with C3 and C4 photosynthetic pathways in cattle diets . A diet consisting mostly of C3 plants has been shown to generate more depleted δ13CCH4 than a diet of C4 plants . A database of studies found that ruminants fed a diet of more than 60% C4 plants emit CH4 with δ13CCH4 signatures of -54.6 ± 3.1‰, whereas ruminants fed a C3 diet emit CH4 with δ13CCH4 signatures of -69.4 ± 3.1‰ . This ~15‰ difference is about the same difference between 13C of C3 and C4 feeds. Furthermore, there is a ~41‰ difference between feed and CH4 regardless of ruminant species and diet . Future studies could explore the relationship between diet and CH4 isotope composition across seasons from different cattle production groups. To improve source apportionment of regional CH4 emissions in top-down studies, it is important to consider direct measurements of δ13CCH4 of enteric methane given that it varies depending on diet composition. We have shown that δ13C measurements of atmospheric CH4 using a mobile platform can be used for source attribution of enteric and manure methane. Our findings show that CH4 from manure lagoons is more enriched in δ13C than CH4 from enteric fermentation across seasons on average by 14 ± 2‰. This has implications to track the effectiveness of mitigation strategies by measuring δ13CCH4 to quantify enteric:manure ratios over time. In addition, drying cannabis this study contributes to a body of knowledge dedicated to investigating the sources and processes responsible for the increasing global mole fraction of atmospheric methane.

Future work could explore whether δ13CCH4 signatures change with mitigation efforts. Additional measurements using δ13C and δ2H of CH4 and δ13C-CO2 could elucidate which methane generation processes drive manure lagoon emissions.Major differences in δ13CCH4 from dairy farms among regions underscore the importance of δ13CCH4 measurements at local scales for global analyses.Livestock agriculture is a major source of ammonia and greenhouse gas emissions, such as methane and nitrous oxide . In the United States, livestock contributes an estimated 66% of total agricultural GHG emissions . Methane is more efficient at trapping infrared radiation than carbon dioxide , 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 . Ammonia is a gas-phase precursor to fine particulate matter, impacting human health and posing a threat to terrestrial and aquatic systems . As such, there is a need for accurate observations of GHG and NH3 emissions from the agricultural sector are imperative to address poor air quality and climate change. The San Joaquin Valley of California is a region with significant CH4, N2O, and NH3 emissions . Currently, there is disagreement whether state inventories accurately represent these gases across spatial and temporal scales. For example, atmospheric studies often report dairy CH4 emissions in California up to two times higher than bottom-up inventories . Meanwhile, other studies have reported that CH4 observations were comparable to inventories during the summer but not winter seasons, or using ground observations but not airborne measurements . A similar case is observed for NH3 in the SJV, where chemical transport models substantially underestimate gas-phase NH3 observations compared to airborne and satellite measurements . These results suggest that inventories likely underestimate and misrepresent agricultural NH3 emissions across spatial and temporal scales . There are limited N2O observations in the SJV of California, where most N2O emissions is expected from the agriculture sector . These studies show that top-down observations of N2O are at least two times higher than bottom-up inventories . In addition, these studies use either short-term airborne or tower observations, which provide limited seasonal and spatial information on N2O emission trends. The dairy sector is an important source of GHG and NH3 emissions in the SJV. Methane emissions from dairy farms is primarily emitted by enteric fermentation from ruminant gut microbes and anaerobic decomposition of dairy manure in storage ponds . Dairy manure management contributes a substantial fraction ofCH4, N2O, NH3 emissions and the relative magnitudes depends on manure management practices . Solid manure management includes storing manure in piles, deep pits, open lots, and daily spreading of dairy waste. In contrast, in a liquid manure management system, waste from barns and other dairy infrastructure, such as milking parlors, are washed and collected in slurry ponds or anaerobic lagoons . Anaerobic conditions, such as found in anaerobic manure lagoons, promote the production of CH4, and to a lesser extent N2O and NH3 emissions . Solid manure storage systems have reportedly higher N2O emissions than CH4 and NH3 emissions relative to manure lagoons. Nitrous oxide is generated from denitrification and nitrification reactions in manure-amended soils, manure storage, and direct N deposition by animals . In general, denitrification accounts for most of N2O emissions under anaerobic conditions. Nitrous oxide, along with NH3 and NO, is indirectly emitted through volatilization of manure N from nitrification and denitrification in soil after redeposition . Ammonia emissions, on the other hand, are primarily a byproduct of urea hydrolysis during the decomposition of urine and feces, which is mostly found in animal housing . Ammonia volatilization at liquid-surface interface occurs under high pH conditions since the pKa of NH4 + /NH3 is 9.25 . Storage of animal feed, such as silage piles, also emit NH3 and N2O As California moves towards meeting GHG and air pollution reduction goals, it is critical to gain a better understanding of the magnitude, temporal patterns, and source of emissions from dairy farms in the SJV region.Ground-based mobile lab measurements were collected in autumn of 2018 , spring , summer , and autumn of 2019 , and winter of 2020 . Table 3.2 shows a summary of these measurements and associated environmental conditions. Atmospheric measurements were performed with a mobile platform outfitted with multiple trace gas analyzers based on cavity ring down spectroscopy and an isotopic N2O analyzer based on off-axis integrated cavity output spectroscopy . In addition, a global satellite positioning unit recorded geolocation and vehicle speed and a weather station measured wind direction, wind speed, air temperature and relative humidity. A stationary 3 m meteorological tower with a 3-D sonic anemometer mounted was used to collect ambient temperature, wind speed, and wind direction. Atmospheric measurements of CH4, NH3, and N2O were collected from an inlet height of 2.87 m above ground level. Greenhouse gas measurements were corrected using high and low gas mixtures before and after each measurement period. The gas mixtures were tied to the NOAA Global Monitoring Division scale.The highest ΔNH3:ΔCH4 maxima were observed during the summer and autumn seasons, when air temperatures were high, for free stall barns, corrals, manure lagoons, and silage. In animal housing, NH3 emissions are a byproduct of urea hydrolysis from the decomposition of urine and feces. In general, NH3 volatilization increases with higher concentrations of NH4 + /NH3, substrate temperature, pH, wind speed and turbulence . When temperatures are high, this dairy farm increases the ventilation and moisture of free stall barns with ceiling fans and cools milking cows with periodic cooling water mist. Increased wind speed and ventilation rates tend to decrease CH4 emissions in animal housing . Increased turbulence and moisture conditions during the summer months potentially promoted more NH3 emissions in the free stall barns and decreased CH4 emissions. Methane emissions from animal housing are impacted by weather conditions and management practices. The quantity and quality of manure deposited onto the housing floor affects whether methanogenesis is promoted.

A 2013 University of California, Berkeley study, for example, found that across the United States, Blacks were 52% more likely, Asian Americans 32% more likely, and Latinos/as 21% more likely to live in conditions with increased heat related risk as compared to whites. Furthermore, low-income people and people of color are also less likely to have air conditioning. In the Los Angeles-Long Beach Metropolitan Area, for example, approximately twice as many Blacks do not have access to air conditioning compared to the general population. The cumulative impact of such circumstances is that Blacks in Los Angeles are twice as likely to die from a heat wave as other residents. Significantly, Blacks and other communities of color are also less likely to own cars to escape extreme weather events: nationally, 19% of Blacks reside in households without a single car, compared to 13.7% of Latinos/as and 4.6% of whites. Furthermore, climate change will lead to higher prices for energy, food and water, exacerbating the fact that low-income communities and communities of color already spend a greater portion of their income on basic necessities. Households in the lowest income bracket use more than twice the proportion of their total expenditures on electricity, and twice the proportion of their total expenditures on food, than do those households in the highest income bracket. Finally, due to climate change, low-income communities and communities of color will have fewer or shifting job opportunities. Low-income people of color hold the majority of jobs in sectors that will be significantly affected by climate change, such as agriculture and tourism.

In California, as of 2014, for example, there were 739,000 agriculture laborers, 49.2% of whom were Latinos/ as. Workers in these industries, growing benches particularly agricultural laborers, would be the first to lose their jobs in the event of an economic downturn due to climatic troubles. Additionally, people of color already own the most marginal farmland and benefit the least from support programs, thus leaving certain producers themselves at greater risk due to climate change. Corporations, furthermore, stand to benefit by way of the impacts of climate change and a Farm Bill that serves corporate interests. In the 2014 Farm Bill, the crop insurance program expanded to cover specialty crops and account for the higher value of organics. Due to extreme weather, however, the program’s costs have grown even without changes to the Farm Bill. After the 2012 drought, for example, the Federal Crop Insurance Program paid out $17.3 billion in losses, the highest ever, breaking the earlier record set in 2011, yet taxpayers covered nearly 75% of the payouts, minimizing any cost to crop insurance corporations. The public thus subsidizes not only the destructive type of agriculture but also the insurance payouts themselves caused in part by such destructive methods—a resilient arrangement that leaves corporations benefitting the most.Corporate Consolidation and Control: Corporate consolidation and control have become central features of the US food system, and 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 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. 

Food System Worker Disparity: Racial and economic inequity is a central feature of the industrial and corporate-controlled food system. At every level of the food chain, for example, from food production to food service, workers of color typically make less than white workers. On average, white food workers earn $25,024 a year while workers of color make $19,349 a year.Significantly, women of color in particular suffer the most, earning almost half of what white male workers earn. In some contexts, a majority of farm workers who receive “piece-rate” earnings frequently earn far less than minimum wage—an exploitative practice deeply tied to immigration policy. For example, as of 2014, twice as many restaurant workers were food insecure compared to the overall US population; as of 2011, in Fresno County, California, 45% of farmworkers were food insecure, and in the state of Georgia, 63% of migrant farmworkers were food insecure. Beyond wages, few people of color hold management positions in the food system, with white people holding almost three out of every four managerial positions in the food system. As of 2012, 11.8% of executive and senior level officials and managers, and 21.0% of all first- and mid-level officials and managers in 2012 were people of color. One result of this disparity is that non-white food system workers experience greater food insecurity.Food Equity and Nutrition: Food insecurity in the US continues unabated, affecting low-income communities and communities of color in particular. 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 for Black and Latino/a households, households with incomes near or below the federal poverty line, and households with children headed by single women or single men. Within this social, political, and economic climate, recent cuts to the Supplemental Nutrition Assistant Program and other meal support programs continue to disproportionately hurt communities of color, as they are frequently over represented in the lowest-paying sectors of the labor market. Land Access: In 1920, 14% of all US farmers were Black . By 1997, fewer than 20,000 US farmers 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, between 1920 and 1997, the number of US farms operated by Blacks dropped 98%, while the number of US farms operated by whites dropped 65.8%. Although in 1982 the US Commission on Civil Rights concluded that the USDA was the primary reason Black farmers continued to lose their land at such astonishing rates. In 1983 President Reagan eliminated the division of the USDA that handled civil rights complaints. The USDA Office of Civil Rights would not re-open until 1996 during the Clinton Administration. The increasing influence of corporations inside and outside the food system since the early 1980s exacerbated such trends for communities of color, and marked the complex ties between the federal government and corporate interests. Farm Labor and Immigration Policy: The Farm Bill itself does not deal directly with immigration. However, the combination of an immigration system easily exploited by employers, and workers’ low income, limited formal education, limited command of the English language, and undocumented status, gives such farm laborers little opportunity for recourse within—or options outside of—the unjust working conditions that the Farm Bill has helped make possible. For example, as of 2009, 78% of all farmworkers were foreign born; 70% said they could not speak English “at all,” or could only speak “a little”; the median level of completed education was sixth grade; and 42% of farmworkers surveyed were migrants, a third of whom having traveled between the United States and another country, primarily Mexico. 

Significantly, many agricultural workers fear that challenging the illegal and unfair practices of their employers will result in further abuses, loss of their job, and, ultimately, deportation. Worse yet, bud drying system few attorneys are available to help poor agricultural workers, and federal legal aid programs are prohibited from representing undocumented immigrants. Ultimately, corporate control of the food system secures and exacerbates the unjust treatment of the predominately non-white and migrant agricultural workforce of the United States. Climate Change: In the United States, the relationship between disparity in exposures to environmental hazards and socio-economic status has been widely documented. As a major contributor to global climate change and the racialized distribution of its impacts, conventional agricultural production practices, in particular, have been instrumental toward this end. In 2013, for example, the US Environmental Protection Agency reported that greenhouse gas emissions from agriculture accounted for approximately 9% of total US greenhouse gas emissions—an increase of approximately 17% since 1990. Low-income communities and communities of color in the United States experience the brunt of the effects of climate change than other Americans: they breathe more polluted air, suffer more during extreme weather events, and have fewer means to escape such extreme weather events. Rising energy, food, and water costs also disproportionately effect low-income communities and communities of color, as such communities already spend a greater portion of their income on basic necessities than white communities. Finally, low-income communities and communities of color hold the majority of jobs in sectors that will be significantly affected by climate change, such as agriculture and tourism. Workers in these industries would be the first to lose their jobs in the event of an economic downturn due to climatic troubles. Significantly, this report found a number of structural barriers to addressing these racial/ethnic, gender, and economic inequities. Part I found that the Farm Bill—from its inception in 1933 to the Farm Bills of the 1980s onward— is defined by the long term shift from the subsidization of production and consumption to the subsidization of agribusiness itself. In this light, low-income communities and communities of color have been structurally positioned on the losing side of such shifts, and of US food and agriculture policy more broadly. They have also been given few options for recourse, given the ways in which the Farm Bill has been designed and re-designed to be insulated from democratic influence, particularly by way of countless layers of committees. Part II found that, despite the benefits of joint SNAP and Unemployment Insurance for low-income communities and communities of color, such of the benefits of both during the recession precipitated by the 2007–2008 financial crisis, supporting public nutrition assistance programs and fighting poverty and racial/ethnic inequality, are antithetical. Specifically, while such public assistance programs do indeed support, in some ways, the most marginalized communities, they ultimately maintain structural inequity by way of the major profits that corporations such as Walmart and other large retailers reap by distributing such benefits. These corporations are the same ones that funnel profits back to their corporate headquarters, outside their respective retail sites, and that force low wages and poor working conditions onto workers at all levels of the food system. Finally, Part III and Part IV found that supporting the inclusion of producers of color into current payment schemes and fighting poverty and racial/ethnic inequity are also antithetical, despite recent gains in terms of USDA Civil Rights settlements and slowly increasing participation in such programs by such producers. Specifically, while such disparities may be addressed, in part, by way of more representative Farm Service Agency committees—or by better outreach and assistance such payment programs, and their successor, crop insurance programs—ultimately they maintain structural inequity. They do so, for example, by re-entrenching existing property regimes that consistently push producers, be they of any racial/ethnic background, to cut costs where possible. Specifically, while these disparities may be addressed, in part, by way of more representative Farm Service Agency committees—or by better outreach and assistance— such payment programs, and their successor, crop insurance programs, they ultimately maintain structural inequity. Furthermore, such property regimes set the stage for corporations to fare best, and to grow in size, profit, and influence by way of the multiple mechanisms outlined in both Part III and Part IV. These short term policy interventions must be aligned with the long term strategy of challenging the structural and racialized barriers to a fair and sustainable food system, and thus the existing social, political, and economic frameworks that make such barriers possible. That is because structural change must arguably begin with the tools that are available at the moment, in this case the US Farm Bill, in order to address the most immediate needs for some. Yet, history has shown that such tools can only address the needs of some.

The percentage of the population with income below 130% of the federal poverty line—the income limit for SNAP eligibility—increased substantially during the period of the Great Recession, from 54 million in 2007 to 60 million in 2009, and 64 million in 2011. During this period, the rate of SNAP participation rose among eligible households from 65% in 2007 to 75% in 2010, up to 83% in 2012, with the program expanding at a record pace of 20,000 people per day. By the end of 2014, more than 46 million people, over 14% of all Americans, were using SNAP. SNAP eligibility and use, however, varies significantly by race/ethnicity, with communities of color experiencing the highest rates of eligibility for, and use of, SNAP, particularly during economic downturns. For example, by end of 2009, SNAP was used by 12% of the US population , 28% of all Blacks and 15% of Latinos/as nationwide were using SNAP. On the other hand, only 8% of whites were using SNAP, substantially below the national average. Such trends follow racial/ethnic and economic geographies as well, with SNAP use greatest where poverty and racial/ethnic stratification runs deep. Across the ten core counties of the Mississippi Delta, for example, 45% of Black residents receive SNAP support, while in larger cities such as St. Louis, with a population of 353,064, the percentage of Black residents receiving SNAP support rises to 60%. Even in the largest cities, those with over 500,000 people, such trends re-main: white SNAP use peaks at 16% in the Bronx, New York for example, cannabis racks while Black SNAP use peaks at 54% in Kent, Michigan. Significantly, there are 20 counties across the United States where Blacks are at least 10 times as likely as whites to be SNAP beneficiaries, and 26 counties in the United States where over 80% of Blacks were SNAP recipients.

Conversely, there are only 5 counties with more than 39% of white receiving SNAP benefits. The growth of SNAP use amidst the Great Recession has been especially rapid in locations worst hit by the housing bubble burst, and particularly in suburbs across the United States where SNAP use has grown by half or more in dozens of counties. Furthermore, this is the first recession in which a majority of low-income communities and communities of color in metropolitan areas live in the suburbs, giving SNAP and other federal aid new prominence there. The increase in SNAP eligibility and use thus mirrors the impacts of the crisis in housing and employment, and the racialized distribution of impacts of such crises. Specifically, SNAP use was found to have increased by the greatest amount in places characterized by increased poverty, increased unemployment, more home foreclosures, and increased Latino/a populations. A 2012 Congressional Budget Office report confirmed such findings and estimated that although 20% of the growth in SNAP spending was caused by policy changes, including the temporarily higher benefit amounts enacted in the American Recovery and Reinvestment Act of 2009 , the housing crisis and weak economy were responsible for about 65% of the growth in spending on benefits between 2007 and 2011, with the remainder caused by other factors, including higher food prices and lower incomes among beneficiaries. Such has been the case historically: when unemployment rose, SNAP use always did too, signaling how SNAP use has long played a role in alleviating periods of economic distress. As such, SNAP is heavily focused on the poor. According to a 2015 Center on Budget and Policy Priorities report, about 92% of SNAP benefits go to households with incomes below the poverty line, and 57% go to households below half of the poverty line . Because families with the greatest need receive the largest benefits, and because households in the lowest income bracket use twice the proportion of their total expenditures on food than do those households in the highest income bracket, SNAP is a powerful anti-poverty tool.

SNAP, when measured as income, kept 4.8 million people out of poverty in 2013, including 2.1 million children, and lifted 1.3 million children above half of the poverty line in 2013. Furthermore, SNAP is also effective in reducing extreme poverty. A 2011 National Poverty Center study found that SNAP, when measured as income, nearly halved the number of extremely poor families with children in 2011 by 48% and cut the number of children in extreme poverty by more than half . That the increase in SNAP eligibility and use during the start of the Great Recession mirrored larger trends in the economy—and was patterned after long-standing racial and economic inequality—signals the need to again assert that the experience of food insecurity is one part of a larger structure that continues to affect the most historically marginalized populations. A 2010 Census Bureau report found that the recession not only grew the wealth gap between rich and poor; it also exacerbated the gap between different racial/ethnic groups. Between 2007 and 2009, the wealth gap between whites and Blacks nearly doubled, with whites having 22 times as much household wealth as Blacks and 15 times as much as Latinos/as. By 2010, the median household net worth for whites was $110,729 while for Blacks it was $4,995 and for Latinos/as it was of $7,424. Between 2005 and 2010, furthermore, median household net worth for Blacks, Latinos/as, and Asian Americans fell by roughly 60%, while the median net worth for white households fell by only 23%. Many people of color were pushed into bad mortgages by the nation’s biggest banks, while the loss of 600,000 public sector jobs during the recession also had a significant impact on communities of color, as Black and Latino/a workers are more likely to hold government jobs than their white counterparts. Although the current slow economic recovery is not unusual, the cumulative and sustained impacts of unemployment, income loss, and housing loss disproportionately experienced by low-income communities and communities of color signal the value of a safety net that protects such marginalized communities from sustained poverty and food insecurity. Two major parts of the recessionary safety net are the USDA’s Supplemental Nutrition Assistance Program and the Unemployment Insurance program of the US Department of Labor, which provides financial support to workers who become unemployed through no fault of their own. As with SNAP, pipp drying racks expenditures for UI generally expand during economic downturns and shrink during times of economic growth, primarily because economic downturns result in wider eligibility and participation. Significantly, households that participate jointly in both SNAP and UI can improve their ability to sustain food expenditures, nutrition, and overall standard of living during times of economic challenge and are an indicator of the strength of the recessionary safety net itself.

Toward this end, a 2010 USDA study found that the recession not only increased the number of SNAP households but also increased the extent of joint SNAP or UI households: an estimated 14.4% of SNAP households also received UI at some point in 2009—nearly double that of 7.8% in 2005. Moreover, an estimated 13.4% of UI households also received SNAP at some point in 2009, an increase of about one-fifth over the estimate of 11.1% from 2005. Significantly, people of color, hardest hit during the economic downturn, benefitted the most from the safety net. In 2009, the estimated joint SNAP and UI use for Blacks and for Latinos/as exceeded joint use by whites by about 16.6 and 9.8%, respectively. Together, SNAP and UI help sustain aggregate household spending and national production in economic downturns, making the impact of such downturns less severe than they would be in the absence of the programs. Such benefits are particularly pronounced for communities of color who not only experience relatively greater degrees of poverty, but also are hardest hit during economic downturns.In April 2012, the Congressional Budget Office estimated that temporarily higher benefit amounts enacted in the American Recovery and Reinvestment Act of 2009 accounted for about 20% of the growth in SNAP spending during the Great Recession. New legislation can thus affect safety net programs such as SNAP or UI and provide additional support for household spending and national production. Historically, there has been some form of federally financed SNAP and UI benefit extensions during recessions that build upon the benefits they already provide. In 2008, for example, national legislation provided a temporary increase in SNAP benefits for all SNAP participants and expanded eligibility for jobless adults without children. Similarly, UI benefits were extended by the Emergency Unemployment Compensation 2008 program. Together, such efforts highlight the potential benefit of strategic program extensions, particularly during pronounced times of need for communities that are already marginalized. Along with the federally financed temporary benefit extensions, these programs have the potential to have a substantial impact in cushioning the negative effects of recessions on the US population and economy. Ultimately, however, such program expansions are neither a long term nor a structural solution. While SNAP and other federal safety net programs are useful during times of economic hardship and pronounced food insecurity, or as potential anti-poverty tools, such programs only superficially act as efficient and effective forms of local economic stimulus. According to the USDA, for example, SNAP spending yields a substantial local multiplier effect, with every $1 of SNAP benefits spent in a community generating an additional $1.80 in local spending. Yet because many larger grocery retailers have non-local corporate headquarters, sales revenue is transferred outside the community, a phenomenon called “leakage.” For example, in 2008, the City of Oakland, CA estimated that approximately $230 million in grocery store spending is leaving the city. Thus, although it has the potential to help millions of Americans feed their families during economic crises and keep many out of extreme poverty, investing in SNAP is a questionable long term economic stimulus policy and social and economic equity tool because of the benefits accrued by corporations, and the injustices such corporations perpetuate with regard to the exploitation of their employees. Despite these limitations, however, both SNAP and UI have indeed had positive effects on both Gross Domestic Product and on job growth, as well as long term effects on beneficiaries. Research has shown, for example, that access to SNAP in childhood leads to a significant reduction in the incidence of obesity, high blood pressure, and diabetes, and, for women, on the other hand, an increase in economic self-sufficiency. Thus, such costs and benefits ultimately beg the question of whether SNAP, and the Farm Bill more broadly, are the best long term approach to challenging structural poverty, particularly as it is perpetuated by corporate control itself.THE STRUCTURE OF US AGRICULTURE determines and reflects the challenges faced by US farmers and rural communities. This includes farm size, type, cropping patterns, and ownership. Moreover, the federal food and agricultural policies, including the Farm Bill, affect the structure of US farmland through multiple forces and drivers, including taxes, lending programs, environmental and safety regulation, rural development programs, research and development funding, and commodity programs. In this light, Part III examines how such programs have shaped the structure of US farmland and, in turn, how they have affected the socio-economic well-being of low-income farmers and communities, as well as farmers and communities of color. It does so, first, by providing a snapshot of the structure of US farmland, including the outcomes of structural racialization with regard to farmland ownership and government payments . It then outlines the historical significance of change in the structure of US agriculture over the 20th century, and examines three federal rural and agricultural support programs in particular: Farm Service Agency lending programs, Farm Bill commodity programs, and Farm Bill Rural Development programs. Ultimately, Part III argues that such programs have historically undergirded white farmland ownership at the expense of farmland ownership by people of color. Significantly, these programs also highlight how white agricultural land ownership was held up amidst, and by way of, increasing consolidation and specialization, with farmers of color on the losing side of such shifts in the structure of US farmland. In the push for the dismantlement of corporate control and structural racialization, such trends thus require greater attention with regard to their role in intensifying marginality that low-income communities and communities of color face in terms of wealth, access to program benefits, and land access.

Subsequently, under the 1938 Farm Bill, the federal government, and not a processor’s tax, would finance such subsidies, thus relieving corporations of any responsibility to maintain high commodity prices or profitable farms. Significantly, this funding structure was held in place during the shift in agricultural policy from the support of production to the support of prices by way of the doctrine of parity. The ongoing erosion of the doctrine of parity from 1952 onward, which included the lowering of price floors and reduction of supply management practices, sent farm prices crashing and ushered in a period of agricultural policy driven by agribusiness. Specifically, corporations such as Archer Daniels Midland and Cargill were instrumental in helping replace New Deal-era loan programs and land-idling arrangements with direct subsidies that supported low prices for corporate purchasers themselves. Anticipating the 1973 Farm Bill, for example, and alongside Secretary of Agriculture, Earl Butz, Cargill and the Farm Bureau argued that crashing farm prices would be a plus. They argued that not only would greater exports and new uses such as ethanol and sweeteners remedy the drop in price, but also that farms would remain profitable with the support of government subsidies. The winners and losers were clear under such policies: corporate buyers could acquire commodity crops for record low prices that were subsidized by the federal government while farmers continued to lose their lands and their income. Such policies, furthermore, cannabis racking systems constituted part of the larger trend in corporate growth, not limited solely to agribusiness.

For example, according to a 2013 Bureau of Economic Analysis, corporate profit as a percentage of GDP more than doubled between 1980 and 2013, rising from less than 5% to over 10%; before tax, corporate profit, as a percent of GDP, rose from less than 8% to over 12.5% between 1980 and 2013. Both periods, from the Great Depression and New Deal farm programs, to their erosion over the following decades, were characterized by structural racialization. Although New Deal-era legislation was geared toward pulling Americans out of poverty, it was itself a project of racial exclusion, with Black communities and other communities of color systematically barred from such supports. Southern committee members in Congress, for example, blocked efforts to include agricultural workers and domestic workers in the Social Security Act—the New Deal’s centerpiece legislation—largely because of the high concentration of black workers within those lines of work. In the 1930s, 60% of Black workers held domestic or agricultural jobs nationally while, in the southern United States, domestic and agricultural occupations employed almost 75% of Black workers, and 85% of Black women. Furthermore, although the National Recovery Administration set wages within the cotton industry at $12 a week, many Black workers had jobs that were not covered by the law and thus had their wages reduced by employers so that white workers could be paid more. Finally, Black agricultural workers were also left out of New Dealera agricultural union programs—namely the National Labor Relations Act, enacted and signed into law on July 5, 1935—while Black landowners in particular were excluded from federal farm support under the Agricultural Adjustment Administration. Significantly, the distribution of federal support during this period resulted in the dramatic decrease in the number of Black farms, from about 900,000 in 1930 to 682,000 in 1939. 

Although these programs were slowly eroded over the next few decades, farmers of color continued to face great hardship relative to white farmers. The period of agricultural mechanization and industrialization after World War II, marked by the widespread adoption of scientific and technological innovations is usually credited with weeding out supposedly “non-productive, inefficient” farmers. Yet farmers of color and particularly Black farmers, in the context of the uneven application of New Deal era supports and years of discriminatory practices, were at a great disadvantage during this period because they were prevented from attaining the requisite access to capital and thus economic stability for such a transition. The Emergence of the Neoliberal Corporate Food System From the late 1970s and early 1980s until today, corporations have taken on a new and more deeply entrenched set of relationships within the food system. In short, this period is defined by neoliberal capitalist expansion and corporate control that began with the global economic shocks of the 1970s and 1980s During the 1980s, and working for the interests of multinational corporations in securing markets abroad for agricultural commodities produced domestically, Structural Adjustment Programs broke down foreign tariffs, dismantled national marketing boards, and eliminated price guarantees in the Global South. Alongside this destructive guarantee of foreign markets, the 1950s-onward trend of dismantling domestic safety net programs for farmers, guaranteeing low prices for commodity purchasers , and making up the potential loss for farmers with government direct payments continued. Such trends culminated in the 1996 Farm Bill—the “Freedom to Farm” bill. This Farm Bill eliminated the structural safety nets that had long protected producers during lean years. Corporate buyers and groups such as the National Grain and Feed Association, composed of firms in the grain and feed industry, pushed the 1996 Farm Bill to completely eliminate price floors, the requirement to keep some land idle, and the grain reserves that were meant to stabilize supplies and therefore stabilize prices, while simultaneously encouraging farmers to plant as much as possible. The 1996 Farm Bill thus marked the culmination of the shift from the federal government subsidizing production and consumption to diminishing price supports and the subsidization of agribusiness itself.

The dismantling of such price controls drove prices down and allowed corporate buyers to profit off heavily subsidized commodities while securing their power over producers. Specifically, deregulation left farmers increasingly vulnerable to market fluctuations caused by speculation, price volatility, and the profit-motives of corporate buyers. The shifts under the 1996 Farm Bill were deemed a failure by both farmers and legislators, and by 1997, rapidly falling farm prices resulted in direct government emergency payments to farmers, despite the fact that the legislation was designed to completely phase out farm program payments. Between 1996 and 1998, expenditures for farm programs rose dramatically, from $7.3 billion to $12.4 billion. They then soared to $21.5 billion in 1999 to over $22 billion in 2001. From 1996 to 2001, US net farm income dropped by 16.5% despite these payments. Rather than address the underlying cause of the price drop—overproduction—Congress voted to make these “emergency” payments permanent in the 2002 Farm Bill. As outlined below, neoliberal corporate influence remains particularly salient within two domains: the first is food production, processing, distribution, and service, and the second is education, research, and development.Commodity Supports: One major way corporations continue to profit and exert their influence on food production, distribution, and consumption is through commodity support programs. Once the safety nets of the New Deal farm programs were cut back during the 1980s and 1990s, and completely eliminated in the 1996 Farm Bill, farmers began to produce much more corn, soybeans, wheat, and other commodity crops. Specifically, harvest drying rack the 1996 Farm Bill eliminated the requirement to keep some land idle, which encouraged farmers to plant far more than they had before. As a result, the higher supplies of these crops brought down their prices, which drastically hurt farmer incomes and greatly increased the profits corporate purchasers reaped from purchasing even cheaper commodities. These low prices undermined the economic viability of most crop farms in the late 1990s, and subsequently, Congress provided a series of emergency payments to farmers. Furthermore, because continued oversupply kept prices from recovering, Congress eventually made such payments permanent in the 2002 Farm Bill. The dismantling of direct payment support for farmers thus ushered in another form of federally subsidized cheap commodities for corporate buyers that still leaves farmers themselves relatively vulnerable: disaster assistance programs and other emergency aid. The 2014 Farm Bill in particular cut funding allocated to direct payments by about $19 billion over 10 years—the most drastic policy change in this Farm Bill—with much of this money going into other types of farm aid, including disaster assistance for livestock producers, subsidized loans for farmers, and, most significantly, the crop insurance program. Crop Insurance: As fundamental as direct payments and emergency payments have been for subsidizing agribusiness profits, under neoliberal political and economic restructuring, crop insurance has surpassed them as the most egregious and expensive subsidy for agribusiness. For decades, farmers have been able to buy federally subsidized crop insurance in order to protect against crop failure or a decline in commodity prices. However, private insurance corporations and banks that administer the program, such as Wells Fargo, benefit the most from crop insurance subsidies. In 2011, these corporations received $1.3 billion for administrative expenses with $10 billion in profits over the past decade. In order to help cushion the blow from the reduction of direct payments, under the 2014 Farm Bill, $90 billion over 10 years will go toward crop insurance, which is $7 billion more than the previous farm bill. However, much of this money will go to private insurance corporations and banks instead of farmers. On the production side, the increase in government support will be directed toward the deductibles that farmers have to pay before insurance benefits begin. In other words, unlike non-farm insurance policies , crop insurance insures not only the crops, but also the expected revenue from selling those crops. Thus, Agricultural Risk Coverage and Price Loss Coverage only pays out when prices drop below a certain threshold. As of early 2015, corn crops have already reached this threshold. There exists a risk that this insurance program could cost far more than expected depending on how crop prices continue to shift: therefore, this is one of the more contentious aspects of the 2014 Farm Bill. Another contentious part is the uneven distribution of benefits. A 2014 report by the Environmental Working Group estimates that 10,000 policyholders receive over $100,000 a year in subsidies, with some receiving over $1 million, while the bottom 80% of farmers collect only $5,000 annually. In short, under the guise of cutting subsidies by repealing unpopular direct payments to farmers, the 2014 Farm Bill instead increases more costly crop insurance subsidies. Food Chain Workers: The pressure for corporate profit and the history of corporate consolidation with regard to the food system, both vertical and horizontal, has driven corporations to continue to lower wages for millions of food system workers and accumulate more wealth. A 2011 national survey of over 630 food system workers conducted by the Food Chain Workers Alliance found that the median hourly wage was $9.65 per hour. More than 86% of food system workers were paid poverty wages while 23% of food system workers were paid less than the minimum wage. Despite their significant role in every part of the food system—from production to processing to distribution and service—food system workers experience a greater degree of food insecurity than the rest of the US workforce. For example, according to the Food Chain Workers Alliance report, food system workers use SNAP at more than one and a half times the rate of the remainder of the US workforce. Additionally, as of 2014, twice as many restaurant workers were food insecure compared to the overall US population; as of 2011, in Fresno County, the country’s most productive agricultural county, 45% of farmworkers are food insecure. The situation is even worse in other parts of the country: in 2011, 63% of migrant farmworkers in Georgia were food insecure. Women and people of color disproportionately feel the economic pressure experienced by food system workers as a result of corporate consolidation. A comprehensive 2011 study of food workers and economic disparity found that people of color typically make less than whites working in the food chain. It found that half of white food workers earn $25,024 a year while workers of color earn $19,349. The study found that women of color in particular suffer the most, earning almost half of what white male workers earn. Furthermore, workers of color experience wage theft more frequently than white workers. More than 20% of all workers of color reported experiencing wage theft, while only 13.2% of all white workers reported having their wages misappropriated. Significantly, the study found that such discrepancies exist in all four sectors of the food system: production, processing, distribution, and service. Furthermore, such trends hold across the overall workforce.

This model incorporates segregation costs in the processing and marketing sector under regulations. The regulations of interest are imposed at the point of purchase within the regulating jurisdiction, so the identity of compliance must be preserved at all stages along the supply chain. For traceability, segregation between the restricted products and the other products is required. The segregation costs will be transferred mainly to final consumers of covered products in the regulating jurisdiction through a higher retail product price. The model yields several interesting results. First, the more adoption there is, the higher the farm compliance cost at equilibrium. Second, restrictions raise profits for some inframarginal adopters whose compliance costs are low. Third, restrictions have spillover effects on the unregulated share of the market. Fourth, processing and distribution costs are higher for products covered by regulations, and the magnitude of incremental costs is affected by the size of the regulated jurisdiction. Fifth, competition implies that the cost increases must be borne fully by covered products, and the product coverage of regulations affects its magnitude.The parameters of the primary supply and demand functions were calibrated such that the functions fit the 2018 market values for North America, based on Canadian and U.S. government statistics, vertical air solutions and have the corresponding price elasticity given those values. I used a price elasticity of supply of hogs at the farm of 1.8 from Lemieux and Wohlgenant , which was used in subsequent work .

To parameterize the primary demand functions, I began with a base retail price elasticity of demand for all pork of -0.68 from Okrent and Alston , a value that compares closely to values of -0.69 and -0.79 used by Buhr and -0.65 reported by Wohlgenant and Haidacher . The demands for covered and non-covered pork products will be more price elastic than the demand for pork as an aggregate category based on consumers’ willingness to substitute between the two types of pork products in response to price signals. After reviewing the relevant literature, I chose a base value of -0.9 for covered pork and -1.1 for non-covered pork. Given Okrent and Alston’s estimate of the price elasticity of demand for all pork and the market shares for C and N pork, these values imply a cross-price elasticity of 0.36 for N pork demand in response to a change in the price of covered pork and a cross-price elasticity of 0.26 for C pork demand in response to a change in the price of non-covered pork.Based on the size of the California pork market relative to the total market for covered pork products produced in North America, about 7% to 8% of North American sow housing needs to be compliant with Prop 12 standards to meet California’s demand. Generally, compliance would be less costly for farms already using group housing than for farms using gestation stalls. Therefore, Prop-12 compliant farms will mostly come from the set already using group housing. Hence, the relevant one-time cost of conversion to Prop 12’s requirements is that which applies to group housing operations. Variable costs for group-housing operations that become Prop-12 compliant are also compared with those that remain non-compliant.

Based on information from the industry, about 20 square feet of usable space per sow is allowed among typical operations using group housing, with some variation below that space per sow. Capital costs of housing per sow for those mostly likely to convert will, thus, rise by about 20% to increase the space allowance per sow from 20 to 24 square feet. Based on farm cost data , the implied increase in capital costs were assessed to be $3 per piglet produced in a farrowing operation, when converted to a marketed weight basis this corresponds to in my model. As noted, compliance costs vary across farms based on farm-specific characteristics such as housing facilities and managerial expertise. Given that less than 10% of North American hogs are destined for California consumption, I assumed that farms covering roughly 30% of the total North American sows might seriously consider the option to produce Prop 12-compliant sows. I use $2 per pig as the lowest conversion cost and $5 per pig as the cost for the 30th percentile of farrowing operations . The calculated value for group housing with 20 square feet per sow, therefore, is consistent with the lower 10th percentile of the uniform distribution. Note that hog farms with a higher cost of conversion, say those using gestation stall housing, are irrelevant to the calculations because they are far outside the range of farms that might convert to compliance.Prop 12 raises variable costs per pig produced in several ways. These include higher sow mortality, lower farrowing rates, fewer live pigs per sow, higher veterinarian costs, and higher farm labor costs all assessed on a marketable per pig basis. To compare costs, I used as the baseline costs calculated by university specialists .

Based on productivity information from producers, including declarations from dozens of producers included in the Petitioners Complaint in UNITED STATES DISTRICT COURT SOUTHERN DISTRICT OF CALFIORNIA CASE NO. 19CV2324W AHG National Pork Producers Council and the American Farm Bureau Federation v. Ross Dated: December 5, 2019. Given that California comprises less than 10% of the North American retail pork market, many primary processing operations will choose not to acquire the costly Prop 12-compliant hogs. These plants will avoid added costs of identifying, segregating, tracing, and labeling the compliant pork separately from the rest of their production. Most primary processing operations that do acquire and process the more expensive compliant hogs will also continue to utilize non-compliant hogs to exploit economies of size,access hogs within a reasonable distance of the plant to reduce transport costs and utilize plant capacity efficiently. These firms thus incur additional costs for identification, segregation, and tracing to enable sales of compliant pork into the California market. Such costs include separate holding pens, more complicated and less flexible scheduling, interruption in plant operation between processing the compliant and non-compliant hogs, additional storage capacity so that the up-to-double SKUs of fresh pork can be kept in distinct lots, a more complicated labeling process, and more complex shipping of labeled products. The costliest among these factors is likely to be the interruption of plant operations and reduced throughput during the change-over from handling compliant to non-compliant hogs. Compliant hogs will be processed on different days and/or at different times on a given day from other hogs to assure that non-compliant pork is not comingled with uncooked cuts of pork that are destined for California.  Further, my simulation projects that the average price of uncooked cuts of pork in California will rise by 6.9%, or about $0.23 per pound . California consumers will buy 6.2% less of the covered pork products as a consequence, given the baseline price elasticity of demand. Accordingly, vertical weed grow the share of North American hogs that provide pork products destined for California will decline from about 7.6% to 7.1%.6 This reduction in the share of market hogs destined for California drives much of the small impact of Prop 12 on the rest of the North American market. Because covered pork products cost more in California post Prop 12 and consumers buy less of it, less of North American pork production is used to feed California with Prop 12 than without Prop 12 regulations in place. This means that more of the pork production capacity is available to supply the rest of the market, causing non-compliant hog prices to fall by about 0.3% . Retail prices for non-compliant pure pork products to decline by about 0.2%. The lower consumer price causes a small percentage increase in quantity demanded, but, given that the non-California share of the market exceeds 90%, this increase largely offsets the decrease in consumption in California, so that the model predicts only a 0.2% decline in hog production due to Prop 12.

As noted, the more efficient operations that convert to Prop 12 compliance can expect to increase profits from conversion, while marginal converters should on expectation break even from conversion. The model estimates that converting operations gain about $0.2 million annually in producer surplus in 2018 dollars from converting to compliance with Prop 12 and supplying pork to California. Those that continue to produce for the unregulated market will lose a small amount of surplus due to the slightly lower hog price. I estimate that this aggregate loss to those that do not supply the California market about $44.8 million annually. The resulting in a net annual loss to producers of about $44.6 million or about $0.16 loss per hog. Despite significant industry opposition, Prop 12 will not impose much negative impact on producers on average. The California covered pork price increase implies that California consumers of covered pork products will have a $258 million consumer surplus loss annually through paying more for less covered pork. However, the higher price of covered pork causes an increase in the California demand of the substitute, non-covered pork. California buyers of non-covered pork are now willing to pay more for non-covered pork and quantity demand rises by about 2.4%. With our base-case parameters, the consumer surplus gain from the shift in demand for non-covered pork is about $69 million annually. Therefore, the total annual consumer surplus loss for California consumers of the two types of pork is about $188 million or about $4.70 each if all Californians were to eat pork. The per capital impact of Prop 12 on pork consumers outside California will be minimal due to the tiny projected decline in prices uncooked pork cuts outside of California and essentially no change in the price of non-covered pork products.The model assumes that the implementation of Prop 12 does not shift California’s demand for covered pork products. The projected decrease in consumption arises from movement along the static demand curve due to higher prices. The resultant decrease in California quantity of pork demanded causes the small decrease in hog prices for non-compliant producers and loss of producer surplus. It is possible that Prop 12 or animal welfare regulations more generally could increase demand for the covered products. For example, some non-consumers of pork in California could become consumers and some who consume pork only occasionally could become more regular consumers upon implementation of Prop 12 because they believe pork for sale California is now more humanely produced. I explore this potential demand expansion by considering a rotation of the demand function of covered pork in the California market. This is implemented simply by adjusting the coefficient on the covered pork product quantity term in equation . I rotate this demand curve enough to generate sufficient increases in prices and quantities at the new equilibrium such that the producer surplus of non-compliant hog producers is unchanged under Prop 12. The full results from this exercise are shown in Table 4.2. The large and important changes are that the quantity of California uncooked pork now rises by about 8.3% rather than falling by about 6.2% as was the case in the Table 4.1 results. Also, the quantity of non-covered pork in California falls by 3.2% rather than rising by 2.4% because of the shift in preferences for covered pork production under the assumption that consumers now believe such pork is more humanely produced. Notice also that producers that supply the California market for covered products gain even more producer surplus so that producers as a group gain about $4.6 million per year.Although one major requirement of Prop 12 is no use of gestation stalls for sows that produce pigs destined to supply the California covered pork market, my work shows that Prop 12 will have negligible effects on the conversion of stall housing operations. Given that about 30% of breeding pigs in North America are already confined in group housing, and only about 7-8% of the North American hog production is needed for the California market, operations converting to Prop 12 requirements will come from this group of producers. Prop 12 will provide more space to breeding pigs in those operations that convert to compliance because the space allowance per sow in typical group housing is smaller than California’s 24 square feet minimum requirement.

Urban planners have an opportunity to address food insecurity and other urban food system challenges including production, consumption, waste management and recycling by co-creating solutions with urban farmers through participatory processes and investing in community-led solutions. In our systematic review of the literature on whether urban agriculture improves urban food security, we found three key factors mediating the effect of UA on food security: the economic realities of achieving an economically viable urban farm, the role of city policy and planning, and the importance of civic engagement in the urban food system . A radical transformation toward a more equitable, sustainable and just urban food system will require more responsible governance and investment in UA as a public good, that is driven by active community engagement and advocacy.The effects of natural events such as a disease outbreak are typically difficult to measure since simultaneous shifts can occur along several dimensions. The analysis of longitudinal data may reveal dynamic change that would be hard to recognize when only cross sectional data are used . With panel data, one can examine when depression on production occurs -if there is any-, vertical grow weed either at the time of the outbreak, soon after or even before, and for how long such depression occurs.

Previous studies have addressed the impact of porcine reproductive and respiratory syndrome , but have not provided detailed information regarding how the disease affected farm performance. Here, we used longitudinal data routinely collected from sow farms from a US firm between 2014 and 2015 to explore the intensity and extension of outbreaks of PRRS. We then evaluated the effects on revenue due to a decrease on output production using the pre-, during, and post-outbreak periods. This approach allows us to evaluate if outbreaks were reported on time, as well as the extension and length of the impact on production. Endemic animal diseases can affect farm profit by reducing output, increasing production costs, and reducing product price . For example, PRRS, which was first identified in the 1980s, has become one of the most important endemic animal diseases in the US . It affects the swine industry and disease control is difficult due to factors inherent to the disease and the nature of the swine production system. The causal agent is an RNA-virus from the Arteriviridae family that is highly mutagenic and resistant to the low temperatures registered in Midwestern areas of the US, where a significant proportion of the US swine industry is located. On the other hand, the disease is highly transmissible and can persist for long periods in chronically infected animals and in the environment, if contaminated through secretions and excretes . Because PRSS has no effect on humans and has little impact on international trade, PRRS is a non-reportable disease in the US. There are no official programs for its control, but producers in some regions have begun collaborative programs to exchange information on PRRS outbreaks in the hope that coordinated action might reduce disease effects .

PRRSV spreads between and within farms via airborne transmission, the introduction of infected animals and contaminated fomites, often associated with the failure of bio-security protocols . PRRS may increase abortion and mortality rates in pre- and post-weaning pigs, lead to reproductive failure in sows, and lower feed conversion in feeder pigs , thus affecting several stages of the swine production cycle. However, the severity and length of the impact at each production stage are still unclear. In high farm density areas, PRRSV eradication is not the main target. Indeed, farmers prefer to maintain homogeneous levels of immunity in breeding herds using vaccination or, although less common, exposing animals to live virus . Herd closure and rollover is one of the most common strategies to eradicate PRRSV from sow farms. It consists in stopping introducing new sows as replacements in addition to remove seropositive animals for at least 24 weeks . A study showed that production of PRRSVnegative pigs was reached 27 weeks after herd closure started, although an important variation between farms was observed . Alternatively, whole-herd depopulation and repopulation strategy is the most effective strategy described but in many cases is financially impracticable . Two studies have estimated the economic impact of PRRS using data from a set of farms and then extrapolating their results to the entire US swine industry . They reported total annual losses of ~$560 million and ~$664 million, respectively . Although the two studies estimated losses similar in magnitude, they significantly differed in the proportion attributed to losses on sow farms. While Holtkamp et al. estimated that 46% of total losses occurred on sow farms , Neumann et al. estimated that only 12% of total losses occurred on sow farms. The causes of the differences in their loss estimates are not explicitly explained, but may occur because of differences in the epidemiology of the disease at different times, the diversity of clinical outputs in infected animals and/or differences in types of farms. We observe that the effects of disease vary slightly across the farms in our study despite a common management approach, but the availability of data from multiple farms is likely to provide a better estimate of impact than would the use of data from only one farm.

We measure the effect of disease using data prior to the outbreak as the baseline and find that disease impact varies over time, with output declining rapidly initially following the outbreak and then recovering slowly and non-monotonically. We gain additional insights into the progression and recovery of disease by measuring changes in seven other performance indicators. Our methodology can be used to characterize disease impact at the farm and/or firm level, as it provides information on the timing of disease effects, the pathways through which PRRS affects production, and the total time needed for recovery. We anticipate that the results presented will help in the development of more accurate models for evaluating alternative PRRS prevention and control strategies in the US.We screened production records from a large, vertically integrated swine firm that includes farms in each stage of the swine production cycle, i.e., breeding and growing. All of the farms are located in the Midwestern region of the US. Numerous of the sow farms experienced PRRS outbreaks during 2014–2015. An outbreak was reported when animals showed PRRS-compatible clinical signs that were subsequently confirmed through PCR testing. We chose for analysis only sow farms that had not experienced a PRRS outbreak for at least one year prior to the outbreak studied in this analysis. In addition, we excluded from the analysis any farm that experienced cases of porcine epidemic diarrhea virus during the eight months before the PRRS outbreak to avoid confounding disease effects. Thus, all farms were classified as positive-stable without undergoing elimination at the time of this study. This firm also used a common disease management protocol for all its farms.f weaned pigs , which subsequently was used to estimate the decline in the value of output due to a PRRS outbreak. Likewise, we used weekly data for seven statistics, drying rack cannabis referred here as performance indicators, to more comprehensively assess how the disease affected weaned pig output. These indicators are: the number of live births per litter -or litter size- , the number of stillbirths per litter , the number of pre-weaned pigs dead , the number of sows dead , the number of sows aborting , the number of sows with repetition of service , and the number of sows farrowing .The estimated baseline is used to measure PRRS’ effect on production after the outbreak, i.e., between t and t + 35. We then used the same procedure to analyze, separately, the baseline values and post-outbreak effects for each performance indicator . Using longitudinal data allows us to reveal PRRS dynamics that might be difficult to identify if using cross-sectional data. In this case we evaluate the net effect of the outbreak on production within a selected set of farms. The use of fixed effects also permits us to manage the unobserved heterogeneity within farms whose omission could bias the estimated coefficients. We assume that time invariant effects are unique to each farm and are not correlated with effects on other farms. In addition, the expectation that individual farms have stable characteristics over time and the recognition our sample set has not been selected randomly led us to prefer a fixed effects rather than a random effects model. We used the Hausman test to determine whether the unobserved effects are distributed dependently of the regressors . We used Stata Statistical Software V13.1 to perform all these statistical computations and graphic designs .Dummy coefficients for weeks were statistically different than 0 indicating that the inclusion of Tt as fixed time effects in equation is appropriate. Although we found no significant trend in weaned pig production during the pre-outbreak period, holding the number of sows and season constant when using equation , we observed a consistent decrease in weaned pig production relative to the baseline in the week t − 1, immediately before the outbreak was reported in week t . The decreases ranged between 1 and 12%. As our regressions based on equation 1) showed no significant trend in weaned pig production even when week t − 1 was included, we did not remove week t − 1 from the baseline period.

Had we done so, the baseline would have been very slightly higher and the estimated damages from PRRS slightly greater, as discussed subsequently. We estimate that aggregate weaned pig production for the 16 farms decreased from the baseline production of 2,094 per week to 1,600 in week t + 5, when output was a full 23% lower than the baseline. Table 1. The results show that farm production decreased monotonically from t − 1 to t + 5, and then began to recover . Output recovered moderately from t + 5 until t + 11, at which point another significant decline in production occurred to t + 17 . Eight of the 16 farms then recovered monotonically to their baseline production levels by t + 33, but a slight drop occurred again in t + 34 and t + 35 with 15 farms producing lower than the baseline. In the aggregate, observed production approached the baseline value by the end of t + 35, when our sample ended. Estimated output appears slightly lower than the pre-outbreak level, but the difference is not statistically significant . Similar to the production of weaned pigs, the seven performance indicators did not fully recover to their pre-outbreak means. Week-to-week comparisons revealed changes in all performance indicators, with some variation in timing and intensity. For each performance indicator, the recovery of each farm fluctuated around a rising trend estimated for all farms, and again showed a non-monotonic recovery . As expected, some performance indicators presented a lag with respect to the trend observed in weaned pig production. A significant increase in the number of preweaned pigs dead was detected at t with an average expected rise of 79 deceased animals relative to the baseline, reaching a maximum increase at t + 1, with 143 expected extra losses . While litter size did not show a significant decline at t, the expected number of live births decreased by around 1 animal between t + 1 and t + 18, reaching a maximum decline at t + 2 and t + 3 and a new deterioration at t + 14. The number of stillbirths increased between t and t + 16, reaching a maximum at t + 12, with 2 stillbirths per litter . Although there was no immediate increase in the number of sows designated for repeated service the week of the outbreak report, by t + 6 the number of sows that were designated to repeat service increased from 11 sows in the estimated baseline to 31 sows. Likewise, the number of pigs farrowed declined after t + 1. The number of abortions significantly increased at week t− 1, doubling the number of sows that aborted prior to t − 1. The number of sows with abortions peaked in the week of the outbreak report at a level five times higher than the baseline level.

While these may be useful in providing novice farmers with a starting point as to how to even begin putting together information regarding environmental, resource, and operational data, most of the farmers we spoke with end up restructuring their previously collected data to fit policy and/or certification requirements.The population of farm workers includes a large Spanish speaking population and a diverse range of literacy levels, as well as a varied set of communication needs across stakeholders. Such diversity is not always accommodated. Models may include multiple languages or a visual focus to overcome barriers to communication in the social context of the farm.There is a lack of consistency and technological capacity across the spectrum of sustainable agriculture as a result of the sparse attention that has been paid to the information management needs and requirements of sustainable farmers. Their agricultural systems cannot be adequately represented and analyzed using current modeling tools and methods, and many environmental data sources have not been designed to integrate with the tool kits that such farmers currently use.Artifact trails spanned across disparate data structures: from hand-drawn maps containing crop locations, rolling benches spreadsheets varying in structure from year to year even within a farm, to custom databases to handle portions of the farmer’s workflow.

Maintaining coherence and consistency across data structures is key to stitching together the information workflow of sustainable farmers; the heterogeneity of data structures within farms provides a significant stumbling block in effective workflows. One potential avenue for future work involves the design of better models to encapsulate the types of data that are collected and fit with the data collection techniques that farmers engage in. Models are apt devices for communication of a system’s composition and performance, as well as useful artifacts for reflection. In particular, a domain-specific modeling language would enable system stakeholders to gain perspective on their systems, perform environmental analyses, and create abstract yet grounded models that they can manipulate before changing the real world systems that are represented.The switching between data structures is accompanied by several transitions between different mediums of technology, resulting in a messy information management experience. Five genres of technology were used across one or more farms: physical or paper-based artifacts; communication technologies like email; external regulation- or certification-specific software; farm-specific custom software; and office productivity software such as Microsoft Word and Excel.Farm models varied in level of abstraction, formality, and granularity, both within an individual farm and across agricultural system types. For example, on some farms, resource use was tracked at the whole farm level, while on others there were intricate interconnections between subsystems requiring tracking at a granular level.

The commodity and process complexity of the farm also affected the level of abstraction, formality, and granularity of models. Models need to be flexible enough to capture whole-farm activities as well as fine-grained data about specific farm components. Ideally, farmers should be able to create both informal and formal models depending on the type of data they have and analyses they plan to conduct. Consequentially, relevant tooling must be capable of representing the varying spatial and temporal complexity present throughout the spectrum of sustainable agriculture.Many of the models used for internal assessments by farmers are created in an ad hoc manner and for a specific purpose For example, a map created for communication to visitors may be opportunistically used for coordination among farm workers. However, this is a one-off reuse, and is not inherently supported by the models. Representations cannot always be easily repurposed, resulting in a significant reusability gap.Further, data is often isolated in purpose-specific models. For example, input logs used to track resource application for organic certification are not necessarily connected to inventories that are used to track expenditure on materials. Data and effort are duplicated as data is tracked separately for inventory management and organic certification. Farm data needs to be captured in a general enough form such that it can be manipulated and transformed on demand. There is an opportunity to reduce the reusability gap by enabling farmers to create modular component-based farm models. There is also a significant amount of publicly available environmental data, such as data on soil composition throughout the United States, global weather data, and California-wide water quality and availability. These data can and should be incorporated into information management tools for sustainable farmers to reduce their data collection burden.The transience of agriculture means that not all data collected is necessarily archived. This is further exacerbated by the rate of change on farms: the more often the farm changes, the easier it needs to be to update the model. This also affects the formality and reusability of farm models. Use of model configuration management may allow for farmers to track changes in their farm models just as they use models to track changes in their farms. This would also allow farmers to compare models over time and reduce the effort involved in updating models to reflect changes. We found workarounds implemented by farmers to mimic such a workflow. By designing with the intent to archive historical data and track changes, the inherent dynamism of the farm can be captured.There is a mismatch between the causes and goals of data collection, particularly as the farm evolves. While initial causes for data collection may be for regulatory reporting and system understanding, eventually farmer goals can expand to include communicating to diverse stakeholders , environmental analyses, and monitoring.

The mismatch between the collection trigger and emergent goals results in a lack of coherence in farm models and data workflows. We must explicitly consider both the causes and goals of data collection in the design of information management tools for sustainable farmers to ensure that appropriate data is aggregated and connected. Supporting easy transformation of the information management structures and practices would facilitate synchronicity among causes and goals.Farm stakeholders are constantly using, sharing, and communicating various data for daily decision-making and problem solving. However, many of the technologies we found in use were not designed to meet the coordination and collaboration needs of varying farm stakeholders. Any redesign of the information workflow of sustainable farmers must provide these stakeholders with the capacity to conduct environmental assessments and other forms of analysis; coordinate among farm workers and other stakeholders; and communicate with regulators, certifiers, and consumers as needed. Explicit attention to the differing privacy and access characteristics of data would be critical.They have been used in software design as stand-ins for real users, allowing people to engage in human-centered design, where the user experience and interaction of the human with the system is the key focus. In this dissertation, I flip the traditional persona, and instead use it to describe a system , that a human , is interacting with, cannabis drying room the result of which is a Farm Persona. These Farm Personas were created based on the data and findings of Chapters 3 and 4. Methodological detail on the design of these personas is available in Section 6.1 of Chapter 6, as the Farm Personas are explicitly presented during the evaluation of MoSS. The Farm Personas were used to engage in Persona Driven Design. Partial MoSS models were created to represent various activities, components, and aspects of hypothetical farms. These models were then used to refine the Pseudo-Software Models and thus refine MoSS. For example, Figure 5.8 below shows how a Farm Persona called Blackbird Gardens was used to explore the representation of crop layout and field management. Persona Driven Design thereby allowed for the farm-centered design of MoSS.Personas are detailed constructions of fictitious yet archetypal users of a software system that allow for human-centered design. There is growing interest in using personas to engage in design that considers other forms of system interaction. For example, collaboration personas have been used for the design and evaluation of tools for use by groups of humans. Non-human animal personas were proposed in recognition that the human stakeholder should not entirely dictate prototype development: a cow is a user of a robotic milking device, and the cow’s needs, welfare, and experience in mind, should dictate the design of the tool. Farmers are integral actors in agricultural systems. Often, in addition to being the farm’s owner/operator, a farmer is also: a manager of staff, the primary decision maker regarding on-farm activities, a stakeholder of farm data, and a farm data collector. While future work involves the design of a modeling tool to allow the farmer to interact with MoSS, the scope of this dissertation is to design a mechanism for modeling sustainable agricultural systems. I therefore consider farmers as primary actors in a System Persona. I define a System Persona as a detailed construction of a fictitious yet archetypal system. As the focus of this dissertation is on the holistic representation of the characteristics of small- to medium-scale sustainable agricultural systems, I created a set of Farm Personas, where each persona represents a hypothetical small- to medium-scale sustainable farm in California.The goal of this activity was to flesh out the Farm Personas with enough details so as to be used as part of the Persona Driven Design of MoSS, as described in Section 5.1 of Chapter 5. The Farm Personas needed to be grounded in both the academic literature regarding formal modeling in sustainable agriculture , as well as the findings regarding sustainable farms in reality . Overview tables were created to map out the key characteristics of the 16 farms from Chapter 4, Farms Atwood to Pullman . These led to the creation of design parameters for the MoSS Farm Personas. These parameters reflect greater nuance regarding the characteristics of the farms. Once the base Farm Personas were created, they were augmented through the addition of characteristics from the 16 farms resulting in artificial yet data-rich composite farms. Two primary characteristics were used to augment the Farm Personas to engage in the design work that resulted in the MoSS framework: complexity and dynamism. This process is briefly described next.A scenario is a hypothetical set of activities to be enacted by actors interacting with a system of interest in the real world. Scenarios have been used in software engineering and HCI research both as a means to explore potential designs of a system, as well system evaluation. For example, Sutcliffe describes how scenarios may be used in requirements engineering, in particular, to check abstract models through use of scenarios as a substitute for formal verification. Scenarios are often implemented when the problem domain is “squishy”, i.e., the problem boundaries are not distinct, the interactions are complex, and the problem does not lend itself well to linear design work or structured evaluations. It follows that there is evidence of the use of scenarios for design and evaluation in the environmental assessment and sustainable agriculture communities. In LCA research, scenarios have even been used to represent hypothetical agricultural systems to explore, for example, the environmental impacts of various pig production systems. The University of California Cooperative Extension has been conducting “Sample Cost of Production Studies” for various farm commodities since 1928. While the early reports are written in the style of a handbook, later reports in the mid-2000s begin to list study assumptions in the style of a scenario. For example, the 2009 cost production report for organic leaf lettuce in the central coast region of California, begins with a description of hypothetical farm with specific produc-tion activities; labor, interest and equipment; and how cash and capital are spent and obtained. It is these characteristics of a fictitious farm that provide context for the various cost estimates that are subsequently listed. This structure is powerful as it allows the reader insight to the rationale for listed costs, contextualizes the calculations, and provides a human readable example that a reader may work through. These cost production studies also proved an invaluable model for tailoring the software-style scenarios for sustainable agriculture.There are eight scenarios described in this chapter that were used to evaluate MoSS. The primary basis for scenario creation were the findings of the qualitative study involving 16 sustainability-oriented farms in California, described in depth in Chapter 4. To ensure scenario accuracy, interview data were also checked against relevant literature prior to being incorporated into a scenario. To avoid overspecialization and simply designing for those 16 farms , I created a set of scenarios, applicable to farms throughout a spectrum of sustainable agriculture.

As some of these foods are produced primarily outside of the regions of interest in this paper , they were cross referenced with the top ten most produced agricultural commodities in each of those regions to create a list of commodities to be considered in this paper.Table 3.1 shows which of the globally most produced food and agricultural commodities are also in the top ten most produced commodities of the United States, Europe and Australia. Note that some commodities, such as rice, vegetables, and cassava, are not widely produced in any of these geographic regions. Therefore, commodities of interest were those that existed in the both the global top ten most produced and the top ten list of at least two out of three regions. Figure 3.3 shows that enteric fermentation and three manure related activities , contribute 66.1% of agriculture’s emissions, resulting in significant interest in better understanding the environmental impacts of livestock farming. Beef is one of the top ten most produced commodities in both the United States and Australia. Similarly pork is one of the most produced commodities in Europe. As they are therefore responsible for a large proportion of global agricultural emissions, these two livestock farming commodities, vertical grow beef and pork, were added to the list of commodities of interest in this paper.Most of the LCA studies found, 18 of the 25, were comparative studies. Many looked at the differences between conventional and organic farming systems.

As there is a current push toward both growing and buying more organic food, it makes sense that the community is interested in finding empirical evidence to support the environmental benefits of organic food. Comparisons of different cropping systems that result in a similar processed product is also common: For example, three cropping systems are compared, as all three result in the production of vegetable oil. In all of the studies in this category, the agricultural systems are manually compared. Surprisingly, there were only two connective studies and one update study found. It may be the case that updates to LCI data are not commonly published in academic literature, and are instead directly updated in LCI databases . The lack of papers in the connective category may be evidence that there truly is a lack of connectivity across the plethora of LCA models created. Four methodology studies were found. There is substantial literature on improving LCA methods. The search criteria that were used aimed to find papers that specifically involved one or more LCA studies and their details. The papers in this category did conduct an LCA study, but the purpose was to test proposed methods.LCA Methods allow for the modeling of different types of agricultural systems . Some methods, such as EIOLCA, have been developed to try to reduce the overhead inherent in LCA, by allowing analysts to calculate estimates. However, the process is very involved, requires expertise in the method, and it is difficult to reuse models, thereby taking advantage of effort already expended. In general, accurate, thorough, and rigorous LCAs are effort-intensive. Each of the LCA studies presented in this chapter provide further insight into how the LCA technique is customized for and commonly used in the assessment of agricultural systems,such as the creation of hybrid LCA methods and streamline LCAs.

In this section, I articulate these findings as seven observations regarding LCA for agriculture.The most common type of LCA used in the analysis of agricultural systems is a cradle-to gate analysis, since once the product is ready for shipping, the storage, variety of packaging, distribution methods, preparation, and consumption, among others, vary widely. Table 3 lists the LCA types used in the representative sample of LCA studies, highlighting those that involved a cradle-to-gate LCA. The scope and boundary of the cradle-to-gate agricultural system LCAs are very similar to each other. The final product of the agricultural system is usually some form of raw product, such as a meat, grain, fruit or vegetable. The amount of processing this food product undergoes within the system of interest also varies widely in some cases, the final product is frozen, ground, deboned, packaged, or transformed into some derivative transportable product such as sugars from sugarcane.Table 3 overviews the functional units that are used in each LCA study. For instance, “1 ha of land used” is a popular metric, which allows for the calculation of energy intensity with respect to land use, demonstrating how much strain the system puts on the land. For single product systems, a functional unit is often in terms of produce weight, as it allows for the calculation of energy, emissions or impacts per unit weight of the product at the gate. The decision of where the gate lies depends the system boundary, i.e., which of these processing techniques occurs on the farm. For example, in beef production, the farm-gate may be pre-slaughter or post-slaughter. This also determines the functional unit: for pre-slaughter the functional unit would be live-weight, while post-slaughter, a common unit is Hot Standard Carcass Weight.Sometimes, midpoint functional units are used to analyze system subcomponents or to allow for the discretization of processes. For example, HSCW , is used.

It represents the end of the production process in the pork supply chain, i.e., the weight of the product at the slaughterhouse gate. This makes the unit incomparable to other pork or meat product LCA studies that may define the endpoint at the consumer side To address this issue, the analysts in the Australian Pork study also use two midpoint functional units: 1 live piglet and 1 live slaughter pig at the farm gate. These units allow the findings to be used in comparative studies. For reference, another pork production study, by Basset-Mens & Van der Werf, has a functional unit of 1kg of live slaughter pig as well, in addition to a land use unit . However, not all LCA studies have a midpoint functional unit or a functional unit that can be used to compare the models produced in the study with other studies, even if they are ISO compliant. Functional units can also be highly specific to the system of interest, product, or location. For example, the functional unit is “1kg of soybean meal produced in Argentina and delivered to Rotterdam Harbor”. The level of granularity is non-negotiable. While there must have been intermediate steps in the LCA that separated the different processes , these numbers are not always released or easily accessible. Various levels of detail are lost to the reader, and more importantly the system cannot easily be broken down into reusable components. Unfortunately, while the functional unit is meant to make LCA studies more comparable and reusable, it is not always the case.Renouf et al. perform a comparative assessment of the production of sugars for fermentation in Australia, corn production in the US, and sugar beets in the UK. The product of interest was a sugar suitable for fermentation, as its bio-products have wide use, including as an alternative energy source. Here, in addition to systems based in different locations, the initial crop is different too. The functional unit in this study is 1 kg of monosaccharide , as this enables comparability across sugarcane, corn and sugar beets. As opposed to conducting a separate LCA study based on the specific sugarcane farms, the researchers used data from a variety of Australian inventory databases, local survey data, and other academic publications that have looked at different processes within the sugarcane production system . Similarly, for the U.S. corn, best way to cure cannabis and the British sugar beet impact numbers, the researchers looked at two sets of studies for each case, and converted their functional units into 1 kg of monosaccharide, based on the yield numbers reported. As all the U.S. corn and British sugar beet studies had a high level of detail available in the report, the resulting analysis is precise comparison between the three sugar production systems.Heller et al. perform a very broad review of the United States food system by using a life cycle perspective to connect systems within different sectors of the industry. They use a product life cycle approach to analyze sustainability indicators across different life cycle stages: resource origin, growing and production, food processing, packaging, and distribution, preparation and consumption, and end of life management. This study is unique in that it attempts to address the entire US food system, connecting different agricultural systems without resorting to a sector based approach like EIO-LCA. Heller et al. did not conduct a new LCA, instead opting to review LCAs in published literature and connect information about the impacts that occurred at each stage to provide a holistic view of the food system.

It is still one of a small number of papers that attempts to connect impacts across products and agricultural sectors over a large region, thereby encompassing a sizable portion of the industry.Another massively-scaled LCA study is available in a report by the Center for Environmental Strategy at the University of Surrey by Mila i Canals et al.. The paper details a series of comparative LCAs, which combined aim to compare the environmental impacts of domestic versus imported vegetables. They compared broccoli production in the United Kingdom and Spain, salad in the UK, Spain, and Uganda, and finally, legume production in the UK, Uganda, and Kenya. The life cycle of each product is geographically disparate, therefore they break it down into three projects/reports chunked as follows: “cradle-to-central-depot”, “retail-to-plate”, and “consumption-to-waste” , The report highlights the importance of connecting LCAs across products, production systems, regions, up to connecting the entire industry. It is because of this highly detailed, connected set of LCA models that they can come to the surprising conclusion that local is not always more environmentally friendly.Once a flow diagram has been created, and the analysts have a feel for how resources move within the system, they gather all the data required to calculate different environmental impacts. This process involves decomposing the high level steps in the flow diagram into individual sub-flows or processes. The basic unit of LCI data that is collected is the “unit process”, defined by ISO 14044 as: “the smallest element considered in the life cycle inventory analysis for which input and output data are quantified” . For each unit process, inputs and outputs , and the associated environmental impact with it are listed. The question to be answered is: how does actually performing this step affect the environment? The data may be collected in several ways: primary data collection , data obtained from published literature , data obtained from the results of simulations of approximately similar systems, or through lookup in a Life Cycle Inventory database. Due to the scope of the LCA, the number and size of the agricultural systems under study, the type and level of detail of the LCA to be conducted, and the availability of existing data, the LCI phase can consume the most time, money, and effort. They contain structured collections of objects representing unit process data. An overview of some LCI databases used in Agricultural LCAs is available in Table 3.4 .Within the LCA studies surveyed in this chapter, the national or regional LCI databases that analysts interact with are: the United States Life Cycle Inventory database, the Australian National Life Cycle Inventory database, and the European Life Cycle Database. Additional databases are listed in Table 3.4. Although this list is not exhaustive, others exist, many of which contain smaller, specialized datasets. Many proprietary databases are populated via primary data collection performed by consultants in partner organizations. These proprietary databases often aggregate existing free databases, and/or resell other proprietary databases as part of a package deal. ecoinvent is such an example, and is popularly used to supplement data regarding machinery, infrastructure, or capital goods in an agricultural LCA. These data are often international in scope. Most databases , contain data gathered during process-based LCAs. Some new database initiatives focus specifically on agricultural and food systems, some of which are also included in Table 3.4. Some databases, such as USLCI and ecoinvent, only release data in the ecoSpold format, ELCD and GaBI only use ILCD. Whereas others, such as AusLCI,, have versions of their data in both formats.