Monthly Archives: January 2025

Each data point is then projected into the resulting low dimension linear space

Any days where less than 75% of the herd was successfully recorded in the parlor were also dropped. This left a total of 80 days of milk order observations ± 26 recorded while cows remained overnight in their pen, and 54 after the transition to overnight pasture. Finally, cows that were not present in at least 50% of the remaining milkings were excluded from further analysis. Of the 177 cows with sufficient records, 114 had no recorded health events.With this metric, the more consistently a smaller set of cows are observed in a given segment of the queue, the smaller the entropy values becomes to reflect less stochasticity in the system. In standard statistical models, the nominal value of estimators such as log likelihood and AIC scale with the size of the data set, and must be interpreted relative the value of equivalent terms assessed against a null model. Analogously, the nominal value of the entropy estimates scales with the number of discrete categories used. The maximum theoretical value occurs when no underlying deterministic structures are present and all categories are equally likely to occur, which algebraically simplifies to the log of the number of discrete categories used . Here the maximum theoretical entropy value would be log2 = 6.83. To visually contrast differences in stochasticity across the queue, the observed entropy values were plotted against the median entry quantile of the corresponding queue segment using the ggplot2 package, vertical grow racks with maximum theoretical entropy added as a horizontal reference line . Nonrandom patterns in queue formation could also be explored by tracking the entry position of individual cows over time. As entry quantile has a numerical value, we can now also use variance to quantify and contrast stochasticity between animals.

As with all analytical approaches reviewed in this paper, there are both strengths and shortcomings to either approach . In this system there are two potential drawbacks to this conventional summary statistic. The first is that variance estimates are quite sensitive to outliers, making it difficult to empirically distinguish between cows that occupy a wider range of queue positions and animals who typically occupy a narrower range but might have gotten jostled far from their normal position on one or several occasions. The second drawback is that, because variance quantifies dispersion about a central value, it cannot distinguish between cows that demonstrate little consistency in entry position and multimodal queuing patterns. For example, if a cow always entered the parlor either first or last, we would intuitively determine that this pattern is nonrandom, but the corresponding variance estimate would be the largest in the herd. Having recovered evidence of nonrandom patterns, the next step was to begin characterizing the behavioral mechanisms driving this heterogeneity. The most fundamental question that need be answered to inform further analysis was the degree to which queueing patterns were driven by individual or collective behaviors. Because cows jockey for position with one another in the crowd pen, where they are pushed up to enter the parlor, we know intuitively that entry quantile records cannot be considered truly independent observations. If cows move through this melee as independent agents, such that their position within the queue is determined by individual attributes ±preferences, dominance, etc ±then a linear model may still provide a reasonable approximation of the underlying system. Early observational work on milking order, however, has suggested that cows may form consistent associations when entering the milking parlor, particularly when heifers are reared together .

If cows move into the parlor in cohesive units, such that queue position is more determined by clique-level than individual attributes, then network analyses may be a more appropriate. Principal Component Analysis is commonly employed to visualize relationships between observational units in high dimensional datasets. In this approach, redundancy between variables, here each milking record, is captured using either covariance or correlation assessed across all data points, here all animals. An eigenvector decomposition is then used to linearly compress the information contained in the data via rotation of the orthogonal axes. New axes are added iteratively such that each new dimension is pointed in the direction of greatest remaining variability until only noise remains . PCA was here performed only on animals with no recorded health in order to prevent any anomalous queuing behaviors recorded from acutely or chronically ill animals from obscuring the queuing patterns of the broader herd. The correlation matrix was constructed using all pairwise complete observations, and a scree plot was used to determine the dimensionality of the resulting space . The plotly package was then used to visualize the final embedding. While PCA provides a computationally expedient means of visualizing high dimensional data, the underlying assumption of linearity is not always appropriate . In some data sets complex geometric constraints, such as those commonly found with images or raw accelerometer data, and other latent deterministic features may project data points onto high dimensional geometric surfaces collectively called manifolds .

When these topologies are nonlinear , the spatial relationships between data points cannot always be reliably maintained when projected directly into a linear space, which can lead to incorrect inferences . Imagine, for example, you had a round globe of the world and wanted instead a flat map. Applying PCA to this task would be analogous to smooshing the globe flat on a table. Some of the original geographic relationships would be discernable, but some locations would appear erroneously close, and some landscapes would be entirely obscured. Modern manifold learning algorithms strive to more reliably project the complex geometric relationships between observational units into a standard Some geographic features will still be lost, particularly over sparsely sampled regions like the oceans, but the spatial relationships between landmarks would collectively prove more representative of the original topography. To further explore the underlying structure of this data absent assumptions of linearity, and thereby potentially accommodate any complex geometric constraints imposed on milk order records by latent social structures within the herd, a diffusion map algorithm was implemented using functions provided in base R . This was done here by first calculating the Euclidean distance between temporally aligned vectors of parlor entry quantiles for each pairwise combination of cows, scaled to adjust for missing records, and then inverting these values to create a similarity matrix. From this similarity matrix a weighted network was created by progressively adding links for the k = 10 nearest neighbors surrounding each data point. A spectral value decomposition was then performed on the corresponding graph Laplacian matrix . The resulting eigenvalues were used to select the appropriate number of dimensions, and the corresponding eigenvectors visualized using the 3D scatter tools from the plotly package . Finally, as a means of comparing geometric structures identified in the observed dataset with those of a completely randomized queuing process, the permutated dataset generated in the previous section was also embedded and visualized using plotly graphics .Having determined from the previous visualizations that a linear model might be a reasonable representation of the underlying deterministic structures of this system, the next step was to explore the temporal dynamics of this dataset. In a standard repeated measures model, multiple observations from the same animal are assumed to be identically and independently sampled, mobile shelving system implying that sampling order should not affect the observed value. If the observation period is sufficiently long to allow the underlying process to shift or evolve over time, however, stationarity cannot be assumed. Failure to statistically accommodate a temporal trend can not only lead to spurious inferences due to incorrect estimation of error variance, but also risks overlooking dynamic features of the behaviors under consideration . In practice temporal trends are often assessed by first fitting a stationary model and analyzing the resulting residuals. This may suffice when the temporal trend is uniform across animals, but risks overlooking more complex nonhomogeneous temporal affects. This could occur if only a subset of the larger group displays a non-stationary pattern, a risk that is likely heightened in large socially heterogeneous groups.

In this physically constrained system, where we know that every cow moving forwards in the queue must force other cows backwards, compensatory trends could also be easily overlooked in collective assessment of residuals. We first assessed temporal trend using two conventional EDA techniques. First, the ggplot2 package was used to generate scatter plots of entry quantile values against the corresponding observation date for each individual cow, with pasture access annotated with a verticalline. Plots were visually inspected for non-stationary, and are provided in supplementary materials. Next, to further explore the impact of the shift from pen to overnight pasture access on morning queueing patterns, median queue positions from the two subperiods were plotted against using the ggplot2 package , and Pearson correlation and Kendall Tau were computed using the stats package . While these preliminary visualizations were easy to both generate and interpret, both treat cows as independent and somewhat isolated units. With such a large number of animals to consider, the capacity for human pattern detection is quickly overwhelmed, making it difficult to contextualize trends within the broader herd. Further, this approach fails to leverage non-independence between animals entering the parlor, and thus risks overlooking subtler collective responses. Data mechanics visualizations were implemented to simultaneously explore systematic heterogeneity in milk entry quantiles both between animals and across the temporal axis. This was done by first using entry quantile values to compute two Euclidean distance matrices: one quantifying the similarity between pairwise combinations of cows, the second quantifying similarity between pairwise combinations of daily milking sequences. These distance matrices were then used to generate two independent hierarchical clustering trees using the Ward D2 method . By cutting both trees at a fixed number of clusters, observation days and cows were both partitioned into empirically defined categories, and a contingency table was then formed with cow clusters as the row variable and day clusters as the column variable. The original distance matrices were then updated, using the clustering structure between cows to create a weighted distance matrix between days and vice versa, thereby allowing mutual information to be shared between the temporal and social axes of the dataset . After several iterations of this algorithm, clusterings converged towards a contingency table with minimal entropy, wherein the entry quantile values within each cell were as homogenous as possible. When the entry quantile values were subsequently visualized using a heat map, this highly generalizable entropy minimization technique served to visually enhance heterogeneity within the data driven by nonrandom patterns along either axis. Further, by facilitating the transfer of information between axes, interaction effects between the social and temporal dimensions of this system were magnified, which here provided a means to explore nonhomogeneous temporal non-stationary between subgroups within the herd . The data mechanics pipeline was used to analyze the temporal dynamics present in both the complete milking order dataset and the subset of animals with no recorded health events. Instead this algorithm was applied on a grid from 1 to 10 clusters for either axis. The resulting 100 heat maps scanned visually to determine the clustering granularity required to bring into resolution any interactions between social and temporal mechanisms. While this process may be computationally cumbersome, it is empirically analogous to systematically varying the focus of a light microscope to bring into resolution microbes of unknown size ±a tedious but effective means of identifying all relevant structures within a sample . Finally, the RColorBrewer package was used to add color annotations to the column margin, to clarify temporal patterns, and to the row margins, which served to visualize potential relationships between queue position, a selection of individual cow attribute variables, and the onset of recorded health complications.Having thoroughly characterized the stochastic structures present in this dataset, the insights gleaned from the preceding visualizations were incorporated into a linear model to evaluate the relationship between queue position and several cow attributes. The four days identified as outliers by the data mechanics visualizations were first removed and the dataset converted to long format to be analyzed as a repeated measures model using the nlme package . Cow was fit as a random intercept via maximum likelihood method. Guided by the results of entropy and data mechanics visualizations, VarIdent was used to estimate separate error variance terms for each cow, and the necessity of this data-hungry heterogeneous variance model confirmed via likelihood ratio test against the null model with homogenous variance .

We multiplied temperature and soil scores to make a preliminary suitability map

As California’s temperatures get hotter and precipitation becomes increasingly variable with climate change , we expect a further systematic overestimation of suitable areas identified based on the past 30 years of weather data. For the suitability analysis we assigned temperature and soil texture to three categories that were each associated with a score: good , tolerable , and intolerable , while precipitation was divided into ranges that were suitable with no additional irrigation, suitable with additional irrigation, and unsuitable.For temperature, we considered the average maximum temperature in the three hottest months of the growing season , categorizing them separately with the scores described above . We then multiplied these three categorized scores together and took the cube root to get temperature suitability scores for the state, also excluding any areas whose monthly 30-year minimum temperature was above 59o F. We followed a similar procedure for soil texture, using SSURGO estimates of clay content averaged across soil horizons at a 90m resolution . Because farmers did not give numeric estimates of how much clay was needed in dry farm soils, we made sure our defined ‘tolerable’ range encompassed the full range of clay content observed in participating farms’ soils . To define the ‘good’ range , we excluded the farm with the lowest clay content, which was also the only farm where farmers stated that they could not grow tomatoes of a high enough quality to consistently market them as “dry farm.” This multiplication reflects the interaction between temperature and soil texture, grow rack in which good texture can compensate for higher temperatures by increasing soil water holding capacity, and lower temperatures can lessen the evapotranspirative demand that would be particularly problematic for plants growing in sandier soils with a lower soil water holding capacity.

We then separated the dataset into three areas based off of farmers’ understandings of where tomato dry farming could occur with no added irrigation and where it could occur with supplemental irrigation , and excluding areas that would not get enough winter rain to grow a suitable winter cover crop . The final map shows suitability scores in all areas that are categorized a ‘cropland’ in the 2019 National Land Cover Database . These areas are superimposed onto groundwater basins categorized as high priority in California’s Sustainable Groundwater Management Act . Crop totals on land that was deemed suitable for tomato dry farm management in these areas were calculated using the 2021 Cropland Data Layer .By focusing on the characteristics that limited water can give a tomato, these farmers highlight a recurring theme in understanding the functional definition of dry farming tomatoes. As the Central Coast faces increasingly limited water availability, the idea of dry farming has gained traction among policymakers purely by virtue of offering a means to continue farming while maintaining a restricted water budget. However, these farmers are quick to recognize that dry farming is only a management style that they can afford to choose for their operations insofar as it can excite customers and return a reasonable profit. In this way, the product that dry farming creates, which is valuable enough to consumers that they are willing to pay a significant premium for it, is the outcome that defines the management approaches farmers can use. Farmers know that they could alter the schedule for the minimal irrigation they do put on their dry farm tomatoes to increase yields . However, while defining the practice by some maximum threshold of water application, and then choosing to allocate irrigation water to maximize yields, may be appealing from a water savings perspective, farmers recognize that they must define the practice in terms of outcomes and not inputs.

Farmers must produce what consumers have come to expect from a dry farm tomato if they are going to make dry farming an economically viable choice for their operation.To better understand where tomatoes might conceivably be farmed in California given the environmental constraints identified above, we modeled dry farm suitability on California cropland as a function of precipitation, temperature, and percent clay in soil. The resulting map shows what lands could potentially support a dry farm crop, with and without supplemental irrigation, using constraints that are relaxed to encompass the least restrictive farmer-elicited constraints . The map therefore errs on the side of including land that is not an ideal candidate for dry farming, rather than leaving off land that may potentially be a good fit. With rising temperatures and less reliable rainfall, this map, which is based off of 30-year normals, likely also systematically overestimates what areas might fall into these thresholds when projecting into future climatic conditions. All areas in blue indicate land that meets a threshold where dry farming could be considered in a non-drought year without adding any irrigation. Areas in orange indicate that, while there is likely enough rain to sustain a winter cover crop, some amount of irrigation would often be needed to grow a successful dry farm crop. Areas in darker colors connote land that falls in conditions that are closer to ideal, whereas lighter colors indicate that more conditions are tolerable, rather than ideal, for dry farming.It is crucial to note that areas that show up as “suitable” on the map–including the most ideal locations–will likely require years of diversified management for soils to build the water holding capacity and fertility that allow for peak dry farm performance. These areas should therefore be considered candidates for long-term dry farm management, rather than ready-to-go dry farm fields. Because the constraints used to build the model were elicited specifically with regard to tomatoes, this of course is not a comprehensive map of everywhere that might be considered for dry farming non-tomato crops.

Particularly when it comes to grains and perennials , the range of possible locations is likely much broader. In the case of grains, winter varietals can be planted that take advantage of rain in winter months, while tree crops have far more extensive root systems that can reach water well beyond that which might be available to a tomato, in both cases relaxing the temperature and precipitation constraints that tomatoes need to survive without irrigation. Tomatoes are likely a better proxy for other vegetable crops , though each will have its unique requirements . As we imagine a shift towards dry farm agriculture in California, it is also important to consider how land that is suitable for dry farming is currently being used. Combining areas that are suitable for tomato dry farming with and without irrigation, we compiled a list of the top ten crops by area that are currently grown on these lands . Some of them are currently being dry farmed with some regularity in the state and could signal particularly easy targets for a shift to low-water practices. Others are dry farmed in other Mediterranean climates and suggest an important opportunity for management exploration in lands that might be particularly forgiving to experimentation. The remaining crops are some of the most water intensive in the state and would therefore lead to substantial water savings if the land could be repurposed. While unrealistic in the near future, calculating potential water savings from a complete conversion of suitable lands to dry farming allows for comparison with other water saving strategies. Even assuming that an acre-foot of irrigation is added to each acre of dry farm crops every year , vertical racks if all the land listed in Table 3 were converted to dry farming and irrigated to the statewide averages listed in the table , California would save 700 billion gallons of water per year, or nearly half the volume of Shasta Lake, the largest reservoir in the state. Given the overlap between suitable dry farm areas and high priority groundwater basins, these potential water savings are especially valuable as water districts scramble to balance their water budgets in light of SGMA. Perhaps the largest caveat to these potential water savings–and any analysis of dry farm suitability that relies solely on environmental constraints–is the economic reality in which conversions to dry farming currently occur. As discussed above, while a dramatic reduction in irrigation inputs might be feasible from a crop physiological perspective, whether farms can remain profitable through such a transition is an entirely different question. Given a dramatically increased supply of dry farm tomatoes, the profits that current dry farmers rely on could easily crumble. When considering other, less charismatic crops that could be good candidates for dry farming , customers’ likely hesitance to pay as steep a premium for high quality produce as they do for tomatoes also casts doubt on the viability of a large-scale dry farm transition given current profit structures for farmers.Our suitability map shows potential for vegetable dry farming to be practiced on California croplands that are currently irrigated, though its expansion is inherently limited.

Even if markets could be adapted to support an influx of dry farmed vegetables, our map indicates that climatic constraints will largely require dry farming to be practiced in coastal regions or other microclimates that can provide cool temperatures and sufficient rainfall. However, the Central Coast’s tomato dry farming offers principles–but not a blueprint–for low water agriculture in other regions. Based on themes from our interviews, these principles show a cycle of water savings that connect reduced inputs, management diversification, and market development . The cycle begins with lower irrigation , which can be accomplished in concert with soil health practices that build soil water holding capacity and increase long-term fertility. Reduced weed pressure and lower biomass production can then lead to reducing other inputs, such as labor and fertilizers, while also allowing for further water savings. The combination of reduced inputs and soil health practices then gives rise to a product that is unique in its water saving potential, and may also be of unusually high quality. By encouraging consumers to appreciate the products, or through novel policy support, farmers can develop markets that will provide a premium for these low-water products–or payment for the practice itself–which in turn creates an opportunity to expand the practice, further lowering inputs.As we ask how policies may impact dry farm production systems, we find a forking path in what types of expansion may result from different policies. An increase in production can be accomplished through both scaling size and scaling number . Both options can tap into the water saving cycle to decrease water usage; however, the search for just, agroecological transitions has pointed time and again to the need for scaling number . On the Central Coast, small, diversified farms have used this water saving cycle to both cut water use and develop a specialty product that allows growers to farm in areas with high land values by increasing their land access, profits, and resilience to local water shortages. Through these principles, small-scale operations have differentiated their management from both industrial farms and even other small farms in the region by creating a system based in localized knowledge, soil health practices, and thought-intensive management. However, it cannot be taken as a given that this water saving cycle will continue to uplift the small scale operations on which it started. Recent work highlights the potential for biophysical and sociopolitical conditions to combine to shrink–rather than grow–the use and viability of agroecological systems . In the case of dry farm tomatoes, socio-political attention is already beginning to target the biophysical need to decrease water consumption. If well-intentioned policy interventions designed to decrease irrigation water use build markets that value the fact of dry farming, rather than the high quality fruits it produces , growers will be able to scale the size of dry farm operations without needing to rely on the highly localized knowledge required to produce high quality fruits. As large grocers scale up dry farm produce sales without worrying about quality-based markets that may quickly saturate at industrial scales, the agroecological systems that originally produced dry farm tomatoes may be edged out of the market. On the other hand, if policies build guaranteed markets for small farms growing dry farm produce, dry farming may grow by scaling out to more small-scale operations. Policies focused on water savings may then favor industrial or small-scale farms, depending on how interventions shape the “Market Development” aspect of the cycle.

It is also possible that Sordariomycetes themselves are improving dry farm outcomes

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

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

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

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

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

Other experiments offered restricted credit that can only be used to purchase agricultural inputs

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

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

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

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

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

It normally takes a longer time to reach equilibrium in a temperate soil than in a tropical soil

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

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

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

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

Several techniques are used to estimate CH4 emissions from large area sources

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

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

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

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

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

Many of those sites were listed on a map from the City of Long Beach Office of Sustainability

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

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

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

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