Hemp Growing

Direct observation of feed bunk behaviors would ultimately be needed to positively determine the underlying mechanism for this behavioral pattern among this subset of animals.In order to account for this informational redundancy, age in days at start of trial and the 95th quantile of daily milk yield were here scaled to uniform variance and jointly encoded using the ecodePlot utility . Visualizations of this encoding can be found in Supplemental Materials. Bivariate tree tests, however, returned no significant associations between this age-yield encoding and any of the tensor plot encodings of daily time budgets. This result differed slightly from analyses of overall time budgets, wherein encodings that accounted for heterogeneity in sensor error and plasticity in daily response also returned no significant association, but encodings that accounted only for sensor noise found that heifers were significantly under represented among the most moderate time time budgets . Collectively, these results suggest that plasticity in daily time budgets might be obscuring links to age and productivity, but that this relationship cannot be brought into resolutions by accounting for systematic responses to environmental factors alone. This could occur if there were heterogeneity in the lag time between an environmental stressor and the behavioral response, or if there are more complex interactions occurring between transient environmental variables and more persistent internal biological states. If this in fact the case, then this result may suggest that an encoding strategy capturing only the variability in the response, trim tray screens and not systematic fluctuations, may in fact be a more effective means of pinning down this bivariate association.

Visualizations of these associations are provided in Supplemental Materials. As with overall time budgets, these resultsrevealed that cows with no health complications were over represented among the most moderate time budgets while sick cows were over represented among cows with low time spent eating. Overall, however, these associations were stronger with encodings of overall time budgets than with daily time budgets. Bivariate associations were also evaluated against health records disaggregated into five broad categories: healthy, mammary infection and injury, hoof and leg lameness, infections of the reproductive tract, and digestive and metabolic diseases. Significant associations were recovered for all three dissimilarity estimators used to encode daily time budgets. Visualizations of these associations can again be found in Supplemental Materials. Again, these results mirror the bivariate patterns recovered for overall time budgets, with no clear evidence that temporal dynamics notably modified these associations. Figure 4 displays the strongest association recovered for the ensemble-weighted encoding, wherein the optimal metaparameter combination for the tensor mechanics encoding employed no re-weighting along the temporal axis. As with overall time budgets, mammary infections were over-represented among cows with consistently extremely high time spent eating, while digestive and metabolic diseases were over-represented among low eating time budgets . Infections of the reproductive tract were also again over-represented among the most moderate time budgets, but in this encoding with daily time budgets, it is more visually apparent that these same time budgets are also the most variable over time. As these observations occurred fairly early in the lactation period, it seems unlikely that temporary perturbations in behavioral patterns attributable to estrus behaviors could fully explain so much variability in eating times .Finally, significant bivariate associations were recovered between patterns in milking order and encodings of daily time budget using all three dissimilarity estimators.

Visualizations of these bivariate patterns are provided for all encodings in Supplemental Materials. As a whole, the bivariate associations recovered against encodings created using overall time budget were stronger than for encodings using daily time budgets . Results for the unweighted Euclidean distance and the KLD distance largely mirrored patterns recovered using overall time budgets, with extremely low eating times being under represented among cows entering at the end of the queue and over represented among animals just ahead of these caboose animals. Visualizations of the bivariate association recovered against the ensemble-weighted Euclidean distance encoding are provided in Figure 5. Here the pattern between time budgets and cows that enter nearer the rear of the milking queue are brought into slightly higher resolution. This visualization shows that it is cows that consistently enter at the end of the queue, but not the last handful of cows, that are over represented among time budgets with moderate-to-high time spent eating. Time budgets with consistently low time spent eating, on the other hand, are significantly over represented among cows that routinely jumped between the middle and rear of the milking queue. Collectively, these results confirm that a later milking position does not preclude a cow from investing a significant amount of time at the feed bunk, though it should be noted that these results may not extrapolate to larger milking groups with longer waiting times in the milk parlor . As systematic temporal patterns in daily time budgets do not, however, appear to play a significant role in further distinguishing these bivariate patterns, future work might consider if these asynchronous perturbations in queuing patterns and time budgets might be more directly linked within individual, and consider latent biological causes of these isolated behavioral responses.Drawing complete and holistic quantitative inferences is always challenging when working with multivariate data streams. When a temporal component is also present, information compression via data aggregation can simplify the analytical pipeline, but a considerable amount of behavioral complexity can be lost.

In analyses of Cow Manager rear tag accelerometer records available in the Organilac dataset, tensor mechanics analyses revealed that cows in this experimental herd were remarkably consistent in their overall time budgets. Never the less, these algorithms were still able to capture subtle shifts in the tradeoffs between the eating and nonactive axes and the eating and highly active axes as cows progressed in their lactations, even though such temporal patterns were only found amongst a subset of the herd. While the resulting encodings did not differ substantially from overall time budgets, these analyses helped to better visualize differences between animals in the relative plasticity of their behavioral responses. While tensor mechanics algorithms are designed to extract systematic changes over time by leveraging collective responses of animals to a shared environment, these results have served to highlight the need to look beyond the mean value and more fully explore the richness of PLF datasets, which can provide repeated measurements on individuals at a scale never before possible .Future work should consider how to further leverage these approaches to not only explore patters in such plastic responses that can be extracted by leveraging group level information, but also variability attributable only to the individual, and subsequently any complex interactions that may occur between these systematic and fleeting perturbations in behavioral responses. Rice is the most widely consumed staple food for a large part of the human population, especially in Asia, trimming tray with screen providing more than one fifth of the calories consumed by humans worldwide . In many Asian countries, rice accounts for more than 70% of human caloric intake. China is the world’s largest rice producer, accounting for 30% of the total world production, followed by India , Indonesia , and Bangladesh . The traditional method of rice cultivation in the world used to be transplanting rice , which ensured a steady yield during the long history of mankind . However, in all climatic zones, human labor represents more than 50% of the cost of TPR farming , followed by the cost of other inputs such as water and fertilizers . With the development of rice cultivation science and the requirements of different climatic zones, many other methods of rice cultivation gradually emerged, such as dry or wet direct rice seeding . Direct-seeded rice , which is cultivated by directly broadcasting seeds onto the topsoil of paddy fields without needing to raise and transplant seedlings, provides an opportunity to save both labor and time .

This farming method also enables earlier crop establishment, providing an opportunity to make better use of early season rainfall, while increasing crop intensification in some rice-based systems . Moreover, the development of early-maturing varieties and improved nutrient management techniques, along with increased availability of chemical weed control methods, have encouraged many farmers in Asia to switch from transplanted to direct-seeded rice culture . During the last two decades, the change in the method of crop establishment from manual transplanting of seedlings to directseeding has occurred in many Asian countries in response to rising production costs, especially those of labor and water . In the Taihu Lake Basin of east China, many farmers have accepted the cultivation of DSR, although the average yield of DSR is not as stable and is still slightly lower than that of TPR . The area with DSR cultivation has rapidly increased and has already exceeded 50% of the total farmland in many TLB regions . In accordance with this development trend, the direct-seeding approach will likely continue to remain popular in the TLB. Water flow in paddy fields with cultivated rice involves the interaction of very complex processes, and their observation and evaluation under field conditions is relatively difficult, costly, and time consuming. Therefore, a large number of scientists increasingly use computer models to study the complex processes in the soil and to provide management and planning guidance. Hydrus-1D and Hydrus are numerical models that have often been used by many researchers to simulate water flow in agricultural fields with different crops and various irrigation schemes , including TPR fields . However, the Hydrus-1D model has not yet been used for simulating water flow in DSR fields. Compared to traditional TPR, DSR requires different water management, which provides rice with a different growth environment, particularly during its seedling stage . During the first two weeks after seeding, rather than being flooded as with TPR, the top soil only needs to keep sufficient moisture to allow for seed germination . As a result, the root mass of DSR is distributed shallower than that of traditional TPR, which consequently produces different vertical profiles of the water content. Furthermore, compared to TPR, DSR prefers an alternative drying and wetting soil environment during the middle late season when multiple smaller irrigations can benefit both the plant growth and deeper root growth . This water management produces distinctly different characteristics of the water flow regime and water losses from DSR fields compared to TPR fields. In this study, field observations in a DSR field in the TLB during two consecutive rice-growing seasons are evaluated using Hydrus-1D, and the main characteristics of the water flow regime and water losses are discussed.The agricultural land in the Taihu Lake Basin is used for very intensive production of the rice crop. The basin area, which is located south of the Yangtze River, is approximately 36,900 km2, with rice fields accounting for about 34.8% of this area. Rotations of rice with either wheat or rape are the most popular cultivating modes in this region. The basin has a subtropical monsoon climate with average annual rainfall of 1181 mm, 60% of which occurs from May through September. The annual PAN evaporation from the water surface is approximately 822 mm, and the average annual air temperature is 15–17 ◦C. The study site is in the Dangyang region , upstream of Taihu Lake . The dominant soil type in this region is classified as a hydromorphic paddy soil, and the parent material is a lacustrine deposit. The physical and chemical properties of the soil at the site are listed in Table 1.In our experiments, the variety of rice used for the DSR cultivation was Wuxiangjing 14 , a type of Japonica rice thatis predominantly cultivated in the Dangyang region. The observations were carried out in the same field during two growth seasons. After the mechanical field preparation, the seeds were evenly broadcasted by hand on the soil surface at 75 kg/ha, without prior soaking, on June 8th in 2008 and June 11th in 2009. After seeding, the fields were irrigated until the surface soil was saturated . The harvest dates were on November 1st in 2008 and November 5th in 2009. The total growing periods during these two years were thus 147 and 149 days, respectively. The water management in the DSR field followed instructions from the local Agricultural Technical Guidance Station and drew from the farmers’ own experience.

If instead some cows were less consistent in their time budgets across days, then the sampling error imposed by the subsampling routine would be greater, resulting in a larger ensemble variance estimate. Thus, we would expect a stronger penalty to be applied to cows whose demonstrated greater plasticity in their behavioral response to both transient and persistent changes in the production environment. For small datasets with limited number of replications, the number of subsamples could be set quite close to the size of the complete sample, and would thus emulate a jackknife approach to variance estimation . For larger datasets, however, the subsample size could be set smaller to make the resulting ensemble variance estimates progressively more sensitive to the uncertainty in the underlying behavioral signal. To evaluate the empirical performance of these dissimilarity estimators, distance matrices were calculated for the 177 cows with complete CowManager time budget records. Euclidean distance and KL Distance were calculated using base R utilities, with speed up options utilizing the Rfast package . An ensemble-weighted dissimilarity matrix was first calculated using simulated values accounting only for measurement error using the most conservative joint Dirichlet-Multinomial sampling scheme, hereafter referred to as Noise-Penalized Distance. A second ensemble-weighted dissimilarity matrix was then calculated using the same sampling scheme for measurement noise but aggregated over a 14-day subsample to account for behavioral plasticity in daily time budgets,hereafter referred to as Plasticity-Penalized Distance.

The LIT package provides users a clustering visualization utility, weed trimming trays which converts dissimilarity matrices into a dendrogram using the hclust utility in base R with default Ward D2 linkage and the generates heatmap visualizations of the resulting clustering results using the pheatmap package . HHaWPaSV ZHUH JHQHUaWHG RQ a JULG RI cOXVWHU YaOXHV IURP N = 1«10 IRU HacK RI WKH IRXU dissimilarity estimators, with complete results provided in Supplemental materials, and the results for k = 10 clusters compared in Figure 2. The LIT package also provides users a plotting utility to visually contrast the broader patterns between behavioral encodings. Outputs from the clustering utility are passed in to create a contingency matrix generated using ggplot2 with cells colored by their corresponding cell count . The heatmap visualizations for each encoding are then added to the row and column margins of the contingency matrix using the ggpubr package , and arranged such that each row cluster in either heatmap matches the order of the contingency matrix reading either up-down or left-to-right, allowing for direct and detailed visual comparison of the discretized behavioral patterns. Comparisons between the Noise-Penalized and Plasticity-Penalized encodings are provided in Figure 3.An optimal encoding strategy seeks to minimize the loss of relevant information by retaining as much of the underlying deterministic signal as possible while hemorrhaging only noise . In a hierarchical clustering framework, this is achieved by pruning thedendrogram built from the dissimilarity matrix where the branches cease to represent differences in the underlying signal. Standard pruning strategies typically either 1) to allow the user to provide a dissimilarity cutoff, below which value all further branches are grouped into the same bin, or 2) allow users to extract the first K branches of the tree .

As with the default Euclidean distance dissimilarity estimator, this approach may be appropriate for datasets with relatively homogenous variance structures. For data drawn from intrinsically heterogenous distributions, however, the branch lengths cannot be directly compared across the domain of support, making globally-defined pruning rules a suboptimal strategy for analysis of time budget data. More fundamentally, a homogenous pruning strategy may be too simplistic for many PLF sensor datasets, for which the underlying signal often represents a complex composite of behavioral mechanisms that operate at multiple scales. While some environmental factors might be expected to have an impact on cattle behaviors that is fairly uniform across the herd, other factors might elicit responses that differ in magnitude for different subgroups within the larger populations, or even become isolated within smaller social cliques. For example, we might expect the number of times cows are moved each day for milking will place similar constraints on the time left to lie down across all animals, but overstocking with respect to stall spaces might have a much larger magnitude of impact on the lying patterns of subordinate heifers than the more dominant older cows . In such a complex system, we would expect the heterogeneity imposed by the underlying biological signal to differ in scale across the dataset. Subsequently, in attempting to employ a global cutoff decision to encodeinformation for such a dataset, we would always be faced with the difficult decision to either ignore the subtler behavioral patterns present in some branches of the tree, or else allow noise to contaminate our encoding of other branches with intrinsically coarser behavioral patterns.

While all the components that contribute to the signal in a complex livestock system might be difficult to anticipate a priori, we propose that a more dynamic pruning algorithm might still be achieved by again employing flexible simulation-based approaches to emulate the comparably simpler sources of uncertainty. If each branch of the dendrogram is viewed as a pairwise contrast between two groups of animals, then we need only to determine whether the bifurcation under inspection represents a difference in the underlying signal can be reliably distinguished from noise. By implementing such a branch-level test recursively, we can gradually work our way down the tree with locally-defined pruning decisions. To evaluate the reliability of the behavioral signal encoded at each bifurcation of the tree , our branch test utility utilizes two mimicries. The first set of simulations are generated under the alternative hypothesis that assumes a branch contains an underlying deterministic signal that is only partially obscured by stochastic noise. Here then we can simply repurpose the ensemble of simulated data sets used previously to calculate the ensemble weighted dissimilarity metrics by mimicking the uncertainty in the observed data. As the null implies that animals demonstrate equivalent patterns of behavior within the resolution of the sample, this mimicry can be generated quite efficiently using a standard bootstrapping routine , wherein time budgets simulated under the alternative are unconditionally resampled from amongst all animals in a given branch. HClustering is then performed independently on each data mimicry in either ensemble and the first k branches are extracted to create an ensemble of discrete encodings. Under the alternative hypothesis, a strong signal should produce a robust tree structure such that, even after the addition of simulated noise, the resulting encoding would still closely mirror that of the original observed data. As the stochastic component of a dataset becomes stronger relative to the signal, these bifurcation points will become progressively less stable and the subsequent encodings less reliably aligned with the original data. When the signal falls below the resolution of the data, the tree structures of the simulated data would then seldom match that of the original data, and so would become poorly distinguished from encodings generated under the null with no signal component. We propose that mutual information, which can be calculated without any additional distributional assumptions, can be used to quantify the similarity between of the observed data and each mimicked dataset, and subsequently used to determine if simulations under the alternative are distinguishable . Equipped with an appropriate encoding to discretely represent the heterogeneity in overall time budgets within this herd, trimming trays for weed and provided the encoding of longitudinal patterns in parlor entry position from previous work with this data set, a potential question to ask would be: There are a number of nonparametric and parametric techniques available to evaluate the overall strength of association between two discrete variables by evaluating the distribution of animals in the joint encoding .

There is, however, perhaps greater practical utility in characterizing low and high points within the joint encodings, which would provide more detailed insights into the tradeoffs between specific behavioral patterns recovered from the data streams in these distinct farm contexts. Towards this end, information theory offers a more comprehensive approach to decomposing the stochasticity within discretely encoded variables, and thus may provide a more holistic approach to evaluating both the global and local features of a joint encoding, while employing few structural assumptions . First, to evaluate the strength of overall relationship between two discretized behavioral responses, the LIT package provides users a permutation-based bivariate testing utility thatuses the mutual information estimator to quantify the amount information entropy that is redundant between the two encodings . We can anticipate, however, that the efficacy of this test in recovering significant relationships between the underlying biological signals will be affected by the resolutions of the encodings. Suppose that a single latent biological factor impacts the behavioral responses collected by both PLF data streams, creating informational redundancy between the two encodings. If we cut the trees above the intrinsic magnitude of its impact on a given behavior, its influence may be overlooked and mutual information under-estimated. On the other hand, if we prune the tree far below the magnitude of its impact, our inferences can lose power as bins sizes in the joint encoding become progressively smaller, weakening the empirical estimation of the joint probability distribution and thereby increasing estimation error in the MI estimator. The resolution of our encodings must, therefore, be optimized to match the dynamics of the system, or a false negative result may be returned. To further complicate matters, however, we cannot necessarily assume that the magnitude of impact of a given latent factor will be uniform across behaviors, nor should we expect in a complex farm environment that behaviors will be influenced by a single latent factor. To overcome this logistical challenge without falling back on dubious a priori assumptions, the LIT package implements mutual information-based permutation tests on a grid, varying the cluster resolutions across both behavioral axes . Under the null hypothesis that no significant bivariate relationship exists between data streams, cow ID labels are randomly permuted within each tree, preserving the marginal distribution of the data along each axis but destroying any latent bivariate relationships. These permuted trees are then cut and the mutual information of the joint encoding estimated for each combination of cluster counts on the grid. A p-value is then generated by comparing the observed MI value of the joint encoding at each grid point against the corresponding distribution of MI values simulated under the null. Just as a scientist varies the focus of a microscope to bring microbes of different size into resolution, we can expect that geometric features of the joint probability distribution imposed by latent deterministic variables that vary in scale of impact will come into and fall out resolution as these meta-parameters are varied across the grid of cluster counts. To help the user visually identify where such features have come into resolution, the LIT package also returns a heatmap visualization of the observed MI value for each grid point that is centered and scaled relative to the distribution of MI values under the null. For behavioral measurements subject to the influence of multiple biological and environmental factors operating simultaneously, this exhaustive approach to parameterization enables users not only to build a more complete picture of a complex behavioral system, but may also provide insights into the hierarchy of these behavioral responses. Unfortunately, as the resolution of the encodings is increased, MI estimates not only become less precise, but they may also become less accurate. Bias is introduced when empirical estimates of the joint probability distribution become so granular that regions with low but nonzero probabilities go unsampled. These zero-count bins cause the total entropy calculated from the empirical joint probability distribution to be under-estimated, which in turn causes the relative amount of redundant information to be over-estimated. While the magnitude of this bias is partially dependent on the total sample size, it is also contingent on the structure of the joint probability distribution itself, namely the number of low-probability cells. Given that the joint probability distribution under the null, which is randomly permuted to intentionally remove any nonrandom features in the sample, can be expected to have a more uniform distribution of probability than the observed dataset, we can anticipate that the magnitude of the bias may differ between these two distributions as the sample becomes more granular, preventing MI estimates from being directly comparable.

After centering and scaling cow attribute variables, linear fixed effects were added for cow age , calving date , and peak milk yield . Interaction effects were created for each combination of these linear terms, and a categorical effect added for the control and treatment groups of the fat supplementation trial. Models were generated for both the complete dataset and the subset of animals with no recorded health events, which consisted of 160 and 104 cows respectively after removing animals with incomplete attribute records. While UML insights served to improve the specification of model variance structures within animal, the validity of statistical insights made at the between-animal level is still contingent upon the correct estimation of model degrees of freedom. A fundamental assumption of frequentist tests is that observations must be independently sampled. When observations are not independent, the effective degrees of freedom present in the model may be lower than the nominal value. This causes the model to be overconfident in its estimation of error terms, increasing the risk of a false positive result. Non-independence due to repeated sampling has here been accounted for by fitting arandom effect for each cow, but non-independence between animals has not been accommodated. The results of the diffusion map and data mechanics visualizations did not recover overwhelming evidence of coordinated movements between animals through the queue, which would have signified non-independence due to social cohesion ; however, mobile racking we both visualized via data mechanics and know intuitively that in this physically constrained system any cow moving forward in the queue must be countered with other cows being forced backwards and vice versa.

If this effect extends beyond isolated fluctuations in daily formation of the queue, then the presence of some animals in the herd might systematically dampen or even completely prevent other animals from demonstrating behavioral patterns that they would otherwise display independently or in another herd with a different social composition . This would not only serve to confound the behavioral mechanisms at play, but such cows whose behaviors are suppressed by their herd mates cannot be said to be contributing fully to the model, potentially reducing the effective sample size. This could allow sampling fluctuations to produce misleading statistical inferences, even in this large sample of animals . UML algorithms cannot recover information about behaviors that were never expressed, and so are also not immune to the biasing effects of non-independence between animals. These tools can, however, provide model-free tests of association that may serve as a sanity check for statistical inferences when degrees of freedom may be uncertain. We explore this option here by again combining modern clustering tools with a flexible information theoretic approach to pattern detection . First, independent clustering tress were used to subdivide the herd based on queuing records and each of the cow attributed variable. The resulting categorical variables were then used to form contingency tables between queue subgroups and each of the candidate predictor variables. If no relationship existed between these two axes, then a cow belonging to a given row category based on queue records would be just as likely to belong to any of the column categories based on cow attribute and vice versa.

If instead an underlying biological mechanism was present linking these axes, then cows within a range of cow attribute values would be spread unevenly among queue subgroups. Such heterogeneity in cell counts was quantified by calculating a weighted mutual conditional entropy value across first the rows and then the columns of the contingency table and averaging the results, which reflected the amount of mutual information shared between the two variables. To determine if the observed MCE value was significantly smaller than would be expected from random fluctuations in the sample, row and column classifiers were randomly permuted across cows to remove any underlying bivariate relationship and MCE recalculated. This randomization procedure was repeated over 2000 iterations, and the observed entropy value compared to the resulting empirical CDF to produce a p-value for the significance of the bivariate association. Mutual conditional entropy tests were performed for all significant or marginally significant linear effects for both regression models. As this herd was also fitted with ear tag accelerometers, it is here also possible to explore relationships between queue position and behavioral patterns displayed between milkings. Due to the size of these datasets, however, this small step beyond the bounds of the existing literature constitutes a considerable leap in statistical complexity within a linear modeling framework. A multivariate mixed model that considers all observations from either dataset would exceed the capacity of many solvers . A simpler approach to exploring this relationship might therefore be to compress the information available in parlor entry records into a grouping variable and then attempt to identify differences in the various home pen behaviors across the resulting subsections of the herd.

We implement this strategy here by using the nlme package to fit linear mixed models, with cow fit as a random intercept, against each of the five behaviors recorded by the CowManager platform and also average body temperature . To avoid the risk of anomalous behaviors that might skew model inferences, only cows with no recorded health events were used. Hour of the day was fit as a categorical variable to capture cyclical patterns. Days on trial was also fit as a categorical fixed effect to allow for non-smooth longitudinal changes in behaviors due weather and also the shift to pasture. Finally, queue groups were determined by arbitrarily dividing the herd into quartiles based on median entry position. The resulting categorical variable was then fit as both a main effect and an interaction effect against both cyclic and longitudinal time effects. Due to the size of the model, temporal correlation and heterogeneous variance models both exceeded the capacity of this package to converge. Comparisons of the cyclic and longitudinal trends in behavioral patterns between queue groups were made using the plotting utility available in the emmeans package , with the complete results provided in the supplemental materials. While linear models provide an expedient means to statistically evaluate targeted experimental hypotheses, the more open-ended approach to knowledge discovery provided by UML algorithms may offer an advantage in exploratory data analysis problems such as this. We explore the utility of this alternative strategy here by again employing a mutual conditional entropy test to identify significant associations between these two behavioral axes . The flexibility of hierarchical clustering tools allows this technique to be directly extended from the previous section, which compared repeated measures of queue position against a univariate covariate, to accommodate both high dimensional datasets. For each parameter recorded by the CowManager platform, this model free test of association was performed on the complete sensor record, on subsets of the records corresponding to each of the three lounging periods , vertical grow system and finally on a subset of the records where observations from all three lounging periods had been aggregated. As in the previous section, the number of clusters used to discretize queue and sensor data were evaluated on a grid, here from tree depths 2-10. To characterize the divergent behavioral patterns across queue groups identified by significant tests of association, tube plots were created by plotting each within day subgroup median on a circular grid and then stacking rings to form a tube using the 3D plotting tools in the plotly package .Looking first at the entropy calculations for each segment of the queue visualized in Figure 1, it is clear that all parlor entry positions are not stochastically equivalent. The same animals are seen consistently at the very front and back of the queue, such that the resulting entropy values are far lower than would be seen with a purely random queueing process. Moving towards the middle of the queue, however, there is progressively less consistency in the animals present across milkings, such that the observed entropy values approach a random process. Looking next at the stochasticity demonstrated by each individual cow in Figure 2, we see there is again a clear gradient.

Cows with median entry quantiles at the front and rear of the herd again show far greater consistency in their entry positions. As their median quantile position moves towards the center of the herd they become more variable intheir entry positions over the observation window. This gradient is seen using both entropy and variance as estimators of stochasticity, but is more visually distinct using entropy estimates. While discretizing an intrinsically continuous parameter results in a loss of information, we see here that this sacrifice has excluded extraneous noise in the system to bring the underlying stochastic pattern into clearer resolution. This data thus highlights the potential upside of amending entropy estimates to the traditional cadre of summary statistics, particularly when working with outcome variables that are prone to extreme or anomalous values.In examining the results of the permutation tests, nearly all animals demonstrated significantly less stochasticity in their entry positions at the standard 0.05 significance level as compared with a completely randomized queueing process. Only 3 cows out of 114 overlapped with the empirical distribution of entropy estimates under a randomized queueing pattern, and only 1 cow overlapped when variance was used as the estimator of stochasticity. This suggests that nearly all animals in the herd might contribute some information about the underlying nonrandom patterns in queue formation to subsequent analyses; however, the amount of information they contribute may not be equal, as there is considerable heterogeneity between cows. Of greater concern, this heterogeneity is systematic, as there are no cows showing high consistency in entry quantile in the center of the queue. If this pattern is not driven by variability in the underlying predictors of queue position, but instead reflects either an underlying behavioral mechanism or something even more fundamental to this system such as the inherent domain constraint , this could lead to inaccurate statistical inferences. Toavoid such risks, these simple visualizations provide clear evidence that a nontrivial variance model should be incorporated into the model specification phase to accommodate the heterogeneous variance structures in this dataset. Finally, the insights gleaned from these entropy-based visualization techniques agree well with the prior literature. Previous studies have repeatedly determined milk order records to be significantly more consistent than would be expected from a random queuing process using an array of correlation and regression-based approaches . Fewer papers, however, have explored differences in the consistency of entry positions between animals. Gadbury observed that only a subset of his herd seemed to demonstrate clear preferences for parlor entry positions. Such preferences do not appear to have been constrained to the front or back of the queue, however, as Gadbury also reported animals with a preference for the middle of the queue. In a more recent analysis with large commercial herds, however, Beggs et al. reported a nearly identical parabolic relationship between mean entry quantile and variance. With clear and consistent evidence of nonrandom patterns having been recovered from this dataset, further investigation of the behavioral mechanisms that might give rise to such heterogeneity in milk order records was clearly warranted.Visual inspection of the scree plot produced from PCA analysis revealed only one significant dimension was recovered from the original 80-dimensional dataset. To visualize the resulting projections, the first two principal components were plotted . In two dimensions points also appeared randomly scattered with no clear clustering. Thus, the PCA results revealed no compelling visual evidence of social cohesion. As this was the only significant dimension, this may suggest that a linear model to predict variations in central moment would be a reasonable representation of this dataset. This feature of the dataset was not, however, self-evident in the geometric relationships between data points revealed by the PCA projection, and thus might have been overlooked without specification of color encoding by median entry quantile value a priori.Evaluation of eigenvalues returned by the diffusion map embedding identified five significant dimensions. The 3D visualizations of these axes in Figure 3:B and provided in supplemental materials revealed quite clearly the underlying linear geometry of this dataset. Color encodings showed that the relative positions of animals along this narrow geometric band were determined by median entry quantile, further reinforcing that central moment was the most defining feature of this dataset.

We therefore examine this cycle not only as a means to save water, but ask if and how it can enhance the viability of nonindustrial farming operations as the food system adapts to restricted water availability. We consider the relevant policy recommendations outlined in Blesh et al.’s analysis of how institutional pathways can act synergistically with farmer networks to enable agricultural diversification , asking which have the potential to point future dry farming towards scaling size vs scope.To better situate these policy options in the local context, we first look to the outcomes of institutional intervention in organic strawberry production in a very similar region on the Central Coast, and consider the analogous options for dry farm tomatoes. Similar to dry farm tomatoes, organic strawberry production was launched into the spotlight by government-mandated input curtailments . For strawberries, the development of an organic strawberry production system also coincided with the adoption of an organic certification process by the US Department of Agriculture. Growing public interest in organic strawberries and the methyl bromide ban led to the rapid expansion of industrial-scale organic strawberry production– blatantly scaling size of production . As production increased, organic strawberry markets saturated and prices crashed, leaving an economic landscape where only the largest operations could remain viable selling strawberries at market prices . At this point, agroecological growers had to redouble their efforts to target local consumers with direct marketing strategies, weed drying rack as the organic label no longer added the necessary value to profitably sell their product.

In an analogous case for dry farm tomatoes, it is easy to see the immediate appeal of establishing a “dry farm” label that can incorporate the social value added to dry farm tomatoes into the price of the product without relying on consumers trusting and paying a premium based solely on higher qualities. However, by divorcing dry farm practices from quality premiums and trusting relationships with customers, a dry farm label would make it much easier for large-scale growers to enter the dry farm market. These larger operations–which may struggle to produce high quality fruits or maintain direct relationships with customers but can still decrease water usage enough to produce a certified dry farm tomato–could easily grow dry farm produce at large enough scales to edge smaller growers out of the label. As has been seen in the organic program, industrial growers could also lobby for an official relaxation–a literal watering down–of label standards . This sidestep of the dry farm practices described in the above interviews would not only further advantage large scale farmers, but would also undermine the very water savings that they are meant to encourage.Larger scale growers may also be favored when farmers are paid to implement specific practices. Administrative costs involved in enrolling in payment-for-practice programs can be a cumbersome barrier to entry, while low payouts at small scales dissuade small farmers who implement the practice from enrolling . These patterns are currently seen in programs offering cost shares for cover cropping, where farm size is significantly larger for participants than non-participants .Given farmers’ interest and current experimentation with dry farming non-tomato vegetables, expanding the set of crops that can be dry farmed and adapted to local conditions is a clear target for future policies. Support for research and participatory breeding programs/variety evaluation could spur development of locally-adapted dry farm varietals.

By compensating farmers for experimentation with diversified dry farm rotations and development of locally adapted varietals, policymakers can also absorb some of the risk inherent to on-farm experimentation and encourage innovation on the farms that are most familiar with the practice, while simultaneously lowering barriers for farmers new to the practice. To create a policy environment where experimentation feels more accessible to farmers, minimum lease terms could be set for farmland, allowing farmers to feel more secure in investing in localized practices .Priority could also be given to creating programs that connect farmers–particularly new farmers and those who hold underrepresented identities–to available farmland. Without the burden of securing water access, lands that would otherwise be impossible to farm with summer crops could become arable, particularly in conjunction with the concurrent support of the other policies discussed here. Though many areas will still require some access to water to successfully dry farm , crops’ need for water coincides with points in the season when surface water is most available , making areas with inconsistent water access over the course of the season likely candidates for dry farm success. Priority might initially be given to areas shown as suitable on the map, but as new and locally adapted crop varieties emerge, access may also extend.What is the difference between data and information? Although these two terms are often used interchangeably in biology, entire sub-fields of data science are dedicated to solving this riddle . While mathematicians have developed formal definitions for these terms , we will attempt to illustrate the conceptual differences with a simple example. Suppose a graduate student wants to analyze the gait dynamics of a dairy cow using a 120 Hz leg-mounted accelerometer.

In developing their data collection protocol, they surmise that if one accelerometer would be good, then two accelerometers must be even better. For their pilot study, they strap two sensors onto the same leg of their favorite cow and record her walking normally down the alley way in a straight line. If both sensors were calibrated and synchronized properly, then these sensors might produce data that looks something like the plot in Figure 1A. From this graph, can we learn anything about this cow from the blue sensor that we could not have inferred from the red sensors ? Since these sensors are quite precise, the two signals are virtually identical. Thus, even though this graduate student has doubled the number of data points recorded by adding a second sensor, they have not really collected any additional information about the gait dynamics of this cow.As this example has hopefully highlighted, data can be considered a physical resource ± something that can be measured in bytes and memory space. Information, on the other hand, should be regarded as a more nebulous unit of measure that represents how much can be learned from a dataset. In experimental settings, collecting measurements is typically costly, so sampling strategies are often developed to minimize redundancy between data points, such that data and information are often functionally equivalent terms . With sensor technologies, however, sampling density is often dictated by the hardware, which can result in a considerable amount of redundancy between data points . With such datasets, information compression strategies are often implemented in an effort to reduce the size of a dataset without losing any potentially useful information about the system. Returning to our previous example, suppose the graduate realizes they have not requisitioned enough hard drives to store data from both accelerometers. They decide to average the output of the two sensors at each time point, as illustrated in Figure 1B. This simple information compression step reduces the size of their dataset by half, but no details about the signal have been sacrificed. In the struggle that ensues to attach the accelerometers, one sensor is accidentally strapped on upside down. As a result, the two signals become inverted, as illustrated in Figure 1C. Subsequently, if the previously validated rolling average filter were applied to this new dataset, mobile rack the signals would cancel out, leaving only the measurement noise as shown in Figure 1D. Thus, this example serves to illustrate just how easily biologically relevant patterns can be lost when the assumptions employed in an information compression strategy do not match the data to which it is applied. As the range of Precision Livestock Farming technologies that are available to farmers to herds continues to expand , sodoes the need for robust algorithms that are capable distilling the large quantities of data that these systems produce down to useable knowledge . In data science, there LV ³QR fUee OXQcK´ ±no single algorithm that can be expected to perform optimally in all scenarios . In this paper, however, we will demonstrate how model-free unsupervised machine learning approaches can be used to recover and encode complex behavioral patterns from datasets generated outside of controlled experimental contexts, where an appropriate model may not be immediately obvious.

We will then explore how model-free information theoretic approaches may be used to recover complex nonlinear associations between farm records and multi-sensor systems in order to provide more comprehensive ethological insights.Animal scientists are seldom forced to work with raw accelerometer data, as in the previous example. Therefore, to further illustrate the fundamental differences between model-dependent and model-free approaches to information compression and knowledge discovery, lets consider a more practical example. Suppose a farmer has a group of 100 cows that are currently overstocked at 200% stocking density in a free stall barn. Concerned they might fail the outcome measures on their upcoming welfare audit, the farmer contracts a consultant to analyze data from the leg mounted accelerometer system that the farm uses in its estrus detection program.The farmer would like to know if there is any clear evidence of animals with compromised welfare in 60 days of archival records? They begin by implementing standard exploratory data analysis techniques, and produce the plot in Figure 2, wherein each dot represents the proportion of time a given cow is recorded lying down on a given observation day, with a LOESS curve fitted to reflect the mean lying time for the entire herd at each time point. From this visualization they glean that there is a considerable amount of variation between cows, and that there certainly are animals on any given day that are not spending a sufficient amount of time lying down . There is not, however,any clear evidence in this graph that would indicate that lying time patterns are changing at the herd level over time – quite the opposite actually, the herd mean is incredibly stable over the observation interval.Based on this visualization, the consultant constructs a mixed effect model with ³cow´ as the random effect, to account for repeated measurements and avoid pseudo-replication. Since the data appears stationary, they add ³day of observation´ as a simple continuous fixed effect. Finally, based on their preexisting knowledge of factors that may affect lying time, they add a categorical fixed effect to distinguish between ³heifers´ and ³cows´ within the herd. The results of this modelare provided in full in Supplemental Materials. As anticipated from the EDA visualizations, the fixed effect for day is neither biologically nor statistically significant . The consultant is, however, a bit surprised to find that the difference in average lying time between heifers and cows is also neither statistically or biologically significant . Thus, the consultant determines that the expected proportion of time spent lying for any animal within this herd for any given day within the observation window is 40% . In appraising the variance estimates for the random effects in this model, the consultant also sees that between-cow variance is considerably smaller than the within-cow error term , from which they conclude that there is also no evidence of consistent individual differences in lying time within this herd, and so these results also do not indicate that any individuals are consistently above or below the expected lying time for this herd. Subsequently, they might confirm that, while cows might not get to lie down an adequate amount of time every day, and that the average lying time could certainly be improved, there is also no evidence of cows in this herd that are consistently, and critically, under-rested. Let¶ s now suppose that the milk buyer is dubious of this result, and so they send the data to a new consultant who specializes in welfare audits. They ask the farmer some additional questions about this herd before constructing their own model and learn that the start of grazing season fell somewhere in the middle of this observation window, at which point cows had free access to pasture from their free stall barn. This consultant, suspecting that heifers and cows might react differently to this pasture access, develops a similar model to the first consultant, except this time an interaction term is included to allow the temporal dynamics to differ between these subgroups. With this small modification to the fixed effects matrix, this model now tells an entirely different story.

The experiment was conducted on seven certified organic dry farm tomato fields in Santa Cruz and San Mateo counties in California during the 2021 growing season. Five blocks were established on each field over the course of a full growing season , for a total of 70 experimental plots. These fields are managed by six farms; one farm contributed two fields at two separate sites. Each farmer continued to manage their field for the duration of the experiment according to their typical practices. Each dry farm crop was preceded by a crop in the winter prior to the experiment, either in the form of a cover crop , or continuous winter production . All fields were disked prior to planting, and two fields additionally ripped down to 60-90cm. Each field’s plant and bed spacing, plant date, and tomato variety are listed in Table 1, along with amendments added to the soil. Fields also varied in their rotational history . The mapped soil series, measured texture, and soil pH are listed in Table 3. From March 2 to October 27 there were 15 rain events greater than 1 mm recorded at the De Laveaga CIMIS weather station , none of which occurred between the months of May and October . Monthly weather data is summarized in Table 4. A nested experimental design was used to account for management and biophysical differences across fields. Ten plots were established at each field site within three days of tomato transplant. Each plot contained 12 plants, and plots were divided across two beds with a buffer row between . Plots were randomly selected to be inoculated in the first experimental row and then paired with a counterpart in the second experimental row that received the opposite inoculation condition to achieve a randomized complete block design with five blocks per field. Here we refer to a pair of inoculated and control plots as a block. There were three non-inoculated buffer plants between each plot and at least twenty buffer plants at the start and end of each experimental row.

This inoculum has been shown to impact crop physiology and improve plant water status in various field and greenhouse applications. Each of the 12 plants in plots in the inoculation condition received 0.2 g of inoculum, rack heavy duty which was mixed with 40 mL of water and then poured at the base of the plant within three days of transplanting, as per manufacturer instructions.Harvests began when farmers indicated that they were beginning to harvest the portion of their field that included the experimental plots. Each field was harvested once per week from its start date to its end date, with the exception of Farm 5, which was harvested twice per week, in accordance with farmer desires. All red tomatoes were harvested from each plot and sorted into marketable, blossom end rot, sunburnt, or “other unmarketable” fruits and then weighed. Harvests stopped when there were no remaining tomatoes in the field or when farmers decided to terminate the field. Fruit size and quality were assessed on the third, sixth, and ninth week of harvest at a given field. Ten representative marketable tomatoes were taken from each plot, weighed, dried at 70 degrees C and then weighed again to establish the percent dry weight . PDW was used as a proxy for fruit quality, with fruits with a lower water content increasing fruit quality up to a certain point. Extension research has linked dry farm fruit quality with lower fruit water content, as opposed to specific compounds that are elevated in dry farm tomatoes, and we expect PDW to correlate highly with the concentration of flavors previously found to create dry farm fruits’ superior quality. After eliciting quality categorization from farmers in the study, we determined that fruit quality increases up to a PDW of 8%, peaks between 8 and 12%, and falls above 12%.

Soil samples were taken three times over the course of the field season: once at transplant , once mid-season , and once during harvest . Each time samples were taken from four depths at each plot. Samples were homogenized and a subsample was immediately put on ice for transport to the lab. Each sample was then divided into fresh soil , dried at 60 degrees C , and dried at 105 degrees C . Ammonium and nitrate levels were measured after using 2M KCl to extract samples from transplant , midseason , and harvest samples using colorimetry35,36. As soil pH was close to neutral, Olsen P37 was used to measure plant-available phosphate on samples from transplant and midseason . Gravimetric water content was assessed for all samples. Samples from transplant were composited by depth at each field, and texture was assessed using a modified pipette method. At transplant, a soil core was taken with a bucket auger down to one meter from a central plot in each field and used to calculate bulk density at each depth increment. We then took a weighted average of GWC at each plot to calculate available water using bulk density and a pedotransfer function based on soil texture. Potentially leachable soil nitrate levels were calculated for each field using nitrate concentrations from the top 15cm at the harvest sampling event, which occurred within the first three weeks of harvest. Though the plants continued to grow for the duration of the harvest, it is unlikely that nitrate from the top 15cm were used due to the soil’s low water content, and no precipitation orirrigation occurred for the duration of harvest. Bulk density in the top 15cm was assumed to be 1.2 g soil/cm3 as experimental bulk density was measured with 1m of soil and likely overestimated the bulk density at the surface of the soil.Soil subsamples taken from 0-15cm and 30-60cm at midseason were set aside for DNA analysis. In addition to the experimental plots, samples were also taken from both depths at the nearest irrigated crop production areas and non-cultivated soils, such as hedgerows, field sides, etc. . Gloves were worn while taking these samples and the auger was cleaned thoroughly with a wire brush between each sample.

Roots were also collected from one plant per plot and were dug out using a trowel from the top 15 cm of soil. These samples were stored on-site in an ice-filled cooler and transferred to a -80 degree C freezer immediately upon returning to the lab . Roots were later washed in PBS Buffer/Tween20 and ground using liquid N.The ITS2 rRNA region was selected for amplification and fungal community analysis. This region has been successfully utilized in recent AMF community studies. Though AMF-specific primers exist , we chose the more general ITS2 fungal primers for several key reasons. First, in the field, SSU primers detect more taxa in nonGlomeraceae families but give lower resolution in the Glomeraceae family. Because the four species in our inoculant are in the Glomeraceae family and this family is dominant in agricultural systems and clay soils, we prioritized species resolution in Glomeraceae over other families. More broadly, the higher variability in the ITS2 region can lead to more unassigned taxa, but does not run as much of a risk that distinct taxa will be lumped together. Third, and of particular importance in our root samples, these primers are better able to select for fungal over plant material than other ITS primer options. Finally, ITS2 allowed us to also examine the broader fungal community in our samples, whereas SSU and LSU options are AMF specific and cannot be used to characterize other fungi.Qiime2 was used for all bioinformatics. Reads without a primer were discarded, and primer/adapter sequences were trimmed off reads using cutadapt. Samples were denoised with DADA2, and taxonomy was assigned using the UNITE version 9 dynamic classifier for all eukaryotes. Taxa outside of the fungal kingdom were removed from all samples and SRS normalization was used to reduce each sample to 7190 reads. 7190 was chosen as a cutoff due to a natural break where no samples fell between 4000 and 7190 reads. Because depths below 4000 retained less than 90% of sample richness, 7190 was chosen, cannabis drying system retaining over 95% of richness. The 22 samples out of 301 samples that fell below this cutoff were discarded. These samples included all 5 blanks, 3 samples from field 1A , 4 samples from field 1B , 2 samples from field 2 , 4 samples from field , and 4 samples from field 4 .In addition to the variables of interest, each model had a random effect of field and block within field. Yields were modeled using the total marketable fruit weight harvested from each plot at each harvest point, while BER was modeled using the proportion of fruits that were classified as non-marketable due to BER from each plot at each harvest point. Yield models and BER models treated weekly harvests as repeated measures, adding random effects of plot within block and harvest number. For hurdle models, random effects were treated as correlated between the conditional and hurdle portions of the model. Because PDW was measured at three time points, the initial PDW model treated the time points as a repeated measure and added a random effect of plot within block. However, given the nonlinear relationship between PDW and fruit quality described by farmers, further models used only PDW at the 6th harvest when fruit quality was at its peak and therefore did not include any repeated measures.The initial model for each outcome variable included plant spacing and PC1 for soil texture , along with PC1 for GWC and PCs 1 and 2 for nutrients at all four depths , as well as the interaction between texture and GWC. In this initial model, only one depth showed a statistically clear relationship with each outcome variable .

To improve model interpretability, we then replaced the two PC’s from the depth of interest with the scaled transplant values of nitrate, ammonium and phosphate at that depth, also adding the ratio of nitrate to ammonium and an ammonium-squared term to allow for non-linearities in outcome response to nitrogen levels. Because all nutrient variables had variance inflation factors over 5 in this model , we dropped nutrient PC’s for each depth that was not of interest, leaving only the transplant nutrient values at the depth of interest in the model. All nutrient VIF values were below 5 in the resulting model. Reported models were run using unscaled nutrient values for ease of interpretation. Transplant nutrient levels were used rather than midseason/harvest both because they are the most relevant to farmer management and because their interpretation is more clear than later timepoints, when low levels can either indicate lower initial nutrient levels, or that plants have more thoroughly depleted those nutrients.Two fungal community descriptors were calculated for each soil depth and root fungal community: the Shannon index and the count of OTUs in the class Sordariomycetes, which was identified as an indicator of dry farm soils . Counts were scaled, and both community descriptors were added to the final model described in the “Variable selection” section to determine the impact of fungal community structure while controlling for water, nutrients, and texture. Because the metrics between roots and the two depths of soil fungal communities were highly correlated, three separate models were run: one with both fungal community metrics from 0-15cm, one with metrics from 30-60 cm, and one with root community metrics.A PERMANOVA using Bray distances showed statistically clear differences in fungal community composition in irrigated, dry farm, and non-cultivated bulk soils as well as communities at 0-15cm and 30-60cm when stratifying by field and controlling for water, texture and their interaction, which also significantly differentiated between communities . Though dry farm, non-cultivated and irrigated soils each had more unique taxa than taxa shared with another location, dry farm and non-cultivated soils each had nearly twice as many unique taxa as taxa shared with a single other location, while irrigated soils had more taxa shared with dry farm soils than unique taxa . Abundance analysis showed that there were 466 taxa that significantly discriminated between the three soil locations. We then set the LDA threshold to 3.75 to highlight only the most stark differences, resulting in 13 discriminative taxa . All of the taxa identified as being enriched in dry farm soils were sub-taxa of Sordariomycetes, a fungal class that is highly variable in terms of morphology and function.

An interesting idea is that farmers themselves have a better idea of how they will benefit from the new technology. And this should be reflected in their willingness-to-pay to experiment with the new technology . Experimentation has shown that incentivizing contact farmers with the payment of bonuses proportional to their success with diffusion of the innovation in their community may be necessary and can be effective . It remains however unclear whether this recommendation is scalable. Experiments include sending SMS reminders to farmers on agronomic practices and using SMS to make recommendations customized to farmers’ own idiosyncratic soil conditions . In some specific cases, there may be simple techniques that can substantially improve farmers’ ability to obtain information through self-experimentation. An interesting experiment provides farmers with leaf color charts that guide them in their fertilizer doses decisions . The extension models described above rely on what can be called a “push approach”, whereby contact farmers are expected to pass information to others in their social networks. There is also potential for a “pull approach” to the diffusion of information through social learning. In this case, information is broadly broad casted in the community that something is to be learned from informed farmers. This “buzzing” can motivate farmers to seek information from informed farmers that may be inside or outside their normal social networks. Dar et al. thus show that “buzzing” through visible demonstration plots that use a counterfactual can be just as effective as seeding information through central farmers .

Under this approach, farmers know that there is something to be learned and they know with whom to engage in conversation to be informed. Banerjee et al. found a similar result in helping people understand the demonetization process in India: inform selected individuals in the community and widely tell the community who they are to induce information-seeking conversations. The advantage is lower cost and ability to reach less privileged farmers who do not belong to well informed social networks. In general, drying rack weed results show that information remains a serious constraint on SHF modernization. Extension services are even more under-funded than Research-and-Development and in need of new approaches that may work. Social learning can be made more efficient by a better choice of contact farmers and giving them high-powered incentives to diffuse information. Social learning can be reversed from push to pull for broader impact. Using IT services such as Digital Green and digital platforms offer interesting new options . Innovative approaches in addressing the information problem are in need of conceptualization and experimentation, with significant opportunities to make a large difference on SHF modernization.Incentives to adopt require good access to well performing markets. In contrast, SHF typically face poor infrastructure and high transaction costs, limited access to information on prices, lack of competition on local markets, and problems with quality recognition for inputs and outputs. Distance to market and poor infrastructure are major contributors to higher input prices and lower product prices for net sellers, which in turn act as a tax that discourages the adoption of innovations. Aggarwal et al. thus show that distance to market is equivalent to a 6% advalorem tax per kilometer for villages in Tanzania. Reducing travel cost by 50%, which is said to be equivalent to paving rural roads, would in this case increase local maize prices and double fertilizer adoption.

Improved infrastructure may however increase or decrease the prices of local crops depending on the competitiveness of local goods with those from further away. For Sierra Leone, Casaburi, Glennester, and Suri show that improving rural roads lowers prices on rural markets, benefiting consumers and hurting producers. They find that only when cell phone services help traders reach markets further away does improved infrastructure raise local prices, benefiting producers. For teff in Ethiopia, Vendercasteelen et al. show that proximity to cities increases the price received by farmers as well as the use of fertilizer and improved seed, resulting in higher yields, especially proximity to primary as opposed to secondary cities. Improved price information can also have mixed effects on farmer welfare. The role of IT services in reducing search costs, lowering local price volatility, raising average producer prices, and lowering consumer prices was observed by Jensen for fish in India and by Aker for grains in Niger. Better information on prices was also observed by Svensson and Yanagizawa in Uganda to have a positive effect on the level of prices received by farmers. However, better price information may have no effect on prices received by farmers if they have no option to sell on these markets and no bargaining power with local merchants . Local markets may also not be competitive, with the possibility of extensive collusion among merchants as shown by Bergquist in Kenya using an experiment exogenously varying the number of merchants on local markets with no consequent impact on price. This finding is however not supported in an extensive literature review by Dillon and Dambro who found that local markets in Sub-Saharan Africa overall tend to be competitive.Finally, quality recognition is a major issue on local markets, even though urban consumers may be willing to pay a price premium for higher quality, particularly for higher phytosanitary standards.

Quality recognition via third party certification, as for onions in Senegal, resulted in higher prices for good quality produce and created incentives for farmers to adopt quality enhancing technology . On input markets, Bold et al. find that there is extensive cheating on the quality of fertilizers, contributing to low adoption in Uganda. An experiment by Hasanain, Khan, and Rezaee shows that quality recognition in services markets via IT ratings can lead to improved veterinary services for artificial insemination in Pakistan. This was due to increased veterinarian effort once success rates were known to cattle owners. Lack of quality recognition for domestic production is a major issue for the competitiveness of SHFs with imported food on urban markets. It creates an increasing disconnection between what farmers produce and what urban households consume. The large number of SHFs and aggregation of production by traders high in the value chain prevents creating incentives for farmers to increase the quality of what they produce. This contributes to rising dependency of urban consumers on imported foods and low prices for domestic producers, discouraging technological upgrading. The frequent poor performance of markets due to high transaction costs, partial transmission of information, frequent lack of competitiveness, and lack of quality recognition remains a major obstacle to profitability and hence to technological upgrading. Addressing these constraints requires not only institutional innovations, but also costly public investments in infrastructure and marketing facilities.The supply-side approach to constraint removal and value chain development has helped identify a large number of technological and institutional innovations with potential to enhance adoption. In spite of this, technology adoption and modernization has been modest. As shown by continuously rising cereal yield gaps, and in spite of many local success stories, a global Green Revolution for Africa is still in the waiting. A major difficulty for technology adoption under rainfed conditions is heterogeneity of conditions. At the household level, this applies to three dimensions: farmer circumstances, farmer objectives, and farmer capacity. If these dimensions are immutable or too costly to change, technological innovations must be customized to fit these dimensions. Farmers’ circumstances such as agro-ecological conditions vary widely over short distances and across years in particular regarding rainfall patterns and soil fertility . For Zambia, Burke et al. show that only 8% of farmers can profit from basal chemical fertilizer applications due to lack of a complementary factor, in this case lime to achieve the desirable level of soil acidity.

In Western Kenya, Marenya and Barrett find that only 55% of plots can profitably use chemical fertilizers due to lack of a complementary factor, racking system in this case soil organic matter as measured by carbon content. Barghava et al. similarly find that there is complementarity between soil organic carbon and modern inputs. For adoption to go beyond farmers with complementary factors in place, technological innovations must either be customized to fit heterogeneous contextual conditions, or complementary factors must be delivered jointly with the technological innovation. Farmers’ objectives are different from breeders’ who typically focus on maximum yields in experimental plots with highly favorable controlled conditions . Farmers maximize profit or utility weighting return and risk. They may also have labor calendar objectives such as labor-saving at peak periods and labor-smoothing in the rest of the year. Labor constraints on farming may come from involvement in rural non-farm economy activities and seasonal migration, requiring to fit farming systems to accommodate complementarities between on- and off-farm labor engagements, including a gender division of tasks. The household will have nutritional objectives if part of the harvest is home consumed, and diversity of diets matter for the choice of farming systems. These specific objectives must feed into the design of new customized technological innovations. Farmers’ capacity may be improved through the acquisition of information and skills, but other dimensions of capacity are fixed factors to which technological innovations must adapt. T.W. Schultz and Foster and Rosenzweig famously showed that farmers’ education matters for technology adoption. Low skills may reduce the capacity and the speed of learning . Again, limits on capacity must be taken into account on the supply side of technology if it cannot be addressed as a demand-side constraint that can be relaxed. Technology must be kept relatively simple to use. An example is SwarnaSub1 that requires the same agronomic practices as the widely used Swarna rice variety. Another is the leaf color chart to adjust the quantity and timing of fertilizer applications. It is thus possible that available technology is not adapted to the circumstances and demands of a majority of farmers. Either it has to be adapted to the lack of key complementary factors, or the complementary factors have to be jointly delivered as a technological package. Unless this is done, lack of technological upgrading for a majority of farmers may not be an adoption issue but a supply-side issue concerning the availability of technologies that are profitable and adoptable by a majority of farmers. Lack of investment in Research-and-Development to address the specificity and heterogeneity of SubSaharan conditions noted above adds credibility to this interpretation. This is documented by Pardey et al. who shows that there is both under-investment in agricultural research in Sub-Saharan Africa as revealed by an estimated average internal rate of return of 42% for 25 countries over the 1975-2014 period, well in excess the expected return on public investment, and a continuing deterioration of the situation. Goyal and Nash document a net decapitalization of agriculture Research-and-Development capacity in Sub-Saharan Africa over the last decade. Conclusion is that an approach to using Agriculture for Development that seeks to remove constraints on adoption of existing technology from the supply side is essential, but likely to hit a low ceiling due to heterogeneity of conditions, lack of complementary factors, and diverse farmers’ objectives and capacity. Lack of quality recognition also creates increasing disconnectedness between what farmers produce and what urban consumers demand. A complementary approach to address these issues is development of inclusive value chains and constraint removal starting instead from the demand side.A demand-side approach consists in creating incentives for SHF to modernize through their participation in vertically coordinated value chains that provide links to markets for products with a profitable effective demand, while at the same time potentially offering solutions to market and institutional failures. The advantage of a demand-side approach is that it does not predetermine the solution to adoption but seeks instead broad complementarities in the ways of achieving modernization that are specific to the agent in question. Referring to the Byerlee and Haggblade classification mentioned above, we include under the category of vertically coordinated value chains, resource providing contracts and the more complex multi-stakeholder structure. Which elements are included in contracts and the specific structure and institutional form that vertical coordination will take is endogenous, depending on the particular needs of producers, the end buyers, and the context of market failures and institutional deficits.A key element of modern value chains that can result in SHF modernization is implementation of resource-providing contracts .

The effect of zero tillage is dependent on climate, especially on rainfall, and the effect is more pronounced in drier areas . The energy requirements of zero tillage and reduced tillage are less, so GHG emissions are lower . GHG emissions were reduced by 1.5 Mg CO2-e ha 1 year 1 in zero tillage-based wheat and maize systems .Crop residue return has positive impacts on SOC, however, its effectiveness varies with tillage practices . Retaining residues on the soil surface increases the soil C sequestration , whereas residue incorporation with inversion tillage may lead to higher N2O and CH4 emissions . Amount of residue return is positively related to the C sequestration . Residue return with optimum fertilizer input, paddy-upland rotation, improved crop cultivars, and use of legumes in rotation are some of the improved management practices for enhancing amounts of crop residue return to the soil . Crop retention can reduce the requirement of fertilizer and therefore, may limit the GHG emission. The application of bio-char to soil has the potential to offset 12% of global GHG emissions, as it can stabilize decaying organic matter and associated CO2 release, and can remain in soil for hundreds or even thousands of years . The retention over longer period is due to reduction in mineralization rate by 10–100 times from that of crop biomass . A meta-analysis reported that bio-char can either increase or decrease soil C depending on the types of bio-char/soil and duration . In addition to its effect on SOC, bio-char application may decrease soil N2O emissions to an extent of 9–12% or even 50% .Improved water management enhances C sequestration by increasing NPP and the subsequent addition of biomass to soil . It is estimated that improved water management could mitigate 1.14 t CO2-e ha 1 year 1 of GHG emissions .

In dryland agricultural system, drying weed crop productivity and the above- and below-ground inputs of C to the soil can be improved through efficient water management practices which enhances the plant-available water . However, drip irrigation with frequent wetting-drying cycles may promote soil CO2 emission through greater microbial activities . Micro-irrigation/fertigation also reduces N losses and hence lower GWP . In rice cultivation, soil flooding is known to emit a large amount of CH4 , which can significantly be reduced from improved water management such as alternate wetting and drying , also called intermittent flooding . However, the intermittent flooding may result in higher N2O emission , which necessitates water management to be in synchrony with inorganic fertilizer and organic matter inputs. Reduced water application reduces the C footprint of pumping water .The application of N fertilizer from the right source, at the right dose, right time, and in the right place enhances crop yield, N use efficiency, and SOC storage, and mitigates GHG emissions . Optimum and balanced doses of nutrients maximize crop yields, resulting in relatively more C inputs from both above- and below-ground plant biomass to the soil. Nitrogen can be applied effectively by correlating the leaf greenness with the leaf N content, and this can be done with a chlorophyll meter, leaf color chart, or optical sensors . Decision support systems like Nutrient Expert and Crop Manager are becoming popular for efficient nutrient management . ‘Nutrient Expert’-based management reduced on average 13% of GHG emissions from rice, wheat, and maize compared with farmers’ fertilizer practices. Studies conducted by Gaihre et al. reported that in Bangladesh, the deep placement of urea in a rice-rice cropping system reduced N loss as N2O and improved the crop yield. Thus, deep placement of urea can mitigate global warming and improve SOC by producing more biomass than traditionally applied urea. Enhanced fertility management can improve SOC content at the rate of 0.05–0.15 Mg ha 1 year 1 . In a meta-analysis conducted by Ladha et al. , it was reported that N fertilization promotes SOC storage in agricultural soils throughout the world. Benbi and Brar reported that the application of balanced fertilization positively impacted the soil C sequestration due to its effects on crop growth.

Balanced fertilization improved SOC concentration in rice-wheat and maize-wheat cropping systems because of the greater C input associated with enhanced primary production and crop residues returned to the soil . To improve soil health and soil productivity through balanced fertilization, the Government of India has started a “Soil Health Management ” program under the National Mission for Sustainable Agriculture . In India, the Soil Health Card has been useful in assessing the status of soil health, and when used over time. The SHM program aims to promote Integrated Nutrient Management through the judicious use of chemical fertilizers including secondary- and micro-nutrients in conjunction with organic manures and bio-fertilizers. The SHC-based recommendations have shown an 8–10% reduction of chemical fertilizer use with a 5–6% increase in crop yields .In India, the availability of manure as a source of nutrients and C in agricultural practice reduced from 70% of the total manure produced in the early 1970s to 30% in the early 1990s . Three hundred and thirty-five Mt of dung is produced per annum in India, out of which 225 Mt is available for agricultural use . This is only one third of the FYM requirement of the country that is needed to achieve the full C sequestration potential . Use of organic manure such as compost can enhance soil C stocks but may also result in higher CO2 emissions . Application of organic manure can improve SOM by supplying enzyme-producing microorganisms with C and N substrates , thus enhancing the structure and diversity of the microbial community . However, application of inorganic nutrients with FYM sequestered C at the rate of 0.33 Mg of C ha 1 yr 1 compared to 0.16 Mg of C ha 1 yr 1 in NPK application alone . Even in a hot, semi-arid climate, balanced and integrated nutrient management along with FYM could increase SOC in soil . Regmi et al. , in a long-term study, reported the accumulation of soil C in a triple-cereal cropping system with organic amendment. In a rice-wheat cropping system, compared to NPK, the use of organic material increased SOC ranging from 18 to 62% . Likewise, Duxbury reported SOC accumulation from 0.08 to 0.98 Mg C ha 1 yr 1 in rice-wheat cropping systems through addition of FYM in India and Nepal. Several researchers have reported higher GHG fluxes in different types of soil when manures were added .

In a soybean-wheat cropping systems with an organic amendment, Lenka et al. reported increases in SOC stocks and N2O and CO2 emissions but the annual GWP was lower.Deep-rooted crops and crop varieties can sequester more CO2 in lower soil profiles . Growing deep-rooted crops also reduces nitrate leaching to the groundwater and thereby reduces N2O emission , curing weed improves SOC stocks, and extracts nutrients and moisture from deeper soil layers . Deep-rooted perennial crops could also significantly decrease the requirement for tillage . Plants with improved root architecture can improve soil structure , hydrology , drought tolerance , and N use efficiency . Van de Broek et al. compared the amount of assimilated C that was transferred below ground and potentially stabilized in the soil from old and new wheat varieties. The authors reported that old wheat cultivars with higher root biomass transferred more assimilated C down the soil profile over more recent cultivars. Recently, Dijkstra et al. proposed a new ‘Rhizo-Engine framework’ emphasizing a holistic approach for studying plant root effects on SOC sequestration and the sensitivity of SOC stocks to climate and land-use changes. Mycorrhizal association is another important trait that could play a crucial role in moving C into soil through active participation with plants. It is reported that plants with mycorrhizal associations can transfer up to 15% more C to soil than their non-mycorrhizal counterparts . The most common mycorrhizal fungi are marked by thread-like filaments, hyphae that extend the reach of a plant, increasing its access to nutrients and water. These hyphae are coated with a sticky substance called glomalin which are known to improve soil structure and C storage. Glomalin helps the organic matter bind with silt, sand, and clay particles, and it contains 30–40% C and helps in forming soil aggregates . Averill et al. using global data sets, observed 70% more C per unit N in soil dominated by ectomycorrhizal and ericoid mycorrhizal-associated plants than arbuscular mycorrhizal-associated plants. Another recent synthesis by Verbruggen et al. opined that the mycorrhizal fungi can increase C sequestration through “enhanced weathering” of silicate rocks through intense interactions.The excessive use of pesticides in crop production has amplified to fight against insect pests and diseases. While the use of pesticides captures more C from improved crop production, it also increases GHG emissions from the processes involved in the use of synthetic pesticides . Integrated pest management can reduce pesticide use and increase crop yields. A study conducted in 24 countries of Asia and Africa has shown that the use of IPM to control pests can increase crop yields by more than 40%, and can reduce pesticide use by 31% . Research has shown that any pest management practices that lessen foliar spraying are able to reduce GHG emissions . CSPM is proposed by the FAO , and its aim are to reduce crop losses due to pests, improve ecosystem services, reduce GHG emissions, and make the agricultural system more resilient .A cover crop used to cover the ground surface during the fallow period prevents nutrients leaching from the soil profile, and provides nutrients to the main crops . Poeplau and Don reported a reduction in SOC loss by cover cropping. A significant area in South Asia, where cultivation of a single crop is the practice, provides an opportunity for cover cropping. Likewise, in intensive double-cropping areas, a short-duration cover crop such as sesbania can be grown to improve soil fertility including soil C . In a meta-analysis, Poeplau and Don estimated that using cover crops in 25% of the world’s farmland could offset 8% of GHG emissions from agriculture. Cover cropping has also been reported to reduce N2O emissions . Aryal et al. reported that cover crops and fallow rotation in warm and moist climates can reduce a net loss of 0.98 Mg C ha 1 in 7-year period. Creating borders of permanent vegetation along the edges of the field is another way to provide continuing live cover for agricultural soils . The possible effect of no-till in increasing SOC is more prominent when cover cropping is included in the system . Cheng et al. and Dignac et al. reported improvement in SOC stocks through rhizodeposition and root litter addition, which is greater with perennial crops than with annuals. In a policy analysis report on soil health and C sequestration in US croplands, Biardeau et al. reported that agroforestry, in which crop cultivation is intermixed with growing trees and sometimes with grazing livestock, has the highest potential to hold C, ranging from 4.3 to 6.3 MT CO2-e per ha annually.Inclusion of a dual- or multi-purpose legume in a rotation is likely to balance the organic and inorganic fertilizer inputs and its effect on SOC stocks . In South Asia, several researchers have shown similar benefits at the system level of optimizing crop rotations in CA mode in rice-wheat and rice-rice rotations . Legumes with the ability to fix atmospheric N benefit subsequent crops by increasing biomass production, crop residue inputs, and subsequently the total SOC in legume-cereal crop rotations . Reducing overgrazing ; balancing SOM decomposition through manures, crop residues and litter; and enhancing the mean annual NPP, are known to improve SOC in agricultural soils . Greater SOC stocks and more stabilized SOC can be obtained by increasing soil biodiversity . Havlin et al. reported that instead of continuous soybean cultivation, the inclusion of grain sorghum in a rotation increased soil organic C and N and that growing high residue crops along with reduced tillage could increase productivity. Ladha et al. reported that in different parts of the Indo-Gangetic Plains, the implementation of CA along with intensive crop diversification resulted in a 54% increase in grain energy yield, with 104% more economic returns, a 35% reduction in total water input, and a 43% lower global warming potential intensity compared to farmers’ conventional management practices. Improved agronomic practices can lead to SOC changes which are often higher than the proposed 0.4% .

The eddy covariance method provides valuable information to better understand temporal variability and estimates an annual CH4 emission average that can be compared to inventories. Only a select number of studies have conducted in situ field measurements of CH4 from California dairy manure lagoons. The magnitude and temporal patterns of CH4 emissions from manure lagoons often vary depending on the method used to estimate emissions. There is also an important role of seasonality of CH4 emissions that might confound comparison of atmosphere-based estimates with inventory. For example, Arndt et al. showed that summer CH4 emissions were comparable to inventory estimates, but not during winter measurements. In addition, emissions from manure liquid storage were 3 to 6 times higher during the summer measurements than during the winter measurements using three different techniques . In a recent study, statewide emission factors were comparable to ground-level measurements during the summer and fall seasons, but airborne measurements were 8% higher than the statewide inventories . Methane emissions from dairy manure lagoons may also differ by as much as a factor of two using different dispersion models . Other important gaseous emissions are also co-emitted with CH4 at dairy farms, but have different spatial patterns because they are coming from different sources . Additional observations at the seasonal and diel scales are needed to address uncertainties in CH4 emissions from dairy manure lagoons in California. In this study, we investigate seasonal and diurnal CH4 fluxes from manure lagoons at a dairy farm in Southern California using the eddy covariance technique. We pair our CH4 fluxes with micrometeorological measurements, air racking including wind speed, surface pond temperature, air temperature, among other parameters.

We then discuss the impact of lagoon agitation events, such as precipitation and manure management practices, on CH4 fluxes. Finally, we compare our CH4 flux estimates using the eddy covariance technique with other methods deployed at the same location. We hypothesized that manure lagoon CH4 emissions would follow seasonal patterns, with higher fluxes in spring and summer when manure substrate availability and temperature are higher. We also surmised that higher wind speeds would increase CH4 fluxes through increased turbulence and mixing of the lagoon surface. Finally, we hypothesized that manure management practices would have a measurable impact on measured CH4 emissions.Our study site is a manure storage lagoon on a typical dairy in southern California, located near 33.8º, -117.0º . The site has a semi-arid climate, with a mean annual temperature of 19⁰C and mean annual precipitation of 0.5 ± 2.6 mm that mostly falls between November and March. It is an open dry lot dairy—meaning that milk cows are housed in open corrals with dirt surfaces, and manure deposited in feed lanes is primarily scraped off the lot rather than flushed with water. The manure that is scraped from the corrals is stored as dry manure piles south of the dry lot. Water is used to flush out manure deposited in the milking parlor into manure ponds via the subsurface and above ground channels . Corral runoff flows to the channels via drainage pits,with four weeping walls present to retain solids. Approximately 227,100 L of storm water runoff from corrals and feed lanes , milk parlor wash down water, and wash pen water enters the manure pond system daily. Manure ponds receive about 38,000 L of fresh dairy flush manure daily. From December 2016 to June 2018, 56,775 L per day of green waste digestate was also introduced to the manure lagoons for testing their Ag Waste Solutions system that converts cow manure into bio-fuel, primarily diesel fuel, and bio-char . Occasionally, solids are removed from the above ground channels and stored as dry manure storage piles . The dairy farm’s population consist exclusively of Holstein cows. Demographics are relatively stable between seasons since it is a closed herd—births are on site and cows only leave once they retire or pass away.

There are approximately 1066 milking cows, 200 dry cows, 685 heifers, and 370 calves. The dairy manure flush system only receives input from the milking cows and calves. The total annual manure produced from dry corral production is 6300 tons.The manure pond system consists of five manure ponds , wherein the liquid manure navigates from manure pond 1 to manure pond 5 via gravity, decreasing the content of suspended volatile solids through anaerobic decomposition and settling as it navigates from one manure pond to the next. Throughout the study period , the surface of manure pond 1 underwent a drastic change in vegetation and surface variation . To quantify the percentage change in crust/vegetation,we calculated the change in vegetation/crust area using Google Earth satellite imagery between 2019 and 2021. There was a 147% increase in area covered by the crust layer and vegetation on manure pond 1 from June 2019 to June 2021. Peak vegetation growth occurred during the summer months , followed by a dry period. We define the pre-sedimentation stage occurring from June 2019 to May 2020 and the postsedimentation stage occurring from June 2020 and June 2021 when a substantial crust and sediment layer formed on the surface of manure pond 1. A common practice is to dredge dairy manure ponds periodically. However, the Southern California dairy farm has not dredged their manure ponds since it was constructed in 2006, thus solids also accumulated throughout this study period. The solids in the channel leading to the manure pond system were dredged in March 2020 following rain events and December 1, 2020 . Typically, the channels are dredged twice a year.We installed an eddy covariance flux tower at a height of 4 m on the southeastern edge of Lagoon 1 . The eddy covariance flux tower consisted of an open-path CH4 analyzer , integrated CO2 and H2O Open-Path Gas Analyzer and 3-D Sonic Anemometer . The analyzers measured at a rate of 10 Hz. They were calibrated before and after the field measurements using zero air and custom gas mixtures that were tied to the scale set by the NOAA Global Monitoring Division by measurement against NOAA certified tanks. We also measured air temperature and relative humidity , the surface temperature of the pond with an infrared radiometer , and precipitation with a rain gauge.

The data were recorded using a CR3000 datalogger. Instruments were powered using three solar panels, seven deepcycle. Dust was removed using an automatic cleaning system.The footprint of an eddy covariance flux measurements represents the upwind area that contributes to the fluxes at the location of measurements. The extent of the footprint depends on the micrometeorological conditions such as stability of the boundary layer and wind speed. A flux footprint model by Kljun et al. was used to estimate the footprint of the eddy covariance flux measurements. The algorithm uses the following inputs to calculate the footprint: mean wind speed, wind direction, weed dryer standard deviation of the horizontal wind speed, friction velocity, planetary boundary layer height, and Obukhov length. Figure 4.3 shows the upwind area that contributes to the flux observations with friction velocity greater than 0.1 m s-1 , wind direction between 270⁰ and 340⁰, and wind speed greater than 0.2 m s-1 . The distance of footprint contributions were calculated for each half-hour flux using the EddyPro software. The extent of the footprint captures manure pond 1, manure pond 2, and a portion of manure pond 3. As shown in Figure 4.3, 70% of the footprint primarily covers less than 50% of the area of manure pond 1.On August 28, 2019, we sampled the manure lagoon complex for various biophysical parameters using a boat at three different locations and depths. We sampled at three locations shown in Figure 4.4 and Figure 4.10. L1 and L2 were sampled at 0 and 0.3 m and L3 was sampled at surface level, 0.3, and 0.8 m. L1 and L2 were only sampled at the surface level and 0.3 depth since the high volatile content limited the instrumentation’s reach. We measured pH and temperature with an Oakton PCTS 50, PCSTestr 35 or pHTestr 30. Oxidation-reduction potential was measured with anOakton ORP Testr 10 that was calibrated with Zobell’s solution from VWR Scientific in the lab 24 hours prior to field work. Electrical conductivity was measured on each liquid sample in the laboratory using an Oakton Con 100 series meter and conductivity probe. The probe was calibrated according to manufacturer’s recommendations with 1413 uS standard solution from Fisher Scientific. Samples were removed from the 4 °C cold room and each was inverted gently 2-3 times to mix contents just prior to measurement. The probe was calibrated after every 10-15 readings to reduce drift. Total solids concentration , which is the solid concentration of biomass, was determined by weighing and drying 15-25 ml aliquots of each sample in triplicate in a 120 °C oven for 4- 16 hours, weighing the residual, then dividing by the wet weight. Aliquots were made using the shake and pour method . Fixed solids concentration , which is the inorganic fraction of total solids, was determined by further combustion of the dried samples in a muffle oven at 540 °C for 4 hours, weighing the residual, then dividing by the dry weight . Volatile solids concentration , which is the organic fraction of total solids, is the difference between TS and FS divided by wet weight. On August 14, 2018, stationary measurements of CH4 mole fractions downwind of manure pond 1 were collected with a cavity-ring down spectrometer . Dispersion models were then used to estimate CH4 emissions and showed that CH4 emissions were heterogenous, with higher CH4 emissionsnear the manure stream inlet . In a pilot study on August 27, 2019, CH4 emissions were estimated using an auto-ventilated floating chamber connected to a CRDS .

Figure 4.5 shows the timeline of measurements conducted at manure pond 1.During the study period, observed air temperatures were on average 19 ⁰C, with the highest temperatures measured during the summer . Sensible heat flux was on average 41 W m-2 . Mean surface pond temperatures were comparable to mean air temperatures with 20 ⁰C. Friction velocity was on average 0.2 ± 0.1 ms-1 . Lastly, incoming shortwave radiation near the manure ponds was 75±71 Wm-2 , on average . In our study site, precipitation events were highest during the winter and spring seasons. The highest precipitation events occurred during March and April in the year 2020. Daily CH4 fluxes were also highest during this time . Surface and pond temperatures were on average highest during the summer months of August and September. Similarly, incoming shortwave radiation was strongest during the summer months of August and September in the year 2020. There were no overall seasonal patterns observed for friction velocity and wind speed.At the diurnal scale, the micrometeorological factors that had the strongest correlations with CH4 fluxes were air and surface pond temperature, wind speed, and friction velocity based on linear regression models. The micrometeorological factors that had the strongest effects differed between the pre-sedimentation stage and post-sedimentation stage . There was a strong diurnal relationship between CH4 fluxes and surface pond temperature fluxes, especially during the pre-sedimentation stage of manure pond 1 . However, the diurnal connection between CH4 fluxes and pond temperature weakens postsedimentation . Methane fluxes and latent heat fluxes follow a similar diurnal pattern , with peaks during the early afternoon, when pond and air temperatures were also the highest . Wind speed also had a significant effect on diurnal CH4 fluxes during both pre-sedimentation and post-sedimentation conditions. Friction velocity had a stronger influence on CH4 fluxes during the post-sedimentation phase than during the pre-sedimentation phase of manure pond 1. During our study period, CH4 fluxes from manure pond 1 decreased from 2019 to 2021, with the highest CH4 fluxes observed during the spring period in 2020 . Spring CH4 fluxes decreased on average by 70% from 2020 to 2021 and summer CH4 fluxes decreased on average by 57% from 2019 to 2021. Monthly CO2 fluxes increased during the spring season and then decreased during the summer months, when there was vegetation growth in manure pond 1, driving photosynthesis and carbon uptake. Methane fluxes and CO2 fluxes followed a similar seasonal pattern . In contrast to diurnal CH4 fluxes, seasonal CH4 fluxes were not significantly correlated with seasonal latent heat flux . Monthly latent heat fluxes increased during the summer, whereas monthly CH4 fluxes decreased during the summer.

The other by-products, acetate and butyrate, help induce methanogenesis. Manure management systems vary among dairy farms but generally consist of dry and wet management practices . Dry manure management consists of deep pits, solid manure storage, dry lots, and daily spread . In a wet manure management system, manure waste from animal housing areas are washed and typically collected in manure lagoons, where anaerobic conditions produce CH4 . Dry manure handling practices reduce anaerobic conditions since they do not flush waste with water. So far, however, there are only two studies on seasonal CH4 emissions from anaerobic lagoons in California, but none have studied emissions from all four seasons . Measuring and modeling emissions from dairy manure management are challenging given the variability of practices . It is only recently that mobile measurement campaigns measured CH4 emissions from a small number of dairies with anaerobic lagoons . Given that field data is still variable, the majority of N2O emissions is estimated to originate from barns, unlike dairy CH4 emissions, which are mostly expected from anaerobic lagoons and slurry systems . The next largest emitter of N2O from dairy manure management is estimated to come from corrals and solid manure piles . Corrals include loafing pens, hard standings, and dry lots. Studies have also measured N2O emissions from anaerobic lagoons and slurry stores, which was unexpected since anaerobic conditions are dominant in wet manure storage . In anaerobic wet manure, nitrogen is mostly found in the form of ammonium and organic nitrogen, cannabis curing but denitrification is possible at inlets from wet manure storage systems if aerobic conditions are present . Nitrous oxide can also form through the denitrification of nitrate generated by Feammox, Mnammox, or anammox in the cases where NO3 – is present .

Nitrification can also occur under aerobic conditions, where N2O is emitted as a by-produced when NH4 + is first oxidized to nitrite and then converted to NO3 – . Ammonia is formed and volatilized from dairy manure almost immediately after urine and feces are excreted. Ammonia travels to the manure surface via diffusion and is released to the atmosphere via convective mass transfer . In general, NH3 volatilization increases with higher concentrations of NH4 + /NH3, substrate temperature, wind speed and turbulence . Ammonia emissions are highest between a pH of 7 to 10 and decrease with lower pH and is impacted by the pKa of the reaction .Methane is the second most important anthropogenic greenhouse gas after carbon dioxide and is increasingly becoming a critical priority for near-term climate action, given its relatively short lifetime and substantial potential for rapid mitigation . Over the last several decades, the growth rate of atmospheric CH4 has significantly changed, reaching stable zero growth from 1999 to 2006, followed by an increase beginning 2007 . This rise in the global mole fraction of atmospheric CH4 has been the subject of several studies that focus on explaining this phenomenon, without a definitive explanation. A rise in CH4 emissions could be indicative of changes in total emissions from various sources, including from biogenic, thermogenic, and pyrogenic CH4 and/or changes in the atmospheric sink of CH4 . The isotopic signature of CH4 is an important tool to diagnose the source of this increase in CH4 . The global stable carbon isotope ratio of atmospheric CH4, expressed as δ 13CCH4, has shifted towards more negative values simultaneously with the rise of the atmospheric mole fraction of CH4 . Recent isotopic evidence suggests that this rise in CH4 is likely dominated by increased emissions of biogenic CH4, which are more depleted in 13C relative to fossil and pyrogenic CH4 sources. Based on this explanation, possible biogenic sources responsible for the rise in atmospheric CH4 include ruminants, rice paddies, and wetlands, among others. Previous work have shown that isotopic signatures of CH4 emitted by enteric fermentation depend on the carbon isotopic ratio of diet composition, driven by the proportion of plants with C3 and C4 photosynthetic pathways, with estimates δ 13CCH4 of about -60‰ for C3-fed ruminants and about -50‰ for C4-fed ruminants . Other conflicting hypotheses about the CH4 budget include an underestimate of fossil-derived sources in CH4 inventories based on an isotope mass balance . Further studies, however, show that an increase in fossil-derived CH4 emissions is inconsistent with the observed trend in atmospheric δ13CCH4 . Additionally, there are large uncertainties in the magnitude and trends of atmospheric sinks of CH4 . Given that our understanding of the CH4 budget remains incomplete, there is a clear need for sufficient in situ isotopic characterization of CH4 at the local level to identify the location and type of sources that dominate the current rise in global CH4 emissions . Even at local to regional scales, the budgets of both CH4 and its stable carbon isotope remain uncertain . Improved knowledge is particularly important for ensuring effective mitigation of CH4 at scales where policies to reduce CH4 are being enacted . In California, there are statewide efforts underway to reduce CH4 emissions, but it remains challenging to accurately monitor progress given the large inconsistencies between atmospheric observations and greenhouse gas inventories . Atmospheric observations have inferred higher CH4 emissions than reported in GHG inventories at the statewide and regional levels and from individual sectors, including dairies . However, there is little information about the processes that produce this apparent discrepancy.

The California Air Resources Board GHG inventory estimates that dairies contribute about half of statewide CH4 emissions, with contributions from enteric fermentation by ruminant gut microbes and manure managed in anaerobic conditions. However, these estimates are based on emission factors derived from a few pilot and lab-scale studies conducted outside of California and thus likely not representative of California’s climate and unique bio-geography . Given that mitigation practices are targeted towards the bio-geochemical and management processes that produce CH4, new tools for source apportionment and process understanding are required . Stable isotopes of CH4 may be a promising way forward. The few studies that have measured isotopic signatures of CH4 from dairies in California were done in the Los Angeles Basin. Townsend-Small et al. investigated the isotopic signature of major sources of CH4 in the Los Angeles megacity and found that isotopic values of δ13CCH4 from fields applied with cow manure were characterized by values between -62.1 per mil to -59.2‰, whereas δ13CCH4 of manure bio-fuel from a manure digester facility ranged from -52.4‰ to -50.3‰. Cow breath, on the other hand, had more depleted δ13CCH4 source signatures between -64.6‰ and -60.2‰. A more recent study by Viatte et al. measured isotopic signatures of δ13CCH4 from the largest dairy farms in Southern California, and observed values between -65‰ to -45‰, attributing the most depleted observations to enteric fermentation. In Europe, grow room previous research has shown that δ 13CCH4 signatures vary dependent on the type of dairy manure storage. In Heidelberg, Germany, Levin et al., observed more enriched δ13CCH4 from manure piles and a biogas generator than liquid manure . Two recent studies used mobile surveys to measure δ 13CCH4 in Europe. In Germany, Hoheisel et al. conducted mobile measurements to determine δ 13CCH4 signatures around Heidelberg and in North RhineWestphalia. The δ13CCH4 signatures ranged from -66.0‰ to -40.3‰ for three dairy farms with biogas plants. More enriched δ13CCH4 signatures were observed from plumes downwind of the biogas plant relative to plumes downwind of the animal housing. In Northern England, Lowry et al., found that methane plumes downwind of dairy farms had δ 13CCH4 signatures from -67‰ to -58‰. Atmospheric measurements downwind of manure piles were more enriched in 13CCH4 with values close to -50‰ relative to cow breath, which were close to -70‰.

Isotopic endmembers were variable downwind of animal housing dependent on the cattle population and amount of manure waste present. In general, CH4 from barns with fewer cows and more manure waste were more enrichedin 13C. In comparison, beef cattle feedlots have isotopic signatures within the range of expected enteric fermentation, with δ13CCH4 signatures of -66.7 ± 2.4‰ in Alberta, Canada to -56.2‰ ± 1.2‰ in the Colorado Front Range, USA . Beef cattle are generally pasture raised until they are sent to feedlots, where their diet is primarily maize with varying proportions of wheat . In this study, we present seasonal atmospheric measurements of δ 13CCH4 from dairy farms located in the San Joaquin Valley, California, where 91% of the state’s dairy herd resides . Our primary objective was to measure δ 13CCH4 emitted from anaerobic manure lagoons and enteric fermentation source areas across seasons. Our second objective was to use δ13CCH4 source signatures from enteric fermentation and anaerobic lagoons to identify the dominant source responsible for CH4 hotspots detected from downwind plume sampling of other dairies in the region. We hypothesized that the δ 13CCH4 signatures from dairy anaerobic manure lagoons and enteric fermentation can be used to apportion CH4 emissions between these two dairy farm source processes. These isotopic signatures can help contribute to the body of knowledge that aims to resolve the CH4 budget in California and globally.Ground-based mobile measurements were collected at a dairy in Tulare County , California, in the fall, spring, summer, and winter seasons from 2018 to 2020. Hereafter, we will refer to this dairy as the reference test site farm. Figure 2.1shows a schematic of the reference test site farm layout. The reference test site has on average 3070 milking cows that spend most of their time in free stall barns, with an additional ~400 dry cows and ~3000 heifers that are primarily in open lots . Wet manure management is used for waste deposited in the free stall barns, where manure waste is flushed from barn floors and diverted to a processing pit. Wastewater from the milking parlor also enters the processing pit. Processing pit water is reused to flush lanes or is pumped over stationary inclined screen . A manure separator then removes coarser solids from liquid effluent, which gravity flows into cell 1. The liquid manure navigates from separation cell 1, cell 2, the primary lagoon, and finally into a holding pond via gravity, decreasing the content of suspended volatile solids through anaerobic decomposition and settling as it moves from one component to the next. Water waste from the holding pond is later used as irrigation water for cropland. Hereafter, manure lagoons refer to cell 1, cell 2, primary lagoon, and the holding pond. Dry manure management refers to the fraction of waste that is separated from the liquid waste stream, which is spread out on the ground and solar dried. Once dry, this manure is distributed into free stall beds or stacked and covered in the dry bedding. The primary forages are wheat and maize preserved as silage. Silage piles are covered with a double layer of plastic. The feed composition for different seasons was obtained by weighing each feed ingredient as it was included into the mixer wagon. All weights were transferred electronically to feed management software . FeedWatch data were retrieved once monthly for ingredient identification, quantity fed per pen, pen population and dry matter composition. Each ingredient was identified as C3 or C4 except for distiller’s grain, which could be a changing combination of C3 and C4 sources. Sum of dry weights by pen for C3, C4, distillers feeds were calculated. The feed composition by cattle production group is presented in .We also made measurements at other dairies within a 10 x 10 km region of agricultural land in the same county, which includes additional dairy farms, beef feedlots, poultry farms, and a landfill that are also emitting CH4 . Other potential sources of emissions surround the region, including a wetland, plugged and abandoned oil and gas wells that are permanently sealed, and a wastewater treatment plant. Residential land is primarily located south of the region and contains an extensive natural gas pipeline network. Globally, the δ 13CCH4 signatures from fossil fuel sources are typically around – 44‰ , with δ 13CCH4 signatures between −50‰ to −36‰ from fugitive natural gas in urban settings . Urban studies also use ethane to CH4 ratios as a tracer to distinguish between sources in mixed source regions .

Significantly, such payments are not entirely mitigated by higher rental payments for land. That is, the potential for profit is still great, with only 25% going toward rent, and with only 64% of farmland itself rented. Finally, increasing corporate influence, which has further entrenched and profited off large scale, specialized, and commodity crop-oriented production—and ensured that it does so by way of federal commodity support programs—subsequently exacerbates such trends in wealth accrued by white farmland owners. That is, corporate influence, which has pushed for increasingly specialized and large-scale commodity crop production on prime farmland, has facilitated and secured further accumulation of wealth by whites, particularly by way of plentiful government payments. Thus, despite the widely experienced loss of farmland by way of consolidation and specialization, such trends ultimately undergird white land ownership and wealth in the United States, and exacerbate the marginality that people of color face in accumulating wealth in relation to white people.The third major channel within the Farm Bill and other federal food and agricultural policies that has played a historic and ongoing role in structural racialization is the Farm Bill’s Rural Development programs, which are intended to help strengthen small communities by investing in water systems, housing, new businesses, infrastructure, and similar projects. Because many farms owned by people of color are in counties with little wealth and limited opportunities for non-farm employment, and because many rural and small town communities of color are faced with persistent poverty, Rural Development programs have the potential to promote socio-economic well-being for people of color and other historically marginalized communities. As of 2012, there is a larger percentage of whites in rural communities than in urban communities . Yet, cannabis drying rack ideas within rural communities, people of color face higher rates of poverty: while only 14% of rural whites live in poverty, 34% of rural Blacks live in poverty. Additionally, as of 2010, Latinos/as, Blacks, and Pacific Islanders have the lowest homeownership rates compared to homeownership rates for whites.  Thus, it is unsurprising that, according to a 2013 Tuskegee University study, farmers and rural communities of color have had particularly high participation rate in three major Rural Development programs: Rural Housing and Community Facilities; Rural Business; and Rural Utilities. 

Even though the Farm Bill’s Rural Development programs hold great potential for farmers of color and rural communities of color, barriers to participation reflect those that characterize other Farm Bill programs, marking how such support programs actually contribute to structural racialization. The Tuskegee University study, for example, found that regarding the delivery of such programs, farmers of color experience five major barriers: lack of program knowledge, impersonal workplace environment, “facially neutral eligibility requirements” that do not address the historic and systemic exclusion, remote locations, and sub-par outreach efforts. The Value Added Producer Grant program, for example, is a major Rural Development program that supports innovative marketing and product development strategies for the added processing of agricultural goods that can generate additional income. The VAPG program could be of great benefit to producers of color who grow a variety of nongrain and oilseed crops with value-added potential.Yet despite the Farm Bill itself requiring the USDA to prioritize projects by socio-economically disadvantaged farmers, a short application period and complex application form, and a requirement that recipients provide 1:1 matching funds, puts the VAPG program out of reach for some farmers of color. Finally, insufficient funding has long marked the Farm Bill’s rural development title and programs. Although, overall program spending within the 2014 Farm Bill averages $95.6 billion per year for the next ten years, the rural development title will receive less than 0.024% of that, only $22.8 million per year. The Value Added Producer Grant program, in particular, although originally authorized in 2000 to receive $20 million per year in funding, has been cut to $12.5 million annually under the 2014 Farm Bill. Collectively, such barriers limit the potential benefit of the Farm Bill’s Rural Development programs with regard to the dire situation many farmers of color and rural communities of color face.

Ultimately, they highlight the central contradiction that farmers of color face with regard to such commodity support programs and other support programs for farmers and rural communities: inclusion in the benefits of such programs does little to destabilize the historic and structural outcomes that they have reinforced, to undergird the wealth of whites in the United States, and to ensure that it is white communities that fare best regardless of what happens to the structure of US agriculture.PART III OUTLINED HOW LENDING,commodity, and rural development programs have historically undergirded white farmland ownership at the expense of people of color farmland ownership, and how long term changes in the structure of US farmland—the consolidation and specialization of agricultural production, in particular—have exacerbated such trends. Part IV continues this line of argumentation regarding the structure of US farmland and examines how programs geared toward supporting supposedly environmentally sustainable management practices also shape the socio-economic well-being of and farming and rural communities of color relative to white farming and rural communities. First, this part does so by providing a snapshot of the racialized distribution of costs and benefits regarding programs under the conservation title of the Farm Bill . It then outlines the significance of the historical continuity between environmentally-oriented programs and commodity support programs. Finally, it outlines the significance of four federal rural and agricultural support programs in particular—the Conservation Reserve Program , Environmental Quality Incentives Program , organic agriculture programs, and outreach and assistance programs—as well as recent corporate-backed trends in increased bio-fuel production. Part IV argues that, because of their inseparability from commodity crop production, and the consolidation and specialization of agricultural production, and despite the countless environmental benefits they produce, Farm Bill programs under the conservation title also undergird white farmland ownership at the expense of farmland ownership by people of color. Ultimately, they do so by funneling benefits primarily to white large-scale landowners on high quality land and keeping even low quality white-owned farmland profitable—an inadvertent result of the history of farmland ownership in the United States that cannot be seen as separate from the history of racial discrimination. This part argues, furthermore, that this is the case not only with commodity crop and acreage-based conservation programs , but that management practice-based conservation programs have similar effects. Furthermore, a fourth program, commercial weed the Outreach and Assistance for Socially Disadvantaged Farmers and Ranchers and Veteran Farmers and Ranchers Program, contributes to the social and economic inequities that characterize commodity and conservation programs alike, yet holds great potential as a strategic rallying point against structural racialization.

Finally, Part IV then addresses the relationship between structural racialization, industrial agriculture, environmental degradation, and climate change, and argues that farmers of color and communities of color bear the brunt of such environmental change.Conservation programs within the Farm Bill not only emerged from and remain tied to commodity crop production, but also maintained white communities as the primary benefactors of such modes of production in terms of both wealth accumulation and land ownership. The Farm Bill began by joining the re-establishment and maintenance of farm income at fair levels with the promotion of soil conservation and profitable use of agricultural resources. The first Farm Bill, the 1933 Agricultural Adjustment Act, for example, aimed to restore the purchasing power of agricultural commodities by encouraging voluntary acreage reduction of such crops through agreements with producers as well as the use of direct payments for participation in acreage control programs. Five years later, the 1938 Farm Bill was significant for a number of reasons: it secured these acreage restrictions; included new provisions where the federal government—and not corporations—would pay farmers who planted “soil-conserving” crops instead of “soil-depleting” crops ; and it established a series of credit programs that provided farm storage facility loans, purchases, and income support payments. By the mid-20th century, conservation programs were not only tied to, but also upheld, commodity crop production. Years of acreage reductions offset by increased farm productivity after World War II led to the 1956 Farm Bill’s Soil Bank program, a key conservation measure that set aside 4.9 million acres of select commodity crops. The land that the Soil Bank program was applied to, however, was already low-productivity land. In this light, with white landowners holding the vast majority of grain and oilseed farmland, the Farm Bill’s premier conservation program upheld white land ownership by keeping even the least productive grain and oilseed farmland profitable. Later programs, such as the Feed Grains Act of 1961, continued such trends, with farmers often diverting the least productive acres and realizing higher yields on those planted acres.  By the 1970s and 1980s, acreage reduction programs were all but abandoned as farmers began planting “fencerow to fencerow” to meet growing domestic demand for grain, precipitating massive environmental degradation and low prices that bolstered corporate profit. These changes ultimately prompted a new approach to conservation over the next two decades: starting with the introduction of the conservation title and programs in the 1985 Farm Bill; the addition of the Wetland Reserve Program and the Agricultural Water Quality Program in the 1990 Farm Bill; and the eventual separation of commodity programs from conservation programs in 1996 Farm Bill. These programs, as outlined below, however, maintain the structural benefits historically afforded to whites while keeping people of color at a structural disadvantage.One major Farm Bill conservation program that has undergirded white farmland ownership at the expense of farmland ownership by people of color is the Conservation Reserve Program . The CRP is the largest federal, private-land retirement program in the United States, with 27.5 million acres covered at a cost of $20 billion over the next 10 years. It provides financial compensation for landowners to voluntarily remove land from agricultural production for 10 to 15 years in order to improve soil and water quality and create wildlife habitat. Acres enrolled in CRP have indeed shown a number of environmental gains, including reduced soil erosion, water quality improvements, and wildlife population improvement. However, a number of factors shape the purpose the CRP serves and for whom: first, enrollment is considered to be undesirable by some land owners, primarily because of the cost of compliance and the potential loss of farm income due to the prevention of the use of such land for agricultural production. Thus, as with the 1956 Soil Bank Program from which the CRP grew, it is the least productive land and lowest income households that are often enrolled and kept profitable. Second, studies have shown that conservation compliance does not present a strong economic deterrent for landowners who want to crop former CRP acreage after the CRP term is over, thus highlighting the potentially temporary nature of such economic relief. Third, and perhaps most importantly, only lands planted with commodity crops, especially, corn and wheat are eligible for CRP and not fruits or vegetables, or lands used for livestock. Because white farmers have historically owned large-scale grain and oilseed farmland while farmers of color have been relegated to smaller, non-commodity crop farmland, the Conservation Reserve Program potentially undergirds white farmland ownership, both during times of economic hardship and on marginal land. A 2005 Texas A&M University survey study, for example, found that white landowners were more likely to have land qualified for reserve programs—as well as programs such as the Stewardship Incentives Program and the Forestry Incentives Program . Such landowners not only received more favorable program outreach and assistance, as will be addressed below, they also had more incentives to participate due to the economies of scale and tax savings. Toward this end, the study found that white landowners, on average, were enrolled in the CRP longer and signed up more acres than landowners of color .Another Farm Bill conservation program that secures white farmland ownership more so than farmland ownership by people of color is the Environmental Quality Incentives Program . EQIP underwrites part of the cost when farmers and ranchers implement environmentally sound practices tied not only to wildlife habitat but also to nutrient runoff, pest control, water irrigation, and livestock grazing. Eligible land includes cropland, rangeland, pasture, non-industrial private forest land, and other farm or ranch lands, with 60% of total EQIP funding set aside for livestock operations at the national level.