Category: hydroponic agriculture

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Hypatia automatically deploys clustering experiments to account for all of these challenges

Local tasks fetch from the file system or co-located database, remote tasks fetch their data over the network between EC and PC. Hypatia estimates the input/output dataset transfer time to the remote in two ways. We use the first when Hypatia has no history on the job type in the database, i.e., when the job is run for the first time. knowledge of job’s tasks is available, Hypatia uses the job’s input and output dataset sizes and the number of concurrent connections, to estimate transfer time using the iPerf lookup table with values representing time in seconds needed to transfer data from the edge to remote cloud for a dataset of a size given in kilobytes and a number of concurrent connections. The lookup table provides fast access but may introduce error because the data in the table is a “snapshot” in time. Table 5.1 shows a snapshot of a Hypatia lookup table. Files of different sizes, listed on the left, are sent over the network with a variable number of concurrent connections, listed across the top. The data is produced using iPerf to measure the network performance between the EC and PC , when Hypatia is first started. Each of the numbers in the table is an average of over 10 profiling runs. For job types that Hypatia has seen , Hypatia uses the average time measured across past tasks of the same type to estimate the transfer time. This computation takes longer than a simple table lookup but uses recent history to makes predictions . Hypatia launches a set of virtual machine instance types to the EC and PC,plant pot with drainage the number of each is specified by the user in the job description. Instance type names map to the amount of memory and compute resources that each provides. We deploy one Hypatia worker to each processor. Thus, we view each compute resource in terms of the number of workers it can support. The Hypatia queue is placed on the local instance and has two queues: local and remote. Based on the load split ratio, Hypatia places tasks in the local or remote queues. Workers then pull tasks from their assigned queue for execution on a first-come-first-served basis.

Given a Job J with n tasks and D dataset, di is the amount of data that must be transferred if the job is to use the PC. The scheduling plan uses this value, the network bandwidth between the EC and PC , the number of concurrent connections required to saturate the link, the number of available workers in each cloud, and the current state of the queues. The scheduler computes the ratio of the number of tasks that will execute locally and remotely such that time to completion for the job is minimized. Hypatia estimates the time to completion for EC tasks as the average time to complete past tasks of a similar type , and the state of the EC queue, which is expressed as the lag caused by unfinished tasks in the EC queue . The estimate for PC tasks includes a time estimate for data transfer. In addition, Hypatia uses the lag in the PC queue instead of the EC queue for this estimate.To evaluate the efficacy of the Hypatia scheduler, we execute multiple workloads across multi-tier deployments and measure the time to completion per job. For the experiments, we consider one edge instance and two sets of public cloud instances. On the edge, we use an m3.xlarge instance with 4CPUs and 2GiB memory. In the public cloud, our first instance set consists of a single “free tier” t2.medium instance type with 2 CPUs and 4GiB of memory. Our second public cloud set consists of an m5ad.xlarge instance type with 4 CPUs and 16GiB of memory. Our experiments consider deployments with 2, 4, 8, and 12 CPUs in the public cloud. Our workloads consist of two machine learning applications that we developed in the previous chapters of this dissertation: linear regression and k-means clustering. For each experiment, we evaluate the performance of the mixed load against executing all of the jobs on the edge cloud or all of the jobs in the public cloud . We refer to Hypatia deployments as mixed because Hypatia will schedule job tasks on both the edge cloud and the public cloud when doing so reduces time to completion.Since our jobs consist of tasks with different parameters, their time to transfer data and to perform the computation differs across tasks. To empirically evaluate this effect, for each set of machine learning models, we use two sets of jobs: uniform containing tasks having the same parameters, and variable containing tasks having different parameters . The statistics for each machine learning algorithm, for the variable or uniform sets, for EC and PC workers are listed in Table 5.3.

We present mean and standard deviation for the time it takes to transfer the data needed for the task and to process the task .The first experiment uses jobs that have 900 tasks, where each task is given a set of parameters that it then uses to compute the coefficients of a linear regression model based on two time series. The length of the time series defines the dataset size, and thus the computation time per task. Jobs are either uniform or variable task sets as defined above. The uniform linear regression tasks take as input a one month time series of 5-minute interval measurements . The variable job tasks take as input time series that vary between one day and one month worth of 5-minute measurements. For this experiment, the EC has 4 workers, and the PC has 2, 4, or 6 workers. Figure 5.3 shows the average time in seconds it took to complete a job with 900 uniform tasks using 4 EC workers and either 2 , 4 , or 6 PC workers , on average across 5 jobs. For each set of results, the first three bars show the average time in seconds that it takes to complete a task using mixed , local , and remote deployments, respectively. The second three bars per set show the average time in seconds that Hypatia estimates that each deployment should have taken, which we discuss later in this chapter. In every case, the mixed Hypatia workload performs best for time to completion for the workload. For uniform jobs, the mixed load using 2 remote workers finishes in 176 seconds on average, while EC-only takes 207s and PC-only takes 1160s on average, respectively. With 6 PC workers, the mixed workload takes 144s, EC-only takes 199s, and PC-only takes 387s on average. The results also show that as the number of PC workers increases, Hypatia is able to accurately split jobs between the two resource sets and time to completion decreases: 176s for PC2, 160s for PC4, and 144s for PC6 on average. Data for linear regression experiments is stored in the database running on a separate instance within the local edge cloud . This makes data transfer from an EC worker much faster than the transfer from the PC worker. The average data transfer time for EC workers is 0.85s and for PC workers is 2.42s. Processing time is 0.03s for EC and 0.13s for PC workers on average. For the variable jobs in Figure 5.3 ,black plastic planting pots the mixed workload takes an average of 102s with 2 remote workers, 91s with 4 remote workers, and 80s with 6 remote workers. Even though we expect EC workload to be the same across all 3 experiments in this figure since local workers don’t change, we still see some variation with workloads taking on average 110s in PC2, 105s in PC4 and 105s in PC6. The total time of the workload is significantly less than for uniform workloads because many jobs employ much smaller input data sets sizes.

The average data transfer time is 0.47s for EC and 1.6s for PC workers, while computation takes 0.02s for EC and 0.12s for PC workers, respectively. We see that for this particular job type, the PC workers take more time to fetch and process tasks. To investigate this further, we next change the instance type from the free tier t2.medium to use m5ad.xlarge instead – which we note is 4.4 times more expensive monetarily. Figure 5.4 shows results for the linear regression application with uniform jobs and with remote instances having 4, 8, and 12 workers, respectively. Only using 12 PC workers, can we observe remote experiments outperforming EC-only workloads. Similarly, with the increased number of workers the scheduler picked a correct split to minimize the time to completion, which was 158s, 122s, 100s, for 4, 8 and 12 PC workers, respectively. Local only experiments took on average 195s, while remote took 566s, 296s, and 187s respectively.The mean computation time of the uniform workload is 0.19s for EC and 0.31s for PC workers, with a standard deviations of 0.04s and 0.05s respectively. For the variable workload, the mean computation time for EC workers was 2.86s with a standard deviation of 5.09s, while PC workers have a mean of 4.36s and a standard deviation of 8.13s. Like for the linear regression application, Hypatia is able to deploy the Kmeans workload to achieve the shortest time to completion on average. As the number of PC workers increases, Hypatia adapts to employ the extra computational power by using the high-overhead communication link sparingly. The average time to completion is 306s, 238s, and 198s for mixed variable workload for 4, 8, and 12 PC workers, respectively. For EC-only variable workload, we see 423s, 430s, and 424s for three different repeats of the same experiment. PC-only workloads took 778s with PC4, 423s with PC8, and 316s with PC12. For uniform mixed workloads, the average time to completion is 69s, 56s, and 43s for 4, 8, and 12 PC workers, respectively. EC-only workload took 84s on average while PC-only workloads took 250s, 121s, and 82s on average.With more remote workers we observe that PC-only outperforms EC-only.

These results reflect the importance of considering both data transfer and computation time when deciding when to use remote, public/private cloud resources in multi-tier settings. Moreover, they show that Hypatia is able to adapt to different resource configurations to achieve the best time to completion for different machine learning applications automatically.With this dissertation, we present Hypatia – a scalable system for distributed, data-driven IoT applications. Hypatia ingresses data from disparate sensors and systems , and provides a wide range of analytics, visualization, and recommendation services with which to process this data and extract actionable insights. With a few examples of commonly used machine learning algorithms, like clustering and regression, we provide abstractions that make it easy to plug in different algorithms that are of interest to agronomists and other specialists who work with datasets that can benefit from their locality. Hypatia integrates an intelligent scheduler that automatically splits analytics applications and workloads across the edge, private, and public cloud systems to minimize the time to completion, while accounting for the cost of data transfer and remote computation. We use Hypatia to investigate K-means clustering consider different methods for computing correlation, using large numbers of trials , and cluster degeneracy.It then scores the clustering results using Bayesian Information Criterion to provide users with recommendations as to the “best” clustering. We validate the system using synthesized data sets with known clusters for validation, and then use it to analyze and scale measurements of electrical conductivity of soil from a large number of farms. We compare our approach to the state of the art in clustering for EC data and show that our work significantly outperforms it. We also show that the system is easy to use by experts and novices and provides a wide range of visualization options for analysts. We next extend the system with support for data ingress from sensors and develop a new approach for “virtualizing” sensors to extend their capability. Specifically, we show that it is possible to estimate outdoor temperature accurately from the processor temperature of simple, low-cost, single-board computers .

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Doing so poses challenges for k-means clustering and all variants mis-classified some points

Advanced users can modify the following Centaurus parameters: maximum number of clusters to fit to the data ; number of experiments per K to run; number of times to initialize the k-means clustering ; type of covariance matrix to use for the analysis ; whether to scale the data so that each dimension has zero mean and unit standard deviation . Centaurus considers each parameterization that the user chooses as a “job”. Each job consists of multiple tasks that Centaurus deploys. Users can check the status of a job or view the report for a job . The status page provides an overview of all the tasks with a progress bar for the percentage of tasks completed and a table showing task parameters and outcomes available for download and visualization. Centaurus has a report page to provide its recommendation. The recommendation consists of the number of clusters and k-means variant that produced the best score. This page also shows the cluster assignments and spatial plots using longitude and latitude . For additional analysis, users can select “advanced report” to see the correlation among features in the dataset, scores for each k-means variant, best clusterings for each one of the variants, etc. We implement Centaurus using Python and integrate a number of opensource software, packages, and cloud services. These services include the Python Flask Flask  web framework, RabbitMQ RabbitMQ  and Python Celery Celery  for messaging and queuing support, and an PostgreSQL SQL database PostgreSQL and MongoDB Community Edition NoSQL database MongoDB , which we use to store parameters and results for jobs and tasks. Other packages include Numpy Walt et al. , Pandas McKinney et al. , SciKit-Learn Pedregosa et al. , and SciPy Jones et al. for data processing and Matplotlib Hunter and Seaborn Seaborn for data visualization. Centaurus can execute on any virtualized cluster or cloud system and autoscales deployments by starting and stopping virtual servers as required by the computation. In our evaluation in this chapter, we deploy Centaurus on a private cloud that runs Eucalyptus v4.4 Nurmi et al. , Aristotle ,growing strawberries vertical system which has multiple virtual servers with different CPU, memory, and storage capabilities. We build upon, generalize, and extend this sys- tem in Chapter 5 of this dissertation.

Centaurus stores the cluster assignments for each experiment, which is the result with the largest log-likelihood value across initial assignments. This Centaurus instance only considers clustering results when all clusters have at least 30 points, in its computation of BIC and AIC. Finally, as described above, Centaurus reports the result with the highest average BIC score the “best” clustering across every K considered for all variants. Note that Dataset-1 was generated using a GMM where all dimensions are independent of each other and are identically distributed. Thus the “perfect” classification results generated by the Full and Diagonal methods indicate that they correctly disregard any observed sample variance or covariance. The results for Full-Untied with Dataset-2 and Dataset-3 illustrate Centaurus ’s ability to correct for cross-dimensional correlation. The generating GMM in both cases is untied . Also, unlike in Dataset-1 where there are three distinct clusters with separated centers, we purposefully placed the cluster centers of Dataset-2 and Dataset-3 near each other and generated distributions that overlap.To visualize the effect of different k-means variants on BIC score, we perform 2048 single k-means runs for each variant for synthetic datasets described in 3.4Figure 3.2 shows histograms of the BIC scores for each the of three synthetic datasets. We divide the scores among 100 bins. For each dataset, we present six histograms, one for each of the k-means variants, represented in different colors, where each variant has a total of 2048 single k-means runs. The X-axis depicts BIC scores from experiments – farther right corresponds to larger BIC and thus higher quality clusterings. This corresponds to a clustering with four clusters having cardinality of 2188, 531, 308, and 205, respectively. The second-best clustering has BIC score of -8925.4 and three clusters with cardinality 1733, 973, and 526, respectively, as shown in . Figure 3.4c shows the difference between these two clusterings. A specific data point is shown if it has a different cluster number assignment when we rank clusters by cardinality. For this data, clearly these clusterings differ. Thus, doubling the number of experiments from 1024 to 2048 allows Centaurus to find a clustering with a better BIC score.

The Sedgwick dataset has a more stable outcome in terms of the best BIC score when increasing the number of experiments. Figure-3.3b shows that even with 256 experiments , we achieve the same maximum BIC score as with 2048 experiments. The best result has a BIC score of -7468.0 and three clusters with 1111, 996, and 568 elements . This result is consistent over many repeated jobs with a sufficiently large number of experiments i.e. any job with more than 256 experiments produced this same clustering as the one corresponding to the largest score. The second-best clustering agrees with the best result on the number of clusters with cluster cardinalities of 963, 879, and 833, and a BIC score of -7529.8 . While these clusters do differ, Figure 3.5c shows thatthe differences are scattered spatially. Thus the best and second-best clusterings may not differ in terms of actionable insight. For the UNL field, the best and second-best clusterings are shown in Figure 3.6. These are both from job-2048. The best clustering has six clusters with cardinalities 2424, 1493, 1138, 561, 111, and 70, respectively. The second-best clustering has four clusters with cardinalities 2730, 1615, 838, and 614, respectively. From these features and the differences shown in Figure 3.6c it is clear the best and second-best clustering are dissimilar. Further, the second-best clustering from job-2048 is the best clustering in job-64, job-512, and job-1024 respectively. As with the Cal Poly data , doubling the number of experiments from 1024 to 2048 “exposed” a better and significantly different clustering. Unlike the results for the synthetic datasets, the best clustering for the Veris EC datasets is produced by the Full Untied variant for sufficiently large job sizes. This result is somewhat surprising since the Full Untied variant incurs the largest score penalty in the BIC score computation among all of the variants. The score is penalized for the mean, variance, and covariance estimates from each cluster. The other variants require fewer parameter estimates . Related work has also argued for using fewer estimated parameters to produce the best clustering Fridgen et al. leading to an expectation that a simpler variant would produce the best clustering, but is not the case for these datasets.

Because Centaurus considers all variants, it will find the best clustering even if this effect is not general to all Veris data.To evaluate the differences among clustering variants as applied to farm datasets, we present three largest jobs with their best BIC scores and the variant that produced the best score . For each farm dataset we present results from the three largest experiments: Job-512, Job-1024, and Job-2048. The results show that for most of the multivariate datasets,growing vegetables in vertical pvc pipe the best clusterings came from the Full-Untied variant with a very small number of exceptions: The ALM dataset and Job-1024 for TC1 dataset . For the univariate datasets , the only variant possible is spherical since there are no additional dimensions with which to compute the covariance. For these datasets, the Spher-Untied variant performs best.Degenerate clusters are a surprisingly frequent, yet under-studied, the phenomenon when clustering farm datasets. We next investigate the frequency with which degeneracy occurs for different datasets and different numbers of clusters. Figure3.7 illustrates the search space for the best BIC score with the estimated joint distribution of BIC scores and the number of elements in the smallest cluster . The figure represents a Job-2048 for CAP dataset, with all six variants and all values of K . Darker colors on the graph represent higher density regions. Our system uses all of the k values and all of the variants when choosing the model with the highest BIC score. Per-component distributions are available on the sides of the graph. The graph indicates that the highest BIC scores often come from the clusterings that have one or more almost empty clusters. Particularly for variants that rely on an estimate of co-variance between dimensions, inferences made about the means of these clusters are suspect when their sizes are small. Note that for larger values of k, such clusterings can be common. For example, this particular job had 50961 or 41.5% degenerate and 71916 non-degenerate experiments. To illustrate this effect more fully, we divide the experiments based on the number of clusters, k, and illustrate how degeneracy behaves with increasing k in Table 3.6. The total number of experiments for multivariate datasets was 12288 for each k and all six variant types. The univariate datasets had 4096 experiments and include only two variants for each k. For each farm, the results show the number of non-degenerate clusterings for each k. In some cases, the number of non-degenerate clusterings decreases as k increases. To emphasize the overall degeneracy across all Job-2048 experiments for each dataset, we summarize the percentages of experiments with fewer than 30 elements in their smallest cluster in Table 3.7. The smallest percentage of degenerate clusters is 6% for the GR1 dataset and the largest percentage was 72% for TC2 dataset. We have chosen 30 as a reasonable rule of thumb for a cluster size from which to make an inference about the mean of each cluster in the experiments having three dimensional data. Because k-means converges to a locally optimum solution for non-convex solution spaces, the choice of initial assignment can effect the clustering it finds. Often, users of k-means will run it once, or a small number of times assuming that the local minimum it finds is “close” to the global minimum. In this subsection, we investigate the validity of this conjecture for soil EC data across farms. Figure 3.8 presents the best BIC scores for different experiment sizes for all of the farm datasets. In some of the jobs, the best BIC score occurs only once amongst all of the experiments while in others the best BIC score is more common among multiple experiments . Thus a small number of trials is likely to result in the most common clustering rather than the best one. More importantly, an increase in the number of experiments increases the chance of finding the best BIC score.

The x-axis for each graph in Figure 3.8 is the power of 2 in the number of experiments. For most of the graphs, k-means finds the best BIC consistently beyond some large number. However, for a few of them, it appears that an even greater number of experiments may be necessary before a single consistently large BIC is determined. The graph in Figure 3.3c, for example, seems to indicate that an even larger number of experiments may yield a larger BIC. Thus, for the EC available to our study, it is clear that the best clustering is often rare and thus requires a large number of independent trials to determine. Even though the best clustering may be rare, it may also be that it differs from the most common clustering by so little as to make the effort required to find it unnecessary or wasteful. Table 3.8 compares the clustering determined by the best BIC scored cluster to the most common clustering for each of the data sets across their largest jobs. We limit the clusterings to those with at least 30 elements in each cluster to prevent degenerate clusterings from clouding the results. For each data set we show two rows. The row marked “B” shows the BIC score, the value of k, the cardinality of each of the k clusters, and the number of occurrences of this clustering for the clustering having the best BIC score. The row marked “MC” shows the same information for the most common clustering.

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Community Supported Agriculture connects farmers and the consumers of their products

The Mask R-CNN model produced an AP of 0.8996. Results were not stratified by size category. This result is 8 AP percentage points higher than the Mask R-CNN results on the Nebraska dataset for the medium sized category. This could be due to a number of factors. First, the imagery used was 0.26 meter, which is much higher resolution than Landsat 5’s 30 meter resolution. Therefore, each building instance could have a higher amount of pixels with which to compute informative features. A larger model with more parameters was also used, Resnet-101, which increases learning capacity as well as the tendency to overfit. Finally, some figures referenced in Wen et al. indicate that bounding boxes encompass multiple individual buildings. If these groups of buildings were used to represent a single instance to calculate mAP, the score would be higher than if each building was considered as a separate instance.Deines et al used a random forest model to classify irrigated pixels across the HPA using spectral indices, GRIDMET precipitation, SSURGO soil water content, a topographic DEM, and other features. The random forest model was trained on 40% of the data, validated on 30%, and tested on the remaining 30%. Since this method classified pixels and not objects, metrics are not directly comparable, however, the result shows that the model was quite successful in accurately mapping irrigated area across a wide climatological gradient. Pixelwise, overall accuracy for classified irrigated area was 91.4%, and results were visually assessed to correspond well with GCVI computed from Landsat scenes. Given that this model performed so well and that we are able to inspect feature importance for random forest models more easily than with CNN models, it is useful to determine if the features that were successful in the random forest model were or were not shared by the Mask R-CNN model so future models may take advantage of both approaches. The top six most important features in the random forest model that led to this accurate classification were,lettuce vertical farming in order of importance: latitude, slope, longitude, growing degree days , minimum NDWI, and the day-ofyear at peak greenness.

The authors note that the random forest model uses latitude and longitude to separate scenes by climatological gradients, which can improve detection as different climatological areas contain different agricultural patterns. Of these features, only minimum Normalized Difference Water Index can be directly learned by Mask R-CNN, though the model likely recognizes scene qualities such as bare soil that are correlated with a drier climatology. This indicates that pixel-wise features besides reflectances can contribute, and be even more important, than reflectance alone. Future approaches based on CNN’s could make use of not just reflectance information, but also the features named above. However, this would currently require training CNN models from scratch. This could be prohibitively expensive, increase training times, or lead to lower accuracies since weights are initialized randomly and training from scratch may not lead to as informative features as those that are arrived at after pretraining. The results from Deines et al. do not separate individual fields from each other; many clearly distinct fields are mapped as a single irrigated area unit. The method used here is useful for tracking pixel level changes in irrigation, yet it cannot be used for tracking field level statistics from year to year. The amount of training data was the second largest factor in determining the resulting performance metrics. Decreasing the amount of training data by 50% decreased performance metrics by a substantial amount. The large performance drop for the small size category, which had an 11.4% difference in terms of AP percentage points, indicates that more training data is especially important for more uncommon center pivot size categories, which highlights the importance of using as much training data as possible when training CNN-based models. Using a NIR-R-G vs RGB composite did not affect the validation AP, which is expected since center pivots are visually defined by shape rather than spectra. Correct preprocessing choices were also important in order to achieve good results, and the most important of these was to convert image values to 8-bit integers before normalizing by the mean. This step clips the very large values present in Landsat 5 OLI scene chips from high reflectance from snow and clouds and adjusts the range of values of the training set to more closely match that of the pretrained model’s original dataset, which in this case was Imagenet. Models trained on the original TIFF imagery performed very poorly even when these images were normalized by the mean .

Adjusting other hyperparameters did not affect the model performance as much as converting the data type of each image sample to 8 bit integer and using the largest training data size. Examples of hyperparameters tested include doubling the number of region proposals generated during the training stage and increasing the amount of non-max suppression to prune low-confidence region proposals. I expected that more region proposals and more aggressive pruning of low confidence proposals would lead to a better performing model, however making these adjustments did not impact model performance, which is likely because the default number of regions generated was sufficient to intersect with most potential instances of center pivots in each sample.These results clearly show that segmenting small center pivots will be a challenge for Landsat RGB imagery, given it’s coarse resolution and the less resolving power for smaller boundaries. However, Figure 13 shows that in a scene where there are many fields in full cultivation, they are for the most part all accurately mapped. This implies that inaccuracies from misdetected small center pivots in a stage of pre-cultivation could be remedied by applying the model to multiple dates and merging the output results in order to capture center pivots in a stage of cultivation during the growing season. The results from the medium category are overall much more accurate, though even in this size category, Figure 11 shows that there are many false negatives due to many pivots being in a stage of pre-cultivation or showing half in cultivation and half pre-cultivation. Figure 11 also highlights another source of error in segmenting center pivots, where corner areas surrounding center pivots are also cultivated. While these regions are not annotated in the Nebraska dataset as belonging to part of the center pivot, they could either be irrigated along with the center pivot as a single field using an extension to the irrigation apparatus or separately. This has relevance for accurately estimating individual field water use, and highlights the difficulty in resolving individual units using satellite imagery. Center pivots that are composed of multiple sections will be hard to segment, given the limited amount of training data that contains similar representations. As with small center pivots in stages of pre cultivation, one approach to segmenting these could be to apply a model to multiple dates in a growing season and then merge the highest confidence detections in order to reduce the amount of false negatives.

Because the dataset contained 52,127 instances of center pivots, it was infeasible to examine each with respect to 32 Landsat scenes as an image reference. In some cases existing center pivots went unlabeled . This impacted both the model training, leading to lower performance due to greater within class variability and similarity between the center pivots category and background category,vertical grow shelf and less certain evaluation metrics, since the results were evaluated on an independent test subset of the reference dataset. Furthermore, center pivots as a detection target represent some of the most visually distinct agricultural features, and therefore it is expected that these models would perform more poorly on small agricultural fields that do not conform to such a consistent shape and range of sizes. Finally, the reference dataset for Nebraska used a broad interpretation of center pivots to include center pivots in multiple stages of cultivation . This led to a considerable amount of within class variability during training which impacted model performance. This limitation could be handled better by assigning more specific semantic categories to the center pivot labels based on greenness indices, in order to distinguish between different developmental stages. Or, the model produced from the original dataset could be applied for multiple dates throughout the seasons and results could be merged based on detection confidence in order to map pivots when they are at their most discernible, i.e. cultivated.In the original CSA model, members support a farm by paying in advance, and in return they receive a share of the farm’s produce; members also share in production risks, such as a low crop harvest following unfavorable weather. An important social invention in industrialized countries, Community Supported Agriculture addresses problems at the nexus of agriculture, environment and society. These include a decreasing proportion of the “food dollar” going to farmers, financial barriers for new farmers, large-scale scares from food borne illness, resource depletion and environmental degradation. Together with farmers markets, farm stands, U-picks and agritourism, CSAs constitute a “civic agriculture” that is re-embedding agricultural production in more sustainable social and ecological relationships, maintaining economic viability for small- and medium-scale farmers and fulfilling the non–farm-based population’s increasing desire to reconnect with their food . The first two CSAs in the United States formed in the mid-1980s on the East Coast . By 1994, there were 450 CSAs nationally , and by 2004 the number had nearly quadrupled to 1,700 . There were an estimated 3,637 CSAs in the United States by 2009 . This rapid expansion left us knowing little about CSA farmers and farms and raised questions about their social, economic and environmental characteristics. Knowing these features of CSAs would allow for more-precise policy interventions to support and extend these kinds of operations, and could inform more in-depth analyses, in addition to giving farmers and the public a better understanding of them.We conducted a study of CSAs in 25 counties in California’s Central Valley and its surrounding foothills — from Tehama in the north to Kern in the south, and Contra Costa in the west to Tuolomne to the east. The valley’s Mediterranean climate, combined with its irrigation infrastructure, fertile soil, early agrarian capitalism and technological innovation have made it world renowned for agricultural production . In addition to its agricultural focus, we chose this region because we wanted to learn about how CSAs were adapting to the unique context of the Central Valley. Many of the region’s social characteristics — relatively low incomes, high unemployment rates and conservative politics — differ from those in other regions where CSAs are popular, such as the greater San Francisco Bay Area and Santa Cruz . An initial list was compiled from seven websites that list CSAs in the state: Biodynamic Farming and Gardening Association, California Certified Organic Farmers, Community Alliance with Family Farmers, Eat Well Guide, LocalHarvest, the Robyn Van En Center and Rodale Institute. Of the 276 CSAs that we found, 101 were in our study area. We contacted them by e-mail and phone. It became evident that some did not correspond, even loosely, to the definition of a CSA in which members share risks with the farm and pay in advance for a full season of shares. As the study progressed, we revised our definition of a CSA to mean an operation that is farm based and makes regular direct sales of local farm goods to member households. We removed some CSAs that did not meet the revised definition, based on operation descriptions on their websites or details provided by phone or e-mail if a website was not available. Some interviews that we had already completed could not be used for our analysis because the operations did not meet the revised definition. As the study progressed, we augmented the initial list with snowball sampling by asking participating farmers about other CSAs, which added 21 CSAs. Of these 122 farms, 28 were no longer operating as CSAs, seven turned out to be CSA contributors without primary responsibility for shares and 13 did not meet our revised CSA definition. We called the 28 CSAs no longer operating “ghost CSAs” because of their continued presence on online lists.

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The federal government has had an agricultural guest worker program for most of the past century

In response to the plaintiff’s standing argument, the defendant food company or grocery store can reply that the WTO Agreements specifically contemplate allowing compensation and retaliation for injuries inflicted upon private commercial interests. Defendant would argue that it is only presenting a defense based on explicit WTO language. Moreover, defendant would argue that, if the doctrine of standing blocks the raising of the WTO-based defenses, it would face administrative actions or consumer damages under a law that very likely violates either the SPS Agreement or the TBT Agreement. Defendants would argue that such a result is unjust and legally indefensible because nobody should be held legally accountable under a law that may be itself demonstrably invalid.The hired workers who do most of California’s farm work are primarily employed to produce crops, which accounted for almost three-fourths of the state’s $37 billion in farm sales in 2010. Within crops, most hired workers are employed to produce the so-called FVH commodities, fruits and nuts , vegetables and melons , and horticultural specialties such as flowers and nursery products that generated almost 90 percent of California’s crop sales and two-thirds of the state’s total farm sales. California requires all employers with $100 or more in quarterly wages to pay unemployment insurance taxes on worker earnings. Over the past two decades, UI data show stable average annual agricultural employment of just under 400,000, but crop support employment, primarily employees of farm labor contractors, recently surpassed the number of workers hired directly by crop employers . Average annual employment is a measure of the number of year-round job slots, not the number of farm workers, because of seasonal peaks and worker turnover. For example, agricultural employment averaged 389,500 in 2011, with a peak of 452,800 in June and a second peak of 449,600 in September; the low was 311,700 in January. The employment peak-trough ratio was almost 1.5,nft vertical farming meaning that 50 percent more workers were employed in June than in January. According to Khan et al.,an analysis of individual social security numbers reported by agricultural establishments in the 1990s found almost three individuals for each year-round farm job, suggesting 1.1 million unique farm workers.

Even though the analysis removed SSNs reported by 50 or more employers in one year and jobs that generated less than $1 or more than $75,000 in quarterly earnings, some observers believe that UI data may exaggerate the number of unique farm workers. If the three-to-one ratio of workers to year-round jobs is correct, there are about 1.1 million farm workers; at two-to-one, there are almost 800,000. For most workers, farm work is a job rather than a career. A conservative estimate is that at least 10 percent of farm workers leave the farm work force each year, so that farmers rely on an influx of new entrants to replace those who leave for non-farm jobs or return to Mexico. If California has a million unique farm workers, this means 100,000 newcomers are required to replace those who exit; for the 2.5 million unique hired farm workers across the United States, 250,000 newcomers a year are required. Mexico-U.S. migration has slowed, providing fewer new entrants to replace farm workers who exit. About 10 percent of the people born in Mexico have moved to the United States, some 12 million, and 30 percent of the 40 million foreign-born U.S. residents were born in Mexico, making Mexico the largest source of U.S. immigrants. Mexican born U.S. residents have spread throughout the United States, but almost 60 percent live in California and Texas. Between 2005 and 2010, the Pew Hispanic Center estimated zero net Mexico-U.S. migration; that is, almost 1.4 million Mexicans moved to the United States over this five-year period and 1.4 million Mexicans moved to Mexico . Many of those who returned to Mexico were deported, and some took their U.S.-born children with them. There are still Mexicans moving to the United States, but returns to Mexico outnumbered new Mexican entrants to the United States by four to one in recent years. Reasons for the slowdown in Mexico-US migration include high U.S. unemployment, border violence and more fences and agents that raise smuggling costs and risks, and improving conditions in Mexico. California agriculture is feeling the effects of slowing Mexico-U.S. migration because of its revolving-door labor market, which relies on newcomers from abroad to replace workers who exit.

If Mexico-U.S. migration does not increase with the expected U.S. economic recovery, where will California farmers get replacement farm workers? The answer depends on immigration policy: will currently unauthorized farm workers be legalized and required to continue to work in agriculture or will replacement workers be guest workers from abroad? The current H-2A program certified 7,200 U.S. farmers to fill over 90,000 farm jobs with guest workers in FY11, including 250 California farmers to fill 3,000 farm jobs. The H-2A program requires farm employers to try to recruit U.S. workers under federal and state supervision, offer guest workers free housing, and pay them a super minimum wage called the Adverse Effect Wage Rate of $10.24 an hour in California in 2012. California farm employers assert that the H-2A program is too cumbersome and bureaucratic because of the state’s diverse and perishable crops. They urge three major employer-friendly changes. First, employers would like attestation to replace certification, meaning that employers would attest or assert that they tried and failed to recruit U.S. workers while offering appropriate wages, and their attestations would allow them to recruit and employ guest workers. Second, farm employers would like to offer housing vouchers worth $200 to $300 a month instead of free housing, adding $1 to $2 an hour to current wages. Third, to help offset the cost of housing vouchers, the AEWR would be rolled back by $1 or more an hour and studied to determine how well it achieves its goal of protecting U.S. workers from any wage-depressing effects of guest workers. The Clinton Administration blocked efforts to enact these employer-friendly changes to the H-2A program during the 1990s. However, in December 2000, farm employer and worker advocates negotiated the Agricultural Job Opportunity Benefits and Security Act , which would legalize currently unauthorized farm workers and make these three employer-friendly changes to the H-2A program. They hoped that Congress would enact AgJOBS in the waning days of the Clinton Administration, but AgJOBS was blocked by those opposed to “amnesty.” Most farm employers and worker advocates continue to urge enactment of the 12-year old AgJOBS bill.

Senator Dianne Feinstein introduced a version in 2009 that would grant Blue Card temporary legal status to up to 1.35 million unauthorized foreigners who did at least 150 days or 863 hours of farm work in the 24-month period ending December 31, 2008. If Blue Card holders continued to do farm work over the next three to five years, they and their families could become legal immigrants. AgJOBS’s employer-friendly changes to the H-2A program include the Big 3 desired by farm employers: attestation, housing vouchers, and a reduced AEWR. Farm employers and workers today are in a period of uncertainty. There is unlikely to be any immigration reform that includes earned legalization or amnesty in 2012, and legalization may face continued obstacles in a Republican controlled House in the next Congress. However, employer-friendly changes to the H-2A program or a new guest worker program could accompany a federal mandate that all employers use E-Verify to check the legal status of newly hired workers. Representatives who favor mandatory E-Verify have proposed new guest worker programs administered by USDA rather than the Department of Labor that include attestation, reduced or no housing requirements,indoor vertical farming and lower minimum wages without legalizing currently unauthorized workers. Just months after tainted cantaloupes caused the deadliest U.S. outbreak of food borne illness in a century, 270 consumers were sickened and three killed this summer by cantaloupes carrying Salmonella. The FDA has linked the contaminated cantaloupe to unsanitary conditions at Chamberlain Farms of Owensville, Indiana. Like the Holly, Colorado farm implicated in the deadly Listeria outbreak in cantaloupes last fall, Chamberlain is a relatively small grower in a region with no obvious comparative advantage in cantaloupe production. Moreover, climatic conditions predispose the area to bacterial contamination. Recent outbreaks of food borne illness and an increase in infections from most pathogens monitored by the CDC in 2011 have occurred alongside growth in consumer demand for locally sourced produce that has led even multinational discount retailers like Walmart to seek out local suppliers. Growing reliance on local production sacrifices the benefits of specialization according to comparative advantage and scale economies that more concentrated production affords.

In addition, dependency on smaller, local producers may come at the expense of food safety.Voluntary and regulation-induced investments in food safety, including process control, inspection, and traceability, often include fixed-cost components and “lumpiness” that gives an advantage to larger firms, which can spread the costs over larger quantities of production. High fixed costs of food safety can cause small firms to exit. Food safety processes and technologies appear to impose higher costs per unit of output on small operations. This fear was articulated during the 1998 implementation of pathogen reduction and Hazard Analysis and Control Point systems in the red meat- and poultry-slaughter industries. More recently, small farms successfully lobbied for exceptions to similar rules for produce growers under the Food Security Modernization Act, citing concern that the higher cost of regulatory compliance to small operations would force local and direct to-consumer operations to exit. Though there are few empirical analyses of the cost of food safety investments among produce growers, studies of the 1998 regulatory changes in the meat packing industry have documented a considerable disadvantage to small firms. An ex ante analysis of the planned regulations, for instance, estimated the costs to small beef slaughter plants as a share of the value of shipments would be 100 times greater than the cost to large firms. Many of the costs associated with the regulations, including monitoring, record keeping, and sanitation equipment are fixed, at least over a range of outputs, so that the cost per unit of production decreases in the scale of production. Moreover, the Economic Research Service of the USDA estimated that smaller plants had variable and fixed costs of regulatory compliance that were three and six times higher, respectively, than those of larger firms. Output inspection costs, for instance, may vary proportionally to output over a range of outputs, so that inspection technology exhibits constant returns to scale and does not disadvantage small firms. Plant inspection costs, however, are largely fixed, so that average plant inspection costs decline with output, favoring larger operations. Large firms, for instance, may find it optimal to operate their own testing laboratories and, thus, exploit increasing returns to scale. Small firms, however, are likely to contract testing out to third parties and, consequently, bear a relatively high cost per unit output. An ERS survey of the meat- and poultry-processing sectors concluded that testing costs, in particular, were higher for smaller firms. Likewise, the cost of regulatory enforcement exhibits increasing returns to scale because of the fixed costs associated with inspection of a firm’s facilities and procedures. These include travel costs and testing that increase in the number of facilities but not in the level of output. Thus, given a fixed enforcement budget, a decline in industry concentration resulting from demands for local production is likely to lower the level of safety.Food producers face food safety costs imposed by regulation, but may also make voluntary investments in pathogen control in order to meet contractual demands of buyers, reduce the risk of crop losses, avoid liability judgments, and protect brand value. Optimal private investment in exante food safety mechanisms equates the marginal expected reduction in damage and liability costs to the marginal cost of private risk reduction. This condition is likely to induce disproportionately greater investments from large operations than from small ones. The USDA estimated that as much as two-thirds of the reduction in Salmonella contamination in meat and poultry facilities is attributable to private voluntary investments rather than regulation-induced effort. When outbreaks of food borne illness occur, those firms responsible for contamination are typically obligated to recall all or some of the affected product.

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Black pepper can be planted in tandem with shade trees

Kerala is the second largest coffee producing region in the country, responsible for 23% of India’s total, and the district of Wayanad produces 90% of all the coffee grown in Kerala.Wayanad is located in the Western Ghats mountain range, along the southwest coast of the Indian subcontinent, and receives a majority of its precipitation from the southwest monsoon period.Thus it is an effective region in which to examine the potential effects of climate change, and alterations in the South Indian monsoon, on coffee production in India. In the following analysis, current trends in the local climatic conditions of Wayanad are examined and discussed as they pertain to coffee production. The roles of global warming and large-scale climate variability modes such as El Niño Southern Oscillation and Indian Ocean Dipole as influential factors in Wayanad’s local trends are investigated. Data suggests strong weather trends during the month of June, which may be significant for the production cycle of the coffee plant. Other factors examined include early Spring precipitation, which triggers the blossoming of the coffee flower, and November precipitation, which provides the moisture that sustains coffee plants during the immediately proceeding dry season. As seen above, daily maximum temperatures were found to be increasing over time in the months of June, July, and August. Daily maximum temperature was found to be decreasing over time by a negligible amount in September. This temperature increase could be attributed to the global-scale warming trend, but could also be due to other local factors, and further study would be necessary to determine this conclusively. Monthly precipitation values were found to be decreasing significantly in June,tower garden and no significant precipitation trends were found in other monsoon months. This implies a delayed onset of the monsoon season, which is consistent with other findings. Another metric for monsoon onset, which is the first day of June with a recorded precipitation of over 25mm, also aligns with this finding.

Total precipitation for the entirety of the monsoon period also displays a decreasing trend, suggesting an overall weakening of the monsoon that is also consistent with other findings for the region.Thus it can be concluded that the local climate of Wayanad is experiencing changes in the monsoon that are consistent with the broader region. While canopy trees can reduce annual yields of coffee and other crops, integrated multi-species systems are very likely to be more resilient to climate change in the near future. According to Prasanth one of the farmers interviewed during this study, while a full sun monoculture of robusta coffee in Kerala will yield approximately 900 kg / acre, a shade-grown system will yield only 700 kg / acre. However, inter cropping with canopy trees provides cooler air temperatures, which is likely to protect under story crops such as coffee from future heat waves. Shade trees have been shown to decrease leaf temperature of coffee plants by up to 4˚C in sub-optimally hot conditions . In addition, shade creates cooler soil temperatures, which increases microbial and mycorrhizal activity. This in turn increases uptake of nutrients in crop plants, which promotes better plant health and can increase yields . A robust canopy layer has also been shown to decrease both soil evaporation and crop transpiration, which can substantially increase soil moisture available for crops to use during photosynthesis . Canopy layers also reduce precipitation velocity, which reduces erosion and crop damage . If adopted at a large enough scale, reinstating a living canopy layer across Kerala is likely to be the state’s single most effective strategy for agricultural resilience in the face of climate change. Shade trees can also provide supplemental income to buffer or even surpass coffee yield losses, if commodity-producing trees are used. Shade trees well suited for coffee agroforestry in Kerala include rubber,mango , areca nut , teak , silver oak , and jackfruit . It has been recently suggested that mango trees are particularly well suited for growth alongside coffee due to their tendency to draw water from the deeper subsoil level, thereby reducing competition for water, as coffee draws the majority of its water from the topsoil layer .

Studies indicate that optimal shade levels for coffee production are approximately 20-40%, however this can vary based on local ecological and economic conditions . If species are selected carefully based on local conditions, shade trees can provide substantial economic synergies to coffee agroforestry systems. In conjunction with a canopy layer, additional crop species can be utilized to increase farm productivity-per-acre.The pepper plant, which is a vine, can be made to grow using the shade trees for support. Pepper also has a different harvest season than coffee, which is a useful synergy from a logistic perspective. An under story of turmeric, ginger, and other spices can be planted as well, or alternatively an under story of nitrogen-fixing cover crops to increase soil fertility.Building soil health, which for these purposes can be translated to increasing soil carbon content, water holding capacity, and microbial richness, can significantly buffer a farmer’s vulnerability to economic and environmental uncertainties. An increasing number of studies suggest that the single best metric for long-term soil fertility is carbon content. While fertilization with synthetic nitrogen, phosphorous and potassium compounds can lead to short-term yield boosts, building organic content in soil vastly improves long-term yields by increasing water retention, reducing erosion, and building healthy microbial ecosystems. . A review paper encompassing a multitude of crop species in agricultural systems across the globe concluded that soils with higher carbon content consistently produced higher yields, especially in dry years . Soil carbon content can be increased by mulching, composting, or cover cropping. All of these practices require little to no technology and can be implemented at very low cost to the farmer. Organic matter can be added to soils in the form of manure, crop residues, or waste pulp from coffee processing. In Kerala, these products are often dried and burned for use as cooking fuel. However, some products, most notably coffee pulp, often goes to waste. Art Donnelly, founder of the Estufa Finca project, has invented a low-cost cooking stove that uses a dualchambered, oxygen-restricted combustion system to burn organic coffee waste products in a way that produces both a clean-burning cooking flame and carbonrich charcoal, or biochar. This charcoal can be applied to fields directly, and a number of studies find this application to boost both microbial activity and crop yields . The biochar can also be fermented to produce a microberich fertilizer. This process is also low-cost and low-tech. A 2017 account of a Costa Rican farmer named Echeverría describes the process: “This low-tech microbial inoculant employs a sourdough-like starter to brew up a bio-fertilizer rich in mycorrhyzal fungi.

To get it going, Echeverría collects a bag full of organic matter from the forest floor, making sure that it contains white fungal hyphae. Next, she removes all the big leaves and mixes it up with rice bran and molasses. She then covers it with a plastic bag in 60-liter, screw-top containers and lets it ferment for a month and a half.” . An account of Gabriel Umaña, an agent for the Costa Rica Ministry of Agriculture,stacking flower pot tower attests that application of fermented biochar even “synchronizes maturity among coffee bushes. This helps farmers with a major logistical problem, as coffee beans must be sold when ripe.” . Notably, biochar relies on the synthesis of carbon-based plant matter via photosynthesis, and the process of incomplete combustion returns a portion of this carbon to the soil. As such, it is the only known form of energy production that is net carbon negative. In addition to building soil carbon content, employing no-till farming practices, robust mulching or cover cropping, and diverse crop rotations are essential to promoting soil health. Frequent tillage has been shown to result in decreased soil carbon and loss of fertility . Covering the soil surface with mulch, cover crops, or other organic residues has been proven to conserve soil moisture and reduce erosion . Surface mulch from crop residues has been shown to affect crop yields due to its variety of physical, chemical, and biological impacts on soil characteristics . Long-term studies have shown that agricultural practices that combine crop rotations, consistent surface residue, and reduced tillage result in soils with higher levels of carbon and nitrogen . Crop rotation is also associated with increased microbial diversity in soils, which in turn reduces risk of pest outbreaks due to natural biological control mechanisms . Through MSSRF, the State of Kerala’s Ministry of Agriculture already provides stipends to farmers to keep native species of shade trees on their properties. In addition, a separate Tree Stipend Program has recently been developed that engages the private sector. In this program, the government sponsors banks to give financial support to farmers who plant trees. In exchange for the initial investment to plant the trees, the banks then own the assets associated with the value of the tree on the farmer’s property, much like a lien. However, these programs should be provided much more funding. According to Prasanth, a typical stipend to keep a rosewood tree is approximately 700 rupees per year , whereas the profits from cutting the tree and harvesting its timber is closer to 70,000 rupees . As such, the tree stipends should be increased by at least two orders of magnitude in order to be a policy that is effective at retaining participants. In lieu of providing frivolous crop insurance payouts that subsidize bad farming practices, it is recommended to incentivize practices that build topsoil and increase soil carbon content.

During the course of this study, soil carbon content was found to be the single most applicable metric correlated with sustainable and resilient farming practices. Establishing simple incentives based on a single metric reduces organizational costs for the governing body, while simultaneously encouraging a diverse array of carbon-sequestering farming practices that work synergistically to build economic and climate resilient agriculture. Measuring soil carbon content is low-effort and low-cost, and the monitoring efforts that would be required to award financial incentives responsibly are likely to be fairly cost-effective relative to other similar incentive efforts. This policy could be achieved through subsidies, stipends, or other incentive measures. In order for any of these policies to be effective, awareness and education must be brought directly to the farmers. Studies indicate that one of the most effective methods to encourage the adoption of new farming practices is the establishment of full-scale demonstration farms within the agricultural communities . MSSRF’s Botanical Garden presents a fortuitous opportunity in Kerala, as it is already established, staffed, and well known within the community. This would be an excellent location to establish a pilot program for carbon farming workshops, in which information and hands-on lessons about regenerating topsoil and soil carbon can be provided. If successful, these demonstration farms could be replicated in other communities. In an effort to increase consumer awareness of the current state of the coffee commodity market and its effects on smallholder producers, an amended version of the Introduction section of this report is being published and distributed among coffee shops in the San Diego area, along with a link to the full report. In addition to the article, a list of San Diego coffee shops that implement ethical coffee purchasing practices is included, to promote awareness of socially responsible coffee companies. The publication encourages consumers to support importers with business models that don’t undermine the livelihoods of producers and the ecosystems that sustain them. Engaging consumers in a way that changes their preferences is one of the most effective ways to enact change in market economies.In addition to consumer outreach for the sake of increasing awareness, a fundraising effort was established to support the M. S. Swaminathan Research Fund and the work they are doing to improve conditions for farmers in Kerala. During the month of July 2019, a portion of the proceeds from merchandise sold at Bird Rock Coffee Roasters will go to MSSRF. This partnership represents an easily replicable business model for any coffee business that wants to connect consumers with producers in a way that builds resilience and ethical practices into their supply chain.

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Improving agricultural production and profits is an important component of poverty alleviation

Randomized evaluations of the agronomic productivity gains from new crops or agricultural techniques have been common in the agricultural field for many years. More recent is an approach to agriculture that aims to conduct ‘effectiveness’ trials, incorporating real-world issues of access and adoption among smallholder farmers, rather than the idealized ‘efficacy’ trials produced using experimental test plots. Tackling the impacts of agricultural interventions outside of the test plot introduces issues at the heart of economics, such as transaction costs, social interactions, marketing, finance, and contracting as we think carefully about the decision to adopt. Thinking of the smallholder farm as a small business, this decision should be driven by profitability. The core contribution of RCTs is their ability to clearly trace causality between the constraints to agricultural technology adoption, adoption itself and final outcomes . Randomized experimental evaluations allow researchers to isolate the causal impact of a program from other confounding factors—such as price, weather, or access to credit—which are simultaneously changing over time and across regions 2 . Carefully designed experiments allow us to identify whether specific constraints to adoption are binding, and measure the impacts of a technology when adopted in farmers’ actual fields. These evaluations speak to the effectiveness of specific approaches to achieving agricultural technology adoption for improved smallholder productivity and welfare.The Agricultural Technology Adoption Initiative was founded in 2009 to increase the quantity and quality of experimental evidence in developing-country agriculture. ATAI aims to serve as a mechanism to generate, aggregate,livestock fodder system and summarize research for policy outreach on the adoption of agricultural innovations by smallholders in Sub-Saharan Africa and South Asia.

ATAI exclusively funds randomized controlled trials, and pilot work that lays the groundwork for future RCTs, and was organized intellectually around understanding how a set of specific constraints held back technology adoption. Because of this methodological focus, the resulting evidence is primarily on interventions targeted at the individual or household level, although we also report on studies in areas such as input and output markets that attempt to drive outcomes at more aggregated levels. Even within this domain, we have a distribution of studies that is purposive, driven by the questions asked by our affiliated investigators, and by the technical feasibility of running randomized trials. We use the structure of the ATAI constraints to adoption to help summarize the experimental evidence, aggregating individual, internally valid studies around these common themes. This produces an evidence base that is far from comprehensive in terms of the important issues in agricultural development, but is broader than would have been produced by a more tightly structured replication-focused research initiative and does provide a relatively clear guide to what makes specific interventions attractive in terms of evidence-based funding. Throughout the world, 63% of those living under $1.25 per day are working in agriculture .Ligon and Sadoulet show the importance of economic growth in the agriculture sector for the livelihoods of the poorest households: a one percent growth in GDP that originates from agriculture correlates with a 5.6 percentage point increase in expenditures among the poorest decile of the population, a 4.45 percentage point increase for the bottom 30%, while “growth from non-agriculture sectors does not appear to have a significant effect on expenditure growth for the poorest 50%.” The Green Revolution of the 1960s saw the spread of agricultural technologies to less industrialized nations, and large agricultural productivity gains particularly in East Asia.

Yet technological innovations have not similarly spread to transform agricultural productivity in Sub-Saharan Africa and parts of South Asia as evident in the lagging adoption of modern varieties and a persistent yield gap between regions. Many African countries have rising private sectors developing agricultural technologies, and research and implementation groups including the CGIAR centers and AGRA continue to develop improved inputs and interventions designed to improve the resilience, profits, and nutrition of African smallholders in particular. Yet these innovations do not appear to have translated into meaningful improvements in yields at the macro-level. FAOSTAT data shows a large gap between low per hectare cereal yields in Africa and South Asia which are on average roughly one third of the per hectare yields in East Asia and OECD countries. Sub-Saharan Africa is particularly lagging behind. In South Asia, land use for cereal production has increased 20% while yields have tripled. In Sub-Saharan Africa, land use for cereal production has more than doubled, while yields have increased by just 80% . The macro picture of fertilizer use over time similarly looks unchanged, with low and stagnant use of fertilizers in mainly rainfed areas like SubSaharan Africa. Fertilizer consumption remains extremely low in SubSaharan Africa compared to other regions. Roughly 16 kilos of fertilizer are used per hectare in SubSaharan Africa, and among all developing countries the average is 26.75 kg/hectare. This figure is much higher in other regions: 344 kg/hectare in East Asia/Pacific, and 159 kg/hectare in South Asia.This clearly demonstrates that the status quo of agricultural production, particularly in Sub-Saharan Africa, remains far below the technological frontier, suggesting missed potential in terms of yields, income, and welfare improvements to food security and nutrition. The specific reasons behind lagging adoption of productivity enhancing technological innovations and persistent yield gaps in rainfed Sub-Saharan Africa and South Asia relative to the rest of the world have been a puzzle in need of policy solutions. Field experiments help us move beyond test plots to explain the continuing puzzle of low technology adoption by smallholder farmers in rainfed areas where agriculture is performing well below the technological frontier. Focusing at the micro-economic level of this challenge, we focus on technology adoption as an outcome that inherently requires smallholder farmers to change their practices.

Behavior changes can include, for example, the adoption of resilient and high-yielding crop varieties or a shift to high-value crops, the purchase and application of complementary inputs such as fertilizers, and the adjustment of farm labor allocated toward specific agronomic practices. Many smallholder farmers face barriers to adopting effective agricultural technologies. These constraints to adoption may be driven by standard economic factors , or may be behavioral . Standard economic explanations consider smallholder farmers as economic agents, building from the conception that “in a well-functioning economy where markets perfectly capture all costs and benefits, and individuals are fully informed and unconstrained, farmers will adopt a technology if they make a profit from adopting it” . This is an important distinction from a world where farmers focus their efforts to maximize their productivity, for example, their crop yields, given increased yields do not necessarily lead to improved welfare. Profitability can be limited by input costs, credit constraints, and market access. Information and labor constraints are also relevant — how well do farmers understand the properties of new technologies, in the absence of opportunities to experiment? What are the additional labor requirements for the use of these new technologies, and how do farmers value their time in input decisions? Jack reviews in detail other dimensions that mediate whether certain technologies “meet the expected profitability condition” for specific farmers. This varies temporally and spatially . This also varies between and within households,hydroponic nft gully particularly when complementary asset or capital investments are needed, or new technologies challenge individual tastes and preferences. Even where markets are functioning well, accessible and profitable technologies may not be adopted for behavioral reasons, such as risk or uncertainty aversion or procrastination, which challenge decision-making even in the best of circumstances. Smallholders’ decision-making is highly complex and conducted in risky and low resource environments. Farmers make interconnected choices over long time frames that are characterized by risks and uncertainty. One of many choices is among a range of potential inputs to production , in contexts with highly variable land, wide ranging and seasonal climatic variation that is growing increasingly extreme given climate change, and unpredictable shocks to their livelihood. New technologies may change the risk or payoff profiles of farming in ways that require us to incorporate other social science insights, for example expected utility theory and behavioral economics, in order to understand perceived benefits at the farmer level. Motivated by addressing the constraints hindering the adoption of new agricultural technologies, ATAI has worked to fund and structure the experimental evidence base across seven primary market inefficiencies that constrain adoption. These are credit5 , risk, information, input and output markets, labor and land market inefficiencies, as well as externalities . These may operate through supply or demand channels, for example by limiting the availability of technologies, information, or financing, and/or dampening demand by lowering expected profits. Lessons from psychology and behavioral economics are considered where they are particularly relevant. Jack motivates the focus on constraints to adoption, rather than specific technologies, as a framework that helps identify effective strategies to address common inefficiencies and constraints in order to encourage the adoption and use of more than one technology. ATAI uses this conceptual framework of seven constraints to drive its research competitions.

Randomized evaluations are selected for ATAI funding based not only on methodological rigor, logistical viability, and innovation, but also on their potential for both a significant contribution to public knowledge, and practical influence and scalability in related contexts. Field experiments require, by their very nature, durable partnerships with real-world implementation groups that are working directly with smallholder farmers in order to randomize interventions and deliver credible results. Partner organizations may work as agro-dealers, contract farming groups, extension agents, financial service providers, technology developers, or otherwise. ATAI views more favorably studies that evaluate questions of key importance to large-scale program and policy partners, particularly those that are difficult to address without causal evidence, and those that have received less research attention to date. To meet these criteria, technologies under investigation are those where there is credible field data signaling that adoption would prove neither distasteful nor ineffective in target farmers’ contexts, and that the take-up and use of a technology is likely to prove utility-enhancing, profitable, and welfare-increasing for smallholder farmers and others along agricultural value chains. For such promising under-adopted technologies, ATAI funds social science field experiments to provide evidence on the strategies that work in helping farmers adopt, and ultimately benefit from, these technologies. In the sections that follow, we summarize particular components of the evidence base given the accumulation of ATAI-generated experimental evidence in four areas: credit and savings, risk, information, and input and output market inefficiencies. This does not imply that the latter three constraints to adoption, i.e. externalities and land and labor markets, are excluded from this chapter because they do not bind or do not deserve further investigation. These topics are not covered here simply because there is less rigorous micro-evidence given the difficulty of examining them through the lens of RCTs6 . This is not intended to be an exhaustive review. ATAI-funded studies are often presented in greater detail given our familiarity with their contributions. Each section begins by motivating the specific constraint to Agricultural income streams are characterized by large cash inflows once or twice a year that do not align well with specific times when farmers need access to capital to either make agricultural investments or, for example, pay school fees. If there is limited access to credit in an area, farmers may not have cash on hand to make agricultural productivity investments unless they are able to save, or can afford the potentially high interest rates of informal lending. However, saving can be difficult for farmers given their limited resources, a variety of demands on their money, and the seasonal cycle of production and prices of their agricultural production. Credit and saving products could help farmers make investments in inputs and other technologies by making cash available when needed. Yet many developing countries, and particularly rural areas, have limited access to formal financial services that could provide this liquidity. Credit constraints have been reflected in farmers self-reports , and are associated with less use of productive inputs like high-yielding varieties . On the supply side, formal financial service providers are often unwilling or unable to serve smallholders.

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Each enclosure contained an array of six rectangular PIT antennas arranged in the same orientation

Given that the proposed California threshold is 0%, a scenario in which both GM and non-GM products are offered side-by-side in the market seems unlikely. Some non-GM products may remain unlabeled if food companies are able to find substituting ingredients that are not at any risk of containing GM. But certified non-GM products will mostly disappear. As U.S. corn, canola, and soybean production uses primarily GM varieties, Prop 37 labeling standards will force change in the composition of retail products offered. As the initiative applies only to California, it may not be profitable to undergo a reduction of GM inputs for one state. If this is the case, then the vast majority of food products that are not completely GM-free will bear the new label. As a consequence, a fraction of consumers now wary of the label may shift their consumption towards organic. Such a transition implies potential gains for organic growers but potential losses for conventional growers. Today, a move towards “non-GM” or “naturally grown” labels is underway, especially with natural grocers. Some organic corn and soybean growers in the U.S. have converted back to conventional with non-GM seeds, thereby saving labor and other costs, while still getting similar price premia. The “non-GM” or “natural” products are the closest competition for organic products now; but they will be reduced or eliminated with Prop 37 due to forced relabeling and the prohibition of terms such as “naturally grown” on food labels . Table 4 outlines the likely impacts of Prop 37 on various categories of food and beverages.conducted a series of field studies during 2012-2017. To test fish and food web responses within different land-management scenarios, we conducted our project on standard rice and winter wheat fields, adjacent fallow lands,stackable planters and rice fields with different harvest practices or other experimental modifications. This work yielded several publications that provided insight into habitat conditions in flooded rice fields for fish and invertebrates . The focus of our effort was on rearing habitat for young Chinook Salmon, but this work may also be relevant to other native fishes.

The goal of this paper is to summarize the key lessons learned from 6 years of research on the feasibility of using farm fields as rearing habitat for juvenile Chinook Salmon in the Yolo Bypass and other Central Valley locations. Our hope is that our summary will provide guidance to future researchers, as well as inform managers as they evaluate potential management approaches. An important caveat is that our studies were not intended as a proof of concept for any specific management actions. Rather, our research was intended to examine some of the attributes that could reduce limitations to rearing conditions identified in early research, and gain insight into some of the key considerations for potential future agricultural floodplain management. A second major caveat is that we had to rely on juvenile hatchery Chinook Salmon as a surrogate for wild Chinook Salmon, our ultimate target for habitat restoration. We recognize that there are several potential differences in the behavior of hatchery and wild Chinook Salmon . However, hatchery salmon were the only feasible alternative in this case since downstream migrating wild juvenile Chinook Salmon were mostly cut off from the Yolo Bypass because of extreme drought conditions. Nonetheless, hatchery salmon have been used successfully as a research tool in many types of ecological studies, so many of the lessons learned here should have at least some relevance to wild Sacramento River Chinook Salmon. Finally, our project was separate from a number of other fish management research projects in agricultural parcels, such as current efforts to investigate whether invertebrates grown on flooded rice fields can be used as a food subsidy for adjacent river channels . The Yolo Bypass is a 24,000-ha, partially leveed flood basin that is used to safely convey floodwaters away from Sacramento Valley communities . The Yolo Bypass contains a suite of habitats including agricultural lands, managed wetlands, upland habitat, and perennial ponds and channels, with broad open-water tidal wetlands at its downstream end where it joins the Sacramento-San Joaquin Delta .

The basin receives seasonal inflow from the Sacramento River, Colusa Basin , Cache Creek, and Putah Creek, as well as substantial perennial tidal flow from the San Francisco Estuary via the lower Sacramento River at the downstream end of the floodplain . The Yolo Bypass floods to various degrees in approximately 80% of water years, but inundation events are often relatively short and sometimes driven entirely by inflow from the west-side tributaries. The most substantial flow events come from the Sacramento River, which enters the Yolo Bypass via Fremont Weir and Sacramento Weir. However, in drought periods, such as during 2012-2015, there is little or no flooding.For each year, we evaluated water quality , food web responses , and fish growth and condition . Water temperature in fields was recorded continuously at 10- to 15-minute intervals with Onset HOBO® loggers, and a suite of other water-quality parameters was measured and recorded using handheld and continuously installed multi-parameter sondes. We included plankton sampling with the broad goal of characterizing the communities and densities of phytoplankton and zooplankton in the study fields. Because long-term monitoring of the Yolo Bypass includes weekly plankton sampling in both the perennial Yolo Bypass channel of the Toe Drain and the Sacramento River, we could compare our experimental fields to productivity across habitats. Because the study fields were shallow compared to canal and riverine channel environments, sampling methods had to be slightly modified compared to the Toe Drain and Sacramento River. As a result, we used hand-tosses of a smaller 30-cm zooplankton net , recording the length of the toss, and the relative percent of the net mouth that was submerged during net retrieval. Detailed methods for zooplankton sampling are described in Corline et al. . Fish used in the experiments were primarily fall-run Chinook Salmon parr obtained from Feather River Fish Hatchery; however, small numbers of wild Sacramento River Chinook Salmon were also studied in 2013 and 2013, 2015, and 2016 . The majority of the study fish were free swimming throughout the flooded fields, but mesh cages were also used as a tool to compare hatchery salmon growth and survival across substrates in 2012 or habitats in 2016 and 2017. The initial study year was a pilot effort to evaluate whether managed flooding of a rice field could provide suitable habitat for juvenile salmon rearing, and to assess associated growth and survival. A single 2-ha field contained a patchwork of four agricultural substrate types, including disced , short rice stubble , high rice stubble , and fallow vegetation. Approximately 10,200 juvenile salmon were released in the field, with a subset implanted with passive integrated transponder tags, so individuals could be identified, and individual growth rates could be measured. Twenty PIT-tagged fish were also released in each of eight enclosures placed over patches of the different substrate types, to determine if growth rates differed .Substrates in flooded rice fields differ from those that juvenile salmon may encounter in natural floodplains or riverine systems. Thus, the goal of the second study year was to investigate whether juvenile salmon had differential growth and survival rates across agricultural substrates,stacking pots and whether they would preferentially use a specific substrate type when given a choice. Our logic was that understanding these responses could provide insight into whether some agricultural practices provide more suitable salmon rearing conditions than others.

To compare growth and survival rates across rice stubble, disced, and fallow substrates, we created a series of nine 0.8-ha experimental fields with individual inlets and outlets, with three replicates of each substrate . We placed approximately 4,600 hatchery origin juvenile Chinook Salmon in each field for 40 days and measured weekly during the study period to estimate average growth rates. To examine substrate preference, we used PIT-tag technology to track individual fish in two large circular enclosures . In addition to examining the potential for preference among agricultural substrates, this study also investigated whether newer and smaller PIT tags were viable for detecting juvenile salmon movements in these habitats. One enclosure included three habitat treatments , and the other served as a comparison with only the disced treatment.Fish remained in the enclosures for 14 days, during which occupancy data were collected. Detailed methods can be found in Conrad et al. .As an engineered floodplain, the Yolo Bypass is designed to drain efficiently. During moderate inundation events, availability of floodplain habitat can be brief—persisting for a week or less. In 2016, our focus was to test the feasibility of using agricultural infrastructure to extend the duration of a small to moderate flood event, increasing the length of time flooded habitat was available to fish. We called this idea “flood extension.” We planned similar studies in other study years, but extreme weather events prevented implementation . Landowner partners in the Yolo Bypass at Knaggs Ranch, Conaway Ranch, and Swanston Ranch agreed to maintain shallow inundation for 3 to 4 weeks in a designated experimental field after a natural flood event. At Knaggs Ranch, the landowner made modest to extensive modifications to the drainage infrastructure to allow more control over the drainage rate from the inundated field to the Toe Drain. At Swanston and Conaway ranches, inundation was maintained with flash boards, which could be removed once it was time to drain the field. During the first week of flood extension, we held stocked hatchery salmon and any entrained natural-origin salmon, allowing us to estimate growth and survival rates upon drainage. Thereafter, we allowed salmon to leave fields if they chose to do so. We outfitted field drains with a plastic mesh live-car trap, where we captured and measured emigrating individuals before they proceeded downstream. In 2016, the attempt to test a “flood extension” concept was unsuccessful because inundation occurred late in the season, resulting in unsuitably warm water temperatures for juvenile salmon in our experimental fields. We therefore made a second attempt to conduct a flood extension pilot in 2017 at Knaggs Ranch, Conaway Ranch, and Swanston Ranch, and at a new site in the Yolo Bypass Wildlife Area located south of Interstate 80 between the cities of Davis and Sacramento . Field infrastructure was identical to 2016, with the YBWA utilizing flash boards to hold water in similar fashion to Conaway and Swanston ranches. As we describe below, high flows made it infeasible to complete the flood extension work, although we were still able to conduct water-quality and food-web sampling, along with the use of experimental cages to evaluate salmon growth comparatively across experimental sites.Previous research has shown that inundated Yolo Bypass floodplain habitat typically has substantially higher densities of phytoplankton, zooplankton, and drift invertebrates than the adjacent Sacramento River across a suite of water year types . Our studies consistently showed that managed inundation of agricultural fields supported statistically higher levels of phytoplankton and invertebrates than the Sacramento River . Also notable was that phytoplankton and zooplankton densities in our flooded experimental fields in Yolo Bypass were higher than those measured during river inundated flood events and in the Toe Drain, a perennial tidal channel . In addition, the invertebrate community in flooded rice fields was completely dominated by zooplankton , particularly Cladocera, whereas drift invertebrates such as Diptera were found in higher concentrations in study sites at Conaway Ranch and Dos Rios. Drift invertebrates are often a more substantial part of the food web in natural flood events in Yolo Bypass . Nonetheless, zooplankton densities can be relatively high in Yolo Bypass during dry seasons and drought years . The specific reasons for these differences include longer residence time and shallower depths in the Yolo Bypass than in adjacent perennial river channels . Water source also may have been important for quantity and composition of invertebrates, including zooplankton, since all the managed flooding work was conducted using water from Knights Landing Ridge Cut, not the Sacramento River.Given the high densities of prey in the flooded fields, along with the low metabolic costs of maintaining position in a relatively low-velocity environment, it is not surprising that growth rates of juvenile salmon were comparatively high . This result was consistent across approaches used: cages, enclosures open to the substrate, and free-swimming fish.

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We turn now to the estimation of the hedonic farmland value equation

This is because, although non-federal surface water is generally not subsidized, it is priced on the basis of historic cost, which is generally far below the current replacement cost of this capital. In summary, the economic effects of climate change on agriculture need to be assessed differently for counties on either side of the 100th meridian, using different variables and different regression equations. Because of data constraints, our analysis here focuses on the effect of climate on farmland values in counties east of the 100th meridian. Our sample comprises approximately 80 percent of the counties and 72 percent of all farmland value in the United States.The dependent variable in our hedonic model is the county average value of land and buildings per acre as reported in the 1982, 1987, 1992, and 1997 Censuses of Agriculture. We have translated all numbers into 1997 dollars using the GDP implicit price deflator to make them comparable. It has been customary in the hedonic literature to use as explanatory variables soil and climatic variables evaluated at the centroid of a county. However, soils and climatic conditions can vary significantly within a county and the estimated value at the centroid might be quite different from what farmers experience. To more accurately reflect this reality, we therefore average the soil characteristics over all the farmland area in a county.The agricultural area is used as a cookie-cutter for our exogenous variables, i.e., we average the climate and soil variables over all farmland areas in a county. All soil variables are taken from STATSGO, a country-wide soil database that aggregates similar soils to polygons.The climatic variables are derived from the PRISM climate grid,10 litre plant pots a small-scale climate history developed by the Spatial Climate Analysis Service at Oregon State University and widely used by professional weather services. It provides the daily minimum and maximum temperature and precipitation averaged over a monthly time-scale for a 2.5 mile x 2.5 mile grid in the coterminous United States, i.e., more than 800,000 grid cells, for the years 1895 to 2003.

The 2.5 mile x 2.5 mile climate polygons are intersected with the agricultural area to derive the agricultural area in each polygon. The climatic variables in a county are simply the area-weighted average of the variables for each climate grid. In this analysis we use the monthly average temperature and precipitation for the 30 years preceding each census year.The existing literature has generally represented the effect of climate on agriculture by using the monthly averages for January, April, July and October.However, from an agronomic perspective, this approach is less than optimal. First, except for winter wheat, most field crops are not in the ground in January; most are planted in April or May and harvested in September or October . Second, plant growth depends on exposure to moisture and heat throughout the growing season, albeit in different ways at different periods in the plant’s life cycle; therefore, including weather variables for April and July, but not May, June, August or September, can produce a distorted representation of how crops respond to ambient weather conditions. The agronomic literature typically represents the effects of temperature on plant growth in terms of cumulative exposure to heat, while recognizing that plant growth is partly nonlinear in temperature. Agronomists postulate that plant growth is linear in temperature only within a certain range, between specific lower and upper thresholds; there is a plateau at the upper threshold beyond which higher temperatures become harmful. This agronomic relationship is captured through the concept of degree days, defined as the sum of degrees above a lower baseline and below an upper threshold during the growing season. Here we follow the formulation of Ritchie and NeSmith and set the lower threshold equal to 8◦C and the upper threshold to 32◦C. In other words, a day with a temperature below 8◦C results inzero degree days; a day with a temperature between 8◦C and 32◦C contributes the number of degrees above 8◦C; and a day with a temperature above 32◦C degrees contributes 24 degree days. Degree days are then summed over the growing period, represented here by the months from April through September.Following Ritchie and NeSmith , the level beyond which temperature increases become harmful is set at 34◦C.

A complication with degree days is that the concept is based on daily temperature while our climate records consist of monthly temperature averages. Thom develops the necessary relationship between daily and monthly temperature variables under the assumption of normality. This relationship is used to infer the standard deviation of daily temperature variables from monthly records. Degree days are then derived using the inverse Mills ratio to account for the truncation of the temperature variable.Before we present our regression results we first examine whether the spatial correlation of the error terms as described in the previous section is indeed present. We conduct three tests of spatial correlation for all counties east of the 100th meridian using the same set of exogenous variables as in the estimation of the hedonic equation in Table 3 below, including state fixed effects. One test is the Moran-I statistic . However, since this does not have a clear alternative hypothesis, we supplement it with two Lagrange-Multiplier tests involving an alternative of spatial dependence, the LM-ERR test and LM-EL test. The results are shown in the first three rows of Table 2. Note that they are rather insensitive to the chosen weighting matrix.The spatial correlation of the error terms is quite large and omitting it will seriously overstate the true t-values. For example, the t-values using standard OLS that does not correct for the spatial correlation or the heteroscedasticity of the error terms are up to nine times as large, with an average value of 2.2 for the model presented in the first column of Table 3. In the following we use a two stage procedure. In the first stage we estimate the parameter of spatial correlation and premultiply the data by . In the second stage we estimate the model and use White’s heteroscedasticity consistent estimator to account for the heteroscedasticity of the error terms. In previous climate assessments, it has been customary to estimate a linear regression model. Since farmland values have to remain non-negative, and given the highly skewed distribution of farmland values in Table 1 a semi-log model appears preferable. To determine which model better fits the data, we conduct a PE-test . We use 10,000 bootstrap simulations to get a better approximation of the finite sample distribution of the estimate. The t-value for rejecting the linear model in favor of the semilog model is 873, while the t-value of rejecting the semi-log model in favor of the linear model is 0.01.

We therefore focus the remainder of our analysis on the semi-log model.Results of the log-linear hedonic regression under the Queen standardized weighting matrix are displayed in the first two columns of Table 3. We present results with and without state fixed effects. The reason for including fixed effects is that this can control for the possibility that there are unobserved characteristics common to all farms within a state,40 litre plant pots such as state-specific taxes and uneven incidence of crop subsidies due to differences in cropping patterns across states. The concern is that the identification of the climate coefficients in the hedonic model might otherwise come primarily from variation in government programs that target specific crops. However, it should be noted that since we rely on a nonlinear functional form, the estimation procedure still uses variation between states in the identification of the coefficients. We find that inclusion of fixed effects does not reduce the significance level of the climatic variables. At the same time, the parameter of spatial correlation is virtually unchanged when we include fixed effects, suggesting that there are indeed spill-over effects that are based on spatial proximity rather than an administrative assignment to a particular state. The estimated coefficients on the climatic variables are consistent with the agronomic literature. The optimal number of growing degree days in the 8◦C − 32◦C range peaks at 2400 degree days for the pooled model in column 1 of Table 3. This is close to the optimal growing condition for many agricultural commodities when one adjusts for the length of the growing season . Degree days above 34◦C are always harmful.13 Precipitation peaks at 79 cm or approximately 31 inches, which also is close to the water requirements of many crops, when adjusted for the length of the growing season. Other variables have intuitive signs as well. Income per capita and population density are important and highly significant determinants of farmland value: higher population pressure translates into higher farmland values, albeit at a decreasing rate. Similarly, higher incomes drive up the price of farmland. Two soil variables are significant at the 5% level in the pooled model: better soils, as measured by a soil quality index, result in higher farmland values; and a lower minimum permeability, which indicates drainage problems, reduces farmland value. The effect of the former is quite large: farmland with 100% of soils in the best soil class categories are 35% more valuable compared to farmland with 0% in the top soil classes. The variable K-factor is significant at the 10% level in three out of the five regressions using state fixed effects. It indicates higher erodibility of the fertile top soil, which is harmful. Percent clay frequently switches sign and is not significant in most models; neither is the average water capacity of the soil. We have suggested that degree days and precipitation over the growing season better represent the effect of climate on agriculture than the alternative specification of monthly averages of untransformed temperature and precipitation.

To assess this claim, we conduct an encompassing test to determine which model is better at predicting the effects of climate change. In order to do so, we split the sample into two subsets: the northern-most 85% of the counties in our sample are used to estimate the parameters of both models in order to derive the prediction error for the southern-most 15%, i.e., we see which model calibrated on moderate temperatures is better at predicting the values for warmer temperatures. The results offer clear confirmation of the superiority of the degree days model. Even though this model has less than one third the number of climate variables included in the alternative, we can reject the null hypothesis of equal forecasting accuracy in favor of the degree days model with a t-statistic of 2.94 .14 Kaufmann emphasizes that the parameter estimates in the model using undemeaned climate variables often vary between models. This is not surprising in light of the strong multi-colinearity between the climate variables that leads to frequent switching of the parameter estimates, sometimes with large marginal effects. This can be seen in our data as well, as shown in an appendix available from the authors on request. Summarizing briefly, when the monthly climatic variables for January, April, July and October are included, the only variables which are significant in the pooled model between all census years are July temperature and April and October precipitation ; none of the other 10 monthly climate variables in the pooled model is significant even at the 10% level. Further, the coefficients on July temperature imply that farmland value peaks at an average temperature of 22◦C ≈ 72◦F, which seems rather low given agronomic research showing that plant growth is linear in temperature up to about 32◦C. There are other anomalous results, but as the coefficients are not significant, we do not discuss them further here. The results of the degree days model are very reasonable in the light of the agronomic literature. But how robust are they across plausible alternative specifications of variables and data? Here we briefly describe several sensitivity tests. A more complete discussion is given in the appendix available on request. We test the stability of the five climatic coefficients across the several census years in our pooled model. During this period there were some significant changes in farmland values east of the 100th meridian; the overall farmland value in this region declined by 32% between 1982 and 1987 in real terms, and increased by 13% between 1987 and 1992, and 14% between 1992 and 1997.

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Changing hydrologic trends throughout California will directly impact San Diego’s water supply

Offering relief from the consistently hot, dry, drought conditions of summer, CLCF play an important role in hydrological regime and ambient temperature . Although CLCF are a distinct component of San Diego’s climate, it relies on highly variable, complex factors. Thus, the net effect of climate change on CLCF remains uncertain . However, observational records exhibit that California CLCF has declined over the last decade, and that this decline can be attributed to urban warming . Future research on the response of San Diego’s CLCF to climate change is critical in understanding the implications for coastal and inland ecosystems and human communities.Despite intensified extreme events, it is likely that droughts will increase in both frequency and intensity . San Diego will experience more dry years as the subtropical zone expands and leads to a decrease in the number of wet days . More dry days will intensify already depleting soil moisture content. This will cause earlier spring soil drying and extended drying through the late fall into winter, and thus elongate seasonal dryness in California. The combination of longer periods of dryness, expanding subtropical zones, and warming temperatures, will lead to more dry years. With more dry years and dry antecedent conditions, it is projected that future droughts will increase in duration, severity, and frequency, which will also increase the region’s vulnerability to wildfire occurrence. The relative impacts of drought are likely to be more intense as well, as increased temperatures continue to create drier conditions. As the climate warms, drought conditions worsen, and Santa Ana wind events continue, it is likely that wildfire risk will also increase . Given San Diego’s water supply portfolio and its dependence on imported water,30 planter pot it is critical to consider the climate change impacts on the regions that supply much of San Diego’s water supplies.

These regions, largely Northern California and Colorado, are likely to experience changes in precipitation , temperature, and thus altered snow pack and runoff patterns .Water from Northern California, specifically snow pack in the Sierra Nevada Mountains, is expected to decrease due to higher rain and snow elevations, and earlier snow melt and spring runoff. Snow pack will be reduced by more than 60% by the middle of the century, with positive feed backs further exacerbating these warming and snow melt trends . Increased evapotranspiration and decreased snow pack will also cause decreases in water supply in the Colorado Basin . Over the next fifty years, droughts lasting five or more years are projected to occur fifty percent of the time. These impacts will reduce the water in these areas which supply San Diego, as well as counties across California and along the Colorado Basin, resulting in worsened water resource challenges. Thus, it is imperative that San Diego consider alternative sources of water and infrastructure developments, in addition to enhanced water-use efficiency across all sectors.As a sector that is greatly dependent on climate and highly sensitive to environmental conditions , agriculture is exceptionally vulnerable to the effects of a warming world. With ongoing shifts in natural processes that dictate agricultural practices, productivity, and costs, the future of agriculture is one with distinct and palpable challenges. Because the effects of climate change on agriculture are highly dependent on variables such as climate, geography, soils, and customary agricultural practices, the net impact felt by regions will vary greatly. In some areas, it is projected that climate change may result in beneficial consequences for agriculture, while in others, consequences could be detrimental. Therefore, it is necessary to develop regionally and locally unique solutions for these changes . Most Mediterranean regions, such as San Diego, will feel the greatest impacts from increasing variability in precipitation compounded with increasing temperatures. Precipitation and temperature are deeply embedded within the hydrologic cycle, and thus, as these climate variables continue to shift, they will alter many hydrologic processes. Furthermore, increased climate variability making adaptation increasingly difficult for the farming community .

Ongoing changes, such as the timing and frequency of precipitation, reduced snow pack levels, and earlier snow melt, present several challenges for the region’s water resources . These water-related challenges are inextricably linked to the overall functioning and viability of agriculture, and are thus paramount in determining the persistence and growth of San Diego agriculture. There are several key hydrologic variables that play a role in the overall functioning of a landscape, and as these hydrologic variables change, agricultural lands are impacted. Table 2 outlines these hydrologic variables, their impact on a landscape, and projections for future climate scenarios. These hydrologic variables include: CWD, AET, runoff and recharge. One of the major hydrologic variables San Diego’s landscape is climatic water deficit . CWD is the amount of additional water that would have evaporated/transpired if soil water was not limiting, combining the effects of evapotranspiration, solar radiation, and air temperature on watersheds, given the soil moisture level from precipitation . CWD can be translated to direct impacts for agriculture. In Mediterranean climates, it is considered a proxy for water demand based on irrigation needs . Another important hydrologic variable that heavily impacts landscapes is actual evapotranspiration , which is the amount of water actually lost by the vegetated surface. For the farming community, AET translates to above ground net primary productivity and is used as a proxy for productivity of a landscape . Changes in AET and CWD can be used in quantifying the additional water necessary to maintain vegetation or crops in a landscape , effectively identifying the amount of irrigation demand needed to cover seasonal deficit . Critical to the relationship between climate change and natural landscapes is understanding the contribution of agriculture to increasing GHGs and in turn, climate change. Degrading and eroding soil from intense grazing, plowing, and clear-cutting, has throughout time, played a significant role in the increasing concentration of atmospheric GHGs . Long-term degradation of important features of natural lands, such as soils, forests, and wetlands, is one of the key drivers of a warming world . Relative emissions and impact, however, vary with region depending on soil properties and agricultural practices. In the San Diego region, agriculture contributes approximately five percent of total unincorporated county emissions . In general,plastic planters bulk most farm-related carbon dioxide emissions result from a variety of soil, livestock, and manure management practices, including soil tillage, overgrazing, farm equipment, livestock and fertilizer use .

The world’s soils play a critical role in food production, water resources, both quantity and quality, and increased net primary productivity. Enriched with soil organic matter , soil has the ability to recycle dead matter into mineral-rich nutrients vital for plants and other organisms. Additionally, soil provides the distinct and critical service of removing gases from the atmosphere. Through the biological process of carbon sequestration, carbon dioxide is removed from the atmosphere and stored as sinks in soils. This service helps keep terrestrial and atmospheric carbon levels in a balance . Carbon is the primary component of SOM and provides soil with defining characteristics such as water-retention capacity, filtering capabilities, structure, and fertility . Because pools of soil organic carbon aggregates are stable and robust, they provide the largest store of terrestrial carbon and have the ability to be sequestered for up to a millennia . The length of time and amount of carbon that remains in the soil is largely influenced by ecosystem and environmental processes, depending on vegetation, soil properties, water drainage, and climatic conditions. Thus, levels of SOC varies on large-scale global patterns and on smaller-scale regional and sub-regional basis . The unique capability of soil to nourish vegetation and capture carbon long-term helps buffer the implications of climate change for both society and ecosystems alike. Additionally, with the likelihood of increased flood events, agriculturally managed lands could play a role in retaining flood waters for flood risk reduction as well as possible groundwater recharge. However, the ability of soils to provide these services is contingent on its overall quality. The length of time and amount of carbon that remains in the soil is largely influenced by management practices, in addition to ecosystem and environmental processes . If soils are poorly managed with unsustainable agricultural practices, soils can release CO2, contributing to atmospheric concentrations. Alternatively, if healthy, soils can enhance sequestration and continue to play an essential role in climate change mitigation. Thus, promoting healthy soil is critical to ensuring the resilience of landscapes, agriculture, and society. Recently research has focused on the potential of enhancing SOC sequestration to help moderate high levels of atmospheric carbon. On a large scale, SOC sequestration could hypothetically sequester all current annual GHG emissions globally, at approximately 52 gigatonnes of CO2 equivalent . This research highlights the ability of soils to offset increasing atmospheric CO2, where restored SOC pools could promote productivity, fertility, and resiliency to a variety of climate extremes. There are a variety of carbon farming practices that farmers can adopt in order to achieve these benefits .

Table 6 outlines some of the common on-farm conservation practices recognized by the Natural Resource Conservation Service to improve soil health, sequestration rates, and associated co-benefits . From permanent crops, compost and mulch application, windbreak renovation, no-till row crops, to cover cropping, the agricultural community has several options when it comes to implementation . Overall effectiveness, in terms of sequestration rate, GHG reduction, and benefits will depend on various climatic and environmental factors. Thus, suitability of practices vary by region and individual agricultural context. Many studies have shown that compost application is one of the most impactful practices for carbon sequestration rates. Field and model results from a report within California’s Fourth Climate Change Assessment indicate that a one-time ¼ “ application of compost to California’s range and croplands can lead to increased carbon sequestration and net primary production rates in soils maximized after 15 years . In another study conducted by the Marin Carbon Project, it was shown that a one-time application of a ½ layer of compost on grazed range land was able to increase carbon storage by 1 ton of carbon per hectare. This resulted in both increased forage production and water holding capacity . This study uses down scaled statewide modeling data to analyze hydrologic response in San Diego as it relates to agricultural land evaluation, based on a report for California’s Fourth Climate Change Assessment by Flint et al. 2018. The Basin Characteristization Model is a grid-based model that combines climate inputs, watershed, and landscape characteristics to calculate the water balance. By combining fine-scale data, the BCM can generate detailed assessments of coupled climate and hydrologic response . Precipitated water can act in various ways as it enters into a landscape, from evaporation and transpiration, recharge, or runoff. Given climate data, governed by latitude, longitude, elevation, slope, and aspect, in addition to soil properties, and characteristics of deep soil materials, the BCM can effectively model the response of these hydrologic factors . Flint et al. 2018 utilized a revised version of the BCM to include SOM percentage for calculations of WHC. Using this modified version of the BCM, Flint et al. calculate how increases in SOM changes hydrologic response to climate . The study assesses changes in WHC as a result of additional SOM, and the impact that changes in WHC have for hydrologic variables such as recharge, runoff, AET, and CWD throughout the state. Table 6 outlines the predicted hydrologic response to these changes, however, response is dependent on several factors, including precipitation. Figure 10 shows the high variability of potential hydrologic benefits from increases in SOM for the period of 1981-2010 across the state’s working lands . These results showcase the diversity of climates and soil properties throughout the state’s landscape, and the impacts these factors have for potential benefits in forage production, landscape stress, irrigation demand, and water supply .Hydrologic benefit is calculated using a hydrologic index from changes in these variables, specifically increases in AET and recharge, and decreases in CWD . The hydrologic index is binned into three classifications of benefit based on index value, including “no benefit”, “minimum benefit”, “moderate benefit”, and “maximum benefit”. Hydrologic benefit is mapped for the entire county , the unincorporated county excluding the incorporated areas , and the incorporated county .

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Passive RFID sensors have a relatively short range compared to other communication protocols

Healthy soil is rich with microbial life, and over time, the microbial communities will adapt and digest what is more likely to be available to them. As an important note for polymers – when a polymer is described formally as a ‘biodegradable polymer,’ it contains hydrolyzable bonds – meaning they are affected by hydrolysis . Therefore, their most crucial degradation mechanisms are chemical degradation by hydrolysis or microbial/enzymatic digestion. The latter effect is often referred to as bio-degradation, meaning that the degradation is mediated at least partially by a biological system. Our strategy for controlling the degradation rate of our device is to apply both principles of passive geometry and material selection. We make devices out of ‘shells’ of materials that degrade at different rates. More specifically, we paired fast degrading printed conductors with slow-degrading, wax-based encapsulation that degrades uniformly by surface erosion. Figure 5.15 describes the performance of such a device over time, with cross-sections at critical intervals in the degradation process. Material selection was determined by literature review and experimentation. Lee et al. have investigated the use of electrochemically-sintered zinc in a water-soluble polyvinyl propylene binder as a naturally-degradable printed conductor material. Meanwhile, natural waxes have an exciting opportunity as naturally degradable encapsulation material. They have been able to retain the operation of underlying degradable electronic systems for weeks to months. Figure 5.16 shows the accelerated degradation of wax blends held at elevated temperatures in an incubation chamber over 28 days.Unfortunately, it is impractical to make a nitrate sensor node 100% degradable. For example, the ISM,grow blueberries in containers which provides the operating mechanism for the nitrate sensor, necessitates a hydrophobic polymer backbone to function.

Because of this, it is impossible to make this component naturally degradable by the current mode of operation. Fortunately, the mass of this component is minimal – only about 0.5 mg. To put that into perspective, it would take 10,000 ion-selective membranes to produce as much plastic pollution as a single credit card. Table 5.3 shows all of the components in a wireless nitrate sensor node and what naturally degradable materials they can be substituted with.Some components of a conventional wireless sensor node are difficult or even impossible to replace with naturally-degradable materials, as shown in Table 5.3. For example, degradable batteries or other energy storage devices exist in literature, but none are resilient or low-cost enough for our application. Similarly, using onboard energy storage and harvesting necessitates a higher complexity micro-controller, which corresponds to larger and more costly micro-controllers. One method of circumnavigating these components is using passive sensor nodes, such as passive RFID sensors. Passive RFID sensors comprise an antenna, an RFID IC, and a sensor. Of note, there is no onboard energy storage, meaning an external power signal must be sent to the node to take a measurement. In the case of RFID, an RF signal is transmitted by an external RFID reader. The antenna receives the wave and transduces it into an electric signal which ‘wakes up’ and powers the RFID IC. The RFID IC acts as the micro-controller, communications IC, and power management. When it receives the wake-up signal, it uses the power in that signal to read the sensor and modulate a return signal through the antenna to the reader corresponding to the sensor measurement. By designing a sensor node using this passive RFID scheme, we estimated that we can make the naturally-degradable nitrate sensor nodes 99.99% degradable by mass. Ag/AgCl strips were fabricated using the same parameters with Engineered Materials Systems, Inc. CI-4001 ink. Afterward, they were cured in an oven at 120◦C for two hours. After curing, the carbon and Ag/AgCl strips were cut into six equal-sized electrodes. Each electrode was then sandwiched between two patterned wax sheets and heated in an oven at 55◦C for thirty minutes. The wax sheets were made by soaking untreated plywood sheets in water before dipping them in molten wax and removing the waxy film that forms on the surface. The thin water layer on the surface of the saturated plywood sheet acts as a barrier to the hydrophobic wax, allowing for easy removal. The thickness of the wax sheets was controlled by dipping the saturated plywood sheets multiple times in quick succession, obtaining wax sheet thicknesses of 350 µm, 700 µm, and 1.25 mm for one, two, and three dip cycles, respectively.

The wax sheets used for encapsulating the bottom of the sensors were used as-is, while the sheets used for encapsulating the top of the sensors had 12.5 µm windows for the membranes removed using a laser cutter. An image of an ISE immediately after the encapsulation step is shown in Figure 5.17B. ISE membranes were fabricated by mixing 5.2 wt% Nitrate Ionophore VI, 47.1 wt% dibutyl phthalate, 0.6 wt% tetaroctylammonium chloride, and 47.1 wt% PVC. A total of 0.2 g of this mixture was dissolved in 1.3 mL of THF. 180 µL of the membrane solution was drop-cast on the ISE surface and dried in a fume hood for 15 minutes. The REs employed a CNT transducer layer between the Ag/AgCl electrode and the membrane. This transducer was composed of 0.01 g of CNT and 0.05 g of F127 -block-poly-block-poly diacrylate dissolved in 10 mL of THF, which were sonified for 1 hour in an ice bath using a Branson Digital Sonifier probe. 120 µL of the resulting transducer cocktail was deposited onto the RE surface. The salt membrane was made by dissolving 1.58 g of Butvar B-98 , 1.00 g of NaCl, and 1.00 g of NaNO3 in 20 mL of methanol. The mixture was sonified for 30 minutes in an ice bath, and 180 µL of the resulting salt membrane cocktail was deposited on top of the CNT transducer. Unless otherwise noted, all chemicals used in ISE and salt membranes were obtained from Millipore Sigma. After each electrode was made, they were cold-sintered to 22 AWG wire using 8331D silver conductive epoxy and en-capsulated with multiple layers of Gorilla 2-part Epoxy . Figure 5.17 shows an image of the fabricated naturally-degradable nitrate sensors. Wireless sensor networks are becoming more and more relevant in agriculture. Researchers have made agricultural WSNs to monitor weeds, evapotranspiration, crop disease, and water use. However, there are limited examples of agricultural WSNs for monitoring nitrate. The design of a wireless sensor network in agriculture has a host of unique challenges. Issues like energy consumption for autonomous operation of sensor nodes dictate design and development issues, including communication protocols and deployment. Furthermore, the placement of sensor nodes in open, uncontrolled environments presents another host of unique challenges, such as damage accumulation from weather or wildlife. Finally,blackberry plant pot the scale it takes to implement WSNs in agricultural settings is much larger than in commercial or industrial environments. Cropland accounts for about 11% of the habitable land globally, and in the United States, the average crop farm is 445 acres. This dictates the placement and quantity of sensors needed, as discussed in Section 5.2, and shows that large numbers of sensor nodes are required. Different researchers have adopted different strategies for circumventing these challenges. Ding and Chandra investigated using Wi-Fi for measuring soil moisture and electrical conductivity.

Syrovy et. al. utilized Long Range, Wide Area Network communications to transmit data from paper-based soil moisture sensors. Yu et al. deployed a system where the sensors connect directly to a person’s phone over Bluetooth Low Energy. Here, we propose an agricultural WSN explicitly designed for the precision management of soil moisture and soil nitrate. The naturally-degradable nitrate sensor nodes demonstrated in Section 5.4 can be deployed at minimal cost and without the need for maintenance throughout any agricultural field using the techniques outlined in Section 5.2.Hence, a reader needs to be brought to within a few meters of the sensor to sample data from the sensors. Because many sensors need to be distributed across an agricultural field to acquire granular enough data to capture soil variability, drones offer a unique advantage over other existing methods to sample data from the sensors. With drones and drone accessories becoming less expensive, using multiple drones to simultaneously map sensors has become an attractive route to efficiently gather data. Machine-learning algorithms are a promising approach for generating flight path maps due to their ability to solve highly non-convex problems quickly, and even operate in real-time as a digital twin. We developed an agent-based dynamics model to generate flight paths for the drones to scan each sensor in the field while circumventing obstacles and avoiding crashes. The coordinated effort of multiple drones working towards a common objective has similarities to swarms found in nature, such as bees and ants, where the accumulation of each agent’s actions and reactions can give rise to phenomena and emergent behavior where the system becomes more than the sum of its parts. Unlike bees and ants, it is atypical for a drone swarm to contain a ‘leader.’ In the context of field mapping, the drone swarm adapts to changes within the system, such as the disablement of a few drones due to collisions or other unforeseen causes. We developed a robust agent-based model capable of optimizing the flight paths of each drone within a swarm to scan all sensors within a simulated agriculture field. The simulations determine each drone’s aerial route for optimal flight path planning. Each drone within the simulated framework – an ‘agent’ – has its own characteristics that determine how it interacts with its surroundings, such as its environment and other drones. These characteristic parameters take inspiration from the physics of molecular dynamics, where each agent is modeled as a point mass particle that is attracted and repelled by other objects within the system. A genetic algorithm determines the direction of propulsion. The framework inputs are the field’s shape, the number of agents, and the positions of sensors . This framework can be used for various sizes and shapes of agriculture fields. Depending on the field geometry and the locations of sensors within that field, the framework will output several suggestions of each drone’s flight path trajectory. AUTONOMOUS agricultural mobile robots become increasingly more capable for persistent missions like monitoring crop health and sampling specimens across extended spatio-temporal scales to enhance efficiency and productivity in precision agriculture. An autonomous robot needs to perform certain tasks in distinct locations of the environment subject to a specific budget on the actions the robot can take . During in-field operations, the actual costs to complete tasks can be uncertain whereas expected costs may be known. Also, some tasks can be more urgent than others, and have to be prioritized. It is often the case that there exists some prior information about a required task that can bias robot task assignment. Hence, it is necessary to develop approaches that utilize limited prior information to plan tasks with uncertain costs and priority level. There exist two key challenges for efficient robot task allocation in precision agriculture. First, prior maps can indicate biases in task assignments, but may not be trustworthy. This is because conditions in the agricultural field can change rapidly, are dynamic, and may be hard to predict ahead of time. Second, as the budget is being depleted, the robot needs to periodically return to a base station . Addressing these two challenges simultaneously poses a two-layer intertwined decision making under uncertainty problem: How to perform optimal sampling given an approximate prior map, and how to decide an optimal stopping timeto avoid exceeding a given task capacity? This paper introduces a new stochastic task allocation algorithm to balance optimal sampling and optimal stopping when task costs are uncertain. A direct approach for persistent sampling is to survey the entire space and perform the desired task sequentially. The main drawback is that the robot would then exhaustively visit all sampling locations without prioritizing those that would yield a higher gain or would be more time-critical. Orienteering can address part of this drawback by determining paths that maximize the cumulative gain under a constant budget. The robot prioritizes visiting adjacent locations if they jointly yield higher gains than isolated high-gain locations, and provided that any budget constraints are not violated. However, this strategy can be insufficient for missions where some tasks are more urgent than others. For instance, several existing robot task allocation strategies, albeit for distinct application domains, typically consider a deadline or user-defined importance levels.