We also evaluate the impact of using different amounts of training data and calibration durations

Measuring and predicting temperature accurately is challenging due to variation across farm micro-climates where local temperature can deviate from the surrounding area which is typically measured at mesoscale. Measuring temperature for a large number of micro-climates on a farm can be prohibitively expensive with extant weather stations and sensors . In this chapter, we explore the use of sensor synthesis to estimate outdoor temperature on farms using the processor temperature of simple, inexpensive single-board computers or micro-controllers such as those in the Arduino family Arduino. Our approach estimates outdoor temperature from the on-board processor temperature sensor that these devices support and which is available via their respective hardware/software interfaces. Such devices cost around $5, are battery or solar-powered, and can be packaged in small, inexpensive, weatherproof enclosures, making them practical for use in moderate and large scale geographic deployments. To investigate how well the processor temperature of these devices can be used to predict outdoor temperature, we have developed an on-farm IoT system in which we place single-board computers in-situ throughout the farm. The devices transmit measurements of CPU temperature wirelessly to wall-powered, indoor, edge cloud systems Elias et al. . We first calibrate the device CPU temperature against a co-located,vertical growing racks high-quality temperature sensor using linear regression.

We then remove the temperature sensor at each remote location. The edge cloud computes a prediction of outdoor temperature for each device/location for each CPU measurement that it receives from the device. It does so by applying the regression coefficients from the calibration period to the CPU temperature measurement. To account for autocorrelation in the time series, we investigate the use of Single Spectrum Analysis Golyandina & Zhigljavsky to extract a smooth “signal” from the data prior to performing linear regression and compare this approach to non-smoothing.Finally, we integrate different outdoor temperature sources that include device-attached sensors , high-end, on-farm weather stations, and remote Weather Underground Weather Underground stations. We first consider two different configurations. The first is a “limit study” in which we continuously update the regression coefficients using a co-located temperature sensor, to compute a one step ahead prediction. This configuration represents an upper bound on the efficacy of predicting outdoor temperature from processor temperature. Using a second configuration, we consider a practical application of our approach in which the edge cloud estimates the outdoor temperature using information from the initial calibration period and the CPU temperature measurements reported by the device every 5 minutes. Next, because sensor synthesis is based on computed estimates rather than actual measurement, it introduces the possibility of additional error beyond measurement error. To address this, we examine how a larger ensemble of measurements improves the accuracy of “synthetic” temperature measurement while, at the same time, not requiring the use of powerful computational resources.

Reducing the prediction error is not only academically interesting, rather, precision has a direct impact on the cost and efficiency of what has become known as precision agriculture or precision farming. In precision agriculture, farmers use technology to increase the efficiency of farming techniques increasing crop yields and reducing costs. Having more precise temperate data reduces the cost of frost prevention and prevents excessive resource use without negatively impacting crop production. Consequently, we believe that our approach can contribute to improved farming outcomes, enable water and energy savings, and help reduce carbon emissions, by providing high-quality data to data-driven, IoT-based agricultural applications. We then extend our approach to use a combination of processor temperatures from multiple devices and outdoor temperature from high-quality, remote weather stations to train a multiple linear regression model. We use this model to estimate the future outdoor temperature at a particular device location that is not part of the model. We also investigate the efficacy of computationally simple smoothing techniques to reduce noise. We also investigate how well our approach performs when the processors on the devices experience load. The load may affect processor temperature and thus negatively impact the accuracy of our outdoor temperature estimates. To do so, we develop techniques that successfully deal with the perturbations caused by load variability, which is an important requirement to make our sensor synthesis practical in the field . In this chapter, we investigate the relationship between processor temperature embedded in single-board computers, and the atmospheric temperature that surrounds them. Our goal is to place these computers in-situ in agricultural settings for use as thermometers. By doing so, we can leverage their measurements to actuate and control a wide range of IoT-based farm operations, while driving down the cost of implementing such solutions at scale.

Examples of such farm operations include irrigation scheduling and frost damage mitigation strategies. For automatic irrigation scheduling, real-time temperature measurements are used to compute localized estimates of evapotranspiration , which indicates the amount of water that has been lost and that must be replaced via irrigation. Both under and over-watering can decrease productivity, destroy crops, and degrade soil health. Irrigation scheduling is the most common form of IoT and data-driven decision support system on farms and is especially important for managing farms in drought-stricken regions. The terms “frost” or “freeze” are used by the public to describe a meteorological event that causes freezing injury to crops and other plants, when the air temperature falls below the tolerance level of the specific plant Levitt et al. . The ability to predict the onset of frost, its duration, and the specific locations where frost will occur is of tremendous value to the agricultural industry. In the USA, there are more economic losses to frost damage than to any other weather-related phenomenon White & Haas . Active frost protection strategies include application of water, use of wind engine-driven machines and heaters, and/or some combination of these methods, all of which are extremely labor-intensive and costly for growers. If the onset or duration of frost is mis-predicted, the cost of any mitigation strategies applied is lost. Alternatively, incorrectly predicting that a freeze will not occur to save these costs can devastate a crop. For this reason, current practice is conservative, passing any unnecessary mitigation costs on to the consumer in exchange for a low risk of crop loss. In both operations, accurately measuring and predicting the temperature in real-time is required. However, the temperature is not uniform and can vary widely across a farm,vertical farming in shipping containers requiring that operations account for very localized differences to obtain measurable outcomes. Micro-climates can occur in large numbers due to topographic differences, surrounding structures, ground cover, plant maturity, and nearby bodies of water. Measuring temperature across vast numbers of micro-climates is costly and labor-intensive given the price of high-quality sensors and complexity of sensor management . Many IoT vendors provide managed services to reduce this complexity for growers, but these services are expensive, require that data be transmitted off-farm to cloud-based applications via cellular, and impose a recurring subscription fee on farmers in order to view their data. As a result, IoT advances have not achieved widespread uptake in agriculture, despite their potential.As part of the UCSB SmartFarm effort Krintz et al. , we have investigated ways of reducing cost and complexity of temperature-based IoT solutions, while maintaining accuracy and robustness. SmartFarm implements a low cost, on-farm edge cloud comprised of multiple Intel Next Unit of Computation machines Int . Using open-source cloud software and Eucalyptus Nurmi et al., we design the edge clouds to be self-managing and to perform a wide range of data analytics on-farm data, thereby precluding the need to transmit data off-farm and keeping cost, complexity, and latency low Krintz et al. , Elias et al. . We use SmartFarm and single-board computers to provide accurate, real-time estimates of micro-climate temperature across a farm.

To do so, we place battery or solar-powered devices in-situ in various settings and configurations within inexpensive enclosures. The devices transmit their CPU temperature wirelessly to an on-farm edge cloud every 5 minutes. As ground-truth, we consider co-located DHT digital sensors, high-end, on-farm weather stations, and Weather Underground remote weather service Weather Underground , which farmers commonly use to estimate temperature. Figure 4.1 shows a two-week time series trace of CPU temperature from a Raspberry Pi Zero, the outdoor temperature from an attached digital DHT22 temperature sensor , and the outdoor temperature from a nearby Weather Underground station . WU measures outdoor temperature at 10 meters and the Pi Zero is at a 1 meter altitude. The Pi Zero is in a plastic enclosure with a small, covered hole from which the DHT wires exit; the DHT sensor is outdoors and hanging freely. The device is located outdoors under constant shade in Goleta, CA. We refer to this device as Pi1 in later sections of the paper. The average CPU temperature on the Pi Zero during this period is 99.71 ◦F with a standard deviation of 4.69. The mean and standard deviation for the DHT sensor and WU station are 61.93 and 60.20 , respectively. DHT and WU temperature is similar but WU exhibits data dropout , more variance, and more extreme temperatures. From this graph, there appears to be a correlation between CPU temperature and both outdoor temperature measures for this location. The CPU values exhibit small oscillations or noise . A sub-portion of the CPU data alone is shown in Figure 4.2 using a different scale. We note that there are some discrepancies in the shape of different curves. We observe similar relationships using other types of devices, locations, and sources for ground-truth temperature measurements. We next investigate how accurately we can predict outdoor temperature using CPU temperature of these devices.The data in Figure 4.1 is typical of the outdoor SmartFarm installations we have deployed suggesting that linear regression would be an effective way to predict outdoor temperature from CPU temperature. Because each single-board computer is running a multi-user operating system , however, the CPU temperature exhibits fluctuations that we do not observe in the outdoor temperature. Further, because these fluctuations are caused by programs that are running on the computer, they are autocorrelated in time. To account for this autocorrelated “noise” in the CPU temperature series, we apply Single Spectrum Analysis Golyandina & Zhigljavsky to the CPU series before performing regression. SSA decomposes an auto correlated time series into “basis time series” which are analogous to principle components Abdi & Williams , Wold et al. . By summing the most significant basis series , SSA can extract a smooth “signal” from a noisy time series. To do so, SSA requires the number of lags over which auto correlation is significant to be supplied as a parameter. To investigate the accuracy with which it is possible to predict outdoor temperature, our system runs multiple smoothing passes, each with a successively larger number of lags up to 12 . During daylight and nighttime hours, outdoor temperature can be auto correlated for several hours, but during the early morning or early evening the significant auto correlation duration is significantly less. For each lag we compute the coefficient of determination for a regression covering a previous window of time and choose the number of lags that generates the highest R2 value. We refer to this window as the training window . Typically the best R2 value is for 6 lags indicating that the significant auto correlation in the CPU temperature series covers about 30 minutes. The method recomputes both the smoothed series and the regression coefficients every time a new outdoor measurement is generated . Thus the approach is a “piece wise” linear regression approach where the data is re-smoothed using the “best” number of lags before each regression. When a new CPU value arrives, we use the regression coefficients to compute a prediction of outdoor temperature. Prior to applying smoothed regression coefficients, we append the new CPU value to the training window . We compute the prediction using the smoothed CPU value . We then compare this value to the actual outdoor measurement to compute the absolute difference and square difference as the error.We refer to this configuration as a “limit study” because we believe that it provides us with an upper bound on the efficacy of our approach.

Plants were carefully harvested and transported in dry ice to the laboratory

Community Supported Agriculture appeals to an increasing number of people. In recent decades, CSA farm and member numbers have grown rapidly in the United States and in California’s Central Valley and foothills. CSA numbers in our study area grew from a few in the early 1990s to 74 in 2010. The loss of 28 CSAs found in our initial online search, which were actually defunct when contacted, merits further research. Membership growth has similarly exploded: CSA membership in our sample increased from less than 700 in 1990 to almost 33,000 expected members in 2010. CSA membership characteristics also deserve further study. The CSA expansion has been accompanied by innovation in CSA types. The CSA concept appears to be both robust and flexible, and different CSA operations are using it to address different challenges. The motivations of farmers for creating CSAs are diverse; ideological predispositions vary greatly, as do farmer attitudes around CSAs as a business and their practices for paying themselves. The diversity of CSA types, and the loose adherence to many of the features of the original concept of CSA, brings into question whether the original model met the needs of the California population. Expanding market opportunities for CSA farmers could involve further adaptations to reach consumers not commonly involved, such as participants in USDA’s nutritional assistance programs,vertical farming companies including the Special Supplemental Nutrition Program for Women, Infants and Children . Despite the diversity of types we identified, CSAs in our study retained a number of core characteristics.

Namely, the vast majority of CSA farmers in the Central Valley cultivated high levels of agrobiodiversity, were committed to agroecological practices and embodied an ethic of reducing off-farm resource use. CSA farmers in our study were also dedicated to enhancing the environment on and off their farms and to providing healthy food to their communities. Our study also revealed that CSAs in the Central Valley and surrounding foothills share characteristics with CSAs nationwide: Smaller-scale CSA farmers are more dependent upon the CSA as a market outlet; CSAs are less dependent upon off-farm work than U.S. agriculture generally; CSA farmers are younger, less diverse ethnically, more likely to be women and more formally educated than the general farming population; and CSA farming practices demonstrate strong commitments to environmental ethics . CSAs are an increasingly important form of direct marketing, crucial for smaller farms. The gross sales per acre of CSAs were considerably higher in our study than of almost all other agricultural endeavors, even in California where gross sales per acre are high. Although most CSAs are profitable, CSAs are like other forms of U.S. farming in often requiring farm partners to work off farm. Even though a CSA is hard work, farmers tend to fi nd it rewarding. The vast majority were happy with their work and continued to view the CSA as a viable option for small- and medium-scale farmers. Overall, CSAs provide an increasingly important marketing option for Central Valley and foothill farmers. However, the extent to which existing and new CSAs will be able to expand the movement and collectively increase their market share, rather than increasingly compete with one another for a limited number of members, remains to be seen. With the numerous economic, social and environmental benefits of the CSA model and its growing popularity, it would seem wise to explore the creation of policy instruments, informational clearinghouses, and additional UC Cooperative Extension efforts to support the needs of CSA farmers and members.

Microorganisms inhabiting the rhizosphere play a key role in plant health and defense , stress response , nutrition and promoting plant growth . The rhizosphere is considered a dynamic ‘hotspot’ of microbial diversity and ecological interactions across the plant–soil system. It comprises a thin layer of soil surrounding and sometimes adhered to the roots of superior vascular plants . The roots release a variety of exudates, mucilage and other compounds to the rhizosphere, via rhizodeposition , serving as source of carbon and energy to microorganisms, as well as chemotaxis signals that lead members of microbial communities in the surrounding soil, called bulk soil, to recognize and occupy niches in this region . Thus, microbial diversity and abundance in the rhizosphere zone can be largely higher compared with its main source in free-roots surrounding soil . Moreover, the microbial community structure in the rhizosphere soil can be largely different from that in the bulk soil, resulting in a potential functional differentiation . Except for some endophytes that come to soil adhered to or even colonizing the seeds of cultivated plants, the main source of microbial diversity in the rhizosphere is the bulk soil . Niche occupancy in the rhizosphere is believed to be dependent on the source and quantity of those substrates released by the roots , root architecture , plant species or genotype and development stage . In some cases, authors have found that the influence of the plant root system can surpass the effect of soil type or management for both assembly and functional potential . Forest-to-agriculture conversion is often found to be detrimental to microbial diversity . However, literature is controversial when linking taxa trade-offs with the consequences to functional potential and ecosystem services . Furthermore, the low number of environmental variables generally measured leads to the assumption that the same set of soil factors, markedly pH , rule microbial shifts when converting forest to agriculture systems.

A multidimensional approach, linking taxonomy, functions and a broader set of environmental variables, could enable researchers to depict correlations among diversity and niche occupancy, as well as to define the real factors modulating ecological patterns in agriculture soils . Thus, it is possible for microbial ecologists to depict those combinations, resulting in a better understanding of changes in microbial community assembly related to disturbances such as deforestation , changes in soils management or even natural ecosystems transitions over time . In this study, we hypothesize that soybean roots act as filters, selecting microbial communities via taxa trade-offs according to niche, to maintain functional resilience. Thus, we aimed to identify the microbial community patterns, in bulk soil and soybean rhizosphere, in a long-term forest-to-agriculture conversion chronosequence, in Eastern Amazon.Sampling fields are located into the Amazon Rainforest Biome, within the ‘Alto Xing ´u’ water basin, which is currently recognized as ‘the last agricultural border’ in the Southeastern Brazilian Amazon. Bulk soil samples were collected in January 2013, in agricultural fields, located in the municipality of Quer ˆencia , Mato Grosso State, Brazil . The climate of the region is Am ,vertical garden indoor with annual average temperature of 27◦C and annual precipitation of 1400 mm in 2013, composed of well defined periods of wet and drought . In order to evaluate long-term microbial dynamics we established a chronosequence varying from 1-year cultivation after deforestation, to 10- and 20-year cultivation in a no-till cropping system, with successive rotation of cultures. All areas were deforested via slash-and-burn, followed by cultivation with common rice for one season, in order to prepare the soil for further cropping. Since that, the selected areas have been cultivated in a no-till cropping system, with successive rotation, including: millet , ryegrass and black oat in the winter season, as cover plants, and maize and soybean in the summer season, as main crops. After deforestation, both areas received liming in the first year and each fifth year to increase and keep pH around 6. Fertilizers and pesticides had been regularly applied over time, according to cultivation demand and technical recommendation. We collected soil samples from the 0–20 cm profile, between lines of soybean plants at V6 stage, in a cartesian-geogrid scheme . Eight samples were mixed to form one composite sample × six replicates × three areas, totalling 18 composite soil samples. The straw layer was removed from topsoil and used as cover in the further greenhouse experiment, in order to keep soil cover conditions. All samples were transported to the laboratory within 48 h after sampling for implementation of the mesocosms experiment.Soil samples collected in the field were used to grow soybean plants in mesocosms at CENA-University of S˜ao Paulo, Brazil. The experiment was carried out in greenhouse in order to normalize the influence of environmental parameters, such as temperature and moisture. The vases were filed with 5 kg of soil from each composite sample. Then, six soybean seeds were sown in each vase. The straw collected in each chronosequence area was distributed in the vases according to the quantity found in each sampling point in the field. The experiment was carried out with 36 vases, consisting of 18 vases with plants, to evaluate the rhizosphere effect, and 18 vases with no plant, to evaluate the bulk soil effect, totalling 36 vases . The soil moisture in all vases was corrected for 60% of water holding capacity in the beginning of the experiment, and maintained via irrigation with deionized water . Soybean seeds were germinated at 28/20◦C and 12-h photoperiod. Ten days after germination, seeds with lower vigor were removed from the vases, keeping three plants per vase. The experiment was conducted until stage R1 , 65 days after sowing, from January to March 2013.Immediately, roots were briefly shaken to separate bulk from rhizosphere soil.

The soil that remained attached to the roots was defined as rhizosphere soil and extracted from the roots with the aid of a sterile brush. Soil samples from the control vases, with no plant, were collected and considered as bulk soil.A total of 10.7 million sequences were obtained by high throughput shotgun metagenomics, for 36 soil samples. Shannon’s α-diversity did not vary across bulk soil and rhizosphere, but did across the chronosequence, with samples from 1-year being less diverse than samples from 10- and 20-year, with no differences between 10- and 20-year no-till . Regardless of time, α-diversity was higher in bulk soil compared with the rhizosphere. Whittaker’s global β-diversity decreased along the chronosequence, in both bulk soil and rhizosphere, indicating that the communities in the same fraction became more similar in both sides of the interface. Despite that, regardless of time, β-diversity was always higher in rhizosphere compared with bulk soil. Based on results that showed a clear reduction of β-diversity, in both bulk soil and the rhizosphere over time, we asked whether community structure presented distinct patterns across time and soil fractions. Taxonomic NMDS plot revealed a separation of microbial communities from bulk soil and rhizosphere. Microbial community structure changed over time of no-till cropping, with separation of 1-year bulk soil samples from 10- and 20-year samples , the same as found for 1-yearrhizosphere samples, which differed from 10- and 20-year samples , with no differences between 10- and 20-year, in both bulk soil and rhizosphere . The taxonomic variation from the whole set of samples was 41%. We investigated possible shifts in microbial community composition through relative abundance of taxonomic profiles. From a total of 32 observed bacterial and archaeal phyla, 15 presented significant abundance differences between bulk soil and rhizosphere in 1-year, 19 in 10-year and 10 in 20-year no-till cropping . In almost all cases, relative abundances were higher in bulk soil than in rhizosphere, except for Proteobacteria and Bacteroidetes in 1-year, which presented higher abundance in rhizosphere. We also evaluated the microbial dynamics along the chronosequence. In bulk soil, 19 out of 30 bacterial phyla had significant changes in abundances after 20-year no-till cropping . Acidobacteria and Actinobacteria abundances decreased after 20-year no-till cropping, while 17 other phyla increased in abundance, markedly Proteobacteria , Planctomycetes , Gemmatimonadetes , Bacteroidetes , and both archaeal Euryarchaeota and Crenarchaeota . Yet for rhizosphere, from 1- to 20-year no-till cropping, 16 bacterial phyla had changes in abundance . Proteobacteria and Acidobacteria decreased their abundances, while 14 other phyla increased, with emphasis on Planctomycetes , Nitrospirae , Gemmatimonadetes , Firmicutes , Cyanobacteria , Aquifcae and both archaeal Euryarchaeota and Crenarchaeota . Owing the fact that Proteobacteria increased in bulk soil and decreased in rhizosphere after 20-year no-till cropping, we depicted its variability at class level . α– and β-Proteobacteria presented higher abundances in rhizosphere, while δ– and γ -Proteobacteria had higher abundances in bulk soil.

Non-CNN approaches have also been used to map irrigated and rainfed agriculture

My goal is to determine whether Mask R-CNN and FCIS produce substantially different results when applied to the task of segmenting center pivots, and if these differences are as pronounced as their performance on segmentation tasks in true color photography. This can inform the field of remote sensing on the value of adopting new CNN based architectures and to what extent performance on datasets like COCO translates to the remote sensing domain. The FCIS model is tested on the same Nebraska dataset with minimally altered hyperparameters based on Rieke , which applied FCIS to the task of segmenting large agricultural fields in Denmark. It is evaluated on a validation set, as the validation set was not used to make decisions to change hyperparameters. Both the FCIS model and the Mask R-CNN model performances on the Nebraska dataset are compared relative to their results on Common Objects in Context, a large image dataset similar to Imagenet but with added annotations for segments of objects. Imagenet is a dataset with 3.2 million images labeled with categories appearing in the images, while COCO or Common Objects in Context, contains 328,000 images with 2.5 million labeled instance segments . Answering these questions can help inform users of these models of their compared to one another and whether differences in performance are consistent across image domains. The ultimate goal of this research is to improve the mapping of field boundaries that are either actively cultivated, recently cultivated,vertical home farming or soon to be cultivated using the public Landsat record.

The dataset used does not allow for classifying particular crop types, though the Mask R-CNN method allows for this. Nevertheless, accurate field boundaries obtained from CNNs could make it much easier to classify crop type by allowing crop type classification procedures to focus on known field locations. The popularity of center pivots stems from their low energy consumption relative to the amount of water applied, low maintenance costs, and uniformity. These advantages make center pivot irrigation productive in both temperate regions and drylands with access to groundwater. However, while center pivots have been highly productive, their unchecked expansion in water-scarce regions has contributed to water shortages, over-tapped aquifers, and even political conflict. These risks are exemplified by Saudi Arabia in the 1970s and 80s, when center pivot development expanded and into the present day, where domestic production has slowed and must eventually decline . Like many of the world’s most agriculturally productive regions that depend on fossil water from aquifers, Saudi Arabia’s overall rate of irrigation has long since eclipsed groundwater recharge rates back to the Arabia Aquifer. As early as 1951, it was noted that the first government agricultural project at Al Kharj was dependent on ancient aquifer waters, and that parts of the project were already “… operating close to the margin of safety in regard to the current water supply.” . Since then, the government of Saudia Arabia has transformed large tracts of it’s deserts into fields of center pivots , resulting in a tripling of total water use between 1980 and 2006, 936% of total renewable water . This boom in irrigated area has been followed by a curtailing of domestic production, with the government pledging to eliminate water intensive wheat production by 2016 , though this commitment has since been postponed. Though researchers have called for more investment in demand side controls on water resource sustainability, the government has invested heavily in direct foreign investments in water intensive agriculture abroad.

Some of these investments are ongoing in areas experiencing extreme water scarcity, including southern Arizona, which depends on declining flows from the Colorado river, and Gambela, Ethiopia, where a government mandate to relocate smallholder farmers for a Saudi agriculture project led to armed conflict . There is a lack of public information on the distribution and ownership of large scale commercial agriculture, particularly in developing countries. Rectifying this knowledge gap will allow us to make more informed decisions of how to allocate water resources to best serve ecosystems, industry, and local populations. In regions where this information is available, it can enable improved monitoring of water rights compliance and experimentation with potentially more sustainable water management practices. A potentially successful strategy for mapping agriculture fields in developing countries where data is scarce is to train machine learning models using geospatial labels of fields located in regions with similar climates. The High Plains Aquifer region encompasses Nebraska, Kansas, Colorado, and Texas, spanning a climatological gradient that contains semi-arid and humid regions and supplies nearly one third of all groundwater irrigation applied in the United States. Since the invention of center pivot irrigation in Nebraska in the 1950s, the HPA has experienced significant water level declines as a result of groundwater pumping for irrigation . These were noted as early as the 1980s , yet substantial expansions in irrigated area continued to occur into the 2000’s, with Nebraska adding 1.3 million hectares of irrigated area, a 16.3% increase in total irrigated area for the state . Projections indicate that, assuming pumping at the same linearly extrapolated historic rate, overuse of groundwater will leave 50% of the southern and central HPA dry by 2025 and 2065 respectively . Since the 1980s, management strategies have been evaluated on the basis of whether or not they increase the sustainability of groundwater resources in the HPA while preserving local economies.

These strategies fall into two broad categories: water conservation strategies for increasing water use efficiency or limiting irrigation, and water productivity strategies that focus on increasing yields. Specifically, these strategies have ranged from switching irrigation methods from high pressure applicators to low energy precision agriculture or subsurface drip irrigation , converting land use from water intensive wheat to cotton , converting irrigated farmland to dryland agriculture , and variable rate irrigation. However, found that the dominant crop for drop strategy in Kansas between 1995 and 2005, switching from highly pressurized applicators to low energy precision agriculture systems, still coincided with an unsustainable increase in the total volume of irrigation. The increased profits and water savings generated by the technology switch enabled growers to expand irrigated acreage as well as switch to water intensive corn, alfalfa and soybeans, contributing to a consistent declining trend in the southern and central HPA. Colaizzi et al. 2009 also recounts that for the southern HPA in Texas between 1958 and 2000, despite many center pivot systems switching to more efficient LEPA irrigation methods,vertical grow total irrigation volumes increased with total irrigated acreage. Deines et al. underscores the consequences of a continued present trend of unsustainable groundwater management; they estimate that of the current irrigated acreage across the HPA, possibly 24% would undergo a forced transition to dryland agriculture by 2100, 13% of which is likely unsuitable for crop production, resulting in substantial negative impact to local economies. This body of research suggests that strategies to increase water use efficiency or yields are not by themselves sufficient to sustainably manage finite ground water resources. Groundwater management areas that have the power to tax, educate, and regulate jurisdictions were evaluated on their effectiveness throughout the HPA in Nebraska, Kansas, and Texas. This analysis used well hydrographs, which were missing or absent in some jurisdictions, making a complete analysis of management area effects difficult . This data gap can be supplemented by satellite and machine learning derived estimates of field locations and water use, where irrigation supplied is sourced from groundwater. In regions of the world without good, publicly available records of well hydrographs, remote sensing and machine learning will need to play a role in monitoring and evaluating groundwater use and groundwater management policies. Understanding the spatial location and extent of center pivot agriculture is critical to monitoring, enforcement, and evaluating the effectiveness of groundwater management.

Machine learning models trained on remotely sensed imagery can provide real time predictions on location and extent. In 1986, Strahler et al. identified two classes of models remote sensing – low resolution models where the pixel size is larger than the object of interest and high resolution models where an object is resolved by many pixels . For machine learning problems in remote sensing, the scale of the object of interest determines whether a low resolution or a high resolution model can be employed to locate and classify the object of interest. In many cases, low resolution approaches have been used to classify pixels using only spectral information, even in cases where the high resolution method is available to map objects of interest in finer spatial and semantic detail. Random forest has been the most popular pixel based classifier of choice in remote sensing and has been used to produce global and regional, coarse resolution LULC maps from MODIS imagery . The spatial resolution of the imagery used for global land cover mapping obfuscates any spatial features that can be used to distinguish land cover classes. For global, coarse, land cover mapping, random forest is a more appropriate method than a CNN model pretrained to interpret hierarchies of spatial features. Random Forest has also been applied for regional mapping of particular land cover categories, including irrigated area. While pixel-wise, irrigated area maps are useful, the method used to generate them discards valuable information embedded in the labels, which distinguish between unique fields. Instead of using pixel-wise methods, which only consider spectral information at a point as a feature to classify a pixel, object oriented classification methods that make use of spatial features provided by higher spatial resolution can be used to map objects in finer spatial and semantic detail. These techniques require information about the size, distribution, temporal pattern, and ranges of reflectance in order to be effective, and this can lead to overfitting to particular regions. For circular feature detection, Hough transform methods have been employed, which require prior knowledge of the radii of the objects. However, the Hough transform must be tuned to changes in illumination that happen over a scene or multiple scenes, and also has problems handling changing topographic relief. While it has been successfully used to extract very visually distinct, perfectly circular oil tanks, as well as circular geologic features, each case required visual inspection, manual tuning . In the case of , the scenes tested were very small , allowing for easier manual tuning and less scene diversity for the method to handle. In the case of Cross 1988, there were substantial commission errors. Generally, center pivots are not amenable to this family of techniques since they are not always perfect circles, come in a varying range of sizes which makes the Hough transform more computationally burdensome, can have variable brightness within the field, and are present across the world in varying topographic relief and illumination. All of the aforementioned techniques have a limited capacity to learn complex patterns for object recognition relative to the current state-of-the-art, CNNs. This was demonstrated clearly in 2012 on the task of classifying photographic scenes, with CNNs outperforming the previous state of the art . There is still a need to evaluate how CNN based instance segmentation models perform on coarser resolution sensors like Landsat OLI, given the more limited size of labeled datasets compared to large image datasets like Imagenet and COCO. The simplest model of a neural network can be described as a pipeline which takes as input training data and performs a sequence of linear regressions and nonlinear functions, with each layer in each sequence operating on the output of the previous layer . When used in a supervised machine learning task, the goal in training a neural network is to learn the set of all parameters w and b across all layers of the neural network in order to achieve minimal loss relative to a reference dataset. The learning process is conducted through gradient descent, where a model’s parameters are first initialized and then the model is tested on data that has corresponding reference labels. The difference between the results and the reference labels are then used to adjust model parameters by a learning rate through a method called back propagation, a method for efficient auto differentiation. Model weights are either randomly assigned or taken from a previously trained model, which is referred to as “pretraining” a neural network model.

The emphasis has been on empirical analysis of listorical phenomena

Some argue that a major historical strength of agricultural economics has been its tolerance for a range of methodological approaches. Early agricultural economists drew on production agriculture, accounting and business, classical, neoclassical, and institutional economics. Some have even argued that the very parochialism and fragmentation of agricultural economics have been the basis for many of its most important contributions . In a different vein, Bonnen has argued that “… agricultural economics has been drifting toward an anti-empirical and a disciplinary outlook, away from the great empirical tradition around which the profession was built and upon which its reputation ·still rests.” – While some have identified excessive fragmentation along geographic and sub-disciplinary lines as a factor limiting the effectiveness of agricultural economics , others have taken refuge in the glowing account expressed by Leontief in his presidential address to the American Agricultural Economic Association: “An exceptional example of a healthy balance between theoretical and empirical analysis and of the readiness of professional economists who cooperate with experts in the neighboring disciplines is offered by agricultural economics as it developed in this country over the last fifty years.” Few would argue that Leontiefs observations which focus on the period of 1920 through 1970 still hold with equal force today.

Does the diversity within agricultural economics enhance or detract from the creation of knowledge? An appropriate degree of diversity creates cross fertilization of ideas and a healthy tension which exposes inferior applications. But has the diversity become excessive? Given the degree of diversity within the AAEA,indoor growers do the current policies and practices of the association enhance or detract from the creation of useful knowledge? Do the media products of the AAEA promote and encourage new ideas, methods, institutions, theories, data, or articulation of important problems? Do they foster scientific inquiry, dialogue, and debate? What are the research values of our collective organization, the AAEA? The objective of this paper is to assess the above questions. Professions are clubs whose tribal behavior should be examined from time to time in order to evaluate whether they are on a path to extinction.. Accordingly, o~ purpose here is to make an assessment of our current professional state which reflects not only our views but the collective views of the AAEA membership. In making our assessment and evaluation, the current configuration of the profession is taken as given . The paper begins with a review of some anecdotal evidence in the next section followed by some data based evidence on the current state of the profession.

Some selected problems for which little or no empirical evidence is available are highlighted. The argument is made that the value of the profession and role of data in advancing knowledge has led to a number of serious self. imposed limitations. The tendency of the profession toward solution-rich or technique-oriented approaches is examined. These problems have hindered creativity a~d left the profession unable to take advantage of the opportunities for innovations that have been available. In essence, we shall argue that many missed opportunities are the result of the profession tinkering at the margins rather than designing, reforming, and promoting more effective institutions . These themes largely reflect subjective interpretation of the anecdotal evidence. To support or refute this interpretation, results of a survey of the AAEA membership are presented. This survey was conducted in the spring of 1989 and provides the database for analytically judging the interpretations and perspectives of anecdotal episodes presented herein.A number of major shocks have occurred in both U. S. and world agriculture over the last few decades. None of these shocks or their impacts were anticipated by publications of the profession. For example, the huge commodity price explosion of the early 1970s surprised all interested observers. No ex ante analysis was conducted prior to 1971 that even weakly suggested such a price explosion was a realistic possibility.

Many ex post analyses have now been conducted that isolate the Soviet grain deal, the deregulation of the overvalued dollar, trade barriers, and worldwide economic growth as some of the explanations for the events of the early 1970s. In fact, not until three years after the fust devaluation of the dollar and two years after its deregulation did anyone in the profession attempt to evaluate its implications for U. S. agriculture . It is important to note that this study was based on personal understanding and experience and involved the heuristic application of basic economic principles. The study did not formally analyze any secondary or primary data. Furthermore, if secondary time series data had been utilized at that time, no.significant effect would have been isolated between exchange rates and any performance measures for U. S. agriculture because of limited data availability following devaluation. In the early 1980s those concerned with U. S. and world agriculture were again surprised. Although there were studies In the late 1970s of the relationship between the macroeconomic environment and U. S. agriculture, few if any serious ex ante analyses were reported in the literature. Perhaps more importantly after the Volcker Federal Reserve Policy Announcement of 1979, no ex ante analysis was reported by the profession on the potential effect of real interest rate increases on U. S. agriculture. It was not until commodity markets plummeted in 1981 that the potential effects of monetary and fiscal policy on U. S. agriculture were seriously evaluated. Since the macroeconomic environment had been reasonably stable over much of the 1960s and 1970s, ex post historical analysis could not identify a significant relationship between nominal or real interest rates and the U. S. agricultural sector . To address this difficulty, Freebairn, Rausser, and de Gorter developed a simulation model with some empirically estimared and some hypothetical parameters to explain the events of 1981. Similarly, Just demonstrated that an extended capitalization formula calibrated to preVolcker events could have predicted the land price decline beginning in 1982 in terms of interest rate and inflation phenomena. But these types of approaches could have been undertaken as early as late 1979 or early 1980.

Given the vulnerability of U. S. agriculture in 1980 to optimistic expectations, why did the profession not provide some crisp but qualified warning signals? Conventional wisdom today is that U. S. macroeconomic policy in the early 1980s helped destroy U. S. agricultural export potential while escalating its costs and leaving it in the deepest financial crisis since the great. depression. Why was this possible outcome not even remotely entertained in the forums of the profession in the early 1980s? Again, it is important to note that the early studies which began to sort out the role of new phenomena affecting agriculture were based on personal understanding and experience and involved the heuristic application of basic economic principles. The lesson of these war stories is that when undue weight is placed on ex post data analysis, future events will always present surprises. These same points arise in a number of current topical problems. For example, with respect to the General Agreement on Tariffs and Trade negotiations,danish trolley there have been no serious evaluations of the dynamic path that might result from any proposals that- have been tabled by the U. S. Trade Representative.The profession has imposed a number of limitations on what constitutes acceptable research.The philosophical base for much of this focus is provided by Popper. Popper emphasizes explanation of observable phenomena and introduces the notion of falsification as the rigorous standard for scientific procedure. Kuhn, in his study of scientific progress, found no support for Popper’s idealization for science-falsifying instances seldom lead to the revocation of theory. Among economists, McCloskey has advanced the view that economic research is basically essays in persuasion. One of the dominant characteristics of the profession is its insistence on objectivity. Objectivity is much like motherhood and apple pie; if it could be achieved, we would all warmly welcome its presence. The difficulty, however, is that in principle an infinite number of hypotheses are capable of explaining a given finite body of non-experimental data. Accordingly, the only objectivity that exists emanates from the clash of individual subjectivities. As Keynes argued long ago, “It;is astonishing what foolish things one can temporarily believe if one thinks tou long alone….” Discussion and debate with colleagues provide a useful defense against one’s own foolish subjective beliefs. In the context of falsification and the explanation of observable phenomenon, a number of solution techniques have been developed from mathematical statistics, econometrics, operations research, etc. This technology has been utilized sometimes wisely and sometimes unwisely. In general, the technology imposes a logic which limits the role of intuition. In contrast, it is interesting to observe how many members of the profession that trade in futures markets do so on the basis of formal econometric models as opposed to intuition and heuristic application of economic principles.

The technology that has been embraced by’ the profession is largely computer based. In many research applications, this technology has been used as a substitute for creativity and seriolls thought. In fact, available technology along with its standardized solutions often leads to a “have model will travel” mentality.- For some years now, the AlAE and AAEA meetings have been dominated by solution-oriented or technique approaches. This professional behavior has severely limited originality. Many of our recent graduates spend most of their time wondering about the applications they can make of standardized solution frameworks rather than finding interesting problems that require the development of customized frameworks. Given the small weight our profession places on case studies and induction, this is not surprising. Due to self-imposed limitations and the promotion of a solution rich environment, our profession has missed many opportunities for creativity. This is especially in the field of new institutional economics. As Ruttan and Hayami have argued, the largest payoff to the public interest is to the area of institutional innovation. For example, throughout the world there is a serious problem of financing public good and infrastructure investments in agriculture. In the case of the United States, Bonnen argues “… that responsibility for coordination of agricultural science policy is shifting from a predominantly public function to more of a shared public and private responsibility, making both policy and its coordination more complex.” What institutional frameworks have been advanced by our profession to determine sustainable burden sharing arrangements between the public and private sector to finance various quasi-public goods? Does our profession encourage and reward its members for designing such institutions? On the methodological front, why has so little effort been undertaken to explain collective organizational behavior. Why have no basic propositions been empirically tested that focus on the distribution of power in collective groups? We always find members with unequal influence being compensated by collective organizations. Why have we not exploited our traditional relationships with rural sociologists and other disciplines to advance the frontiers of knowledge in this area of inquiry?From the anecdotal evidence outlined above, a number of questions and hypotheses emerge. Some of these hypotheses relate to the linkages among academic, extension, industry, and government – components of the profession. Do the applied components of the AAEA find different approaches effective than are emphasized by academic components and the med!a ? How does the importance of f0011al models and econometric analysis vs. heuristic application of economic principles and intuition differ among the various components of the profession? Are the channels of communication among these various groups within the Association highly integrative and interactive or are they channeled and separate? How well are the problems faced in the applied components of the profession communicated to the academic community and how well do the products of the academic community serve the applied components? In acquiring human capital, what is the best relative emphasis of training on various types of techniques, conceptual frameworks, and case studies, and how does that compare with the training that has been received? Many other questions naturally arise. What empirical evidence is used in the analyses conducted by members of the Association? Are the frameworks of analysis used by the various components of the ‘profession formal or informal?

Extension workers are often expected to cover large areas with limited staff

Although farmers’ trust and understanding surely influences demand for weather index insurance, resolving these concerns has not proven sufficient to solve the demand issues that beset this financial product. Demand for insurance does increase when farmers observe payouts over time ; receiving payouts in the previous year has a strong effect on increasing subsequent demand, increasing demand by almost 30% . However, not receiving payouts when a fair price has been paid has a strong negative effect on subsequent demand. Since the latter state is the ‘normal’ year for insurance consumers, this does not bode well for the adoption of insurance and its commercial viability. Cole et al. explicitly tests whether efforts to improve trust can improve take-up of insurance, finding that “in Andhra Pradesh, households were more likely to purchase insurance if an agent from a well-known microfinance institution endorsed the product, but in Gujarat, a similar endorsement in a marketing video had no effect” . In another study from Gujarat, Gaurav et al. found that receiving an invitation to a financial literacy training increased take-up by 5.3 percentage points, but the cost of the training was more than three times the full cost of premiums. Basis risk, the risk that the official weather observation will not accurately reflect a farmer’s loss,hydroponic net pots can also dampen take-up. For example, a farmer may experience poor weather conditions that damage their harvest, but if rainfall at the weather station is adequate, there would be no pay out.

Mobarak and Rosenzweig find in Uttar Pradesh that for every kilometer increase in perceived distance from the weather station, demand for weather index insurance decreases by 6.4% . This suggests that improvements in index design could help resolve basis risk, using improved data to more closely align the experienced conditions of smallholders’ plots with the measured conditions at data collection facilities used to set the index. Since basis risk is largely covariate for a geographic area, another promising approach appears to be the provision of insurance to groups that are already providing informal risk pooling of idiosyncratic risks among their membership . Index insurance can be a complement to informal risk mitigation where these informal risk pooling arrangements are smoothing idiosyncratic risks.Where insurance projects have been successful in achieving widespread uptake they tend to increase the appetite for activities vulnerable to risk . Magruder emphasizes, however, that these studies are “a handful of promising results that suggest the potential for risk reduction to spur technology adoption,” necessarily drawn from exceptional contexts where insurance take-up rates were sufficient to detect the impacts of insurance on productive technology adoption. This shift to higher risk and potentially more profitable production can have the somewhat counter intuitive effect of increasing the overall exposure of agricultural activity to rainfall volatility.

Insured households are better financially insulated , but landless laborers, whose income relies on harvesting crops, may become more exposed to risk as a result . This is concerning if the poorest rural households have limited alternatives should agricultural wage labor opportunities disappear.Scientists have developed stress-tolerant crops to protect farmers and help the broader agricultural system cope with extreme weather. These breeder-selected varieties of common seeds are agronomically designed to maintain high yields if a drought or flood occurs. Dar et al. conducted a two-year randomized evaluation with the International Rice Research Institute to study the effects of a flood-tolerant rice variety, Swarna-Sub1, on rice yields and farmer behavior in Odisha, India. Switching from Swarna to SwarnaSub1 cultivation does not require significant changes in farmer behavior. Flood-tolerant Swarna-Sub1 rice reduced risk for smallholder farmers and encouraged additional investment in their farms, resulting in substantially increased yields in both flood and non-flood years . Yields of this flood resistant rice variety were as good as regular varieties in normal conditions and superior during floods, and the yield gain went disproportionately to low-caste farmers because of the less-desirable risk-prone location of their lands due to generations of social marginalization . Higher yields increased farmers’ revenue by approximately US$47 per hectare relative to farmers in comparison villages, and 36% of this additional revenue was reinvested in their land. The results show how farmers respond to risk reduction by crowding in other investments and technological changes, which effectively double farmers’ expected gains: first from the agronomic benefits of the improved seed itself, and an equal benefit reaped from unlocking more productive practices when protected from risk.

These researchers are now evaluating the long-term effects of Swarna Sub-1, as well as the yield, welfare, and labor market impacts of other drought- and saline-tolerant crop varieties. Yet longer-term analysis shows that even improved varieties like SwarnaSub1, with demonstrated impacts and effective demand, have not been widely adopted. This shifts attention to other constraints, particularly the importance of seed supply and extension systems for technology diffusion, which will be discussed in the remaining sections in this chapter.Given insufficient demand for individual-level insurance, and the limited availability of agronomic technologies that could single handedly protect farmers across a wide range of agro-climatic conditions, ongoing research is focused on adjustments to, and combinations of, these risk mitigation approaches . Could institutions engage in risk-sharing to move risk away from particularly vulnerable smallholder farmers? For example, perhaps financial institutions or governments could serve as clients for meso-level insurance to see if that benefits smallholder farmers. “Under this arrangement, [e.g. the World Bank Group’s Global Index Insurance Facility and African Reinsurance Corporation plan], a government or institution would reimburse insurers above a set loss ratio. This decreases risk and costs for insurers and could lead to lower premiums for farmers” .Or, given willingness to pay for individual insurance has been a challenge, perhaps free or subsidized insurance could be offered as a form of social protection, achieving a multiplier effect by releasing farmers’ production decisions from risk constraints. Ongoing research is testing strategic combinations of financial products including index insurance, precautionary savings, and emergency credit , to understand if these bundled products can protect smallholders across a spectrum of risks of varying severity . Or perhaps financial products could be combined with risk-protective seeds in an attempt to better mitigate risk under a wide range of conditions. An evaluation is underway by Carter et al. that offers a combination of drought tolerant maize and index insurance in Tanzania and Mozambique, given index insurance could protect against the extremely adverse events that prove so taxing that even stress-resistant seeds fail .Farmers face a range of potential production technologies and practices to choose from, each of which may have different risk profiles and different suitability for a farmer’s own plots. Many technologies have heterogeneous returns that vary based on local plot characteristics and complementary input choices or agronomic practices . In addition,blueberry grow pot any single year farmers can only observe the performance of the technology under one weather realization, and understandably have trouble predicting outcomes under a range of different conditions they could experience in the future. A variety of specific information is therefore necessary for farmers to make good decisions as to which technologies to use at which specific points in time. Extension services have been a common approach used to inform farmers and encourage technology adoption, and have traditionally been one of national agriculture ministries’ main types of expenditure . The available literature has compiled summary statistics on the type of learning outcomes that we should hope to see as a result of large investments in extension systems. These statistics match anecdotal understanding that often status quo extension systems are characterized by limited supply of extension agents in communities, and even where available, low engagement with services, low adoption of recommendations, as well as low information diffusion beyond a select few contact farmers .For example, in Mozambique, countrywide extension coverage is as low as 1.3 agents per 10,000 rural people , while in Malawi approximately 50% of government extension positions remain unfilled .

Glendenning et al. estimated that fewer than 6% of the agricultural population in India reported having received information from the Government of India’s decades-long extension program. Extension workers may simply shirk responsibilities, or choose to focus their attention on villages or individuals based on their convenience to reach or perceived potential , and may neglect more marginalized farmers from poorer or female-headed households . Rural extension services are difficult and costly to monitor, limiting the potential to hold extension workers accountable and providing little direct incentive to show up for work or complete their duties . Duflo et al. shows another, perhaps intuitive, reason that extension can be ineffective – if it is promoting the use of a technology that is not profitable. They found that test plots using fertilizer recommendations from the Kenyan Ministry of Agriculture did not encourage fertilizer adoption by farmers. In this case, Duflo et al. calculate that these fertilizer recommendations would have increased farmers’ yields if applied, but would have actually reduced farmer profits. For food security and broader economic reasons, governments are often interested in maximizing yields, and thus extension programs may make regional-level fertilizer and other input use recommendations that target yield outcomes that farmers disregard as unlikely to be profitable. Gearing extension service recommendations based on what is profitable is important to drive adoption. Traditional extension models that directly train “contact” farmers typically do so given budget limitations, in the hopes that these contact farmers will share information and encourage new practices among other farmers. Analyses of panel data suggest that farmers learn from observing the decisions and experiences of people in their social networks . Conly and Udry in particular examine the importance of heterogeneity in observable characteristics of demonstrators for the transferability and diffusion rate of a particular technology, and Rogers discusses how “trialability,” the degree to which a potential adopter can try something out on a small scale first before adopting it completely, can also be an important factor for adoption. We present findings from randomized evaluations that show how agricultural extension can be more effective in both the initial design of channels and pedagogy, and the use of social networks to encourage the spread of technology adoption.Evidence presented in this section shows that well-designed information provision attuned to smallholders’ information needs and social networks can encourage poor farmers to invest in new technologies. There is a need to support farmers’ decision-making when introducing unfamiliar inputs, and early evidence demonstrates how to adapt information provision systems to support smallholder farmers’ in technology adoption. The inefficiencies that cripple extension systems and lead to information constraints can also impede the physical availability of technologies. In Uganda, Bandiera et al. worked alongside BRAC’s roll out of their female extension worker model in Uganda to understand how social networks, credit constraints, and expectations about the returns to technology affect adoption decisions of improved seed varieties and modern farming practices such as zero tillage, line sowing, and disease prevention. Increases in agricultural productivity were achieved by effectively targeting the key “accessibility” constraint in this context — farmers previously had little access to quality improved seeds. However, social connections mediated input market access: extension workers play an important role in selling seeds, and farmers in their networks had better access to technologies compared to less-connected farmers. Emerick et al. are extending their work on improved seed and extension in Odisha to understand how agents in input value chains can effectively provide both information and technology. Given that a lack of information is unlikely the primary constraint to technology adoption where inputs are not locally accessible, and improved quantities, qualities, or varieties of output may not be profitable in current market structures, we turn our attention to supply chains and markets.Output market dynamics can also affect smallholder decision-making. Information about market conditions and prevailing prices could influence farmers’ decisions of how, when, and where to sell their harvest, while search and transport costs, and relationships with traders, mediate producers’ access to potential points of sale. In Sub-Saharan Africa in particular, farmers are limited in their ability to access lucrative options for sale .

We measured the child’s height and weight at the time that spirometry was performed

Ultimately, addressing the question of population-level effects will likely depend on a combination of measured field data, incorporating new methodologies for assessing survival to adulthood , and fish population models.Our observations must be considered within the constraints of the infrastructure in the Yolo Bypass during the time of our field work. In the intervening years since our field studies, there has been a substantial amount of progress in improving Yolo Bypass infrastructure to support native fishes. During 2017-2018, an inflatable dam fish barrier and collection facility was constructed at Wallace Weir at Knights Landing Ridge Cut . This facility can enhance potential water distribution options for managed flooding studies under relatively low flow conditions when only Yolo Bypass tributary flows are available, including sources from Colusa Basin, which may not always have suitable water quality for juvenile salmonids. It is important to note, however, that these local water sources are not useful unless there is improved connectivity with the Sacramento River, allowing wild juvenile salmon to access seasonal habitat throughout the Yolo Bypass. To that end,fodder growing system the joint Environmental Impact Statement/Report was finalized in 2019 for a project that will improve connectivity between the Sacramento River and Yolo Bypass with a proposed notch in Fremont Weir .

This proposed facility would allow managed flows at lower Sacramento River stages than the current weir structure, thereby increasing the frequency and duration of seasonal inundation, and providing improved access to the floodplain from the Sacramento River fish migration corridor. This upgrade is required as a condition of the 2009 Biological Opinion for Salmonids for long term operation of the federal and state water projects . Our study did not specifically address these new facilities or their operations, and how the concept of managed agricultural floodplain habitat can be integrated into the primary purposes of these improvements. Hence, potential use of flooded agricultural fields as juvenile salmon rearing habitat should be evaluated in light of both a modified hydrology and local land use and infrastructure changes. Additional research is needed to address the efficacy and suitability of different potential water sources, hydrology timing, connectivity with the Sacramento River, and related issues, such as the effects of operations on land use and other species or life stages .Metam sodium, 1, 3-dichloropropene , methyl bromide, and chloropicrin are high use agricultural fumigants that account for about 20% of the annual pesticide usage in California . These fumigants are known respiratory toxicants and were the top four pesticides ranked by chronic health risk based on a risk assessment conducted in the early 2000s . Methyl bromide, 1, 3-DCP and chloropicrin have also been identified as the top three pesticides of public health concern used near schools .

An evaluation of pesticide drift-related illnesses in 11 states found that the largest percentage of cases were related to fumigant applications, indicating the particularly hazardous nature of these substances . Methyl bromide was banned by the Montreal Protocol due to harmful effects on the ozone layer and is currently being phased out of use, resulting in increased usage of chloropicrin, metam sodium and 1,3-DCP in recent years . Cases of acute methyl bromide exposure in adults and children have produced symptoms such as shortness of breath, pulmonary edema, cough, respiratory irritation and respiratory arrest . In the Agricultural Health Study, which examines pesticides and health in a cohort of pesticide applicators and their families, methyl bromide application was associated with higher prevalence of chronic bronchitis in nonsmoking wives of farmers . Metam sodium degrades into methylisothiocyanate, which is known to irritate respiratory tissue , and then further breaks down into methylisocyanate, the active ingredient responsible for the Bhopal tragedy that killed more than 3500 people . In the Bhopal tragedy the most common and serious problems were related to respiratory symptoms . Cases of metam sodium-related illnesses have involved minor respiratory symptoms including coughing and dyspnea . A metam sodium spill in California resulted in persistent respiratoryhealth problems for nearby residents . In a case study of drift from a metam sodium application in California, an association between cases of respiratory illness in nearby residents and proximity to the application area was observed . Increased respiratory symptoms have been reported as a result of community exposure to chloropicrin following application .

A larger analysis of chloropicrin-related illness in California from 1992–2003 found that 54% of cases involved respiratory irritation . Toxicology studies conducted on rodents have shown that 1, 3-DCP exposure is related to benign lung tumor incidence as well as enlargement of the respiratory epithelium. Several epidemiological studies have found an association between occupational exposure to pesticides and an increased risk of respiratory symptoms and asthma . No research to date has been conducted on fumigant exposure and respiratory health in children, who are particularly vulnerable to inhalation risk due to relatively higher inhalation-rate-to-body-weight ratios . There are no biomarkers available to assess human exposure to fumigants in epidemiologic studies . Residential proximity to fumigant use is currently the best method to characterize potential exposure to fumigants. California has maintained a Pesticide Use Reporting system which requires commercial growers to report all agricultural pesticide use since 1990 . A study using PUR data showed that methyl bromide use within ~8 km radius around monitoring sites explained 95% of the variance in methyl bromide air concentrations, indicating a direct relationship between nearby agricultural use and potential community exposure . In the present study, we investigate associations of residential proximity to agricultural fumigant usage during pregnancy and childhood with respiratory symptoms and pulmonary function in 7-year-old children participating in the Center for the Health Assessment of Mothers and Children of Salinas , a longitudinal birth cohort study of primarily low-income Latino farm worker families living in the agricultural community of the Salinas Valley, California.We enrolled 601 pregnant women in the CHAMACOS study between October 1999 and October 2000. Women were eligible for the study if they were ≥18 years of age, <20 weeks gestation, planning to deliver at the county hospital, English or Spanish speaking, and eligible for low-income health insurance . We followed the women through delivery of 537 live-born children. Research protocols were approved by The University of California, Berkeley,chicken fodder system Committee for the Protection of Human Subjects. We obtained written informed consent from the mothers and children’s oral assent at age 7. Information on respiratory symptoms and use of asthma medication was available for 347 children at age 7. Spirometry was performed by 279 of these 7-year-olds. We excluded participants from the prenatal analyses for whom we had residential history information for less than 80% of their pregnancy. We excluded participants from the postnatal analyses for whom we had residential history information for less than 80% of the child’s lifetime from birth to the date of the 7 year assessment. Prenatal estimates of proximity to fumigant applications and relevant covariate data were available for 257 children and postnatal estimates of proximity to fumigant applications and relevant covariate data were available for 276 children for whom we obtained details of prescribed asthma medications and respiratory symptoms. Prenatal estimates of proximity to fumigant applications and relevant covariate data were available for 229, 208, and 208 children for whom we had FEV1, FVC and FEF25–75 measurements, respectively. Postnatal estimates of proximity to fumigant applications and relevant covariate data were available for 212, 193, and 193 children with FEV1, FVC and FEF25–75 measurements, respectively.

A total of 294 participants were included in either the prenatal or postnatal analyses. Participants included in this analysis did not differ significantly from the original full cohort on most attributes, including maternal asthma, maternal education, marital status, poverty category, and child’s birth weight. However, mothers of children included in the present study were slightly older and more likely to be Latino than those from the initial cohort. Women were interviewed twice during pregnancy , following delivery, and when their children were 0.5, 1, 2, 3.5, 5, and 7 years old. Information from prenatal and delivery medical records was abstracted by a registered nurse. Home visits were conducted by trained personnel during pregnancy and when the children were 0.5, 1, 2, 3.5 and 5-years old. At the 7-year-old visit, mothers were interviewed about their children’s respiratory symptoms, using questions adapted from the International Study of Asthma and Allergies in Childhood questionnaire . Additionally, mothers were asked whether the child had been prescribed any medication for asthma or wheezing/whistling, or tightness in the chest. We defined respiratory symptoms as a binary outcome based on a positive response at the 7- year-old visit to any of the following during the previous 12 months: wheezing or whistling in the chest; wheezing, whistling, or shortness of breath so severe that the child could not finish saying a sentence; trouble going to sleep or being awakened from sleep because of wheezing, whistling, shortness of breath, or coughing when the child did not have a cold; or having to stop running or playing active games because of wheezing, whistling, shortness of breath, or coughing when the child did not have a cold. In addition, a child was included as having respiratory symptoms if the mother reported use of asthma medications, even in the absence of the above symptoms.Three identical EasyOne spirometers were used . Routine calibration was performed every morning and 92% of tests were conducted by the same technician. The expiratory flow-volume curves were reviewed by two physicians experienced in pediatric spirometry, and only adequate quality data were included in the statistical analyses. Some participants with adequate quality data for FEV1 did not provide adequate quality data to calculate FVC or FEF25–75. Young children have difficulty sustaining forceful exhalation after a deep breath that is required to produce a plateau in airflow and calculate FVC and subsequently FEF25–75. Each child performed a maximum of eight expiratory maneuvers and up to three best acceptable tests were saved by the spirometric device software. Latitude and longitude coordinates of participants’ homes were collected during home visits during pregnancy and when the children were 0.5, 1, 2, 3.5 and 5 years old using a handheld Global Positioning System unit . At the 7-year visit, mothers were asked if the family had moved since the 5-year visit, and if so, the new address was recorded. We used Geographic Information System software to geocode the new addresses and obtain coordinates. Residential mobility was common in the study population. We estimated the use of agricultural fumigants near each child’s residence using a GIS based on the location of each child’s residence and the Pesticide Use Report data . Mandatory reporting of all agricultural pesticide applications is required in California, including the active ingredient, quantity applied, acres treated, crop treated, and date and location within 1-square-mile sections defined by the Public Land Survey System . Before analysis, the PUR data were edited to correct for likely outliers with unusually high application rates using previously described methods . We computed nearby fumigant use applied within each buffer distance for combinations of distance from the residence and time periods . The range of distances best captured the spatial scale that most strongly correlated with concentrations of methyl bromide and 1,3-DCP in air . We weighted fumigant use near homes based on the proportion of each square-mile PLSS that was within each buffer surrounding a residence. To account for the potential downwind transport of fumigants from the application site, we obtained data on wind direction from the closest meteorological station . We calculated wind frequency using the proportion of time that the wind blew from each of eight directions during the week after the fumigant application to capture the peak time of fumigant emissions from treated fields . We determined the direction of each PLSS section centroid relative to residences and weighted fumigant use in a section according to the percentage of time that the wind blew from that direction for the week after application. We summed fumigant use over pregnancy , from birth to the 7-year visit and for the year prior to the 7-year visit yielding estimates of the wind-weighted amount of each fumigant applied within each buffer distance and time period around the corresponding residences for each child.

Several studies have documented the benefits of direct markets to small-scale farmers

The rising temperature will severely reduce the snow pack in the Sierra Nevada, which currently provides almost as much storage at the beginning of the irrigation season as the state’s man-made reservoirs. By the end of the century, the spring snow pack is projected to decline by 30-70% under the B1 scenario, and by 70-90% under the A1FI scenario. This would drastically reduce water supply available in the late spring and summer, when roughly 75% of all water use in California occurs. Moreover, the rise in summer-time temperatures will cause an increase in the water demand during this period both in agriculture and for urban uses.The result is likely to be an increased scarcity of water in California.Obviously, this can be dealt with in a variety of ways, including water marketing, increased conservation, water rights re-allocation, the improved operation of existing reservoirs, the construction of new reservoirs, and the development of conjunctive use schemes. The likely mix of solutions and their potential cost are being assessed in a case study now being conducted for the State of California. Although we do not cite quantitative results here because the work is still in progress, it seems likely that economic losses could be quite substantial in the aggregate. In the face of an increasingly consolidated, industrialized, and often faceless food system,plant pot drain many researchers and activists have looked to alternative food institutions as ways to improve the food system for both producers and consumers.

AFIs include urban gardens, food policy councils, alternative education programs, farmers’ markets, and community supported agriculture . These last two are of particular interest because many perceive them to be win-win for both producers and consumers. That is, even though farmers’ markets and CSAs were originally developed to provide markets for the increasingly beleaguered small-scale and family farmers, recently the goals of food security have been attached to these AFIs. At least since the 1995 Farm Bill discussions, the community food security and sustainable agriculture movements have made a strategic alliance, combining the goals of farm security and food security in their platforms and projects . Among their strategies for achieving these goals is the continued development of alternative agrifood institutions, including farmers’ markets and community supported agriculture arrangements. The hope is that eliminating market intermediaries will improve the food system for both disadvantaged small-scale farmers and low-income consumers. Is it possible to simultaneously make fresh, nutritious food affordable to low-income people while providing a decent return to small-scale farmers through farmers’ markets and CSAs? Certainly, direct marketing opportunities like farmers’ markets and CSAs can be economic lifelines for small-scale growers, particularly those using sustainable farming practices. Not only do direct markets enable growers to avoid transactions with middlemen and sell products at retail prices , they also often provide the only space through which small-scale farmers with limited production can enter the market.

Less is known, however, about how well they serve low-income consumers. The goal of our research, therefore, was to see how and to what extent California CSAs and farmers’ markets are addressing food security in both concept and practice. We focused our research on these two types of AFIs because they best exemplify the idea of an economic “win-win” situation for farmers and consumers. Also, unlike farm-to-school programs and community gardens, which tend to operate as public-private partnerships, farmers’ markets and CSAs operate more fully under the constraints and opportunities of the market. The majority of our data were gathered through a survey conducted of farmers’ market and CSA managers in California during 2004– 2005. We wanted to get the views of market and CSA managers because they best know the constraints under which their operations must function. In addition, their intentions, decisions, and efforts play a key role in the ways and degree to which food security is addressed in these institutions. Questionnaires were sent to all CSA and farmers’ market managers for whom we could find mailing addresses. CSA contact information came from a variety of online sources. Farmers’ market manager contact information came from USDA, California Certified Farmers Markets, and Local Harvest websites. Managers who were in charge of multiple markets received multiple questionnaires.1 Thirty-seven CSA questionnaires were returned of 111 sent, for a response rate of 33 percent. For farmers’ markets, 157 questionnaires were returned by 101 managers, for a response rate of 35 percent of 443 questionnaires sent to 294 managers. Respondents are fairly representative of the scope of farmers’ market and CSA operations in California .

Most surveyed CSAs were run by single families or individuals. Others were run by non-profits , government institutions , a non-family partnership , and a cooperative .3 There was similar variation among farmers’ markets . Of the managers who answered our question about the type of organization that runs their market, a large majority indicated that they were run by nonprofits ; others were run by government institutions , chambers of commerce , private businesses , or private individuals, groups of growers, and other miscellaneous institutions . In addition, the geographic scope was quite wide. Farmers’ market respondents came from 41 of California’s 58 counties, including counties in all major regions of the state, and CSA respondents came from the major, highly regionalized pockets of CSA activity. We also conducted interviews with a purposive sample of CSA and farmers’ market managers during May through August 2004. To gain more background on the use of entitlement programs by AFIs, we also interviewed key individuals with the USDA’s Food and Nutrition Service and one non-profit group involved with managing and promoting entitlement programs for farmers’ markets. While our sample is limited to California, and may therefore reflect particular regional sensibilities, it nevertheless captures a broad range of the possible configurations and characteristics of CSAs and farmers’ markets in the United States as a whole. Despite regional differences in growing seasons, crops produced, and ethnic diversity, our sense is that CSAs and farmers’ markets tend to develop and thrive in particular socioeconomic environments that are likely to be very similar from place to place. We can at least say that our data are representative of the diversity of CSAs and farmers’ markets in California.We asked CSA and farmers’ market managers to rank on a six-point scale how important they thought it was for their AFI type to address issues of food access and affordability for low income people. The vast majority of CSA and farmers’ market managers believe that these institutions should be paying attention to issues of food security. Eighty-one percent of farmers’ market managers and 77 percent of CSA managers considered it important to extremely important . We further asked if they would be willing to employ strategies that other farmers’ markets or CSAs had used to serve low income people. We found significant support for the idea of trying out new Percentage of managers rating access and affordability as important Market managers CSA managers 1–3 18.6 22.9 4–6 81.4 77.1 Mean rating 4.59+1.463 4.46+1.442 a Note: 1=not at all important; 6=extremely important Table 2. Manager perceptions of importance of addressing issues of food security through farmers’ markets and CSAs Willing to consider using new strategies Percentage of market managers Percentages of CSA managers Yes 72.1 64.5 Maybe 10.5 25.8 No 17.4 9.7 Table 3. Manager willingness to consider using new strategies to serve low-income people in farmers’ markets and CSAs strategies to reach low-income people . Ninety-one percent of CSA managers and 83 percent of farmers’ market managers said they would or might consider employing additional tactics to serve low-income people. These data suggest a strong commitment among managers to improving food security through farmers markets and CSAs as a broad concept. We wanted to learn what strategies are used to serve low-income people, especially in light of strong support for the goal of inclusion. On the questionnaires we listed several possible strategies for reaching low-income consumers and provided space for respondents to write in strategies of their own. These data demonstrate that a tremendous amount of effort has been devoted to working to address both access and affordability. Among CSAs, 83 percent of respondents had attempted at least one strategy to attract low-income people ; among farmers’ markets,30l plant pots the figure was 87 percent . In coding responses, we organized these strategies into four categories: direct outreach, discount, food recovery, and entitlement. The strategies and categories for both CSAs and farmers’ markets are found in tables 4 and 5.

There were substantial differences between farmers’ markets and CSAs in strategies used to improve low-income access. Farmers’ markets relied most on the use of entitlements and CSAs relied most on food recovery. As shown in table 5, 82 percent of farmers’ markets have used at least one entitlement tactic, dwarfing the other strategies by a considerable margin. Use of Farmers’ Market Nutrition Program coupons accounted for the largest percentage of entitlement strategies by far; 78 percent of markets indicated that they accept FMNP coupons.On the other hand, none of the CSAs we surveyed use entitlements. CSAs most frequently looked to food recovery as a means to serve low-income consumers, with 61 percent of CSAs having attempted at least one food recovery tactic, usually food donations. A number of examples of internal redistribution exist in CSAs and farmers’ markets. Indeed, the idea of having a community or organization cross subsidize , was, actually, the original vision of CSA. As one CSA manager put it, “the CSA community commits to financially supporting the farm . . . and everyone gets together in a room, the farm budget is shared, and people go around and pledge what they can afford,” but as she noted, it was unlikely to work as a business model in the United States. As an implicit operationalization of this sort of subsidy, half of those CSA managers surveyed have offered discounts of some kind, as illustrated in table 4, although to our knowledge, none have implemented the strong form of redistribution mentioned above. While the analog of this sort of redistribution was not mentioned by our farmers’ market respondents, there are examples of between-market redistribution. For example, the Victory Park market in Pasadena, California was set up specifically to help subsidize growers selling at the lower-income market in nearby Villa Park; growers cannot sell produce at Victory Park without selling at Villa Park as well. The San Francisco Heart of the City’s Wednesday farmers’ market, which draws hundreds of office workers, subsidizes its Sunday market, which draws many more low-income Asians, Latinos, and African Americans from the Tenderloin neighborhood . We also note some success with the indirect subsidies of the nonprofit sector. For example, Tierra Miguel CSA, a non-profit, uses grant funds to supply shares to low-income diabetics through the Indian Health Council and funding from the Los Angeles Unified School District’s Nutrition Network to deliver boxes of produce to nearly 1,000 Los Angeles classrooms. Full Belly Farm CSA, a for-profit partnership, provides donations of produce to a Sacramento area shelter and food kitchen, which are subsidized by a local church congregation. Despite these efforts, managers reported low rates of participation by low-income people, both in farmers’ markets and CSAs .* Other data support these perceptions. For example, it appears that few of those who are eligible for federal food assistance programs participate in these market-oriented AFIs. In 1998, less than 25 percent of food stamp recipients reported shopping at a farmers’ market, and food stamp redemptions at farmers’ markets accounted for only 0.02 percent of overall redemptions . In the case of CSA, a number of studies have found that CSAs primarily serve members with high incomes . There remains low participation on the part of low-income people in farmers’ markets and CSAs despite the intentions and efforts of their managers. Yet AFI managers generally support the idea of improving the affordability of the food they provide, and most have made an effort to put their convictions into practice. How do managers explain the low rates of participation? Given that existing research already shows that CSAs tend to serve a disproportionately affluent clientele, we asked CSA managers, “What do you think are some of the reasons that it is primarily affluent people who seem to participate in CSAs?” We coded the responses to this open-ended question into six categories .

It is critical that San Diego leverage these advanced scientific tools to further maximize existing and developing programs

Reducing barriers and streamlining viability of opportunities are key factors in advancing sustainable agricultural practices. Economic incentives and supportive programs, at both the state and local levels, are key components to encouraging farmers to engage with these practices. Given that the average age of farmers in the region is 62 , we need to do more to recruit and retain the next generation of climate-smart farmers. Implementation of conservation practices requires that farmers shift their management methods, while also assuming they pay the costs and repercussions of trial-and-error periods. Thus, incentivizing these actions with the appropriate financial safeguarding is necessary. California recognizes the economic value of carbon sequestering abilities in forests and working lands. The state has initiated a cap-and-trade market that promotes the selling of offsets for GHG emissions , In the past, carbon markets have generally failed to involve agriculture because the protocols for standardized offsets does not exist. However,plastic round pots for plants as new economic programs and protocols are increasingly established, landowners have the ability to generate financial returns from their conservation efforts.

Recently, the rangeland compost application has been approved as a viable practice for generating carbon offsets by the American Carbon Registry and the California Air Pollution Control Officers Association GHG Reduction Exchange . Incorporating agriculture in carbon markets gives farmers, ranchers, and forestland owners the opportunity to mitigate and adapt the impacts of climate change that directly affect them. Carbon markets encourage landowners to shift their practices to enhance carbon sequestration, generating quantifiable benefits such as enhanced carbon sequestration and soil health . The inclusion of agriculture in the carbon market can be expanded to encompass more land conservation practices, for instance, by approval of carbon farming as California Environmental Quality Act GHG mitigation . While the county’s CAP has proposed developing a local offsets registry, approving carbon farming methods as CEQA certified GHG mitigation practices could reduce costs from credit development and 3rd party verification . Compliance Offset Protocol Rice Cultivation Projects highlight a case study program for encouraging sustainable farming methods by quantifying reductions in GHG emissions. The protocol develops a market solution for a rice carbon offset protocol that helps facilitate agricultural Programs administered by the Natural Resources Conservation Services and the California Department of Food and Agriculture , such as the Healthy Soils Program and Conservation Innovation Grants , present grant opportunities that can drive agricultural innovation in resource conservation . Although there remain barriers that have ultimately limited farmer involvement, carbon credit programs have the potential to financially encourage the adoption of sequestering agricultural practices.

With several options for carbon crediting and market involvement, large-scale implementation of carbon farming throughout the county is becoming more viable. In addition to these economic incentives, there are programs that also offer financial support for transitioning land management methods. As part of San Diego’s Climate Action Plan , the county proposed establishing a Local Direct Investment Programs that aims to fund and implement local direct investment projects approved as GHG emission reductions protocols by the state . The CAP strategy is an opportunity for conservation projects such as carbon farming protocols to directly feed into CAP GHG reduction goals while also financially backing landowners. As local carbon farming protocols continue to develop and more are approved as acceptable recipients of investment funds, this can help provide economic incentive, promote carbon farming, and increase recognition of these practices as investments in county-wide resilience . As these programs that directly support farmers and offer funding, farmers are increasingly willing and able to invest in the transformation of land into carbon sinks. Thus, there is an opportunity to capitalize on these already existing programs and economic tools to further implementation efforts. As analysis result indicate, San Diego’s agricultural lands are at great risk of conversion to urban use in the coming years. Thus, programs that promote the growth of agricultural land are critical to safeguarding these valuable lands. The Urban Agricultural Incentive Zone is an example program that utilizes incentives to encourage the growth of agriculture in the city’s urban neighborhoods.

UAIZ allows for private landowners in urban areas to receive a tax incentive for leasing lands to growers, farmers, and/or gardeners for agricultural use. To receive these incentives, zones must be designated for agricultural use for at least five years . In addition to these economic and scientific resources, it is critical that farmers are supported with a network of agencies and partners that can provide both the guidance and political will needed to encourage local carbon farming initiatives. As carbon farming is increasingly recognized as a viable method of climate adaptation and mitigation, entities have started to join forces to facilitate carbon farming ideas and initiatives to on-the-ground implementation and demonstration projects. The county’s Resource Conservation Districts are integral in providing technical assistance, advice, and planning . Through comprehensive planning and monitoring, the RCDs are working to help farmers implement effective practices on their land . RCDs of Greater San Diego are following in the footsteps of RCDs statewide, developing carbon farming plan templates for assessing opportunities and related practices for landowner implementation of conservation systems that address resource concerns . In addition, San Diego has a Task Force specifically designed to promote and scale out carbon farming throughout San Diego . The Task Force is comprised of diverse stakeholders from over 40 multi-sector organizations, representing producers, industry, local independent farmers, scientists, and philanthropic groups . Expanding carbon farming from demonstration projects to region-wide implementation requires that farmers and landowners are well supported by these resources and agencies. As this network continues to grow, it presents a promising opportunity for sizeable carbon farming application and thus tangible GHG reductions and co-benefits.The combination of incentive programs, carbon farming projects, and scientific research presents a powerful opportunity to make tangible change.

If the county can capitalize on these opportunities, it will help ensure that agricultural lands continue to exist and contribute value for the region in future years. Fine-scale hydrologic models at the regional level can provide useful information about water-shed specific hydrologic response. While the BCM is able to generate confident, high resolution projections, the results are dependent on underlying NRCS soil properties used as an input. As these NRCS soil maps may not accurately reflect existing conditions,plastic garden planter it is important that conditions are assessed locally and case-by-case to help achieve projected benefits . It is thus vital that science-based decision support tools, such as the BCM and model input data, are updated, maintained, and improved to continue to inform stakeholders and decision makers. Integrated analysis helps portray where areas of benefit coincide with existing agricultural lands, and areas of benefit at risk of conversion. However, it is important to note that in many ways, San Diego lacks a comprehensive understanding of the region’s agricultural landscape. With many small, dispersed farms, data illustrative of all agricultural land in the region is limited. As a result, the “agricultural land” area is likely not representative of the entirety of San Diego’s working lands. The lack of recognition of the agricultural lands throughout the county presents many challenges for planners, natural resource managers, and entities at the forefront of San Diego county planning. Without accurate spatial records and data of agricultural lands, it is difficult to target both land preservation and carbon farming efforts. If the county plans on continuing its efforts to preserve these valuable lands in the years to come, it is critical to utilize tools, such as the BCM, that highlight the potential benefits of maintaining lands in partnership with soil management practices. For BCM results to be best utilized in targeting carbon farming and preservation planning, there is a need for accurate agriculture data. In addition, understanding the status of lands and the agencies involved in ownership and preservation can help stream light efforts. As shown in figure 19, there are lands listed as protected under CPAD and CCED that are also planned for urban development, highlighting a need for more synthesis between land use planning and programs aimed at protecting these agricultural lands. These analyses illustrate areas that exhibit natural resource characteristics amenable to carbon sequestration that coincide with existing agriculture and thus help identify lands with existing opportunities for building resiliency of agriculture against climate change impacts in the San Diego region. Results can be leveraged a tool to inform how farmers consider possibilities for farming strategies, and for designing and directing programs to best support these efforts. Additionally, studies can depict the unparalleled role that these agricultural lands play in providing critical ecosystem services throughout San Diego, especially taking into account the threats of climate change and urbanization. Agricultural lands not showing hydrologic benefit under a 3% BCM soil management scenario are not necessarily unable to produce hydrologic benefit, and thus should not be disregarded from considerations of carbon farming implementation. However, areas showing the highest degree of potential benefit may be prioritized with the most feasible and immediate opportunity for implementation. While results may not give exact quantification of hydrologic benefits, due to potential misrepresentation of soils and agricultural land, these local applications help conceptualize the potential value of our region. The rapidly changing climate necessitates that conservation efforts are more immediate and poignant than ever. With projections showing a future of water related challenges, how San Diego acts now will have implications regarding resiliency of agriculture for both the short and long-term future.

This study has highlighted the important and unique role that San Diego agriculture can play in addressing these growing challenges. Results indicate that San Diego’s agricultural lands contribute many benefits throughout the region’s institutional and natural landscapes. When managed sustainably, these lands have the potential to improve water resources across the region and become more resilient to forecasted climate impacts. Carbon farming practices can not only result in immediate benefits, but can also ensure that lands sustain these benefits for years to come. To ensure that agricultural lands and their benefits remain in the future, supportive programs and economic incentives must continue to assist farmers in implementing and bearing the costs of carbon farming practices. Additionally, quantifying benefits at a regional scale can help identify opportunities and direct these efforts to be most effective. Regional application of advanced, fine-scale hydrologic modeling can inform policy and programs, while encouraging farmers to adopt practices. It can also serve to quantify the value lost and opportunity cost if future development plans are pursued. The region’s agricultural sector, which is directly impacted by the impacts of climate change, has the ability to become a source of regional climate resilience. With advanced climate modeling, a strong agricultural community, and a network of supportive entities, partnerships, and programs, San Diego is poised to lead the way in California’s carbon farming movement. Soil compaction is a long-recognized soil sustainability threat with reports of its associated problems worldwide . Soil compaction issues have been exacerbated over the past century in agriculture by mechanization and continued growth in average tractor size . When soils are compacted, soil particles are forced closer together, diminishing porosity and pore connectivity , which impedes soil water and air movement necessary to the many services soils provide to societies and the environment, such as crop production and preventing surface runoff . Soils are more susceptible to compaction when moist, because soil strength increases as soils dry . For example, in California trials, compaction increased when the time interval between irrigation and tillage operations was shortened . In California, agricultural managed aquifer recharge is an active area of interest and research amongst farmers, policy makers, and scientists searching for solutions to groundwater depletion , 2018. This practice involves flooding agricultural fields during the winter and early spring when most crops are dormant or fields are fallow. But one concern is that Ag-MAR may increase the risk of soil compaction during subsequent field operations, especially since the practice involves deep soil saturation during periods of relatively low potential evapotranspiration . There are two concepts related to the capability of soil to support agricultural field operations: trafficability, the capability of a soil to support traffic without wheel slippage and compaction, limited mostly by excessive moisture and, work ability, the capability of the soil to be worked by tillage to achieve a specific goal , occurring optimally between some upper and lower soil moisture threshold, since soils can be either too wet or too dry for ideal work ability .

NBA-P ensures that urgent tasks are prioritized subject to both energy and resource budgets

In precision agriculture, overhead imagery can help pinpoint locations that appear to be under water stress, in which case sampling leaves or soil in those areas should be prioritized. We formalize the notion of tasks with distinct urgency by assigning a priority level to tasks. Besides the task priority level, deciding a next task for a robot to complete is also dependent on available budget, which can be of multiple types. For instance, the number of locations that a robot can visit and sample from in one ‘trip’ is constrained by both the energy capacity to move between locations and the robot’s sample payload capacity.Exceeding the energy budget can prevent the robot from returning to the base station to recharge and drop collected samples, whereas exceeding the sample payload capacity may cause potential robot and sample damage. Here we consider an energy budget for the robot moving between locations, and a resource budget linked to task execution. The two budgets are independent of each other, and both can be reset to their initial values when the robot returns to the base station. The actual amount of resources consumed to execute a task can differ from what is the expectation in practice. In fact, the actual amount of resources consumed for task execution is revealed only after the task has been completed. To model this,plastic pots large we consider the cost to complete a task to be a stochastic random variable that follows some known distribution. The cost to move between locations, however, is considered to be deterministic. Specific details are given in the following.

This paper introduces a new stochastic task allocation approach, termed Next-Best-Action Planning , for task planning under uncertainty in precision agriculture. The paper also contributes a new Stochastic-Vertex-Cost Aisle Graph . SAG is an extension of the aisle graph, which is often used to describe agriculture-related environments.Using SAG, our proposed NBA-P algorithm simultaneously determines 1) how to optimally schedule which tasks to perform at run-time, and 2) when to optimally stop performing new tasks and return back to the base station also at run-time. Further, it can be extended to multi-robot teams. We test in single- and multi-robot cases using both simulated data and 10 real-world datasets collected in a commercial vineyard at central California. In all cases, NBAP achieves higher efficiency than naive lawnmower, informed lawnmower, and series Greedy Partial Row planners in terms of more return per visited vertices, less resources wasted because of aborted tasks, and less total visited vertices. Aisle graphsmodel motion constraints emerging when robots navigate in structured environments like agricultural fields. Vertices denote task locations, and edges represent connections between locations. Any two rows connect to each other only via the two end vertices. Moving backwards is not allowed; if a robot enters a row, it will have to reach the row’s end before moving to another row. In the original aisle graph, vertices and edges are associated with known and constant reward and movement costs, respectively. Our extension, SAG, can also represent uncertain task costs. Orienteering can tackle persistent sampling on aisle graphs. Orienteering is NP-hard, and thus greedy heuristics are often employed. Recent efforts on stochastic orienteering associate stochastic costs to graph edges and propose a time-aware policy for a robot to adjust its path to avoid exceeding a certain budget. However, addressing cases that involve uncertain task cost on vertices for aisle graphs remains open. NBA-P tackles the problem by simultaneously considering uncertain task costs on vertices and deterministic costs on edges.

Optimal stopping can be used to find the criteria to terminate a process while incorporating uncertainty. Often, data arrive in sequence, and irrevocable decision has to be made as to when the expected return is maximized. Optimal stopping has been used in robotics applications like target tracking and marine ecosystem monitoring. However, no motion constraints, like those imposed by aisle graphs, apply to robot actions, and hence existing methods cannot be ported over to operations on aisle graphs. Paths planned with NBA-P fill the gap, as they directly apply to environments with motion constraints captured by aisle graphs. Our method applies when: 1) motion constraints in the environment can be captured by a SAG; 2) the cost of completing tasks follow exponential distributions; and 3) the obtained gain by completing a task is proportional to the actual task cost. Anthropogenic landscapes and their associated biodiversity are novel ecosystems that are expanding globally. Urbanization and agriculture, particularly, are the products of growing human populations and have accelerated rates of land conversion . These landscapes are typically associated with transformations in habitat structure, climate, and connectivity as well as the establishment of non-native species . However, even with their increasing dominance as a land use, many questions remain regarding the impact of these anthropogenic landscapes on ecological communities. Particularly, although there are differences between these ecosystems and their less human-modified equivalents, there is evidence for the potential of these anthropogenic landscapes to support biodiversity in unexpected ways that in some cases may be equivalent, or even surpass the biodiversity in surrounding natural landscapes . With the continued expansion of human-modified ecosystems coupled with the inability of reserves to support more than a fraction of the world’s biodiversity , understanding the dynamics of communities in these landscapes is necessary to evaluate their conservation potential and opportunities for restoration and management . Specifically, the temporal dynamics, or phenologies, of species have been observed to be vulnerable to disruption .

Changes in the temporal activity of species can lead to mismatches in the presence of species and their biotic or abiotic resources, which can impact the functioning of ecosystems . Like global climate change which has been found to result in phenological shifts , the urban heat island effect is a well-documented local phenomenon experienced as significantly warmer temperatures in cities relative to the surrounding landscape due to higher energy use and impervious surface area . As a result, plants bloom earlier and more densely and bird migration advances earlier in urban contexts .Human-altered landscapes may also shift the activity of wildlife by altering the variety and timing of resource availability. Many natural areas in temperate environments experience a large burst of diverse plant growth in the spring,planting pot seedlings and by the end of the summer, there are very few resources available . Urban areas, while likely having less vegetative cover than less human-modified landscapes, often support many exotic plants, which are supplemented with water and nutrient inputs, allowing for an extended vegetative and flowering season. As a result, urban areas may support more limited but constant vegetative resources throughout the year . In contrast, agricultural landscapes, due to the phenology of monoculture crops, have large patches of dense vegetative resources that fluctuate greatly from early spring to the end of the summer . Such within-year differences in vegetative availability between land-use types have been documented through the use of remote sensing and may affect the seasonal population dynamics of the animal communities that rely on floral resources . Focusing on wild bees, organisms that are highly dependent on floral resources, we ask whether there are differences in the seasonal population dynamics of bee communities in urban, agricultural, and natural landscapes. Although bee seasonality and movement has been documented in urban and agricultural landscapes , differences in the phenological dynamics of bee communities between urban, agricultural, and natural land-use types have not been explored. Here, we evaluate the hypothesis that neighboring land-use types exhibit different patterns in bee community phenology throughout the year. We predict the temporal dynamics of bee communities in urban landscapes will be less variable than in agriculture and less human-modified areas because resources are more stable. We also predict that because the pulse of resources availability in less human-modified and agricultural area occurs at different times, the bee community phenology will shift in the different land uses to track that availability. Understanding how the novel communities in human-modified landscapes function will help to better inform restoration and conservation efforts.Our study landscape was located in east Contra Costa County around Brentwood, California, where natural, agricultural, and urban areas intersect with each other within a 20 9 20 km region . Large areas of land remain protected from development within this region by regional and state parks as well as the local water district’s watershed. This undeveloped land consists mainly of grasslands and oak woodlands, some portions of which are managed for grazing.

East Contra Costa County has had a farming community presence since the late 19th century. The agricultural areas of Brentwood, Knightsen, and Byron mostly consist of orchards , corn, alfalfa, and tomatoes . A housing boom in the 1990s led to massive residential growth in the area. The city of Brentwood has grown from <2500 people in the 1970s to over 50 000 today , and nearby Antioch has over 100 000 residents . Using NOAA’s 2006 Pacific Coast Land Cover data set , a 500-m buffer was created around each site, and the number of pixels classified as agricultural, urban, natural, water, or bare land was extracted. We classified each site based on the dominant land-use type within its 500 m buffer. In 2010, we had 18 sites, with six each classified as types ‘urban’, ‘agricultural’, and ‘natural.’ In 2011 and 2012, we increased to have a total of 24 sites, with eight of each land use classification. Sites were selected to be at least one km away from all others, based on assumed maximum bee foraging ranges . Although certain bee species have been recorded foraging over a kilometer , most bees have nesting and foraging habitat within a few hundred meters of each other . At each site, we designed a standardized pan-trapping transect of 15 bowls spaced five meters apart in alternating colors of fluorescent blue, white, and fluorescent yellow following a modified version of established protocols for pan-trapping bees . Bowls were filled to the brim with soapy water . In 2010, transects were set up during peak bee flying hours for the four-hour period between 10:30 and 14:30 , with four sites sampled per day, and all sites sampled on consecutive days. These 2010 transects were sampled at two collecting periods: once in the early summer and once in the late summer. In 2011 and 2012, transects were left out for a 24-h period , so that more sites could be sampled simultaneously and therefore, 24 sites could be sampled during each collecting period. All 24 sites were sampled within 4 days of each other, during four collecting periods: early spring, late spring, early summer, and late summer. There were a total of 228 collecting events . Long-term sampling methods such as this have not been found to affect bee community structure . The goal of collection was to sample the bee community that was flying through the landscape searching for resources. For this reason, the human-altered sites were deliberately selected so as not to be adjacent to any mass-flowering plants of agricultural crops or gardens to reduce potential pan-trapping biases of bees actively foraging on immediately local resources . All sites were selected in easily accessible, open areas that received full sun. Natural areas were in grassland habitat, so we selected agricultural sites that were either weedy field margin edges or fallow fields, and urban sites that were vacant lots or green ways. The weedy flower margins in urban and agricultural landscapes generally had equivalent flowering levels as those in the natural areas, making the adjacent floral resources similar, allowing the floral availability on a landscape scale to be the primary differentiating factor. Bee specimens were identified to species . The only exception was for bees of the genus Lasioglossum, which due to their overwhelming abundance combined with difficulty of identification, were identified to the genus level. The vast majority of Lasioglossum collected were primitively social generalist species of the sub-genera Dialictus and Evylaeus. Voucher specimens are deposited at the Essig Museum of Entomology at the University of California, Berkeley.

Hydroponics Rain Gutter Strawberries

Hydroponic rain gutter systems can be an effective way to grow strawberries and other plants. This method combines hydroponic principles with rain gutter technology to create a space-efficient and controlled environment for plant growth. Here’s how you could set up a hydroponic rain gutter system for growing strawberries:

Materials Needed:

  1. Rain gutters: These will serve as the main containers for growing the strawberries.
  2. Support structure: You’ll need a frame or structure to hold the rain gutters in place at an appropriate height.
  3. Growing medium: Hydroponic growing media like coconut coir, perlite, or a mixture of both.
  4. Nutrient solution: A hydroponic nutrient solution rich in essential minerals for plant growth.
  5. Strawberry plants: Purchase disease-free strawberry plants suited for hydroponic growth.
  6. pH and EC meters: To monitor and adjust the pH and electrical conductivity (EC) of the nutrient solution.
  7. Irrigation system: Drip irrigation or a similar system to deliver the nutrient solution to the plants.

Steps:

  1. Prepare the Rain Gutters: Clean the rain gutters thoroughly to remove any debris. You can use rain gutters of appropriate length to fit your available space. Ensure they have proper drainage holes to prevent waterlogging.
  2. Install Support Structure: Set up a sturdy frame to hold the rain gutters horizontally. The gutters should have a slight slope to facilitate water flow from one end to the other.
  3. Fill with Growing Medium: Fill the rain gutters with your chosen hydroponic growing medium, leaving enough space for the strawberry plants to be inserted.
  4. Plant the Strawberries: Plant the strawberry seedlings into the growing medium at equal intervals. Make sure the crown of the plant (the area where the stems meet the roots) is level with the growing medium’s surface. This will prevent rotting.
  5. Set Up Irrigation: Install a drip irrigation system along the length of the rain gutters. This system will deliver the nutrient solution to the plants. The nutrient solution should be properly mixed according to the manufacturer’s instructions.
  6. Monitor and Maintain: Regularly monitor the pH and EC levels of the nutrient solution to ensure optimal plant health. Adjust the solution as needed. Keep an eye on the plants for signs of nutrient deficiencies or diseases.
  7. Lighting: If you’re growing strawberries indoors or in a controlled environment, provide adequate lighting using grow lights. Strawberries require a certain amount of light for healthy growth and fruit production.
  8. Pruning and Training: As the strawberry plants grow, consider training them to ensure good air circulation and light penetration. Prune any dead or diseased leaves.
  9. Harvest: Once the strawberries start to ripen, carefully harvest them. Since the rain gutter system elevates the strawberries, they’ll be less prone to pests and diseases.
  10. Cleaning and Replanting: After a growing season, clean the rain gutters and the system thoroughly. You can then replant new strawberry seedlings for another cycle of growth.

Remember that successful hydroponic gardening, including rain gutter systems, requires attention to detail, proper nutrient management, and a good understanding of the specific needs of the plants you’re growing.