Short-term finance is typically only available at abnormally high rates of interest

The large decrease in sales by florists with only a small change in farm level sales is due to a significant change in retail market shares for floral products. Specifically, other outlets such as supermarkets gained market share for floral products at the expense of individual florists. The situation for lawn and garden equipment and supplies stores is much different than florists or other retailers of nursery products. While total sales decreased after the peak occurring in 2007, the number of retail licenses continued to increase. This is not the case for other retailers handling nursery products. As shown in Table 2, there are fewer producers as well as incidental and specialized nursery retailers. The number of retailers licensed to sell nursery stock decreased from a total of 6,471 in 2003 to 3,022 in 2013, a 3,449 reduction in number of outlets. Given much smaller reductions in wholesale nursery sales, the surviving retailers are larger on average and probably have smaller operating margins than was typical for florists. This very significant reduction in the number of California retailers handling nursery and floral products has implications for both producers and consumers. Some producers undoubtedly lost their major retail customers while many lost important retail outlets. The impact of the loss of outlets was not uniform but it was widespread. This consolidation of outlets may offer some economies in distribution but the short-run impact on floral and nursery product sales will be negative. Products are not as available at the consumer level as previously, which tends to reduce consumer choice and negatively impact impulse buying. A change from specialized to multi-product retailers tends to reduce customer service and may reduce product assortments. And, finally, the changes noted may be associated with more market power in the hands of surviving retailers. With varying degrees of enthusiasm, the governments of the central and eastern European Countries all aspire to join the European Union . These aspirations were given strong encouragement at the EU’s 1993 Copenhagen Summit,blueberry container size at which time associated CEECs were told they would eventually gain membership.

Along the path to accession, however, lie difficult policy choices and delicate negotiations concerning the pace and terms of economic integration. Of these, among the most challenging are those affecting the fate of agriculture in the emerging market economies. Accession to the EU has historically implied the integration of the new member into the community’s Common Agricultural Policy , a complicated system of interventions whose most prominent and expensive features are designed to support prices of program commoditiesl through intervention purchases, and to shield markets from external competition through tariff barriers. As in previous accession negotiations, EU negotiators will be concerned about the impact of accession agreements on the EU treasury, while CEEC governments will be attentive to their implications for national budgets. Furthermore, many producer groups in the West will be nervous about granting market access to Eastern competitors; the political clout of these interests will constrain the negotiations. As with the accession of southern members Greece, Portugal, and Spain, the new members would be substantially poorer and less technically developed than those currently in the Union, raising the possibility of the need for substantial technical assistance. In the case of the CEECs, other issues arise that have no clear precedent. First, there is the unusual size and importance of agriculture in these countries. Depending on the chosen measure, these nations would increase the size of the Union’s agricultural economy by roughly one third. 1 In each nation, agriculture accounts for a larger share of employment and GOP than is typical in the current Union. Second, these countries share with their western neighbors a similar continental, temperate climate, and similar growing conditions. In the long run, after a period of restructuring, their agricultural sectors could display patterns of comparative advantage similar to those in the current EU member states, a prospect that makes concerns about competition even more pronounced than in past expansions . Third, these countries are presently going through a profound process of economic transformation that hopes to shed the legacy of the socialist period in favor of a market-based system of production. Eastern governments will have to consider how an accession agreement will affect the ongoing process of market development and enterprise restructuring currently unfolding in these emerging economies.

Finally, the requirements of the Uruguay Round of the GAIT -will be an important new factor regulating agricultural trade, imposing new constraints on allowable treaty terms. The overall success of the accession accords may be determined primarily by factors outside agriculture. Nonetheless, the treatment of agriculture promises to playa central, and delicate, role in the accession negotiations. Nearly a decade after the region embraced market economics, their agricultural sectors continue to struggle with the transition from a socialist production system. While it is problematic to make generalizations across the entire region, we can identify a few of the key characteristics of today’s CEEC agriculture that are likely to have first-order impacts on the prospects for long term performance . Farm enterprises in these countries can be broadly grouped, by size, into two types: large enterprises that are primarily the successors to state and collective farms organized during the socialist period; and smaller, usually privately-owned, operations. These latter farms, sometimes covering less than one hectare, have often been established by former members of the collective farms who have taken their land out of collective enterprises in an attempt to “make it on their own.” Both types of farms are typically under capitalized, or have a mix of capital goods inappropriate to the kind of production in which they are engaged. In the face of woefully imperfect capital markets, farms are typically unable to undertake investments to improve their efficiency, even in cases in which such investment would be profitable , depending, of course, on the cost of debt.Credit constraints are a particularly severe problem for the smaller farms, which tend to lack either demonstrable collateral or social clout. Persistent problems with land titling, and generally with the development of a market for land, impede the ability to offer land as collateral, further exacerbating problems in the market for long-term credit. Capital market imperfections are, therefore, one of the key barriers preventing an improvement in the technical efficiency of East European farms, which consistently lags that in the EU.

These problems are aggravated by the poorly developed state of public goods in rural areas, including transport and storage infrastructure and market information . In the socialist period, much of this rural infrastructure was provided from within the large enterprises. A system of infrastructure supporting independent farms has not yet emerged. These features the split between large and small farms, the low level of technical development on most farms, imperfections in market for agricultural fmance, poor provision of public goods, and a history of government-controlled prices-define the landscape of agriculture in Central and Eastern Europe. These are the initial concerns that government policymakers in the region have to consider as they chart their agricultural strategies over the coming years. Official statements from CEEC policymakers have expressed multiple goals for agriculture during the transition. To the Czech Ministry of Agriculture, for example, an ideal scenario would include the transformation of agriculture along free-market lines; preparation for eventual integration to the EU’s CAP program, and maintenance of a “domestic equilibrium” that would keep farm incomes and output from collapsing during an excessively violent transition . A central motivation for the present paper is the observation, under-appreciated in policy circles, that these goals may be Inconsistent, and that there are points of tension between the goal of creating agricultural economies that respond rationally to market signals,growing raspberries in container and the desire to bring agriculture into alignment with the heavily-regulated CAP programs of the EU. In particular, a single-minded focus on convergence to EU norms can inappropriately distract policymakers from steps that create incentives to improve productive efficiency. Policies that encourage the restructuring of agricultural enterprise during the interim period prior to joining CAP allow factors to flow toward efficient uses. The terms of agriculture under the treaties of accession will have important implications for CEEC decision makers choosing pre-accession agricultural support policies. If CAP is maintained substantially unchanged from its current form , then producers in the new environment will enjoy higher prices, supported through commodity subsidy programs and trade barriers. If a version of CAP covered Central and Eastern Europe, the current owners of land would reap windfall profits, as these benefits became capitalized into land values . . CEEC governments have a number of instruments that they can deploy in order to encourage such transformation. They can adopt policies to encourage the reorganization of agricultural enterprises, to move from a system dominated by huge state and cooperative agricultural enterprises into one more responsive to market signals, including a mix of large and small farms. CEEC governments can also control spen~ing on relevant public goods such as public information and rural infrastructure. They can vary the degree of the economy’s openness to foreign trade, through the erection of tariff and import quotas, export subsidies, and other trade management activities. Commodity price supports and other market manipulation schemes will also continue to offer their rent-seeking temptations.

Indeed, price supports and tariff barriers can have desirable effects, from the theory of the second-best: in the presence of a distortion in one input market-that for credit-a government imposed distortion in the output market can have beneficial effects, by transferring resources to producers that are able to use it efficiently. At the same time, however, distortive policies can create price instability. In this context, free trade can substitute for price supports as a market stabilizing mechanism, operating more effectively and at lower cost. Both distortive and laissezfaire approaches may, however, compare unfavorably with policies that address market imperfections directly. Of course, use of any instruments has associated costs, both directly taxing the government treasury and indirectly imposing adjustment burdens on society. Thus, in bargaining over the treatment of agriculture in accession, and in selecting appropriate pre-accession policies, CEEC policymakers must therefore be prepared to juggle a complicated set of interactions and trade offs. The nature of these trade offs can be clarified through a heuristic version of a comparative statics exercise. Suppose that a government knew with certainty the date and terms under which it would join the CAP, and was cbntemplating a restructuring program that would appropriately position the agricultural sector for successful entry. For a given date of entry, a relatively aggressive restructuring program would create multiple effects, including an increase in the efficiency and flexibility of the agricultural sector; an increase in producer profits and aggregate national wealth in the long term following CAP integration; a short-term decrease in output, as established patterns of production are disrupted; an ambiguous effect on output in the long term; and an increase in the short-term costs of adjustment, including social costs such as unemployment. The government’s fundamental decision problem is how to balance these trade offs, i.e., how to deploy judiciously the policy instruments at its disposal in order to position the agricultural sector for a successful entry into CAP while keeping it robust during the interim period and, perhaps, subsequent to a major reform in the CAP. To be sure, a number of questions concerning the interaction between the terms of accession to the EU and pre-accession policies naturally arise. Let us assume that the CAP will not be altered in the near term and, therefore, that the program’s current form represents a credible policy commitment by the EU, both to its own farmers and to prospective member states of Eastern Europe. 2 How will alternative accession scenarios impact the budgets of the EU and the CEEC national governments, respectively? Under what forms of the accession contract, if any, should the CEECs use the pre-accession period to mimic the EU by adopting CAP-like policies? Do price supports encourage or inhibit efficiency-enhancing restructuring of farm enterprises? Should the restructuring process receive public subsidy? In other words, how should the burdens of the restructuring process be divided between the public and private sectors?

The AVIRIS results are analyzed for portability and band importance

MASTER is a thermal sensor that captures 8 bands of emissivity between 4-12 μm, used to represent the proposed SBG thermal bands . The AVIRIS data was resampled to a resolution of 18 m while the MASTER data was resampled to a 36 m resolution. This paired dataset was flown over a portion of the Southern Central Valley seasonally while the state experienced severe drought effects. This unique dataset allows for study of remote sensing capabilities while also providing valuable information as to the response of crops in California to drought. The goal of this dissertation is to use data from the HyspIRI Airborne Campaign to evaluate how hyperspectral and thermal imagery can be used to improve upon current initiatives to account for and manage food and water resources in the face of a changing climate. This research will study patterns of agriculture and crop water use in the Central Valley as they shift throughout the course of an intense drought period from 2013-2015. These patterns will be investigated using imaging spectrometry from AVIRIS and thermal imaging from MASTER by mapping crops into relevant water use groups and then analyzing three indirect measures of crop water use from the imagery: choice of crop plantings, land surface temperatures, and water vapor patterns. Moreover,large plastic pots this dissertation will serve as a proof of concept for actively monitoring and measuring agriculture from space when the proposed SBG satellite is launched. In Chapter 2, I use three hyperspectral images acquired from AVIRIS over the course of the 2013-2015 drought in the Central Valley of California to both evaluate the performance of hyperspectral imagery for crop classification and to study farmer decision making with drought. A random forest classifier is run on the AVIRIS imagery to classify crops into groups of similar water use. Results are then compared to equivalent classifications using Landsat Operational Land Imager and Sentinel-2 imagery.

The results of this classification are then used to study the prevalence of crops as they change with increasing drought. Analysis highlights the economic and environmental drivers of planting decisions, and what this means for the future of California agriculture. In Chapter 3, I use spatially coincident AVIRIS and MASTER imagery from 2013, 2014 and 2015 to study the health of perennial crops over drought. First, I use a mixing model on AVIRIS imagery to decompose the scene into its fractional makeup of green vegetation , non-photosynthetic vegetation , and soil. Next, I model the expected temperature of each pixel as the fractional linear sum of its thermal components. I then calculate a thermal residual for each pixel as the difference between its measured temperature from MASTER and the modeled temperature. This method strips away thermal variability due to air temperature, time of day, fractional cover, structure, and moisture to allow for direct thermal comparisons between pixels and crop species. Thermal variability within agricultural fields is quantified and crop health is assessed. In Chapter 4, I evaluate spatiotemporal patterns of water vapor as they occur over agricultural fields in the Central Valley to evaluate the potential of this imagery to assist with agricultural applications. I use pixel-level column water vapor estimates derived from AVIRIS radiance imagery, surface characteristics obtained from AVIRIS reflectance imagery, and interpolated maps of wind to investigate relationships between the atmosphere and the surface. I propose and test a set of hypotheses for how water vapor will interact with the landscape in a diverse and complex agricultural scene at the pixel, field and scene scales. Results and analysis further knowledge of opportunities and limitations for using water vapor imagery to better understand crop water use. Although California faces substantial variability in inter annual precipitation and is accustomed to multi-year dry periods, the 2012 to 2016 drought was exceptional in its severity, and may be emblematic of greater shifts in California’s climate associated with anthropogenic warming .

Climate projections for California indicate that mean and extreme temperatures are likely to increase over the next century, which will increase the risk of experiencing future droughts of the severity of the 2012–2016 event . Future droughts will undoubtedly continue to put strain on water supplies, but the magnitude and extent to which these events impact water resources will depend not only on the characteristics of the drought, but also on the adaptive responses of people . In California, where the agriculture sector uses roughly 80% of the state’s managed water , agriculture simultaneously shows high vulnerability to a warming climate while also offering the greatest opportunity to mitigate the intensity of future drought impacts through adaptation strategies . Consequently, it is critical to study how we can monitor crop management response in real-time in order to assist with policy making during drought and analyze the ways in which the long-term sustainability of food and water security can be improved. This research used annual hyperspectral remote sensing imagery to assess the accuracy at which imaging spectroscopy can be used to map crops into categories of similar water demand and analyze changes in cropping patterns in a portion of the Central Valley. The study takes advantage of data collected over three years of a multi-year drought as a unique opportunity to measure agricultural response and adaptation in times of drought. Climate change is likely to significantly affect regional agricultural patterns and crop yields , in part due to management decisions such as fallowing fields or switching crop varieties or species . Therefore, monitoring how crop patterns change during droughts is a direct measure of adaptive response. Cropping decisions impact society in multiple ways by altering regional water requirements , food yields , economic production , and pesticide exposure . Consequently, accurate and timely crop maps are necessary to support long-term adaptation planning for a broad range of sectors, and are of use to farmers, managers, policymakers, and scientists.

Remote sensing has the potential to map crops and monitor changes in crop area more efficiently and frequently than time and labor-intensive on-the-ground crop accounting. Hyperspectral imagery, which samples hundreds of spectrally contiguous wavelengths, has the potential to identify crops at a single time point with a single sensor at higher accuracies than a broadband sensor . This ability is critical to enabling managers and scientists to stay abreast of rapidly changing planting choices and assess current risks, which is a need that current mapping initiatives with remote sensing are unable to fulfill. Most remote sensing mapping initiatives in the United States rely on satellites such as Landsat and the Moderate Resolution Imaging Spectrometer because of their large spatial and temporal coverage, ease of accessibility, and free availability . The National Agricultural Statistics Service ’s Cropland Data Layer is the most comprehensive current agricultural mapping initiative for the United States with an easily accessible crop map published at yearly intervals at a 30-m resolution . It relies on data from Deimos-1,raspberry container the United Kingdom’s Disaster Monitoring Constellation 2 , and Landsat 8 Operational Land Imager and produced an overall accuracy of 81.1% in California in 2016, with accuracies of crop groups ranging from a low of 32.8% for berries to 77.6% for forage crops. Although widely used and highly useful, the CDL has limitations concerning reproducibility and timeliness. First, by using three sensors, not all of which produce publicly available data, reproducing this map or using this methodology on a different study area or at a different time would not be possible. Furthermore, with maps published at the end of each year, the CDL does not offer near real-time or mid-growing season assessments of crop area. Another method of crop mapping uses multi-temporal MODIS imagery to classify crops using annual crop phenology for identification . These studies illustrate the ability of time series datasets to produce detailed and accurate crop classification maps at the end of an agricultural year in a single study area, but this methodology also faces challenges that hinder its practical and scientific usefulness in California. First, the spatial resolution of MODIS is not fine enough to individually classify many fields. For example, the average size of a field in the area of this study is approximately 0.2 km2 . Therefore, even at its finest resolution of 250 m, most MODIS pixels will result in mixtures of different fields or crop types, and are therefore best suited for croplands at larger scales . Second, multitemporal crop mapping is limited in its spatial scope due to a spatial variation in phenology that would decrease the accuracy if it was applied over a large spatial area . Third, the co-registration of multiples images and the need for cloud-free images create challenges for time-series analysis that single-data hyperspectral analyses do not face .

Finally, the need for multiple images throughout time obviates the ability to conduct real-time crop assessments. Hyperspectral imagery can act as a complement to these current crop-mapping initiatives, as it has the potential to identify crops at a single time point with greater accuracy than broadband sensors, and therefore can provide mid-season assessments of crop area without a yearly time-series . Discriminating crop types is challenging due to differing biophysical traits, development stages, variable management practices, regional weather and topography, and the timing of plantings . Despite these complications, various studies have successfully shown the ability to use hyperspectral imagery to classify crops and cultivars . By discriminating crop types with a single image from one time point, hyperspectral imagery can serve as a time-critical agricultural management tool, providing scientists, farm managers, and policymakers with improved information regarding the agricultural landscape and on-the-ground food and water needs. This study uses airborne hyperspectral imagery over a portion of the Central Valley to assess the accuracy of imaging spectroscopy for agricultural classifications and conducts a case study to display the utility of these classifications for analyzing changes in farming decisions. The results of this study, while limited in their spatial scope due to the use of airborne imagery, are salient in light of recently available Sentinel-2 data and the proposed HyspIRI mission, which would provide repeat, global hyperspectral imagery. In order to separate soil or fallow pixels from those of agricultural plant matter, a spectral mixture analysis was run on each of the three images to obtain fractional green vegetation cover. Multiple End member Spectra Mixture Analysis uses a linear mixture model to unmix pixels into fraction images while allowing the number and types of end members to vary on a per-pixel basis, thus better accounting for end member variability. Pixels were modeled as a mixture of green vegetation , soil, nonphotosynthetic vegetation , and shade. Image end members were chosen from each of the three images from 2013, 2014, and 2015 by selecting pixels with high overall reflectance from each of the three end member categories that were well-distributed spatially throughout the image in order to capture the variability from north to south along the flight line. A combined library of all of the chosen end members, consisting of eight NPV, 10 Soil, and 21 GV endmembers, was used for analysis in order to obtain consistent results throughout the years. MESMA was partially constrained by requiring shade fractions to vary between 0–0.8, and setting a maximum allowable root mean squared error of 0.025. The spectral mixture result was then shade normalized by dividing each non-shade component, GV, NPV, and soil by the sum total of all of the non-shade components in that pixel to obtain physically realistic fraction estimates . Only those pixels that contained 50% or more shade-normalized GV were chosen for training and validation, as this was decided as the threshold for classifying a pixel as a crop.Due to the high diversity of crop species in the Central Valley, we focused on a smaller set of crop classes that would be of the most practical use to stakeholders such as water managers, farmers, and scientists. Crops were classified into categories defined and used by the California Department of Water Resources to estimate water use . The crops within each category have similar rates of development, rooting depths, and soil characteristics, and are therefore presumed to have similar water requirements. Categories were included in the classification if they were prominent in the area, defined as ≥20 fields of that category, each of which contained ≥50% green vegetation, in the validation layers .