hydroponic vertical farming – Nature Ecrogrow

The financial value of publicly traded firms has also been shown to suffer from food scares

The risk of contamination, thus, typically includes the cost of withdrawing product from the market and lost revenue from a portion of harvest. Larger operations, therefore, have greater risk from contamination, though the risk from forgone revenues and the associated costs of product recalls are presumed to increase proportional to sales. This component of risk is likely to be scale-neutral, suggesting that the optimal private investment in food safety is independent of whether a product is produced in a concentrated industry or by many small firms. But large firms are more likely than small operations to have brand capital, which is threatened by food scares traced to their products. The loss of a firm’s good reputation can occur as the consequence of a food scare independent of the magnitude of the scare and the firm’s market share. With the loss of reputation, a brand-name product would lose its price premium. Sales and margins for products produced by the firm unrelated to the food scare are also likely to suffer.Large firms with good reputations, therefore, stand to incur losses from lapses in food safety that are disproportionately higher than small firms. Losses to a firm from an outbreak of food borne illness often also include product liability for related illness and death. Judgments can easily reach into tens of millions of dollars. If food contamination occurs in the field, then the magnitude of the outbreak may be independent of the market share of the responsible producer. However, if contamination were to occur in a processing facility,hydroponic vertical farming then the greater quantities handled in the facility and distributed through a wider network could cause illnesses and fatalities to be greater for outbreaks caused by larger firms.Liability is limited to the assets of the firm; thus, it increases as production increases because the assets of larger producers are greater.

Limited liability explains, in part, why firms that operate in hazardous industries with latent damages tend to be smaller than firms in other industries. Divestiture is recognized as a mechanism to limit liability. Small growers, then, face an upper bound on the risk of food contamination that lowers their incentives to invest in food safety. For instance, the Colorado cantaloupe grower whose melons are implicated in 32 deaths filed for bankruptcy in May 2012, listing its net worth at -$400,000. The farm’s owners, therefore, avoid potentially tens of millions of dollars in liability. Because large firms generally have more assets, they face greater exposure from product liability, and, therefore, demand greater protection against food contamination. Large firms and small firms alike can insure against product liability, but premiums are positively correlated with coverage limits and with past lapses in food safety, so that large firms demand greater ex ante prevention of food contamination even if they are insured. Large firms face more-than-proportionally greater risk from selling tainted food, and, consequently, have greater demand for food safety. They also face lower average variable costs in supplying food safety. Therefore, the optimal level of investment, which equates the marginal benefit of an incremental increase in food safety with the marginal cost of achieving it, is increasing at an increasing rate in firm size. Holding market supply of food constant, then, the provision of food safety declines as the number of producers grows. Empirical evidence from the meat and poultry-processing sectors indicates that firm size is an important determinant of firm-level investment in food safety. For instance, research suggests that firm size has more impact on the adoption of safety and quality assurance practices than any other firm characteristic. Large firms are also more likely to have adopted a range of food safety technologies.The losses stemming from an outbreak of food borne illness often are not limited to those firms implicated in food contamination. Indeed, food safety regulators issue broad warnings about food products irrespective of where they originated if the origin of contamination cannot be immediately identified.

For instance, when in 2006 an outbreak of E. coli was linked to consumption of bagged fresh spinach, the FDA issued a blanket warning to consumers to avoid the product altogether. Fifteen days later, the alert was scaled back to include a warning against consumption only of specific brands of spinach packaged in California on specific days, but the industry had already experienced a dramatic decline in sales. The outbreak, blamed for 199illnesses and three deaths, cost spinach producers $202 million in sales over a 68-week period, a 20% decline. By 2008, demand for California spinach remained below pre-outbreak levels. Because a food scare can induce a negative demand shock, there exists a reputational externality associated with food safety provision, which, consequently, exhibits public good characteristics. The benefits of an individual firm’s food safety investments accrue, in part, to competing firms. Because the firm does not capture the full benefits of its food safety provision, it will under invest in food safety relative to the efficient level. Industry-wide private provision may be much too low. A firm’s share of the benefit from an investment in food safety, however, is increasing in its market share. The more concentrated the industry, therefore, the closer is the equilibrium level of food safety provision to the efficient level. A monopolist would internalize the full benefit of his investments, and, therefore invest in the efficient level of food safety. As the number of small firms increases, however, the equilibrium market-wide provision of food safety falls increasingly short of the efficient level.Because agronomic and climate conditions impact the optimal handling and processing of crops, they afford some firms and regions a comparative advantage in producing safe food. For instance, the hot and dry conditions during the cantaloupe-growing season in California reduce the crop’s exposure to contaminants that can be transferred to melons in wet fields. Moreover, because the dry conditions keep California cantaloupe relatively clean, most are packed directly in the field, requiring less handling and avoiding exposure to food pathogens in shed packing operations that rinse and dry the produce.

As retailers seek to market local produce, however,vertical hydroponic garden comparative advantage in the provision of food safety is forsaken. The Listeria outbreak last summer was linked to unsanitary conditions at the Jensen Farms packing shed. And an FDA investigator identified unsanitary practices at the Chamberlain Farms packing shed that has been associated with this summer’s Salmonella outbreak. Concentration of production in regions with comparative advantage creates agglomeration advantages for the mutual provision and certification of food-safety practices. Because of the potential losses from food scares and the market-wide externalities from food safety investments, grower organizations have adopted voluntary process standards to mitigate risk and avoid shirking among their members. Some growers have also created marketing orders to enforce handling practices and require audits of all operations covered by the agreement. The California Leafy Green Product Handler Marketing Agreement was implemented in 2007, following the 2006 E. coli outbreak linked to spinach from California’s Salinas Valley. The California Cantaloupe Advisory Board responded to last summer’s Listeria outbreak by imposing mandatory certification by state auditors of all growers in the state. Such industry-wide cooperation and self policing is likely to be lost when production is fragmented and spread across wide geographical areas in the quest for local production.Irrigated agriculture exerts strong controls on global food production yields and the water cycle while accounting for 85-90 percent of human consumption . However, little is known about the spatial distribution of agricultural fields, their crop types, or their methods of irrigation. Spatially explicit knowledge of these field attributes is necessary in order to implement more water-efficient agricultural practices and plan for more sustainable economies . However, most remote sensing based mapping efforts cover limited political boundaries, on the order of U.S. states or smaller, and usually only cover a snapshot in time. Agriculture maps in developing countries are even more lacking in semantic detail, coverage, and resolution, which is a particularly acute problem given that agricultural expansion in these regions tends to be decentralized and without a guiding management plan for water sustainability. While a fine scale and up-to-date census of global agriculture does not yet exist, it is feasible that we can map a subset of fields that are spectrally and visually distinct from surrounding land cover and numerous enough to train a machine learning model to map a substantial subset of global agriculture. Center pivot irrigation fits these criteria; they are relatively uniform in shape, have a narrow range of sizes within particular geographies, and in drylands, can be strongly contrasted with the surrounding lack of vegetation .

Like all remote sensing applications for agriculture detection, image obstruction by clouds, fallow periods and the growth of non-agricultural natural vegetation in the landscape all pose challenges for models that detect center pivots. In more humid environments, center pivots have distinctive growing season patterns and larger amplitude changes in vegetation “greenness” compared to other vegetation types, making them readily identifiable with time series of multi-spectral images. However, it’s difficult to assemble a comprehensive time series of imagery over many parts of the world, primarily due to cloud occlusion and scene availability. Scene availability is particularly low for dates prior to the launch of the Sentinel-2, so a method to map center pivots using single date imagery in many parts of the world is desirable. Such a method is particularly valuable given that center pivots are one of the most ubiquitous irrigation sprinkler systems employed in large scale commercial agriculture and make up a considerable fraction of the unplanned agricultural expansion in developing countries. There are a variety of methods for mapping objects using remotely sensed imagery, but object based classification has been shown to outperform per-pixel classifiers in cases where the object of interest contains enough pixels to distinguish objects by textural or shape properties . Because center pivots are large relative to the resolution of public sensors like Landsat, they are amenable to being mapped using object based classifiers. The traditional approach to object based classification in remote sensing has been to use manually tuned algorithms to delineate edges or engineered features that capture texture and shape properties combined with per-pixel machine learning algorithms . However, traditional object based classifiers in remote sensing have a tendency to over fit and must be manually tuned or supplemented with region specific post processing to arrive at a suitable result. Another class of methods which make use of convolutional neural networks have achieved great success on complex image recognition problems in true color photography. These have not been thoroughly evaluated for mapping agricultural fields as instances across a large climate gradient using Landsat imagery. The goals of this research are twofold. First, I evaluate the performance of convolutional neural network based instance segmentation models on Landsat imagery. This experiment determines if CNN based models can make use of Landsat’s 30 meter resolution to provide reliable predictions of the locations and extents of center pivot agriculture in various states of development, including cleared, growing, and fallow. I test this approach by using the current most popular and near state of the art Mask R-CNN model, an approach based on a lineage of regional CNNs which jointly minimize prediction loss on region proposals, object class, refined object bounding boxes, and an object’s instance mask. This model is tested on the 2005 CALMIT Nebraska Center Pivot Dataset, which is divided into geographically independent samples that were partitioned into a training, validation and testing set. Multiple model runs with varying hyperparameters and preprocessing steps were conducted to arrive at the most accurate result on the validation set, and the final most accurate model was applied to the test set to produce the final reported accuracy. The model is also evaluated on the full training data set and 50% of this dataset in order to examine the effect of reduced training data on model accuracy over a large region. Second, I compare these results to the Fully Convolutional Instance Aware Segmentation model, which was previously the state of the art in instance segmentation in true color photography prior to Mask R-CNN.

The tools to facilitate such an accounting can only be developed within a whole-systems perspective

Our educational and research institutions tend to mirror this shortcoming,8 with the result that the larger system contexts of research questions are infrequently investigated and poorly understood. Difficulties in apprehending and resolving problems whose constituents are grounded in several interrelated systems are compounded by the international community’s disparate, competitive political and economic systems. Nations act to promote their own priorities but affect, often negatively, globally shared resources and globally interdependent societies. Although nations and other sociopolitical groups generate impacts beyond their borders, they are generally incapable or unwilling to assess and react equitably to the results of their actions. Pierre Crosson and Norman Rosenberg 18 note the inadequacy of information feedback about significant environmental problems in modern societies, an inadequacy which characterizes feedback about social problems as well. Accounting for the system-wide implications of local actions should be a primary objective for sustainable agricultural systems. The definition of sustainability offered here places a priority on broad-based equity considerations. We believe it is inadequate to exclude social justice as a priority and that there is an ethical requirement for greater equity in the agricultural system. Some have combined concern for how we treat the environment with how we treat our fellow human beings.19, 20, 21,22 For those focusing on the latter, it is essential to look beyond sustaining our environmental and economic ability to produce agricultural goods. It is equally important to ensure that those goods are produced and distributed in an equitable manner. A concern with this human values aspect of agriculture involves a sweeping rather than localized concept of who constitutes “us.” Typically, resource conservation is dis- cussed in terms of its implications for farmers’ profit- ability or our descendants’ food-producing capabilities. The sustainability definition offered in this paper does not limit equity considerations to these groups. A concern with equitable social relations in agriculture requires defining “us” in terms of all fellow humans – not only farmers and future generations, but also farm workers, consumers, non-farm rural residents, Third World urban poor, and others.

Sustainability in this sense is framed in terms of both intergenerational and intragenerational equity. Thus,greenhouse vertical farming issues such as farm worker rights and inner-city hunger are as central as issues of soil erosion and groundwater contamination to the goals of agricultural sustainability. One of the most profound challenges facing agriculture is creating a decision-making process which will fairly resolve equity issues. Such a process must assess competing interests; evaluate agriculture’s costs and benefits, and the recipients of each; decide fairly what the compromises must be; recognize and encourage shared goals and common ground. In most discussions of sustainability either environmental quality or social justice issues are emphasized, but neither can be sup- ported wholly at the expense of the other. Nourishing humans, ensuring social justice, and providing a reasonable quality of life cannot be accomplished if agriculture’s resource base and environmental constraints are neglected. Likewise, few would argue that environmental considerations should be pursued at the expense of satisfying basic human needs. An equitable agricultural system must foster a decision-making process which is truly democratic, one which identifies not only what the costs and benefits are but how to distribute them fairly among all sectors of society.Many sustainability definitions, particularly those which guide applied sustainable agriculture programs, are based on the primacy of farm production and short-term profitability. As sustainable agriculture programs have increasingly been incorporated into long-established agricultural institutions they have manifested the largely unquestioned intellectual assumptions and infrastructural constraints which characterize their parent institutions. This is problematic because conventional agricultural institutions have fostered many technologies and policies counter to sustainable agriculture goals.Such institutions have, for example, contributed to concentration within agriculture; have not generally benefited agricultural labor; and have systematically failed to examine their impact on the environment, the structure of rural households and communities, and the consequences of rural resident displacement.

To situate new pro- grams designed to address these problems within the framework which produced them is of questionable value unless steps are taken to change the nature of that framework, for it determines the way its re- searchers see the world, pose questions, and define problems. When agriculture is viewed in a whole-systems context and sustainability is defined comprehensively, it is clear why the current popular focus on farm production practices is insufficient for achieving agricultural sustainability. Developing non-chemical pest management methods, for example, will effectively reduce pesticide use only if economic structures and policies encourage their adoption by farmers. More importantly, one cannot conclude that improved production practices will transform the agricultural system into one that meets all environmental, economic, and social sustainability goals. Social goals must be addressed explicitly. This is why production techniques such as organic farming, while a likely component of a sustainable food and agricultural system, cannot be thought of as synonymous with sustainable agriculture. Given the conventional institutional context of most state and federal sustainable agriculture programs it is not surprising that they tend to focus research on conventional priorities such as production practices and efficiency and have not, for the most part, aggressively addressed social and economic issues. Sustainability priorities – and the definitions which embody them – must be expanded to encompass the many factors affecting production and distribution as well as the larger environmental, economic, and social systems within which agriculture functions. This has been the focus of the Agroecology Program since its inception in 1982. Through conferences and publications* we have worked to expand the discussion and practice of integrating these aspects of sustainability. Recently, the University of California Sustainable Agriculture Research and Education Program has broadened its agronomic focus to include social, economic, and policy issues. SAREP defines sustain- able agriculture as integrating “…three main goals – environmental health, economic profitability, and social and economic equity.”Their grant program, which encourages research and education on social, economic, and public policy issues affecting food and agriculture, could become a model for other sustain- able agriculture programs such as LISA. We believe that it is important to continue exploring the meaning of agricultural sustainability. Before an improved agricultural system can be developed the biases and structures that have led to agricultural problems must be closely examined and concrete goals articulated, based upon a broadened concept of agricultural sustainability. The concept of sustainability offered in this paper emphasizes that social goals are as important as environmental and economic goals, and widens the opportunity to move beyond the narrow agricultural priorities expressed in the past.

It is based upon the whole-systems, interactive nature of all aspects of the agricultural system – that problems and their resolutions must be conceived not only in terms of their immediate time frames and local impacts, but just as importantly, in terms of their future time frames and their global impacts. It encourages emphasis on optimum production over maximum production, the long term along with the short term, the public’s best interest over special interests, and the contextualization of disciplinary work within interdisciplinary frameworks. Our hope is that this definition helps advance the discussion on developing a food and agriculture system that is sustainable for everyone. While aggregate growth in an economy may improve the welfare of both wealthy and poor households,vertical agriculture the latter are most usually rural, and rural households have employment and incomes that depend disproportionately on agriculture. It is natural to wonder if growth in aggregate agricultural income has a different effect on the welfare of poorer households than does growth elsewhere in the economy. The question is an important one for many policy issues. Faced with continuing extensive poverty, many development agencies and scholars have suggested the need to refocus growth on agriculture , arguing that the alternatives of redistributing income generated outside of agriculture or migration out of agriculture to urban areas are difficult to achieve and create other problems. Of course, we are not the first to wonder whether growth in agriculture may be more effective than growth in the rest of the economy in reducing poverty; an extensive theoretical and empirical literature already exists on the subject which we discuss in Section 2. The theoretical literature focuses on the different transmission mechanisms of an exogenous gain in agricultural productivity on poverty, while the empirical literature analyzes the reduced form relationship, and generally documents a stronger association between poverty reduction and growth originating in agriculture compared to growth originating in non-agriculture, with the exception of Latin American countries. In this paper we tackle this question by comparing changes in the level and distribution of household expenditures due to growth in both aggregate agricultural and aggregate non-agricultural income. We use growth in household expenditures as the outcome of interest because we believe expenditures to be the best available indicator of material well-being; also, these are the data generally used for poverty calculations for most low-income countries. However, our analysis differs from most other studies in several aspects. First, we consider growth in expenditures across the entire distribution rather than the simple poverty headcount ratio, giving a richer picture of the effect of sectoral growth on welfare. Second, we use the deciles as defined within each country, rather than a common international benchmark of expenditures.

To correct the underlying assumption that deciles of very different countries have similar relationship with agriculture, we then pursue some heterogeneity analysis. Finally, we tackle the issue of simultaneity between sectoral income and expenditures using an instrumental variable approach, allowing us to take a stand on the causality of sectoral growth on welfare. The simple regression we would like to estimate relates expenditure growth for differently positioned households to growth in sectoral income, the latter weighted by its share in total aggregate income; this is described in Section 4. The question of whether the poor benefit more from agricultural income growth than growth in other sectors could then be answered simply by examining the relative size of the coefficients on aggregate income growth from agriculture and from other sectors. In practice, there is a series of challenges we must face before estimating such a regression. First, we do not have household level data that would allow us to make comparisons across countries. Instead, we use data from the World Bank’s Povcal Net project and consider estimates of household expenditures from different expenditure deciles; in effect we construct a panel of ten representative ‘households’ for each country, each representing an expenditure decile.1 We discuss these data in Section 3.1. Second, the resulting ‘panel’ is extremely unbalanced, since the underlying expenditure surveys are conducted at irregular intervals. This creates some important accounting issues when we turn to estimation, treated in Section 4.1. Third, some countries, some years, and perhaps some deciles can naturally be expected to have different expenditure growth rates for reasons unrelated to sectoral income growth. A global financial shock may cause expenditure growth to slow for everyone; households’ risk attitudes or time preferences may imply different rates of expenditure growth across deciles ; the endowments of a particular country or some aspect of the structure of its economy may imply systematically different rates of expenditure or income growth even over long periods; and variation in the global price of agricultural commodities will change the composition of income across sectors for many countries. We attempt to deal with these kinds of alternative sources of variation in expenditure and income growth in a fairly agnostic manner, by using fixed effects and related methods for dealing with what Wooldridge calls “unobserved effects.” So: we account for aggregate cross-country shocks using a collection of time effects; and for systematically different rates of expenditure growth across the distribution we use a set of decile fixed effects. We would be inclined to also use a complete set of country fixed effects to deal with differences in endowments, but with these we reach the limits of our dataset; instead we employ a set of continent fixed effects, which in practice seems to be effective. Fourth, the stochastic process governing country-level agricultural income exhibits more time-series variance than does income from other sectors.