September 2022 – Page 2 – Nature Agriculture

September 16, 2022

Regulated agricultural sources will be provided a financial incentive to aid in compliance

According to the California League of Conservation Voters , “Because agriculture has gone unregulated for so long as a source of air pollution while other sectors have been subject to air quality rules, there exist many viable opportunities to reduce air pollution from agricultural sources.” Thus, ending the exemption not only helps avert national sanctions, but will help the state clean the air . This mirrors Florez’s stated intent in offering the bill in the first place. According to Pollard , “Florez said he introduced [SB 700] because agriculture is a major contributor to air pollution that is related to epidemic levels of asthma in children and other health problems in the Central Valley.” In an attempt to put a “face” on the victims, Florez had residents from across the valley testify on behalf of SB 700. As reported by Grossi in the Fresno Bee , “Caleb Schneider, 16, of Hanford, said he has asthma, and he wants to see every effort made to clean the air. ‘When you can’t breathe’, he said, ‘nothing else matters.’” This narrative depicts the agriculture industry as a villain deserving of public policy burdens. The numbers and comparisons define the level of the burden. Since the contribution of the agriculture industry is “significant,” their responsibility in the cleanup should be proportionate. The proposed solution will make agricultural sources a part of the regulatory process just like every other industry in the California. For supporters of SB 700, this creates a sense of equity in the treatment of all sources of pollution. While this harkens back to the complex cause narrative, there is no doubt that supporters have emphasized the role of agricultural sources in the air pollution, allowing others to fade into the background. There is a strategically constructed link between the agricultural sources and the exemption . This adds to the somewhat negative construction of the agriculture industry. There is also some concern about the arbitrary and capricious nature of the regulatory structure of SB 700.

Many in agriculture do not see themselves as being like other industries. According to Roger Isom,vertical hydroponic garden vice president of the California Cotton Ginners and Growers Association, “It’s not like ag is an industrial source that’s going day after day. It’s seasonal. The question is how can we do our share and not be put out of business” . An editorial in the San Francisco Chronicle makes the case for differential treatment, “The farmers have a decent case for special consideration. A range of 200 crops call for different farming methods, making rule-making tricky. In a struggling economy, new costs should be minimized. As always, water, land prices and import figure, too” . The last component of this narrative is the potential consequences of imposing an unfair and overly broad regulatory approach on agriculture. Opponents of SB 700 argue the agricultural community will not be able to “absorb the additional operation costs resulting from new regulatory fees imposed by LADs, given the international competition in the marketplace for most agricultural operations” . While increased costs and decreasing competitive advantage will plague the industry, there will also be impacts felt at the level of individual small farms. According to state Senator Chuck Poochigian , “They are not corporate magnates. They are ordinary people trying to make a living. They are losing their farms. They are making no money at all in some cases. . . . [The bill] punishingly exceeds federal regulations” . This narrative uses very different language than the previous one . Here the agriculture industry is more often referred to as farmers, growers, ranchers, and dairymen. This “puts a face” on the seemingly faceless, corporate agriculture industry. It is these individuals that face the unfair and overreaching regulations of SB 700.

There is also a different interpretation of the multi-causal narrative. Instead of emphasizing the contributions of agriculture, all of the other sources are placed front and center. This is especially true of passenger vehicles and sprawling development. It is simply inequitable to single-out agriculture for regulation when this will result in increased costs with little or no improvement in air quality. It is only by using a modest approach to address California’s air exemption that this pain can be avoided. Hence, it is the regulatory approach of SB 700 that is the problem in this narrative, not air pollution in the valley. The narratives, as captured by the NPF, have a strong link to the theories of policy design discussed by Schneider and Ingram.Both the portrayal of characters and proposed policy solutions fit with the social construction of target populations and their hypothesized links to elements of policy design. This allows the use of these narratives to hypothesize about what kind of policy tools, agents, and implementation structures will be contained in SB 700. Both the “complex-cause” and “agriculture as significant contributor” provide a characterization or social construction of agriculture as villain in the narrative of causing pollution harmful to the health of citizens. While the “complex cause” narrative has many more villains, “agriculture as significant contributor” has only one and tells a damning tale of intentional causation. Thus, one should expect to see policy design elements used on negatively constructed target populations. The “agriculture as victim” narrative portrays agricultural interests in a much different light. This narrative shows agriculture as the victim of punitive and overly broad attempts to regulate their activities. It provides a more positive construction of this target population. So, given this portrayal, we should expect to see policy designs reflective of a positively constructed target population. All of these narratives coexist with one another in the larger debate surrounding air quality policy and SB 700. Agriculture and its interests are characterized as both villain and victim in the policy discourse.

Different policy solutions are linked to these different characterizations. These varying constructions as agriculture result in seemingly contradictory elements of policy design that both benefit and burden the agriculture industry. The choice of policy tools reflects the social constructions policymakers have used to construct target populations. These policy tools direct the treatment of both targets and agents . Schneider and Ingram argue that different types of policy tools contain different behavioral assumptions about the group being targeted by the policy. Thus, just as the narratives suggest, we should expect to see a mix of policy tools in SB 700 that seek to force the agriculture industry to comply; and those that seek to aid them in achieving compliance. The structure of the regulatory framework itself is based on the premise that the agricultural industry is a significant contributor and will not voluntarily comply. All agricultural sources are required to meet the most stringent technology standards , as well as the best available control measures for mitigation purposes. The required standards reflect what Schneider and Ingram term an authority tool. The expectation is that industry will obey the requirements. A locally administered permit system is another part of the regulatory structure. Agricultural sources emitting 50% or more of major source emission levels for PM-10 and ozone are required to pay a fee to operate or construct facilities. According to Schneider and Ingram , “User fees, rates, and charges also are used as incentives, but these do not carry as much positive valence as inducements.Charges can also be distinguished from sanctions in that they do not intend to convey social disapproval of an activity.” Thus, the regulatory structure itself reflects a somewhat negative to ambivalent tone concerning the agriculture industry. There are a host of other policy tools that will aid agricultural sources in their attempts to comply with the new regulatory framework. These tools echo themes from the agriculture as victim narrative. The first of these is the information clearinghouse on mitigation strategies. This fits the description of a capacity-building tool . These kinds of tools are supposed to “enlighten, remove impediments, and empower action by the target group or agency itself” . The agriculture industry is portrayed as a group that simply needs to learn about the best mitigation strategies available. This suggests a more positive social construction of the agriculture industry. It is not a question of willful neglect, but one of needed education.Specifically, financial institutions that provide service to agricultural interests will be granted access to additional monies in order to make it easier to provide loans to fund air pollution control measures. This inducement implies “respect for the target population and portray[s] a positive valence of the behavior that is desired” . Agriculture will receive financial resources to aid compliance with the new rules developed under SB 700’s regulatory framework. This suggests a positive tool for a positively constructed target group. The nature of the relationship between agent and target reflects themes of the agriculture as victim narrative. The clearest illustration of this relationship lies in the rule-making process for SB 700 . The policy tools utilized here are learning tools. This approach coincides with the “consensus-building” or “support-building” implementation structure . This design is “intended to provide a forum for participation and discussion that will enable lower-level agents or target populations to determine what should be done. Statutes usually allocate discretion to lower-level agents or even target populations” . This implementation structure sets the stage for the negotiation of both PM-10 and ozone rules developed by the SJVAPCD.

Although a large literature describes how recessions affect non-agricultural labor markets, few studies examine the effects of recessions in the seasonal agricultural labor market.1 We examine how the last three recessions affected hourly earnings, the probability of receiving a bonus,vertical home farming and weekly hours in agricultural labor market. We compare those results to those in three non-agricultural labor markets that rely on immigrants. We empirically test five hypotheses. First, we expect seasonal agricultural workers’ earnings to rise during major recessions. Because the income elasticities of demand for seasonal agricultural products such as fruits and vegetables are relatively inelastic, recessions cause a small, possibly negligible leftward shift of the labor demand curve in seasonal agriculture. In contrast, a recession’s may cause a significant leftward shift of the labor supply curve. Roughly half of hired, seasonal agricultural workers are undocumented.2 The Great Recession significantly reduced the number of new, undocumented immigrants entering the United States , causing a substantial leftward shift of the agricultural labor supply curve.3 Given a substantial leftward shift of the supply curve and only a minimal shift of the demand curve, agricultural workers’ earnings rise. Second, while we hypothesize that hourly earnings and the probability of receiving a bonus rose during the Great Recession, 2008–2009, we expect these earnings measures to rise by less or possibly fall in the earlier, relatively minor 1990–1991 and 2001 recessions. The Great Recession caused much larger decreases in new immigrant labor supply than in these earlier recessions . Third, we expect recessions to affect undocumented workers differently than documented workers because their labor markets are partially segmented. Evidence that these markets are partially segmented comes from earlier studies that show that, compared to documented workers, undocumented workers are more likely to be employed by farm labor contractors as opposed to farmers, and because their pay differs . Fourth, we expect weekly hours of employed agricultural workers to increase to compensate for the reduced flow of new immigrants during major recessions. Fifth, we expect recessions to have larger earnings effects in agricultural labor markets than in construction, hotel, and restaurant labor markets. These non-agricultural labor markets are more likely to have sticky wages due to union and other contracts and minimum wage laws. The first section discusses how recessions affect the supply curve of agricultural labor. The next section describes our two data sets. The third section presents our empirical results. The final section discusses our results and draws conclusions.In contrast, during a major recession, fewer undocumented immigrants enter the United States from Mexico and other countries. Passel, Cohn and Gonzalez-Barrera reported a large drop in the number of undocumented immigrants during the Great Recession relative to the recovery years afterward and to preceding years, which include milder recessions. They estimated that the number of undocumented immigrants rose monotonically from only 3.5 million in 1990 until it peaked at 12.2 million in 2007. However, the number of immigrants fell to 11.3 million by 2009 during the Great Recession. In contrast, they found that the supply of immigrant labor rose during relatively mild 2001 recession.These results are consistent with U.S. border patrol reports from the Department of Homeland Security’s Office of Immigration Statistics.

September 15, 2022

Agriculture’s reciprocal relationship with the overall economy is clear

Many of these assumptions and priorities also influence sustainable agriculture programs. Such an examination is critical if we are to avoid reproducing the problems engendered by conventional decision-making processes in the re- search, education, policy, and business institutions which determine agriculture. KennethDahlberg 9 notes that assumptions and biases which may occlude the development of sustain- able agriculture concepts include: separating ourselves from nature and viewing it as something which must be dominated; measuring progress in increasing applications of science and technology; emphasizing technology and formal social institutions over natural systems and less formal aspects of society; and failing to see how human societies fit into and are dependent upon larger natural systems. We would add to Dahlberg’s list the tendency to overlook the needs of human beings who are separated from us, whether it be by distance, by socioeconomic status, or by time. These types of assumptions govern how we understand the world and have been institutionalized in educational and research pro- grams. MacRae et al. note that many characteristics of the research process responsible for conventional agriculture’s great productivity create obstacles to developing sustainable agriculture. Among these are over reliance on reductionism and quantification, scientists’ belief in objective “truth,” and the divorce of research from its potential social consequences . Along with Patricia Allen those authors also cite obstacles posed by a peer review system and publishing process which tend to reward individual “isolated” achievement while discouraging long-range interdisciplinary work and innovative ideas. This is aggravated by research funding from private sources, which encourages research on technology development rather than social analysis. The same assumptions and biases which govern research and education are also embedded in much of U.S. agricultural policy. They are expressed primarily as short-term economic considerations such as maximizing production, minimizing production costs and consumer prices, vertical aeroponic tower garden and maximizing the market share of certain agricultural commodities. These priorities have largely been those of the agricultural sector, and not necessarily those that are best for society at large.

To address these types of whole-system issues we believe that sustainable agriculture concepts must go beyond placing top priority on environment and production practices and give greater emphasis to social issues. Current definitions are often based on two assumptions that we believe to be problematic: 1) that the farm is the primary locus for achieving agricultural sustainability and 2) that short-term micro-economic profitability is paramount.Major institutions promulgating “sustainable” agriculture often focus on the farm level rather than on the whole system. This is clear from the priorities of the U.S. Department of Agriculture’s Low Input Sustainable Agriculture program. LISA focused on “low input technologies [which] provide opportunities to reduce the farmer’s dependence on certain kinds of purchased inputs in ways that increase profits, reduce environmental hazards, and ensure a more sustainable agriculture for generations to come.”As these priorities demonstrate, agriculture is often thought of almost purely in terms of farms and farmers, a perspective traceable to the period in which most Americans were involved in farm production but which no longer reflects agriculture’s true scope. Even though the on-farm transformation of resources into food and fiber is a core process of the food and agriculture system, it is but one of many components. The system includes not only generating agricultural products, but also distributing those products and the infrastructure which affects production and distribution at regional, national, and global levels. Interactions among the larger environmental, social, and economic systems in which agriculture is situated directly influence agricultural production and distribution. The following briefly describes how these larger systems affect agriculture yet remain unaccounted for in many sustainable agriculture programs.Agricultural practices ranging from the development of irrigation projects to the use of agrichemicals have often had negative environmental impacts such as wildlife kills, pesticide residues in drinking water, soil erosion, groundwater depletion, and salinization. Substituting environmentally sound inputs for those which are damaging is an important step in addressing these problems. But ecological sustainability re- quires intensive management and substantial knowledge of ecological processes which go far beyond substitution and cannot be achieved merely by substituting inputs.

Such substitutions need to account for their complex and long-term ecological consequences. Otherwise they may engender secondary and perhaps more serious problems in the same way that conventional solutions frequently have been shown to do. Viewing agricultural systems as true ecosystems can serve as a model for bringing the whole-systems perspective to bear on social and economic issues as well. Instead, however, sustainability programs often take conventional approaches to solving these problems by changing the production practices which are directly at fault without addressing the total ecosystem context of either the problems or the alternative production practices which show promise as solutions. An example is the current emphasis on input substitution. Most projects funded by the USDA Low- Input Sustainable Agriculture program in its first two years, for instance, explore how inputs which cause environmental damage or incur expensive costs for the farmer can be replaced with more environmentally or economically benign inputs . In most cases single components of farming systems are being analyzed and little attempt is made to place these analyses in the context of whole agroecosystems.Agriculture both affects and is affected by the larger society. Farmer production decisions, for example, determine the diversity and quality of foods available to consumers, and farm size and technologies have been associated with the economic and social vigor of rural communities.At the same time, society deter- mines what is possible at the farm level. Farmers lose valuable farmland when encroaching urbanization creates zoning problems, inflates land values, and generates urban pollution which lowers crop productivity. Production decisions are heavily influenced by consumer decisions. A recent example is farmers’ voluntary discontinuation of Alar on apples. Although farmers continued to endorse the safety of Alar, they realized that this position was untenable in the face of consumer concerns. The international scope of agriculture also plays an important role. Social and economic conditions in other countries and global food supplies can greatly affect the viability of farming in local regions, as evidenced when the world grain shortages of the 1970s led to enormous expansion in U.S. grain production. When foreign demand for U.S. grain subsequently declined, many American farmers’ incomes fell, often to the point where debts incurred to expand production could not be paid, and major social and economic dislocations in the grain belt occurred.

Efforts in sustainable agriculture are not unlike those of their conventional counterparts in that they tend to serve certain clientele selectively and generally do not evaluate the social consequences of the technologies that sustainable agriculture encourages. For example, organic farming strategies are often sup- ported because they are environmentally sound, and in terms of the prices organic foods command, are profitable for farmers. An unintended and unaddressed social consequence of this is that people with low incomes often cannot afford organic products and thus are denied access to food containing fewer pesticide residues.The agricultural industry is a significant portion of the nation’s economy: in 1984 about 20 percent of U.S. jobs were in some aspect of food and fiber production, distribution, or service and these workers and their industries contributed 18 percent of the gross national product. The importance and volatility of food prices have made most governments reluctant to let market forces alone set these prices. Thus, a host of institutional measures have been implemented to address agricultural prices in order to manage their effects on consumer welfare, public coffers, farmer income, foreign exchange, food security, nutrition, and food distribution. Such policies include commodity programs, water and reclamation programs, import/export policies, and research and extension programs. Larger economic factors indirectly affect the agricultural system, factors such as interest rates, trade policy and negotiations, the exchange value of the U.S. dollar, and environmental regulations. In the context of these economic policies, agriculture is subject to non-agricultural constraints and conditions, a fact acknowledged broadly in the literature of both conventional and sustainable agriculture. Yet most research and extension programs in both conventional and sustainable agriculture do not recognize or address thesmacrofactors. Sustainable agriculture efforts generally concentrate on environmentally sound farm-level technologies which are economically profitable for farmers to adopt. Less commonly do such efforts address how the technologies they generate will affect or be affected by larger economic concerns in the long run.A second assumption behind many sustainable agriculture definitions, that short-term profitability is of ultimate importance, is also common. This is a central tenet of LISA, forming the first of its ten Guiding Principles: “If a method of farming is not profitable, it cannot be sustainable.”This is problematic, particularly since there is little acknowledgement that profitability is determined by policies, fiscal procedures, vertical gardening in greenhouse and business structures that can obstruct sustainability. We recognize that short-term profit- ability is important in commercial agricultural systems; clearly, if growers are to adopt sustainable agricultural practices, these must be profitable in the short run as well as the long run. The problem lies in pursuit of short-run profitability at the expense of environmental and social goals. In conventional agriculture, the drive to maximize short-term profit has meant that many pressing problems have been ignored or exacerbated. Natural resources have often been treated as expendable commodities , and agriculture has functioned more for financial gain than for human need.

The social costs of production have generally been neglected: chronic hunger, inequitable economic returns and unsafe working conditions for farm labor, possible negative health effects related to nutrition and agrichemical use, and the decline of socioeconomic conditions in rural communities associated with large-scale industrial agriculture. Subsuming social goals to economic goals may easily be reproduced in sustainability programs unless sustainability concepts address the fact that profitability and social goals are often not compatible in current economic systems.A useful concept of agricultural sustainability needs not only to acknowledge social issues as priorities equivalent to those of production, environment, and economics, but to recognize the need for balance among those disparate but highly interactive elements which comprise agriculture. Toward this, we offer the following perspective: A sustainable food and agriculture system is one which is environmentally sound, economically viable, socially responsible, non-exploitative, and which serves as the foundation for future generations. It must be approached through an interdisciplinary focus which addresses the many interrelated parts of the entire food and agriculture system, at local, regional, national, and international levels. Essential to this perspective is recognition of the whole-systems nature of agriculture; the idea that sustainability must be extended not only through time, but throughout the globe as well, valuing the welfare of not only future generations, but of all people now living and of all species of the biosphere.This sustainability concept moves beyond emphasis of farm-level practices and microeconomic profitability to that of the entire agricultural system and its total clientele. Richard Lowrance, Paul Hendrix, and Eugene Odum16 describe a model which approximates a whole-systems approach. They see four different loci or subsystems of sustainability: 1) farm fields where agronomic factors are paramount; 2) the farm unit wherein micro-economic concerns are primary; 3) the regional physical environment where ecological factors are central; and 4) national and international economies where macroeconomic issues are most important. Their model demonstrates that focusing on only one level of the agricultural system neglects others that are equally essential. A whole-systems perspective fosters an understanding of complex interactions and their diverse ramifications through- out agriculture and the systems with which it articulates. This understanding is at the root of sustainability. Vernon Ruttan17 describes an ever-widening comprehension of “whole system” as he delineates three waves of social concerns which have arisen about natural resource availability, environmental change, and human well-being. In the late 1940s and early 1950s the first wave focused on whether resources such as land, water, and energy were sufficient to sustain economic growth. The second wave, in the late 1960s and early 1970s, focused on the effect of growth-generated pollution on the environment . The most recent concerns, manifest since the mid-1980s, also center on adverse environmental effects, but the key distinction is the transnational issues such as global warming, ozone depletion, and acid rain. As agriculture and its impacts become increasingly globalized, the need for a whole-systems perspective,particularly in terms of decision-making, become increasingly critical. Dahlberg 9 observes that although the impacts of modern industrial society are global, the data and analytical tools we use to assess those impacts are limited by national, disciplinary, or sectoral boundaries.

September 14, 2022

Processing efforts congruent to the cloud infrastructure cannot be provided by the farm server

This documentation directly needs to fit for cross-compliance procedures with public authorities and personal calculations of the farm which requires a standardized format.Furthermore, the billing of tasks shall be processed simultaneously for contractors.To enable task or route planning for the MR or contractor a RRN is required which allows communication to the farmers also without internet.A digitally managed farm becomes resilient if it is characterized by utter independence of external internet connection and power supply.Like this, the FDFS is able “to prevent disasters and crises as well as to anticipate, absorb, accommodate or recover from them in a timely, efficient and sustainable manner”.Nonetheless, all online features and functions are used comprehensively in normal conditions but are replaceable by farm particular systems in case of intermitted power and internet connection.Consequently, a hybrid system was developed, combining cloud-based systems and farm-specific solutions.Likewise, it is for power supply, backed up by an emergency power generator, which is already mandatory for livestock farming.By its installation on the farm, it ensures the decentrality of the data repository.Storing data redundantly on different servers is the main aspect of configuring an FDFS in a resilient manner.Moreover, decentrality results in a higher level of data safety by securing data against external access and loss.All necessary data for the applied precision farming solutions are available in any circumstance.To ensure the connection of sensors, machinery, farm server, and farm management, during an internet outage, an LWN is installed.Less mechanized farms obtain digital farming technology through contractors or machinery rings.When a task is ordered by such latter farmer the contractor’s machinery connects to the LWN of the farm when it is in reach.Data transfer of e.g.prescription maps or documentation of tasks is then enabled.Communication between farm and contractor/machinery ring for task disposition can proceed before task execution with a minimum amount of data within an RRN of long-range like LoRaWAN.The single components of the FDFS in Fig.2 are explained in detail in the following sections.First a farm managed by an FDFS needs a power generator for a redundant power supply.

With technical hardware solutions,ebb and flow table it has to be guaranteed that in case of a power failure no voltage drop occurs.This can be achieved by bridging voltage drops with a UPS implementable for edge computing applications.Its capacities need to be selected according to the data and processing rates of digital sensors and devices running on the farm and the needed degree of resilience of the farm.Farmers have to decide which digital functionalities are fundamental for a similarly effective and sustainable maintained production without the internet thus balancing investment cost and necessary level of resilience.Fig.3 summarizes the general functions which could be covered by the farm server.The first main function of the farm server is to store relevant data for farm management.different data partitions are to mention: Basic data like field boundaries for guidance and calculations, AB-lines for controlled traffic, management zones for variable rate application, flight plans for drones, machinery settings, etc.Open Geodata, provided by public authorities, can be stored in an automated updated version or even several older versions if necessary for specific applications of farmers.Needed versions of satellite images are stored automatically corresponding to actual calculations of e.g.management zones.In level five this function can be conducted via AI, selecting and downloading the currently available satellite images according to the needs of the planned crop rotation.Depending on the cache function of each machine or sensor the server also is supposed to be used as a cache in case machinery or sensor has lost internet connection to send data to the cloud.Data sets of tasks that got interrupted by internet outages are completed in the cloud with cache data from the farm server when an internet connection is reset or over the landline.Some specific data, individual for every farm, might be needed in real-time where no delay by internet connection is acceptable.By receiving and storing these data, redundant to the proprietary cloud service, on the farm server ensures constant access to it.The format of these in real-time needed backup data on the farm server must be read- and processible for offline desktop software.Here ISOXML, JSONLD, and for some applications, Shapeles are the suggested formats.Shapefile and ISOXML are the most common in the actual applications of OEMs.

Data for later use could also be stored in manufacturer proprietary formats and be processed after reconnection to the internet in the proprietary cloud.When purchasing technology, which disadvantageously only offers proprietary formats it is favourable to see if there exists a plugin for converters like ADAPT.Generally, here a clear farm specific definition of necessary data has to be made by farmers because the duration of internet blackouts is never predictable.Among others because of a lack of interacting functionalities between different clouds.Nevertheless, some applications of predictions, machine learning, or simple AI algorithms are possible.In this case, farmers are reliant on the offline performances of their OEMs which evince a great deficit in this sector.Accordingly, secondary processing software needs to be chosen wisely.Focusing on long-term internet interruptions each farm needs to decide about which processed data in which time gap is indispensable.These diagnoses will reveal which proprietary raw data are expected to be made available by manufacturers in open and standardized formats for farmers to be able to use the data in secondary software while the usual FMIS is not available and a desktop version does not exist.Within the suggested FDFS, a desktop version of the FMIS is intended.This is a decisive contribution for maintaining effective, efficient, and sustainable crop production during any internet outage.The FMIS desktop version maintains the necessary functionalities of the specific farm needs.The ideal case of a resilient FMIS would be redundant to the online version which is closely linked to the processing efforts in the former section.Furthermore, interfaces to the LWN connect FMIS and machinery and ensure data transmission and documentation.The FMIS of the presented FDFS provides export functions of common, standardized, and open-source formats which ensures interoperability to secondary software like a GIS for example.When investing in a FMIS farmers need to consider which services their FMIS needs to fulfil in a situation when no internet connection is available.Data communication in between farm server and sensors/ machinery during internet outages is maintained by an LWN.Depending on farm size , topography, data traffic , and required latency of executed applications a corresponding network gets installed.

Farmers who depend on contractors or MRs additionally need to be able to receive and send a radio signal to communicate tasks while internet communication is not available.Otherwise, machinery rings or contractors cannot manage their orders to maintain maximum service capacity.This network does not necessarily need high bandwidth because for management purposes the contractor or machinery ring only needs basic information about the task.The range of this signal is defined by the catchment area of the contractor and MR.To establish and ease the installation of such systems only view standards with low installation costs should be taken into consideration.MRs and contractors, who work for the farmers, need to be able to connect with their machinery and sensors to the LWNs of their customers.When the executing vehicle is in reach of the LWN, the task data can be completely uploaded for task execution.When the job is completed, as-applied and further documentation data are sent to the FMIS through the farm server over the LWN.To complete the independence of the FDFS from external signals, the LWN also covers an alternative for positioning signals.That means all items on a farm, which use position data can navigate or track their position by calculating with signals of the LWN.In case the area covered by the LWN is too small for the farm extension, georeferenced marker points can be distributed on the farm area or mobile antenna stations can be set up.The main use case in the project MRdigital in Germany was in the field of slurry application by a subsidiary company of two machinery rings,flood table which acts as a contractor.A self-propelled slurry applicator is used.Via NIRS technology nutrient contents of the substrate can be measured which enables the system to conduct variable rate application using prescription maps.Such an applicator in addition to digital nutrient measurement is seldom purchased by single farmers and a fortiori, not by small and middle scaled farms.The business model to run such a machine within the organization and the management of machinery rings makes precision farming technology accessible and affordable also for small and middle scaled farms.According to the latter sections, usually, tasks and prescription maps are sent via the internet from the FMIS to the machinery ring or directly to the machine and vice versa.Farmers using an FDFS at their needed level can create and send tasks and prescription maps also during internet connectivity problems.The RRN enables the transmission of simple task data in advance.The prescription map and the as applied documentation data get transferred when the slurry applicator is in reach of the LWN of the farm.The machinery can read data from any kind of FMIS and write it back in the same format.The approach of categorizing farmers’ needs of resilience into five levels tried to meet the majority of conditions farms are exposed to.Nevertheless, many farms might prioritize another sequence of upscaling their level of resilience.For example, a farm with many sensors in the field acquiring low-volume data might prioritize the installation of an LWN before investing in a farm server.The five-level classification is supposed to give orientation for digitizing farms to prepare for crises.Technical setups are very individual and require therefore adapted solutions securing the most important digitized applications from failure.To set up an individual FDFS and choosing the right technical components might over strain the IT skills of most of the farmers, simply because it’s not their profession.Lachia et al.for example, found that the infield use of yield maps is too complex for over 50% of French farmers.

Assembling the components of an FDFS might be even more challenging.But having once set up an FDFS, the maintenance of its components, in addition, can also be expected as very demanding for farmers alone.Consulting and support from independent institutions are needed.A farm server as a central component of the FDFS covers many functionalities from storage to processing and AI to the management of access rights for third parties.This is also supposed to guarantee data ownership and control by farmers on whom has access and uses their data for which purposes.The more extensive these server functionalities get, the harder it can be for farmers to overview and control them.A lot of time might be needed for farmers to incorporate extensive server functionalities.But this should balance itself, the more functionalities the server covers, the more farmers will profit and the more time and money they might be willing to invest and vice versa.Considering that sensors and machinery have cache storage capacities to store raw data when cloud connection is lost, a large on-farm storage server might not be needed, especially if the farm is located in an area with a rather reliable internet supply.But here too, it depends on the applications farmers deploy.Areal imaging for example needs much storage capacities and sometimes low latency which makes it necessary to store and process the images on the farm server to be in time with the following application.The proposed FDFS can be a chance for small and middle scaled farmers in employing digital farming technologies.Only the investment of administrative components, like an FMIS, is sufficient and can, under certain circumstances, be built up modularly according to farmers’ needs and investment possibilities.Machinery and technology are rented or ordered from MRs and contractors.Here we meet the problem of lacking interoperability.When choosing digital components, farmers need to look out for solutions that use international standards on semantics and ontologies, in addition, to open APIs and data formats to ensure interoperability with future or external solutions.Developing such customer- or branch-specific solutions remains the responsibility of established agricultural software developers and solution providers.Solutions including proprietary data formats should be avoided.Also, the MRs and contractors need to take this into account when investing in new technologies.What is not defined in this paper is the detailed organization of data flow at the moment of an interruption of cloud connection.

September 13, 2022

Applying AI to the agricultural sector is not a homogenous challenge since the sector varies substantially

All interviews were held through digital meeting platforms and all but two were recorded with the permission of each respondent.The codes mentioned in Table 1 are used throughout this article to refer to the respective interview respondents.Remote sensing technology enables detection and monitoring of physical characteristics of the earth’s surface.Remote sensing data is collected from a distance, commonly from satellites and drones.The three most common properties of remote sensing data are spatial, spectral, and temporal resolutions.Spatial resolution is the pixel size of an image, a property that affects the ability to detect objects through imagery.differently, spectral resolution refers to the spectral sampling intervals size and number which affect the ability of the sensors to detect objects in electromagnetic regions.The temporal resolution regards the frequency of acquired data.The availability and economics of using remote sensing data collection is addressed by Khanal et al., which present remote sensing technology alternatives both open-accessed and for some cost.However, the resolution of the data varies, where the trend is that medium-resolution data is free whereas the prices for high resolution and very high resolution data increase in proportion to their increasing quality.Regarding data resolution, Meier et al. opine that site-specific smart farming depends on high resolution, as detection of anomalies are impossible or insipid with too large pixel sizes.Of course, depending on what kind of analysis the data aims to contribute to, the need for resolution varies.For example, predicting the crop yield within a field can accomplish a high accuracy despite a coarse resolution while detection of plant diseases through hyperspectral imaging requires a detailed resolution.Internet-of-Things is a collective concept for objects with incorporated electronics and connections that enable remote control and information sharing.In agriculture, IoT is mainly used for collecting data through different types of sensors.

By further data analysis,flower pot valuable information can be derived as decision support, e.g.for farmers.Kamienski et al. define four main challenges for IoT development in smart farming.First, the IoT system must have a high level of adaptability.Since the needs of farmers often significantly vary, the IoT system must be customizable to local circumstances but still not increase the required work for the farmer.Secondly, the IoT deployment must be efficient.As Kamienski et al. write, “there is no ‘one size fits all’ in IoT systems”.Thus, each system needs to be configured, the Internet connection and farm infrastructure must be reliable, and the farmer must deploy enough human and economic resources into this process.Furthermore, the scalability is affected by the previous factors but also depends on whether the system, and the models learned, are supposed to work for just one farm or entire agricultural consortiums.Lastly, the complexity of the IoT system can be interpreted as a trade-off between making the middleware broker complex and the software application simple, or the reverse.Another aspect to IoT in smart farming is security.Since the data often is valuable for the farmer and is regarded as a business secret, Kleinschmidt et al.describe the need for end-to-end encrypted communication from the sensor to the application.In practice, this means that the IoT sensor network must have a synced security strategy to the cloud database and the potential fog computing network.By ensuring security, the probability that the farmer trusts the IoT system increases.Still, trust in IoT systems does not just depend on security but also on the precision of the sensors.Without ensuring that there are no systematic measurement errors in the sensors, few farmers would trust the learned model or the real-time data.The potential of smart farming in animal husbandry, such as dairy-, beef- and fur production, is largely constituted by increasing productivity and profitability by streamlining and automating tasks and information.Much research consists of ways to monitor and look after the animals automatically or semi-automated.These articles suggest that devices, both wearable and non-wearable, may be incorporated in the animal stable and that these devices can gather data that can give indicators on the health of the animals.The data that may be gathered through these devices vary, but the wearable devices can measure heat, hormone levels, rut etc.The non-wearable devices more typically are 3D cameras for body condition scoring and infrared imaging, sensors that monitor environment and weather as well as automatic weighing scales and gates.In arable farming, an important feature of smart farming is to be able to calculate the vegetation index of fields or areas to be able to monitor when it is time for harvest and other activities.This may be done by both remote sensing and IoT solutions.

Viljanen et al. train a machine learning model aimed to optimize the “balance between the highest possible yield quantity and an adequately high digestibility for feeding”.By using an inexpensive drone system that can get multi-spectral data from an RGB camera and an infrared camera, traditional physical tools for predicting ley yield can be replaced by smart machine-learned models with higher accuracy.Furthermore, the research of predicting yield and quality of silage can also be accomplished through satellite data, as presented by Griffiths et al..The study shows that it is possible to detect mowing events of grasslands, and therefore characterize the land use intensity by looking at satellite imagery.In terms of yield prediction, Feng et al. stress the importance of incorporating biophysical characteristics of the crop in machine learning algorithms.This means that to learn a model with high precision, it is important to simulate the growing process of the crop to ensure that the model learns the crop characteristics in different stages of the growing process.Furthermore, Matos-Moreira et al.uses manual soil samples to further improve their model.By including manual sampling and analysis with a variety of existing data sources one may learn a model to predict the quality of a crop or the concentration of some matter at a given place and time.Another application of precision farming is to detect sickness or pests among crops.Torai et al. study how diseases can be detected in crops by classifying, or labeling, areas in pictures as “healthy”, “infected”, “diseased” or “aged”.Thereafter, methods such as hyperspectral imaging, Bayesian networks, and an analysis through probabilistic latent semantics are applied to detect the diseases .This study is a good example of a remote sensing technology applied to agriculture which needs a very high resolution of data, preferably on a scale of centimeters.One dilemma when applying artificial intelligence to arable challenges is how to use the different types of available data.Kerkow et al. use fuzzy mathematical modeling to solve this problem.This approach allows for mixing machine learned climate models with wind data and expert knowledge of the landscapes to build precise models .The literature review also brings up some interesting aspects regarding the implementation of AI in agriculture.Medvedev and Molodyakov highlight both theoretical and practical knowledge of smart farming as requirements for successful implementation.Unfortunately, seldom farmers have either the economic resources or the time to attend longer educations within the subject.To meet the lack of technical education within smart farming, Medvedev and Molodyakov propose smaller model-based courses that should cover technical, economic and management aspects to smart farming.

A crucial part of the education is that the courses are on-demand, so that busy farmers can access it whenever it suits them.Both business cases and clear driving forces are named as critical components to spreading the use of smart farming technologies in society.Barriers that hinder the drive towards smart farming are categorized as economic, institutional behavioral, and organizational as well as market.Furthermore, they identify social and moral drivers to play a key role in terms of creating a societal demand for smart farming.Without the support from society at large, innovations will not be adopted by key actors, they conclude.Other research aims to map the barriers to implementing and diffusing smart farming technologies.Kernecker et al., describe that farmers approach smart farming technologies differently given how much smart farming technologies the farmers have already adopted.The so-called adopters perceive the barriers to adopt smart farming technology as high investment costs, a difficulty in interpreting data, a lack of interoperability or precision in devices, that farmers cannot see the added value of the new technology or the relative advantage of the system, as well as a lack of neutral advice from advisors and other actors.The non-adopters also perceive high investment costs and unclear added value as barriers.Additionally, they regard too demanding complexity of use, that the technology is not appropriate for their context or farm size, as well as a lack of access to proof of concept from a neutral point of view, as obstacles.Finally, the literature review highlights the importance of data presentation and visualization, both in arable farming and livestock farming.Beside identifying possible applications of the technology in agriculture, several research groups argue that methods within machine learning and AI require decision support tools that visualize the data in comprehensive ways.One pattern, stated by a farmer respondent,berry pots is that farmers of different agricultural sectors almost always believe that the implementation of smart farming technologies has come further in other sectors than in their own.The agricultural sector that most farmers highlight as currently the most technologically advanced is the milk production.Milk robots were introduced to the commercial market decades ago, and with the milk robots the fodder of an individual cow can be customized, increasing its health status and production capacity.Due to the milk robots, the dairy industry is regarded notably data driven.One important aspect to consider when evaluating the success of the milk robots is the short feedback loop.Since cows are both fed and milked daily, the machines can adjust quickly depending on the latest input.Furthermore, Swedish dairy farmers have a long history of collecting data by being part of the so-called Kokontrollen, a cow data collection application owned by Växa Sverige.Even if Kokontrollen today is web-based, Swedish dairy farmers have been reporting to it for more than 100 years.Previously, all data was collected manually but today almost all data connected to milk production is automatically gathered by the milking robots.Contrasting to milk production, arable farming is diverse with different crops requiring distinct machines and technologies.Hence, a single successful machine is difficult to implement for the entire arable farming sector, making its technological development more complex.

However, it is possible to create effective technology for specific crops.As a rule of thumb, crops with high manual work, such as vegetables, use lots of technology since they operate on small, more controlled areas.In such environments, such as green houses, the feedback loop is faster and there are less uncontrollable factors, such as weather or wild hogs, which makes the application of new technology and AI easier.Of the three agricultural sectors compared in this study, beef production is considered by the respondents as the least technologically developed.Nevertheless, one respondent at a major company believes that meat production will have a central role in the development of the Swedish primary food production.The list of possible innovations includes making the value chain digital by automatically transferring information to the slaughterhouses regarding characteristics of the animals they will receive.By mandatory RFID tags for all cattle, the respondent argues there is an enormous potential, since the development of the animals could be followed in real time throughout the value chain.With such a system, the slaughterhouse could plan far in advance for incoming meat quality and volume.Simultaneously, a grocery store could send data to the farmers regarding the current popularity of different kinds of meat, enabling the farmers to adjust their production to the current consumer behavior.Furthermore, if one could autonomously and automatically weigh the cattle, their growth curves can be predicted which would enable optimization of the timing for sending animals to slaughter.By this optimization, one could avoid having full-grown animals that both drain economical resources and emit environmentally damaging methane gas.Regarding data and the activity of collecting data, the responses from the interviews reflects different realities within the agricultural sector.On the one hand, some respondents say that farmers generally are positive towards gathering data on their farm.On the other hand, some responding farmers state that they collect almost no data on their farms, although they say that they understand that data could add value to them.In-between is a spectrum of attitudes towards data gathering and implementation of technology in the farms.

September 7, 2022

Consequently both the rate of successful introductions and the volume of susceptible crops rise

Most of the land trusts in our study were created through the grassroots efforts of a few community leaders or environmental activists, organizing effectively to pursue shared conservation goals. Other land trusts and open space districts originated more directly through local government action, including voter approval.Few of the established programs in California enjoy a steady revenue stream for building large agricultural easement portfolios. Programs with substantial acquisitions have relied largely on fluctuating and opportunistic revenue sources, primarily state government funds and foundation grants. They generally lack the certainty that an ongoing, dedicated local tax source could provide. As a result, most programs acquire easements in fits and starts, limiting their ability to plan and work quickly with interested landowners.Landowners cannot be compelled to enter into an easement transaction by government regulation or eminent domain; selling or donating an easement is entirely voluntary. This means that programs must rely on each landowner’s understanding of the technique and personal estimate of benefits and costs. For many landowners, easements are a foreign or confusing concept. They offer the unwelcome prospect of having less control over their land and create uncertainties about the long-term consequences for immediate heirs and later generations of owners. Landowners located near rapidly urbanizing areas are especially reluctant to consider the easement option, as they believe that they will be able to prosper by selling their parcels for residential or commercial development sometime in the future. Finally, even with a supply of willing landowners, there is the challenge of fitting the available properties into a program’s criteria for location,ebb and flow agricultural quality and easement price. Nonetheless, the successful programs demonstrate that a few early transactions with landowners respected in the local farm community can break the ice for subsequent deals .

Primarily because they are non-regulatory and voluntary, easements on farmland increasingly appeal to landowners and communities attempting to protect open space and agriculture. With about 120,000 farm acres covered statewide, agricultural easements have become an important farmland protection tool in California in less than two decades. A small number of local land trusts and open space districts, assisted by funding opportunities and entrepreneurship, have established successful programs. Yet the active programs operate in only a minority of California’s major agricultural counties. Many of these areas lack easement programs because of the absence of citizen interest and mobilization combined with local government inaction. Most established programs also are limited in their easement acquisitions, largely because of unsteady revenues, limited entrepreneurship and reluctant landowners. Undoubtedly, the few successful programs will continue to grow and expand their easement holdings. But expanding agricultural easements to major agricultural regions is the key to making optimal use of the technique in California.Introductions of non-indigenous species of plants, animals, and microbes cause significant ecological and economic damage worldwide. A 1993 report from the Office of Technology Assessment estimates the monetary costs associated with biological invasions in the US alone is between $4.7 and $6.5 billion annually ; subsequent research revises that estimate for the US upward to over a hundred billion dollars a year . Moreover, since these estimates derive mostly from costs calculated for agriculture1 , there is consensus that these numbers are lower bounds. There are numerous pathways by which non-indigenous species enter a country: contaminated agricultural products, timber, potted plants, ballast water and packing materials are primary conduits for unintentional introductions2. We focus on introductions facilitated by commodities trade and explore how changes in agricultural protection affect patterns of trade and subsequent damage to local agricultural and ecological systems from exotic species introductions. In our stochastic model, exotic species introductions, success, and damage are functions of the volume of trade and agricultural production.

The model is coupled with results from international trade theory that link volumes of production and trade to agricultural policies such as output subsidies. This simple structure generates several compelling insights. First, we show that increases in agricultural subsidies may improve overall ecosystem health despite common opposition to agricultural protectionism by environmental advocates. This arises because protectionist trade policies in agriculture importing countries reduces imports, often from species-rich tropical regions, such that the rate of exotic species introductions will likely fall with reduced subsidies. Second, we establish that crop damages are a poor proxy for overall damages associated with biological invasions. Crop damage arising from biological invasions may rise as a result of increased protectionism, either because there are more crops available to be damaged or because there is more agricultural land available on which non-native species can gain a foothold. But because increased protectionism will reduce the volume of imports in agriculture importing countries, ecological—and hence total—invasion-related damage may nonetheless fall. Third, we argue that the ecological impacts of increasing agricultural subsidies may be markedly different for agriculture exporting versus agriculture importing countries. For countries that initially export agricultural products, increases in production subsidies will lead to an increase in both the volume of agricultural output and in the volume of trade, with unambiguously negative consequences for overall ecological health. In sum, the ecological consequences of raising agricultural subsidies are reversed in agriculture importing countries. In section 2 we discuss the relevant economic and ecological literatures. We then describe our model in section 3, derive results in section 4, and briefly conclude in section 5. Ecologists have studied the consequences of invasive species extensively; see Drake et al. , di Castri , Parker et al. , and Shogren for overviews. This research has established several patterns governing successful exotic species introductions.

For example, successfully introduced exotic species tend to be native to nonisolated habitats within continents and their success is enhanced with the similarity in physical environments between the original and exotic locations . Furthermore, species that inhabit disturbed environments tend to be successful at invading human-modified environments . These and other empirical observations suggest that predictions of the frequency and severity of exotic species introductions can be made on the basis of factors such as similarity in physical environments between trading partners, trade volume, and the extent to which the home country modifies its natural environment. Despite the extensive study of the ecological impacts of non-indigenous species, rigorous economic treatment of the problem is lacking. The little attention paid by economists to the problem of invasive species has focused largely on case studies and analysis of control and risk reduction methods . With the exception of Dalmazzone , none explicitly incorporate the role of commodity trade in their analysis. Using a linear regression model, Dalmazzone finds a strong, positive and statistically significant relationship between the ratio of exotic to native species in a region and both its GDP/capita and its population density. Indicators of disturbance such as percentage of land devoted to agriculture and pasture are also positively correlated . Dalmazzone finds weaker evidence of a link between susceptibility to biological invasions and engagement in trade: although she finds a negative and statistically significant relationship between import duties and presence of non-native species, the influence of other measure of openness such as trade as a percentage of GDP, volume of merchandise imports and tourism are all statistically insignificant. We believe these results underplay the importance of trade volumes for rates of exotic species introduction. Given the biological rules of thumb governing invasions, a superior econometric specification would decompose imports by type and country of origin, as recognized by Dalmazzone. Moreover, we believe empirical testing would benefit from a more thorough understanding of the mechanisms in which trade policies and flows affect introduction and damage rates. The present paper serves as a first pass at establishing these relationships theoretically. We explore the effects of agricultural protectionism,greenhouse benches via an increase in subsidies to domestic agricultural producers, on expected damage arising from biological invasions. It is shown that the magnitude of change in expected damage depend critically on two things: the responsiveness of damages to changes in agricultural output and the response of imports to agricultural subsidies. We show that offering subsidies in an agriculture importing country will reduce both its rate of introductions and the ecological damage caused by the introductions. We further demonstrate that changes in crop damage are a misleading proxy for the effects of protectionist policy on ecological and total damages arising from biological invasions. This latter claim is particularly important if we believe pecuniary losses to agricultural production are more easily observed than ecological damage from exotic pests and hence form the basis for policy decisions.

Proposition 1 makes the simple point that increased support for Home’s agriculture industry may reduce the rate at which exotic species are introduced because of the effects agricultural subsidies have on the volume of trade. For countries that import agricultural goods, production subsidies lead increased output of locally produced agricultural goods to displace imports, thereby reducing the overall volume of trade as the country moves toward self-sufficiency. As reduced volume of trade reduces the size of the platform for non-native species introductions into a country, the expected rate of introductions N, and consequently the number of non-native species that take hold J, is thereby reduced. Alternately, if a country instead initially exports agricultural goods, an increase in S raises the volume of trade since it induces greater agricultural output, exports of which finance greater imports of manufactured goods. Although there is a tendency to equate species introductions with imports of agricultural goods, trade in non-agricultural goods also frequently serves as a conduit for biological introductions, either through contaminated ballast water from ships, or infestations of packing materials and manufactured goods themselves. It is possible that λ may depend differently on imports of different types—we abstract from this issue in the interest of simplicity—nonetheless the larger volume of trade arising from subsidies in agriculture exporting countries increases the platform for introductions and so raises the expected values of N and J. Some simple interpretations of these classifications are useful at this point. Damages arising from loss of crops —either through infiltration of crop and pasture land by weeds or predation on crops and livestock by pests— increase as the level of agricultural activity increases. Commonly referred to as crop damage, these types fall under the definition of Augmented damage. Other types of invasion related damage are unlikely to depend directly on the level of agricultural activity. Introductions affecting marine and aquatic systems are good examples of this: invading mollusks foul water intake systems at power generation facilities; introduced fish out compete native species, creating losses to recreational activities such as sport fishing. In addition, there are numerous examples of exotic species displacing native species, with consequences for non-monetized assets such as ecosystem health and biodiversity. These examples meet the definition of Neutral damage. In subsequent discussion we will also refer to these types as ecological damage. If instead introduction rates are sufficiently sensitive to the volume of trade, then an increase in agricultural subsidies may instead reduce expected invasion related damage. This latter possibility raises an interesting problem. Since Neutral and Augmented type damages may change in different directions following an alteration in agricultural policy, then estimates of invasion related damage that are based on one type of damage serve as poor—even misleading—indicators of total damage. As noted in the introduction, most real-world estimates of invasion related damage derive predominately from estimates of damage to crops and livestock. However, as proposition 2 indicates, crop damage may be increasing while Neutral and Diminished damages, and hence total damages, are decreasing. This insight confirms our earlier conjecture: economic measures of crop damage are a misleading indicator of total damages arising from biological invasions. For the policy change considered here, an increase in the agricultural output subsidy S changes in crop damage overestimate changes in total damage, and may indicate an increase in total damage even when total damage is in fact declining. As outlined in proposition 3, the effects on an increase in S on damages in an agricultural exporter differ from the effects felt by an importer. Again higher S spurs local agricultural production, however for an agricultural exporter this finances greater imports.

September 6, 2022

Empirical results include the impact of UCCE’s expenditure stock on individual counties

Therefore, our results agree with the existing literature, which suggests that old expenditures impact current productivity positively, and their exclusion tells us only a partial story. The coefficients we have obtained in this study indicate that there is room for improvement in extension research and outreach, and that introduction of new research-based knowledge and technology improves productivity. Results also suggest that primary-occupation farmers may be less efficient than those who are able to maintain more than one profession. Efforts could be focused towards improving any existing gaps in efficiency among farmers in different counties.The results of our analysis can guide policymakers during a period of political pressure to reduce public spending for agricultural extension in the state. The county fixed effects results allow a more targeted policy intervention on higher and lower performing regions .By controlling for individual county and fixed-year effects that may be driving productivity in that county, we find that some of the major agricultural counties in California record high positive impacts of UCCE expenditures stock. Out of the 50 county offices in our study, we observe that UCCE expenditures stock has a significant impact on 21 counties for all values of knowledge depreciation. We observe a statistically significant negative impact on a few counties, such as Amador, Calaveras, Humboldt-Del Norte, Modoc, and Siskiyou. For two counties, the impact is not statistically different from 0. In terms of policy, these coefficients can be used as reference points for allocating budgets to different counties. Extension efforts could be targeted to the counties with inconclusive or negative impacts. Monetary impact of cutbacks on county productivity could also be calculated, using the estimates of extension expenditures in this paper. The analysis driven by county performance helps design policies with heterogenous focus, which has been more relevant when public funds have to be allocated among heterogeneous performing recipients of these funds. And finally, as shown in Section 5.3 extension introduces substitutability of traditional inputs with extension knowledge so that higher expenditure on extension in some of the lower-performing counties can substitute for other traditional inputs, dutch buckets for sale which may be scarce in supply.

In particular, our analysis highlighted and measured substitution of extension knowledge for labor and chemicals.Both biodiversity and the human activities that threaten it are unevenly distributed around the globe. Thus, evaluating whether they are spatially congruent and choosing the best areas for conservation actions given the distribution of these conflicts are central problems in conservation bio-geography . The magnitude of the current biodiversity crisis, coupled with the limited resources available for protecting biodiversity, implies that prioritization is unavoidable. Spatial prioritization seeks to identify the areas that are likely to yield the best benefits for biodiversity given a particular conservation investment. It may be applied at a variety of scales, including global , regional , national and sub-national levels. Spatial conservation prioritization analyses can be based solely on the distribution of the biological features to be protected . Alternatively, prioritization analyses can include socioeconomic variables that represent threats to biodiversity or opportunities for conservation, such as human population density, land cost and land use . Agriculture is the human activity that represents the main threat to the environment . It constitutes the largest land use on the planet, using 38% of Earth’s ice-free land surface and 70% of global human freshwater uptake. Food production accounts for 19% of Earth’s net primary productivity and 30-35% of global greenhouse gases, with direct impacts on biodiversity . The burden on the environment may be higher in the future as the human population is expected to increase to more than 10 billion by 2050 . Moreover, a billion people are currently chronically malnourished as a result of lack of access to food . Given the value of biodiversity for human well-being , understanding the potential impacts of future agricultural expansion on biodiversity is a key issue for humanity. The general aim of my PhD thesis was to evaluate the potential impact of agricultural expansion on biodiversity conservation during the 21st century. Specifically, I evaluated four interrelated issues: conservation conflict between agricultural expansion and the global biodiversity conservation priorities and the Brazilian system of protected areas ; the effect of incorporating agricultural expansion data into spatial prioritization models for the conservation of world carnivores ; and the benefits of a globalized conservation strategy for food production and for biodiversity conservation . The impact of future socioeconomic development pathways, including land-use trends, on biodiversity can be accessed by means of quantitative scenarios . For all analyses presented here, I obtained future scenarios of agricultural expansion from land cover maps produced by the Integrated Model to Assess the Global Environment .

IMAGE forecasts, at a resolution of 0.5° × 0.5°, the number of years that each area will be cultivated during the 21st century for six socioeconomic scenarios . For chapter IV, I also included an estimation of potential agricultural productivity in each grid cell, based on climate, relief, soil constraints and irrigation impact . For the first chapter, I overlaid the spatial polygons of the Global Biodiversity Conservation Priorities onto a grid with a spatial resolution of 0.5° × 0.5°. I tested whether areas defined by their higher vulnerability were more affected by agriculture in the year 2000. The opposite was expected for areas with low vulnerability . I also tested whether these priority areas would be more affected by agricultural expansion during the 21st century than expected by chance . To address the aims of chapter II, I overlaid the IMAGE’s land-use model with Brazilian protected areas to calculate the conflict between these two land uses. I obtained Brazilian protected areas’ polygons from the World Database of Protected Area . I also included 10 km buffers around each protected-area polygon to represent the legal buffer zone usually used in Brazil, which is an area where human activity is restricted. I then tested whether these areas were more affected in the present and in the future than expected by chance. Additionally, I tested whether there was difference between the integral protection protected areas and sustainable use protected areas . In both chapters I and II, I evaluated the probability of such conflicts to be found by chance using spatial randomization tests developed in R , considering 1000 iterations . To meet the objectives of chapters III and IV, I performed global spatial conservation prioritization using Zonation . Zonation’s algorithm provides a nested hierarchical ranking of the sites, maximizing the representation of species’ distributions. To define the ranking of importance of sites for conservation, Zonation analyses can also incorporate costs such as potential agricultural production. For all prioritization analyses, I defined the target proportion of areas to be protected as 17%, following the Convention on Biological Diversity , which proposed this percentage as the goal to be met by 2020. I obtained information about mammal species’ distributions from the International Union for Conservation of Nature’s Red List of Endangered Species. I overlaid the spatial polygons onto a grid with a spatial resolution of 0.5° × 0.5°. For chapter III, I focused on 245 terrestrial carnivore species. In chapter IV, I used 5216 terrestrial mammals. These taxonomic groups have been the focus of many conservation programs and they are often considered to represent a potential surrogate for other taxonomic groups . To test whether there is a spatial conflict between the global carnivore conservation solutions obtained in chapter III and the agricultural expansion, I performed spatial correlation analyses using the Spatial Analysis in Macroecology software .

The objectives of chapter IV were achieved by defining global conservation priorities considering three levels of political integration: individual countries, regions , and globalized . I also evaluated the effect of considering, or not, agricultural costs for spatial conservation prioritization. The different conservation solutions were evaluated in terms of the relative amount of food production lost by setting aside sites for conservation and the representation of the geographic distribution of species within those sites. I also evaluated whether the most underdeveloped countries would be subject to higher losses in food production under the global strategy. For this, I correlated the percentage of food production and area lost to sparing land for biodiversity conservation with three development indicators: the Human Development Index , the per-capita gross domestic product , and the percentage of GDP added by agriculture . I found that reactive global biodiversity priorities had about 49% of their area impacted by agriculture in the year 2000 . Conversely, proactive schemes had a low intersection with the agricultural distribution . By the end of the 21st century, there will be an overall increase in world agricultural area from 26.5% of the analyzed area in 2000 to 34.6% in 2100, according to IMAGE, and the difference between the proactive and reactive schemes is predicted to hold true. However, High Biodiversity Wilderness Areas, a proactive scheme,hydroponic net pots is predicted to suffer agricultural impact similar to the reactive schemes, with 73.5% of its area affected, if the worst-case scenarios are realized . In Brazil, a megadiverse country in which agribusiness is the pillar of economy, agricultural expansion is a major conservation concern . According to IMAGE, agricultural land use represented 22% of Brazilian land coverage in 2000 and is predicted to increase up to 40% by 2100, according to a business-as-usual scenario. Moreover, the percentage of protected areas affected is predicted to increase from 11% to 30%, with no difference between IPPAs and SUPAs . I found spatial conflicts between the best areas for terrestrial carnivore conservation and agricultural expansion in the 21st century . These conflicts were alleviated when I incorporated agricultural expansion information into the spatial prioritization process . Nevertheless, accounting for agricultural expansion resulted in a lower representation of species’ geographical ranges: the average proportion of represented ranges was reduced from 58% to 32%. This reduction affected mainly those species with small geographic distributions. In addition, the best solution for global carnivore conservation changed from a spatial distribution closer to that of the reactive global conservation priority schemes to one more like proactive ones. Looking at the impact of globalization for conservation and food production, I found that combining the use of agricultural expansion data and integrating countries in a globalized conservation blueprint to meet the 17% target for terrestrial protected areas, resulted in a 78% reduction in the costs of food production . Furthermore, this globalized conservation approach represented an increase of 30% in the representation of the species in the protected areas network.

The regional-scale conservation solution resulted in similar losses in food production, compared to the globalized solution, and an increase of 17.5% in terms of representation of mammals’ geographical ranges .Conservation actions in the different areas of the world should be planned according to the expected agricultural expansion in the 21st century. Some areas can hold mega-reserves , while other areas should focus on the development of wildlife-friendly agricultural practices. Within Brazil, my findings suggest that the risk of agricultural expansion should be included in the management of protected areas and associated buffer zones. Globally, conservation actions for carnivores should consider agricultural expansion because this may significantly influence the distribution of areas where conservation actions could be more effective in the future . The regional scale may represent an intermediate step towards the global integration. Economic agreements may evolve to common conservation policies, since this has already been done in the European Union by means of the Natura 2000 network . By comparing differences in the distribution of protected areas among countries in the different scenarios, I found that the poorest countries will not be negatively affected by participating in this globalized conservation blueprint. However, the particular cases in which poor countries would be impaired in their development process should be a focus of compensatory policies in order to guarantee the participation of these countries within the global approach. Moreover, such compensatory policies may help to overcome socioeconomic problems such as poverty and inequality, which are known to be detrimental to the success of conservation actions . Feeding an increasing human population, with rising per-capita consumption, while managing the environmental impacts of agriculture, is one of the greatest challenges for global policy. In my thesis, I demonstrated that agricultural expansion will continue to represent an important threat to biodiversity throughout the 21st century. Reducing food waste, increasing agricultural resource efficiency, closing yield gaps, and fostering organic agriculture are tools available for solving this challenge .

September 5, 2022

Prime-age adult mortality affects their production since their family business is labor-intensive

In the regions affected by Human Immunod efficiency Virus / Acquired Immune Deficiency Syndrome , prime-age adult mortality negatively affects household welfare by decreasing household income and consumption. Previous studies on the effects of prime-age adult mortality on household agricultural production show that the mortality decreases household size and productive assets such as land and livestock. In this study, we further ask whether prime-age adult mortality due to HIV/AIDS decreases the endowment of knowledge for agricultural production in Kagera, Tanzania, reducing total factor productivity. Equivalently, we ask whether prime-age adult mortality due to HIV/AIDS destroys household agricultural production by magnitude beyond the decreases in observed productive assets such as household members, land, and livestock. We also quantify how much decreased TFP growth contributes to the decrease in long-term household agricultural income growth compared to the decreased accumulation of each productive asset. Kagera was estimated to be one of the regions in Tanzania most affected by the HIV/AIDS epidemic , Beegle. Kagera is also the region where AIDS cases were reported first in hospitals in Tanzania. In 1983, the first 3 AIDS cases were reported and the number of cases increased rapidly to 5,116 cases in 1994. On the other hand, the share of reported AIDS cases in Kagera to Tanzania decreased from 100% in 1983 to 10% in 1994. In 2003, the percentage of HIV positive in Kagera among age 15-49 is 3.7% while the figure in Tanzania is 7.0% and thus HIV/AIDS pandemic in Kagera has been alleviated compared to other regions in Tanzania. We use the Kagera Health and Development Survey which collects the detailed information on households in Kagera in 1991-94 and 2003. The survey samples households hit by prime-age adult mortality more than households without the mortality and the data allow us to study the long-term effects of prime-age adult mortality on agricultural production. In the data, 36.7% of prime-age adult mortality is considered to be due to HIV/AIDS by deceased individuals’ families.

We will focus on agricultural production among other income generating activities and we will study the effects of prime-age adult mortality on agricultural production in the region. Agriculture is the major income source in Kagera and also in Tanzania. In Kagera, 85% of household heads engage in agriculture in 2000/01 while 70% in Tanzania , Tanzania NBS. In Kagera,grow lights households engage in subsistence and traditional agriculture. Male adult members produce coffee and banana with or without cattle manure. Female adult members produce crops such as maize and yams mainly for own consumption.As shown below, households hit by prime-age adult mortality between 1990 and 2003 have less increase in household members by 1 person from 1991 and 2003 than households without the mortality.They also accumulate less other productive assets; land and livestock. As a consequence, their agricultural income growth is also smaller. However, we do not find such clear differences in per capita asset accumulation and income growth between households with prime-age adult mortality and those without it. In order to explore the effects of the mortality on agricultural production more, we will study the difference in TFP growth. We study the hypothesis that a household hit by the mortality cannot increase TFP as much as a household not hit by it. We also decompose agricultural income growth into the contribution of the accumulation of each productive asset and TFP growth and compare the differences in those factors between households with and without the mortality. The remainder of this paper is organized as follows. Section 2 reviews the previous studies on the effects of prime-age adult mortality on households’ welfare based on household level micro data and the differences between the previous studies and this study. Section 3 outlines our conceptual model, hypothesis, and framework of empirical methods. Section 4 explains the characteristics of the original data, especially with respect to prime-age adult mortality, how we construct our data for the analysis from the original KHDS data, and discuss the relevancy of our specification of the model to study the data. Our empirical methods are explained in more details in Section 5 and the empirical results are shown and discussed in Section 6. Section 7 concludes this paper.Whether and how much HIV/AIDS epidemic affects a household welfare is the important topic. We can categorize the literature of the effects of prime-age mortality due to HIV/AIDS on household welfare into consumption studies and production studies. Beegle, de Weerdt and Dercon studies the effects of prime-age mortality on long-term consumption growth based on KHDS.

Their regression equations have change in logarithm of per capita consumption from 1991 to 2003 as the dependent variable and dummy variables for deaths as explanatory variables. They use household fixed effects methods in order to control unobserved time-invariant characteristics and relax the endogeneity and self-selection problem of HIV/AIDS as other previous studies based on panel data do. They take into account which year each death occurred by using dummy variables for deaths in 1991-1995, 1996-1999, and 2000-2004. Their results show that the coefficients of dummy variables for deaths are negative but only dummy variables for deaths in 2000-2004 are statistically significantly different from zero. This characteristics of the results are robust in various specification of regression equations. Their results imply that there are negative effects of prime-age adult mortality on consumption growth but households may recover from the negative shock of the mortality after 5 years. They find that a prime-age adult death results in a 7% drop in consumption in the first 5 years after the death. Carter, May, Aguero and Ravindranath use KwaZulu-Natal Income Study , South Africa data and study the effects of prime-age mortality due to HIV/AIDS on long-term growth rate of per capita consumption and find the negative coefficients for dummy variables for deaths although they are not statistically significantly different from zero. They also find the large magnitude of the negative effects: a prime-age adult death lowers a household’s 5 year growth rate by 21%. Although the consumption studies above find the negative effects of prime-age adult mortality on household 5-year consumption growth, channels of the causality has not been made clear. Production studies analyze some potential channels of the causality. Beegle uses the first 4 waves of KHDS from 1991 to 1994 and studies the short-term effects of prime age adult mortality in a household on the household members’ labor supplies. She constructs dummy variables for male and female deaths in future and past 0-6 months and 7-12 months and uses them as explanatory variables in regression equations. The dependent variables are the probabilities of being in wage employment, non-farm self-employment, working on coffee production, banana production or maize, cassava, or beans production. She finds coefficients of some dummy variables for deaths are negative and statistically significantly different from zero in regression equation of being in wage employment, working on coffee production and maize, cassava, or beans production.

Yamano and Jayne use two-year panel of rural Kenyan households and study the effects of prime-age adult mortality on households’ size and composition, crop production, asset levels and off-farm income. They find the mortality decreases households’ size, area under high-valued crops, gross and net outputs, farm equipment, small animals, and off-farm income. They find that the death of a male household head is associated with a 68% reduction in the net value of the household crop production implying large negative effects of the mortality on households welfare and that channels of the causality are decreases in productive inputs above. Chapoto and Jayne use nationally representative 3-year panel data in Zambia and find the results similar to Yamano and Jayne . These production studies show that the negative effects of prime-age adult mortality on household income and channels of the causality. HIV/AIDS also increases an household’s expenditure for medical care for the sick and funeral for the deceased. Tibaijuka finds that this expenditure is almost equivalent to the cash income for the 10 households in her data from Kagera, Tanzania. We can think that decreased income and increased expenditure for health care and funeral due to HIV/AIDS and prime-age adult mortality contribute to the decreased consumption which is found in the consumption studies above. Households hit by HIV/AIDS have to face tighter budget constraints and invest less in productive assets than the other households. Smaller investment in productive assets brings smaller income in the future. We contribute to the literature with the following three points. First, we provide an answer to the question whether prime-age adult mortality decreases total factor productivity in the long run. Previous studies do not ask this question although it is an important question to study the channels from prime-age adult mortality to decreased income and welfare. This question is closely linked to the question how important an adult’s knowledge stock of agriculture is for his/her household income generation. Since subsistence agriculture in Kagera, Tanzania depends on weather and is erratic,led grow lights the knowledge may be important. On the other hand, its agriculture is traditional and does not depend on new technologies and new market opportunities so much, the knowledge may not be important. If the knowledge is important, prime-age adult mortality destroys not only household members but also the quality of household as an agricultural enterprise. Second, we decompose the agricultural income growth into TFP growth and the contribution of each productive asset. Previous production studies analyze the effects of prime-age adult mortality on each productive asset separately and cannot show how much change in each productive asset due to prime-age adult mortality contributes to change in agricultural income or output.

We quantify this channel from change in each productive asset to change in agricultural income by estimating an agricultural production function and decomposing the long-term change in agricultural income growth into TFP growth and change in contribution of each productive asset for households with and without prime-age adult mortality. Third, we study the effects of prime-age adult mortality on long-term agricultural production and link the previous studies on long-term consumption with the previous studies on short-term change in production mentioned above in this section.We can categorize channels through which the mortality changes the investment decision into two: First, the household changes future asset accumulation path as a response to changes in current asset levels due to the mortality and inheritance. For example, the household may sell land and livestock in order to achieve efficient and smaller productive asset level as a response to decreased household members and productivity due to the mortality. Second, the household’s budget constraint becomes tighter due to the mortality and the household has to change its allocation of income into consumption and investment over time. The household lost labor for income generation since the member who was sick and deceased did not and will not contribute to the household as labor and other members take care of the sick and thus the household income decreases. Furthermore, the household faces expenditure for medical care and funeral. Tibaijuka finds that this expenditure is almost equivalent to the cash income for the 10 households in her study. Although there is no consensus on what adult age range we should use to study the effects of adult mortality on household welfare1, we set the age range for prime-age adults is from 15 and 50. In this subsection, we discuss the relevancy of this age range. Our focus is the effects of prime-age adult mortality on agricultural production. We will focus on prime-age adult’s death rather than other household members’ deaths since prime-age adults contribute to their household as main labor force for agricultural production and they are the age group who are affected by HIV/AIDS directly.We set the lower bound of prime-age adult to be 15 since 15 year old individuals is physically adult and start to face the risk of HIV/AIDS through heterosexual sex. Although under 15 year old children can contribute to their households with their labor, we do not think that decreasing the lower bound would change the results since most of them do not die due to HIV/AIDS shown below. On the other hand, we set the upper bound of the age range at 50. Figures 1 and 2 show the distribution of age by gender in the data. Figures 3 and 4 show the distribution of deceased individuals’ age by gender. Figures 5, 6, 7, and 8 show the distribution of age of deceased individuals due to HIV/AIDS .

September 2, 2022

Unpredictable climatic conditions could also influence the intensity of fertilizers

The high rates of urbanization and environmental degradation caused in the last decade have negatively impacted on the quality and quantity of food production.Besides the above challenges, there is a problem of nutrient depleted soils and water scarcity across the globe and these are expected to exacerbate in the face of the increasing population especially in urban areas.Traditional farming is generally faced with problems of weather changes, water pollution, soil degradation and soil infertility.Africa alone continues to fight the problem of food insecurity where improved yield and sustainability in the agriculture sector can best be achieved through climate smart agriculture.CSA has been defined as an intervention vital for maintainace of global food security and nutrition through changing and readjusting agricultural practices within the new era of climate change.In order to conserve sustainable crop production systems, there is need to utilize spaces like: non-arable fields that do not support crop cultivation and develop alternative cultivation methods.This justifies the increasing use of various smart agricultural technologies to meet these rising levels of food insecurity.Emami et al.described smart agriculture as the use of technology that has the capability to increase food security if well streamlined to the domestic levels.On other hand, CSA synchronizes actions by researchers, policy maker, private institutions, societies and farmers to promote climate resilient systems, practices and technologies.Arshad Mahmood et al.described hydroponics as an agriculture system for growing crops in water composed of mineral nutrients supported by medium.This system which uses less water as compared to soil farming has successfully been used for cultivation of different vegetables like: lettuce, spinach, cucumbers,hydroponic dutch buckets tomatoes among other crops as these respond well to hydroponics due to low nutrient demands and short growth period.

New drifts in agriculture have shown hydroponics as one of the new innovative soilless farming systems to realize satisfactory outcomes and has the potential to produce more yields in minimal space and promote food security through production of food vertically thus should be considered as a better farming option for East Africa facing a quandary of challenges as earlier discussed.Hydroponic farming has different types which include: Nutrient Film Technique , Wick system, Drip system, Ebb and Flow and Deep water culture.Wick system is the simplest hydroponic method which uses wicks to draw nutrients from the reservoir without use of pumps or timer while NFT hydroponics is a method where shallow channels are used to supply the nutrient solution to the bare plant roots through re-circulation process.DWC is a method of hydroponics in which plant roots are suspended directly into the nutrient rich water solution while drip system uses micro emitters to drip the nutrient and water directly to the plant roots with the help of a pump.Ebb and Flow involves flooding the plant tray with the nutrient solution using a pump that is connected to the solution tank at given time intervals with the use of a timer.The solution is later drained back to the nutrient tank.Adoption of hydroponics in East African countries like: Uganda and Tanzania, where this technology might offer a profitable agri-business and food security solution for urban dwellers by tapping into the growing demand for local produce, is still very low.The potential of hydroponic farming in these developing countries hasn’t yet been fully established.It is likely to be more complicated to provide sufficient food for the fast-growing population using traditional agriculture in future, therefore soil-less cultivation is the right substitute technology to adapt effectively.There has also been a lot of attention given to urban agriculture among researchers, scientists and the general public which calls for more attention into hydroponics as it is considered an urban farming technology.Based on the impasse of challenges presented by conventional farming practices, urbanization and the increasing urban population as well as the ability of hydroponics to tackle these challenges, this study focused on examining the status and perception of soilless farming in Central Uganda and Northern Tanzania as an alternative sustainable cropping system to increasing food security and agdribusiness opportunities around urban and peri-urban areas.

Focus was specifically put on a couple of influential factors majorly socio-economic and agricultural factors surrounding the urban and semi-urban farmers and farms practicing hydroponics in these countries.The study assessed and categorized the benefits, challenges and recommendations for enhancing the implementation of this technology.It focused specifically on vegetable production because research has shown vegetables to be one of the most easy-to-cultivate crops under hydroponics as earlier mentioned.The study was carried out in the months of April-July 2021 in the urban and periurban areas of Meru district located in Northern Tanzania and Wakiso district located in Central Uganda.Tanzania and Uganda are both located in East Africa and experience tropical climate conditions.Tanzania has an estimated population of 58 million while Uganda has approximately 44 million people.Northern Tanzania was selected as study site because it is one of the vegetable growing hot spots in the country and also has a couple of large hydroponic farms in the country while the Central Uganda was selected because it has majority of the urban and periurban farmers engaging in soilless farming.A total of 150 farmers/firms/farms were identified using snowball sampling through farmers groups and recommendations from expert farmers and agricultural bodies.Only 51 participants who practice vegetable production soilless farming technology majorly hydroponics around urban and periurban areas took part in the study.These participants included both farm owners of the hydroponic vegetable farms that as well as managers of firms that produce vegetables using hydroponics for either seed production or vegetables for sale.A pre-tested semi-structured questionnaire using both closed and open-ended questions was designed to capture socio-economic and agricultural factors related to hydroponic farming as well as the benefits and challenges faced by the farmers and farms at large.Socio-economic factors included: age, gender, education level, labor used at the farm, whether the farmer received financial support to implement the technology or not, market for the hydroponic produce and if hydroponics is the main economic activity engaged in by the farmer.

The agricultural factors captured included: vegetables grown, type of hydroponic system used, medium used, size of land used, planters used to grow the crops, kind of fertilizer used, and the environmental setting used to grow the hydroponic crops.Furthermore, it also included questions to capture information on benefits and challenges of using soilless farming as well as the recommendations that can be put in place to enhance the adoption of the technology.Based on the COVID-19 challenges and restrictions, the questionnaire was designed and answered using Google forms and face-face interviews with key informants especially with companies that were engaging in seed production using soilless farming.Due to the limited sample size, data collected was coded and summarized into frequencies using the Statistical Package for Social Sciences version 26.0 and presented using tables and graphs.Previous research has pointed out hydroponic farming to have a number of benefits as compared to other traditional farming system.Hydroponic in general promotes environmentally friendly measures with the ability for improved commercial food production and perform better than traditional open field farms.One of its advantage is the production of good quality crops.Approximately 24% of the farmers recognized this advantage stating that hydroponic vegetables are clean with good color, taste, uniformity in texture and size, and pesticide residue free.Results from a study in Trinidad similarly reported a high willingness to pay greenhouse-“hydroponic tomatoes” compared to “open-field” tomatoes based on being free of pesticides.Hydroponically grown crops have more mineral composition than soil grown plants.About26% of respondents also reported hydroponics to be a CSA system that is not dependant on weather conditions and also environmentally friendly which aspect was also pointed out by Zhigang and Qinchao.Farmers established that hydroponic food production is not dependant on rainfall seasons and neither does existence of drought conditions deter an individual from cultivation hence offers an opportunity for all year crop production.24% of the respondents noted that hydroponics allows production of high harvests within a small space or areas with unfertile soils through vertical farming as compared to the ancient farming system where farmers need huge chunks of fertile land to get big harvests.This makes it a very suitable urban farming system in areas faced with scarcity of arable land.Gholamreza et al.similarly noted that hydroponics gives the opportunity to grow crops in non-arable areas.This farming system can take place in areas with non-fertile soils and can be implemented using vertical farming which increases crop production per unit area through vertical crop cultivation means.Another advantage noted by approximately 20% of the participants was the absence of soil borne pests and diseases with the farming system as compared to soil farming.

The controlled nature of the environment setting for hydroponics, no use of soil for cultivation,bato bucket use of insect traps for both indoor and outdoor systems all play huge roles in dettering pests like white flies hence reducing use of pesticidies.Richard, Charles reported that soilless faming has the benefit of restricted occurrence of pests and diseases.The use of soilless farming gives a unique chance for controlled environment seed production with limited pests and diseases.Approximately 4% reported having control over the environment of the vegetables through monitoring climatic and environmental conditions such as: temperature, Electrical Conductivity , pH and humidity, majorly those who were cultivating under fully automated green houses.With hydroponic farming, there is control over the climatic conditions within the greenhouse environment.Other advantages for hydroponic farming noted by about 4% of the farmers were: no weeding is required, source of income from sale of vegetables and training other farmers, provides supply of fresh vegetables, require little attention during growth and production of surplus food for home consumption.Fig.5att link=”no” categorizes the advantages of hydroponic farming within Tanzania and Uganda.Rice is one of the most important crops for food security and rural livelihoods in many developing countries, especially in the Southeast Asia region.Rice farming plays a crucial role in income generation and ensuring food security for millions of rice farmers in Southeast Asian countries and contributes to food security at the global level through rice export.However, the current rice farming practices heavily rely on synthetic fertilizers and pesticides for higher productivity through improving soil health and preventing damages caused by crop pests and diseases.

Synthetic fertilizers and pesticides are the inputs that farmers cannot self-produce and have to rely on purchase, and the expenses on these inputs usually account for a high proportion of production costs.Therefore, overuse of these inputs lowers rice production efficiency and income from rice farming.Furthermore, overusing chemical inputs poses a major threat to agricultural sustainability , negatively affecting both underground and surface water, and creating eutrophication and losses of biodiversity.Empirical evidence for explaining the overuse or inappropriate application of synthetic fertilizers and pesticides points out several factors such as farmers’ lack of knowledge about optimal levels of input use, significant influence of input suppliers, weak management from authorities, and risk aversion under uncertainties caused by fake products, asymmetric market information of inputs, soil quality, pests and diseases, and climatic variability.For instance, Feder reported that a lack of information about the degree of pest infestation and pesticide’ effectiveness was driving risk-averse farmers to apply more pesticides to reduce the impact of risks.Supporting this finding, Khor et al.indicated that the fear of low-quality fertilizers might be an uncertainty encouraging farmers to apply more fertilizers.Under risk aversion, these uncertainties become significant determinants of fertilizer and pesticide use.Hence, examining the influence of risk attitude in uncertain contexts on the application of fertilizers and pesticides deserves attention.Rural households in developing countries live in a vulnerable context , frequently facing different types of shocks such as weather shocks and crop pests/diseases.These shocks create uncertainties that influence farmers to use more/less inputs.For instance, frequent weather shocks such as floods, landslides, and storms might, on the one hand, discourage rural households from applying an adequate amount of inputs because of their fear of losses.On the other hand, droughts might indirectly and adversely affect systemic insecticides’ performance that leads to an increase in pesticide use.Consequently, farmers might disregard recommended optimal input application rates in the context of uncertainties.Unfortunately, a limited number of studies account for these shocks in examining the relationship between farmers’ risk attitude and input application.

September 1, 2022

Studies have found that people readily use stereotypes to fill in details about strangers

Tightness and collectivism are separate constructs, while collectivistic cultures tend to have tighter norms, the correlation is far from perfect.Across 26 nations, collectivism and tightness correlate at r =.49.In other words, 75% of the variation in tightness is separate from collectivism.For example, Brazil and Thailand score high on collectivism but low on tightness , thus the rationale to test the constructs separately.We focus on China’s farming histories as a source of cultural variation.Recent research has found that China has large-scale cultural differences between the north and south that trace back to wheat versus rice farming.Paddy rice farming required twice the labor per hectare as dryland crops like wheat, corn, and millet.To deal with labor demands, rice farmers in southern China formed cooperative labor exchanges.Paddy rice also involved shared irrigation systems and these irrigation systems increased labor demands and forced farmers to coordinate water use and labor for upkeep.There is evidence that the tight inter-reliance in historical rice farming has made parts of southern China more interdependent.In contrast, historically, wheat required less labor and relied mostly on rainfall, which reduced farmers’ need to coordinate.Why would these farming differences be related to pandemic responses?Some researchers have argued that the long-term prevalence of infectious diseases shapes cultural differences.For example, cultures in areas with more disease may be less open to outsiders as a way of defending against people with diseases that are not already present in the community.Because China is such a large country,planting gutter covering climate zones from the tropics to Russia, it has significant variation in climates and disease rates.We tested for the influence of different regions’ experience with diseases by analyzing data on provinces’ SARS cases per population and for the long-run prevalence of flu cases per population.We chose SARS cases because the SARS outbreak captured public attention and thus may have influenced culture.

We chose flu cases as an indicator of more stable differences in respiratory infections across China.Flu cases catch less media attention than SARS, but they affect far more people.If rice farming influences behavior and social coordination, it can potentially explain cultural differences in mask use outside China.By early February, 2020, mask use became widespread in Japan, South Korea, and Vietnam.Newspaper articles in the US and the UK have argued that culture played a role in mask use in East Asia.Thus, it’s possible that countries with histories of rice farming like China, Japan, South Korea, and Vietnam might have been quicker to adopt masks and were thus less affected by COVID-19.However, comparing nations makes it hard to pinpoint the effect of culture.For example, is South Korea’s strong response related to its history of rice farming or the policies Korea put in place after the local MERS outbreak? This is why exploring cultural differences within China is empirically valuable.By looking at differences within a single nation, we can compare people who share the same national government, healthcare system, language family, and other factors.Although this does not eliminate confounding variables, it limits them far more than cross-country comparisons.If rice farming can explain differences within a single country, we may gain insights that could be applied to “messier” cross-country comparisons.The epicenter of the outbreak also provided a rare opportunity to test for regional differences.This is because Wuhan is located in the middle of China.Had the epicenter been in the north or the south, the relationship between proximity and mask use, as well as other cultural factors, would have been harder to gauge.To measure rice farming, we used the percentage of farmland devoted to rice paddies per prefecture.To represent historical rice farming, we used the earliest prefecture-level rice data available from provincial statistical yearbooks.For Study 3, because the search engine data are reported by province, we used provincial rice statistics from the 1996 Statistical Yearbook—the earliest statistical yearbook we could find.To test whether the rice statistics represent historical farming patterns, we compared the statistics to rice data available for a subset of regions from 1914.The 1914 data correlate highly with modern rice statistics.This suggests that the more complete, recent data adequately represent historical rice-farming patterns.Additional analyses found that the results were robust to alternative operationalizations of rice: using provincial rice data instead of prefectural rice data and a simple dichotomous rice-versus-wheat variable.As time progressed, regional governments introduced mask policies.

Mask policies arose first in Wuhan on January 22.Yet even then, many people publicly questioned the effectiveness of masks.At the same time, most reported cases were confined to Wuhan, in Hubei Province.By January 24 , most cities in Hubei were quarantined.Other provincial governments began to introduce their own mask policies afterwards.We measured top-down policies by gathering news reports of official city-level notices requiring residents to wear masks, as well as regulations banning public gatherings and events.Table S8 lists the policy dates and newspaper reports for each observation area.Section S5A describes the data collection in more detail.One plausible alternative explanation to rice theory is that mask shortages determined mask use.Since masks were in short supply, perhaps people in some regions did not wear masks because they could not find one.We investigated this possibility by tracing reports of mask shortages across China.We searched newspaper reports that documented when masks first sold out in different regions.We searched for the word “masks shortage” using Chinese search engines and were able to trace reports and dates for all observation sites.We coded the earliest day a mask shortage was mentioned in the local news.In this approach, we followed the methodology previous researchers used to retrace unfolding events like the 2008 financial crisis and were able to identify that masks were in short supply across all of our observation window.This allows us to explore the possibility of how mask shortages might impact mask use during the early days of the outbreak.As a robustness check, we tested whether rice farming predicted searches for common search terms not related to COVID-19.we ran the same analyses using four of the most popular internet search terms: weather, map, calculator, and translate.This can rule out methodological artifacts.For example, perhaps internet activity was generally higher in rice provinces during this time.If so, rice would predict more searches in general, not just for masks.The results for these common search terms showed no evidence of a spike in rice regions during the initial outbreak.For example, people from rice-farming provinces were no more likely to search for “weather” before the outbreak, in the early days, or in the later days.People from rice provinces did search for “translate” more than people in wheat provinces, but this difference was consistent before, during, and after the emergency declarations.In sum, these results suggest that rice-wheat differences in the early days of the COVID-19 outbreak were not the result of general search differences.

The three complementary studies suggest that rice-wheat differences impacted mask use early in the pandemic.This is partly explained through the mediation of tightness.Rice-farming provinces in China have tighter norms , and people in places with tighter norms wore masks more.Tight norms may have helped pre-modern rice farmers deal with the large labor burdens of rice and coordinate shared irrigation networks.Although this emphasis on social norms emerged for farming, it seems to have helped people in rice regions react faster to the COVID-19 pandemic.However, tight norms explain only a portion of the cultural differences.There are other reasons to think that people from interdependent cultures would wear masks more.For one, research has found that people in interdependent cultures are more focused on risk prevention, whereas people in independent cultures focus more on potential gains and positive outcomes.The data here fits with prior evidence that East Asia is more concerned about virus than people in other parts of the world.A study of the Swine Flu outbreak East Asians reported greater concern about the virus than Westerners, and East Asian air traffic decreased much more severely.A more recent mask observation study found that even with more than 40-days without local COVID- 19, nearly 60% of Shanghai residents still wore masks in public settings , thus pointing to the continual ‘concern’ for others even with no risk of infection.In addition, seeing society through an interdependent lens may make people see the dual purpose of masks: to protect not just the self, but others.Although our studies found little evidence for provincial collectivism measures explaining the differences, it is possible that self-report measures of collectivism do not reflects cultural differences.The fact that cultural differences diminished as the pandemic progressed suggests three theoretical implications: First, cultural differences may help fill in the blanks during ambiguous times.In the early days of the outbreak, the virus was surrounded with unknowns: Is it dangerous? Can it spread from human to human? Is this a true crisis or are people overreacting? It was during this period of uncertainty that cultural differences were the largest.At first, rice areas wore masks.Over time, as cases spread and risk increased, places with looser norms caught up.Culture seemed to help fill in the blanks, before policy, science, and media come into play.There is some evidence that people use stereotypes in the same way.But when people have access to richer, gutter berries individuating information , they rely on stereotypes less or not at all.Second, the results suggest a boundary condition for the influence of culture.Policy efforts can override cultural differences.

While regions responded differently at the beginning of the outbreak, they converged as awareness spread and the government enforced mask policies.This finding can help policymakers understand when cultural differences are likely to influence human behavior and when external circumstances will override initial differences.Third, the data are also consistent with the idea that people do not respond strictly to objective risk factors when they decide how to respond to a public health crisis.For example, death rates were higher for older people, yet they were less likely to wear masks.In addition, COVID-19 cases were less common in places farther from Wuhan.People in wheat farming regions responded to that distance—more people wore masks in places closer to Wuhan, and fewer people wore masks in places farther from Wuhan.Yet people in historically rice-farming regions wore masks at the same rate regardless of distance, local COVID-19 cases, and history of infectious diseases.In sum, ground data from China during the early days of COVID-19 revealed some behaviors that fit with risk calculations and some that did not.It is rational to expect that more people would wear masks in places with more cases, places closer to the epicenter, and places with denser populations.It is also rational to expect that mask use would approach 100% as the outbreak spread and local governments required masks.Yet objective risk factors explained only a portion of human behavior.Non-obvious cultural factors also predicted whether people wore masks, how early people started wearing masks, and whether people searched for masks online.Although COVID-19 was only discovered in 2019, people’s reactions mapped onto their long-run cultural histories.The definition and status of smart farming, sometimes referred to as digital farming , varies from country to country.Smart farming solutions apply information and technologies to increase the economic yield of crop and livestock production, and to optimize farming inputs and processes that extend to the transportation, distribution, and retail phases of the food supply chain.These technologies rely on Big Data Analytics and include cyber systems that afford monitoring, smart predictions, decision support, automated control and future planning.Although there are many definitions for smart farming, the main conceptual elements found in the literature are similar and include combining Big Data Analytics and information communication technologies such as the Internet of Things , and Edge and Cloud computing with farm equipment, GIS technology, robotics, satellite images, unmanned aerial vehicles and algorithms to accomplish farming practices innovatively and efficiently.In addition, smart farms are expected to optimize food production by improving the application of nutrients to the soil, reducing the use of pesticides and water consumption in irrigation.Precision agriculture, precision irrigation and AgInformatic systems are prime examples of current technologies that could integrate ICT for optimizing farm inputs.Yet, the smart farming concept was meant to be more holistic and include frameworks for establishing optimal farm processes, networking of on-farm systems , monitoring the distribution of farm products and marketing food commodities.Finger et al.predicts that smart farming solutions could narrow the productivity gap between developing and industrial countries.