Tag: hydroponic

The data for workers in these sectors come from the March Current Population Survey

Apprehensions by the U.S. border patrols dropped from 876,803 in 2007 to 556,032 in 2009. Because immigrants often send money home, we can use remittances from the United States to Mexico to infer whether the number of immigrants changed substantially during a recession. Figure 2 shows quarterly remittances to Mexico in millions of U.S. dollars as reported by Banco de México . The figure shows that remittances increased during the relatively mild 2001 recession but decreased substantially during the 2008–2009 Great Recession. These data again support the view that the number of Mexican immigrants to the United States fell during the Great Recession but not during the previous, milder recession. Moreover, Warren and Warren estimated that the net change of undocumented immigrants was negative during the Great Recession, which was related to a sharp decrease of new undocumented immigrants. The United States Department of Agriculture, Economic Research Service estimated number of full- and part-time agricultural workers fell from 1.032 million in 2007 to 1.003 million in 2008 and 1.020 million in 2009, before rising to 1.053 million in 2010.5 That is, the number of workers in 2008 was 3% to 5% lower than in the years before and after the Great Recession. Presumably the share of workers dropped by even more in seasonal agriculture, which employs most of the undocumented workers.Our agricultural workers data comes from the National Agricultural Workers Survey.The NAWS is a national, random sample of hired seasonal agricultural employees, who work primarily in seasonal crops.The NAWS is an employer-based survey. That is, it samples worksites rather than residences to overcome the difficulty of reaching migrant farm workers in unconventional living quarters.

These employers are chosen randomly within the U.S. Department of Agriculture’s 12 agricultural regions .Surveyors randomly select 2,500 employees of these growers to obtain a nationally representative sample of crop workers. Surveyors interview the more than 2,500 crop workers outside of work hours at their homes or at other locations selected by the respondent. The NAWS has a long,vertical growers visible history within farming communities, and the survey design incorporates questions aimed at data validation about legal status. Respondents receive a pledge of confidentiality and a nominal financial incentive for participation. As a result, only one to two percent of workers in the overall sample refuse to answer the legal status questions. The NAWS contains extensive information about a worker’s compensation, hours worked, and demographic characteristics such as legal status, education, family size and composition, and workers’ migration decisions. We dropped workers from the sample who were missing any relevant variable, 23% of the original survey sample. The NAWS is conducted in three cycles each year year to match the seasonal fluctuations in the agricultural workforce. Unfortunately, the public-use data, which we use, suppresses information about the cycle and aggregates the 12 regions into 6 regions. As a result, our data set consists of repeated annual cross sections of workers from 1989 through 2012. Column 1 of Table 1 presents national summary statistics for the variables used in our empirical analysis. Columns 2 and 3 provide data for California and for the rest of the country, because 37% of the sample works in California. Compared to workers in the rest of the country, Californian workers tend to have less education; have more farm experience; are more likely to be non-native, Hispanics; and are more likely to work in fruit and nut crops and less likely to work in horticulture.

After analyzing the effects of recessions on agricultural workers, we replicate the analysis for workers in construction, hotels, and restaurants, which also employ many immigrants.In March of each year, workers in the basic CPS sample are administered a supplemental questionnaire in which they are asked to report their income such as hourly wage rate and additional labor force activity such as hours worked in the previous week.8 Because information on immigration is available only since 1994, our sample period is 1994–2013. We include all workers who are 18 years and older.Three recessions occurred during our 1989–2012 sample period . The economy recovered quickly from the first of these recessions in 1990–1991. The second, 2001 recession was also relatively mild. However, the third recession, the 2008–2009 Great Recession, was much more severe and had longer-lasting economic and labor market effects than the first two. We analyze the effects of recessions on hourly earnings, the probability of receiving a bonus, and weekly hours of work of employed workers. For workers paid by time, hourly earnings are a worker’s hourly wage. For piece-rate workers, we use the workers’ reported average hourly earnings. The bonus dummy equals one for workers who receive a money bonus from an employer in addition to the wage, and zero otherwise. Weekly hours of work are the number of hours interviewees reported work at their current farm job in the previous week. The explanatory variables in all these equations are the same. The explanatory variables include all the usual demographic variables: age, years of education, years of farm experience, job tenure , gender, whether the workers is Hispanic, whether the worker was born in the United States, and whether the worker speaks English.The specification uses a legal status variable to capture the bifurcated labor markets for documented and undocumented workers. It also includes crop and regional dummies.

We have seven main explanatory variables: dummies for each of the three recessions, the recession dummies interacted with the legal status dummy , and regional unemployment rates for workers in all sectors of the economy. We use separate dummies for each recession to allow for differential effects across the recession . The interaction terms capture whether employers treat undocumented workers differently than legal workers during a recession. We include the unemployment rate because it peaks after the end of each recession . We do not report the unemployment rate interacted with the undocumented dummy because we cannot reject that its coefficient is zero in any equation. We treat all these variables as exogenous to the compensation and weekly hours of individual agricultural workers. We start by examining the effects of recessions on NAWS workers’ hourly earnings. Column 1 of Table 2 presents regression estimates for the ln hourly earnings equation. The coefficients on the demographic variables have the expected signs and are generally statistically significantly different from zero at the 5% level. Undocumented workers’ hourly earnings are 2.1% less than those of documented workers. Females earn 6.4% less than males. Hispanics earn 4.9% less than non Hispanics. Unlike most previous studies, we find a statistically significant effect of education. English speakers earn 3.9% more than non-English speakers. The coefficients on the recession dummies reflect the effect of the recession on documented workers. Documented workers’ hourly earnings rose 1.8% during the 1990–1991 recession, 4.2% during the 2001 recession, and 6.9% during the Great Recession. We draw two conclusions about the effects of recessions on documented workers. First, the hourly earning effect of the Great Recession was larger than that of the relatively minor recessions, which is consistent with literature on business cycles and the farm labor market in the 1970s . Second, in all recessions, documented workers’ wages rose, which suggests that recessions cause the hired-agricultural-worker supply curve to shift leftward relatively more than did the demand curve. The sum of the coefficients on the recession dummy and its interaction with the undocumented dummy captures the effect of a recession on undocumented workers. The 1990–1991 recession did not have a statistically significant effect on undocumented workers.

Hourly earnings for undocumented workers rose by 3.4% during the 2001 recession and 1.9% during the Great Recession. In contrast to the pattern for documented workers, the undocumented workers’ earnings rose by less during the Great Recession than during the 2001 recession. Thus, not only do undocumented workers earn less than documented workers do in general, but their hourly earnings rise less during recession than do the earnings of documented workers. That is, the wage gap between documented and undocumented workers widens during recessions. In addition to hourly earnings, 28% of the workers in our sample receive bonus payments , which supplement relatively low wage payments . These deferred payments play a similar function to that of efficiency wages in other sectors . We use a binary indicator equal to one if a worker receives a money bonus. Column 2 of Table 2 shows the results of a regression using a linear probability model . For documented workers, the probability of receiving a bonus did not rise during the two relatively minor recessions but increased by 5.8 percentage points during the Great Recession. Thus, the Great Recession not only raised documented workers’ hourly earnings, but it raised the probability that they received a bonus substantially. For undocumented workers, the probability of receiving a bonus fell by 2.9 percentage points during the 1990–1991 recession and rose by 9 percentage points during the Great Recession. Again, this result is consistent with the theory that the Great Recession caused a large supply side shock. Thus,vertical grow for both documented and undocumented workers, the Great Recession had a larger, positive effect on the probability of receiving a bonus than did earlier recessions. The unemployment rate has a statistically significant effect on the probability of receiving a bonus payment. A one percentage point increase in the unemployment rate raised the probability of receiving a bonus by 0.9 percentage points.Because our data set includes information about only employed workers, we cannot directly observe the effect of a recession on total employment. However, we can examine the effect on workers’ weekly hours. When employers have difficulty recruiting workers, they have employees work more hours per week to compensate for an unusually small workforce. For documented workers, weekly hours fell by 2.2 hours during the 1990–1991 recession, but rose by 1.1 hours during the 2001 recession, and 2.3 hours during the Great Recession. For undocumented workers, weekly hours were not statistically significantly affected during the two relatively minor recessions, but rose by 2.6 hours during the Great Recession—more than for documented workers. An increase in the overall unemployment rate by 1 percentage point lowered the weekly hours by 0.3 hours. Thus, an increase in the overall unemployment rate lowered weekly hours, but weekly hours rose during relatively large recessions.We conducted five robustness checks. First, we estimated all three equations separately for documented and undocumented workers. That is, we allowed all the coefficients to vary between these two groups instead of only the recession dummies.

The coefficients on our seven key recession variables were virtually unchanged . Second, we estimated all three regressions eliminating all newcomers , about 3,300 people or 7.5% of the sample, to see if compositional changes in the workforce during recessions are driving our results. However, the coefficients were virtually unchanged . Third, we estimated all three regressions leaving out the unemployment rate. Doing so had negligible effects on the other recession variable coefficients . Fourth, we excluded the crop dummies, in case they are endogenous. The recession variable coefficients were unaffected .Do recessions have different effects in agriculture than in other sectors of the economy that employ many undocumented immigrants, such as construction, hotels, and restaurants? To answer this question, we constructed a comparable data set based on the March Current Population Survey for 1994–2013. We can look at the effects from only two recessions, 2001 and the Great Recession, because the CPS does include certain key variables prior to 1994. It also lacks a variable on bonus payments. In contrast to the NAWS, the CPS data does not record whether an immigrant is undocumented. Therefore, we focus on immigrants in general and form interaction terms between immigrant status and the recession dummies. Otherwise, we use as similar a set of demographic variables as possible. Table 4 presents the regression results for the ln wage and weekly hours in the three sectors. In none of these three sectors did either recession affect the wages of non-immigrants or of immigrants. Presumably, wages are sticky in these sectors, partially due to union and other contracts and minimum wage laws. The unemployment rate had a statistically significant effect only in the construction sector, and that positive effect is small, as in the agricultural sector. The 2001 recession did not affect the weekly hours in these sectors.

A prominent example of NbS in agriculture is the coconut -based farming system

Depending on the data, cache memory is a bridging solution for yield data for example.Acquired in-field moisture or temperature data which need to be displayed to the farmer with low latency a direct switch to the suggested resilient infrastructure must be given.Concrete solutions for machine data, which have been tested in the field, were shown in the iGreen project with the so-called “Machine Connector”.For data, only allowing low latencies, the LWN directly has to be used in case of an interrupted internet connection.Again, here farmers have to diagnose and define which data they need, with which latency, and accordingly design the FDFS.In any case, if farmers have to calculate with interruptions, a parallel, hybrid data acquisition, like in the suggested FDFS, seems best practice.On-farm data storage on the farm server can be erased if cloud computing of a certain task is completed and data safety is guaranteed.The digitization span amongst farmers reaches from no network coverage at all, to farms that use autonomous robots controlled with real time data.For the latter, our approach in the FDFS at Level V makes perfect sense.However, most farmers in a worldwide perspective have no internet at all or only a low bandwidth landline connection to the office area.Solutions that use, and should use, the prior way over an internet connection but without providing desktop solutions, are strongly limited from the start on such remote farms.These farms indicate most reasonable the concern of this paper and might directly take level four or five into account of their digitization process.Last but not least, it is difficult for farmers who already invested in and implemented proprietary solutions of a few OEM brands to switch to or integrate open, standardized, and flexible solutions.APIs and converter plugins are needed for seamless data exchange which is often in conflict with the business model of the manufacturer.Once more a case where it is the responsibility of the OEMs to provide interoperable solutions.Advantages of strengthened interoperability not just for the farmers are expected, but also for the OEMs who might integrate their innovations in the part wise proprietary environment of another OEM.Farms, as mentioned here, seem to be in the same situation as the partners of the iGreen project who decided on the following strategy to ensure interoperability: “iGreen touches on so many actors, that a traditional top-down, up-front standardization of document formats and APIs would be so costly and time-consuming that it would be impossible to realize within the frames of the project.Instead,rolling benches the iGreen project used semantic technologies as an attractive alternative to costly and time-consuming standardization efforts by committee”.

Nature-based Solutions seek to maximize nature’s ability to provide ecosystem services that help humans address issues such as climate change adaptation, disaster risk reduction, and food security.The IUCN defines NbS as “actions to protect, sustainably manage, and restore natural and modified ecosystems that address societal challenges effectively and adaptively, simultaneously providing human well-being and biodiversity benefits”.A key challenge in ecosystem management is the loss of agrobiodiversity as a result of agricultural intensification.NbS in agriculture can reduce the adverse environmental impacts of intensive modern agriculture and sustain agricultural production.Many traditional agricultural production systems, such as agroforestry, have the potential to address natural resource management challenges, provide societal benefits, and conserve biodiversity.They create complex and diversified farmsteads with the goal of producing sustainable and long-term outputs, as in ecological or sustainable agriculture.Low external input usage, integration of different life forms and sustainable intensification are the hallmarks of these cultural systems.Such traditional land use systems also represent the accumulated wisdom and insights of farmers who have engaged with the environment without recourse to outside in-puts, capital, or scientific skills over millennia of cultural and biological transformations and are often regarded as time-tested examples of sustainable land use practices; the tropical home gardens are a case in point.These are traditional multi-strata agroforestry systems , which provide a range of ecosystem services such as provisioning, regulating, supporting, and cultural services.Although coconut is cultivated in several parts of the tropics, it is the most important crop in Kerala – “the Land of Coconut Trees”.Indeed, the euphonious Malayalam word, Keralam , is derived from two root words: Kera, which means coconut tree, and Alam, which means land.Kerala is the south-western state of the Indian Union.The coconut palm is, in fact, the “nucleus” of the Kerala home gardens , around which the other constituents are orchestrated .Although agroecology emerged as a distinct branch of science in the early twentieth century, the ecological underpinnings of agriculture in Kerala are much older.

In fact, Krishi Gita, a 15th-century Malayalam poem, explains the environment-friendly cultivation systems of medieval Kerala, including that of coconut palms.This paper examines the autecological characteristics of coconut, besides the role of CBFS in providing nature-based solutions to various ecological challenges, with special reference to Kerala.It focuses on three specific questions: what natural resource challenges CBFS addresses, what ecosystem services CBFS provides, and what biodiversity outcomes CBFS offers.It also examines the functional dynamics and vegetation structure of complex coconut-based land use systems.Aspects like varietal development, cultural practices, and pest and disease management, which are discussed in detail elsewhere, are, however, not focused here.Coconut is one of the earliest among the domesticated plants.Based on the occurrence of two genetically distinct sub-populations corresponding to the Pacific and the Indo-Atlantic oceanic basins, Gunn et al.postulated two geographical origins of the coconut palm: Southeast Asia and the southern margins of the Indian subcontinent.India has a long history of coconut cultivation spanning over three millennia.The crop is inseparably intertwined with the socio-cultural heritage and economic well being of the people of Kerala, as in other coconut-growing regions of the world.It is ingrained in folklores and has been celebrated by poets over centuries.For instance, Krishi Gita, the 15 century text, describes the importance of coconut growing in the livelihood of the residents of medieval Kerala.Apart from being an oilseed crop of enormous significance , it also yields food, drinks, timber, and fibre, besides being an ornamental species of prominence.This astounding range of products and services from the palm justifies the sobriquet “Tree of Life” or “Kalpavriksha”.Being a portable source of diet, water, and fuel, it is thought to have played a pivotal role in pre-historic migrations and the growth of civilization in the wet tropics.According to the FAO statistics , the Philippines, Indonesia, and India are the three largest coconut-producing countries in the world, with 3.5, 3.0, and 2.2 million hectares, respectively.

With over 80% of the area and 62% of the global output, South and Southeast Asia and the Pacific Islands dominate the scene.Coconut is also popular in many other tropical and subtropical nations, including those along the African coasts and in LAC , where they grow naturally as well as in planted and managed stands.A large proportion of such planted and managed stands of the palm are in smallholder farms of size less than 5 ha; the farms in Asia, the main coconut-growing region of the world, are, however, much smaller.And in Kerala, more than 98% of the operational holdings are either small or marginal.Most coconut-growing areas were once forested and, in some regions like the Pacifific Islands, where coconuts are produced, the crop is still the primary cause of deforestation.For example, in Vanuatu , the development of large “coconut estates” became a dominant land-use activity during the 20th century by the Europeans, and forests and old tree-fallows were transformed into coconut plantations.A large number of smallholder coconut plantations that substantially altered the indigenous farming systems followed this.Thaman et al.reported a gradual shift away from the traditional mixed agroforestry systems in the Pacific islands in which fruit trees and other culturally useful trees,ebb and flow bench such as coconut, breadfruit , traditional banana and plantain clones , citrus , Malay apple and Polynesian vi-apple were dominant, to monocultural production of commodities.Likewise, detrimental environmental effects of coconut monoculture have been noted in Western Samoa, central Indonesia, and Vanuatu.Although tropical deforestation caused by palm oil production is well-known , deforestation by coconut oil production and its biodiversity implications are rarely discussed.Furthermore, the majority of coconut is produced in tropical island nations, where “endemism richness” – an index that combines endemism and species richness – exceeds mainland regions by a factor of 9.5 and 8.1 for plants and vertebrates, respectively, and deforestation may result in the extinction of the endemic species.Furthermore, conservationists classify coconut as an invasive species that threatens biodiversity in the Chagos Archipelago.However, such evidence is scarce elsewhere, and coconut plantations are an important part of the cultural landscape in many countries providing employment, food, and artisanal products, as well as playing an important role in ecological restoration.In Kerala, coconut palm is the most extensively cultivated crop.It grows virtually everywhere in the state.Kerala has a diverse range of land forms that includes mountains, riverine deltas, wetlands, and ecoclimatic conditions that range from high rainfall zones to rain-shadow regions.The soil, climate, flora and fauna of these ecoregions are also correspondingly diverse.The principal crops of the state, including coconut, are cultivated in most of these ecoregions since time immemorial.Coconut is a major crop in the lowlands of Kerala, but the midlands and the slopes of the highlands are also suited for its cultivation.The western seaboard, the shorelines of lagoons and backwaters, and the banks of creeks in Kerala are profusely flecked with this palm.

The palm abounds on the fringes of the meandering valleys that surround the numerous hills – a distinctive feature of the state’s topography.Despite being a prominent crop in the lowlands and midlands, coconut cultivation has gradually expanded to the high-altitude regions , which may not be ideally suited for the crop in terms of its eco-climatic requirements.Consistent with the importance of the palm in the bio-cultural legacy and livelihood of the people of Kerala, there was a dramatic increase in the area of coconut in the state during the second half of the 20th century.In fact, area under coconut increased by 106% between 1955 and 2000.Conversion of paddy fields and other croplands has contributed much to this so-called “coconut boom”, which, however, faded subsequently.Indeed, the state’s coconut area decreased dramatically between 2010–11 and 2015–16, but it increased significantly after that, by about 1,00,000 ha in 2018–19.It should be noted, however, that it is difficult to estimate the area under coconuts precisely due to a lack of standardized procedures for estimating areas when the species grows at different densities and is planted and nurtured as a crop either alone or in combination with other species.In multi-strata systems, extinction of incoming solar radiation by the tree canopies warrants the use of shade-tolerant or sciophytic species as inter-crops.Factors such as stage of development of coconut palms, growth habit/crown characteristics of the associated tree components and their planting geometry, determine stand leaf area index, and in turn, the magnitude of light extinction.Optical density of multi-species systems especially involving woody perennials are clearly lower than that of monocropping systems owing to the higher stand leaf area index in the former.In line with this, Kumar and Kumar, in an experimental study involving 17-year and 8-month-old coconut palms and three 3 year and 9-month-old dicot multipurpose trees, found that the stand leaf area index varied from 5.24 to 7.15 for coconut+ dicot multipurpose tree systems as opposed to 4.9 for coconut monoculture.Reduced light availability beneath the multi-strata canopy may reduce sub-canopy yields of some crops , although yield levels may also increase or remain the same in some situations, reflectsing differential understory performance of crops.Shade-loving/tolerant crops maintain positive net photosynthesis even when the understory irradiance is relatively low.Phenotypic plasticity in certain plant traits, particularly those morphological features for optimizing light capture, is also high in shadetolerant species, which helps to explain their improved understory performance.In an exploratory attempt, the understory species that are widespread in the CBFS were classified as “shade sensitive,” “shade intolerant,” “shade-tolerant,” and “shade-loving”.However, there may be varietal and cultivar differences in adaptability to shade even within the same species, which obscures such classification schemes.Wright et al.postulated that there are a few extremely shade-tolerant and a few extremely light-demanding species, with the bulk of species, however, having intermediate and hence overlapping light preferences.Herbs like colocasia or taro , elephant foot yam , ginger , tannia , turmeric , yams , and many medicinal and aromatic plants are widely recognized as examples of shade-loving/tolerant crops.

The respondent further underscores the need for precise models dealing with biology and living animals

Some respondents from the larger companies and cooperatives suggest that the attitudes might be affected by the perceived inconvenience that data gathering causes.They all believe that more farmers would have a positive view on it if it was made easier for them to collect it.However, there is also a sense that the data is not used optimally, partly because it is saved in different databases that are not interconnected.The responses from the respondents indicate that data is being gathered differently depending on the agricultural sector.For instance, many respondents in the dairy section state that there is a lot of data gathered, to a high degree on an individual level, on the farm animals.In contrast, arable farmers also collect data on almost all farms, but that data is not always as detailed.An arable farmer may collect remote sensing satellite data on its farm, but sometimes not with a resolution of square meters, but rather on a field or even farm level.The inputs, i.e.the resources added to the soil, are what would be interesting for the farmer to get decision support on, if one could see a beneficial correlation between input and output.One responding farmer with previous experience from the tech industry, believes that the problem with applying AI to arable farming is the lacking volume of interconnected data.The whole data chain is not connected today, he states.In practice, the input data taken during, for example, arable seeding is not properly connected to the output of the harvest.Additionally, the insights from the harvest are not used as a decision basis for the next seeding.Thus, the data loop is not closed, which it would need to be for AI to be efficient.This data gap combined with the large amount of uncertainty factors, such as unpredictable weather, is a technical hindrance to the learning of AI models.In the field of AI and machine learning, there is an important tradeoff between bias and variance.In the interviews, the respondents had different opinions on the matter.The concept was discussed with the respondents as ‘generalizability’ and ‘precision’ instead of their technical terms.Some respondents say that precision is extremely important since a technical solution that only predicts or detects something half of the time is useless.At the same time,hydroponic grow system other respondents say that as long as the predictions are slightly better than human predictions or detections then the model can be as general as one wants.

In fact, many respondents claim that there is a much larger market for standardized models than the ones that are too adapted after local needs.There is a tendency among arable farmers and corporations that they tolerate a higher degree of generalizability while livestock farmers need more precision.A respondent in the livestock farming sector claims that a farm would never really benefit from a technical solution that could only detect rut among the animals one out of three times.Of course, many respondents bring up that there is a need for balance between generalizability and precision, and that it would be optimal if there was some degree of customizability in that aspect so that each solution can fit each farm.One key concern for the development of smart farming technologies is ownership of the data.Most smart farming systems are created as closed technological ecosystems, with limited possibilities of sharing data in between each other.This technological segregation hinders the systems to share data with each other and is thereby an obstacle to the interconnection between systems.Descending from the rivalry between the major transnational agricultural technology companies, including the quest to both pin the users to their specific technological ecosystems and avoid giving their rivals a chance to create competitive technology, this structure is difficult to change.With that said, two respondents note a tendency for transnational agricultural technology companies to move away from technology that ensnares the user to their ecosystem, to more open data flow.Such open data flow is believed to create more value for the businesses and their users.Consequently, a higher degree of data is expected to be on open standards.Even if the companies providing the technology make some progress towards open data sharing, a couple of projects are created to facilitate the data sharing compatibility.GigaCow, a research project by the agricultural university SLU on data for dairy farms, aims to enable data sharing by automatically exporting the data from different milk robots over time.Such initiatives are welcome to most farmers.However, this is a third-party work-around solution and not as straight-forward as if all machines would automatically be open for data sharing.

Some respondents lift the potential threat towards online IT systems as a risk when implementing new smart farming technology.The risk of being hacked poses a threat both to farmers and to society at large.Focusing on society at large, a respondent from a governmental agency describes cyber security as a particularly important aspect of digitalization in agriculture.This respondent believes that such a data platform probably would be classified with an extremely high security and secrecy label and be managed by the Swedish Security Service SÄPO.Therefore, this could be regarded as a clear barrier for the development process of a common data platform.Nevertheless, the respondent adds that in case of potential cyber-threats it would be better to have the data stored on a common platform than with individual farmers, since people would be managing and looking after the platform to a much higher degree than farmers currently are securing their data.Even though these issues are mostly raised by the larger organizations and authorities, the threat is also acknowledged by some farmers.They believe that connected data platforms with weak security make the farm quite vulnerable to threats.However, one farmer commented that “it is not worse than having all money in a bank account, and that I trust today.”.Other respondents, both governmental agencies and farmers, recognize the IT systems as possibly vulnerable but are not necessarily worried.Instead, they reject the belief that lacking cyber security would pose a greater threat to agriculture than to any other sector in society.When it comes to digitalization of such a fundamental societal system such as the agricultural sector, many strategic decisions are of nationwide interest.Some of the interviewed respondents from larger organizations and authorities believe that there is a wide interest that the agricultural sector becomes smarter.However, farmers are themselves accountable for making this technological transition.Two respondents argue that there is a lack of initiatives from the state or from the large organizations to drive the propagation of digitalization forward in a structured manner.One respondent, working at a governmental authority, addresses the topic of nationwide interest in digitalizing the agricultural sector , stating that AI in agriculture is a natural step moving forward.The respondent says that there are a lot of internal discussions in governmental agencies regarding if and how they should take a more active leadership role in the digitalization of Swedish agriculture.The governmental official thinks that Sweden is behind with its digital development compared to other countries with weaker economic conditions and budgets for agriculture.

A natural first step, according to this respondent, is to create a common national data platform for all agricultural data to be compiled on.Still, this respondent sees no clear political ambition driving this change, while this could speed up the digital transition tremendously.Although there is no wish to ‘force’ farmers into using agricultural technology and digitalizing their businesses, it is a likely progress if there is a nationwide and political interest in going in that direction.As in any other industry, the agricultural sector is driven by the quest for increased profit.Money is a motivator, not only for larger agricultural enterprises but also for farmers.Therefore, the general low profitability in agriculture is a major problem for farmers.Optimization plays an important role for the often unprofitable Swedish agricultural farms to be competitive on the world market.Even though there are lots of subsidies connected to food in the European agricultural system, no farmer respondents recognize any subsidies for investments in new technologies at a farm-level.Instead, the technological transition that is supposed to lead to more sustainable food production or larger output is financed by the individual farmer.different farmers have distinct economic incentives to implement smart farming technologies in their work.Generally, there is one group of farmers that have less reason to care about implementing new technologies since they will have structures in place to reach their revenue in any case.This group often owns their own property and farmland.On the other hand, there are farmers that lease their farmland and therefore constantly must become more and more effective.It is not only a matter of farm ownership though, also the size of the farm affects the probability that smart farming technologies will increase profitability.With a small farm, farmer respondents believe it is difficult to profit from smart farming techniques.A farmer with a small farm describes that he cannot afford buying new equipment, such as a new tractor, himself.Upgrading the machine park is necessary for smart farming technologies to gather enough useful data.This can be linked to the major macro trend of consolidation of farms.Basically, this means that smaller farms cannot afford to compete with the larger ones that can use their competitive advantages of being larger.There is simply not enough profit in managing most small farms, a problem which forces many farmers to merge with neighboring farms.Another trend that impacts the agricultural sector is how technologies are sold and distributed.Today, indoor garden most technology is bought as a hardware which is often a huge expense for the farmer.However, slowly things are changing.There is a transition happening towards services being bought as ‘Software as a Service’ solutions.This allows for business models in which the sold hardware is much cheaper than today or even provided at no cost, while the farmer pays a fee to subscribe for using the set of hardware and software.One respondent from an agricultural cooperative foresees that this change will have major implications and wonders whether, in ten years from now, tractors will be sold solely as a rental service instead of as a product.To enable this, an enormous amount of data will be needed.

One communicated and discussed concern about implementation of smart farming technologies is the dependency it might create towards technology.Dependency on technology refers to a system that relies on automated or semi-automated activities based on often incomprehensible software, a constant power supply or Internet-access.The system itself is not problematic to any of the respondents.However, there are some concerns regarding the cases when this type of system fails.One respondent, from an organization, states that the usefulness of the system would be compromised if the communication infrastructure would somehow break.The concern is expressed in different ways and with different urgency.Livestock farmers express their concern about this since their activities revolve around living beings, whose comfort and health rely on the technological systems continuing to operate.Also, when it comes to dependency on technology, another aspect that several respondents mention is that some practical knowledge among farmers and advisors might be forgotten.One responding farmer believes that if he applies too much technology to his farm he would risk losing some of the local, tacit knowledge of the farm.Particularly, some local variations of the farmland he finds difficult to represent correctly with data.Since there are a vast number of connected parameters affecting how a crop at a specific place will grow, he fears that a program could miss some critical aspects.This may be linked to a certain expressed mistrust towards technology, that it needs to be double checked to make sure it is doing the right thing while working autonomously.In general, there is a positive attitude towards smart farming and what it could mean, to the agricultural sector as a whole and to farmers specifically.Incorporating smart farming technologies could mean that time and costs for activities, such as irrigating and fertilizing, are reduced.Therefore, farmers can better manage their time when using well-functioning new technology.One positive side effect of this is an improved work environment for the employees.With that in mind, researcher respondent R2 states that farmers are generally bad at valuing their time spent compared to the economic return.

The primary problems cited in dominant discourse on sustainable agriculture relate to these crises

Combined these two effects lead to an unambiguous increase in both crop and ecological damage in the agricultural importer. For the case of a simple production subsidy this suggests that, for agriculture exporting countries, invasion related crop damage serves as an adequate proxy for the sign of ecological and total invasion related damage. However, since more complex policies—for example a combination of subsidies to producers and consumers of agriculture—may instead generate changes in crop and ecological damage of opposite signs, we reiterate our general concern over the use of crop damages as a proxy for total invasion related damages. In this section we discuss the likely consequences of relaxing some of the important assumptions of our model. The distribution of inter arrival times for successive introductions is stationary in this model. More appropriately, perhaps, we can think of the arrival rate as dependent on the number of successful introductions in the past. This would be appropriate, for example, if there was a finite pool of exotic species which was being “whittled away” as introductions became successful. In real life, the pool of exotic species is orders of magnitude larger than, say, the expected number of successful introductions in a given year—suggesting that our approximation of the process as homogeneous with respect to time is appropriate. We have also made several simplifying assumptions concerning the nature of the commodities trade: Home is a small, undistorted economy that does not engage in intra-industry trade. If Home is instead a large country in the market for agricultural goods, then changes in the Home subsidy rate that spur local production also affect world prices. Under general conditions9 it can be shown that an increase in S lowers the world price of agricultural goods if Home initially imports agricultural goods. This price change induces a change in local consumption such that overestimates the magnitude of the change in Home imports: as the world price of agricultural goods falls, Home consumers want to buy more, so Home imports fall by less than the increase in Home production of agricultural goods. Indeed, if the elasticity of import demand in Home’s trade partner is less than unity, Home imports of agricultural goods actually rise with an increase in S.

Interpreting Propositions 2 and 3 in this context reveals that the usefulness of agricultural subsidies as an indirect means of reducing successful introductions of non-native species is limited,ebb flow or even reversed, when prices on world markets are responsive to local policy changes. Finally, suppose that countries engage in intra-industry trade in goods. In such a case, changes in net imports misrepresent the true impacts of trade policy changes since rates of exotic species introductions depend not on net imports but gross imports. For example, while the United States is a net exporter of agricultural goods , its imports of agricultural goods are substantial: $37,755 million in 2000 . Cross-hauling of goods can arise for a variety of reasons, and the implications for the validity of propositions 2 and 3 depends on the underlying source of the cross-hauling. First, agricultural commodities include a large variety of goods, from coffee to corn to vegetables and fruit. Some of these goods the US predominately imports and some of these it predominately exports . Reinterpreting S in our model as a subsidy to a single agricultural industry—corn—and subsuming the non-subsidized sector—coffee—in the Y industry would be sufficient to generalize our model to include such cases. However some goods are both imported and exported, such as vegetables and fruit. Some of this cross-hauling can be explained easily by the fact many countries are geographically large and diverse. For example, although apples are grown in Washington State, it may be cheaper for Alaskans to import them from British Columbia. Cross-hauling derived from this source could also be accommodated easily into our model by making the state, rather than the country, the unit of analysis.As discussed earlier, one of the means by which exotic species impose damage on the host country is through destruction of crops. In the interest of simplicity, throughout this paper we have assumed that industrial mix responds to producer prices but not to net harvest rates, such that producers do not engage in “averting behavior.” Farmers planting more corn and less wheat in response to the establishment of the Russian Wheat Aphid in the United States, or using costly pesticides to combat wheat aphids, are examples of averting behavior.

In an economy in which producers face undistorted—i.e. world—prices such averting behavior would reduce the magnitude of, but not change the sign of, crop damages imposed by biological invasions. If, however, producers initially faced distorted prices then biological invasions may actually generate net benefits to an economy. For example, the provision of subsidized water to agriculture in the US’s southwestern states induces the cultivation of water intensive crops, despite that region’s dry climate. Introduction into that region of a pest that preys on water intensive crops would induce a re-orientation of agriculture away from water intensive crops, offsetting at least to a partial extent the effect of the water subsidies and possibly even raising welfare.10 Of course we do not promote such introductions, as it would be superior to eliminate the inefficient subsidies to begin with. We offer this example merely to re-iterate the point from the literature on environmental double-dividends that pre-existing distortions alter the welfare impacts of policy changes, even possibly to the extent of changing the signs of those welfare impacts.Only a few years ago sustainable agriculture was considered peripheral to conventional agriculture and its institutional framework. Today, however, sustainability programs and efforts have been initiated all over the world and sustainability has become a major theme of many groups, including local and national agricultural research institutions, farmer associations, policy makers, and nongovernmental citizens organizations. This institutionalization is manifest in a number of ways – new books and journals devoted to sustainability; sustainable agriculture research and education programs in many agricultural universities and governmental agencies; organic food laws and certification programs; legislative initiatives that mandate various changes toward sustainability; increased popular consciousness about food safety; and higher sales of organic produce. Yet we shouldn’t let this widespread progress convince us that it is time to close off discussion on the meaning of sustainable agriculture. Too many key questions remain at the core of the sustainability debate.

The most fundamental of these is, “Who and what do we want to sustain?”1 Those within the sustainability movement answer this and related questions differently, based on their various positions in the food and agriculture system. Currently, there are many diverse goals and ideas included in the term “sustainable agriculture.”SUSTAINABILITY IN THE BALANCE This diversity presents an opportunity. As a relatively new concept, sustainable agriculture does not yet reflect a coherent vision of what is possible and preferable in agricultural production and distribution. This emerging discourse on sustainable agriculture thus represents a chance for a fundamental paradigm shift in the way we think about food and agriculture and an opening to develop a comprehensive vision of sustainability. It is important to continue to discuss sustainability’s meaning in this context because, “In adopting certain categories for social inquiry we also adopt a certain view of the social world, of its problem areas and of its fixed points, of the actions it makes available and ways in which their results are constrained.” Thus, the language of sustainable agriculture has a direct effect on our form of practical response and action in sustainable agriculture. How we conceptualize sustainability today will determine the extent to which sustainable agriculture will differ from conventional agriculture in the future.We find there is contention over which sorts of problems can legitimately be called sustainability problems, and there are differing viewpoints on the causes of non-sustainable agriculture. There are disagreements over the vision of sustainable agriculture, primarily over who should be the beneficiaries of sustainability. And there is debate over which strategies and practices will be most effective for developing sustainable agriculture. After discussing these view- points we offer our ideas on how we can begin to reformulate sustainable agriculture.Sustainable agriculture arose as a critique of and an alternative to conventional agriculture. A focus on agricultural sustainability first emerged in the U.S. during the energy crisis of the 1970s as people began to recognize the petroleum dependence of industrialized agriculture. The movement grew in response to the farm crisis of the 1980s and an increasing awareness of agriculturally related environmental problems. The primary problems cited in dominant discourse on sustainable agriculture relate to these crises. “Notable among these problems are the contamination of the environment by pesticides, plant nutrients, and sediments; loss of soil and degradation of soil quality; vulnerability to shortages of nonrenewable resources,plant benches such as fossil energy; and most recently the low farm income resulting from depressed commodity prices in the face of high production costs.”Some would add concerns about pesticides’ effects on consumer and worker health and on wildlife as problems leading to demands for agricultural sustainability.In sustainable agricultural science, the main problem addressed is that of the environment and conservation’s role in maintaining profits: “There is a growing awareness about the need to adopt more sustainable and integrated systems of agricultural production that depend less on chemical and other energy-based inputs. Such systems can often maintain yields, lower the cost of inputs, increase farm profits, and reduce ecological problems.”

While all sustainability advocates address the importance of preserving the environment and natural resources, social issues are less often cited as sustainability problems. For example, although many sustainability advocates are concerned with preserving family farms, the larger issue of systemic economic concentration in food and agriculture is rarely addressed. While the dominant discourse on sustainable agriculture raises important problems, there is a tendency to overlook issues such as hunger, poverty, gender subordination, and racial oppression – problems that also contribute to a lack of sustainability in food and agricultural systems. In general, we find that problems identified in dominant U.S. sustainability perspectives are usually framed without questioning the current economic and social structure within food and agriculture systems.Although the United Nations Food and Agriculture Organization explicitly recognizes the link between socioeconomic and agroecological prob- lems,7 the causes of non-sustainable agriculture are often not discussed in scientific texts on sustainability. Family farm and food safety advocates do, however, provide explanations of the problems they identify. Wes Jackson, for example, criticizes corporate agriculture for the concomitant destruction of the environment and the family farm and blames the lack of an ecological approach for an agriculture characterized by soil loss, fossil fuel dependence, and heavy chemical use.8 Another advocate of family farms, Marty Strange, suggests that “the most serious environmental problems in agriculture are those caused by technologies that make large-scale farming possible, and that sever the rewards of farming from the rewards of stewardship and husbandry.” In the same tradition, Wendell Berry decries the industrialization and mechanization of corporate agriculture and asserts that the current U.S agricultural system is unsustainable because of the continual attempt to get the highest possible production with the smallest number of workers.10 Particularly important for Berry is the erosion of cultural values associated with family farming, such as hard work, respect for place, respect for nature, and commitment to home and community. Food safety advocates cite the failure of government to adequately regulate pesticides 11 and lack of consumer awareness as primary causes of food contamination.We wonder, though, if these causes cited for non-sustainability, such as corporate agriculture, inadequate government regulation, and loss of respect for nature, do not themselves need to be explained. Why has corporate agriculture superseded family farming? Why isn’t an ecological approach standard in agricultural research? Why are environmental regulations insufficient or poorly enforced? In our view, there is a need to examine the relationship between the logic of current political economic structures and the causes of agricultural non-sustainability to find the answers to such questions. What role, for example, does the current mode of agricultural production, based on maximizing short-term profits and foreign exchange, play in causing agricultural problems? We must also examine the connection between non-sustainability and present power and decision-making structures at levels ranging from the individual farm to national policies. Who makes decisions in food and agriculture and who do they represent?

In most regions of the world farmers do not pay for the real value of irrigation water

I propose that considering future agricultural expansion data and promoting globalized conservation solutions for defining spatial priorities should be included in this toolbox for sustainability. Only by the careful analysis of future scenarios of agricultural expansion and other human activities will it be possible to predict their impacts on biodiversity and, most importantly, act effectively to reduce the worst impacts of human land use on the environment. Water is a crucial resource for life on Earth because it is irreplaceable in its role of sustaining the functioning of environment and societies. Humankind uses water resources for drinking, municipal needs, and a number of economic activities. Among them, agriculture is the most water-demanding, claiming more than 85% of human water consumption . Despite its important impacts on crop production, food security, and rural livelihoods, water often remains hidden in the economic valuation of agricultural assets. Unlike oil, it is seldom treated as a commodity and traded in the marketplace to generate revenues . Rather, it remains underpriced because users do not pay for its real value . Oftentimes farmers do not even pay for the provision costs associated with withdrawal and delivery . Thus, while crops use huge amounts of water, the price of agricultural products seldom accounts for the cost of water consumption. What is the value of water? How can it be determined? The valuation of water remains a difficult task because this natural resource is rarely traded and therefore its value cannot be determined from a market price. Of course, there are exceptions, such as bottled water, which accounts for less than 1% of human appropriation of water resources worldwide , the pricing of municipal water supply , or the few water markets existing around the world . In some of these cases, the market value reflects the extrinsic value of water, expressed both by the users’ willingness to pay and the willingness of water rights holders to accept compensation for relinquishing their water allocations . Water markets and water trading can be found in Australia, the United States, Mexico, Chile, China, Spain, and South Africa .

These are more exceptions than the rule because in most of the world there are no tradable water rights , the “conditio sine qua non” for the emergence of water markets . In other words,blueberry grow pot in many regions there are no water entitlements that can be sold or acquired through market transactions separately from the land. Rather, water is either tied to land’s property rights or treated as a public good, “res nullius” , or a common pool resource . Although not properly priced, water availability shapes the global patterns of agricultural production and trade and the associated flows of embodied or “virtual” water , which is the water consumed in the production of goods such as crops . In fact, water-scarce regions need to import agricultural commodities to meet their food demand . Even when water is not directly commodified, the goods it contributes to produce are. The value of the associated virtual water, however, is seldom accounted for . Likewise, water is implicitly acquired with agricultural land in the form of rainwater and sometimes also irrigation water when blue water resources are inherently appropriated with the land . This happens in regions where land ownership includes water rights or unregulated access to adjacent or underlying freshwater resources . Interestingly, while there are well-established methods to calculate the water resources that are virtually acquired with agricultural land , their economic value remains difficult to assess . Because water pricing is often viewed as a mechanism to promote a more efficient use of water resources, an international agreement on water valuation is sometime considered to be crucial to the achievement of an efficient and sustainable global water use, a point that has been discussed at the World Water Forum in the last two decades . The value of irrigation water in agricultural areas is an important piece of information for investors and financial groups engaged in the acquisition of land and water resources. Even in the absence of a water market,land and agribusiness investors would benefit from knowing more about the potential economic value of the water resources they are virtually acquiring with the land.

Indeed, the decision to invest along the banks of the Nile River or in areas suitable for rain-fed agriculture instead of targeting arid lands within the same regions would benefit from a combined hydrologic and economic analysis of the availability, productivity, and value of irrigation water. On the other hand, it could be argued that the valuation of water may favor its growing transnational control through the acquisition of water and land entitlements by self-interested agribusiness corporations. This may happen if, as a result of the valuation and commodification of land and water resources, peasants decide to sell land and water rights to realize short-term profits without having the opportunity to plan for the long-term economic development of their communities . At the same time, a major factor impeding planning for rural development is lack of awareness of the value of natural resources such as land and water. Indeed, local communities engaged in the negotiation of land and water concessions need to know the current and potential contribution of water resources to the creation of value in their farmland. Unbalanced power relations and asymmetry in the knowledge of the economic value of these assets are often major obstacles to the informed negotiation of land and water deals . Likewise, investments in irrigation infrastructure require an assessment of the increase in production and associated profits resulting from the use of irrigation. Indeed, farmers’ decision to adopt irrigation depends—among other factors—on the value generated by irrigation in the production process . There is a need for reliable and reproducible water valuation methods that—in the absence of markets—can be used to determine the value of water embodied in agricultural land and its products. The estimate of the value of water in the absence of market is often based on the marginal value produced by a unit volume of water . The literature on this subject is often based on inductive statistical/econometric methods determining the value of water from empirical data, or on deductive models that are fitted to the data . Both approaches typically require a wealth of data that are seldom available, particularly in the developing world .

These classes of methods fail to capitalize on process-based understanding of the underlying hydrological processes determining the role of water as a factor of production . More recently, some studies have proposed a mixed model in which one of the factors of production is estimated with biophysical models while the shadow price of groundwater is determined either by fitting a function of production to empirical data or by simulating the dynamics of crop growth accounting for their dependence on soil moisture and irrigation technology . Here we use a completely mechanistic biophysical method for the valuation of water in agriculture that can be used even when tradable water rights do not exist. We carry out this valuation analysis for the 16 major crops at the global scale on a 10-km grid and then map and critically analyze the results. Our approach allows for the worldwide valuation of water in agriculture and can be used to determine water’s contribution to the value of both crop production and agricultural land.ently planted in each location allows for an estimate of the maximum price farmers might accept to pay for irrigation water. If we look at the four major staple crops , we find that the global mean water values are $0.05, $0.16, $0.16, and $0.10/m3 for wheat, maize, rice, and soybean, respectively . The value of water for the production of maize, soybean, and rice is consistently higher than for wheat. These differences are the result of the combined effect of differences in crop price and in crop water use efficiency . The values of water for maize and rice are substantially higher in East Asia than in other regions of the world . Interestingly, for maize and rice the within-region variability in water value tends to be smaller than the variability among regions, potted blueberries while for wheat and soybean the water value variability tends to be relatively small both within region and across regions . Results presented in this manuscript refer to water withdrawals because farmers are more likely to be allocated—and consequently account for and keep track of—volumes of water withdrawals than water consumption . Values of water based on consumption are presented in SI Appendix as well as in Fig. 1B. As expected, the water values determined with reference to water withdrawals are lower than those determined with reference to water consumption and the difference depends on the efficiency of the irrigation system .

Expanding the analysis to the 16 major crops [≈70% of global food production ], we see that for all of them the global median and mean roughly range between $0.05 and $0.25/m3. The only exception is represented by potatoes, which consistently exhibit a much greater water value than the other crops with a median value of $0.67/m3 . The higher values of water for potatoes is due to their higher yields per unit volume of water application and their higher price compared to the other crops; however, despite their widespread use, potatoes contribute to only 2.1% of the global food calorie production and account for only 1.1% of the global irrigated areas . Variability in the mean water value across regions is overall smaller than that across crops and ranges from $0.09/m3 in South Asia to $0.42/m3 in Europe . With the current crop distribution, the global median and mean water values are $0.13 and $0.23/m3, respectively . Interestingly, even though the within-region water value can substantially vary , globally, the spread around these median and mean value is relatively small, with the 25% and 75% quartiles being $0.08 and $0.42/m3 smaller and greater than the median, respectively . We also provide an estimate of the maximum water values obtained considering—among all of the crops currently cultivated in every 10-km × 10-km pixel—the crop associated with the maximum local water value. These results show that the current crop distribution does not maximize water value . In this analysis we have considered the global areas cultivated with the 16 major crops. Each crop has its own irrigation water requirements, yield, and price, which leads to different water values, depending on the crop. In Fig. 4B we show the results for the crop that realizes the maximum value. Thus, while with the current crop distribution the median water value is $0.13/m3 , if we consider only the crops with the maximum value, the median of the maximum values around the world becomes $0.54/m3 . Interestingly, the variability in water value is greater for the maximum values than for the median values both across regions and within regions . The crops that maximize water value are potatoes in many regions of the world and sugarcane in South and Southeast Asia .The economic valuation of water is a sensitive matter because it can be the premise to water pricing, commodification, and privatization, which are often contentious issues . In fact,a large part of the public tends to think that water should be publicly owned because it is a natural resource that, like air, is essential for human life . Therefore, the valuation of water becomes particularly difficult when this resource is used not only for economic activities but also for environmental needs or the fulfillment of human rights such as drinking or sanitation. Instead of dealing with these uses, here we explicitly focused on the value of water in agriculture. In fact, in many cases they do not even pay for costs of water infrastructures and their maintenance and operation , which are often subsidized by governmental agencies . In addition to costs associated with the supply, treatment, storage, and distribution of freshwater resources, it is often argued that water itself should be sold to its users to avoid that it goes wasted or is used in economically inefficient ways .

We also drop households which have outliers in variables used in our analysis

These figures show that most of deceased due to HIV/AIDS are 22-45 years old males and 15-50 years old females. This observation and the fact that age 15-50 are main labor for household production are the two main reasons why we set the age range to be from 15 to 50. Another reason why we set the upper bound of the age range at 50 is that KHDS did not ask mortality or illness for below 15 or above 50 when KHDS chose sample households. As we discuss in the following subsection, 33% of prime-age adult mortality in the data is enumerated when KHDS chooses sample households. We need to set the upper bound at 50 or less to include these data into our analysis consistently.Here, we show the characteristics of prime-age adult mortality in the data. There are 6,681 individuals are surveyed in wave 1, 2, 3, or 4 . Out of these 6,681 individuals, 988 died between 1991 and 2004 and their deaths are recorded in the KHDS. Note that since wave 5 in 2003 asks mortality only for individuals who were household members in wave 1-4 , there can be other deaths which are not recorded in the KHDS. While these 6,681 individuals have individual ID for KHDS, KHDS records other 377 individuals who do not have individual ID since some of them died in the 12 months just before wave 1 and others joined a survey household and died between waves. Thus, KHDS records the details of total 1,365 deaths. Among 1,365 deaths, 844 deaths are deaths of individuals whose ages are between 15 and 50 when they died. Out of these 844 prime-age adult deaths, 743 deaths are as the result of illness. Out of these 743 illnesses, 398 illnesses are diagnosed by a health professional and 188 are reported as HIV/AIDS. Thus, 47.2% of diagnosed illnesses are reported as HIV/AIDS. KHDS also asks a respondent in a household what illness the respondent think the died person was suffering from. Out of 743 illnesses, 36.7% illnesses are thought as HIV/AIDS. Out of 844 prime-age adult deaths, 32% deaths are due to HIV/AIDS although respondents may not have enough knowledge about health to understand the cause of death correctly.

As mentioned above, KHDS intended to sample households hit by adult mortality more than other households. KHDS calls the sampling stage before main survey as “enumeration”. The enumeration before wave 1 asks whether any adult with age of 15-50 has died in the past 12 months. Then, if so,strawberry gutter system it asks the ages of each adult and the cause of the death. The cause of the death has only 4 categories: illness, accident, child birth, and other. It does not ask gender of each adult nor any further individual characteristics. The enumeration recorded 499 deaths. We checked the duplication of deaths between one in the enumeration and one in wave 1. The enumeration was implemented between March 15 and June 13, 1991 while wave 1 was implemented between September 30, 1991 and May 10, 19922. We found 83 duplications although we could rely on only household ID and the age of died adult to find duplications. Thus, the enumeration before wave 1 provides information on 416 adult deaths. Figure 9 shows the age distribution of these died adults. Out of these 416 died adults, 413 adults died due to illness. Figure 10 shows the age distribution of these adults died due to illness.We think we should include these mortality in analysis since our focus is effects of adult mortality and there are huge numbers of adult mortality in the enumeration and before wave13. As we mentioned in the previous subsection, one of the reasons why we set upper limit of prime-age adult at 50 is that the enumeration does not record mortality of individuals whose ages are more than 50. The reasons why we do not distinguish adult mortality due to HIV/AIDS and one due to other causes are the sample size is not so large, whether the cause is HIV/AIDS is not clear, and the enumeration does not ask whether the cause is HIV/AIDS. Previous studies mentioned that HIV/AIDS is more harmful than other mortality or illness since a household suffers from the longer period of sick before death and other members’ care for the sick. Since we do not think we have proper data to study the difference in the effects of HIV/AIDS and those of other illness and mortality, we focus on the effects of prime-age adult mortality on long-term agricultural production. Table 1 shows the number of prime-age of adult deaths by cause and by year. Most of deaths recoded in the data are in 1990 and 1991. This characteristic is due to KHDS’s unique sampling strategies. First, KHDS intentionally sample households which suffered from prime-age adult mortality, more precisely, 14 out of 16 households have prime-age adult mortality in the last 12 months, prime-age adult who is too sick to work or both in the enumeration. Second, in wave 5 , KHDS does not ask death of individuals who were not household members in previous waves even if an individual was a household member when he or she deceased.

We should take into account that even we call prime-age adult mortality between 1990 and 2003, most of death occurred in 1990 and 1991. Table 2 shows the number of households by year and by number of prime-age adult death. As we explain in Section 4.3, we use 401 households out of all households in the original data. There are households which suffer multiple deaths. The number of households which has 0, 1, 2, 3, 4, 5, and 6 deaths are 152, 117, 82, 38, 10, 1, and 1, respectively as shown in Table 2. 56% households have prime-age adult mortality between 1990 and 2003. This table also show that most of prime-age adult death in the data occurred in 1990 and 1991, which is due to KHDS’s sample selection scheme as mentioned above. Wave 5 of KHDS asked households whether each of the past ten years was a very bad year or not, if so, why it was, and if so, how did they cope with it. As the answer to for year 2003, 25% of 376 individual singled out death of family member, 22% did poor harvest due to weather and 20% did serious illness. As the answer to , each individual could answer at most two and there are 525 answers for 2003 from 376 individuals. The content and percentage of each answer is as follows: rely on support from family and friends , reduce consumption , take casual employment , introduce other crops , sell livestock , sell other assets , start other business , start selling processed food , and sell land . These results imply that mortality and illness are the most serious negative economic shock for the households and households respond to it in various ways. We do not study short-term responses although Beegle studies short-term labor responses to prime age adult mortality as mentioned in Section 2. Instead, we study the long-term consequences in agricultural production after being hit by prime-age mortality and responding to it.We need homogeneity in households in the sense that households solve the same or at least a similar economic problem. In this subsection, we discuss what sub-sample of households we choose from the original data. In summary, we choose households which engage in agriculture mainly and we exclude households which emigrate from the original location and new households which split from the original households over a decade from our analysis. Wave 5 of KHDS tracks households and their members who emigrated between 94 and 03. However, investigators do not ask those emigrated households about their agriculture less than non-emigrated households in order to reduce work load for tracking phase and thus the data on agriculture are much less complete compared to non-emigrated households. Since the data on agricultural outputs and productive assets for emigrated households are not collected, we simply drop emigrated households from our analysis. Unfortunately, the number of emigrated household are large: there are 1,413 emigrated households out of all 2,774 households in 2003.

However, we should not say 51% households emigrated. First, these 2,774 households in 2003 includes split households from the original 919 households in 1991 and 1992. Second, 540 out of 1,413 emigrated households emigrated to nearby villages. If we take household unit in 1992, total 830 households are resurveyed in 20034. Out of them, 733 households have at least one new household unit which remained in the same village. 46 households do not have any new household units which remained in the same village but have at least one new household unit which emigrated to a nearby village. The remaining 51 households emigrated in the most restricted definition, that is, do not have any new household units which remained in the same village or emigrated to a nearby village. We exclude households in the most urbanized four clusters since the model does not have occupational choice and poverty dynamics in urban area is very different from the one in rural area we study. The ratio of employment income compared to agricultural income increased a lot in these four most urbanized clusters from 1994 to 2003. Although one fourth of households in wave 1 live in urban zone as mentioned above,hydroponic fodder system we include households in urban zone except households in the most urbanized four clusters since urban zone except the most urbanized four clusters seems to be as agriculture-oriented as other zones in 1991-19945. We drop 55, 51, and 41 households in these four clusters in 1991, 1992, and 2003, respectively. In order to focus on agricultural households, we drop households whose non-agricultural income or transfer income is larger than agricultural income.We exclude households which split from the original household between 1992 and 2003 and which do not seem to be continuing households from 1992. More particularly, we exclude the following households: If there is a main household where household head is the same over 1992 and 2003 and there is another household which was split from the main household between 1992 and 2003, for example, a son’s new household, we exclude the split household and focus on the main household.

If a household head passed away between 1992 and 2003 and there are two households in 2003, for example, older brother’s new household and younger brother’s new household, we choose only one household as the continuing household and exclude the other household from our analysis. Table 3 shows the results of this selection of households. See Appendix A.1 for the detail on how to choose a continuing household.In this subsection, we discuss the relevancy of our specification of agricultural production function . We use the sub-sample of households whose income is mainly from agriculture for our analysis. We think household members, land and livestock are the three main productive factors/assets for the agricultural production in Kagera region. We use the number of household members instead of labor hour input into agricultural production. Although main labor input is household member’s labor, some household use hired labor. For example, in the original KHDS data, 26% of and 33.3% of households used hired labor on their shamba in the past 12 month in wave 1 and wave 5 , respectively. Also, 10.9% of households used paid labor for herding in the past 12 month in wave 5 . In order to control this heterogeneity among households, we subtract the cost of hired labor from agricultural output/sale. We exclude a household from analysis if its agricultural income is smaller than non-agricultural income in order to focus on household income generation with subsistence agriculture. Although we do not take into account 1) that household members use some labor hours in non-agricultural activity and 2) the differences in gender and age among household members, we do not think it is a shortcoming for our purpose. Our objective is to understand the effects of prime-age adult mortality on long term income generating power of subsistence agricultural households and production function is a reduced form of household income generation.

Fertilizers are generally considered risk-increasing inputs

Adverse shocks might have a direct impact on the production of rural households by destroying output and physical assets.They might also have an indirect effect by altering farmers’ behavior towards risks.Under dysfunctional and flawed insurance markets, rural households in developing countries have become more risk-averse after experiencing co-variate and idiosyncratic shocks.However, just a few studies take shock experience and farmers’ risk attitude in examining their impacts on crop production.While these previous studies provide important insight, there are a number of research gaps that need further investigation.First, the endogeneity of risk aversion has not been addressed.Second, while rural households in developing countries have to cope with a wide range of shocks and production risks, previous studies mainly considered droughts and crop pests in the analysis disregarding other shocks such as floods, storms, and diseases.Third, previous studies did not examine how changes in farmers’ risk attitude impact farming efficiency to validate whether farmers’ application of pesticides and fertilizers is efficient, especially for risk-averse farmers.Against this background, we use a panel dataset collected in Thailand to examine the impacts of risk attitudes on fertilizer and pesticide use, and investigate the effect of adverse shocks and risk attitudes on technical efficiency in rice production.Thailand is relevant because agricultural production plays an important role in its rural economy.Addressing these research questions is necessary for policy responses to the harmful impacts of the inefficient application of synthetic fertilizers and agrochemicals on rural households’ production and the environment.The rest of the paper is as follows.Section 2 reviews the literature.Section 3 introduces the study sites and data.Section 4 describes the methods for data analysis.Section 5 discusses the findings.Section 6 concludes with policy recommendations.Although the relationship between risk attitude and input application has been examined in a few studies,dutch bucket hydroponic the findings on the roles of pesticides and fertilizers show mixed directions.

However, they could also play a risk decreasing role.For instance, Rajsic et al.found that nitrogen was a risk‐increasing input, implying that risk‐averse farmers tend to apply less nitrogen.This finding is supported by Möhring et al..On the contrary, Khor et al.stated that less wealthy farmers had a lower level of fertilizer use when their risk aversion increased.This finding aligns with Salazar and Rand that fertilizers are risk-decreasing inputs.Farmers who are more unwilling to take risks might overuse fertilizers because they think the crops need an additional amount of fertilizers.With regard to pesticides, a key motivation behind the application of pesticides is to provide a means of insurance against yield losses/damages caused by pests and diseases.These studies revealed that the higher the degree of uncertainty regarding pests’ damages, the higher the volume of pesticide application, despite any given levels of pest infestation and pesticide costs.Liu and Huang confirmed the risk-reducing role of pesticides.Nevertheless, pesticides could also play a risk-increasing role.Möhring et al.pointed out that risk attitudes affect differently on pesticide use depending on the types of pesticides.Recently, Salazar and Rand examined the impacts of production risks on pesticide use and concluded that pesticides are risk increasing inputs when more risk-averse rice producers apply fewer pesticides.Although these previous studies provide important insight on the association between risk attitude and input application, there are a number of research gaps that need further investigation.First, farmers in developing countries live in a highly vulnerable environment with a wide range of adverse shocks.However, only a few studies simultaneously take these aspects into account when estimating the impact of risk attitude on crop production.Rural households’ behavior under risks might explain low agricultural productivity, vicious cycles of poverty, and determination of risk-aversion in the loss domain to maximize investment decisions.Uncertainties caused by adverse shocks affect rural households’ risk attitudes that might lead to improper applications of inputs and, therefore, reduce technical efficiency.In this case, their fear of uncertainties may encourage them to apply more inputs than efficient levels, and this overuse is wasteful and harmful for the environment and their health.As a result, farmers with high levels of risk aversion could culminate in economic decisions that lead to relatively less income.Thus, accounting for diverse shock types in estimating input application still deserves further attention.Second, farmer’s risk attitude is endogenous.There is a significant and robust linkage between risk aversion and wealth levels in the form of income or assets of the households.

Farmers’ risk attitude can also be affected by household characteristics such as age, education, and gender.Externalities can further influence the risk aversion of rural households in the form of adverse shocks.Therefore, estimations of input use and risk preferences ignoring these aspects might produce biased results due to the problem of endogeneity.Third, farmers’ risk aversion might change overtime; however, most previous studies on risk attitude and input application in developing countries relied on cross-sectional data because long-term panel data with information on risk aversion might not be available.Thus, using panel data for this type of study is relevant to produce more reliable evidence since it allows to control for unobserved sources of heterogeneity.Hence, our study contributes to filling these research gaps.We simultaneously examine the impact of risk attitudes and shocks on input application and technical efficiency in rice production.By employing a balanced panel dataset of rice producers in Thailand, we first investigate the association between risk attitude and input use in the context of shocks.We control for the potential endogeneity of risk attitude by employing an instrumental variable regression.Then, we estimate the technical efficiency in rice production through a stochastic frontier model for panel data proposed by Greene to justify the effects of improper input application caused by farmers’ risk attitudes and shocks.One of the advantages of this model is that it allows us to estimate time-variant efficiency and can distinguish the unobserved heterogeneity from the inefficiency component.The findings are expected to enrich the literature on risk attitude and chemical input application and provide useful insight for formulating public policies to mitigate the negative impacts of shocks, improve production efficiency, and reduce the harmful effects of chemical overuse on the environment.Data for this research are from the “Poverty dynamics and sustainable development: A long-term panel project in Thailand and Vietnam ”, funded by the German Research Foundation.This project aims to generate a better and in-depth understanding of income and vulnerability to poverty dynamics in rural regions of the emerging economies of Thailand and Vietnam.Following the guidelines of the Department of Economic and Social Affairs of the United Nations , the sampling process included a three-stage stratified random sampling procedure based on the administrative system of each country.In Thailand, the survey was conducted in three provinces, namely Buriram, Nakhon Phanom, and Ubon Ratchathani , where majority of the households live in rural area and are dependant on agriculture for their livelihood.In the first stage, sub-districts were selected in each province.Then, two villages were chosen with a probability proportional to the size of the population.At the third stage, a random selection of ten households was made based on the list of all households in the sampled villages with equal probability,Klasen and Waibel for detailed information of the survey’s designation and implementation.

For this research, we use a balanced panel of 1220 rice farmers collected in 2013 and 2017.In this survey, the information of risk attitude is a self-assessment scale similar to the one in the German Socioeconomic Panel conducted by the German Institute for Economic Research.In this self-assessment, the respondents were asked to self-evaluate their risk attitude on a shown scale ranging from zero to ten.Although this kind of self-assessment might not perfectly reflects risk attitude, it has been validated as an appropriate indicator for respondents’ risk preferences and has been widely applied in studies on risk preferences.With regard to shock experience, the respondents were asked to report shock events that they experienced in the reference period “Was your household affected by any of the following [events] between 1st May 20XX to 30th April 20XX”.The length of the reference period was defined by the gap between the current and previous waves.In this research, we focus on weather shocks , crop pests and diseases.We take the respondents’ exposure to shocks in the last 12 months into account as indicators of shock impacts such as production costs, yield, and efficiency are based on a 12-month recall period.We prevent misreported shocks of respondents by cross-checking between reported shocks and their losses due to the events.Then, we generate a dummy variable of households who are exposed to weather shocks,dutch buckets system crop pests and diseases.These reported shocks are strongly relevant to agricultural production in rural areas in developing countries.In the TVSEP data, input costs are recorded with a wide range of cost categories such as land preparation, seedling, weeding, fertilizers, pesticides, irrigation, harvest costs, and other costs.The other costs include additional costs that do not fit any in the listed cost categories, for example, of pre-processing before selling.This study uses fertilizer volume, fertilizer expenditure, and pesticide expenditure as key variables to analyse the impacts of farmers’ risk attitudes on input applications.We use the expenditure on pesticides instead of quantity use because the data do not record the amount of pesticides.We control for price differences by using constant monetary values adjusted to 2005 prices.Besides key variables, namely farmers’ risk attitudes, rice production, and shocks, we control for other characteristics of rice farm households such as household’s demographic characteristics, farming characteristics, physical capital, and village characteristics.Table 1 provides a descriptive summary of the data.The descriptive statistics show significant differences in rice output, expenditures on fertilizers, pesticides, seedling, weeding, irrigation, and other costs, but not the fertilizer quantity, land preparation costs, and harvest costs between 2013 and 2017.While the use of inputs is higher, the rice productivity was lower in 2013 than in 2017.

The average farming area of rice farmers in Thailand is about 3.24 hectares , and approximately two household labourers engage in farming activities.The experience of shocks appears to be different over time.Particularly, farmers reported more weather shocks in 2013 but almost the same level of crop pests in 2013 and 2017.Overall, farmers who experience shocks appear to significantly have lower rice yield, lower expenditure on land preparation, higher expenditure on fertilizers, pesticides, seedling, and other costs, while fertilizer use and expenditures on weeding, irrigation, and harvest are not significantly different.Households experiencing shocks have larger farming areas and more household members engaging in agriculture than non-shock households.Households with shock experience also tend to have a lower level of willingness-to-take risks than the households without shock experience.Table 2 shows the demographic characteristics, farming characteristics, physical capital, and village characteristics of rice farmers in Thailand.The average age of the households’ head is about 60 years old with around five years of schooling.The household size and dependency ratio are significantly different both between 2013 and 2017 and between shock and non-shock groups.On average, rice farm households in Thailand have about five members.The average distance from farmers’ house to all land plots is 2.23 km.The village characteristics show that the vast majority of households in rural Thailand have access to electricity , but only a small percentage of them have cable internet at home.The instrumented risk attitude variable shows a negative impact on input applications with a significance at less than 10% level.This implies that both fertilizers and pesticides can be considered risk-reducing inputs in rice production in Thailand.The estimations of fertilizer use in both quantity and monetary values show almost the same effect of farmers’ risk attitudes on the application of fertilizers.In other words, the more the farmers avoid risks, the more they apply fertilizers and pesticides.This also points out that becoming more risk-averse influences them to apply more inputs, even though these applications are improper.Our results remain consistent with lagged values of risk attitudes from the previous waves.Compared with a similar rice exporting country, our results of the correlations between risk attitude and input use support the findings from Salazar and Rand that fertilizers are risk-decreasing inputs in Vietnam, but pesticides have an opposite role.This difference can be because of the intensive level in rice production between the two countries or the biased results from the endogeneity problem unaddressed in their estimation.In short, uncertainties motivate rice farmers to use more fertilizers to enhance crops production because of their aversion behavior to losses.Besides, Salazar and Rand found that droughts negatively affect pesticides use.This is contrary to our findings.

Dietary changes are driving the percent land use changes for rice and specialty crops

Several articles discuss how smart farming practices could narrow the productivity gap between developing and industrial countries by increasing competition and raising the standard of living Though much of the focus of smart farming constructs is on the fusion of analytical and mechanical innovations and the potential benefits for agricultural production, smart farming will also drive changes in societal structures, the economy, business models, and public policy as it relates to agriculture.Lombardi et al.and Klerkx et al.argue that social innovation initiatives brought about by smart farming could provide opportunity to strengthen relationships among rural populations, improve social networking and engender a new sense of ‘responsible professionalism’, which may prevent rural marginalization.On the other hand, innovative changes could have negative socio-ethical implications, such as widespread technical unemployment due to automation, cultural changes in farming practices from a “hands-on” approach to a data driven approach.Furthermore, farmers may experience an identity crisis, especially if they do not provide input to data driven decision-making.Other misgivings expressed by Bronson are that research and investment in smart farms are biased towards large-commodity crop farmers,strawberry gutter system and do not address the needs of medium-sized and small-sized farm holders.Smart farming solutions in the U.S.and Canada have created ‘lock-in’ technologies, for example a packaging of proprietary crop seeds, specialized fertilizer and pesticide combinations, sensor monitoring systems and software that contains hidden algorithms to manage the data from the sensors and have been used to maximize crop production.Today, the product service system is a common business model in many industries and is closely linked to innovation and sustainability of businesses.The PSS facilitates monopolistic opportunities for large agrochemical companies.

Rotz et al.warns that historically, the consequences of advanced technologies cause deleterious effects such as land consolidation and cost-price squeeze that adversely impact small scale and marginalized farmers.Marketing and distribution are critical towards a smooth transition from traditional farming to smart farming and must also be addressed to ensure successful transfer of farm-holders’ rights.Existing reviews on smart farming tend to have either a singular focus on the advanced technologies or have a heavy slant towards the political economic aspects of smart farming.This review juxtaposes technological advantages and disadvantages of smart farming with social benefits and social challenges by comparing the status of smart farming solutions between the U.S.and South Korea, 1) beginning with a discussion of agricultural resources and production systems; 2) briefly describing the challenges facing sustainable agricultural production; 3) investigating the frameworks and reasonings for the smart farming solutions developed; and 4) identifying the potential positive and negative impacts that could result from the implementation of smart farming solutions.A discussion of each of these four topics as they pertain to either the U.S.or South Korea provides insight as to reasoning for each country’s approach to smart farming solutions, predicted benefits and potential negative impacts that smart farming could have on the actors involved in agricultural production.The research method used in this study was a literature survey, searching on Scopus and Science Direct databases using “Smart Farming” in the title and key words of published journals.Agricultural data was also collected from FAOSTAT, USDA-NAS and USDA-FAS, news articles, country reports, and books.The data was used to provide a comparison of agricultural resources, challenges, and approaches to smart farm solutions between the U.S.and South Korea to understand each country’s reasoning for pursuing smart farming solutions.Because there is a dichotomy in opinion regarding the positive impacts from the technological advances of smart farming and the potential negative societal impacts, this article includes a description of the positive and potential negative impacts from the two different approaches pursued by the U.S.and South Korea.Information is also provided from the field experience and communication that the authors have in working with producers and agriculture industry members within their own country.

In 2020, approximately 363 million ha, 37% of total land area in the U.S., was under agricultural production with more than 2 million open-field farms in operation.At least 34% of the farmed area was cultivated with grain crops for animal feed, such as corn and sorghum, while acreage in soybean and wheat were roughly 25% and 13% of the total cultivated area, respectively.Acreage for orchards, vegetables and melons represented less than 3% of total acreage in production, but these crops contributed to more than 24% of the value of the principal crops grown in the U.S..Spatial distribution of these major crops shows that grain crops are grown mostly throughout the Midwest and in the Northern and Southern Plains regions.Cotton and soybeans are grown mainly in the southern region, while specialty crops are more abundant in the coastal regions near California and Florida.The average U.S.farm size in 2020 was 180 ha , and the trend continues towards larger-sized farms.Organic farming is important to mention as it represents 5% of agricultural sales and annual sales have increased by 31% between 2016 and 2019.Certified organic acres operated in the U.S.in 2020 totaled 2.23 million ha.Of this acreage, approximately 1.42 million ha produced organic crop commodities.The reported area dedicated to food crops under greenhouse production was 1,321 ha.Most crop producing farms in the U.S.are family owned , and many families are members of agricultural cooperatives, existing as independent private businesses to enable better access to financing, supplies and markets.In South Korea, approximately 22% of land is arable, while the remaining land is mountainous or urbanized.Agriculture in South Korea strives to combine cultural heritage, societal needs, while emphasizing adaptation to local conditions and maintaining rural livelihoods.The total area cultivated for agriculture in South Korea in 2019 was 1.58 million ha, representing a decrease of 29% from 1975 mainly due to land development for industrial complexes and residential housing.While agricultural acreage overall is decreasing in South Korea, farm size in the past 45 years has been increasing from 0.94 ha to 1.57 ha.Acreage for rice paddy fields has also experienced a downward trend in the past 45 years.However, rice continues to be the dominant crop grown in South Korea.In 2020, 52% of the total agricultural area was planted with rice and the remaining 48% of agricultural acreage was diversified towards production of other grains, vegetables, fruits, specialty crops, and flowers , data is from FAOSTAT.While the cultivated area in the open fields decreased, the cultivated area in protected facilities increased by 7.2% per year since 1979, and the absolute acreage in 2016 was approximately 83,629 ha.

Fifty percent of the greenhouse acreage is dedicated to vegetable and fruit production, 27% is relegated to condiment and root vegetables, 10% is dedicated to leafy and stem vegetables, 9% is devoted to fruit trees, and the remaining 4% is for flowering plants.The spatial distribution of the main crop types produced within the major provinces are shown in Fig.4.In the U.S., river systems, reservoirs and aquifers play an important role in supplying water for everyday life.Total water withdrawals from surface and groundwater sources in the U.S.per day in 2015 were approximately 1.22 billion m3.Roughly 70% of the freshwater withdrawals are from surface-water sources making precipitation and snow pack data essential for supply forecasting of surface-water sources.Major withdrawals in the west are predominately for irrigation, while those in the east are for thermoelectric power.Daily withdrawals for agriculture represented 39.7 % of total water use in the U.S.in 2015, of which nearly 50% are from groundwater sources.Dam structures have been used to increase water storage capacity and distribution for agricultural production and to decrease climate uncertainty.Pressurized irrigation systems, mostly center pivot sprinklers, dominate the method of application to irrigated acres across the U.S..Total annual water resources in South Korea amount to approximately 132.3 billion m3.Annual water use in 2014 was reported to be 37.2 billion m3.Water use among agricultural, industrial and household sectors were 40.9%, 6.2 % and 20.4 % of the total annual water used.Since two-thirds of the topography in South Korea is mountainous, most rivers drain into reservoirs built to store runoff and supply water during the dry season.However, a constant supply of quality water is difficult to manage as roughly 43% of surface water is lost through evaporation and soil penetration, while during the rainy season,grow strawberry in containers run off is lost in floods and estuaries.Data summarizing natural resources of land and water are shown in Table 1.Throughout the U.S.there is competition for water between sectors and states.Governance of water is different in each of the fifty states.Historically state laws address statutory guidance for water use and quality, but governance policies, ownership type , and levels of enforcement vary from state to state.In many states, groundwater management districts comprised a variety of interest groups and local farmers establish management plans for conservation, recharge and preservation of groundwater resources for municipal and agricultural water use.Limited quality water resources due to the depletion of groundwater from the Ogallala Aquifer in the Great Plains region in south of Nebraska, and drought conditions in the western and south-central U.S.continue to threaten crop production and reduce natural stream flow and snow pack.

In South Korea, rural regions are vulnerable to water deficits in irrigation districts due to seasonal variations in precipitation and water quality issues.Estimation of agricultural water demand is critical for long-term planning and management.In recent years, available agricultural water resources were gradually diminished due to water shortages caused by drought and heat waves.Climate variability also makes it difficult to estimate supply and demand.Climate variability and climate change have altered the distribution of water storage and water fluxes in the U.S..Hydrologic vulnerability maps show that temperature and potential evapotranspiration consistently project a high vulnerability of the western states to climate conditions.Direct effects of climate change on crops and livestock include an increase in: annual average and seasonal air temperatures, growing season length, number of hot days and hot nights, variable precipitation patterns, and higher concentrations of CO2..It is estimated that these effects on crop production will continue to be spatially and temporally variable across the continental U.S., especially across counties in the Midwest where grain crops are the predominant crop type.It is generally accepted that in some regions, predicted yields will increase while in other regions, yields will decline.States in the northern part of the country are expected to see an increase in precipitation along with an increase in air temperature and growing season length.Yu et al.projected that by 2050, increasing air temperature due to climate change will lead to a yield decrease in corn and soybeans in the U.S.by at least 13% and 57%, respectively.This forecast assumes that climate-neutral bio-technical changes will continue to increase corn and soybean yields at annual rates like those in the past 45 years.Suttles et al., using SWAT simulations, projected that stream flow would increase causing flooding, while base flow will decrease leading to extremely low flows in all future scenarios of land use and climate change in the southeast U.S.Changes in climate and groundwater storage will affect future irrigated areas and likely affect public policy.The Korean peninsula is also highly impacted by climate change.For the past century, the average ambient temperature in South Korea has risen by 1.1 °C , and precipitation has increased by almost 160 mm annually.Furthermore, there is a growing trend of longer summer and shorter winter seasons.Currently, South Korea experiences a 4 to 6-year cycle of extreme droughts and rainfall events that result in extreme heat waves and flooding under the East Asian monsoonal circulation.The country’s exposure to extreme conditions including total annual precipitation, daily maximum rainfall, drought duration and drought severity is projected to continue to be spatially variable and occurrences are likely to increase if greenhouse gases continue to be released at their current rate.The agricultural sector contributes nearly 3.4% to the total GHG emissions in South Korea, of which 58% is from crop cultivation and 42% is attributable to livestock farming.Using long-term spatial and temporal data, Nam et al.showed that significant differences in annual reference evapotranspiration have occurred in the Midwest and Southwest regions of the peninsula since the early 1970’s.Considering the current status of temperature, precipitation and extreme climate events in South Korea, a long-term outlook suggests marked differences in the South Korean agricultural geography after 2050.Unexpected environmental variables increase year by year and continue to threaten food security in South Korea.The scientific and Technological Prediction Survey suggests that water and food shortages are linked to the intensifying trend of climate warming, and that the current situation of abnormal climates are megatrends, because they are ultimately related to agricultural production.

Articles studied either one or various arthropodrelated ES and EDS

While these changes have a positive effect on the ability of lower caste groups to attain resources and engage in dairy farming , it also shows that 48% of the HHs participating in this study had no livestock, and 6.8% kept livestock only temporarily in contrast to the past.This also suggests that those who cannot afford intensive livestock production tend to reduce their livestock rearing or to rear small ruminants as needed, thus indicating marginalization.In view of the above, it is necessary to re-assess current approaches in ongoing WDPs as intensification and specialization, do not necessarily result in higher economic performance, especially in biophysically constrained environments such as dryland areas.Our reason for emphasizing the biophysical aspect is that, despite the better standards of socio-economic and infrastructural conditions in Telangana , the lower economic performance in farming is still observed and across all farming systems.We therefore suggest considering alternative development strategies for HHs, such as “area-wide integration”, feed self-sufficiency, or farm diversification to triggering better economic results or enhance the viability of farms in the long term , particularly in environmentally constrained regions.Further, to manage the dynamics of intensification and specialization in farming systems , the institutional capacity-building at the village level in WPDs should be strengthened with new information and approaches.This is well demonstrated by some civil society organizations, using community engagement approaches and tools.Such approaches, combined with science-based evaluations of ongoing programs,flood tray could help avoid the implementation of conflictive technological development and create knowledge about complex social-ecological processes.

This approach could also facilitate an interactive learning space and promote local innovations by tapping local or traditional knowledge systems to improve the management of dryland environments.In all, we urge the need for interdisciplinary research to assess the relative feasibility of varied farming systems in dryland conditions, the socio-economic impact of agricultural intensification in dryland ecosystems e.g., indebtedness and access to credit, HH dietary diversity or gender implications.Also, we encourage the implementation of mechanisms that can facilitate continuous research on farming systems development and their economic and environmental performance.This will help to better anticipate farming systems trajectories and the potential effects of development strategies, also those within the WDP operational framework.Worldwide, agriculture is facing a double challenge of increasing productivity and developing more sustainable ways of food production.Small-scale farming practiced on relatively small plots of land is the most dominant form of agriculture, constituting more than 70% of the global food production entities.Family farmers with small landholdings represent about 80% of the world’s farms and account for 85% of global population involved in agriculture , mostly in low and middle-income countries , with strong strain on natural resources and pressing concern for food security ; and addressing multiple goals and targets contributing to achieve the Sustainable Development Goals.Although widely used, a unique and unambiguous definition of smallholder farming still remains to be established.It currently relies on several criteria, mostly related to land endowment , labor productivity and income.The definition of smallholding is however context-dependent and can vary according to socio-economic, technological and agroecological realities.

SHF systems are highly diverse in terms of climatic, ecological and socioeconomic conditions as well as in their structure and functioning.Still, these agroecosystems share certain properties like high levels of biodiversity and complex landscape composition , key role of family-managed farms in supporting local livelihoods , management methods tightly related to rich local knowledge system or shared cultural values in common social organization and strong adaptability to changes, sometimes in high risk environments.These agrosystems are also a leading representation of human-nature interactions and feed backs, encompassing material and non-material benefits for humans as well as threats or unfavorable outputs.As for other ecosystems, long-standing interactions within SHF and their ecological functions provide direct and indirect fundamental benefits to humans, through supporting, cultural, provisioning and regulating ecosystem services , 2005.Because of the strong interconnected natural and agricultural features in SHF, unsustainable practices may undermine ES on which smallholders depend to meet urgent needs in contexts of great vulnerability and weak institutional support.Food production on SHF is strongly linked to biodiversity-derived services as increasing the levels of artificial inputs is not economically viable for resource-constrained households.Therefore, options to maintain or improve production are rather linked with improvement of the amount and integrity of ecosystem regulation and supporting services , 2013.Food production, especially in SHF, depends on a wide range of ecosystem functions including nutrient and water cycling, pollination, competitive interactions, and matter decomposition.These functions are fulfilled by several agrobiodiversity components, particularly arthropods.

To date, research on arthropodrelated ES has mainly focused on well-known functions and performed by charismatic or iconic groups such as butterflies, hymenoptera or beetles , even though a large part of global crop production depends on pollination from bees and wild pollinators.Pollination also contributes to economic welfare and to rich and meaningful cultural and spiritual life for a large population.Along with pollination, biological control is one of the most studied services as it implies high economic impacts for agriculture because parasitoids and predatory arthropods contribute to controlling pest insects in crops.In contrast to ES, ecosystem disservices are defined as ecological elements, functions and processes affecting negatively human well-being, directly , by intermediate of negative impacts on ES or by reinforcing other EDS.EDS scope on ecological phenomenon linked to negative outcomes affecting human well-being, which must be differenciated from the associated detriments or costs resulting from human actions on ecosystems.In agricultural systems, EDS affect functions and productivity, leading to important crop losses.These disservices such as herbivory or competition for resources have also been extensively studied, establishing a dominant viewpoint where insects are predominantly perceived as crops pest and harmful to anthropogenic environments.Nevertheless, as stakeholders’ actions may be largely driven by greater perception and willingness to reduce EDS , arthropod management for either mitigating EDS or enhancing ES can also be a powerful driver for transition towards sustainable agriculture in smallholder systems.In particular, promising results on agroecosystem management towards more sustainable agriculture have been reported when including ES-EDS synergies and trade-offs.To date published evidence on the relationships between arthropod related ES and the sustainability of agricultural practices has been largely based in research from high-income countries and temperate regions.

Moreover, a combined analysis of services and disservices of arthropods in SHF systems has still to be performed for balancing positive and negative impacts of nature on human well-being and for reframing entomological research to achieve the SDGs.To address this issue, we performed a literature review capturing research trends in insect-related ES and EDS in SHF, detecting knowledge gaps and exploring to what extent these studies are conducted within a transdisciplinary framework.In particular, we were interested in research practices in SHF considering ES and EDS in a multidimensional view of agroecosystems and bringing together diverse knowledge systems, especially between academic and farmer communities.We conducted a systematic multilingual review of the scientific literature in peer-reviewed journal articles published between January 2015 and January 2021.We followed the systematic literature review approach and the six steps protocol commonly used for scientific review.Detailed steps of the process are described in Appendix A.We first determined the research scope with the PICOC framework.We identified concept groups for keywords from the terminology identified in PICOC and then ran a ‘naïve search’ for identifying search terms through an automated approach using the litsearchr R package version 1.0.0.Then identified terms in the three languages were searched in different databases covering a broad range of academic contexts: Web of Science , Scopus , BASE , and Scielo.The search string was a compilation of keywords of four main domains: Arthropods, Agriculture, Ecosystem services and disservices,ebb and flow tray and Smallholder farming.Keywords were searched in aggregated quests, progressively filtering articles, thereby giving us an idea of the shared publications of each sub-theme in the overall literature on arthropods.Overall, we retrieved 454,703 records on arthropods, of which 40,720 were related to agriculture.Among them 14,967 articles were related to ES or EDS, of which 1564 concerned SHF.As diversified international databases and collection of published scientific research help cover citations more widely , especially for countries in L&MIC, we included bibliographic resources from other scientific search engines, scientific libraries and scholarly journals platforms as Dialnet, PKP Index and AJOL , using the four main keywords groups repeatedly in the search process.Finally, we conducted a complementary approach of citation tracking by backward snowballing using articles’ reference lists.We retrieved 57 additional references, leading to a total of 1621 articles.All references were compiled into a unique bibliographic database organized and arrayed to eliminate duplicates and misreferenced entries using the revtools v.0.4.1 and synthesisr v.0.3.0 R packages.Article titles and abstracts in the resulting database were subsequently screened to complete inclusion-exclusion procedure according to predefined criteria.

We excluded publications whose focus was not relevant to SHF systems or for which insect sampling was not done under real world conditions.This also implied excluding studies about intensive and high-input farming systems and those located in HICs.Moreover, we excluded papers in which insects were not associated to any disservices or EDS.After this selection process, our database included 172 publications.These were selected for full screening and qualitative assessment, after which 122 publications were kept.The remaining 42 articles were excluded in the last full-text reading step when arthropods were not explicitly mentioned or ES and EDS were not clearly addressed.For the final data extraction step, we registered in separate subset datasets all information related to ecosystem services , entomofauna and farmer knowledge and perceptions.Besides bibliographic default metadata, we registered data about country, income level and study system as well as scientific methodology variables.We defined four main thematic to analyze the articles listed in the final database and extracted information on arthropods, their services and disservices, farmers’ knowledge and actions related to arthropod management; the transdisciplinarity approach of the research.First, we examined the taxonomy of arthropod communities and at which spatial scale they were studied.This issue is important when assessing arthropod-related ES and EDS as understanding arthropod dynamics typically requires studies at the landscape scale.For this, we reported which habitats were included in the study.Second, we used the four Millennium Ecosystem Assessment’s EDS were visualized through a network analysis using the R bipartite package.In addition, we extracted diversity data of arthropod taxa related to ES or EDS.Third, we gathered information on the type of farmers’ knowledge and associated management practices regarding arthropods in their farming systems.We also recorded all actions mentioned in the studies for subsequent classification of values based on arthropod management strategies  and whether chemical pesticides were used.Fourth, we analyzed to what extent the research works had been developed through a transdisciplinary approach.Transdisciplinarity addresses relations between science and society, making transformations from science building process and involving stakeholders since the first stages of research process to better target problems.To assess whether research processes encompassed knowledge co-construction and sharing, we set a farmers’ participation index adapted from the typology proposed by Pretty and Brandt et al..The five levels of the FPI reflect the degree of involvement of farmers in research process, from an absence of farmers or no implicit participation to a shared and coordinated implication of farmers in research.In addition, we identified the person involved in arthropod identification.All statistical analyses and graphs were performed using R 4.0.4.The 122 selected studies were conducted predominantly in SubSaharan Africa , Latin America & Caribbean and East Asia & Pacific.Overall, 44% of the studies were conducted at a regional scale, 39.0% focused on local scale and 15.0% covered national or transnational scales.In total, 79.5% of the publications were English-language performed, followed by Spanish or bilingual version English/Spanish and French.Research disciplines concerned mainly “Agriculture and Agronomy” , Ecology-Biologyand Entomology , with a low occurrence of studies belonging to social sciences, economics or multidisciplinary approaches.The majority of publications focused either on crop fields , agroforests or crop storages , encompassing 68 different crops.In most cases , those systems were polycultural with monoculture and mixed systems representing 22.2% and 17.1% of the studies, respectively.Most works studied insect-plant relationship only at the plot-level and only 29.8% included the surrounding habitats.Because several services could be analyzed in a single study, the total number of studied ES and EDS was higher than the total number of studies.Most studies focused on regulating ES and EDS.Only 6.86% of services referred to cultural services, and even fewer to provisioning and supporting services.Overall, 16 main categories of ES and EDS were covered.

Weather and climate-induced costs on social and economic systems are substantial

Access to infrastructure is considered to influence the feasibility and efficacy of aid distribution programs in response to disasters and used to represent physical capital.Given that better access to power services may reduce the impacts of winter storms by providing alternative or additional assistance, access to facilities was used to represent physical capital.GIS data on power plants and facilities were obtained from the U.S.Environmental Protection Agency’s Facility Registry Service and Iowa Facility Explorer.The interviewed farmers also reported that a major winter storm loss on farms was from animal death caused by inadequate feed.Thus, feed supply was also considered as a physical capital indicator and represented by the 2012 feed expenditure data collected from USDA QuickStats.Human capital.Labor is considered to make a positive impact on vulnerability reduction because more family members can increase work efficiency during both events and subsequent recovery.This study used household size and labor expense as human capital indicators to represent the availability of labor engaged in adaptation.Education level, which is considered to increase the adaptive capacity by enhancing access to information , was also included to estimate human capital.The more skills and knowledge acquired, the more capability households have for emergency planning, recovery, and decision-making.Data on household size, labor expense, and education level were collected from the US Census Bureau.Social capital.Social organizations can improve adaptive capacity by enhancing social networking.Households with a membership to farm-related organizations are more likely to receive support or benefit from the professionals.To obtain information on membership with the agricultural organizations, a request was submitted to the contact on the Practical Farmer of Iowa website.Interview results also reveal that the reduction of storm losses can be attributed to the registration of insurance packages and government programs.More investment in government programs could provide more support during the storm recovery process.

The government program expense used in this study was retrieved from USDA QuickStats.Overall,mobile vertical farm a total of 12 adaptivity variables, 2 sensitivity variables, and 2 exposure variables were selected for the assessment of rural winter storm vulnerability.Socioeconomic statistics and spatial information were all aggregated to the census county level and standardized to Z-scores in SPSS before further analysis.There are 29 out of 60 significantly correlated pairs with a p-value of less than 0.050, indicating strong interrelationships between indicators.Hence, these indicators are considered suitable for factor analysis to extract principal components accounted for by the variable correlations.The correlation coefficients range from − 0.459 for farm income and natural shelter to 0.788 for farm income and labor expense.Counties planting more trees appear to receive lower income.Labor can increase farming productivity and, at the same time, require more investment, leading to the strongly positive relationship between farm income and labor expense.There is also a strong correlation between membership counts and education, indicating that counties with higher education levels are more likely to subscribe to farming associations.Among the selected 12 variables, poverty, energy, internet operations, and household size yielded low community values , suggesting that they would be weakly reflected via the extracted factors and thus be removed from factor analysis.Finally, with the remaining 8 variables, factor analysis extracted the first 3 factors that could yield a total of 85.124% of total variance explained , with an acceptable KMO value of 0.627.The Bartlett’s Test was statistically significant, indicating the high independency among the 8 variables.The loadings matrix in Table 5 shows the correlations of each variable with the three extracted components.Those with loadings greater than 0.800 are considered as salient indicators representing the three underlying dimensions of adaptive capacity determinants.The first factor is interpreted as farming economic status based on its salient indicators of labor expense, farming facilities, and farm income.This factor is considered to project adaptive capacity more accurately as it accounts for the largest total variance of the input variables.Economic conditions may be the most important determinant of adaptive capacity, probably because economic resources can facilitate technology implementation, ensure training opportunities, and lead to political influence.The second factor has high loadings on natural shelter and government programs, hence it is explained as environmental institutional capital.This factor may suggest a strong correlation between institutional efforts and the enhancement of environmental services.

For example, through general or continuous funding, the state of Iowa has a variety of conservation programs aimed to provide cost-sharing for tree planting on a highly erodible row crop and pasture land , potentially increasing farmers’ adaptive capacity to winter storms.The third component is highly correlated with education and organization membership.These indicators representing human capital and social capital are considered to affect innovative performance.Therefore, innovative capital is reasoned as the theme for the third component of adaptive capacity.The overall exposure rates are high in Northwest and Southeast Iowa due to high event frequency.This is consistent with the long history of severe winter storms and blizzards recorded for these regions.In contrast, eastern Iowa shows the lowest exposure scores.Sensitivity indicator scores were calculated by summing the standardized variable scores for animal sale and building age.As shown in Fig.4, counties peripheral to central Iowa tend to be more sensitive due to a high percentage of the total sale from animal commodities.From East to Central Iowa, the counties are light-colored, indicating low rates for building age and animal sale.This contributes to the notably least overall sensitivity for Polk County and its surrounding counties.Several counties score high in animal sale and/or building age, leading to their high overall sensitivity scores.Fig.5 shows the overall adaptive capacity and individual factor scores.Figure 5a shows that the adaptive capacity is low in most northwestern counties in Iowa and high in central Iowa and northeastern margins.It is noted from Fig.5b that counties in northern Iowa have higher rates for farming economic status as they have higher labor expense, farm-related income, and farming facilities than counties in the southernmost part of Iowa.Sioux appears to have the best farming economic status, as opposed to the metropolitan regions where farming-related investments are low.Fig.5c shows that the northwestern quarter of Iowa is low in environmental institutional capital, with limited natural shelter and low expense on government programs.This may be because the long-standing large tracts of wetlands concentrated in the northwest and north-central parts of Iowa have provided rich farmland for growing intensive crops.The increase of mono-cultures and the decrease in livestock pastures in the northwest could lead to the destruction of windbreaks.The patchwork of small, diversified fields that once were common remains in southeastern Iowa.

In northeastern Iowa, the rugged landscape with more wooded areas may have prevented farms from expanding to large industrialized operations, resulting in high index scores for environmental institutional capital.Fig.5d shows a concentration of innovative capital in the metropolitan areas of central Iowa and cold spots in northwestern and southeastern Iowa.Fig.6 illustrates the overall vulnerability for all Iowa counties calculated using the overall exposure, sensitivity, and adaptive capacity scores.In general, southern counties such as Adams and Union are remarkably vulnerable to winter storms, perhaps because much of their land areas in southern Iowa is used for perennial pastures , increasing their sensitivity.Highly vulnerable counties are also clustered in the Northwest where winter storm events are more frequent and in the Southeast where winter temperature deviation is higher, both reflecting high exposure.The vulnerability is low in central Iowa due to low sensitivity from East to Central Iowa, in particular in Polk and its adjacent metropolitan areas.Counties with low vulnerability are also found in northeastern Iowa where adaptive capacity is higher.Among different disaster types, winter storms receive limited attention, while they cause non-negligible costs.In Iowa, there appears a generally increasing trend in experiencing winter storm events, indicated by more above-average event occurrences in the recent past.Evaluating the vulnerability of farming communities to winter storms in Iowa has implications for identifying counties’ agricultural production prone to winter storms and thus reducing farm loss during winter storms by managing the vulnerability components, namely, exposure, sensitivity, and adaptive capacity.Exposure can be influenced by the increased population and assets at risk as a result of population growth in locations at risk from natural hazards , and storm impacts are likely to be worse in more populous areas than others.However, Polk County – the most populous county in Iowa – rated the least vulnerable to winter storms,vertical farming racks whilst it has relatively high exposure.Its low score in vulnerability may be due to their industry-oriented development that is more resistant to winter storms than farming activities.This indicates the severity of weather events is not necessarily consistent with the population pattern alone as it may vary depending on the specific disasters or economic structure.To explore the issue further, the difference between vulnerability level and factual on-farm loss in 2012 per county was calculated and illustrated in Fig.8.After scaling to the range of 0–1, the overall difference ranged from 0.009 for Johnson County to 0.88 for Van Buren County.

Counties graphed in the left half of Fig.8 show almost identical distributions of farm loss and vulnerability.This implies the selected indicators for winter storm vulnerability in the current study may be used to effectively evaluate the general farm losses for these counties for a given year.It is found the metropolitan county of Story has non-negligible farm loss and underpredicted vulnerability.This suggests the limitation in the current model that is unable to capture all critical factors to determine the area’s general farm loss.For example, farming intensity may scale the loss but is not considered in the model.Agricultural production characteristics such as the quantity of products vulnerable to other storm events as well as meteorological variability such as winter storm occurrence may also contribute to the discrepancy between empirical farm losses and predictions.To account for all counties’ general loss characteristics determined by factors not included in the current winter storm vulnerability model, the 2002-2017-census-year average farm loss was calculated.Several counties in the left half of Fig.8 show small differences between farm loss in 2012 and average farm loss, indicating these counties have relatively stable farm loss patterns and the current model can be used to evaluate their long-term general farm losses.On the other hand, counties displayed on the right half of Fig.8 reveal large differences between the predicted vulnerability and farm loss in 2012.This may be due to meteorological variability and generally low farming loss.For example, Hamilton County has a high difference value between the predicted vulnerability and farm loss in 2012 but a low difference between the predicted vulnerability and average farm loss, suggesting the model may not be suitable to predict farm loss for certain years due to variable winter storm occurrence.Van Buren County shows a high difference value between the predicted vulnerability and farm loss in 2012.Yet its average farm loss and farm loss in 2012 are equally low perhaps due to its low farming intensity resulting in consistently low farm losses.Key ways to reinforce adaptive capacity and reduce sensitivity include providing incentives for diversification and tree planting programs as well as enhancing innovative capital, facility investments, and subsidies.The high winter storm vulnerability may be reduced in northwestern and southeastern Iowa, where farms rely heavily on pastures and receive more winter extremes and anomalies through increasing environmental institutional capital, such as engaging more nursery professionals in vulnerable areas to assist livestock farmers who want to plant trees and shrubs.Innovative livelihood strategies such as diversifying income into other sources may be helpful for economic development in the Southeast.In southern Iowa with poor farming economic status, subsidies and facilities can also play an important role in offsetting the negative impacts of financial problems.Previous studies have shown that the spatial resolution of census administrative boundaries is the principal factor affecting map accuracy.Indicators presented at an aggregated level may be unclear or distorted.As a result, the use of census data at the county level which includes metropolitan areas can affect vulnerability patterns for farming communities as it fails to distinguish urban-rural contrast in terms of farming characteristics.To address the issue, the three vulnerability components scores for rural Iowa were also calculated and mapped exclusively for rural counties.By comparing it with Figs.3–5 that include non-rural counties, it is observed that the exposure pattern remains the same and few significant pattern changes are found for sensitivity.