One such simplification will be to focus on a single crop for any given region

The relationships between crop yields, weather and climate have been the focus of a great deal of attention in the Earth system science literature.This is due to concerns about securing food supplies for our growing populations and the potential challenges that climate change poses.Most studies have been concerned with establishing the current relationships between climate and crop yields, or making projections about changes in crop yields due to future climate change rather than extending this approach back into the past.Where historical information is used, it tends to be on a relatively recent time scale.Recently, researchers have attempted to infer the location and intensity of agricultural production during the Holocene on a global scale.These estimates are ultimately derived from estimates of past population sizes and make assumptions about how human populations use land for agriculture.Although such studies should be applauded for their ambitious scale, they have a number of features that make them less-than-ideal for our purposes.First, in order to test certain theories it is desirable to separate out achieved production and population from potential production and population.A number of interesting hypotheses about human social and political evolution invoke “population pressure” as a key variable in causing changes in human societies.For example,grow table hydroponic demographic-structural theory , argues that state instability and societal collapse is a result of the pressures on resources from population growth, which, in turn, leads to population decline.

Boserupian models of agricultural change, mentioned above, see agricultural innovations themselves as resulting from population pressure.Second, this approach does not make full use of the historical and archaeological information about past agricultural systems that could potentially inform estimates of productivity.Finally, the data on past population are fairly rough estimates, and are typically made at the coarsegrain level of a province or whole country.There is always some degree of uncertainty associated with these estimates, and unless handled with care, such an approach can indicate a false level of precision, given the data that are being used as inputs.In order to understand the impact of agriculture and increasing productivity on human societies, we need a “bottom-up” approach that estimates productivity or potential productivity independently of population size.Of key theoretical interest is using this information to estimate the carrying capacity of a given region.For our purposes, we define carrying capacity as the maximum human population size that can be supported in a given unit of space.It is a function of the physical and biological characteristics of the region being examined and is also dependent on the types of agricultural technology and techniques possessed by the population that affect the productivity of the crops grown in that region.Carrying capacity is something that can be calculated across agricultural systems and, therefore, facilitates comparisons between different time periods and regions.Furthermore, it is an important variable because it enables us to compare the actual population to the size of the population that could possibly inhabit such a region, including cases where there is a substantial mismatch between these two estimates.This can provide a measure of the population pressure a society experiences.

Mismatches could also reflect cases where a surplus is produced in order to guard against shortfalls in some years or where a substantial proportion of productivity is diverted to elite members of society.In the former case, we would expect actual population and a measure of carrying capacity that took into account annual fluctuations to converge over longer time periods, whereas this would not be the case in the latter example.The measure of carrying capacity can include technological or other cultural features that affect crop productivity.Therefore, over suitably long time periods and geographic scales, this estimate of carrying capacity will also provide a measure of relative agricultural productivity.In other words, in the absence of direct assessments of actual productivity, this measure is still likely to be informative about which regions and time periods were more productive than others.Such a measure is also extremely useful for testing many hypotheses about socio-cultural evolution.Previous work has attempted to calculate carrying capacity for hunter gatherers , which is a somewhat more straightforward task than for agriculturalists.This is because foragers’ sources of food are determined primarily by external climatic conditions and other characteristics of the physical environment, such as “unearned” sources of water, including rivers, which enable plant growth in otherwise arid environments.Although such climatic and environmental considerations are obviously important for agriculturalists, calculating agricultural carrying capacity has a number of added complications.One such factor is the characteristics of crops.Hunter-gatherer population densities tend to be highest in tropical regions with high temperatures and greater amounts of rainfall, i.e.where net primary production is high.On the other hand, large agricultural populations can be supported by grain crops derived from wild grasses.

Cereal productivity, and, therefore, agricultural population density, tends to be greatest when annual patterns of rainfall create seasonal climates that allow grains to dry properly , which is generally at higher latitudes.For example, in island Southeast Asia, rice productivity is highest in regions such as Java, where monsoon conditions create a more distinct dry season.Humans are also niche constructors par excellence , and agriculture is probably one of the most dramatic representations of our ability to substantially modify our environment and, thus, reduce or ameliorate the impact of external environmental factors.Artificial selection has also been a key process in improving crops and increasing yields over time, so having information about historic cultivars and varieties is of great importance.In addition to these crop characteristics, another important determinant of agricultural productivity is the level of agricultural technology and the specific agricultural practices that enhance productivity, which have varied dramatically in time and space.We return to this issue below.The fundamental idea behind this approach to estimating carrying capacity is to construct a function that predicts crop productivity based on a variety of theoretically informed inputs, the parameters of which will then be estimated and empirically validated.This estimate in terms of energy can then be converted into a population estimate based on an understanding of the energy requirements of human populations.In both cases, calibration and validation will require historical information about past crop productivities, ideally with as broad a geographic and temporal distribution as possible.Figure 2 shows examples of changing productivities of two cereal crops in two regions in Europe.In both cases, productivity has increased, but to what degree these changes are due to changes in climate, technology, or genetics needs to be assessed.Obviously, estimating potential agricultural productivity on a global scale and over long time periods is not an easy task.In order to make this task manageable, it will be important to employ a number of simplifying assumptions and strategies.

Because we are interested in assessing the amount of energy produced, a reasonable starting point is to focus on the major carbohydrate source grown.For example, based estimates of potential pre-Hispanic productivity in the valley of Oaxaca using only information on a single crop, maize.Previous experience with calculating carrying capacity in Europe suggests that reasonably accurate estimates can be obtained just by using a single crop such as wheat or rye.The focal crop will, of course, vary from region to region due to different histories of domestication and the spread of different crops.In some cases, when different crops seriously affect the estimate, it may be advisable to estimate carrying capacities based on more than one crop.In some places, ecological conditions may vary over a relatively small distance, such that one crop does well where another one does poorly.For example, Pacific islands are characterized by wet conditions on the windward sides,grow table where taro does best, and drier conditions on the leeward side, which favors sweet potato.Agricultural productivity varies in space and, importantly, in time.In recent years, a large amount of work has been conducted on historical climate change and the effects of climate on crop productivity.This work needs supplementing with information about historical crop yields and the cultural and technological factors that affect agricultural productivity.Unfortunately, such data are not readily available in the kind of systematic manner on a global scale that would aid these endeavors due to the general turn away from broad-scale theorizing and comparative perspectives in disciplines such as anthropology, archaeology, and history.Here, we demonstrate how initiative that we have developed, Seshat: The Global History Databank 2, can provide a framework for collecting the necessary information to model agricultural productivity in the past and, more generally, to test comparative hypotheses about cultural evolution and human history.Most historians and archaeologists studying agricultural systems or other aspects of human societies tend to be experts in particular time periods and/or tightly defined regions.Although there are some who argue that there are broadscale patterns and general processes shaping human history, their claims tend to rely on illustrative examples and are not systematically tested in the manner that is common in the natural sciences.However, in order to test competing ideas properly, a more rigorous way of adjudicating between alternative hypotheses is required.A barrier to such an endeavor is the lack of data of suitable quantity and quality in the kind of systematic format that is required.It is for these reasons that the Seshat project aims to work directly with historians and other relevant experts to construct a large-scale database that collates the most up-to-date knowledge and understanding of past human societies in a systematic manner.Importantly, the information is coded into well-defined variables suitable for statistical analyses so that different hypotheses can be rigorously tested.Although the Seshat approach can be applied to any aspect of human societies, in this paper, we focus in on the variables of relevance to agriculture.

As a sampling strategy, we have selected 30 regions of roughly 10,000 square kilometers from around the world that are delimited by natural geographic features, such valleys, plains, mountains, coasts, or islands.Examples of these Natural Geographic Areas, or NGAs, include Latium , Upper Egypt, Hawaii, and the Kansai region of Japan.We have employed a stratified sampling strategy such that the NGAs are broadly distributed geographically and exhibit substantial variation in the polities that inhabited these NGAs in terms of the degree and timing of the appearance of the first large-scale, complex societies.For information related to agricultural systems for each NGA, we are gathering data on variables that relate to the NGA itself and the forms of agriculture practiced there, going back as far as possible in the Holocene.In related projects, we are capturing information about all the polities that occupied the NGA during this time.This will allow us to match different sources of information about different aspects of human societies and enable us to test a range of different hypotheses about human social and cultural evolution.What information do we need to capture about past societies in order to estimate the productivity of agricultural systems? Over the last two years, members of our research team have been developing a codebook to describe the variables relating to agricultural productivity.Typically, variables in the codebook relate to the presence or absence of certain features , naming of specific features that were present , or a quantitative measure of certain features.The development of this codebook has been an iterative process, and has improved through discussing these issues with experts on agriculture in past societies.For each NGA, we examine the variables of interest during the time since agriculture was first practiced until the present day.Research assistants work with expert historians and archaeologists to identify the most relevant literature, attempt to code the variables in the codebook from these sources, and, where possible, indicate the time at which features appear or change.These codings are then ultimately checked for accuracy by experts in the appropriate region and/or time period.Currently, the variables we are coding relate to Land Use, Features of Cultivation, Technology & Practices, Conventions & Techniques, Post-Harvest practices, Food Storage and Preservation, Social Scale of Food Production, Agricultural Intensity, and Major Carbohydrate Sources.We describe each of the categories below and illustrate the kinds of variables we are capturing within them.Land use variables relate to the areas of the NGA that were either used for agriculture or that could potentially be cultivated.To give a couple of modern examples, according to the CIA World Factbook , around 25% of the total area of the United Kingdom is given over to crop production, whereas Japan, with its much more mountainous terrain, devotes only 12% of its land to producing crops.

Gillin provides interesting insight into the importance of water for the people of Moche

In Gillin’s ethnographic account of the town of Moche, he observed that many dishes were cooked or boiled over an open flame, either in ceramic or metal containers placed on an adobe brick stove or on rock supports placed on the ground. Gillin lists a variety of one pot meals, including soups, stews, or gruels, which often contained meat, maize , manioc, and/or beans. Typical kitchens contained ceramic cooking vessels, water storage jars, chicha fermentation jars, cooking hearths , fuels , woven reed or cane fans for igniting or intensifying cooking fires, grinding stones and pestles , wooden utensils, and various serving implements made from gourds including scoops, plates, and bowls. Gillin also points out that essential ceramic and ground stone implements were commonly acquired from nearby archaeological sites, and praised by the local population at the town of Moche as being the best quality kitchen tools. In my research in the Moche Valley for this dissertation project, I have been invited into homes and served meals in kitchen setups mirroring those described above, including stews of meat , maize, and beans cooked over open hearths in Chimu pots that smallholders recovered in their fields. It is likely that a variety of food preparation and processing techniques were implemented in the Moche Valley during the EIP, including boiling , roasting, steaming, parching, toasting , drying, soaking, and grinding.

Rowe describes how toasted maize, or cancha, was a popular food at the time of Spanish conquest in Peru .Water was considered important for irrigation,mobile vertical grow table food preparation, and bathing, but not for drinking; distaste for drinking water has been documented widely in the Andes . According to Gillin , many families drank chicha rather than water, and many women also used chicha for boiling meats and vegetables. It is likely that chicha production occurred regularly at domestic habitation sites in the Moche Valley in the past, for quotidian uses in addition to feasting events. Indeed, chicha would have remained potable longer through the process of boiling, and also would have reduced sickness due to contamination of the water supply. Chicha production would have required a specific set of tasks associated preparation/processing; to brew maize chicha, germinated maize is dried, ground, mixed with water, and fermented, to create an alcoholic liquid .Alongside maize, similar shifts in ubiquity values are noted for members of the Fabaceae family. Some members of the Fabaceae family present in the five Moche Valley assemblages could be identified to the genus or species level, including domesticated legumes , along with a number of weedy legumes. However, some remains only could be identified to the family level if they lacked clear diagnostic attributes to aid in more specific identification. For example, common beans and peanuts share many of the same attributes; if an attachment scar was not present, then it was impossible to determine the difference between these two taxa. As the common bean and peanut represent different genera, these specimens were recorded as “Fabaceae,” although noted as probable domesticated beans.

Domesticated Fabaceae, including common beans, lima beans, and peanuts, have low ubiquity values across the study sites , likely due to preservation bias. As beans are consumed in their entirety after cooking, they are less likely to appear in archaeological assemblages than plant foods that require processing . Indeed, no clear domesticated beans were identified in the La Poza or MV-83 assemblages . A number of partial or complete domesticated bean fragments were present in the MV-224, MV-225, and MV-83 assemblages; in addition, some specimens that could only be classified to the family level of Fabaceae likely represent domesticated forms, but lacked diagnostics to distinguish between common bean, peanut, and pacay. However, the lack of domesticated beans at La Poza and MV-83, when considered in relation to overall Fabaceae presence, may have some implication for cropping strategies. If we group all of the Fabaceae for each assemblage together and chart ubiquity values through time , we see an increase from 23.5 percent ubiquity at La Poza to 58.1 percent at MV-224. This ubiquity trend remains fairly consistent across the remaining three study sites through time, with Fabaceae ubiquity values of 56.2 percent, 55.6 percent, and 50 percent for MV-225, MV-83, and Galindo, respectively. I interpret these trends along two lines, suggesting that the increases in Fabaceae may represent increased collection/incidental intrusion of weedy leguminous taxa that grow in and along fields as maize production increased, and possible intercropping of maize and beans. As discussed above, intercropping Phaseolus beans with maize would have provided benefits to both plants; nitrogen fixation from beans benefits maize plants, and beans benefit from having the maize stalks to climb during growth .

This pattern gives us pause to reconsider rigid taxonomic distinctions that give a taxon like chenopod a quintessential ‘highland’ identity in cuisine ; rather, interaction, melding, and movement between the coast and highlands, which likely involved the exchange of resources as well as knowledge of plant cultivation strategies, contributed to the formation of middle valley chaupiyunga cuisines. Furthermore, as food ways often are divided by social status, identity/ethnicity, or context, it seems problematic to attribute such a singular identity category as ‘highland’ to a particular food taxon. Another taxon of note is cotton. While ubiquity values for cotton are low at La Poza and MV-83 , no cotton seeds were recovered in the MV-224 and MV-225 assemblages. In contrast, cotton seeds have a very high ubiquity value in the Galindo samples. This trend is noteworthy in that it sheds light on practices related to an important economic activity, spinning and weaving. The fact that no cotton seeds were recovered in either the MV-224 or MV-225 assemblages indicates that cotton fiber textile production may not have been practiced widely at these sites. This issue may be a result of preservation bias, as cotton seeds may be less likely to enter fires than food taxa; however, carbonized cotton seeds were recovered in the other middle valley assemblages , including in very high ubiquity at Galindo. Ringberg reports the presence of ceramic disk spindle whorls known as torterosand pirurosin patio spaces at MV-225, suggesting that women, or possible children and elderly of both genders, used open, well-litpatio spaces for spinning and weaving.

Although the sample size of torteros at MV-225 was small, ethnographic evidence suggests that large tortero whorls were used on the Peruvian north coast to ply heavier fibers into rope or twine . Wooden spindle whorls may have been used for this purpose as well . The lack of cotton seeds in the archaeobotanical assemblage at MV-225 may indicate that camelid fiber spinning took precedence over cotton fiber spinning during the Gallinazo/Early Moche phases. Indeed, the highland occupants of MV-225 houses and tended camelids, a tradition that continued at MV-83 and Galindo. Amber VanDerwarker found that camelids were the main source of meat at MV-83, and that households processed the whole animal for consumption, in contrast to obtaining dried meat or leg meat. These animals were likely used for their wool in addition to meat. The presence of camelids at these middle valley sites also challenges long-standing typologies that categorize such as animals as exclusively ‘highland’ in nature —as the local costeño occupants of MV-224 appear to have interacted and likely intermarried and cohabitated with serrano colonists, they likely bred and herded camelids as well for wool and meat. Future analyses of faunal assemblages from MV-224 and MV-225 will likely clarify the nature of these dynamics. With respect to ubiquity overall, Galindo witnessed a greater range of taxa that are highly ubiquitous in the assemblage as compared to the other assemblages, which are dominated by five or less taxa. However, the Galindo archaeobotanical dataset is made up of only ten samples. While this sample number meets Hubbard’s minimum threshold for ubiquity calculation , having fewer samples more severely skews frequency scores of rare taxa. As a result, I interpret rare taxa ubiquity values with caution. I will use the taxon of coca as an example. Coca is a special use taxon; it is neither ubiquitous nor abundant in the Moche Valley samples. Indeed, only one coca seed was recovered in the MV-225 assemblage,mobile vertical farm and four coca seeds total were recovered from the Galindo assemblage .

The paucity of coca in these deposits likely is related to preservation biases. Coca leaves are chewed raw , and stems and seeds are separated before the bola, or wad of leaves, is placed in the mouth for chewing. In the of context quotidian routines, coca chewing likely would have been done along walks to agricultural fields or when laboring in fields, to provide energy and to act as an appetite suppressant. Coca seeds are therefore unlikely to be burned and dropped in cooking fires and therefore are less likely to leave behind carbonized remains at domestic habitation sites. It is likely that the residents of these Moche Valley sites grew and consumed coca, particularly the residents of the Middle Valley sites; indeed, the middle valley sites are located within primary production zones for coca for the valley determined by agroecological zonation models . While conducting research for this dissertation in the Moche Valley, I frequently noticed the presence of coca in family smallholdings and community gardens throughout the middle valley . In their analysis of oral health indicators and phytoliths from dental calculus, Gagnon et al. argue that coca use decreased among the coastal skeletal population buried at Cerro Oreja from the Salinar to Gallinazo phases; they attribute this pattern to the occupation of the coca-growing regions of the Moche Valley by highlanders during the Gallinazo phase. Ethnohistorical research has documented that control of limited coca fields was an important source of wealth and a site of conflict between coastal and highland groups in this region , dynamics Billman argues extended deeper into the past . It would be intriguing to compare the coastal skeletal population at Cerro Oreja to an EIP highland burial population to test this hypothesis .

Regardless, coca probably was an important resource consumed by residents of the Moche Valley during the Gallinazo/Early Moche phases, including highland colonists; the fact that coca is unlikely to be preserved in carbonized form appears to be the reason for its paucity in the Moche Valley samples. Returning to the ubiquity problem noted above, the single coca seed recovered at MV-225 out of 143 samples produced a ubiquity value of 0.7 percent, whereas the four coca seeds recovered at Galindo out of 10 samples produced a ubiquity value of 10 percent. Represented by one and four specimens at MV-225 and Galindo, respectively, it cannot be said that coca was truly more abundant or used more widely at Galindo than MV-225, although ubiquity values might cause a reader to infer otherwise. In summary, a basic assessment of the plant assemblages from the five Moche Valley sites reveals some broad similarities in the types of plants collected and produced; and the importance of maize relative to other taxa at the sites. Despite these similarities, however, quantitative analysis reveals significant differences in terms of the standardized counts of different plant food categories, differences that allow us to offer insight into the nature of subsistence shifts related to maize and other cultigen intensification. To further explore changes in plant use through time, I turn to an exploratory data analysis using box plots to assess statistical difference between the five Moche Valley assemblages. As discussed above, if the notched areas of any of the boxes do not overlap, then the distributions are significantly different at the 0.05 level. Outliers are depicted as asterisks and far outliers as open circles. In some cases, distributions of smaller sample sizes will cause notched boxes to overextend and then fold back on themselves. All plots are logarithmically transformed. I initially began my analysis by comparing densities of maize, other cultigens, fruits, and miscellaneous/wild resources across the five different sites. What I found was that every single plant category was represented in greater density at MV-83 than at the other sites. I therefore calculated total plant density, finding that there was a significant difference in the overall density of plant remains between MV- 83 and the other study sites . This pattern may reflect several things: better plant preservation, a change in the manner of plant deposition, a difference in disposal patterns, a reflection of higher settlement population in the areas sampled at MV-83 compared to the other study sites, etc. What is clear, however, is that density measures cannot speak to differences in plant diet/use in this particular comparison.

It is likely that some of these rodents survive baiting by consuming a sub-lethal dose

The higher incidence in the western states may suggest that workers in this region are at higher risk of drift exposure; however, it may also have resulted from better case identification in California and Washington states through their higher staffed surveillance programs, extensive use of workers’ compensation reports in these states, and use of active surveillance for some large drift events in California.Nonoccupational exposure.This study found that more than half of drift-related pesticide poisoning cases resulted from nonoccupational exposures and that 61% of these nonoccupational cases were exposed to fumigants.California data suggest that residents in agriculture-intensive regions have a 69 times higher risk of pesticide poisoning from drift exposure compared with other regions.This may reflect California’s use of active surveillance for some large drift events.Children had the greatest risk among nonoccupational cases.The reasons for this are not known but may be because children have higher pesticide exposures, greater susceptibility to pesticide toxicity, or because concerned parents are more likely to seek medical attention.Recently several organizations submitted a petition to the U.S.EPA asking the agency to evaluate children’s exposure to pesticide drift and adopt interim prohibitions on the use of drift-prone pesticides near homes, schools, and parks.Contributing factors.Soil fumigation was a major cause of large drift events, accounting for the largest proportion of cases.Because of the high volatility of fumigants, specific measures are required to prevent emissions after completion of the application.Given the unique drift risks posed by fumigants, U.S.EPA regulates the drift of fumigants separately from non-fumigant pesticides.

The U.S.EPA recently adopted new safety requirements for soil fumigants, which took effect in early 2011 and include comprehensive measures designed to reduce the potential for direct fumigant exposures; reduce fumigant emissions; improve planning, training,dutch bucket for tomatoes and communications; and promote early detection and appropriate responses to possible future incidents.Requirements for buffer zones are also strengthened.For example, fumigants that generally require a > 300 foot buffer zone are prohibited within 0.25 miles of “difficult to-evacuate” sites.We found that, of the 738 fumigant-related cases with information on distance, 606 occurred > 0.25 miles from the application site, which suggests that the new buffer zone requirements, independent of other measures to increase safety, may not be sufficient to prevent drift exposure.This study also shows the need to reinforce compliance with weather-related requirements and drift monitoring activities.Moreover, applicators should be alert and careful, especially when close to non-target areas such as adjacent fields, houses, and roads.Applicator carelessness contributed to 79 events , of which 56 events involved aerial applicators.Aerial application was the most frequent application method found in drift events, accounting for 249 events.Drift hazards from aerial applications have been well documented.Applicators should use all available drift management measures and equipment to reduce drift exposure, including new validated drift reduction technologies as they become available.Limitations.This study requires cautious interpretation especially for variables with missing data on many cases.This study also has several limitations.First, our findings likely underestimate the actual magnitude of drift events and cases because case identification principally relies on passive surveillance systems.Such under reporting might have allowed the totals to be appreciably influenced by a handful of California episodes in which active case finding located relatively large numbers of affected people.Pesticide-related illnesses are under reported because of individuals not seeking medical attention , misdiagnosis, and health care provider failure to report cases to public health authorities.

Data from the National Agricultural Workers Survey suggests that the pesticide poisoning rates for agricultural workers may be an order of magnitude higher than those identified by the SENSOR-Pesticides and PISP programs.Second, the incidence of drift cases from agricultural applications may have been underestimated by using crude denominators of total population and employment estimates, which may also include those who are not at risk.On the other hand, the incidence for agricultural workers may have been overestimated if the denominator data under counted undocumented workers.Third, the data may include false-positive cases because clinical findings of pesticide poisoning are nonspecific and diagnostic tests are not available or rarely performed.Fourth, when we combined data from SENSOR-Pesticides and PISP, some duplication of cases and misclassification of variables may have occurred, although we took steps to identify and resolve discrepancies.Also, SENSOR-Pesticides and PISP may differ in case detection sensitivity because the two programs use slightly different case definitions.Lastly, contributing factor information was not available for 48% of cases, either because an in-depth investigation did not occur or insufficient details were entered into the database.We often based the retrospective coding of contributing factors on limited data, which may have produced some misclassification.Anticoagulant rodenticides are the most common baits used in agricultural and domestic areas to manage rodent pests.They are generally classified as first- or second-generation anticoagulants based on their toxicity relative to the amount of bait a rodent must eat.The first-generation anticoagulants such as chlorophacinone, diphacinone, and warfarin usually require multiple feedings over several days to be lethal.The second-generation anticoagulants, such as bromadiolone, brodifacoum, and difethialone, are more persistent in animal tissues and in many situations can be lethal from only one feeding.In California, only firstgeneration anticoagulants are registered for agricultural uses.

Almost 1 million pounds of formulated chlorophacinone and diphacinone baits are sold annually by California Agricultural Commissioners to control agricultural ground squirrels, voles, and some other rodent pests.Additional firstgeneration anticoagulant bait is sold by commercial outlets for agricultural protection and some commensal use, but use data are not readily available.A much larger quantity of second-generation anticoagulants is sold to homeowners, structural pest control operators, and others for control of commensal rodents in and around structures.All of these uses have the potential of creating primary and secondary poisoning risks to pets, domestic animals, and wildlife including birds of prey.Various predators and scavengers in California have tested positive for second-generation anticoagulants, while a much lower number of first-generation exposures have been detected.However, without information on anticoagulant use patterns in the areas where these animals were collected, we cannot paint a complete picture of the exposure risks and impacts of anticoagulant use in agricultural production areas.Yet, in the absence of such data, persons concerned about pesticide residues in wildlife often assume that anticoagulant rodenticides used in agriculture cause widespread risk to non-target wildlife, particularly predators and scavengers of rodents.This study was undertaken to help understand the extent of raptor exposure to anticoagulants, particularly in relation to anticoagulant uses for protecting agriculture.Data were utilized from raptors that were collected as part of the public health surveillance programs of the County Veterinarian and/or Departments of Environmental Health, as well as by submission from other organizations such as California Fish and Game and the United States Department of Agriculture – Wildlife Services.None of the raptors analyzed were initially suspected of having anticoagulant exposure or poisoning.The ultimate goal was to determine possible raptor exposure to first- and second-generation anticoagulants by evaluating the relationship between the use of these materials in agricultural versus urban settings and the presence/absence of residues in raptor tissues collected from each region.A second objective was to determine if wild rodents captured as part of a county Hantavirus surveillance program would show any signs of exposure to anticoagulants.

While anticoagulant residues have been found in many carnivores, few reported data exist demonstrating the occurrence of residues in rodents found in areas where anticoagulant materials are used.The data that are available originates from rodents targeted by specific baiting programs.In turn, these survivors could have some anticoagulant residue remaining in their tissues, providing a possible exposure route for raptors and carnivores.San Diego County has a robust public health surveillance program that includes testing of raptors and other birds found dead throughout the County.This provided a large number of raptors for potential analysis.Since San Diego County is fairly urban, we wanted to compare data from these birds with birds from more rural and agricultural counties.The top 5 agricultural counties with the highest quantity of total agricultural pesticide use in California in 2007 were Fresno, Kern, Tulare, San Joaquin, and Madera.Of these, Fresno, Kern, and Tulare Counties were selected because we have worked on extensive ground squirrel problems in these areas for the past 30 years.We sought to compare anticoagulant residue data from raptors collected in these counties to those from the more urban San Diego County, where we assume most rodenticides applied are used by homeowners for the control of commensal rodents.California has been faced with a shortage of farm labor in recent years , primarily attributed to a decline in the number of Mexican migrant workers coming to the United States, who compose the majority of the labor force.Compounding the decline from abroad, migration within the United States has also dropped as farm labor has undergone a demographic transition: workers are more likely to be older, female and living with children.Labor shortages appear to have especially affected support activities,blueberry grow pot such as labor contractors.For example, the Napa County vineyard industry experienced an estimated 12% shortage of laborers in 2017.The agricultural industry is responding to this labor shortage in three ways.First, growers are increasingly relying on machines to stretch worker productivity or as a substitute for hand labor.Second, they are seeking to replace lost workers with a new labor source — for example, women and H2-A guest workers, although the complications of providing housing in coastal California have limited the viability of the H2-A guest workers option.The third way is the focus of this study: offsetting the labor shortage by boosting retention of existing workers through increased job satisfaction.High job satisfaction, defined as a “pleasurable or positive emotional state resulting from one’s…job experience” , is linked to positive effects on both employees and organizations, with evidence of a causal relationship.Benefits include lower worker turnover , increased work performance , lower absenteeism and healthier workers.Job satisfaction has been categorized in numerous ways, but core categories include the type of work performed, rewards, professional growth or promotional opportunities, supervision, and coworkers.

Additional categories may be included under specific circumstances , and the most salient categories often differ between occupations.Conversations on how to address satisfaction in the agricultural workplace understandably tend to focus on pay and benefits, with some acknowledgment that reducing harassment and favoritism is also beneficial.Because the nature of the relationship between job satisfaction and turnover goes beyond financial compensation, companies may seek to reduce turnover by adopting strategies that carry a lower financial burden.This includes respectful treatment of workers, ensuring a safe workplace, providing workers a diversity of tasks and promotional opportunities, and formalizing labor relations procedures.Despite decades of research on job satisfaction in other occupations , there has been a paucity of research on agricultural workers.To date, the few studies of satisfaction in California agriculture have been primarily based on interviews of workers.Building on this qualitative work, we developed a quantitative survey to identify and describe the job satisfaction categories that drive turnover in a population of Napa County vineyard workers.We investigated how satisfaction may vary by three key demographics — employment status , gender and age.And we conducted a limited set of follow-up interviews with a selection of participating workers to explore specific issues raised in the survey.Collectively, these results provide feedback to agricultural employers from their workers on how their company is performing in various aspects of job satisfaction, which strategies and activities they should invest in to boost job satisfaction, and how they can adapt their strategies to target specific worker demographics.We envision the agricultural industry adopting this survey tool to formally evaluate their progress toward improved job satisfaction and increased workforce sustainability.In summer 2018, we surveyed 611 vineyard crew members and 54 of their immediate supervisors from 14 companies operating out of Napa County.There were an estimated 10,000 vineyard workers in Napa County in 2018, and our survey therefore captured approximately 6.5% of the workforce.Participating employers learned about the study through contact with or recruitment by the UC Cooperative Extension research team or by advertisement at industry meetings.Under previous arrangements with their employer, survey participants completed the questionnaire in small groups while at work and were paid their normal hourly rate while they participated.Since all participants were Spanish speaking, the study was conducted in Spanish by a bilingual research assistant who displayed the questions on a flipchart and also read them aloud in Spanish.

Individuals working on a resources or database should be named on the website

Distributed and independent genome projects produce assemblies and annotations that can be beneficial to research on related species, if researchers can discover them. However, even a multi-species database that manages gene families may not contain all gene data of interest to the communities it serves. Services that assign new data, supplied by researchers or by other sites, to gene family memberships can help with discovery across databases by putting new sequence data into an evolutionary context, but then the data must be discoverable broadly.Applications that can operate where the data exists, to support comparative access for pre-publication and privately maintained genomes, can reduce the need to move large data sets among locations. For example, a group might generate a draft assembly of an accession with a novel phenotype that they have mapped to a certain genomic region. They may then wish to compare the scaffolds that contain the region of interest to a reference assembly for a different accession or for a related species, to find candidate genes that may be novel to their accession. Existing services such as CyVerse can be used to analyse data from many sources. Being able to do the comparison where the different genomes are located would save moving and duplicating large genome files, but requires considerable investment in distributed computation. Another solution is for GGB databases to host a local Galaxy instance connected to a Tripal database with public and private data sets. This is effective if a researcher with phenotypic,nft hydroponic genotypic and environmental data needs a place to house the data both before and after publication, but is not an expert in genomic analyses or data management.

Analysis pipelines tailored to the needs of a particular community, hosted through that community’s database, allow researchers to upload, search and visualize private data and public data, select these data and parameterize an association mapping workflow and execute that workflow locally. In order to execute the analysis remotely, data will need to move efficiently from the database to a remote analysis platform.Scientists often want to discover all that they can about a particular entity , but the data are distributed across multiple resources, many of which may be unfamiliar. Each data element on its own is not large, but the total space to be searched is. A hypothetical workflow is as follows: a researcher who works on one species comes to a participating database with a sequence of interest, wanting to find out what biological functions their sequence might be involved in. The researcher identifies homologous sequences in the new database by running BLAST. The database converts the BLAST results to an exchangeable token and queries other databases for information about orthologs. The product of these requests could be as simple as a gene name/symbol and a URL to point the user to the data display at the external database, or could also include provenance and database information for attribution, sequence, publications and many other types of information. For data discovery to work, databases with relevant data and compatible APIs must be discoverable and well documented, and a method should be in place to track usage across different services.There are several mechanisms for outreach to researchers. The most common form of outreach is meeting and conference attendance. With a large number of researchers at meeting and conferences GGB databases can use these opportunities for workshops, presentations or a database booth. GGB database brochures can be handed out during the meeting and conferences. However, there are a number of researchers that are unable to attend meeting and conferences so it is important that GGB database also use other forms of outreach. These include newsletters, mailing lists, blog posts and social media to inform researchers about new tools or data, webinars, workshops and videos.

These forms of outreach can be used together to reach a broader audience. Using social media during conferences and meetings with the appropriate hashtag can send information about new tools and data to researchers who cannot attend the conference. A prime example of this is the Plant and Animal Genome Conference, which has a strong social media presence.Many online resources and databases do not mention the people on their teams and only provide an anonymous contact form.Being anonymous creates a barrier to communication, and if contact/feedback forms don’t generate a response, there is no further recourse for the researcher to get help. Providing individual staff contact information and even photographs makes it easier for researchers to target questions to the appropriate person. Photos can enable researchers to find curators at meetings, and in general encourage communication by putting, literally, a human face on the GGB resources. Building in dedicated time at workshops for a ‘meet the team’ event, well advertized in advance to the research community, is also recommended to increase engagement opportunities.Overcoming the challenge of reliable data submission will require communication among representatives from the appropriate journals, GGB databases and funding agencies to establish guidelines and an easy-to-submit and police system for researchers and the journals/funding agencies and databases. This would likely be best initiated through an inter-agency sponsored workshop, followed up by regular meetings and assessment of effectiveness. Such a workshop could also develop ways to ensure journal publishers and editors are aware of all relevant GGB databases so they can direct authors of each accepted paper to the proper repository, nomenclature clearing house etc.

Providing access to centralized cyber infrastructure where databases, journals and funding agencies could sign off on successful data submission for projects would help make this process easier for all parties and ensure accountability.The GGB databases that currently comprise the AgBioData Consortium were created to serve the needs of researchers for access to curated and integrated data and analysis/visualization tools to aid scientific discovery, translation and application. The funding for these databases, however, is limited and not stable. Maintaining these resources in the longer term so that invaluable data are kept up-to-date and do not get lost is a major issue facing almost all AgBioData databases, their researcher communities and funding agencies.AgBioData databases are supported through a variety of sources. Generally these fall into one of four categories: primarily supported through line-item government funding, such as the USDA-ARS databases MaizeGDB, SoyBase, GrainGenes, Legume Information System and GRIN; primarily supported through competitive federal grants, such as TreeGenes, Hardwood Genomics, Gramene, Planteome, Solanaceae Genomics Network and Araport; supported through a combination of competitive federal grants, commissions and industry, such as the Genome Database for Rosaceae, AgBase, PeanutBase, AnimalQTLdb and CottonGen; and supported primarily through a user subscription model, such as TAIR. With long-term government funding, the USDA-ARS databases enjoy the most stable financial support of the AgBioData databases. They typically represent high-value commodity crops serving a large research and industry community. While the level of support provided by USDAARS generally allows for continuation of base activities and curation, it typically does not provide resources for technical innovation or more resource-efficient systems to be implemented. For these, funding through competitive grants is increasingly necessary,nft system as in the case of the NSF funded Legume Federation award. At the other extreme lies TAIR, which after a phased withdrawal of support by NSF, successfully implemented a subscription-type funding model under a not-for-profit organizational structure.

As the model plant for functional genomics, TAIR also has a large user community making this funding option more feasible to implement than for the databases represented in categories 2 and 3. Many of the AgBioData databases have reported willingness of the scientific stakeholders to budget some funds in their grants to support data deposit and access to their community databases, similar in how they budget for peer reviewed, open access publications costs. Unfortunately, most of the databases do not have organizational structures or processes that would allow them to accept these funds.How can studies of agricultural systems and the ways that people interact with foods they produce, eat, and discard lead us to new understandings about social relations in the past? How do labor roles, gender relations, and status-based inequalities relate to these types of interactions? This dissertation addresses these themes through the lens of food ways in the prehispanic Moche Valley of north coastal Peru. The Peruvian north coast witnessed a profound series of social and political changes during a time period that archaeologists refer to as the Early Intermediate Period, or EIP , with far-flung consequences for members of various social standing, from rural households to political centers. The EIP was marked by an increase in political complexity, with clear shifts in settlement and site reorganization accompanied by an increase in social stratification . These cultural and political changes occurred in a vertically compressed environment that also witnessed periodic El Niño events, which had significant and varied impacts on people’s subsistence practices. Indeed, substantial changes in elevation over the relatively short distance from the coast to the highlands, in the Moche and neighboring river valleys, create different micro-environments within close proximity to one another. Fertile interande an valleys have constituted a prime interaction zone between people of the highlands and the densely populated Peruvian coast, a contact dynamic that initiated in prehistory and continues today.The beginning of the EIP, which includes the Salinar and Gallinazo phases, witnessed the abandonment of earlier ceremonial centers; population increases and expansion of irrigation systems; political fragmentation and the appearance of formal fortifications and settlements in defensive locations; and cooperation and conflict between coastal and highland groups and among polities of various coastal valleys . Between approximately 300 and 800 A.D., the iconic Moche culture flourished on the Peruvian north coast.

The large adobe pyramid complex of the Huacas de Moche was constructed, accompanied by the emergence of a new regional political economy in which Moche rulers exercised significant economic, military, and ideological power over the population of the Moche and adjacent valleys. How did these periods of profound social change affect the prehispanic residents of the Moche Valley in terms of gender relations, status, and the organization of labor in ancient rural households? Foodways data provide a critical lens for examining these issues. Foodways represent a fundamental axis along which identity is constructed and maintained, and are increasingly recognized as having played a prominent role in the emergence of social hierarchies and the negotiation of status and power . In this dissertation, I incorporate archaeobotanical, environmental, and ethnohistorical evidence to address changes in food production, processing, and consumption during the EIP, a period that included the consolidation of the Southern Moche polity, one of the largest and most complex pre-Columbian political systems in the New World. Conducted inconjunction with MOCHE, Inc., a 501c3 nonprofit dedicated to protecting archaeological sites through community heritage empowerment, this project involved a large-scale comparative analysis of paleoethnobotanical data sampled from five EIP habitation sites that span the period of political transformation and state formation in the Moche Valley. The data presented in this dissertation derive from three major projects conducted in the Moche Valley in collaboration between North American and Peruvian archaeologists since 2000: the Moche Origins Project , directed by Brian Billman and Jesus Briceño Rosario ; el Proyecto de Evaluación Arqueológico con Excavaciones en las Lomas de Huanchaco , directed by Gabriel Prieto and Victor Campaña ; and the Galindo Archaeological Project , directed by Gregory Lockard and Francisco Luis Valle . I employ diachronic and spatial analyses of archaeobotanical data from 225 soil samples recovered from five domestic habitation sites excavated within the contexts of these projects to address key issues that have largely remained untested with direct subsistence data. Through these analyses, I trace changes in food production and wild plant food collection during the EIP, considering issues of agricultural intensification and the resulting impacts on labor relations, gender roles, and social inequality for the pre-Columbian inhabitants of rural households in the Moche Valley. The question of scale looms large in this dissertation. The Moche civilization of northern Peru is one of the best-known and most intensely studied archaeological cultures of the ancient New World. The ancient Moche have captured the imagination of scholars and the public alike, characterized by a series of elaborately decorated temple complexes, wealthy elite burials, and exquisite ceramics found over ten river valleys on the desert coast.

The 20th century brought significant changes to the economics of global agriculture

Beginning in the 1970s, economic researchers began to study the potential impacts of bans on the use of sub-therapeutic antibiotics on the pork, poultry, and beef sectors and on U.S. consumers, but there has been little study of how heterogeneity impacts antibiotic use, and in turn, how it impacts returns to using antibiotics in U.S. livestock operations. I concentrate on U.S. pork and poultry operations since they are the largest users of sub-therapeutic antibiotics by volume in the U.S., and explore the existing literature on the economics of sub-therapeutic antibiotic use for glimpses of heterogeneity in the returns to antibiotic use. Perhaps the most interesting source of heterogeneity in returns to antibiotic use may be heterogeneity in management and/or the use of potential substitutes for antibiotics, such as improved sanitation practices and more modern facilities. Productivity and use of technologies that substitute for STA use vary amongst producers, and likely by region and farm size. Thus, the marginal abatement costs of reducing STA use vary across industries, producers, production systems, and regions.In more developed countries such as the United States, the face of agriculture was once that of the small family farmer. Today, the agricultural landscape in developed—and to some extent developing— countries is dominated by agribusiness and large farming operations. While many of these operations are still family-owned and farm size, management, and production methods remain diverse, on the whole, farms are larger and more mechanized and specialized than ever before . This transition is a direct result of the increase in relative price of labor and changes in domestic and global agricultural policies , and was spurred by dramatic improvements in agricultural productivity, and a shift from more labor-intensive agriculture to more capital- and technology-intensive agricultural practices that employed new varieties,growing vegetables in vertical pvc pipe synthetic inputs, and irrigation.

While agricultural production in much of Asia, Africa, and Latin America is more heterogeneous and more labor-intensive in general, specialization, mechanization, and technological change have increased productivity of agricultural commodity crops such as soybeans and sugarcane in Brazil, wheat and rice in China and India, palm oil in Indonesia and Malaysia, and others . Incorporating and disseminating technological advances that improve productivity and incomes in smallholder farming systems remains a challenge throughout the developing world . In spite of—or perhaps in response to—this shift toward specialization and mechanization, there has been renewed momentum on the part of a vocal contingent of consumers, producers, researchers, and policy makers who draw attention to the social, environmental, and economic implications of this transition . They envision a new model of agriculture that employs fewer synthetic inputs, incorporates practices which enhance biodiversity and environmental services, and takes into account the social implications of production practices, market dynamics, and product mixes. Components of this movement are taking hold in the economic and cultural mainstream in the United States, Europe and other countries. Evidence of this shift includes the rise of organic, “fair trade”, and other production and certification schemes, and the growth of consumer willingness-to-pay for these differentiated food products. The prevalence of local farmers’ markets and slow and local food movements, and the emergence of Payments for Ecosystem Services and multifunctional agriculture within agricultural landscapes are also supporting this change . While closely related to the concepts of sustainable, multifunctional and organic agriculture, diversified farming systems have emerged as a separate agricultural model.

Diversified farming systems share much in common with sustainable, multifunctional, organic and local farming systems, but are unique because they emphasize incorporating functional biodiversity at multiple temporal and spatial scales to maintain ecosystem services critical to agricultural production. These ecosystem services include but are not limited to pollination services, water quality and availability, and soil conservation . Our aim is to provide an economists’ perspective on how a range of existing and emerging factors drive profitability of DFS at the farm level and how these relate to the adoption and emergence of diversified farming systems at larger scales. We begin with an overview of the factors that impact the profitability of agricultural systems, follow with a discussion of the economic factors that support and run counter to diversified farming systems, and conclude with our thoughts on how technological innovation and market trends must continue to evolve if DFS are to become economically sustainable and widespread.How profitable is it to farm? The answer depends upon the choices a farmer makes about what crops to grow and where, what technologies to use, and many other short- and long-term management decisions. Economists assume that farmers make choices so as to improve their utility, or well-being. In particular, farmers tend to pursue activities that increase their income, reduce their financial and physical risk, reduce labor requirements, and are convenient or enjoyable. A variety of constraints play into farmers’ decisions, including constraints with respect to available production technologies, biophysical or geophysical constraints, labor and input market constraints, financial and credit constraints, social norms, intertemporal trade offs, policy constraints, and constraints to knowledge or skills . The literature on technology adoption at the farm level tells us that many factors—in particular, variables that vary across farms and are sources of heterogeneity—influence farmers’ choices about what crops to grow, whether to use a new technology, and how to manage their land. Just as individual consumers have different preferences about products they consume, farmer characteristics, asset endowments, risk preferences, and intertemporal considerations affect their choices.

Farmer attitudes, resource availability, and education and knowledge are especially important; farmers may be risk-averse toward making changes in cropping decisions or adopting new agricultural practices, or might have very conservative attitudes toward technology or lower or higher levels of concern for the natural environment . A farmer’s income or resource base and ability to obtain credit will also influence his/her choice of crops, farming systems, and willingness to invest in new crops, systems, or technologies . A risk-averse farmer or one who is credit or income-constrained may be less likely to adopt new technologies, even if they are likely to reduce his susceptibility to risk or increase productivity or income over the long-run . Lack of knowledge and information about the costs and benefits of adopting new technologies or conservation practices or lack of knowledge about how to implement such technologies or practices will also affect a farmer’s propensity to adopt them . Even if farmers have full information and can implement new technologies efficiently and at low cost, differences in intertemporal preferences or credit constraints may mean that farmers are unwilling to sacrifice current profits or income for long-term improvements in soil fertility, risk-reductions, or improved yields . Biological and geophysical factors and input and output market conditions are important variables that also impact farmer decision-making and adoption of land use practices or technologies. Biological and geophysical factors that influence production can include water availability, soil fertility, and risks of floods, droughts, frost, or pest or weed infestations, and the importance of each of these factors varies with the types of crops planted. Input market conditions can shape farmer production decisions in a number of ways; dynamics of local and seasonal labor availability may mean that it is not profitable to grow a crop with a very narrow harvesting window in a month where the overall demand for agricultural labor is high in the region .

Input price volatility and economies of scale with respect to inputs or technologies can also contribute to farmers planting different mixes of crops, or planting more land in one crop than another.Similarly, output market conditions including prices, price variability, transportation costs, and supply chain transactions costs are important determinants of how profitable it is for farmers to grow a crop. Many of these variables are influenced by location; Rogers notes that communities closer to urban centers are likely to adopt new technologies more quickly. Consumer attitudes and willingness to pay for differentiated crops or particular attributes, such as organic or local production or pesticide-free varieties,vertical greenhouse also affect the agricultural systems that emerge in response to the demands of a changing market. Finally, policies and regulations can impact the profitability and evolution of different agricultural systems by facilitating or hindering trade in particular types of agricultural products, by influencing farmer decisions about what crops to grow or how much land to farm using policies such as price supports or set-aside programs, or by making different types of production or land use relatively more or less “expensive” via regulations, taxes and subsidies, or standards . In addition, many policies that do not specifically target agriculture, such as labor and immigration or water policies, have a significant effect on the costs of agricultural production. For example, laws such as those that regulate pesticide usage and application or limit water use can make it more costly to produce using synthetic pesticides or inefficient irrigation systems . While in the short-run such regulations may have a negative impact on farmer welfare, they also serve to stimulate innovation and adoption of new technologies in order to comply with regulations and reduce the costs of production . How can we describe trends in adoption and diffusion of agricultural technologies at landscape, regional, or global scales? Early studies on adoption noticed that the number of adopters, or the cropped area of using the new technology, were S-shaped as a function of time. They explained this pattern by imitation behavior among farmers; adoption is slow until enough farmers begin using the technology, and then rates of adoption speed up rapidly before they plateau.

The more profitable the new technology, the faster the rate of adoption and the higher the level of adoption after the diffusion process has played out . Farmers are heterogeneous, however, which impacts how and when they make decisions. In light of this heterogeneity, David and Feder et al. introduced the threshold model of adoption which characterized adoption within a community as a dynamic process whereby farmers make decisions according to explicit economic decision rules. Differences in when and how farmers adopt new technologies, then, arise due to heterogeneity among farmers and differences in other factors, such as their location and land quality. Larger farmers, for example, are often early adopters of mechanized technologies that exhibit increasing returns to scale. There is an interplay between farmer heterogeneity and the biological and geophysical factors that influence adoption that we mentioned earlier in this section; farmers in areas with soils with lower water-holding capacity will reap greater benefits from adopting irrigation technologies, and pest control strategies are adopted first in regions with high pest pressures. Over time, technologies and practices diffuse as producers gain knowledge and experience, or “learning by doing,” and as more and more farmers begin to use the technology, or “learning by using.” More and more farmers will adopt a technology as the fixed costs of adoption decline with time, and for some technologies, the gains from adoption increase with time as the network of producers using the technology increases in size . These basic principles that guide producer adoption choices provide a background for analyzing the factors that will affect whether farmers adopt diversified farming systems. Within the context of farmer decision making, there are a number of ways that diversified farming systems can help farmers maximize their utility, including through their roles in mitigating different types of risks, providing complementary inputs and optimizing production in the face of different biophysical or input and output market constraints, and through providing income or non-pecuniary benefits from ecosystem services or other benefits of using DFS practices. In this section, we focus on how these factors might make diversification an economically optimal choice for the farmer. Farmers are typically risk-averse . They face many different types of risk including price risk , yield risk , input supply risk and other types of risks . Many of these types of risk contribute directly to profit risk, which is ultimately most important to the producer. Farmers and their families can respond to risks in many ways, and can respond ex ante in precautionary ways, or ex post to try and minimize their losses. Strategies for coping with risk include finding off-farm employment , saving or using credit markets, informal borrowing , adopting risk-reducing technologies such as seed varieties with properties such as drought or herbicide resistance that emerged during the green revolution , engaging in contracts such as those that ensure that the farmer will have a buyer for his product at the end of the season at a set price , and diversification of production.

We use digital agriculture for its semantic breadth and increasing currency

The ‘urbanization of hinterland’ requires the ability to observe, interpret, and manage processes of extended urbanization from zones of concentration. We then “bring information back in” by introducing a more materialist analysis of the role of information in global capitalist space, which centers on computation capital: the infrastructure necessary to transport and make legible enormous amounts of data. In this framework, digital agriculture can be reinterpreted as a “data fx” for multiple entangled crisis tendencies of urbanization. These include the well-documented ecological crisis caused by industrialized agriculture—necessary to keep food prices, and therefore wages, low enough to generate profits in the traditionally ‘urban’ secondary and tertiary sectors—as well as a potential crisis of the over-accumulation of computational capital. This crisis response, in turn, reconfigures the concentrated–extended dialectic of urbanization. The digitalization of agriculture further consolidates agrarian knowledge and decision-making away from the felds and among agribusiness and, newly, technology actors. We note how this of-siting transforms agrarian land tenure and deskills agricultural workers. This connects directly to the concept of ‘depeasantization’ , what is vertical farming which can be understood as the mirror of urban agglomeration. We conclude with some suggestions for future research on digital agriculture’s effects on the urban/rural divide. The intensive use of information technologies in agriculture has received limited attention from social scientists.

As recently as 2016, Bronson and Knezevic, in taking a critical look at how such tools affect the power dynamics between farmers and corporations, noted that “there has been no attention given to Big Data’s implications in the realm of food and agriculture” . In the years since, a steady trickle of publications has begun addressing this gap: on a “data grab” ; on the unequal ability between farmers and firms to use data ; on digital agriculture’s transformation of farmers into consumers ; on the racialized exploitation of labor ; on the embedded norms of digital agriculture ; and on alternatives . A variety of labels have been used for this emergent industry: precision agriculture, e-agriculture, smart agriculture, and digital agriculture, among others. Despite early critical use of precision agriculture, the term tends to be used in the industry to signify a specific suite of production-oriented technologies.However, information technologies are also used to open new markets and new territories for production. For example, digital platforms have become increasingly important for individual producers to bring their goods to market. Figure 1 shows how information technologies are intertwined throughout the cycle of agricultural production and sale.In our taxonomy, precision agriculture is a subset of digital tools which improve efficiency through careful management of inputs. Three other types of tools—marketplace and financial platforms, e-extension, and smallholder management—are typically platform-based systems that mediate the social relation between farmers and the outside world. Marketplace and financial technologies help farmers access new credit lines and optimize their market behavior.

E-extension is the digitalization of the practice of implementing technological innovations through farmer education, particularly in the international development context. E-extension, like the analog version that preceded it, is largely reliant on insights produced far from the farm. Finally, smallholder management platforms allow larger agribusinesses to exert control over smallholder farmers through close management of their inputs, products, and so forth. This may allow major actors to divest themselves of the risk inherent in owning land and instead subcontract smallholders in a relationship analogous to other platforms in the gig economy.For digital agriculture’s boosters, it has the potential to be the much-needed “fourth agricultural revolution” . In particular, it is framed as a climate-friendly way to feed the world and improve the lot of farmers around the world. By making the application of inputs more efficient, digital agriculture can indeed lessen the environmental impact and yield of agriculture. By increasing input efficiency and improving knowledge of market demand, digital agriculture may indeed improve the fortunes of producers. The rhetoric is not dishonest, but it is incomplete.Optimizing inputs enables the continued use of ecologically-harmful chemicals and practices, which would otherwise be abandoned if their effects were not actively mitigated . Digital agriculture’s marketing claims it will improve efficiency, increasing yield and minimizing the use of inputs—many of which are harmful and unsustainable. The externalities produced by using these inputs are the “un- and undervalued costs of industrial capitalist agriculture” . A team at Cornell, for example, has developed a model that recommends ideal fertilizer application rates for each section of a farmer’s feld in order to minimize nitrogen run of into the Gulf of Mexico, which causes algal blooms, depletes oxygen levels in the water, and kills fish and wildlife.3 While optimization limits the short-term damage of unsustainable practices, it also makes those practices more politically permissible and financially feasible. Thus, by making unsustainable practices appear sustainable, the necessity of adopting more ecologically and socially sustainable and just practices is delayed.

By focusing on input management, these technologies advance a limited interpretation of sustainability that still depends on of-farm inputs, rather than a more radical shift to permanently sustainable practices . Just as digital agriculture promises to minimize inputs, it also promises to maximize yield—yet yield is not the problem. In the 1970s Amartya Sen noted that while starvation was increasing globally, food per capita was also increasing —as population grew, food production grew at a greater rate, not only globally but even regionally. While some scholars have taken issue with Sen’s empirical basis, an updated analysis using 2010 statistics found the same results . The direct relationship between hunger and food per capita, when we would expect an inverted one, betrays the simple thesis that hunger is due to a lack of food availability. Instead, Sen attributes hunger to an inability to exchange for food. Davis similarly notes the disconnect between food availability and hunger, finding that famine can occur in areas of grain surplus because it is more attributable to rural food management and exploitation than to production . The “solution” to hunger, then, lies not in yield. Yield has increased; food per capita has increased; hunger persists. Therefore, stretching yield through digital agriculture is insufficient and does not address the political-economic basis of systemic hunger.The third key claim made by digital agriculture’s boosters is that it will improve farmers’ welfare, in particular their profits. profits may be found in better decision-making, better yields, and better access to market information . In the Global North, such increased profits may be plausible. However, a primary mode for digital agriculture, the platform service, means that the data produced typically becomes the property of the platform provider. Weersink et al. note that a key challenge for digital agriculture is making this data useful; this, in turn, may favor larger companies with the capacity to process the data. Bronson notes this dynamic and warns that it may reproduce the distributional effects of the Green Revolution—that is, to concentrate wealth and power in the hands of major agribusinesses. In the Global South, digital agriculture presents a different set of problems for farmers’ welfare.

Technological innovation that increases a crop’s yield in turn increases supply and undercuts the socially necessary labor time required to produce it. This dynamic lowers the crop’s exchange value at the expense of those at the bottom of global commodity chains, in particular the growers’ compensation per unit of crop. As this price drop is not accompanied by any increase in production for farmers without access to this technological innovation, this drop translates to lower overall compensation and to “exchange entitlement decline” . If they depend on exchange for subsistence, the decreased compensation translates to hunger as well. Digital agriculture’s strategy of overcoming hunger by increasing yield thereby may even exacerbate it. In reffecting on these mainstream claims, a different theme emerges. Rather than sustainability, nourishment,vertical farming supplies or farmer welfare, digital agriculture is fundamentally about securing the conditions to generate profit in the food system. Crucially, however, this is not about profit in food production alone, but in the wider capitalist economy for which food is obviously a fundamental input. Therefore, we submit that digital agriculture must be understood as addressing a specific set of crisis tendencies that have emerged at a particular juncture in the social, ecological, and spatial history of capitalism. This juncture is defined by interlocking moments of ecological disaster; enormous advances in information production, gathering, and processing; and “hypertrophic” urbanization . In this section we argue that rather than a solution to the climate crisis, hunger, or farmer welfare, the rise of digital agriculture can better be understood as an attempt to overcome crisis tendencies of “the relentless growth imperatives of an accelerating, increasingly planetary formation of capitalist urbanization” . Afer briefy excavating the informational dynamics latent within the framework of extended and concentrated urbanization, we describe how digital agriculture functions as a “data fix” by allowing the intensification of agricultural industrialization and the extraction and enclosure, for eventual profit, of the data produced by digital agriculture technologies. An early theme in globalization literature was a tendency to embrace the rise of information technologies in a way that dematerialized the now planetary systems of extraction, production, and consumption . Such concepts, however, have largely been absorbed by analyses which show that a deterritorialized “information society” is not displacing traditional modes of production and social relations as much as emerging as a financial-managerial stratum in a “new international division of labor.” Another major theme in globalization studies is the ‘global city network,’ a set of nodes in the global space of flows from which the global economy could be commanded and controlled . In describing such cities as “strategic sites where global processes materialize” , they appear to be material sites foating in a sea of immaterial processes.

In this model, cities are simultaneously the result of, yet alienated from, specific material processes— such as agricultural production—taking place beyond their bounds. In both concepts the informational nature of globalization is over-emphasized at the expense of its material effects. In an era of climate crisis, this shortcoming is glaring.One response has been to radically reframe globalization as a material process of urbanization, which unfolds as the product of dialectically-entwined moments of extension and concentration . Concentrated urbanization signifies the moment of agglomeration where the material flows of global capitalism accumulate into cities, megalopolises, and mega-regions. On the flip side, extended urbanization is the moment where remote territories are enclosed and transformed into operational landscapes that funnel energy, materials, and food into areas of accumulation. Both moments cause and are caused by the other: “The urban unfolds into the countryside just as the countryside folds back into the city” . Global capitalist urbanization is a metabolic process of moving and consuming the material world . This involves both fragmentation and homogenization —for example, the simultaneous expansion of monoculture agriculture and of liberal private property regimes. At the same time, enclosure and technological advances deprive peasants of their livelihoods; ‘depeasantization’ is the mirror of urbanization. However, the desire to develop a more materialist model of globalization leads to the black-boxing of information‘s role in facilitating vast networks of production and exchange. To bring information back in requires recognizing that something happens at the moment of concentration which sets the stage for extension. In the present framework, production and the growth imperative drive a search for more raw materials. But extension also depends on informational infrastructure to make a massively decentralized network of global supply chains profitable. Indeed, another way to describe capitalist geography is as “a skein of somewhat longer networks that rather inadequately embrace the world on the basis of points that become centers of calculation” . Information, along with material, is being drawn inwards in the moment of concentration; the processing of raw information—which is “what remains after one abstracts from the material aspects of physical reality” —into actionable knowledge informs extension processes. “Information processing” is computation, and computation at the scale required to make legible the vast amounts of data produced in the contemporary economy involves enormous physical infrastructural investment in data centers, undersea cables, and satellite networks . Such computational capital consists also of intellectual and human capital in the form of models, algorithms, and the expertise to deploy them. There is a potential for the over-accumulation of computational capital, however; as a result, there is a constant drive for firms to find productive outlets. This is what leads firms like Amazon, Micros of, Google, Oracle, and Cisco—as well as funds invested in and consultancies hired by them—into digital agriculture.

The proportion of retail commodities sold at market prices has kept rising

The Great Recession reduced weekly hours by 1.3 hours in the hotel sector but not in the other sectors. Thus, for most employed workers in these three sectors, weekly hours remained constant during recessions. This result contrasts with that in the agricultural sector where weekly hours rose substantially during recessions.China’s economic liberalization and structural change have proceeded for several decades. Since the economic reforms were initiated in the late 1978, China’s economy has grown substantially. For example, the annual growth rate of GDP was 8.5% in 1979-84 and 9.7% in 1985-95 . Moreover, despite the Asian financial crisis, China’s economy continued to grow at 8.2% annually between 1996 and 2000. Foreign trade has been expanding even more rapidly. China’s trade to GDP ratio increased from 13% in 1980 to 44% in 2000 . Although reform has penetrated throughout the whole economy since the early 1980s, most of the successive transformations began and in some way depended on growth in agricultural sector . After 1978, decollectivization, price increases, and the relaxation of local trade restrictions on most agricultural products accompanied the take off of China’s agricultural economy in 1978-84. Grain production increased by 4.7% per year. Even higher growth was enjoyed in horticulture, livestock and aquatic products . Although agricultural growth decelerated after 1985 after the one-off efficiency gains from the decollectivization, the country still enjoyed agricultural growth rates that have outpaced the rise in population . Despite the healthy expansion of agriculture,vetical aquaponics system the even faster growth of the industrial and service sector during the reform era has begun to transform the rural economy, from agriculture to industry and from rural to urban.

During this process, the share of agriculture in national economy has declined significantly. Whereas agriculture contributed more than 30% of GDP before 1980, it fell to 16% in 2000 . During this same time, agriculture’s share of employment fell from 81% in 1970 to only 50% in 2000. The rapid economic growth, urbanization and food market development have boosted demand for meats, fruits and other non-staple foods, changes that have stimulated sharp shifts in the structure of agriculture . For example, the share of livestock output value more than doubled from 14% to 30% in 1970 to 2000 . Aquatic products rose at an even more rapid rate. One of the most significant signs of structural changes in the agricultural sector is that the share of cropping in total agricultural output fell from 82% to 56%. Moreover, the most significant declines in crop-specific growth rates have been experienced in the grain sector . Changes in the external economy for agricultural commodities have paralleled those in domestic markets. Whereas the share of primary products in total exports was over 50% in 1980, it fell to only 10% in 2000 . Over the same period, the share of food exports in total exports fell from 17% to 5%. The share of food imports fell from 15% to 2%. Disaggregated, crop-specific trade trends show equally sharp shifts and suggest that exports and imports increasingly are moving in a direction that trend toward products in which China has a comparative advantage . The net exports of land-intensive bulk commodities, such as grains, oil seeds and sugar crops, have fallen; exports of higher valued, more labor-intensive products, such as horticultural and animal products, have risen.

The proportion of grain exports, which was only around 20% of total agricultural exports in the 1990s, is less than half of what it was in the early 1980s. By the late 1990s horticultural products and animal and aquatic products accounted for about 80% of agricultural exports . These trends are even more evident when reorganizing the trade data grouping them on the basis of factor intensity.Taken as a whole, we believe the trends of China’s economic structure and agricultural trade over the past two decades reveals that the changes that are expected to be experienced as a result of WTO are not new. Changes in the structure of economy and agricultural production and trade suggest that China was already moving towards a point that was more consistent with its domestic resource endowments. To the extent that the new trade agreements reduce barriers to allow more land-intensive products into the domestic market and the fall in restrictions overseas stimulates the export of labor-intensive crops, WTO main impact will be to push forward trends that were already happening on their own. The commitments that China provided in its WTO Protocol of Accession are largely consistent with the nation’s long-term reform plan. Despite the continuity with the past, few can dispute that the terms of China’s WTO accession agreement pose new challenges to the agricultural sector. In some cases, there will likely be large impacts on rural households, and will undoubtedly elicit a sharp behavioral response . However, the nature and severity of the impacts will not only depend on how households respond. Perhaps of even greater importance will be how China’s agricultural policy makers will manage their sector as the new trade regime takes effect. To examine this set of issues more carefully, in this section we first review agricultural policy during the reform era. In the next section we will then see how WTO measures will change the environment in rural China.

While government expenditures in most areas of agriculture have increased gradually during the reform period, the ratio of agricultural investment to agricultural gross domestic product has monotonically declined since the late 1970s. In 1978, officials invested 7.6 percent of AGDP . By 1995, the proportion of AGDP committed to investment fell to 3.6 percent. Exceptions were only recent years in the late 1990s when this ratio rose. Moreover, a significant capital outflow from agriculture to industry and rural to urban has occurred during the last two decades through the financial system and government agricultural procurement . China’s policies governing the external economy have played a highly influential role in shaping the growth and structure of agriculture for many decades. During the entire Socialist Period , the overvaluation of China’s domestic currency destroyed incentives to export effectively isolating China from international exporting opportunities.After the reforms were initiated, however, officials allowed the real exchange rate to depreciate by 400% between 1978 and 1994. Except for during the past few years when the exchange rate has experienced a slightly re-appreciation, adjustments in the exchange rates throughout most of the reform period have increased export competitiveness and contributed to China’s export growth record. These, in turn, have helped the overall expansion of the national economy. Perhaps more than anything, China’s open door policy, including its exchange rate policy, has contributed to the rapid growth in the importance of the external economy.The shift of labor from the rural sector to the urban sector lies at the heart of a country’s modernization effort and China has been experiencing this primarily two ways: by the absorption of labor into rural firms and by movement of massive amounts of labor into the off farm sector in cities. Rural industrialization has played a vital role in generating employment for rural labor, raising agricultural labor productivity, and farmer’s income. The share of rural enterprises in GDP rose significantly from less than 4% in the 1970s to more than 30% by 1999.

REs have dominated the export sector throughout the 1990s . And, perhaps most importantly, REs employ 35% of the rural labor that works off the farm. In addition to formal wage earning jobs in rural areas, a large and rising part of the rural labor force also works in the self-employed sector. At the same time, although China’s factor markets still contain a number of structural imperfections, such as employment priority for local workers, housing shortages,farming vertical and the urban household registration system, labor has poured into the cities during the last 20 years and labor markets emergence are transforming the economy.According to a nearly national representative survey of 1200 households across China, it is found that more than 100 million rural workers found employment in the urban sector in the late 1990s . In fact, to an extent never found before, China’s labor markets have allowed migration to become the dominant form of off-farm activity; been increasingly dominated by young and better educated workers; expanded fastest in economies or areas that are relatively well-off; and recently begun to draw workers from portions of the population, such as women, that earlier had been excluded from participation. According to the work on some researchers, if China continues to change at the pace it has in the past 20 years, and other provinces experience the same changes that have already occurred in the richest provinces, China’s economy will continue to follow a healthy development path and be on the road to modernization.China has a strong agriculture research system that has generated technologies adopted by millions of farmers to meet the increasing demand of food and agricultural products in the most populous country in the world . All previous studies consistently show that research-led technological change is the main engine of agricultural growth.Technology produced by China’s agricultural research system accounts for most of the rise in the cropping sector’s total factor productivity between 1980 and the late-1990s . Despite this past record, China faces considerable challenges. Although as a publicly funded agricultural research system, it functioned well and addressed many important problems, its expenditures have been tied to public budgets. Falling fiscal support has taken its toll. Currently, there is much concern that agriculture research investment intensity has declined since the early 1980s and reached a dangerously low level, only 0.44 in 1999 . At the same time, the increasing evidence of overlapping, inefficiency, over-staffing, and inappropriate technology make fundamental reform of the current research system an essential task.Price and market reforms were key components of China’s policy shifts from a socialist to a market-oriented economy. The reforms associated with China’s policy reforms, however, began slowly and have proceeded gradually. Market liberalization began with non-strategic commodities such as vegetables, fruit, fish, livestock, and oil and sugar crops. Little effort was made on the major crops. And, although the aims of the early reforms were to raise farm level prices and gradually deregulate the market, most of the significant early reforms were done by administrative measures .

However, as the rights to private trading were expanded in the early 1980s, and official allowed traders the to buy and sell the surplus output of almost all categories of agricultural products, the foundations of the state marketing system began to be undermined. Since the mid-1980s, market reforms have continued though only in a stop and start way. For example, after record growth in agricultural production in 1984 and 1985, a second stage of price and market reforms was announced in 1985 aimed at radically limiting the scope of government price and market interventions and further enlarging the role of market allocation. Because of the sharp drop in the growth of agricultural production and food price inflation in the late 1980s, however, implementation of the new policy stalled. Mandatory procurement of grains, oil crops, and cotton continued. After agricultural production and prices stabilized in 1990-92, another attempt was made in early 1993 to abolish the grain compulsory quota system and the sale at low prices to consumers. The state distribution and procurement systems were substantially liberalized, but the policy was reversed when food price inflation reappeared in 1994: government grain procurement once again became compulsory. As well, a provincial governors’ grain responsibility system was introduced in 1994-95, aimed at encouraging greater grain self-sufficiency at the provincial level. Further retrenchments followed; in 1998 the central government initiated a controversial policy change prohibiting individuals and private companies from procuring grain from farmers . Grain quota procurement prices were set more than 20% higher than market prices, which meant a transfer in favor of those farmers able to sell at that price . Not surprisingly, stocks started to accumulate and procurement and market prices had to come down relative to international prices in 2000. Despite these periodic cycles in the reform process, markets have gradually emerged in rural China.According to Lardy , the share for agriculture was just 6% in 1978 but had risen to 40% by 1985, 79% by 1995 and 83% by 1999. Moreover, the state’s intervention was unable to halt the flow of grain across provincial boundaries. Huang and Rozelle find that agricultural prices for all major commodities, including rice, wheat, and especially for maize and soybeans have moved together across far reaching localities within China.

There is also a literature that challenges the dominant role of agricultural growth for poverty reduction

From the point of view of our exercise, this greater variance produces a ‘bias’ in the resulting estimates of the connection between agricultural income and welfare, since we are interested not in the short-run effect of things like weather shocks on expenditures but on the longer-run effects of things like improvements in agricultural productivity. We are also concerned about the related issue of endogeneity; even the simplest general equilibrium models with investment imply simultaneity in the determination of income and expenditures. We address these issues using a simple instrumental variables strategy, using averages of neighboring countries’ sectoral income growth as instruments for own-income growth . Fifth, even after controlling for time, continent, and decile fixed effects in growth, we are concerned that there may be heterogeneity across countries in the way agricultural income growth affects households in different parts of the expenditure distribution. We explore this possible heterogeneity by interacting various fixed or pre-determined country characteristics with income growth from different sectors, reporting those results in Section 5.4. We summarize our main results. First, poorer households’ expenditures grow more in response to growth from agriculture than do the expenditures of wealthier households, and this holds across all deciles. We call this result monotonicity, and it is both very robust and important.

Monotonicity also holds for growth from non-agricultural sources, but in the opposite direction, vertical farming aeroponics with wealthier households’ expenditures responding more than poorer households’. Second, it is not just across deciles that we see an effect: within poorer deciles, households benefit significantly more from growth in agriculture than they do from growth in other sectors. Third and finally, the connection between expenditure and sectoral income growth is importantly and significantly different across different groups of countries. In particular, it is the poorest households in the poorer countries for whom agricultural income growth is most important.From a theoretical standpoint, a long tradition of dual economy models that aggregate the economy into two sectors—agriculture and non-agriculture—has served to identify the transmission mechanisms of an exogenous agricultural productivity increase on welfare . Transmission mechanisms include employment, food prices, real wages, and the demand for non-tradable goods produced in the rural non-farm economy. The tradition in the dual economy literature is to assume that consumption expenditures are equal to real income and that labor income is the source of expenditures while capital income is saved and invested. An increase in growth in one sector would affect the welfare of only the part of the population actually employed in that sector. If expenditures are distributed differently across households in the two sectors, then an increase in employment in one sector will have an effect on the aggregate distribution of expenditures. If, for example, households employed in the agricultural sector tend to be poorer, an increase in agricultural employment will have an equalizing effect on the entire distribution of expenditures .

For a country with a closed economy , an increase in agricultural productivity induces a decrease in food prices. All consumers benefit from lower food prices, but most particularly the poor, who typically spend a larger share of their income on food . If there is surplus labor and wages are tied to the cost of living to secure a fixed real subsistence wage, lower food prices can induce a decrease in the nominal wage, fostering employment and growth in the non-agricultural sector . When workers are mobile and wages are equated across sectors, differences in the rate of growth of different sectors can result in changes in the distribution of expenditures through the employment effect. For example, Loayza and Raddatz formulate a model in which expenditures of the poor are equal to the prevailing wage, while non-poor households can borrow or lend to smooth away the effects of variation in labor income on expenditures . The model shows that the effects of sectoral growth on real wages are larger for sectors with larger employment and a lower elasticity of demand for labor, namely agriculture and services. Another strand of literature is based on a three-sector aggregation of the economy, with a non-tradable sector in addition to the agricultural and, say, manufacturing sectors. A key determinant of the overall effect of an initial growth impetus in agriculture is the linkages created in fostering demand for the non-tradable sector products . To the extent that labor is not fully mobile, then in addition to asymmetric effects on the functional sources of income any growth that originates in the rural economy stands to have a more direct impact on the rural population, where many of the poor live. Much of the empirical support to the claim that agricultural growth is good for aggregate growth, employment, and welfare is based on simulation models that rely on demand and supply elasticities that are not estimated.

Thorbecke and Jung use social accounting with postulated elasticities applied to Indonesia, thus finding that agriculture and services contribute more to poverty reduction that the industrial sectors. Within-country or within-region studies arguably offer the best evidence we have on the connection between aggregate agricultural income growth and household welfare, perhaps because in these contexts one can construct a proper panel dataset. In an important series of papers Datt and Ravallion use panel data for states in India and show a systematic and relatively uniform association between agricultural growth and poverty reduction, but a very heterogeneous relationship between non-agricultural growth and poverty change. With province-level panel data for China over the period 1985–1996, Fan et al. find that agricultural growth is associated with a reduction of rural poverty while non-agricultural growth is associated with an increase in rural poverty. With provincial data for 1983–2001, Montalvo and Ravallion show that the primary sector was the driving force behind the spectacular decrease in poverty in China. Suryahadi et al. conduct an exercise similar to that of Ravallion and Datt but for Indonesia, and are able to distinguish between the rural and urban poor. They find that growth in services is good for both the rural and urban poor, with the effects of agricultural growth focused more specifically on the rural poor. In a similar spirit, Warr uses national data from four Asian countries from the 1960s to 1999 in a panel analysis and finds similar results, in that growth in agriculture and services were associated with a decrease in poverty, with the estimated coefficient on agriculture substantially smaller than the coefficient on services, and the coefficient on manufacturing not significantly different from 0. Looking at the 25 countries with the greatest success at reducing extreme poverty under the period of the Millennium Development Goals, Cervantes-Godoy and Dewbre find that while economic growth was a key determinant, growth in agricultural incomes was especially important. Bresciani and Valdes provide evidence of the role of agricultural growth on poverty reduction through rural labor markets, farm incomes, food prices, and economy-wide multipliers in different country case studies.

Other studies have resorted more systematically to cross-sectional country-level time series data, thus looking for average effects across a large set of countries and hence economic structures. Using data from 80 countries spanning 1980 to 2002, Christiaensen et al. find a stronger association between overall poverty decrease and growth originating in agriculture than growth originating in either of the other two sectors. With higher participation, slower growth of agriculture may still deliver more poverty reduction than the growth of non-agriculture. In contrast, using a slightly different method, Bravo-Ortega and Lederman find that in Latin America, it is the non-agricultural sector that has the strongest effect in reducing poverty. Focusing on the role of the unskilled labor market, Loayza and Raddatz find evidence that growth in income from sectors with high unskilled labor shares has a disproportionate effect in reducing poverty rates. In a somewhat different specification, Dollar et al. regress growth rates in incomes of the poorest 20 percent on growth in average income and on changes in the share of agriculture in GDP. The significance of the coefficient on the agricultural variable suggests that, even controlling for aggregate growth, faster growth in agriculture is likely to disproportionately benefit the poor. Lanjouw et al. for example argue that it is the non-agricultural sector in the rural areas that is both more dynamic and more pro-poor,vertical indoor hydroponic system and hence the most important contributor to poverty reduction in rural India. Collier and Dercon note that productivity in agriculture, and especially in the smallholder sector, is so low that economic development and poverty alleviation in Africa will have to come from a radical transformation of the agricultural sector and massive exodus from agriculture. They also cite works on the role of migration in the reduction of poverty in rural areas. Most of the literature that cautions against the importance given to agriculture for poverty alleviation however relates to a different argument: while the relatively strong poverty impact of agricultural growth seems to be a fairly robust result, the cost of investing to obtain a given growth is far higher in agriculture than in other sectors, making it an inefficient instrument for growth and welfare . Our paper does not address this issue at all, but aims at contributing to the literature on the sectoral growth-poverty linkage. An issue in almost all of the studies we have discussed is simultaneity between sectoral growth and the welfare indicator used in the analysis. A contribution of this paper is to tackle this issue by using an instrumental variable approach to try to measure the effect of an exogenous increase in sectoral growth on welfare.

We use the same database collected by the World Bank as do other cross country analyses, although we only select the countries for which welfare is measured by consumption expenditures.2 We also use data on all deciles, rather than only on e.g., poverty rates, as in Christiaensen et al. and other studies described above. When using cross-country evidence on changes in the distribution of income or expenditures one has to make an early choice regarding whether it is better to consider the distribution of these welfare measures within countries or across countries. The former choice leads to an empirical strategy that groups together different welfare quantiles across countries, so that for example, one imagines that the poorest 10 percent of households in Tanzania are similarly positioned to the poorest 10 percent of households in China, despite the substantial differences in the level of real expenditures of the quantile across these two countries. The latter choice construes distribution as a global phenomenon, with the result that the poorest 10 percent of all households globally may all be located in a very small number of countries. If what we want to measure is the global distribution of welfare one also logically ought to weight countries by their populations in any cross-country analysis. different researchers have made different choices.3 In this paper we take the country focused approach, and analyze the relationship between welfare and sectoral growth of all deciles of the distribution within countries, rather than on a measure of poverty level or distribution across countries.4 Over the last several decades, the World Bank has accumulated a large number of datasets from a large number of developing countries which are based on household-level surveys, statistically representative of the populations of those countries, and which include data on non-durable goods expenditures. Though the micro-data from these surveys are not generally available, the World Bank provides data on aggregate expenditures by decile for many of these countries. Our sample is restricted to the countries and years for which we have information on expenditures data for at least three points in time . The sample covers 62 countries, with variable numbers of observations over 1978 to 2011, totaling 310 surveys. This sample of countries and years is not a random sample of the countries of the world. Instead, it is a sample of countries where household expenditure surveys have been conducted . It has however a large coverage, including 81% of the population in low and middle-income countries in 2000. In terms of continents, the sample includes 97% of the population of South Asia, 70% of Sub-Saharan Africa, and 20% of Latin America and Caribbean.There is no clear bias in this sampling of developing countries except for the obvious and egregious absence of all but one Latin American countries.

The process of carbon sequestration can be accelerated by coconut plantation and inter crop management

Based on a field study, Kumar et al.reported that the presence or absence of over-canopy had little effect on the rhizome yield of galangal , a medicinal plant, implying its shade tolerance.Being a shade-tolerant crop, galangal yield remained steady across a wide range of light availability conditions, from full light to a photosynthetic photon flux density as low as 18% of that in the open.Woody perennials such as cacao , cinnamon , clove , coffee and nutmeg , and vines and creepers such as sweet potato and vanilla can also tolerate shade to varying degrees.Species are also grouped into obligate or facultative shade plants and obligate or facultative sun plants based on their light requirements.Nonetheless, rigorous studies on the nature, mechanisms, inheritance, and management of shade adaptability of understory species in CBFS are lacking.Being a single-stemmed woody perennial with oucambium, the palm’s main stem does not develop radially with age.The crowns are likewise rather narrow, measuring 5 to 6 m in breadth.This unique growth form of the coconut palm allows significant light infiltration into the understory in an even-aged stand.A related aspect is the uniform spatial arrangement of the palms.In Kerala, coconut palms are typically planted at 7.6–9 m apart, with a population density of 120 to 170 palms per hectare.Likewise, an average density of 148 trees per hectare was reported from Melanesia’s smallholder coconut plantations.Although designed to meet the growth requirements of mature palms, this wide spacing typically results in inefficient use of site resources and a lack of full site occupancy by the main crop throughout the majority of its life cycle.

In the field study mentioned above, Kumar and Kumar found that understory light transmittance for mixed coconut+multipurpose tree stand ranged between 6 and 75% of that in the open, depending on the time of the day, tree species involved and planting geometry.According to Thomas et al.,4x8ft rolling benches only around25% of the land is properly utilized, when monocropping is practiced in coconut gardens.Furthermore, the grower receives little or no returns from the palms throughout their immature stage, which can last up to 10 years, while the intercrops provide some returns.As a result, mixed species agroforestry systems aimed at increasing spatial and/or temporal complementarities in resource utilization, as well as providing additional returns, have become a unique feature of the coconut-growing regions in the tropics.What explains the functioning of such sophisticated agroecological models is perhaps the “Niche-complementarity hypothesis”.It implies that a bigger suite of species occupying a site may lead to better resource partitioning and utilization making the system more productive than systems involving fewer number of species.Consistent with this, Liyanage and Dassanayake reported increased nut yields when pasture species , black pepper and coffee were inter cropped with coconut.Such beneficial effects of inter cropping have been attributed to improved nutrition of the palm through complementary resource sharing, better retention of soil moisture, reduced weed competition and improved soil quality.

Competition for site resources between coconuts and the associated plants, however, could be a potential problem.Such interactions may be either above ground or below ground.Section 5.1.4 describes the below ground interactions.Furthermore, the nature of inter specific interactions will vary depending on the stage of coconut stand development.A synthesis of the published reports, nevertheless, indicates that growing trees in the inter spaces does not have a strong adverse impact on the yield of coconut palms, except in situations where such trees impede light availability of the palms.Species mixtures generally ensure spatial complementarity in resource use as the components occupy different niches, although the tree-crop interactions may change with time and planting geometry.Although coconut-based polycultural systems are ubiquitous, below ground interactions of woody perennials in such mixed-species systems are rarely studied due to methodological challenges.Furthermore, results from the available studies are also not consistent, implying that the interactions may be either complementary or competitive.Nelliat et al.reported horizontal and vertical stratification of coarse roots in “adequately and separately fertilized multi-storied combination of coconut, cacao and pineapple”.Conversely, Pandey et al.found that the coconut root systems were at close proximity to the intercrops in well fertilized polycultural systems involving three 20-year-old tree species,implying competitive nutrient withdrawal by the coconut palms.Using the 32P soil injection technique, Kumar et al.and Gowda and Kumar investigated root competition in the coconut + dicot MPT agroforestry system.According to Kumar et al., 32P uptake by coconut palm in a species mixture was higher than that of a sole coconut stand, owing to increased subsoil root activity in the former, implying that the coconut root system may grow deeper in mixed-species systems compared to sole coconut systems.Gowda and Kumarexamined root interactions between coconut and dicot trees along a soil fertility gradient.

Notwithstanding major differences in the nutrient status along the gradient as well as dicot tree root characters, uptake of 32P by the coconut palms was not substantially different, signifying non-competitiveness of the associated dicot tree components for P.Nevertheless, the interplanted dicot trees captured significant quantities of the radio-label supplied to the coconut palm, implying a “scavenging effect” by these trees that, in turn, minimizes the potential for lower leaching of nutrient elements.Coconut-based farming systems often involve mixtures of trees that occupy different soil strata and this may entail a certain degree of spatial complementarity in resource use.Occurrence of two or more woody species in mixtures also favors diminished lateral spread and/or facilitates deeper root penetration of the components.In the coconut+dicot tree system investigated by Gowda and Kumar mentioned above, the interplanted dicot trees absorbed considerable quantities of the radio-label applied to the palm, which declined log-linearly with distance from the palms, signifying a substantial potential for “capturing” the lower leaching nutrients, at proximal distances.Proximity of the associated tree component, therefore, is a strong determinant of such plastic responses in tree root distribution.Gowda and Kumar also reported that some dicot species in the coconut+dicot tree mixture developed deeper root systems , while others produced increasingly spreading root systems , denoting that root architecture of mixed tree plantations is species dependent.Thus, there is a need for proper selection of the component crops and their manipulation to optimize productivity in coconut ecosystems.An array of ecosystem services such as provisioning, regulating, supporting, and cultural services are provided by the coconut based multi-strata, multi-species ecosystems.This includes crop species yielding food, fiber, fuel, fodder, timber, medicine, and other basic necessities , besides cash returns.The diverse range of crops integrated into CBFS producing fruits, nuts, drinks , edible oils and cakes, fiber, foliage, timber, bio-fuels, vegetables, spices, and medicinal plants justifies the sobriquet “coconut-based food forests”.The coconut palm also yields organic coconut water, virgin coconut oil, functional foods and health drinks like neera , coconut sugar, cosmeceuticals, oleochemicals, and bio-lubricants and is a popular ingredient in the cuisines of many countries in South and Southeast Asia.Furthermore, the coconut palm produces edible copra for the extraction of coconut oil, as well as desiccated coconut powder, fermented sap, and sap jaggery, among other culinary items.

Also available in both domestic and international markets are a variety of value-added products from coconut oil such as soap, body oil and perfumed hair oil, and kernel-based products such as coconut chips, coconut cream, coconut milk powder, white soft coconut cheese, coconut yoghurt and so on.Tender coconut water is a healthier alternative to many carbonated beverages due to its nutritious properties.Apart from being an important dietary component, the coconut palm and the associated species yield diverse range of aesthetic and artisanal products.Coconut wood is an excellent structural material that is used in the construction of buildings, furniture, flooring, and paneling, and the fabrication of high-end products like handcrafted, biodegradable,flood and drain table and sustainable coconut bowls, as well as for the making of charcoal, chemicals, pulp, and paper.In experimental studies, the mechanical properties of coconut wood compared quite well with those of other structural timbers such as teak , wild jack and the like.Coconut wood thus supplements the supply of raw materials for the wood industry and provides low-cost and durable construction materials.Because of its availability and renewability, coconut’s sustainability can add value to this construction material and thus help to conserve the remaining natural forests, by offsetting the pressure on them.Additionally, CBFS provides byproducts such as coconut shells and fibre, which are presumably underutilized but constitute vital raw materials for cottage enterprises.Coconut shell is a useful bio-fuel as well, despite its relevance as an alternative fuel in homes and small businesses.In addition to offering an alternative and better source of fuel than fuel wood and other traditional fuels, using coconut shell as a fuel reduces CO2 emissions and sanitizes the environment of the harmful hard shell.The husk usually forms 35–45% of the weight of the whole nut when ripe.About 30% of the husk is fibre and 70% is coir dust.The industry uses just about 35% of the total husk available, while there is scope for economically utilizing at least 50% of the husk produced.Coir fiber and coir pith are two important products made from coconut husk.The fibers are used for spinning into yarn for manufacturing mats and mattings, ropes, twines, etc.Pith, which is usually mixed with short fifibers and contains mainly lignin, cellulose, and hemicellulose, is used as a manure and has a variety of industrial applications too.Agrobiodiversity being the critical feature of NbS, CBFS offers innumerable opportunities for integrating diverse forms of crops in the same land management system.

Such systems have provided sustenance, nourishment and livelihood security to large segments of Kerala’s rural and peri-urban populations for millennia, as in other parts of South and Southeast Asia.As described in Table 2 and Section 5, many functional groups of plants, such as food crops , permanent plantation crops, medicinal plants, multipurpose trees, and others, are associated with CBFS, implying their potential to conserve biodiversity in managed ecosystems.Such integrated farming systems generally outperform mono specific production systems in all major aspects of multifunctional agriculture, including food security, environmental functions, economic functions, and social functions.The coconut palm is also very resilient as it can withstand natural calamities like typhoons and flooding.In general, woody perennial-based mixed-species land use systems have the potential to address natural calamities such as droughts, floods, and high temperatures as a consequence of climate change.Improvements in soil organic matter status and water holding capacity, and the resultant yield gains, are also integral features of the coconut-based ecosystems.Osei-Bonsu et al.observed higher soil moisture retention in cacao + coconut mixture in Ghana compared to cacao + Gliricidia sepium system.From Sri Lanka, Arachchi and Liyanage also reported improved soil organic matter status, bulk density, aeration, and water content in the soil profiles of acacia and gliricidia interplanted plots compared to that of sole coconut and Calliandra calothyrsus and L.leucocephala intercropped plots.Although global warming and the consequential faster soil organic matter turnover may exacerbate the deterioration of nutrient-poor tropical soils, such obstacles are less likely in coconut-based multi-strata production systems than in mono specific stands, emphasizing the CBFS’s sustainability.Another major characteristic of CBFS is enhanced carbon capture and storage in soil-crop systems, which has the potential to minimize CO2 emissions.This includes carbon sequestration in soil and biomass , as well as the substitution of fossil fuels with bio-diesel made from biomass or coconut oil.However, only few studies have characterized the carbon sequestration potential of coconut-based ecosystems.The available reports suggest that tree plantations signify remarkable carbon pools as trees hold much more carbon per unit land area than other categories of vegetation, and CBFS has huge potential as a carbon sink.Consistent with this, Navarro et al.reported that coconut plantations exhibit high productivity typical of the tropical humid evergreen forest ecosystems.Ranasinghe and Thimothias estimated that the ecosystem carbon stock of CBFS in Sri Lanka ranged from 32 to 72 Mg C ha–1, while the net carbon balance ranged from 0.4 to 1.9 Mg C ha–1 month–1 under various growth conditions.Carbon storage by coconut palms in mixed stands is clearly greater than that of sole stands, especially when the species-mix involves trees.For instance, in a system involving different inter cropped fruit trees such as guava , litchi , sapota and custard apple grown in association with coconut, Manna et al. reported higher soil carbon sequestration for mixed-species systems than sole coconut.Nutrient management of CBFS is yet another important determinant of soil carbon sequestration, and improved nutrient management may augment the carbon sequestration potential.

Most stakeholders interviewed in this study share a demand for applying AI to agriculture

Agricultural farms are extremely pressed to get a rewarding return on their investments which leads to, at times of the year with high workload, farmers not getting much sleep at all.This is confirmed by a farmer who says that since he works so much, some hours are nearly unpaid.Implementing AI in agriculture could potentially mitigate these intense periods of large workloads somewhat, which would give social values back to the farmers.Another dimension of investments and implementation of new technology in agriculture, is that investments in smart farming are not always viewed as necessary by farmers but rather something neat and trendy.Thus, such investments are described to be paid by the “amusement account”.This is confirmed by a farmer that states that most of the technological investments made on his farm are motivated by his interest and fascination with technology.Respondent C4 says that a lot of farmers gladly spend money on new and exciting tools and machines, for instance new tractors.From this, it seems like many farmers think that the charm of running an agricultural business is to be able to tailor and adapt the farm according to one’s liking.While some respondents like doing things very manually others like to develop their way of working consistently with new types of technology.To summarize the results of this interview study, the themes and topics are divided into what appears to be the demands or opportunities for AI in agriculture, as well as the barriers or hurdles that hinder the use of it.Furthermore, based on the contrastive responses and views of different groups of respondents,hydroponic farming the demands and barriers are differentiated by the respondent groups that all have distinct roles in the agricultural sector.

Table 2 shows an overview of the most important points from the interviews, divided over the different respondent groups.To begin with, the responses from farmer respondents show that there are many opportunities linked to the usage of AI and smart farming technologies in agriculture.Most importantly, according to them, new smart farming technologies have the potential of increasing their profitability, either by contributing to higher revenues or freeing time spent on some tedious tasks.On the other hand, the large initial costs to set up the technologies are identified as a barrier.However, if economical means allow for investing in such solutions, farmers believe that the investments will pay off in terms of profitability and competitiveness.Other factors that act as demands for smart farming technologies are their potential to be more sustainable and that they make farming more fun.Further barriers according to farmers are the complex solutions and lack of interoperability, as well as the poor prerequisites and opportunities of continuous education regarding technology in agriculture.Also, the fickle market makes smart farming risky to invest in for farmers.From a commercial enterprise point of view, there are many opportunities connected to smart farming, but also some critical barriers to overcome.The respondents of this group see potential in increased cooperation between companies as well as with farmers, business cases in providing Software as a Service and additionally to streamline logistics connected to agriculture.Nevertheless, data sharing and cybersecurity are seen as large hurdles to the use of these technologies.Respondents from research institutes also express a positive view on accelerated use of AI in agriculture.They believe such a development would result in more data collected by the farmers, which would decrease the time researchers themselves spend on gathering data.This would, according to the researcher respondents, lead to a faster and better research on agriculture.However, data sharing hinders, once again, the scientific development since high-paced research is hard to conduct without proper access to data from different sources.An additional identified barrier for smart farming is the mistrust from farmers that the scientifically developed solutions mirror a real agricultural demand and are not just developed for the sake of technology.

Finally, the respondents from governmental agencies claim that there is a great interest and demand for propagating smart farming technologies for national competitiveness as well as other economic reasons.Still, they are not sure how to position themselves in this transition, which slows down the process of digitizing the agricultural sector.This respondent group also views cybersecurity and data sharing as critical barriers to overcome.This paper provides a review of the main opportunities and hurdles for applying AI to agricultural businesses.By conducting a structured literature review and an interview study with 21 respondents from various parts of the agricultural industry, data has been gathered to get a holistic view on the use of smart technology in agriculture.The scope of the thesis is deliberately wide, focusing on three agricultural sectors: arable farming, milk production and beef production.Furthermore, the respondents are categorized by their role in the sector, ranging from governmental authorities, commercial enterprises, researchers as well as farmers.This broad view allows to acquire knowledge that ranges over several production sectors, as well as over several kinds of organizations with different views on the agricultural sector.Driving the farmers towards smart farming technologies are the needs for increased profitability, reduced workload and often a genuine curiosity for new technology.Surprisingly, all these aspects are not completely captured in the literature review.For example, there are studies about the impact smart farming can have on the relation between humans and animals on a farm, but they did not show in the literature review search.On the contrary, some expected drives for smart farming were not expressed by the respondents, such as the advantageous impact that smart farming can have on the environment through less nutrient loss.Instead, profitability stands out as the most influential factor which makes a clear business case an essential requirement connected to the propagation of smart farming technologies.

Since more and more agricultural products become available in the form of SaaS, allowing for sharing and renting equipment, the business case is changing for both farmers and machine producers, opening new possibilities.Nevertheless, for smart farming to really transform the agricultural sector, governmental agencies and commercial enterprises might need to take a more active role in the transition.Such aspirations are especially important to ensure that the governmental and societal demand for reduced emissions and increased sustainability is met in the technological shift.For the transformation to be successful, it is essential that the structures, allowing farmers to apply the smart farming technologies, are modern.One key requirement is that farmers have continuous and easy ways to acquire up-to-date knowledge of how to apply smart farming.Therefore, there is a need to ensure technical, agricultural education which is easily accessible through for example flexible, on-demand courses.Additionally, the smart farming techniques need to be modifiable to match the varying transparency and adaptability demands that different farmers have.Regarding how implementation and propagation of AI in agriculture might be hindered, this study identifies some factors that act as barriers.The most prominent one is how data is managed, which can be further specified to data sharing and ownership as well as cybersecurity.This is a complex question that as of now does not have a clear solution, neither technically nor legally.Here lays an important role for research institutes as well as authorities.However, there is a consensus among respondents that to transition the agricultural sector into a more data-driven and digital environment, the technical infrastructure must be secure.The solution must be able to guarantee that sensitive data is not available for intruders while at the same time guaranteeing access for the intended users.Furthermore, for the end users to be able to benefit from the digitalizing transition of the sector, the data models require a high degree of flexibility.This stems from the wide variety of machinery at farms as well as the varying level of technological interest and knowledge among the farmers.Moreover, an important aspect that slows down the process of implementing smart farming technologies and AI in agriculture is the economical dimension expressed by the respondents.

A large part of this are of course the high investment costs, but other economic aspects also play a part in this barrier.For example, the fickle market demands, the general low profitability in agriculture as well as the trend towards consolidation of farms all contribute to making investments full of risk.Other identified barriers that hinder the spread of AI in agriculture are some social factors, for example the concerns about technological over-dependency and insufficient end user trust towards technology.The lacking trust seems to stem from over-selling from developers of technology as well as a gap between the technology that is developed and the real market demands.As for the technical solutions that could potentially solve the demand for AI and smart farming technologies, there are many possible ways.In this study, findings show that a lot of the data and sensors types already exist.The problem that remains to be solved is to connect the input data to the output data by developing the datasets, and thereby closing the data cycle.Today, the dairy sector generally holds a closed and elaborate data cycle whereas generally the meat and arable sector have less developed data gathering and therefore less precise decision support tools.This is highlighted in both the interviews and the literature review, as high-resolution data allows for more precise and detailed decision support.Although, after a thorough process of data gathering from input to output, one can build models and evaluate which one of them performs best with some specified evaluating metrics.Additionally, a general problem and difficulty in building machine learning models is that models tend to take too many variables at the same time.The results show the importance of ‘starting small’ when building the models, i.e.using few input variables to begin with and then tune the model adding only one more variable at a time.It is also found that all possible use cases and technical solutions demand a high precision for classification model output as well as low prediction errors for regression models.Decision support in agriculture manages and affects core parts of the agricultural business, and therefore it is important that estimations and predictions are accurate.Interestingly, respondents from the arable sector express that they, as of now,hydroponic equipment accept higher levels of total error in the model.However, for future purposes and solutions with increased complexity, the total error must decrease which is likely to affect the bias- variance trade-off.A requirement for achieving precise supervised machine learning models, adapted to the local farm, will be easy pre-processing of the data.Thus, the data labeling process must either be simplified by developers or offered to the farmers as a service by consultants.

Technologically, the agricultural sector has developed for decades, but the shift towards smart farming techniques and data-driven agriculture might be one of the greatest transitions.Applied AI in agriculture has the potential to optimize and streamline agricultural activities in all sectors in agriculture.By data-driven decision support, and even tasks performed completely automatically, farmers hope to improve their output both in terms of quantity and quality, mitigate carbon emissions, decrease work time, and increase profits.For commercial enterprises and governmental agencies, the transition allows for updated supply chains and planning models, improving the agricultural industry on a macro-level.Still, several challenges remain unsolved, jeopardizing the speed of the transition.Here, there are important tasks for companies, authorities and research institutes.Nevertheless, with such strong incentives, the long-term trend towards increased usage of AI in agriculture is clear.The question is no longer if smart farming will continue to develop, but how the hurdles will be resolved, and which stakeholders will benefit from its radical transformative effects.Growing urban populations and the reduction of arable land, increase the need for productive, efficient, and environmentally friendly ways of agricultural production.For more than three decades, agriculture changed towards an increasing degree of automation.Today numerous digital solutions already exist to support farmers’ everyday lives.Examples can be found within the monitoring of crops and soils as well as for data analysis and storage including decision support.Most solutions today need a connection to farm external cloud systems where data and information are being received from and transferred to.Farmers are motivated to actively use this information technology to benefit from increases in farm input efficiencies, from decreases in negative environmental impacts as well as from automated operation documentation.However, farmers in Europe are diverse in their farm produces and many digital solutions only cover partly the activities within farms.This leads to the problem that farmers experience the lack of interoperability of different digital products.